A THESIS SUBMITTED FOR THE DOCTOR OF PHILOSOPHY

INFORMATION, SEARCH EFFICIENCY, AND NEIGHBORHOOD SOCIAL INTERACTIONS IN RESIDENTIAL HOUSING CHOICE QIU LEIJU NATIONAL UNIVERSITY OF SINGAPORE 2015 INFORMATION, SEARCH EFFICIENCY, AND NEIGHBORHOOD SOCIAL INTERACTIONS IN RESIDENTIAL HOUSING CHOICE QIU LEIJU (B. SCI., NJU; M. Eng., NUS) A THESIS SUBMITTED FOR THE DOCTOR OF PHILOSOPHY DEPARTMENT OF REAL ESTATE NATIONAL UNIVERSITY OF SINGAPORE 2015 DECLARATION I hereby declare that this thesis is my current work and it has been written by me in its entirety. I have duly acknowledged all the sources of information which have been used in the thesis. This thesis has also not been submitted for any degree in any university previously. __________________ Qiu Leiju Feb, 2015 Acknowledgements With joy together with challenge, it comes to the end of my Ph.D. study. This thesis is the witness of an important step as a Ph.D. student, and it will also become a milestone in my future journey of academic research. This thesis would not have been completed without the support, encouragement, and help of the people around me. First of all, I would like to express my sincere thanks to my supervisor A/P Tu Yong for her endless guidance and encouragement towards the completion of this thesis. I am really grateful for her efforts in imparting knowledge and experience on researching in real estate economics, and also for her inspirational advice to my topic on housing choice behavior. My deep appreciation also goes to my thesis committee members A/P Fu Yuming and Dr. Lee Kwan Ok for their critical comments and invaluable support throughout my research. I would like to thank A/P Yu Shi Ming and Professor Ong Seow Eng for the discussion when I choose my research topic, Professor Deng Yongheng and Dr. Lee Nai Jia for their comments when I propose my research topic, and Dr. Li Pei for his comments and suggestions throughout the research. I also would like to thank A/P Liao Wen-Chi for his useful comments on this work, especially for the help on the coding of the third part of this work. I wish to give my truly thanks to all the faculties in the department of real estate who have taught me in or out of the classroom, because they widely open my view and promote my understanding. i I am indebted to all my seniors and postgraduate peers in the department of real estate and all my colleagues in the department of physics. All of them are extremely helpful with their assistance and friendship. They are including but not limited to the following: Li Mu, Wei Yuan, Dr. Liang Lanfeng, Dr. Xu Yiqin, Dr. Liu Bo, Dr. Wang Yourong, Dr. Guo Yan, Dr. Li Qing, Zhou Xiaoxia, Zhang Liang, Dr. He Jia, Xuan Xu, Tang Yuhui, Luo Chenxi, Lai Xiongchuan, Dr. Deng Xiaoying, Wang Yonglin, Zhang Bochao, Dr. Zhong Yun, Dr. Yang Junjin, Jiang Mingxiu, Dr. Nidhi Sharma, Tao Ye, Lim Yen Kheng, and etc. I would also like to thank the administrative staff members for their effort to help during my study period, especially Zainab Binte Abdul Ghani, Nor’Aini Binte Ali, and Zheng Huiming. The financial support provided by National University of Singapore is gratefully acknowledged. Last but not least, heartfelt thanks will be given to my family for their kind understanding and unconditional care, support and love. Thanks, my parents, my sister, my brother-in-law, and my beloved niece. Thanks, my husband, Daxuan. And thanks, my lovely sons, Lecheng and Peicheng. ii Table of Contents Acknowledgements . i Table of Contents . iii Summary . vi List of Tables viii List of Figures x Chapter Introduction 1.1 Research Background and Research Questions 1.2 Significance . 15 1.3 Organization of the Dissertation . 17 Chapter Literature Review . 19 2.1 Introduction . 19 2.2 Housing Search and Information . 21 2.3 Heterogeneous Characteristics of Households in Housing Choice . 23 2.4 Stochastic Frontier Approach 25 2.5 Residential Location Choice . 27 2.6 Social Interactions and Neighborhood Effect . 30 2.7 Summary . 34 Chapter Background of Tianjin Housing Market and Data . 36 3.1 Background of Tianjin Housing Market . 36 3.2 Data . 41 Chapter Information and Housing Choice . 44 4.1 Introduction . 44 4.2 A Housing Search Model 46 iii 4.3 Data . 57 4.4 Empirical Results 63 4.5 Robustness Test . 70 4.6 Further Discussion . 73 4.7 Conclusion . 75 Chapter Search Efficiency and Housing Choice . 78 5.1 Introduction . 78 5.2 Econometric Implementation 83 5.3 Data . 87 5.4 Empirical Results 92 5.5 Robustness Test . 104 5.6 Conclusion . 112 Chapter Neighborhood Social Interactions and Housing Location Choice 116 6.1 Introduction . 116 6.2 A Residential Sorting Model with Neighborhood Social Interactions 120 6.2.1 Model . 120 6.2.2 Econometric Implementation . 123 6.3 Data . 126 6.4 Empirical Results 141 6.5 Conclusion . 150 Chapter Conclusion . 154 7.1 Review of the Research . 154 7.2 Contributions . 157 iv 7.3 Limitations and Future Work 161 Bibliography 165 Appendix I List of Notations . 172 Appendix II Hedonic Regressions for Subsamples 174 v Summary This thesis analyzes the residential housing choice behavior from three different angles, based on the features of housing market. First, information is imperfect in housing market. Households have imperfect housing market information; and different housing buyers may have different market information levels. Housing search is a process in which households gather information about the attributes of each choice alternative. The difference in households’ information level plays a role in their housing choice. To study this role, I examine the varied behaviors between informed and uninformed households in housing market theoretically and empirically. In a housing search model, it is found that the informed households are more likely to secure a good deal in housing market. With the data from Tianjin city in China, hedonic estimation is implemented to quantify the impacts of information difference on housing search output. The results are consistent with the theoretical predictions. Second, households are heterogeneous in housing market. With heterogeneous characteristics, households have different levels of information, different ability to collect and assimilate information, different search costs, and different bargaining power, so they would be likely to perform differently in a housing search and to deliver different search outcome. This study defines search efficiency as the probability of a household to secure a good deal to measure its caliber in housing search. I address the factors determining the performance of a household in its housing search. I adopt a modified stochastic frontier approach to study the impact of households’ characteristics vi on their search efficiency using the data from Tianjin commercial housing market in China. The results show that the probability of securing a good deal is higher for a better informed household and households with lower income, less education, lower ranks in occupation. Third, households are always surrounded by neighbors, because their homes are in a neighborhood. Their economic choice can be affected by their neighbors, which researchers address as neighborhood effect. I study the housing location choice behavior with the consideration of neighborhood social interactions. I propose a collective choice model with different tastes of households to show that households’ destination neighborhood choice is impacted by their current neighbors. With the data from Tianjin China, the estimation results show that social interactions among the current neighbors significantly impact their destination neighborhood choice. The results also show that old, high educated and rich households relatively value social interactions more when they choose their destination neighborhoods. Overall, this dissertation enriches the literature on housing choice behavior by quantifying the impacts of information level, households’ characteristics, and neighborhood social interactions. It also provides some policy implications to the housing market in China. Housing market institution should be built to reduce the market friction and increase households’ welfare. During the urban redevelopment, more human factors should be considered by city planners, because it is not only constructing housing units, but also building communities, in which neighbors interact and further affect their economic choice behaviors. vii single family housing in the western housing market and high-rise apartment units in Chinese housing market could have different impacts on the decay speed of information, as well as the interactions among neighbors. Without this information, the empirical result could not manage to capture this type of difference of the housing market. Second, my study focuses on the searching process in housing market in the theoretical model. But in the empirical estimation, I don’t have the detailed information of housing search process for each household. Ideally, the effort home buyers put in housing search should be controlled. But the search times, i.e. the number of housing units that households visit or duration before they make the decision are not available. Third, the classification of jobs and education level is limited in the sample. The characteristics of households are majorly based on the head of households. Actually, the other members of households are also important and their characteristics should impact the housing choice. For example, whether there is a school-age kid should vary the preference to school. Unfortunately, such information is not recorded in the dataset. Fourth, the sellers’ information is also not available in the data. The second part of the research considers information level and bargaining power as two important channels through which households’ characteristics impact on the search efficiency. If sellers’ characteristics are available, I can further analyze their bargaining power, which is also critical on whether buyers can find a 162 good deal. Fifth, in the study of housing choice with social interactions, the sample used is not full sample. Which means, demographic characteristics of each neighborhood, such as the population, the average income, and the average education level, and etc. in each neighborhood, are not available. Thus, it is difficult to directly include the contextual social influence of the neighbors in the destination neighborhood. Sixth, the challenge of studying on social interactions is that it is not observable to researchers. All the measures are proxies, because whether households really know each other or discuss with each other before they make the housing choice decision is normally not recorded. This research assumes households know each other because most of the households in the sample are possibly lived in housing units provided by SOEs before they move. But whether the original neighborhoods are the fang gai fang neighborhoods or real estate development complexes are not available. The current results might overestimate the neighborhood effect, since there might exist some unobserved attributes that attract employees of the same working unit to the same neighborhood (e.g., group buying of the same working unit). I have included the distance between the destination neighborhood and the working place, Distance to Workplace, as the control for it. Ideally, working unit dummies as the control could be better. But this information is also not available. 163 Last but not least, there are all kinds of social interactions occurring during the complicated housing choice decision making process. Some households find their housing units through housing agents, but some may find their housing units via their friends. This kind of social factors is more important because it is directly related to the housing choice. A fully understanding of the housing choice behavior requires data with more information on social factors. But normally this kind of information is not recorded. In the future, I will work in this direction, and if possible, surveys will be conducted. 164 Bibliography Aigner, Dennis J., C. A. Knox Lovell and Peter Schmidt, 1977. Formulation and Estimation of Stochastic Frontier Production Function Models, Journal of Econometrics 6, 21–37. Albrecht, James, Axel Anderson, Eric Smith, and Susan Vroman, 2007. Opportunistic Matching in the Housing Market, International Economic Review 48, 641-64. Albrecht, James, Pieter A. Gautier, and Susan Vroman, 2010. Directed Search in the Housing Market, Working paper. Alonso, William, 1964. Location and Land Use: Toward a General Theory of Land Rent, Harvard University Press, Cambridge. Anglin, Paul M., 1997. Determinants of Buyer Search in a Housing Market, Real Estate Economics 25(4), 567-589. Bayer, Patrick, Robert McMillan, and Kim Rueben, 2009. An Equilibrium Model of Sorting in an Urban Housing Market, NBER Working Paper 10865. Bayer, Patrick and Christopher Timmins, 2005. On the Equilibrium properties of Locaitonal Sorting Models, Journal of Urban Economics 57, 462-477. Bayer, Patrick and Christopher Timmins, 2007. Estimating Equilibrium Models of Sorting Across Locations, Economic Journal 117(518), 353-74. Bayoh, Isaac, Elena G. Irwin, and Timothy Haab, 2006. Determinants of Residential Location Choice: How Important are Local Public Goods in Attracting Homeowners to Central City Locations? Journal of Regional Science 46, 97-120. Besstremyannaya, Galina, 2011. Managerial Performance and Cost Efficiency of Japanese Local Public Hospitals: A Latent Class Stochastic Frontier Model, Health economics 20(S1), 19-34. Börsch-Supan, Axel, 1987. Econometric Analysis of Discrete Choice: With Applications on Demand for Housing in the U.S. and West-Germany, Springer-Verlag, Berlin. Börsch-Supan Axel and John Pitkin, 1988. On Discrete Choice Models of Housing Demand, Journal of Urban Economics 24, 153-172. Breen, John Mc, Florence Goffette-Nagot, and Pablo Jensen, 2009. Information and Search on the Housing Market: A Multi-agent Model, Working paper. Brock, William A. and Steven N. Durlauf, 2001. Discrete Choice with Social Interactions, The Review of Economic Studies 68(2), 235-260. 165 Brock, William A. and Steven N. Durlauf, 2002. A Multinomial Choice Models of Neighborhood Effects, The American Economic Review 92, 298303. Brock, William A. and Steven N. Durlauf, 2007. Identification of Binary Choice Models with Social Interactions, Journal of Econometrics 140, 52-75. Calvó-Armengol, Antoni and Matthew Jackson, 2004. The Effects of Social Networks on Employment and Inequality, The American Economic Review 94(3), 426-454. Carriazo, Fernando, Richard Ready, and James Shortle, 2013. Using Stochastic Frontier Models to Mitigate Omitted Variable Bias in Hedonic Pricing Models: A Case Study for Air Quality in Bogotá, Colombia, Ecological Economics 91, 80-88. Clark, W.A.V., 1992. Residential Preferences and Residential Choices in a Multiethnic Context, Demography 29, 451-466. Clark, W.A.V. and W.F.J. Van Lierop, 1987. Residential Mobility and Household Location Modelling, In P. Nijkamp, editor, Handbook of Regional and Urban Economics. Volume I. North-Holland: Elsevier B.V., 97-132. Colwell, Peter F. and Henry J. Munneke, 2006. Bargaining Strength and Property Class in Office Markets, Journal of Real Estate Finance and Economics 33, 197-213. Cronin, Francis J., 1982. The Efficiency of Housing Search, Southern Economic Journal 48(4), 1016-1031. Deng, Yongheng, 2008. The Emerging Mortgage Markets in China. BenShahar, D., Leung, C.K., and Ong, S.E., editors. Mortgage Market Worldwide. Blackwell Publishing, pp. 1-33. Deng, Yongheng, Stephen L. Ross, and Susan M. Wachter, 2003. Racial Differences in Homeownership: The Effect of Residential Location, Regional Science and Urban Economics 33, 517-556. Diamond, Peter A., 1984. A Search-Equilibrium Approach to the Micro Foundations of Macroeconomics, MIT Press, Cambridge, MA. Durlauf, Steven N. and Marcel Fafchamps, 2005. Social Capital, in Philippe Aghion and Steven N. Durlauf edited Handbook of Economic Growth, Elsevier, vol 1,1639-1699. Ellickson, Bryan, 1971. Jurisdictional Fragmentation and Residential Choice, American Economics Review 61(2), 334-39. 166 Epple, Dennis, Brett Gordon, and Holger Sieg, 2009. Drs. Muth and Mills Meet Dr. Tiebout: Integrating Location-specific Amenities into Multicommunity Equilibrium Models, Presented at Journal of Regional Science 50th Anniversary Conference, Federal Reserve Bank of New York, April 2324. Follmer, Hans, 1974. Ramdom Economics with Many Interacting Agents, Journal of Mathematical Economics 1, 51-62. Fu, Yuming and Stuart A. Gabriel, 2012. Labor Migration, Human Capital Agglomeration, and Regional Development in China, Regional Science and Urban Economics, 42(3), 473-484. Fu,Yuming and Wen-Chi Liao, 2013. What Drive the Geographic Concentration of College Graduates in the US? Evidence from Internal Migration, Working paper. Fu, Yuming, David K. Tse, and Nan Zhou, 2000. Housing Choice Behavior of Urban Workers in China's Transition to a Housing Market, Journal of Urban Economics 47(1), 61-87. Glaeser, Edward, David Laibson, and Bruce Sacerdote, 2002. An Economic Approach to Social Capital, The Economic Journal 112, F437-F458. Glaeser, Edward and José Scheinkman, 2000. Nonmarket Interactions, NBER Working papers no. W8053. Goette, Lorenz, David Huffman, and Stephan Meier, 2012. The Impact of Social Ties on Group Interactions: Evidence from Minimal Groups and Randomly Assigned Real Groups, American Economic Journal: Microeconomics 4(1), 101-115. Gyourko, Joseph, 1991. Impact Fees, Exclusionary Zoning, and the Density of New Development, Journal of Urban Economics 30, 242–256. Harding, John P., John R. Knight and C. F. Sirmans, 2003. Estimating Bargaining Effects in Hedonic Models: Evidence from the Housing Market, Real Estate Economics 31(4), 601-622. Harding, John P., Stuart S. Rosenthal and C. F. Sirmans, 2003. Estimating Bargaining Power in the Market for Existing Homes, The Review of Economics and Statistics 85(1), 178-188. Herfindahl, O.C., 1950. Concentration in the Steel Industry, doctoral thesis on file with the California Law Review, 16-18. Hirschman, A.O., 1945. National Power and the Structure of Foreign Trade, Berkeley: University of California Press, 98-99, 155-162. 167 Hu, Jin-Li, Chia-Ning Chiu, Hwai-Shuh Shieh, and Chia-Hui Huang, 2010. A Stochastic Cost Efficiency Analysis of International Tourist Hotels in Taiwan, International Journal of Hospitality Management 29(1), 99-107. Ioannides, Yannis M. and Jeffrey E. Zabel, 2003. Neighborhood Effects and Housing Demand, Journal of Applied Econometrics 18, 563-584. Ioannides, Yannis M. and Jeffrey E. Zabel, 2008. Interactions, Neighborhood Selection and Housing Demand, Journal of Urban Economics 63, 229-252. Ioannides, Yannis M. and Kamhon Kan, 1996. Structural Estimation of Residential Mobility and Housing Tenure Choice, Journal of Regional Science 36(3), 335-363. Ihlanfeldt Keith and Tom Mayock, 2012. Informaiton, Search, and Housing unitPrices: Revisited, Journal of Real Estate Finance and Economics 44, 90115. Jovanovic, Boyan, 1979. Job Matching and the Theory of Turnover, Journal of Political Economy 87(6), 1246-1260. Kan, Kamhon, 2002. Residential Mobility with Job Location Uncertainty, Journal of Urban Economics 52, 501-523. Kan, Kamhon, 2003. Residential Mobility and Job Changes under Uncertainty, Journal of Urban Economics 54, 566-586. Kan, Kamhon, 2007. Residential Mobility and Social Capital, Journal of Urban Economics 61, 436-457. Kumbhakar, Subal C., and Christopher F. Parmeter, 2010. Estimation of Hedonic Price Functions with Incomplete Information. Empirical Economics 39(1), 1-25. Lagos, Ricardo and Rocheteau Guillaume, 2007. Search in Asset Markets: Market Structure, Liquidity, and Welfare, American Economic Review 97, 198–202. Lambson, Val E., Grant R. McQueen and Barrett A. Slade, 2004. Do Out-ofState Buyers Pay More for Real Estate? An Examination of AnchoringInduced Bias and Search Costs, Real Estate Economics 32(1), 85-126. Lancaster, Kelvin J., 1966. A New Approach to Consumer Theory, Journal of Political Economy 74,132-157. Ling, David C., Andy Naranjo and Milena Petrova, 2013. Search Costs, Behavioral Biases, and Information Intermediary Effects, Working paper. Luce, R. Duncan, 1959. Individual Choice Behavior, Wiley, New York. 168 Martín, Juan Carlos, Concepción Román, and Augusto Voltes-Dorta, 2009. A Stochastic Frontier Analysis to Estimate the Relative Efficiency of Spanish Airports, Journal of Productivity Analysis 31(3), 163-176. Manski, Charles F., 1993. Identification of Endogenous Social Effects: The Reflection Problem, Review of Economic Studies 60, 531-542. Manski, Charles F., 2000. Economic Analysis of Social Interactions, The Journal of Economic Perspectives 14(3), 115-136. McCall, John J., 1970. Economics of Information and Job Search, Quarterly Journal of Economics 84 (1), 113–126. McFadden, Daniel, 1978. Modeling the Choice of Residential Location, in A. Karlqvist, L. Lundqvist, F. Snickars, and J. Weibull, eds., Spatial Interaction Theory and Planning Models North-Holland, Amsterdam, 75–96. McFadden, Daniel and Kenneth Train, 2000. Mixed MNL Models of Discrete Response, Journal of Applied Econometrics 15, 447–470. Meeusen, Wim and Julien Van Den Broeck, 1977. Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error, International Economic Review 18, 435–444. Mills, Edwin S., 1972. Studies in the Structure of the Urban Economy, The Johns Hopkins Press, Baltimore. Montgornery, James D., 1991. Social Networks and Labor-Market Outcomes: Toward an Economic Analysis, The American Economic Review 81(5), 14081418. Muth, Richard F., 1969. Cities and Housing: The Spatial Pattern of Urban Residential Land Use, The University of Chicago Press, Chicago and London. Nechyba, Thomas and Robert P. Strauss, 1997. Community Choice and Local Public Services: A Discrete Choice Approach, Regional Science and Urban Economics 28, 51–73. Nijkamp, Peter, Leo van Wissen, and Annemarie Rima, 1993. A Household Life Cycle Model for Residential Relocation Behaviour, Socio-Economic Planning Sciences 27(1), 35-53. Pissarides, Christopher A., 2000. Equilibrium Unemployment Theory, Second edition, MIT Press, Cambridge, MA. Quigley, John M., 1985. Consumer Choice of Dwelling, Neighborhood and Public Services, Regional Science and Urban Economics 15(1), 41-63. Read, Colin, 1993. Tenants’ search and Vacancies in Rental Housing Markets, Regional Science and Urban Economics 23, 171-183. 169 Read, Colin, 1997. Vacancies and Rent Dispersion in a Stochastic Search Model with Generalized Tenant Demand, Journal of Real Estate Finance and Economics 15(3), 223-237. Reschovsky, Andrew, 1979. Residential Choice and the Local Public Sector: An Alternative Test of the Tiebout Hypothesis, Journal of Urban Economics, 6, 501-520. Rosen, Sherwin, 1974. Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition, The journal of political economy 82(1), 34-55. Rosenthal, Stuart S., John V. Duca, and Stuart A. Gabriel, 1991. Credit Rationing and the Demand for Owner-occupied Housing, Journal of Urban Economics 30, 48–63. Rosko, Michael D., and Ryan L. Mutter, 2011. What Have We Learned from the Application of Stochastic Frontier Analysis to US Hospitals? Medical Care Research and Review 68(1) 75S-100S. Samaha, Stephen A. and Wagner A. Kamakura, 2008. Assessing the Market Value of Real Estate Property with a Geogaphically Weighted Stochastic Frontier Model, Real Estate Economics 4,717-751. Schelling, Thomas, 1971. Dynamic Models of Segregation, Journal of Mathematical Sociology 1, 143-186. Schelling, Thomas, 1978. Micromotives and Macrabehavior, Norton, New York. Scotti, Davide, Paolo Malighetti, Gianmaria Martini, and Nicola Volta, 2012. The Impact of Airport Competition on Technical Efficiency: A Stochastic Frontier Analysis Applied to Italian Airport, Journal of Air Transport Management 22, 9-15. Shimer, Robert and Smith, Lones, 2000. Assortative Matching and Search, Econometrica 68, 343–370. Straszhem, Mahlon, 1987. The Theory of Urban Residential Location, In E.S., Mills, editor, Handbook of Regional and Urban Economics volume II. NorthHolland, Elsevier B.V., 717-57. Tiebout, Charles M., 1956. A Pure Theory of Local Expenditures, Journal of Political Economy 64(5), 416-424. Turnbull, Geoffrey K. and C.F. Sirmans, 1993. Information, Search, and Housing unitPrices, Regional Science and Urban Economics 23, 545-557. Watkins, Craig, 1998. Are New Entrants to the Residential Property Market Informationally Disadvantaged? Journal of Property Research 15(1), 57-70. 170 Wheaton, William C., 1977. Income and Urban Residence: An Analysis of Consumer Demand for Location, American Economic Review 67(4), 620-631. Wheaton, William C., 1990. Vacancy, Search, and Prices in a Housing Market Matching Model, The Journal of Political Economy 98, 1270-1292. Wu, Jing, Joseph Gyourko, and Yongheng Deng, 2012. Evaluating Conditions in Major Chinese Housing Markets, Regional Science and Urban Economics 42(3), 531-543. Zhang, Honglin Kevin and Shunfeng Song, 2003. Rural–urban Migration and Urbanization in China: Evidence from Time-series and Cross-section Analyses, China Economic Review 14(4), 386-400. Zheng, Siqi, Fu Yuming, and Liu Hongyu, 2006. Housing-choice Hindrances and Urban Spatial Structure: Evidence from Matched Location and Locationpreference Data in Chinese Cities, Journal of Urban Economics 60, 535–557. Zhou, P., B. W. Ang, and D. Q. Zhou, 2012. Measuring Economy-Wide Energy Efficiency Performance: A Parametric Frontier Approach, Applied Energy 90(1), 196-200. 171 Appendix I List of Notations Table A-1 List of Notations Notation Definition Notations Used in Chapter a vector of the attributes of the housing unit 𝑖 𝑥𝑖 the price of the housing unit 𝑖 𝑝𝑖 the housing unit 𝑖 𝑖 the indirect utility function (i.e. the surplus value) of a home 𝑣𝑖 buyer for the housing unit 𝑖 the utility function of a home buyer staying in a housing unit 𝑢(∙) the distribution of the surplus value of the housing units 𝐺 the density function of 𝐺 𝑔(𝑣) the location or neighborhood 𝑚, which is assumed to be 𝑚 continuously from to 𝐷. 𝑚 = is the current location of a household, thus 𝑚 can also be understood as the distance to the current location the information level in location 𝑚 𝑠(𝑚) the information level of a buyer at his current location 𝑎 the slope indicating how fast the information level decays 𝑏 with the distance to the current location the distribution of housing units for home buyers with the 𝑓(𝑣) information level 𝑠(𝑚) search cost for one search 𝑐 ∗ home buyer’s reservation value 𝑣 the probability of the home buyer to choose a housing unit in 𝑃𝑚 location 𝑚 Notations Used in Chapter the maximum housing price (Pareto) frontier 𝑃𝑖∗ the maximum housing price as a function of characteristics 𝑉(𝑋𝑖 ) of housing unit 𝑖 a bunch of characteristics of housing unit𝑖 𝑋𝑖 the housing unit 𝑖 (𝑖 = 1. . . 𝑛) 𝑖 −𝑢𝑖 an inefficient component 𝑒 a nonnegative random variable with positive half-normal 𝑢𝑖 distribution 𝑢𝑖 ~𝑁 + (0, 𝜎𝑢,𝑖 ). 𝑢𝑖 = 𝑙𝑛𝑉(𝑋𝑖 ) − 𝑙𝑛𝑃𝑖 , which provides the measure of efficiency of the household 𝜎𝑢,𝑖 standard deviation of 𝑢𝑖 , 𝜎𝑢,𝑖 = 𝑢(𝑌𝑗 ), which is a function of the households’ characteristics the observed transaction housing price 𝑃𝑖 a vector of household’s characteristics for household 𝑗 𝑌𝑗 a random variable with normal distribution 𝑣𝑖 ~𝑁(0, 𝜎𝑣2 ) 𝑣𝑖 constant and coefficients on attributes of housing unit 𝛽0 , 𝛽 constant and coefficients on households’ characteristics 𝛼0 , 𝛼 172 Notations Used in Chapter household 𝑖,( 𝑖 = 1. . . 𝐼) 𝑖 the total number of households 𝐼 the current neighborhood 𝑗,( 𝑗 = 1. . . 𝐽) 𝑗 the total number of current neighborhoods 𝐽 𝑘, (𝑜𝑟 𝑝) the destination neighborhood 𝑘 (𝑜𝑟 𝑝),(𝑘(or 𝑝) = 1. . . 𝐾) the total number of destination neighborhoods 𝐾 the utility of household 𝑖 from the current neighborhood 𝑗 𝑈𝑖,𝑗,𝑘 moves to the destination neighborhood 𝑘 the strength of social interactions of a household 𝑖 with its 𝑆𝑗,𝑘 neighbors in the current neighborhood 𝑗 who move to the same destination neighborhood 𝑘 a vector of characteristics of neighborhood 𝑘 𝑋𝑘 a vector of characteristics of household 𝑖 𝑌𝑖 error term indicating the individual idiosyncratic preferences 𝜖𝑖,𝑗,𝑘 the total number of households who move out from current 𝑁𝑗 neighborhood 𝑗 the total number of households who move in the destination 𝑃𝑗,𝑘 neighborhood 𝑘 from the current neighborhood 𝑗 the probability of household 𝑖 from the current 𝜋𝑖,𝑗,𝑘 neighborhood 𝑗 to choose neighborhood 𝑘 a state of the system in which households distribute among 𝜎 𝐾 destination neighborhoods A vector of social interactions terms corresponding to the 𝑆(𝜎) state 𝜎, and 𝑆𝑗,𝑝 (or 𝑆𝑗,𝑘 ) is the elements of vector 𝑆(𝜎) a function to predict the probability of the household 𝑖 𝑔 chooses neighborhood 𝑘 household 𝑞 who moves out from the current neighborhood 𝑞 𝑗,( 𝑞 = 1. . . 𝑁𝑗 ) the indirect value function, which is the specification of 𝑉𝑖,𝑘 𝑈𝑖,𝑗,𝑘 the unobservable amenity in neighborhood 𝑘 𝑀𝑘 the distance between the current neighborhood 𝑗 and the 𝑑𝑗,𝑘 destination neighborhood 𝑘 the distance between the destination neighborhood 𝑘 and the 𝑑𝑘,𝑤 work place of the household 𝛽𝑖 , 𝛾𝑖 , 𝛼𝑖 , 𝜎𝑖 , 𝜏𝑖 household’s characteristic-dependent coefficients 𝛽𝑖 = 𝛽𝑌𝑖 ; 𝛾𝑖 = 𝛾𝑌𝑖 ; 𝛼𝑖 = 𝛼𝑌𝑖 ; 𝜎𝑖 = 𝜎𝑌𝑖 ; 𝜏𝑖 = 𝜏𝑌𝑖 the price of the housing unit chosen by household 𝑖 𝐻𝑃𝑖 the characteristics of the housing unit chosen by household 𝑖 𝑍𝑖 a vector of dummy variables to indicate neighborhood 𝑘, 𝐷 the element in the vector 𝐷 𝐷𝑘 a vector to capture the unobservable amenity of different 𝑀 neighborhoods, and 𝑀𝑘 is the elements of vector 𝑀, where 𝑀𝑘 is the coefficient on 𝐷𝑘 173 Appendix II Hedonic Regressions for Subsamples Table A-2 Hedonic Model with Market Information Premium with Subsample of High Education Group. LOG_HP VARIABLES (1) (2) (3) (4) (5) LOG_AREA 0.905*** 0.905*** 0.906*** 0.905*** 0.907*** (0.005) (0.005) (0.005) (0.005) (0.005) LOG_CBD -0.033*** -0.033*** -0.033*** -0.033*** -0.033*** (0.005) (0.005) (0.005) (0.005) (0.005) SHOP 0.037*** 0.037*** 0.037*** 0.037*** 0.037*** (0.002) (0.002) (0.002) (0.002) (0.002) SCHOOL 0.016*** 0.016*** 0.016*** 0.016*** 0.016*** (0.001) (0.001) (0.001) (0.001) (0.001) HOSPITAL -0.009*** -0.009*** -0.009*** -0.009*** -0.008*** (0.001) (0.001) (0.001) (0.001) (0.001) 0.022*** 0.022*** 0.022*** 0.022*** 0.022*** (0.002) (0.002) (0.001) (0.001) (0.001) 0.005 0.006 0.005 0.005 0.005 (0.008) (0.008) (0.008) (0.008) (0.008) RESALE -0.210*** -0.211*** -0.209*** -0.209*** -0.209*** (0.009) (0.009) (0.009) (0.009) (0.009) B_TYPE1 0.077*** 0.077*** 0.077*** 0.077*** 0.077*** (0.006) (0.006) (0.006) (0.006) (0.006) 0.003 0.003 0.003 0.003 0.003 (0.014) (0.014) (0.014) (0.014) (0.014) -0.014*** -0.019*** SUBWAY SALE1 B_TYPE2 RURAL_IMM 0.006 (0.005) RURAL_BIRTH 0.002 (0.006) PRE_STAY -0.015*** (0.003) MOVE_1 (0.004) MOVE_12 (0.004) -0.016*** (0.004) CONSTANT 9.206*** 9.209*** 9.210*** 9.209*** 9.208*** (0.027) (0.027) (0.027) (0.027) (0.027) Obs. 9,455 9,455 9,455 9,455 9,455 R-squared 0.876 0.876 0.876 0.876 0.876 Notes: (1) This group of households is with high education level, which is university or above. All the variables are defined in Table 4-1; (2) The dependent variable is LOG_HP; (3) Standard errors are in parentheses; (4) *** p[...]... individual to make location choice is a function of the characteristics of others making the same choice (Bayer, McMillan and Rueben, 2009; Bayer and Timmins, 2007) Ioannides and Zabel (2003, 2008) estimate a model of the continuous housing services demand that is influenced by the average of one’s neighbors’ housing demand Kan (2007) estimates the impact of social capital on residential mobility behavior,... and Smith, 2000) Search has greater importance in housing markets than in any other economic markets because of the information asymmetry in housing market Search theory is suitable for the market without perfect information A stream of literature on search theories in housing market has emerged, starting with 2 For example, Gyourko (1991) points out fiscal zoning restricts the types of home available... information about the locational environment, especially some soft information, such as the characteristics of neighbors, and etc., is usually not reflected Housing agency might not be sophisticated enough to provide all the detailed information of the housing unit and the neighborhood To gather information of a housing unit, a personal visit is normally required for a home buyer However, because of. .. Glaeser and Scheinkman, 2000; Ioannides and Zabel, 2003) The details of this stream of literature can be found in a comprehensive review in Section 2.6 The empirical work with the consideration of the collective location choice behavior has overall lagged behind the theoretical analyses, and they are from different viewpoints Bayer and his co-authors estimate a sorting model in which the propensity of an... taxes, neighborhood amenities, availability of public services, dwelling characteristics, and so forth, and then picking the optimal housing unit (e.g McFadden, 1978; and Quigley, 1985) On the other hand, imperfect information is pervasive in a residential housing market The advertisement for the sale of housing units does not necessarily convey information essential for buyers For example, the detailed... between intra-market movers and immigrants However, Lambson, McQueen, and Slade (2004) use a large sample from Phoenix metropolitan area and find that non-Arizona residents pay a premium of about 5.5% in comparison with the within-Arizona 5 counterpart Ihlanfeldt and Mayock (2012) identify the distance between the next housing unit and the previous address of households in a large number of single-family... Lambson, Macqueen and Slade, 2004; Turnbull and Sirmans, 1993) The bargaining power literature compares the impacts of different bargaining powers between buyers and sellers on property transaction price (Colwell and Munneke, 2006; Harding, Knight and Sirmans, 2003; Harding, Rosenthal and Sirmans, 2003; Ling, Naranjo, and Petrova, 2013) A detailed review of these streams of literature can be found in Chapter... Organization of the Dissertation This dissertation is organized as follows Chapter 1 introduces the background and research questions, and significance of this research 17 Chapter 2 reviews the related literature comprehensively Chapter 3 introduces the background of Tianjin housing market and the housing transaction data Chapter 4 examines the role of location-dependent information of a household in... Rosenthal and Sirmans, 2003; Ling, Naranjo, and Petrova, 2013) Previous literature tries to quantify the impacts of home buyers’ heterogeneous characteristics on their performance in a housing search Empirically, the duration and the number of search times are used to measure search efficiency 6 in Cronin (1982) and Anglin (1997) They find that various home buyers’ characteristics may influence the duration... 2,854 apartments in Phoenix metropolitan area and find that non-Arizona residents pay a premium of about 5.5% in comparison with the within-Arizona buyers Ihlanfeldt and Mayock (2012) identify the distance between the next housing unit and the previous address of households in 6,666 single-family home transactions from Florida, and show that distant buyers pay more for nearly identical homes There . QIU LEIJU NATIONAL UNIVERSITY OF SINGAPORE 2015 INFORMATION, SEARCH EFFICIENCY, AND NEIGHBORHOOD SOCIAL INTERACTIONS IN RESIDENTIAL HOUSING CHOICE QIU LEIJU. also not been submitted for any degree in any university previously. __________________ Qiu Leiju 9 Feb, 2015 i Acknowledgements With joy together with challenge, it comes to