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UBC Theses and Dissertations

An analysis of the effects of M.U.R.B. legislation on Vancouver’s rental housing market Wicks, Anne Patricia 1982

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AN ANALYSIS OF THE EFFECTS OF M.U.R.B. LEGISLATION ON VANCOUVER'S RENTAL HOUSING MARKET by ANNE PATRICIA WICKS B.COMM., UNIVERSITY OF BRITISH COLUMBIA, 1978  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN BUSINESS ADMINISTRATION  in  THE FACULTY OF COMMERCE AND BUSINESS ADMINISTRATION (URBAN LAND ECONOMICS DIVISION)  We accept this thesis as conforming to the required standard  THE UNIVERSITY OF BRITISH COLUMBIA April, 1982 c \ Anne P a t r i c i a Wicks, 1982  In  presenting  requirements of  British  it  freely  agree for  this  thesis  f o r an advanced  Columbia, available  that  i n partial  I agree  that  f o r reference  permission  scholarly  degree  may  at the University shall  and study.  I  copying  be g r a n t e d  o r by h i s o r h e r r e p r e s e n t a t i v e s .  understood  that  for  f i n a n c i a l gain  or publication  shall  n o t be a l l o w e d  permission.  Department  O f  URBAN LAND ECONOMICS  The U n i v e r s i t y o f B r i t i s h 1956 Main M a l l Vancouver, Canada  V6T 1Y3 APRIL  2 5 , 1982  Columbia  make  further  of this  by t h e head  department  copying  of the  the Library  f o rextensive  purposes  fulfilment  thesis  o f my  I ti s  of this without  thesis my  written  A B S T R A C T  The purpose of this paper is to examine the. impact of federal M.U.R.B.I legislation on Vancouver's rental housing market, and to see what conclusions can be drawn about the effectiveness of this subsidy policy in achieving its objective, which was to increase the allocation of resources to the housing sector of the economy by stimulating the construction of rental units.  It is the thesis of this paper that the M.U.R.B. legislation was not effective in achieving its objective, since the inelasticity of the land supply function, as imposed by junior levels of government through  zoning and other  supply  constraints, prevented the rental market from responding to these incentives in the form of increased production.  It is hypothesized that the real effect of the  program was to create windfall gains for existing owners of multiple family zoned land at the time the legislation was passed. It is argued further that real estate markets are more efficient than they are generally given credit for, in the sense that tax shelter benefits associated  with  M.U.R.B. properties will be fully  capitalized into the value of such properties, thus preventing M.U.R.B. investors from earning rates of return superior to those earned by owners of comparable nonM.U.R.B. properties.  The paper begins with a brief history of M.U.R.B. legislation, and an analysis of the magnitude and cost of the program to the Canadian government. This is followed by a graphical analysis of the impact of M.U.R.B. legislation on the multiple family housing market, and a discussion and review of the literature pertaining to the (ii)  theory o fefficient markets, the capitalization of costs and benefits into value, and the various models o f land value which have been formulated.  Two  theoretical  models are then presented a sthe underlying basis for the e m p i r i c a l r e s e a r c h in the paper.  The first is a valuation function for apartment  investments, where t h e  d e p e n d e n t v a r i a b l e is t h e selling p r i c e o f a n a p a r t m e n t b u i l d i n g ; t h e s e c o n d is a model o f the determinants o f multiple family land values, where the  dependent  v a r i a b l e is t h e p r i c e of a site.  T h e two theoretical models are tested using multiple regression techniques. T h e data results provide evidence which contradicts the general case for the operation of  t h e multiple family  housing market, where  renters should receive  b e n e f i t s o f the M . U . R . B . p r o g r a m in the f o r m o f l o w e r rents.  t h e full  The research  shows  t h a t t h e f u t u r e t a x s h e l t e r b e n e f i t s a s s o c i a t e d w i t h M.U.R.B. p r o p e r t i e s a r e f u l l y capitalized  into t h e m a r k e t values o f c o m p l e t e d  M.U.R.B. buildings, and  that  M . U . R . B J investors d o not earn rates o f return superior to those o f investors i n non-M.U.R.B. expected  apartment  M.U.R.B./ t a x  properties. shelter  T h e research  benefits  were  suggests  over-capitalized  further  that t h e  into higher land  value p r e m i u m s during the life o f the p r o g r a m , thus creating windfall gains f o r existing landowners at the time the program was introduced.  The results suggest that the full capitalization o f M . U . R . B . benefits into both land a n d a p a r t m e n t b l o c k values r e s u l t e d in a l l o f t h e b e n e f i t s o f t h e subsidy p o l i c y not filtering through to renters.  Some benefits most likely did reach renters, since it is  u n r e a l i s t i c t o a s s u m e no s u b s t i t u t i o n in t h e p r o d u c t i o n f u n c t i o n for a p a r t m e n t s , but the actual distribution o f policy benefits between renters and existing cannot be measured within the scope of this research.  (iii)  landowners  T A B L E O F CONTENTS Page Number ABSTRACT  (ii)  T A B L E OF CONTENTS  (iv)  LIST OF TABLES  (v)  ACKNOWLEDGEMENTS  (vi)  1.0  INTRODUCTION  1  2.0  HISTORY OF THE M.U.R.B. P R O G R A M  3.0  THEORETICAL FRAMEWORK 3.1 The Multiple Family Housing Market 3.2 Efficient Markets 3.3 Capitalization 3.4 Land Price Models  10 10 17 19 24  4.0  D A T A BASE 4.1 M.U.R.B. Resale Sample 4.2 Land Sale Sample  32 32 33  5.0  ESTIMATION OF A P A R T M E N T VALUATION FUNCTION 5.1 Capitalization 5.2 Rates of Return to M.U.R.B. vs. Non-M.U.R.B. Investors  36 36  6.0  ESTIMATION OF MULTIPLE FAMILY LAND PRICE MODEL 6.1 Description of Variables 6.2 Empirical Results  45 45 52  7.0  CONCLUSIONS AND IMPLICATIONS  65  FOOTNOTES  70  BIBLIOGRAPHY  72  APPENDIX "A" - VARIABLE LISTS  74  APPENDIX "B" - DESCRIPTIVE STATISTICS  87  APPENDIX "C" - SCATTER PLOTS  94  APPENDIX "D" - RESIDUAL PLOTS  100  APPENDIX "E" - DATA FILE LISTINGS  139  (iv)  3  40  LIST OF TABLES  ESTIMATED M.U.R.BJFEDERAL REVENUE LOSSES (1975- 1980) A P A R T M E N T BLOCK VALUATION FUNCTION RATES OF RETURN: M.U.R.B./VS. NON-M.U.R.B.iAPARTMENTS THE REGRESSION EQUATIONS FOR MULTIPLE FAMILY LAND VALUES C O R R E L A T I O N MATRIX - F U L L MODEL VARIABLES  (v)  ACKNOWLEDGEMENTS  There are many individuals and organizations who have assisted me during the course o f this research, either through their explicit participation in i t or through their general encouragement and support.  It is not possible to acknowledge a l l of  them, but some do deserve special recognition here.  I would like to thank the B.C. Assessment A u t h o r i t y and B.C. Land Title O f f i c e for allowing me to search their records to gather my data, and special thanks should go to Canada Mortgage and Housing Corporation, f i r s t l y for the graduate scholarship which they awarded me i n 1979, and secondly for the s t a t i s t i c a l data which was provided by Ted M i t c h e l l and Helmut Pastrick. I would like to thank my c o m m i t t e e chairman, Professor George W. Gau, for his invaluable guidance and encouragement throughout the somewhat lengthy period of this research, and I would also like to thank the other members of my c o m m i t t e e , Professors Dennis R. C a p o z z a and Norman Carrothers, for their assistance.  For  their  assistance  in data  c o l l e c t i o n , I would l i k e to thank  Paul Smith, C r a i g Homewood and Helen Evans.  Darryl Y e a ,  For their assistance i n coding,  thanks should go to Melaney Gleeson-Lyall and M i c h a e l Wicks. F o r their e x c e l l e n t word processing and production assistance, I would like to thank K e l l y  Gariepy,  Renate Vetter and 3oan Choo. I would also like to say a special thank you to my sister, Mary Gleeson, for her valuable assistance and stamina i n data c o l l e c t i o n , coding, proofing and production.  (vi)  F i n a l l y , there are two people who deserve special mention for their long-standing moral support throughout my university career.  I would like to thank my  Mrs. K a t h l e e n W. L y a l l , for her continual encouragement. to  thank  my  mother,  Most of a l l , I would like  husband, M i c h a e l , for his undying tolerance and understanding,  throughout what sometimes seemed an endless task.  (vii)  1.0  I N T R O D U C T I O N  In 1972, the Federal Government removed the t a x shelter benefits available to owners o f residential r e a l estate investments, resulting in severe c r i t i c i s m f r o m the real estate industry, and in claims that construction of rental housing would decline or even terminate as a result. Canada's population continued  Since the demographic c h a r a c t e r i s t i c s of  t o put pressure  on the market for rental accom-  modation (given that the peak of the baby boom cohort was s t i l l only 1<V years o f age at the t i m e , and the front end was 27 years o f age),* the public pressure which ensued prompted the Federal Government in 1974 to reinstate tax shelter benefits on a l i m i t e d basis.  This took the f o r m of the Multiple Unit Residential Building  (M.U.R.B.) Program, whose objective was to increase the allocation of resources to the housing sector of the economy by stimulating the construction of r e n t a l units. The  purpose of this paper  is t o examine the impact o f this legislation on  Vancouver's r e n t a l housing market, and to see what conclusions c a n be drawn about the effectiveness of the policy in achieving this objective.  It is the thesis of this paper that the M.U.R.B. legislation was not e f f e c t i v e i n achieving imposed  its objective, since the inelasticity of t h e land supply by junior  levels of government  through  zoning  function, as  and other  supply  constraints, prevented the rental m a r k e t f r o m responding to these incentives in the f o r m o f increased production.  It is hypothesized  that the real e f f e c t o f the  program was t o create w i n d f a l l gains for e x i s t i n g owners of multiple f a m i l y zoned land a t the t i m e the legislation was passed.  It is argued further that real estate  markets are more e f f i c i e n t than they are generally given credit f o r , in the sense  . - 1-  that tax shelter benefits associated with M.U.R.B. properties will be fully capitalized into the value of such properties, thus preventing M.U.R.B. investors from earning rates of return superior to those earned by owners of comparable nonM.U.R.B. properties.  The paper begins with a brief history of M.U.R.B.J legislation in Canada, and an analysis of the magnitude and cost of the program to the government.  This is  followed by a graphical analysis of the impact of M.U.R.B. legislation on the multiple family housing market, and a discussion and review of the literature pertaining to the theory of efficient markets, the capitalization of costs and benefits into value, and the various models of land value which have been formulated. Two theoretical models are then presented as the underlying basis for the empirical research in this paper.  The first is a valuation function for  apartment investments, where the dependent variable is the selling price of an apartment building; the second is a model of the determinants of multiple family land values, where the dependent variable is the price of a site.  The empirical testing of the two theoretical models utilizes multiple regression techniques.  The objective will be to estimate the magnitude and significance of  the M.U.R.B. tax shelter benefits in the determination of the capital value of apartment buildings sold during 1979 and 1980 and in the determination of multiple family zoned land values in the City of Vancouver from 1972 to 1978.  The final section of this paper will discuss the implications of the empirical findings in the context of Vancouver's rental housing market. implications for government policy will also be addressed. - 2-  The broader  HISTORY OF THE F E D E R A L M.U.R.B. P R O G R A M  2.0  The new  Canadian Income Tax A c t which came into e f f e c t on January 1,  1972,  introduced major changes in tax law relating to c a p i t a l gains tax provisions, income 2 tax rates, income averaging, corporate tax treatment, and resource taxation.  The  tax change most pertinent to the analysis in this paper was the elimination of tax sheltering of "other" income by c a p i t a l cost allowance ( C C A ) deductions c l a i m e d on residential investment properties. C C A  deductions could only be used to offset  any positive income on a particular investment property, rather than other non-real estate income of the taxpayer. The new A c t also eliminated the "pooling" of assets with  values  exceeding  $50,000, thus  preventing a  taxpayer  from  deferring  recapture on disposition of one asset by adding new assets to the same class. The federal budget presented in November, 1974 re-introduced l i m i t e d provisions for the f u l l deductibility of c a p i t a l cost allowances on properties f r o m any income source of the taxpayer.  residential  investment  As a consequence, two  new  —/  asset classes were created under the Income Tax A c t which were exempted f r o m the 1972 tax reform removal of tax shelter benefits:  Class 31 f r a m e buildings  (10 per cent annual C C A  on a declining balance) and Class 32 concrete buildings  (5 per cent annual C C A  on a declining balance).  These two asset classes were  obtainable only through C.M.H.C. c e r t i f i c a t i o n on new  residential construction  containing at least two units, and where at least 80 per cent of the gross f l o o r area 3 of the proposed building was to be allocated to residential use.  - 3-  The  M.U.R.B. designation provided  annual c a p i t a l cost allowance  deductibility  against any income of the taxpayer, as w e l l as the traditional deductibility of front-end "soft costs" associated with the development of the building. These soft costs, which generally comprise between 15 and 25 per cent o f the t o t a l c a p i t a l value of a building, include the following: •  survey, engineering and architects' fees  •  legal and accounting fees  •  property taxes  •  interim financing  •  marketing and a d m i n i s t r a t i v e expenses  •  landscaping costs  •  l i m i t e d servicing costs  Although i n i t i a l l y the M.U.R.B. program was intended to remain in e f f e c t only until December 31, 1975, subsequent revisions to the Income Tax A c t extended i t on an annual basis u n t i l the end of 1979.  A s of December 31,1979, the A c t was amended  in such a way that any transfer of a M.U.R.B.-designated building w i t h Class 31 status (10 per cent C C A ) would a u t o m a t i c a l l y move the building into the Class 32 asset class (5 per cent C C A ) , this presumably a first step towards the e l i m i n a t i o n of the program altogether.  However, the program was reintroduced in late 1980  w i t h a termination date of December 31, 1981.  The federal budget announced on  November 12, 1981, and subsequent revisions, indicated that the M.U.R.B.'program would not be continued beyond 1981, but that c e r t i f i c a t i o n would s t i l l be available u n t i l May 31, 1982 to those developers/investors who had submitted application for M.U.R.B. designation prior to the budget date.  -4-  As a supplement to the M.U.R.B. program in stimulating construction of m u l t i p l e unit rental buildings, the Federal Government, in 1975 and in 1976, in concert w i t h several p r o v i n c i a l governments, introduced the Assisted Rental Program (A.R.P.)., This program i n i t i a l l y involved an outright monthly per-unit grant of $900 to t h e developer  through  Canada  Mortgage  and Housing  Corporation,  with  annual  reductions in the grant over a ten-year period and accompanying escalations in economic rent, e f f e c t i v e l y allowing the developer a constant rate of return on equity f r o m operating flows over the ten-year p e r i o d  The involvement of provincial governments in 1976, most notably B . C f a n d Ontario, resulted in variations in A.R.P. program provisions, so that CM.H.C's c o m m i t m e n t was in the f o r m of a second mortgage which accumulated over a ten-year interestf r e e period, while t h e p r o v i n c i a l government provided  annual per-unit grants.  Units were rented at market levels, while annual reductions in both the f e d e r a l second mortgage and provincial grant ensured a constant rate of return on equity to the developer over the ten-year c o n t r a c t period.  Thus, the combination of the M.U.R.B. and A.R.P. program provisions c r e a t e d very a t t r a c t i v e t a x incentives to p o t e n t i a l investors and developers residential properties.  The question  of multi-unit  i s , o f course, whether the t a x revenues  foregone by the Federal Government induced  an increase in the construction of  rental units which would not otherwise have o c c u r r e d  Unfortunately, the a c t u a l cost to the Federal Government of the estimated 195,000 units built under the M.U.R.B. program is not publicly available, although future  -5-  lost t a x revenue has been estimated at $514 million in 1981 dollars, assuming a discount rate o f 12 per cent and an average marginal tax rate o f 40 per cent for M.U.R.B./investors (Clayton Research, 1981). However, this estimate is based on the unrealistic assumption that none o f the tax shelter benefits w i l l be recaptured by the government upon disposition of these properties over the next 30 years.  Table 1 sets out a n estimate o f accumulated f e d e r a l tax losses associated w i t h M.U.R.B.; units built f r o m 1975 to 1979. Due to the "off and on" nature of the program in both 1980 and 1981, estimates for these years have been o m i t t e d  Based on estimates in the C l a y t o n Research Associates study, 60 per cent of those units w i t h M.U.R.B. c e r t i f i c a t i o n actually were built and operated as M.U.R.B.!s. Assuming an average f e d e r a l marginal  t a x rate of 40 per cent, a one-year  construction period, an average C C A rate o f 7.5 per cent (reflecting an equal weighting of properties in the 5 per cent and 10 per cent asset classes), and an opportunity cost o f 12 per cent per annum to the government on foregone t a x revenues f r o m 1975 to 1979, the f e d e r a l t a x losses accumulated by 1981 as a result of the M.U.R.B. program amounted to some $304 million.  These estimates assume further that one-half of t h e C C A deductions are claims that c a n be attributable only to the M.U.R.B. program. The exact portion of C C A that c a n be deducted solely due to the M.U.R.B.I exemption is quite d i f f i c u l t t o estimate since i t would d i f f e r among properties depending on financing arrangments and among investors depending on their other real estate holdings. For nonM.U.R.B. properties, the general rule is that "annual c a p i t a l cost allowances on a l l  -6-  Table 1 ESTIMATED M.U.R.B. F E D E R A L T A X REVENUE LOSSES (1975 - 1980)  1975 Multiple starts  1978  1979  107,527  138,890  137,321  117,638  87,932  8,517  35,219  82,265  80,089  76,550  5,110  21,131  49,359  48,043  45,930  $35,000  $ 39,000  $42,650  $46,650  $51,350  $13,414 $12,408 $11,478 $10,617 $ 9,821  $ $ $ $  61,809 57,174 52,886 48,919  $157,887 $146,046 $135,092  $168,127 $115,517  $176,888  $ 2,683 $ 2,482 $ 2,296 $ 2,123 $ 1,964  $ $ $ $  12,362 11,435 10 ,577 9,784  M.U.R.B. c e r t i f i cates issued 3 R e n t a l units P e r unit construction cost  1977  1976  Present Value of Foregone Revenue 1981 ($000)  Deductible C C A ($000) 1976 1977 1978 1979 1980 Tax Loss ($000) 1976 1977 1978 1979 1980  $31,577 $29,209 $27,018  $33,625 $31,103  $35,378  $ 4,728 $ 23,357 63,654 94,750 $117,877 $304,366  1) 2) 3) 4)  C.M.H.C., Canadian Housing S t a t i s t i c s , 1976-1980. C l a y t o n Research Associates, T a x Expenditures - Housing, p. B./2. Assumed to be 60 per cent o f M.U.R.B.1 c e r t i f i c a t e s , based on results o f C l a y t o n Reasearch Associates study. C.M.H.C., 1975 - Table 90; 1980 - Table 100. -7-  of the taxpayer's r e n t a l properties combined is l i m i t e d to the amount, if any, of his net income minus losses on those properties computed before deducting cost allowance" (Harris, 1979:  capital  223). Thus, the incremental C C A benefit o f a  M.U.R.B. is dependent on the d i f f e r e n c e between the available C C A c l a i m and the remaining aggregate rental income of the investor a f t e r deducting operating and interest expenses (the C C A that could be c l a i m e d on a non-M.U.R.B.).  In this  analysis, it is assumed that half o f the t o t a l C C A deductions would have been claimed in the absence of M.U.R.B.Is, o f f s e t t i n g positive rental income f l o w i n g f r o m rental properties.  O f f s e t t i n g the estimated f e d e r a l tax losses in the future w i l l be the f e d e r a l t a x revenue resulting f r o m any recapture of the C C A a t the t i m e o f sale of the M.U.R.B./properties. claimed  during  Since 1972, the Income Tax A c t requires t h a t any C C A  an investment  which  i s greater  than  the a c t u a l  economic  depreciation of the asset be recaptured upon sale and taxed as ordinary income. This "excess" C C A is e f f e c t i v e l y an interest-free loan provided by the government during the holding period of t h e investment.  The net cost over time of t h e  incremental C C A c l a i m e d under the M.U.R.B. program is therefore an interest expense to the federal government o f this C C A loan.  With interest rates for  government borrowings at the 15 per cent l e v e l , and assuming a l l the incremental CCA  w i l l be recaptured, the annual f e d e r a l interest expense of the above foregone  tax revenue is approximately $46 m i l l i o a  In the context of the Vancouver rental market, which is the subject of the analysis in this paper, 448 M.U.R.B. projects were started during the period 1975 through  -8-  May  31, 1981.  If each of these properties contained  an average of 20 suites, a  t o t a l of about 9,000 multiple family rental units in the c i t y would be presently under the M.U.R.B. program.  This represents approximately 10 per cent of t h e  current stock of multiple f a m i l y dwellings.*'  The next section of this paper presents a t h e o r e t i c a l framework for analysing the impact o f the M.U.R.B. program on Vancouver's rental housing market.  Two  models w i l l be presented. The first is a valuation function for apartment buildings, while the second is a model of the determinants of multiple f a m i l y land v a l u e s  -9-  3.0  THEORETICAL FRAMEWORK  This section o f t h e paper presents impact  o f t h eM.U.R.B.  a theoretical  program o n Vancouver's  framework  for analyzing the  rental housing market.  The  theoretical discussion begins with a graphical analysis o f the expected impact o f M.U.R.B./legislation  o n t h esub-sectors  under various assumptions.  o f t h e multiple family housing  The concept o f efficient  markets is then  market  examined,  followed b y a discussion o f capitalization and a review o f the land price  models  which have been formulated through previous empirical research.  3.1  T H E M U L T I P L E FAMILY HOUSING M A R K E T  Within the multiple f a m i l y housing m a r k e t , there are three groups who would react to o rbenefit f r o m t h eintroduction o f the preferential t a x treatment  associated  with M.U.R.B.'s - landowners, investors and renters.  developers  In t h i s c o n t e x t ,  merely act a s the m i d d l e m e n b e t w e e n landowners and investors, thus they can play either o f t w oroles i n t h e m a r k e t , depending upon whether  they owned the land  prior to development, o rwere expecting to hold the property over the long t e r m after completion.  The following paragraphs review the impact which M.U.R.B./legislation should have on each o f the three sub-groups within t h emultiple family housing m a r k e t , under  - 10 -  various assumptions concerning the e l a s t i c i t y of t h e supply and demand schedules for each of these groups.  Case 1; General Case  In the general case, the supply arid demand schedules f o r landowners, investors and renters a r e a l l neither p e r f e c t l y elastic nor p e r f e c t l y inelastic.  Under these  conditions, the introduction of M.U.R.B. legislation should cause a shift in the demand schedule of landowners (as shown in Figure 1), resulting i n higher prices f o r multiple  family  sites and an increased supply  of such  sites available f o r  development. S i m i l a r l y , the demand schedule of apartment block investors should shift upward in response to the special M.U.R.B. t a x benefits, resulting in higher apartment block prices and higher production of units. This increase in supply of apartment units w i l l cause a corresponding shift in the supply schedule for renters, who w i l l thus benefit in the form of lower rents.  Landowners  Investors  FIGURE 1  - 11 -  Renters  In the above case, because of t h e slope of t h e demand and supply schedules, the increase in the price o f both land and apartment blocks should be less than the a c t u a l present value of t h e M.U.R.B. t a x shelter benefits to either landowners or investors.  Furthermore, i f there is substitution in the production function f o r  apartment blocks, l e . i f t h e increase in demand induces substitution of c a p i t a l f o r land and higher density apartment blocks a r e produced, the supply investors should be more elastic  curve f o r  than that f o r landowners, and the increase in  apartment block prices should be lower than the increase in land prices as a result of the M.U.R.B. program, assuming of course that the slope of both demand curves is equivalent. i  C a s e 2; P e r f e c t l y E l a s t i c Investor Demand  In this case, the demand horizontal investment  curve  f o r investors i s p e r f e c t l y e l a s t i c , Le. i t is  This would be the case where there is a totally e f f i c i e n t apartment market, where an increase i n expected  profits causes an equivalent  increase in the p r i c e of apartments (see Figure 2). The shape of t h e supply schedule for investors under such conditions would have no e f f e c t on the magnitude of t h e increase in p r i c e of apartment blocks, although it would certainly a f f e c t the magnitude of increased production of apartments as a result of the shift in demand.  - 12 -  Landowners  Investors  Renters  FIGURE 2  The  impact of t h e M.U.R.B. on landowners and renters in this case should not  vary from Case 1, since i t does not follow that their demand curves would also be p e r f e c t l y elastic.  Case 3:  Inelastic Land Supply  If t h e multiple family land supply function were p e r f e c t l y inelastic, a shift in the demand schedule for landowners as a result of higher demand for sites by investors would induce a proportionate increase in prices of such land, and no additional land would be made available for the production of multiple f a m i l y housing (see Figure 3). However, i f there is substitution in production, the higher demand for apartments by investors would result in production t o higher densities of e x i s t i n g multiple f a m i l y sites, through demolition of structures not presently representing f u l l capacity on these sites.  - 13 -  Thus, the supply curve for  investors would be f l a t t e r than the land supply curve; t h e a c t u a l slope of this curve would of course depend upon the magnitude of substitution and hence the shape of t h e production function.  FIGURE 3  Case 4; Inelastic Land Supply and Perfectly Elastic Investor Demand  If the supply of multiple f a m i l y land were inelastic and the demand f o r apartment blocks by investors were p e r f e c t l y e l a s t i c , t h e i m p a c t of M.U.R.B. legislation should be as shown in Figure 4.  No additional land would be made available f o r  production, and land prices should rise at least by the amount of t h e present value of the M.U.R.B. benefits. If there is substitution i n production, more sites should be redeveloped  to higher densities, but to a lesser extent than in Case 3, since  more of the M.U.R.B. tax benefits w i l l go into higher prices because of the f l a t investor demand curve.  Renters should s t i l l benefit under these conditions, but  again to a lesser degree than in Case 3.  - 14 -  The magnitude of their benefit would  depend upon the slope of t h e investors' supply curve, and hence on the degree of substitution in production.  Landowners  Investors  Renters  FIGURE *  Case 5:  Inelastic Land and Apartment Supply  Figure 5 illustrates the impact o f M.U.R.B. legislation where both the land supply and  apartment supply  functions are inelastic.  Under these circumstances, no  increase in apartment production would result f r o m the introduction of M.U.R.B.!s, and land and apartment block prices would rise by the equivalent of the present value of t h e M.U.R.B. benefits.  There would be no benefits accruing to renters  because of M.U.R.B.'s; however, i t does not follow that the renters supply curve is inelastic, since there c a n be tenure changes in existing multiple f a m i l y properties f r o m condominium to rental.  - 15 -  p,  h-  FIGURE 5  The conditions necessary for this case to be true, however, are not very realistic. In order for apartment supply to be p e r f e c t l y inelastic, there c a n be no substitution of c a p i t a l for land in the production of apartments.  This implies that a l l existing  multiple f a m i l y sites are built to c a p a c i t y , which is c l e a r l y not the case in many metropolitan areas, and Vancouver is no exception.  It is hoped that the research in this paper w i l l provide some evidence as t o the f i l t e r i n g of the M.U.R.B. tax shelter benefits through to the various sub-groups of the multiple f a m i l y housing market.  The following paragraphs review previous  work which has been done in real estate and other c a p i t a l markets relating to these concepts.  - 16 -  EFFICIENT M A R K E T S  Considerable research has been undertaken to test the e f f i c i e n c y of  competitive  speculative markets, p a r t i c u l a r l y with respect to the stock market (Fama, 1970X An e f f i c i e n t market has been defined as one in which current market prices "fully r e f l e c t " available information, and it is assumed that such information is fully and rapidly c a p i t a l i z e d into prices.  F a m a has distinguished three types of market  efficiency:  •  weak f o r m - where market prices are a r e f l e c t i o n of h i s t o r i c a l p r i c e information;  •  semi-strong  form  -  where  market  prices  fully  reflect  public  information, e.g. dividend declarations;  •  strong f o r m - where market prices take into account a l l available information, even that held by those with special knowledge, such as professional speculators or management.  The e m p i r i c a l research of stock market prices which has been done to test the e f f i c i e n t markets model has generally supported both the weak and  semi-strong  concepts of e f f i c i e n c y (Fama, 1970). However, strong f o r m e f f i c i e n c y has not held up w e l l , as seen in the work of F i g l e w s k i , who  found that "when there is a wide  range of forecasting ability or a diversity of expectations among the participants, the market may  deviate relatively far f r o m e f f i c i e n c y " (1978:  - 17 -  597X  E f f i c i e n c y in the context of r e a l estate markets is to date a relatively untested concept, although i t has been argued that numerous c h a r a c t e r i s t i c s of r e a l estate markets preclude their e f f i c i e n t operation, c h a r a c t e r i s t i c s such as:  •  the l o c a l orientation of r e a l estate markets;  •  a lower incidence of transactions for specific properties;  •  the uniqueness and  lack of comparability  of various parcels of r e a l  estate vis-a-vis d i f f e r e n t common stocks;  •  the importance of financing and the specialized nature of some real estate financing techniques;  •  the lack of sophistication of investors;  •  a dearth of disciplined analysis of future events and the use of crude rule-of-thumb techniques;  •  the divergence between expectations and  actual accomplishments of  participants, and their widely varying investment objectives;  •  the extreme v o l a t i l i t y in construction a c t i v i t y , which leads to sharp swings in vacancy factors and (Roulac, 1976).  related short-term cash f l o w yields  Although real estate markets do suffer f r o m these deficiencies,  i t nevertheless  seems reasonable that prudent investors would compare expected  investment  returns on real estate assets to expected returns on other c a p i t a l assets, and that public information, such as the announcement of the M.U.R.B. program i n 1974, would be reflected in subsequent transactions prices of both multiple f a m i l y zoned land, whose future cash f l o w benefits would be considerably enhanced, as w e l l as the transactions prices o f M.U.R.B. properties once built. Although this paper w i l l not  test the speed of the market's reaction to M.U.R.B. legislation, i t w i l l  nevertheless seek some evidence of semi-strong f o r m e f f i c i e n c y to the extent that the change in expected cash flows associated with M.U.R.B. properties caused a proportionate  increase  in their  values  relative  to comparable  non-M.U.R.B./  properties of similar risk, and that investors in M.U.R.B. properties earn equivalent rates of return to investors in non-M.U.R.B. properties in the same risk class.  CAPITALIZATION  The e m p i r i c a l research which has been undertaken on the concept of c a p i t a l i z a t i o n does have some overlap with the e f f i c i e n t markets concept, in the sense that it is measuring the extent to which (although not the speed with which) market values of assets have c a p i t a l i z e d changes in expected future costs or benefits. Tullock  Work by  (1975) suggests that changes in government regulation which  create  p r e f e r e n t i a l benefits f o r c e r t a i n groups (e.g., owners of t a x i c a b medallions) e f f e c t i v e l y create windfall gains f o r persons already in the group, since these benefits a r e competed away and hence become c a p i t a l i z e d into the value of the  - 19 -  commodity by other  market participants t r y i n g to obtain i t . As Krueger has  pointed out in her study of import licenses in India, the e f f o r t s of persons t r y i n g to obtain the special benefits associated with import licenses a c t u a l l y change the optimal a l l o c a t i o n of goods in the domestic economy, since resources a r e diverted f r o m other sectors in the a t t e m p t to a t t a i n those "rents".  Hence, as argued by  Posner, the competition to obtain special rights of a monopolistic nature results in the transformation of p o t e n t i a l monopoly profits into social costs (1975:  807X  The property tax literature lends support to the concept of c a p i t a l i z a t i o n of future benefits into real estate  values.  Hamilton's (1976) study of the e f f e c t s of  interjurisdictional differences in tax rates supported previous work 1969, 1972) on the c a p i t a l i z a t i o n of varying  rates of property  (Mieszkowski, taxation into  property values across l o c a l i t i e s , although he demonstrated further that it is these tax rate d i f f e r e n t i a l s r e l a t i v e to public sector benefits which should be c a p i t a l i z e d rather than just the tax d i f f e r e n t i a l s themselves.  His model r e f l e c t s an arbitrage  process whereby the f i s c a l surplus or d e f i c i t created by the d i f f e r e n c e between actual tax rates and the l e v e l of public sector benefits results i n proportional variations in the values of properties owned by high and low income households.  He  concludes, among other things, that in communities containing a v a r i e t y of highvalue and low-value dwellings, land value differentials between those properties w i l l exactly r e f l e c t the present value of their f i s c a l surplus differentials.  Further work on property  taxation by Mills (1981) indicated that the nature of  property taxes does have an e f f e c t on the land-use allocation of land, as a result of the impact of t h e tax on the income streams of d i f f e r e n t land uses. F o r example, a  - 20 -  property tax on the value of land rather than the income generated f r o m that land w i l l favour the construction of projects w i t h earlier income streams, since  the  e f f e c t i v e discount rate or required rate of return is increased by the property tax on vacant l a n d  Thus, the research relating to the c a p i t a l i z a t i o n concept has shown that some r e a l estate markets have responded to d i f f e r e n t i a l future costs and  benefits and  to  changes in public regulation, by bidding up the prices of c a p i t a l assets to yield returns similar to those which existed before the change occurred.  In the context  of this paper, the impact of the M . U . R . B . program on the value of  M.U.Re-  c e r t i f i e d apartment buildings can be analyzed through a valuation model, in which the value of a real estate investment is equal to the present value of i t s future g  a f t e r - t a x cash flows. In the f o r m of a before-financing framework:  n  V  0  {  -(Oj  -  (1+k)  1  i = 1  where  n  +  V  m a r k e t value of the property;  0.  net operating income in year i ;  C  c a p i t a l cost allowance in year i ;  - 21 -  net sales p r i c e of t h e property at the e n d o f t h e i n v e s t o r ' s h o l d i n g p e r i o d (i=n); T  The  operating  n  taxes resulting f r o m sale of property;  t  marginal tax rate;  k  market rate of return.  flows  received  each  year  during the  investor's  holding  period  ( i = 1,. . . , n ) a r e e q u a l t o t h e n e t r e n t a l i n c o m e a f t e r o p e r a t i n g e x p e n s e s , Ch , m i n u s taxes (determined by the taxable  i n c o m e , 0. - C j , a n d t h e t a x r a t e ) .  f i n a l c a s h f l o w a t t h e e n d o f t h e h o l d i n g p e r i o d is t h e a f t e r - t a x p r o c e e d s  The  resulting  f r o m the sale of the property.  If a n i n v e s t o r a c q u i r e s a p r o p e r t y f o r a p r i c e e q u a l t o V , h i s e x p e c t e d  rate  of  r e t u r n , r , is e q u a l t o k, the r e t u r n r e q u i r e d in the m a r k e t g i v e n the r i s k l e v e l o f the investment.  If, h o w e v e r , t h e p r i c e is l e s s t h a n V , r w o u l d b e g r e a t e r t h a n k .  a l l f l o w s h e l d c o n s t a n t , t h e r e is a n i n v e r s e r e l a t i o n s h i p b e t w e e n  the  With  acquisition  price of a property and an investor's expected rate of return.  A s discussed e a r l i e r , the purpose o f the M . U . R . B . p r o g r a m was to i n c r e a s e  the  allocation of resources to multiple family rental housing and stimulate construction by increasing the rates of return of investors in properties under the p r o g r a m . terms of the valuation framework,  the  M . U . R . B . / p r o g r a m , b y r a i s i n g C. i n  equation, causes the taxable i n c o m e of the investment to be negative and increases the after-tax  cash flows to the M . U . R . B .  - 22 -  investor.  In the  thereby  If t h e p r i c e s o f t h e s e  properties were  not a f f e c t e d  investments would be greater  by being  in the program, the r of M.U.R.B.  than k (the rate of return expected  on other  investments of equivalent risk), and investors would be encouraged to a l l o c a t e more funds to rental housing.  However, in c o m p e t i t i v e real estate markets, there is l i t t l e reason to believe that the market value of M.U.R.B. properties would be unaffected by the t a x subsidies available under the program. If investors recognize and respond to the additional cash-flow benefits, they should increase their demand for these properties and bid up the p r i c e of t h e investments.  A c q u i s i t i o n prices would rise until r equals k.  R e a l estate investors should not be able to earn abnormal or superior returns f r o m the benefits of publicly-known  tax incentive programs. R e a l estate markets would  be expected to c a p i t a l i z e into property values the tax benefits of this program by competing away the excess cash flow benefits until the rate of return of M.U.R.B. investors is the same as the rate expected on other investments of s i m i l a r risk. Thus, the benefits of t h e program would accrue to e x i s t i n g owners a t the time of its introduction.  The  a n a l y t i c a l framework f o r assessing the impact of t h e M.U.R.B. program on  land values  (as apart  f r o m completed M.U.R.B. apartment buildings) w i l l be  addressed in the next subsection of this paper.  - 23 -  L A N D PRICE MODELS  There have been numerous e m p i r i c a l investigations of the determinants of urban land values, but there are four studies which seem most relevant to the topic o f this paper - Brigham (1965), Adams (1968), Witte (1975), and Diamond (1980X A l l of these studies examine residential land value determinants, albeit for single f a m i l y dwellings rather than medium or high density residential properties.  Brigham's study is the most pertinent to the present analysis, since it is a crosssectional study of residential land values within a single metropolitan area.  The  underlying model in his study assumes that the value of an urban site is functionally related to its accessibility to economic a c t i v i t i e s , to its topography, to i t s present and  future  (1968:  use, and to c e r t a i n h i s t o r i c a l  factors that a f f e c t  its utilization  325). He employed multiple regression to analyze a sample of land values  by census t r a c t within the Los Angeles metropolitan area in 1964.  His estimation  of residential land values was able to explain 79 to 89 per cent o f variations in p r i c e , where the independent variables included  accessibility  t o employment  opportunities and the c e n t r a l business d i s t r i c t , the level o f median family income, a measure o f crowding, average value o f dwellings in the neighbourhood, and a dichotomous topography dummy variable. The major d i f f i c u l t i e s he faced were the highly collinear nature of some of the data, and the instability of some of t h e coefficients.  Adams e t a l (1968) also developed an intrametropolitan model o f the determinants of peripheral land value, using a series o f over 1,100  - 24 -  transactions in Philadelphia  between 1945 and 1962.  Their e m p i r i c a l results showed that v a r i a t i o n in the p r i c e  of residential sites during this period could be 60 per cent explained by  distance  f r o m the c e n t r a l business d i s t r i c t , distance to public transportation, location on a major a r t e r i a l , zoning, and "state of the land" variables (e.g. servicing availability). However, the authors do not employ any measures of income or population over the 17-year period, factors which one would expect to have an i m p a c t on residential land values, particularly at the periphery of an urban area.  Witte (1975) develops and estimates  a derived demand model for single f a m i l y  residential sites, in an a t t e m p t to explain differences in residential land across SMSA's f r o m 1966 to 1969.  values  Her model, which explained 78 per cent of the  variation in average prices per square foot of residential sites across  SMSA's,  suggests that average residential site values are determined p r i m a r i l y by average size of sites in various urban areas, the value  of a g r i c u l t u r a l  the land,  population density, current annual f a m i l y income levels, and the rate of change in population (as a proxy for households).  It is interesting to note that average site  size is a significant determinant, possibly a reflection of economies of scale or decreasing marginal returns as site sizes rise above what is considered "essential" for the average homebuyer.  The  absence of location or amenity variables as  determinants of residential land value is explained by the very aggregate nature of her d a t a (e.g. average price of land in an SMSA).  Further work on intraurban land values was done recently by Diamond, who  utilized  bid-price theory to strengthen the e m p i r i c a l relationship between land prices and locational amenities  (1980:  32).  His results were similar to previous studies in  - 25 -  terms of t h e importance of proximity to the c e n t r a l business d i s t r i c t and public transit.  However, his amenity variable measures differed somewhat f r o m those  used in other studies - they included c r i m e rates, particulate pollution levels, distance to a lake, and topography, a l l of which were important determinants of land value ( R = 0.75). 2  In the context of t h e present analysis, the t h e o r e t i c a l framework for e s t i m a t i n g the determinants of multiple f a m i l y zoned (RM-3)^ land values is a derived demand model similar to that employed by Witte (1975), since the value of multiple f a m i l y zoned land w i l l be derived f r o m the demand for multiple f a m i l y housing.  Since land  is a residual in the development process, we would expect the value of land to vary with the expected future costs and benefits associated with  multiple f a m i l y  housing, which in turn w i l l be a f f e c t e d by broad demand and supply variables influencing the rental market.  The basic land residual equation states that land  value is equal to the d i f f e r e n c e between the selling p r i c e of the l o t fully developed and the cost to construct the building on the s i t e :  Land Value = (Selling P r i c e - Construction Costs)  The selling price of the fully developed site w i l l be a function of the net income accruing to the property, the investor's required rate of r e t u r n , C C A  deductions,  the investor's marginal tax rate, and the expected c a p i t a l gains accruing to the property over the investor's holding period.  The valuation function described in  Section 3.2 represents the selling p r i c e portion of the land value equation above,  - 26 -  while construction costs are deducted f r o m this selling price to yield the residual land value. Thus, using the net cash f l o w equation, residual land value is equal to:  Oj -(OJ - ) t  S„ - T n n  C i  (1+k)  i =1  The  (l+k)  1  CC  r  f o l l o w i n g paragraphs break, down the land value equation into the various  demand and supply variables which w i l l a f f e c t the net cash flows accruing to multiple f a m i l y zoned sites, and hence the residual value of such sites.  The dependent variable in this model of t h e determinants of multiple f a m i l y land value is the price of a multiple f a m i l y zoned site ( P ) , which is a function of both g  d s the demand for (Q ) and the supply of (Q ) such sites, as shown in (1):  (1)  P =f(Q ,Q ) d  s  s  o  The quantity demanded of multiple family zoned sites w i l l be a function of the net cash flows accruing to the developer, including the income generated f r o m the property ( I ), the cost of construction ( C C ), and the price of t h e land (P ), as shown in (2):  (2)  Q  d  = g( I, C C , P ) s  - 27 -  The income generated f r o m multiple f a m i l y housing w i l l be a function of numerous cash f l o w variables: the p o t e n t i a l rents attainable by any s p e c i f i c property ( R ), the cost of debt ( i ) , apartment vacancy rates ( V R ), the cost of equity or required rate of return ( k ), which w i l l determine the present value of the future income stream to the investor, and any special tax benefits available, in this case the M.U.R.B. benefits. A l l of these variables w i l l a f f e c t the holding period cash f l o w of a multiple f a m i l y property, as shown in (3):  (3)  I = h( R, i , VR, k, M U R B )  Thus, I represents the e n t i r e bracketed  expression in the L V cash f l o w  equation  presented above.  The  rent component of income c a n be divided into two functions - current rents  (R  ) and expected future rents (R^ ). Current rents w i l l be a function of general  c  market rent levels per unit (R r  m  ), the number of units which can be built on the  site ( UNITS ), market vacancy rates ( V R ), and the location of the site ( L ):  (4)  Future  R  c  = j ( R , UNITS, V R , L ) m  rents ( R^ ) w i l l be a function of growth in the number of non-family  households ( A . H H ), who are the primary demanders of multiple f a m i l y growth in future income levels o f non-family  households ( A Y ) —  housing,  under t h e  assumption that the marginal propensity to consume housing is positive ~ and a supply constraint ( S T A R T S ), which w i l l a f f e c t the level of future competition f o r  -28-  multiple f a m i l y housing and hence future rents.  Growth in household income •X-  ( A Y ) c a n be specified either in nominal ( A Y )  or real ( A Y  ) terms.  The  equation, which represents a growth function, is shown as (5) : (5)  R  =m  f  ( Z i HH,A.Y , S T A R T S )  S T A R T S are expected to have a negative influence on land value since future competition  increased  implies lower future rent levels attainable by the developer,  and hence lower residual land value.  On the other hand,  HH, U N I T S and A  Y  are expected to have a positive influence on land value, since as they rise, so does the demand for multiple-family housing and hence future rent levels.  The nominal c a p i t a l i z a t i o n rate or required  rate of return on multiple f a m i l y  properties, k, has three components: i n f l a t i o n (1V), the real interest rate ( i ) or return on risk-free c a p i t a l assets, and the excess return or risk premium required to invest in multiple f a m i l y properties ( E R while (7) and (8) show the derivation of E R  , which is simply the real c a p i t a l i z a t i o n a  rate ( k  ) minus i .  (6)  k = 1f + i * + E R * a  (7)  k = k* + T  (8)  E R * = k*- i *  f  d  - 29 -  ). This relationship is shown i n (6),  The real c a p i t a l i z a t i o n rate is equal to the nominal c a p i t a l i z a t i o n rate ( k ) minus If" , where the nominal c a p i t a l i z a t i o n rate represents the o v e r a l l rate of return on t y p i c a l multiple f a m i l y investment properties, Le. gross income divided by selling price.  Turning to the supply side of equation (1), the quantity of multiple f a m i l y sites supplied w i l l be a function £(9)j  of the price of sites ( P  g  ), the size of sites ( SS ),  and zoning ( Z ).  (9)  Q  s  = m( P , SS, Z) s  The zoning variable w i l l constrain the supply of multiple f a m i l y land, since unless a site is included  in the appropriate zoning category, i t is not available to  be  developed as multiple f a m i l y housing. The size of sites a f f e c t s supply in the sense that a large number of s m a l l sites would prevent economies of scale in development and hence the e f f e c t i v e supply of sites would be reduced.  By substituting a l l of the supply and demand equations into (1), one obtains:  (10)  P  g  = f ( R , UNITS, L, A Y * , A H H , STARTS, i , m  VR, ER*, M U R B , C C , SS, Z) cL  - 30 -  B y dividing through by SS and converting  to real terms where appropriate, the  reduced t h e o r e t i c a l model of multiple f a m i l y land values, where the dependent variable is the real price per square foot of sites, becomes:  (11)  UNITS, L, A Y*, A H H , STARTS, i * ,  P*/SS = f ( R * O  ill  j  VR, ER*, M U R B , C C * , Z) 3,  In the context of the land residual equation discussed previously,  LV = ( SP - C C )  4  a l l of the variables except C C i n equation (10) w i l l determine the selling price or value of a fully developed site. Thus,  P  and f r o m (11)  P  g  s/  = ( SP - C C )  S  S  = ( SP - C C ) * /SS  Equation (11) is thus the land model t o be tested in the e m p i r i c a l section of this paper, where the c r i t i c a l concern w i l l be the significance and sign of t h e M U R B variable in the land value equation.  - 31 -  4.0  D A T A BASE  The major portion of the data for this research was c o l l e c t e d during the summer and f a l l of 1980; i t was later supplemented in the spring and summer of 1981. The primary sources of data were the B.C. Assessment A u t h o r i t y , the B.C. Land T i t l e O f f i c e , Canada Mortgage and Housing Corporation, and Statistics Canada. separate samples were c o l l e c t e d :  Two  a sample o f sales transactions of M.U.R.B.  apartment buildings and a matching set o f non-M.U.R.B.iapartment buildings (the " R E S A L E S " f i l e ) , and a sample of multiple f a m i l y land sale transactions (the " L A N D S A L E S " file).  The R E S A L E S sample w i l l be used to estimate the apartment  block valuation function presented in Section 3.2, while the L A N D S A L E S sample w i l l be used to estimate  the land p r i c e model presented in Section 3.3.  The  c h a r a c t e r i s t i c s of the data contained in each of these files w i l l be discussed in turn in the next two subsections.  4.1  M.U.R.B. R E S A L E S A M P L E  A sample of 46 apartment block transactions in the C i t y of Vancouver in 1979 and 1980 were identified through B.C. Assessment  Authority records.  This  sample  consists of 7 M.U.R.B. apartment buildings and 39 matching non-M.U.R.B apartment buildings sold during the same t i m e period.  These non-M.U.R.B.  properties  are s i m i l a r to the M.U.R.B.'s in terms of number of suites in the building, l o c a t i o n within the c i t y and holding p e r i o d . ^  Although the non-M.U.R.B. buildings in the  - 32-  matching sample do vary in age, and in a l l cases are older than the M.U.R.B.'s, they are nevertheless considered comparable in terms of t o t a l investment risk.  For each of these apartment blocks in the sample, information was obtained f r o m the B.C. Assessment Authority and B.C. Land Title O f f i c e concerning the physical c h a r a c t e r i s t i c s o f the property (e.g., number of suites, lot size, number of storeys, etc.), construction and land costs, and the rental incomes earned during the investor's holding period. variable basis is included  A complete list of the data c o l l e c t e d on a variable-byas Appendix Table A - l .  The data were s u f f i c i e n t to  allow for an accurate measurement of the actual operating cash flows and c a p i t a l gains received by owners of both M.U.R.B. and non-M.U.R.B. properties, as w e l l as an analysis of the determinants of the market prices of these properties.  Appendix Table B - l contains the descriptive statistics for a l l variables in this sample, which includes for each variable the minimum value, maximum  value,  mean and standard deviation. The average sales price in the sample is $785,220, the average building size 27 suites, the average l o t size 10,566 square f e e t , and the average age 26 years.  L A N D SALE S A M P L E  A sample of 115 arms length sales transactions of RM-3 zoned land which occurred in  the C i t y of Vancouver  from  1972 t o 1978 were  identified  B.C. Assessment Authority and B.C. Land T i t l e O f f i c e records.**  - 33 -  through the  The site-specific  i n f o r m a t i o n for each transaction includes the l o t size, l o c a t i o n , selling price and date of sale, on a quarterly basis. The location variable is disaggregated  into four  sub-areas:  Vancouver Sub area  Number of Observations  West End  10  Kitsilano  33  Marpole  2  East Vancouver  70 115  In addition to the site-specific information on the land sales, the 90 variables in the " L A N D S A L E S " data  file  include  measures, ^ in varying  forms, of a l l of t h e  determinants of multiple f a m i l y land values identified in the t h e o r e t i c a l model (refer to Appendix Table A-2).  D a t a were gathered f r o m Statistics Canada and  Canada Mortgage and Housing Corporation  on measures of population, income,  unemployment, interest rates, housing construction a c t i v i t y , rent levels, construction costs, vacancy rates, and various measures of inflation.  The existence of M.U.R.B. legislation is specified in this data set as a dichotomous dummy variable w i t h a value of 0.0 during the period preceding the introduction of the M.U.R.B. program in 1974 (0.0 for sales during the first quarter of 1972 through the fourth quarter o f 1974), and a value of 1.0 during the period f o l l o w i n g the legislation. Since the 1972 to 1974 period was one c h a r a c t e r i z e d by a slowdown in  - 34 -  apartment construction a c t i v i t y , i t was d i f f i c u l t to find a large number of land transactions during that period. Nevertheless, 19 transactions were identified from 1972 to 1974, with the remaining 96 transactions in the sample occurring a f t e r the M.U.R.B. program was introduced.  This 5 to 1 ratio of non-M.U.R.B. t o M.U.R.B.  transactions in the sample is not considered large enough to bias the data results.  As Appendix Table B-2 indicates, the average real sales p r i c e (indexed by the general C P I f o r Vancouver) of the land transactions is $10.81 per square foot, while the average lot size is 9,285 square feet.  - 35 -  5.0  ESTIMATION O F A P A R T M E N T VALUATION FUNCTION  This section of the paper presents two methods for analyzing the i m p a c t of the M.U.R.B. program on the resale values of M.U.R.B. apartment buildings.  These  analyses were done using the matching sample in the " R E S A L E S " f i l e which, as discussed,  contains  7  M.U.R.B.  properties  and 39  matching  properties, a l l of which have holding periods terminating in 1979 and  5.1  non-M.U.R.B. 1980.  CAPITALIZATION  In order to measure the extent of t h e c a p i t a l i z a t i o n of M.U.R.B. t a x shelter benefits  into market values of M.U.R.B. properties, a valuation  function is  estimated f r o m the " R E S A L E S " sample using multiple regression analysis.  This  valuation function shows the relationship between the selling price of an apartment block and its physical and f i n a n c i a l c h a r a c t e r i s t i c s , such as age, number of suites, gross income, and cost of debt.  Numerous runs on the dependent variable (selling price) were made using various combinations of the variables in the data f i l e in an attempt to find the best f i t of variables which fully represents which best explains  the determinants of apartment block value, and  the variation i n prices of apartment blocks, i n both a  s t a t i s t i c a l l y significant and intuitive sense. Table 2 presents the best estimation of the apartment block  valuation function, which explains 84.5 per cent of the  - 36 -  Table 2 A P A R T M E N T BLOCK VALUATION FUNCTION  SP = 481600. + 10.831 GI + 63526. M U R B - 13758. I - 452.32 A G E (2.084)*  (14.342)*  R  (2.082)*  (-1.239)  2  =  .845  F  =  SE  =  293010.  n  =  (-1.864)  51.202 46  =  sales p r i c e  MURB  =  0-1 variable with 1 = M.U.R.B.  =  gross income a t time of sale  AGE  =  number of years since construction  =  interest rate  s t a t i s t i c s in parentheses, c o e f f i c i e n t s significant a t .05 l e v e l  - 37 -  variation in apartment block prices.  A c c o r d i n g to this equation, the p r i c e of an  apartment building w i l l be determined by i t s gross income, the existence of M.U.R.B./tax shelter benefits, the cost of debt (the weighted average interest rate on a l l financing a t the t i m e of sale), and the age of the building; as expected, the former two variables have a positive and significant e f f e c t on value (at the .05 level), while the latter t w o have a negative  e f f e c t on value.  MURB  is a  dichotomous dummy variable, with a value of 1.0 for M.U.R.B. properties and 0.0 for non-M.U.R.B. properties.  In the context of t h e t h e o r e t i c a l apartment block valuation function discussed in Section 3.3, G I in the estimated equation is a measure of O j , M U R B is a measure of C. , A G E w i l l have an impact on operating expenses and hence CX , and the cost of debt, I, represents one component of the investor's required rate of return, k. The disposition term does not enter into the estimated equation, since there is no way of measuring an investor's expectations concerning either his optimal holding period  or the a n t i c i p a t e d selling  p r i c e of t h e property.  Presumably, such  expectations are part of the 15.5 per cent of selling price variations which cannot be explained by the estimated equation.  These regression results c l e a r l y show that the presence of M.U.R.B. benefits in an apartment investment do have a significant impact on value. Given the magnitude of the M.U.R.B. c o e f f i c i e n t , M.U.R.B. investors in this sample apparently paid an average premium of $63,526 to acquire these properties. A t the 5 per cent l e v e l of significance, this represents a confidence interval of $63,526 + $59,800. The a c t u a l  - 38 -  value of the M.U.R.B. benefits to an apartment block investor can be c a l c u l a t e d in terms of the following present value equation:  PV  CCA  MURB i = 1  Recapture  MURB  (1 +r )  ( l r )  l  S P  MURB"  S P  nonMURB  (l + r )  where  PV,, MURB  Recapture  S P MURB  n  = the present value of the M.U.R.B. tax shelter . benefits.  I i r i n  ^^MURB  n  +  =  t  ' 8 ^ ^ C C A deductions allowed on a M.U.R.B. building over and above what would be available on a non-M.U.R.B. building. i em a r  n a  = the recapture of the marginal M.U.R.B. C C A deductions upon disposition of the property.  = the sales premium which the investor . £ , .. . . .. .. „ _, receives because of the remaining M.U.R.B benefits available to the purchaser of his building. r = the investor's discount rate. M  i  m  n  SP ,, ,r,n nonMURB t  n = the investor's holding period. Based  on an average depreciable basis of the properties in the sample of  io  $560,000,  a C C A rate of 5 per cent (all are Class 31 properties), a marginal tax 13  rate o f 55 per cent,  and a discount rate o f 12 per cent, the present value a t the  - 39 -  t i m e of purchase of the future M.U.R.B. t a x shelter benefits, assuming a sevenyear holding period, is $61,972. M.U.R.B. premium estimated  This is extremely close t o (and lower than) the  in the equation; however, i t does not take into  account the sales premium on disposition, since this is not possible to measure. This is nevertheless strong evidence that M.U.R.B. subsidies are fully c a p i t a l i z e d into the market values of M.U.R.B. properties.  RATES O F R E T U R N TO M.U.R.B. VS. NON-M.U.R.B. INVESTORS  In order to determine whether M.U.R.B. investors actually achieved rates of return superior  to those  of non-M.U.R.B. investors, the seven M.U.R.B. apartment  properties identified as having complete holding periods by the end of 1980 (Le., they were built, rented and sold to investors by that year) are matched with seven comparable  non-M.U.R.B. apartment properties  with  s i m i l a r holding  periods.  Again, comparability was defined in terms of s i z e of building and location. To eliminate the influence on the rates of return of specific financing arrangements by investors, t h e before-financing rates of r e t u r n are c a l c u l a t e d recognizing the income, c a p i t a l appreciation, and, i f applicable, the t a x shelter benefits accruing to each investment in the sample.  The returns are a f t e r - t a x , assuming a  marginal tax rate of 55 per cent.  The comparative rates o f return for the M.U.R.B. and non-M.U.R.B. properties are presented in Table 3.  The average returns earned on the t w o groups of real estate  investments are essentially equivalent - 12.8 per cent for the M.U.R.B. properties  - 40 -  Table 3 RATES O F RETURN: M.U.R.B. VERSUS NON-M.U.R.B. A P A R T M E N T S  Non-M.U.R.B.  M.U.R.B. Average Rate of Return Standard Deviation t-Value Number of Observations  12.8%  13.2%  5.4  8.0  .13 14  and 13.2 per cent f o r the non-M.U.R.B. properties. The following s t a t i s t i c a l test supports the hypothesis that the means of these two samples are not s i g n i f i c a n t l y different:  Let  1 5  Y = the mean return on M.U.R.B. properties Z = the mean return on non-M.U.R.B. properties  Let  ( Y - Z ) be an estimator of u^ - U 2 = -0.4.  2  Let  s  _ ( Y - Z ) be the estimator of t h e variance  of the sampling distribution of ( Y - Z*) where:  s ( Y - 1) = s 2  1  2 n  l  I  + +  n  2  and where s is the estimator of the common variance:  2 2 s + s c  2  s  2  = n  Therefore  l  + n  2 "  2  2 s = 7.763  s  2  ( Y - Z ) = 2.218  - 42-  hence  s ( Y - Z ) ='1.489  Since  t (.975 ; 12) = 2.179  The hypothesis that u  lu, holds where:  A  i  A  where  ^  (Y - Z ) ^  A  2  = - t (0.975; 12) s ( Y - Z )  {  A = 2  Therefore, since  (  t (0.975 ; 1 2 ) s ( Y - Z " )  A j = -2.179 (1.489)  =-3.24  the condition holds that  since  A  2  = +3.24  A  1  ^  (Y -Z )  -3.24 ^  A  2  -0.4 =^ 3.24  - 43-  = .05)  These results indicate that M.U.R.B. investors do not necessarily receive a higher rate of return, but rather earn the same returns as a r e experienced on other apartment investments of s i m i l a r risk.  Hence, the proposition that superior rates  of return on M.U.R.B. properties w i l l shift the a l l o c a t i o n of resources  into the  rental housing sector is not supported, since the program does not in f a c t c r e a t e superior rates of return for investors. This occurs essentially because the market competes away any special profits expected f r o m M.U.R.B. properties and the future tax benefits associated with M.U.R.B.'s become fully c a p i t a l i z e d into higher apartment transactions prices, as supported by the e m p i r i c a l results in Section 5.1. Thus, the apartment valuation and comparable rates of return results y i e l d t h e same conclusion — M.U.R.B. apartment block investors do not appear to have earned preferential ex ante rates of return as a result of the M.U.R.B. program.  There are two groups other than investors who may have benefitted f r o m t h e M.U.R.B. program - landowners and renters.  The next section of this paper tests  whether i t was landowners who benefitted by estimating legislation on multiple f a m i l y land values.  -44-  the impact o f t h e  6.0  ESTIMATION O F MULTIPLE FAMILY L A N D PRICE MODEL  The objective of this analysis of the determinants of multiple f a m i l y land value is to find an estimation of the land p r i c e model which fully represents those f a c t o r s which influence land values, so that the estimated impact of the M.U.R.B. variable on land values w i l l be unbiased.  To this end, c a r e f u l selection of variables for  inclusion in the regression equation was made, both in terms of the actual measure of s p e c i f i c variables in the t h e o r e t i c a l model and in terms of how those variables were represented in e m p i r i c a l terms. The structure of the M.U.R.B. variable as a 0-1 dummy, which is 0.0 during the pre-1975 period and 1.0 thereafter, meant that any t i m e trend existing in other independent variables would have t o be eliminated in order to c l e a r l y identify the M.U.R.B.  impact.  Hence, a l l variables are  specified in real terms (where applicable).  Before discussing the regression results, the following paragraphs describe the 12 variables which have been included in the preferred estimation of the t h e o r e t i c a l model.  6.1  DESCRIPTION O F VARIABLES  6.1.1  Location Variables  As previously discussed, the two location variables which were s i g n i f i c a n t in estimating multiple f a m i l y land values (West End and East Vancouver) - 45 -  a r e 0-1 d u m m y v a r i a b l e s i n d i c a t i n g w h e t h e r o r n o t t h e l a n d took place in that sub-area o f t h e city. ^  transaction  S i n c e t h e W e s t E n d d i s t r i c t is  m u c h c l o s e r t o V a n c o u v e r ' s c e n t r a l b u s i n e s s d i s t r i c t , a n d h e n c e is a m u c h m o r e established a p a r t m e n t district, one would expect sites in this area  to  be greater in value than sites in East Vancouver.  6.1.2  M.U.R.B. Variable  T h i s 0-1 d u m m y v a r i a b l e i s e x p e c t e d t o h a v e a p o s i t i v e e f f e c t o n m u l t i p l e family land values, since t h e presence of M . U . R . B . increases t h e expected future n e tafter-tax cash flows to t h edeveloper of a n apartment building.  6.1.3  Vacancy R a t e s  T h e first  measure  of vacancy  r a t e s u s e d i n t h e f i n a l e s t i m a t i o n is t h e  overall vacancy rate i n apartment buildings in t h e C i t y of Vancouver which have been c o m p l e t e d f o r a t least six months. tested  was the vacancy  rate  Another measure which was  in the sub-area  in which  the land  sale  o c c u r r e d , b u t this m e a s u r e d i d n o t p e r f o r m w e l l , L e . t h e c o e f f i c i e n t w a s positive a n d significant  rather than negative  One would expect the vacancy  in preliminary  rate coefficient  regressions.  to have a negative  sign  since an increase in potential vacancies would reduce expected future cash flows to t h e developer and hence reduce t h e amount h e would b e willing to pay f o r apartment zoned land. perform as expected  A possible reason w h y this m e a s u r e did n o t  is t h e f a c t t h a t t h e v a c a n c y  - 46 -  rate was higher int h e  West End throughout the study period, but land values were also higher in that area. Some c o r r e l a t i o n problem may thus have occurred.  The second measure of vacancy rates used in the analysis is the vacancy rate in new completed  multiple f a m i l y dwellings, defined as the stock of newly and  unoccupied  multiple f a m i l y dwellings in the  City  of  Vancouver divided by multiple f a m i l y completions over the previous four quarters.  An  increase in this vacancy rate should have even greater  negative impact on developers' expectations regarding future cash f l o w s than the overall vacancy rate in existing apartments, since new  apartments  w i l l represent developers' strongest competition in the marketplace.  6AA  Income  The income measure used in this analysis is real per c a p i t a income (indexed by the general C P I for Vancouver) i n British Columbia over the period.  study  Ideally, one would use the average income of non-family house-  holds in the Vancouver metropolitan area, since such households are the primary market f o r multi-unit housing.  However, income i n f o r m a t i o n at  the metropolitan level is severely d e f i c i e n t , 1971.  p a r t i c u l a r l y as f a r back as  Although taxation statistics are available at the metropolitan and  municipal levels, such statistics do not account household  f o r changes i n average  income, since they are on an individual taxpayer basis rather  than on a household basis. Hence, although the B.C.  per c a p i t a income  measure is not specific to non-family households, i t is considered the best  - 47 -  information available which can represent real changes in income over the required t i m e period.  6.1.5  Interest R a t e s  The interest rate measure employed in this analysis is the real interest rate (adjusted by the general C P I for Vancouver) on N H A properties ( i ).  This  rate is considered  approved lender rental  more appropriate  than  the  conventional mortgage rate, which would be more representative of rates on single f a m i l y dwellings than on apartment properties.  6.1.6  "Excess" A p a r t m e n t Returns  It is reasonable to assume that one  inducement for developers to  buy  multiple f a m i l y land is the rate of return expected on apartment properties over and  above the rate of return on a risk-free asset.  This "excess  returns" variable is thus defined as the real c a p i t a l i z a t i o n rate minus the real interest rate on N H A  approved lender rental properties, which in a  sense represents the leverage opportunities available to apartment block investors.  The measure of c a p i t a l i z a t i o n rates is derived f r o m a data f i l e  containing the universe of arms length apartment block transactions in the City  of  Vancouver  from  1969  to  1981.*  7  Since  this data  included  information on a quarterly basis on the selling price and gross income for each apartment block transaction, a standard  - 48 -  representative apartment  block was  selected in each quarter, f o r which the c a p i t a l i z a t i o n rate  was  used.  6.1.7  Rents  One  would » expect  developers' decisions concerning  the p r i c e they  are  w i l l i n g to pay for multiple f a m i l y zoned land to r e f l e c t current market rents being achieved on new  apartment buildings. However, there is no  public or private source of such information over the seven-year study period of this paper.  Therefore, the measure of rental rates used is the  Statistics Canada rent index for Vancouver (adjusted by the general C P I f o r 18 Vancouver).  A second variable which should have an impact on developers' expectations about future rents is the l e v e l of apartment dwelling starts in the C i t y of Vancouver.  As  the number of potential c o m p e t i t i v e units rises, other  things being equal, a developer should expect this competition to reduce future market rents and hence the rents he w i l l be able to achieve in his building. Therefore, this variable is expected to have a negative e f f e c t on multiple f a m i l y land values.  6.1.8  C o n s t r u c t i o n Costs  The construction cost variable represents the Statistics Canada construction cost index for British C o l u m b i a (adjusted by the general C P I for  - 49 -  Vancouver).  This variable is expected to have a negative impact on  multiple f a m i l y land values, since increases in this variable would decrease expected future cash flows to the developer.  6.1.9  Population  The  measure used as a proxy for growth in households is the quarterly  growth i n British Columbia's t o t a l population which, although probably  too  macro a measure to fully represent the e f f e c t of increasing numbers of households and decreasing household size in the Vancouver region, is the only measure available on a quarterly basis. were attempted, one being  Several alternate measures  the change i n households in the C i t y of  Vancouver interpolated between census years, and the other the annual  20 change in main residence telephone Neither of these  listings in the C i t y of Vancouver.  measures performed w e l l i n the equation for various  reasons. In the case of t h e census information on households, the interpolation between five-year intervals created t i m e trend problems w i t h the M.U.R.B. variable, since the t o t a l change in households in Vancouver between 1976 21  and 1981 was higher than the change between 1971 and 1976;  thus, the  structure of the variable resulted in high collinearity with the M.U.R.B. variable, preventing e f f i c i e n t estimation of their individual e f f e c t s on multiple f a m i l y land values.  - 50 -  When added to the equation, the regression c o e f f i c i e n t for the telephone listings variable was negative, which is contrary to the expected positive e f f e c t of growth in households on land values.  There are two possible  explanations for this result. F i r s t l y , using the C i t y of Vancouver statistics may  be too narrowly  defining  how  the housing  market  operates.  Presumably, demand for multiple f a m i l y housing, and hence pressure on multiple f a m i l y land values, is coming f r o m migration and undoubling within the entire Vancouver region, rather than just i n the C i t y of Vancouver.  Secondly, the growth in t o t a l households includes f a m i l y as  w e l l as non-family households, hence the e f f e c t on non-family housing and land values may not be c l e a r l y represented.  6.1.10  The Zoning Issue  The underlying assumption throughout this analysis w i l l be that the zoning variable in the t h e o r e t i c a l model remains constant.  It seems appropriate  to address this issue d i r e c t l y and to present evidence that i t is indeed a valid assumption.  If during the study period of this paper, any major change in the supply of multiple f a m i l y zoned land occurred, this would c l e a r l y bias the representation of the multiple f a m i l y land market, and hence the M.U.R.B. and other c o e f f i c i e n t s i n the equation.  However, discussions w i t h  planning  o f f i c i a l s in the Greater Vancouver region has revealed that the supply of multiple f a m i l y zoned land on a regional basis was f a i r l y constant throughout the 1972 to 1978 period.  Although the West End was downzoned in  - 51 -  1975, reducing build-out c a p a c i t y in that area by 5,000 to 10,000 units, other parts of the c i t y were upzoned to increase t o t a l c a p a c i t y , as  were  other m u n i c i p a l i t i e s in the region, most notably Richmond, Burnaby and 22  Surrey.  Thus, i t appears reasonable to assume that the multiple f a m i l y  zoning variable is a constant over the study period of this research. Since zoning is assumed to be constant, both Z and UNITS w i l l drop out of the t h e o r e t i c a l model ( equation (11)); the number of units per square foot w i l l be constant for a l l sites because of the constant floor space ratio.  EMPIRICAL RESULTS  The results of the estimation of the multiple f a m i l y land value model are shown in Table 4.  Each of the regression equations is discussed in turn in the paragraphs  which follow.  .2.1  Run Number 1  This equation represents the estimation of the t h e o r e t i c a l model w i t h a l l variables measured as extreme c o l l i n e a r i t y  expected  problem  theoretically.  with  two  However, there  variables, real rents and  is an real  income, whose correlation w i t h the M U R B variable are greater than .90, as shown in Table 5 (for R L R E N T 2 and R E A L I N C ) . This c o l l i n e a r i t y prevents an e f f i c i e n t estimation of the true e f f e c t of each of these three variables  - 52-  on land values; t h e M U R B variable consequently shows a negative sign, contrary to what is expected.  An  examination of t h e scatter plots of R L R E N T 2 and R E A L I N C  versus  Q U A R T E R included in Appendix "C" provides an explanation for the high c o l l i n e a r i t y of these two variables with M U R B . Essentially, the very small number of data points in the middle of the study period, which was the t i m e when M.U.R.B.'s were introduced, compared to the larger number of data points a t the t w o extreme ends of the study period, results in high v  positive and negative variable.  c o r r e l a t i o n between M U R B  and any time  trend  This does introduce a bias into the data results, but i t is an  unresolvable problem in terms of a v a i l a b i l i t y of transactions data, due to the paucity of land sales in the C i t y of Vancouver during the 1973 t o 1975 period.  The objective of this analysis is to find an e f f i c i e n t and unbiased estimate of the significance of the M U R B multiple f a m i l y land values.  and other  variables i n determining  The c o l l i n e a r i t y problem i d e n t i f i e d above  creates an e f f i c i e n c y problem; however, the usual remedies for reducing c o l l i n e a r i t y , i.e. c o l l e c t i n g more data and taking first differences on both sides of the regression equation, are not available in this case.  An  a l t e r n a t i v e representation of the rent and income variables is their change f r o m quarter to quarter, which removes the t i m e trend interference with M U R B , as c a n be seen f r o m  Table 5 (for G R R L I N C and L A G R E N T ) .  However, i t must be recognized that although some gain in e f f i c i e n c y is  - 53-  54.  Table * THE REGRESSION EQUATIONS FOR MULTIPLE FAMILY LAND VALUES  Regression Coefficients of the Independent Variables (t-values in parentheses) Dependent Variable  Constant  we  Real price per square foot of multi-farnily Run Number 1 zoned sites 1.81 in the City -252.63 . of Vancouver (1.81) (-4.12) during the 1972 to 1978 period.  MURB  VR  Rm  NEWVRATE  STARTS  -3.26 . (-5.52)  -5.6* (-1.44)  -1.66 (-1.60)  0.05 (0.60)  0.06 . (4.41)  -0.35 ( -0.72)  0.84 _ (3.53)  -0.004, (-3.90)  2.77 (2.66)  -3.13 (-4.97)  8.37 (3.41)  0.18 (0.11)  0.14 (1.53)  0.69* (1.96)  -0.26 (-0.43)  0.23ab (2.24)'  -0.002 (-1.91)  2.48 (2.29)  -3.20 » (-5.14)  0.65 (0.20)  -1.94 (-1.23)  0.33 . (3.38)  0.12° (0.42)  0.99 (1.84)  3.20 (3.12)  -3.22 , (-5.20)  5.20 (2.22)  -2.46 (-1.68)  0.35 , (3.76)  0.20° (0.66)  0.97 (1.82)  3.21 , (2.89)  -3.14 , (-4.97)  4.74 (1.89)  -2.84 (-1.93)  0.40 , (4.10)  0.10° (0.27)  1.33 , (2.27)  2.17 , (1.96)  -3.18 , (-4.95)  1.72 (0.38)  -0.67 (-0.42)  0.20 . (2.35)  0.30" (0.95)  0.42 (0.72)  CC  -0.30 (-3.89)  k.POP  ER  n  F-stat  DW  SE  1.42 (1.34)  -0.37 (-2.18)  ,592  112  11.98  2.11  2.44  2.23 . (2.00)  -0.65 . (-2.79)  .532  112  9.38  1.88  2.61  Run Number 2 -8.66 (-1.65)  m  0.07 . (2.41)  Run Number 3 10.36 (1.46)  -0.004. (-3.46)  -0.22" (-2.30)  2.75 , (2.42)  -0.35 (-1.51)  .540  112  9.70  2.05  2.59  -0.004, (-3.23)  -0.27, (-2.98)  3.22 (2.95)  -0.44 (-1.89)  .545  112  9.90  1.97  2.57  -0.29° (-0.47)  -0.004, (-3.19)  -0.23" (-2.11)  3.16 , (2.84)  -0.42 (-1.77)  .530  112  9.30  2.10  2.61  -0.13 (-1.31)  -0.002, (-2.47)  0.02 (0.44)  2.19 (1.92)  -0.47 (-1.88)  .517  112  8.82  1.94  2.65  -0.11 (-1.57)  Run Number 4 0.56 (0.16)  #  0.18'ab (1.89)  Run Number 5 -1.31 (-0.23) Run Number 6 9.28 (0.73)  55  Tablet (cont'd) THE REGRESSION EQUATIONS FOR MULTIPLE FAMILY LAND VALUES  Regression Coefficients of the Independent Variables (t-values in parentheses) Dependent Variable Real price per square foot of multi-family zoned sites in the City of Vancouver during the 1972 to 1978 period.  Constant  we  MURB  VR  STARTS  Rm  NEWVRATE  CC  /\POP  ER.  F-stat  DW  SE  Run Number 7 1.52 (0.17)  3.04 (3.06)  -3.21 (-5.20)  4.11 (2.48)  -3.10 (-2.81)  0.38 , (4.47)  1.18 , (2.78)  3.03 (3.02)  -3.17 (-5.08)  4.30 (2.57)  2-81 . (-2.57)  0.38 . (4.50)  1.20 . (2.82)  0.16 (1.78)  ab  -0.004. (-3.58)  -0.28°. (-3.06)  3.28 , (3.02)  -0.33 , (-1.97)  .543  112  10.82  1.95  2.56  -0.40 , (-3.46)  -0.25 . (-2.80)  3.20 (2.93)  -0.40 (-2.42)  .529  112  11.34  1.93  2.59  .125  112  16.20  1.48  3.35  .446  20  1.38  2.75  3.57  ,104  20  2.10  1.68  3.71  Run Number 8 0.73 (0.23)  b  Run Number 9 3.54 , (4.02)  7.79 (9.58) Average real Run Number 10 price per square foot 23.48 0.16 of land sale (1.04) (0.20) transactions in quarter x. c  -5.27 (-0.76)  -0.04 (-0.02)  -0.21 (-1.21)  0.13" (0.22)  0.03 (0.30)  -0.04 (-0.14)  c  Run Number 11  "  .  9  3  2  *  (6.64)  2.52 (1.45)  Lagged by one quarter. Change since last quarter. . Location index variable, weighted by sub-area. Expectations variable, defined as the lag over the past six quarters in Vancouver rents. , Real capitalization rate variable. Indicates t-value significant at .05 level.  achieved by taking these first differences, there is also some loss in unbiasedness in the results.  The following paragraphs describe the data  results using these new specifications f o r rent and income.  Run Numbers 2 and 3  Number 2 includes Zik R  Run  m  and  in the regression equation, both  of which are significant and have t h e expected positive sign.  Other  variables in this equation which are significant and have the expected sign are the two l o c a t i o n variables (West End and East Vancouver), M U R B , population growth ( ^ P O P ), and excess returns on apartment investments ( ER rate  ). Variables which do not have the expected sign are the vacancy ( V R ),  the vacancy  rate  in new  multiple  family  dwellings  ( N E W V R A T E ), and construction costs ( C C * ).  Since the s p e c i f i c a t i o n of the rent variable is in quarterly change terms rather than the actual l e v e l , i t would seem more consistent to include construction costs in quarterly change terms as w e l l , particularly since the c o e f f i c i e n t for the level of construction costs does not make i n t u i t i v e -x  -x-  sense. than  Run Number 3 replaces C C  with «£k C C  •x  R » to see what the e f f e c t of £s» C C  independent of £ ± R  rather  m  is in t h e equation,  -x  . As Table 4 shows,  CC  and negative as expected in this equation, but equations i n the following paragraphs w i l l CC  -x-  m  -X-  ^  , but keeps R  is also negative. specify both  , as this is considered to be most consistent. -56 -  is in f a c t significant  £± R  m  The and  6.2.3  Run Number k  This equation represents my preferred estimation of the t h e o r e t i c a l model, since a l l but two of t h e variables have t h e expected  sign and t h e  c o l l i n e a r i t y between independent variables has been m i n i m i z e d (refer to Table 5 ) .  A c c o r d i n g to this equation, the most s t a t i s t i c a l l y s i g n i f i c a n t  determinants (at the 5 per cent level) of multiple f a m i l y land values are: location (the West End having a positive e f f e c t and East Vancouver a negative  effect),  MURB,  t h e level  of apartment  construction costs, and population growth. statistically  dwelling  starts,  Other variables which are not  significant, but which do contribute to the explanation of  multiple f a m i l y land values, hence m i n i m i z i n g bias in the estimation of the M U R B c o e f f i c i e n t , are:  vacancy rates, change in real income, the real  23 interest rate, a one quarter lag in the change in rents,  and excess returns  expected on apartment investments. The N E W V R A T E and i variables do not have the expected negative sign in this equation, although these measures are consistent with the t h e o r e t i c a l determinants of multiple f a m i l y land values. Furthermore, neither of these variables is severely  collinear with other independent variables in the  equation, although the c o r r e l a t i o n c o e f f i c i e n t of . 7 5 between N E W V R A T E and  M U R B , and . 7 9 between R E A L I N T and V A C R A T E , may be causing  s t a t i s t i c a l problems.  - 57 -  58.  Table 5 CORRELATION MATRIX FULL MODEL VARIABLES  Correlation Coefficients  Variable  39.NEWREALP  1 .OOOO  7. WESTEND  .3899  8. KITS 9. EASTVAN 1O.MARP0LE 25.MURBSTAT 22.VACRAJE  1 OOOO . 1910  1 .0000  -.3227  .3675  - . 8035  1 .OOOO  .0692  .0399  - .0871  - . 1676  .3592  .0335  - .4362  .3730  .0570  1 .0000  - . 165S  .0725  .3697  -.3638  -.0799  -.4390  .0931  92 NEWVRATE  .3794  .0178  - . 3200  42.REAL INC  . 4204  . 1252  - . 3930  -.2679  . 1790  52.GRRLINC  .2359  .2582  1.0000  . 1 126  1 .0000  .7542  - . 3859 -.3073  . 2936  .0130  .9143  -. 1188  -.0065  - .6092  1 .0000 .6911  I .OOOO  . 1562  -.2042  • .6280  .0000  • .1436  . 3289  1 .0000  55.REAL INT  - . 1810  .0147  .2124  - . 1S86  - . 1395  -.2924  . 7956  -.2820  93.RLRENT2 •  -.4081  . 1094  . 4340  -.3380  - .0229  -.9554  .4469  -.6997  • .9736  .6742  .3162  -.4 105  . 1034  - . 3492  -.0298  - . 9649  . 46 1 1  -.7407  • .9711  .6141  .3015  .9954  1 .0000  . 1092  - . 1 107  - . 1036  97.RLRNTLAG  . 4444  1.0000  1.OOOO  60.LAGRENT  . 1821  .0505  -.0819  .0561  - .0285  . 1991  .0976  . 1557  . 1578  .3394  94.STARTS  .0273  .0563  -.1136  . 1562  -.0695  .4122  . 1489  . 2860  .5768  • .2515  .2758  -.4800  - . 4576  O80O  .2123  . 1814  - .0096  . 1827  . 2233  . 1098  .5279  • 2 5 16  .3523  - . 3532  - . 3346  .0356  .3039  .0161  -.6018  - . 0913  1 .0000  95.REALCOST  - .0323  -.0682  63.CCOSTBC2  - .0439  .0861  .0269  -.1281  51.POPGRTH  -.2766  .0432  .4330  -.371 1  70.RLAPTRTN  -.1325  .0774  . 1 160  -.0780  .2029  -.2522  - .0496  - .0333  -.8013  .450O  .0475  - . 1747  .2318  . 1285 -.7042 .14 16  .4573  - .0172  - .6895  .4488  .2004  .7351  .7576  -.2551  -. 1030  -.0779  .0170  -.1811  .7039  . 3305  .2672  . 1976  -.2041  . 1078  - .0184  .3307  .2938  1 .0000  . 5264  .4309  -.3558  -.7208  - .2190  - . 1812  - . 3548  -.0998  -.4498  - . 3687  . 1 146  - . 4135  .3180  .0154  . 9482  - . 3639  .6945  .9919  •6595  -.2156  - .9929  - .9901  53.INFLATIO  .2539  .0251  -.3353  .3030  .0880  .5281  -.88 16  . 4358  .3539  • .3854  - .9273  - .5347  -.5321  9: EASTVAN  10. MARPOLE  25. MURBSTAT  22 . VACRATE  1 .0000  - .337 1  .4130  8. KITS  . 4897  .3578  12.BCP0P  39. NEWREALP  1 .OOOO  42 . 92 . NEWVRATE REALINC  52 GRRLINC  55 REALINT  93. RLRENT2  . 1433 -.0560  97 . 60. RLRNTLAG LAGRENT  94. STARTS  1 .0000  63 . 51 . 95. REALCOST CC0STBC2 POPGRTH  1.0000 .4378  12. 70. RLAPTRTN BCPOP  53 . INFLATIO  It is worthwhile noting that the R-squared of .545 for this equation is quite acceptable for cross-sectional studies of this nature.  Although the sample  contains transactions which occurred over a seven-year period, the removal of the t i m e trend, and the conversion to real terms changes the data to a cross-sectional sample. One would expect a much higher R-squared in time series studies such as those by Witte and Adams et a l .  These data results suggest that the introduction of the M.U.R.B. program in 1974 had a significant i m p a c t on multiple f a m i l y land values, and that developers paid a premium of $5.20 per square foot (compared to the average real sales p r i c e per square foot of $10.81) to obtain such land over the period of the program. Hence, a developer would pay an e x t r a $52,000 (in 1971 dollars) for a t y p i c a l 100' x 100' apartment site (this is somewhat smaller than the average apartment block in the R E S A L E S sample). A t the 5 per cent l e v e l of significance, this represents a confidence  i n t e r v a l of  $52,000 + $45,891. If one assumes that the developer built a t y p i c a l (Class 31) 25-unit apartment block on this s i t e , with a marginal tax rate of 55 per cent, a real discount rate of 2 per cent and a seven-year holding period, the present value of the future marginal tax shelter benefits associated with the M.U.R.B. c e r t i f i c a t i o n is $23,180 (in 1971 dollars). Converting to 1980 dollars, the equation estimates acquire  that a developer would pay $108,680 t o  a site with future t a x shelter benefits worth $48,446.  estimate is again based on the P V  - 59 -  M  n  R  R  equation described previously:  This  I n  PV  MURB  ~  CCA,  i =1  Recapture  MURB  ( 1 +r)  (1  l  + r )'  Thus, the M.U.R.B. premium on land price estimated in these data results represents a significant o v e r - c a p i t a l i z a t i o n of future M.U.R.B. tax shelter benefits.  Run Number 5  This equation  shows the impact of using  lagged form.  Although the R-squared is only moderately a f f e c t e d (it is  reduced, however), the sign of  R  m  R  m  in current rather than  is i n c o r r e c t , and « ^ Y  is no longer -x-  -x  significant. ^  POP,  It also reduces slightly the significance of i , ^ \ C C  and  although i t only marginally a f f e c t s the M U R B c o e f f i c i e n t .  This  equation shows that the specification of the rent variable in current terms is not as good behaviour.  a measure as lagged  rents i n explaining  developers'  This may result f r o m information lags, or i t may be that  developers are merely slow in r e a c t i n g to changes in the market.  - 60 -  6.2.5  Run Number 6  Run Number 6 show the regression results where income is s p e c i f i e d as a quarterly change, but rents and construction costs are specified in level terms.  Only three variables are s t a t i s t i c a l l y significant - WESTEND,  E A S T V A N , and S T A R T S , while four variables do not have the c o r r e c t sign N E W V R A T E , i * , R* , and C C * . Thus, Run Number 4 is s t i l l considered the preferred estimate of the t h e o r e t i c a l model, although this run could be considered more theoretically appealing.  6.2.6  Run Numbers 7 and 8  These two equations show the variables which remain in equation four a t the .10 and .05 significance levels, respectively. In the former equation, only A . Y  drops out of the equation, while in the l a t t e r , A R  out. In both cases, N E W V R A T E and i results are generally encouraging,  f f l  also drops  s t i l l have the i n c o r r e c t sign. These in that t e n variables remain i n the  estimated equation even a t the 5 per cent level of significance, with an acceptable R-squared of 53 per cent.  6.2.7  Run Number 9  A run was made including only M U R B as the independent variable. It is interesting to note that the M U R B c o e f f i c i e n t in this equation i s 3.54  - 61 -  (significant at .05  level), and  although  the R-squared is only .125,  the  regression is significant at the .05 l e v e l (F = 16.20).  6.2.8  Run Number 10  In an a t t e m p t to reduce the bias in the regression equation which  may  result f r o m the large number of land sale observations in some quarters compared to others during the study period, an additional run on the data was  made on  a quarterly rather than on  a transactions basis.  This  e f f e c t i v e l y reduced the sample size to 20, since of the 27 quarters over the 1972  to 1978  t i m e period, 7 quarters had  no  occurrence of land sales  transactions.  The dependent variable in this equation is the average real p r i c e per square foot of a l l land transactions during a quarter.  A  l o c a t i o n index  was  created, which gave a weighting of 3.0 for sales in the West End, - 3.0 for sales in East Vancouver, and 0.0 for sales occurring in either K i t s i l a n o or Marpole.  These weights are based on the earlier regression results, which  showed quite stable coefficients for W E S T E N D and E A S T V A N , while KITS and M A R P O L E were never significant in the equations (see Appendix "C" for these regression results).  Due  to the dramatic reduction in the sample size for this run, the number  of variables included in the equation existing  vacancy  rate  was  was  reduced to seven.  included, while  - 62 -  an  expectations  Only  the  variable,  NEW R M L A G ,  which represents  a six-quarter moving average of the real  growth of rents in Vancouver, replaces A 1  and E R  &  Y  , S T A R T S and A  POP.  The  variables are collapsed into one rate of return variable, the  real c a p i t a l i z a t i o n rate.  As  can be seen f r o m Table 4, the results of this regression are quite  disappointing, since not one variable is significant at even the 20 per cent level of significance, nor is the regression as a whole significant, although the R-squared is a surprising 44.6 per cent.  A n examination of the residual  plot and histogram of residuals for this regression (refer to Appendix "D", page 127), reveals a possible outlier in the data, which may be causing standard errors and thus biasing the results.  A regression  high  was run  excluding this possible outlier (refer to Appendix "D", page 134); however, the  results are very  comparable  to Run Number 10,  although the  c o e f f i c i e n t s of the M U R B , R E A L C O S T and V A C R A T E variables  switch  signs, and the standard error is reduced somewhat and t values improved, 24 as would be expected.  A possible explanation  for these small sample  results is that the number of variables is s t i l l too large for this sample size. However, to remove more independent variables would bias  the f u l l  representation of the multiple f a m i l y housing market.  6.2.9  Run Number 11  This regression equation shows the small sample results where only M U R B is included as the independent variable. Although M U R B is not significant,  - 63 -  its c o e f f i c i e n t has a value of 2.52, which is reasonably clsoe to the value in Run 9. The R-squared is again very low (.102), while the F s t a t i s t i c is not significant at the .05 level.  - 64 -  CONCLUSIONS A N D IMPLICATIONS  The  results of the foregoing  general  analysis provide  evidence which c o n t r a d i c t s the  case f o r the operation of t h e multiple f a m i l y housing market, where  renters should receive the f u l l benefits of the M.U.R.B. program in the f o r m of lower rents.  This  research  has shown that the future t a x shelter benefits  associated with M.U.R.B. properties are fully c a p i t a l i z e d into the market values of completed M.U.R.B. buildings, and that M.U.R.B. investors do not earn rates o f return superior to those of investors in  non-M.U.R.B. apartment properties.  Similarly, these results do not support the widely made argument that adverse t a x revisions (such as reductions in tax shelter benefits) cause inferior ex ante rates of 25 return in real estate investment. comparative  returns  among  In c o m p e t i t i v e c a p i t a l markets, equilibrium  a l t e r n a t i v e investments  Government subsidies or d i f f e r e n t i a l t a x treatments.  are not determined by Expected rates of return  among assets of equivalent risk must be equal; otherwise, investors w i l l enter o r leave a specific investment market, causing prices to rise or f a l l until t h e returns among the assets are similar.  The only  way government programs e f f e c t  d i f f e r e n t i a l returns is through any investment risk created by having a f l u c t u a t i n g or uncertain tax or subsidy policy. This research suggests further that the expected M.U.R.B. t a x shelter benefits were over-capitalized into higher program.  land value premiums during the l i f e of the  Thus, using Tullock's (1975) terminology, a major e f f e c t of the program  - 65 -  was  to create transitional gains for existing landowners a t the t i m e the program  was introduced.  The expected favourable tax shelter benefits were thus competed  away, resulting in higher multiple f a m i l y land prices.  Although the data results  show c l e a r l y the impact of the M.U.R.B. on land values, a weakness in the data, i.e. there were very f e w land sales occurring immediately before and a f t e r t h e introduction of the program, must be recognized, since i t may be biasing the results to some extent.  The data results nevertheless suggest that one of two cases discussed in Section 3.1 holds. with  The o v e r - c a p i t a l i z a t i o n of M.U.R.B. benefits into land values, combined the f u l l  capitalization  of M.U.R.B. benefits into the resale values of  apartment blocks, would result i f the land supply function were inelastic and the investors demand function were p e r f e c t l y elastic (Case 4). This would imply that there  is substitution  in the production  function  f o r apartment  blocks,  i.e. developers w i l l substitute c a p i t a l for land and incrase the density on existing sites as a result of the increase in demand caused by M.U.R.B. legislation. This is also evidence of semi-strong f o r m e f f i c i e n c y of r e a l estate markets, since the tax shelter benefits were fully c a p i t a l i z e d into resale values of M.U.R.B. apartment properties.  However, since this research has not tested the speed with which the  market reacted to the introduction of the M.U.R.B. program, i t does not  provide  conclusive evidence of r e a l estate market e f f i c i e n c y .  The data results also cannot reject the conditions under Case 5, where both t h e land and apartment supply functions are p e r f e c t l y inelastic, since the  - 66 -  confidence  i n t e r v a l of the M U R B c o e f f i c i e n t includes the case where the present value of the M.U.R.B. benefits is equal to the estimated increase in land values which occurred as a result of M.U.R.B. legislation.  However, this would imply no substitution in  production, which is not very likely. Furthermore, conclusive evidence of this case could only be found by either observing the movement i n rents a f t e r M.U.R.B.'s were introduced i n comparison t o what would have occurred  in t h e absence of  M.U.R.B.'s., or by deriving structural estimates of the supply and demand curves in the land and apartment markets.  C l e a r l y , such a comparison is not possible with  these data.  A third possible market situation which is supported by the data results is where the demand schedules of both investors and landowners are p e r f e c t l y e l a s t i c , a consequence of both an e f f i c i e n t land and apartment investment market.  A  definitive answer is not possible, however, without some knowledge of the change in apartment rents which resulted f r o m the M.U.R.B. program.  What the results do suggest, however, is that the f u l l c a p i t a l i z a t i o n of M.U.R.B. benefits into both land and apartment block values resulted in the f u l l benefits of the M.U.R.B. program not f i l t e r i n g through to renters. Some benefits most likely did reach renters, since i t is unrealistic to assume no substitution in production, but the extent of renters' benefits cannot be measured within the scope of this research.  If the supply of multiple f a m i l y land is in f a c t inelastic and government assistance programs which increase the demand for rental housing or apartment zoned land,  -  67  -  are not accompanied by policies at junior levels of government which concurrently increase the supply of developable  land, these assistance programs c a n become  marginally e f f e c t i v e tools for increasing the allocation of resources to the housing sector.  This research has in f a c t shown that the M.U.R.B. was a very expensive  subsidy policy and that i t s effectiveness in achieving i t s objective was l i m i t e d by the nature of the multiple f a m i l y housing market.  The evidence regarding t h e  slope of the supply and demand curves for landowners and investors suggests that the f u l l impact of the M.U.R.B. tax shelter benefits was split between w i n d f a l l gains to landowners and decreased rents f o r renters. However, the distribution of the benefits between these two groups is not clear f r o m these data results.  If the introduction of the M.U.R.B. program i n 1974 created w i n d f a l l gains for existing landowners, then it follows that the termination of the program w i l l create windfall losses. It also follows that the "off and on" nature of the program over the past seven years should have created considerable uncertainty for prospective land purchasers and developers,  resulting i n increased  risk of holding real estate.  Nevertheless, after termination of the program, once the market has adjusted t o the lower costs of production, future market participants should earn "normal" market rates of return on apartment investments.  This research has shown that there is s t i l l much to be learned about how housing markets operate.  It would be instructive t o do a similar  study  i n another  metropolitan area, p a r t i c u l a r l y where land sales between 1973 and 1975 were not so scarce, in order to compare market reactions i n another l o c a l marketplace.  - 68 -  Before more d e f i n i t i v e conclusions can be drawn concerning the behaviour of various market participants, more research needs to be done on rent movements and on the speed with which real estate markets r e a c t to changes in information.  - 69 -  FOOTNOTES  Statistics Canada, V i t a l S t a t i s t i c s , Catalogue Number 84-204. See Harris (1979 : 4-14) for a discussion of the tax reform process. See Interpretation B u l l e t i n IT-367R2, September 7, 1981. A f t e r 1978, with f e w exceptions, a l l new M.U.R.B.-certified buildings c a m e under the 5 per cent C C A asset class (Class 32X There w i l l also be foregone provincial t a x revenues, which w i l l vary f r o m province to province. Based on information obtained f r o m Helmut Pastrick at C M H C i n Vancouver. C i t y of Vancouver Planning Department estimates. Stock markets also suffer f r o m some of these deficiencies, such as lack of sophistication, and divergence between expectations and a c t u a l accomplishments. The e f f i c i e n c y of stock markets nevertheless has been e m p i r i c a l l y supported. For additional i n f o r m a t i o n on this type of framework f o r real estate investment analysis, see Gau and Kohlhepp (1976, 1978). The RM-3 zoning classification in the C i t y of Vancouver allows a maximum floor space ratio (FSR) of 1.5, Le., the ratio o f t o t a l gross building area to lot size. Comparable holding period in terms of acquisition and sales date. Although the present data f i l e contains 112 transactions, the original data c o l l e c t e d comprised some 496 transactions which occurred f r o m 1963 t o 1978. However, the pre-1972 data were not useable due to constaints in other data and because o f problems which arose w i t h the representation o f the 1971 t a x reform legislation in the modeL Assuming a t y p i c a l structure-to-property value ratio of 70 per cent on the average selling price in the sample. In 1980 in B r i t i s h Columbia, a 55 per cent marginal t a x rate would apply to investors w i t h a taxable income of $70,000 or more.  - 70-  For the M.U.R.B. developments, t h e analysis assumes that 15 per cent o f t h e construction costs are soft; in other words, outlays that could be expensed when incurred as opposed to being c a p i t a l i z e d into the depreciable basis of the property. The 15 per cent figure is the average soft cost r a t i o (after e l i m i n a t i n g syndicationtype fees) found in a survey of t e n registered M.U.R.B. syndicates o f f e r e d in Western Canada in the third quarter of 1981. R e f e r to Neter and Wasserman (1974:  12-13) for a discussion of this test.  The other two l o c a t i o n variables, K I T S and M A R P O L E , d i d not have significant c o e f f i c i e n t s in preliminary regressions. This data was also c o l l e c t e d f r o m B.C. Assessment A u t h o r i t y records, under the supervision of Professor George W. Gau. Catalogue Number 62-010. Catalogue Number 62-007. Obtained f r o m the B.C. Telephone Company. Statistics Canada, Census of Canada, for 1971 and 1976, and preliminary census counts for 1981. Based on information obtained f r o m t h e Planning Departments of t h e C i t y of Vancouver, the Municipalities of Richmond, Burnaby and Surrey, and the P r o v i n c i a l Land Commission. A l l independent variables were tried with a lag to see if t h e s p e c i f i c a t i o n of the model improved, but A R was the only variable which performed better when specified on a lagged basis. This same observation was excluded f r o m a separate run on the large sample (in Runs 1 and 4), and s i m i l a r l y , the regression results changed only marginally. An example of such an argument c a n be found in Smith (1981).  - 71 -  BIBLIOGRAPHY Adams, F. Gerard; M i l g r a m , Grace; Green, Edward W.; and Mansfield, Christine, "Undeveloped Land Prices During Urbanization: A M i c r o - E m p i r i c a l Study Over Time", Review of Economics and S t a t i s t i c s , Vol. 50, No. 2, May, 1968, pp. 248-258. B a i l e y , Martin 3., "Progressivity and Investment Y i e l d s under U.S. Journal of P o l i t i c a l Economy, VoL 82, No. 6,1974, pp. 1157-1175.  Income Taxation",  Baxter, C h e r y l , "The Impact of Government Policies and Programs on Land Values", The R e a l Estate Appraiser and Analyst, Vol. 45, May-June, 1979, pp. 42-45. Brigham, Eugene F., "The Determinants of Residential Land Values", Land Economics, VoL 41, August, 1965, pp. 325-334. Canada Mortgage and Housing Corporation, Canadian Housing S t a t i s t i c s , Ottawa. Clayton Research Associates Ltd., Tax Expenditures-Housing; research paper prepared for C.M.H.C.{ March, 1981, Ottawa. Diamond, Douglas B. Jr., "The Relationship Between A m e n i t i e s and Urban Land P r i c e s " , Land Economics, Vol. 56, No. 1, February, 1980, pp. 21-32. Fama, Eugene F., " E f f i c i e n t C a p i t a l Markets: A R e v i e w of Theory and E m p i r i c a l Work", Journal of Finance, 25 (May) 1970, pp. 383-423. F i g l e w s k i , Stephen, "Market 'Efficiency' i n a Market with Heterogeneous Information", Journal of P o l i t i c a l Economy, Vol. 86, No. 4, 1978, pp. 581-597. Fisher, Ted L., "Tax L e v e r a g i n g and R e a l Estate Tax Shelters", The Appraisal Journal, July, 1980, pp. 414-422. Gau, George W., and Kohlhepp, D.B.£ "Estimation of Equity Y i e l d Rates Based on C a p i t a l Market Returns", The Real Estate Appraiser and A n a l y s t , Vol. 44, November-December, 1978, pp. 33-39. Gau, George W., and Kohlhepp, D.B./' "Reinvestment R a t e s and the Sensitivity of Rates of Return in Real Estate Investment", A R E U E A Journal, Vol. 4, Winter, 1976, pp. 69-83. Goldman, M. Barry, and Sosin, Howard B., "Information Dissemination, M a r k e t E f f i c i e n c y and the Frequency of Transactions", Journal of Financial Economics, Vol. 7,1979, pp. 2961. H a m i l t o n , B r u c e W., " C a p i t a l i z a t i o n of Intrajurisdictional D i f f e r e n c e s in L o c a l Tax P r i c e s " , The A m e r i c a n Economic Review, December, 1976, Vol. 66, No. 5, pp. 743-753. H a r r i s , E.C., Canadian Income Taxation, Toronto, 1979.  - 72 -  Janssen, Christian T.L., and Hoskins, C o l i n G., "Analysis of A R P and C C A Projects", Appraisal Institute Magazine, May, 1980, pp. 26-32. Krueger, Anne O., "The P o l i t i c a l Economic Review, June, 1974.  Economy of the Rent-Seeking Society", A m e r i c a n  Linnemann, P., "The Demand f o r Residence Site C h a r a c t e r i s t i c s " , Economics, March, 1981,9(2), pp. 129-148.  Journal o f Urban  M i l l s , David E. "The Non-Neutrality of Land Value Taxation", N a t i o n a l Tax Journal , Vol. 34, No. 1, March, 1981, pp. 125-129. Needham, B a r r i e , "A Neo-Classical Supply-Based Studies, VoL 18, No. 1, February, 1981, pp. 91-104.  Approach  to Land  Prices",  Urban  Posner, R i c h a r d A., "The Social Costs of Monopoly and Regulation", Journal o f P o l i t i c a l Economy, August, 1975, pp. 807-827. R i c k s , R. Bruce, "Imputed Equity Returns on R e a l Estate Financed with L i f e Insurance Company Loans", The Journal of Finance, December, 1969, pp. 921-937. Roulac, Stephen E.. "Can R e a l Estate Returns Outperform Journal of P o r t f o l i o Management, Winter, 1976, pp. 26-43.  Common Stocks?', The  Shenkel, W i l l i a m M., "The Valuation of Multiple F a m i l y Dwellings by Inference", The Real Estate Appraiser, January-February, 1975, pp. 25-36.  Statistical  Smith, L.B.J "Federal Housing Programs and the A l l o c a t i o n of C r e d i t and Resources", in Government in Canadian C a p i t a l Markets: Selected Cases, edited by J.E.' Pesando and L.B./Smith, C D J H o w e Research Institute, Montreal, 1978. Smith, L.B./ "Canadian Housing P o l i c y in the Seventies", Land Economics, VoL 57, August, 1981, pp. 338-352. Tullock, Gordon, "The Transitional Gains Trap", B e l l Journal o f Economics, Autumn, 1975, pp. 671-678. Valachi, Donald J. "The A r i t h m e t i c of R e a l Estate Tax Shelter", Journal of Property Management, Vol.44, July/August, 1979, pp. 209-215. Von Furstenberg, G.M., "The Impact of Government Housing and C r e d i t Programs on the Cost of Housing", in The Cost of Housing, Federal Home Loan Bank of San Francisco, San Francisco, 1977. Wendt, P a u l F., and Wong, S u i N., "Investment Performance: C o m m o n Stocks Versus Apartment Houses", The Journal of Finance, December, 1965, pp. 633-646. White, Wilbert L., " P r i c e Indexing for Time Adjustments", The Appraisal Journal, VoL 48, January, 1980, pp. 15-23.  -73-  W i t t e , Ann Dryden, "The Determination of Interurban Residential Site P r i c e Differences: A Derived Demand Model with E m p i r i c a l Testing", The Journal of Regional Science, VoL 15, No. 3,1975, pp. 351-364. Witte, Ann Dryden, "An Examination of Various E l a s t i c i t i e s f o r R e s i d e n t i a l Sites", Land Economics, Vol. 53, No. 4, November, 1977, pp. 401-409. Zerbst, Robert H. and Eldred, Gary W., "Improving Multiple Regression Valuation Models Using Location and Housing Q u a l i t y Variables", Assessors Journal, VoL 12, No. 1, March, 1977, pp. 1-15.  - 74-  APPENDIX "A" V A R I A B L E LISTS  - 75  -  Table A - l LIST OF VARIABLES A P A R T M E N T "RESALES" FILE Variable Number  Symbol  Description  1  FILENOl  F i l e reference  2  MONTH  Quarter of sale: 1 = 01/77 19 = 04/81  3  PRICE  Selling price of building  4  FINANCE  T o t a l mortgages outstanding  5  FILEN02  F i l e reference  6  INTPMO  Interest payable on demand note  7  INTRATE  Weighted average interest rate on T o t a l FINANCE.  8  PURCHTYPE  Type of purchaser: 1 = Individual 2 = 2 or more individuals. 3 = Holding or management co. 4 = Developer or construction co. 5 = Couple 6 = Co-operative 7 = F i n a n c i a l institution 8 = Miscellaneous co.  9  PURCHLOC  Address of purchaser: 1 = Vancouver westside 2 = Vancouver eastside 3 = CBD 4 = West Vancouver 5 = N o r t h Vancouver 6 = Richmond 7 = Burnaby 8 = Elsewhere in G V R D 9 = Elsewhere in B.C. 10 = Elsewhere in Canada  AGE  Y e a r Building was completed.  10  - 76 -  Table A - l (cont'd) Variable Number  Symbol  11  SUITES  Number of suites in building  12  AREA  Total gross floor area of building  13  AVERAGE  Average suite size  14  LOTSIZE  Square footage of site  15  BACHS  Number of bachelor suites  16  ONES  Number of one-bedroom suites  17  TWOS  Number of two-bedroom suites  18  THREES  Number of three-bedroom suites  19  FOURS  Number of four-bedroom  20  CONSTN  Type of construction: 0 = Frame 1 = Concrete  21  HEATING  Type of heating: 0 = Oil 1 = Electric 2 = Gas  22  STOREYS  Number of storeys  23  PARKING  0 1 2 3  24  LAUNDRY  Dummy variable: 1 = Yes 0 = No  25  ELEV  Number of elevators in building  26  BALC  Dummy variable: 1 = Yes 0 = No  27  POOLREC  Dummy variable: 1 = Yes 0 = No  Description  suites  = None = Above Ground = Underground = Both  - 77 -  Table A - l (cont'd) Variable Number  Symbol  Description  28  SAUNA  Dummy variable: 1 = Yes 0 = No  29  PENTHS  Number of P H suites  30  FILEN03  File reference  31  TAXSHELT  M.U.R.B. dummy variable  32  GI  Gross income of building  33  EXPENSES  Operating expenses  34  NOI  N e t operating income  35  RENTCONT  Dummy variable: 1 = Yes 0 = No  36  MTGPMT  Annual pmt on F I N A N C E  37  ARPSUPP  Amount of A R P subsidy (if applicable)  38  ECC  39  RVALUE  Estimated construction cost of building (per building permit) Replacement value of building (per B.C. assessment)  40  ACC  A c t u a l construction cost (per owner)  41  ARPSTAT  Dummy variable: 1 = Yes 0 = No  42  INCDATE  Date GI applicable  43  FINDATR  R e g i s t r a t i o n date of financing  44  FINDATC  C a n c e l l a t i o n date of financing  45  FINAMT  Financing amount  46  LVRATIO  F I N A N C E / P R I C E x 100  47  REALAGE  No. of years since completion  -78 -  Table A - l (cont'd) Variable Number  Symbol  48  SPPSF  Selling price per square foot of building area  49  SPPSTE  Selling price per suite  50  OERATIO  Operating expense ratio  1)  Description  Sources of data: B.C. Assessment A u t h o r i t y records, B.C. Land T i t l e O f f i c e , Statistics Canada, R e a l Estate Board of Greater Vancouver. This f i l e contains M.U.R.B. apartment block resales and a matching sample of non-M.U.R.B. apartment block sales in the same t i m e period.  - 79 -  Table A-2 LIST O F V A R I A B L E S "LANDSALES" FILE Variable Number  Symbol  Description  1  FILENOl  F i l e Reference: #5001-6050  2  QUARTER  Quarter in series: 1 = 1st Q t r , 1963  72 - 4th Q t r , 1980 3  PRICE  Selling price of l o t  4  LOTSIZE  Total square footage of l o t  5  FRONTAGE  Frontage of lot in f e e t  6  DEPTH  Depth of l o t in f e e t  7  WESTEND  Dummy variable: 1 = Yes 0 = No  8  KITS  as above  9  EASTVAN  as above  10  MARPOLE  as above  11  KERRISDL  as above  12  BCPOP  Estimate of B.C. population in quarter x.  13  BCPERINC  E s t i m a t e of per c a p i t a personal income for B.C. during quarter x.  14  UNEMPLUA  Unadjusted unemployment r a t e in B.C. during quarter x.  15  UNEMPLSA  Seasonally adjusted unemployment r a t e in B.C. during quarter x.  16  COMPLVAN  Total dwelling completions in the C i t y of Vancouver during quarter x.  17  FILEN02  F i l e reference: #5001-6050  - 80 -  Table A-2 (cont'd)  Symbol  Description  COMPLBC  T o t a l dwelling completions in B.C.  CPIALL  Consumer P r i c e Index - A l l items; C i t y of Vancouver, during quarter x.  CPIHOUSG  NONFAMHH  Consumer P r i c e Index - Housing Component; C i t y of Vancouver, during quarter x. Non-family households as a proportion of t o t a l households in quarter x; nonf a m i l y households defined as those in the 15-19, 20-24, and 65+ age groups; extrapolation of census data used t o arrive at estimates.  VACRATE  A p a r t m e n t vacancy rate (in buildings completed f o r at least 6 months) in the C i t y of Vancouver during quarter x.  NHARATE  N.H.A. interest rate on approved lender rental properties during quarter x.  CONVRATE  Conventional mortgage lending rate during quarter x.  MURBSTAT  Dummy variable (0=No; 1+Yes) indicating whether M U R B legislation was in e f f e c t (or pending) during quarter x.  CCASTAT  Dummy variable indicating whether C C A . allowances were permitted as tax shelters on a l l rental properties during quarter x.  CCANEW  Dummy variable indicating whether C C A . allowances were permitted as tax shelters on new rental properties; this r e f l e c t s both pre-1971 and post 1974 situations.  CCANEWWP  Same as variable 27, except that allowance is made for the White Paper re-leased in the 4th quarter of 1969, which introduced the f i r s t possibility that tax shelters on rental properties might be removed.  ARPSTAT  Dummy variable indicating whether A R P benefits were available during quarter x.  - 81 -  Table A-2 (cont'd) Variable Number  Symbol  30  RENTCONT  Dummy variable indicating whether rent control legislation (of any form) was in e f f e c t in British C o l u m b i a during quarter x.  31  HOLDPER  Holding period of l o t x prior to construction  Description  of apartment building (in years). 32  SPPERSF  Selling price per square foot of l o t x.  33  SPPERFF  Selling price per front foot of l o t x.  34  SPPERDF  Selling price per foot of depth of l o t x.  35  DEFLATOR  A p a r t m e n t Sales price index (from Transactions File).  36  REALSP  PRICE/DEFLATOR  37  REALPPSF  REALSP/LOTSIZE  38  CPINEW  CPIALL/100  39  NEWREALP  SPPSF/CPINEW  40  RLINTRTE  NHARATE/CPINEW  41  POPGRRTE  G r o w t h in B.C. population since 01/71.  42  REALINC  43  INCGRRTE  BCPERINC/CPINEW. G r o w t h in real B.C. income per c a p i t a since 01/71.  44  NEWQTR  C a t e g o r i c a l variable for Q U A R T E R .  45  RENTLEVEL  Average nominal monthly rents in Vancouver apartments, weighted by l o c a l area.  46  GRRTRENT  G r o w t h rate in R E N T L E V E L since 01/71.  47  CONSTNCOST  Construction cost index for Canada.  48  GRRTCOST  G r o w t h rate in C O N S T N C O S T since 01/71.  49  RENTGRTH  G r o w t h since last quarter in nominal rent levels. - 82 -  Table A-2 (cont'd)  Symbol  Description  COSTGRTH  G r o w t h since last quarter in construction cost index f o r Canada.  POPGRTH  G r o w t h since last quarter i n B.C. population.  GRRLINC  G r o w t h since last quarter in B.C. real per c a p i t a income.  INFLATION  G r o w t h since last quarter in C P I A L L , on an annualized basis.  GRRLRENT  G r o w t h since last quarter in real rents in Vancouver apartments.  REALINT  N H A R A T E - INFLATION  REALRENT  Average monthly real rent ( R E N T L E V E L / C P I N E W ) in Vancouver apartments.  CCOSTBC  G r o w t h since last quarter in construction cost index f o r B.C.  RNTGRTH2  G r o w t h since last quarter i n rent index for Vancouver (Statistics Canada).  RNTGRTH3  G r o w t h since last quarter in rents in a sample of Vancouver apartments less than 5 years old (from Transactions File).  LAGRENT  One quarter l a g in real rent index growth since previous quarter ( R N T G R T H 2 - I N F L A T I O N ) .  LAGCOSTS  One quarter lag in real construction cost index growth since previous quarter ( C C O S T B C - INFLATION).  RNTGRTH4  R e a l growth since last quarter i n Vancouver rent index ( R N T G R T H 2 - I N F L A T I O N ) .  CGOSTBC2  R e a l growth since last quarter in B.C. construction cost index ( C C O S T B C - I N F L A T I O N ) .  CAPRATE  The real c a p i t a l i z a t i o n rate (nominal I N F L A T I O N ) being achieved by a standard Vancouver apartment block in quarter x (from Transactions File).  - 83 -  Table A-2 (cont'd) Variable Number  Symbol  65  CAPRTLAG  One quarter l a g in C A P R A T E .  66  NOMCAPRT  The nominal c a p i t a l i z a t i o n rate being achieved by a standard Vancouver apartment block in quarter x (from Transactions File).  67  CAPGAIN  R e a l capital gain f r o m a sample of Vancouver apartment blocks since last quarter (from Transactions File).  68  CAPGNLAG  One quarter lag in C A P G A I N .  69  POPLAG  One quarter lag in P O P G R T H .  70  RLAPTRTN  Excess returns earned on Vancouver apart-  Description  ment blocks ( C A P R A T E - R E A L I N T ) . 71  RLINCLAG  One quarter lag in G R R L I N C .  72  VACRTLAG  One quarter l a g in V A C R A T E .  73  RLINTLAG  One quarter lag in R E A L I N T .  74  APRTNLAG  One quarter l a g in R L A P T R T N .  75  INFLALAG  One quarter l a g in I N F L A T I O N .  76 77  INTCHGE APTCOMCH  Change since last quarter in N H A R A T E . Net change since last quarter in apartment stock in the C i t y of Vancouver (defined as apartment completions minus apartment demolitions).  78  HHCHANGE  Q u a r t e r l y increase in t o t a l households in the C i t y of Vancouver (based on interpolation of census data).  79  RLINTCHG  Change since last quarter in R E A L I N T .  80  ERCHANGE  Change since last quarter in R L A P T R T N .  81  APTSTSCH  Change since last quarter in apartment starts in the C i t y of Vancouver.  82  NEW WE  C a t e g o r i c a l variable for WESTEND.  - 84 -  Table A-2 (cont'd)  Symbol  Description  NEWKITS  C a t e g o r i c a l variable for KITS.  NEWEV  C a t e g o r i c a l variable for E A S T V A N .  NEWMAR  C a t e g o r i c a l variable for M A R P O L E .  NEWKERR  C a t e g o r i c a l variable for K E R R I S D L .  SUBVACRT  Vacancy rate in apartments 6 months or older in the sub-area and quarter in which the land sale observation occurred.  NEWVACCH  Change since last quarter in the stock of newly completed (in past six months) and unoccupied apartment and row dwellings in the C i t y of Vancouver.  REZONING  Dummy variable r e f l e c t i n g the major downzoning of the West End enacted in August, 1975.  VACRTCHG  Change since last quarter in V A C R A T E .  NEWHH  Change since the last quarter in the number of main residence telephone listings in the C i t y of Vancouver.  NEWVRATE  Vacancy rate in newly completed multiple f a m i l y dwellings in the C i t y of Vancouver - defined as the stock of newly completed and unoccupied multiple f a m i l y dwellings divided by multiple f a m i l y completions over the previous four quarters.  RLRENT2  L e v e l of the Statistics Canada Rent Index for Vancouver (in real terms).  STARTS  The number of apartment dwellings starts in the C i t y of Vancouver in quarter x.  REALCOST  L e v e l of the Statistics Canada Construction Cost Index for B.C. (in real terms).  NEWVRLAG  A one quarter lag in N E W V R A T E .  RLRNTLAG  A one quarter l a g in R L R E N T 2 .  - 85 -  Table A-2  (cont'd)  Variable Number  Symbol  98  RLCSTLAG  A one quarter lag in R E A L C O S T .  99  NEWVACMF  The number of newly completed and unoccupied multiple f a m i l y dwellings in the C i t y of Vancouver in quarter x.  1)  Description  Sources of data: B.C. Assessment A u t h o r i t y records, B.C. Land T i t l e O f f i c e , Statistics Canada, R e a l Estate Board of Greater Vancouver.  - 86  A P P E N D I X D E S C R I P T I V E  - 87  " B "  S T A T I S T I C S  -  Table B - l DESCRIPTIVE STATISTICS "RESALES" FILE  Variable  N  Minimum  Maximum  Mean  Standard Deviation  1 . FILEN01  59  103.00  1112.0  816.19  344.92  2 . MONTH  59  4 . OOOO  16 .000  13 . 458  2 .5415  3. PRICE  59  66666.  .30000 +7  .78522 +6  .70338 +6  4. FINANCE  58  0.  .75000 +7  .60342 +6  .10686 +7  5 . FILEN02  59  103.00  1112.0  816.19  344.92  6.INTPMO  37  0.  26250.  2404 .4  5248 . 5  7.INTRATE  58  0.  17.200  10. 993  3 . 5687  8. PURCHTYP  57  0.  8.OOOO  2.4561  1 .5592  9. PURCHLOC  44  1.0000  10.000  2.9091  2 . 2805  10.AGE  59  5.OOOO  79.000  53.915  24.883  11.SUITES  59  5.OOOO  93.000  27.220  19.111  12. AREA  59  4200.0  57229.  18760.  13480.  13. AVERAGE  59  196.00  124 1 .0  674.41  169 . 16  14. LOTSIZE  58  3050.0  35000.  10566.  6184.2  15. BACHS  54  0.  60.000  7 . 2593  12.189 •  16 . ONES  54  0.  68.000  17.463  14.457  17. TWOS  54  0.  21.000  2. 1852  4 . 2563  18. THREES  54  0.  7.OOOO  .27778  1 . 2 196 .13608  4  19. FOURS  54  0.  1.OOOO  .18519 - 1  20. CONSTN  56  . 0.  1.OOOO  . 19643  .40089  1 .3276  .80324  3 . 5763  2.6209  1 .4746  1 .0061  .92000  .27405  .49153  .59807  .50909  .50452  .51724 -1  . 22340  21. HEATING  58  0.  2.OOOO  22.STOREYS  59  1 . OOOO  17.000  23. PARKING  59  0.  3.OOOO  24. LAUNDRY  50  0.  1.OOOO  25. ELEV  59  0.  2.OOOO  26 . BALC  55  0.  1.OOOO  27 . POOLREC  58  0.  1 . OOOO  - 88 -  Table B - l (Cont'd) DESCRIPTIVE STATISTICS "RESALES" FILE  Standard Deviation  Minimum  28.SAUNA  58  0.  1.0000  .51724- - 1  . 22340  29.PENTHS  58  0.  2 .0000  . 12069  . 37825  .30.FILEN03  59  103.00  1112.0  816.19  344.92  31 . TAXSHEI.T  59  b.  1O.OOO  .45763  1 . 3432  32. GI  55  . 25000  .25348 +6  72979.  59494 .'  33. EXPENSES  28  2904 .0  56240.  21305.  18268.  34. N0I  27  9697 .0  .14212 +6  39329.  30589.  35. RENTCONT  58  0.  1 .0000  .74138  .44170  36. MTGPMT  56  o.  .32562 +6  60456.  824 1 1 .  37. ARPSUPP  11  o.  76263 .  19802. .  254 19 .  .10000 +6  .43600 +7  .85451 +6  .11690 +7  74150.  .20690 +7  .51253 +6  .41474 +6  .25250 +6  .17800 +7  .99806 +6  .59411 +6  38. ECC 39. RVALUE 40. ACC  • 12 49 5  Maximum  Mean  N  Variable  I  41. ARPSTAT  59  0.  1 .0000  .13559  .34529  42.INCDATE  55  0.  127.00  49.436  23.899  43. FINDATR  51  1 .0000  193.00  98.275  33.404  44. FINDATC  5  42.000  131.00  104.60  37.753  45. FINAMT  50  21000.  .23100 +7  .38676 +6  .42146 +6  46. FILEN04  59  103.00  1112.0  815.83  344.62  47 . S0LD77  59  0.  1.0000  . 16949 - 1  .13019  48.S0LD78  59  0.  1.0000  . 16949 - 1  .13019  49.S0LD79-  59  O.  1.0000  .32203  .47 127  50.S0LD80  59  0.  1.0000  .64407  .48290  51 .REALAGE  59  0.  75.000  26.492  25.549  52.LVRATIO  58  0.  1 12.50  2 . 5335  14.'698 .  53.SPPSF  59  3.1503  93.329  40.343  17.129  54.SPPSTE  59  3072 . 7  61O00.  27606.  10766.  55 . OERATIO  28  . 13158  .21638 +6  7728.3  40893.  56.GIPERSTE  55  .71429 -2  6231 . 3  2766.9  1101.6  - 89 -  Table B-2 DESCRIPTIVE STATISTICS "LANDSALES" FILE  Mean  Standard Deviation  Variable  N  Minimum  Maximum  1 . FILENO1  1001.0  6025.0  2220.5  : 1992 . 2  2. QUARTER  1 15 115  .37 .000  63 .OOO  54 .652  7 . 1390  3. PRICE  1 15  18000.  .15000 +7  .15293 +6  .20603 +6  4. LOTSIZE  1 15  2950.0  67054 .  9285 . 1  1 1446.  5. FRONTAGE  1 15  25.000  400.00  72.817  75.698  6. DEPTH  1 15  100.OO  168.OO  123.06  9 . 0934  7. WESTEND  1 15  O.  1.OOOO  .86957 -1  .28300  8. KITS  1 15  0.  1.OOOO  .28696  .45432  9 . EASTVAN  1 15  0.  1.OOOO  .60870  .49018  10. MARPOLE  1 15  0.  I. OOOO  . 17391 -1  .13130  11. KERRISDL  1 15  0.  0.  0.  12. BCPOP  1 15  2223.6  2533 . 2  2451 .8  86.515  13. BCPERINC  1 15  3859 . 3  8677 . 8  7094 . 3  1360.9  14. UNEMPLUA  1 15  6.OOOO  9.6300  8.1999  .6271 1  15. UNEMPLSA  1 15  5.5300  9 . 0700  8.3520  .52081  16. COMPLVAN  1 15  104.00  1353.0  755.77  219.22  17. FILEN02  1 15  1001 .0  6025.0  2220. 5  1992.2  18 . COMPLBC  1 15  5846 .0  12091.  8187 . 6  1201 . 5  19. CPIALL  1 15  102.70  176.27  150.83  20.648  20. CPIHOUSG  1 15  101.33  170.50  147 . 17  21.184  21 .NONFAMHH  1 15  26.640  28.870  28.297  .60357  22. VACRATE  1 15  .10000  2 . OOOO  .84000  .49187  23. NHARATE  1 15  8 . 8900  1 1 . 880  10.620  . 83352  24. CONVRATE  1 15  8 .9800  II. 980  10.583  .82093  25. MURBSTAT  1 15  0.  1.OOOO  .85217  . 35648  26. CCASTAT  1 15  O.  1 . OOOO  .34783 -1  .18403  27. CCANEW  1 15  0.  1 . OOOO  .86087  .34760  28. CCANEWWP  1 15  O.  1 . OOOO  .85217  .35648  29. ARPSTAT  1 15  O.  1 . OOOO  .85217  •. 35648  30. RENTCONT  115  0.  1 . OOOO  .86957  .33826  31. HOLDPER  102  O.  ^5 . OOOO  1 . 2255  .70229  -90  T a b l e B-2 (Cont'd) DESCRIPTIVE STATISTICS "LANDSALES" FILE  Variable  Minimum  N  Maximum  Mean  Standard Deviation  32 . SPPERSF  1 15,  4 . 2853  33.898  16 . 585  . 6.2763  33.SPPERFF  1 15  539 . 77  4242.4  2039.7  794.80  34.SPPERDF  1 15  150.00  8928 . 6  1 198 .0  35. DEFLATOR  1 15  1 . 2470  2 .6520  2 .0897  .45541  36. REALSP  1 15  14320.  .70588 +6  75246.  .10029 +6  37. REALPPSF  1 15  2 . 1200  15.863  7 .9269  2.7262  38. CPINEW  1 15  1 .0270  1.7627  1 . 5083  ' 1399.7  .20648 :  3.5675  39. NEWREALP  1 15  2 . 7578  21 .541  10.807  40. RLINTRTE  1 15  5.9454  8 . 7537  7.1474  4 1 . POPGRRTE  1 15  1 .0084  1.1488  1.1119  ' .39234 -1  42.REALINIC  1 15  3757 . 8  4923.0  4658 . 8  ! 336.06' .  43.INCGRRTE  1 15  1 .0168  1 . 3320  1.2605  .90929 -1  44. NEWQTR  1 15  37.000  63.000  54.652  7.1390  45. RENTLEVE  1 12  168.80  . 278.OO  251.02  32.821  2.5102  .3282 1  .87049 i  46. GRRTRENT  1 12  1 .6880  2 . 7800  '4 7 .CONSTNCO  1 12  105.10  194.50  162.67  48. GRRTCOST  1 12  1 .0510  1 . 9450  1 .6267  ' .24040  49. RENTGRTH  1 12  0.  14.900  5.6250  2.8106  50. COSTGRTH  1 12  2 .0000  i20.100  8.3714  5.2952  51. POPGRTH  1 12  .77000  4 .0100  1.5454  .63257  52. GRRLINC  1 12  - 1 . 8000  9 .0400  3.5627  2 .6694  12.230  7.5704  1 .9916  ;  24.040  53.INF LAT 10  1 12  3.8500  54 .GRRLRNT  1 12  -8.0200  1 1 .840  - 1 .8899  3 . 2739  55.REALINT  1 12  - 1 . 6 100  5 . 2900  3 .0391  1 . 4552  56 . RE ALRENIT  1 12  157.7 1  ' 182 .02  166.31  3. 1433  57. CCOSTBC  1 12  3.2600  21 . 320  9.1174  4 . 9600  58. RNTGRTH2  1 12  1 . 1900  7 . 9500  4.91 19  1 . 9262  59. RNTGRTH3  1 12  17.410  7.6050 -  7.2952  -14.890  60. LAGRENT  112  -9.9900  1 . 7700  61. LAGCOSTS  1 12  -7.8800  7.6400 91  -2.0659 3.4312  2.9628 4.6865  Table B-2 (Cont'd) DESCRIPTIVE STATISTICS "LANDSALES" FILE  Variable  N  Minimum  Maximum  Mean  Standard Deviation  -2.6585  1.1913  62. RNTGRTH4  1 12  -5.8300  63. CC0STBC2  1 12  -3 . 8 100  12.370  1 .5471  4.7458  64. CAPRATE  1 12  -4.9000  10.520  5.0375  3.1156  65. CAPRTLAG  112  -5.1000  10.410  5 . 1224  3.3439  66. NOMCAPRT  112  1 1 .000  13.780  12.735  .73109  67. CAPGAIN  1 12  - 5 . 9000  13.030  5.7 133  6.1479  68. CAPGNLAG  1 12  -6.9400  13.030  3.2793  5.0261  69. POPLAG  1 12  3 . 4000  1 .537 1  .77142  70. RLAPTRTN  1 12  -5.4300  5 . 2300  1 .9984  2 . 3157  71. RLINCLAG  1 12  - 1 . 8000  9 . 6900  3.9503  2.471 1  72 . VACRTLAG  1 12  . 10000  2 . 1000  .84241  . 55905  73.RLINTLAG  1 12  -5.8900  6 . 3800  3.4554  2 . 5880  74 . APRTNLAG  1 12  -.88000  5 . 1400  1.6670  1 . 4522  75.INFLALAG  1 12  15.900  7.2872  2 . 8396  76.INTCHGE  1 12  -.65000  .83000  -. 13321  .27534  77 . APTCOMCH  112.'  -908.00  6 1 3 . 00  19.491  258.36  78. HHCHANGE  1 15  341.00  663.00  559.40  151.08  79. RLINTCHG  1 12  -8 . 8200  10.010  -.54357  3.7617  80. ERCHANGE  1 12  - 2 . 3 100  2 . 1300  , .45866  8 1 .APT5TSCH  1 12  -737 .00  698.00  - 1 . 1800  . 77000  2 . 7300  243.66  82. NEWWE  10  1 . OOOO  1 . OOOO  ; 1.OOOO  83. NEWKITS  33  1 . OOOO  1 . OOOO  1.OOOO  84. NEWEV  70  1 . OOOO  1 . OOOO  i 1.OOOO  1 . OOOO  1.OOOO  1.OOOO  1 . 2049 427.35  85 . NEWMA.R  2  86 . NEWKERR  O  87.SUBVACRT  1 12  - . 70000  .55000  .15670  .22546  88. NEWVACCH  1 12  -221.00  156.00  -13.911  80.628  89. REZONING  1 15  90. VACRTCHG  1 12  O. - . 7 5000 92  1.OOOO  . 77391  .42013  .30000  . 174 1 1 - 1  .19759  T a b l e B-2 (Cont'd) D E S C R I P T I V E STATISTICS "LANDSALES" FILE  N  Minimum  Maximum  Mean  Standard Deviation  91. NEWHH  1 12  -418.00  2133.O  19.2 .93  760.19  92. NEWVRATE  1 12  1 . 1 200  29.200  16.625  7 . 1638  93. RLRENT2  1 12  34.070  91 .020  48.012  16.272  94.STARTS  1 12  32.000  1385 .0  822 . 13  437.97  95. REALCOST  1 12  1 12 . 24  153.67  131.27  1 1 . 740  96. NEWVRLAG  1 12  1 . 1200  29.200  17.184  7 . 8095  97. RLRNTLAG  1 12  35.450  95.160  49.755  16.754  98 . RLCSTLAG  1 12  103.11  151 .46  129.91  14.404  99.NEWVACMF  1 12  3 1 .000  553.00  349.99  137.33  Variable  - 93 -  APPENDIX "C" SCATTER PLOTS  - 94-  REALINC 4923.0  N = 115 OUT OF 115 42.REALINC VS. 2.QUARTER  X '•  +  4690.O  +  ^ ^  * * *  +  9 X  6 6  •  8  47 +  4456.9  6  +  3  4223.9  3990.9  + 3 +  3 3757.8  +6 +  37 OOO  ON  +  + ---- +  42.200  +  47.400  +  +  52.600  +  +  57.800  COMMAND 7SCATTER V=93,2 CASES=380-388,390-396,398-496 SCATTER PLOT CASES=CASE#:380-388,390-396,398-496 N= 112 OUT OF 115 93.RLRENT2 VS. 2.QUARTER RLRENT2 9 1.020 +6 33  79.630  68.240  + *  +  +  QUARTER 63.000  +  56.850  + +  45.460  6  +  4 7 8  34.070  + +37.OOO  66  X9  X  -+  42.200  47.400  52.600  57.800  COMMAND 7SCATTER V=93,25 CASES=380~388,390-396,398-496 SCATTER PLOT CASES = CASE# : 380-388,390-396,398-496 N= 112 OUT OF 115 93.RLRENT2 VS. 25.MURBSTAT RLRENT2 91 .020 +6  79.630  + 2  68.240  +  56.850  +  45.460  +  34.070 • + +-  + .  QUARTER 63.000  o.  .40000  20000  .80000  •60000  MURBSTAT 1•0000  COMMAND 7SCATTER V=22,92 CASES=380-388,390-396,398-496 SCATTER PLOT CASES = CASE# : 380-388,390-396,398-496 N= 112 OUT "OF 115 22.VACRATE VS. 92.NEWVRATE VACRATE 2 .0000 +6  1.6200  +  1.2400  +  . 86000  +  I ON  I  . 48000  10000  +  6  + 4  -+  1 . 1200  .  6.7360  +  +  12.352  +  +  17.968  y  V  23.584  +  COMMAND 7SCATTER V=12,2 CASES=380-388,390-396,398-496 SCATTER PLOT CASES = CASE# : 380-388,390-396,398-496 N= 115 OUT OF 115 12.BCP0P VS. 2.QUARTER BCPOP 2533.2 +  6  X 9  +  NEWVRATE 29.200  2471.3  +  8 6 47  +  3 6  2409.4  +  2347 . 4  2285 . 5 +  2223.6  3  3  +6 +37.000  -+  42.200  47.400  52.600  57.800  QUARTER 63.000 I  COMMAND 7SCATTER V=51,2 CASES=380-388,390-396,398-496  00 ON  I  SCATTER PLOT CASES = CASE/5': 380-388 , 390-396 , 398-496 N= 112 OUT OF 115 51.POPGRTH VS. 2.QUARTER POPGRTH 4.0100 + *  3.3620  +6  2.7140  +  2.0660  + 3  3  2 6  X  1.4180  + 66 X  .77000  +  7  +  37 000  +  +  42.200  +  +  47.400  +  +  52.600  +  +  57.800  +  +  QUARTER 63.000  COMMAND 7SCATTER V=12,25 CASES=380-388,390-396,398-496 SCATTER PLOT CASES=CASE#:380-388,390-396,398-496 N= 115 OUT OF 115 12.BCP0P VS. 25.MURBSTAT BCPOP 2533.2 +  247 1 . 3 I ON ON  I  2409.4  +  2347.4  +*  2285.5  +  2 +  2223.6  3 3 +6  -+ -  . 20000  .40000  •60000  .80000  COMMAND 7SCATTER V = 51,25 CASES = 380-388 , 390-396 ,'398-496  MURBSTAT 1•0000  APPENDIX "D" RESIDUAL PLOTS  - 100  -  SCATTER PLOT CASES=CASE# : 380-388,390-396,398-496 N = 112 OUT OF 115 51.POPGRTH VS. 25.MURBSTAT POPGRTH 4.0100 +* +  3.3620  +6 +*  2.7 140  + +  2.0660  +5 3  1.4180  X 4 * 8  +  I — <  O  .77000  + 0  +  7  +  +  +  + + .40000  .20000  +  +  •60000  I  + + + .80000 MURBSTAT 1•OOOO  COMMAND ?REG V = 39,7,9.25,22,92,42,55,93,94,95,51,70 CASES = 380-388 , 390-396 , 398-4.96LEAST SQUARES REGRESSION CASES=CASE#:380-388,390-396,398-496 SUM SQRS N=ME AN O SU QT R OF 115 SOURCE ANALYSIS OF VARIANCE OF DF 39.NEWREALP 112 12 852.31 99 586.76 1 1 11439 . 1  REGRESSION ERROR TOTAL MULT R= .76959 VARIABLE CONSTANT  71.026 5.9268  F-STAT 1 1 . 984  SIGNIF . OOOO  R-SQR= .59227 SE=2.4345  PARTIAL  COEFF -252.63  STD ERROR 61.277  T-STAT  SIGNIF  -4.1228  .0001  Run Number 1  7 WESTEND . 9. EASTVAN 25 .MURBSTAT 22 .VACRATE . 92 . .NEWVRATE 42 .REALINC 55 .REALINT 93 .RLRENT2 94 .STARTS 95 .REALCOST 51 .POPGRTH 70 .RLAPTRTN  .17877 -.48526 -.14320 -.15886 .06005 .40488 -.07 194 .33421 -.36522 -.36422 .13345 -.21386  1.0009 1.8078 1.8095 .59069 -5.5220 -3.2618 3.9203 -1.4397 -5.6441 1.0392 -1.6010 - 1.6637 .4994 1 -1 .83433 -1 .59857 .58387 -1 .13252 -1 4.4057 .48247 -.71767 -.34626 .23949 3.5282 .84497 -.35193 -2 .90157 -3 -3.9035 .77618 -1 -3.8912 -.30203 1.0589 1.3398 1.4186 .17140 -2.1783 -.37337  .0737 .0000 .1531 .1126 .5508 .0000 .4746 .0006 .0002 .0002 .1834 .0318  COMMAND ? SAVE V100=RESIDUAL LABEL FOR THE RESULT VARIABLE(S) = RESI DUAL CASES TO SELECT =380-388,390-396,398-496 RESIDUAL USING: REGRESS VARIABLE  TOTAL  100.RESIDUAL  CASES=CASE#:380-388,390-396.398-496  VALID  115  MISS  112  3  COMMAND 7SCATTER V=100,39 CASES=380-388,390-396,398-496  I  O  SCATTER PLOT CASES=CASE#:380-388,390-396,398-496 N= 112 OUT OF 115 100.RESIDUAL VS. 39.NEWREALP RESIDUAL 6 . 2029  * 3.2202  + .23750  + +  -2.7452  * 2  + *  * *  * 3 *2  *  *2 * * ** 2* 2522* * 3 22 * 2 * 53 * *2*** 2 *2 * * * 2 * *  ***  ***  + *  * 2  *2  *  2 *  -5.7279  +  -8.7106  +* +  +  2  7578  +  +  6.5144  +  10.271  +  +  +  14.027  +  17.784  +  +  NEWREALP 21.541  COMMAND 7SCATTER V=100,2 CASES=380-388,390-396,398-496 SCATTER PLOT CASES=CASE#:380-388,390-396,398-496 N= 112 OUT OF 115 100.RESIDUAL VS. 2.QUARTER RESIDUAL 6.2029 + *  2  .3.2202  *  +  *  *  *  * * *  *  *  .23750  + * 3 2 2 *  -2.7452  +  -5.7279  +  *  2 2 4 * **2 32 4 4 3* * 5 * . *2 * 5 * 2 2* * * 3 3 * ** * 2  I  CO O  +  -8.7106  + +  37 000  +  +  42.200  +  +  47.400  COMMAND ?HISTOGRAM V=100 INT=10 OP=HIST% CASES TO SELECT =380-388,390-396,398-496  +  +  52.600  +  + -'  57.800  +  +  QUARTER 63.000  HISTOGRAM  CASES=CASE#:380-388,390-396,398-496  MIDPOINT  HIST% COUNT FOR 100.RESIDUAL  -8.7106 -7.0535 -5 . 3965 -3 . 7394 -2 .0824 - . 42532 1 . 2317 2.8888 4.5458 6.2029  9 0. 3..6 2..7 16 .. 1 34 . 8 26 .8 9 .8 4. 5 . 9-  (EACH X= 1)  1 +X 0 + 4 + XXXX 3 + XXX 18 +XXXXXXXXXXXXXXXXXX 39 +XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX. 30 +XXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 1 1+XXXXXXXXXXX 5 +XXXXX 1 +X 3 1 15 (INTERVAL WIDTH= 1.6571)  MISSING TOTAL  COMMAND 7TRANS V101=V100/2.4345 LABEL FOR THE RESULT VARIABLE(S) =STANDRES CASES TO SELECT =380-388,390-396,398-496 DIVIDE TRANSFORMATION VARIABLE  TOTAL  101.STANDRES  115  CASES=CASE#:380-388,390-396,398-496 VALID 112  •3-  MISS  O  3  COMMAND 7SCATTER V=101',2 CASES = 380-388 , 390-396 , 398-496 SCATTER PLOT CASES = CASE/f : 380-388,390-396,398-496 N= 112 OUT OF 115 101.STANDRES VS. 2.QUARTER STANDRES 2.5479 + *  1.3227  + *  .97556 -1+ * 3 2 2 *  2 2 4 * **2 32 4 * * 4 3 5 *' *2 * 5 * 2 2 * * * 3 3 * ** * 2  • 1 . 1276  -2.3528  +  -3.5780  +  +  +  37.OOO  +  +  4-  +  47.400  42.200  +  +  +  52.600  +  57.800  +  +  QUARTER 63.000  COMMAND ,„„ ?REG V = 39,7,9,25,22,92,52, 55,60,94,95,51 ,70 CASES=380-388,390-396,398-496 LEAST SQUARES REGRESSION  CASES=CASE#:380-388,390-396,398-496  ANALYSIS OF VARIANCE OF 39.NEWREALP N= 112 OUT OF 115 SUM SQRS  DF  SOURCE  12 765 .63 99 673 . 44 1 1 11439 . 1  REGRESSION ERROR TOTAL  MEAN SQR  F-STAT  63.803 6.8024  9 . 3794  SIGNIF .0000 I  O  MULT R= .72941 R-SQR= .53203 SE=2.6081 VARIABLE CONSTANT 7 .WESTEND 9 . EASTVAN 25 .MURBSTAT 22 .VACRATE 92 .NEWVRATE 52 .GRRLINC 55 .REALINT 60 .LAGRENT 94 .STARTS 95 .REALCOST 5 1.POPGRTH 70 . RLAPTRTN  COEFF  PARTIAL .25848 - .44710 .32397 .01123 .15198 .19347 -.04331 -.21939 -.18856 .23555 .19694 -.27027  STD ERROR  -8.6634 2 .7661 -3.13 17 8.3651 .17959 .13919 .68528 -.26336 .23084 -.17540 -2 .74282 -1 2.2256 -.65161  T-STAT  SIGNIF  -1 .6461 5.2630 1.0390 2.6623 .62970 -4 .9733 2.4551 3 .4072 1.6069 1 1 177 .90979 -1 1. 5299 . 34927 1.9621 .61060 -.43131 .10317 2 . 2374 .91811 -3 - 1.9104 .30802 -1 2.4116 1.1136 1.9986 .23329 -2 . 7931  . 1029 .0091 .0000 .0010 .9112 . 1292 .0526 .6672 .0275 .0590 .0177 .0484 .0063  COMMAND 7SAVE V100=RESIDUAL LABEL=RESIDUAL CASES=380-388,390-396,398-496 RESIDUAL USING: REGRESS VARIABLE  TOTAL  CASES=CASE#:380-388,390-396,398-496  VALID  MISS  Run Number 2  100.RESIDUAL  115  112  3*  * CASES CHANGED IN EXISTING VARIABLE COMMAND 7SCATTER V=100,39 CASES=380-388,390-396,398-496 SCATTER PLOT CASES = CASE# : 380-388,390-396,398-496 N= 112 OUT OF 115 100.RESIDUAL VS. 39.NEWREALP RESIDUAL 7.0373 +  4.0374  +  1.0375  +  *  -1.9625  +  3 )  2 2* *** * 2 4 2 *3 * 2 53 * * *2 *3* 2* *3 3 3 2 * ** * * * ** ** 2 *  NO  o I  -4.9624  -7.9623  +  +* +  2.7578  6 . 5144  10.271  14.027  17.784  COMMAND 7SCATTER V=100,2 CASES = 380~388,390-396 , 398-496 SCATTER PLOT CASES = CASE# : 380-388,390-396,398-496 N= 112 OUT OF 115 100.RESIDUAL VS. 2.QUARTER RESIDUAL 7 .0373 + * +  NEWREALP 21 . 541  4.0374  +  *  +* *  *  * + ** * * +  1 .0375  3 +2  -1.9G25  *  *  *  *  2 ** 4 * .* 2 * 4 2 3*4 3 2 5 * 2 3 * 2 3* 4 5 * * * ** * 2  * * 2  *  * +  * *  -4.9G24  i  * *  *  +  -7.9623  +  *  +  +  +  37.000  +  +  +  +  +  47.400 42.200  +  57.800 52.600  +  +  QUARTER 63.000 o  COMMAND ?HISTOGRAM V=100 INT=10 OP=HIST% CASES TO SELECT =380-388,390-396,398-496 HISTOGRAM  CASES = CASE# : 380-388.390-396,398-496  MIDPOINT  HIST% COUNT FOR 100.RESIDUAL  -7.9623 -6.2957 -4.6290 -2.9624 - 1 .2958 .37081 2.0374 3.7040 5.3707 7.0373 MISSING TOTAL  9 18 . 2..7 7.. 1 33 .0 31 . 3 13 . 4 4. 5 2.7 2 .7  (EACH X= 1)  1 +X 2 + XX 3 + XXX 8 +XXXXXXXX 37 +XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 35 +XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 15 +XXXXXXXXXXXXXXX 5 +XXXXX 3 + XXX 3 +XXX 3 115  (INTERVAL WIDTH= 1.6666)  COMMAND 7TRANS V101=V100/2.6081 CASES=380-388,390-396,398-496 LABEL FOR THE RESULT VARIABLE(S)  =STANDRES DIVIDE TRANSFORMATION VARIABLE 101.STANDRES  TOTAL 115  CASES=CASE#:380-388,330-396,398-496 VALID 112  MISS 3*  * CASES CHANGED IN EXISTING VARIABLE COMMAND 7SCATTER V=101.2 CASES=380-388,390-396,398-496 SCATTER PLOT CASES = CASE# : 380-388,390-396,398-496 N= 112 OUT OF 115 101.STANDRES VS. 2.QUARTER STANDRES 2.6982 + *  1.5480  .39778  + * *  .75245  3 +2  2 * * 4 * * 2 * 4 2 3*4 3 2 5 * 2 3 * 2 3* 4 5  1.9027  -3.0529  + +37 000  42.200  47.400  52.600  57.800  QUARTER 63.000  COMMAND 7REG V=39,7,9,25,22,92,52,55,93,94,63,51,70 CASES=380-388.390-396,398-496 LEAST SQUARES REGRESSION  CASES = CASE/S': 380-388 , 390-396 , 398-496  00 O  ANALYSIS OF VARIANCE OF 39.NEWREALP N= 112 OUT OF 115 DF  SOURCE  SUM SQRS  12 777 . 58 99 66 1 . 48 1 1 11439 . 1  REGRESSION ERROR TOTAL  MEAN SOR  F--STAT  SIGNIF  64.799 6.6816  9 .6980  .0000  T-STAT  SIGNIF  MULT R= .73508 R-SQR= .54034 SE=.2.5849 VARIABLE CONSTANT 7. WESTEND .9. EASTVAN 25 . MURBSTAT 22 .VACRATE 92 .NEWVRATE 52 .GRRLINC 55 .REALINT 93 .RLRENT2 94 .STARTS 63 .CC0STBC2 51 .POPGRTH 70 .RLAPTRTN  COEFF  PARTIAL  10.362 .22442 2.4837 -.45866 -3.1962 .01987 .64773 -.12310 -1.9362 .32202 . 33336 .04208 .12366 . 1822 1 .99055 -.15570 -.11392 -.32828 -.41391 -2 -.22526 - .21,603 .23632 2.7463 -.15033 -.35266  STD ERROR  1.457 1 2.2914 -5 . 1356 19774 -1 . 2342 -1 3. 3843 41901 1.8438 -1 -1 .5683 -2 -3 .4580 -1 -2 . 3004 2 .4198 - 1.5 129  7.1117 1.0839 .62236 3.2757 1.5687 .98500 .29512 .53722 .72638 .11970 .93908 1.1349 .23309  . 1483 .024 1 .0000 .8437 . 2200 .0010 .6761 .0682 . 1200 .0008 .0235 .0174 . 1335  COMMAND 7SAVE V100=RESIDUAL LABEL = RESIDUAL CASES=380~388,390-396,398-496 RESIDUAL USING: REGRESS VARIABLE 100.RESIDUAL  TOTAL 115  CASES=CASE#:380-388,390-396,398-496  VALID 112  MISS 3*  * CASES CHANGED IN EXISTING VARIABLE COMMAND 7SCATTER V=100,39 CASES=380-388,390-396,398-496 SCATTER PLOT CASES = CASE# : 380-388,390-396,398-496 N= 112 OUT OF 115 100.RESIDUAL VS. 39.NEWREALP RESIDUAL 8.2391 + +  5.1610  + +  *  *  2 * *  Run Number 3  i  ON  o  2.0830  +  *  3 * * * * * ** * 3 * *4* * * * * * **2 *** * * * *622* * *2 ** 4 * * * 2 3 ** * 2 2 * 32* *  + -.99504  +  *  4-  -4.0731  *  *  +  -7.1511 2 7578  6.5144  10.271  17.784  14.027  NEWREALP 21.541  COMMAND 7SCATTER V=100,2 CASES=380-388,390-396,398-496 SCATTER PLOT CASES = CASE# : 380-388,390-396,398-496 N= 112 OUT OF 115 100.RESIDUAL VS. 2.QUARTER RESIDUAL 8.2391 + * +  5 . 1610  2.0830  +  * *  + * * * *  -.99504  + +  -4.0731  +  *  * * 2  *  * 2 3*2 2 6 * 2 2 * * * 22 3 7 *  3 2  *  *  2  **  4 * 2  *  **  *  *  * 5 3 *2 *  *  -7.1511  + + 37.OOO  H  1  +  42.200  +  47.400  +  +  +  52.600  +  57.800  +  r  QUARTER 63.000  COMMAND ?HISTOGRAM V=100 INT=10 OP=HIST% CASES TO SELECT =380-388,390-396,398-496 HISTOGRAM  CASES=CASE#:380-388.390-396,398-496  MIDPOINT  HIST°/„ COUNT FOR 100.RESIDUAL  -7.1511 -5.44 1 1 -3.7311 -2.0210 -.31103 1 . 3990 3 . 1090 4.8190 6.5291 8.2391  .9 3. .6 4. 5 18 . 8 35 . 7 25 .0 3 .6 6. 3 .9 .9  PASSING TOTAL  (EACH X= 1)  i +X  4 + XXXX 5 +XXXXX 21 +XXXXXXXXXXXXXXXXXXXXX 40 +XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 28 +XXXXXXXXXXXXXXXXXXXXXXXXXXXX 4 +XXXX 7 +XXXXXXX 1 +X 1 +x 3 1 15  (INTERVAL WIDTH=  1.7100)  COMMAND 7TRANS V101=V100/2.5849 LABEL=STANDRES CASES=380-388,390-396,398-496 DIVIDE TRANSFORMATION VARIABLE 101.STANDRE S  TOTAL 115  CASES=CASE#:380-388,390-396,398-496 VALID 112  MISS 3*  * CASES CHANGED IN EXISTING VARIABLE COMMAND 7SCATTER V=101,2 CASES=380-388,390-396,398-496 SCATTER PLOT CASES = CASE# : 380-388,390-396,398-496 N= 112 OUT OF 115 101.STANDRES VS. 2.QUARTER STANDRES 3.1874 + *  1.9966  +  * 2 *  * *  .80583  + *  *  * * * *  +  * * 2  *  ^  -.38494  **  *  *  *  2  *  *  4 * * 5 ** 3 2 * 2  *  2 +  -2.7665  * 3*2 2 6 * 2 2 * * 22 3 7  + 3  •1.5757  *  *  *  +  + + ___._ +  37 000  * +  +  42.200  +  47.400  +  +  +  52.600  +  57.800  +  +  QUARTER 63.000 I  COMMAND „ „„„ 7REG V = 39.7,9,25,22,92.52. 55,60,94,63.51.70 CASES = 380-388,390-396,398-496 LEAST SQUARES REGRESSION  CASES=CASE#:380-388,390-396,398-496  ANALYSIS OF VARIANCE OF 39.NEWREALP N= 112 OUT OF 115 SOURCE REGRESSION ERROR TOTAL  DF  SUM SQRS  12 784 . 78 99 654.29 1 1 11439. 1  MEAN SQR  F-STAT  65.398 6.6090  9. 8953  SIGNIF .0000  MULT R= .73847 R-SQR= .54534 SE= 2.5708 VARIABLE CONSTANT 7 .WESTEND 9.EASTVAN 25 .MURBSTAT 22 .VACRATE 92 .NEWVRATE 52 .GRRLINC 55 .REALINT 60 .LAGRENT 94 .STARTS  PARTIAL . 29935 -.46335 .21785 -.16632 .35359 .066 19 .17967 .18669 -.30850  COEFF .56185 3.2022 -3.2222 5.2002 -2.4589 .35300 .19922 .96650 . 18379 -.38300 -2  STD ERROR  T-STAT  1571 1 3.5761 1.0258 •3.1216 .61937 -5 . 2024 2.34 14 2 . 2210 1 .4652 - 1.6783 .93856 -1 3. 76 1 1 .30184 .65999 .53185 1.8172 .97205 -1 1.8908 . 1 1869 --3 2 . 2270  SIGNIF .8755 .0024 .OOOO .0286 .0965 .0003 .5108 .0722 .0616 .0017  Run Number 4  63.CC0STBC2 51.POPGRTH 70.RLAPTRTN  -.28695 .28434 -.18637  -.27091 3.2185 -.43530  .90895 1.0907 .23063  -1  -2.9804 2.9509 -1.8875  .0036 .0040 .0620  COMMAND 7SAVE  V100=RESIDUAL  RESIDUAL  *  USING:  LABEL = RESIDUAL  REGRESS  CASES=CASE#:380-388,390-396,398-496  VARIABLE  TOTAL  VALID  100.RESIDUAL  115  112  CASES  CHANGED  IN  CASES = 3 8 0 " 3 8 8 , 3 9 0 - 3 9 6 . 398-496  MISS 3*  E X I S T I N G VARIABLE  COMMAND 7SCATTER SCATTER RESIDUAL 9..2064  V=100,39 PLOT  CASES=380~388,390-396,398-496  CASES=CASE#:380-388,390-396,398-496  N= 112 OUT OF  115  100.RESIDUAL  VS.  39.NEWREALP  + I  5.9503  +  2 2.6942  *  + +  .56190  *  +  *  +  24  *  4** * 23 * 72 * * * ** * 3 2 * * 4 ** 2 * * * * * * 2  *  *  *  *  *  2  **  *  *2 2  ***  -+  +-  ,7578  *  2*  *  2  *  -7 .0741  *  *  +  -3.8180  * *  *  6.5144  10.271  14.027  17.784  NEWREALP 21.541  I  COMMAND 7SCATTER V=100,2 CASES=380-388,390-396,398-496 SCATTER PLOT CASES = CASE# : 380-388,390-396,398-496 N= 112 OUT OF 115 100.RESIDUAL VS. 2.QUARTER RESIDUAL 9.2064 + *  5.9503  + * *  * *  2 * *  2.694 2  +  * * * * *  2 2  .56190 +  5 -3.8180  *  2 2 2 * 2 * * 3 6 * 2 4 3 5 * 2 2**3 * 3 * 3 * 4 3 2 * 2  + *  + +  -7.0741 37.000  42.200  47.400  52.600  57.800  QUARTER 63.000  COMMAND ?HISTOGRAM V=100 INT=10 OP=HIST% CASES TO SELECT =380-388,390-396.398-496 HISTOGRAM  CASES=CASE#:380-388,390-396.398-496  MIDPOINT  HIST% COUNT FOR 100.RESIDUAL  -7.074 1 -5.2652 -3.4562 -1 . 6473 . 16168 1 .9706  +X 1 +X 16 +XXXXXXXXXXXXXXXX 23 +XXXXXXXXXXXXXXXXXXXXXXX 40 +XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 19 +XXXXXXXXXXXXXXXXXXX 1  14 20 35 17  (EACH X= 1)  3 . 7796 5.5885 7 . 3975 9 . 2064  8.. 0 1. 8 0 .9  9 +XXXXXXXXX 2 + XX 0 + 1 +x 3 1 15 (INTERVAL  MISSING TOTAL  COMMAND 7TRANS V101=V100/2.5708 LABEL=STANDRES CASES=380-388,390-396,398-496 DIVIDE TRANSFORMATION VARIABLE  TOTAL  101 . ST ANDRES  CASES=CASE#:380-388,390-396,398-496 VALID  115  MISS  112  3*  * CASES CHANGED IN EXISTING VARIABLE COMMAND ?HISTOGRAM V=101 INT=10 CASES=380-388,390-396,398-496 OP=HIST% HISTOGRAM  CASES=CASE#:380-388,390-396,398-496  MIDPOINT  HIST% COUNT FOR 101.STANDRES  -2 . 7517 -2.0481 - 1 . 3444 -.64076 .62893 -1 . 76655 1 . 4702 2.1739 2.8775 3.5812  .9 .9 14 ..3 20 . 5 35 .. 7 17 .0 8.O 1.8 0 .9  (EACH X= 1)  1 +X 1 +X 16 +XXXXXXXXXXXXXXXX 23 +XXXXXXXXXXXXXXXXXXXXXXX 40 +XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 19 +XXXXXXXXXXXXXXXXXXX 9 +XXXXXXXXX 2 + XX 0 + 1 +X 3 1 15 (INTERVAL WIDTH= .70365)  MISSING TOTAL  COMMAND . 7SCATTER V=101,2 CASES=380-388,390-396,398-496 SCATTER PLOT CASES=CASE#:380-388,390-396,398-496 N= 112 OUT OF 115 101.STANDRES VS. 2.QUARTER STANDRES 3.5812 + *  2.3146  +  I  1.0480  +  * * * 2 2  * * .21857  +  -1.4851  +  -2.7517  +  * 2 * * 4 2  37.000  47.400 42.200  COMMAND 7REG V=39,25  LEAST  57.800 52.600  REGRESSION  MULT R=  SUM  DF 1 113 1 14  REGRESSION ERROR TOTAL .35405  I  CASES=CASE#:380-388,390-396,398-496  OF VARIANCE OF 39 .NEWREALP  SOURCE  SQRS  181.88 1269 .0 1450.9  N = 115 OUT OF 115 MEAN SQR  F-STAT  SIGNIF  181.88 1 1 .230  16.195  .0001  T-STAT  SIGNIF  9.5817 4.0243  .0000 .0001  R-SQR = .12535 SE= 3.3512 :  VARIABLE  PARTIAL  COEFF  CONSTANT 25.MURBSTAT  35405  7.7878 3.5432  STD  ERROR  .81278 .88046  COMMAND 7REG V = 3 9 , 7 , 9 , 2 5 , 2 2 , 9 2 , 5 2 , 5 5 , 6 0 , 9 4 , 6 3 , 5 1 . 7 0 , 3 8  LEAST  SQUARES REGRESSION  ANALYSIS  QUARTER 63.000  CASES=380-388,390-396,398-496  SQUARES  ANALYSIS  *  2 2 2 2 * 3 6 2 4 3 5 2 * * 3 3 * 3 3 * 2  OF VARIANCE  „„_ CASES=380-388,390-396,398-496  CASES=CASE#:380-388,390-396,398-496  OF 39.NEWREALP  N = 112 OUT OF 115  *  CASES  CHANGED  VARIABLE  *  IN  E X I S T I N G VARIABLE  TRANSFORMATION  STRAT=NEWQTR:51  VARIABLE  TOTAL  VALID  98.RLCSTLAG  3  3  CASES  End of  CHANGED  IN  MISS 0*  E X I S T I N G VARIABLE  command f i l e  "*SOURCE*"  at  line  999  (}  COMMAND ?REG V = 3 9 , 2 5 C A S E S = 3 8 0 ~ 3 8 8 , 3 9 0 - 3 9 6 , 3 9 8 - 4 9 6 STRATA=NONE LEAST  SQUARES' REGRESSION  A N A L Y S I S OF VARIANCE  OF 39.NEWREALP  SOURCE  DF  REGRESSION ERROR TOTAL M U L t R=  CASES=CASE#:380-388,390-396,398-496  1 113 114  .35405  R-SQR=  N= 115 OUT OF  SUM SQRS 181.88 1269.O 1450.9  MEAN SQR 181.88 11.230  . 1 2 5 3 5 SE=  VARIABLE  PARTIAL  COEFF  CONSTANT 25.MURBSTAT  .35405  7.7878 3.5432  115 F-STAT  16.195  SIGNIF  .0001  3.3512 STD ERROR  T-STAT  SIGNIF  .81278 .88046  9.5817 4.0243  .0000 .0001  COMMAND 7SAVE  V200=RESIDUAL  RESIDUAL  USING:  VARIABLE  OPTION=TEST  REGRESS  LABEL=RESIDUAL C A S E S = 3 8 0 - 3 8 8 , 3 9 0 - 3 9 6 , 3 9 8 - 4 9 6  CASES=CASE#:380-388,390-396,398-496  TOTAL  VALID  MISS  115  115  O  200.RESIDUAL  DW  #VAR  1.4775  1  COMMAND ?HISTOGRAM V=200 INT=20 .  HISTOGRAM MIDPOINT -8.5732 -7.4127  OP=HIST% C A S E S = 3 8 0 - 3 8 8 , 3 9 0 - 3 9 6 , 3 9 8 - 4 9 6  CASES=CASE#:380-388,390-396,398-496 HIST% .9 O.  COUNT FOR i +X 0 +  200.RESIDUAL  (EACH  X=  1)  Run Number 9  i  -6.2522 -5.0918 -3.9313 -2.7708 - 1 .6103 -.44979 .71070 1 .8712 3.0317 4 . 1922 5.3527 6.5132 7.6737 8.8341 9.9946 11.155 12.316 13 . 4 7 6  .9 1. 7 3.5 13 . 9 28 . 7 16 . 5 7 .0 7 .8 5.2 2. 6 3 .5 4.3 .9 .9 .9 0 0 .9  115  TOTAL COMMAND SCATTER V = 2 0 0 , 2 SCATTER RESIDUAL 13.476  1 2 4 16 33 19 8 9 6 3 4 5 1 1 1 0 0 1  +x + XX + XXXX +XXXXXXXXXXXXXXXX +XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX +XXXXXXXXXXXXXXXXXXX +XXXXXXXX +XXXXXXXXX +XXXXXX + XXX + XXXX +XXXXX +x +x +x + + +x (INTERVAL  WIDTH=  1.1605)  CASES= 3 8 0 - 3 8 8 , 3 9 0 - 3 9 6 . 3 9 8 - 4 9 6  CASES=CASE# : 380-388 , 390-396 , 398-496 PLOT 2 0 0 . R E S I D U A L V S . 2.QUARTER N = 115 OUT OF 115 +  *  I 00  * 9 .0662  •  +  *  2 3 4.6564  2 *  + * *  .24651  + 3 * +2 *  * *  2 -4.1634  +  -8.5732  +  * 5 2 * * 2 2  *  * * 3 4 * * * *  2 *  4 *  4 7 7 * 3 * 3  +  +  37.000  +  +  42.200  +  47.400  +  +  +  52.600  +  57.800  +  +  QUARTER 63.000  COMMAND ?REG V = 3 9 , 7 , 9 , 2 5 , 2 2 , 9 2 , 5 2 , 5 5 , 6 0 , 9 4 , 6 3 , 5 1 , 7 0 , 5 3 LEAST  SQUARES REGRESSION  A N A L YSOURCE S I S OF VARIANCE  R=  CASES=CASE#: 380-388 , 390-396 , 398-496  STAT SQR OF F-115 SUM SQRS N= MEAN OF DF 39.NEWREALP 112 OUT  SIGNIF  1Ci. 706  .0000  .76603  T- STAT  SIGNIF  3 . 0293 10.921 1.0204 3 . 9806 .5999 1 -4 . 9123 3.4179 3 . 8875 1.7027 -3 . 2188 .97577 -1 4 . 8348 .361 14 2 . 4299 1.1186 -1 . 9278 .95951 -1 2 . 6693 . 1 7 4 8 6 -2 -4 . 5726 .15176 -4 . 3535 1.0907 3 . 8486 .26737 - 3 . 3936 1 . 1924 -3 . 1362  .0031  R-SQR=  CONSTANT 7 . WESTEND 9 . EASTVAN 2 5 ..MURBSTAT 22 . VACRATE 92 .NEWVRATE 52 . GRRLINC 55 . REAL INT 60 .LAGRENT 94 . STARTS 63 . CC0STBC2 51 .POPGRTH 70 .RLAPTRTN 53 . I N F L A T I O  .58681  SE= 2 .4632 STD  COEFF  PARTIAL  VARIABLE  64.958 6.0675  844.46 594.61 1439 . 1  13 98 111  REGRESSION ERROR TOTAL MULT  CASES=380~388,390-396,398-496  33.083 4.0617 -2.9469 13.287 -5.4805 .47177 .87753 -2.1565 .25613 -.79959 -.66067 4 . 1977 -.90736 -3.7397  .37307 -.44450 .36552 -.30921 .43885 . 23838 -.19115 .26034 - .41933 -.40256 . 36235 -.32428 -.30201  -2  ERROR  .0001 .0000 .0002 .0017 .0000 .0169 .0568 .0089 .0000 .0000 .0002 .0010 .0023  COMMAND 7SAVE  V200=RESIDUAL OPTION=TEST LABEL=RESIDUAL  RESIDUAL  *  USING:  REGRESS  CASES=CASE#:380-388,390-396,398-496  VARIABLE  TOTAL  VALID  200.RESIDUAL  115  112  CASES- CHANGED  IN  CASES=380-388,390-396,398-496  MISS 3*  DW  #VAR  2.0430  13  E X I S T I N G VARIABLE  COMMAND • ?HISTOGRAM V = 200 HISTOGRAM  INT = 20 OP=HIST°/„ CASES = 380~388 , 390-396 , 398-496  CASES=CASE#:380-388,390-396,398-496  With INFLATION  variable  MIDPOINT  HIST% 9  -6.7265  COUNT  0. 0. 1 .8 4 .5 6 .3  0  -2.0805 - 1 .3062  8 .9 10. 7 1 2 . .5 17 . 9 13 .. 4 7 .. 1 4 ,5  10 12 14  2.5654 3.3397 4.1141 4.8884 5.6627 6.4371  2 5 7  20 15  8  6 . 3 3 .6  5 7 4  0 0  0 d .9  1  .9  o i  0  7 . 2.1 14 7.9857  MISSING TOTAL  (EACH X= 1)  1 +X + O +  -5.9522 -5.1779 -4.4035 -3.6292 -2 . 8549  -.53189 .24243 1 . oi'68 1 .7911  FOR 2 0 0 . R E S I D U A L  +XX +XXXXX +XXXXXXX +XXXXXXXXXX +XXXXXXXXXXXX +XXXXXXXXXXXXXX +XXXXXXXXXXXXXXXXXXXX +XXXXXXXXXXXXXXX +XXXXXXXX +XXXXX +XXXXXXX +XXXX +  + + +x  3 1 15  (INTERVAL  WIDTH=  .77433)  COMMAND 7SCATTER SCATTER RESIDUAL 7.9857  V=200,2 PLOT N=  CASES=380-388,390-396,398-496  CASES = CASE/? : 3 8 0 - 3 8 8 , 3 9 0 - 3 9 6 , 3 9 8 - 4 9 6 112 OUT OF  115  200.RESIDUAL  VS.  2.QUARTER  +  5 .0433 *  *  * * *  2 . 1008  2  * *  *  * 2  2  -.84162  *  +3 2 -3.784 1  *  2  5  3  * * * * 3 *  2 6 2 * 6  * * 4 2 3 2  2 2  *  *  *  5  2  O CM  +  -6.7 265  + 37.000  47.400  42.200  57.800  52.600  QUARTER 63.000  COMMAND ?REG V = 39,7,9,8,25,22,92.52,55,60,94,63,51 .70 CASES = 380-388.390-396,398-496 LEAST SQUARES REGRESSION  CASES^CASE*:380-388,390-396,398-496  ANALYSIS OF VARIANCE OF 39.NEWREALP N= 112 OUT OF 115 SUM SQRS  DF  SOURCE  13 784.79 ,98 654.28 i 1 1 1439 . 1  REGRESSION ERROR TOTAL  MEAN SQR  F -STAT  SIGNIF  60.368 6.6763  9 .0421  .0000  T-STAT  SIGNIF  MULT R= .73847 R-SQR= .54534 SE=2.5839 VARIABLE CONSTANT 7 .WESTEND 9 .EASTVAN 8.KITS 25 .MURBSTAT 22 .VACRATE 92 .NEWVRATE 52 .GRRLINC 55 .REALINT 60 .LAGRENT 94 .STARTS 63 .CC0STBC2 51 .POPGRTH 70 .RLAPTRTN  COEFF  PARTIAL  .62439 .14929 3.1392 - . 16813 -3.2857 -.00348 -.68919 -1 .21712 5.1946 - . 16621 -2.4621 . 35253 .35330 .06616 .19913 .17873 .96881 .18669 . 1839 1 -.30657 -.38353 -2 -.27979 -.27169 .28410 3.2206 -.18640 -.43538  STD ERROR  15505 4.0269 2.1004 1.4946 1.9460 - 1.6884 2.0010 34441 -1 2.3591 2. 2019 1 .4756 -1 .6686 .94738 -1 3 .7293 .30339 65635 .53873 1. 7983 .97763 -1 1.8812 .12029 -2 -3 . 1884 .94174 -1 -2 .8850 1.0979 2.9333 .23181 -1 .8782  .8771 . 1382 .0945 .9726 .0300 .0984 .0003 .5131 .0752 .0629 .0019 .0048 .0042 .0633  7SAVE V200=RESIDUAL OPTION=TEST LABEL=RESIDUAL CASES=380~388,390-396,398-496 RESIDUAL USING: REGRESS VARIABLE 200.RESIDUAL  TOTAL 115  CASES=CASE#:380-388,390-396,398-496  VALID 112  MISS 3*  * CASES CHANGED IN EXISTING VARIABLE COMMAND  DW  #VAR  1.9642  13  With KITS v a r i a b l e  ?HISTOGRAM V = 200 HISTOGRAM MIDPOINT  INT=20 OP=HIST%  CASES=380-388,390-396,398-496  CASES=CASE#:380-388,390-396,398-496 HIST%  -7.0724 -6.2154 -5.3585 -4.5015 -3 . 6446 -2 . 7876 - 1 .9306 - 1 .0737 -.21672 .64024 1 . 4972 2.3542 3.2111 4.0681 4.9250 5 . 7820 6 . 6390 7 . 4959 8.3529 9.2098  9 0. 0. 3 .6 2 .7 10. 7 8 .0 14 . 3 15 . 2 17 . O 9 ..8 7.1 1 .8 . 6. 3 .9 0 .9 0 0 .9  COUNT FOR 2 0 0 . R E S I D U A L  1 +X 0 + 0 + 4 + XXXX 3 + XXX 12 +XXXXXXXXXXXX 9 +XXXXXXXXX 16 +XXXXXXXXXXXXXXXX 17 +XXXXXXXXXXXXXXXXX 19 +XXXXXXXXXXXXXXXXXXX 1 1 +XXXXXXXXXXX 8 +XXXXXXXX 2 +XX 7 +XXXXXXX 1 +X 0 + 1 +x O + 0 + 1 +x 3 1 15 (INTERVAL  MISSING TOTAL  (EACH X= 1)  WIDTH=  .85696)  COMMAND 7SCATTER SCATTER  V=200,2 PLOT  CASES=CASE#:380-388,390-396,398-496  N=  RESIDUAL 9.2098  +  5.9534  +  CN  CASES=380-388,390-396,398-496  112 OUT OF  115  200.RESIDUAL  VS.  2.QUARTER  *  2 * 2.6969  +  *  2 2 .55950  +  *  *  * *  *  *  4  *  2 3  3 2 3 2 *  2 2 2 2 6 3 6 * 3 * *  3  3  -3.8159  -7.0724  +-  37.000  47.400  42.200  57.800  52.GOO  -+  QUARTER 63.000  COMMAND 7REG V = 3 9 , 7 , 9 , 1 0 , 2 5 . 2 2 , 9 2 , 5 2 , 5 5 , 6 0 . 9 4 , 6 3 , 5 1 , 7 0 LEAST  SQUARES REGRESSION  A N A L YSOURCE S I S OF VARIANCE  R=  SIGNIF  9 .0421  .0000  T -STAT  SIGNIF  15434  .8777  7SAVE  .73847  .55547 3.2081 -3.2168 . 6 8 9 1 9 -1 5.1946 -2.4621 .35330 .19913 .96881 . 18391 - . 3 8 3 5 3 -2 -.27169 3.2206 -.43538  OPTION=TEST  REGRESS  SE= 2 . 5 8 3 9  COEFF  .29616 - . 45 155 .00348 .21712 - . 1662 1 .35253 .06616 .17873 .18669 -.30657 -.27979 .284 10 -.18640  V200=RESIDUAL USING:  .54534  R-SQR=  PARTIAL  CONSTANT .WESTEND . .EASTVAN .MARPOLE .MURBSTAT .VACRATE .NEWVRATE .GRRLINC .REALINT .LAGRENT .STARTS .CC0STBC2 .POPGRTH . RLAPTRTN  RESIDUAL  60.368 6.6763  784.79 13 98 654.28 1 1 1 1439 . 1  VARIABLE 7 9 10 25 22 92 52 55 60 94 63 51 70  CASES=CASE#:380-388,390-396,398-496  SQR OF F--STAT SUM SQRS N= MEAN OF DF 39.NEWREALP 112 OUT 115  REGRESSION ERROR TOTAL MULT  CASES=380-388,390-396,398-496  STD ERROR 3.5990 1.0451 .64207 2.0010 2.3591 1.4756 .94738 .30339 .53873 .97763 .12029 .94174 1.0979 .23181  LABEL=RESIDUAL  3 .0696 -5 . 0 1 0 0 3444 1 -1 2 . 2019 - 1. 6686 3 . 7293 65635 1 . 7983 1 .8812 -3 . 1884 -2 . 8850 2 .9333 - 1.8782  -1 -1 -2 -1  CASES=380-388,390-396,398-496  CASES=CASE#:380-388,390-396,398-496  VARIABLE  TOTAL  VALID  200.RESIDUAL  115  112  MISS 3*  DW 1.9642  .0028 .0000 .9726 .0300 .0984 .0003 .5131 .0752 .0629 .0019 .0048 .0042 .0633  #VAR 13  With MARPOLE v a r i a b l e  * CASES CHANGED  COMMAND ?HISTOGRAM  HIST%  O. 0. 3 . 2 10. 8 14 15 17 9 7 1 .8 6 . 3  COUNT FOR 200.RESIDUAL 1 0 O 4 3 12 9 16 17 19 11 8 2 7 1  (EACH X= 1)  +X + + + XXXX + XXX +XXXXXXXXXXXX +XXXXXXXXX +XXXXXXXXXXXXXXXX +XXXXXXXXXXXXXXXXX +XXXXXXXXXXXXXXXXXXX +XXXXXXXXXXX +XXXXXXXX +XX +XXXXXXX +x  0 + O. O.  1 +x 0 +  d+  1 +x  3 1 15  MISSING TOTAL  SCATTER  CASES=380-388,390-396.398-496  CASES=CASE#:380-388,390-396,398-496  0724 2154 3585 5015 6446 7876 1 . 9306 1.0737 .21672 .64024 1 . 497 2 3542 2 111 068 1 9250 7820 6390 4959 8.3529 9.2098  COMMAND 7SCATTER  VARIABLE  V=200 INT=20 OP=HIST%  HISTOGRAM MIDPOINT  IN E X I S T I N G  •aCM  (INTERVAL  WIDTH=  .85696)  V=200,2 CASES=380-388,390-396,398-496  PLOT N=  RESIDUAL 9.2098  +  5.9534  +  CASES=CASE#:380-388,390-396,398-496 112 OUT OF 115 200.RESIDUAL VS. 2.QUARTER *  * * * *  2 * *  2.6969  +  * * *  *  * 2 2 2 2 3 6  2 2  *  2 . 3 3 6 * 2 2 * * 3 * * 3 * 3 * 4 3 2 * 2 *  -.55950  2 -3.8159  +  -7.0724  + 3 7 . O O O  +  +  +  42.200  +----+ 47.400  +  +  52.600  + 57.800  +  + QUARTER 63.000  COMMAND 7REG V = 3 9 . 7 , 9 , 2 5 , 2 2 , 9 2 , 5 2 , 5 5 . 9 3 . 9 4 , 6 3 , 5 1 , 7 0 LEAST  SQUARES REGRESSION  ANALYSIS OF VARIANCE  REGRESSION ERROR TOTAL MULT  R= . 7 3 5 0 8  VARIABLE CONSTANT 7 .,WESTEND 9 ..EASTVAN 25 ..MURBSTAT 22 .VACRATE 92 ..NEWVRATE 52 .GRRLINC 55 . R E A L I N T 93 .RLRENT2 94 . S T A R T S 63 . C C 0 S T B C 2 5 1.POPGRTH 70 . RLAPTRTN  CASES=CASE#:380-388,390-396.398-496  OF 39.NEWREALP  SOURCE  CASES=380-388,39Q-396,398-496  N= 112 OUT OF 115  DF  SUM SQRS  MEAN SQR  F-STAT  SIGNIF  12 99 1 11  777.58 661.48 1439 . 1  64.799 6.6816  9.6980  .0000  R-SQR=  PARTIAL . 22442 -.45866 .01987 - . 12310 .32202 .04 208 . 1822 1 - . 1557.0 - . 32828 - . 22526 .23632 -.15033  .54034 COEFF 10.362 2 . 4837 -3.1962 .64773 - 1.9362 .33336 .12366 .99055 -.11392 -.41391 -.21603 2.7463 -.35266  (N  SE= 2 . 5 8 4 9 STD  ERROR  7.1117 1.0839 .62236 3.2757 1.5687 .98500 . 29512 .53722 .72638 .11970 .93908 1.1349 .23309  -1 -1 -2 - 1  T-STAT 1 .4571 2 .2914 -5 . 1356 19774 - 1 . 2342 3 . 3843 .41901 1 .8438 -1 .5683 -3 . 4580 -2 . 3004 2 .4198 - 1. 5 1 2 9  SIGNIF . 1483 .0241 .0000 .8437 . 2200 .0010 .6761 .0682 . 1200 .0008 .0235 .0174 . 1335  COMMAND 7SAVE  V200=RESIDUAL OPTION = TEST  RESIDUAL U S I N G :  REGRESS  LABEL = RESIDUAL CASES = 3 8 0 - 3 8 8 , 3 9 0 - 3 9 6 , 398-496  CASES=CASE#:380-388,390-396,398-496  Run Number 3  VARIABLE 200.RESIDUAL  TOTAL  VALID  115  112  MISS 3*  DW  #VAR  2.0508  12  * CASES CHANGED IN EXISTING VARIABLE COMMAND ?HISTOGRAM V=200 INT=20 OP=HIST% CASES=380-388,390-396,398-496 HISTOGRAM  CASES= CASE#: 380-388,390-396.398-496  MIDPOINT  HIST% COUNT FOR 200.RESIDUAL  -7.1511 -6.34 1 1 . -5 . 531 1 -4.7211 -3.9111 -3.1011 -2.291 1 -1.4810 - .67103 .13898 .94899 1.7590 2 . 5690 3.3790 4.1890 4 . 9990 5.8090 6.619 1 7.4291 8.2391  9 0. 2. 7 18 . 18 . 2. 7 89 . 13 .. 4 9..8 21 . 4 13..4 1 1.6 1.8 1. 8 4.5 1.8 0 .9 0 .9  1 +X 0 + 3 +XXX 2 + XX 2 + XX 3 + XXX 10 +XXXXXXXXXX 15 +XXXXXXXXXXXXXXX 1 1+XXXXXXXXXXX 24 +XXXXXXXXXXXXXXXXXXXXXXXX 15 +XXXXXXXXXXXXXXX 13 +XXXXXXXXXXXXX 2 +XX 2 + XX 5 +XXXXX 2 + XX 0 + 1 +x 0 + 1 +x 3 115  MISSING TOTAL  (EACH I  (INTERVAL WIDTH= .81001)  COMMAND 7SCATTER V=200,2 CASES=380"388,390-396,398-496 SCATTER PLOT CASES=CASE# : 380-388,390-396,398-496 N= 112 OUT OF 115 200.RESIDUAL VS. 2.QUARTER RESIDUAL 8.2391 + * *  5.1610+  * * *  2 *  2.0830  +  *  * 2 3 * 2 2 6 * 2 2 * * 22 3 7 * * * * 4 * * 5 ** 3 2 * 2  *  .99504  * *  -4.0731  -7.1511  •i  +  37.000  +  +  47.400  42.200  +  + 52.600  COMMAND 7REG V = 3 9 , 7 , 9 , 2 5 , 2 2 , 9 2 , 5 2 , 5 5 , 9 3 , 9 4 , 9 5 , 5 1 , 7 0 LEAST  SQUARES R E G R E S S I O N .  A N A L Y S I S OF VARIANCE  MULT  R=  VARIABLE CONSTANT 7.WESTEND 9.EASTVAN 25.MURBSTAT 22.VACRATE 9 2 . NEWVRATE 52.GRRLINC 55.REALINT 9 3 . RLRENT2 94.STARTS 95.REALCOST 51.POPGRTH 70.RLAPTRTN  OF 39.NEWREALP SUM SQRS  743.57 12 99 695.50 1 1 1 1439 . 1  REGRESSION ERROR TOTAL .71882  R-SQR=  PARTIAL .19305 - .44551 .03837 -.04253 .23020 .09476 .07257 -.13018 -.24088 .04391 . 18965 - . 18555  + + 57.800  + QUARTER 63.000  ,„„ CASES=380-388,390-396,398-496  CASES=CASE#:380-388,390-396,398-496  DF  SOURCE  +  N= 112 OUT OF 115 SIGNIF  MEAN SQR  F  61.964  8.8202  .0000  T-STAT  SIGNIF  7.0252  . 5 1 6 7 0 SE= 2 . 6 5 0 5 COEFF 9 . 2826 2 . 1747 -3 . 1841 1 .7167 - .66655 .20433 . 30075 . 422 19 - . 12725 - . 24046 . 17583 2 . 1902 - .46718  STD ERROR 12.666 1 . 1 109 . 64309 4 . 4936 1 . 5736 .86815 -1 .31753 . 58313 . 9 7 4 0 0 -1 . 9 7 3 7 7 -3 .40203 -1 1 . 1396 . 24866  . 73290 1 .9577 -4 .9513 .38204 - . 42358 2.3537 .947 14 . 72399 -1 .3064 -2.4694 .43735 1 .9218 -1 .8788  .4654 .0531 .0000 .7033 .6728 .0206 . 3459 . 4708 . 1944 .0152 .6628 .0575 .0632  Run Number 6  i  COMMAND  7 S A V E  V 2 0 0 = R E S I D U A L  R E S I D U A L  U S I N G :  T O T A L  200.RESIDUAL C A S E S  V A L I D  115  C H A N G E D  I N  L A B E L = R E S I D U A L  C A S E S = 3 8 0 - 3 8 8 , 3 9 0 - 3 9 6 , 3 9 8 - 4 9 6  CASES=CASE#:380-388,390-396,398-496  R E G R E S S  V A R I A B L E  *  O P T I O N = T E S T  M I S S  112  D W  3*  E X I S T I N G  # V A R  1.9386  12  V A R I A B L E  C O M M A N D ? H I S T O G R A M  V = 2 0 0  H I S T O G R A M  M I D P O I N T  I N T = 2 0  H I S T %  C O U N T  1  9 1  0  0 .  F O R  2 0 0 . R E S I D U A L  9  1  + x  - 5  9  1  + x  .. 7  3  + XXX  . 9  1  + x  . 3 9 8 8 2  - 4 . 5 8 9 2 .  7 7 9 6  - 2 . 9 7 0 0  5 .. 4  6  - 2 . 1 6 0 3  5 ..  6  -  4  1 3  . 4  17  . O  1 9  . . 2 6 8 5 3  1 9  . 6  2 2  1 .  0 7 8 2  1 4  .  3  1 .  8 8 7 8  6  .  3  3  . 6  1  . 3 5 0 7  - . 5 4 1 0 9  2 . 6 9 7 4 3  .  5 0 7 0 2  4 . 3 1 6 6  1 5  1 6 7 4 1  + x  .  3  + XXX  1  + x  5 . 1 2 6 3  . 9  5 . 9 3 5 9  1 . 8  2  6 . 7 4 5 5  1 .  8  2  7 . 5 5 5 1  .  9  1  X =  1)  +XXXXXX +XXXXXX +XXXXXXXXXXXXXXX +XXXXXXXXXXXXXXXXXXX +XXXXXXXXXXXXXXXXXXXXXX +XXXXXXXXXXXXXXXX +XXXXXXX +XXXX  . 9 7  ( E A C H  +X +  - 6 . 2 0 8 4  - 3  C A S E S = 3 8 0 - 3 8 8 , 3 9 0 - 3 9 6 , 3 9 8 - 4 9 6  C A S E S = C A S E # : 3 8 0 - 3 8 8 , 3 9 0 - 3 9 6 , 3 9 8 - 4 9 6  - 7 . 8 2 7 7 - 7 . 0 1 8  O P = H I S T %  + XX +XX + x  3  M I S S I N G  ( I N T E R V A L  1 1 5  T O T A L  W I D T H =  . 8 0 9 6 2 )  C O M M A N D 7 S C A T T E R  S C A T T E R  V = 2 0 0 , 2  P L O T N=  C A S E S = 3 8 0 - 3 8 8 , 3 9 0 - 3 9 6 , 3 9 8 - 4 9 6  C A S E S ^ C A S E * : 3 8 0 - 3 8 8 , 3 9 0 - 3 9 6 , 3 9 8 - 4 9 6 1 1 2  O U T  O F  1 1 5  R E S I D U A L 7 . 5 5 5 1  4.4786  +  *  2 0 0 . R E S I D U A L  V S .  2 . Q U A R T E R  1 .4020  *  +  * *  +  +  * * *  • 1 .674G  4 2 5 8 + 3 * 3 3 2 * * * 2 5 2 * * 3  • 2 * *  * *  3  2  *  2 3  *  +2  *  *  2  *  -4 . 751 1  -7.8277  +  37.000  47.400  42.200  52.600  QUARTER 63.000  57.800  COMMAND CORRELATE  V = 3 9 , 7 , 8 . 9 , 1 0 , 2 5 , 2 2 , 9 2 , 4 2 , 5 2 , 5 5 , 9 3 , 9 7 , 6 0 , 9 4 , 9 5 , 6 3 , 5 1 , 7 0 , 12,  CORRELATION MATRIX  CASES = C A S E # : 3 8 0 - 3 8 8 , 3 9 0 - 3 9 6 , 398-496  N=  .0500=  112  DF = 110  R@  .1857  R<a .0100=  .2425  VARIABLE 39.NEWREALP  1.OOOO  7.WESTEND  . 3899  1.0000  8 . KITS  .0931  - . 1910  1.0000  - . 3675  -.8035  1.0000  -.0399  -.0871  -.1676  -.4362  9.EASTVAN  - . 3227  10.MARPOLE  .0692  25.MURBSTAT  . 3592  .0335  22 . VACRATE  - . 1656  .0725  92.NEWVRATE  . 3794  42.REALINC  1.0000  . 3730  .0570  1.0000  . 3697  - . 3638  -.0799  - .4390  .0178  -.3200  . 2582  . 1 126  . 7542  . 4204  . 1252  -.3930  . 2936  .0130  .9143  GRRLINC  - . 2679  - . 1790  . 2359  - . 1 188  -.0065  -.6092  55.REALINT  - .1810  .0147  .2124  - . 1686  - . 1395  - . 2924  52  CASES = 3 8 0 - 3 8 ' 8 , 3 9 0 - 3 9 6 , 3 9 8 - 4 9 6  ON CM  X  +  5 . 5260  6 3.8500  3  +  3  37 ..000  +  47.400  42.200  ?REG ^101.102,25,22,64,93,103,95  57.800  52.600  +  +  QUARTER 63.000  CASES=380,386,390,393.394,396,398,399.405,408,412,419,427.433.439.450,459,494,495,496  M  LEAST SQUARES REGRESSION CASES = CASE#: 3 8 0 , 3 8 6 , 3 9 0 , 3 9 3 , 3 9 4 , 3 9 6 , 3 9 8 , 3 9 9 , 4 0 5 , 4 0 8 , 4 12,4 1 9 , 4 2 7 , 4 3 3 , 4 3 9 , 4 5 0 , 4 5 9 , 4 9 4 , 4 9 5 , 4 9 6 A N A L Y S I S OF VARIANCE OF  N= 20 OUT OF  20  SOURCE  DF  SUM SQRS  MEAN SQR  F-STAT  SIGNIF  REGRESSION ERROR TOTAL  7 12 19  123.60 153.28 276.88  17.658  1.3824  . 2966  .44642  SE= 3 . 5 7 3 9  MULT R=  102 25 22 64 93 103 95  101.RLAVGSP  .66814  R-SQR=  VARIABLE  PARTIAL  COEFF  CONSTANT .LOCINDEX .MURBSTAT .VACRATE .CAPRATE .RLRENT2 .NEWRMLAG .REALCOST  .05842 -.21313 -.00503 -.04032 -.33074 .06222 .08560  23.479 .16037 -5.2688 -.42969 -.41806 -.21299 .13219 .25976  7SAVE V200=RESIDUAL A  OPTION=TEST  12.773  o  STD ERROR  -1 -1 -1  22.602 .79108 6.9723 2.4660 .29907 .17544 .61214 .87283  T-STAT  SIGNIF  1.0388  .3194  .20272 -.75567 - . 1 7 4 2 4 -1 -.13979 -1.2140 .21595 -1 .29760  .8428 . 4644 .9864 .8911 . 2481 .8327 .7711  LABEL = RESIDUAL CASES = 3 8 0 , 3 8 6 . 3 9 0 , 3 9 3 , 3 9 4 , 3 9 6 , 3 9 8 , 3 9 9 , 4 0 5 , 4 0 8 . 4 1 2 , 4 1 9 , 4 2 7 . 4 3 3 , 4 3 9 . 4 5 0 , 4 5 9 , 4 9 4 , 4 9 5 . 496  RESIDUAL U S I N G : REGRESS CASES = C A S E # : 3 8 0 , 3 8 6 , 3 9 0 , 3 9 3 , 3 9 4 , 3 9 6 , 3 9 8 . 399 , 405,408,412,419,427,433,439,450,459,494,495,496  *  VARIABLE  TOTAL  VALID  200.RESIDUAL  20  20  CASES CHANGED  IN  Run Number 10  MISS O*  E X I S T I N G VARIABLE  DW  .  2.7524  #VAR 7  'HISTOGRAM V=200  INT=20 OP=HIST%  CASES=380,386,390,393,394,396,398,399,405,408,412,419,427,433,439,450,459,494,495,496  HISTOGRAM CASES=CASE# : 3 8 0 , 3 8 6 , 3 9 0 , 3 9 3 , 3 9 4 , 3 9 6 , 3 9 8 , 3 9 9 , 4 0 5 , 4 0 8 , 4 1 2 , 419,427,433,439,450,459,494,495,496 MIDPOINT  HIST%  -4.2056 -3.5245 -2 . 8433 - 2 . 1622 - 1 . 48 1 1 - . 79993 - . 1 1880 .56234 1 .2435 1 . 9246 2.6057 3 . 2869 3 . 9680 4.6491 5 . 3303 6.0114 6 . 6925 7.3737 8 .0548 8 . 7359  5 .0 0. 5 .0 15 . 0 0. 35 ..0 0. 25 ..0 0, 0. 5 .0 0 0 0 5 .0 0 0 0 O 5 .0  COUNT FOR 1 0 1 3 0 7 0 5 0 0 1 0 0 0 1 0 0 0 0 i 20  TOTAL  200.RESIDUAL  (EACH X=  1)  +X + +x  +XXX + +XXXXXXX + +XXXXX + + +x  + + + +x  + + + + +x  (INTERV  .681 13)  l  COMMAND 7SCATTER  V=200,2  CASES=380,386,390,393,394,396,398.399,405,408,412,419,427,433,439,450,459,494,495,496  SCATTER PLOT CASES = C A S E # : 3 8 0 , 3 8 6 , 3 9 0 , 3 9 3 , 3 9 4 , 3 9 6 , 3 9 8 , 3 9 9 , 4 0 5 , 4 0 8 , 4 1 2 , 419,427,433,439,450,459,494,495.496 N= 20 OUT OF 20 2 0 0 . R E S I D U A L V S . 2.QUARTER RESIDUAL 8.7359 + * +  6 . 1476  3.5593  97 102  I  +  +*  * * * •1.6173  +  -4.2056  +  H  37.000  47.400  42.200  ?REG V=101, 1 0 2 , 2 5 , 9 2 , 6 4 . 9 3 , 1 0 3 . 9 5 M  57.80O  52.600  QUARTER 63.000  CASES = 3 8 0 , 3 8 6 . 3 9 0 , 3 9 3 , 3 9 4 , 3 9 6 , 3 9 8 , 3 9 9 . 405 , 4 0 8 , 4 1 2 . 4 1 9 . 4 2 7 , 4 3 3 , 4 3 9 , 4 5 0 , 4 5 9 , 4 9 4 , 4 9 5 , 496  LEAST SQUARES REGRESSION CASES=CASE#:380,386,390,393,394,396.398,399 405,408,412,419,427,433 , 439,450,459,494,495,496 A N A L Y S I S OF VARIANCE  101.RLAVGSP  N= 20 OUT OF  20  SOURCE  DF  SUM SQRS  MEAN SQR  F-STAT  SIGNIF  REGRESSION ERROR TOTAL  7 12 19  124 . 13 152.75 276.88  17 .733  1.3932  . 2927  .44833  SE= 3 . 5 6 7 8 T-STAT  SIGNIF  MULT  R=  .66957  R-SQR=  CONSTANT . LOCINDEX . MURBSTAT .NEWVRATE .CAPRATE .RLRENT2 .NEWRMLAG .REALCOST  22.467 .18308 -5.4127 .34752 -.62205 -.20337 .14289 .27510  .08121 - . 221 12 .05898 -.06024 -.31196 .08587 .09215  7 S A V E V 2 0 0 = RESIDUAL A  OPTION = TEST  STD  -1 -1 -1  ERROR  22.865 .64868 6.8914 .16978 .29756 .17880 .47860 .85813  .98259  . 3452  .28224 -.78544 .20469 -.20905 -1.1374 .29855 -1 .32058  . 7826 .4474 .8412 .8379 . 2776 .7704 .7540  TOTAL  VALID  200.RESIDUAL  20  20  * CASES CHANGED  IN  With NEWVRATE v a r i a b l e  LABEL = RESIDUAL CASES = 3 8 0 , 3 8 6 , 3 9 0 , 3 9 3 , 3 9 4 , 3 9 6 , 3 9 8 . 399 , 4 0 5 . 4 0 8 , 4 1 2 , 4 1 9 . 4 2 7 . 4 3 3 , 4 3 9 . 4 5 0 , 4 5 9 . 4 9 4 , 4 9 5 . 4 9 6  RESIDUAL U S I N G : REGRESS CASES=CASE#:380,386,390,393,394,396,398,399, 405 . 4 0 8 , 4 1 2 , 4 1 9 , 4 2 7 , 4 3 3 , 4 3 9 , 4 5 0 , 4 5 9 , 4 9 4 , 4 9 5 , 4 9 6 VARIABLE  CM  12.729  COEFF  PARTIAL  VARIABLE 102 25 92 64 93 103 95  OF  ,  MISS 0*  E X I S T I N G VARIABLE  DW 2.7901  #VAR 7  ?H?STOGRAM  HISTOGRAM  V=200 INT=20 OP=HIST% C A S E S = 3 8 0 , 3 8 6 , 3 9 0 , 3 9 3 , 3 9 4 , 3 9 6 , 3 9 8 , 3 9 9 , 4 0 5 . 4 0 8 . 4 1 2 , 4 1 9 , 4 2 7 , 4 3 3 , 4 3 9 , 4 5 0 . 4 5 9 , 4 9 4 , 4 9 5 , 4 9 6  CASES=CASE#:380.386,390,393,394,396,398,399,405,408.412,  419.427,433,439,450,459,494,495,496 MIDPOINT -4.3017 • -3.6085 -2.9152 -2.2220 -1.5287 -.83546 - . 14221 .55104 1 .2443 1 . 9375 2.6308 3 . 3240 4 .0173 4.7105 5.4038 6 .0970 6 . 7903 7.4835 8.1768 8 . 8700  HIST% •5 .0 0. 5 .0 10. 0 5 .0 35 .0 0. 25 . .0 0. 0. 5 . .0 0. 0 5 .0 0. 0 0 0 0 5 .0  TOTAL  COMMAND 7SCATTER V = 200,2  COUNT FOR  200  1 0 1 2 1 7 0 5 0 0 1 0 0 1 0 0 0 O 0 1  +X + +X + XX +x +XXXXXXX + +XXXXX + + +x + + +x + + + + + +x  20  (INTERV  .69325)  CASES=380,386,390,393.394,396,398.399,405,408,412,419,427,433,439,450,459,494,495,496  SCATTER PLOT CASES = CASE# : 3 8 0 , 3 8 6 , 3 9 0 , 3 9 3 , 3 9 4 , 3 9 6 , 3 9 8 , 3 9 9 , 4 0 5 , 4 0 8 , 412 , 419.427,433,439,450,459,494,495,496 N= 20 OUT OF 20 200.RESIDUAL VS. 2.QUARTER RESIDUAL 8.8700 + *  6 . 2357  3 .6013  ro  .96699  +*  •1.6674  +  -4.3017  y  37.000  COMMAND RELATE 7C0R  42.200  47.400  57.800  52.600  V=101,102,25,22,92,64,93,103,95  QUARTER 63.000  CASES=380,386,390,393,394,396,398,399,405,408  CORRELATION MATRIX CASES = C A S E # : 3 8 0 , 3 8 6 , 3 9 0 , 3 9 3 , 3 9 4 , 3 9 6 . 3 9 8 408,412,419,427,433,439,450,459,494,495,496 = 2Ci  DF = 18  R@  .0500=  .4438  R@  .0100=  , 412,419,427,433.439,450.459,494.495.496  , 399 , 405 ,  .5614  VARIABLE 101 . RLAVGSP  CO  1.OOOO  102 . LOCINDEX  . 1777  1.OOOO  25 . MURBSTAT  . 3230  -.3980  1 .0000  .1987  . 2010  1.OOOO  . 3067  - .3673  . 8094  - . 1610  1.OOOO  64 . CAPRATE  -.3466  -.1202  .1811  . 5806  -.0774  1 .0000  93 . RLRENT2  -.5363  .1566  .9047  . 3221  -.7573  . 3989  103 . NEWRMLAG  -.0149  - .3542  . 1849  .5312  95 . REALCOST  .3561  .3531  . 1082  .0703  101 .  25. 102. LOCINDEX MURBSTAT  22 . VACRATE 92 . NEWVRATE  -.1968  RLAVGSP 93 .RLRENT2  1.OOOO  103 .NEWRMLAG  -.057 1  1.OOOO  95 .REALCOST  -.1337  .1925  93 .  103 .  1 . OOOO 95 .  22. VACRATE  . 1092 -.0613 92 .  .4183 .0563 64 .  NEWVRATE CAPRATE  Correlation Matrix S m a l l Sample V a r i a b l e s  RLRENT2 NEWRMLAG REALCOST TREITV^IOI^S  CASES = 380,386,390,393.394,396,398,399,405,408,412,419,427,433, 439.450.459.494,495.496  LEAST SQUARES REGRESSION CASES = CASE#:380,386,390,393,394,396,398 , 399 , 405,408,412,419,427,433,439,450,459,494,495,496 ANALYSIS OF VARIANCE OF 101.RLAVGSP N= 20 OUT OF 20 SOURCE  DF SUM SQRS  REGRESSION ERROR TOTAL  1 28.895 18 247.99 19 276 . 88  MULT R= .32304 VARIABLE CONSTANT 25.MURBSTAT  MEAN SQR 28.895 13.777  F-STAT 2.0973  SIGNIF . 1648' Run Number 11  R-SQR= .10436 SE= 3.7117 COEFF 9. 3184 2.5200  PARTIAL .32304  STD ERROR 1 .4029 1.7401  T-STAT  SIGNIF .0000 6 .6422 1 .4482 . 1648  7SAVEV200=RESIDUAL OPTION=TEST LABEL=RESIDUAL CASES=380,386.390,393,394,396,398,399,405,408.412.419,427,433,439,450,459,494,495.496 A  RESIDUAL USING: REGRESS CASES = CASE# : 380,386,390,393.394.396,398,399 , 405,408,412,419,427,433,439,450,459,494,495,496 VARIABLE 200.RESIDUAL  TOTAL VALID 20  MISS  20  0*  DW  0VAR  1.6831  1  * CASES CHANGED IN EXISTING VARIABLE 7HIST0GRAM V=200 INT=20 OP=HIST% CASES=380,386,390,393.394,396,398,399.405,408,412,419,427.433,439,450,459,494,495,496 HI STOGRAM CASES = CASE/5': 380, 386 , 390, 393 , 394 , 396 , 398 , 399 , 405 , 408 , 4 12 , 419.427,433,439,450,459,494,495,496 MIDPOINT HIST% COUNT FOR 200 2 +XX -3 . 665 1 10.0 -2.8435 0. 0 + -2.0219 30.0 6 +XXXXXX - 1 .2003 20.0 4 +XXXX -.37866 10.0 2 +XX .44295 O. 0 + 1 .2646 10.0 2 +XX 2.0862 5.0 1 +X  2.9078 3.7294 4.5510 5.3726 6.1943 7.0159 7.8375 8 . 659 1 9.4807 10.302 1 1 . 124 11.946  0. 0. 10.0 0. 0. 0. 0. 0. 0. O. 0. 5.0  TOTAL  0 0 2 0 0 0 0 0 0 0 0 1  + + + XX + + + + + + + + +x  20  (INTERVAL (I  WIDTH=  .82161)  COMMAND 7SCATTER  V = 200,2  CASES = 3 8 0 , 3 8 6 , 3 9 0 , 3 9 3 , 3 9 4 , 3 9 6 , 3 9 8 , 3 9 9 , 4 0 5 , 4 0 8 , 4 1 2 , 4 1 9 , 4 2 7 , 4 3 3 , 4 3 9 , 4 5 0 , 4 5 9 , 4 9 4 , 4 9 5 , 4 9 6  SCATTER PLOT CASES=CASE#:380,386,390,393,394,396,398,399,405,408,412, 4 19,427,433,4 39,450,459,494,495,496 N= 20 OUT OF 20 2 0 0 . R E S I D U A L V S . 2.QUARTER RESIDUAL 11.946 + *  8.8234  +  5.7013  +  2.5791  +  -.54299  +  -3.6651  +  VD  H  37.000  47.400 42.200  52.600  57.800  QUARTER 63.000  A N A L Y S I S OF  VARIANCE  101.RLAVGSP  SOURCE  DF  REGRESSION ERROR TOTAL  125.89 7 1 1 39.157 18 165.04  MULT  R=  .87335  R-SQR=  N = 19 OUT OF  SUM SORS  .76275  SE =  16.14 1 . 57781 .38000 . 1 1942 1 .5448 . 1 1670 .50876 -.07805 - . 4 0 9 9 6 -1 -.28716 - . 9 4 4 11 - 1 .22198 .24446 -.01928 - . 2 9 6 5 1 -2  CONSTANT .LOCINDEX .MURBSTAT .VACRATE .CAPRATE .RLRENT2 .NEWRMLAG .REALCOST  ?SAVE V200=RESIDUAL A  0PTI0N=TEST  MEAN  SOR  17.984 3.5597  COEFF  PARTIAL  VARIABLE 102 25 22 64 ,.93 103 95  OF  19 F-STAT  SIGNIF  5.0520  .0088  T-STAT  SIGNIF  1.8867 STD  ERROR  12.002 .42408 3.8725 1.3055 .15788 .94955 .32376 .46360  . 2058  1 .3448  1.3625 . 39892 .38971 -.25966 -1 - . 9 9 4 2 7 .75505 -1 - . 6 3 9 5 8  -1  . 2003 .6976 . 7042 . 7999 .3415 . 4661 .9502  LABEL=RESIDUAL C A S E S = 3 8 0 . 3 8 6 . 3 9 0 , 3 9 3 , 3 9 4 . 3 9 8 , 3 9 9 , 4 0 5 , 4 0 8 , 4 1 2 , 4 1 9 . 4 2 7 , 4 3 3 , 4 3 9 , 4 5 0 , 4 5 9 , 4 9 4 . 4 9 5 , 4 9 6  RESIDUAL U S I N G : REGRESS CASES = C A S E # : 3 8 0 , 3 8 6 . 3 9 0 , 3 9 3 , 3 9 4 , 3 9 8 , 3 9 9 408.412,419,427,433.439,450,459,494,495,496 VARIABLE  TOTAL  VALID  MISS  200.RESIDUAL  19  19  0  7HIST0GRAM V = 200  DW  , 405 ,  #VAR  2.2702  7  INT=20 OP = HIST% CASES = 3 8 0 . 3 8 6 , 3 9 0 . 3 9 3 , 3 9 4 , 3 9 8 , 3 9 9 . 4 0 5 , 4 0 8 , 4 1 2 , 4 1 9 , 4 2 7 , 4 3 3 , 4 3 9 . 4 5 0 , 4 5 . 9 , 4 9 4 , 4 9 5 , 4 9 6  HISTOGRAM CASES = C A S E * : 3 8 0 , 3 8 6 , 3 9 0 , 3 9 3 , 3 9 4 , 3 9 8 , 3 9 9 , 4 0 5 , 4 0 8 , 4 1 2 , 4 19 , 4 27,433,439,450,459,494,495,496 MIDPOINT  HIST%  -2.0720 - 1 .7236 - 1 .3753 - i.. 0 2 6 9 -.67858 -.33022 . .18 134 - 1 .36649 .71484 1.0632 1 . 4.1 16 ' 1.7599 2.1083 2.4566  5 .3 0. 5 .3 15 . 8 10. 5 21 . 1 '15 . 8 IO. 5 5 .3 0. 0. O. O. 0.  Run Number 10 (excluding o u t l i e r )  COUNT 1 0 1 3 2 4 3 2 1 0 0 0 0 0  FOR  200.RESIDUAL  +x + +x + XXX + XX + XXXX + XXX + XX +x + + + + +  (EACH X=  1)  STR  2 .8. 0501533 0 5.. 30 1+ X 3 3 . 501 70 . 0 3 . 8 5 0 0 0 . 0 4 . 1 9 8 4 0 . 4.5467 5 . 30 1 TOTAL 1 9 (INTERVAL WIDTH= .34836) 19 ( C O M M A N D 7SCATTER V=200,2 CASES=380,386,390.393.394.398,399.405,408.412,419.427,433,439,450,459,494,495,496 SCATTER PLOT CASES=CASE#:380,386,390,393,394,398,399,405,408,412,419, 427,4.3 3,439, 4 50,4 5 9,4O 9U 4T ,495 , 49619 200.RESIDUAL VS. 2.QUARTER , N = 1 9 O F RE 4S .I 5D 4U 6A 7L • + * + + + + +x  3.2230 00 CO  1 .8993 + . 57550 *  .74825 + -2.0720 + 37.OOO  •  *  *  *  * *  T E R 42.200 47.400 52.600 57.800 Q 6U 3A .R 0 0 0 ?R'EGT'01 . 102 , 25 . 22 , 64 , 93 , 103 , 95 C ASES = 380, 386 , 390, 393 . 394 . 398 ,399 , 405 , 408 , 4 12 L0 E8 AS R E G R E S S I5 O9 N,49C =96CASE#:380,386,390,393,394,398,399,405 , 4 ,T 412,S 4Q 1U 9A ,R 4E 2S 7,43 9 , 4 5 0 , 4 4A ,S 4E 9S 5,4 +  1  +  +  +  +  +  +  +  +  +  +  APPENDIX "E" D A T A FILE LISTINGS  139  NITS (DH4A/ANNA/10267 ) #SIG GAU #Enter user password. ? # * * L a s t s i g n o n was: 14:14:37  H User "GAU." NO MESSAGES -P -• -T --w -- u -- s -< -  CTL CTL CTL CTL CTL  CTL CTL CTL CTL  -D •-E -C -A -  signed  *PRINT* FORT.TABS *STATUS %DUPLEX SIG $ %WF %WL DEL LINE DEL END EOF INSERT  on a t  14:19:06  C T L - F - *FTN CTL-R - R MIDAS C T L - 0 - PORTRAT CTL-L - *LISTER CTL-X - CANCEL - %WB=36 a > - %WR=60 PROUTE = ANGS CTL-B - PAUSE CTL-M - RETURN CTL-K - HOME  on Tue CTL-0 CTL-G CTL-Y CTL-Z CTL-V  CTL-H CTL-I CTL-N CTL-J  Apr -  20/82  %P AGE %WF=36 %UC %LC S $ = 0N  - LEFT - RIGHT - UP - DOWN  #SET PROUTE=CNTR tt $.04, $.07T ^CONTROL * P R I N T * LANDSCAPE ONESIDED C0PIES=3 #*PRINT* a s s i g n e d j o b number 523752 #*PRINT* RM523752 h e l d H $.09, $.15T /fLIST RESALES LA W53'B98,DL264A,P5738 1 0 0 4 3 2 4 6 4 6 2 1 2 0 5 8 2 5 EAST 7TH 1 6466 1004 9 7 5 9 1 3 . 7 5 0 0 3 277 36 24 1 17 670 2 35489 600000 804550 147548 1004 1111900 1 3 1004 0 0 0 0 0 0 01 03 3. L 5-12 B49,DL264A.P430 1 0 1 1 3 1 7 6 0 7 2 3 4 5 0 1 5 5 6 CHARLES 4 719 25254 101 1 .1.0. 7500 2 277 47 33792 5 0000005850001155100 1011 1151620 0 211597 6  > >  6. 7 8 9 9. 10 1 1 12 12 . 13 14 15 15 . 16 17 18 18 . 19 20 21 21 . 22 23 24  1011 0 0 0 0 0 0 01 03 1 0 1 6 3 2 1 2 3 4 6 3 3 0 1 1 9 2 5 WOODLAND 0 . 0 0 0 0 4 277 30 21 152 1016 0 123662 1016 1 8 9 2 2 0 1016 00 00 00 01 03 1 0 3 4 3 1 7 6 3 1 2 3 4 4 3 1 5 4 5 E 2ND 1034 1 1 . 7 5 0 0 2 276 48 26698 1034 1134940 0 188549 1034 00 00 00 01 04 1037263 144832998777 HUDSON ST 1037 0 . 0 0 0 0 2 278 75 52022 1037 1251880 O 320475 1037 0 0 0 0 0 0 01 02 1042337770235701574-78KINGSWAY 1042 1 0 . 6 6 6 6 4 278 10 6536 44923 1042 1 2 4 0 0 0 O 1042 0 0 0 0 0 0 01 02 1046606606116651331 NELSON 1046 6 1 2 1 5 . 0 0 0 0 4 178 17 21 162 19058 1046 1 7 5 1 2 0 O 1046 00 0 0 00 01 02 1048323644 19454334 E 5TH 1048 97 1 9 1 2 . 5 0 0 0 2 276 32 23752 1048 1102282 18948 O  L17-20.B74,DL264A,P442 705 16104 32844360000  161 1785821000000 14 2 1 1 10 0 0 1 44101 369148 0  161876000 963287 2 4 2 1 1 1 1 0 0 1 34184 328248  0  1511380000000000 2 4 2 1 1 1 0 0 0 1 29193 901246  L ' A ' ,B65,DL264A,PL16291 1613900001257988 556 20130 29 19 0 0 0 0 1 3 2 1 1 1 0 0 0 52775 757000 921895 1 24 95 308900 L ' D' , B 9 , D L 3 1 8 , P 1 7 4 9 & 16599 1630000000000000 694 35000 3 68 4 0 0 1 2 4 2. 1 1 1 0 O 0 762631500000 1780000 1 40 24 4 2 2 3 1 0 0 1 8 L' 1&2' , B 7 , 9 & 1 1 , D L 3 5 2 , P L 2 1 7 0 15 327000 322052 654 4925 0 8 0 0 0 0 1 3 3 1 0 1 0 0 0 8883 100000 1 44 103 L' 1 7 ' 715  ,B34,DL185,P92 8646 260000  0  15 666667500000 13 2 1 1 10 0 0 0 46  L'G',B28,DL200A,P15786 13 985000 130900 742 18117 3 27 2 0 0 0 2 4 2 1 1 1 0 0 0 320OO0 673000 0 37 96 21000  > > > > > > > > > . > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >  24 . 5 25 26 27 27 . 5 28 29 30 30. 5 31 32 33 33 . 5 34 35 36 36 . 5 37 38 39 39 . 5 40 41 42 42 . 5 43 44 45 45 . 5 46 47 48 48 .. 5 49 50 51 51 . 5 52 53 54 54 . 5 55 56 57 57 . 5 58 59 60 60 . 5 61 62 63 63 . 5 64 65 66 66 . 5 67 68 69  1048 0 0 0 0 0 0 01 04 1050207648095951905 W 8TH 1050 1 6 0 5 1 2 . 2 3 5 6 1 278 5 6092 1050 1 3 0 3 0 0 0 27228 1050 01 00 00 00 00 1060212683146972885 SPRUCE 1060 7 9 7 4 1 0 . 7 5 0 0 4 277 40 26878 1060 1 0 183755 1060 0 0 01 00 00 01 106512969003853 3663 W 16TH 1065 0 1 0 . 6 2 5 0 0 3 0 3 7 8 30 18060 1065 0 1 8 6 9 4 0 4482014212000 0 1065 0 0 00 00 01 02 0 4 1 5 3 1 7 6 3 1 2 3 2 0 5 1421 E 2ND 04 15 1 0 . 2 0 6 6 0 1 0 6 7 7 33 18022 041501100848 00 100764 04 15 00 00 01 00 02 0 4 2 1 3 2 1 6 4 1 2 3 4 1 6 1602-06 E 6TH 0421 1 1 . 0 0 0 0 0 1 0 1 7 9 12 7356 042101 36000 7269 28731 0 21368 04 21 00 00 01 00 00 0 1 4 5 2 1 1 6 5 4 1 2 0 9 5 1705 W 10TH 0145 0.0000020125 5 4739 014501 15600 3000 12600 1 O 0145 00 00 01 00 54 0 1 2 3 2 1 2 14665492 1195 W 11TH 0123 1 0 . 4 5 6 1 0 1 0 1 7 8 18 14878 012301 75372 65892 0 1 2 3 0 0 OO 01 00 01 0 1 0 3 2 1 2 1 3 4 6 5 0 9 3 2555 HEMLOCK 0103 1 0 . 5 0 0 0 0 1 0 2 7 9 24 15267 010301 0 68187 0 1 0 3 00 00 01 00 00 0 4 1 2 3 1 7 2 3 4 6 0 6 0 5 1209 WOODLAND 0412 012.7500010178 5 4780 0412 1 22800 3000 19800 0 0 0412 00 0 0 01 00 01 0 1 2 2 2 1 1 6 8 3 1 2 4 6 7 1535 W 13TH 0122 0 9 . 7 6 1 7 0 1 0 4 4 1 17 10891 0122 O 37878 1 17172 0122 00 00 01 00 38 0 1 3 6 2 1 5 6 8 6 1 2 4 7 0 1536 W 14TH 0136 0 1 0 . 2 5 0 0 0 2 0 1 2 8 21 13632 0136 0 51084 14727 36357 1 0136 0 0 00 01 00 51 0 3 0 5 2 6 0 1 4 4 8 3 0 0 4 8 6 0 6 - 2 0 HUDSON 0305 0 0.0000010144 6 24167 0305 0 8820 1 0 0305 0 0 00 01 00 35 0 4 4 2 3 2 7 6 7 0 2 3 4 8 3 1657 E 12TH 0442 0 1 0 . 9 7 6 8 0 1 0 4 1 2 11 6327 0442 O 29000 1 1626 0442 00 00 01 00 67 0 4 3 0 3 2 4 6 5 0 2 1 4 3 8 938 E BROADWAY 0430 0 0 1 3 . 4 7 6 2 0 1 0 2 5 5 10 8200 0 4 3 0 O 26328 5809 20518 1 0 4 3 0 0 0 0 0 01 00 24 0 4 1 4 3 1 7 6 3 0 2 3 0 2 6 1344 E 1ST 0414 0 1 0 . 6 7 4 9 0 1 0 2 7 0 30 19833 04 14 0 7 3 9 8 0 1 90958  L'11',B306.DL526,P590 1218 6000 0 0 2 3 0 260000 252496  04 80000 27228 1 3 2 1 0 1 0 1 0 0036  L ' A ' , D L 5 2 6 , B 4 1 4 , V R 5 4 2 , P 1 7 0 6 5 07 19910001697994 672 18750 0 39 1 0 0 0 1 4 2 1 1 1 0 0 0 1362920 O L ' A ' B112 602 27000  DL540 P17099 0 30 0 0 885510  13 4 180001131135 302 01 OOOO 0 01 42 97 1131135  1211730001170000 L21&22&24 B66 DL264A P448 546 16104 6 27 0 0 00001 30201 101000000 00 34 92 961066618 656400 888950 L13 B154 DL264A P1 141 613 9863 6 6 0 0 339700 L11 B348 DL526 947 6250 LA-C 827  P1949 142350  12 275000 184250 00001 3 0 2 0 1 0 0 0 1 0 0 0 0 0 0 00 64 94 185000 12 155000 0 0002 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 5812 1 600000  19S20 B374 DL526 P2014 11 800000 621382 12400 0 14 4 O 000010302010101000000 755775 775750 00 58 97 623295  LA 17&18 B351 DL526 P2334 121060000 643707 636 12500 3 20 1 0 00001 40201 101000000 851600 00 106 645000 L5 B 41 DL264A 956 6250 0 L19&20 B410 640 9375  P399&1771 11 197000 128000 0 5 0 00001 2 0 1 0 1 0 0 0 1 0 0 0 0 0 0 191600 00 52116 128000  10 390000 1574 17 DL526 P1949 00002 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 16 0 0 00 34 90 160000 334400  L7&8 B450 DL526 P11949 649 9375 10 10 1 O 322300  11 435000 245700 00002 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 59 94 250000  L1 18.12 B17 DL318 P1749 587 11500 0 0 0 5 164800  10002 00  L88 B161 DL264A P222 575 6350 11 0 0 0 3750 2 16800  12 240000 187751 OOOO10200010000000000 00 56 95 900O0  LC B158 DL264A 820 6710 0  P9068 9 1 0 178300  12 2 12000 126000 00002 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 00 55 55 120000  L9-12 B67 DL264A P442 632 15708 6 24 0 0 548700  11 630000 590993 00002 20201 101000000 OO 51 7 1 201000  12 190000 0 101000000000000 00  I -3-  > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >  69 .5 ' 70 71 72 72 .5 73 74 75 75 .5 76 77 78 78 .5 79 80 81 8 15. 82 83 84 84 .5 85 86 87 87 .5 88 89 90 90.5 91 92 93 93 .. 5 94 95 96 96 . 5 97 98 99 99 .5 100 101 102 102 .5 103 104 105 105 . 5 106 107 108 108 . 5 109 1 10 111 1 1.15 1 12 1 13 1 14  0414 00 00 01 00 09 12 788000 499000 L1-3 B24 DL184 P178 040931058026504 2200 DUNDAS 0409 011.3191010169 35 22514 640 18117 6 28 1 0 00002 202 1 000000 00 116 460000 642390 040910.2500 54096 1 4908 0409 00 00 01 00 10 12 190000 45056 L2 B600 DL526 P2976 060321917070205 3707 CAMBIE 647 3757 2 6 0 0 00000 200010001000000 0603 9.5000 000149 8 5176 00 60 4 1 55000 143500 0603 0 15852 6154 9697 1 6072 0603 OO 00 01 00 30 11 109000 95693 L8 B4 DL634 P1426 043733918872899 4899 QUEBEC 0437 011.555901 10 6 4200 700 3848 0 5 1 0 00002 201010000000000 OO 57 93 78300 1500 105000 928 0437 O 15900 1 0437. 00 00 01 00 69 10 153000 110000 L33 B145 DL264A P222 041832163423411 1515 E 4TH 0418 011.7243010955 7 5180 740 6100 1 5 i 0 00002 201000000000000 00 57 18 65000 0 111500 0418 O 14880 3030 11849 1 1640 04 18 00 00 01 00 24 12 155000 79160 L12 B55 DL 302 P198 040221469018667 137 E 16TH 595 5440 2 5 1 0 00002 201010001000000 0402 012.0000030164 8 4765 142850 00 43 67 82500 0402 O 17472 3656 13816 1 10216 0402 00 00 01 00 15 12 430000 L13&14 B6 DL318 P1749 031226313883350 8860 MONTCALM 630 14000 9 14 1 0 00000 302010001000001 0312 0 1.0000010264 24 17917 OO 00 464200 0312 0 3 1980 1 0312 00 00 01 00 15 1 1 440000 414378 L8S9 B'P' DL318 P1903 030726014583046 8650 SELKIRK 680 10996 2 18 1 0 00002 201010000000000 0307 011.2590010859 21 20952 00 47 103 290000 398300 0307 O 49936 13004 36932 1 32304 0307 00 00 01 00 20 15 275000 106142 L18 B372 DL526 P991 104221266413425 1373 W 11TH 548 6250 2 2 4 0 00000 201 OOOOOOOOOO 104 2 15.5000080307 8 4384 00 49 83 110000 164150 1042 0 28400 5153 21297 1 13836 1042 00 00 00 01 7 1 16 389000 84000 L14 B355 DL526 P991 105521265414965 1035 W 10TH 764 6250 1 8 1 0 00002 201010000000000 1055 011.5000051060 10 7645 00 00112 84000 232200 1055 O 17856 1 10572 1055 00 00 00 01 20 1425000002500726 L24-28 B58 DL185 P92 102460260611357 1655 NELSON 1024 1312512.6697030372 75 53507 713 26959 24 43 8 0 00002 402010101000000 00 63 119 1500000 1629650 1024 0253476 1 325620 1024 00 00 OO 01 08 1326070002385077 L18 E HLF&19 B58 DL185 P92 103660661511805 1320 BUTE 1036 10.143903 67 93 57229 615 17292 60 33 0 0 001011703010201010100 00 01 01 725000 2068950 1036 0179276 1 63645 1036 00 00 00 01 13 1625000000575074 L11&12 B26 DL185 P92 103760260611063 1735 NELSON 00100110301020101 02 615 12969 O 62 O 0 1037 281311.0000020269 62 38114 00 56 101 598500 1361300 1037 0108822 4 1740 67082 1 160424 1037 00 OO 00 01 11 15 75000 0 L13 B43 DL196 P196 108030757919862 662 ALEXANDER 00002 300 O O O O O O O O O O 356 3050 1 2 0 0 0 1080 014.7768011012 12 4276 00 56128 50000 74 150 1080 O 14280 2904 11376 1 0 1080 00 00 00 01 68 151200000 950000 L1 B 48 DL185 P92 102760360711596 1091 BROUGHTON 1027 477311.5000030312 40 49657 1241 8646 0 28 12 0 00002 60001 100000000 00 67 75 130000 939400 1027 0174763 1 61476 1027 00 00 00 01 68 14 169000 135000 L7 B91 DL196 P196 107331159619834 634 E GEORGIA 196 3050 42. 13 0 0 00002 400 OOOOOOOOOO 1073 013.864201 05 55 10800 00 4 1 123 95000 158500 1073 0 49200 15277 33923 1 13793  CM  > >  > >  > >  114.5 115 116 117 117.5 118 119 120 120.5 121 122 123 123.5 124 125 126 126.2 127 128 129 129.5 130 131 132 132.5 133 134 135 135.5 136 137 138 138.5 139 140 141 14 1.5 142 143 144 144.5 145 146 147 147.5 148 149 150 150.5 151 152 153 153.5 154 155 156 156.5 157 158 159  1073 0 0 OO 00 01 75 101060661311647 1345 BURNABY 1010 011.250005 60 15 9133 1010 O 36448 8153 28295 1 23580 1010 OO 0 0 00 01 20 1 0 1 4 6 0 3 6 0 5 1 1 7 6 5 1231 BARCLAY 1014 O 8.000005 62 21 13149 1014 O 61584 23402 38182 1 23100 1014 OO 00 00 0 1 . 1 8 101560260511425 1549 BARCLAY 1015 29 1 6 1 4 . 0 0 0 0 0 3 0 3 5 8 21 12077 1015 0 5 6 2 8 0 24200 32080 1 0 1015 0 0 00 00 01 22 110030923558826 322S326 WOODLAND 1100 03 10 32 11880 1070 1100 O 3 2 5 2 0 14756 17764 1 1100 0 0 00 00 01 70 111232568319404 310 E 13TH 60 26 15732 1112 .12.000001 1 35516 1112 0 7 0 8 0 0 1112 0 0 00 00 01 20 107820964816318 686 W 8TH 9936 26 18 1078 13.000001 35988 1078 0 48384 15240 33144 1 1078 OO 00 00 01 54 111121468718604 3122 QUEBEC 8920 1111 01.1.500001 59 13 13860 1 1 1 1 1 0 33600 1090 0 0 0 0 0 0 01 70 109031058825536 2026 FRANKLIN 8 4484 10 1090 011.255205 14832 1090 O 20904 3842 17062 1 1090 0 0 00 00 01 70 1007603607 1 1796 10658.1085 BUTE 1007 1 3 . 6 2 2 9 0 3 0 3 3 9 26 14088 47292 1007 0 62145 26722 35423 1 1007 OO 0 0 . 0 0 01 41 101660260711317 1675 COMOX 1016 14.000002 61 21 13250 37008 1016 O 58884 25320 33564 1 1016 0 0 0 0 . O O 01 19 1077208648 13045 1455 W 8TH 1077 2 6 2 5 0 1 2 . 4 4 8 3 0 4 0 3 1 2 25 1761 1 26412 1077 O 5 1 2 2 0 1 1077 OO 00 0 0 01 68 103460660911784 1222 PENDRELL 1034 1 0 . 2 8 7 9 0 3 0 3 6 5 43 25653 1034 0 1 1 9 7 0 9 1 83131 1034 OO 00 00 0 1 . 1 5 1 0 3 5 6 0 3 6 0 3 1 1 8 2 5 1155. HARO 1035 1 2 . 3 9 8 8 0 4 0 3 6 8 50 25240 1035 0 1 4 8 0 0 0 56240 91760 1 313904 1035 0 0 00 0 0 01 12 106012064208485 2211 W 5TH 1060 0 7.750003 67 35 23242 1060 0 1 3 1 9 4 0 52776 79164 1 21972 1060 OO 00 00 01 13 106112064808727 2185 W 8TH 106 1 014.000005 66 20 12696 1061 O 72162 28865 43297 1 24620  0  15 394000 148055 00002 201010001000001 00 36 91 152000  L24 B33 DL185 P92 625 8646 2 19 O 0 383500  15 625000 5 2 1 0 0 0 OOOOO 3 0 1 0 1 0 0 0 0 0 0 0 0 0 0 00127 107 200000  L12 B45 DL185 575 8646 2  16 460000 250000 00002 3 0 1 0 1 0 0 0 0 0 0 0 0 0 0 00 72130 250000  L16 B38 DL185 608 8646 0  P92 15 0 247500  P92 18 1 339500  0  LB B15 S U B ' C DL183 P5443 08 170000 114361 383 4455 32 0 0 0 OOOOO 300 0000000000 156500 00 58 95 80000 L1&2 B112 DL301 P187 605 12078 2 24 0 0 439800  16 735000 00002 2010100 0 0 71107  605000 000000 285000  L2 B339 DL526 P7916 552 5850 0 18 0 0 286650  14 368000 257500 00002 300 0000000000 00 66122 189500  LD OF SUB1&2 B55 DL302 P6105 15 365000 228000 000000 0 0 OOOOO 2010100 685 7663 1 12 120000 00 69107 263700 L3 B39 DL184 P178 560 6034 0 8 0 0 128600  15 164000 123218 00101 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 00 72 96 126000  L1 B36 DL185 P92 542 8646 21 5 0 0 372900  16 585000 407000 00002 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 00 67 129 307000  L23 B59 DL185 P92 630 8646 O 21 0 377300  6  16 6 6 0 0 0 0 65000 00002 201010000000000 00 77122 263000  L11 B311 DL526 P590 653 6000 5 16 4 0 332150  14 400000 291756 OOOOO 3 0 0 0 1 0 0 0 0 0 0 0 0 0 0 00 17 7 1131 203000  L2 B37 DL185 P92 596 8646 1 40 2 0 884500  151440000 769069 00102 90301 101000001 00 56101 600000  L14 B19 DL185 P 92 505 8646 34 15 1  1618000001254882 00100 803010100 01 00 75101 281972  0  131060000 190564 202010101000000 74  L23-26 B243 DL526 P590 664 16800 7 27 1 0 657550  00002 00  L18&19 B304 DL526 P590 633 12000 5 13 2 0 389700  14 600000 350000 00002 203010001000001 00 75 65125 213150  r*N  159.5 1061 0 0 0 0 0 0 01 14 160 1 0 2 9 6 0 3 6 0 7 1 1 9 5 5 1041 C0M0X 161 1029 12.6600030311 34 32768 10548 162 1029 0 1 0 3 8 8 4 1 162.5 1029 OO 0 0 0 0 01 69 163 1 0 3 1 6 0 2 6 0 6 1 0 9 2 0 1872 NELSON 164 1031 O 9 . 0 0 0 0 0 3 0 3 5 9 35 25176 165 1031 0 1 0 5 5 6 4 41170 64394 1 28814 165.5 1031 0 0 0 0 0 0 01 21 166 1030603607 11697 1075 J E R V I S 167 1030 6 2 0 8 1 7 . 2 0 0 0 0 4 0 3 5 8 37 28663 168 1030 O 57924 1 74496 168.5 1030 OO OO 0 0 01 22 169 1032602605 10796 2 0 1 0 BARCLAY 170 1032 015.0000 52 39 34801 171 1032 0 1 3 3 8 8 4 52215 81669 1 17 1.5 1032 0 0 0 0 0 0 01 28 172 102851659711704 610 J E R V I S 173 1028 3208 1 4 . 0 7 4 8 0 3 0 3 1 0 51 47149 174 1028 0 1 167304 > 174.5 1028 OO 0 0 0 0 01 70 > 175 1042 > 176 1042 10.7500 78 10 6536 > 177 1042 1 0 44923 > 178 1042 0 0 0 0 0 0 01 02 #End o f F i l e H $.09, $.25T #$C *MSOURCE*@SP * P R I N T *  L14 B8 DL185 P92 963 8646 7 6 21 O 689400  1610800000785809 00100 400010100000000 00 58106 470490  L6 B69 DL185 P 92 716 8646 14 21 0 0 667050  15 9 3 0 0 0 0 24924 1 00102 70301 101000000 00 7 1  L19 B35 DL185 P92 772 8646 0 30 7 0 698800  151075000 750000 0 0 1 0 0 803 0 1 0 0 0 0 0 0 0 0 0 0 00117 129 6 5 0 0 0 0  LB 14-16 B68 DL185 P8501 161070000 350000 892 12965 18 15 6 O 00102 403010100000001 755150 0 0 72130 370000 LC B30 DL185 P92 922 9108 0 25 19 7 607100 654 4925 O 8883 100000  O  161550000 955088 00100 600 0200000000 0 0 12 1 1 1 815000 16 6 1 0 0 0 0 268000 0 0 1 3 3 1 0 1 0 0 0 1  U n i v e r s i t y o f B r i t i s h C o l u m b i a C o m p u t i n g C e n t r e - D e v i c e : DSE7 Task: THE SYSTEM WILL BE IN UNATTENDED MODE FROM MIDNIGHT TO 4 AM T O N I T E * * * H s i g gau H Enter user password.  168  H  ?  . .  * * L a s t s i g n o n was: 18:37:27 ..User " G A U . " s i g n e d o n a t 1 9 : 1 8 : 5 4 o n F r i A p r 23/82 NO-MESSAGES If set.proute=cntr U $.02, $.03T H c o n t r o l * p r i n t * landscape onesided copies = 2 * * * , THE XEROX 9 7 0 0 IS TEMPORARILY DOWN* * * * H * P R I N T * a s s i g n e d j o b , n u m b e r 533737 H * PR INT* RM533737 h e l d $ . 10, $.13T If landsales(759,992) H list 0120000 0 1 8 0 0 0 150 120 00 01 00 00 0 0 2 2 2 3 . 6 > 759 6004 037 > 760 6004 06639 1 0 2 . 7 0 1 0 1 . 3 3 2 6 . 6 4 2 . 0 0 0 8 . 9 9 0 8 . 9 8 0 . 0 0 . 0 > 761 6005 037 . 0 1 1 2 5 0 0 0 1 8 0 0 0 150 120 00 01 00 00 0 0 2 2 2 3 . 6 > . 762 6005 06639 1 0 2 . 7 0 1 0 1 . 3 3 2 6 . 6 4 2 . 0 0 0 8 . 9 9 0 8 . 9 8 0 . 0 0 . 0 0 1 3 0 0 0 0 0 1 2 0 0 0 100 120 00 01 00 0 0 0 0 2 2 2 3 . 6 > 763 5007 037 > 764 5007 06639 1 0 2 . 7 0 1 0 1 . 3 3 2 6 . 6 4 2 . 0 0 0 8 . 9 9 0 8 . 9 8 0 . 0 0 . 0 > 765 5038 037 . 0 1 1 2 5 0 0 0 1 8 0 0 0 150 120 00 01 00 00 00 2 2 2 3 . 6 > 766 5038 06639 1 0 2 . 7 0 1 0 1 . 3 3 2 6 . 6 4 2 . 0 0 0 8 . 9 9 0 8 . 9 8 0 . 0 O . O 0 1 2 0 0 0 0 0 1 8 0 0 0 150 120 00 01 00 0 0 0 0 2 2 2 3 . 6 > 767 5039 037 > 768 5039 06639 1 0 2 . 7 0 101.3.3 2 6 . 6 4 2 . 0 0 0 8 . 9 9 0 8 . 9 8 0 . 0 0 . 0 0 0 2 0 0 0 0 0 0 3 0 0 0 025 120 00 01 00 00 0 0 2 2 2 3 . 6 > 769 5039 037 > 770 5039 06639 102.70 101.33 26.64 2.00 0 8 . 9 9 0 8 . 9 8 0 . 0 0 . 0 > 771 5058 038 , 0 0 1 8 0 0 0 0 0 3 0 0 0 025 120 00 01 00 0 0 0 0 2 2 3 5 . 3 0.0 > 772 5058 Off 5 31 1 0 3 . 7 0 1 0 1 . 7 3 2 6 . 7 7 1.90 0 8 . 8 9 0 9 . 1 9 0 . 0 0 0 2 0 0 0 0 0 0 3 0 0 0 025 120 00 01 00 00 00 2235 > 773 5058 038 > 774 5058 06531 1 0 3 . 7 0 1 0 1 . 7 3 2 6 . 7 7 1.90 0 8 . 8 9 0 9 . 1 9 0 . 0 0 0 0 5 3 0 0 0 0 0 6 2 5 0 0 5 0 125 00 01 00 00 OO 2235 > 775 5304 038 , > 776 5304 06531 1 0 3 . 7 0 1 0 1 . 7 3 2 6 . 7 7 1.90 0 8 . 8 9 0 9 . 1 9 0 . 0 0 0 7 6 0 0 0 0 023793 198 131 01 00 00 00 0 0 2235 > 777 6008 038 > 778 6008 06531 1 0 3 . 7 0 1 0 1 . 7 3 2 6 . 7 7 1.90 0 8 . 8 9 0 9 . 1 9 0 . 0 0 0 0 3 1 5 0 0 006534 0 5 0 132 0 0 0 0 01 0 0 OO 2245 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O 8 . 0 7 8 . 6 0 0791 0080400 0 0 6 0 0 0 O50 120 00 01 OO 00 OO . 0 0 . 0 1.0 1.0 1.0 1 . 0 02 163.13 161 . 17 2 8 . 4 8 1.05 10.51 1 0 . 3 5 1 2 5 0 6 . 4 8 1 2 8 . 0 8. 10 8 . 5 3 0751 6000 50 120 0 0 01 00 0 0 OO 800O0 .0 0 . 0 1.0 1.0 1.0 1.0 01 1 6 6 . 2 0 164 .17 2 8 . 4 0 1.00 10.51 10.34 1 2 5 0 6 . 4 8 1 2 8 . 0 8 . 1 0 8 . 5 3 0751 122 OO OO 01 00 00 6039 50 125000 .0 0 . 0 1.0 1.0 1.0 1 . 0 01 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1 2 5 0 6 . 4 8 1 2 8 . 0 8 . 10 8 . 5 3 0751 5319 44 122 0 0 00 01 00 00 76000 . 0 0 . 0 1.0 1.0 1.0 1.0 01 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 1 0 . 3 4 1 %  > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >  •923 924 925 926 927 928 929 9.30 931 932 933 934 935 936 937 938 939 940 94 1 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 . 957 958 959 960 . 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982  1029 1029 1019 1019 1064 1064 1064 1064 1005 1005 1005 1005 1005 1005 1005 1005 1063 1063 104O 1040 1040 1040 1040 1040 1031 1031 1031 1031 1031 1031 1031 103 1 1031 1031 1031 1031 1024 1024 1024 1024 1024 1024 1024 1024 1024 . 1024 1024 1024 1021 1021 1019 1019 1019 1019 5092 5092 5025 5025 5025 5025  060 08198 060 08198 060 08 198 060 08198 060 08198 060 08 198 060. 08198 060. 08198 060 . 08 198 060, 08 198 060 08 198 060 08 198 060 . 08 198 060 . 08 198 060 08 198 060 08 198 060 08 198 060 08198 060 . 08 198* 060 08 198 060 08 198 060 08 198 060 08198 060 . 08 198 060 . 08 198 060 08198 060 08 198 0 6 0 .. 08198 060 08 198 060 . 08198  8 1 2 8 . 0 8 . 1 0 8. 53 0751 1.0 I . 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O 01 8 1 2 8 . 0 8 . 1 0 8..53 0751 0 .01 1.0 1 .O 1.0 .1 8 1 2 8 . 0 8 . 1 0 8..53 0751 . 0. 01 1.0 1.0 1.0 1 8 1 2 8 . 0 8 . 1 0 8..53 0751 1.0 1.0 1.0 1.. 0 01 8 1 2 8 . 0 8 . 1 0 8..53 0751 1 . 0 1 . 0 1.0 1.. 0 01 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 8 1 2 8 . 0 8 . 1 0 8..53 0751 75000 4026 33 122 00 00 01 00 00 2506 1.0 1 O 1.0 1 .0 01 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 O 8 1 2 8 . 0 8 . 1 0 8 .53 0751 63000 4026 33 122 00 00 01 0 0 00 2506 1.0 1.0 1.0 1.. 0 01 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 8 1 2 8 . 0 8 . 1 0 8 .53 075 1 63000 4026 33 122 00 0 0 01 0 0 0 0 2506 1.0 1.0 1.0 1 .0 01 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 8 1 2 8 . 0 8 . 1 0 8 .53 0751 63000 4026 33 122 00 00 01 00 00 2506 1.0 1.Q 1.0 1 . 0 01 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.OO 10.51 10.34 1.0 O 8 1 2 8 . 0 8 . 1 0 8 .53 0751 70500 4026 33 122 00 00 01 00 00 2506 1.0 1 O 1.0 1 . 0 01 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.OO 10.51 10.34 1.0 O 8 1 2 8 . 0 8 . 1 0 8 .53 0751 66500 4026 33 122 00 0 0 01 00 00 2506 1.0 1.0 1.0 1 .0 01 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 O 8 1 2 8 . 0 8 . 1 0 8 .53 0751 105000 4026 33 122 00 0 0 01 00 0 0 2506 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 .0 1.0 1.O 1.0 1 .0 01 63250 4026 33 122 0 0 ! 0 0 01 0 0 0 0 2506 .4 8 1 2 8 . 0 8 . 1 0 8 .53 0751 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 O .0 1.0 1.O 1.0 1 .O 01 69000 4026 33 122 00 00 01 0 0 0 0 2506 . 4 8 1 2 8 . 0 8 . 1 0 8 .53 0751 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 . 0 1.0 1.0 1.0 1 .0 01 68000 4026 33 122 0 0 00 01 0 0 00 2506 .4 8 1 2 8 . O 8 . 1 0 8 .53 0751 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 O .0 1.0 1.0 1.0 1 . 0 01 68000 4026 33 122 0 0 0 0 01 00 0 0 2506 . 4 8 1 2 8 . 0 8 . 1 0 8 .53 0751 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 .0 1.0 1.0 1.0 1 . 0 01 75000 4026 33 122 00 00 01 0 0 0 0 2506 .4 8 1 2 8 . 0 8 . 1 0 8 .53 0751 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.OO 10.51 10.34 1.0 O .0 1.0 1.0 1.0 1 . 0 01 365000 20149 165 122 00 00 01 0 0 0 0 2506 .4 8 1 2 8 . 0 8 . 1 0 8 .53 0751 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 . 0 1.0 1.0 1.0 1 .0 01 52000 4026 . 3 3 122 0 0 00 01 0 0 00 2506 .4 8 1 2 8 . 0 8 . 1 0 8 .53 0751 1 6 6 . 2 0 164.1.7 2 8 . 4 0 1.00 10.51 10.34 1.0 O . 0 1.0 1.0 1.0 1 . 0 01 68500 4026 33 122 00 0 0 01 OO OO 2506 .4 8 1 2 8 . O 8 . 1 0 8 .53 0751 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 . 0 1.0 1.0 1.0 1 .0 01 0090000 0 0 6 0 0 0 050 120 OO 01 00 00 00 2506 .4 8 1 2 8 . 0 8 . 1 0 8 53 0751 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 . 0 1.0 1.0 1.0 1 0 01 0068500 0 0 2 9 5 0 025 118 00 01 00 00 00 2506 .4 8 1 2 8 . O 8 . 1 0 8 53 0751 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 .0 1.0 1.0 1.0 1 O 01 0064000 0 0 2 9 5 0 025 118 00 01 00 00 00 2506 .4 8 1 2 8 . O 8 . 1 0 8 53 0751 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 . 0 1.0 1.0 1.0 1 O 01  80000 6039 50 122 00 00 01 0 0 00 2 5 0 6 . 4 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 . 0 57000 4026 33 122 00 0 0 01 00 0 0 2 5 0 6 . 4 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 . 0 200000 8646 66 131 01 0 0 0 0 0 0 0 0 2 5 0 6 . 4 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 . 200000 8646 66 131 01 0 0 00 00 00 2 5 0 6 . 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 . 63500 4026 33 122 00 00 01 00 00 2 5 0 6 . 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 . 63000 4026 33 122 00 0 0 01 00 00 2 5 0 6 . 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 . 58000 4026 33 122 00 0 0 01 00 0 0 2 5 0 6 . 1 6 6 . 2 0 1 6 4 . 1 7 . 2 8 . 4 0 1.00 10.51 10.34 1.0 0 . 90000 4026 33 122 00 00 01 0 0 00 2 5 0 6 . 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 . 215000 12480 125 100 00 01 00 00 00 2 5 0 6 . 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 . 68500 3000 25 120 00 01 00 0 0 00 2506 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 . 64000 3000 25 120 00 01 00 0 0 OO 2506 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 0 112000 6000 50 120 00 01 00 0 0 00 2506 4  00  •> 984 5025 0 8 1 9 8 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 . 0 1.0 1.0 1.0 1.0 01 > 985 6019 060 0 2 1 5 0 0 0 0 1 2 4 8 0 125 100 00 01 00 0 0 0 0 2 5 0 6 . 4 8 1 2 8 . 0 8 . 1 0 8 . 5 3 0751 > 986 6019 0 8 1 9 8 1 6 6 . 2 0 1 6 4 . 1 7 2 8 . 4 0 1.00 10.51 10.34 1.0 0 . 0 1.0 1.0 1.0 1.0 > 987 6023 061 0337000 0 1 8 6 4 9 173 108 00 00 01 00 00 2 5 1 7 . 6 8 3 1 1 . 3 9 . 4 3 8 . 4 0 1353 > 988 6023 0 6 7 0 8 1 6 9 . 1 0 1 6 6 . 3 3 2 8 . 3 2 1.10 10.34 10.32 1.0 0 . 0 1.0 1.0 1.0 1.0 > 989 6017 062 0 5 1 0 0 0 0 0 1 8 0 0 0 150 120 00 01 0 0 0 0 0 0 2 5 2 4 . 1 8 4 9 4 . 5 7 . 7 3 7 . 8 0 1003 > 99.0 6017 0 8 1 0 5 1 7 3 . 0 3 1 6 8 . 3 7 2 8 . 2 4 1.20 1 0 . 4 5 1 0 . 3 9 1.0 0 . 0 1.0 1.0 1.0 1.0 > 991 6024 063 0 1 5 0 0 0 0 0 0 6 2 8 8 048 131 01 00 00 00 00 2 5 3 3 . 2 8 6 7 7 . 8 7 . 6 7 8 . 2 7 1057 > 992 6024 0 7 9 5 0 1 7 6 . 2 7 1 7 0 . 5 0 2 8 . 1 6 1.15 1 0 . 4 8 1 0 . 4 3 1.0 0 . 0 1.0 1.0 1.0 1.0 > 993 6022 063 0 3 3 5 0 0 0 0 2 0 1 3 0 165 122 00 00 01 00 0 0 2 5 3 3 . 2 8 6 7 7 . 8 7 . 6 7 8 . 2 7 1057 > - 994 6022 0 7 9 5 0 1 7 6 . 2 7 1 7 0 . 5 0 2 8 . 1 6 1.15 1 0 . 4 8 1 0 . 4 3 1.0 0 . 0 1.0 1.0 1.0 1.0 #End o f File H i$.07, $.17T #LIST L A N D . M I D A S . 2 > 1 $RUN M:MIDAS > 2 READ VAR=1-31 CASES=1-496 FILE=LANDSALES F O R M A T = ( F 4 . 0 , 2 X , F 3 . 0 , 3 X , F 7 . O , & > 3 1X,F6.0.1X,F3.0,1X,F3.O.5(1X.F2.0),2(1X,F6.1).1X.F4.2,1X,F4.2,1X,F4.0,& > 4 2 X . / . F 4 . 0 . 2 X . F 5 . 0 . 2 ( 1 X . F 6 . 2 ) . 1 X . F 5 . 2 , 1 X . F 4 . 2 . 1 X , F 5 . 2 1 X . F 5 . 2 . 1 X . F 3 . 1,& > 5 5( 1 X . F 3 . 1 ) , 1X, F 2 . 0 ) LABELS = F I L E N O 1 , Q U A R T E R , P R I C E , L O T S I Z E . F R O N T A G E , D E P T H , & > 6 W E S T E N D , K I T S . EASTVAN,MARPOLE,KERRI SDL,BCPOP,BCPERINC,UNEMPLUA,UNEMPLSA,& > 7 C O M P L V A N , F I L E N 0 2 , C O M P L B C , C P I A L L . C P IHOUSG.NONFAMHH,VACRATE.NHARATE,S > 8 CONVRATE.MURBSTAT.CCASTAT.CCANEW.CCANEWWP,ARPSTAT,RENTCONT.HOLDPER > 9 TRANS V32=V3/V4 LABEL=SPPERSF > IO TRANS V33=V3/V5 LABEL=SPPERFF > 11 TRANS V34=V3/V6 LABEL=SPPERDF > 12 TRANS V 3 5 = 1 . 0 0 0 CASES=287-320 LABEL=DEFLATOR > 13 TRANS V 3 5 = 1 . 1 3 7 CASES = 32 1-333 LABEL=DEFLATOR . > 14 TRANS V 3 5 = 1 . 1 7 1 CASES=334-344 LABEL=DEFLATOR > 15 TRANS V35= 1 . 160 CASES = 345~348 LABEL=OEFLATOR > 16 TRANS V 3 5 = 1 . 1 4 2 CASES=349-352 LABEL=DEFLATOR > 17 TRANS V 3 5 = 1 . 2 3 1 CASES=353-355 LABEL=DEFLATOR > 18 TRANS V 3 5 = 1 . 1 7 0 CASES=356 LABEL=DEFLATOR > 19 TRANS V 3 5 = 1 . 1 4 2 CASES=357 LABEL=DEFLATOR > 20 TRANS V 3 5 = 1 . 0 7 7 CASES=358-372 LABEL=DEFLATOR > 21 TRANS V 3 5 = 1 . 1 0 4 CASES=373-374 LABEL=DEFLATOR > 22 TRANS V 3 5 = 1 . 1 9 3 CASES=375-377 LABEL = DEFLA TOR > 23 TRANS V 3 5 = 1 . 1 0 1 CASES = 378-379 LABEL=DEFLATOR > 24 TRANS V 3 5 = 1 . 2 4 7 CASES=380~385 LABEL=DEFLATOR > 25 TRANS V 3 5 = 1 . 2 5 7 CASES=386-389 LABEL=OEFLATOR > 26 TRANS V 3 5 = 1 . 3 1 0 CASES=390-392 LABEL=DEFLATOR > 27 TRANS V 3 5 = 1 . 2 9 3 CASES=393 LABEL=DEFLATOR > 28 TRANS V 3 5 = 1 . 3 8 2 CASES=394-395 LABEL=DEFLATOR > 29 TRANS V 3 5 = 1 . 5 7 6 CASES=396-397 LABEL=DEFLATOR > 30 TRANS V 3 5 = 1 . 6 8 1 CASES=398 LABEL=DEFLATOR > 31 TRANS V 3 5 = 1 . 6 6 2 CASES=399-404 LABEL=DEFLATOR > 32 TRANS V 3 5 = 1 . 8 0 3 CASES=405-407 LABEL=DEFLATOR > 33 TRANS V 3 5 = 1 . 7 5 2 CASES=408-411 LABEL=DEFLATOR > 34 TRANS V 3 5 = 1 . 9 4 4 CASES=412-418 LABEL=DEFLATOR > 35 TRANS V 3 5 = 1 . 8 8 7 CASES=419-426 LABEL=DEFLATOR > 36 TRANS V 3 5 = 2 . 0 6 3 CASES=427-432 LABEL=DEFLATOR > 37 TRANS V 3 5 = 2 . 1 5 2 CASES=433-438 LABEL=DEFLATOR > 38 TRANS V 3 5 = 2 . 0 8 7 CASES=439-449 LABEL=DEFLATOR > 39 TRANS V 3 5 = 2 . 1 2 5 CASES=450-458 LABEL=DEFLATOR > 40 TRANS V 3 5 = 2 . 6 5 2 CASES=459-493 LABEL=DEFLATOR > 41 TRANS V 3 5 = 2 . 1 9 6 CASES=494 LABEL=DEFLATOR > 42 TRANS V 3 5 = 2 . 1 1 8 CASES=495 LABEL=DEFLATOR > 43 TRANS V 3 5 = 2 . 2 1 8 CASES=496 LABEL=DEFLATOR > 44 TRANS V36=V3/V35 CASES=ALL LABEL=REALSP > 45 TRANS V37=V36/V4 LABEL=REALPPSF > 46 TRANS V 3 8 = V 1 9 / 1 0 0 . 0 LABEL=CPINEW  '> > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >  47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 .81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106  TRANS V39=V32/V38 LABEL=NEWREALP .TRANS V40=V23/V38 LABEL=RLINTRTE TRANS V 4 1 = V 1 2 / 2 2 0 5 . 1 LABEL=POPGRRTE TRANS V42=V13/V38 LABEL=REALINC TRANS V 4 3 = V 4 2 / 3 6 9 5 . 8 5 LABEL=INCGRRTE CODE V44=V2 LABEL=NEWOTR TRANS V 4 5 = 1 G 7 . 5 STRATA= V 4 4 : 3 6 LABEL=RENTLEVEL TRANS V 4 5 = 1 6 8 . 8 STRATA=V44:37 LABEL=RENTLEVEL TRANS V 4 5 = 1 7 0 . 0 STRATA=V44:38 LABEL=RENTLEVEL TRANS V 4 5 = 1 7 6 . 0 STRATA=V44:39 LABEL=RENTLEVEL TRANS V 4 5 = 1 8 2 . 0 STRATA=V44:40 LABEL=RENTLEVEL TRANS V 4 5 = 1 9 4 . 0 S T R A T A V 4 4 : 4 2 LABEL=RENTLEVEL TRANS V 4 5 = 2 1 4 . 0 STRATA=V44:45 LABEL=RENTLEVEL TRANS V 4 5 = 2 2 0 . 0 STRATA=V44:47 LABEL=RENTLEVEL TRANS V 4 5 = 2 2 9 . 0 STRATA=V44:50 LABEL-RENTLEVEL TRANS V 4 5 = 2 4 7 . 0 STRATA=V44:53 LABEL=RENTLEVEL TRANS V 4 5 = 2 5 3 . 0 STRATA=V44:54 LABEL=RENTLEVEL TRANS V 4 5 = 2 5 6 . 5 STRATA=V44:55 LABEL=RENTLEVEL TRANS V 4 5 = 2 6 0 . 0 STRATA=V44:56 LABEL=RENTLEVEL TRANS V 4 5 = 2 6 3 . 5 STRATA=V44:57 LABEL=RENTLEVEL TRANS V 4 5 = 2 6 7 . 0 STRATA=V44:58 LABEL=RENTLEVEL TRANS V 4 5 = 2 6 9 . 8 STRATA=V44:59 LABEL=RENTLEVEL TRANS V 4 5 = 2 7 2 . 5 STRATA=V44:60 LABEL=RENTLEVEL TRANS V 4 5 = 2 7 5 . 3 STRATA=V44:61 LABEL^RENTLEVEL TRANS V 4 5 = 2 7 8 . 0 STRATA=V44:62 LABEL RENTLEVEL TRANS V 4 5 = 2 7 8 . 0 STRATA=V44:63 LABEL=RENTLEVEL TRANS V46 = V 4 5 / 1 0 O . O STRATA = NONE LABEL = GRRTRENT TRANS V 4 7 = 1 0 3 . 5 STRATA=V44:36 LABEL=CONSTNCOST TRANS V 4 7 = 1 0 5 . 1 STRATA=V44:37 LABEL=CONSTNCOST TRANS V 4 7 = 1 0 8 . 2 STRATA=V44:38 LABEL=CONSTNCOST TRANS V 4 7 = 1 1 1 . 1 STRATA=V44:39 LABEL=CONSTNCOST TRANS V 4 7 = 1 1 6 . 3 STRATA = V 4 4 : 4 0 LABEL = CONSTNCOST TRANS V 4 7 = 1 2 3 . 6 STRATA=V44:42 LABEL=CONSTNCOST TRANS V 4 7 = 1 3 0 . 3 STRATA=V44:45 LABEL=CONSTNCOST TRANS V 4 7 = 1 3 7 . 0 STRATA=V44:47 LABEL=CONSTNCOST TRANS V 4 7 = 1 4 1 . 5 STRATA = V 4 4 : 5 0 LABEL=CONSTNCOST TRANS V 4 7 = 1 5 4 . 5 STRATA=V44:53 LABEL=CONSTNCOST TRANS V 4 7 = 1 5 9 . 3 STRATA=V44:54 LABE L = C0NSTNC0ST TRANS V47= 1 6 2 . 6 STRATA = V 4 4 : 5 5 LABEL =CONSTNCOST TRANS V 4 7 = 1 6 5 . 6 STRATA=V44:56 LABEL = CONSTNCOST TRANS V 4 7 = 1 6 8 . 5 STRATA=V44:57 LABEL=CONSTNCOST TRANS V 4 7 = 1 7 4 . 0 STRATA=V44:58 LABEL=CONSTNCOST TRANS V 4 7 = 1 7 9 . 5 STRATA=V44:59 LABEL=CONSTNCOST TRANS V 4 7 = 1 8 0 . 4 STRATA=V44:60 LABE.L=CONSTNCOST TRANS V 4 7 = 1 8 4 . 2 STRATA = V 4 4 : 6 1 LABEL = CONSTNC0ST TRANS V 4 7 = 1 8 9 . 9 STRATA=V44:62 LABEL=C0NSTNC0ST TRANS V 4 7 = 1 9 4 . 5 STRATA=V44:63 LABEL=CONSTNCOST TRANS V 4 8 = V 4 7 / 1 0 0 . 0 STRATA=NONE LABEL=GRRTCOST TRANS V 4 9 = 2 . 9 STRATA=V44:36 LABEL=RENTGRTH TRANS V49=3.1 STRATA=V44:37 LABEL=RENTGRTH TRANS V 4 9 = 2 . 9 STRATA=V44:38 LABEL=RENTGRTH TRANS V 4 9 = 1 4 . 9 STRATA=V44:39 LABEL=RENTGRTH TRANS V 4 9 = 1 4 . 3 STRATA=V44:40 LABEL=RENTGRTH TRANS V 4 9 M 3 . 4 STRATA = V44 : 42 LABEL=RENTGRTH TRANS V 4 9 = 5 . 8 STRATA=V44:45 LABEL=RENTGRTH TRANS V49 = 5 . 6 STRATA = V 4 4 : 4 7 LABEL = RENTGRTH TRANS V 4 9 = 5 . 4 STRATA=V44:50 LABEL=RENTGRTH TRANS V 4 9 = 1 0 . 3 STRATA=V44:53 LABEL=RENTGRTH TRANS V 4 9 = 1 0 . 1 STRATA=V44:54 LABEL=RENTGRTH TRANS V49 = 5 . 6 STRATA = V44 : 55 LABEL = RENTGRTH 3  3  . '  O  > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >  107 108 109 1 10 1 1 1 1 12 1 13 114 1.15 1 '1.6 1 I? 118 1 19 120. 121 122 123 124 125 126 127 128 129 130 131 132 133 134 136 137 138 139 140 14 1 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167  TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS  V49-= 5 6 . STRATA=V44:56 LABEL=RENTGRTH V49-= 5 5 . STRATA=V44:57 LABEL=RENTGRTH V49 = 5 4 . STRATA=V44:58 LABEL=RENTGRTH V49 = 4 3 . STRATA=V44:59 LABEL=RENTGRTH V49 = 4 .1 S T R A T A = V 4 4 : 6 0 LABEL=RENTGRTH V49 = 4 2 . STRATA=V44:61 LABEL=RENTGRTH V49 = 4 0 . STRATA=V44:62 LABEL=RENTGRTH V49 =0. 0 STRATA=V44:63 LABEL=RENTGRTH V50 = 10. 7 STRATA=V44 36 LABEL=COSTGRTH V50 = 6 3 . STRATA=V44:37 LABEL=COSTGRTH V50 = 1.51 STRATA=V44 38 LABEL=COSTGRTH V50 = 1. 1 2 STRATA=V44 39 LABEL=COSTGRTH V50 = 2 0 . 1 STRATA=V44 40 LABEL=COSTGRTH V50 = 17. 2 STRATA=V44 42 LABEL=COSTGRTH V50 = 6 4 . STRATA=V44:45 LABEL=COSTGRTH V50 = 4 5 . STRATA=V44:47 LABEL=COSTGRTH V50 = 18. 2 STRATA=V44 50 LABEL=COSTGRTH V50 = 9 .3 STRATA=V44:53 LABEL=COSTGRTH V50 = 13.0' STRATA=V44 54 LABEL=COSTGRTH V50 = 8 .5 STRATA=V44:55 LABEL=COSTGRTH V50 = 7 6 . STRATA=V44:56 LABEL=COSTGRTH V50 = 7 .2 STRATA=V44:57 LABEL=COSTGRTH V50 = 13. 7 STRATA=V44 58 LABE L = C0STGRTH V50 = 13. 3 STRATA=V44 59 LABE L = C0STGRTH V50 = 2 0 . S T R A T A = V 4 4 : 6 0 LABEL=COSTGRTH V50 = 8 .7 STRATA=V44:61 LABEL=C0STGRTH V50 = 13. 0 STRATA=V44 62 LABEL=COSTGRTH V50 = 1 0 . 0 STRATA=V44 63 LABEL=COSTGRTH V51 = 3 .13 STRATA=V44 36 CASES=ALL LABEL=P0PGRTH V51 = 3 .40 STRATA=V44 37 LABEL=POPGRTH V51 = 2 .12 STRATA=V44 38 LABEL=POPGRTH V5 1 = 191. STRATA=V44 39 LABEL=POPGRTH V51 = 2 .77 STRATA=V44 40 LABEL=POPGRTH V51 = 2 .10 STRATA=V44 42 LABEL=POPGRTH V51 = 4 01 . STRATA=V44 45 LABEL=POPGRTH V51 = 3 08 . STRATA=V44 47 LABEL=POPGRTH V51 = 160. STRATA=V44 50 LABEL=POPGRTH V51 = 151. STRATA=V44 53 LABE L = POPGRTH V5 1 =0. 77 STRATA=V44 54 LABEL=POPGRTH V5 1 = 1 27 STRATA=V44 55 LABEL=POPGRTH V5 1 = 106 STRATA=V44 56 LABEL=POPGRTH V51 = i 05 STRATA=V44 57 LABEL=POPGRTH V5 1 = 1 07 STRATA=V44 58 LABEL=POPGRTH V51 = 1 18 STRATA=V44 59 LABEL=POPGRTH V51 = 1 60 STRATA=V44 •60 LABEL=POPGRTH V51 = 180 STRATA=V44 •61 LABEL=POPGRTH V51 = 104 STRATA=V44 :62 LABEL=POPGRTH V51 = 145 STRATA=V44 :63 LABEL=POPGRTH V52 = 6 20 STRATA=V44 : 36 LABEL=GRRLINC V52 = 6 88 STRATA=V44 : 37 LABE L = GRRLINC V52 = 8 10 STRATA=V44 : 38 LABEL=GRRLINC V52 = 9 04 STRATA=V44 : 39 LABEL=GRRLINC V52 = 8 17 STRATA=V44 : 40 LABE L = GRRLINC V52 = 8 20 STRATA=V44 :42 LABEL=GRRLINC V52 = 6 84 STRATA=V44 :45 LABEL = GRRLINC V52 = 151 STRATA=V44 :47 LABEL=GRRLINC V52 = 3 86 STRATA=V44 : 50 LABEL=GRRLINC V52 = 4 21 STRATA=V44 : 53 LABEL=GRRLINC V52 . 8 0 STRATA=V44:54 LABEL=GRRLINC V52 = 7 15 STRATA=V44 : 55 LABEL=GRRLINC  I —«  I  > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >  168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227  TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS • TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS  V 5 2 = 6 . 2 2 STRATA=V44:56 LABEL=GRRLINC V52 = 4 . 7 6 STRATA = V 4 4 : 5 7 LABEL = GRRLINC V 5 2 = 2 . 2 0 STRATA=V44:58 LABEL=GRRLINC V 5 2 = 3 . 0 1 STRATA=V44:59 LABEL=GRRLINC V 5 2 = 1 . 9 7 STRATA=V44:60 LABEL=GRRLINC V 5 2 = 2 . 0 2 STRATA=V44:61 LABEL=GRRLINC V 5 2 = - 0 . 4 7 STRATA=V44:62 LABEL=GRRLINC V 5 2 = 1 . 1 3 STRATA=V44:63 LABEL=GRRLINC V 5 3 = 3 . 9 6 STRATA=V44:36 LABEL=INFLATION V 5 3 = 4 . 1 6 STRATA = V 4 4 : 3 7 LABEL = INFLATI ON V 5 3 = 4 . 1 9 STRATA=V44:38 LABEL=INFLATION V53 = 3 . 8 5 STRATA = V 4 4 : 3 9 LABEL=INFLAT ION V 5 3 = 3 . 8 5 . STRATA=V44:40 LABEL=INFLATION V53 = 5.91 STRATA = V 4 4 : 4 2 LABEL=INFLAT I ON V53 = 9 . 6 4 STRATA = V 4 4 : 4 5 LABEL = INFLAT ION V 5 3 = 1 2 . 2 3 STRATA = V 4 4 : 4 7 LABEL = INFLATI ON V53=1 1.02 STRATA = V 4 4 : 5 0 LABEL = INFLATI ON V53 = 9 . 7 8 STRATA = V 4 4 : 5 3 LABEL=INFLAT ION V 5 3 = n . 3 5 STRATA = V44 : 51 LABEL = INFLATION V 5 3 = 9 . 0 6 STRATA=V44:55 LABEL=INFLATION V 5 3 = 8 . 6 8 STRATA=V44:56 LABEL=INFLATION V 5 3 = 8 . 3 3 STRATA = V 4 4 : 5 7 LABEL=INFLAT ION V53 = 6 . 4 4 STRATA = V 4 4 : 5 8 LABEL = INFLAT I ON V 5 3 = 6 . 7 1 STRATA=V44:59 LABEL=INFLATION V 5 3 = 7 . 0 7 STRATA = V 4 4 : 6 0 LABEL = I NFLAT I ON V53 = 7 . 4 5 STRATA = V 4 4 : 6 1 LABEL = INFLAT I ON . V53 = 7 . 8 5 STRATA = V 4 4 : 6 2 LABEL = INFLAT I ON V53 = 8 . 0 5 STRATA=V44:63 LABEL=INFLAT ION V 5 4 = - 0 . 1 7 STRATA=V44:36 LABEL=GRRLRNT V54 = - 2 . 3 7 STRATA = V4"4 : 37 LABEL=GRRLRNT V 5 4 = - 1 . 0 4 STRATA=V44:38 LABEL=GRRLRNT V 5 4 = 1 1 . 8 4 STRATA=V44:39 LABEL=GRRLRNT V 5 4 = 1 0 . 7 8 STRATA=V44:40 LABEL=GRRLRNT V 5 4 = 3 . 0 5 STRATA=V44:42 LABEL=GRRLRNT V 5 4 = - 4 . 3 8 STRATA=V44:45 LABEL=GRRLRNT V54 = - 8 . 0 2 STRATA = V44 :47 LABEL=GRRLRNT V 5 4 = - 3 . 7 4 STRATA=V44:50 LABEL=GRRLRNT V54 = 3 . 1 2 STRATA = V44 :53 LABEL = GRRLRNT V 5 4 = - 5 . 0 3 STRATA=V44:54 LABEL=GRRLRNT V 5 4 = - 0 . 1 4 STRATA=V44:55 LABEL=GRRLRNT V 5 4 = - 0 . 7 1 STRATA=V44:56 LABEL=GRRLRNT V 5 4 = - 0 . 1 2 STRATA=V44:57 LABEL=GRRLRNT V 5 4 = - 2 . 3 9 STRATA=V44:58 LABEL=GRRLRNT V 5 4 = - 2 . 4 8 STRATA=V44:59 LABEL=GRRLRNT V 5 4 = - 3 . 4 1 STRATA=V44:60 LABEL=GRRLRNT V 5 4 = - 2 . 8 0 STRATA=V44:6 1 LABEL=GRRLRNT V54 = - 5 . 1 3 STRATA = V44 :62 LABEL=GRRLRNT V 5 4 = - 7 . 1 7 STRATA=V44:63 LABEL=GRRLRNT V55=V23-V53 STRATA=NONE LABEL=REALINT V56=V45/V38 STRATA=NONE CASES=378-496 LABEL=RE V 5 7 = 8 . 1 7 STRATA=V44:36 CASES=ALL LABEL=CCOSTBC V 5 7 = 6 . 0 6 STRATA=V44:37 LABEL=CCOSTBC V 5 7 = 9 . 4 4 STRATA=V44:38 LABEL=CCOSTBC V 5 7 = 1 0 . 3 7 STRATA=V44:39 LABEL=CCOSTBC V 5 7 = 1 6 . 2 2 STRATA=V44:40 LABEL=CCOSTBC V 5 7 = 1 1 . 5 2 STRATA=V44:42 LABEL=CCOSTBC V 5 7 = 1 3 . 9 2 STRATA=V44:45 LABEL=CCOSTBC V 5 7 = 1 1 . 8 4 STRATA=V44:47 LABEL=CCOSTBC V57=2 1.32 STRATA=V44:50 LABEL=CCOSTBC V 5 7 = 1 1 . 5 8 STRATA=V44:53 LABEL=CCOSTBC  > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >  > >  >  228 229 230 23 1 232 233 234 235 236 237 238 2 39 240 24 1 242 243 244 245 246 247 248 249 250 25 1 252 253 254 255 256 257 258 259 260 26 1 262 263 264 265 266 267 268 269 270 27 1 272 273 274 275 276 277 278 279 280 28 1 282 283 284 285 286 287  TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS . TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS  V57 =8 .02 STRATA=V44: 54 LABEL=CCOSTBC V57 = 10. 77 STRATA=V44 : 55 LABEL=CCOSTBC V57 = 1 1 . STRATA=V44 52 : 56 LABEL=CCOSTBC V57 =9 . 94 STRATA = V44 :57 LABEL=CCOSTBC V57 = 1 3 . 1 7 STRATA=V44 :58 LABEL=CCOSTBC V57 = 13 . 72 STRATA=V44 : 59 LABEL=CCOSTBC V57 =3 . 26 STRATA=V44: 60 LABEL=CCOSTBC V57 = 1 0 . 8 4 STRATA=V44 :61 LABEL=CCOSTBC V57 = 13 .03 STRATA=V44 :62 LABEL=CCOSTBC V57 =8 .72 STRATA=V44: 63 LABEL=CCOSTBC V58 =1 . 20 STRATA=V44: 36 LABEL=RNTGRTH2 V58 = 1 . 19 STRATA=V44: 37 LABEL=RNTGRTH2 V58 = 1 . 59 STRATA=V44: 38 LABEL=RNTGRTH2 V58 = 1 .98 STRATA=V44: 39 LABEL=RNTGRTH2 V58 = 1 . 97 STRATA=V44: 40 LABEL=RNTGRTH2 V58 =3 . 53 STRATA=V44: 42 LABEL=RNTGRTH2 V58 =3 .81 STRATA=V44: 45 LABEL=RNTGRTH2 V58 =7 . 15 STRATA=V44: 47 LABEL=RNTGRTH2 V58 =7 . 95 STRATA=V44: 50 LABEL=RNTGRTH2 V58 =7 . 12 STRATA=V44: 53 LABEL=RNTGRTH2 V58 =5 .97 STRATA=V44: 54 LABEL=RNTGRTH2 V58 =7 . 57 STRATA=V44: 55 LABEL=RNTGRTH2 V58 =7 . 43 STRATA = V44 :56 LABEL=RNTGRTH2 V58 =6 . 96 STRATA=V44: 57 LABEL=RNTGRTH2 V58 =5 . 26 STRATA=V44: 58 LABEL=RNTGRTH2 V58 =4 . 88 STRATA=V44: 59 LABEL=RNTGRTH2 V58 =3 . 90 STRATA=V44: 60 LABEL=RNTGRTH2 V58 =2 . 37 STRATA=V44: 61 LABEL==RNTGRTH2 V58 =2 . 95 STRATA=V44: 62 LABEL=RNTGRTH2 V58 =3 . 82 STRATA=V44: 63 LABEL=RNTGRTH2 V59 = 1 .31 STRATA=V44: 36 LABEL=RNTGRTH3 V59 =: —I0.94 i STRATA=V44l : 37 '' LABEL = RNTGRTH3 V59 =0 . 50 STRATA=V44: 38 LABEL=RNTGRTH3 V59 =••- 7.62 STRATA=V44:32I LABEL=RNTGRTH3 V59 = 8 . 1 2 S T R A T A = V 4 4 : 4 0 LABEL=RNTGRTH3 V59 == - 13.96 STRATA=V44:42 LABEL=RNTGRTH3 V59 == 1 0 . 2 7 S T R A T A = V 4 4 : 4 5 LABEL=RNTGRTH3 V59 = 6.21 I STRATA=V44:47' LABEL=RNTGRTH3 V59 == - 2 . 0 0 S T R A T A = V 4 4 : 5 0 LABEL=RNTGRTH3 V59 =M O . 2 0 STRATA=V44:53 LABEL=RNTGRTH3 V59 == 1 . 0 8 STRATA=V44: 54 LABEL=RNTGRTH3 V59 ==17.41I STRATA=V44:55 LABEL=RNTGRTH3 V59 ==15.54 STRATA=V44:56 LABEL=RNTGRTH3 V59 ==15.13 STRATA=V44:57 LABEL=RNTGRTH3 V59 ==11.57 STRATA=V44:58 LABEL=RNTGRTH3 V59 = =10.92 STRATA=V44:59 LABEL=RNTGRTH3 = 9. 29 STRATA=V44::60 LABEL=RNTGRTH3 V59 = = 9. 18 STRATA=V44::61 LABEL=RNTGRTH3 V59 = V59 = = 6. 0 9 STRATA=V44;:62 LABEL=RNTGRTH3 = - 1 4 . 8 9 STRATA=V44:63 LABEL=RNTGRTH3 V59 = V60= 2 . 8 8 STRATA=V44:36 LABEL=LAGRENT V60== - 2 . 0 2 STRATA=V44:37 LABEL=LAGRENT V60=--- 4 . 3 3 STRATA=V44:38 LABEL=LAGRENT --- 2 . 3 6 STRATA=V44:39 LABEL=LAGRENT V60 = V60=--- 0 . 7 5 S T R A T A = V 4 4 : 4 0 LABEL=LAGRENT V60=--- 5 . 8 6 STRATA=V44:42 LABEL=LAGRENT V60=--- 4 . 0 7 STRATA=V44:45 LABEL=LAGRENT V60=--- 9 . 9 9 STRATA=V44:47 LABEL=LAGRENT V60=- - 2 . 6 3 S T R A T A = V 4 4 : 5 0 LABEL=LAGRENT V60==G1 . 1 6 STRATA=V44 : 53 LABEL=LAGRENT  CO  > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >  288 289 290 29 1 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 31 1 312 313 314 315 3 16 317 3 18 3 1.9 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347  TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS  V60= 0 . 1 1 STRATA=V44:54 LABEL=LAGRENT V60 =- 9 . 9 3 S T R A T A = V 4 4 : 5 5 LABEL=LAGRENT V60 = 1.77 STRATA=V44:56 LABEL=LAGRENT V60= 1.11 STRATA = V44 :57 LABEL = LAGRENT V 6 0 = 1.33 S T R A T A = V 4 4 : 5 8 LABEL=LAGRENT V60 =- 2 . 7 5 S T R A T A = V 4 4 : 5 9 LABEL=LAGRENT V60 =- 2 . 0 2 S T R A T A = V 4 4 : 6 0 LABEL=LAGRENT V60= - 3 . 8 4 STRATA=V44:61 LABEL=LAGRENT V60= - 4 . 7 9 STRATA=V44:62 LABEL=LAGRENT V60= - 6 . 6 8 STRATA=V44:63 LABEL=LAGRENT V6 16=. 3 7 S T R A T A = V 4 4 : 3 6 LABEL=LAGCOSTS V6 1 4=. 9 5 STRATA=V44:37 LABEL=LAGCOSTS V6 10=. 5 4 S T R A T A = V 4 4 : 3 8 LABEL=LAGCOSTS V6 1 5=. 4 9 S T R A T A = V 4 4 : 3 9 LABEL=LAGCOSTS V6 1 7=. 6 4 S T R A T A = V 4 4 : 4 0 LABEL=LAGCOSTS V6 1 5=. 1 7 S T R A T A = V 4 4 : 4 2 LABEL=LAGCOSTS V6 1 2=. 2 4 S T R A T A = V 4 4 : 4 5 LABEL=LAGCOSTS V61 =- 5 . 1 5 STRATA=V44:47 LABEL=LAGCOSTS V6 1 -=7 . 1 2 S T R A T A = V 4 4 : 5 0 LABEL=LAGCOSTS V6 1 -=0 . 2 4 STRATA=V44:53 LABEL=LAGCOSTS V6 1 4=. 5 7 STRATA = V 4 4 : 5 4 LABEL = LAGCOSTS V6 1 -=7 . 8 8 STRATA=V44:55 LABEL=LAGCOSTS V6 14=. 9 7 S T R A T A = V 4 4 : 5 6 LABEL=LAGCOSTS V6 1 5=. 2 0 STRATA=V44:57 LABEL=LAGCOSTS V6 1 4=. 3 1 S T R A T A = V 4 4 : 5 8 LABEL=LAGCOSTS V6 1 5=. 1 6 S T R A T A = V 4 4 : 5 9 LABEL=LAGCOSTS V6 1 6=. 8 2 S T R A T A = V 4 4 : 6 0 LABEL=LAGCOSTS V61 =- 4 . 4 8 STRATA=V44:61 LABEL=LAGCOSTS V6 1 3=. 6 8 S T R A T A = V 4 4 : 6 2 LABEL=LAGCOSTS V61 =3 . 4 0 S T R A T A = V 4 4 : 6 3 LABEL=LAGCOSTS V62 =V58-V53 LABEL = RNTGRTH4 STRATA = NONE V63 =V57-V53 LABEL = CC0STBC2 STRATA = NONE V64 = 10.41 STRATA=V44:36 LABEL=CAPRATE V64 =7 . 72 -STRATA = V 4 4 : 3 7 LABEL = CAPRATE V64 =9 . 8 3 STRATA = V44 : 38 LABEL = CAPRATE V64 = 1 0 . 2 0 S T R A T A = V 4 4 : 3 9 LABEL=CAPRATE V64 = 1 0 . 5 2 S T R A T A = V 4 4 : 4 0 LABEL=CAPRATE V64 = 1.74 S T R A T A = V 4 4 : 4 2 LABEL=CAPRATE V64 = 1.20 S T R A T A = V 4 4 : 4 5 LABEL=CAPRATE V64 =- 2 . 7 6 STRATA=V44:47 LABEL=CAPRATE V64 =3 . 6 4 S T R A T A = V 4 4 : 5 0 LABEL=CAPRATE V64 =4 . 8 1 S T R A T A = V 4 4 : 5 3 LABEL=CAPRATE V64 =- 4 . 9 0 STRATA=V44:54 LABEL=CAPRATE V64 =6 . 4 8 S T R A T A = V 4 4 : 5 5 LABEL=CAPRATE V64 =6 . 6 3 S T R A T A = V 4 4 : 5 6 LABEL=CAPRATE V64 =6 . 2 0 STRATA=V44:57 LABEL=CAPRATE V64 =5 . 3 4 STRATA=V44:58 LABEL=CAPRATE V64 =5 . 0 6 S T R A T A = V 4 4 : 5 9 LABEL=CAPRATE V64 =5 . 4 4 S T R A T A = V 4 4 : 6 0 LABEL=CAPRATE V64 =6 . 6 1 STRATA=V44:61 LABEL=CAPRATE V64 =2 . 7 3 S T R A T A = V 4 4 : 6 2 LABEL=CAPRATE V64 =5 . 7 3 STRATA=V44:63 LABEL=CAPRATE V65 =9 . 8 4 S T R A T A = V 4 4 : 3 6 LABEL=CAPRTLAG V65 = 10.41 STRATA=V44:37 LABEL=CAPRTLAG V65 =7 . 7 2 S T R A T A = V 4 4 : 3 8 LABEL=CAPRTLAG V65 =9 . 8 3 S T R A T A = V 4 4 : 3 9 LABEL=CAPRTLAG V65 = 1 0 . 2 0 S T R A T A = V 4 4 : 4 0 LABEL=CAPRTLAG V65 =6 . 3 4 STRATA=V44:42 LABEL=CAPRTLAG V65 =4 . 4 3 S T R A T A = V 4 4 : 4 5 LABEL=CAPRTLAG V65 =- 5 . 1 0 S T R A T A = V 4 4 : 4 7 LABEL=CAPRTLAG  v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v ti Ju ^ ^ A ft U C J t J U C J U C J Q U U U . 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155 -  -  > > > co co CO m- m I r- m n ti r 2 Z II -  II  > > > 00 CO CO 03 m CO 03 00 t n m m r~  >  m m m  i—  ti  ti  it n  II z z Z Z z z z> o o o O O O 73 ooo 2 2 3 2 2 73  3 CD > T3 73  3 CD O > > 73 TJ T! 73 —4  CD 2 3 CD > O CD > TJ > > T l TJ TJ TJ 73 H 73 73 H  CD CD —I >  >  I  -  73 TJ > 73 73 CD —4 H  > >  Ul  ft U l Ul CJ ft Ul  cn Ul  > I 03 CO CO 00 >  -  3> »  CO CO CO tn tn m m m m t — r- i — i — 00 m m rII i— iCD CD CD CD CD CD 11 CD  > > > > > >  73 T> TJ "0 TJ TJ 73 T l TJ TJ 73 TJ  Ul  O  |— |— I  -  >  CO CO m m IIr II CD CD  > > >  T l 73 73 73 73 73  > i > > > j > > r - > > > CDCDCDCDCDCD»CDCDCD CD  ' > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >• > > > > > > > > > > > > > > > > > >  408 409 410 411 412 413 414 415 416 417 4 18 419 420 421 422 423 424 425 426 427 428' 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467  TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS  V 6 8 = 7 . 6 9 S T R A T A = V 4 4 : 5 0 LABEL=CAPGNLAG V 6 8 = - 1 . 0 0 STRATA=V44:53 LA8EL=CAPGNLAG V 6 8 = - 1 . 4 1 STRATA=V44:54 LABEL=CAPGNLAG V 6 8 = - 5 . 9 0 STRATA=V44:55 LABEL=CAPGNLAG V 6 8 = 2 . 7 2 S T R A T A = V 4 4 : 5 6 LABEL=CAPGNLAG V 6 8 = 3 . 8 4 STRATA=V44:57 LABEL=CAPGNLAG V 6 8 = 9 . 2 1 STRATA=V44:58 LABEL=CAPGNLAG V 6 8 = 3 . 8 7 S T R A T A = V 4 4 : 5 9 LABEL=CAPGNLAG V 6 8 = 6 . 4 9 S T R A T A = V 4 4 : 6 0 LABEL=CAPGNLAG V 6 8 = 1 3 . 0 3 STRATA=V44:61 LABEL=CAPGNLAG V 6 8 = 7 . 7 3 STRATA=V44:62 LABEL=CAPGNLAG V 6 8 = 2 . 4 4 STRATA=V44:63 LABEL=CAPGNLAG V 6 9 = 1 . 8 9 STRATA =V44:36 LABEL = P0PLAG V 6 9 = 3 . 1 3 STRATA=V44:37 LABEL=P0PLAG V 6 9 = 3 . 4 0 STRATA=V44:38 LABEL=P0PLAG V 6 9 = 2 . 1 2 S T R A T A = V 4 4 : 3 9 LABEL=P0PLAG V 6 9 = 1 . 9 1 S T R A T A = V 4 4 : 4 0 LABEL=P0PLAG V69 = 3 . 3 9 STRATA = V 4 4 : 4 2 LABEL = P0PLAG V 6 9 = 3 . 3 9 STRATA=V44 : 45 LABEL=P0PLAG V 6 9 = 2 . 5 3 STRATA=V44:47 LABEL=P0PLAG V 6 9 = 2 . 9 5 S T R A T A = V 4 4 : 5 0 LABEL=P0PLAG V 6 9 = 1 . 7 2 STRATA=V44:53 LABEL=POPLAG V 6 9 = 1 . 5 1 STRATA=V44:54 LABEL=POPLAG V 6 9 = 0 . 7 7 STRATA=V44:55 LABEL=P0PLAG V 6 9 = 1 . 2 7 STRATA=V44:56 LABEL=P0PLAG V 6 9 = 1 . 0 6 STRATA = V 4 4 : 5 7 LABEL = POPLAG V 6 9 = 1 . 0 5 STRATA = V44 : 58 LABEL=POPLAG V 6 9 = 1 . 0 7 STRATA = V 4 4 : 5 9 LABE L = POPLAG V 6 9 = 1 . 1 8 S T R A T A = V 4 4 : 6 0 LABEL=POPLAG V 6 9 = 1 . 6 0 STRATA=V44:61 LABEL=POPLAG V69= 1 . 80 STRATA = V 4 4 : 6 2 LABEL = POPLAG V 6 9 = 1 . 0 4 STRATA=V44:63 LABEL = POPLAG V70=V64-V55 STRATA=NONE CASES=378-496 LABEL=RLA V 7 1 = 5 . 5 5 STRATA=V44:36 LABEL=RLINCLAG CASES=ALL V 7 1 = 6 . 2 0 STRATA=V44:37 LABEL=RLINCLAG V 7 1 = 6 . 8 8 STRATA=V44:38 LABEL=RLINCLAG V 7 1 = 8 . 1 0 STRATA = V 4 4 : 3 9 LABEL = RLINCLAG V 7 1 = 9 . 0 4 S T R A T A = V 4 4 : 4 0 LABEL=RLINCLAG V 7 1 = 9 . 6 9 STRATA=V44:42 LABEL=RLINCLAG V 7 1 = 7 . 5 6 STRATA=V44:45 LABEL=RLINCLAG V 7 1 = 1 . 4 7 STRATA=V44:47 LABEL=RLINCLAG V 7 1 = 5 . 1 6 S T R A T A = V 4 4 : 5 0 LABEL=RLINCLAG V 7 1 = 3 . 8 9 STRATA=V44:53 LABEL=RLINCLAG V 7 1 = 4 . 2 1 STRATA=V44:54 LABEL=RLINCLAG V 7 1 = - 1 . 8 0 STRATA=V44:55 LABEL=RLINCLAG V 7 1 = 7 . 1 5 STRATA=V44:56 LABEL=RLINCLAG V 7 1 = 6 . 2 2 STRATA=V44:57 LABEL=RLINCLAG V 7 1 = 4 . 7 6 STRATA=V44:58 LABEL=RLINCLAG V 7 1 = 2 . 2 0 STRATA=V44:59 LABEL=RLINCLAG V 7 1 = 3 . 0 1 S T R A T A = V 4 4 : 6 0 LABEL=RLINCLAG V71 = 1 . 97 STRATA = V 4 4 : 6 1 LABEL = RLINCLAG V 7 1 = 2 . 0 2 STRATA=V44:62 LABEL=RLINCLAG V 7 1 = - 0 . 4 7 STRATA=V44:63 LABEL=RLINCLAG V 7 2 = 2 . 9 0 STRATA=V44:36 LABEL=VACRTLAG V 7 2 = 2 . 1 0 STRATA=V44:37 LABEL=VACRTLAG V 7 2 = 2 . 0 0 STRATA=V44:38 LABEL=VACRTLAG V 7 2 = 1 . 9 0 S T R A T A = V 4 4 : 3 9 LABEL=VACRTLAG V 7 2 = 1 . 1 5 S T R A T A = V 4 4 : 4 0 LABEL=VACRTLAG V 7 2 = 0 . 5 0 STRATA=V44:42 LABEL=VACRTLAG V 7 2 = 0 . 2 0 S T R A T A = V 4 4 : 4 5 LABEL=VACRTLAG  VD  > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >  468 469 470 47 1 472 473 474 475 476 477 478 479 480 48 1 482 483 484 485 486 487 488 489 490 49 1 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 5 10 51 1 512 513 514 515 516 517 518 519 5 20 521 522 523 524 525 527 528  TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS  V72 =0. 20 STRATA = V44 :47 LABEL= VACRTLAG V72 =0. 10 STRATA = V44 :50 LABEL= VACRTLAG V72 =0. 10 STRATA = V44 :53 LABEL= VACRTLAG V72 =0. 15 STRATA = V44 :54 LABEL= VACRTLAG V72 = 0 . 20 STRATA = V44 :55 LABEL= VACRTLAG V72 =0. 35 STRATA = V44 :56 LABEL= VACRTLAG V72 =0. 50 STRATA = V44 :57 LABEL= VACRTLAG V72 =0. 80 STRATA = V44 :58 LABEL= VACRTLAG V72 = 110 . STRATA = V44 :59 LABEL= VACRTLAG V72 = 105 . STRATA = V44 :6 0 LABEL= VACRTLAG V72 = 100. STRATA = V44 :6 1LABEL= VACRTLAG V72 = 110 . STRATA = V44 :62 LABEL= VACRTLAG V72 = 120 . STRATA = V44 :63 LABEL= VACRTLAG V73 = 5 .22 STRATA = V44 :36 LABEL= RLINTLAG V73 = 6 .1 1STRATA = V44 :37 LABEL= RLINTLAG V73 = 3 .47 STRATA = V44 :38 LABEL= RLINTLAG V73 = 4 .94 STRATA = V44 :39 LABEL= RLINTLAG V73 = 6 .38 STRATA = V44 :40 LABEL= RLINTLAG V73 = 120 . STRATA = V44 :42 LABEL= RLINTLAG V73 = 160 . STRATA = V44 :45 LABEL= RLINTLAG V73 = - 5 . 8 9 STRATA=V44:47' LABEL =RLINTLAG V73 = 3 .18 STRATA = V44 :50 LABEL= RLINTLAG V73 = 3 .93 STRATA = V44 :53 LABEL= RLINTLAG V73 = 4 .80 STRATA = V44 :54 LABEL= RLINTLAG V73 =-4.02 S T R A T A = V 4 4 : 5 5 LABEL =RLINTLAG V73 = 5 .99 STRATA = V44 :56 LABEL= RLINTLAG V73 = 5 .44 STRATA = V44 :57 LABEL= RLINTLAG V73 = 5 .48 STRATA = V44 :58 LABEL= RLINTLAG V73 = 2 .49 STRATA = V44 :59 LABEL= RLINTLAG V73 = 3 .6 1STRATA = V44 :60 LABEL= RLINTLAG V73 = 2 .77 STRATA = V44 :6 1LABEL= RLINTLAG V73 = 3 .18 STRATA = V44 :62 LABEL= RLINTLAG V73 = 0 . 82 STRATA = V44 :63 LABEL= RLINTLAG V74 = 4 .62 STRATA = V44 :36 LABEL= APRTNLAG V74 = 4 .30 STRATA = V44 :37 LABEL= APRTNLAG V74 = 4 .25 STRATA = V44 :38 LABEL= APRTNLAG V74 = 4 .89 STRATA = V44 :39 LABEL= APRTNLAG V74 = 3 .82 STRATA = V44 :40 LABEL= APRTNLAG V74 = 5 .14 STRATA = V44 :42 LABEL= APRTNLAG V74 = 2 .83 STRATA = V44 :45 LABEL= APRTNLAG V74 = 0 . 79 STRATA = V44 :47 LABEL= APRTNLAG V74 = 2 .06 STRATA = V44 :50 LABEL= APRTNLAG V74 = 199 . STRATA = V44 :53 LABEL= APRTNLAG V74 = 0 . 01 STRATA = V44 :54 LABEL= APRTNLAG V74 =-0.88 STRATA=V44:55 LABEL =APRTNLAG V74 =0. 49 STRATA = V44 :56 LABEL= APRTNLAG V74 = 119 . STRATA = V44 :57 LABEL= APRTNLAG V74 = 0 . 72 STRATA = V44 :58 LABEL= APRTNLAG V74 = 2 .85 STRATA = V44 :59 LABEL= APRTNLAG V74 = 145 . STRATA = V44 :60 LABEL= APRTNLAG V74 = 2 .67 STRATA = V44 :6 1LABEL= APRTNLAG V74 = 3 .43 STRATA = V44 :62 LABEL= APRTNLAG V74 = 191 . STRATA = V44 :63 LABEL= APRTNLAG V75 = 4 .08 STRATA = V44 ':36 LABEL= INFLALAG V75 = 3 .22 STRATA = V44 :37 LABEL= INFLALAG V75 = 5 .52 STRATA = V44 :38 LABEL= INFLALAG V75 = 3 .95 STRATA = V44 :39 LABEL= INFLALAG V75 = 2 .73 STRATA = V44 :40 LABEL= INFLALAG V75 = 7 .82 STRATA = V44 :42 LABEL= INFLALAG V75 = 7 .91 STRATA = V44 :45 LABEL= INFLALAG  •A  I  > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >  529 530 531 532 533 534 535 536 537 538 539 540 54 1 542 543 544 545 546 547 548 549 550 55 1 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 58 1 582 583 584 585 586 587 588  TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS  V75 = 1 5 . 6 8 STRATA=V44:47 LABEL=INFLALAG V75 =7 . 7 0 S T R A T A = V 4 4 : 5 0 LABEL=INFLALAG V75 =7.81 STRATA=V44:53 LABEL=INFLALAG V75 =7.01 STRATA = V44 : 54 LABEL = INFLALAG V75 = 1 5 . 9 0 STRATA=V44:55 LABEL=INFLALAG V75 =5 . 8 0 S T R A T A = V 4 4 : 5 6 LABEL=INFLALAG V75 =6 . 3 2 STRATA = V44 : 57 LABEL=INFLALAG V75 =5 . 6 3 STRATA=V44:58 LABEL=INFLALAG V75 =8.01 S T R A T A = V 4 4 : 5 9 LABEL=INFLALAG V75 =6 . 9 0 S T R A T A = V 4 4 : 6 0 LABEL=INFLALAG V75 =7 . 7 4 STRATA = V44 :61 LABEL = INFLALAG V75 =7 . 1 6 STRATA=V44:62 LABEL=INFLALAG V75 =9 . 6 3 STRATA=V44:63 LABEL=INFLALAG V76 = . 0 3 STRATA = V44 : 36 LABEL = INTCHGE V76 =- . 3 4 STRATA=V44:37 LABEL=INTCHGE V76 =- . 1 0 STRATA=V44:38 LABEL=INTCHGE V76 = .22 S T R A T A = V 4 4 : 3 9 LABEL=INTCHGE V76 = . 0 3 S T R A T A = V 4 4 : 4 0 LABEL=INTCHGE V76 =- . 0 5 STRATA=V44:42 LABEL=INTCHGE V76 = . 1 6 STRATA=V44:45 LABEL=INTCHGE V76 = . 8 3 STRATA = V44 :47 LABEL = INTCHGE V76 =- . 5 8 S T R A T A = V 4 4 : 5 0 LABEL=INTCHGE V76 = . 0 7 STRATA = V44 :53 LABEL = INTCHGE V76 = . 0 7 STRATA=V44:54 LABEL=INTCHGE V76 =- . 0 9 STRATA=V44:55 LABEL=INTCHGE V76 =- . 0 3 STRATA=V44:56 LABEL=INTCHGE V76 =- . 6 5 STRATA = V44 : 57 LABEL=INTCHGE V76 =-.61 STRATA=V44:58 LABEL=INTCHGE V76 = .01 STRATA = V44 : 59 LABEL=INTCHGE V76 = . 0 0 S T R A T A = V 4 4 : 6 0 LABEL=INTCHGE V76 =- . 1 7 STRATA = V 4 4 : 6 1 LABEL=INTCHGE V76 = .11 STRATA=V44:62 LABEL=INTCHGE V76 = . 0 3 STRATA = V44 :63 LABEL = INTCHGE V77 =-69 STRATA=V44:36 LABEL=APTC0MCH V77 =-209 STRATA=V44:37 LABEL=APTCOMCH V77 = 174 S T R A T A = V 4 4 : 3 8 LABEL=APTCOMCH V77 = 126 STRATA=V44:39 LABEL=APTCOMCH V77 =-123 S T R A T A = V 4 4 : 4 0 LABEL=APTCOMCH V77 =283 STRATA=V44:42 LABEL=APTCOMCH V77 = 156 STRATA=V44:45 LABEL=APTCOMCH V77 =313 STRATA=V44:47 LABEL=APTCOMCH V77 =245 S T R A T A = V 4 4 : 5 0 LABEL=APTCOMCH V77 =-908 S T R A T A = V 4 4 : 5 3 LABEL=APTCOMCH V77 =497 STRATA=V44:54 LABEL=APTCOMCH V77 =-57 STRATA=V44:55 LABEL=APTCOMCH V77 =64 STRATA=V44:56 LABEL=APTCOMCH V77 =208 STRATA=V44:57 LABEL=APTCOMCH V77 =-163 STRATA=V44:58 LABEL=APTCOMCH V77 =- 1 4 0 S1RATA=V44:59 LABEL=APTCOMCH V77 =54 S T R A T A = V 4 4 : 6 0 LABEL=APTCOMCH V77 =613 STRATA=V44:6 1 LABEL = APTCOMCH V77 =-331 STRATA=V44:62 LABEL=APTCOMCH V77 = 13 S T R A T A = V 4 4 : 6 3 LABEL=APTCOMCH V78 =341 S T R A T A = V 4 4 : 3 6 - 5 4 LABEL=HHCHANGE V78 =663 STRATA = V44 : 5 5 - 6 3 LABEL = HHCHANGE V79 = . 8 9 STRATA=V44:36 LABEL=RLINTCHG V79 =- 2 . 6 4 STRATA = V44 :37 LABEL = RLINTCHG V79 = 1.47 STRATA=V44:38 LABEL=RLINTCHG V79 = 1.44 S T R A T A = V 4 4 : 3 9 LABEL=RLINTCHG V79 =- . 4 6 S T R A T A = V 4 4 : 4 0 LABEL=RLINTCHG  00  I  > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >  589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 61 1 612 613 614 615 6 16 617 6 18 6 19 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 64 1 642 643 644 645 646 647 648  TRANS V79 =- 2 . 2 9 STRATA=V44:42 LABEL=RLINTCHG TRANS V79 =- 2 . 5 9 STRATA=V44:45 LABEL=RLINTCHG TRANS V79 = 1.64 STRATA=V44:47 LABEL=RLINTCHG TRANS V79 =- 2 . 3 9 S T R A T A = V 4 4 : 5 0 LABEL=RLINTCHG TRANS V79 = . 8 7 STRATA=V44:53 LABEL=RLINTCHG TRANS V79 =- 8 . 8 2 STRATA=V44:54 LABEL=RLINTCHG TRANS V79 = 10.01 STRATA=V44:55 LABEL=RLINTCHG TRANS V79 =- . 5 5 STRATA=V44:56 LABEL=RLINTCHG TRANS V79 = .04 STRATA=V44:57 LABEL=RLINTCHG TRANS V79 =- 2 . 9 9 STRATA = V44 : 58 LABEL = RLINTCHG . TRANS V79 = 1.12 S T R A T A = V 4 4 : 5 9 LABEL=RLINTCHG TRANS V79 =- . 8 4 S T R A T A = V 4 4 : 6 0 LABEL=RLINTCHG TRANS V79 = .41 STRATA=V44:61 LABEL=RLINTCHG TRANS V79 =- 2 . 3 6 STRATA=V44:62 LABEL=RLINTCHG TRANS V79 = 1.96 STRATA=V44:63 LABEL=RLINTCHG TRANS V80= - . 3 2 STRATA=V44:36 LABEL=ERCHANGE TRANS V80 =- . 0 5 STRATA=V44:37 LABEL=ERCHANGE TRANS V80= .64 STRATA=V44:38 LABEL=ERCHANGE TRANS V80= - 1 . 0 7 S T R A T A = V 4 4 : 3 9 LABEL=ERCHANGE TRANS V80 = . 7 8 S T R A T A = V 4 4 : 4 0 LABEL=ERCHANGE TRANS V 8 0 =- 2 . 3 1 STRATA=V44:42 LABEL=ERCHANGE TRANS V80 =- . 6 4 STRATA=V44:45 LABEL=ERCHANGE TRANS V80= . 7 0 STRATA=V44:47 LABEL=ERCHANGE TRANS V80 = . 7 9 S T R A T A = V 4 4 : 5 0 LABEL=ERCHANGE TRANS V80= - 1 . 9 8 STRATA=V44:53 LABEL=ERCHANGE TRANS V80= - . 8 9 STRATA=V44:54 LABEL=ERCHANGE TRANS V80= 1.37 STRATA=V44:55 LABEL=ERCHANGE TRANS V80 = . 7 0 STRATA=V44:56 LABEL=ERCHANGE TRANS V 8 0 =- . 4 7 STRATA=V44:57 LABEL=ERCHANGE TRANS V80= 2 . 1 3 STRATA=V44:58 LABEL=ERCHANGE TRANS V80= - 1 . 4 0 S T R A T A = V 4 4 : 5 9 LABEL=ERCHANGE TRANS V80 = 1.22 S T R A T A = V 4 4 : 6 0 LABEL=ERCHANGE TRANS V80 = . 7 6 STRATA=V44:61 LABEL=ERCHANGE TRANS V 8 0 =- 1 . 5 2 STRATA=V44:62 LABEL=ERCHANGE TRANS V80 = 1.04 S T R A T A = V 4 4 : 6 3 LABEL ERCHANGE TRANS V8 1 -87 = STRATA=V44:36 LABEL=APTSTSCH TRANS V8 1 -329 = STRATA=V44:37 LABEL=APTSTSCH TRANS V8 1 -303 = STRATA=V44:38 LABEL=APTSTSCH TRANS V8 1 131 = STRATA=V44:39 L A B E L A P T S T S C H TRANS V8 1 173 = S T R A T A = V 4 4 : 4 0 LABEL=APTSTSCH TRANS V8 1 659 = STRATA=V44:42 LABEL=APTSTSCH TRANS V8 1 -61 = STRATA=V44:45 L A B E L A P T S T S C H TRANS V8 1 -689 = STRATA=V44:47 L A B E L A P T S T S C H TRANS V8 1 342 = S T R A T A = V 4 4 : 5 0 LABEL=APTSTSCH TRANS V8 1 -444 = STRATA=V44:53 L A B E L A P T S T S C H TRANS V8 1 130 = STRATA=V44:54 LABEL=APTSTSCH TRANS V8 1 575 = STRATA=V44:55 LABEL=APTSTSCH TRANS V8 1419 = STRATA=V44:56 LABEL=APTSTSCH TRANS V81 =-456 STRATA=V44:57 LABEL=APTSTSCH TRANS V8 1 30 = STRATA=V44:58 LABEL=APTSTSCH TRANS V8 1 -11 = S T R A T A = V 4 4 : 5 9 LABEL=APTSTSCH TRANS V81 =698 S T R A T A = V 4 4 : 6 0 LABEL=APTSTSCH TRANS V8 1 -737 = STRATA=V44:61 LABEL=APTSTSCH TRANS V81 =-616 STRATA=V44:62 LABEL=APTSTSCH TRANS V8 1 167 = STRATA=V44:63 LABEL=APTSTSCH CODE V82 = V7 LABE L = NEWWE CASES = ALL STRATA=NONE CODE V83=V8 LABEL=NEWKITS CASES=ALL STRATA=NONE CODE V84 = V9 LABEL = NEWEV CASES = ALL STRATA =NONE CODE V85=V10 LABEL=NEWMAR CASES=ALL STRATA=NONE CODE V86 = V11 LABE L = NEWKERR STRATA =NONE 3  3  3  3  3  I ON  > > > > > > > > > > > > > > > > > > > > > > > •> > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >  649 651 656 663 665 668 669 .670 671 672 673 674 675 677 679 680 682 684 685 687 692 694 697 698 699 700 701 702 703 704 705 706 717 724 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 752 753 754 755  TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS  V87=-.3 STRATA V44:36*V82:1 LABEL=SUBVACRT V 8 7 - - . 7 STRATA = V 4 4 : 3 8 * V 8 2 : 1 LABEL-SUBVACRT V87=.00 STRATA=V44:47*V82:1 LABEL=SUBVACRT V87=.55 STRATA=V44:58*V82:1 LABEL=SUBVACRT V87=-.1 STRATA=V44:60*V82:1 LABEL=SUBVACRT V87=-.15 STRATA=V44:63*V82:1 LABEL=SUBVACRT V87=-1.5 STRATA=V44:36*V83:1 LABEL=SUBVACRT V87=.25 STRATA=V44:37*V83:1 LABEL=SUBVACRT V87=.25 STRATA=V44:38*V83:1 LABEL=SUBVACRT V87=-.5 STRATA=V44:39*V83:1 LABEL=SUBVACRT V87=-.5 STRATA=V44:40*V83:1 LABEL=SUBVACRT V 8 7 = . 0 5 STRATA = V 4 4 : 4 2 * V 8 3 : 1 LABEL=SUBVACRT V87=.05 STRATA=V44:45*V83:1 LABEL=SUBVACRT V87=.00 STRATA=V44:50*V83:1 LABEL=SUBVACRT V87=-.05 STRATA=V44:54*V83:1 LABEL=SUBVACRT V87=.05 STRATA=V44:55*V83:1 LABEL=SUBVACRT V87=.1 STRATA=V44:57*V83:1 LABEL=SUBVACRT V87=-.1 STRATA=V44:59*V83:1 LABEL=SUBVACRT V87=-.1 STRATA=V44:60*V83:1 LABEL=SUBVACRT V 8 7 = . 0 0 STRATA = V 4 4 : 6 2 * V 8 3 : 1 LABEL = SUBVACRT V87=-.7 STRATA=V44:39*V84:1 LABEL=SUBVACRT V 8 7 = . 3 5 STRATA = V 4 4 : 4 2 * V 8 4 : 1 LABEL = SUBVACRT V87=.00 STRATA=V44:50*V84:1 LABEL=SUBVACRT V 8 7 = . 1 5 STRATA = V 4 4 : 5 3 * V 8 4 : 1 LABEL = SUBVACRT V87=.15 STRATA=V44:54*V84:1 LABEL=SUBVACRT V87=.3 STRATA=V44:55*V84:1 LABEL=SUBVACRT V87=.3 STRATA=V44:56*V84:1 LABEL=SUBVACRT V 8 7 = . 2 5 STRATA = V 4 4 : 5 7 * V 8 4 : 1 LABEL = SUBVACRT V 8 7 = . 2 5 STRATA = V 4 4 : 5 8 * V 8 4 : 1 LABEL = SUBVACRT V87=.3 STRATA=V44:59*V84:1 LABEL=SUBVACRT V87=.3 STRATA=V44:60*V84:1 LABEL=SUBVACRT V87=-.05 STRATA=V44:61*V84:1 LABEL=SUBVACRT V87=.00 STRATA=V44:50*V85:1 LABEL=SUBVACRT V 8 7 = - . 1 5 S T R A T A = V 4 4 : 5 9 * V 8 5 : 1 LABEL=SUBVACRT V88=-161 STRATA=V44:36 LABEL=NEWVACCH V88=-78 STRATA=V44:37 LABEL=NEWVACCH V88=76 STRATA=V44:38 LABEL=NEWVACCH V88=-38 S T R A T A = V 4 4 : 3 9 LABEL=NEWVACCH V88=-17 S T R A T A = V 4 4 : 4 0 LABEL=NEWVACCH V88=33 STRATA=V44:42 LABEL=NEWVACCH V88=-14 STRATA=V44:45 LABEL=NEWVACCH V88=97 STRATA=V44:47 LABEL=NEWVACCH V88=25 S T R A T A = V 4 4 : 5 0 LABEL=NEWVACCH V88=-172 S T R A T A = V 4 4 : 5 3 LABEL=NEWVACCH V88=119 STRATA=V44:54 LABEL=NEWVACCH V88=131 STRATA=V44:55 LABEL=NEWVACCH V88=56 STRATA=V44:56 LABEL=NEWVACCH V88=-57 STRATA=V44:57 LABEL=NEWVACCH V88=-1 S T R A T A = V 4 4 : 5 8 LABEL=NEWVACCH V88=4 S T R A T A = V 4 4 : 5 9 LABEL=NEWVACCH V88 = -76 STRATA = V 4 4 : 6 0 LABEL =NEWVACCH V88=156 STRATA=V44:61 LABEL=NEWVACCH V88=27 STRATA=V44:62 LABEL=NEWVACCH V88=-221 S T R A T A = V 4 4 : 6 3 LABEL=NEWVACCH V 8 9 = 1 . 0 S T R A T A = V 4 4 : 5 3 - 6 3 LABEL=REZONING V 8 9 = 0 . 0 STRATA=V44:36-51 LABEL=REZONING V 9 0 = - . 8 0 S T R A T A = V 4 4 : 3 6 LABEL=VACRTCHG V 9 0 = - . 1 0 STRATA=V44:37 LABEL VACRTCHG V 9 0 = - . 10 STRATA = V 4 4 : 3 8 LABEL = VACRTCHG V 9 0 = - . 7 5 S T R A T A = V 4 4 : 3 9 LABEL=VACRTCHG 3  3  O  > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >  > > > > > > > > > > > > > > > > > > > > > > >  756 757 758 759 760 76 1 762 763 764 765 766 767 768 769 770 77 1 772 773 774 775 776 777 778 779 780 78 1 782 783 784 785 786 787 788 789 790 79 1 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 8 10 81 1 812 813 8 14 815  TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS  V 9 0 =- . 7 5 STRATA=V44: 40 LABEL=VACRTCHG V 9 0 = . 1 0 STRATA=V44:42 LABEL=VACRTCHG V 9 0 = . 0 0 STRATA=V44:45 LABEL=VACRTCHG V90= - . 0 5 STRATA=V44: 47 LABEL=VACRTCHG V90= . 0 0 S T R A T A = V 4 4 : 5 0 LABEL=VACRTCHG V90 = . 0 5 STRATA=V44:53 LABEL=VACRTCHG V90 = . 0 5 STRATA=V44:54 LABEL=VACRTCHG V90= .15 STRATA=V44:55 LABEL=VACRTCHG V 9 0 = .15 STRATA=V44:56 LABEL=VACRTCHG V 9 0 = . 3 0 STRATA=V44:57 LABEL=VACRTCHG V 9 0 = . 3 0 STRATA=V44:58 LABEL=VACRTCHG V90= - . 0 5 STRATA=V44: 59 LABE L =VACRTCHG V90= - . 0 5 STRATA=V44: 60 LABEL=VACRTCHG V90= . 1 0 STRATA=V44:61 LABEL=VACRTCHG V90= . 1 0 STRATA=V44:62 LABEL=VACRTCHG V90= - . 0 5 STRATA=V44: 63 LABEL=VACRTCHG V9 1 1277 = STRATA=V44: 37 LABEL=NEWHH V9 1 1277 = STRATA=V44: 38- 40 LABEL = NEWHH V9 1 1184 = STRATA=V44: 42 LABEL=NEWHH V91 =1426 STRATA=V44: 45- 47 LABEL=NEWHH V91 =1381 STRATA=V44: 50 LABEL=NEWHH V91 =424 S T R A T A = V 4 4 : 5 3 - 5 6 LABEL=NEWHH V91 =-418 STRATA=V44: 57- 60 LABEL=NEWHH V91 =2133 STRATA=V44: 61- 63 LABEL=NEWHH V92 = 1.12 STRATA=V44: 37 LABEL=NEWVRATE V92 =5.4 1 STRATA=V44: 38 LABEL=NEWVRATE V92 =5 . 4 2 STRATA=V44: 39 LABEL=NEWVRATE V92 =4 . 0 2 STRATA=V44: 40 LABEL=NEWVRATE V92 =5 . 5 3 STRATA=V44: 42 LABE L = NEWVRATE V92 =2.71 STRATA=V44: 45 LABEL=NEWVRATE V92 =9 . 4 7 STRATA=V44: 47 LABEL=NEWVRATE V92 =2 5 . 6 1 STRATA=V44 : 5 0 LABE L = NEWVRATE V92 =6 . 7 8 STRATA=V44: 53 LABEL=NEWVRATE V92 = 1 3 . 1 3 STRATA=V44 :54 LABEL=NEWVRATE V92 =2 0 . 5 8 STRATA=V44 :55 LABEL=NEWVRATE V92 =2 6 . 0 3 STRATA=V44 :56 LABEL=NEWVRATE V92 =2 9 . 2 0 STRATA=V44 :57 LABEL=NEWVRATE V92 = 1 9 . 8 3 STRATA=V44 : 58 LABEL=NEWVRATE V92 = 1 9 . 5 5 STRATA=V44 : 59 LABEL=NEWVRATE V92 = 16._44 STRATA = V44 : 6 0 LABEL=NEWVRATE V92 =2 3 . 8 1 STRATA=V44 :61 LABEL=NEWVRATE V92 =2 1 . 49 STRATA = V44 :62 LABEL=NEWVRATE V92 = 1 1 . 9 9 STRATA=V44 :63 LABEL=NEWVRATE V93 =9 1 . 0 2 STRATA=V44 : 37 LABEL=RLRENT2 V93 =8 8 . 8 9 STRATA=V44 :38 LABEL=RLRENT2 V93 =8 8 . 2 2 STRATA=V44 : 39 LABEL=RLRENT2 V93 =8 7 . 1 2 STRATA=V44 : 4 0 LABE L = RLRENT2 V93 =7 6 . 6 6 STRATA=V44 :42 LABEL=RLRENT2 V93 =6 5 . 7 1 STRATA=V44 :45 LABEL=RLRENT2 V93 =5 4 . 5 8 STRATA=V44 :47 LABEL=RLRENT2 V93 =4 9 . 0 4 STRATA=V44 : 5 0 LABEL=RLRENT2 V93 =4 6 . 3 6 STRATA=V44 :53 LABEL=RLRENT2 V93 =4 1 . 76 STRATA = V44 :54 LABEL=RLRENT2 V93 =4 2 . 5 0 STRATA=V44 :55 LABE L = RLRENT2 V93 =4 2 . 9 7 STRATA=V44 : 56 LABEL=RLRENT2 V93 =4 3 . 5 4 STRATA=V44 :57 LABEL=RLRENT2 V93 =4 2 . 3 4 STRATA=V44 :58 LABEL=RLRENT2 V93 =4 1 . 4 9 STRATA=V44 :59 LABEL=RLRENT2 V93 =3 8 . 9 0 STRATA=V44 : 6 0 LABEL=RLRENT2 V93 =3 7 . 9 8 STRATA=V44 :61 LABEL=RLRENT2  VD I  > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >  816 8 17 8 18 819 820 82 1 822 823 824 825 826 827 828 829 830 83 1 832 833 834 835 836 837 838 839 840 84 1 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 86 1 862 863 864 865 866 867 868 869 870 87 1 872 873 874 875  TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS  V93 V93 V94 V94 V94 V94 V94 V94 V94 V94 V94 V94 V94 V94 V94 V94 V94 V94 V94 V94 V94 V95 V95 V95 V95 V95 V95 V95 V95 V95 V95 V95 V95 V95 V95 V95 V95 V95 V95 V95 V96 V96 V96 V96 V96 V96 V96 V96 V96 V96 V96 V96 V96 V96 V96 V96 V96 V96 V96 V97  3 5 . 4 5 STRATA=V44:62 LABEL=RLRENT2 3 4 . 0 7 STRATA=V44:63 LABEL-RLRENT2 473 S T R A T A - V 4 4 : 3 7 LABEL=STARTS 170 S T R A T A - V 4 4 : 3 8 LABEL-STARTS 301 S T R A T A = V 4 4 : 3 9 LABEL-STARTS 474 S T R A T A = V 4 4 : 4 0 LABEL=STARTS 807 S T R A T A - V 4 4 : 4 2 LABEL-STARTS 222 S T R A T A - V 4 4 : 4 5 LABEL-STARTS 193 S T R A T A - V 4 4 : 4 7 LABE L-STARTS 508 S T R A T A - V 4 4 : 5 0 LABEL-STARTS 160 STRATA=V44:53 LABEL=STARTS 290 STRATA=V44:54 LABEL=STARTS 705 S T R A T A - V 4 4 : 5 5 LABEL-STARTS 1124 S T R A T A - V 4 4 : 5 6 LABEL-STARTS 668 S T R A T A - V 4 4 : 5 7 LABEL-STARTS 698 STRATA = V 4 4 : 5 8 LABEL = STARTS 687 STRATA=V44:59 LABEL-STARTS 1385 S T R A T A = V 4 4 : 6 0 LABEL=STARTS 648 STRATA=V44:61 LABE L-STARTS 32 S T R A T A - V 4 4 : 6 2 LABEL -STARTS 199 S T R A T A - V 4 4 : 6 3 LABEL=STARTS 1 12 24 STRATA = V44 37 LABEL -REALCOST 1 1840 STRATA -V44 38 LABEL =REALCOST 127 45 STRATA -V44 39 LABEL -REALCOST 144 01 STRATA -V44 40 LABEL =REALCOST 153 67 STRATA = V44 42 LABEL =REALCOST 147 6 0 STRATA -V44 45 LABEL =REALCOST 135 76 STRATA = V44 47 LABEL -REALCOST 1 15 28 STRATA -V44 50 LABEL -REALCOST 122 14 STRATA -V44 53 LABEL -REALCOST 1 1251 STRATA = V44 54 LABEL -REALCOST 1 1810 STRATA = V44 55 LABEL -REALCOST 1 2425 STRATA = V44 56 LABEL -REALCOST 129 6 0 STRATA = V44 57 LABEL -REALCOST 1 3629 STRATA -V44 58 LABEL -REALCOST 145 58 STRATA = V44 59 LABEL -REALCOST 139 06 STRATA -V44 60 LABEL -REALCOST 144 18 STRATA -V44 61 LABEL -REALCOST 149 08 STRATA = V44 62 LABEL -REALCOST 150 60 STRATA -V44 63 LABEL -REALCOST 3.51 S T R A T A - V 4 4 : 3 7 LABEL-NEWVRLAG 1.12 S T R A T A - V 4 4 : 3 8 LABEL-NEWVRLAG 5.41 S T R A T A - V 4 4 : 3 9 LABEL-NEWVRLAG 5 . 4 2 S T R A T A - V 4 4 : 4 0 LABEL-NEWVRLAG 3 . 2 5 S T R A T A - V 4 4 : 4 2 LABEL-NEWVRLAG 4 . 1 8 S T R A T A - V 4 4 : 4 5 LABEL-NEWVRLAG 2 . 2 0 S T R A T A - V 4 4 : 4 7 LABEL-NEWVRLAG 2 2 . 1 9 S T R A T A - V 4 4 : 5 0 LABEL-NEWVRLAG 1 3 . 6 8 S T R A T A - V 4 4 : 5 3 LABEL-NEWVRLAG 6 . 7 8 S T R A T A - V 4 4 : 5 4 LABEL-NEWVRLAG 1 3 . 1 3 S T R A T A - V 4 4 : 5 5 LABEL-NEWVRLAG 2 0 . 5 8 S T R A T A - V 4 4 : 5 6 LABEL-NEWVRLAG 2 6 . 0 3 S T R A T A - V 4 4 : 5 7 LABEL-NEWVRLAG 2 9 . 2 0 S T R A T A - V 4 4 : 5 8 LABEL-NEWVRLAG 1 9 . 8 3 S T R A T A - V 4 4 : 5 9 LABEL-NEWVRLAG 1 9 . 5 5 S T R A T A - V 4 4 : 6 0 LABEL-NEWVRLAG 1 6 . 4 4 S T R A T A - V 4 4 : 6 1 LApEL-NEWVRLAG 2 3 . 8 1 S T R A T A - V 4 4 : 6 2 LABEL-NEWVRLAG 2 1 . 4 9 S T R A T A - V 4 4 : 6 3 LABEL-NEWVRLAG 9 5 . 1 6 S T R A T A - V 4 4 : 3 7 LABEL-RLRNTLAG  CN VO I  > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >  875 877 878 879 880 88 1 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 . 902 903 904 905 906 907 908 909 910 91 1 912 913 914 9 15 916 917 918 919 920 92 1 922 923 924 925 926 927 928 929 930 931 932 933 934 935  TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS  V97 =91 . 02 STRATA=V44: 38 LABEL=RLRNTLAG V97 =88 . 89 STRATA=V44: 39 LABEL=RLRNTLAG V97 =88 . 22 STRATA=V44: 40 LABEL=RLRNTLAG V97 =82 . 01 STRATA=V44: 42 LABEL=RLRNTLAG V97 =7 0 . 54 STRATA=V44: 45 LABEL=RLRNTLAG V97 =59 . 14 STRATA=V44: 47 LABEL=RLRNTLAG V97 =49 . 81 STRATA=V44: 50 LABEL=RLRNTLAG V97 =46 . 31 STRATA=V44: 53 LABEL=RLRNTLAG V97 =46 . 36 STRATA=V44: 54 LABEL=RLRNTLAG V97 =4 176 . STRATA=V44: 55 LABEL=RLRNTLAG V97 =42 . 50 STRATA=V44: 56 LABEL=RLRNTLAG V97 =42 . 97 STRATA=V44: 57 LABEL=RLRNTLAG V97 =43 . 54 STRATA=V44: 58 LABEL=RLRNTLAG V97 =42 . 34 STRATA=V44: 59 LABEL=RLRNTLAG V97 =4 149 . STRATA=V44: 60 LABEL=RLRNTLAG V97 =38 . 90 STRATA=V44: 61 LABEL=RLRNTLAG V97 =37 . 98 STRATA=V44: 62 LABEL=RLRNTLAG V97 =35 . 45 STRATA=V44: 63 LABEL=RLRNTLAG V98 = 1 1 .64 1 STRATA=V44 : 3 7 LABEL=RLCSTLAG V98 = 1 12 . 24 STRATA=V44 : 38 LABEL=RLCSTLAG V98 = 1 18 . 40 STRATA=V44 :39 LABEL=RLCSTLAG V98 = 127 . 45 STRATA=V44 : 40 LABEL=RLCSTLAG V98 = 15 1 . 46 STRATA=V44 : 42 LABEL=RLCSTLAG V98 = 142 . 94 STRATA=V44 : 45 LABEL=RLCSTLAG V98 = 140 . 0 0 STRATA=V44 : 4 7 LABEL=RLCSTLAG V98 = 103 . 1 1STRATA=V44 : SO LABEL=RLCSTLAG V98 = 1 16 . 80 STRATA=V44 : 53 LABEL=RLCSTLAG V98 = 122 . 14 STRATA=V44 :54 LABEL=RLCSTLAG V98 = 1 12 .51 STRATA=V44 :55 LABEL=RLCSTLAG V98 = 1 18 . 10 STRATA=V44 : 56 LABEL=RLCSTLAG V98 = 124 . 25 STRATA=V44 : 57 LABEL=RLCSTLAG V98 = 129 . 6 0 STRATA=V44 : 58 LABEL=RLCSTLAG V98 = 136 . 29 STRATA=V44 : 59 LABEL=RLCSTLAG V98 = 145 . 58 STRATA=V44 : 6 0 LABEL=RLCSTLAG V98 = 139 .06 STRATA=V44 :61 LABEL=RLCSTLAG V98 = 144 . 18 STRATA=V44 :62 LABEL=RLCSTLAG V98 = 149 .08 STRATA=V44 :63 LABEL=RLCSTLAG V99 =31 STRATA=V44:37 LABEL=NEWVACMF V99 = 107 STRATA=V44:38 LABEL=NEWVACMF V99 =69 S T R A T A = V 4 4 : 3 9 LABEL=NEWVACMF V99 =52 S T R A T A = V 4 4 : 4 0 LABEL=NEWVACMF V99 =74 STRATA=V44:42 LAB EL = NEWVACMF V99 =39 STRATA=V44:45 LABEL=NEWVACMF V99 = 138 STRATA=V44:47 LABEL=NEWVACMF V99 =449 S T R A T A = V 4 4 : 5 0 LABEL=NEWVACMF V99 = 194 STRATA=V44:53 LABE L = NEWVACMF V99 =313 STRATA=V44:54 LABEL=NEWVACMF V99 =444 STRATA=V44:55 LABEL=NEWVACMF V99 =5 0 0 STRATA=V44:56 LABEL=NEWVACMF V99 =443 STRATA=V44:57 LABEL=NEWVACMF V99 =442 STRATA=V44:58 LABEL=NEWVACMF V99 =446 S T R A T A = V 4 4 : 5 9 LABEL=NEWVACMF V99 =3 7 0 S T R A T A = V 4 4 : 6 0 LABE L = NEWVACMF V99 =526 STRATA=V44:61 LABEL=NEWVACMF V99 =553 STRATA=V44:62 LABEL=NEWVACMF V99 =332 STRATA=V44:63 LABEL=NEWVACMF V 1 0 0 = 7 .222 STRATA=V44 : 37 LABEL=AVGPPSF V 1 0 0 = 7 .429 STRATA=V44 :38 LABEL=AVGPPSF V 1 0 0 = 5 .902 STRATA=V44 : 3 9 LABE L = AVGPPSF V 1 0 0 = 6 .250 STRATA=V44 : 4 0 LABEL=AVGPPSF  vo i  > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >  936 937 938 939 940 94 1 942 943 944 945 946 947 948 949 950 95 1 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 97 1 972 973 974 975 976 977 978 979 980 98 1 982 983 984 985 986 987 ' 988 989 990 991 992 993 994 995  TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS TRANS  V 1 0 0 =8 . 201 STRATA = V44 :42 LABEL=AVGPPSF V100= 25 . 0 0 0 STRATA=V44 :45 LABEL=AVGPPSF V100= 13 .518 STRATA=V44 : 47 LABEL=AVGPPSF V 1 0 0 = 13 . 254 STRATA=V44 : 5 0 LABEL=AVGPPSF V100= 13 .887 STRATA=V44 : 51 LABEL=AVGPPSF V100= 15 . 377 STRATA=V44 :53 LABEL=AVGPPSF V 1 0 0 = 16 .688 STRATA=V44 : 54 LABEL=AVGPPSF V 1 0 0 = 15 .627 STRATA=V44 : 55 LABEL=AVGPPSF V 1 0 0 = 16 .029 STRATA=V44 : 56 LABEL=AVGPPSF v i o o = 26 . 175 STRATA=V44 : 57 LABEL=AVGPPSF V 1 0 0 =20 . 522 STRATA=V44 : 58 LABEL=AVGPPSF V100= 18 .314 STRATA=V44 : 59 LABEL=AVGPPSF V100= 17 . 867 STRATA=V44 : 6 0 LABEL=AVGPPSF V100= 18 .071 STRATA=V44 :61 LABEL=AVGPPSF V100= 28 . 333 STRATA=V44 : 62 LABEL=AVGPPSF V 1 0 0 =23 . 855 STRATA=V44 : 63 LABEL=AVGPPSF V101 =V 1 0 0 / V 3 8 STRATA = N0NE LABEL=RLAVGSP V102 =0 . ' 00 STRATA=V44:37 LABEL=LOCINDEX V102 =0 . ' 00 STRATA=V44:38 LABEL=LOCINDEX V102 =-2 . 0 0 STRATA = V44 :39 LABEL=LOCINDEX V102 =O . i 00 S T R A T A = V 4 4 : 4 0 LABEL=LOCINDEX V102 =- 1. 50 STRATA = V44 :42 LABEL=LOCINDEX V 102 =O . i 00 STRATA=V44:45 LABEL=LOCINDEX V102 =3 . i 00 S T R A T A = V 4 4 : 4 7 LABEL=LOCINDEX V102 =-2 . 0 0 STRATA = V44 :50 LABEL=LOCINDEX V102 =- 1 . 0 0 STRATA=V44 : 51 LABEL=LOCINDEX V102 =-3 . 0 0 STRATA=V44: 53 LABEL=LOCINDEX V102 =-2 . 57 STRATA = V44 :54 LABEL=LOCINDEX V102 =-2 .63 STRATA=V44: 55 LABEL = LOC INDEX V102 =-3 . 0 0 STRATA=V44: 56 LABEL=LOCINDEX V102 =- 1. 5 0 STRATA=V44: 57 LABEL=LOCINDEX V102 =-0 . 27 STRATA=V44: 58 LABEL=LOCINDEX V102 =- 1 .67 STRATA=V44: 59 LABEL=LOCINDEX V102 =- 1. 8 0 STRATA = V44 :60 LABEL=LOCINDEX V102 =-3 . 0 0 STRATA = V44 :6 1LABEL=LOCINDEX V102 =0 . 0 0 S T R A T A = V 4 4 : 6 2 LABEL=LOCINDEX V102 =3 . 0 0 S T R A T A = V 4 4 : 6 3 LABEL=LOCINDEX V103 =-2 . 454 STRATA=V44 : 37 LABEL=NEWRMLAG V103 =-3 .609 STRATA=V44 : 38 LABEL=NEWRMLAG V103 =-3 .463 STRATA=V44 : 39 LABEL=NEWRMLAG V103 =-3 . 0 9 0 STRATA=V44 :40 LABEL=NEWRMLAG V103 =-3 . 703 STRATA=V44 : 42 LABEL=NEWRMLAG V103 =-5 . 528 STRATA=V44 : 45 LABEL=NEWRMLAG V103 =-7 . 988 STRATA=V44 : 47 LABEL=NEWRMLAG V 1 0 3 =-8 . 430 STRATA=V44 : 50 LABEL=NEWRMLAG V103 =-7 . 166 STRATA=V44 : 51 LABEL=NEWRMLAG V 103 =-4 .051 STRATA=V44 : 53 LABEL=NEWRMLAG V103 =-2 . 389 STRATA=V44 : 54 LABE L = NEWRMLAG V103 =-4 . 3 1 9 STRATA=V44 : 55 LABEL=NEWRMLAG V103 =-3 . 343 STRATA=V44 : 56 LABEL=NEWRMLAG V103 =- 1 . 599 STRATA=V44 : 57 LABEL=NEWRMLAG V103 =- 1 . 324 STRATA=V44 : 58 LABEL=NEWRMLAG V103 =-2 . 109 STRATA=V44 : 59 LABEL=NEWRMLAG V103 =-0 . 0 9 9 STRATA=V44 : 6 0 LABEL=NEWRMLAG V103 =- 1. 562 STRATA=V44 :61 LABEL=NEWRMLAG V103 =-3 .062 STRATA=V44 : 62 LABEL=NEWRMLAG V103 =-5 . 0 9 0 STRATA=V44 : 63 LABEL=NEWRMLAG V 9 2 = 1 7 . 8 7 STRATA=V44:51 LABEL=NEWVRATE V64 = -2 . 8 4 STRATA=V44:51 LABEL=CAPRATE V66= 12. 12 STRATA=V44:51 LABEL=NOMCAPRT  -3-  

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