Open Collections

UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Measures of investment performance in real estate investment analysis : current practice and predictive… Arthur, David Douglas 1977

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
831-UBC_1977_A4_6 A78.pdf [ 9.7MB ]
Metadata
JSON: 831-1.0093953.json
JSON-LD: 831-1.0093953-ld.json
RDF/XML (Pretty): 831-1.0093953-rdf.xml
RDF/JSON: 831-1.0093953-rdf.json
Turtle: 831-1.0093953-turtle.txt
N-Triples: 831-1.0093953-rdf-ntriples.txt
Original Record: 831-1.0093953-source.json
Full Text
831-1.0093953-fulltext.txt
Citation
831-1.0093953.ris

Full Text

MEASURES OF INVESTMENT PERFORMANCE IN REAL ESTATE INVESTMENT ANALYSIS: CURRENT PRACTICE AND PREDICTIVE UTILITY by DAVID DOUGLAS ARTHUR S. (Urban and Regional Planning), Univers ity of Waterloo, 1975 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Commerce and Business Administration) We accept th i s thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA October, 1977 <g) David Douglas Arthur, 1977 In presenting th i s thes is in pa r t i a l fu l f i lment of the requirements for an advanced degree at the Un ivers i ty of B r i t i s h Columbia, I agree that the L ibrary sha l l make it f ree l y ava i l ab le for reference and study. I fur ther agree that permission for extensive copying of th is thes is for scho lar ly purposes may be granted by the Head of my Department or by his representat ives. It is understood that copying or pub l i ca t ion of th is thes is for f inanc ia l gain sha l l not be allowed without my wr i t ten permission. Department of OpMiVlEgCP, tygTi "Rl j^klFPS, A^U,NU<STRmQVa The Univers i ty of B r i t i s h Columbia 2075 W e s b r o o k P l a c e V a n c o u v e r , C a n a d a V6T 1W5 Date r v ^ p p g Sjioni ( i ) ABSTRACT Real estate offers substantial returns to astute investors but the real estate investment decision i s not generally well understood and as a con-sequence, th i s ignorance often causes inadequately prepared investors to venture into poorly conceived and subsequently f i n anc i a l l y disastrous projects. One of the most important considerations in the analysis of real estate invest-ments i s the measure of investment performance or return. Over the years, many d i f fe rent measures have been used to indicate the re la t i ve p r o f i t a b i l i t y of real estate investments with the interest ing dilemma that d i f fe rent measures produced d i f fe rent re la t i ve rankings of investment d e s i r a b i l i t y and d i f fe rent absolute indicators of investment return. In the past, real estate analysts have emphasized " t r a d i t i o n a l " or " f i r s t generation" measures of investment return which are mathematically unsophisticated, contain few f i nanc ia l variables and give no e x p l i c i t consideration to the time value of money. Analysts in the f i e l d s of investments and corporation finance widely use the more sophist icated discounted cash flow or "second generation" measures of analys is. Academic researchers in real estate support the theoret ical super ior i ty of discounted cash flow measures and claim that the i r use i s wide-spread among the real estate community. The purpose of th i s study is twofold: (1) to develop an understanding of the current pract ice regarding real estate investment analysis with pa r t i cu la r emphasis on the measures of return used and the degree of sophist icat ion in investment analysis according to the s ize and type of investor; and (2) to test the predict ive u t i l i t y of the various measures of return. The fol lowing hypotheses are to be tested: 1. Real estate investors remain r e l a t i v e l y unsophisticated in the i r approach to investment analys is. 2. The degree of sophist icat ion in investment analysis i s pos i t i ve l y related to the s ize and type of investor. 3. Fa i lure to use avai lable measures of investment return i s due to a lack of understanding on the part of the neglecting investors and d i f f i c u l t i e s a r i s ing from the estimation of the necessary input data. 4. The a b i l i t y of the real estate community to make more accurate and hence, more prof i tab le real estate decisions i s d i r ec t l y related to the use of more sophist icated measures of return. A questionnaire survey, the f i r s t in Canada, was developed and administer-ed to the fol lowing samples: (1) 300 real estate equity investors and (2) 150 ICI real estate brokers. The findings strongly suggest that real estate investors remain unsophisticated in the i r approach to investment analysis r e l a t i ve to other business f i e l d s . The real estate community continues to re ly on f i r s t generation or t rad i t i ona l measures of return or performance to evaluate real estate investment opportunities. The most popular before-tax measures of return employed by those surveyed were Return on Investment (net operation income/purchase price) and the Equity Dividend Rate (net operating income - debt service/equity). For those respondents employing an af ter - tax measure, the most popular were After-tax Cash Flow ( f i r s t year)/Equity and the Internal Rate of Return. I t was shown that many of the respondents lacked the knowledge and understanding required of the more sophisticated measures of performance common to other business f i e l d s . They appeared to select a par t i cu la r measure and then f a i l to adhere to the spec i f i c methodology of the chosen measure. Sophist icat ion in real estate investment analysis was shown to be a function of the type of company and por t fo l i o s ize since large publ ic real estate corporations employed more sophist icated methods and techniques with a greater frequency re l a t i ve to other investors. ( i i i ) An empirical test was developed to examine the predict ive u t i l i t y of the various measure of investment return i den t i f i ed in theory (Chapter 2) and in practice (Chapter 3). The approach taken was to compare the ex ante returns from a sample of 15 apartment properties over the period 1970 to 1977 with the ex post returns over the same period. Thus, i t was possible to measure the deviations between the predicted and actual returns each with a corresponding investment ranking as a.test of the accuracy and r e l i a b i l i t y of predict ion using each measure of return. The findings suggest that f i r s t generation measures of return w i l l predict investment returns and re l a t i ve investment rankings as c losely correlated to that which actual ly occurred as w i l l the more sophist icated second generation or discounted cash flow (DCF) measures of return. This resu l t can be traced to the i m p l i c i t requirements of the methodologies of f i r s t generation measures necessitating only one year forecasts of input parameters. The f a i l u r e of the DCF models to provide c l ea r l y superior predictions of investment performance was largely due to the inaccurate fore-casts of key input parameters, pa r t i cu l a r l y sales pr ice at the end of the holding period. The empirical test revealed that d i f fe rent measures of return produced d i f fe rent re l a t i ve rankings of investment d e s i r a b i l i t y and d i f fe rent absolute indicators of investment return. The results do not allow any spec i f i c measure or group of measures ( f i r s t generation or DCF) to be hai led as a more accurate and r e l i a b l e predictor of investment return in real estate. Indeed, the poor predict ive u t i l i t y of the various measures raise a question as to the r e l i a b i l -i t y of predict ion into the future for real estate investment analysis in general. Thus, i t i s c lear that the results of the sample test do not lend support to the hypothesis that the a b i l i t y of the real estate community to make more accurate and r e l i a b l e , and hence more p ro f i t ab le , real estate decisions i s d i r e c t l y r e l a t -ed to the use of the more sophist icated discounted cash flow measures of return. ( iv) TABLE OF CONTENTS Chapter Page 1. INTRODUCTION: THE REAL ESTATE INVESTOR AND THE INVESTMENT ANALYSIS PROBLEM 1 1.1 Purpose of the Study 5 1.2 Organization of the Study 6 2. REAL ESTATE INVESTMENT ANALYSIS: OUR CURRENT KNOWLEDGE 8 2.1 Character ist ics of the Real Estate Market 9 2.2 Investment Decisions in Real Estate 11 2.3 Contributions from Other D i sc ip l ines 15 2.4 Methods of Measuring Investment Return in Real Estate 17 2.4.1 F i r s t Generation (Tradit ional ) Methods of Analysis 18 Capital ization-of-Income (Value) Approach 18 Rate of Return Approach 22 2.4.2 Second Generation (Discounted Cash Flow) Models of Analysis 25 Mortgage-Equity and Cap i ta l i za t ion and Equity Y ie ld Models 26 Present Value Models 27 Discounted Cash Flow Rate of Return Models 29 3. REAL ESTATE INVESTMENT ANALYSIS: CURRENT PRACTICE 40 3.1 Review of Pr ior Research 40 3.2 Sample Selection and Administration 49 3.3 Sample Character i st ics 51 3.4 Investment Return Measures 54 3.5 Forms of Return Included in Measures of Performance 60 3.6 Required Rates of Return for Investment 61 3.7 Adjustments for Uncertainty 64 3.8 Computer Usage 66 3.9 Planned Holding Periods 69 3.10 Summary of Pr inc ipa l Findings 71 4. EMPIRICAL TEST OF THE PREDICTIVE UTILITY OF METHODS FOR MEASURING INVESTMENT PERFORMANCE 74 4.1 Property Data 75 4.2 Model to Evaluate Measures of Investment Performance 78 4.2.1 Framework of the Model 78 4.2.2 Operation of the Model 84 4.3 Analysis o f Input Variables and Assumptions 89 4.3.1 Predicted Performance Case 89 4.3.2 Actual Performance Case 97 (v) Chapter Page 4.4 Results of the Test 99 4.5 Estimation of Input Data in the Test 107 4.6 Summary of Pr inc ipa l Findings 110 5. CONCLUSIONS, IMPLICATIONS AND SUGGESTIONS FOR FURTHER RESEARCH .... 112 5.1 Implications for Real Estate Investment Analysis 114 5.2 Suggestions for Further Research 116 SELECTED REFERENCES 118 APPENDIX 129 A: Real Estate Investor Survey 129 Covering Letter and Questionnaire 130 Follow-up Letter 131 Complete Tabulated Results 132 B: Real Estate Broker Survey 152 Covering Letter and Questionnaire 153 Complete Tabulated Results 154 C: Property Sample 168 General Information and 1971 Appraisal Report S t a t i s t i c s 169 Amenity Features 170 Net Income Mu l t ip l i e r s 171 (v i ) LIST OF TABLES Table Page 2- 1 Impact of A l ternat ive Def in it ions of "Return" 23 3- 1 Table of Studies of Real Estate Investment Performance 42 3-2 CMHC Study: Average Annual After Tax Rates of Return Calculated for Investors in Apartments Pre and Post White Paper on Taxation 44 3-3a HUD Study: Measures of Return Used by Investors 45 3-3b HUD Study: % of Investors Including Each Form of Return in Measure of Performance 47 3-4A Size of Real Estate Por t fo l io s 51 3-4B Average Value of ICI Transactions 52 3-5B Location of Head Of f i ces , By Type of Investor 53 3-8A Types of Property Held, By Size of Investor 53 3-1IA Before-Tax Measures of Investment Return Used Most Often, By Type of Investor 55 3-11B Before-Tax Measure of Investment Return Used Most Often 55 3-13A After-Tax Measure of Investment Return Used Most Often, By Type of Investor 56 3-13B Afters-Tax Measure of Investment Return Used Most Often 56 3-15A Form of Investment Return Considered, By Type of Investor 60 3-17A % of Investors Including Each Form of Return in Measure of Performance 60 3-18A Rates of Return Expected By Investors 62 3-18B Rates of Return Expected By Brokers 62 3-20A Uncertainty Adjustment Techniques 64 3-20B Uncertainty Adjustment Techniques 65 3-23A Computer Usage, By Type of Investor 67 3-24A Computer Usage, By Size of Investor 67 3-25A Evaluation of Reasons for Using the Computer 68 3-26A Evaluation of Reasons fo r Not Using the Computer 68 3-31A Planned Holding Periods, By Type of Investor 70 3- 31B Planned Holding Periods 70 4- 1 Size of Apartment Projects, Measured in Units 76 4-2 Input Data: Computer Model 85 4-3 Output Data: Computer Model 86 4-4 Results of the Test - 'Actua l * Versus Predicted Returns and Ranking Using Dif ferent Measures of Return 101 4-5 ' Ac tua l ' Return Versus Predicted Return - Adjusted IRR 103 4-6 Property Acceptance or Rejection: Actual Versus Predicted Measures 104 4-7 Correlat ion of Predicted Ranking to Actual Ranking: Spearman Rank Correlat ion Coef f ic ient 105 4-8 Average ' A c tua l ' Rates of Return for the Apartment Properties By Each Measure 107 ( v i i ) LIST OF FIGURES AND MAPS Figure Page 2-1 The Investment Decision 13 2-2 F i r s t Generation Methods for Measuring Investment Return in Real Estate 24 2-3 Second Generation Models of Measuring Investment Return in Real Estate 37 4-1 Project Analysis - Investment Return Model 80 4-2 Flowchart of Computer Model 88 Map 4-1 Location of Property Sample 77 (vi i i ) ACKNOWLEDGEMENTS I t i s v i r t u a l l y impossible to enumerate a l l those who have "contr ibuted" in one form or another to the successful completion of th is study. Much appreciation is due to those members of the real estate community across Canada whose cooperation and assistance formed the basis of th i s research e f f o r t . Pa r t i cu la r thanks i s extended to several members of the real estate community in Toronto who w i l l i n g l y provided deta i led information on the i r property investments. I would l i k e to express my appreciation to two of my friends and classmates, Blake Al lan and Chris Graham. Blake provided invaluable a s s i s t -ance towards the computerization of the survey results as well as encourage-ment throughout. Credit and appreciation is due Chris for his assistance in the modif ication of the investment analysis program used to analyze the property sample. Financial support for the study came from the Urban Land Economics Divis ion and the Univers ity of B r i t i s h Columbia. This assistance i s grate-f u l l y acknowledged. Sincere thanks are extended to the members of my thesis committee, pa r t i cu l a r l y Professors Larry Jones and Mike Goldberg. Larry readi ly accepted the role of advisor at short notice and provided much in the way of helpful comments and d i rect ion in sp i te of the time constra ints. Mike offered his encouragement, his signature (700^ times), and the necessary support f a c i l i t i e s to make a l l th is a r e a l i t y . A formal acknowledgement is due Sandra, Joyce and Louise who pat ient ly endured the process of typing, and re-typing the manuscript. Above a l l , hea r t - f e l t thanks i s due someone very close to me, Sue, who supported me throughout a very try ing period. No more can be said than the s i gn i f i can t s ac r i f i ce s and hardships she underwent w i l l not be forgotten and cannot be repaid. September 15, 1977. 1 1.0 INTRODUCTION AND SETTING:' THE REAL ESTATE INVESTOR AND THE INVESTMENT  ANALYSIS PROBLEM Real estate i s a favourite investment of Canadians. In 1974, the value of a l l real estate transactions t o ta l l ed between $18 b i l l i o n and $20 b i l l i o n . In the same year, the federal government's tota l spending was $22.6 b i l l i o n whi le, for further comparison, the value of trading on a l l stock exchanges in Canada in 1974 was just over $5.4 b i l l i o n . " ' There has also been a remarkable appreciation in property values in recent years. The value of a l l real estate sales in 1970 was about $7,000 m i l l i o n , compared with 19741s $18 b i l l i o n to $20 b i l l i o n . By comparison, the value of trading on a l l Canadian stock exchanges in 1974 was only about $70 m i l l i o n higher than in 1970. 2 The real estate boom has manifested i t s e l f not only in soaring res ident ia l property values, but in the growing interest in acquiring income properties and undeveloped land. This interest has been reinforced by numerous accounts 3 of the more successful real estate ventures. However, l i t t l e i s ever written about the many investors who make l i t t l e or no p r o f i t from the i r real estate investments. While the myths of high p ro f i t s in real estate have persisted over the years, l i t t l e attention has been given to the importance of the real estate investment decis ion. R a t c l i f f observed that th is decision and: 1. Robert H. Catherwood, Real Estate for P r o f i t , (Toronto: Maclean-Hunter, 1975), p . l . 2. Ib id. 3. Popular real estate l i t e r a t u r e describes in some deta i l the steps that an investor should take in order to "make a m i l l i on do l l a r s " or "buy real estate for p r o f i t . " For example, see Will iam Nickerson, How I  Turned $1000 Into a M i l l i o n in Real Estate in My Spare Time, (New York: Simon and Schuster, 1963), and Will iam Zeckendorf and Edward McCleary, Zeckendorf, (New York: Holt, 1970). ... the investment transaction which ensues, i s the c r i t i c a l act in the process of urban development. This transaction i s the generating force in urban growth and change; i t i s „ the determinant of land use and.thus builds the urban st ructure. One of the most important considerations in the analysis of real estate investments i s the measure of investment p r o f i t a b i l i t y or performance. The subject of " rate of return" or " y i e l d " i s often a confusing one because the term is subject to many d i f fe rent meanings. A l l too often, what i s represented as a " return " on closer analysis in fact proves to be something e l se. Over the years, many d i f fe rent methods have been used to indicate the r e l a t i ve p r o f i t -a b i l i t y of real estate investments with the interest ing dilemma that d i f fe rent methods produced d i f fe rent re l a t i ve rankings of investment d e s i r a b i l i t y and d i f fe rent absolute indicators of investment return. In the past, investment decisions of business firms in general have tended to be based on the i n tu i t i on of decision makers, rules of thumb, or investment c r i t e r i o n with fau l ty theoret ical foundations which y ie lded i n -correct answers in a large percentage of the decisions. A survey of the l i t e ra tu re on real estate investment analysis supports the v a l i d i t y of th i s claim. For example, according to one well-respected author in the f i e l d of real estate investment analys i s ; "the most overwhelming conclusions that one must almost invar iably reach a f te r reviewing both the l i t e r a t u r e and the practice of real estate investment analysis are twofold: 1. Almost a l l methods of analysis re ly upon general industry " ru le of thumb" calculat ions fo r analys is. These guidelines range from the appl icat ion of a gross income mu l t i p l i e r to determine "rate of return" on a project to s imp l i s t i c calculat ions of a " rate of return" based upon the antic ipated results of the f i r s t year of the project ' s l i f e . Almost no ana lyt ica l methods employ present value ca lcu lat ions . 4. Richard U. R a t c l i f f , Real Estate Analys is , (New York: McGraw-Hill, 1961), p.21. 3 2. V i r t ua l l y no methods of analysis advocated in the l i t e r a tu re . or u t i l i z e d by the industry apply sophist icated computational techniques or s t a t i s t i c a l tests of v a l i d i t y which are readi ly avai lable today.§ In the past, real estate analysts have emphasized " t r a d i t i o n a l " or " f i r s t generation" methods of measuring investment return. These methods are mathematically unsophisticated, contain few f inanc ia l variables and give no e x p l i c i t consideration to the time value of money. Analysts in the f i e l d s of investments and corporation finance widely use the more soph i s t i cat -ed discounted cash flow or "second generation" methods of analys is. These methods can incorporate many f inanc ia l variables and give e x p l i c i t consider-ation to changes in f inanc ia l conditions over time. At an academic l e v e l , un ivers i t ies and colleges have employed few real estate educators and researchers.^ Consequently, inadequate research has been undertaken to examine the complexities of the real estate decis ion-making process. The need for improving investment decision making s k i l l s in real estate i s described by P e l l a t t : . . . real estate offers substantial returns to astute investors but ... the real estate market is not generally well understood, and as a consequence, th i s ignorance often causes inadequately prepared investors to venture into poorly conceived and sub-sequently f i n anc i a l l y disastrous projects ... the level of sophist icat ion of decision making in th is investment area i s normally low and there i s substantial opportunity to improve the qual i ty of investment ana ly s i s . ' 5. Peter G. K. P e l l a t t , "The analysis of Real Estate Under Uncertainty", Journal of Finance 27, (May 1972), p.459. 6. See for instance: Jerome Dasso, "Real Estate Education at the Univers ity Leve l " , in Recent Perspectives in Urban Land  Economics,. Michael A. Goldberg (ed.), Vancouver: University of B.C., 1976) pp. 171-179. 7. Peter G. K. P e l l a t t , "A Normative Approach to the Analysis of Real Estate Investment Opportunities under Uncertainty and the Management of Real Estate Investment Po r t f o l i o s , " (Ph. D. D i ssertat ion, University of Ca l i f o r n i a , Berkeley, 1970), p . l . 4 Contrary to developments in real estate, investment analysts in the f i e l d s of business finance and investments have rigourously studied invest-ment planning and decision-making processes. A growing volume of l i t e r a t u r e in these areas has resulted in a var iety of ana lyt ica l frameworks and sophisticated computational techniques for the evaluation of investment projects and po r t f o l i o s . In contrast to the l i t e r a t u r e lamenting the lack of sophist icated methods of investment analysis in real estate r e l a t i ve to other business f i e l d s , several recent a r t i c l e s by notable authors in the real estate f i e l d claim widespread support for the more sophist icated methods for measuring investment return. According to Wendt and Wong: " theor i s t s and pract i t ioners (are) generally agreed on the 'discounted cash f low' method as the correct tool for 9 measuring investment performance. Roulac lends support to th i s view: Although the real estate industry has in the past tended to re ly on s imp l i s t i c "broker 's y i e l d s " to select investment opportunities and evaluate investment re su l t s , s i gn i f i can t progress has been made in the l a s t few years toward adoption of more sophisticated p r o f i t a b i l i t y measures s im i la r to those generally in use in other f inanc ia l and corporate settings.10 This lack of consensus concerning the usage of the various methods of measuring investment performance has hampered ef for t s to increase the sophist icat ion and qua l i ty of investment analysis in real estate. It i s against th i s problem se t t i ng , that the present study was undertaken. 8. For example, see James C T . Mao, Quantitative Analysis of  Financial Decisions, (New York: Macmillan, 1969) and Harry M. Markowitz, Po r t fo l i o Se lect ion; D i ve r s i f i ca t i on of In- vestments , (New York: John Wiley and Sons, Inc., 1959). 9. Paul F. Wendt and Sui N. Wong, "Investment Performance: Common Stocks versus Apartment Houses," Journal of Finance 15, (December 1965), p.633. The discounted cash flow method calculates the rate of return on an investment project by f inding that rate of compound discount which equates the net cash inflow stream att r ibutab le to a project to i t s i n i t i a l cash outlays. 10. Stephen E. Roulac, Modern Real Estate Investment: An I n s t i t u t i on - al Approach, (San Francisco: Property Press, 1976), p.353. 5 1.1 Purpose of the Study The overal l purpose of the study was to gain some pract ica l rather than theoret ical insights into real estate investment analysis by examining the investment decision-making process through the eyes of the real estate investor. Two spec i f i c objectives were seen for the study: (1) to develop an understand-ing of the current pract ice regarding real estate investment analysis with par t i cu la r emphasis on the measures of return and the forms of return consider-ed according to the s ize and type of investor; and (2) to test the predict ive u t i l i t y of the various methods of measuring investment return. The research objectives were accomplished by the fol lowing two steps: 1. Developing and administering a questionnaire survey to a representative sample of real estate investors and i n d u s t r i a l , commercial and investment (I.C.I.) brokers in Canada. The survey attempts to determine the current ' s tate of the a r t ' of real estate investment analysis with respect to components of return, measures of return and rates of return by s ize of investor and class of property. 2. Developing and implementing an ana lyt ica l framework to test the predict ive u t i l i t y over time of various measures of investment return. A case analysis i s developed and various tests performed to analyze the usefulness of each measure. The study has set up the fol lowing hypotheses to te s t : 1. Real estate investors remain r e l a t i v e l y unsophisticated in the i r approach to investment analys i s ; 2. The degree of sophist icat ion in investment analysis i s pos i t i ve l y related to the s i ze and type of investor. 3. Fai lure to use avai lable measures of investment return is due to a lack of understanding on the part of the neglecting investors and d i f f i c u l t i e s a r i s ing from the estimation of the necessary input data for many of the measures. 4. The a b i l i t y of the real estate community to make more accurate and hence, more prof i tab le real estate decisions i s d i r e c t l y related to the use of more sophist icated measures of return. 1.2 Organization of the Study An overview of the unique character i s t i c s of the real estate market i s presented in Chapter 2. Reference i s made to the contributions of other d i s c ip l i ne s to the development of real estate investment analys is. A synthesis of ana lyt ica l methods and models used by real estate investors i s then presented. General c l a s s i f i c a t i on s of methods are defined and case examples are employed to i l l u s t r a t e the use of the more t rad i t i ona l r' methods for evaluating real estate investment proposals. A s im i la r approach i s pursued in the f i na l sections where the discounted cash flow models are discussed. Chapter 3 i s an explanation of the current state of the art with respect to real estate investment analysis in Canada. The results of two questionnaire surveys are presented. The f i r s t deals with a sample of 300 real estate investors (publ ic, pr ivate, i n s t i t u t i ona l and a f f i l i a t e d ) from across Canada. The second concerns a sample of 150 I ndus t r i a l , Commercial, and Investment (I.C.I.) brokers in the Greater Vancouver area. The findings and l im i ta t ions of these surveys are then compared to s im i la r studies done in the U.S. In Chapter 4 an ana lyt ica l framework i s developed in order to examine the predict ive u t i l i t y of various methods of measuring investment return in real estate. Data i s derived from a sample of apartment properties in Metropolitan Toronto. Inputs into the empirical analysis in order to compute the predicted performance are developed from operating h i s tor ies 7 for each property, ex i s t ing market data, and subjective evaluations of future economic and market conditions. The predicted performance and the corresponding investment ranking i s then compared to the actual performance rea l i zed for each property over the holding period. The process and problems of data estimation encountered are also discussed. Furthermore, the actual returns calculated for the property sample are interest ing re l a t i ve to the returns indicated in previous studies. The f i n a l chapter is devoted to analyzing the differences between the status of real estate investment analysis as seen through the surveys and the accuracy of the various methods of measuring investment return as tested through the model. Implications and l imi tat ions of the study are discussed and suggestions for further research are presented. 2.0 REAL ESTATE INVESTMENT ANALYSIS: OUR CURRENT KNOWLEDGE 8 Part ic ipat ion in real estate investment offers substantial returns but i t also enta i l s high exposure to r i sk . Real estate i s known for i t s lack of an extensive body of theory and professional research. Thorne contends that real estate investment analys i s , as i t has developed to date, suffers from two general def i c ienc ies ; Two of the more pers istent facts of the real estate industry are the ubiquitous use of incorrect terminology and so-cal led "simple rules of thumb" or "shortcuts" in capita l investment analysis ... No other economic a c t i v i t y i s so embroiled in esoter ic and complicated measures of f inanc ia l f e a s i b i l i t y as real estate investment.^ Consequently, the real estate market i s not generally well understood and this ignorance often causes inadequately prepared investors to venture into poorly conceived and subsequently f i n anc i a l l y disastrous projects. Models developed to measure the worth of income producing real estate properties can be categorized as ! ( l ) capita l izat ion-of- income, or "va luat ion" models or (2) rate of return models. In the past, inordinate attention has 12 been given capital izat ion-of- income models by appraisal theor i s t s . Recently, the emphasis has sh i f ted to rate of return analysis as witnessed 13 by the current writ ings of Pyhrr, P e l l a t t , Wendt and Cerf, and others. 11. Oakleigh Thorne, "Real Estate Financial Analysis - The State of the A r t , " Appraisal Journal 42, (January, 1974), p.7. For a recent discussion of general rules of thumb in real estate including those dealing with f inanc ia l analys i s , see Ronald E. Get te l , Real Estate Guidelines and  Rules of Thumb, (New York: McGraw - H i l l , 1976). 12. Often appraisal value i s confused with investment value, the former being market value, and the l a t t e r being value unique to aii investor. See Paul F. Wendt, Real Estate Appraisal : Review and Outlook, (Athens: Univers ity of Georgia Press, 1974), pp. 16-35. 13 Stephen A. Pyhrr, "A Computer Simulation Model to Measure Risk in Real Estate Investment, "Real Estate Appraiser, (May-June, 1973) pp. 13-31, P e l l a t t , "The Analysis of Real Estate Under Uncertainty", pp. 459-471, and Paul F. Wendt and Alan Cerf, Real Estate Investment  Analysis and Taxation, (New York: McGraw - H i l l , 1969). 9 Both the r e l a t i v e l y unsophisticated " t r a d i t i o n a l " or " f i r s t generation" and the more sophist icated discounted cash flow or "second generation" methods of analysis w i l l be examined using both approaches l a te r in th is chapter. However, f i r s t i t would be appropriate to discuss the unique character i s t ics of the real estate market, the investment decis ion-making process in real estate, and the contributions made by other d i sc ip l ines to th is process. 2.1 Character ist ics of the Real Estate Market It i s often argued that the unique character i s t i c s of real property cause real estate markets to somehow operate d i f f e ren t l y from the manner in which markets for other commodities operate. Certainly real estate has i t s unique character i s t ic s which influence the real estate market. Nevertheless, the economic pr inc ip les which are at work in these markets are the same pr inc ip les which apply in other commodity markets. The study of the economics of real estate is not a study of an anomaly of economics but rather the operation of the pr inc ip les of economics in a spec i f i c market. Business decisions in real estate are based on the same pr inc ip les and involve many of the same techniques as other business decisions but in many aspects, real estate decisions are d i f fe rent . This d i s t i nc t i on arises from the character i s t ic s of real estate i t s e l f . Real estate is a highly fragmented commodity because every indiv idual 14 property i s unique. Consequently, there are no standardized, ident ica l units in real estate s im i l a r to consumer goods or common stock shares. This makes comparison for the purposes of se lect ion among a l ternat ive 14. See R. U. R a t c l i f f , Urban Land Economics (Toronto: McGraw - H i l l , 1949), pp. 281-282. investments pa r t i cu l a r l y complex and d i f f i c u l t . Therefore, i t t y p i c a l l y requires more judgment on the part of decision-makers than is true of 15 other types of business decisions. Real estate is also a very durable, long-term asset. As a r e su l t , decisions to invest in real estate frequently necessitate longer-term commitments than are involved in other types of business or consumer decisions. In such circumstances, committing resources into an uncertain future requires an investor to re ly heavily on long-term forecasts of changes in the real estate market. Another unique aspect of real estate i s i t s immobility. Due to i t s f ixed l ocat ion , real estate i s very much influenced by i t s environ-ment: phys ica l , economic, legal (governmental), and s o c i a l . Each parcel of real property i s vulnerable to environmental factors outside i t s borders. The immobility of real estate prevents s h i f t i ng i t to a better market. Changes in nat iona l , prov inc ia l or loca l economic or l e g i s l a t i v e conditions change the context in which market part ic ipants operate. Thus, a great deal of importance must be placed on market analysis and market forecasts in making decisions about the purchase., use or development of real estate. The market environment i s i t s e l f unique. It is r e l a t i v e l y uninformed, and market data i s often d i f f i c u l t to c o l l e c t . This i s due to the lack of any one centra l ized market, or subset of such markets, l i k e the stock market. Consequently, th i s provides an opportunity for the investor who possesses superior management s k i l l s and keeps well-informed of the market to outperform an investor lacking these advantages. F i na l l y , the r e l a t i v e l y large amount of f inancing required for real estate transactions (due in part to the long payout period for benefits) 15. For a good discussion of the d i f f e ren t i a t i ng features of real estate decisions see Arthur M. Weimer in "Real Estate Decisions are D i f fe rent " , Harvard Business Review 44, (November-December, 1966). 11 makes real estate financing decisions (especia l ly the debt-equity ra t i o ) c r i t i c a l . Uncertainty ex ists in real estate because investors are unable to make perfect forecasts. I t may be defined as the chance or probab i l i ty that the investor w i l l not receive his expected or required rate of return on investment. For the purposes of th i s study, i t i s assumed that un-certainty is i m p l i c i t l y ref lected in the investor ' s required return necessary to induce investment ( i . e . the investor has added a premium su f f i c i en t to compensate him for the perceived uncertainty). 2.2 Investment Decisions in Real Estate Any simulation model of a real world system must incorporate the properties of that real world system in order to produce v a l i d conclusions from which accurate predictions can be made. To date, a main source of the i n a b i l i t y of the various models used to predict investment performance accurately l i e s in the i r i n a b i l i t y to incorporate the varied goals and motivations of a pa r t i cu la r investor in real e s t a t e . ^ These goals may range from a desire for a short-run p r o f i t and/or tax shelter to that of a long-run maximization of return on investment. For a corporation, the goal may be the maximization of a pos i t ive e f fect on earnings per 18 share. Important in the context of th i s study, management must have the time and expertise to decide when to s e l l , manage, removate, bu i ld 16. In contrast to uncertainty, r i sk may be defined as the known chance or probab i l i ty that an investor w i l l not receive his expected or required return on investment. 17. Ricks documents the unique investment goals of three types of i n s t i t u t i ona l real estate investors: insurance companies, colleges and un i ve r s i t i e s , and corporate pension funds. R.„Bruce Ricks, Recent Trends in In s t i tu t iona l Real Estate Investment, (Berekley, University of Ca l i f o r n i a , 1964). 18. Cooper presents over twelve d i f fe rent objectives prevalent in real estate investment analys is. James R. Cooper, Real Estate Investment  Analys i s , (Lexington, Mass.: D.C. Heath & Co., 1974), pp. 4-10. 12 19 and knowledge of ana lyt ica l methods in real estate investment analysis. Investment decisions in real estate can range from on-the-spot snap judgments founded on a hunch to detai led ana ly t i ca l evaluation based on months of background research. Figure 2-1 is a schematic representation of the ana ly t i ca l process for a typ ica l investment decis ion. The f i r s t phase in the investment decision consists of a projection of the cash flow and a f ter - tax income. This requires research to develop complete f inanc ia l projections over the ent i re investment cyc le. Forecasts are made of revenues, vacancies, operating expenses, and possible future sales pr ices. Mortgage terms and depreciation schedules are ca lcu lated. The second phase converts the project income statement into a measure or measures of p r o f i t a b i l i t y along with an estimate of the r i sks involved. It i s to th i s phase of the investment decision which th i s study addresses i t s e l f . There are a wide range of techniques currently employed to measure the p r o f i t a b i l i t y of real estate investment from the unsophisticated techniques based on f i r s t year, before-tax estimates to the highly sophist icated techniques using computer simulation models to ca lcu late the discounted cash flows from an investment given a var iety of outcomes and the i r associated p robab i l i t i e s . The f i n a l phase i s the actual investment decision or in many cases, a 20 decision to replan or renegotiate. 19. Roulac contends that since real estate markets are less than e f f i c i e n t r e l a t i ve to for instance, the stock market, profess ional ly managed real estate por t fo l io s can out-perform those that are "randomly" or nonprofessionally managed. Stephen E. Roulac, "Can real estate outperform common stocks?" Journal of Po r t fo l i o Management, (Winter 1976), pp. 32-33 20. Sherman Maisel and Stephen E. Roulac, Real Estate Investment  and Finance, (Englewood Wood C l i f f s , N.J.: McGraw-Hill, 1976), pp. 335-337. 13 Figure 2-1: The Investment Decision Phase 1  Investment cycle  Origination Operations Termination  Projections of The investment Scheduled gross revenue Net sales pr ice cash flow and Tax structure Ef fect ive revenues Taxable income after - tax income Financing Operating expenses Tax on sales income Total investment Operating income Use of funds After - tax invest-ment Debt Service Loans Sources of funds Tax effects Estimated return schedule Cash flow Cash flow Cash flow Tax e f fect Tax e f fect Tax e f f ec t Total current Total current benefit Total.current benefit benefit Phase II  Measuring p r o f i t a b i l i t y and Calculat ion of p r o f i t a b i l i t y and rate-rates of return of-return estimates Estimate of r i sks and s e n s i t i v i t y Phase III The investment decision Decision to purchase, negotiate, or r e jec t : Adjust costs Increase potential income A l te r f inancing Reallocate r i sks Source: Stephen Roulac and Sherman Maisel, Real Estate Investment and  Finance, (Englewood C l i f f s , N.J.: McGraw-Hill, 1976), p.336 Having defined his objectives and the investment decision process, the investor i s now in a pos it ion to measure the return ava i lable from invest-ment in a part icular-property. Real estate propert ies, l i k e other invest-ments, are usually characterized by a commitment of funds at the beginning 14, of the investment, then a series of benefits from operations over time and, hopeful ly, a gain from sale at the termination of the investment. However, real estate returns are r e l a t i v e l y unique due to the importance of both leverage, tax considerations, and the great var iety of possible changes in market value. There are three basic components of real estate returns: cash f low, tax e f f e c t , and changes in equity value. Cash flow or "cash ava i lable for d i s t r i bu t i on " i s tota l revenue from operations minus expenses including debt service and reserves. Tax e f fec t or tax benefits are due to the fact that taxable income because of depreciation deductions, i s often less than cash flow. It i s calculated by mult ip ly ing the taxable income by the spec i f i c e f fect i ve tax rate. Changes in equity value ar i se from increases in the value of the property over time as well as amortization of the mortgage debt. They should be thought of as deferred, cond i t iona l , and tax-contingent returns. I t i s important to i dent i f y the considerations that should be incorporated into an accurate and r e l i ab l e measure of return. These include: 1. A l l cash flows from operations over the ent i re l i f e of the investment. 2. The amount and timing of a l l tax e f f ec t s , both shelter and tax l i a b i l i t y , over the ent i re l i f e of the investment. 3. The cash proceeds (or obiigations) from sale a f te r giving e f fect to a l l tax considerations including cap i ta l gains tax and ordinary income tax on the recapture of cap i ta l cost allowances. 4. Recognition of the time value of money. Simply, a do l l a r received today has more value than a do l l a r to be received tomorrow. 21. Roulac, Modern Real Estate Investment: An In s t i tu t iona l Approach, p. 354 5. Expression of the return as an index figures that permits comparison of d i f fe rent projects. 2.3 Contributions from Other D i sc ip l ines Real estate, as mentioned previously, i s known for i t s lack of an extensive body of theory and professional research. It borrows much of i t s theory, methods and techniques from several other d i sc ip l ines including 22 investments, accounting and finance. Real estate investment decisions are a spec i f i c type of capita l budget-ing problem. Capital a l locat ion to an investment in land and buildings must be evaluated by considering the benefits or returns expected over the l i f e of the project. Any theory of optimal investment decisions i s founded on the existence 23 of an objective function which the f i rm maximizes. Current f inanc ia l theory generally assumes that a f irm should maximize i t s value to i t s share-holders. Value i s represented by the market price of i t s common shares, which, in turn, i s a r e f l ec t i on of the f i rm ' s investment, f inancing, and dividend decisions. There i s a growing body of l i t e r a t u r e in finance and with recent extensions to real estate that explains how value i s determined 24 under conditions of uncertainty. For example, in a perfect capita l market with homogeneous investor expectations, the r i s k of an investment in a po r t fo l i o i s measured by the weighted average of the variance of i t s return 22. Wendt, Real Estate Appraisal : Review and Outlook, Chapter 2. 23. James C. Van Home, Financial Management and Po l i cy , (Englewood C l i f f s , New Jersey: McGraw - H i l l , 1974), p.6. 24. For recent extensions of finance theory to real estate see Harris Friedman, "A Po r t f o l i o Approach to Real Estate" , Journal of Financial  and Quantitative Analysis (March, 1971), pp. 861-874, and Steven D. Kapplin, "F inancia l Theory and The Valuation of Real Estate Under Conditions of Risk," Real Estate Appraiser, (Sept. - Oct., 1976), pp. 28-37. and the covariances between i t s return and other returns. The market pr ice of an investment, then, i s a function of i t s expected earnings, the rate of i n te re s t , the pr ice of r i s k , and r i sk measured in th i s way. Real estate decisions are frequently of a capita l rat ioning nature. That i s , there i s a budget c e i l i n g , or constra int, on the amount of funds that can be invested during a spec i f i c period of time. The investor must select the "best" mix of investments which can be obtained with l im i ted c a p i t a l . On the subject of investment decis ions, an important contr ibution is the well-known a r t i c l e by Lorie and Savage which discusses three problems that ar i se when a f irm al locates i t s l i q u i d resources among:- competing invest-25 ment opportunit ies. Their problem concerns a f i rm confronted with a var iety of possible investment projects and a f ixed capita l budget. The cash flows associated with each project are known or estimated and assumed to be independent of the investment decisions. These assumptions make i t possible to compute for each project a net present value defined as the sum of i t s stream of cash receipts and outlays discounted by the cost of c a p i t a l . The objective i s then to se lect from among the projects with pos i t ive present values the pa r t i cu la r projects which lead to the highest present value for the f i rm. Nevertheless, although theor ists have advocated the use of present value methods of measuring return, many businesses s t i l l use the payback period and/or the accounting p r o f i t c r i t e r i o n . 25. J . H. Lorie and L. J . Savage, "Three Problems in Capital Rationing," Journal of Business, 28, (October, 1955), pp. 229-239. 26. National Association of Accountants, Financial Analysis to Guide Capital  Expenditure Decisions, Research Report 43 (New York, 1966), p. 66. The payback period may be defined as the number of years required to return the i n i t i a l investment. The accounting p r o f i t or average rate of return is the ra t i o for the holding period of the average book earnings to average net investment including depreciation. 1Z 2.4 Methods of Measuring Investment Return in Real Estate Given the nature of the real estate market, a set of spec i f i c object ives, and the nature of real estate returns, i t now becomes the task of the potential investor to a l locate his capita l to possible investment proposals. One of the most c r i t i c a l considerations in the analysis of real estate investments i s the measure of investment d e s i r a b i l i t y or return. Numerous methods have been used in connection with real estate investment analysis with the in terest ing resu l t that d i f fe rent indicators of r e l a t i ve investment d e s i r a b i l i t y were produced depending upon the method of measurement applied. It was noted pre-viously that ana lyt ica l models for measuring the return from income producing real properties can be categorized a s : ( l ) capital izat ion-of- income or "value" 27 models or (2) rate of return models. To s t a r t , " f i r s t generation" or " t r a d i t i o n a l " methods of incorporating these two approaches are discussed. These methods are mathematically unsophisticated, contain few f inanc ia l v a r i -ables, and ignore the time value of money. In the l a t t e r parts of th i s section "second generation" on discounted cash flow models are studied. These methods which can incorporate many f inanc ia l variables and changes in these var iables, are widely used to analyze investments in the f i e l d s of investment finance and secur ity valuat ion, and are gaining increasing attention in real estate 1 i terature. 27. The capital izat ion-of- income or value approach evaluates income-producing real estate investments by f i r s t estimating the investor ' s required rate of return ( cap i ta l i za t i on rate) and then cap i t a l i z i n g net income to f ind value. Investment value may be defined as the present worth to the investor of the future net returns cap i ta l i zed at a rate acceptable to him in l i g h t of his evaluation of the investment character i s t i c s of the property. In contrast, the rate of return approach allows the expected rate of return to be computed before the required rate of return. Investment is undertaken i f the expected rate of return i s equal to or greater than the required rate of return. 18 2.4.T: F i r s t Generation Methods of Analysis Capitalization-of-Income (Value) Approach Modern economic value theory as introduced by Marshall and Fisher holds that a capita l asset such as real estate derives i t s value from some 28 future stream of benefits. Babcock, often regarded as the founder of modern appraisal theory, developed further the thesis that value represents the present worth of future returns from property through the reject ion of cost as evidence of value and his emphasis upon the capita l izat ion-of- income method. 2^ Real estate analysts have frequently been charged with f a i l i n g to cor rect ly apply the concept of investment value. They have sometimes confused this concept with "market p r i c e " , "market value" and investment cost " . Notable real estate researchers, such as Wendt and R a t c l i f f , have 30 argued that investment value does not correspond to market value. The capita l izat ion-of- income approach requires the estimation of both investment value and investment cost. The general decision rule f i r s t  enumerated in investment finance, is that an investment should only be  undertaken i f the investment value i s equal to or greater than the invest-ment cost."^ 28. Wendt and Cerf, Real Estate Investment Analysis and Taxation, pp. 17-23. 29. Ibid. 30. Wendt and Cerf, and Richard U. R a t c l i f f , "Cap i ta l i zed Income i s not Market Value", The Appraisal Journal 38, (January, 1968), pp. 33-40. Investment value may be contrasted to investment cost, the replace-ment cost of a property, and market value, the probable s e l l i n g price of a property i f exposed for sale on the open market - both the l a t t e r terms common in appraisal l i t e r a t u r e . 31. Van Home, Financial Management and Po l i cy , pp. 60-61 19 Real estate l i t e r a t u r e attests to the importance of the fol lowing general model in the determination of investment value: V=j^  where: V = investment value I = net income before debt service and depreciation R = the cap i t a l i z a t i on rate (required rate to induce investment) Gibbons emphasizes the fundamental importance of th i s general model in the capital izat ion-of- income approach: The income, or economic approach to value is a very simple, straightforward procedure - not a complicated, d i f f i c u l t s i tuat ion as so often i t has been portrayed. It bo i l s down to a few essent ia l s . F i r s t , there must be a ca re fu l , r e a l i s t i c estimate of a property 's gross income potent i a l . Then, i t i s necessary to accurately forecast the burden of expense entai led in operating. Resulting therefrom is a projection of net income which i s reasonably to be expected. At th i s po int, the appraiser must research the real estate market to ascertain the rate of return which prudent investment cap i ta l i s demanding to be attracted to the purchase of such an ant ic ipated net income stream with a l l i t s attendent r i s k s . Upon completion of these steps, valuation i s a simple mathematical ca l cu la t i on . The general formula could have been wr i t ten : 1. R = net income before in teres t payments and depreciation allowances, f i r s t year  purchase pr ice of the property 2. cash down payment = net a f te r tax cash f low, f i r s t year, investor ' s required rate of return 3. purchase pr ice = net income before depreciation less recapture provision on the bu i ld ing , f i r s t year the rate of return. 4. the cash flow a f te r retirement of any mortgage debt (pr inc ipal and interest as a percentage of equity or tota l cap i ta l invested). The variat ions in the model depend only on the pa r t i cu la r objectives of the investor. Two of the more popular var iat ions are (1) the gross rent m u l t i -p l i e r (GRM) which i s equivalent to purchase pr ice divided by gross income 32. James E. Gibbons, Mortgage-Equity Cap i t a l i z a t i on : Ellwood Method, (Chicago: American Ins t i tute of Real Estate Appraisers, 1967), p. 3. 20 f i r s t year,.and (2) the net rent mu l t i p l i e r (NRM) which i s purchase pr ice divided by net income f i r s t year before in teres t and depreciation. While the general investment value model i s conceptually simple, i t s use is complicated due t o : ( l ) the many mathematical expressions developed for use with the general model, (2) the many variables which must be estim-ated including gross income, expenses, and cap i t a l i z a t i on rates, and (3) the 33 problems of forecasting income and expenses in the future. Due to the problems facing the analyst attempting to use the general investment value model, modifications focusing on two-basic problems were made: (1) how to d ist inguish between land, bu i ld ing , and tota l property returns and values, and (2) how to provide for capita l recovery or recapture of a depreciating asset over the remaining economic l i f e of the property. B r i e f l y reviewing the f i r s t problem, the general model V = I/R makes no e x p l i c i t d i s t i n c t i on between the land and bui lding components of real property. However numerous authors, led by Babcock, have argued that the returns from land and bui lding should be treated separately and cap i ta l i zed 34 at d i f fe rent rates to r e f l e c t the varying r i sk levels associated with each. This led to his exposition of the "res idual techniques" which separated land and bui ld ing incomes and cap i ta l i zed them. The three techniques: (1) land re s idua l , (2) bui lding re s idua l , and (3) property re s idua l , and his methods of estimating cap i t a l i z a t i on rates and depreciation provided the 35 basic structure upon which most modern appraisal theory rests. 33. Sanders A. Kahn, Fred E. Case, and A l f red Schimmel, Real Estate Appraisal  and Investment, (New York: Ronald Press, 1963), pp. 79-80. 34. Frederick M. Babcock, The Valuation of Real Estate, (New York: McGraw -H i l l , 1932). 35. For a complete analysis of these techniques see the American Ins t i tute of Real Estate Appraisers, The Appraisal of Real Estate, (Chicago: 1967), pp. 287-295; and Kahn, Case and Schimmel, pp. 146-153. 21 The second basic problem resu l t ing in addit ional differences between value models is the appropriate treatment of property recapture. Since buildings are "wasting" assets, some provision must be made for recapturing the capita l o r i g i n a l l y invested in these structures. Cooper suggests four modern methods that have been developed for dealing with property recapture over an assumed useful of the property: (1) d i rec t market c a p i t a l i z a t i o n , (2) s t r a i gh t l i ne c a p i t a l i z a t i o n , (3) sinking fund (or Hoskold) C a p i t a l i -ze zat ion, and (4) annuity (or Inwood)capitalization. Each of the four methods attempts to account for the complete recovery of the capita l through 37 adjustments to the cap i t a l i z a t i on rate (R), Discounted cash flow methods of measuring return and most computerized real estate investment models as w i l l be shown l a t e r , stem from the fourth type of c ap i t a l i z a t i on method. Uncertainty factors are often incorporated into the f i r s t generation 38 "value" methods by adjusting the cap i t a l i z a t i on rates. While the methods and techniques for incorporating uncertainty are of varying complexity, they a l l require the analyst to make subjective judgments. Since the judg-ments may vary from analyst to analyst and from time to time, the techniques as used are not very accurate. Other factors , besides uncer ta in ty "an^^ca^ptu re , " ^ 6 _g 0 1 s l de red byanalysts estimating cap i t a l i z a t i on rates. These include;(1) an allowance for manage-ment, and (2) d i f fe rent debt-equity combinations to finance the property. To accommodate the d i f fe rent factors considered to influence cap i t a l i z a t i on rates, the summation, market comparison, and band of investment methods 36. Cooper, Real Estate Investment Analys i s , pp. 14-15 37. For deta i led descr ipt ive treatments of these methods, see American Inst i tute of Real Estate Appraisers, The Appraisal of Real Estate, pp. 278-317; Wendt and Cerf, Real Estate Investment Analysis and  Taxation, pp. 178-185; and Kahn, Case and Schimmel, Real Estate  Appraisal and Investment, pp. 134-135. 38. For example, see Wendt and Cerf, Real Estate Investment Analysis and  Taxation, pp. 150-162. have been developed. J J Rate of Return Approach An a l ternat ive approach to the valuation of income-producing real estate investments is through rate of return analys is. Here the s t a t i c type of valuation problem of the capital izat ion-of- income models in which projects could not be ranked without estimating f i r s t the required rate of return ( cap i t a l i z a t i on rate) and then c ap i t a l i z i n g net income to f ind value, i s avoided. A more pract ica l decision-making technique is provided so that investment a lternat ives can be compared and ranked without f i r s t estimating the required rate or return. There are three steps in the rate of return approach: (1) to estimate the expected rate of return, (2) to f ind the required rate of return, and (3) to compare the expected and required rates of return. The general dec i - sion rule i s that i f the expected rate of return i s equal to or greater than  the required rate of return, the proposal should be accepted. The expected rate of return has t r a d i t i o n a l l y received more attention in real estate l i t e r a tu re than the required rate of return. Three " ru le of thumb" models for measuring the expected rate of return are: 1. rate of return = net operating income minus debt serv ice, f i r s t year purchase pr ice Commonly known as "return on investment", th i s i s a basic perfor-mance measure of tota l capi ta l invested, thus el iminating the e f fec t s , i f any, of leverage. 2. Rate of return = net operating income minus debt serv ice, f i r s t year equity Frequently referred to as the "equity dividend rate" or "broker 's y i e l d " , such a formulation introduces the concept of leverage and a "cash-on-cash" return. I f the cash flow considered i s a f t e r -tax, the model is known as the " a f t e r tax return on equity " . 39. See for instance, Will iam N. Kinnard, Income Property Valuat ion, (Lexington, Mass.: D. C. Heath, 1971). 23 3. rate of return = net operating income minus interest payments + pr inc ipa l repayment, f i r s t year  equity On an af ter - tax basis, th is i s ca l led the "gross y i e l d on equity " . To i l l u s t r a t e the impact of a l ternat ive def in i t ions of " return" consider the following example. For an apartment bui ld ing requir ing a $200,000 equity investment, there was a $14,000 cash flow af ter operating expenses, $8,000 of tax benefit (due to depreciation deductions, taxable income i s greater than cash flow by $16,000 resu l t ing in a taxable loss of $8,000 to an investor in a 50 percent tax bracket), $7,000 of equity build-up from pr inc ip le repayment of the mortgage, and $8,000 of appreciation ( re f lec t ing a one percent increase in value on the i n i t i a l $800,000 purchase p r i ce ) . TABLE 2-1: IMPACT OF ALTERNATIVE DEFINITIONS 0F"RETURN" Dollar Return Return on $200,000 Equity Investment (1) Cash flow 14,000 7% (2) Cash flow + tax benefit 14,000 11% (3) Cash flow + tax benefit + 14,000 + 8,000 equity buildup 7,000 14.5% (4) Cash flow + tax e f fect + 14,000 + 8,000 + equity buildup + appreciation 7,000 + 8,000 18.5% Obviously, the de f i n i t i on of " return" has a s i gn i f i can t impact on what the return on investment turns out to be. The required rate of return measurement i s not discussed extensively in real estate l i t e r a t u r e . This neglect i s surpr is ing in l i g h t of viewpoints such as those of Wendt and Cerf who contend that "the rate of return provides 40 the pr inc ipa l c r i t e r i on for most investment decis ions". Possible measurement methods s im i la r to those used i n the capita l izat ion-of- income approach include summation, band of investment, or comparative approaches for measuring 40. Wendt and Cerf, Real Estate Investment Analysis and Taxation, p. 13. 24 " c ap i t a l i z a t i on rates " . Uncertainty i s e x p l i c i t l y considered in the models 41 presented as a premium in the required rate of return. Figure 2-2 summarizes f i r s t generation methods of measuring investment return in real estate. In summary, i t i s apparent that f i r s t generation or t rad i t i ona l c a p i t a l i -zation-of-income and rate of return models have received substantial attention in real estate l i t e r a t u r e . However, they do not sa t i s f y the c r i t e r i a for an accurate and r e l i ab l e measure of investment performance enumerated previously. Figure 2-2 FIRST GENERATION METHODS FOR MEASURING INVESTMENT RETURN IN REAL ESTATE Method Gross Rent Mu l t i p l i e r Net Rent Mu l t i p l i e r Return on Investment Payback Period Average Annual Return on Equity Equity Dividend Rate Formula GRM=PP Gl NRM=PJP NOI R0I=N0I Input Parameters PP = purchase pr ice Gl = gross income NOI = net operating income PP # of years to return i n i t i a l investment AROE = NOI Equity EDR = CTO Equity After - tax Return on Equity ATROE = ATCF Equity Gross Y ie ld on Equity GROE = ATCF+Principal Repayment DS = debt service payments CT0=cash throwoff (NOI-DS) ATCF=after-tax cash flow (NOI-DS+taxes) Equity F i r s t , the time value of the cash flows i s ignored. This can be c r i t i c a l to the success of an investment since cash flows received at d i f fe rent times in the future have varying value to the investor today, depending on the timing of the cash flows. Second, no consideration i s given to uneven cash flows. Smaller cash flows received today are averaged with larger cash flows which 41. For an example of a t rad i t i ona l analysis of a proposed investment see Will iam J . Beaton, Real Estate Investment, (Englewood C l i f f s , N.J.: P rent i ce -Ha l l , 1971), pp. 230-237. 25 may not be received un t i l l a te in the holding period. Third, the a f ter - tax value of the equity reversion received at the sale of the property i s not considered. S i gn i f i cant tax impl ications come with this reversion and i t can a f fect the cash flow in each period. Further, substantial annual tax benefits from depreciation and interest are often not incorporated into the models thereby reducing the i r capacity to r e f l e c t investors ' objectives. I t has been shown that such tax shelters may comprise a large part of the rate of 42 return. A f i f t h l im i ta t i on of several of the t rad i t i ona l methods i s that f inancing and refinancing are not considered. Thus, th i s fact ignores the tremendous impact which leverage may have on the p r o f i t a b i l i t y of an invest-ment. Yet another c r i t i c a l l im i t a t i on of these models i s the lack of attention given to d i f fe rent possible holding periods. This has been 43 demonstrated to have a s i gn i f i c an t impact on p r o f i t a b i l i t y . F i na l l y , other factors ignored in many f i r s t generation models include the uncertainty associated with the rates of return, transaction costs, and i n f l a t i o n . Because f i r s t generation or t rad i t i ona l rate of return and value models have excluded from consideration many important var iables, the i r use as tools in the decision-making process of real estate investment i s l im i ted . Second generation or discounted cash flow models presented in the fol lowing sections attempt to incorporate some of these important variables. 2<;4.2 Second Generation Models of Analysis The many shortcomings of f i r s t generation or t rad i t i ona l methods of analyzing real estate investments have promoted increased usage during the l a s t decade of "second generation" or discounted cash flow methods of analys is. The discounted cash flow method may be defined as that method which: 42. See Paul F. Wendt and Sui N. Wong, "Investment Performance: . Common Stock Versus Apartment Houses," The Appraisal Journal 20, (December, 1965) 43. See R. Bruce Ricks, "Imputed Equity Returns on Real Estate Financed with L i fe Insurance Company Loans," Journal of Finance 24, (December, 1969), pp. 901 - 937. 25 . . . calculates the rate of return on an investment project by f inding that rate of compound interest discount which equates the net-cash-inflow stream att r ibutab le to a project to i t s i n i t i a l cash outlays. The theory behind th i s method is that the economic worth of a cap i ta l good equals the present value of i t s expected income stream.44 This method permits each investment a l ternat ive to be evaluated as to the magnitude and timing of expected future cash flows r e l a t i ve to the i n i t i a l cost. Investment proposals may then be compared by ranking each by rates 45 of return. Also, many variables not considered in f i r s t generation models have been incorporated in discounted cash flow models. These have been adapted to computer systems to perform the necessary computations quickly and e f f i c i e n t l y . The t rans i t i on from f i r s t generation models to discounted cash flow models was provided by L. W. Ellwood, who developed mortgage-equity cap i ta l -46 i za t i on and equity y i e l d models. Mortgage-Equity Cap i ta l i za t ion and Equity Y ie ld Models Ellwood, while maintaining the t rand i t iona l format for value determi-nat ion, V=1/;R, set forth a mathematical procedure for deriving overal l c ap i t a l i z a t i on rates, given selected mortgage terms, and assumed equity rates and depreciation or appreciat ion. Ellwood's methods measure invest-ment value and "equity y i e l d " and assume that the primary c r i t e r i a of an 47 investor is to obtain a certa in y i e l d on his equity. 44. Cooper, Real Estate Investment Analys is , p. 15. For a general discussion of the discounted cash flow approach, see Sam R. Goodman, S impl i f ied Use  of the Discounted Cash Flow Method of Evaluation, (Englewood C l i f f s , N.J.: P rent i ce -Ha l l , 1972. 45. Ibid. 46. L. W. Ellwood, Ellwood Tables fo r Real Estate Appraising and Financing, (Chicago: American Inst i tute of Real Estate Appraisers, 1970). 47. The equity y i e l d rate i s the discount rate which equates the present value of a l l expected cash inflows with the present value of a l l expected cash outflows ( internal rate of return on equity c ap i t a l ) . 11 The essence of the Ellwood formula i s to estimate the cap i t a l i z a t i on rate (R) assuming a constant net operating income. Income i s then cap i ta l i zed using the general formula V = I/R to f i nd investment value. The equity y i e l d required on a project can be calculated by means of a minor adjustment to the capital izat ion-of- income method. Redefining V as the "investment cost" and with a l l variables except the equity y i e l d rate given, the l a t t e r can be defined as an expected value and calculated by t r i a l A 48 and error. Important shortcomings of these models include the l im i t a t i on to a constant cash flow, and a lack of recognition of the tax implications on cash flows and reversion. This has brought researchers back to discounted cash flow models which are more f l e x i b l e and contain fewer l im i t i n g assumptions than the mortgage equity models. Present Value Models Present value models attempt to overcome several of the shortcomings of the Ellwood models by incorporating the fo l lowing: 1. Cash flow defined as a f ter - tax cash throwoff. 2. Consideration of returns in s pec i f i c time periods. 3. Reliance on discounting reversion at a simple compound interest rate rather than at s t a t i c annuity factors. The general present value model may be defined as fo l lows: PVF = ATCF, ATCF ? ATCF SP-GT-UM t L_ + + + H + 1 2 ••* n n (l+y) (i+y) (i+y) (l+y) where: PV^ = present value of equity returns ATCF,, AtCF^, ...ATCF = annual a f ter - tax cash flow from the investment over the holding period (n) y = investor ' s required rate of return on equity SP = sales pr ice at end of holding period (n) GT = capita l gains tax at t = n UM = unpaid mortgage at t = n 48. Kinnard, Income Property Valuat ion, p.67. This formula adjusts a l l f lows, both pos i t ive and negative, to the present value, using the investor ' s required rate of return (y). The general dec i - sion rule i s that i f the present value of the cash inflows exceeds the 49 present value of the cash outflows, the project w i l l be accepted. While the present value approach recognizes the time value of money, i t does not f a c i l i t a t e comparison of one investment proposal with another, since each investment has a unique value. The p r o f i t a b i l i t y index (PI) operational izes the present value model by expressing a l l p r o f i t a b i l i t y in terms of a common denominator. I t i s the ra t io of the present value of a l l pos i t ive flows divided by the present value of a l l negative flows at the investor ' s required rate of return. I f the PI = Present Value of Cash Inflows Present Value of Cash Outflows p r o f i t a b i l i t y index i s greater than 1, the project is accepted. Ranking investment proposals i s thus made poss ible 1 ;ar id~W~optima^ selected. For example, the present value of the fol lowing cash flow stream at a required rate of return of 10 percent i s $1,917. n $ 10% Discount Factor Present Value 0 ($1 ,617) 1.000 ($1,617) 1 100 .909 91 2 400 .826 330 3 900 .751 676 4 1200 .683 820 $1,917 49 For examples of computer adapted models, see: James A. 6raaskamp,_ "A Pract ica l Computer Service for the Income Approach", The Appraisal Journal 37, (January 1969), pp. 50-57; Will iam M. Shenkel, "Cash Flow Analys is: An Appl icat ion of Conversational Computer Programming", Journal of Property Management, (July/August 1969), pp. 165 - 172; Paul B. F a r r e l l , J r . , "Computer-Aided Financial Risk S imulat ion", The Appraisal Journal 37, (January 1969), pp. 58 - 73. 29 Thus, the p r o f i t a b i l i t y index i s : PI = 1,917 1,617 PI = 1.2 The primary weakness of the present value model i s the determination of the required rate of return (Y). R a t c l i f f notes that i t may be some average market y i e l d determined by analyzing "comparables" or ta lk ing to investors, or i t may be a "personal" c ap i t a l i z a t i on rate which represents an investor ' s "own evaluation of uncertainty and l i q u i d i t y and his personal f inanc ia l 50 circumstances. R a t c l i f f and Schwab have argued for the el iminat ion of uncertainty from the cap i t a l i z a t i on rate through the use of u t i l i t y theory 51 where cash flows are adjusted for perceived uncertainty. Discounted Cash Flow Rate of Return Models An investor is concerned w i th , in addition to the complete return of his invested c a p i t a l , an adequate return orchis investment. This " rate of return" from a real estate investment i s an important measure of investment d e s i r a b i l i t y because i t provides a basis for se lect ing among a l ternat ive investments. The internal rate of return (IRR) method may be defined as that rate of discount at which the present worth of future cash inflows 52 i s exactly equal to the present value of a l l expected cash outflows. The IRR method of ca lcu lat ing the rate of return of an investment i s that of solving the fol lowing equation for " r " : 50. Richard U. R a t c l i f f , Real Estate Analys is , (New York: McGraw-Hill, 1961), p. 135. 51. Richard U. R a t c l i f f and Bernhard Schwab, "Contemporary Decision Theory and Real Estate Investment", Appraisal Journal 38, ( A p r i l , 1970), pp. 165 - 187. This i s s im i la r to the certa inty - equivalent approach employed in cap i ta l budgeting - see Van Home, p. 138. 52. Essent ia l ly , the IRR i s a var ia t ion of the p r o f i t a b i l i t y index (PI). The PI i s f i xed at 1 and the unknown i s the discount rate required to make the present value of these two flows equal to each other. 30 Reversion c n r 0 . - I . - A T - V SP„ - GT - IW t _ r - T. Z Z Z n  0 = £ (l+r)1 ( l + r ) n where: E = equity at time of investment Oj, = net operating income in period t I t = interest on mortgage in period t = amortization of mortgage in period t T t = income tax in period t SP n = s e l l i n g pr ice at end of holding period (n) GT = capita l gains tax payable upon sale at t=n UM = unpaid mortgage at t=n r = internal rate of return. It represents a measure of performance of invested c a p i t a l . Such a ca lcu-l a t i on re f lec t s the effects of leverage and tax on the return of return. The general decision rule i s to accept only those investment proposals with 53 an IRR greater than, or equal to, a required rate of return. Rate of return models may also be employed to calculate rates of return on tota l capi ta l invested. The model would then be wr i t ten : P,_ C F 1 + C F 2 + C F 3 , + C F n + S P n 0 ( 1+r ) 1 ( 1 + r ) 2 ( 1 + r ) 3 ( l + r ) n ( l + r ) n where: P Q = tota l investment cost CF n , CF o s , CF = annual cash flows before debt serv ice. 1 2 ' n This rate of return measure i s widely recognized by f inanc ia l analysts in the f i e l d of corporation finance as a measure of basic product iv i ty of invested cap i t a l . I t i s not influenced by the type or cost of f inancing. 53. This minimum acceptable rate of return i s also known as a cutoff or hurdle rate. 31 However, due to the impact of leverage on real estate investment, i t would seem log ica l for an investor to calculate the rate of return on his 54 equity investment rather than on the tota l capita l invested. Wendt and Cerf state further that i t " i s generally recognized that the a f ter - tax 55 return on equity is the most s i gn i f i can t measure of real estate returns". I t e x p l i c i t l y considers the a b i l i t y of the investor to ' " l everage 1 the y i e l d to an unusually high degree and shelter the return from income taxes". Messner and Findlay a t t r ibute the popularity of the internal rate of return model in real estate investment analysis to the apparent advantages i t o f fe r s : 1. I t i s simple to understand and to compute. 2. The calculated solut ion would appear to be unique and unambiguous. 3. The measure is the "standard" among most f inanc ia l i n s t i t u t i o n s , and has been widely used fo r mortgage loan rates, bond rates, etc. 4. The measure provides a solut ion in a convenient form - a rate -which can be readi ly used as the c r i t e r i on of comparison with a l ternat ive investments.56 I t i s the l a s t advantage, the a b i l i t y to rank and compare investments without f i r s t ca lcu lat ing the required rate of return, a procedure not possible with the present value model, that has made the internal rate of return models popular among academics for comparing investments. The fol lowing example i l l u s t r a t e s the ca lcu lat ion of the IRR for an investment of $15,000 that produces the fol lowing cash flows and revers ion: 54. Leverage results when the cost of debt cap i ta l i s lower than the rate of return on tota l capita l invested. The e f fect of leverage is to magnify the percentage gain or loss on the owner's investment. 55. Wendt and Cerf, Real Estate Investment Analysis and Taxation, p. 26. 56. Stephen D. Messner and M. Chapan Findlay, "Real Estate Investment Analys is: IRR Versus FMRR", Real Estate Appraiser, (July/August 1975), p.6 32 n $ 1 3,000 2 3,000 3 3,000 4 2,000 5 2,000 6 2,000 7 13,700 A l ternat ive Discount Rates 5% 10% 15% Present Value of Cash Flows $22,611 $18,228 $15,003 $12,581 Indicated i s the present value of these var iable cash flows at d i f fe rent discount rates. C lear l y , the 15% discount rate reduces the future cash flows quite c lose ly to the i n i t i a l investment of $15,000. Current l i t e r a t u r e in finance and investment analysis has tended to reject the use of the internal rate of return as a measure of the re la t i ve 57 attractiveness of various projects. Five problems are apparent in the 58 use of the IRR as a measure of investment performance. 1. Mult ip le rates of return - the p o s s i b i l i t y of more than one rate of return arises when the future cash flow stream contains both net cash inflows and net cash outflows. 2. Reinvestment of intermediate cash flows - while the IRR i m p l i c i t l y assumes that intermediate cash flows generated from the project can be reinvested at the calculated IRR, such investment opportunities may not ex i s t . 3. Discounting negative cash flows - Negative cash flows are discounted at the same internal rate as pos i t ive cash flows. Instead, i t would appear reasonable to discount future negative cash flows at an a f te r -tax rate ava i lable to the investor. 57. J . H i r s h l e i f e r , "On the Theory of Optimal Investment Decis ion", Journal of P o l i t i c a l Economy 66, (August, 1958), pp. 329 - 352. In a survey of decision-making methods in business f inance, Mao found that the payback period and the accounting return were preferred to present value and internal rate of return models - James C. T. Mao "A Survey of Capital Budgeting: Theory and P rac t i ce " , Journal of  Finance 25, (May, 1970), pp. 349 - 360. 58. Messner and Findlay, pp. 6 - 1 2 present a deta i led, numerical analysis of the problems associated with the use of the IRR as a measure of investment performance. 33 4. Size differences in investments - The IRR i m p l i c i t l y assumes that investments being compared are both d i v i s i b l e and rep l i cab le . Not only i s th is not l i k e l y in a real world system, but also the s ize of an investment w i l l a f fect the rate of return. 5. Time d i spar i ty - when the IRR i s used to choose between mutually exclusive investments and the timing of the cash flows d i f f e r s among the investments being compared, the IRR may provide i n va l i d indicators of investment d e s i r a b i l i t y to the decision-maker. Very recent work has further modified the discounted cash flow ( i n te r -nal ) rate of return models in view of the various shortcoming and def ic ienc ies outl ined above. Wendt and Cerf have modified the IRR formula to permit ca lcu lat ion of the required reinvestment rate necessary to maintain any re-59 quired rate of return over a period of years. Strung has also addressed the reinvestment rate problem by providing an internal rate of return fin model which frees the user of the troublesome reinvestment rate problem. Messner and Findlay have constructed a new measure, the Financial Management Rate of Return (FMRR), to overcome problems with the IRR.^ Friedman has introduced his " Pu l l Factor" to better attempt to determine whether to hold fi? or s e l l an investment. Yet s t i l l further modifications have taken place 59. 60. 61, 62. Wendt and Cerf, pp. 32-35. Known as the Adjusted Internal Rate of Return (ARR), i t may be expressed as fo l lows: Where: I t=l R ICF Rev x ATCF. (1+r) I t=x R(ICF) 1 (1+r) ICF + Rev (1+r) reinvestment rate intermediate cash flows revers ion, (SP n - GT - UM) f i r s t year of return on invested cap i ta l Joseph Strung, "The Internal Rate of Return and the Reinvestment Pre-sumption", Appraisal Journal 44, (January 1976), pp. 22 - 33. The FMRR was developed by M. Chapman Findlay and Stephen D. Messner of the School of Business Administrat ion, the University of Connecticut, Storrs , Connecticut in 1973. Mr. Jay W. Levine of the Realtron Corpor-at ion, ass isted in the adaption of the model to the real estate invest-ment market. An explanation of th i s model was f i r s t presented as Determination and Usage of FM Rate of Return, 1973, by Realtron Corporation, Detroit . Jack P. Friedman, "The Internal RAte of Return Plus the Pu l l Factor " , Real Estate Appraiser 42, (March-Apri l, 1976), pp. 29 - 32. 34 to create an investment model, the Holding Period Rate of Return (HPR), for which the length of the holding period may be varied according to the needs of the a n a l y s t . 6 3 Of these addit ional models, the FMRR has received the most attention in real estate l i t e r a t u r e . The FMRR i s a spec ia l ized form of geometric mean rate of return where i t i s assumed that: 1. Only cash flows a f te r f inancing and taxes from the property under evaluation are considered. 2. Funds can be invested at any time in any amount at a safe a f te r -tax rate of i ^ and withdrawn when desired. 3. Funds can also be invested in "run of the m i l l " real estate projects of comparable r i sk at i ^ . Such funds must be in minimum quantit ies of R do l l a r s , however, and may not be withdrawn during the period to meet other requirements. To i l l u s t r a t e the FMRR model, the fol lowing example is provided. n $ 0 ($10,000) 1 ($50,000) 2 ($50,000) 3 $30,000 IRR = 25.2% 4 ($20,000) 5 $30,000 6 $250,000 Step 1. Remove a l l future outflows by u t i l i z i n g pr io r inflows where possible. In th i s example, i f $19,048 of the $30,000 received by E0Y 3 were invested at a safe rate ( i ' L ) of 5%, i t would grow to $20,000 by E0Y 4 and be ava i lab le to meet the $20,000 outflow requirement at that point in time. The cash flows are changed as fo l lows: n $ 0 ($10,000) 1 ($50,000) 2 ($50,000) 3 10,952 4 -0 -5 $30,000 6 $250,000 63. George W. Hettenhouse and John J . Dee, "A Component Analysis of Rates of Return in Real Estate Investment", American ReaV Estate and Urban  Economics Journal, (Spring, 1976), pp. 7 - 2 1 . Step 2. Discount a l l remaining outflows at the present at the safe rate. In th is example, the $50,000 payments at EOY's 1 and 2 are discounted to the present at a rate of 5%. The cash flows are changes as fo l lows: n $ 0 ($10,000)+($92,971)=($102,971) 1 -0-2 -0-3 $10,952 4 -0-5 $30,000 6 $250,000 Note that th i s re f l ec t s the fact that the actual investment amount to be made or assumed by the investor is $102,971, not $10,000. Step 3. Compound forward those pos i t i ve cash flows remaining at the appro-pr iate rate. In th i s example, assume R i s equal to 10,%.- Thus, cash flows received at EOY's 3 and 5 w i l l be compounded forward at i R = 10%. The cash flows are changed as fo l lows: n $ 0 ($102,971) 1 -0-2 -0-3 -0 -4 -0-5 -0-6 $250,000+$14,577+$33,000=$297,577 FMRR = 19.4% (the rate at which $102,971 grows to $297,577 in 6 years) IRR = 25.2% (the rate which discounts a l l future cash flows such that the sum of the present values i s equal to $10,000). The difference between the two measures of y i e l d i s en t i re l y explained by the e x p l i c i t estimates made for the various rates u t i l i z e d with in the FMRR ca lcu lat ion as compared with the assumption of the IRR approach in which a l l 64 relevant rates are exactly equal to the calculated IRR. 64. Messner and Findlay, "Real Estate Investment Analys is: IRR Versus FMRR", pp 13-14. Modifications of the above technique may also be employed to determine optimal holding periods, se lect among mutually exclusive investment a l t e r n -65 t i v e s , and even deal with simple cases of rat ion ing. Figure 2-3 summarizes the more widely known second generation models of measuring investment return in real estate. Attention has sh i f ted away from the estimation of r i sk or uncertainty as a component of the required rate of return. Appl icat ion of computer technology to internal rate of return and present value models has resulted in several models appearing to i l l u s t r a t e the concept of real estate invest-ment under conditions of r i sk . However, none of these are operational. Early uncertainty proposals are found in Ricks (1964) and Wendt and Cerf (1969) - both are expected-value, determinist ic approaches. R a t c l i f f and Schwab (1970) extend expected value and i l l u s t r a t e u t i l i t y theory. Friedman (1970) incorporates real property investments into ex i s t ing port-f o l i o theory; the model i s not operational because, "Risk in real e s ta te . . . w i l l be relegated to the f i e l d of real estate investment analys i s , which l o g i c a l l y must precede po r t fo l i o se lec t i on " . Two l a t e r a r t i c l e s attempt to bridge that gap: Pe l l a t (1972) and Phyrr (1973) both use monte carlo sim-ulat ion but d i f f e r in treatment of input-output f l e x i b i l i t y , se r i a l c o r r e l -a t ion, autocorrelat ion and dependence, and stochastic convergence. Neither model i s operat ional, but together they provide a basis for f i e l d adaptation of the techniques. 65. I b id . , pp. 18-19. C r i t i c i sm of the FMRR centres around the require-ment that two rates, i L and i R , must be selected by the analyst which could introduce bias into the outcome. 37 Figure 2-3 SECOND GENERATION MODELS OF MEASURING INVESTMENT RETURN IN REAL ESTATE Model 1) Present Value* Formula PV 11 I ATCF Rev 2) Equity Y ie ld Rate (before-tax IRR) t n •t=i (i+y) d+y) CTO. SP-UM I  f t=l ( l+r)* ( l + r ) n Input Parameters ATCF=after-tax cash flow (NOI-DS+taxes) y = required rate of return Rev=Reversion (SP-GT-UM) CT0=cash throw o f f (NOI-DS) 3) Return on Invest-ment (before-f in-ancing IRR) 4) Internal Rate of Return (IRR) SP I t=l n I t=l n I t=l 6) Financial Management Rate of Return (FMRR) FMRR = n NOI^taxes. SP-GT ICF= intermediate cash Z H flows (1+r) ATCF, ( l + r ) n x = f i r s t year of return on invested cap i ta l Rev. R; - reinvestment rate T*=reversion +positive 5) Adjusted Internal Rate of Return (ARR) EQ ( l + r ) t ( l + r ) n ATCF. n R(ICF) ICF+Rev _1 +V + (l*r)tZ 0*r)1 ( l + r ) n reinvested CF's t=x D* = modified i n i t i a l n outlay * PV may be calculated on to ta l invested cap i ta l The computation of DCF models for real estate investment analysis has made i t possible to quickly determine the interact ions of the investment model's var iables. Computer simulation techniques allow the investor or the analyst to explore the ef fects of a wide range of assumptions about the input variables upon the rate of return. Thus, ins ights may be gained into the i n te r re l a t i on of the level of vacancies, net operating income, operating expenses and the future equity reversion. Sen s i t i v i t y analysis i s another ana ly t i ca l technique made eas i l y operation-al -v by computerization. This technique permits the investor to compare the impact on p r o f i t a b i l i t y of the investment by d i f fe rent assumptions about such input variables as growth rates in gross income and operating expenses, loan-to-value r a t i o , mortgage term, equity investment and so on. The var iable unde study i s changed while holding a l l other input variables constant. The use of computer technology to calculate rates of return in real estate investment analysis has led to several problems of app l i ca t ion . F i r s t , too many people place undue rel iance on f inanc ia l information and rat ios output by the computer without s u f f i c i e n t regard for the qua l i ty of the model, the accuracy of the input data, and the intent ion of the analyst. Second, conceptual errors can occur in the derivat ion of the mathematical algorithm and programming errors in t rans lat ing the mathematical l og ic to program language. Another problem of both computer models and hand calculat ions is the tendency to treat the f i r s t and l a s t year of the investment cycle as i f i t i s always a f u l l year. Depending on the tax character i s t ics of the investment and the holding period, th i s misrepresentation may or may not be s i g n i f i c an t . A f i n a l problem with the appl icat ion of the discounted cash flow rate of return models i s the timing of the recognition of cash flow and tax consequences. Few 66 computer programs d i r e c t l y accommodate var iat ions beyond an annual basis. In summary, i t i s apparent that a var iety of models or methods have been developed to simulate properties found in real world systems. Their usefulnesses l im i ted only by the a b i l i t y of those developing the model to: (1) recognize the real world propert ies, (2) incorporate these properties into model s t ructure, and (3) make accurate decisions based on model outputs in f u l l l i g h t of the l imi tat ions e x p l i c i t l y and i m p l i c i t l y incorporated in 66. For further deta i l see Roulac, Modern Real Estate Investment: An In s t i tu t iona l Approach, pp. 429 - 432. 39 the model.. Indeed, investment analysis for real estate decisions i s most c r i t i c a l when there i s a p o s s i b i l i t y of using the results to influence decisions. Although recent l i t e r a tu re on real estate investment analysis has favoured the adoption of more sophist icated models of investment performance s im i l a r to those generally in use in other f inanc ia l and corporate sett ings , several problems and issues remain unsolved. These are (1) disagreement on which method w i l l produce the most accurate and r e l i ab l e measure of investment performance, and (2) a lack of consensus on the extent of the usage of the various methods of measuring return by real estate investors. This chapter demonstrated that in a theoret ica l context, discounted cash flow models incorporated many of the requirements of an accurate and re l i ab l e measure of return. In Chapter 4, most of the methods of measuring investment return discussed w i l l be evaluated in a pract ica l appl icat ion involving a sample of apartment properties. It i s to the second issue, the actual use of the various investment models by the real estate community today, that the next chapter addresses i t s e l f . 67. In every instance, the accuracy of the answer ult imately depends on the accuracy of the input data. 3.0 REAL ESTATE INVESTMENT ANALYSIS: CURRENT PRACTICE Numerous methodologies have been developed for analyzing the performance or return avai lable to real estate investments, although investors have generally r e l i ed in the past on naive " ru le of thumb" measures in the i r evaluation. In Chapter 2, capital izat ion-of- income and rate of return frame-works were u t i l i z e d to trace the development of ana lyt ica l models for invest-ment analysis. While models t r ad i t i o na l l y focused on the assessment of value through income-capital izat ion i t was noted that a preference for rate of return models has been indicated in recent publ icat ions. However, there ex ists disagreement on the extent of the usage of the various models of investment return by the real estate community. The intent ion of th is study i s to acquire pract ica l rather than theoret ical information which w i l l provide a sounder basis fo r the analysis of real estate investments in the future. In l i ne with this overal l goal, the aim of Chapter 3 i s to provide a current and comprehensive analysis of the actual use of ana ly t i ca l models of investment return by the real estate community. However, pr ior to the presentation of the findings of two questionnaire surveys, i t would be appropriate to b r i e f l y review the results of previous studies. 3.1 Review of Pr io r Research The ex i s t ing l i t e r a tu re on the subject of ana lyt ica l models fo r measuring investment return in real estate i s sparse. For the most part real estate studies are concerned with the actual returns received and often compare real estate returns to those avai lable on the stock or bond market. Table 3-1 summarizes the studies and the i r f indings. Most of the studies suffer from a common shortcoming that diminishes the i r r e l i a b i l i t y - the use of misleading measures of r e t u r n . " 0 In Chapter 2 i t was noted that 'the lack of agreement on a de f i n i t i on of " return" or on a method of measuring return leads to the interest ing dilemma that d i f fe rent methods.of measuring performance produced both d i f fe rent r e l a t i ve rankings and d i f fe rent indicators of investment return. ' Nevertheless, a few studies have been conducted which do warrant some attent ion as they w i l l provide a perspective in which to view the present study. Two of the "rate of return" studies u t i l i z e Canadian data. The f i r s t , Comparative Study of the Rates of Return on Apartment and Common Stock  Investment 1960 - 1969, was conducted by Woods, Gordon and Company for Central Mortgage and Housing Corporation (CMHC). The purpose of the CMHC study was to invest igate the comparative average annual a f ter - tax rates of return on investments in common stocks and rental apartments and the extent to which return would be affected by the adoption of the proposals contained in the White Paper on Taxation. Despite the emphasis on the tax ef fects a r i s i ng from real estate and common stocks investments, the rates of return ca lcu lated, as shown on Table 3-2, are of interest for th i s study. 68. Roulac i den t i f i e s three other shortcomings with real estate return studies: (1) investment strategies and time periods are often unspecified and uncomparable, (2) i n s u f f i c i e n t data points are used, and (3) data i s sometimes l imi ted to a s ingle property type or geographic region, thereby i t lacks representativeness - Roulac "Can Real Estate Return Outperform Common Stocks", pp. 26 - 30. 42 TABLE 3-1 TABLE OF STUDIES OF REAL ESTATE INVESTMENT PERFORMANCE Study Finding 1. Wendt and Tul ly Hodges 5. 6. 7. 8. 10 11. Ricks 4. Achtenhagen Ricks Case Davis Update of Wendt and Wong research and changed assumptions: indicated higher returns i f double decl in ing balance depreciation used. A survey of 17 apartments and commercial o f f i c e property sales in the Washington, D.C. region, occurring between Ju ly , 1966 and June, 1970, showed returns for most properties concentrated around a 9% f igure. Hypothetical 11.4% macro-level return based on census data; inputed equity returns on real estate financed by l i f e insurance companies in the 8 to 9% range. Results for 15 "closed-out" real estate syndi-cations that range from a mean rate of return of 20.9% at the 0% tax bracket to 25.7% at the 50% bracket. Net investment y ie ld s for 14 l i f e insurance com-panies in real estate of s l i g h t l y in excess of 4° Net income as percent of purchase pr ice averaged 12.4% for 1950 - 54 period, a substantial im-provement over e a r l i e r periods (4.3% in 1935 -39); study of 108 properties. Average 7.8% return on investment for 114 Fresno propert ies. Study on Tax Consideration 137 investors holding 434 properties in 6 c i t i e s in Multi-Family Housing reported median "net cash return from operations' of 10%. Less than 5 percent of investors were found to use DCF methods of analys is. Investments (HUD) Lowenstein and Recht Hippaka Grebler 370 mult i - fami ly transactions, presumably in the San Francisco Bay Area, showed returns de-c l i n i ng from 10.1% in 1953 - 55 to 7.9% in 1962 - 1964. San Diego apartment investors in the mid-1960's reported 7-8% net annual returns as being "average". Reported median y ie ld s ranging from 0.94% for 5 elevator apartments to 5.04% for 8 walk-up apart-ments, and 1.06% for 11 properties acquired be-tween 1925 and 1929 and 4.98% for 3 properties acquired between 1910 and 1914; New York properties. 43 TABLE 3-1 continued 12. Dale-Johnson 13. CMHC 14. Wiley 69 Apartment properties in the Vancouver area were analyzed with respect to the i r operation-al costs and y i e ld s . Returns averaged between 17.8 - 26% but poor investment decisions were found to be due to use of " ru le of thumb" methods of analys is . Reported mean y ie lds ranging from 50.2% for 63 corporately owned apartments to 7.74% for i nd i v idua l l y owned apartments p r io r to the 1971 tax changes. Including the tax changes, the y ie ld s ranged from 25.7% to 7.7%. From a sample of 159 real estate equity investors, 45% used no af ter - tax measure of return; 27% used some form of a f te r - tax discounted cash flow model; and only 27% used the computer. 1. Wendt and Tu l l y , "Investment Performance: Common Stocks Versus Apartment Houses," The Appraisal Journal (January 1972). 2. Hodges, "Computer Progress in Valuation of Income Propert ies, " The  Appraisal Journal (January 1971). 3. Ricks, Real Estate Investment: The Investment Process, Invest Per- formance and Federal Tax Pol icy (Report of the Real Estate Investment Project for the United States Treasury Department, 1968). 4. Achtenhagen, An Investor-Based Marketing Plan for Sale of Real Property Investment Securit ies to Indiv iduals, " (unpublished doctoral d i s se r ta -t i o n , Stanford Univers i ty, 1974). 5. Ricks, Recent Trends in In s t i tut iona l Real Estate Investment (Centre for Real Estate and Urban Economics, Univers ity of Ca l i f o r n i a , Berkeley,1964). 6. Case, Los Angeles Real Estate: A Study of Investment Experience (Real Estate Research Program, Univers ity of C a l i f o r n i a , Los Angeles, 1960). 7. Davis, A Study of Real Estate Investment Returns to Capital and Management (Bureau of Business Research and Service: Ca l i f o rn ia State Un ivers i ty , Fresno, 1973). 8. Study on Tax Consideration in Mult i-Family Housing Investments (U.S. Department of Housing and Urban Development, 1972). 9. Lowenstein and Recht. "Var iat ions in Rates of Return in an Urban Area", unpublished paper on f i l e at Center for Real Estate and Urban Economics Univers ity of Ca l i f o r n i a , Berkeley. 10. Hippaka, Factors Contributing to Successful Investment Experience in  Mult ip le Unit Housing (Bureau of Business and Economic Research: Sah Diego State College, 1965). 11. Grebler, Experience in Urban Real Estate Investment: An Interim Report  Based on New York City Properties (Columbia University Press, New York, 1955). 12. Dale-Johnson, "Returns on Apartment Properties for the Period 1960 to 1970 in the Greater Vancouver Area", (unpublished Master's d i s se r ta t i on , Faculty of Commerce and Business Administrat ion, Univers ity of B r i t i s h Columbia, 1972). 44 TABLE 3-1 continued 13. Comparative Survey of the Rates of Return on Apartment and Common Stock  Investments 1960-69, (Ottawa: Central Mortgage and Housing Corporation, 1970). 14. Wiley, "Real Estate Investment Analys is: An Empirical Study," Appraisal  Journal 44, (October, 1976). TABLE 3-2 CMHC STUDY: AVERAGE ANNUAL AFTER TAX RATES OF RETURN CALCULATED FOR INVESTORS IN APARTMENTS PRE AND POST WHITE PAPER ON TAXATION 1960 - 1969 Closely Held Widely Held Individuals Direct Corporations Corporations Death Option Se l l i ng Option Pre Post Pre Post Pre Post Pre Post 0/ 01 01 01 01 01 01 01 10 10 10 10 10 10 10 10 50.22 22.61 19.24 10.14 44.41 25.70 57.21 8.55 The rates of return were found to probably be somewhat higher than those that were actual ly received. This i s due to (1) the assumption that investors currently had s u f f i c i en t non-property income to u t i l i z e any deductions they were allowed to make for tax purposes, (2) the small s ize of each sample, and (3) the use of the IRR method which assumes reinvestment of intermediate cash flows at the discount rate. The second Canadian study was completed in 1972 by Frederick Dale-Johnson and en t i t l ed Returns on Apartment Properties for the Period 1960 - 1970 in  the Greater Vancouver Area. The aim of Dale-Johnson's study was to analyse a representative sampling of properties with respect to the i r operational costs over a period of time and the y ie lds which investors obtained on these properties. He found that using the gross y i e l d on equity method (a f ter -tax cash flow + pr inc ipa l repayment/equity), average rates of return ranged from 17.8 to 26.0 percent. Of greater interest to the present study, the analysis of y ie lds showed that many properties were extremely good buys when purchased while others were very bad buys and others were sold at a return re f l ec t i ng a normal market rate. Consequently, Dale-Johnson concluded that 45 part:of th i s large discrepancy can be based upon misinformation, incorrect statements and dishonesty, but the majority of the discrepancy appeared to be caused by the fact many buyers and se l l e r s do not know the i r business and so re ly upon a number of " rules of thumb" currently in use to make the i r 69 investment decis ion. Only two studies attempted to determine the methods of investment analysis actua l ly employed by the real estate community to calculate return. The f i r s t such study was conducted for the U. S. Department of Housing and Urban Develop-ment (HUD) and en t i t l ed Study on Tax Considerations in Multi-Family Housing  Investments. While the overal l purpose of the study was to explore the e f fec t of tax considerations on investment decis ions, other variables were examined which are relevant in the present context. S p e c i f i c a l l y , these were (1) the d i f ferent measures of " p r o f i t a b i l i t y " used to evaluate an investment, and (2) the d i f fe rent forms of return included in measuring return. The findings are presented below. HUD STUDY: Average * Annual Rate of Return (74) Total Dol lar * * Return (41) Payback Period (23) Discounted Rate of Return (IT) Other(lO) TABLE 3-3a MEASURES OF RETURN USED BY INVESTORS A l l Measures of Return Used (Number of Respondents in Parentheses) 3 29% 5fl% 24% 0 10 20 30 Active Investors-Sample s ize 93 40 44% m 80% 3 50 60 70 80 90 100% i — | Passive Investors-Sample s i ze 42. 69. Dale-Johnson, P. 78. 46 Key Measures of Return Used Average* Annual Rate of Return (62) Total Do l la r s * * Return (12) Payback Period (10) Di scounted Rate of Return (4) 0ther(5) 77% al 3% -,14% ,11% 5% ! 4% h 2% 5% b"2% 0 10 20 30 40 50 i 60 70 80 90 100% *Average Annual Rate of Return i s annual a f ter - tax cash flow divided by equity. * *Total Dol lar Return i s the a f ter - tax dol lars earned from investment.. As can be seen in Table 3-3a, more than two-thirds of the act ive and passive investors ca l led the average annual return (Net Operating Income/equity) t he i r key measure while less than 4 percent re ly on the discounted rate of return. The authors claim that th is fact i s worthy of note since " . . . i t i s generally recognized that the discounted rate of return i s a more sophist icated measure of p r o f i t a b i l i t y than the average rate of r e t u r n . T h e average annual return, as generally employed, only considers the cash flow from operations today and thus may not measure the substantial benefits potent ia l l y r e a l i z -able from tax savings and/or sale or, account for the time-value of money. A second consideration of interest i den t i f i ed by the HUD study was the forms of return included in measuring investment return. The results are i l l u s t r a t e d in Table 3-3b. 70. Study on Tax Considerations in Multi-Family Housing Investments, p.40. Cash Tax Amortization Net Cash Flow Shelter of Loan Proceeds of Sale 98% 40% 26% • 9% 100% 61% 39% 33% 91% 45% 18% 9% 100% 80% 20% 60% TABLE 3-3b HUD STUDY: % OF INVESTORS INCLUDING EACH FORM OF RETURN IN MEASURE OF PERFORMANCE (Number of Respondents in Parentheses) Average Annual Return (93) Total Dol lar Return (18) Payback Period ( l l ) Discounted Rate of Return - (IRR) ( 5) The most s i gn i f i can t resu l t in th i s table is that a substantial portion of the investors do not measure a l l of the forms of return from a project. The authors concluded that many investors appear to se lect less sophist icated measures of return and then f a i l to adhere to the spec i f i c methodology of the chosen measure. Such practice may lead to unwise investment dec i s i on s . ^ The major l imi tat ions of the HUD study are (1) the use of a s ingle type of investor - apartment owners, (2) the analysis of only four measures of return, and (3) the concentration on the tax considerations of investment. In 1976, Robert Wiley presented the results of a study which, at least 72 in purpose, more c losely resembles the present work. The purpose of the Wiley study was to determine "the behaviour of part ic ipants in the market for real estate investment propert ies. " Important findings of Wiley 's study include: 1. Over 10 percent of the surveyed companies i den t i f i ed purchase price rather than i n i t i a l equity investment as the base for the i r measures of return. 2. Cash flow (net operating income minus debt service) was used more often than income (net operating income) by most investors to measure return. 3. Over 45 percent of the 158 investors used no af ter - tax measure of return. 71. Ib id . , p. 41. 72. Robert Wiley, "Real Estate Investment Analys is: An Empirical Study," Appraisal Journal 44, (October, 1976), pp. 586 - 592. 4. About 22 percent of the surveyed companies reported using some form of a f ter - tax discounted cash flow model; the internal rate of return was the most popular. 5. Of the 67 percent of the companies adjusting for uncertainty e i ther : (1) adjusted the required rate of return; or (2) adjusted the s i ze of estimated future benefits. 6. About 27 percent of 150 respondents used the computer in real estate investment analys i s , and 7. Ten year holding periods were found to be the most common in invest-ment ana ly s i s .^ 3 The shortcomings with regard to Wiley ' s study are twofold: (1) insurance companies were.by far the dominant group in the sample, and (2) no consider-ation was given to the forms of return considered important to investors. The l a t t e r point has a s i gn i f i can t bearing on the method chosen to measure investment performance. Therefore, in view of the shortcomings of the previous research on 74 real estate investment analysis as i den t i f i ed above and by Roulac i i t was determined that a survey of ana ly t i ca l practices regarding real estate investment should be an integral part of the present study. Two addit ional benefits were seen ar i s ing from such a survey (1) i t would be the f i r s t analysis of real estate investment practices in Canada known to the author and would allow comparisons to be drawn with previous American research, and (2) due to the dramatic increase i n the capab i l i t i e s of and the use of computer technology, s i gn i f i c an t changes could be antic ipated in investment analysis from the results of the most recent study done over 5 years ago. Some of the investment variables considered were: 1. Size of real estate port fo l ios . 2. Types of property held. 73. Ib id. 74 Roulac, "Can Real Estate Returns Outperform Common Stocks," pp. 26 -30. 49 3. The d i f fe rent forms of return included in measuring return. 4. The d i f fe rent measures (before-tax and af ter - tax) of return used to evaluate an investment. 5. The minimum expected rate of return required to a t t rac t investment to a par t i cu la r property type. 6. Methods used to adjust for uncertainty. 7. Extent of computer usage in investment analys is . 8. Reasons for using (or not using), the computer in investment analys is. 9. The holding period considered in making investment decisions. In add i t ion, certa in background data on each investor was co l lected including information regarding his pr inc ipa l business a c t i v i t y and the locat ion of the company's head o f f i c e . 3.2 Sample Selection and Administration This chapter i s based on information about real estate investment analysis co l lected by mail questionnaire administered during June, 1977. The two sources of information were: (1) real estate investors (public and private real estate corporations, insurance companies and other investors) operating in Canada, and (2) Industr ial Commercial and Investment (I.C.I.) real estate brokers pract i s ing within the Greater Vancouver metropolitan area. The i den t i f i c a t i o n of real estate investors to be included in the sample was not an easy task since real estate investors cannot be c l a s s i -f i ed as an industry. Thus, there i s no central reg i s t ry as i s common in most industr ies. The i den t i f i c a t i on of investors was of necessity achieved by ad hoc methods. Membership l i s t s from the Canadian Inst i tute of Publ ic Real Estate Companies (CIPREC), the Directory of Canadian Real Estate Com-panies, and the Housing and Urban Development Association of Canada (HUDAC)^ 75. Only those members of HUDAC with "investment" versus s t r i c t l y "deve l -oper-bui lder" interests in real estate were contacted. 50 were obtained. In addi t ion, a l i s t of insurance companies with an equity interest in real estate was compiled. A senior management executive in each company was sent an introductory l e t t e r explaining the nature of the present study and a copy of the questionnaire with a return envelope (see Appendix A) The " investor " sample eventua l l y . tota l led some 300 real estate investors in Canada. By no means does this represent the tota l real estate investment community. Many investors, pa r t i cu l a r l y those investing in real estate to shelter other income from taxat ion, have l i t t l e or no a f f i l i a t i o n with the organizations mentioned.^ In an attempt to overcome th i s problem and reach many of the smal ler, and perhaps less active investors, a membership l i s t of the I.C.I, d i v i s ion of the Greater Vancouver Real Estate Board was obtained. Each member was sent an introductory l e t t e r and a copy of the questionnaire with a return envelope (see Appendix B). While the "broker" survey was of secondary importance re l a t i ve to the " investor " survey, i t s role was not only to obtain information on the investment practices of smaller investors (who are l i k e l y to use an I.C.I, broker in the i r real estate transactions) but also to obtain information on the s e l l i n g practices of real estate brokers and appraisers. This permits an examination of the extent of the c o r r e l -ation between the practices of the brokerage and appraisal profession and the actual behaviour of part ic ipants in the market for real estate invest-ment properties (as seen through the " investors " survey). Essent ia l ly the same questionnaire was sent to Vancouver area I.C.I, real estate brokers. Of 150 broker-questionnaires, 55 (37 percent) were returned with usable information concerning I.C.I, brokers and the i r 76. The complete tabulated results of both surveys are shown in the Appendices. 77. It is well known that profess ionals, such as doctors and lawyers, and other wealthy indiv iduals often invest substantial amounts of income in real estate (see Dale-Johnson thes i s ) . 5H c l i e n t s . The respondents in both surveys were very candid in the i r answers and any useful f indings a r i s i ng from th is section are the d i rect resu l t of the i r cooperation. 3.3 Sample Character ist ics Although not a s c i en t i f i c , sample, the respondents do represent an imposing group of investors. The re l a t i ve s ize of the real estate port-fo l i o s of the 105 investors that provided information on the i r holdings i s shown in Table 3-4A. The tota l assets of these investors was in excess of $8,150.5 m i l l i o n . Some 74 percent of the respondents had at least $10 m i l l i o n . TABLE 3-4A SIZE OF REAL ESTATE PORTFOLIOS (Percentage of Respondents in Parentheses) 25 51 Public Private 17 12 Real Estate Real Estate Insurance Other A l l 105 Corporations Corporations Companies Investors Investors Under $1 m i l l i o n 1 ( 4.0) 4 ( 7.8) 1 ( 5.9) 2 (16.7) 8 ( 7.6) $1 m i l l i on to 4 (16.0) 11 (21.6) 3 (17.6) 1 ( 8.3) 19 (18.1) $10 m i l l i on $10 m i l l i on to 9 (36.0) 31 (60.8) 8 (47.1) 3 (25.0) 51 (48.6) $100 m i l l i on $100 m i l l i on to 7 (28.0) 4 ( 7.8) 3 (17.6) 3 (25.0) 17 (16.2) $250 m i l l i on Over $250 m i l l i o n 4 (16.0) 1 ( 2.0) 2 (11.8) 3 (25.0) 10 ( 9.5) (100%) (100%) (100%) (100%) (100%) Assets (mi l l ions of $): Total 3,557.2 1,968.7 1,497.1 1,127.4 8,150.5 Average 142.3 38.6 88.1 112.7 Standard Deviation 243.7 58.9 125.0 113.4 in real estate investment properties. That the responses of the investors are representative of the real estate investment community as a whole i s borne out by the s i gn i f i can t portion of tota l real estate assets accounted for by 52 the sample. For example, Price Waterhouse and Company i den t i f i ed the 42 largest publ ic real estate companies in Canada with to ta l assets of over $7,014.1 m i l l i on in 1976. Publ ic real estate companies responding to the survey had tota l assets of $3,557.2 m i l l i on (51 percent of the Price Water-house g roup ) . ^ Turning to the "brokers" survey, there ex i s ts a wide var iat ion in the s ize of the 55 brokerage firms whose representatives returned responses. Table 3-4B presents the average value of I.C.I, transactions. Brokerage firms ranged from small one-man operations to large internat ional companies located in most major c i t i e s and providing a wide range of real estate services including brokerage, appra i sa l , investment counsell ing and f e a s i b i l i t y analys is. TABLE 3-4B AVERAGE VALUE OF ICE TRANSACTIONS (Percentage of Respondents in Parentheses) $_ 55 Brokers Under $500,000 20 (40.8) $500,000 to $1 m i l l i o n 17 (34.7) Over $1 m i l l i o n 12 (24.5) (100%) Geographically, the investor-respondents are located in a l l areas of Canada as i l l u s t r a t e d in Table 3-5A. While the brokers were for the most part located within the Vancouver metropolitan area, many had a s i gn i f i c an t portion of t he i r c l i en t s located across Canada and in other countries (see 78. P r i ce , Waterhouse & Company. The Real Estate Development Industry in  Canada: 1976 Survey of Accounting Reporting and Accounting Development, (Toronto, 1976), p.2. To further establ i sh the representativeness of the survey, the $6,653.3 m i l l i o n of real estate corporation assets represents 24 percent of the assets of real estate 'operators and develop-ers ' i den t i f i ed by S t a t i s t i c s Canada in 1974 - S t a t i s t i c s Canada, Corporation Financial S t a t i s t i c s , 1974, (Ottawa: Department of Supply and Services, 1974). While the value of insurance company equity interests in real estate in Canada could not be obtained, i t i s ant ic ipated a s im i la r asset 'coverage', as that described above would apply. 53 Appendix B, Tables 3-5B/6B) 79 TABLE 3-5B LOCATION OF HEAD OFFICES, BY TYPE OF INVESTOR (Percentage of Respondents in Parentheses) B r i t i s h Columbia Alberta Saskatchewan or Mani toba Ontario Quebec A t l an t i c Provinces Outside Canada 25 Public Real Estate Corporations 9 (36.0) 6 (24.0) (28.0) ( 4.0) ( 4.0) ( 4.0) (100%) 51 Private 17 Real Estate Insurance Corporations Companies 17 (33.3) 7 (13.7) 4 ( 7.8) 20 (39.2) 2 ( 3.9) 1 ( 2.0) 1 ( 5.9) 1 ( 5.9) 11 2 1 1 (100%) (64.7) (11.8) ( 5.9) ( 5.9) (100%) 12 Other A l l 105 Investors Investors (16.7) C 8.3) (58.3) (16.7) (100%) 29 (27.6) 14 (13.3) 5 ( 4.8) 45 7 3 2 (42.9) ( 6.7) ( 2.9) ( 1.9) (100%) The average'proportions of the responding investors ' port fo l ios represented by indiv idual types of property are presented—i-n—T-ab-l-e--3--8A-.-TABLE 3-8A TYPES OF PROPERTY HELD, BY SIZE OF INVESTOR* % for 26 % for 50 % for 25 % for Smal 1 Medium-sized Large A l l 101** 1 Investors** Investors** Investors **Investors Single family 17.8% 5.3% 4.9% 7.1% housing Apartments 21.5 30.1 15.2 24.2 Condominiums 1.2 9.6 3.8 6.0 Off ice buildings 13.0 19.9 24.3 19.2 Shopping Centres 9.8 10.1 16.0 11.5 (retai1) Industr ial buildings 10.8 6.6 10.0 8.5 Hotels/motels 3.1 1.6 2.3 2.1 Undeveloped land 16.8 15.4 18.4 16.5 Other 11.1 1.7 4.6 4.9 *Columns do not sum to 100% since each row is an average. **Refer to Table 3-6A ***The to ta l responses for each question may not correspond to the tota l number of respondents since a l l respondents did not answer every question. 79. Due to the primary emphasis on the behaviour of real estate investors and the l imi tat ions of space, th is section focuses on the results of the investor survey. The "other property type most often mentioned was serviced land awaiting development. Note the small portion of property types comprised of s ingle family housing. This i s due to: (1) the fact many s ingle family dwelling units tend to be developed by firms with a small annual output and not l i k e l y to be members of organizations as CIPREC or the Directory of Real Estate Companies or are they insurance companies, and (2) s ingle family housing i s usually b u i l t for immediate sale rather than investment. S im i l a r l y , there was a wide range of property types involved in the trans-actions of the brokers (see Appendix B, Table 3-7B). In both studies the most common types of income-producing real property were apartments (rental) followed by o f f i ce bui ldings. 3.4 Investment Return Measures A s i gn i f i c an t problem in real estate investment analysis involves the many d i f fe rent methods of measuring investment return ava i lable to investors. Each respondent, both investors and brokers, was asked to indicate from a l i s t of measures along with a correponding de f i n i t i on which before-tax and a f ter - tax measure they used most often to evaluate real estate investments. The results of th i s invest igat ion have been tabulated by investor type in Tables 3-11 A and 3-13A and for the brokers in Tables 3-11B and 3-13B. 55-TABLE 3-1IA BEFORE-TAX MEASURES OF INVESTMENT RETURN USED MOST OFTEN, BY TYPE OF INVESTOR (Percentage of Respondents in Parentheses) 19 44 Public Private 15 10 Real Estate Real Estate Insurance Other A l l 88 Corporations Corporations Companies Investors Investors GRM _ _ _ _ _ NRM 3 ( 6.8) 3 (20.0) 1 (10.0) 7 ( 8.0) Overall ROI 8 (42.1) 13 (29.5) 6 (40.0) 2 (20.0) 29 (33.0) Equity Dividend 5 (26.3) 20 (45.5) 3 (20.0) 3 (30.0) 31 (35.2) Payback - 1 ( 2.3) - - 1 ( L D Equity Y ie ld 3 (15.8) 3 ( 6.8) 1 ( 6.7) 3 (30.0) 0 (11.4) Other 1 ( 5.3) 1 ( 2.3) - - 2 ( 2.3) More than one 2 (10.5) 3 ( 6.8) 2 (13.3) 1 (10.0) 8 ( 9.1) (100%) (100%) (100%) (100%) (100%) TABLE 3- 11B BEFORE-TAX MEASURE OF INVESTMENT RETURN USED MOST OFTEN (Percentage of Respondents in Parentheses) Before-Tax Measure 48 Brokers GRM 6 (12.5) NRM 2 ( 4.2) Overall ROI 22 (45.8) Equity Dividend 16 (33.3) Payback Equity Y ie ld (before-tax IRR) Other More than one 2 ( 4.2) (100%) From a review of these tables, i t i s apparent that over 83 percent of responding investors (87 percent of the brokers) used some form of before-tax return measure. On an a f ter - tax basis, about 57 percent of the responding investors (42 percent of the brokers) used some after - tax measure. Many of the investors using only before-tax measures were pr ivate.rea l estate corporations while public real estate corporations most often reported using a tax-adjusted return analyses. It i s c lear from the sample character i s t i c s outl ined in the previous sect ion, that many of the private real estate 56 TABLE 3-13A AFTER-TAX MEASURE OF INVESTMENT RETURN USED MOST OFTEN, BY TYPE OF INVESTOR (Percentage of Respondents in Parentheses) ATCF ( F i r s t y r equi ty Payback Present Value P r o f i t a b i l i t y Index IRR Adjusted IRR FMRR Other More than One )/ 18 Public Real Estate Corporations 7 (38.9) ( 5.6) (16.7) ( 5.6) 5 (27.8) 1 ( 5.6) (100%) 27 Private 11 Real Estate Insurance Corporations Companies 8 (29.6) (11.1) ( 3.7) ( 3.7) (18.5) ( 3.7) 8 (29.6) rroo%) 1 ( 9.1) 3 (29.1) 1 ('9.1) 5 (45.5) 1 ( 9.1) 1 ( 9.1) 4 Other Investors 1 (25.0) A l l 60 Investors 16 (26.7) 5 ( 8.3) 6 (10.0) 2 ( 3.3) 1 (25.0) 16 (26.7) 1 (25.0) 3 ( 5.0) 1 ( 1.7) 1 ( 9.1) 1 (25.0) 11 (18.3) (100%) TToo%) ( 1 0 0 % ) TABLE 3-13B AFTER-TAX MEASURE OF INVESTMENT RETURN USED MOST OFTEN (Percentage of Respondents in Parentheses) After - tax Measure 23 Brokers ATCF ( f i r s t year)/Equity Payback Present Value P r o f i t a b i l i t y Index IRR Adjusted IRR FMRR Other More than One 12 (52.2) 2 ( 8.7) 6 (26.1) 1 ( 4.3) 1 ( 4.3) 2 ( 8.7) (100%) corporations are small operations re l a t i ve to the public real estate corporations surveyed which may have an impact upon the degree of sophist i-cation in the i r approach to investment analys i s . I t appears l og i ca l that many brokers would not use an a f ter - tax measure since th i s requires tax information pertaining to a spec i f i c c l i e n t who may not ex i s t at the time of analysis or be unwil l ing to divulge such information. Without question the most widely used before-tax measures of return by the respondents in th i s study were the equity dividend rate (net operating income minus debt service/equity) c losely followed by the overal l return on investment (net operating income/purchase p r i ce ) . Next in order of importance for most types of investors was the equity y i e l d rate (before-tax IRR) and the NRM (purchase price/net operating income). For the most part, these findings confirm those of Wiley (1976). The responses of the brokers surveyed were s imi la r except that they reported the overal l return on invest-ment as the i r f i r s t choice, then the equity dividend rate and the GRM ... (purchase price/gross income) as th i rd in importance followed by the NRM. Thus, i t would appear that real estate brokers are influenced to a greater degree than investors by t rad i t i ona l capita l izat ion-of- income methods such as gross and net rent mu l t ip l i e r s commonly used to estimate appraisal value. On an a f ter - tax basis, the most widely used measures of return reported by the investors were a f ter - tax cash flow ( f i r s t year)/equity and the IRR. In the brokers' survey, the present value models enjoyed a greater popularity than the IRR with the l a t t e r method being used by only 5 percent of the ICI brokers. Table 3-13A i l l u s t r a t e s that insurance companies tended to be the most sophisticated group of investors with over 80 percent of th is group using a f ter - tax DCF methods of analys is. This f inding is supported by Ricks (1964) who reported that insurance companies r e l i ed heavily upon DCF methods of analysis and were preoccupied with the tax advantages of owning real estate. The questionnaire contained a general question related to the use of equity or tota l capi ta l invested as the base for the i r measure of return. The responses indicate that 67 percent of both the investors and the brokers surveyed used equity rather than tota l capi ta l invested (see Appendix A, Tables 3-9A/10A and Appendix B, Table 3-1 OB). For a sample of 114 properties 58 in Fresno, Ca l i f o rn i a analyzed in the 1960's, Davis (1973) found only 50 on percent of the investors used equity as an investment c r i t e r i o n . The tables indicate a lack of uniformity in the types of ana lyt ica l return measures used by the 160 respondents. Add i t i ona l l y , many respondents reported using more than one of the methods l i s t e d in the questions. Perhaps one reason for the d i spar i ty of methods used i s the d i f fe rent backgrounds of the investment analysts, with some having had appraisal experience and others having had more corporate capita l budgeting experience. For example, the payback method, which i s a t rad i t i ona l capita l budgeting technique, was found to be used by some of the respondents. The most frequent comment made by the respondents, pa r t i cu l a r l y brokers, was that the more sophist icated DCF models such as the IRR were not useful in many instances because they are not well known in the real estate industry. Consequently, i t i s common practice to emphasize a "cash on cash" y i e l d (equity dividend). Several investors claimed that increased sophist icat ion in real estate investment analysis does not necessari ly mean better decisions because of some of the forecasting assumptions concerning input parameters which must be made. However, one investor f e l t that: in the;future, investment decisions w i l l be s im i l a r to those in other business f i e l d s and use the IRR; the popular equity dividend method w i l l lose i t s un iver sa l i ty s im i la r to that which happened to the GRM; and a new level of sophist icat ion in real estate investment analysis w i l l emerge. Among the implications of the findings presented above would be the fo l lowing: 80. Irving F. Davis, A Study of Real Estate Investment Returns to Capital  and Management, (Bureau of Business Research and Service: Ca l i f o rn i a State Univers i ty, Fresno, 1973). Wiley (1976) found that 90 percent of the companies he contacted used equity rather than to ta l cap i ta l invested. However, his results are highly suspect since the l i s t of return measures from which the respondents selected the i r key measure contained only one which was based on tota l c a p i t a l . Emphasis on cash flows - the findings of the survey indicate that the most widely used re l a t i ve measures of return involve cash flow rather than gross income or other accounting measures. The large number of respondents reporting the use of a f ter - tax cash flow measures that assume the deduction of debt service (DS) payments from net income re f lec t s the concern of the real estate community with the tax shelter features of real property and the impact of f inancing variables including leverage on investment return. Widespread use of DCF models - while the most important investment return measure (on e i ther a before-tax or a f ter - tax basis) was cash flow divided by equity (overall ROI and ATCF ( f i r s t year/ equity) , about 26 percent of the investors surveyed said they used some form of a f ter - tax DCF model; the IRR was the most popular. These findings are s im i la r to those of Wiley (1976) and represent a s i gn i f i can t increase in the use of DCF models over the HUD study (1968) due most l i k e l y to the advent of increased access to computer systems. Results of the investors ' survey indicate that the r e l a t i v e l y sophist icated DCF methods of analysis are in wide use among real estate investors - pa r t i cu l a r l y large investors. Many of these are publ ic real estate corporations who are seeking to maximize earnings per share as soon as possible. Use of a DCF method which weights the dol lars received early in the l i f e of a project more heavily than those received l a te r i s therefore advantageous. Correlat ion between investment and brokerage methods - for the most part, i t i s evident that importance of the measures of investment return chosen by investors i s matched by that indicated by real estate brokers. Thus, i t appears that the actual behaviour of part ic ipants in the market for investment properties i s an important consideration in the process of the brokerage function. 60 3.5 Forms of Return Included in Measure of Performance An addit ional problem in understanding the decision-making process of investors in real estate is the i d en t i f i c a t i o n of the forms of return which are included in measuring the performance of a project. Each respondent was asked what forms of return they considered in the analysis of an investment property. The results are shown below and iin/.Apperidi.xAA> Table..3f 16Ar.for investors and in Appendix B, Table 3-16B for brokers. TABLE 3-15A FORM OF INVESTMENT RETURN CONSIDERED, BY TYPE OF INVESTOR 25 51 Publ ic Pr ivate 17 12 Real Estate Real Estate Insurance Other A l l 105 Corporation Corporation Companies Investors Investors Cash Flow 84.0 % 90.2 % 76.5 % 66.7 % 83. 8 % Tax Shelter 48.0 27.5 35.3 33.3 34. 3 Amortization of loan 32.0 27.5 35.3 33.3 30. 5 Net proceeds of sale 48.0 49.0 29.4 33.3 43. 8 Other 8.0 15.7 5.9 10. 5 The results of th is invest igat ion were then tabulated to indicate the forms of return considered by the respondents in the computation of the four most popular measures of return: overal l ROI, equity dividend rate, ATCF ( f i r s t year/equity, and IRR. The findings are shown in Tables 3-17A and 3-17B. TABLE 3-17A % OF INVESTORS INCLUDING EACH FORM OF RETURN IN MEASURE OF PERFORMANCE Overal1 Equity Dividend ATCF ( f i r s t ROI Rate year)/Equity IRR Cash flow 86.2 % 90.3 % 75.0 % 87.5 % Tax Shelter 13.8 38.7 68.8 68.8 Amortization of Loan 13.8 35.5 37.5 56.3 Net Cash Proceeds 37.9 48.4 62.5 56.3 of Sale Investors in Sample - 105. 61 The most s i gn i f i c an t f inding ref lected in these tables i s that a substantial portion of the respondents, investors and brokers, do not measure a l l the forms of return from a project. It seems that many investors and brokers are only w i l l i n g to measure those forms of return which can be estimated f a i r l y readi ly or perhaps some do not f u l l y under-stand real estate " theor ies " . As expected, over 83 percent of the respondents measure the benefits of cash flow. On the other hand, r e l a t i v e l y few respondents (less than 37 percent of investors and brokers) measure amortization of the loan (equity build-up) which is a non-cash item whose benefits are only rea l i zed upon sa le. The remaining two forms of return, tax shelter and potential net cash proceeds of sa le , were included in the four most popular measures less than 44 percent of the time. L i t t l e difference existed in the extent to which e i ther investors or brokers include the various forms of return ' In the i r measure of investment performance. These findings are for the most part confirmed by the HUD study with the only exception being that only 14 percent of the HUD respondents measured the benefits of potential proceeds of sale. I t i s interest ing to note that many of the respondents in the present study who considered the benefits of tax shelter and net cash proceeds of sale most often used a before-tax measure of investment per-formance that does not include the actual measurement of these benef i t s , (see Table 3-17A). Perhaps th i s i nd i r ec t l y re f lec t s a strong rel iance by investment decision-makers on "gut f e e l " or i n t u i t i on about a project or a lack of understanding as to the correct appl icat ion of the various measures of investment performance in real estate. 3.6 Required Rates of Return Real estate investors and brokers were asked to specify the minimum rate of return (calculated by the before-tax or a f ter - tax method spec i f ied in the previous question) required to a t t rac t them or the i r c l i en t s to 62 invest in d i f fe rent types of properties. The results are shown in Table 3-18A/B. 8 1 TABLE 3-18A RATES OF RETURN EXPECTED BY INVESTORS Overall Equity ATCF IRR ROI Dividend Rate ( f i r s t yr. )/equity Apartments 10.4 % 9.3 % 8.4 % 10.6 Off ice Buildings 11.1 10.6 18.8 10.6 Shopping Centres ( r e t a i l ) 10.9 10.9 10.5 10.0 Industr ial buildings 11.6 10.5 11.6 10.6 Hotels/motels 12.2 11.0 12.5 12.9 Undeveloped land 20.0 23.3 21.3 17.8 Other - 11 .8 12.0 13.0 Investors in Sample: 21 25 12 14 TABLE 3-18B RATES OF RETURN EXPECTED BY BROKERS Overal1 Equity ATCF ROI Dividend Rate ( f i r s t y r . )/equity Apartments 8.3 % 7.1 % 6.1 % Off ice buildings 9.0 8.3 7.9 Shopping centres ( r e t a i l ) 8.7 8.9 8.4 Industr ial buildings 11.9 8.5 9.0 Hotels/motels 13.9 12.0 11.8 Undeveloped land 31.1 19.5 13.7 Other 8.0 22.0 22.0 Brokers in Sample: 18 14 10 In general on both a before-tax and a f ter - tax basis, undeveloped land required the highest rate of return in order to a t t rac t investment, due to the r e l a t i v e l y higher r i sks involved. Consequently, f inancing on undeveloped land i s often d i f f i c u l t to obtain and when ava i lab le , i s at r e l a t i v e l y high interest rates. For s im i la r reasons, although to a lesser extent, hotels and motels had the second highest required rate of return. These were followed in descending order by indus t r ia l bui ld ings, o f f i c e buildings and apartments. The average a f ter - tax required rate of return or cut -of f point for 81. Due to the minimal use of the IRR by the brokers, the results are incomplete. 63 82 property investment was 12.4 percent. This f inding is s im i la r to that reported by the HUD study and Davis (1973) but considerably higher than that found by Ricks (1969). This could be due to the timing of the studies since in the mid-19601s when Ricks conducted his research, real estate prices had yet to escalate at the rate they did in the la te 1960's and early 1970's and i n f l a t i o n had not yet reached the double d i g i t f igures common in the mid-1970's. For the most part, the brokers surveyed reported rates of return required to a t t rac t the i r c l i en t s (presumably some of these c l i en t s are the investors surveyed in th i s study) to invest in s pec i f i c types of properties ranging from 1.0 to 2.5 percent lower than those reported by investors. Whether th is i s due to un rea l i s t i c expectations on the part of investors or brokers i s not c lear . Roulac contends investors are natura l ly op t im i s t i c , bel ieving e i ther that they possess superior expertise or that investment conditions in the 83 future w i l l be better than they have in the past. This contention i s borne out by the HUD study in which investors reported a median cut -of f point of 12.0 percent while they actua l ly experienced a 10.5 percent a f ter - tax return. In add i t ion, rates of return required by investors in th is survey (Tables 3-18A/B) appear high re la t i ve to the rates of return rea l ized by investors surveyed in previous studies as shown in Section 3.1. The comments received from the respondents tended to focus on the d i f f i c u l t y of establ i sh ing required rates of return for s pec i f i c types of property since indiv idual projects have a d i f fe rent net income stream, physical condit ion, l ocat ion , and reversionary value. Nevertheless, several investors said they were w i l l i n g to accept a lower i n i t i a l y i e l d of, for example, 10.5 82. Represents the average rate of return required by both investors and brokers using ATCF ( f i r s t year)/equity. Based on the IRR, the average rate of return required is 12.2 percent. 83. Roulac, "Can Real Estate Returns Outperform Common Stocks", pp. 34-35. 64 to 11.0 percent, with an antic ipated growth in the f i r s t 4 to 6 years increasing the y i e l d to the 12.0 to 14.0 percent range. 3.7 Adjustments for Uncertainty Investors and brokers were asked to give an ind icat ion of the re l a t i ve frequency with which they used several techniques to adjust fo r uncertainty. For each of f i v e methods respondents assigned a level of frequency, ranging from never (0) to always (3). The results of th i s invest igat ion have been tabulated for investors in Table 3-20A and for brokers in Table 3-20B. The mean usage frequency of uncertainty adjustment techniques by investors i s found in Appendix A by type of investor (Table 3-21A) and by s ize of investor (Table 3-22A). TABLE 3-20A RISK ADJUSTMENT TECHNIQUES (Percentage of Respondents in Parenthesis) Never Seldom Frequently Always No (0) (1) (2) (3) Response Mean Deviation Adjust return 22(21.0)18(17.1)36(34.3) 17(16.2) 12(11.4)1.516 1.049 upwards Adjust benefits 23(21.9)22(21.0)35(33.3) 15(14.3) 10 (9 .5 )1 .442 1.028 down Probab i l i ty 50(47.6) 18(17.1) 21(20.0) 5( 4.8) 11(10.5) 0.798 0.968 d i s t r ibut ions Sen s i t i v i t y 57(54.3) 10( 9.5) 17(16.2) 10( 9.5) 11(10.5) 0.787 1.086 analysis Other 17(16.2) - 7( 6.7) 7( 6.7) 74(70.5) 1.129 1.310 Investors in Sample - 105 65 TABLE 3-20B RISK ADJUSTMENT TECHNIQUES (Percentage of Respondents in.Parentheses) Never Seldom Frequently Always No (0) (1) (2) (3) Response Mean Deviation Adjust return 15(13.3) 9(18.8)19(39.6) 5(10.4) 7(12.7 1.292 1.031 upwards Adjust benefits 10(20.8)10(18.2)22(40.0) 6(10.9) 7(12.7) 1.500 0.968 down Probabi l i ty 24(43.6) 9(16.4) 14(25.5) 1( 1.8) 7(12.7) 0.833 0.930 d i s t r ibut ions Sens i t i v i t y 28(50.9) 13(23.6) 2( 3.6) 5( 9.1) 7(12.7) 0.667 0.975 analysis Other 3( 5.5) 1( 1.8) 2( 3.6) 2( 3.6) 47(85.5) 1.375 1.302 Brokers in Sample - 55 From a review of the f i r s t two tables i t becomes c lear that both investors and brokers used the four uncertainty adjustment techniques with about the same frequency. Both groups of respondents reported that they most often e i ther : (1) adjusted upward the required rate of return (the r i sk-adjusted discount r a te ) , or (2) adjusted downward the benefits expected ("conservative" estimates or the certainty-equivalent approach). The only var iat ion i n the results i s that real estate brokers tended more often than investors to adjust the level of benefits rather than the required rate of return. This may i n -d i r e c t l y r e f l e c t a more conservative approach to investment analysis on the part of the brokers. Thesefindings are reinforced by the conclusions reached by Wiley in his 1972 study with respect to the importance of the r isk-adjusted discount rate and conservative estimates of future income as techniques of adjusting for uncertainty in real estate investment analys is . The "other adjustments" mentioned included a subjective evaluation on the part of the respondent as to the s t a b i l i t y of the income stream from the property (tenant rat ing and vacancy l e ve l s ) . Several of the larger companies said they attempted to minimize the i r exposure to uncertainty by achieving a good "mix" 66 of properties in the i r po r t f o l i o . A breakdown of the investor responses to the uncertainty adjustment question by the type of investor (Appendix A, Table 3-21 A) indicates that pub-l i c real estate corporations used a l l of the techniques with greater frequency than other investors. In pa r t i cu l a r , they emphasized the use of s e n s i t i v i t y analysis to a greater re l a t i ve extent. A breakdown of the responses by the s ize of the investors ' por t fo l io s (Appendix A, Table 3-22A) reveals that while the large po r t fo l i o holders used s en s i t i v i t y analysis more extensively than those with smaller po r t f o l i o s , some companies in a l l po r t fo l i o classes made use of both s e n s i t i v i t y analysis and probab i l i ty d i s t r i bu t i on s . With the use of the computer becoming more widespread, i t i s l i k e l y that there w i l l be greater appl icat ion made of probab i l i ty d i s t r ibut ions and s en s i t i v i t y analysis in adjusting for project uncertainty. 3.8 Computer Usage The questionnaire contained three questions re lated to the extent and reasons for using (or not using) the computer in real estate investment analys is. The responses indicate that publ ic real estate corporations are the heaviest users of the computer as shown in Table 3-23A. Grouping the data by the s ize of the investors ' real estate p o r t f o l i o , Table 3-24A, i t i s evident that the extent of computer usage i s in large part a function of the investor ' s po r t fo l i o s i ze . Almost one-half of the larger investors reported using the computer in investment analysis while th i s held true for only 22 percent of the smaller investors surveyed. On an asset basis, 33 percent of a l l investors surveyed used the computer in investment analys is . The findings are reinforced by those of Wiley in 1972. Comparison of the two studies indicates that the extent of computer usage among real estate investors has not changed in f i ve years despite the advances in computer technology and i t s increased use in many f i e l d s of business. This may be due to a greater sophist icat ion on the part of the 67-84 American investors in 1972 than the i r Canadian counterparts. Computer usage among real estate brokers was less than that of the investors as only 8 percent reported i t s use as an ana lyt ica l tool in investment analysis as shown in Table 3-23B. TABLE 3-23A COMPUTER USAGE, BY TYPE OF INVESTOR (Percentage of Respondents in Parentheses) 25 51 Public Private 17 11 Real Estate Real Estate Insurance Other A l l 104 Corporations Corporations Companies Investors Investors Yes No Yes No 10 (40.0) 10 (19.6) 5 (29.4) 3 (27.3) 28 (26.9) 15 (60.0) 41 (80.4) 12 (70.6) 8 (72.7) 76 (73.1) (100%) (100%) (100%) (100%) (100%) TABLE 3-24A COMPUTER USAGE, BY SIZE OF INVESTOR (Percentage of Respondents in Parentheses) 27 51 26 Small Medium-sized Large A l l 104 Investors* Investors* Investors* Investors 6 (22.2) 10 (19.6) 12 (46.2) 28 (26.9) 21 (77.8) 41 (80.4) 14 (53.8) 76 (73.1) (100%) (100%) (100%) (100%) TABLE 3-23B COMPUTER USAGE (Percentage of Respondents in Parentheses) Computer Usage 49 Brokers Yes 4 ( 8.2) No 45 (91.8) (100%) 84. However, as was noted previously in th is chapter, Wiley 's sample had a greater concentration of i n s t i t u t i ona l investors than the present study. Ricks (1964) demonstrated the re l a t i ve sophist icat ion of th i s group of investors. 68 The remaining two questions dealing with computer usage focused on the reasons for using (or not using) the computer in investment analys is. The findings for the investors are shown below in Tables 3-25A/26A, in the Appendix, Tables 3-27A/28A and for the brokers in Appendix B, Tables 3/25B/ 26B. The three types of computer usage generally i den t i f i ed most often, in order of importance, were: rate-of- return ca lcu lat ions , forecasting and simulations. Among the "other" uses indicated most often by the respondents was the computation of pro forma cash flow projections. TABLE 3-25A EVALUATION OF REASONS FOR USING THE COMPUTER (Percentage of Respondents in Parentheses) Computing rates of return Regression Forecasting Simulations Other Unimportant (0) 22(78.6) 8(28.6) 10(35.7) 4(14.3) Fa i r l y Average Important Importance Essential No (1) (2) (3) Response Mean Deviation 3(10.7) 10(35.7) 13(46.4) 2( 7.1) 2.385 0.697 2( 7.1) 5(17.9) 3(10.7) 2( 7.1) 10(35.7) 8(28.6) 1( 3.6) 3(10.7) 5(17.9) 1( 3.6) 2( 7.1) 2( 7.1) 2( 7.1) 27(78.6) 0.231 1.308 1 .308 0.833 0.587 1.050 1.192 1.329 Investors in Sample - 28 TABLE 3-26A EVALUATION OF REASONS FOR NOT USING THE COMPUTER (Percentage of Respondents in Parentheses) Fa i r l y Average Unimportant Important Importance Essential 10) (1) (2) (3) Lack of 46(60.5) personnel No Computer 45(59.2) f a c i l i t i e s Lack of 41(53.9) input data Too expensive 45(59.2) Not useful 24(31.6) (necessary) Other 12(15.8) 12(15.8) 10(13.2) 9(11.8) 10(13.2) 11.14.5) 11(14.5) 10(13.2) 12(15.8) 16(21.1) 16(21.1) 6( 7.9) 10(13.2) 11(14.5) 7( 9.2) 18(23.7) No Response Mean Deviation 2( 2.6) 0.676 0.995 2( 2.6) 0.797 2( 2.6) 0.892 2( 2.6) 0.743 2( 2.6) 1.378 2( 2.6) 10(13.2) 51(67.1) 1.417 Investors in Sample - 76 1.122 1.142 1.048 1.179 1.472 69 Reviewing the reasons for not using the computer, the three primary reasons c i ted by investors were: not useful (necessary), lack of input data and no computer f a c i l i t i e s (see Appendix A, Tables 3-29A/30A). However, brokers c i ted the lack of computer f a c i l i t i e s as the primary reason followed by the fact that they considered the computer not useful (necessary) and the lack of input data (see Appendix B, Table 3-26A). Perhaps th i s somehow i n -d i r e c t l y re f lec t s the r e l a t i ve s ize d i f f e r e n t i a l between most brokerage operations who are not large enough to j u s t i f y computer f a c i l i t i e s or even access to professional computer services compared to the larger operations of many investors. Comments by the respondents centered upon the fact that the frequency of t he i r decision-making i s too low to j u s t i f y spending the time or money to "gear up" fo r computerization. However, several respondents said they were planning or using the computer in the near future. No doubt that as.the re l a t i ve costs of computer access decrease and the benefits become more widely r ea l i zed , the number of computer users w i l l increase. 3.9 Planned Holding Periods The use of some ana ly t i ca l methods of measuring investment performance in real estate, pa r t i cu l a r l y the discounted cash flow (DCF) methods, require the spec i f i ca t ion in advance of a hold period or "planning horizon" fo r the project. Accordingly, the l a s t question asked the respondents to indicate the length of the i r planned holding periods. The findings are shown in Tables 3-31A/32A for the investors, and Table 3-31B for brokers. The data, when grouped by type of investor, reveals some interest ing patterns. Except for insurance companies, the most popular holding period used in investment analysis was from three to f i ve years followed by s ix to ten year periods. This demonstrates the preoccupation of many investors with the tax shel ter benefits of real estate ownership as hypothesized by the HUD study since depreciation allowances are largely exhausted a f te r ten years. However, over 45 percent of the 70 insurance companies reported planned holding periods of greater than ten years and no consideration was given to any period less than f i ve years. Perhaps this re f lec t s the l i q u i d i t y needs of insurance companies and the i r desire to receive stable income streams from long term investments to match the i r r e l a t i v e l y stable l i a b i l i t y requirements. Grouping the data by the s ize of the investors ' real estate por t fo l io s indicates roughly the same planned holding periods (see Appendix A, Table 3-32A). TABLE 3-31A PLANNED HOLDING PERIODS, BY TYPE OF INVESTOR (Percentage of Respondents in Parentheses) 22 50 Public Pr ivate 15 10 Real Estate Real Estate Insurance Other A l l 97 Years Corporations Corporations . Companies Investors Investors 0 - 2 2 ( 9.1) 8 (16.0) - 1 (10.0) 11 (11.3) 3 - 5 8 (36.4) 19 (38.0) 1 ( 6.7) 2 (20.0) 30 (30.9) 6 - 10 3 (13.6) 11 (22.0) 6 (40.0) 3 (30.0) 23 (23.7) 1 1 - 2 0 5 (22.7) 6 (12.0) 2 (13.3) 1 (10.0) 14 (14.4) 20+ 4 (18.2) 6 (12.0) 6 (40.0) 3 (30.0) 19 (19.6) (100%) (100%) (100%) (100%) (100%) For the brokers survey, the findings were, as shown in Table 3-31B, r e l a t i v e l y s im i la r to that for the investors. About 62 percent of those surveyed employed a three to f i ve year planned holding period for investment analysis purposes. TABLE 3-31B PLANNED HOLDING PERIODS (Percentage of Respondents in Parentheses) Years 40 Brokers 0 - 2 7 (17.5) 3 - 5 25 (62.5) 6 - 1 0 5 (12.5) 1 1 - 2 0 2 ( 5.0) 20+ 1 ( 2.5) (100%) 71 3.10 Summary of Pr inc ipa l Findings The primary objective of th i s chapter was to determine from the real estate investment community the key factors a f fect ing the decision-making process of investment analysis and the se lect ion of methods of measuring return. The fol lowing is a summary of the pr inc ipa l f indings outl ined in deta i l e a r l i e r in th i s chapter. 1. Over 83 percent of the investors in th is study (87 percent of the brokers) used some form of before-tax measure of return on per-formance. The two most popular before-tax measures were the over-a l l ROI and the equity dividend rate. 2. Over 57 percent of the investors in the study (42 percent of the brokers) used some form of a f te r - tax measure of return. The most popular a f ter - tax measures were ATCF ( f i r s t year)/equity and the IRR. 3. Twenty-six percent of the investors reported using an a f ter - tax DCF measure of return. In th i s regard, the most sophist icated group of investors were large publ ic real estate corporations followed by insurance companies. While there was a de f in i te cor-re la t ion between the measures used by investors and brokers, the l a t t e r group placed a stronger emphasis upon t rad i t i ona l c a p i t a l -ization-of-income methods common to appraisal . 4. Cash flow was considered as the most important form of return by both groups of respondents in the study. However, many investors and brokers appear to select less sophist icated measures of return and then f a i l to adhere to the spec i f i c methodology of the chosen measure. 5. The average annual a f ter - tax rate of return required to a t t r ac t e i ther investors or brokers' c l i en t s to invest in real property was 12.4 percent. This return i s high r e l a t i ve to the actual return received and to the return avai lable in other investment markets such as stocks, bonds and mortgages which may be due to un rea l i s t i c expectations on the part of investors. Undeveloped land had the highest required rate of return followed in descend-ing order by hotels/motels, indus t r ia l bu i ld ings, o f f i c e buildings and apartments. 6. Adjustments for uncertainty were most frequently through adjust-ments in the required rate of return or the benefits expected from the project. Public real estate corporations and large investors in general tend to use the more sophist icated uncertainty adjust-ment techniques of s e n s i t i v i t y analysis and probab i l i ty d i s t r i -butions more frequently than other groups of investors. 7. Over 27 percent of the investors in the study (11 percent of the brokers) used the computer in investment analysis which appeared to be a function of the po r t fo l i o s i ze . The three most important reasons c i ted for using the computer were: rate-of-return ca lcu-l a t i on s , forecasting and simulations. On the other hand, the three most important reasons c i ted for not using the computer were: not useful (necessary), lack of input data and no computer f a c i l i t i e s . 8. The most popular planned holding period was three to f i ve years followed by s ix to ten years r e f l ec t i ng the importance of the tax benefits of real estate investment. Insurance companies generally preferred longer planned holding periods than other groups of investors. The next chapter w i l l test the predict ive u t i l i t y of the various measures of return using an actual sample of 15 apartment propert ies. These findings and the i r implications with respect to the current status of investment analysis as revealed through the questionnaire surveys w i l l be considered in the f i n a l chapter. 74 4.0 AN EMPIRICAL TEST OF THE PREDICTIVE UTILITY OF MODELS TO MEASURE  INVESTMENT PERFORMANCE The theoret ical discussion of real estate investment analysis presented in Chapter 2 indicated that numerous methodologies have been developed for measuring investment performance or return. Although second generation or discounted cash flow (DCF) models were shown to incorporate many of the requirements of an accurate and r e l i a b l e measure of return, two issues remained unsolved in real estate l i t e r a t u r e . These were: (1) a lack of consensus regarding the extent of the usage of the various measures by real estate investors, and (2) disagreement on which model w i l l produce the most accurate and r e l i ab l e measure of performance. Chapter 3 addressed the f i r s t issue and presented the results of a survey of real estate investors and a survey of real estate brokers. I t was c lear that the majority of those surveyed employed t rand i t iona l or f i r s t generation, before-tax methods in the i r analysis of investment opportunit ies. Many of the respondents appearedto se lect less sophist icated measures of return and then f a i l to adhere to the spec i f i c methodology of the chosen measure. Sophist icat ion in real estate investment analysis was shown to be a function of the type of company and po r t fo l i o s ize since large, public real estate corporations employed sophist icated methods and techniques more often r e l a t i ve to other investors. The objective in th is chapter is therefore, to examine the predict ive u t i l i t y of the various measures of investment return as i den t i f i ed in theory (Chapter 2) and in pract ice (Chapter 3). The approach taken i s to construct a hypothetical investment decision problem facing a real estate investor. The investor has a sample of properties or investment opportunities from which to choose. Using the measures of investment return discussed i n the previous chapters, the investor or his analyst is able to calculate the ex ante returns expected from each property over the expected holding period and subsequently, construct a ranking of the properties. Next the ex post returns rea l ized from each property are calculated based on actual operating records for each property over the holding period and a corresponding ranking 85 i s constructed. Thus, i t i s possible to compare the ex ante returns of the property sample with the ex post returns and to measure the deviations between predicted and actual as a test of the r e l i a b i l i t y of predict ion using each measure of investment performance or return. Inputs into the empirical analysis in order to compute the ex ante returns are developed from operating h i s tor ies for each property, ex i s t ing market data, the subjective evaluations of future economic and market condit ions. The process and problems of data estimation encountered are also discussed. Furthermore, the ex post returns calculated for the properties are interest ing re la t i ve to the returns indicated in previous studies. 4.1 Property Data I n i t i a l l y , an attempt was made to structure the required sample, in terms of: (1) type of property, and (2) s i z e , number and locat ion of properties within several c i t i e s so that the results would be s t a t i s t i c a l l y s i gn i f i can t . However, i t soon emerged that given the l imi ted resources ava i lab le to the study and the fact that the data which was being requested was so conf ident ia l and d i f f i c u l t to obtain, the sample requirements had to be changed and the data obtained from avai lable sources. Thus, the sample eventually consisted of f i f teen apartment projects constructed from 1959 to 1966 in Metro Toronto. Apartments were chosen as the basis for the analysis s ince: (1) they were 85. Ex ante return i s expected, predicted or forecasted return. Ex post return i s rea l i zed or actual return. Economic theory supports the view that ex ante and ex post returns should be ident ica l in the long run - see Paul H. Cootner and Daniel M. Holland, Risk and Rate  of Return (Cambridge, Mass.: Mass. Inst i tute of Technology, 1964). i d en t i f i ed as the dominant type of property held by investors responding to the survey, (2) there exists a substantial amount of information on the apartment market re l a t i ve to other property markets, and (3) th i s would permit comparisons to be drawn with other investment return studies. A s ingle l ocat ion , Metro Toronto, was selected since th i s allows a more accurate simulation of the market variables (income and expense i n f l a t i o n factor s , vacancy rates, f inancing terms, etc.) facing a potential investor than would be possible in a var iety of locat ions. Therefore, the results are typ ica l of the apartment industry ' s experience in Toronto during the period of analysis although because of the way in which the data was c o l l e c t -ed, they w i l l not allow s t a t i s t i c a l l y s i gn i f i c an t inferences to be drawn. The s ize of the apartment projects in the sample measured in units i s shown in Table IV-1. The average number of units per project was 266 with the mode or most popular project s ize being in the 101-200 unit range. Four projects were comprised of two buildings each while two projects each had three bui ldings. Geographically, the d i s t r i bu t i on of the sample is shown on the map. TABLE 4-1 SIZE OF APARTMENT PROJECTS MEASURED IN UNITS Number of Units 0 - 1 0 0 3 101 - 200 5 201 - 300 1 301 - 400 1 401 - 500 501 - 600 3 600 + 2 TOTAL 15 Average Units/Project 266 77 Audited statements were the source of the data which included the fol lowing information for each apartment property: 1. S i ze, l oca t i on , and amenity cha rac te r i s t i c s 2. Land and improvement cost in 1967„ 3. Replacements of bu i ld ing and equipment by year from 1967 to 1977. 4. Additions to bu i ld ing and equipment by year from 1967 to 1977. 5. Income and expense statements year ly from 1967 to 1977. 6. Equipment cost in 1977. 7. Improvement cost in 1977. There i s no income f igure for 1974 because of a change in year end from December 31st to February 28th. The 1975 income i s fo r the 12-month period ending February 28, 1975. Furthermore, a l l costs represent 100 percent interests on the part of the investor. 4.2 Model to Evaluate Measures of Investment Performance 4.2.1: Framework of the Model The model developed in th i s chapter to evaluate the a b i l i t y of return measures to accurately forecast investment performance works within the general framework of wealth maximization as described in Chapter 2. S p e c i f i c a l l y , i t i s assumed that real estate investors have two common object ives: 1. They want the i r return to be r e l a t i v e l y high; they always prefer more of i t to less of i t . 2. They want the i r return to be dependable and s tab le , not subject to uncertainty; i f they must accept uncertainty, they prefer less of i t to more of i t . In th i s context, the investment decision process becomes one of estimat-ing the expected return character i s t i c s of investment a l te rnat i ves , ranking them, and then making the investment decision based on the return and un-certa inty preferences of the investor. Although models to measure the uncertainty associated with real estate investment have been developed and tested, they have not yet reached the QC stage where they are operational or pract ica l for use by most investors. For the purpose of th i s study, i t i s assumed that the uncertainty elements of the subject properties have been for the most part evaluated and r e f l e c t -ed in the investor ' s "required return" necessary to induce investment ( i . e . the investor has added a premium s u f f i c i e n t to compensate him for the per-ceived uncertainty). Thus, the general decision rule for the investor i s to invest in the subject properties i f the expected return i s equal to or 86. See Chapter 2, p. 35 greater than his "required return" . The model used in th i s analysis i s a modif ication of the RE. 1 computer model developed by James R. Cooper and Stephen Pyhrr at the University of I l l i n o i s . Spec i f ic modifications include: 1. Adaption of the model to Canadian tax laws. 2. Provis ion for the analyst to input d i f fe rent land, bui ld ing and equipment rat ios at the end of the holding period than those which existed i n i t i a l l y . 3. Capabi l i ty to input " ac tua l " income figures for each year through the use of i r regu la r income cards. 4. Capabi l i ty to input yearly expenses and additions to bui ld ing and equipment through the use of "extraordinary" expense and capita l cost cards. 5. Provision for the analyst to specify a reinvestment rate for cash-throwoff from the property and ca lcu lat ion of an adjusted IRR. 6. Provision for the spec i f i ca t ion of a safe rate (i,_), a re-investment rate ( i R ) , and a minimum threshold for reinvestment opportunities thereby allowing the FMRR to be calculated. The f i na l output variables generated are the IRR on tota l c a p i t a l , IRR on equity, adjusted IRR, FMRR, and net present value. In add i t ion, numerous intermediate values are generated in the cash flow analysis stage which permit the ca lcu lat ion of a var iety of measures of return including most of the measures discussed in Chapter 2. Figure 4-1 provides in flowchart form an overview of the steps actua l ly taken by the program. 80 FIGURE 4-1: PROJECT ANALYSIS - INVESTMENT RETURN MODEL INVESTMENT OUTLAYS 1. Size and Type oif Apartments 2. Total Bui lding Costs 3. Land Costs 4. Depreciation Method Income-OPERATIONS 1. Rental Income 2. Operating Costs 3. I n f la t ion Factors 4. Vacancy Rates 5. Replacement Costs 6. Addition Costs g. FINANCING 1. Amt. of Equity 2. Amt. of Debt 3. Amortization Period 4. Interest Rate 5. Safe Rate 6. Reinvestment Rate Tax Assumptions Capital Gains REVERSION 1. Holding Period 2. Se l l i ng Price of 3. Debt Ret i re -ment 4. Land/Building| 5. Equipment/ Bui lding Ratio J Annual Cash Flows During Holding Period Reversion Cash Flow at End of Holding Period CASH FLOW ANALYSIS RETURN ON TOTAL CAPITAL INVESTED - Present Value - IRR RETURN ON EQUITY INVESTMENT - Present Value - Equity Y ie ld Rate - IRR - Adjusted IRR - FMRR The role of computer analysis in the model i s of paramount importance. There are t h i r t y - f i v e input variables and th i r ty - th ree intermediate or f i na l output variables in the model. The computer eliminates laborious hand ca lcu-lat ions which otherwise would be necessary. In th i s study, f l e x i b i l i t y was added to what i s e s sent ia l l y a determinist ic investment return model by pre-paring present value and IRR calculat ions under op t im i s t i c , most l i k e l y , 81 and pess imist ic assumptions. A number of assumptions are basic to the investment return model used here: Assumption 1. The assumptions i m p l i c i t in the DCF rate of return methods (IRR, adjusted IRR, and FMRR) are va l i d . S p e c i f i c a l l y , a yearly discounting procedure i s used over the holding period. While the data for the property sample is recorded on an annual bas is, cash flows received during the year are mathematically treated as i f they occurred at the end of the year. For the IRR, intermediate cash flows are assumed to be reinvested at the internal rate; the adjusted IRR assumes reinvestment opportunities ex i s t at a spec i -f i ed rate (ip>); and the FMRR assumes reinvestment p o s s i b i l i t i e s ex i s t at a "safe rate" ( i^) and once a minimum threshold, has been reached, at a re-investment rate ( i ^) . Assumption 2. The values of input variables which are employed in the ca lcu lat ion of yearly cash flows can be estimated with certa inty. This condition i s eliminated for present value and IRR calculat ions prepared under op t im i s t i c , most l i k e l y , and pess imist ic assumptions. Assumption 3. Rental income can increase (or decrease) at a constant annual rate over the holding period of the investment. Assumption 4. Annual operating costs are a function of tota l rental income. A s t ab i l i z ed cost function is provided since the operating cost per-centage must be increased or decreased at an estimated annual rate over the holding period. Assumptions 2, 3, and 4 are eliminated when the " ac tua l " investment results are computed since the inputs are known from the operating h i s tor ies of the properties. 87. In determinist ic models, a l l the data, input and output, are s ingle "point estimate" values. In contrast is the p robab i l i s t i c model which e x p l i c i t l y considers uncertainty whereby values of variables are stated as ranges with associated probab i l i ty d i s t r i bu t i on s , rather than as point estimates. 82 Input Variables - There are t h i r t y - f i v e input variables which must be estimated in the model. They are: 2. 3. 4. 5. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. CLASS = BED = FTBED = ARENT = VAC = GROWR = COSTL = FTCOP = RROROA = PEROP = GROWOC = DEPL = DEPR = YTAX = CAPTAX = YEARS = SELP = SALCOM = STIR = AMLON 1 RAT 1 = TERMA 1 ANS = AMLON 2 RAT 2 = ONLY I = TERMA 2 V3 = PCTL = RCAP8I RCAP8F RATE = RATE 1 RATE 2 LIMIT = type of property (0 = American r e s i d e n t i a l , 1 = American commercial, 2 = Canadian) number of units in project square feet per un i t total, annual rental, income vacancy allowance ( 1 = year 1 , 2 = year 2 ... 5 = year 5 and for remainder of holding period ) annual growth rate of rental income over the holding period land cost square foot cost of a l l improvements required rate of return on equity operating cost as a percent of tota l rental income annual growth rate of operating cost depreciable l i f e of improvements depreciation method - capita l cost allowance (CCA) class (3 = masonary buildings - 5%, 6 = frame buildings - 10%, 8 = equipment - 20%) ordinary income tax rate Capital gains tax holding period of investment i n years (NT) growth rate of property value (actual reversion in year NT may be substituted) sales commission (as a percent) investor ' s short term borrowing rate amount of loan 1 interes t on rate on loan 1 amortization term of loan 1 response to question about secondary financing (1 = yes, 2 = no) Amount of loan 2 interest rate on loan 2 loan 2 interest only check amortization term of loan 2 CCA treatment (-2. = no CCA deductible against other income, - 1 . = only class 8 deductible against other income, 0. to 1. = a l l of class 8 and 1.00 x 100 percent of class 3 or 6 i s deductible against other income) percent of f i n a l sale pr ice that i s assumed to land percent of i n i t i a l bui ld ing value assumed to be class 8 percent of f i n a l bu i ld ing value assumed to be class 8 adjusted IRR reinvestment rate FMRR "safe rate" FMRR reinvestment rate once minimum reinvestment threshold reached FMRR .minimum reinvestment threshold be 83 Output Variables - There are forty-n ine intermediate and f i na l output values calculated by the computer. They are: 1. FTALL = tota l square feet of a l l improvements 2. COSTP = tota l property cost 3. COSTL = land cost 4. COSTB = bui lding cost 5. DEBT = debt borrowed to finance property 6. EQUITY = or ig ina l equity invested in property annual amortization payment (debt service) 7. AMORT = 8. AINT = annual interest expense 9. APRIN = annual amoritzation of pr inc ipa l (reduction in pr inc ipa l amount of loan) 10. ARPRIN = annual amoritzation of pr inc ipa l reduction in pr inc ipa l amount of loan) 11. ATCCA = tota l CCA claimed each year 12. ACCA = CCA claimed on class 3 or 6 each year 13. ACCA 8 = CCA claimed on class 8 each year 14. RCCA = remaining capita l in class 3 pool or class 6 pool 15. RCCA 8 = remaining capita l in class 8 pool 16. CGL = capita l gain on land 17. CGB = cap i ta l gain on bui lding 18. CG 8 = cap i ta l gain on class 8 19. CG = tota l capita l gain 20. RECAP 8 = recapture on bui ld ing 21 RECAP 8 = recapture on class 8 22. RECAP = tota l recapture 23. GPI = annual gross possible income 24. VAC = annual vacancy allowance 25. ARENT = annual gross e f fec t i ve income 26. COSTS = annual operating costs 27. ANIN = annual net operating income 28. AINT = annual interest expense 29. ADEP = annual depreciation expense 30. AINC = annual taxable income 31 ACASH = annual cash throwoff 32. CASH = annual a f ter - tax equity cash flow 33. VCASH = annual a f ter - tax cash flow on tota l cap i ta l invested in property (before financing factors are considered) 34. COVRAT = annual debt coverage ra t i o 35. BEV = annual breakeven point 36. OARETN = annual overal l return 37. ATRETN = annual a f ter - tax return on equity 38. GROSYI = annual gross y i e l d on equity 39. REV = s e l l i n g pr ice of property at end of holding period of the investment (reversion) 40. COM = s e l l i n g commission 41. ARPRIN = remaining debt pr inc ipa l 42. REVNET = before-tax reversionary cash flow 43. REVTAX = tax on reversion at end of holding period 44. REVFLOW = af ter - tax equity reversion 45. YIELD = rate of return (IRR) on equity 46. RORASS = rate of return on tota l capi ta l 47. PVALUE = present value of project 48. IRR = adjusted IRR on equity 49. RET = FMRR 4.2.2 Operation of the Model In th i s sect ion, a b r ie f discussion of the input and output variables in the model i s presented. As noted previously, the primary dependent v a r i -ables in th i s model are the PV, IRR, adjusted IRR,. and the FMRR. Numerous intermediate values are generated in the cash flow analysis stage thus, permitting the ca lcu lat ion of most of the measures of investment return i den t i f i ed in theory (Chapter 2) and in pract ice (Chapter 3). Values for t h i r t y - f i v e input variables must be estimated in the model. These values represent the various conditions under which a property is evaluated. The investor or his analyst would estimate the input data necessary in the model as shown in Table 4-2 for a sample 250 unit apartment complex. There are forty-nine output variables calculated by the computer, the primary being the PV, IRR (on tota l capi ta l and equi ty) , adjusted IRR, and the FMRR. Table 4-3 indicates the output data received from the computer a f ter employing the input data from Table 4-2. The primary dependent variables in the model have been estimated and serve as yardst icks for com-paring a l ternat ive investment projects. A detai led flow chart of the model's operation i s presented in Figure 4-3. It provides a synthesis of the operation of the model ind icat ing the sequence in which the output values are computed. In the fol lowing sections, the focus i s on the analysis of apartment properties using the computer model to a s s i s t in the ca lcu lat ion of the various measures of investment return. The input variables and assumptions are analyzed followed by the forecasted and actual investment performance of each of the f i f t een properties over the holding period. KUN NUMBER 1 1. rtECAPITULATICN OF INPUT DATA THE UNIVERSITY OF B.C. CENTER FOR REAL ESTATE AND URBAN ECONOMICS • I . TYPE PROPERTY (O.*AMER ICAN RESIDENTIAL. I•*AMERICAN COMMERCIAL. 2. •2. NUMBER OF UNITS IN PROJECT * • 3. AVERAGE SQUARE FCCTAGE PER UNIT « • 4. AVERAGE ANNUAL RENTAL PER UNIT • • 5. EXPECTEO OCCUPANCY >RS. 1-5= • 6. ANNUAL GROWTH RATE OF RENTAL INCOME OVER THE HOLDING PERIOD • • 7. TOTAL LAND COST « •0. SQUARE FCOT COST OF ALL IMPROVEMENTS » •9. REQUIRED RATE OF RETURN ON EQUITY -•10. CPEPATING COST AS A PERCENT OF TOTAL RENTAL INCOME » o i l . ANNUAL GROWTH RATE OF OPERATING COST OVER HOLDING PERIOD * .•13. CEPRECIATION IS BY THE CCA METHOD - CLASS* • 14. ORDINARY INCCME TAX RATE = •1 5 . CAPITAL GAINS TAX RATE » • 16. HOLDING PERIOD OF THE INVESTMENT * • 17. ANNUAL GROWTH RATE CF PROPERTY VALUE o • 18. SELLING COMMISSION I PERCENTJ = •19. INVESTORS SHORT TERM BORROWING RATE'* • 20. AMCUNT OF LCAN t » • 21. EFFECT IVF INTEREST RATE ON LOAN 1 « • 22. AMCRTIZATICN TERM CF LOAN 1 * •23. DOES THIS PROJECT INVOLVE SECONDARY FINANCING » •28. HOW IS CCA TO BE TREATED? = - 2 . =N0 CCA IS CEDUCTA8LE AGAINST OTHER INCOME - I . =ONLY CLASS 8 CCA IS DEOUCTABLE AGAINST OTHER INCOME O.TO l.=ALL OF CLASS 8 AND 1.00 X 100 PERCENT OF CLASS 3 OR 6 IS CECUCTABLE AGAINST OTHER INCOME #29. PERCENT OF FINAL SALE PRICE THAT IS ASSUMED TO BE LAND « •30. PERCENT OF I N I T I A L BUILOING VALUE ASSUMED TO BE CLASS 8 « • 31t PERCENT CF FINAL BUILDING VALUE ASSUMED TO BE CLASS 8 • • 44. ADJUSTED I.R.R. REINVESTMENT RATE" •4 5 . F.M.R.R. •SAFE'RATE F.M.R.R. REINVESTMENT RATE MINIMUM AMCUNT OF MCNEY NEEDED TO INVEST AT F.M.R.R. REINVESTMENT RATE SUMMARY OF IRREGULAR CASH FLCWS . CANADIAN* 3."CANADIAN A.R.P.1*2. 1.00 1.00 514473.0000 0.9700 0.9800 0.9800 0.9800 M 0.9800 0.0500 t-g 430609.0000 3210724.0000 0.1300 S n 0.4500 0.0750 3. o 0.5000 o t 0.0 1 ro 7.0 4677255.OOOJO 0.0500 0.0996 s 2913067.0000 o *—4 0.0996 35.00 NO 1.00 0.1500 0.0500 0.0250 0.1300 0.0650 0.1500 50000. YEAR INCOME EXPENSE CAPITAL COST 04C #41 #42 I 0. 1000. 0. 2 0, 1500. 0. i 0. 2000. 0. 4 0. 2500. 0. 5 0. 3000. 0. 6 • 0. 3500. 0. 7 0. 4000. c. 00 [CTAL SQUARE FEET CF IMPROVEMENTS • 1. ICTAL PRGPFRTY COST = 3641333. LAND CCST = 4306C9. dUJLDING COST " 3210724. DEBT BCRHOWED TO FINANCE PROPERTY = 2913067. OKIGINAL ECUITY INVESTED IN PROPERTY * 728266. 3. LOAN INFORMATION LOAN 1 YE A K AMORTIZATION INTEREST AMORTIZATION REMAINING PAYMENT EXPENSE OF PRINCIPAL PRINCIPAL 3C0Se9. 290142. 10848. 2902219. 3CC989. 289061. 11928. 2890290. 3C0989. 287873. 13116. 2877173. 3cc<;ag. 286566. 14423. 2862750. 3CCS89. 285130. 15859. 2846890. 300989. 283550. 17439. 2829451. 3CC989. 281813. 19176. 2810275. CAPITAL CCST ALLOWANCE INFORMATION CCA CLAIMED CCA CLAIMED CAPITAL IN CAPITAL IN TOTAL CCA ON CLASS ON CLASS CLASS 3 OR 6 CLASS8 3 OR 6 8 POCL PCOL 1 184617. 152509. 32107. 3050187. 160536. 2 17C57C. 144884. 25686. 2897677. 128429. 3 158188. 137640. 20549. 2752793. 102743. 4 147197. 130758. 16439. 2615153. 82195. 5 137371. 124220. 13151. 2484395. 65756. 6 128530. 118009. 10521. 2360175. 526C4. 7 120525. 112108. 8417. 2242166. 42084. CAPITAL GAIN ON LAND « 235900. CAPITAL GAIN ON BUILOING * 632274. CAPITAL GAIN ON CLASS 8 » 0. TOTAL CAPITAL GAIN » 868174. RECAPTURE CN BUILCING » 808021. RFCAPTURE CN CLASS fl = 52338, TOTAL RECAPTURE = 860359. 5. I.ASH FLOW ANALYSIS YEAR * I * 2 * 3 * 4 A GROSS POSSIBLE INCOME B VACANCY ALLOWANCE C GROSS EFFECTIVE INCOME (A-B) D NET OPERATING OPERATING INCOME EXPENSES IC-D) *-INCICATES YEARS CONTAINING IRREGULAR CASH FLOWS 514473. 15434. 499039. 232513. 266526. 54C196. 1C8C4. 529392. 250376. 279016. 5672C6. 11344. 555862. 269542. 286320. 595565. 11911. 583654. 290107. 293547. CO * 5 + 6 * 7 625313. 656610. 689440. 12507. 13132. 13789. 612836. 643478. 615651. 312170. 335866. 361293. 300659. 307612. 314358. YEAR * 1 * 2 * 3 * 4 * 5 * 6 * 7 INTEREST EXPENSE 29C142. 209C61. 287e73. 286566. 285130. 283550. 281813. DEPRECIATION EXPENSE 184617. 170570. 158188. 1471S7. 137371. 128530. 120525. H TAXABLE INCOME (E-F-GI •208232. -180614. •159741. •140216. •121842. •104468. -87981. (E-I CASH THROW OFF AMOR.PAY) 34463. 21973. 14670. -7442. -331. 6622. 13368. J • EQUITY CASH FLOW (AFTER TAX) tI-(TAXRATE X HJ) 69653. 68334. 65201. 62666. 60590. 58857. 57359. CASK FLCH CEBT V N TO TOTAL COVERAGE BREAKEVEN OVERALL CAPITAL RATIO POINT RETURN YEAR (AFTER TAX) (E/AMOR.PAY) !<0>AMOR.PAY)/A) (E/ORIG.(DEBT+ECUITY)) * 1 225571. 0.886 1.037 0.073 * 2 224793. C.927 1.021 0.077 * 3 222254. 0.951 1.006 0.079 + 4 220372. C.975 0.992 0.081 * 5 2 19015. C.999 0.981 0.083 * 6 218071. 1.022 0.970 0.0 84 * 7 217441. 1.044 0.961 0.086 6. CALCULATION OF NET PRCCEECS FROM SALE OF PROPERTY SELLING PRICE CF PROPERTY AT END OF HOLDING PERIOD A677255. LESS SELLING COMMISSION 233863. LESS REMAINING CEBT PRINCIPAL 2810275. bEFORE TAX REVERSIONARY CASH FLOW 1633117. LFSS TAX CN REVERSION AT ENO CF HOLOING PERIOD 430180. AFTER TAX EQU17Y REVERSION 1202937. 7, KATE CF RETURN - PRESENT VALUE ANALYSIS TRUE YIELD CM OWNERS EQUITY (INTERNAL RATE OF RETURN) « 0,H78 INTERNAL RATE OF RETURN UN TCTAL CAPITAL INVESTED IN PROPERTY « C.C852 ICTAL PPESFNT VALUE OF ECUITY INVESTMENT * 795630. PLUS ORIGINAL MORTGAGE PALANCE « 2913067. TOTAL PROJECT VALUE « ADJUSTER RATE OF RETLRN » FINANCIAL MANAGEMENT RETURN » 0.1444 0.1482 37C3705. 0 AFTER TAX RETURN ON EOUITY U/EQUITY1 0.096 0.092 0.087 0.082 0.070 0.074 0.071 P GROSS YIELD ON EOUITY C(J*AMOR.PRIN.)/EOUITY) O.lll 0.109 0.104 0.101 0.098 0.096 0.094 FIGURE 4-2 FLOWCHART OF COMPUTER MODEL Read in i n i t i a l values fo r : Unit type, s i ze and rents (BED, FTBED, ARENT); Vacancy allowance (VAC); Cost of improvements (FTCOP); Land cost (COSTL); Required rate of return (RROROA); % operating costs (PEROP); Growth rates of operating costs and property value (GROWOC, SELP); Depreciation factors (DEPR, DEPL); Tax rates (YTAX, CAPTAX); Mortgage Terms (AMLON 1, RAT 1, TERMA 1, AMLON 2, RAT 2, TERMA 2); Percent of bui ld ing value assumed to be class 8 (RCAP8I, RCAP8F); percent of f i n a l sale price assumed land (PCTL); adjusted IRR rate (RATE); FMRR rates (RATE 1; RATE 2; LIMIT); Holding period (YEARS) Output Calculate annual values fo r : Cash throwoff (ACASH); Taxable income (AINC) Calculate annual values f o r : Rental income (GPI); Operating costs (COSTS); Net operating income (ANIN) Income tax laws Calculate i n i t i a l values fo r : Total sq. f t . of improvements (FTALL) Property cost (COSTP); Land cost (COSTL); Bui lding cost (COSTB); Original equity (EQUITY) Calculate annual values fo r : Depreciation expense (ATCCA); CCA in each pool (ACCA, ACCA 8, RCC 8, RCCA 8) Calculate annual values fo r : Amortization payment (AMORT); Interest expense (AINT); Amortization of pr inc ipa l (APRIN); Remaining pr inc ipa l (ARPRIN) Calculate annual values for : A f ter - tax cash flow to equity (CASH); Gross y i e l d of equity (GROSYI) Calculate at end of holding period: Se l l ing pr ice of property (REV); Before-tax equity reversion (REVNET) Calculate tax on reversion at end of holding period (REVTAX) Capital gains tax laws Calculate IRR on to ta l cap i ta l (RORASS) Calculate annual af ter tax cash flowl to tota l capi ta l (VCASH) Calculate present value of project (PV) Calculate a f ter - tax equity reversion (REVFLO) Calculate.on equity:, IRR (YIELD) Adjusted IRR (IRR) FMRR (RET) 89 4.3 Analysis of Input Variables and Assumptions The analysis of input variables in the apartment sample i s s imp l i f i ed by the existence of a three-year (1967, 1968 and 1969) operating history of each property p r io r to i t s consideration by the investor in 1970 as an investment. This operating record enables the investor or his analyst to accurately input property costs and f i r s t year rental and expense levels into the model. However, when the data re la t ing to future time periods was estimated, the trends derived from past operating records of each property were supplemented with the opinions of numerous real estate analysts, investors and appraisers who were f ami l i a r with the Toronto apartment market pr io r to 1970. These opinions were weighed, combined with the past trends for each bu i ld ing, and entered into the model as judgmental "point estimate" forecasts fo r the period from 1970 to 1977. While i t i s acknowledged that i t is d i f f i c u l t to simulate the h i s t o r i c a l behaviour of market part ic ipants from a l a te r point in time when actual market performance i s known, every attempt was made to r e a l i s t i c a l l y simulate the investment market for apartment properties in 1970. 4.3.1 Predicted Performance Case The return model, i t w i l l be reca l l ed , requires the estimation of th i r ty - th ree variables (Table 4-2): Input 1 i s the type of property. Input 2 i s the number of units in each apartment. Input 3 i s the number of square feet in each un i t . p o Input 4 i s the average rental per unit . Given an operating history 88. Should the analyst not want to involve himself with ca lcu lat ions of a per unit basis, the number of units (Input 2) and the square footage per unit (Input 3) may each be set to one and the average rental per unit established as the gross potential income for the ent i re project. This was the method followed in th i s study. 90 for each property from 1967 to 1969, the task of the analyst i s to estimate gross potential income in 1970. This f igure was estimated through an analysis of several factors: (1) previous rental increases, (2) d e s i r a b i l i t y of the apartment from the users' point of view in terms of locat ion and amenity character i s t i c s (see Map 4-1 and Appendix C), and (3) market information avai lable to the analyst in 1970. With regard to the l a s t fac tor , annual rental increases averaged 3.4 percent to 6.2 percent per year in the period 89 1965 to 1970. Once a reasonable growth factor for rental income was ca lcu-la ted, th i s was applied to the actual gross potential income in 1969 to determine gross potential income in 1970. Input 5 i s the expected vacancy rate over the holding period. The vacancy rate speci f ied in the model for the f i f t h year applies for the remainder of the holding period. Average annual vacancy rates in Metropolitan Toronto from 1965 to 1970 were 1.34 percent for buildings of 50 to 99 un i t s , 2.44 percent for buildings of 100 to 199 un i t s , and 2.26 percent for bu i l d -90 ings over 200 units. These results combined with the previous actual vacancy rates produced an expected vacancy forecast for each apartment property over the holding period. 89. For the period 1965 to 1970, the fol lowing rental revenue information was ava i lab le : ( i ) for a l l buildings in Canada, an average increase of 3.5 percent per year - Canadian Housing S t a t i s t i c s 1970, (Ottawa: Central Mortgage and Housing Corporation, 1970); ( i i ) for high-r ise buildings in Canada, an average increase of 6.2 percent per year - Prices and Incomes Commission, Residential  Rentals in Canada: In f la t ion and Restra int, (Ottawa: December, 1970). ( i i i ) fo r elevator apartments in Toronto, an average rental increase of 4.5 percent - Inst i tute; of Real Estate Management, 1969  Apartment Bui lding Income - Expense Analys is , (Chicago: National Association of Realtors, 1969). 90. Apartment Vacancy Survey, Metropolitan Toronto: October, 1970, Toronto: Central Mortgage and Housing Corporation, 1970 (mimeographed). 91 Input 6 i s the expected annual growth rate of rental income over the holding period. This was calculated as for Input 4. Individuals knowledge-able about the market in the la te 1960's were hesitant to make projections far into the future because of the inherent uncertainty. Most, however, expressed the view that the Toronto apartment market was very active during the la te 1960's and builders and investors were basing t he i r business decisions on the continued high demand for rental accommodation resu l t ing from the un-doubling of fami l ies and the "coming-of-age" of the post-war baby boom and they expected i n f l a t i o n trends to continue. Input* 7 and 8 combine to represent the tota l purchase cost of the property in 1970. To determine t h i s , the fol lowing steps were taken. F i r s t , using figures derived from 1971 valuation day appraisals done by an independent appraisal f i rm (see Appendix C), the net income m u l t i p l i e r ^ in 1970 (NIM7g) was calculated as shown NOI operating expenses 7-, % 7 1 = (market va lue 7 , -f GRMy,) x (1 ' gross income Therefore, q ? NIM 7 Q = m a r k e t v a 1 u e 7 1 N0I 7 1 91. While i t i s acknowledged that shortcomings ex i s t in the use of a net income mu l t i p l i e r or cap i t a l i z a t i on rate to estimate value, since investors do use such devices in the i r decision making, i t remains a most useful predict ive t oo l . R a t c l i f f , using a sample of 385 properties in Vancouver, demonstrated that the average percent difference between predicted s e l l i n g price and actual s e l l i n g pr ice as calculated by the gross income mu l t i p l i e r ranged from 4% to 8% - R.L). R a t c l i f f , "Don't Underrate the Gross Income M u l t i p l i e r " , Appraisal Journal 39, (Apr i l 1971), pp. 246-511. Therefore, given information on operating costs for each property and very consistent operating cost/gross income rat ios between 1967 to 1969 and those calculated in the appraisa ls , the net income mu l t i p l i e r was chosen to estimate value. 92. Since appraisals were not ava i lable in 1970, for the purposes of th i s study, the assumption was made that the 1971 appraisal f igures would su f f i ce in the ca lcu lat ion of a net income mu l t i p l i e r fo r 1970. 92 Net income mul t ip l i e r s ranged from 11.58 to 14.78 for the sample with an 93 average of 12.60 as shown in Appendix C. Next, the forecasted NOI in 1970 was computed: operating expenses g 7 gg% forecasted NOI y o * forecasted G I 7 Q x (1 - average g r 0 s s income F i n a l l y , purchase pr ice in 1970 (PPyg) was found as fo l lows: PP 7 Q = forecasted N0I 7 Q x NIM 7 Q While the property i s being sold as one complete package to the investor, a value must be a l located to the land i n order to estimate a depreciable base for improvements. Although an appraiser attempts to estimate a " f a i r value" for the land, an investor generally prefers to minimize th i s r a t i o , subject to the approval of the Department of National Revenue, in order to maximize his depreciable property base. Total land cost, Input 7, was derived by using land and improvement costs in 1967 derived from audited statements of the properties to determine the land/building r a t i o . This ra t io was then applied to the purchase pr ice in 1970 giving tota l land cost, Input 7, with the remainder being tota l improvement cost, Input 8. Input 9 is the investor ' s required rate of return on equity which was assumed to be about 3 percent above the cost of long term f inanc ing, or 13 percent. This represents the rate of return required to a t t rac t the investor to invest in apartment properties in Toronto given the uncertainty of such investment. 93. The average NIM in 1970 of 12.60 y ie lded a cap i t a l i z a t i on factor of 7.94. While the sample properties are for the most part well located and higher-qua l i ty residences, the results compare favourably with several other studies of the Toronto market: ( i ) A sample study of 300 apartment properties in Toronto by Ron Hopper of P ick in and Mason Limited, rea l to r s , indicated an average cap i t a l i z a t i on rate of 8.07 percent (NIM = 12.39). ( i i ) A CMHC study of 13 apartments (5,044 units) the average c ap i t a l i z a t i on rate was 8.48 percent (NIM = 11.80) with a range of 8.99 to 8.0 (NIM of 11.12 to 12.48) Input 10 i s operating cost as a percent of tota l rental income. For the i n i t i a l year, 1970, the operating cost r a t i o was determined by using the average operating cost ra t io for the three years pr ior to 1970. I t was observed that the operating cost r a t i o remained r e l a t i v e l y constant over previous years. Therefore, given a good annual .maintenance schedule, th i s 94 ra t io is not expected to change s i g n i f i c an t l y over the holding period. Input 11 i s the annual growth rate of operating costs over the holding period. Generally as a bui lding ages, the percent of operating costs to gross income r i ses . Most indiv iduals contacted indicated that th i s ra t io i s expect-ed to r i se as a bui lding ages because of r i s i n g maintenance costs, minor improvements and replacement costs, taxes, etc. Data co l lected by the Ins t i tute of Real Estate Management on elevator apartments in Toronto showed 95 an increase in operating costs of 7 percent annually in 1969. Nat iona l ly , the Prices and Incomes Commission documented average annual increases of 9.2 96 percent for the same period. This market information was combined with the average annual increase in operating costs during 1967 to 1969 for each bu i l d -ing to estimate the annual increase in the operating costs to gross income rat io over the holding period. Input 12 i s the depreciable l i f e of the bu i ld ing. It was assumed that the buildings were depreciated on the basis of a t h i r t y - f i v e year l i f e by the previous owner. The age of the bui ld ing at the purchase date (1970) can be determined from Appendix C. 94. Detailed records for each property include a breakdown of operating costs into the fo l lowing: rea l ty taxes, repairs.and maintenance, hydro, water, heating f u e l , s t a f f , advert is ing and rental serv ice, legal and audit , recreation centre allowance, insurance and manage-ment fees (on-site and head o f f i c e overhead). 95. Inst i tute of Real Estate Management, 1969 Apartment Building Income- Expense Analys i s , (Chicago: National Association of Realtors, 1969). 96. Prices and Incomes Commission; Residential Rentals in Canada:  In f lat ion and Restraint, (Ottawa! December, 1970) Input 13 i s the capita l cost allowance (CCA) class or method appl icable to the bui ld ing. In the property sample,,the buildings f a l l into class 3 which i s masonary buildings allowing 5 percent of the undepreciated cap i ta l cost to be claimed each year or class 6 which i s frame buildings allowing a 10 percent claim annually. Input 14 i s the ordinary income tax rate. The investor was assumed to be in a 50 percent tax bracket. Input 15 i s the capita l gains tax rate. Under the federal tax amendments of 1972, an investor i s subject to the taxation of the capita l gain on the sale of property at one-half the ordinary income tax rate. However, since there were no cap i ta l gains tax laws in force at the purchase date (1970), Input 15 was set equal to zero for the purpose of forecasting the ex ante returns. Input 16 i s the holding period of the investment which in th i s analysis i s established to be seven years. Input 7,7 is the annual growth rate of the property value. However, rather than an annual rate, the analyst may specify the expected f i na l sales price of the property. The problem here i s to forecast the rate of change in real estate values l i k e l y to occur in the future. This was accomplished by f i r s t , estimating the average annual compounded increase in net operating income ( N O I ) over the holding period based on past increases and the expected growth factors assigned to tota l rental income and operating costs. Once the forecasted N O I in 1977 was ca lcu lated, i t was mul t ip l ied by the net income mu l t i p l i e r ( N I M ? Q ) as shown below. The f i na l f igure represents the fore-casted sales price (SP 7 7 ) of the property at the end of the holding period. expected forecasted S P 7 7 = NIM 7 n x (N0 I 7 n x compounded increase) i n N 0 I 7 0 _ 7 7 Input 18 i s s e l l i n g commission due the broker upon sale of the property. This was assumed to be 5 percent. Input 79 i s investor ' s short term borrowing rate at which funds may be obtained to cover negative cash flows generated by a project. This was assumed to be the same rate as the rate at which mortgage funds were ava i lab le . Input 20 i s the amount of the f i r s t mortgage. A loan/value ra t i o of 80 percent was assumed for each property. Therefore, the amount of the f i r s t mortgage was derived from the tota l purchase pr ice in 1970, Inputs 7 and 8. Input 27 i s the e f fec t i ve interest rate on the f i r s t mortgage. The assumption was made that any ex i s t ing mortgages were not assumable and financing had to be obtained at the current (1970) market rates. In 1970 National Housing Act (NHA) loans for rental housing were avai lable at 9.96 percent. Input 22 i s the amortization term of the f i r s t mortgage which was assumed to be a typ ica l 35 years for a l l properties. Input* 23 to 27 re late to secondary f inancing. Since f i r s t mortgage financing was avai lable up to the established maximum of an 80 percent loan/ value r a t i o , a second mortgage was not required and these inputs w i l l not be discussed. Input 28 i s the treatment of cap i ta l cost allowance (CCA). The computer model's b u i l t - i n f l e x i b i l i t y permits CCA to be treated in any one of the fol lowing ways: (1) no CCA i s deductible against other income, (2) only class 8 (equipment) i s deductible against other income, and (3) a l l of class 8 and from 0 to 100 percent of class 3 or 6 (improvements) i s deductible against other income. Since our potential investor is a real estate corpor-at ion, option (3) at a 100 percent deduction was chosen. Input 29 i s the percent of the f i n a l property value at sale assumed to land. This was determined subject ively in view of the i n i t i a l land/ building r a t i o , the age of the bui lding and whether an apartment was the highest and best use for the s i t e . 96 Input 30 i s the percent of the i n i t i a l bui lding value assumed to be class 8. Based on a rule-of-thumb developed by the Inst i tute of Real Estate Marketing, th i s value was f ixed at 5 percent of the purchase pr ice in 1970. Input 31 i s the percent of the f i n a l bui ld ing value assumed to be class 8. This f igure was assumed to be 2.5 percent at sale due to the fast depreciation wr i teoffs afforded equipment under our tax laws. Input 44 i s the reinvestment rate or a f ter - tax return on equity due intermediate cash flows generated by the project as required in the ca lcu lat ion of the adjusted IRR. Given the mortgage rates and the uncertainty associated with real estate investments, th i s input was a r b i t r a r i l y set at 13 percent. Input 45 requires three input variables to be spec i f ied in order to calculate the Financial Management Rate of Return (FMRR). F i r s t , the "safe" rate ( i^) represents an investment rate comparable to that avai lable on a savings account. In 1970 the savings rate was 6.5 percent. Second, re invest-ment rate ( i R ) represents the return avai lable on intermediate cash flows in excess of a minimum amount i f invested in "run of the m i l l " real estate projects 97 of comparable r i s k . This was selected to be 15 percent. F i n a l l y , the minimum amount of money necessary to receive the higher FMRR reinvestment rate must be spec i f ied. In th i s study a $50,000 minimum level was establ ished. For the purpose of the analys i s , the output data generated from the above input data is the most probable forecast of the investment performance of each of the 15 properties over the holding period. In order to improve the f l e x i b i l i t y of an e s sent ia l l y determinist ic model, return calculat ions were prepared under pess imist ic and opt imi s t i c assumptions. This was accomplished by assigning a probab i l i ty factor to the fol lowing s ix growth var iables: tota l rental income, expected occupancy, annual growth rate of rental income, 97. Increasing returns to scale were assumed to apply to real estate invest-ment. Therefore, a higher return was assigned to investment in larger projects than investment in 1 any- s i zed 1 project (13 percent - Input 44) or the average required rate of return (13 percent - Input 9). 97 operating cost as a percent of tota l rental income, annual growth rate of operating costs and annual growth rate of property value. Under pess imist ic conditions each of the growth factors were less favourable by 5 percent. Likewise under opt imi s t i c condit ions, each of the growth factors were more favourable by 5 percent. The other inputs were assumed to be known with certa inty. No attempt was made to account for the interdependence of a l l input variables in each year. The findings are presented in section 4.4. 4.2.3 Actual Performance Case It i s the objective of th i s section to present the inputs used to calculate the ex-post returns rea l i zed from each property over the holding period of the investment. This is done using the various measures of return described in previous chapters and ' r ea l l i f e ' operating records for each property. Only the differences between the inputs required for the predicted and actual performance cases w i l l be described here. Input 4, to ta l rental income, and.Input 6, annual growth rate of rental income are f ixed to zero. The occupancy l e v e l , Input 5, i s set to 100 percent. The operating cost/gross income r a t i o , Input 10, and the annual growth rate of operating costs over the holding period, Input 11, are set equal to zero. This allows the actual yearly net operating income from 1970 to 1977 to be input as i r regu lar income. Also actual replacements of bui ld ing and equipment (expenses) and actual additions to bui ld ing and equipment (capital cost) were avai lable on a yearly basis and incorporated in the ca lcu lat ion of the actual performance. Input 15 i s the capita l gains tax rate. While two years of the holding period were not subject to capita l gains taxat ion, for s imp l i c i t y the ent i re appreciation in value between 1970 and 1977 was assumed to be subject to capita l gains tax. Input 17 i s the sales pr ice of the property at the end of the holding period. This was established in the fol lowing manner: F i r s t , indiv iduals knowledgeable about the current Toronto apartment market and the subject properties were asked to indicate typ ica l c ap i t a l i z a t i on rates l i k e l y to apply i f the buildings were sold today. The general consensus was that while the apartment market i s not as strong today as i t was in 1970 due to rent contro l s , income tax changes, and other factors , cap i t a l i z a t i on rates of 8.5 percent (average NIM 7 7 = 11.76) could reasonably be expected. Second, th i s average mu l t i p l i e r was adjusted on an indiv idual property basis by adding or subtracting the deviation of the 1970 NIM from the average NIM 98 (12.60) in 1970. The resu l t i s the NIM in 1977 for each property (see Appendix C). F i n a l l y , the current NIM i s mu l t ip l ied by the actual net operating income in 1977 (N0I 7 7 ) to obtain the actual sales pr ice in 1977 ( SP 7 7 ) . NIM 7 Q ^ + , , , 1 C D - m na + deviation from v w „ . , . i r i T actual S P 7 7 - (11.76 _ a v e > N I M i n l g 7 ( ) ) x actual N0 I 7 7 Input 31 i s the percent of the f i na l bui ld ing value assumed to be class 8. This f igure was taken d i r e c t l y from the equipment/building and improvement cost r a t i o shown for 1977 on the audited statements. Input 40 in the computer model allows the actual net operating income received by the owner annually from 1970 to 1977 to be read i n . Input 41 permits the actual yearly replacement costs of equipment and improvement incurred by the owner to be input. These items are c l a s s i f i e d as an expense for income tax purposes. 98. This methodology assumes the same re la t i ve investment potential ex ists among the sample properties. 99 Input 42 allows the actual y e a r l y a d d i t i o n s to equipment and b u i l d i n g made by the owner over the holding period to be considered i n the return c a l c u l a t i o n s . These are c l a s s i f i e d as c a p i t a l costs ( c l a s s 2, 6 or 8) f o r income tax purposes. 4.4 Results of the Test Once the t h i r t y - f i v e input v a r i a b l e s have been s p e c i f i e d , i t i s a simple matter to read the value f o r each v a r i a b l e i n t o the computer model. In a few seconds the computer generates the f o r t y - n i n e output v a r i a b l e s l i s t e d i n the previous s e c t i o n . The measures of return discussed i n Chapters 2 and 3 are output on an annual b a s i s , or as an aggregate f i g u r e f o r the e n t i r e holding p e r i o d , depending on the method. Those measures o f re t u r n not e x p l i c i t l y generated by the computer model are e a s i l y c a l c u l a t e d using the intermediate cash flow values provided. Before examining i n d e t a i l the f i n d i n g s , i t i s important to r e c a l l the considerations that should be incorporated i n t o an accurate and r e l i a b l e measure of return as i d e n t i f i e d i n Chapter 2. F i r s t generation or t r a d i t i o n a l r e turn and value models (Figure 2-2) of an a l y z i n g r e a l e s t a t e investments were shown to not s a t i s f y these c r i t e r i a . They, f o r the most p a r t , ignore: (1) the time value of cash f l o w s , (2) uneven cash f l o w s , (3) the tax i m p l i c a t i o n s of re a l e s t a t e investment, (4) eq u i t y r e v e r s i o n received a t s a l e , and (5) f i n a n c i n g and r e f i n a n c i n g . Therefore, i n a t h e o r e t i c a l context, f i r s t generation models were concluded to be of l i m i t e d use i n the decision-making process o f real e s t ate investment. Second generation or discounted cash flow methods (Figure 2-3) attempt to overcome many of these shortcomings. In p a r t i c u l a r , the i n t e r n a l r a t e of return (IRR) was shown to meet the c r i t e r i a of an accurate and r e l i a b l e measure of r e t u r n . The IRR was al s o i d e n t i f i e d i n Chapter 3 as the most popular discounted cash flow measure employed by the re a l e s t ate 100 99 community. However, the IRR suffers from several problems, of which the most serious is the i m p l i c i t assumption that intermediate cash flows generated from a project are reinvested at the calculated IRR when in fact such reinvestment opportunities may not ex i s t . To overcome the problems associated with the use of the IRR, several modifications of the IRR have been developed with the most operat ional ly f ea s ib le , and hence, popular models being the FMRR and the adjusted IRR. Returns calculated using the FMRR may be biased due to the required spec i f i ca t ion of: (1) a " safe" rate ( i ' L ) , (2) a reinvestment rate ( i R ) » and (3) the minimum amount of dol lars to invest at i R . Thus, the adjusted IRR was chosen as the measure to calculate the " ac tua l " return or " t rue " y i e l d rea l i zed by the investor over the holding period. The adjusted IRR solves the major problem posed by the IRR, reinvestment at the IRR, and yet does not require the arb i t rary establishment by the analyst of several key rates as with the FMRR. The findings are presented in Table 4-4 (values in parentheses are the correspond-ing property ranking). Examining these findings in d e t a i l , i t i s apparent that the actual returns range from -4.2 percent to 17.4 percent with an average return of 6.8 percent. The three properties with the highest return over the holding period were, in descending order: Property 14, 5, and 9. The average returns using the predicted measures of return ranged from -5.4 percent with the equity dividend rate (EDR) to 16.9 percent using the IRR. The predict ive u t i l i t y of the various measures of investment perform-ance may be tested by: (1) comparing the ' a c t u a l ' returns to the predicted returns (adjusted IRR), (2) examining which measure most c losely accepted or rejected the same properties as those which were ' a c t ua l l y ' accepted 99. Despite the theoret ical super ior i ty of the IRR over f i r s t generation measures of return and i t s popularity among DCF measures, an overal l preference was indicated by the real estate community in Chapter 3 for f i r s t generation measures such as the ROI and equity dividend rate. TABLE 4-4 RESULTS OF THE TEST -. 'ACTUAL' VERSUS PREDICTED RETURNS AND RANKING USING DIFFERENT MEASURES OF RETURN (Rank in Parenthesis) (a) FIRST GENERATION MEASURES Predicted Returns ROI Equity Dividend Rate After-tax Cash Flow to Equity Prop-erty 'ActuaV Return GRM NRM Pessi-mistic Most Prob-able Opti-mistic Pessi-mistic Most Prob-able Opti-mistic Pessi-mistic Most Prob-able Opti-mistic Gross Yield on Equity # 1 0.6% 12) 7.1( 9) 13.7( 4) 6.6% 7.3%(12) 8.4% - 8.6% -4.7X(13) 0.8% 7.6% 9.6%( 9) 12.3% 11.1%( 9) 2 9.6 k 4) 6.4( 14) 13.0( 10) 6.2 7.7 ( 7) 8.7 -10.2 -2.8 ( 7) 2.4 3.8 7.5 04) 10.1 9.0 (14) 3 8.4 [ 6) 7.2( 7) 11.3( 15) 7.5 8.8 ( 1) 9.6 - 3.9 2.7 ( 1) 6.6 9.5 12.8 ( 2) 14.8 14.3 ( 2) 4 7.0 ( 9) 8.5( 1) 13.3( 8) 6.4 7.5 ( 9) 8.2 - 9.4 -3.7 ( 9) -0.4 6.4 9.2 ( I D 10.9 10.7 (10) 5 13.6 ;'2) 7.2( 6) 12.6( 12) 6.6 7.9 ( 4) 8.7 - 8.4 -1.7 ( 5) 3.6 • 8.4 11.8 ( 3) 12.8 13.3 ( 3) 6 6.2. (10) 8.i( 2) 13.. 6 ( 5) 6.2 7.4 (11) 8.1 -10.5 -4.6 (12) 1.0 8.0 10.9 ( 5) 12.7 12.4 ( 5) 7 9.6 ( 5) 7.2( 4) 13.2( 9) 6.2 7.6 ( 7) 8.8 -10.0 -3.6 ( 8) 2.5 4.7 8.0 02) 11.0 9.5 (12) 8 8.4 ( 7) 7.5( 3) 13.41 6) 6.2 7.5 (10) 8.7 -10.3 -4.0 (11) 2.1 4.6 7.8 03) 10.8 9.2 (13) 9 10.2 ( 3) 6.5( 12) 14.0 3) 5.8 7.1 (13) 8.7 -12.5 -5.5 (14) 2.0 5.7 9.2 OO) 13.0 10.7 (11) 10 - (15) 6.7( 10) 15.8 1) 4.9 6.3 (15) 6.7 -75.0 -46.3 (15) -39.0 -0.2 14.2 ( 1) 17.8 16.5 ( 1) 11 8.0 ( 8) 6.5 13) 12.2 '13) 6.7 8.2 ( 3) 9.1 - 8.0 -0.5 ( 4) 4.1 6.7 10.5 ( 7) 12.8 12.0 ( 7) 12 0.0 (13) 6.4 '15) 12.0 [14) .6.9 8.3 ( 2) 9.3 - 7.0 0.5 ( 2) 5.2 7.5 11.3 ( 4) 13.6 12.7 ( 4) 13 1.0 (11) 6.7 [ I D 13.3 ! 7) 6.1 7.5 ( 8) 8.6 -10.8 -3.8 (10) 18.8 6.2 9.7 ( 8) 12.6 11.2 ( 8) 14 17.4 ( 1) 7.2 [ 5) 12.9 [ I D 6.4 7.8 ( 5) 8.5 -10.1 -2.6 ( 6) 11.6 7.1 10.6 ( 6) 12.5 12.1 ( 6) 15 -4.2 (14) 7.1 ( 8) 14.1 [12) 5.8 7.1 (14) 8.1 - 4.7 -0.3 ( 3) 2.8 4.2 6.4 05) 8.0 7.3 (15) Aver- 6.8% 7.1 13.2 7.7% -5.4% 10.05 i 11.5% age Return TABLE 4-4 (cont.) (b) DISCOUNTED CASH FLOW MEASURES: Predicted Returns Property 'Ac tua l ' Return Present Value Index IRR . Before Financing IRR Before Taxes Pessimistic IRR Most Probable Optimistic Adjusted IRR FMRR * 1 0.6*(12) l.K 9) 8.5%( 7) n.zx(io) 10.0% 14.8%( 9) 19.6% 14.4%( 9) 14.8%( 9), 2 9.6 ( 4) 1.3( 8) 9.1 ( 6) 14.6 ( 7) 11.5 17.5 ( 8) 22.3 16.9 ( 8) 16.8 ( 7) 3 8.4 ( 6) 1.3( 7) 9.5 ( 5) 17.9 ( 5) 12.9 19.0 ( 6) 21.6 17.6 ( 6) 18.1 ( 6) 4 7.0 ( 9) 2.K 1) 12.7 ( 1) 25.6 ( 1) 21.7 27.7 ( 1) 30.8 ' 25.6 ( 1) 25.8 ( 1) 5 13.6 ( 2) 1.7( 2) 7.9 ( 8) 22.6 ( 2) 18.9 24.2 ( 2) 26.1 21.8 ( 2) 22.2 ( 2) 6 6.2 (10) 1.5( 4) 10.2 ( 3) 18.4 ( 4) 15.7 20.8 ( 4) 24.6 19.3 ( 4) 19.6 ( 4) 7 9.6 ( 5) 1.4( 5) 9.8 ( 4) 17.6 ( 6) 16.3 19.5 ( 5) 23.8 18.5 ( 5) 18.8 ( 5) 8 8.4 ( 7) 1.0(12) 7.7 (10) 9.8 (11) 5.8 12.3 (12) 17.6 12.4 (12) 12.8 ( 10) 9 10.2 ( 3) 0.9(13) 7.6 (12) 5.8 (12) 3.3 11.0 (14) 17.1 11.4 (13) 11.8 ( 13) 10 - 05) V.3( 6) 6.4 ( 4) - 05) -22.4 18.5 ( 7) 27.9 17.3 ( 6) 16.4 ( 8) 11 8.0 ( 8) 1.0(11) 7.9 ( 9) 11.5 ( 9) 5.1 12.8 (10) 18.3 12.9 (10) 12.8 ( 11) 12 0.0 (13). 1.0(10) 7.7 (11) 12.1 ( 8) 4.3 12.3 (13) 18.0 12.5 (11) 12.4 ( 12) 13 1.0 (11) 0.8(14) 6.8 (13) 5.1 (13) - 7.2 (15) 13.9 8.7 (14) 8.9 ( 14) 14 17.4 ( 1) 1.7( 3) 11.2 ( 2) 21.5 ( 3) 18.5 23.7 ( 3) 27.2 21.8 ( 3) 22.1 < 3) 15 -4.2 (14) 0.5( 5) 4.7 (15) -1.4 (14) -4.2 12.6 (11) 5.4 3.5 (15) 3.3 ( 15) Average Return •6.8% 1.2 8.5% 13.7% 16.9% 15.6% 15.8% 103 or rejected, and (3) s t a t i s t i c a l l y examining how close the ranking suggest-ed by the predicted measures comes to repeating the actual ranking. A comparison of the actual return (adjusted IRR) to the predicted return (adjusted IRR) i s shown in Table 4-5. Omitting the extreme value due Property #1, the average error i s 160 percent. Despite an attempt to be as reasonable as possible in the subjective determination of inputs required for the ca lcu-l a t i on of the adjusted IRR, the results are unanimously excessively op t im i s t i c . Thus, an investor using this approach could eas i ly be led to un rea l i s t i c expectations concerning the performance of the apartment properties. TABLE 4-5 ACTUAL' RETURN VERSUS PREDICTED RETURN - ADJUSTED IRR ' Ac tua l 1 Predicted Percentac Property Return Return Error 1 0.6% 14.4% +2400% 2 9.6 16.9 +76 3 8.4 17.6 +109 4 7.0 25.6 +266 5 13.6 21.8 +60 6 6.2 19.3 +211 7 9.6 18.5 +93 8 8.4 12.4 +48 9 10.2 11.4 +12 10 - 17.3 -11 8.0 12.9 +61 12 0.0 12.5 -13 1.0 8.7 +770 14 17.4 21 .8 +25 15 -4.2 3.5 +183 Average 6.8% 15.6% +331% Following the same procedure but ca lcu lat ing the actual return and the predicted return using the ROI c r i t e r i a to measure return, the percent-age error is only 7 percent. The difference in the percentage error of predict ion between a DCF measure and a f i r s t generation measure is no doubt due to the input requirements. The ROI requires an estimation of only f i r s t year net operating income while the adjusted IRR requires a seven year estimation of a var iety of inputs including growth rates in net-operating 104 income and property values, vacancy rates, and equipment to bui lding rat ios -a l l of which have uncertain future values. An examination of which measure most c losely accepted or rejected the same properties as those which were ' a c tua l l y ' accepted or rejected is i l l u s t r a t e d in Table 4-6. The Gross Y ie ld on Equity most c losely approximates the ' a c t ua l ' acceptance and reject ion l i s t . In both cases, only two properties were accepted of which Property 5 was accepted under both measures. Property 14 was accepted by the actual (adjusted IRR) measure, a DCF measure, but not by the Gross Y ie ld on Equity, a f i r s t generation measure. This i s due to the fact that th i s property increased in value over 66 percent during the holding period which i s only ref lected in a DCF measure since i t incorporates equity reversion. It i s interest ing to note the s i m i l a r i t y among the IRR, adjusted IRR, and FMRR since they a l l produced ident ica l acceptance and reject ion l i s t s . TABLE 4-6 PROPERTY ACCEPTANCE OR REJECTION: ACTUAL VERSUS PREDICTED MEASURES* (13% required rate of return; A = accept, R = reject ) Gross IRR IRR Equity Y ie ld Before Before Adjus-Prop- 'Ac t - Di vi - ATCF/ on Finan- Taxes ted erty ua l ' ROI dend Equity Equity P.V. cing IRR IRR FMRR 1 R R R R R A R R A A A 2 R R R R R A R A A A A 3 R R R R A A R A A A A 4 R R R R R A R A A A A 5 A R R R A A R A A A A 6 R . R R R R A R A A A A 7 R R R R R A R A A A A 8 R R R R R A R A R R R 9 R R R R R R R R R R R 10 - R R A A A R - A A A 11 R R R R R A R R R R R 12 R R R R R A R R R R R 13 R R R R R R R R R R R 14 A R R R R A R A A A A 15 R R R R R R R R R R R * The GRM and the NRM measures of return were not included since the i r output is in the form of a ra t io and therefore, not immediately comparable to percentage measures. 105 The f i na l test of the predict ive u t i l i t y of the various measures of return involves s t a t i s t i c a l l y examining how close the ranking suggested by the predicted measures comes to repeating the actual ranking. For th i s purpose, the Spearman Rank Correlat ion Coef f ic ient was employed to measure the degree of corre lat ion between the r a n k s . 1 0 0 I t varies from 0 for no corre lat ion to 1 or -1 for a perfect pos i t ive or negative corre lat ion, respect ively. The results are shown in Table 4-7. TABLE 4-7 CORRELATION OF PREDICTED RANKING TO ACTUAL RANKING: SPEARMAN RANK CORRELATION COEFFICIENT Measure Spearman Rank of Return Correlat ion Coef f ic ient GRM 0.18 NRM -0.44 ROI 0.42 Equity Dividend 0.15 ATCF/Equity 0.08 Gross Y ie ld on Equity -0.09 Present Value 0.14 IRR Before Financing 0.28 IRR Before Taxes 0.71 IRR 0.34 Adjusted IRR 0.37 FMRR 0.46 Average 0.31 100. For a good treatment of the operation of the Spearman Rank Correlat ion Coef f i c ient , see Robert L. Winkler and Wil l iam L. Hays, S t a t i s t i c s : P robab i l i t y , Inference, and Decision, (New York: Holt, Rinehart and Winston, 1975), pp. 867 - 870. 106 Analyzing Table 4-7, i t i s c lear that the IRR Before Taxes i s the most successful predictor of the ' a c t ua l 1 property ranking. Of the f i r s t generation measures, the ROI emerges as the most successful predictor. Beyond these results no indiv idual measure of return displays the character i s t ic s of a more r e l i ab l e predictor of investment performance. Nor i s any s i gn i f i can t pattern evident between the f i r s t and second generation measures of investment performance. The only consistent pattern i s among the IRR, adjusted IRR, and the FMRR. While these three measures are s imi la r in the i r methodology, and the corre lat ion results r e f l e c t th i s f ac t , the 'improvements' made to the standard IRR appear to have improved i t s predict ive u t i l i t y . On the other hand, f i r s t generation measures show re l a t i v e l y less consistency in the i r corre lat ion coef f i c ient s probably due to the fact that the i r methodology i s much d i f fe rent than that used to calculate the ' a c t ua l ' return - a DCF measure. In general, the lack of corre lat ion between the actual ranking and the predicted ranking raises the question as to the r e l i a b i l i t y of predict ion in real estate investment analysis for any length of time in the future. Turning to the rates of return rea l ized by the apartment properties from 1970 to 1977, these are shown in Table 4-8. These findings are comparable to those i den t i f i ed in previous studies as detai led in Chapter 3. For instance, the HUD study found an average ATCF/Equity return of 9.3 percent while Davis found an average ROI return of 7.8 percent - results very s imi la r to th is study. 107 TABLE 4-8 AVERAGE 'ACTUAL' RATES OF RETURN FOR THE APARTMENT PROPERTIES BY EACH MEASURE Measure of Return* Average Rate of Return NRM 13.1% ROI 7.7% Equity Dividend ATCF/Equity Gross Y ie ld on Equity Present Value IRR Before Financing Adjusted IRR FMRR -5.3% 9.3% 10.0% 0.8% 5.0% 6.8% 7.9% * GRM could not be calculated since net operating income was used to compute ' a c t u a l ' return in the computer model, not gross income. 4.5 Estimation of Input Data in the Test The results of the empirical test suggest that the forecasted p r o f i t -a b i l i t y of the investing in the property sample depends upon the continu-ance of two key var iables: (1) i n f l a t i o n in the economy, and (2) a high demand for apartment accommodation. These in turn determine the values spec i f ied by the analyst for the fol lowing c r i t i c a l growth and i n f l a t i o n factors expected over the holding period. 1. annual increases in tota l rental income, 2. annual increases in operating costs, and 3. annual increases in property values. The expectations used in the model test of pos i t ive increases in these factors are consistent with local and national trends pr io r to 1970. The prevai l ing growth s i tuat ion at that time was one of apartments generating an increasing do l l a r net income and before-tax equity cash flow each year while operating costs were growing at a rate in excess of the growth rate 108 in rental income. This occurs in a period of economic i n f l a t i o n , as in the late 1960's, i f an investor mortgages most of his investment and makes equal yearly amortization payments. The f ixed amortization payment becomes a decreasing percentage of a r i s i ng income over successive years. Thus, as long as the decreases in th i s percentage (which results in increasing net income and equity cash flow) are not exceeded by increases in operating costs and the operating cost percentage, net operating income and equity cash flows w i l l r i se over successive years. This expected growth s i tuat ion was expected to continue in the 1970's in Toronto and have a s i gn i f i can t pos i t ive impact on expected returns. However, from the findings presented in the previous sect ion, i t i s apparent that neither the f i r s t generation or discounted cash flow (DCF) models produced an accurate and r e l i a b l e measure of return. Forecasts made by the f i r s t generation models, pa r t i cu l a r -l y overal l ROI, appeared to be as accurate as those of the more sophist icated DCF measures. Considering the extra time and e f f o r t involved in the use of DCF measures, the question now must be raised as to the i r value to the analyst. The results outl ined above can be traced to the problem of forecasting into an uncertain future as required in the DCF models. The estimates of input data were subjective or judgmental forecasts, sometimes based on l i t t l e market data, and tempered by the i n t u i t i v e feel ings of indiv iduals with varying expectations about future cost and market c o n d i t i o n s . ^ Examining the f indings, there seem to be three s pec i f i c forecasting problems that hamper ef fort s to estimate the necessary inputs for the DCF models: ' 101 Using s en s i t i v i t y analys i s , the two most c r i t i c a l variables a f fect ing an investor ' s return in apartment investments were found to be changes in property value and changes in rental income over time. 109 F i r s t , there i s the cruc ia l problem of a shortage of market data and the questionable r e l i a b i l i t y of that which does ex i s t . Smith notes that data are ju s t not avai lable in the quantit ies and qua l i t i e s required for accurate and r e l i ab l e forecasting in the f i e l d of mult i - fami ly hous ing.* 0 ^ In general, the results of th i s chapter confirm the fact that the real estate investment problem is actua l ly an information problem. Perhaps th i s s i tuat ion w i l l change in the future as government involvement in housing increases and a larger percentage of housing a c t i v i t y i s undertaken by large publ ic companies (see Chapter 3). Second, while judgmental forecasting techniques have been developed in other business f i e l d s , ' 1 these techniques have not been applied to real estate analys is. R a t c l i f f elaborates on the task confronting the analyst: . . .Start ing with the measure of current product iv i ty , he (the analyst) must create a forecast which expresses in the predict ion the composite e f fect of a l l the s i gn i f i can t factors and which represents the most probable level at each point of time up to the end of the productive l i f e of the property.103 Regression and simulation techniques are now jus t beginning to be used in real estate and may be of potent ia l l y greater use in the future. These techniques and other judgmental forecasting techniques are generally subject to l im i ta t ions imposed by the long time horizon of real estate investments and the scant data base mentioned previously. F i n a l l y , the interre lat ionsh ips of input variables over time were not considered. It was assumed that variables could be changed independent of each other. For example, a change in the interest rate was assumed not to a f fec t rental income. Obviously, th i s i s not true for a substantial decrease 102. Wallace, F. Smith, The Low-Rise Speculative Apartment (Berkeley: Univers ity of Ca l i f o r n i a , Center for Real Estate and Urban Economics, 1964), p. 5. 103. R. U. R a t c l i f f , Real Estate Analys i s , (New York: McGraw-Hill, 1961) p. 111. in the interest rate could increase the attractiveness of apartment invest-ment, thereby increasing the supply of apartments and reducing future growth rates in rental income. Additional research i s needed to establ i sh the l i n k -ages between input var iables. Furthermore, i t was not possible to predict certain market changes such as the introduction of rent control in the Fa l l 104 of 1975 or the capita l gains tax in 1972. 4.6 Summary of Pr inc ipa l Findings The primary objective of th i s chapter was to examine the predict ive u t i l i t y of the various measures of return i den t i f i ed in previous chapters. The approach taken was to compare the ex ante returns and to measure the deviations between predicted and actual returns and the subsequent property ranking as a test of the r e l i a b i l i t y of predict ion associated with each measure. The fol lowing is a b r i e f summary of the pr inc ipa l findings outl ined in deta i l e a r l i e r in th is chapter: 1. A large deviation or error ex ists between the ' a c t ua l ' returns rea l i zed on the property sample and those predicted using a DCF measure of return. However, i f the ' a c t u a l ' returns are calculated by a f i r s t generation measure, .the percentage error of predict ion is s i g n i f i c an t l y less than that using a DCF measure. Thus, f i r s t generation measures, pa r t i cu l a r l y the ROI and the ATCF/Equity, are as accurate in forecasting returns as the more sophist icated DCF measures. 2. A substantial portion of the deviation in the accuracy of the return predict ion between f i r s t generation measures and second generation or DCF measures i s a t t r ibutab le to inaccurate forecasts 104. Landlords in Ontario were l imi ted to an 8 percent rental increase without " j u s t i f i c a t i o n " but were en t i t l ed to increases in excess of 8 percent i f permitted by the Rent Review Commission. I l l of sales pr ice at the end of the holding period required for the DCF measures. An average error of 19 percent can be found between the forecasted sales pr ice and the ' a c t ua l ' sales p r i ce . 3. For most of the propert ies, un rea l i s t i c expectations can be i den t i f i ed in the forecasts (a s i tuat ion i den t i f i ed in Chapter 3) since for those properties analyzed using a probab i l i ty d i s t r i b u t i on , the pess imist ic forecasts generally best approximated the ' a c t u a l ' performance. 4. On an accept/reject basis using a 13 percent required rate of return, the Gross Y ie ld on Equity most c losely approximates the ' a c t ua l ' acceptance and reject ion l i s t . 5. An examination of the cor re lat ion between the ' a c t ua l ' ranking and the predicted rankings as calculated by the Spearman Rank Correlat ion Coef f ic ient reveals that the before-tax IRR with a coe f f i c i en t of 0.71 is the best ranking predictor for our sample. Beyond th i s no c lear pattern emerges among the indiv idual measures or between f i r s t generation or DCF groups of measures. 6. The findings indicated three spec i f i c problems associated with the estimation of input var iables: (1) shortage of market data, (2) lack of r e l i ab l e forecasting techniques, and (3) i n t e r -relat ionships of input variables over time. 7. The average returns rea l ized on the property sample from 1970 to 1977 (9.3 percent using ATCF/Equity and 6.8 percent using adjusted IRR) were found comparable to those i den t i f i ed in previous studies (see Chapter 3). C lear ly , the results summarized above do not allow any spec i f i c measure or group of measures ( f i r s t generation or DCF) to be hailed as a more accurate predictor of investment return or re l a t i ve investment d e s i r a b i l i t y . In f a c t , the poor results ra ise the question as to the r e l i a b i l i t y of predict ion in real estate investment analysis for a period of time in the future. 112 5.0 CONCLUSIONS, IMPLICATIONS AND SUGGESTIONS FOR FURTHER RESEARCH The real estate investment decision-making process involves many dynamic, i n te r re l a ted , and uncertain elements. Models developed to aid the decision-maker should simulate properties found in real world systems. Their usefulness i s l imited only by the a b i l i t y of those developing the model to: (1) recognize the real world propert ies, (2) incorporate these properties into the model structure, and (3) make accurate decisions in f u l l l i g h t of the l im i ta t ions e x p l i c i t l y and i m p l i c i t l y incorporated in the model. Indeed, the accuracy and r e l i a b i l i t y of any model ult imately depends upon the accuracy of the input data. Chapter 2 presented a theoret ical discussion of the considerations that should be incorporated into an accurate and r e l i ab l e measure of return or performance. Capital izat ion-of- income and rate of return frame-works were u t i l i z e d to trace the development of ana lyt ica l models for real estate investment analys is. While models t r a d i t i o n a l l y focused on the assessment of value through income c a p i t a l i z a t i o n , these methods do not meet the c r i t e r i a for an accurate and r e l i ab l e measure of return. A preference was noted in recent real estate l i t e r a t u r e for discounted cash flow (DCF) measures of return as these methods incorporate many of the c r i t e r i a of an accurate and r e l i ab l e measure of return. However, two issues remained unsolved by the real estate community, academics and p rac t i t i oner s : (1) a lack of consensus regarding the extent of the usage of the various measures by real estate investors, and (2) disagreement on which model w i l l produce a r e l i a b l e measure of performance. 113 Chapter 3 addressed the f i r s t issue and presented the results of two surveys: (1) a sample of 300 real estate equity investors and (2) a sample of 150 ICI real estate brokers. The findings strongly suggest that real estate investors remain unsophisticated in the i r approach to investment analysis re l a t i ve to other business f i e l d s . The real estate community continues to re ly on f i r s t generation or t rad i t i ona l measures of return or performance to evaluate real estate investment opportunit ies. The most popular before-tax measures of return employed by those surveyed were Return on Investment (net operating income/purchase price) and the Equity Dividend Rate (net operating income - debt service/equity). For those respondents employing an a f ter - tax measure, the most popular were After - tax Cash Flow ( f i r s t year)/Equity and the Internal Rate of Return. I t was shown that many of the respondents lacked the knowledge and understanding required of the more sophisticated measures of performance common to other business f i e l d s . They appeared to se lect a par t i cu la r measure and then f a i l to adhere to the spec i f i c methodology of the chosen measure. Sophist icat ion in real estate investment analysis was shown to be a function of the type of company and por t fo l i o s ize since large publ ic real estate corporations employed more sophisticated methods and techniques with a greater frequency re l a t i ve to other investors. An empirical test was developed in Chapter 4 to examine the predict ive u t i l i t y of the various measures of investment return i den t i f i ed in theory (Chapter 2) and in pract ice (Chapter 3). The approach taken was to compare the ex ante returns from a sample of 15 apartment properties over the period 1970 to 1977 with the ex post returns over the same period. Thus, i t was possible to measure the deviations between the predicted and actual returns each with a corresponding investment ranking as a test of the accuracy and r e l i a b i l i t y of predict ion using each measure of return. The findings suggest that f i r s t generation measures of return w i l l predict investment returns 114 and re l a t i ve investment rankings as c losely correlated to that which actua l ly occurred as w i l l the more sophist icated second generation or discounted cash flow (DCF) measures of return. This resu l t can be traced to the i m p l i c i t requirements of the methodologies of f i r s t generation measures necessitat ing only one year forecasts of input parameters. The f a i l u r e of the DCF models to provide c l ea r l y superior predictions of investment performance was largely due to the inaccurate forecasts of key input parameters, pa r t i cu l a r l y sales pr ice at the end of the holding period. The empirical tes t revealed that d i f fe rent measures of return produced d i f fe rent re l a t i ve rankings of investment d e s i r a b i l i t y and d i f fe rent absolute indicators of investment return. The results do not allow any spec i f i c measure or group of measures ( f i r s t generation or DCF) to be hai led as a more accurate and r e l i a b l e predictor of investment return in real estate. Indeed, the poor predict ive u t i l i t y of the various measures raise a question as to the r e l i a b i l -i t y of predict ion into the future for real estate investment analysis in general. Thus, i t i s c lear that the results of the sample test do not lend support to the hypothesis that the a b i l i t y of the real estate community to make more accurate and r e l i a b l e , and hence more p ro f i t ab le , real estate decisions i s d i r e c t l y related to the use of the more sophist icated discounted cash flow measures of return. 5.1 Implications for Real Estate Investment Analysis The investor contemplating which measure(s) of return to use in the analysis of real estate investment opportunities must now, in view of the l im i ted use of sophist icated discounted cash flow and the lack of evidence to c lea r l y support the i r use as accurate and r e l i ab l e predictors of return, be unconvinced that "discounted cash flow methods are superior" tools of investment analys is. Thus, i t may seem reasonable to question the value of the sophist icated DCF measures considering the extra time involved in the i r use. 115 Examining th i s question, i t i s i n s t ruct i ve to reca l l the att r ibutes of an accurate and r e l i a b l e measure of return. It i s important that the i r c r i t e r i a meet the objectives of the real estate investor. In th i s study, i t was assumed the rate of return i s the most su i table c r i t e r i o n for real estate investment decisions. However, many investors may have objectives other than maximizing rate of return. Some large i n s t i t u t i o n s , for example, have stated objectives re la t ing to the safety of p r i n c i p a l , s t a b i l i t y of 105 current income, and l i q u i d i t y . Others who invest in real estate, pa r t i cu l a r l y public corporations, are concerned about yearly reported earnings and earnings per share. Furthermore, the short-run l i q u i d i t y needs of an investor may not be consistent with the long-run concept of maximizing rate of return. In many cases, the yearly cash flow or spendable income generated by a project may be much more important to an investor than equity reversion at the end of the holding period, pa r t i cu l a r l y for long holding periods. Thus, the existence of numerous investment objectives suggest the select ion of a measure which incorporates the objectives of the investor. Computational d i f f i c u l t i e s in using DCF models for investment analysis ar ise from the sheer number of input variables as well as the numerous steps and repet i t i ve i te rat ions required to calculate the rate of return. Numerous computer appl icat ions of DCF models fo r real estate investment analysis have become commercially avai lable since the late-1960's to eliminate the arduous •j gg manual ca lcu lat ions. However, certain caveats must be remembered when employing these models. F i r s t , the analyst must accept the underlying assumptions of the spec i f i c measure. Second, the analyst must rea l i ze that 105. R. Bruce Ricks, Recent Trends in In s t i tu t iona l Real Estate, pp. 12-16 20-23, 48-50, 55-56, 78-79. 106. For a c r i t i c a l evaluation of several of these computer models, see Paul F. Wendt and Janet Tandy, Evaluation of DCF Computer Models in Real Estate Investment Analys i s , Athens, Georgia: Univers ity of Georgia, 1976 (mimeographed). 116 the c r e d i b i l i t y of DCF models and hence, the re su l t s , rest ent i re l y upon r e a l i s t i c estimates of the key input var iables: rental income, operating expenses, tax l i a b i l i t y , reinvestment rates, and future sales p r i ce . The case study has demonstrated that expected return i s not the same as rea l ized or actual return. Thus, accurate forecasting techniques, combined with a good data base, are prerequisites of a useful return analys i s ; neither appears in the real estate investment f i e l d and perhaps this should receive the greatest degree of attent ion from real estate decision-makers. 5.2 Suggestions for Further Research A prevalent reason c i ted by real estate investors surveyed for not employing more sophist icated measures of return was the d i f f i c u l t y in obtain-ing the necessary input data. The estimation of input values i s the most d i f f i c u l t and challenging aspect of the investment analysis process since forecasts of future economic and market conditions are required. However, the estimation of input data for many of the measures is plagued by the shortage of r e l i ab l e market data, the lack of adequate forecasting techniques, the long time horizon over which forecasts must be made, and the i n te r -relat ionships which occur between input variables. Unfortunately, real estate l i t e r a tu re has not afforded s u f f i c i e n t attent ion to the development of operational techniques for generating the data necessary for investment analys is. A related task facing the real estate analyst i s to quant i tat ive ly determine which variables are most important to investment performance and the tradeoff between f inancing, operating income, capita l gains, tax she l ter , etc. Sens i t i v i t y analysis can be a v i t a l a id to the analyst and decis ion-maker in th i s regard permitting "key" input variables to be i den t i f i ed so more time and e f f o r t may be spent to ensure the i r accuracy and hence, the accuracy of the analys is . 117 No attempt was made to incorporate uncertainty into the study beyond the i m p l i c i t adjustment in the investor ' s required rate of return. While several authors have developed models to incorporate uncertainty into real estate investment decision-making none of these models are operational. However, they provide a basis fo r further research p o s s i b i l i t i e s . The trend toward a greater information flow in the real estate markets and improved forecasting techniques w i l l provide a so l i d basis for the adoption of methods of investment analysis.common to other business f i e l d s . Nevertheless, models are not intended as substitutes for human judgment. In the uncertain environment of the real estate process, models do not make decisions - developers, lenders, and investors do_. Instead the i r basic purpose is to place numerous investment variables in a systematic and log ica l framework, thereby providing the decision-maker with consistent information on which to evaluate a l ternat ive investment opportunit ies. In the long run, improvements in forecasting techniques and the real estate data base w i l l permit the use of properly spec i f ied models with the end resu l t being better and more prof i tab le investment decis ions. P r a c t i c a l l y any indiv idual or f irm can employ the more sophist icated types of models described in th i s study. However, as Pyhrr notes, perhaps the indiv idual entrepreneur w i l l always believe his i n t u i t i v e methods of measuring real estate investment 107 return are superior. 107. Stephen Pyhrr, "A Computer Simulation Model to Measure the Risk in Real Estate Investment," p. 27. 118 6.0 SELECTED REFERENCES  BOOKS Achtenhagen, Stephen H. An Investor-Based Marketing Plan for Sale of Real Property Investment Secur i t ies to Individuals. Unpublished doctoral d i s se r ta t i on , Stanford Univers i ty, 1974. American Inst i tute of Real Estate Appraisers. The Appraisal of Real  Estate. Chicago, 1967. Backstrom, C. H. and Hursh, G. D. Survey Research. Northwestern Univers ity Press, 1963. Beaton, Will iam R. Real Estate Investment. Englewood C l i f f s , N.J., Prent ice-Hal l Inc., 1971. Bierman, Harold J r . and Smidt, Seymour. The Capital Budgeting Decision. New York, MacMillan, 1966. Catherwood, Robert H. Real Estate for P r o f i t . Toronto, Maclean-Hunter, 1975. Cooper, James R. et at. Real Estate and Urban Land Analys is. Lexington, Mass., Lexington Books, D.C. Heath and Co., 1973. . Real Estate Investment Analys is. Lexingtons-Mass., Lexington Books, D.C. Heath and.Co., 1974. Cootner, Paul H. and Holland, Daniel M. Risk and Return. Cambridge, Massachusetts. Massachusetts Inst i tute of Technology, 1964. Dale-Johnson, Frederick. Returns on Apartment Properties for the Period 1960 to 1970 in the Greater Vancouver Area. Unpublished Master's d i s se r ta t i on , Faculty of Commerce and Business Administrat ion, Univers ity of B r i t i s h Columbia, 1972. Dasso, Jerome J . Computer Appl ications in Real Estate. Research Report No. 13, Storrs, Conn., Center for Real Estate and Urban Economic Studies, 1974. Davis, Irving F. A Study of Real Estate Investment Returns to Capital  and Management. Fresno, Bureau of Business Research and Service, Ca l i f o rn i a State Univers i ty, 1973. Ellwood, L. W. Ellwood Tables for Real Estate Appraising and Financing. Chicago, American Inst i tute of Real Estate Appraisers, 19/0. 119 Gau, George, W. and Kohlkepp, Daniel B. Sen s i t i v i t y Analysis in Real  Estate Investment. Working Paper 76:2, Norman, Oklahoma. Univers ity of Oklahoma, 1976. Ge t te l , Ronald E. Real Estate Guidelines and Rules of Thumb. New York. McGraw-Hill, 1976. Goodman, Sam R. S impl i f ied use of the Discounted Cash Flow Method of  Evaluation. Englewood C l i f f s , N.J. P rent i ce -Ha l l , 1972. Grebler, Leo. Experience in Urban Real Estate Investment: An Interim  Report Based on New York C i t y Properties. New York: Columbia Univers ity Press, 1955. Hippaka, Will iam H. Factors Contributing to Successful Investment Experience in Mult ip le Unit Housing. Bureau of Business and Economic Research. San Diego State College, 1965. In s t i tu t iona l Investor Study Report of the Secur i t ies and Exhange Commission. Supplementary Volume 1, 92nd Congress, 1st Session, House Doc. 92-64, Part 6. U.S. Government Pr int ing O f f i ce , Washington, D.C, 1971. Kahn, Sanders A., Case, Fred E. and Schimmel, A l f red . Real Estate  Appraisal and Investment. New York, Ronald Press, 1963. Kinnard, Will iam N. Income Property Valuation. Lexington, Mass., Lexington Books, 1971). Maisel, Sherman J . Financing Real Estate. New York, McGraw-Hill, 1965. Maise l , Sherman J . and Roulac, Stephen E. Real Estate Investment and Finance. Englewood C l i f f s , N.J. McGraw-Hill, 1976. Mao, James C. T. Quantitative Analysis of Financial Decisions. London, MacMillan, 1969. Markowitz, Harry M. Po r t fo l i o Select ion: D i ve r s i f i ca t i on of Investments. New York, John Wiley and Sons Inc., 1959. Messner, Stephen D., Schreiber, Irving and Lyon, V ictor L. Marketing Investment Real Estate: Finance, Taxation Techniques. Chicago, National Association of Realtors, 1975. National Association of Accountants. Financial Analysis to Guide Capital  Expenditure Decisions, Research Report 43. New York, 1966. 120 Nickerson, Wil l iam. How I Turned $1000 into a M i l l i o n in Real Estate  in My Spare Time. New York, Simon and Shuster, 1963. P e l l a t , Peter G. K. A Normative Approach to the Analysis of Real Estate Investment Opportunities under Uncertainty and the Management of Real Estate Po r t fo l i o s . Unpublished doctoral d i s se r t a t i on , Univers ity of Ca l i f o r n i a , Berkeley, 1971. Prices and Incomes Commission. Residential Rentals in Canada: In f lat ion  and Restraint. Ottawa. December, 1970. Ra i f f a , Howard. Decision Analysis - Introductory Lectures on Choices under  uncertainty. Menlo Park, Addison Wesley, 1968. Raj, Des. The Design of Sample Surveys. New York, McGraw-Hill, 1972. R a t c l i f f , R. U. Real Estate Analys is. New York, McGraw-Hill, 1961. • Current Practices in Income Property Appraisa l : A Cr i t ique. Research Report. Berkeley, Univers ity of C a l i f o r n i a , Center for Real Estate and Urban Economics, 1967. . Urban Land Economics. New York, McGraw-Hill, 1949. Realtron Corporation. Determination and Usage of the FM Rate of Return. Detro i t . The author, 1973. Ricks, Bruce R. Recent Trends in In s t i tut iona l Real Estate. Berkeley, Univers ity of C a l i f o r n i a , Center for Real Estate and Urban Economics, 1964. Roulac, Stephen E. Modern Real Estate Investment: an Ins t i tut iona l  Approach. San Francisco, Property Press, 1976. Se ld in , Maury and Swesnik, Richard H. Real Estate Investment Strategy. New York, Wiley- Interscience, 1970. Shenkel, Will iam M. Modern Real Estate P r inc ip le s . Dal las, Business Publ ications Inc., 1977. . Real Estate Investment Decisions. Chicago, Inst i tute of Real Estate Management, 1974. Smith, Wallace F. The Low-rise Speculative Apartment. Berkeley, Univers i ty of C a l i f o r n i a , Center for Real Estate and Economics, 1964. Study on Tax Considerations in Multi-Family Housing. Department of Housing and Urban Development.Washington, May, 1971. van Home, James. Financial Manaqement and Po l icy . Englewood C l i f f s , N.J., Prentice-Hal l Inc., 1974. Walters, David W. (ed.) Real Estate Computerization. Research Report 35. Berkeley, Center for Real Estate and Urban Economics, Univers ity of C a l i f o r n i a , 1971. Weingartner, H. M. Mathematical Programming and Analysis of Capital Budgeting Problems. Englewood C l i f f s , N.J., P rent i ce -Ha l l , 1963. Wendt, Paul F. Real Estate Appraisal. Athens, Univers ity of Georgia Press, 1974. Wendt, Paul F. and Cerf, Alan R. Cases in Real Estate Investment Analysis  and Taxation. Special Report No. 5. Berkeley, Center for Real Estate and Urban Economics, Univers ity of C a l i f o r n i a , 1967. . . Real Estate Investment Analysis and Taxation. New York, 'McGraw-Hil l, 1969. Wil l iams, E. E. and Findlay, M. C. Invest Analys is. Englewood C l i f f s , N.J. P rent i ce -Ha l l , 1974. Zeckendorf, Wil l iam and McCreary, Edward. Zeckendorf. New York, Holt , 1970. 122 ARTICLES Apgar, Mahlon. "Committment Planning: An Approach to Reducing Uncertainty in Real Estate". Appraisal Journal 44. Ju l y , 1976. pp. 413-427. A r t i e , Ronald. "On Some Methods and Problems in the Study of Metropolitan Economics". Regional Science Association Papers 8, 1962. pp. 69-77. Baldwin, Robert H. "How to Assess Investment Proposals". Harvard Business  Review 37, May-June, 1959. pp. 88-97. Bauman, W. Scott. "Investment Returns and Present Values". Financial  Analysis Journal, November, 1969. Bernstein, Peter L. "What Rate of Return Can You Reasonably Expect"? Journal of Finance 28, May, 1973. pp. 273-282. Blume, Marshall E. "On the Assessment of Risk". Journal of Finance 26, March, 1971. pp. 1-10. Brigham, Eugene. "Differences Between the Major Discounted Cash Flow Capital Budgeting Techniques". Readings in Managerial Finance, Ch. 3, 1971. pp. 45-65. Clettenberg, Karel J . and Kronche, Charles D. "How to Calculate Real Estate Return on Investment". Real Estate Review 2, Winter, 1973. pp. 105-109. Cooper, James R. and Morrison, Cathy. "Using Computer Simulation to Minimized Risk in Urban Housing Development". Real Estate Appraiser 39, March-Apri l , 1973. pp. 15-26. Cord, Joe l . "A Method for A l locat ing Funds to Investment Projects when Returns are Subject to Uncertainty". Management Science 16, January 1969. pp. 335-341. Dasso, Jerome. "Real Estate Education at the Univers ity Level". In Recent  Perspectives in Urban Land Economics, Michael A. Goldberg (ed.). Vancouver, Univers ity of B r i t i s h Columbia, 1976. Dyckman, T. R. "A l l ocat ing Funds to Investment Projects when Returns are Subject to Uncertainty". Manaqement Science 11, November, 1964. pp. 348-350. Fogler, H. Russel l . "Ranking Techniques and Capital Budgeting". Accounting Review 47, January, 1972. pp. 134-143. F a r r e l l , Paul B. J r . "Computer-aided Financial Risk Simulation". Appraisal  Journal 37, January, 1969. pp. 58-73. 123 Friedman, Harris C. "Reinvestment and Po r t fo l i o Theory". Journal of Financial  and Quantitative Analys i s , March, 1971. pp. 861-874. Friedman, Jack P. "The Internal Rate of Return Plus the Pul l Factor". Real Estate Appraiser 42, March-Apri l , 1976. pp. 29-32. Gallagher, Edward F. " S en s i t i v i t y Analysis for Apartment House Investment". In Real Estate Computerization, David Walters (ed.). Berkeley, Univers ity of C a l i f o r n i a , 1971. pp. 190-229. Gibbons, James E. "Mortgage-Equity Cap i ta l i za t ion and After-Tax Equity Y ie ld" . Appraisal Journal 37, January 1969. Graaskamp, James A. "A Commercial Computer Service for Financial Analysis of Rental Income Property Decisions". Proceedings of the AREUEA, December, 1968. pp. 173-207. . "A Pract ica l Computer Service for the Income Approach". Appraisal Journal 37, January,1969. pp. 50-57. Graham, Donald H. "Owner's Analysis of Yields on Major Real Estate Investments". Appraisal Journal 33, October, 1965. Hertz, David B. "Investment Po l i c i e s that Pay Off". Harvard Business  Review 46, January-February, 1964. pp. 95-106. Hettenhouse, George W. and Dee, John J . "A Component Analysis of Rates of Return in Real Estate Investment". American Real Estate and Urban  Economics Journal, Spring, 1976. pp. 7-21. Higgins, Robert C. and Cunningham, Hugh R. "Computerized Calculations -Rates of Return and Risks in Commercial Property". Appraisal  Journal 38, January, 1970. pp. 37-49. H i l l i e r , F. S. "The Derivation of P robab i l i s t i c Information for the Evaluation of Risky Investments". Management Science 9, A p r i l , 1963. pp. 443-457. H i r s h l e i f e r , J . "On Theory of Optimal Investment Decision". Journal of  Business 23, October, 1955. pp. 229-239. Hodges, F. "Computer Progress in Valuation of Income Properties". Appraisal  Journal 39, January, 1971. pp. 61-69. J a f f e , Austin J . "Computer-Assisted Instruction and Real Estate Investment Analys is " . Real Estate Appraiser, November-December, 1976. pp. 21-28. 124 Kapplin, Steven D. "F inancial Theory and the Valuation of Real Estate Under Uncertainty". Real Estate Appraiser, September-October, 1976. pp. 28-37. Kinnard, Will iam N. "The Ellwood Analysis in Valuation: A Return to Fundamentals". Real Estate Appraiser 32, May, 1966. pp. 18-24. Lor ie , J . H. and Savage, L. J . "3 Problems in Rationing Cap i ta l " . Journal  of Business, October, 1955. pp. 229-239. Lowenstein, Louis and Recht, Richard. "Variat ions in Rates of Return in an Urban Area". Unpublished paper, Center for Real Estate and Urban Economics, Univers ity of Ca l i f o r n i a . McLean, John G. "How to Evaluate., New Capital Investments". Harvard  Business Review 36, December, 1958. pp. 59-69. Mao, James C. T. "A Survey of Capital Budgeting: Theory and P rac t i ce " . Journal of Finance 25, May, 1970. pp. 349-360. Messner, Stephen D. and Findlay, M. Chapman. "Real Estate Investment Analys is: IRR Versus FMRR". Real Estate Appraiser 41, July-August, 1975. pp. 5-20. Montgomery, J . Thomas. "The Appraiser 1 s Dilemma - Wedding Investor Motivations to Appraisal Techniques". Appraisal Journal 41, October, 1973. pp. 464-475. Morton, Walter A. "Risk and Return: I n s t ab i l i t y of Earnings as a Measure of Risk". Land Economics 45, May, 1969. pp. 229-261. P e l l a t , Peter G. K. "The Analysis of Real Estate Investments under Uncertainty". Journal of Finance 27, May, 1972. pp. 459-471. P r i e s t , Will iam W.; "Rate of Return as a C r i te r ion for Investment Decisions". Financial Analysis Journal, July-August, 1965. Pyhrr, Stephen A. "A Computer Simulation Model to Measure Risk in Real Estate Investment". Real Estate Appraiser 39, May-June, 1973. pp. 13-31. R a t c l i f f , Richard U. "Capi ta l ized Income i s not Market Value". Appraisal  Journal 36, January, 1968. pp. 33-40. ' . "Don't Underestimate the Gross Income M u l t i p l i e r " . Appraisal Journal 39, A p r i l , 1971. pp. 264-271. 125 R a t c l i f f , R. U. and Schwab, Bernhard. "Contemporary Decision Theory and Real Estate Investment". Appraisal Journal 38, A p r i l , 1970. Ricks, R. Bruce. "Computers and the Real Estate Investment Process". In Colloquium on Computer Applications in Real Estate Investment  Analys is. Richard U. R a t c l i f f (ed.), Monograph Series No. 2. Vancouver, Univers ity of B r i t i s h Columbia, February, 1968. pp. 105-112 . "Imputed Equity Returns on Real Estate Financed with Insurance Company Loans". Journal of Finance 24, December, 1969. pp. 921-937. Robichek, Alexander.A. and Myers, Stewart C. "Conceptual Problems in the Use of Risk-Adjusted Discount Rates". Journal of Finance 21, December, 1966. pp. 727-730. Roulac, Stephen E. "Can Real Estate Returns Outperform Common Stocks". Journal of Po r t f o l i o Manaqement, Winter, 1976. pp. 26-43. Shenkel, William.M. "Cash Flow Analys is: An Appl icat ion of Conversational Computer Programming"^ Journal of Property Manaqement, July-August, 1969. pp. 165-172. . "Equity Cap i ta l i za t ion and Investment Decisions: An Appl icat ion of Conversational Computer Programming". Journal of  Property Manaqement 34, March-Apri l , 1969. pp. 76-87. Soelberg, Peer and Stefaniak, Norbert. "Impact of the Proposed Tax Reform B i l l on Real Estate Investments". Appraisal Journal 38, A p r i l , 1970. pp. 188-211. Strung, Joseph. "The Internal Rate of Return and the Reinvestment Presumption". Appraisal Journal 44, January, 1976. pp. 22-33. Sundley, Emil M. J r . "Changes in Depreciation and Recapture - Impact on Real Estate Investments". Appraisal Journal 38, October, 1970. pp. 524-535. Thorne, Oakleigh. "Real Estate Financial Analysis - The State of the A r t " . Appraisal Journal 42, January, 1974. pp. 7-37. van Home, James C. "Capital-Budgeting Decisions Involving Combinations of Risky Investments". Manaqement Science 13, October, 1966. pp. 84-92. Weimer, Arthur M. "History of Value Theory for the Appraiser". Appraisal  Journal 28, October, 1960. pp. 469-483. 126 Weimer, Arthur M. "Real Estate Decisions are Di f ferent" . Harvard Business  Review 44, November-December, 1966. Wendt, Paul F. "Cash-Flow Analysis by Computer". Real Estate Review 1, F a l l , 1972. pp. 63-68. . "Ellwood, Inwood and the Internal Rate of Return". Appraisal Journal 35, October, 1967. pp. 561-572. Wendt, Paul F. and Tandy, Janet. "Evaluation of DCF Computer Models in Real Estate Investment Analysis". Univers ity of Georgia, Athens, 1976. (Mimeographed) Wendt, Paul F. and Tu l l y , Gibert. "Investment Performance: Common Stock Versus Apartment Houses". Appraisal Journal 40, January, 1972. pp. 123-127. Wendt, Paul F. and Wong, Sui N. "Investment Performance: Common Stock Versus Apartment Houses". Appraisal Journal 20, December, 1965. Wiley, Robert J . "Real Estate Investment Analys is: An Empirical Test". Appraisal Journal 44, October, 1976. pp. 586-592. Woods, Donald H. "Improving Estimates that Involve Uncertainty". Harvard  Business Review 44, July-August, 1966. pp. 91-99. REFERENCE WORKS Canadian Housing S t a t i s t i c s . Ottawa: Central Mortgage and Housing Corporation, year ly. Income/Expense Analys is: Appartments, Condominiums and Cooperatives. Chicago: National Inst i tute of Real Estate Management of the National Association of Real Estate Boards, Yearly. P r i ce , Waterhouse and Company. The Real Estate Development Industry in  Canada: 1976 Survey of Annual Reporting and Accounting  Development. Toronto: Canadian Inst i tute of Public Real Estate Companies, 1976. Real Estate Development Annual. Toronto: MacLean-Hunter, year ly. 128 INTERVIEWS Clayton, Frank. Clayton Research Associates L imited, Toronto, Ontario. June 10, 1977. Holder, Ken. Central Mortage and Housing Corporation, Toronto, Ontario. June 10, 1977. Hopper, Ron. Pickin and Mason Realty Company, Toronto, Ontario. July 12, 1977. North, L incoln. Lincoln North and Associates, Appraisers, Toronto, Ontario. Ju ly 14, 1977. S t ra in , Doug. Toronto Real Estate Board, Toronto, Ontario. June 10, 1977. Tomczyk, Barbara. Central Mortgage and Housing Corporation, Toronto, Ontario. Ju ly 12, 1977. APPENDIX A REAL ESTATE INVESTOR SURVEY - Covering Letter and Questionnaire - Follow-up Letter - Complete Tabulated Results 132 CONFIDENTIAL CONFIDENTIAL REAL ESTATE INVESTMENT ANALYSIS QUESTIONNAIRE Your help i s greatly appreciated and your answers w i l l be kept s t r i c t l y c o n f i -d e n t i a l . Section I deals with the s i z e , l o c a t i o n and general a c t i v i t i e s of  your firm. 1. Which of the following does your company represent? (please check the correct answer.) Public r e a l estate corporation (primary a c t i v i t y i s the development of, and/or, investment i n , r e a l property) Private r e a l estate corporation Insurance company A f f i l i a t e of a f i n a n c i a l i n s t i t u t i o n (bank, t r u s t company or mortgage company) Other (please specify) 2. Where i s your company's head o f f i c e located? B r i t i s h Columbia Alberta Saskatchewan and Manitoba Ontario Quebec A t l a n t i c Provinces Outside Canada 3. What i s the approximate book value of your company's r e a l estate p o r t f o l i o ? Book Value of Real Estate P o r t f o l i o = $ . Approximately what percentage of the t o t a l book value shown i n Question #2 i s composed of the following types of property? Type of Property Single family housing Apartments (rental) Condominiums O f f i c e buildings Shopping centres ( r e t a i l ) I n d u s t r i a l buildings Hotels/motels Undeveloped land Other (please specify) Percentage % O f f i c e ' use only 1 - 4 5 7-16 17-8 19-20 21-2 23-4 25-6 27-8 29-30 31-2 33-4 Total 100 % 133 CONFIDENTIAL - 2 CONFIDENTIAL II . Section II deals with the components of investment return and the  methods used to ca l c u l a t e return. 5. When you analyze r e a l estate investment proposals, which of the following component(s) of investment return do you consider? (please check) Net operating income Before-tax cash flow (net operating income minus mortgage payments) After-tax cash flow (net operating income minus mortgage payments minus taxes) Amortization of the loan (equity build-up) Net cash proceeds from sale Other (Please specify) When you analyze r e a l estate investment proposals, do you cal c u l a t e the return p r i m a r i l y based on: (Please check one.) Equity invested, or To t a l c a p i t a l invested. If you analyze r e a l estate investment proposals on a BEFORE-TAX basis, which one of the following methods of c a l c u l a t i n g invest-ment return do you use most often? (please check one) Gross rent m u l t i p l i e r (purchase price/gross income) Net rent m u l t i p l i e r (purchase price/net operating income) Overal l return on investment (net operating income/purchase price) Equity dividend rate (net operating income minus mortgage payments/equity) Payback period (time to recapture i n i t i a l equity) Before-tax i n t e r n a l rate of return Other (please specify) If you analyze r e a l estate investment proposals on an AFTER-TAX basis, which one of the following methods of c a l c u l a t i n g invest-ment return do you use most often? (please check one). After-tax cash flow ( f i r s t year)/equity Payback period (time to recapture i n i t i a l equity) Time adjusted rates of return: Net present value P r o f i t a b i l i t y index Internal rate of return Adjusted i n t e r n a l rate of return (reinvestment rate s p e c i -fied) F i n a n c i a l management rate of return Other (please specify) O f f i c e use only -35 -36 -37 -38 -39 -40 -41 -42 -43 134 CONFIDENTIAL - 3 - CONFIDENTIAL S e c t i o n I I I d e a l s w i t h s p e c i f i c r a t e s o f r e t u r n , a d j u s t m e n t s to t h a t  r e t u r n , and computer u sage . 9 . What i s t he minimum e x p e c t e d r a t e o f r e t u r n ( c a l c u l a t e d by the method s p e c i f i e d i n Q u e s t i o n #7 o r #8) r e q u i r e d to a t t r a c t you to i n v e s t i n t h e f o l l o w i n g t y p e s o f p r o p e r t i e s ? 10. 11. Type o f P r o p e r t y Apa r tment s ( r e n t a l ) O f f i c e b u i l d i n g s Shopp ing c e n t r e s ( r e t a i l ) I n d u s t r i a l b u i l d i n g s H o t e l s / m o t e l s Undeve loped l a n d O t h e r ( P l e a s e s p e c i f y ) Rate o f R e t u r n R e q u i r e d % % % From the l i s t o f r e s p o n s e s i d e n t i f i e d be l ow, p l e a s e choose the one t h a t most c l o s e l y i n d i c a t e s t he f r e q u e n c y w i t h wh i ch you use the f o l l o w i n g methods t o a d j u s t y o u r a n a l y s i s when w o r k i n g w i t h h i g h l y u n c e r t a i n e s t i m a t e s . ( P l e a s e e n t e r the number c o r r e s p o n d i n g to . . t he c o r r e c t r e s p o n s e b e s i d e each method) (0) n e v e r (1) se ldom (2) f r e q u e n t l y (3) a lways (A) A d j u s t upwards the r e t u r n r e q u i r e d f rom t h e p r o j e c t (B) A d j u s t downward the b e n e f i t s e x p e c t e d f rom the p r o j e c t (C) Use p r o b a b i l i t y d i s t r i b u t i o n s (D) Use s e n s i t i v i t y a n a l y s i s (E) O ther a d j u s t m e n t s _____ Do you use the computer i n making r e a l e s t a t e i n v e s t m e n t a n a l y s e s ? ( P l e a s e check ) No Yes O f f i c e use o n l y 12. I f your answer was " n o " i n t he l a s t q u e s t i o n , f rom the l i s t o f r e s p o n s e s i d e n t i f i e d be l ow, p l e a s e choose the one t h a t most c l o s e l y i n d i c a t e s the r e l a t i v e i m p o r t a n c e o f the f o l l o w i n g r e a -sons f o r n o t u s i n g the computer , ( p l e a s e e n t e r c o r r e c t r e s p o n s e b e s i d e each r e a s o n ) (0) u n i m p o r t a n t (1) f a i r l y i m p o r t a n t (2) v e r y i m p o r t a n t (3) e s s e n t i a l 135 CONFIDENTIAL - 4 - CONFIDENTIAL 12. 13. 14. 15. (A) (B) (C) (D) (E) (F) Lack of computer trained personnel Lack of access to computer f a c i l i t i e s I n a b i l i t y to obtain input data necessary for computer programs Too expensive Do not think the computer i s u s e f u l (necessary) Other (please specify) If you use a computer, from the l i s t of responses i d e n t i f i e d below, please choose the one that most c l o s e l y indicates the r e l a t i v e importance of the following ways of using the computer. (Please enter the number corresponding to the correct response beside each use.) (0) (1) "(2) (3) (A) Computing rates of return (B) Regression analysis (C) Forecasting (D) Simulations (E) Other uses (please specify) unimportant f a i r l y important very important e s s e n t i a l In evaluating r e a l estate investment proposals, on what holding period (time horizon) do you base your analysis? (please check) Years 0 - 2 years 3 - 5 years 6 -10 years 11 - 20 years 20+ years Other time periods (please specify) Thank you for your help. The information you have provided has been very h e l p f u l and w i l l be kept i n s t r i c t confidence. We would be very pleased to send you a copy of the survey r e s u l t s i f you wish, (please check) Yes No If your answer was "yes 1 i n the space provided. Name of respondent: Name of firm Street address C i t y or town please enter the following information _ 0-20 21-42 [43-63 64-80 FOR COMMENTSPLEASE USE REVERSE SIDE OF .PAGE REAL ESTATE INVESTOR SURVEY: COMPLETE RESULTS 136 Section I - general structure and organization of investors TABLE 3-4A SIZE OF REAL ESTATE PORTFOLIOS (Percentage of Respondents in Parentheses) 25 51 Public Private 17 12 Real Estate Real Estate Insurance Other A l l 105 Corporations Corporations Companies Investors Investors Under $1 mil 1 ion 1 ( 4.0) 4 ( 7 . 8 ) 1 ( 5.9) 2 (16.7) 8 ( 7.6) $1 m i l l i o n to 4 (16.0) 11 (21.6) 3 (17.6) 1 ( 8.3) 19 (18.1) $10 m i l l i o n $10 m i l l i o n to 9 (36.0) 31 (60.8) 8 (47.1) 3 (25.0) 51 (48.6) $100 m i l l i o n $100 m i l l i on to 7 (28.0) 4 ( 7.8) 3 (17.6) 3 (25.0) 17 (16.2) $250 m i l l i on Over $250 m i l l i o n 4 (16.0) 1 ( 2 . 0 ) 2 (11.8) 3 (25.0) 10 ( 9.5) (100%) (100%) (100%) (100%) (100%) Assets (mi l l ions of $): Total 3,557.2 1,968.7 1. ,497.1 1, ,127.4 8, ,150.5 Average 142.3 38.6 88.1 112.7 Standard Deviation 243.7 58.9 125.0 113.4 TABLE 3-5A LOCATION OF HEAD OFFICES, BY TYPE OF INVESTOR (Percentage of Repondents in Parentheses) 25 Public Real Estate Corporations 51 Private Real Estate Corporations 17 Insurance Companies B r i t i s h Columbia 9 (36.0) 17 (33.3) 1 ( 5.9) Alberta 6 (24.0) 7 (13.7) -Saskatchewan or - 4 ( 7.8) 1 ( 5.9) Manitoba Ontario 7 (28.0) 20 (39.2) 11 (64.7) Quebec 1 ( 4.0) 2 ( 3.9) 2 (11.8) A t l an t i c Provinces 1 ( 4.0) 1 ( 2.0) 1 ( 5.9) Outside Canada 1 ( 4.0) - 1 ( 5.9) (100%) (100%) (100%) 12 Other Investors (16.7) ( 8.3) (58.3) (16.7) (100%)" A l l 105 Investors 29 (27.6) 14 (13.3) 5 ( 4.8) 45 7 3 2 (42.9) ( 6.7) ( 2.9) ( 1-9) (100%) 137 TABLE 3-6A LOCATION OF HEAD OFFICES, BY SIZE OF INVESTOR (Percentage of Respondents in Parentheses) B r i t i s h Columbia Alberta Saskatchewan or Manitoba Ontario Quebec A t l an t i c Provinces Outside Canada 27 Smal 1 Investors* 14 5 (51.9) (18.5) (22.2) (~7.4) (100%) Assets (mil 1 ions of $): Total 86.9 Average 3.2 Standard Deviation 2.5 51 Medium-sized Investors* 12 5 2 25 5 1 1 (23.5) ( 9.8) ( 3.9) (49.0) ( 9.8) ( 2.0) ( 2.0) (100%) 194.5 38.1 23.3 27 Large 6,118.2 244.7 227.8 A l l ,105 Investors* Investors 3 (ll.D 29 (27.6) 4 (14.8) 14 (13.3) 3 (11.1) 5 ( 4-8) 14 (51.9) 45 (42.9) 2 ( 7.4) 7 ( 6.7) 3 ( 2.9) 1 (~3.7) 2 ( 1.9) (100%) (100%) 8,150.5 *Small Investors: up to $10 m i l l i o n p o r t f o l i o ; medium-sized investors: $10 m i l l i on to $100 m i l l i o n ; larger investors: over $100 m i l l i on po r t f o l i o . TABLE 3-7A TYPES OF PROPERTY HELD, BY TYPE OF INVESTOR* % for 25 Public Real Estate Corporations Single family housing Apartments Condominiums Off ice buildings Shopping centres ( r e t a i l ) Industr ial buildings Hotels/motels Undeveloped land Other 7.6 % % for 50 Private Real Estate Corporations 9.0 % % for 16 Insurance Companies %..for 10 Other Investors 7.8 % $ for A l l 101 Investors 7.1 % 16.1 29.9 26.1 12.9 24.2 4.6 7.5 0.5 11.0 6.0 11.8 17.1 39.1 16.9 19.2 15.5 10.6 4.1 17.7 11.5 4.3 8.1 17.9 6.3 8.5 3.6 1.7 0.2 4.0 2.1 30.8 14.3 3.7 12.2 16.5 5.3 2.2 9.1 10.2 4.9 *Columns do not sum to 100% since each row i s an average 138 TABLE 3-8A TYPES OF PROPERTY HELD, BY SIZE OF INVESTOR* % for 26 Smal 1 Investors** % for 50 Medium-si zed Investors** % for 25 Large Investors** % for A l l . 101 Investors Single family 17.8% housing Apartments 21.5 Condominiums 1.2 Off ice buildings 13.0 Shopping centres 9.8 ( r e t a i l ) Industr ial buildings 10.8 Hotels/motels 3.1 Undeveloped land 16.8 Other 11.1 5.3% 30.1 9.6 19.9 10.1 6.6 1.6 15.4 1.7 4.9% 15 3 24 16.0 10.0 2.3 18.4 4.6 7.1% 24.2 6.0 19.2 11.5 8. 2. 16. 4. •Columns do not sum to 100% since each row i s an average. **Refer to Table 3-6A. 139 Section II - measures of investment return and forms of return considered in the measures. TABLE 3-9A EQUITY VS. TOTAL CAPITAL AS A CRITERION FOR MEASURING RETURN, BY TYPE OF INVESTOR (Percentage of Respondents in Parentheses) 25 51 Public Private 17 10 Real Estate Real Estate Insurance Other A l l 103 Corporation Corporation Companies Investors Investors Equity 19 (76.0) 34 (66.7) 10 (58.8) 6.(60.0) 69 (67.0) Total capi ta l 6 (24.0) 16 (31.4) 4 (23.5) 4 (40.0) 30 (29.1) Both - - 3 (15.7) - 4 ( 3.9) (100%) (100%) (100%) (100%) (100%) TABLE 3-1 OA EQUITY VS. TOTAL CAPITAL AS A CRITERION FOR .MEASURING INVESTMENT RETURN, BY SIZE OF INVESTOR (Percentage of Respondents in Parentheses) 27 Smal 1 Investors* 51 Medium-si zed Investors* 25 Large Investors* A l l 103 Investors Equity 17 (63.0) 36 (70.6) 16 (64.0) 69 (67.0) Total capi ta l 10 (37.0) 12 (23.5) 8 (32.0) 30 (29.1) Both - 3 ( 5.9) 1 ( 4.0) 4 ( 3.9) (100%) (100%) (100%) (100%) *Refer to Table 3-6A. 140 TABLE 3-1IA BEFORE-TAX MEASURES OF INVESTMENT RETURN USED MOST OFTEN, BY TYPE OF INVESTOR (Percentage of Respondents in Parentheses) 19 44 Public Private 15 10 Real Estate Real Estate Insurance Other A l l 88 Corporations Corporations Companies Investors Investors GRM NRM Overall ROI Equity Dividend Payback Equity Y ie ld Other More than one 3 ( 6.8) 3 (20.0) 1 (10.0) 7 ( 8.0) 8 (42.1) 13 (29.5) 6 (40.0) 2 (20.0) 29 (33.0) 5 (26.3) 20 (45.5) 3 (20.0) 3 (30.0) 31 (35.2) _ 1 ( 2.3) - - 1 ( 1-1) 3 (15.8) 3 ( 6.8) 1 ( 6.7) 3 (30.0) 10 (11.4) 1 ( 5.3) 1 ( 2 . 3 ) - - 2 ( 2.3) 2 (10.5) 3 ( 6.8) 2 (13.3) 1 (10.0) 8 ( 9.1) (100%) (100%) (100%) (100%) (100%) TABLE 3-12A BEFORE-TAX MEASURE OF INVESTMENT RETURN USED MOST OFTEN, BY SIZE OF INVESTOR (Percentage of Respondents in Parenthesis) GRM NRM Overall ROI Equity Dividend Payback Equity Y ie ld (before-tax IRR) Other More than one 22 Smaller Investors* 43 Medium-sized Investors* 23 Large Investors* A l l 88 Investors 3 (13.6) 10 (45.5) 6 (27.3) 1 ( 4.5) 3 (.7.0) 11 (25.6) 17 (39.5) 1 ( 2.3) 7 (16.3) 1 (~4.3) 8 (34.8) 8 (34.8) 2 ( 8.7) 7 ( 8.0) 29 (33.0) 31 (35.2) 1 ( 1.1) 10 (11.4) 2 ( 9.1) 1 ( 2.3) 3 ( 7.0) 1 ( 4.3) 3 (13.0) 2 ( 2.3) 8 ( 9.1) (100%) (100%) (100%) (100%) *Refer to Table 3-6A. 141 TABLE 3-13A AFTER-TAX MEASURE OF INVESTMENT RETURN USED MOST OFTEN, BY TYPE OF INVESTOR (Percentage of Respondents in Parentheses) ATCF ( f i r s t y r . ) / equity Payback Present Value P r o f i t a b i l i t y Index IRR Adjusted IRR FMRR Other More than one 18 Public Real Estate Corporations 7 (38.9) 1 ( 5.6) 3 (16.7) 1 ( 5.6) 5 (27.8) 1 ( 5.6) 27 Private. Real Estate Corporations 8 (29.6) 3 (11.1) 1 ( 3.7) 1 ( 3.7) 5 (18.5) 1 ( 3.7) 8 (29.6) 11 Insurance Companies 1 ( 9.1) 3 (29.1) 1 ( 9.1) 5 (45.5) 1 ( 9.1) 1 ( 9.1) 4 Other Investors 1 (25.0) 1 (25.0) 1 (25.0) A l l 60 Investors 16 (26.7) 5 ( 8.3) 6 (10.0) 2 ( 3.3) 16 (26.7) 3 ( 5.0) 1 ( 1-7) 1 ( 9.1) 1 (25.0) 11 (18.3) (100%) (100%) (100%) (100%) (100%) TABLE 3-14A AFTER-TAX MEASURE OF INVESTMENT RETURN USED MOST OFTEN, BY SIZE OF INVESTOR (Percentage of Respondents in Parentheses) 19 27 14 Smaller Medium-sized Large A l l 60 Investors* Investors* Investors* Investors ATCF ( f i r s t y r . ) / 5 (26.3) 10 (37.0) 1 ( 7.1) 16 (26.7) equity Payback 1 ( 5.3) 2 ( 7.4) 2 (14.3) 5 ( 8.3) Present Value 5 (26.3) - 1 ( 7.1) 6 (10.0) P r o f i t a b i l i t y Index - - 2 (14.3) 2 ( 3.3) IRR 3 (15.8) 9 (33.3) 4 (28.6) 16 (26.7) Adjusted IRR - 1 ( 3.7) 2 (14.3) 3 ( 5.0) FMRR - 1 ( 3.7) - 1 ( 1.7) Other - -More than one 5 (26.3) 4 (14.8) 2 (14.3) 11 (18.3) (100%) (100%) (100%) (100%) *Refer to Table 3-6A. 142 TABLE 3-15A FORM OF INVESTMENT RETURN CONSIDERED, BY TYPE OF INVESTOR (Percentage of Respondents in Parentheses) Cash flow Tax Shelter Amortization of loan Net proceeds of sale: Other 25 Public Real Estate Corporation 84.0 % 48.0 32.0 48.0 8.0 51 Private Real Estate Corporation 90.2 % .27.5 27.5 49.0 15.7 17 Insurance Companies 76.5 % 35.3 35.3 29.4 5.9 12 Other Investors 66.7 % 33.3 33.3 33.3 A l l 105 Investors 83.8 % 34.3 30.5 43.8 10.5 TABLE 3-16A FORM OF INVESTMENT RETURN CONSIDERED, BY SIZE OF INVESTOR (Percentage of Respondents in Parentheses) 27 51 27 Smaller Medium-sized Large A l l 105 Investors* Investors* Investors* Investors Cash flow 81.5 I 82.4 % 88.9 % 83.8 % Tax shelter 25.9 37.3 37.0 34.3 Amortization of 11.1 41.2 29.6 30.5 loan Net proceeds of 48.2 52.9 22.2 43.8 sal e Other 3.7 15.7 7.4 10.5 *Refer to Table 3-6A. 143 TABLE 3-17A % OF INVESTORS INCLUDING EACH FORM OF RETURN IN MEASURE OF PERFORMANCE Cash flow Tax Shelter Amortization of Loan Net Cash Proceeds of Sale Overal1 ROI 86.2 % 13.8 13.8 37.9 Equity Dividend ATCF ( f i r s t Rate year)/Equity IRR 90.3 % 38.7 35.5 48.4 75.0 68.8 37.5 62.5 87.5 % 68.8 56.3 56.3 Investors in Sample - 105 144 Section III - rates of return, r i s k adjustment, computer usage and holding periods TABLE 3-18A RATES OF RETURN EXPECTED, BEFORE-TAX Apartments Off ice buildings Shopping centres ( r e t a i l ) Industr ial buildings Hotels/motels Undeveloped land Other Overall ROI 10.4 % 11.1 10.9 11.6 12.2 20.0 Equity Dividend Rate 9.3 % 10.6 10.9 10.5 11.0 23.3 11.8 21 - Investors in Sample - 25 TABLE 3-19A RATES OF RETURN EXPECTED, AFTER-TAX Apartments Off ice buildings Shopping centres ( r e t a i l ) Industrial buildings Hotels/motels Undeveloped land Other ATCF ( f i r s t yr.)/equity 8.4 % 18.8 10. 11. 12. 21. 12.0 IRR 10.6 10.6 10.0 10.6 12.9 17.8 13.0 12 - Investors in Sample - 14 145 TABLE 3-20A RISK ADJUSTMENT TECHNIQUES (Percentage of Respondents in Parenthesis) Never Seldom Frequently Always No (0) (1) (2) (3) Response Mean Deviation Adjust return 22(21.0) 18(17.1) 36(34.3) 17(16.2) 12(11.4) 1.516 1.049 upwards Adjust benefits 23(21.9) 22(21.0) 35(33.3) 15(14.3) 10( 9.5) 1.442 1.028 down Probab i l i ty 50(47.6) 18(17.1) 21(20.0) 5( 4.8) 11(10.5) 0.798 0.968 d i s t r ibut ions Sen s i t i v i t y 57(54.3) 10( 9.5) 17(16.2) 10( 9.5) 11(10.5) 0.787 1.086 analysis Other 17(16.2) - 7( 6.7) 7( 6.7) 74(70.5) 1.129 1.310 Investors in Sample - 105 146 TABLE 3-21A MEAN FREQUENCY OF TECHNIQUES USED TO ADJUST FOR UNCERTAINTY, BY TYPE OF INVESTOR 25 51 Public Private 17 12 Real Estate Real Estate Insurance Other Corporation Corporation Companies Investors Adjust return 1.826 1.356 1.400 1.700 upwards 1.188 1.500 Adjust benefits 1.391 1.543 down Probab i l i ty 1.000 0.667 • 1.188 0.300 d i s t r ibut ions Sen s i t i v i t y 1.391 0.689 0.688 -analysis Other 1.667 1.308 0.500 0.833 A l l 105 1.516 1.442 0.798 0.787 1.129 TABLE 3-22A MEAN FREQUENCY OF TECHNIQUES USED TO ADJUST FOR UNCERTAINTY, BY SIZE OF INVESTOR 27 Smal 1 Investors* 51 Medium-sized Investors* 27 Large Investors* A l l 105 Investors Adjust return 1.286 1.574 1.600 1.516 upwards 1.442 Adjust benefits 1.409 1.532 1.308 down Probab i l i ty 1.091 0.696 0.731 0.798 d i s t r ibut ions Sen s i t i v i t y 0.619 0.872 0.769 0.787 analysis Other 0.714 1.077 1.455 1.129 *Refer to Table 3-6A. 147 TABLE 3-23A COMPUTER USAGE, BY TYPE OF INVESTOR (Percentage of Respondents in Parentheses) 25 51 Public Private 17 11 Real Estate Real Estate Insurance Other A l l 104 Corporations Corporations Companies Investors Investors 10 (40.0) 15 (60.0) (Tool) 10 (19.6) 41 (80.4) (100%) 5 (29.4) 12 (70.6) (100%) 3 (27.3) 8 (72.7) (TOOT) 28 (26.9) 76 (73.1) (Too%) TABLE 3-24A COMPUTER USAGE, BY SIZE OF INVESTOR (Percentage of Respondents in Parentheses) 27 51 26 Small Medium-sized Large A l l 104 Investors* Investors* Investors* Investors Yes 6 (22.2) 10 (19.6) 12 (46.2) 28 (26.9) No 21 (77.8) 41 (80.4) 14 (53.8) 76 (73.1) (100%) ( 1 0 0 % ) (100%) (100%) *Refer to Table 3-6A 148 TABLE 3-25A EVALUATION OF REASONS FOR USING THE COMPUTER (Percentage of Respondents in Parentheses) Computing rates of return Regression Forecasting Simulations Other Fa i r l y Average Unimportant Important Importance Essential No (0) (1) (2) (3) Response Mean Deviation 22(78.6) 8(28.6) 10(35.7) 4(14.3) 3(10.7) 10(35.7) 13(46.4) 2( 7.1) 2.385 0.697 2( 7.1) 2( 7.1) - 2( 7.1) 0.231 0.587 5(17.9) 10(35.7) 3(10.7) 2( 7.1) 1.308 1.050 3(10.7) 8(28.6) 5(17.9) 2( 7.1) 1.308 1.192 : 1( 3.6) 1( 3.6) 27(78.6) 0.833 1.329 Investors in Sample - 28 TABLE 3-26A EVALUATION OF REASONS FOR NOT USING THE COMPUTER (Percentage of Respondents in Parentheses) Unimportant (0) Fa i r l y Important (1) Average Importance (2) Essential (3) No Response Mean Deviation Lack of 46(60.5) 12(15.8) 10(13.2) 6( 7.9) 2( 2.6) 0.676 0, .995 personnel 2( 2.6) 0.797 No computer 45(59.2) 9(11.8) 10(13.2) 10(13.2) 1, .122 f a c i l i t i e s Lack of 41(53.9) 11(14.5) 11(14.5) 11(14.5) 2( 2.6) 0.892 1 .142 input data Too expensive 45(59.2) 10(13.2) 12(15.8) 7 (9 .2 ) 2( 2.6) 0.743 1 .048 Not useful 24(31.6) 16(21.1) 16(21.1) 18(23.7) 2( 2.6) 1.378 1 .179 (necessary) Other 12(15.8) - 2( 2.6) 10(13.2) 51(67.1) 1.417 1 .472 Investors in Sample - 76 149 TABLE 3-27A MEAN IMPORTANCE OF REASONS FOR USING THE COMPUTER, BY TYPE OF INVESTOR 10 Public Real Estate Corporation TO Private Real Estate Corporation 5 3 Insurance Other A l l 28 Companies Investors Investors Computing rates 2.200 2.625 2.333 2.667 2.407 of return Regression 0.200 0.250 . 0.200 0.333 0.231 Forecasting 1.500 1.250 1.000 1.333 1.308 Simulations 1.000 1.625 0.800 2.333 1.308 Other 2.000 - - 1.500 0.833 TABLE 3-28A MEAN IMPORTANCE OF REASONS FOR USING THE COMPUTER, BY SIZE OF INVESTOR Computing rates of return Regression Forecasting Simulations Other '6" Small Investors* 2.667 0.333 2.000 1.333 10 Medium-sized Investors* 2.444 0.222 0.778 1.444 12 Large Investors* 2.250 0.182 1.364 1.182 1.250 A l l 28 Investors 2.407 0.231 1.308 1.308 0.833 *Refer to Table 3-6A. 150 TABLE 3-29A MEAN IMPORTANCE OF REASONS FOR NOT USING THE COMPUTER, BY TYPE OF INVESTOR Lack of personnel No computer f a c i l i t i e s . Lack of input data Too expensive Not useful . (necessary) Other 15 41 Public Private 12 8.. A l l 76 Real Estate Real Estate Insurance Other Corporations Corporations Companies Investors Investors 0.571 0.775 0.417 0.750 0.676 0.643 0.975 0.667 0.375 0.797 1.071 0.850 0.833 0.875 0.892 0.929 0.725 0.500 0.875 0.743 1.643 1.375 0.833 1.750 1.378 2.500 1.222 1.250 0.600 1.417 TABLE 3-30A MEAN IMPORTANCE OF REASONS FOR NOT USING THE COMPUTER, BY SIZE OF INVESTOR Lack of personnel No computer f a c i l i t i e s Lack of input data Too expensive Not useful (necessary) Other 21 Small Investors* 1.000 1.200 1.000 1.000 1.381 1.286 41 Medium-sized Investors* 0.625 0.725 0.750 0.725 1.487 1.600 14 Large Investors* 0.357 0.429 1.143 0.429 1.071 1.286 A l l 76 Investors 0.676 0.797 0.892 0.743 1.378 1.417 *Refer to Table 3-6A 151 TABLE 3-31A PLANNED HOLDING PERIODS, BY TYPE OF INVESTOR (Percentage of Respondents in Parentheses) 22 50 Public Private 15 10 Real Estate Real Estate Insurance Other A l l 97 Years Corporations Corporations Companies Investors Investors 0 - 2 2 ( 9.1) 8 (16.0) - 1 (10.0) 11 (11.3) 3 - 5 8 (36.4) 19 (38.0) 1 ( 6.7) 2 (20.0) 30 (30.9) 6 - 1 0 3 (13.6) 11 (22.0) 6 (40.0) 3 (30.0) 23 (23.7) 1 1 - 2 0 5 (22.7) 6 (12.0) 2 (13.3) 1 (1.0.0) 14 (14.4) 20+ 4 (18.2) 6 (12.0) 6 (40.0) 3 (30.0) 19 (19.6) (100%) (100%) (100%) (100%) (100%) TABLE 3-32A PLANNED HOLDING PERIODS, BY SIZE OF INVESTOR (Percentage of Respondents in Parentheses) 26 47 24 A l l 97 Smaller Medium-sized Larger, Years Investors* Investors* Investors* Investors 0 - 2 5 (19.2) 5 (10.6) 1 ( 4.2) 11 (11.3) 3 - 5 12 (46.2) 14 (29.8) 4 (16.7) 30 (30.9) 6 - 1 0 4 (15.4) 13 (27.7) 6 (25.0) 23 (23.7) 1 1 - 2 0 1 ( 3.8) 7 (14.9) , 6 (25.0) 14 (14.4) 20+ 4 (15.5) 8 (17.1) 7 (29.2) 19 (19.1) (100%) (100%) (100%) (100%) *Refer to Table 3-6A APPENDIX B REAL ESTATE BROKERS SURVEY Covering Letter and Questionnaire Complete Tabulated Results CONFIDENTIAL CONFIDENTIAL REAL ESTATE INVESTMENT ANALYSIS QUESTIONNAIRE Your help i s greatly appreciated and your answers w i l l be kept s t r i c t l y con-f i d e n t i a l Section I deals with the general structure and organization  of your firm. 1. How long has your company been involved i n r e a l estate . : brokerage? Number of Years = 2. In which one of the following a c t i v i t i e s i s your f i r m prim- a r i l y involved? (please check the correct answer) I.C.I. Brokerage I.C.I, and r e s i d e n t i a l brokerage Real estate counselling (I.C.I, brokerage plus market analyses, and f i n a n c i a l planning) Other (please specify) 3. Is your firm a subsidiary of a l a r g e r , integrated (develop-ment, investment) r e a l estate company? Yes No 4. Where i s the head o f f i c e of your fi r m located? B r i t i s h Columbia Alberta .Saskatchewan and Manitoba Ontario Quebec A t l a n t i c Provinces Outside Canada 5. Approximately what percentage of the head o f f i c e s of your firm's c l i e n t s (purchasers & vendors) are located i n the following areas? -n 1. / o i • Percentage Location of „ , . Purchaser/Clients '. .. .. „ , .... Vendor/Clients C l i e n t s Head O f f i c e s  % B r i t i s h Columbia % % Alberta % % Saskatchewan & Manitoba % % Ontario % % Quebec % A t l a n t i c Provinces % Outside Canada _% % O f f i c e Use Only 1 - 3 -4-5 CONFIDENTIAL - 2 - CONFIDENTIAL O f f i c e Use On ly What i s the a p p r o x i m a t e v a l u e o f you r f i r m ' s a ve rage I .C. I , t r a n s a c t i o n ? Average V a l u e o f I .C. I . T r a n s a c t i o n = $ A p p r o x i m a t e l y what p e r c e n t a g e o f you r f i r m ' s t o t a l I .C. I , s a l e s i s composed o f t he f o l l o w i n g t y p e s o f p r o p e r t y ? Type o f P r o p e r t y Apar tments ( r e n t a l ) O f f i c e b u i l d i n g s Shopp ing c e n t r e s ( r e t a i l ) I n d u s t r i a l b u i l d i n g s H o t e l s / m o t e l s Undeve loped l a n d O t h e r ( p l e a s e s p e c i f y ) P e r c e n t a g e % % % % % % % 100 % S e c t i o n I I d e a l s w i t h t he components o f i n v e s t m e n t r e t u r n and  the methods u sed t o c a l c u l a t e r e t u r n . 8. When you a n a l y z e r e a l e s t a t e i n v e s t m e n t p r o j e c t s , wh i ch o f the f o l l o w i n g component ( s ) o f i n v e s t m e n t r e t u r n do you con-s i d e r ? ( p l e a s e check ) Net o p e r a t i n g income B e f o r e - t a x c a sh f l o w (net o p e r a t i n g income minus mortgage payments ) A f t e r - t a x c a sh f l o w (net o p e r a t i n g income minus mortgage payments minus t a x e s ) A m o r t i z a t i o n o f the l o a n ( e q u i t y b u i l d - u p ) Net c a s h p r o c e e d s f rom s a l e O ther ( p l e a s e s p e c i f y ) 9. When you a n a l y z e r e a l e s t a t e i n v e s t m e n t p r o j e c t s , do you . c a l c u l a t e the r e t u r n p r i m a r i l y ba sed o n : ( p l e a s e check one) E q u i t y i n v e s t e d o r T o t a l c a p i t a l i n v e s t e d 10 . I f you a n a l y z e r e a l e s t a t e i n v e s t m e n t p r o j e c t s on a BEFORE- TAX b a s i s , w h i c h one o f t h e f o l l o w i n g methods o f c a l c u l a t i n g i n v e s t m e n t r e t u r n do you use most o f t e n ? ( p l e a s e check one) G ro s s r e n t m u l t i p l i e r ( purchase p r i c e / g r o s s income) N e t r e n t m u l t i p l i e r ( pu rcha se p r i c e / n e w o p e r a t i n g income) O v e r a l l r e t u r n on i n v e s t m e n t (net o p e r a t i n g income/ p u r c h a s e p r i c e ) E q u i t y d i v i d e n d r a t e (net o p e r a t i n g income minus mortgage p a y m e n t s / e q u i t y ) payback p e r i o d ( t ime t o r e c a p t u r e i n i t i a l e q u i t y ) B e f o r e - t a x i n t e r n a l r a t e o f r e t u r n O ther ( p l e a s e s p e c i f y ) - — 156 CONFIDENTIAL - 3 - CONFIDENTIAL 11. If you analyze r e a l estate investment projects on an AFTER-TAX ba s i s , which one of the following methods of c a l c u l a t i n g investment return do you use most often? (please check one) After-tax cash flow ( f i r s t year)/equity Payback period (time to recapture i n i t i a l equity) Time-adjusted rates of return: Net present value P r o f i t a b i l i t y index Internal rate of return Adjusted i n t e r n a l rate of return (reinvestment rate specified) F i n a n c i a l management rate of return Other (please specify) I I I . . Section I I I deals with s p e c i f i c rate of return, adjustments to.  that .return, and computer usage. 12, What i s the minimum expected rate of return (calculated by the method s p e c i f i e d i n question #10 or #11) required to a t t r a c t c l i e n t s to invest i n the following types of proper-t i e s ? Type of Property Apartments (rental) O f f i c e buildings Shopping centres ( r e t a i l ) I n d u s t r i a l buildings Hotels/motels Undeveloped land Other (please specify) Rate of Return Required % J J J J % % 13. From the l i s t of responses i d e n t i f i e d below, please choose the one that most c l o s e l y indicates the frequency with which you use the following methods to adjust your analysis when working with highly uncertain estimates, (please enter the number corresponding to the correct response beside each method) (0) (1) (2) (3) never seldom frequently always Adjust upwards the return required from the project Adjust downward the benefits expected from the project Use p r o b a b i l i t y d i s t r i b u t i o n Use s e n s i t i v i t y analysis Other adjustments . O f f i c e Use Only -A3 -44-5 -46-7 -48-9 -50-1 -52-3 -54-5 -56-7 -58 -59 -60 -61 -62 CONFIDENTIAL - 4 - CONFIDENTIAL O f f i c e Use On ly 14. Do you use the computer i n making r e a l e s t a t e i n v e s t m e n t a n a l y s e s ? ( P l e a s e check ) Yes No 15. I f y ou r answer was " n o " i n t he l a s t q u e s t i o n , f rom the l i s t o f r e s p o n s e s i d e n t i f i e d b e l o w , p l e a s e choose the one t h a t most c l o s e l y i n d i c a t e s the r e l a t i v e i m p o r t a n c e o f the f o l -l o w i n g rea sons f o r no t u s i n g t h e computer . ( p l e a s e e n t e r c o r r e c t r e s p o n s e b e s i d e each rea son ) (0) u n i m p o r t a n t (1) f a i r l y i m p o r t a n t (2) v e r y i m p o r t a n t (3) e s s e n t i a l L a c k o f computer t r a i n e d p e r s o n n e l L a c k o f a c c e s s t o computer f a c i l i t i e s I n a b i l i t y t o o b t a i n i n p u t d a t a n e c e s s a r y f o r computer programs Too e x p e n s i v e Do no t t h i n k the computer i s u s e f u l ( n e c e s s a r y ) O t h e r ( p l e a s e s p e c i f y ) 16. I f you use a computer , f rom the l i s t o f r e s p o n s e s i d e n t i -f i e d be l ow, p l e a s e choose the one t h a t most c l o s e l y i n d i - : ... c a t e s the r e l a t i v e i m p o r t a n c e o f the f o l l o w i n g ways o f u s i n g t h e computer , ( p l e a s e e n t e r the number c o r r e s p o n d i n g t o the c o r r e c t r e s p o n s e b e s i d e each u s e . ) (0) u n i m p o r t a n t (1) f a i r l y i m p o r t a n t (2) v e r y i m p o r t a n t (3) e s s e n t i a l Comput ing r a t e s o f r e t u r n R e g r e s s i o n a n a l y s i s F o r e c a s t i n g S i m u l a t i o n s O ther u se s ( p l e a s e s p e c i f y ) 17. In a n a l y s i n g r e a l e s t a t e i n v e s t m e n t p r o j e c t s , on what h o l d i n g p e r i o d ( t ime h o r i z o n ) do you base y o u r a n a l y s i s ? ( p l e a s e check ) Y e a r s 0 -3 -y e a r s y e a r s 6 - 1 0 y e a r s 11 - 20 y e a r s 20 + y e a r s O ther t ime p e r i o d s ( p l e a s e s p e c i f y ) 158 - 5 -Thank you for your help. The information you have provided has been very h e l p f u l and w i l l be kept i n s t r i c t confidence. We would be very pleased to send you a copy of the survey r e s u l t s i f you wish. I f so, please enter your name and address below. (This sheet may be returned to us under separate cover i f you prefer) NAME: ADDRESS: Please use the space below f or any comments you might wish to make. 159 REAL ESTATE BROKERS SURVEY: COMPLETE RESULTS  Section I - general structure and organization of brokers TABLE 3-4B AVERAGE VALUE OF ICI TRANSACTIONS (Percentage of Respondents in Parentheses) $ 55 Brokers Under $500,000 20 (40.8) $500,000 to $1 m i l l i o n 17 (34.7) Over $1 m i l l i on 12 (24.5) (100%) TABLE 3-5B LOCATION OF BROKERAGE FIRMS HEAD OFFICES Location % for 55 Brokers Quebec A t l an t i c Provinces Ontario Alberta B r i t i s h Columbia Saskatchewan or Manitobe 50 (90.9) 1 ( 1-8) 2 ( 3.6) 1 ( 1-8) 1 ( 1.8) Outside Canada 1 ( 1.8) (100%) TABLE 3-6B LOCATION OF CLIENTS' HEAD OFFICES* Purchaser-Clients Location Vendor-Clients 78.2 % B r i t i s h Columbia 78.4 4.3 Alberta 5.7 0.9 Saskatchewan 0.4 or Manitoba 6.9 Ontario 7.0 0.9 Quebec 0.3 - At l an t i c Provinces -8.6 Outside Canada 9.8 *Columns do not sum to 100% since each row i s an average. TABLE 3-7A TYPES OF PROPERTIES IN BROKERAGE TRANSACTIONS* Property Type % for 50 Brokers Apartments 30.3 % Off ice buildings 15.2 Shopping centres ( r e t a i l ) 16.8 Industrial buildings 16.3 Hotels/motels 5.1 Undeveloped land 12.4 Other 4.2 *Columns do not sum to 100% since each row i s an average 161 SECTION II - measures of return and forms of return included in each measure. TABLE 3-9B EQUITY VS. TOTAL CAPITAL AS A CRITERION FOR MEASURING RETURN (Percentage of Respondents in Parentheses) 49 Brokers Equity 35 (71.4) Total Capital 11 (22.4) Both 3 ( 6.1) (100%) TABLE 3-1 IB BEFORE-TAX MEASURE OF INVESTMENT RETURN USED MOST OFTEN (Percentage of Respondents in Parentheses) Before-Tax Measure 48 Brokers GRM 6 (12.5) NRM 2 ( 4.2) Overall ROI 22 (45.8) Equity Dividend 16 (33.3) Payback Equity Y ie ld (before-tax IRR) Other More than one 2 ( 4.2) (100%) TABLE 3-13B AFTER-TAX MEASURE OF INVESTMENT RETURN USED MOST OFTEN (Percentage of Respondents in Parentheses) After - tax Measure 23 Brokers ATCF ( f i r s t year)/Equity 12 (52.2) Payback 2 ( 8.7) Present Value 6 (26.1) • P r o f i t a b i l i t y Index 1 ( 4.3) IRR 1 ( 4.3) Adjusted IRR FMRR Other More than one 2 ( 8.7) (100%) TABLE 3-16B % OF BROKERS CONSIDERING EACH FORM OF RETURN Forms of Return % for 55 Brokers Cash flow Tax shelter Amortization of loan Net cash proceeds of sale Other 77.8 % 22.2 37.0 31.5 11.1 163 TABLE 3-17B % OF BROKERS INCLUDING EACH FORM OF RETURN IN MEASURE OF PERFORMANCE Cash flow Tax shelter Amortization of loan Net cash proceeds of sale Overal1 ROI 100.0 % 8.3 29.2 8.3 Equity Dividend 100.0 % 25.0 56.3 18.8 ATCF ( f i r s t year)/Equity 100.0 33.3 50.0 8.3 % IRR Brokers in Sample - 55 *Since only 1 broker used the IRR, the results are not representative. SECTION III - rates of return, r i s k adjustment, computer usage and holding periods TABLE 3-18B RATES OF RETURN EXPECTED, BEFORE-TAX Apartments Off ice buildings Shopping centres ( r e t a i l ) Industr ial buildings Hotels/motels Undeveloped land Other Overall Equity Dividend ROI Rate 8.3 % 7.1 % 9.0 8.3 8.7 8.9 11.9 8.5 13.9 12.0 31.1 19.5 8.0 22.0 18 - Brokers in Sample - 14 TABLE 3-19B RATES OF RETURN EXPECTED, AFTER-TAX Apartments Off ice buildings Shopping centres ( r e t a i l ) Industrial buildings Hotels/motels Undeveloped land Other ATCF ( f i r s t year)/Equity 6.T % 7.9 8.4 9.0 11.8 13.7 22.0 IRR 10 - Brokers in Sample *Since only 1 broker used the IRR, the results are not representative. 165 TABLE 3-20B RISK ADJUSTMENT TECHNIQUES (Percentage of Respondents in Parentheses) Never Seldom Frequently Always No (0) (1) (2) (3) Response Mean Deviation Adjust return 15(13.3) 9(18.8) 19(39.6) 5(10.4) 7(12.7) 1.292 1.031 upwards Adjust benefits 10(20.8) 10(18.2) 22(40.0) 6(10.9) 7(12.7) 1,500 0.968 down Probab i l i ty 24(43.6) 9(16.4) 14(25.5) 1( 1.8) 7(12.7) 0.833 0.930 d i s t r ibut ions Sen s i t i v i t y 28(50.9) 13(23.6) 2( 3.6) 5( 9.1) 7(12.7), 0.667' 0.975 analysis Other 3( 5.5) 1( 1.8) 2( 3.6) 2( 3.6) 47(85.5) 1.375 1.302 Brokers in Sample - 55 TABLE 3-23B COMPUTER USAGE (Percentage of Respondents in Parentheses) Computer Usage 49 Brokers Yes No 4 ( 8.2) 45 (91.8) (100%) 166 TABLE 3-25B EVALUATION OF REASONS FOR USING THE COMPUTER (Percentage of Resondents i n Parentheses) F a i r l y Unimportant Important Important E s s e n t i a l No (0) (1) (2) (3) Response Mean Deviation Computing ra t e s 1 (16.7) 1 (16.7) 1 (16.7) 3 (50.0) - 2.000 1.265 of r e t u r n Regression 4 (66.7) 1 (16.7) - 1 (16.7) - 0.667 1.211 Forecasting 1 (16.7) 2 (33.3) - 3 (50.0) - 1.833 1.329 Simulations 5 (83.3) - - 1 (16.7) - 0.500 1.225 Other - 6(100.0) Brokers i n Sample - 55 TABLE 3-26B EVALUATION OF REASONS FOR NOT USING THE COMPUTER (Percentage of Respondents i n Parentheses) F a i r l y Unimportant Important Important E s s e n t i a l No (0) (1) (2) (3) Response Mean Deviation Lack of personnel 25(52.1) 10(20.8) 5(10.4) 7(14.6) 1 ( 2 . 1 ) 0 . 8 7 2 1.115 No computer 11(22.9) 6(12.5) 15(31.3) 15(31.3) 1 ( 2 . 1 ) 1 . 7 2 3 1.155 "ffiicil i t i 6 s Lack of input 26(54.2) 7(14.6) 6(12.5) 8(16.7) 1 ( 2 . 1 ) 0 . 9 1 5 1.176 data Too expensive 26(54.2) 4( 8.3) 14(29.2) 3( 6.3) 1( 2.1) 0.872 1.055 Not useful 17(35.4) 5(10.4) 20(41.7) 5(10.4) 1 ( 2 . 1 ) 1 . 2 7 7 1.077 (necessary) Other 3( 6.3) - - 2( 4.2) 43(89.6) 1.200 1.643 Brokers i n Sample - 48 TABLE 3-31B PLANNED HOLDING PERIODS (Percentage of Respondents in Parentheses) Years 40 Brokers 0-2 7 (17.5) 3-5 25 (62.5) 6-10 5 (12.5) 11-20 2 ( 5.0) 20+ 1 ( 2.5) (100%) 168 APPENDIX C PROPERTY SAMPLE - General Information and 1971 Appraisal Report S t a t i s t i c s - Amenity Features - Net Income Mu l t ip l i e r s - 1970 and 1977 PROPERTY SAMPLE: GENERAL INFORMATION AND 1971 APPRAISAL REPORT STATISTICS -operty Year Constructed Date Operational (70% rented) Building Description Fair Market Value (1971) Cap i ta l i zat ion Rates NOI Cash Flow** GRM Operating Costs/ Gross Income 1 _ July 1965 1-14 storey $ 4.100 M. 8.0 6.0 7.5 42% 2 1964 Ju ly 1964 2 low-rise 1.750 8.5 6.0 6.8 45 3 - June 1965 2-25 storey 13.100 - 7.0 7.2 43 4 1965 June 1965 Townhouses & low-rise 4.000 8.0 6.0 8.0 39*** 5 1965 Sept. 1966 1-24 storey 1-20 storey 10.900 - 6.5 7.3 42 6 1965 Aug. 1966 3 groups of townhouses 2.600 8.0 6.0 6.8 3g*** 7 1965 Sept. 1966 2-21 storey 8.800 8.5 6.5 7.2 42 8 1966 Oct. 1966 1-23 storey 1-17 storey 1-12 storey 12.500 8.5 6.5 7.3 41 9 1966 Aug. 1967 1-17 storey 2-6 storey 7.900 8.5 6.5 7.2 44 10* 1962 Jan. 1964 - 1.650 - - 7.2 54 11 1960 - 1-6 storey 0.830 8.5 6.5 6.5 42 12 1963 - 1-6 storey 0.855 - 6.5 6.6 43 13 1963 Jan. 1964 1-9 storey 2.400 8.0 6.5 6.6 47 14 1963 Jan. 1964 1-8 storey 6.000 8.5 6.0 7.5 41 15 1964 - 1-2 storey 1.025 8.5 6.5 6.8 42 •Limited Dividend bui lding under CMHC. **Net Operating Income - debt service ***Tenants pay heat CT) to cn -p» oo ro — ' o w 3 0 o ^ i c r i c n - p » c o r o -a -s o ro -s c+ -a 73 o -o -< OO near transportation near shopping on-site shopping near schools 53 near subway indoor pool outdoor pool sauna health club (exercise room) recreation centre tennis courts a i r conditioning nursery individual heat control penthouses and townhouses cable T.V. • m 3 > m o o o 171 PROPERTY SAMPLE: NET INCOME MULTIPLIERS (NIM) 1970 Used to determine purchase price in 1970 and forecasted sales price in 1977. 1. Derivation market value NIM 71 70 NOI7i from appraisal - e.g., for property 1: = 4,100,000 317,066 = 12.93 1977 Used to determine actual sales price in 1977. = estimated ( M _ average) M 1 I 7 7 average NIM 7 7 U N i m 7 0 NIM 7 Q ; - e.g., for property 1: = 11.76 + (12.93 = 12.60) = 12.09 Values Property Number 1 2 3 4 5 6 7 8 9 10* 11 12 13 14 15 NIM 70 12.93 12.36 12.63 13.11 12.58 13.07 12.40 12.37 12.50 14.78 11.82 11.58 12.45 12.71 11.72 NIM 7 7 12.09 11.52 11.79 12.27 11.74 12.23 11.56 11 11 .53 .66 13.94 10.98 10.74 11.61 11.87 10.88 Average 12.60 Average 11.76 *Limited Dividend project under CMHC. 

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
http://iiif.library.ubc.ca/presentation/dsp.831.1-0093953/manifest

Comment

Related Items