Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Bin, Junchi
Master of Applied Science - MASc
University of British Columbia
Property assessment plays a vital role in not only people's daily life but also the establishment of policies in North America. Therefore, the robust and accurate assessment is thus required. However, there are several challenges to prevent improving performance in property assessment. For example, the to-be-valued property and its neighbors can influence their values mutually, which is named peer-dependence. Besides, the property values also depend on their topography around the house. Moreover, various urban data in the city, i.e., criminal activities and point of interests, also reflects the property values. The accuracy of property assessment can be further improved if the process can fuse the urban data for assessment. Nowadays, deep learning models become the most powerful computational models in the subjects of both electrical engineering and computer science. Therefore, the thesis aims to investigate deep learning techniques to address three specific challenges in property assessment, which are considering peer-dependence considering topography and considering urban data for improving property assessment. In the first study, a recurrent neural network (RNN), peer-dependence valuation model (PDVM), is proposed to converts the peer-dependence based valuation into sequence prediction. Firstly, K-Nearest similar house sampling (KNSHS) algorithm generates sequences from the to-be-valued property and nearby houses. Then, an RNN estimates the property values from given sequences. The experimental results indicate that PDVM outperforms the other novel computational models being used for property assessment. In the second study, a convolutional neural network (CNN) based method, attention-based multi-modal fusion (AMMF), is proposed to estimate property values with house attributes and corresponding street maps which contain features of topography. The experimental results indicate the proposed method outperforms the contemporary approaches. In the third study, to investigate the ways of urban data fusion to property values, a novel deep learning framework, multi-source urban data fusion (MUDF), is proposed to estimate property values from metadata and street-view images of properties. The experimental results with the data collected from the city of Philadelphia demonstrate the accuracy of the proposed model.
INVESTIGATIONS ON DEEP LEARNINGTECHNIQUES FOR PROPERTY ASSESSMENTbyJunchi BinB.Eng., Chang’an University, 2016B.Sc., Northern Arizona University, 2016A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Electrical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)January 2019© Junchi Bin, 2019The following individuals certify that they have read, and recommend to the Col-lege of Graduate Studies for acceptance, the thesis entitled:INVESTIGATIONS ON DEEP LEARNING TECHNIQUES FOR PROPERTYASSESSMENTsubmitted by Junchi Bin in partial fulfillment of the requirements ofthe degree of Master of Applied Science .Dr. Zheng Liu, School of EngineeringSupervisorDr. Eric Li, Faculty of ManagementCo-supervisorDr. Jian Liu, School of EngineeringSupervisory Committee MemberDr. Dimitry Sediako, School of EngineeringSupervisory Committee MemberDr. Nicolas Miguel Peleato, School of EngineeringUniversity ExamineriiAbstractProperty assessment plays a vital role in not only people’s daily life but also theestablishment of policies in North America. Therefore, the robust and accurateassessment is thus required. However, there are several challenges to prevent im-proving performance in property assessment. For example, the to-be-valued prop-erty and its neighbors can influence their values mutually, which is named peer-dependence. Besides, the property values also depend on their topography aroundthe house. Moreover, various urban data in the city, i.e., criminal activities andpoint of interests, also reflects the property values. The accuracy of property assess-ment can be further improved if the process can fuse the urban data for assessment.Nowadays, deep learning models become the most powerful computational modelsin the subjects of both electrical engineering and computer science. Therefore, thethesis aims to investigate deep learning techniques to address three specific chal-lenges in property assessment, which are considering peer-dependence consideringtopography and considering urban data for improving property assessment.In the first study, a recurrent neural network (RNN), peer-dependence valua-tion model (PDVM), is proposed to converts the peer-dependence based valuationinto sequence prediction. Firstly, K-Nearest similar house sampling (KNSHS) al-gorithm generates sequences from the to-be-valued property and nearby houses.Then, an RNN estimates the property values from given sequences. The exper-imental results indicate that PDVM outperforms the other novel computationalmodels being used for property assessment.In the second study, a convolutional neural network (CNN) based method,attention-based multi-modal fusion (AMMF), is proposed to estimate property val-ues with house attributes and corresponding street maps which contain features ofiiitopography. The experimental results indicate the proposed method outperformsthe contemporary approaches.In the third study, to investigate the ways of urban data fusion to property val-ues, a novel deep learning framework, multi-source urban data fusion (MUDF),is proposed to estimate property values from metadata and street-view imagesof properties. The experimental results with the data collected from the city ofPhiladelphia demonstrate the accuracy of the proposed model.ivLay SummaryProperty assessment is a process to estimate market values of houses via compu-tational models. The assessed values of houses not only influence transactions onhouse market but also influence regional and even national economic health. How-ever, contemporary computational models in property assessment are facing manychallenges. In this thesis, the presented research investigates deep learning tech-niques for challenges in property assessment. Experimental results demonstratethe accuracy of the proposed methods.vPrefaceThis thesis is based on the research work conducted in the School of Engineeringat the University of British Columbia, Okanagan Campus, under the supervision ofProf. Zheng Liu. Published works are contained in this thesis.Chapter 3 is based on the following published paper and used with permissionof Springer:• Junchi Bin, Bryan Gardiner, Eric Li, Zheng Liu, Peer-dependence valuationmodel for real estate appraisal, In Data-Enabled Discovery and Applications,pages 1-10, Springer, In Press.Chapter 4 is submitted for the consideration in the form of Springer refereedjournal paper.Chapter 5 is submitted for the consideration in the form of IEEE refereed jour-nal paper.I am the principal contributor to these works. Prof. Zheng Liu helped me withsome innovative ideas, the suggestion to improve the procedure of experiments andpreparation of the manuscripts. Prof. Eric Li provided me with some ideas fromthe perspective of the housing market. Mr. Bryan Gardiner, the senior softwareengineer at the Data Nerds, developed the tools to facilitate the acquisition of thedata used in this thesis. Besides, Mr. Wells Wang proofread this thesis.viTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiGlossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xivAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Background and Motivation . . . . . . . . . . . . . . . . . . . . 11.2 Challenges Description . . . . . . . . . . . . . . . . . . . . . . . 41.2.1 Considering Peer-Dependence . . . . . . . . . . . . . . . 41.2.2 Considering Topography . . . . . . . . . . . . . . . . . . 51.2.3 Considering Urban Data . . . . . . . . . . . . . . . . . . 51.3 Thesis Outline and Contribution . . . . . . . . . . . . . . . . . . 62 Computational Models in Property Assessment . . . . . . . . . . . . 82.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8vii2.2 Contemporary Computational Models . . . . . . . . . . . . . . . 112.2.1 Conventional Approaches . . . . . . . . . . . . . . . . . 112.2.2 Machine Learning . . . . . . . . . . . . . . . . . . . . . 122.2.3 MLP: The Simplest Deep Learning Model . . . . . . . . . 152.3 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.3.1 Recurrent Neural Network . . . . . . . . . . . . . . . . . 172.3.2 Convolutional Neural Network . . . . . . . . . . . . . . . 182.4 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . 192.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Peer-Dependence Valuation Model . . . . . . . . . . . . . . . . . . . 213.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213.2 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . 233.3 Peer-Dependence Valuation Model . . . . . . . . . . . . . . . . . 253.3.1 Overall PDVM Architecture . . . . . . . . . . . . . . . . 253.3.2 K-Nearest Similar House Sampling . . . . . . . . . . . . 253.3.3 Bidirectional Long Short-Term Memory Layer . . . . . . 283.3.4 Fully-Connected Layer . . . . . . . . . . . . . . . . . . . 313.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . 313.4.2 Experimental Results . . . . . . . . . . . . . . . . . . . . 323.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354 Attention-based Multi-Modal Fusion for Property Assessment . . . 364.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364.2 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . 384.3 Attention-Based Multi-Modal Fusion . . . . . . . . . . . . . . . . 404.3.1 Overall AMMF Architecture . . . . . . . . . . . . . . . . 404.3.2 Deep Learning for Multi-Modal Fusion (M1) . . . . . . . 424.3.3 Boosted Regression Trees for Improved Assessment (M2) 444.3.4 Training Process . . . . . . . . . . . . . . . . . . . . . . 454.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464.4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . 46viii4.4.2 Experimental Results . . . . . . . . . . . . . . . . . . . . 464.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 Multi-Source Urban Data Fusion in Property Assessment . . . . . . 505.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505.2 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . 525.2.1 House Attributes . . . . . . . . . . . . . . . . . . . . . . 525.2.2 Human Activities . . . . . . . . . . . . . . . . . . . . . . 525.2.3 Spatial Features . . . . . . . . . . . . . . . . . . . . . . . 565.2.4 Street-view images . . . . . . . . . . . . . . . . . . . . . 595.2.5 Data Collection . . . . . . . . . . . . . . . . . . . . . . . 615.3 Multi-Source Urban Data Fusion . . . . . . . . . . . . . . . . . . 625.3.1 Overall MUDF Architecture . . . . . . . . . . . . . . . . 625.3.2 Feature Extraction and Fusion . . . . . . . . . . . . . . . 645.3.3 BRT as Property Value Estimator . . . . . . . . . . . . . 685.3.4 Training Process . . . . . . . . . . . . . . . . . . . . . . 685.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685.4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . 685.4.2 Experimental Results . . . . . . . . . . . . . . . . . . . . 705.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 736 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77ixList of TablesTable 3.1 Property summary for Chicago and Detroit (in US$) . . . . . . 24Table 3.2 Available real estate attributes in the housing dataset . . . . . . 24Table 3.3 Comparison of Sampling Strategies (1-Layer B-LSTM) . . . . 32Table 3.4 Selected experimental results with different layers of PDVM(K = 7) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33Table 3.5 Comparison of estimating results in terms of MAE and MAPEfor both cities . . . . . . . . . . . . . . . . . . . . . . . . . . 35Table 4.1 The illustration and statistics of the house attributes. . . . . . . 39Table 4.2 Statistical summary of property values in the city of Los Angeles. 39Table 4.3 Experimental results of estimating house price. The letter ’Y’ incolumns 2-3 indicates that the algorithms use the components ofstreet maps, attention block (Attention). Moreover, the column4 indicate the position of attention blocks. . . . . . . . . . . . 47Table 5.1 The illustration and statistics of house attributes. . . . . . . . . 53Table 5.2 Experimental results of contemporary computational models . 70Table 5.3 Experimental results with metadata . . . . . . . . . . . . . . . 71Table 5.4 Experimental results of combination of street-view images andfused matadata . . . . . . . . . . . . . . . . . . . . . . . . . . 73xList of FiguresFigure 1.1 The illustration of property assessment with computational mod-els. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2Figure 2.1 The procedure of training computational model in property as-sessment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9Figure 2.2 The Venn diagram of contemporary computational models inproperty assessment. . . . . . . . . . . . . . . . . . . . . . . 10Figure 2.3 An example of comparable sales method [1]. . . . . . . . . . 11Figure 2.4 The basic regression tree for property assessment [1]. . . . . . 13Figure 2.5 The architecture of multi-layer percetron in property assessment. 16Figure 2.6 A recurrent neural network and the unfolding in time of thecomputation involved in its forward computation [2]. . . . . . 17Figure 2.7 The process of convolutional neural network [2]. . . . . . . . 19Figure 3.1 Spatial distribution of houses in (a) Chicago (b) Detroit. . . . 22Figure 3.2 The overview of the proposed PDVM. The red house indicatesthe to-be-valued house, and the blue houses are the nearbyhouses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26Figure 3.3 The architecture of recurrent neural network (RNN). . . . . . 28Figure 3.4 The architecture of Long-Short Term Memory (LSTM). . . . 29Figure 3.5 The architecture of bidirectional recurrent neural network. . . 30Figure 4.1 he illustration of spatial distribution of houses and latent geo-graphical appearance in the city of Los Angeles on street map. 37Figure 4.2 The example of a street map. . . . . . . . . . . . . . . . . . 40xiFigure 4.3 The framework of attention-based multi-modal fusion (AMMF). 41Figure 4.4 The detailed architecture of components in M1, convolutionallayers, fusion layer and regression layers. . . . . . . . . . . . 42Figure 4.5 The position of attention block. (a) Concatenation first: placethe attention block after concatenation of image features andhouse attributes. (b) Attention first: place individual block oneach types of feature, then concatenate attended features. . . . 48Figure 5.1 The market values of properties also depends on other factorsbesides its attributes. . . . . . . . . . . . . . . . . . . . . . . 51Figure 5.2 An example of calculation of near repeating series [3]. . . . . 54Figure 5.3 The geospatial distribution of criminal activities indicators andmarket values. The criminal indicators are inversely propor-tional to the market values of houses especially in the red el-liptical areas. . . . . . . . . . . . . . . . . . . . . . . . . . . 55Figure 5.4 The spatial distribution of popularity and market values. Thecorrelation between popularity and market values of houses de-pends on where the houses locate. The blue rectangular areasindicate the proportional correlation. On the contrary, the redelliptical area indicates inversely proportional correlation. . . 57Figure 5.5 The spatial distribution between litter index and market value.The red elliptical area indicate the areas with inversely propor-tional correlation. . . . . . . . . . . . . . . . . . . . . . . . . 57Figure 5.6 The spatial distribution of the maximum rating of schools foreach census tract. The pictures represents the rating of ele-mentary, middle and high schools in Philadelphia from left toright. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58Figure 5.7 The examples of street-view images of properties. The picturesrepresents bottom 10%, normal and top 10% houses in termsof market value in Philadelphia from left to right. . . . . . . . 59xiiFigure 5.8 The illustration of POIs in the City of Philadelphia (a) Thespatial distribution of top 12 POIs that affects the market value.(b) The illustration of the correlation between different typesof POIs and market values of properties. . . . . . . . . . . . . 60Figure 5.9 The examples of street-view images before (a) and after (b)pre-trained PSPNet. . . . . . . . . . . . . . . . . . . . . . . . 61Figure 5.10 Statistics in market values of properties. (a) Statistics (b) His-togram of market values. . . . . . . . . . . . . . . . . . . . . 62Figure 5.11 The overall architecture of multi-source data fusion. . . . . . 63Figure 5.12 The architecture of metadata fusion network (M1). . . . . . . 64Figure 5.13 The architecture of image appraiser (M2) based on VGG-16and deep expectation (DEX). . . . . . . . . . . . . . . . . . . 67xiiiGlossaryANN Artificial Neural NetworkAMMF Attention-Based Multi-Modal FusionB-LSTM Bidirectional Long Short-Term MemoryBN Batch NormalizationBRT Boosted Regression TreesCCI Consecutive Crime IntensityCE Crime Occurence EntropyCNN Convolutional Neural NetworkConv Convolutional LayerCPU Central Processing UnitDEX Deep ExpectationDL Deep LearningFC Fully-Connected LayerGPU Graphic Processing UnitHR Hedonic RegressionKNSHS K-Nearest Simiar House SamplingxivKNN K-Nearest NeighbourLSTM Long Short-Term MemoryLWR Locally Weighted RegressionMAE Mean Absolute ErrorMAPE Mean Absolute Percentage ErrorMLP Multi-Layer PerceptronMUDF Mutli-Source Urban Data FusionNMF Non-Negative Matrix FactorizationNNF Neural-Network FusionPCA Principal Component AnalysisPDVM Peer-Dependence Valuation ModelPOI Point of InterestPSPNet Pyramid Scene Parsing NetworkR2 Coefficient of DeterminationRBM Restricted Boltzmann MachineReLU Rectified Linear UnitRF Random ForestRT Regression TreeRNN Recurrent Neural NetworkxvAcknowledgmentsFirst of all, I want to present my highest gratitude to my supervisor, Dr. Zheng Liu,whose guidance, generosity, kindness, and encouragement throughout my graduatestudy have been precious and indispensable. Without his offer, I would not haveany chance to research at the University of British Columbia. He gave me notonly the research opportunities that have enabled me in obtaining my valuableprofessional skills but also the spirit and personality.I would like to thank my co-supervisor, Dr. Eric Li, who was willing to helpas a mentor and a friend. I also present my greatest gratitude to him for givingguidance in the perspective of marketing throughout my graduate studies. He hasbeen a great role model to work with.Thanks also to committee member Dr. Jian Liu and Dr. Dimitry Sediako fortheir willingness to serve on the supervisory committee, and Dr. Nicolas MiguelPeleato for his willingness to serve on the university examiner.In addition, I offer my appreciation to all of my colleagues at the IntelligentSensing, Diagnostic and Prognostic Research Lab (ISDPRL), who have given meencouragement and technical support over the past two years. Besides, I want toshow my special appreciation to Dr. Huan Liu who continuously encouraged, andcomforted me when I was in the darkest time.Finally, I present the highest appreciation to my family, Mr. Feifan M. Li, Ms.Linan Zhou, Mr. Feixiang Huang,Mr. Feixiang Huang, Mrs. Kaiqi Zhang and Mr.Shiyuan Tang for their love, support, and understanding over my graduate studies.xviChapter 1Introduction1.1 Background and MotivationFor most of the consumers in North America, housing has been one of the largestexpenses. Purchasing a house is a high involvement decision. Consumers’ viewon the potential value of properties might influence the decision in purchasing thehouse or their allocation of budget. In terms of price, if the average house priceof an area is low, people tend to move to this area. Namely, the assessed valuesof properties are crucial to people’s plans of the resident. Moreover, it is one ofthe crucial factors to reflect the economic activities. For example, the unstoppableincreasing interest rates since 2005 has significantly slowed down the housing mar-ket in the US Lehman Brothers Holdings Inc., a famous investment bank in USA[4]. The consequence led to the bankruptcy of the bank which finally sparked aglobal financial crisis in 2008. To assist in sustaining the health of national eco-nomic, timely relevant policies of the housing market from both government andcompanies are required. Therefore, from this perspective, the accurate property as-sessment can significantly support the prediction of the future housing market andthe establishment of the policies [4].The computational model is the set of mathematical tools to study and simu-late the behaviors of complex and dynamic systems in the fields of electrical engi-neering and computer science. Real estate brokers have developed computationalmodels to automatically assess the market values or the sale prices of houses based1on the set of features associated with an individual house. For example, Zillow,RF/MAX, and Estated provide web applications of the computational models toevaluate the property values based on house attributes, and area information [5–7].These applications enable potential estate buyers to find their short-listed housesand obtain comprehensive reports about the properties including assessed values.On the other hand, regional or national governments incorporate computationalmodels to estimate the costs of numerous houses across cities or provinces. Usingthe appraisal results, they can evaluate the health of the housing market, and planfor the future establishment of policies as shown in Figure 1.1.Figure 1.1: The illustration of property assessment with computational mod-els.In the last century, the computational models of property assessment are typi-cally comparable sales method and Hedonic Regression (HR) based on properties’attributes [8, 9]. However, the increasing amount of data requires more powerfuldata-driven models for accurate assessment. Therefore, in the early 21st century,researchers started implementing machine learning for property assessment. Ma-chine learning is a branch of electrical engineering and computer science that candiscover patterns and the complicated relationship among features of data for mak-ing decision or prediction. Given a powerful machine learning model, providingmore data may lead to better performance. Therefore, most real estate brokersuse machine learning models to empower the property assessment. Contemporarymethods such as tree-based models (Regression Tree (RT), Random Forest (RF)2and Boosted Regression Trees (BRT)) are prevalent and successful in the compu-tational models of property assessment [4, 10–12].However, there are still challenges remained in the property assessment. Apartfrom the general aspects of the house, the neighbors may influence the values si-multaneously. This impact is named peer-dependence. Without considering suchimpact, the computational model can be a lack of accuracy. Although researchersunderstand its importance to property assessment, there are few models consideringpeer-dependence during computation. Another significant aspect of challenges inproperty assessment is considering more relevant factors to property values. Tra-ditionally, the computational models are fitted by only house attributes like size,number of bedrooms, etc.. With the development of data storage, various types ofdatasets are recorded which can reflect the property values. For instance, the topog-raphy of a house like a mountain and ocean may affect its costs. The aerial imagelike street maps of a house can reveal the topography near the house. Therefore,using such data may improve the performance of the appraisal. However, there area limited amount of studies which uses aerial imagery data to understand the to-pography for property assessment. On the other hand, only a few kinds of researchhas investigated to take advantage of urban data such as criminal activities andstreet-view images. These types of data can reflect the conditions of the commu-nity which are greatly related to the values of houses. However, they are difficultto manipulate due to their heterogeneity. As a result, three specific challenges areinvestigated in this thesis:• Considering Peer-Dependence: The houses influence the values of estatesin the vicinity which is called peer-dependence. Although there are few con-temporary methods to consider this impact in property assessment, the per-formance can still be improved with more novel techniques like deep learn-ing.• Considering Topography: The value of an estate depends on the topogra-phy around the estate. Manual measurement of topography can significantlyincrease the budget of estate brokers. There is no research on how to extractthe topography without manual analysis.3• Considering Urban Data: The estate consumers are always willing to buythe house where belongs to a good community. Currently, there are varioustypes of urban data to reflect the conditions of communities like criminalactivities, street-view images, etc. The accuracy of the assessment can befurther improved if urban data and housing data fuse properly. However, theresearch on how to fuse the urban data and housing data is still lacking.In the last few years, a type of novel computational model, Deep Learning(DL), has achieved state-of-the-art in disciplines of electrical engineering such ascomputer vision, natural language processing, and data fusion [13]. Technically,DL enables computational models to extract the abstracted representations fromlarge-scale data via multiple processing layers. However, the applications of DLin property assessment is still lacking in research. In this thesis, three DL basedmodels are proposed to address the challenges in property assessment.1.2 Challenges Description1.2.1 Considering Peer-DependenceReal estate brokers regard the impact of peer-dependence as one of the essentialfactors in the real estate appraisal, and they have been researching and developingcomputational models based on peer-dependence. A straightforward approach isto compare it with similar houses in the vicinity, which is the core reasoning of thecomparable sales method. In the comparable sales method, a to-be-valued propertyis compared to a similar real estate that has recently been transacted in the samearea [1]. The values of these related transactions are used as a reference, and thecost of the to-be-valued object is estimated by applying a correction to the referenceprices [1]. This correction may be done based on a linear relationship betweenessential attributes and the transaction price, although in practice it is prevalentthat solely the intuition and knowledge of the valuer form the basis [1, 9]. K-Nearest Neighbour (KNN) is another appropriate method to define the similaritiesand estimate the price based on Euclidean distance among houses as similar asLocally Weighted Regression (LWR) [14, 15].41.2.2 Considering TopographyWith the development of big data techniques, more and more image data are col-lected such as street maps and street-view images, especially in urban areas. Therehave been increasing numbers of applications solving the problems of urban studieswith such images. Some researchers have explored the correlation between safety,beauty and wealth of a region and visual features extracted from street-view im-ages [16–18]. Some research intends to understand the environment, functionality,and population in the urban region [19–21]. Three recent studies [22–24] utilizethe image features and house attributes to estimate the house price. Bessigner etal. [23] integrated the front-view images of houses and house characteristics fromthe appraiser to evaluate the house price. On the other hand, Bency et al. [23]estimated the house price with aerial images and house attributes [25]. Front-viewpictures of houses are useful for property assessment because the appearance out-side the house can indicate the beauty of the houses. Aerial images mainly revealthe appearance of neighborhoods in the aerial view. However, none of the aboveimagery datasets are capable of delivering topography surrounding the house.1.2.3 Considering Urban DataDue to the large-scale and varieties of data sources in urban scenarios, both in-dustrial and academic researchers have been researching data fusion techniquesfrom wireless sensors network to remote sensing for understanding the city [26–30]. For example, Filipe et al. proposed a time-series fusion method to fuse tex-tual data from Facebook and metadata like weather and historical taxi demandsfor forecasting the future taxi demand [26]. However, There is lacking researchon data fusion techniques for property value assessment. Many real estate bro-kers still focus on developing computational models to assess the market values ofhouses based on house attributes. Empirical statistical researches indicate that thecommunity condition greatly influence its market values [31–33]. The communityconditions can be reflected by multiple sources of urban data which is recordedas metadata. Some empirical studies use urban data on the problem of pair-wiseranking of houses [3, 34]. Nonetheless, for property value assessment, none of theresearches discusses the application of data fusion for house attributes and com-5munity conditions. As mentioned in the Section 1.2.2, some researchers have in-vestigated methods of integrating imagery data for improving property assessment[23, 25] in the urban area. However, they only concatenate the image features,and house attributes to train a computational model as well as the estimator forproperty assessment. The curse of dimensions may prevent them from improvingthe performance of computational models via directly combining the metadata andimage features. Moreover, none of the methods considered combining all types ofurban data for property value assessment and the practical ways of data fusion.1.3 Thesis Outline and ContributionThis thesis is organized into six chapters.Chapter 1 presents the background of the property assessment, the current so-lutions, and three specific challenges studied in this thesis. Moreover, this chapterprovides descriptions related to the challenges of property assessment and deeplearning.Chapter 2 provides a review of computational models in property assessment.First, conventional approaches and machine learning approaches are introduced.Then, the deep learning techniques are presented which is related to this thesis.Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Convolu-tional Neural Network (CNN) are described in this chapter. Moreover, the evalua-tion metrics are also presented.In Chapter 3, a Peer-Dependence Valuation Model (PDVM) is proposed to es-timate the property values using RNN, where the to-be-valued house and its nearbyhouses are fed into RNN as a sequence. In the proposed method, a specific form ofRNN, Long Short-Term Memory (LSTM), is used to prevent the model from “van-ishing gradient”. Consequently, the experimental results were finally presented tovalidate the accuracy in property assessment. The experimental results show thesuperiority of PDVM over the contemporary computational models in property as-sessment.In Chapter 4, an Attention-Based Multi-Modal Fusion (AMMF) is proposed,which is used to estimate the property values from house attributes and street maps.The CNN is used to extract topography from street maps. Moreover, the attention6mechanism and Boosted Regression Trees (BRT) are used for further improvingthe performance in assessment. The experimental results show that AMMF out-performs contemporary models.In Chapter 5, a Mutli-Source Urban Data Fusion (MUDF) is proposed to as-sess the property values with heterogeneous urban data such as criminal activities,street-view images and so on. The metadata like house attributes and criminal ac-tivities is fused and abstracted as fused metadata by an MLP. For imagery data, aCNN-based model works for generating the expected levels of property values ofthe estate. Finally, a BRT is proposed to assess the property values based on boththe expected levels and the fused metadata. The experimental results validate itsnovel performance comparing with the state of the arts. Moreover, it is the firstapplication of data fusion methods in property assessment.Chpater 6 conclude the thesis, and recommendations of future research.7Chapter 2Computational Models inProperty Assessment2.1 OverviewThe computational model is the set of mathematical tools to simulate the behaviorsof the complex or dynamic system. In the thesis, the computational models areused to simulate the behavior of property assessment as estate appraisers. Themain objective of the computational model is to learn the function mapping oneor more inputs of property data to the output of property value. Generally, theprocedure of computational modeling consists of a training phase, prediction phaseand validation phase as shown in Figure 2.1. The training phase is the first step touse the property data and the corresponding property values to find the projectionbetween patterns of property data and values. After the training phase, the trainedcomputational model is obtained for the prediction phase. In the prediction phase,the trained computational model estimates the values of properties. On the otherhand, the assessed values of houses are evaluated to determine the performance inthe validation phase. If the result is unsatisfied, the parameters of the computationalmodel are fine-tuned until the result is satisfying.In the early stage of property assessment, the conventional approaches weretypically comparable sales method and Hedonic Regression (HR) based on prop-erties’ attributes [8, 9]. With advances in computer science, machine learning mod-8Figure 2.1: The procedure of training computational model in property as-sessment.els are used to discover patterns and complicated relationship of features for mak-ing decision or prediction in many domains [35]. Therefore, real estate brokersstart using machine learning models in property assessment. A good majority ofthe machine learning models are tree-based models like the decision tree. Re-gression Tree (RT), Random Forest (RF), and Boosted Regression Trees (BRT)are the tree-based models which are the most prevalent methods in estimating theproperty values according to house attributes. Another aspect of machine learn-ing application is assessing property values based on peer-dependence. K-NearestNeighbour (KNN) and Locally Weighted Regression (LWR) are investigated to ad-dress the challenge of peer-dependence. The experimental results in the literaturedemonstrate their accuracy in property assessment [9, 36, 37].Deep Learning (DL) is a group of machine learning models based on ArtificialNeural Network (ANN). Around the early 21st century, the simplest architectureof DL, Multi-Layer Perceptron (MLP), was investigated for improved propertyassessment [4, 10–12]. Due to the problems of computational resources and opti-mization, the investigated MLPs were not accurate and robust in property assess-9Figure 2.2: The Venn diagram of contemporary computational models inproperty assessment.ment at the time. With the development of general-purpose computing on GraphicProcessing Unit (GPU) [38], computer can train the DL models on GPU expo-nentially faster than on Central Processing Unit (CPU). On the other hand, theinvention of Rectified Linear Unit (ReLU) solves the problem of “vanishing gra-dient” in optimization [38]. After the two iconic inventions, DL can be addedmore processing layers to increase the computational capacity and obtain more ab-stracted features significantly. In this thesis, two Deep Learning (DL) methods,Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), areinvestigated to address the mentioned challenges of property assessment in Chap-ter 1. The Figure 2.2 illustrates the correlation among contemporary computationalmodels, machine learning and deep learning in property assessment.The rest of the chapter is organized according to the Figure 2.2. Section 2.2introduces the contemporary computational models in property assessment from10conventional approaches to machine learning algorithms. The Multi-Layer Percep-tron (MLP) is also introduced as one of the contemporary computational modelsin Section 2.2. The Section 2.3 introduces the frameworks of Deep Learning (DL)methods, the Recurrent Neural Network (RNN) and the Convolutional Neural Net-work (CNN), in this thesis.2.2 Contemporary Computational Models2.2.1 Conventional ApproachesThe field of property assessment has been developed for decades in governmentand research institutes, especially in economic departments. However, the compu-tational models were restricted to non-advanced techniques in computer science.Comparable sales method and Hedonic Regression (HR) are the major conven-tional approaches in the early stage of property assessment.Comparable Sales MethodFigure 2.3: An example of comparable sales method [1].Comparable sales method is the simplest way to estimate the houses price with-out large-scale data. The approach is to compare it with similar houses in the vicin-ity, which is the core principle of the comparable sales method. In the comparablesales method, a to-be-valued property is compared to a similar real estate that has11recently been transacted in the same area [1]. The values of these related transac-tions are used as a reference, and the cost of the to-be-valued object is estimatedby applying a correction to the reference prices [1]. This correction may be donebased on a linear relationship between essential attributes and the transaction price,although in practice it is prevalent that solely the intuition and knowledge of thevaluer form the basis [1, 9]. A typical example of the comparable sales method isillustrated in Fig. 2.3.Hedonic RegressionHedonic Regression (HR) is an applied linear regression method for property as-sessment based on Hedonic Price Theory in 1974 [8]. The basic model of assess-ment function is defined as below:V = α0+ ∑i=1...nαixi+ ε (2.1)where V is the values of the to-be-valued house, {α0...αn} is the set of coeffi-cients, and {x1...xn} is the set of house attributes and the ε is the assessment error.Comparing with comparable sales method, the HR can incorporate more influentialfactors to have more robust and accurate estimation [1, 9, 14, 15]. The assumptionof HR is linear correlation among house attributes. Although there shows a bunchof more sophisticated models based on HR, the evidence shows that the valua-tion of real estate also depends on non-linearity [9, 14]. With the developments incomputer science, some machine learning methodologies are applied in real estateappraisal to capture non-linearities for achieving more accurate prediction.2.2.2 Machine LearningThe conventional approaches can only map property data and values if they are un-der linear correlation. However, the property assessment also depends on the non-linearity [9, 14]. Machine learning models are capable of deriving the non-linearityfor property assessment. Regarding property assessment, two groups of machinelearning models, the tree-based models, and models based on peer-dependence areintroduced in this section. Concerning property assessment, two groups of machine12learning models, the tree-based models, and models based on peer-dependence areintroduced in the section.Tree-Based ModelsRegression Tree (RT) is the decision tree for the regression task. The RT of prop-erty assessment has several nodes representing each attribute of the estate. Whenthe house attributes are fed into the model, the decision is made at the leaf end asthe estimated values. Figure 2.4 shows the process of regression tree in propertyassessment with three attributes [1].Figure 2.4: The basic regression tree for property assessment [1].Several empirical studies use the regression tree to estimate the real estate sell-ing prices [9, 39]. According to the empirical studies, they note that the regressiontree does not merely depend on the pre-selection features of houses which indi-cates the generality of the regression tree. Moreover, the leaves and nodes of theregression tree enable the model to capture the non-linearity for achieving betterperformance than the Hedonic Regression (HR). However, regression tree is easyto be over-fitting which leads to high variance of prediction if the tree grows toolarge. Therefore, it is challenging to define the depth of trees to obtain the idealmodel.Methods of dealing the high variance of prediction are found in ensemble mod-els: combinations of multiple models for increasing the reliability and accuracy[35]. Random Forest (RF) and Boosted Regression Trees (BRT) are the two majortree-based ensemble models methods in property assessment [4, 37, 40, 41]. Ran-dom forest traditionally combine the regression trees into one average prediction13via 1n ∑ni=1 yˆi where yˆ is the prediction of the model. Unlike RF, BRT initialize aregression tree, and generate next tree via refitting the initial tree. Then, it com-bines all the generated trees with corresponding weights. The algorithm is definedas below.yˆi =K∑k=1hk(xi) (2.2)L(t) =n∑i=1l(yi, yˆi(t−1)+ht(xi))+Ω(ht) (2.3)where hk(xi) is represent a single regression tree at k; xi is the vector of features ini-th instance while yi is the true values of the data; l is the differential loss func-tion; L(t) is the regularized objective function in t-th iteration; and Ω is the regu-larized term for regression tree. Although the tree-based models are dominating inproperty assessment, the computational capacity is limited which preventing fromincorporating more types of data beyond housing attributes. If a dataset containsvarious types of data like imagery data and metadata, the tree-based models typi-cally requires extra techniques to combine and reduce the dimension of the datasetbefore feeding into the tree-based models. Therefore, in Chapters 4 and 5, the BRTis implemented to estimate the property values after proposed methods for moreaccurate and robust performance.Models Based on Peer-DependenceTo include the impact of nearby houses in property assessment, a model needs todefine the similarity or latent influence between the to-be-valued house and nearbyhouses. K-Nearest Neighbour (KNN) is an appropriate method to define the simi-larities, and estimate the price based on the Euclidean distance of features amonghouses same as Locally Weighted Regression (LWR) [14, 15]. KNN and LWRmemorize K similar houses of the to-be-valued house. Then, they fit the K similarhouses to a regression model for estimate the property values. For regression mod-els, the K similar houses fit least squares regression for KNN, or kernel functionfor LWR [42]. The implementation of KNN and LWR is presented to determinethe accuracy of the proposed model in Chapter 3.14DiscussionThe machine learning methods can accurately estimate the values of houses espe-cially for RF and BRT which are regarded as the two most robust methods in theproperty assessment. With accumulating various types of datasets in urban areas,a model may achieve better performance if the model can take advantage of thesedatasets. However, the machine learning methods are not capable of extracting thefeatures from heterogeneous data. Therefore, before feeding heterogeneous datato machine learning models, it typically requires extra data fusion techniques toobtain features or representations from original data such as Principal ComponentAnalysis (PCA) and Non-Negative Matrix Factorization (NMF), etc.. Moreover,the computational capacity of machine learning models is limited if the modelsface large-scale or high dimensional datasets like images. Therefore, these factorsprevent machine learning models from progressing in property assessment.2.2.3 MLP: The Simplest Deep Learning ModelA good majority of the contemporary computational models are acquired in inves-tigating the applicability of Deep Learning (DL). Multi-Layer Perceptron (MLP)is the most common and the simplest model of DL. Multi-Layer Perceptron (MLP)consists of three components: an input layer, hidden layers, and the output layer.Every layer consists of a number of neurons. Furthermore, each neuron is denselyconnected with all the neurons in the previous layer, which is similar to synapsesin the human brain [2, 13]. The calculation process of MLP in property assessmentis defined as the Equation. 2.4:F (x) = ∆(i=m∑i=0;xi∈x(ωixi+bi)) (2.4)where the x = {x1, ...,xm} is the set of property data; the ∆ is the activationfunction, and the details of activation function can be found in [13]; F is the set ofneurons in next layer; ω represents the set of associated weights to each neuron ofprevious layers; b is the bias of the neuron.Figure 2.5 shows the architecture of MLP in property assessment. The inputlayer obtain the property data, where each neuron will adopt one feature in the15Figure 2.5: The architecture of multi-layer percetron in property assessment.property data. Then, the hidden layers process the information from the input layerand extract representations from the last hidden layer. Finally, the output layerestimates the property values.Researchers have been applying MLP for improving property assessment [4,11, 12, 43, 44]. Previous studies reported that the predictions with MLP for houseproperties were not stable [45, 46]. One study suggested that Hedonic Regression(HR) might even be better than MLP with the selected small number of samples[47]. However, MLP is still useful to extract representations from heterogeneousdata [30]. An MLP which is presented in Chapter 5 is used to fuse heterogeneousdata for better property assessment.2.3 Deep LearningDeep Learning (DL) is a type of machine learning methods to learn representa-tions from raw data based on Artificial Neural Network (ANN) which simulatesthe functionality of the human brain. Traditional machine learning methods needto incorporate some sophisticated methods of feature extraction before the trainingprocess. Unlike traditional machine learning methods, DL can be tuned to capture16representations of data for achieving better performance. Currently, deep learninghas demonstrated great potential for pattern recognition thanks to its robust andaccurate feature learning capabilities in image recognition, natural language pro-cessing, multi-modal fusion [13], etc.. Besides Multi-Layer Perceptron (MLP),Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) arethe two advanced architectures of DL. Recent studies show that RNN is dominatingin the natural language processing [2, 48]. And, the CNN is powerful on computervision task [13, 22, 23, 38]. However, there is lacking of research on RNN andCNN in property assessment. The frameworks of RNN and CNN in the thesis areintroduced in this section.2.3.1 Recurrent Neural NetworkSequential data such as natural languages contain non-negligible mutual relationamong data points inside. Currently, The Recurrent Neural Network (RNN) is aclass of ANN where connections among neurons are directed cycle for manipula-tion of sequential data. The “recurrent” means that the network performs the sametask for every part of a sequence. Moreover, the output depends on the previouscomputation. The illustration of RNN is shown as Figure 2.6.Figure 2.6: A recurrent neural network and the unfolding in time of the com-putation involved in its forward computation [2].The Figure 2.6 shows an RNN being unfolded into a full network. The unfold-ing means the computing process throughout a sequence. The formulas are defined17as follows [2]:• xt is the input at step t.• st is the hidden state at time step t. st is derived based on previous hiddenstate and the current input: st = f (Uxt +Wst−1). The f is the activationfunction [13], and the W is the set of weights of neurons.• ot is the output at step t.However, conventional RNN faces the problem of gradient vanishing whentraining the model. Therefore, a specific form of RNN, Long Short-Term Memory(LSTM), is proposed to solve the problem [13]. Some designs of LSTM have beenapplying sequential data like texts and acoustic signal [49, 50]. In this thesis, the to-be-valued property and its nearby properties are as similar as sequential data. Then,the application of RNN can estimate the property values based on peer-dependence[24]. The implementation of RNN and LSTM is presented in Chapter 3.2.3.2 Convolutional Neural NetworkWhen a computer process an image, the image is regarded as a 2-dimensional arrayof pixel values. The Convolutional Neural Network (CNN) is designated to processthe multiple arrays. The process of the convolutional neural network is shown inFigure 2.7. The outputs of each layer are applied to the image of a picture ofa dog. Each image is a feature map corresponding to the output for one of thelearned features [2]. The abstracted imagery features flow from bottom to top. TheRectified Linear Unit (ReLU) is the activation function in Figure 2.7.The calculation of CNN is similar to MLP. Unlike MLP, the convolution istaken among layers which are defined as follows:y j = f ( ∑i∈(M) jxi⊗ ki j +b j) (2.5)where y j is the j-th feature map in the current layer. xi is the i-th feature map inthe last layer while ki j is convolutional kernel and the b j is the corresponding bias[51]. ReLU [52] is used as the nonlinear activation functions for each convolutional18Figure 2.7: The process of convolutional neural network [2].layer, and formulated as:f (x) = max(x,0) (2.6)In this thesis, the CNN is used to extract representations from imagery datalike street maps and street-view images for improved property assessment. Theimplementation of CNN is presented in Chapters 4 and 5.2.4 Evaluation MetricsThroughout the thesis, three standard regression metrics are employed to evaluatethe efficacy of computational models for property assessment. The Mean AbsoluteError (MAE) and Mean Absolute Percentage Error (MAPE) are the conventionalmeasurements of regression models in real estate appraisal. The lower values ofMAE and MAPE indicate the better performance of the model. Meanwhile, Coef-ficient of Determination (R2) is employed to evaluate the robustness of predictionin regression models. The higher value of R2 indicates the better performance ofthe model. Equations (2.7), (2.8) and (2.9) give the definitions of the three metrics,where y is the vector of true value, y¯ is the mean value of y, and yˆ represents thevector of estimated value.19MAE =1NN∑i=1| yi− yˆi | (2.7)MAPE =1NN∑i=1| yi− yˆiyi| (2.8)R2 = 1− ∑Ni=1(yi− yˆi)∑Ni=1(yˆi− y¯)(2.9)2.5 SummaryIn this chapter, the basic principle of computational models is introduced. Thechapter provides a comprehensive review of the contemporary computational mod-els in property assessment from conventional approaches to machine learning meth-ods. Moreover, it provides the theoretical frameworks of deep learning methods forthe research presented in Chapters 3, 4 and 5. Additionally, the BRT and MLP areimplemented in our models to address the challenges of property assessment. Fi-nally, three standard evaluation metrics are presented to evaluate the experimentalresults in Chapters 3, 4 and 5.20Chapter 3Peer-Dependence ValuationModel3.1 OverviewThe process of valuation is significantly affected by house attributes. For example,a small house with only one bedroom and one kitchen is evidently less expensivethan a larger house with three bedrooms and one kitchen. Nonetheless, in addi-tion to the dependence on its attributes, the property value also depends on nearbyhouses [15, 34]. This effect is known as peer-dependence. The price of houses withthe same set of characteristics will fluctuate depending on the community whichthey belong to. If a house is located in a wealthy community, the house will havea higher value. On the contrary, the house price will be lower in a relatively poorcommunity. Figure 3.1 shows the distribution of the house prices in Chicago andDetroit where the darker colored indicators represent, the more expensive proper-ties in the city. In Figure 3.1(a), it indicates that the relatively expensive housesconcentrate on the upper Chicago and along the lakeside of Lake Michigan. Mean-while, the spatial distribution of house prices is relatively uniform in Figure 3.1(b).Nonetheless, here are still some high latent prices clusters in the center and top-leftof the graph. An intuitive understanding is, if the target property locates in one ofthe clusters, it will a higher probability to feature a high value as well.Deep Learning (DL) has demonstrated great potential for pattern recognition21(a) Chicago (b) DetroitFigure 3.1: Spatial distribution of houses in (a) Chicago (b) Detroit.22thanks to its robust and accurate feature learning capabilities. Recent studies showthat the Recurrent Neural Network (RNN) is powerful for natural language pro-cessing [48]. Natural language processing is a field of machine learning concerningwith interactions between computers and human with sequential data in language.The word in sequential data usually depends on the previous and the future context.RNN is a state-of-the-art method in solving the sequences related problem. In thischapter, a method is proposed using Long Short-Term Memory (LSTM), a specificform of RNN, to estimate the values of the to-be-valued house with considerationof peer-dependence. Specifically, the major steps include sampling the nearby to-be-value properties, comparing their similarity, and converting the data of similarhouses into a set of sequences. In order to sample the nearby houses, a samplingalgorithm, K-Nearest Simiar House Sampling (KNSHS), is also proposed to cat-egorize the nearby similar houses according to their Euclidean distance. Then,the proposed Peer-Dependence Valuation Model (PDVM) estimates the values ofproperties from the generated sequences based on RNN. This work provides twomain contributions:• Transforming the aspect of nearby houses into sequences in property valuesestimation, where the impact of peer-dependence among the houses can bemeasured.• Adopting RNN to estimation of property values considering peer-dependence,and achieved with accurate results.3.2 Data DescriptionThe dataset in this chapter contains real-life housing data from several retailers ofestates in 2016. The actual transactions are discrete values in the time domain,which means the dataset is not time-series data. In our study, we extracted housingdata from Chicago and Detroit, and applied the regression models to this dataset.Chicago has more recorded houses than Detroit. The prices in the two cities varysignificantly, and the houses in Chicago are much more expensive. Some houseprices are extremely high or extremely low. To obtain more general predictiveresults, such extreme values are filtered out as outliers. Table 3.1 summarizes thehousing valuations in both cities after filtering.23Table 3.1: Property summary for Chicago and Detroit (in US$)City Numbers Average Price Standard DeviationChicago 5179 213,902 94,619Detroit 1169 40,041 18,864For each house, both the physical attributes and locational attributes are avail-able. The physical attribute is categorized as structure information in this dataset.It contains major characteristics of houses such as the size of the house, construc-tion materials, decoration materials, and other information related to the capitalcost of a house. Another type of information, i.e., site information, features severalgeographic details about the property such as longitude and latitude. Moreover,the dataset doesn’t have any bi-variate variables which mean Euclidean distance isapplicable to our dataset. The attributes in the dataset are given in Table 3.2.Table 3.2: Available real estate attributes in the housing datasetAttributes DescriptionSite Major locational information for estatesProperty type Apartment, single family house, etc.Structure Major properties of estatesValuation The valuation of estates and its confidenceTo identify the major factors which affect the house price, the importance offeatures is computed by using the Boruta method [53]. Typically, the featureswith low importance contain many missing or irrelevant values of the house price.During feature selection, features with too many missing values are excluded. And,the features of moderate and high scores of importance are reserved for furtherexperiments.243.3 Peer-Dependence Valuation Model3.3.1 Overall PDVM ArchitectureThe whole procedure of Peer-Dependence Valuation Model (PDVM) applied tothe real estate dataset is shown in Figure 3.2. There are three major componentsin the computational model: 1) K-Nearest Simiar House Sampling (KNSHS) isapplied to aggregate sequences of t-th house St = { f 1, ..., f t , ..., f K} from housesand their neighbors, where f represents the vectors of house attributes and K is thelength of a sequence; 2) the Bidirectional Long Short-Term Memory (B-LSTM)layer is to extract the representations of the to-be-valued house based on mutualimpacts of each part of a sequence; 3) the Fully-Connected Layer (FC) works asregressor for estimating the house price based on the representations from previousB-LSTM layers.3.3.2 K-Nearest Similar House SamplingOne major impact of the property is the geographical location in the city. Figure 3.1shows the deviation among houses in different locations. Moreover, the differenceof some attributes may create huge price gap of houses in the same community. Forexample, the value of villas is typically more expensive than apartments. Here, wedeveloped an algorithm, K-Nearest Simiar House Sampling (KNSHS), to samplethe nearby houses according to distance and similarity.For obtaining the geographical coordinates of each house, we calculated theorthodromic distance between any pair of houses. Let (φ1,λ1) and (φ2,λ2) be thelatitudes and longitudes of arbitrary two houses, and the distance is given by theequation as follows:∆σ = 2arcsin√sin2(∆φ2)+ cosφ1 · cosφ2 · sin2(∆λ2)(3.1)where ∆σ is the orthodromic distance.Then, we only keep the houses within 2 kilometers as nearby houses so that wecan have relative larger similar houses according to the geographical distribution ofboth cities [24]. Finally, we select the top K−1 similar houses as nearby houses,25Figure 3.2: The overview of the proposed PDVM. The red house indicates the to-be-valued house, and the blue housesare the nearby houses.26and convert the features of nearby houses and to-be-valued house into sequenceof length K. The similarity between the desired house and its neighborhood iscalculated as Euclidean distance as shown below:d(p,q) =√n∑i=1(pi−qi)2 (3.2)where p and q are the vectors of house attributes from two different houses in termsof geographical coordinates. In Equation (3.2), the d(p,q) indicates the Euclideandistance between the house p and q in terms of house attributes. Therefore, thelower values of d(p,q) indicate the higher similarity between the two houses.Algorithm 1: K-Nearest Similar House SamplingInput : Features of n houses F = { f 1, f 2, ..., f n}Coordinates of n houses G = {g1,g2, ...,gn}Indexes of n houses H = {h1,h2, ...,hn}Output : The sequence of ht house S1 Select a desired house ht ;2 Calculate the orthodromic distance between any house and ht depends on G;3 Select the houses within 2 kilometers;4 Calculate Euclidean distance between ht and nearby houses depends on F ;5 Select the K−1 nearest houses from ht based on Euclidean distance;6 Concatenating their features with f t ;7 If the length of sequence is less than K, padding −1 until the length is K;8 Adjust alignment to let f t be in the center of the sequence;9 return St = { f 1, ..., f t , ..., f K}The remaining sets of houses will be directly concatenated as a sequence for thedesired house. The Algorithm 1 shows the process of sequence generation in theexperiment. The houses inside the same sequence have a relatively high correlationwith each other in terms of their location and similarity. In other words, the valueof houses in the same sequence is highly correlated. To be noticed, the feature ofselected house f t is always placed in the center of sequence in order to make useof both previous context and future context.273.3.3 Bidirectional Long Short-Term Memory LayerRecurrent Neural Network (RNN) is a family of neural networks for processingsequential data as Figure 3.3 [13]. {O1, ...,Ot , ...,OK} are the output units ofthe neural network given the input sequence {x1, ...,xt , ...,xK} and hidden units{h1, ...,ht , ...,hK}. There is a one-way flow of information from the input unitsto the hidden units, while another one-way flow of information from the hiddenunits to the output units. ht is calculated based on the output of the current inputlayer and the state of the previous hidden layer ht−1, which is usually estimated inEquation (3.1) [13].Figure 3.3: The architecture of recurrent neural network (RNN).ht = f (Uxt +Wht−1) (3.3)where f is generally a non-linear activation function such as tanh or ReLU withshared parameters U, W [13]. The Ot is the output of step t which depends onactivation function in current neuron as in Equation (3.4).Ot =4(V ht) (3.4)where 4 represents the activation function for the output layer with V as sharedparameters.Therefore, the Ot depends on not only the current input but also the output fromprevious steps. In our scenario, the term of step t indicates the house index in asequence, and the output Ot is the representation of the t-th house. In the proposedmethod, only Ot will be used in further manipulation, and other outputs will be28ignored because our objective is to estimate the value of the selected house in thesequence.Figure 3.4: The architecture of Long-Short Term Memory (LSTM).Theoretically, RNN is capable of handling sequences of arbitrary length witharbitrary features inside the sequence. However, longer length and more featuresof sequence requires more hidden layers which cause vanishing gradient problempreventing RNN from optimization, especially for high-dimensional dataset. LongShort-Term Memory (LSTM) is the architecture to solve the problem. Each LSTMsplits the whole neural networks into multiple cells {C1, ...,CK}. Each cell containsan input gate, forget gate and output gate as illustrated in Figure 3.4, which is capa-ble of memorizing the error in forwarding propagation stage. The forget gate dropsthe error from the cell to solve the vanishing gradient problem. The calculation isdefined as below:29it = σ(W ixxt +Whi ht−1+Wicct−1+bi) (3.5)ft = σ(W fx xt +Wfh ht−1+Wfc ct−1+b f ) (3.6)ct = ft ct−1+ ittanh(W cx xt +W ch ht−1+bc) (3.7)ht = ot tanh(ct) (3.8)ot = σ(W oo xt +Woh ht−1+Woc c(t)+bo) (3.9)In the above equations, i, f ,o and c are corresponding parameters for the inputgate, the forget gate, the output gate, and the cell respectively [54]. The W is the setof weights for each neuron to each gate. The input gate combines the current inputwith previous output with activation function σ and bias b f in the neuron. Then,the tanh creates new candidates for the cell value and compares to the previousvalue for the decision to update with bias bi and bc respectively. The details of theequations are given in [13, 54].Figure 3.5: The architecture of bidirectional recurrent neural network.In natural language process, there is no reason not to exploit future context inthe document [54]. Likewise, the representation of the t-th house should make useof all the features of houses in the sequence. Bidirectional LSTM (B-LSTM) pro-cess sequential data with two directions, which are forward and backward states[55]. As illustrated in Figure 3.5, a B-LSTM computes sequence with two direc-30tions the output unit Ot can benefit from a relevant summary of the past in its htinput and from an appropriate summary of the future in its gt input. Referring to[54], the process can be described as below.ht =H (W hx xt +Whh ht−1+bh) (3.10)gt =H (W gx xt +Wgg ht+1+bg) (3.11)Ot =W Oh ht +WOg gt +bO (3.12)The mechanism of bi-direction can enable the PDVM to consider the previouscontext and future context especially for the central part of the sequence. There-fore, the central placement of t-th house can benefit from the attributes of all nearbyhouses with B-LSTM.3.3.4 Fully-Connected LayerThe architecture of Fully-Connected Layer (FC) is the same as Multi-Layer Per-ceptron (MLP) mentioned in Chapter 2. Here, an MLP is used to extract the rep-resentation of the t-th house, Ot , from output gate of B-LSTM. Then, the MLPcan estimate the final house price from the representations Ot . The computationalprocess for the FC can be defined in Equation. (3.13).F (x) = ∆(i=m∑i=0;xi∈Ot(ωixi+bi)) (3.13)where the Ot = {x1, ...,xm} is set of representations from output gate of B-LSTM;the ∆ is the activation function; F is the set of neurons in next layer; ω representsthe set of associated weight; b is the bias of the neuron.3.4 Experiments3.4.1 Experimental SetupFor each city, the datasets are shuffled and split into training (80%) and testingset (20%) for building the PDVM. The activation units are ReLU [13], in hidden31layers. Otherwise, linear activation units are used in the output layer for regression.The mini-batch size is 50 with 150 epochs of training. Moreover, the learning rateis initialized by 0.01 with decay 1e−10 for optimizers in all the models. To preventover-fitting, we also apply dropout and weight decay regularization strategy fortraining. The dropout rate sets to 0.5 and hyper-parameter for weight decay set to0.0001. In our experiments, we set the size of each B-LSTM hidden layer to be80, and the FCs to be 240 and 160 respectively. The function of mean squarederror is used as a cost function for training. Moreover, we also apply the RMSProp[56] as the optimizer for PDVM, which is capable of automatically adjusting thelearning rates during training. For the training machine, a computer is used withan NVIDIA GeForce GTX 950M GPU, an Intel Core i7 CPU and 16 GB Memorywith Keras as deep learning framework [57].3.4.2 Experimental ResultsComparison of Sampling MethodsTo ensure that PDVM can benefit from the KNSHS method, we develop a modelwith only one B-LSTM hidden layer inside to test the difference between KNSHSand random sampling. The random sampling method is to randomly sample thecorresponding number of houses within 2 kilometers of the desired house. TheTable 3.3 shows the results of different strategies. And, the best results are high-lighted in the Table 3.3.Table 3.3: Comparison of Sampling Strategies (1-Layer B-LSTM)KNSHS Random SamplingLength (K) Chicago Detroit Chicago DetroitMAE MAPE MAE MAPE MAE MAPE MAE MAPENone 60337 31.38% 15548 36.49%3 47912 23.83% 12063 31.35% 55130 26.69% 13618 39.66%5 42566 20.98% 10981 31.00% 51126 24.92% 12759 33.40%7 41503 20.43% 10784 29.49% 46536 22.36% 12671 33.53%9 43062 21.11% 10722 29.75% 47833 23.35% 12187 32.60%11 47941 24.74% 10844 30.20% 56627 27.88% 11753 31.28%32Comparing the experimental results between none-sampling and after-sampling,there is a significant improvement of the model with KNSHS or random sam-pling. It demonstrates that considering peer-dependence can improve the accuracyof the assessment of the house price. On the other hand, comparing with sam-pling method among each number of neighbors, the KNSHS has higher estimationaccuracy. It indicates that the PDVM can benefit from the context with similarnearby houses other than purely nearby houses. Moreover, the experimental resultis the best when we set K as 7 as the number of houses. From the Table 3.3, weacknowledge that the K is not appropriate to the accuracy of the estimation. Ex-ceeding nearby houses may bring the unrelated or low-similarity neighbors into thesequence, and it may bring negative influences the context of the sequence.Optimization of PDVM StructureIn the last experiment, the model achieved the best performance when it sampled 6similar houses in the vicinity (K = 7) with only one B-LSTM layer. To define thebest structure of PDVM, we applied the grid search method to search the architec-ture of the best performance exhaustively. During the grid search, the number ofneurons inside the hidden layer appears to be the same as the description in Section3.4.1.Table 3.4: Selected experimental results with different layers of PDVM (K =7)Structures Chicago DetroitMAE MAPE MAE MAPE1-layer FC 65008 32.00% 10984 31.01%2-layer FC 64419 31.71% 10641 30.04%1-layer B-LSTM 41503 20.43% 10981 31.00%2-layer B-LSTM 41097 20.23% 10953 30.92%3-layer B-LSTM 42844 21.09% 11760 33.20%2-layer B-LSTM + 1-layer FC 40874 20.12% 10358 29.24%2-layer B-LSTM + 2-layer FC 42377 20.86% 11523 32.53%33Evaluating with MAPE and MAE, the overall performance of the model withB-LSTM layers is better than the model without B-LSTM as shown in Table 3.4.This phenomenon reveals that the mechanism of B-LSTM is capable of takingadvantage of the sequence to estimate the house price. On the other hand, Table3.4 shows that simply adding the depth of B-LSTM or FCs won’t increase theaccuracy of the neural network. Finally, we observed that the model achieved thebest performance in both cities when PDVM was featured with 2-layer B-LSTMand 1-layer FC.Comparison with Contemporary Computational ModelsIn this experiment, we compared the proposed model to the following algorithms:• Boosted Regression Trees (BRT) is an ensemble method to combine weakdecision tree for increasing reliability [58, 59]. The BRT demonstrated itscapability to significantly improve the accuracy of estimation in [37, 41]. Inthis experiment, the BRT is fine-tuned based on our dataset, and the winnerin competitions on Kaggle [60, 61].• K-Nearest Neighbour (KNN) is a non-parametric technique which is used tofind the properties that are the least distant in the similarity space [1]. Tworecent articles [15, 36, 62] reported using KNN for property assessment withpeer-dependence.• Locally Weighted Regression (LWR) is the KNN method with kernel func-tion [15, 36, 63, 64].Table 3.5 shows the regression results with different approaches. All the ex-perimental results are the averages after applying 5-fold cross-validation to avoidthe overfitting problem. Moreover, we also used the grid search to tune the hyper-parameters for BRT and KNN. In the contemporary computational models, BRTand KNN achieved the best performance with Chicago and Detroit data sets, re-spectively. However, the proposed model, Peer-Dependence Valuation Model (PDVM),outperforms them with notable improvements for both cities regarding MAE andMAPE.34Table 3.5: Comparison of estimating results in terms of MAE and MAPE forboth citiesMethods Chicago DetroitMAE MAPE MAE MAPEBRT 53713 26.44% 12146 34.29%LWR 62672 30.85% 12469 35.20%KNN 57430 28.27% 11973 33.67%PDVM 40874 20.12% 10358 29.24%3.5 SummaryIn this chapter, a Bidirectional Long Short-Term Memory (B-LSTM) was pro-posed to create a Peer-Dependence Valuation Model (PDVM) for the estimationof the house prices based on the attributes and houses in the vicinity. The pro-posed method can be divided into three steps. Firstly, the K-Nearest Simiar HouseSampling (KNSHS) to generate sequences of targeted houses in the dataset. Thensequences are feeding into B-LSTM layers to extract the representations of to-be-valued houses based their neighbors. Finally, Fully-Connected Layer (FC) worksas a regressor to estimate the property values. Compared with the random sam-pling method, KNSHS can enhance the valuation performance. Moreover, the ex-perimental results demonstrate that it outperforms other contemporary methods inpractical situations of property assessment. Thus, the PDVM will provide the estatebrokers with more accurate estimation of property values.35Chapter 4Attention-based Multi-ModalFusion for Property Assessment4.1 OverviewSome empirical researches find that the topography can influence the properties’market values [65–67]. For example, Figure 4.1 illustrates the spatial distributionof the houses in Los Angeles. The closer the color is to the dark red (away fromyellow) indicates higher property values. Moreover, some highlighted areas whichcontain the top and bottom 10% properties in the house price across the city byzooming in the map. In these highlighted areas, the expensive houses are eitherclose to the water (lakes, ocean, etc.) or the mountain. In contrast, the areas with-out significant topography views usually are with cheaper houses such as Comptonin the Figure 4.1. Although estate brokers understand the importance of topogra-phy, the expensive cost prevents them from fully obtaining the topography of eachhouse across the city. Rather than manually collecting the topography data, thestreet map provides a prevalent type of data incorporating topography as shown inFigure 4.1. The street maps offer an aerial view of the location and the topographynear the house which is not available in the house attributes. From the street maps,people can observe the topography of a house and evaluate the house price based onobservation. Therefore, if a computational model could understand such abstractedinformation, it would have better estimation results on property assessment.36Figure 4.1: he illustration of spatial distribution of houses and latent geographical appearance in the city of Los Angeleson street map.37In this chapter, a multi-modal fusion method is introduced to combine the twoheterogeneous inputs: street-maps and house attributes. Superficially, the methodis to extract the image features of street maps with Convolutional Neural Net-work (CNN), and incorporate it into house attributes to estimate the house price.These image features are learned from street maps as inputs, which contain theabstracted topography around the houses. However, directly combining image fea-tures and house attributes may reduce the performance of appraisal due to the in-crement of feature dimensions. Therefore, the proposed method, Attention-BasedMulti-Modal Fusion (AMMF), is required to fuse better both types of features forimproving the performance of the value assessment. The proposed model first fusesfeatures from house attributes and street maps by applying a CNN with attentionmechanism. Then, a Boosted Regression Trees (BRT) is employed to estimate theoverall house price from the fused features. The main contributions of this paperinclude:• The proposed method incorporates the latent topography into the real estateappraisal by extracting image features of street maps with a CNN. To thebest of the authors’ knowledge, this is the first application of street maps inthe computational model of property assessment.• An attention mechanism in the neural network is employed to better fuse thefeatures of house attributes and street maps, achieving rather accurate androbust results.4.2 Data DescriptionThe data of house attributes provided by Estated [5] includes physical features andsurrounding points of interest of a property in the city of Los Angeles as shownin Table 4.1. To concentrate on the study of the latent impact of street maps, alltransactions are chosen from 2016 records to avoid possible fluctuation of houseprice over time. Moreover, the properties with extremely high and low values arealso filtered out. The statistical summary of the house price after filtering is givenin Table 4.2. Both the logarithmic and min-max transformation are applied tonormalize the numeric variables. This procedure can reduce the skewness before38Table 4.1: The illustration and statistics of the house attributes.Attributes Description Statisticscoordinates Precise location of houses (33.90,-118.50)-(34.14,-118.14)zipcode The zip-code of the house 58(count)year built The year of completion of construction 1923(min)/1930(median)/2015(max)beds count Number of bedrooms 0(min)/3(median)/6(max)baths count Number of bathrooms 1(min)/2(median)/6(max)total size The livable area of houses ( f t2) 593(min)/1372(median)/4773(max)parking type The types of parking lot (garage, detached, etc.) 5(count)architecture type The types of architecture (colonial, conventional, etc.) 9(count)exterior wall The types of exterior wall (wood, siding shingle, etc.) 4(count)foundation type The types of foundation (slab, pier, etc.) 4(count)poi City landmarks within 1 Kilometers of the house 7(count)normalizing the numeric variables. The normalization is formulated as below:xlog = log(x+1) (4.1)xnorm =xlog−min(xlog)max(xlog)−min(xlog) (4.2)where the x is the raw data, and xnorm is the normalized data.Table 4.2: Statistical summary of property values in the city of Los Angeles.# of properties Avg value Min value Max value Std of value3873 $825.5K $225.0K $3740.0K $617.7KUsing the houses’ coordinates, the images of street maps are extracted fromStamen Maps API [68] for each property. The zoom value is set as 11 to coverthe area of approximately 3.35 km2 around the to-be-valued house, The style ofthe street maps is toner-background to remove the labels of streets and buildingsfor remaining only geographical appearance on each street map. Moreover, theimage size is fixed at 224×224 pixels. One example of the street map is shownin Figure 4.2, which contains four types of information: water (ocean, river, ect.),major road, high elevation (typically mountains), and house in the center of thegraph. From the street map, one can understand the topography of the to-be-valuehouses such as “the house is close to the ocean.”39Figure 4.2: The example of a street map.4.3 Attention-Based Multi-Modal Fusion4.3.1 Overall AMMF ArchitectureThis section describes the overall architecture of the Attention-Based Multi-ModalFusion (AMMF) and presents the details of the proposed method. The proposedapproach consists of two parts: the Deep Learning (DL) for multi-modal fusion(M1) and Boosted Regression Trees (BRT) for appraisal (M2) as shown in Fig-ure 4.3.In M1, a Convolutional Neural Network (CNN) is used to extract image fea-tures from street maps. Meanwhile, the house attributes are also fed into the neuralnetwork, and concatenated with image features after attention blocks to obtain thefused feature. The details of M1 are shown in Section 4.3.2. In M2, the fused fea-tures are first aggregated from M1, then apply a BRT to obtain the estimation of thehouse price. The details of M2 is shown in Section 4.3.3. And, the details of thetraining process are given in Algorithm 3.40Figure 4.3: The framework of attention-based multi-modal fusion (AMMF).414.3.2 Deep Learning for Multi-Modal Fusion (M1)In this chapter, the proposed Deep Learning (DL) approach is a multi-modal CNNdesignated to fuse the information of house attributes and street maps. The wholeneural network consists of three parts: convolutional layers, fusion layer, and re-gression layers as illustrated in both Figure 4.3 and Figure 4.4.Figure 4.4: The detailed architecture of components in M1, convolutional lay-ers, fusion layer and regression layers.Convolutional LayersAs shown in Figure 4.4, the proposed network incorporates 3 Convolutional Layer(Conv). The calculation of convolutional layers is defined as:y j = f ( ∑i∈(M) jxi⊗ ki j +b j) (4.3)where y j is the j-th feature map in the current layer. xi is the i-th feature map inthe last layer while ki j is convolutional kernel and the b j is the corresponding bias[51]. ReLu rectified linear units [52], is used as the nonlinear activation functionsfor each Conv and formulated as:f (x) = max(x,0) (4.4)The size of all the convolutional kernels is 3× 3, and the number of channels ofConvs increases from 64 to 256. Moreover, every Conv is followed by maxpooling42(/2) with a stride of 2. The details of maxpooling can be found in [13] and will notbe duplicated here. During the training process, the change of input distributionwill affect the parameters of each layer in the neural network. Although the changeof input is small, the change will be amplified throughout hidden layers, and causedifficulty of convergence [69]. Such ill-effect is named as internal covariate shift[69]. Therefore, a Batch Normalization (BN) layer is placed after each convolu-tional layer to mitigate the impact of internal covariate shift [69]. The algorithm ofbatch normalization is defined in Algorithm 2.Finally, the features after last Conv are flattened and fed into a Fully-ConnectedLayer (FC) of 512 neurons, which densely link all the units of former layer. TheFC is formulated as:hW,b(x) = f (W T x+b) (4.5)where hW,b(x) is the output vector; x is the input vector; b is the correspondingbias; and W is the corresponding weights [51]. Here, the activation function of FClayer is set as ReLU in Conv layers.Algorithm 2: Batch normalization [69]Input : Values of x over mini-batch B = {x1,x2, ...,xn}Parameters to be learned γ,βOutput : yi = BNγ,β (xi)1 µB← 1n ∑i xi mini-batch mean2 σ2B← 1n ∑i(xi−µB) mini-batch variance3 xˆi← xi−µB√σ2B+ε normalize4 yi← γ xˆi+β ≡ BNγ,β (xi) scale and shiftFusion LayerAs shown in Figure 4.4, an attention block is assigned to each input from convolu-tion layer and house attributes. Then, those features from each block are concate-nated as fused features.In our scenario, a simplification of attention mechanism is used to strengthenthe performance of fusion among cross-domain features. As shown in Figure 4.4,43the attention block is a FC with softmax as activation function. Moreover, thedashed line between FC and Multiplication represents the element-wise multipli-cation between the vector before activation in FC and the vector after activation.Therefore, the block turns the raw features into attended features. The calculationis formulated as:hk =∑iwkixi+bi (4.6)yk =φ(hk)xk =exp(hk)∑k exp(hk)xk (4.7)where φ : RK → (0,1)K is the softmax function, x is the input vector, y is theoutput vector, h is the vector of neurons in FC layer, w is the weight. The φ(h)reflects the importance of features from either house attributes or image features,which is the soft attention weighted vector as described in [70]. The deviationamong attended features is significantly larger than the deviation among the rawfeatures by suppressing some of the raw features. In other words, the attentionblock reduces the dimensions of raw features and reserves the important featuresfor estimating property values.Regression LayersThe regression layers consist of three FCs with corresponding size 256, 128 and 1.The first two FCs are all applied with ReLU as activation function and 0.5 as thedropout rate for preventing from overfitting [71]. On the other hand, the last layeris applied with linear function as activation function for assessment of propertyvalues.4.3.3 Boosted Regression Trees for Improved Assessment (M2)To obtain more accurate and robust estimating property values, Boosted RegressionTrees (BRT) is included to assess the house price based on the fused features fromM1. The boosted regression tree is an implementation of gradient boosting machinewhich ensemble all the outputs of many K weak regression trees into one ”strong”regressor for property assessment.44In the proposed method, the regression layers of M1 is regarded as a singleregressor which is sensitive to the changes of the fused features. Therefore, comb-ing the assessment results of multiple regressors into a sole result will be morerobust and accurate than a single regressor. The objective of the regression layersis the supervised training of the Convolutional Layer (Conv) and simple attentionblocks for the acquisition of precise fused features. Therefore, the output from M2is regarded as the final assessed property values.4.3.4 Training ProcessAlgorithm 3: Training processRequire: House attributes X = {x1,x2, ...,xn}The street maps of houses S = {s1,s2, ...,sn}Sale price of houses Y = {y1,y2, ...,yn}Feature aggregator F = { f1, f2, ..., fn }1 Split X , S and Y into mini batches;2 Initial M1 = Neural network;3 repeat4 Feeding the M1 with batches of X and S, calculate the gradient, andupdate the parameters of M1;5 until Converge;6 Initial F = { };7 for i← 1 to n by 1 do8 Extract features fi from the concatenate layer of M1 with xi and si;9 add fi to F ;10 Initial M2 = BRT;11 Train M2 with F as features and Y as truth;12 return M1, M2.Due to the difference between the training process of M1 and M2, each partof AMMF needs to be trained separately. For M1, mini-batch training is adopteduntil the loss is converged. After training M1, the fused features will contain moreuseful and abstracted information of street maps and house attributes. Then, thefeature aggregator F collects the fused features from the fusion layer of M1 byfeeding training data into M1. Finally, features in F is used to train BRT with45corresponding house price. This training process is widely adopted to make up theshortage of neural network in regression related task [22, 72, 73]. The details ofthe training process are shown in Algorithm 3.4.4 Experiments4.4.1 Experimental SetupThe whole dataset is shuffled and split it into training (80%) and testing set (20%).The optimizer of the neural network is Adam [74] with an initial learning rate of0.001. The epochs of training the neural network are 100 with 32 as batch size.For M2, the number of the estimator is selected as 500, and the learning rate is 0.1.Moreover, the M1 is implemented under Keras [57], and the M2 is implementedunder Xgboost in Python [58]. AMMF models were trained on a server configuredwith Xeon E5-2630 CPU and a single NVIDIA K80 GPU.4.4.2 Experimental ResultsComparing with Contemporary Computational ModelsThis experiment is to compare with several contemporary machine learning al-gorithms in real estate appraisal which are fitted by only house attributes. Fouralgorithms are chosen in this particular experiments: Hedonic Regression (HR),Multi-Layer Perceptron (MLP), Random Forest (RF) and Boosted RegressionTrees (BRT). Besides, the hyperparameters and the structure of MLP is set thesame as regression layers described in Section 4.3.2. The number of leaf nodesin RF is found by using grid search, and the hyperparameters of BRT is the sameas M2 in Section 4.4.1. In this experiment, only the dataset of house attributes isincluded, and the results are regarded as the baseline for further experiments.Table 4.3 presents the results based on three evaluation metrics. From the table,the BRT outperforms other contemporary computational models (without letter ‘Y’in the column of street maps) in the dataset of house attributes. Therefore, the BRTis selected as the baseline model to see if the multi-modal fusion can improve theperformance by including street maps.46Table 4.3: Experimental results of estimating house price. The letter ’Y’ incolumns 2-3 indicates that the algorithms use the components of streetmaps, attention block (Attention). Moreover, the column 4 indicate theposition of attention blocks.Method Street maps Attention Position MAE(K$) MAPE R2HR - - - 640.9 54.2% 59.8%MLP - - - 265.9 23.8% 72.3%RF - - - 246.1 20.8% 78.0%BRT (M2) - - - 237.6 20.1% 81.5%M1 Y - - 224.7 19.0% 82.1%M1 + M2 Y - - 212.8 18.0% 83.7%M1 + M2 Y Y Concatenation first 215.4 18.2% 78.3%M1 + M2 (AMMF) Y Y Attention first 206.9 17.5% 85.1%Experiments of Multi-Modal FusionIn Table 4.3, the experimental results are presented in two combinations: directregression with house attributes and street maps based on M1 (M1), and BRT withfused features from M1 (M1 + M2). Comparing the experimental results of BRTand M1 in Table 4.3, the addition of the street maps improves the performance ofappraisal concerning the reduction of MAE and MAPE and improvement of R2. Onthe other hand, the M1 + M2 model has better performance than direct assessmentform M1. This phenomenon proves the BRT is the better estimator than the neuralnetwork in the perspective of regression on the fused features of house attributesand street maps.Optimization of Position of Attention BlocksIn AMMF, the attention mechanism is used to improve the performance of estima-tion. However, the position of the simple attention blocks varies from each other.Therefore, two different positions of simple attention blocks are tested, “concatena-tion first” and “attention first”, to resolve the best position for the proposed methodas shown in Figure 4.5. In Table 4.3, the performance of “concatenation first”is worse in comparison with the attention model first. The performance is evenworse comparing with results in Table 4.3. On the contrary, the model of “attentionfirst”, reaches the best performance in all the evaluation metrics. Thus, the AMMF47achieved the best performance when the simple block is individually assigned toeach type of features.(a) Concatenation first (b) Attention firstFigure 4.5: The position of attention block. (a) Concatenation first: placethe attention block after concatenation of image features and house at-tributes. (b) Attention first: place individual block on each types offeature, then concatenate attended features.On the other hand, it also demonstrates the latent topography from the imagefeatures can improve the performance of property assessment. Moreover, the per-formance can be further improved by using boosted regression trees on the fusedfeatures between house attributes and image features of street maps. Finally, theattention blocks are added to our proposed model to see if the attention mechanismis capable of improving the appraisal. The experimental results suggest that the at-tention mechanism have better performance when both features are attended first.On the contrary, applying attention mechanism after concatenation of features candamage the assessment by reducing MAE, MAPE and R2.4.5 SummaryThis chapter proposes an Attention-Based Multi-Modal Fusion (AMMF) for prop-erty in Los Angeles, which can incorporate the latent topography from street mapsfor improved estimation. The AMMF framework employs the attention-based deepneural network to combine the features of street maps and house attributes. Then,it estimate the property values from the fused features with Boosted RegressionTrees (BRT). The comparative study with and without street maps confirms that48the provision of street maps can achieve better appraisal results, which demon-strates the impact of the topography around properties on their values. The perfor-mance is improved by using BRT on the fused features. Moreover, the attentionmechanism can further increase the appraisal accuracy and enhance the robustnesswith the suitable position of the attention block.49Chapter 5Multi-Source Urban Data Fusionin Property Assessment5.1 OverviewAfter entering the era of big data, there are accumulating data collected from vari-ous sources in urban areas. These types of data are called multi-source urban data.Typically, the multi-source urban data can unveil the values of properties in threeperspectives: (1) human activities (criminal activities, popularity and educationalprofile), (2) spatial features (point of Interests and dirtiness) and (3) the appearanceof the house (street-view images) in the urban area as shown in Figure 5.1.First, empirical researches have determined the importance of criminal activi-ties, popularity and educational profiles of which the house is in [31–33]. Peopleintend to pay higher prices for houses in a safe, well-educated and popular com-munity will increase the market values of houses in such area. These indicatorsare quoted as human activities in this specific research. Second, the dirtiness ofthe block and Point of Interest (POI) around the property significantly influence itsmarket values. It is evident that people are likely to buy a house where the streetsare tidy and clean. On the other hand, the POIs around the house are correlatedwith the market value of the house. For example, the market values of the housenear a beautiful park are higher. The dirtiness and POIs are quoted as spatial fea-tures. Third, the appearance of the house is one of the most important factors to50Figure 5.1: The market values of properties also depends on other factorsbesides its attributes.its market value in property assessment. When people compare houses with sim-ilar attributes, they typically tend to buy the prettier one. Street-view images ofthe property unveils the front-view appearance of the property. People can roughlyevaluate its market values from the pictures. Although multi-source urban data canreflect properties’ values in different perspectives, none of the researches discussthe applied data fusion techniques to such urban data in property assessment.In this chapter, the proposed Mutli-Source Urban Data Fusion (MUDF) algo-rithm fuses all the relevant information to estimate the market values from multiplesources in Philadelphia: house attributes, human activities, spatial features, andstreet-view images. The algorithm consists of two major steps: (1) feature extrac-tion and fusion, and (2) market value estimator. In the collected data, metadata in-clude house attributes, human activities and spatial features which is different fromthe street-view images. Therefore, I employed two deep learning models to extractrepresentations from each type of data and concatenate them into a feature vectorfor fusion operation. For metadata, I designed an attention-based Multi-Layer Per-ceptron (MLP), metadata fusion network (M1), to extract the representations from51the metadata. On the other hand, the image appraiser (M2), a Convolutional NeuralNetwork (CNN), is adapted to extract the expected levels from the street-view im-ages. Then, a Boosted Regression Trees (BRT) market value estimator is used toassess the market value of the house based on the features from feature extractionand fusion. The major contributions of our work are listed below:• Propose and validate the multi-source urban data fusion and investigate dif-ferent data fusion techniques in the field of property assessment. To the bestof the authors’ knowledge, this is the first use of data fusion in the applicationof property assessment.• Propose an attention-based neural-network, i.e., metadata fusion network,to fuse the metadata for the property assessment. In comparison with othercontemporary approaches, the proposed method demonstrates better perfor-mance and robustness.• Propose an image appraiser rather than directly combining the imagery fea-tures to derive the potential levels of market values according to the street-view images. The market value estimation with the fused metadata improvesthe performance of the assessment.5.2 Data Description5.2.1 House AttributesFor each house in the city of Philadelphia, it’s attributes consists of several featureswhich are measured by appraisers as shown in Table 5.1. House attributes are themost common metadata to describe how the property is in real estate industries.5.2.2 Human ActivitiesCriminal ActivitiesIt is obvious that people intend to live in safe areas. Thus, the criminal recordsare helpful to estimate the safety of the community. The raw crime records is52Table 5.1: The illustration and statistics of house attributes.Feature Description Statisticscoordinates Precise location of houses (latitude, longitude) (39.9,-75.27)-(40.13,-74.96)depth The distance betIen house and principal street ( f t) 11.0(min)/65.0(median)/848.70(max)frontage The width of the lot as it abuts the principal street 1.00(min)/15.00(median)/97.20(max)interior The overall condition of the interior 5(count)baths Number of bathrooms 0(min)/1(median)/6(max)beds Number of bedrooms 0(min)/3(median)/8(max)rooms Number of rooms 0(min)/6(median)/12(max)story Number of stories 0(min)/2(median)/4(max)size The total area of houses ( f t2) 270(min)/1134(median)/7826(max)livable area The total livable area of houses ( f t2) 170.8(min)/992.0(median)/3935(max)year built The year of completion of construction 1920(min)/1930(median)/2017(max)denoted by < coordinates, types, time >, where coordinates indicates the preciselocation, types represents types of criminal events such as burglary and thefts, andtime indicates the occurrence time. To explicitly indicate the safety of the area,the statistical methods are simplified from Z. Yao et al. [3] to calculate indicatorsbelow.Crime Occurence Entropy (CE): the CE represents the severity of crimearound the house. In the method of Z. Yao et al. [3], the time of occurrence isdivided into several time slots and defined in two dimensions: 1) two hours of aday and 2) weekday or weekend. Assumed k denotes a time slot, Ck,i is the setof crime happened in kth time slot at ith house [3]. The Ck indicates the set ofcrime occurrence at kth time slot in the history. Pk,i = |Ck,i|/|Ck| is the probabilityof random crime occurrence in the kth time slot at ith house [3]. The criminaloccurrence entropy is defined as follows:CEi =− ∑i:Pk,i 6=0Pk,i · logPk,i. (5.1)Consecutive Crime Intensity (CCI): With a given crime sequence around thehouse, the temporal correlation aims to consider the temporal proximity among thecrime [3]. Therefore, analyzing the temporal correlation of crimes can reflect thesafety of a house. In the method of Z. Yao et al. [3], assumed there is a crimesequence Ci = {c1, ...,c8} with occurence date of a house hi, c1 is the oldest event53Figure 5.2: An example of calculation of near repeating series [3].and c8 is the latest event as shown in Figure 5.2. Assumed tcn is the occurrencetime of a criminal event, the time interval of each consecutive event should be lessthan the maximum time threshold τ:, tcn− tcn−1 ≤ τ , ∀n ∈ [2,N] [3], where N is thetotal number of events in crime sequence Ci. Moreover, the length of series shouldbe more than the minimum size threshold θ : |s| ≥ θ [3]. In the experiment, themaximum time threshold τ is set as 7 days while the minimum size threshold is setas 3 criminal events. Therefore, the set of near repeat series Si = {s1,s2} of hi isobtained as shown in Figure 5.2. According to the method of Z. Yao et al. [3], theconsecutive crime intensity of hi is defined as below:E(s) = max2≤n≤N{τ− (tcn− tcn−1)+1} (5.2)CCIi = ∑s∈SiE(s), (5.3)Figure 5.3 display the spatial distribution of crime indicators and market value.From the Figure 5.3, conspicuous inversely proportional correlation can be ob-served between criminal activities and market value especially in highlighted re-gions.Educational ProfileThe renowned schools have more possibility to cultivate successful students. There-fore, the reputation of schools in the community is one of the most important fac-tors for buyers to especially for those who are living with their children. Some par-ents, whose children are currently or previously in the specific schools, make theircomments and ratings about the schools on social media. House buyers usually54Figure 5.3: The geospatial distribution of criminal activities indicators and market values. The criminal indicators areinversely proportional to the market values of houses especially in the red elliptical areas.55review these comments and ratings to estimate the educational profile in the com-munity before they resolve to purchase. In the experiment, the large sets of gradesfrom elementary schools are collected to high schools in the city of Philadelphiafrom our data provider. From elementary school to high school within 1 kilometerof given house hi, I separately compute the maximum rating of each type of schooland use them as the rating of the corresponding type of schools of hi. The spatialdistribution of the maximum rating of schools is illustrated in Figure 5.6. Unlikethe rating of middle and high schools, most of the regions have 0 scores after ag-gregation because people seldom comments and rates for primary schools on socialmedia.PopularityThe popularity of community also influence the market values of the property.The measurement of popularity is the numbers of check-ins on social media inhistory. If the region has more check-ins in the past, it indicates that the regionis more popular. For each house, the check-ins within 1 kilometer of the propertyare aggregated to show the popularity of the community around the house. Figure5.4 displays the spatial distribution of popularity and market value. In Figure 5.4,there are some regions of proportional relations while some regions of inverselyproportional relations. Therefore, the impact of popularity is non-linear to themarket value of properties.5.2.3 Spatial FeaturesDirtinessThe dirtiness, as well as litter index, is the indicator to measure the cleanness ofthe census tract. The higher value of litter index means the lower of cleanness inthe census tract. Figure 5.5 shows the spatial distribution of litter index and marketvalue. It is obvious that the market values of properties are higher if correspondingcensus tract has lower values of litter index.56Figure 5.4: The spatial distribution of popularity and market values. The cor-relation between popularity and market values of houses depends onwhere the houses locate. The blue rectangular areas indicate the pro-portional correlation. On the contrary, the red elliptical area indicatesinversely proportional correlation.Figure 5.5: The spatial distribution between litter index and market value.The red elliptical area indicate the areas with inversely proportional cor-relation.57Figure 5.6: The spatial distribution of the maximum rating of schools for each census tract. The pictures represents therating of elementary, middle and high schools in Philadelphia from left to right.58Point of InterestThe density of Point of Interest (POI) as well as city landmarks indicate the func-tionality of the community, which are relevant to the market values of houses in theneighborhood. For example, if a property locates in a region near parks, the marketvalue of the house tends to be high. Thus, each type of POIs is aggregated within1 kilometer of the house. The Figure 5.8(b) displays the correlation between POIsand market value of the house, and the Figure 5.8(a) shows the spatial distributionof the top 12 types of POIs that influence the market values of properties, whichare: Animal Clinic, Cemetery, House of Worship, Ice Rink, Monument, MunicipalBuilding, Park, Post Office, School, Senior Care, Swimming Pool, Transportation.5.2.4 Street-view imagesFigure 5.7: The examples of street-view images of properties. The picturesrepresents bottom 10%, normal and top 10% houses in terms of marketvalue in Philadelphia from left to right.The Figure 5.7 are the examples of houses in bottom 10%, normal, top 10% ofmarket values across Philadelphia. For each house in the Philadelphia, there is acorresponded picture in front of the house. However, some of the images do nothave the corresponding house inside the picture, or the house is veiled by trees,59(a)(b)Figure 5.8: The illustration of POIs in the City of Philadelphia (a) The spatial distribution of top 12 POIs that affectsthe market value. (b) The illustration of the correlation between different types of POIs and market values ofproperties.60cars or some other barriers. Moreover, every object is filtered as black, remainingonly the image of the house. To refine the pattern of the house from the images,a pre-trained Pyramid Scene Parsing Network (PSPNet) is applied [75], a novelCNN for schematic segmentation, to filter out the pictures with less 80% of areasof the house. Moreover, every object is deliberately turned to black color excepthouse for merely remaining house inside the image. After the process, the imagesare manually checked to ensure that every image contains a property inside, andthe house is not overlapped by black color. The example of an image after andbefore pre-trained PSPNet is shown in Figure 5.9.(a) (b)Figure 5.9: The examples of street-view images before (a) and after (b) pre-trained PSPNet.5.2.5 Data CollectionAs mentioned above, the experimental data consists of four categories: house at-tributes, human activities, spatial features, and street-view images. Estated [5]provides the house attributes and the data of educational profiles in human activi-ties. The data for criminal activities in human activities and the spatial features arecollected from Open Data Philly [76]. The data of popularity in human activitiesare collected from the Foursquare and Gowalla [77, 78] which covers the check-ins from 2009 to 2011. Finally, all the street-view images are collected from the61Google Developers Street View API [79]. After the data collection and filtering,the experimental dataset has 15815 properties across 384 census tracts of Philadel-phia. The statistical summary of the market values of properties in Philadelphia isshown in Figure 5.10.# of properties Minimum Median Maximum # of census tracts15815 $11,600 $72,900 $336,400 384(a)(b)Figure 5.10: Statistics in market values of properties. (a) Statistics (b) His-togram of market values.5.3 Multi-Source Urban Data Fusion5.3.1 Overall MUDF ArchitectureThis section describes the overall architecture of the framework and presents thedetails of the proposed method called Mutli-Source Urban Data Fusion (MUDF),62Figure 5.11: The overall architecture of multi-source data fusion.63which is based on supervised learning. The framework is shown in Figure 5.11.The proposed approach consists of two major parts: (1) feature extraction andfusion (2) market value estimator.There are multiple sources of data in the experiment, including the house at-tributes, human activities, and spatial features as metadata, while the street-viewimages as the image data. As a result, there are two mechanisms to individuallyprocess the metadata and the images in Figure 5.11. First, metadata fusion network(M1) is an MLP with input attention to fused the cross-domain features. On theother hand, the street-view images are fed into the image appraiser (M2) to evalu-ate the expected levels of market values. The M2 is developed based on pre-trainedCNN, the VGG-16 of Place365 [80, 81] with Deep Expectation (DEX) [82]. Fi-nally, the fused metadata and the expected levels are concatenated and evaluatedby boosted regression trees as the market value estimator to estimate the marketvalues.5.3.2 Feature Extraction and FusionFigure 5.12: The architecture of metadata fusion network (M1).64M1: Metadata Fusion NetworkThe metadata fusion network (M1) is an attention-based MLP with three hiddenlayers as shown in Figure 5.12. In order to better fuse the metadata, a simplificationof input attention mechanism is applied to enhance the performance [83]. The inputattention is a Fully-Connected Layer (FC) with softmax as activation function.The numbers of neurons are equivalent to the numbers of features of the metadata.Therefore, there is no numbers of neurons labeled in F1 of Figure 5.12. The inputattention in the network is formulated as:hk =∑iwkixi+bi (5.4)yk =φ(hk)xk =exp(hk)∑k exp(hk)xk (5.5)where φ : RK → (0,1)K is the softmax function, x is the input vector, y is theoutput vector, h is the vector of neurons in FC layer, w is the weight. The φ(h)reflects the importance of features, which is the soft attention weighted vector asdescribed in [70]. After input attention, the deviation among the weighted featuresis significantly lager than the deviation among the raw features. In other words, theinput attention is proposed to reduce the dimensions of raw features.Then, the weighted features from the input attention are fed into two FC layerswith 256 and 128 neurons respectively. Finally, the final layer (F4) will generatethe market value of the house as an auxiliary output. Here, the representations inF3 are stored as fused metadata for further approach.The FC layer is formulated as:hW,b(x) = f (W T x+b) (5.6)where hW,b(x) is the output vector; x is the input vector; b is the corresponding bias;W is the corresponding weights [51]; and f (.) is the activation function. Here, theactivation function of FC layer is set as ReLU [52] in hidden layers, and linear asan auxiliary output.65M2: Image AppraiserThe image appraiser (M2) applies a CNN with the VGG-16 architecture pre-trainedby the dataset of Place365 [80] which contains numerous photos of properties andbuildings. Therefore, the pre-trained VGG-16 allows the warm start for training.The proposed VGG-16 is shown in Figure 5.13.The original architecture of VGG-16 consists of 13 convolutional layers and 3FC layers. The calculation of convolutional layers is defined as:y j = f ( ∑i∈(M) jxi⊗ ki j +b j) (5.7)where y j is the j-th feature map in current layer; xi is the i-th feature map in thelast layer while ki j is convolutional kernel; the b j is the corresponding bias; and thef (.) as activation fuction [51].The whole convolutional layers are grouped in 4 convolutional blocks. Eachblock consists of two convolutional layers with filters 3×3 pixels and a max pool-ing layer at the end of convolutional layers as shown in Figure 5.13. The details ofmax pooling can be found in [81]. The numbers of neurons of convolutional layersare 64, 128, 256 and 512 from convolutional block 1 to 4. All the convolutionallayers use ReLu as activation function [52].Other than the original VGG-16 of Place365, the global average pooling layeris connected to the final convolutional block to have the compact abstracted rep-resentation of the images [81]. Then, the features after global average pooling arefed into the block of DEX [82], which is defined as below:E(O) =|Y |∑i=1yi ·oi, (5.8)where O = 0,1,2 is the the 3-dimensional output layer as well as value vector;and oi ∈ O is the softmax-normalized output probability of the neuron i [82]. TheO = 0,1,2 represents the market values in bottom 10%, normal, top 10% in thePhiladelphia. The reason of application of DEX is that training a CNN for regres-sion task is unstable due to the sensitivity to the outliers [82]. In addition, contem-porary researches also indicate that direct inference from images to house price is66Figure 5.13: The architecture of image appraiser (M2) based on VGG-16 and deep expectation (DEX).67not applicable [22]. Therefore, the market values are discretized into three levelsfor training the proposed CNN, turning the training process from a regression toa classification. For example, if o0 = 0.2,o1 = 0.4,o2 = 0.4 represents the outputprobabilities of a house from output layer, the E(O) = {0,1,2} · {0.2,0.4,0.4} =1.2 indicates the expected levels of market values of the house.5.3.3 BRT as Property Value EstimatorAlthough artificial neural network dominates in most of the classification tasks, thehigh sensitivity to the changes of features devastates its performance in regressiontask [58]. To obtain more accurate and robust estimation house prices, proposedBoosted Regression Trees (BRT) is to assess the market values of properties basedon the fused-meta data and expected levels. The boosted regression tree is animplementation of gradient boosting machine which additively ensemble all theoutputs of many K weak regression trees into one “strong” regressor for propertyassessment.5.3.4 Training ProcessDue to the difference between M1,M2 and estimator, each part of the MDSF needsto be trained separately. Before training the M2, the market values as ground truthsare divided into three price levels and label them 0,1,2 as bottom 10%, normal, top10% respectively. Then, metadata is used as features and market values as groundtruth to train the M1. On the other hand, street-view images as features and pricelevels as ground truths are used to train the M2. After converging of loss, the fusedmetadata and expected levels are combined as features from M1 and M2 to train theBRT as market values estimator. The detailed process is in the Algorithm 4.5.4 Experiments5.4.1 Experimental SetupThe whole dataset is shuffled and split it into training (80%) and testing set (20%).The optimizer of the neural network is Adam [74] with the initial learning rate of0.001. For all the FC hidden layers, the dropout rate is set as 0.5 to prevent from68Algorithm 4: Training processRequire: metadata X = {x1,x2, ...,xn}Street-view images S = {s1,s2, ...,sn}Market values of houses Y = {y1,y2, ...,yn}1 Initial price levels P = {};2 b = Threshold of bottom 10% of Y ;3 t = Threshold of top 10% of Y ;4 for i← 1 to n by 1 do5 if yi ≤ b then6 pi = 0;7 else if yi ≥ t then8 pi = 2;9 else10 pi = 1;11 add pi to P;12 Split X , S,Y and E into mini batches;13 Initial M1, M2;14 Training M1 with X as features and Y as ground truths;15 Training M2 with S as features and P as ground truths;16 Extract fused metadata F from M1 and expected levels E;17 Initial BRT;18 Training BRT with F ,E as features and Y as ground truths;19 return M1, M2, BRT.overfitting. The epochs of training the neural network are 100 with 32 as batchsize. For BRT in the proposed model, the number of the estimator is selected as2000, and the learning rate is set as 0.1. The metadata fusion network and thebeauty scorer are implemented under Keras [57], and the BRT is implementedunder Xgboost in Python [58]. For the beauty scorer, the image data generator isincorporated in Keras to do the data augmentation for training the image appraiser[57]. The rotation range, width shift, height shift, and zoom range are set as 0.2.The whole model was trained on a server configured with Xeon E5-2630 CPU anda single NVIDIA K80 GPU.695.4.2 Experimental ResultsComparison of Contemporary Computational ModelsBefore verifying the performance of BRT as market value estimator for house at-tributes, the BRT compares with different contemporary machine learning algo-rithms in property assessment besides BRT:• Hedonic Regression (HR) is the traditional computational model in propertyassessment.• In the experiment, the hyper-parameters and layers of MLP are set as sameas M1 without input attention mechanism. Therefore, the MLP consists of 3layers with 256 and neurons in hidden layers and one neuron in the outputlayer. The activation function is set as ReLU for hidden layers and linear forthe output layer.• Random Forest (RF) is an ensemble method by combining multiple regres-sion trees into a fully grown regression tree [4, 37]. Although RF has domi-nant performance in both classification and regression tasks, the problem ofoverfitting prevents RF from widely application in property assessment. Thenumber of trees is set as 1500, and the number of leaves is set as 10 which isdetermined by grid search.Table 5.2: Experimental results of contemporary computational modelsMarket Value Estimators MAE(K$) R2HR 29.54 -71.0%MLP 17.75 62.8%RF 12.33 77.5%BRT 12.23 78.2%In this experiment, only the dataset of house attributes is used in order to selectthe best market value estimator. Table 5.2 presents the experimental results of thebaseline market value estimator. In MAE and R2, the two tree-based models, RF70and BRT, significantly outperform the rest of the contemporary approach in prop-erty assessment. BRT is slightly better than the RF which leads $0.1K in MAE.However, the BRT is more robust than RF because the R2 of BRT is 1.3% betterthan RF. In conclusion, the BRT is the best market value estimator in perspectivesof MAE and R2. Therefore, the BRT is selected as the baseline estimator to see ifthe data fusion can improve the performance of property assessment.Results of Metadata Combination and FusionTable 5.3: Experimental results with metadataMulti-source metadataHouse Attributes Human Activities Spatial Features Fusion MAE(K$) R2Y - - - 12.23 78.2%- Y - - 19.08 58.0%- - Y - 16.89 67.3%Y - Y - 11.80 79.1%Y Y - - 11.45 80.1%Y Y Y - 11.14 80.3%Y Y Y PCA 13.04 71.5%Y Y Y NMF 12.45 75.5%Y Y Y RBM 12.35 77.6%Y Y Y NNF 11.03 80.5%Y Y Y M2 10.76 81.2%In Figure 5.3, the first three columns are the usage of different categories ofmetadata. If “Y” displays in the column, it means the category of data is includedto assess the market value of houses with BRT as an estimator. First, the first threerows compared the performance to see which single category of metadata shouldbe included for assessment. From Figure 5.3, house attributes have the best per-formance comparing to other two categories of metadata. The result indicates thatthe market value still relies upon the own attributes of houses. Then, Comparingdifferent categories of metadata with house attributes. The combination of all cate-gories of metadata provides the better performance without the assistance of otherfusion methods.As shown in Table 5.3, test several fusion methods are also compared in theexperiments besides M2:71• Principal Component Analysis (PCA) is one of the most prevalent unsu-pervised methods to overcome the curse of dimensionality. In real estateindustries, PCA is widely used to have better appraisal results [84, 85].• Non-Negative Matrix Factorization (NMF) is another prevalent unsuper-vised method to reduce the dimensions of data in urban computing [30]which decomposes the data into matrices W and matrices H with given dataV . Here, I used the matrices W as fused data to train the estimator.• Restricted Boltzmann Machine (RBM) is an unsupervised learning methodfor representation learning. Research shows the usage of RBM can improvethe estimation of house price [86]. The structure of RBM is set as 256-128for hidden layers.• Neural-Network Fusion (NNF) is a widely used supervised method in prop-erty assessment and urban computing [87–89]. The structure of the neuralnetwork is set the same as MDFN without input attention.From the Table 5.3, PCA, NMF and RBM are not capable of providing fusedfeatures because all of them cannot outperform the direct concatenation. Usingunsupervised techniques do extract the abstracted features from the original data,but it may cause the loss of critical information during the process. Unlike unsu-pervised fusion methods, both NNF and M1 supervised methods provide the fusedfeatures for estimator regarding MAE and R2. The fact that M1 has better perfor-mance than the NNF also indicates the input attention can improve the effectivenessof fusion.Results with Expected LevelsIn the past few years, some researchers combine the metadata and image featuresfrom CNN to estimate the house price [23, 90]. Therefore, the image features areextracted from the layer of global average pooling, fed them into BRT to estimatethe house price. In addition, the classification accuracy of expected levels is 87.1%on the test set without DEX. The Table 5.4 indicates that pure image features arenot able to estimate the market values of houses. Moreover, the performance is stillnot worse than simple usage of fused metadata even after adding image features.72Table 5.4: Experimental results of combination of street-view images andfused matadataStreet-view ImagesImage Features Expected Levels Fused metadata MAE(K$) R2Y - - 30.43 -131%Y - Y 12.49 78.8%- Y Y 9.75 82.3%On the contrary, using expected levels and fused metadata can further improve theperformance in the assessment of market values. The experimental results indicatethat a simple concatenation of image features and metadata is not the only way toestimate the properties’ market values. Using DEX to reflect the levels of marketvalue based on the street-view image can be another solution for the real estateindustry.5.5 SummaryIn this chapter, a fusion-based model, Mutli-Source Urban Data Fusion (MUDF),is developed to estimate the market values of real estate. The approach takes theadvantages of multiple types of urban data for property assessment with consider-ation of house attributes, human activities, spatial features and the appearance ofthe house. For house attributes, human activities and spatial features as metadata,the metadata fusion network are used to extract the fused metadata empowered byinput attention. For street-view images, the image appraiser is to evaluate the ex-pected levels which are based on VGG-16 and DEX. Finally, BRT is used to assessthe final market values of the house. The experimental results determine the effec-tiveness of data fusion for property assessment. Moreover, the experimental alsosuggests that including image features to estimate the market values may reducethe performance. Fusing metadata and expected levels of market values achievemore accurate and robust performance in property assessment.73Chapter 6ConclusionsIn this thesis, three deep learning based approaches are proposed to address threespecific challenges in the property assessment which are considering peer-dependence,considering topography, and considering urban data for improving property assess-ment. The main contributions can be summarized as follows:• The property value not only depends on its characteristics of itself but alsoof the nearby houses. A novel computational model, Peer-Dependence Val-uation Model (PDVM), was proposed. In this approach, the K-NearestSimiar House Sampling (KNSHS) method was developed to sample theto-be-valued house and its related nearby houses as a sequence. Then, aRecurrent Neural Network (RNN) was used to learn the peer-dependence,and estimate the property values from the sequence. The proposed methodoutperforms most of the contemporary computational models in property as-sessment,• To make the computational model incorporate the topography near the prop-erty, a Convolutional Neural Network (CNN) method is proposed to fuse fea-tures from street maps and house attributes for property assessment. To theauthor’s best knowledge, this was the first method to estimate the propertyvalues, which was capable of understanding topography from street maps.Besides, the attention mechanism and Boosted Regression Trees (BRT) areused to improve the assessment results. Extensive experiments including74default comparison and comparison of baseline methods were carried out,which verifies the performance of the method.• To use heterogeneous data across the urban area, the Mutli-Source UrbanData Fusion (MUDF) method was proposed to combine urban data and prop-erty data for property assessment. To the author’s best knowledge, this is thefirst application of data fusion in property assessment. In this method, aMulti-Layer Perceptron (MLP) is to fuse metadata while a CNN is used toestimate the expected levels of property values based on properties’ street-view images. Finally, Boosted Regression Trees (BRT) is used to assessthe property values. The experimental results demonstrate the accuracy androbustness.This thesis shows that Deep Learning (DL) provides a promising way for prop-erty assessment, which is accurate and robust. The property values are contin-uously evolving through times. On the other hand, each urban areas have dif-ferent ways of evolving due to the geographical and political differences. Thisphenomenon is called spatial-temporal effects. Nonetheless, none of the proposedmodels considers the spatial-temporal effects in this thesis. Moreover, the pro-posed model does not consider the textual features from housing market like es-tates advertisements in this thesis. These can be the potential future studies for theresearches are listed below:• Spatial-Temporal Effects: Contemporary researchers concentrate on theprediction of current property values in spite of the temporal evolution ofprice. However, for some potential buyers, the investment potential of theproperty is another important consideration. They also acknowledge the fu-ture fluctuation of the price. On the other hand, neighbor areas may fea-ture intertwined temporal trend of property values. Therefore, the spatial-forecasting is needed in property assessment, but few researchers realize itsimportance. Moreover, some events may lead to the fluctuation of prices inthe future. For example, the financial crisis of 2007 devastated the estatemarket in the city of Virginia Beach. The house price index indicates thatthe current average sales price is still below the market before 2007. With75consideration of such an event, it is possible to improve the accuracy of fore-casting.• Textual Features: There are numerous advertisements of estates in the web-site of estate brokers. Besides the numerical features like the number ofrooms filled by the brokers, the description of the house provides the majorinformation of the house. Nonetheless, few researchers discussed the usageof such textual data. On the other hand, people are likely to browse the com-ments and news of the community on social media to see if the candidatesof estates are suitable to live. These comments and news may strongly in-fluence the impression of the community for potential buyers. Therefore,including such textual data may increase the performance of the appraisal.Using deep learning methods like RNN to extract the textual features mayachieve better performance in property assessment.To conclude, the deep learning models were investigated to address specificchallenges in property assessment. Each model outperformed the contemporarycomputational models within each particular challenge. These models are not onlyaimed to be an industrial product, but they also provide different perspectives to ad-dress particular problems in property assessment. Although extensive experimentswere discussed, there are still many potential directions of property assessmentfor further research like the manipulation of textual features and spatial-temporalforecasting. These new topics will be studied in the future works.76Bibliography[1] K. 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Thesis/Dissertation
eng
Electrical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Investigations on deep learning techniques for property assessment
Text