摘要
区位作为房地产的重要特征，是形成城市间房地产价格差异和城市内不同区域房地产价格差异的重要原因，但不是唯一原因。国内外学者已经证明人们的预期对房地产价格的变动有着不可忽视的作用，而人们对未来经济和社会发展、收入变动、房价变动的预期更多源于国民经济和社会发展规划、城市规划、生态环境建设规划等的制定和出台，因此，研究区位因素和规划预期因素对房地产价格的作用成为国内外理论界和实务界关注的焦点。
区位是空间位置关系的反映，规划是城市未来发展的蓝图，因此，在房地产评估中需要采用空间数据处理技术、空间统计分析方法来实现房地产价格的空间比较分析和规划预期的数字化模拟，以提高评估结果的客观合理性和科学动态性。本文集成应用了3S技术、面板数据模型、空间计量模型和Matlab、Eviews、Stata统计软件包，构建了城市商品住宅宜居性特征空间评价模型、矿业城市商品住宅价格空间评价模型、市场参与者异质预期对房价影响的双固定效应变截距模型、城市房地产预期评估模型、包含宏观调控虚拟变量的房价与租金变系数面板模型，提出了城市房地产空间预期评估方法，通过实证分析得到市场参与者的异质预期、规划预期、自然区位、政治区位、基础设施建设是影响房地产价格的主要因素，不同规划预期因素对商品住宅价格的影响在方向和提前期上存在显著差异，房价变动对宏观经济的影响、宏观调控下房价与租金的关系存在显著的城市差异等研究结论。主要贡献如下：
1.提出了城市宜居性特征评价体系和矿业城市商品住宅价格影响因素体系，构建了城市商品住宅宜居性特征和矿业城市商品住宅价格的空间评价模型
在国内外关于城市宜居性特征评价体系和商品住宅价格影响因素体系的基础上，通过增加自然、政治、交通和文化四个区位指标和矿业依存度、矿业从业率、资源开采度、矿产资源价格四个资源特征指标，形成了城市宜居性特征评价体系和矿业城市商品住宅价格影响因素体系。通过对2005-2010年中国35个大中城市和2003-2010年中国21个矿业地级市与39个非矿业地级市的实证研究，构建了城市商品住宅宜居性特征空间评价模型和矿业城市商品住宅价格空间评估模型，得出自然区位、政治区位、基础设施投资、气候条件、经济水平是形成城市间商品住宅价格差异的主要原因；环境因素、资源特征、基础设施、自然区位和政治区位是影响矿业城市商品住宅价格的重要因素，而人口数量、交通区位和文化区位是影响非矿业城市商品住宅价格的重要因素。
2.分析了收入异质预期和规划预期对房地产价格的影响，构建了异质预期对住宅价格影响的双固定效应变截距模型和房地产预期评估模型
以Giovanni Favaray&Zheng Song提出的异质预期为基础，分析了异质预期条件下市场参与者的最优住宅使用数量和市场均衡价格，通过对1999-2011年中国31个省、市、自治区的实证研究，构建了市场参与者异质预期对住宅价格影响的双固定效应变截距模型，得出市场参与者的异质预期程度与商品住宅价格呈正相关关系，且住宅市场存在类似于股市的短期动量和长期反转现象。从城市各种规划对房地产价格的影响出发，建立了城市房地产价格预期因素体系，以张所地构建的“城市不动产动态与预期评估模型”为基础，通过对模型的简化，以及对2000-2011年中国31个地区的实证研究，构建了包含先行因素、现实因素和预期因素的房地产预期评估模型，得出建材价格对商品住宅价格有滞后三期的正影响，老年人口抚养比对商品住宅价格有显著的负影响，而少年人口抚养比对商品住宅价格的影响不显著，不同预期因素对商品住宅价格的影响在方向和提前作用期上存在显著差异。
3.系统分析了住房价格变动对居民消费支出、人均GDP、居民人均可支配收入、居民消费水平和开发商住房投资的影响途径，实证研究了住房价格变动对宏观经济影响的区域差异
在国内外关于住房价格变动对GDP、居民消费支出的影响研究基础上，增加了人均可支配收入、房地产投资和居民消费水平对住房价格变动的响应过程，系统分析了住房价格变动对宏观经济的影响途径。以面板向量自回归模型为基础，通过对2000-2011年中国31个省、市、自治区的实证研究，得出城镇居民消费支出、人均GDP、城镇居民人均可支配收入、城镇居民消费水平对住房价格变动在东、中、西部表现出不同的响应过程，只有房地产开发商的住房投资对住房价格变动的响应不存在区域差异。
4.构建了包含宏观调控虚拟变量的房价与租金变系数面板模型，分析了宏观调控影响下房价与租金关系的城市差异
在国内外关于房价与租金关系模型研究的基础上，增加了房地产宏观调控虚拟变量，通过对1998-2010年中国35个大中城市的实证研究，构建了包含宏观调控虚拟变量的房价与租金变系数面板模型，得出城市的人口构成不同、房地产需求类型不同等导致了房价与租金的关系在不同城市有不同的表现；城市的经济状况、物价变动、人口构成、房地产供给结构、其他扶持性政策等导致了宏观调控对房地产销售市场和租赁市场有不同的影响。
As an important character, location is a key factor that causes the difference ofreal estate price between different cities or between different locations in a city, but itisn’t the only factor. Foreign and domestic scholars have proved that expectation cannot be ignored in analyzing real estate price and the expectations for future economicand society development, income change and real estate price change of peoples aremore originated from the planning for national economic and society development,urban planning, ecological environment construction planning. Therefore, it becomesthe focus of attention from theorists and practice experts all over the world to study onthe effect of location and expectation on real estate price.
Location reflects the relationship of spatial position, planning reflects theblueprint of city development in the future. So it is necessary that spatial comparativeanalysis is made and the expectations from planning are digitally simulated by spatialdata process technologies and spatial statistical analysis methods, so as to make theappraisal results more reasonable, objective, scientific and dynamic. In this thesis,3Stechnology, panel data model, spatial econometric model and some statistical softwarepackages including Matlab, Eviews and Stata are used integrately, some models areconstructed that including spatial appraisal model for urban commercial housingamenities, spatial appraisal model for commercial housing price of mining cities,double-fixed effects and variable intercept model for heterogeneous expectations onhousing price, expected appraisal model for urban real estate, panel VAR model forhousing price change on macro economy, variable coefficient panel model for housingprice and housing rent with macro-control dummy variable, spatial and expectedappraisal method is brought forward, The conclusions are drawn by empirical researchthat spatial correlation of housing price between different cities is obvious, mainfactors of housing price are heterogeneous expectations, planning expectations,natural location, political location and infrastructure level, the effect direction andadvanced time of different planning expectation factors on commercial housing priceare obvious different, there are obvious city difference in the effect of housing changeon macro economy and the relationship between housing price and housing rents withmacro-control. The main innovations are as follows.
First, the appraisal system of urban amenities and the appraisal system ofcommercial housing price of mining cities are advanced, the spatial appraisal modelsfor amenities of commercial housing and commercial housing price of mining citiesare constructed. In this sector, natural location, political location, traffic location andculture location are added to the current appraisal system of urban amenities broughtforward by foreign and domestic scholars, which make the appraisal system of urbanamenities more perfect. And four location indexes including natural location, politicallocation, traffic location and culture location, and four resource indexes includingmining dependence, mining employment rate, resource exploitation degree andmining resource price, are added to the current appraisal system of commercialhousing price brought forward by foreign and domestic scholars, which composes theappraisal system of commercial housing price of mining cities. Further, the spatialappraisal model of urban amenities of commercial housing and the spatial appraisalmodel for commercial housing price of mining cities are constructed by empiricalresearch on Chinese thirty-five large and medium scale cities and prefecture-levelcities including twenty-one mining cities and thirty-nine non-mining citiesrespectively. Further, the conclusions are drawn that natural location, political location,investment for public infrastructure, climate and economy condition are key factorsfor the difference of real estate price between different cities, environment, resource,infrastructure, natural location and political location are main factors for commercialhousing price of mining cities, while population, traffic location and culture locationare main factors for commercial housing price of non-mining cities.
Second, the effects of income heterogeneous expectations and planningexpectations on housing price are analyzed, the double-fixed effects and variableintercept model for heterogeneous expectations on housing price and the expectedappraisal model of commercial housing price are constructed. In this sector, theoptimal housing demand quantity and equilibrium price are analyzed on the conditionof heterogeneous expectations from different market participants brought forward byGiovanni Favaray and Zheng Song, the double-fixed effects and variable interceptmodel for heterogeneous expectations on housing price is constructed by empiricalresearch on thirty-one provinces and autonomous regions. The conclusions are drawn that the degree of heterogeneous expectation has positive correlation with commercialhousing price, and momentum trading in the short run and overreaction at longhorizons in stock market are also happened in housing market. The expectation factorsystem of commercial housing price is brought forward, and the expected appraisalmodel of commercial housing price including lead factors, realistic factors andexpectation factors is constructed by simplifying the dynamic and expected appraisalmodel for real estate brought forward by ZHANG Suo di and empirical research onthirty-one provinces and autonomous regions. The conclusions are drawn that thereare three periods lagged positive effect on housing price for building material price,negative effect on housing price for aged-dependency ratio, the obvious difference ineffect direction and advanced effect period on housing price for different expectationfactors, but there is no correlation between child dependency ratio and housing price.
Third, the effect paths of housing price change on consumption expenditure, perGDP, per disposable income, consumption level and housing investment are analyzedsystematically, the regional difference of the effect of housing price change on macroeconomy are discussed by empirical research. In this sector, the response processes ofper disposable income, housing investment and resident consumption level on housingprice change are added to the current researches on the effect of housing price changeon consumption expenditure and GDP, the effect paths of housing price change onmacro economy are analyzed systematically. The conclusions are drawn by empiricalresearch on thirty-one provinces and autonomous regions based on panel VAR modelthat there are different response processes of consumption expenditure, per GDP, perdisposable income, consumption level on housing price change, but there is noregional difference in the response process of housing investment on housing pricechange.
Four, the variable coefficient panel model for housing price and housing rentwith macro-control dummy variables is constructed, and the city difference of therelationship between housing price and housing rent in the condition of macro-controlis analyzed. In this sector, macro-control dummy variable is added to the currentmodel of housing price and housing rent constructed by foreign and domestic scholars,the variable coefficient panel model for housing price and rent with macro-control dummy variables is constructed by empirical research on Chinese thirty-five large andmedium scale cities. The conclusions are drawn that there are different relationshipsof housing price and housing rent between different cities due to different populationcomposition and different housing demand type, there are different effect on housingsale market and housing leasing market of macro control due to different economiccondition, consumer price change, population composition, housing supply structureand other supportive policies.
引文
2丁祖昱.中国城市房地产市场风险排行榜，中国房产信息集团研究报告，2012.10
3陈淮，2010年蓝筹地产峰会报告，2010.11.
10丁祖昱.中国城市房地产市场风险排行榜，中国房产信息集团研究报告，2012.10
[1] Gil Castle. Geographic Information Systems (GIS): The Location, Location,Location Technology[J]. Real Estate Issues,1995,20(2):52.
[2] Peter J. Wyatt. The Development of a GIS-based Property Information Systemfor Real Estate Valuation[J]. International Journal of Geographical InformationScience,1997,11(5):435-450.
[3]张所地.不动产动态评估研究[D].西安交通大学博士学位论文，1998.
[4]张所地，李怀祖.城市土地定级估价综合模型研究[J].中国土地科学，1998,12(5):21-24.
[5]张所地，吉迎东.城镇数字化地产评估系统及应用[J].山西财经大学学报，2003,25(3):104-106.
[6]赵华平，张所地，吉迎东.动态地理信息系统的构建及其在地产评估中的应用[J].太原科技大学学报，2006,27(3):181-184.
[7]张文燕，崔希民.GIS技术在房地产估价中的应用[A].中国测绘学会2010年学术年会论文集[C].南京：中国测绘学会2010年学术年会，2010.11:633-638.
[8]李伟.基于空间数据挖掘技术的房地产估价系统设计与研究[J].现代测绘，2009,32(6):28-29.
[9] LIU Xiao-sheng, DENG Zhe, WANG Ting-li. Real Estate Appraisal SystemBased on GIS and BP Neural Network[J]. Transactions of Nonferrous MetalsSociety of China,2011,21(3):626-630.
[10] Sheeja RV, Sabu Joseph, Jaya DS, Baiju RS. Land Use and Land CoverChanges Over a Century (1914–2007) in The Neyyar River Basin, Kerala: ARemote Sensing and GIS Approach[J]. International Journal of Digital Earth,2011,4(3):258-270.
[11] Nagendran Nagarajan, Poongothai Sampath. Trend in Land Use/Land CoverChange Detection by RS and GIS Application[J]. International Journal ofEngineering and Technology,2011,3(4):263-269.
[12]党安荣，史慧珍，何新东.基于3S技术的土地利用动态变化研究[J].清华大学学报（自然科学版），2003,43(10):1408-1411.
[13]付丽莉，李钢，马瑞尧，周玉宏，卢刚.土地利用变更调查中的“3S”及其集成技术[J].测绘工程，2008,17(1):55-59.
[14]田贵全，曲凯.遥感和GIS支持下的山东省植被覆盖动态变化分析[J].水土保持研究，2008,15(1):75-78.
[15]麻素挺，汤洁，林年丰.基于GIS与RS多源空间信息的吉林西部生态环境综合评价[J].资源科学，2004,26(4):140-145.
[16] Atiqur Rahman, Yogesh Kumar, Shahab Fazal, Sunil Bhaskaran. Urbanizationand Quality of Urban Environment Using Remote Sensing and GIS Techniquesin East Delhi-India[J]. Journal of Geographic Information System,2011,3(1):62-84.
[17] James M. Poterba, David N. Weil, Robert J. Shiller. Housing Price Dynamics:The Role of Tax Policy and Demography: Comments and Discussion[R].Bookings Papers on Economic Activity, Washington,1991.2:143-148.
[18] John M. Quigley, Real Estate Prices and Economic Cycles[J]. InternationalReal Estate Review,1999,2(1):1-20.
[19] Min Hwang, John M. Quigley. Economic Fundamentals in Local HousingMarket: Evidence from U.S. Metropolitan Regions[J]. Journal of RegionalScience,2006,46(3):425-453.
[20] Eloisa T. Glindro, Tientip Subhanij, Jessica Szeto, Haibin Zhu. Determinants ofHouse Prices in Nine Asia-Pacific Economies[R]. Bank for InternationalSettlements (BIS) Working Paper, No.263,2008.
[21] Kieran McQuinn, Gerard O’Reilly. Assessing the Role of Income and InterestRates in Determining House Prices[J]. Economic Modelling,2008,25(3):377-390.
[22] Radha Bhattacharya, Sei-Wan Kim. Economic Fundamentals, SubprimeLending and Housing Prices: Evidence from MSA-level Panel Data[J].Housing Studies,2011,26(6):897-910.
[23] Larry J. Ozanne, Thomas G. Thibodeau. Explaining Metropolitan Housing PriceDifferences[J]. Journal of Urban Economics,1983,13(1):51-66.
[24] Joshua Gallin. The Long-Run Relationship between House Prices and Income:Evidence from Local Housing Markets[J]. Real Estate Economics,2006,34(3):417-438.
[25] Christopher A. Manning. The Determinants of Intercity Home Building SitePrice Differences[J]. Land Economics,1988,64(1):1-14.
[26] Lucie G. Schmidt, Paul N. Courant. Sometimes Close is Good Enough: TheValue of Nearby Environmental Amenities[J]. Journal of Regional Science,2006,46(5):931-951.
[27]龙奋杰，郭明，郑思齐，曹洋.城市发展与住房意愿支付价格[J].城市问题，2008,(9):50-55.
[28]周京奎.城市舒适性与住宅价格、工资波动的区域性差异——对1999-2006中国城市面板数据的实证分析[J].财经研究，2009,35(9):80-91.
[29] Robyn S. Phillips, Residential Capitalization Rates: Explaining Inter-metropolitan Variation,1974-1979[J]. Journal of Urban Economics,1988,23(3):278-290.
[30] Joe Tak-Yun Wong, Eddie Hui, William Seabrooke, John Raftery. A Study ofthe Hong Kong Property Market: Housing Price Expectations[J]. ConstructionManagement and Economics,2005,23(7):757-765.
[31]位志宇，杨忠直，王爱民.房地产价格与宏观经济基本面研究的新视角[J].系统管理学报，2007,16(5):572-575.
[32]罗刚强，赵涛.区域经济基本面与住房价格波动——1999-2008年东、中、西部地区房价动力因素的经验研究[J].西安电子科技大学学报（社会科学版），2010,20(4):73-80.
[33] Richard F. Muth. Expectations of House Price Changes[J]. Regional ScienceAssociation,1986,59(1):45-55.
[34]沈悦，刘洪玉.住宅价格与经济基本面：1995—2002年中国14城市的实证研究[J].经济研究，2004,39(6):78-86.
[35]况伟大.预期、投机与中国城市房价波动[J].经济研究，2010,45(9):67-78.
[36] Jim Clayton. Rational Expectations, Market Fundamentals and Housing PriceVolatility[J]. Real Estate Economics,1996,24(4):441-470.
[37] Jim Clayton. Are Housing Price Cycles Driven by Irrational Expectations[J].Journal of Real Estate Finance and Economics,1997,14(3):341-363.
[38] William C. Wheaton, Real Estate “Cycles”: Some Fundamentals[J]. Real EstateEconomics,1999,27(2):209-230.
[39] Dawit Sisay. Rational Expectation in the Housing Model[R]. DanmarksStatistik Modelgruppepapirer,2011.
[40] David Bigman. Semi-rational Expectations and Exchange-rate Dynamics[J].Journal of International Money and Finance,1984,3(1):51-66.
[41]张金明.论我国房地产市场的预期性质[J].现代财经，2000,20(3):57-59.
[42]高苛，刘长滨.基于预期理论的住宅市场价格调控模型及其仿真分析[J].土木工程学报，2008,41(4):95-99.
[43]王军武，赵玮.基于预期理论的商品住宅价格模型的应用研究[J].土木工程与管理学报，2011,28(1):12-15.
[44] Michael J. Harrison, David M. Kreps. Speculative Investor Behavior in a StockMarket with Heterogeneous Expectations[J]. The Quarterly Journal ofEconomics,1978,92(2):323-336.
[45] Erik A. Hanushek, John M. Quigley. The Dynamics of the Housing Market: AStock Adjustment Model of Housing Consumption[J]. Journal of UrbanEconomics.1979,6(2):90-111.
[46] Giovanni Favaray, Zheng Song. House Price Dynamics with DispersedInformation[R]. Macro-International Seminar,2007.
[47] Giovanni Favaray, Zheng Song. House Price Dynamics with HeterogeneousExpectations[R].1st Nordic Summer Symposium in Macroeconomics,2008.
[48] Paloma Taltavull, Stanley McGreal. Measuring Price Expectations: Evidencefrom the Spanish Housing Market[J]. Journal of European Real Estate Research,2009,21(2):186-209.
[49]徐佳娜.香港房产市场效率及有限理性预期条件下房价波动模型研究[D].香港城市大学博士学位论文，2008.
[50]梁以德，徐佳娜，崔詠芯.住宅市场价格波动的异质有限预期模型[J].应用数学和力学，2009,30(10):1223-1233.
[51]沈悦，周奎省，张金梅.异质有限理性预期与住宅价格动态反馈机制系统仿真[J].经济理论与经济管理，2010,(9):20-28.
[52] William Alonso. Location and Land Use[M]. Harvard University Press,1964.
[53] Se-il Mun, Komei Sasaki. Effects of Urban Transportation System Change onLand Prices in The Setting of Owner-Occupied Residence[J]. Journal of UrbanEconomics,1992,32(2):351-366.
[54] John F. McDonald, Clifford I. Osuji. The Effect of Anticipated TransportationImprovement on Residential Land Values[J]. Regional Science and UrbanEconomies,1995,25(3):261-278.
[55] Kwok-Wing Chau, Fung-Fai Ng. The Effects of Improvement in PublicTransportation Capacity on Residential Price Gradient in Hong Kong[J].Journal of Property Valuation and Investment,1998,16(4):397-410.
[56] Gerrit J. Knaap, Chengr Ding, Lewis D. Hopkins. Do Plans Matter?: TheEffects of Light Rail Plans on Land Values in Station Areas[J]. Journal ofPlanning Education and Research,2001,21(1):32-39.
[57]叶霞飞，蔡蔚.城市轨道交通开发利益的计算方法[J].同济大学学报（自然科学版），2002,30(4):431-436.
[58]陈有孝，林晓言等.城市轨道交通建设对地价影响的评估模型及实证——以北京市轨道交通为例[J].北京交通大学学报，2005,4(9):7-13.
[59]刘贵文，胡国桥.轨道交通对房价影响的范围及实践性研究——基于重庆轨道交通二号线的实证分析[J].城市发展研究，2007,14(2):84-87.
[60]王霞，朱道林，张鸣明.城市轨道交通对房地产价格的影响一一以北京市轻轨13号线为例[J].城市问题，2004,122(6):39-42.
[61] Ching-Yi Yiu, Kevin Siu Kei Wong, The Effects of Expected TransportImprovements on Housing Prices[J]. Urban Studies,2005,42(1):113-125.
[62] John F. Henneberry. Transport Investment and House Prices[J]. Journal ofProperty Valuation and Investment,1998,16(2):144-158.
[63]顾杰，贾生华.公共交通改善期望对住房价格及其价格空间结构的影响——基于杭州地铁规划的实证研究[J].经济地理，2008,28(6):1020-1024,1034.
[64]王琳.城市轨道交通对住宅价格的影响研究——基于特征价格模型的定量分析[J].地域研究与开发，2009,28(2):57-61,70.
[65]杨鸿.城市轨道交通对住房价格影响的理论与实证研究[D].浙江大学，2010.
[66] Ekaterina Chernobai, Michael Reibel, Michael Carney. Nonlinear Spatial andTemporal Effects of Highway Construction on House Prices[J]. The Journal ofReal Estate Finance and Economics,2011,42(3):348-370.
[67]梁云芳，高铁梅，贺书平.房地产市场与国民经济协调发展的实证分析[J].中国社会科学，2006,(3):74-84.
[68]段忠东.房地产价格与通货膨胀、产出的关系——理论分析与基于中国数据的实证检验[J].数量经济技术经济研究，2007,24(12):127-139.
[69]丁珊.房地产价格波动与宏观经济指标关系的实证研究[J].中国物价，2007,(7):48-51.
[70]唐志军，徐会军，巴曙松.中国房地产市场波动对宏观经济波动的影响研究[J].统计研究，2010,27(2):15-22.
[71] Jie Zhang, Jianhua Wang, Aiyong Zhu. The relationship between real estateinvestment and economic growth in China：a threshold effect[C]. The Annals ofRegional Science, DOI:10.1007/s00168-010-0388-2,2010.
[72] Jian Zhou. Testing for Cointegration between House Prices and EconomicFundamentals[J]. Real Estate Economics,2010,38(4):599-632.
[73] Milton Friedman. A Theory of the Consumption Function[M]. Princeton:Princeton University Press,1957.
[74] Franco Modigliani, Richard Brumberg. Utility Analysis and the ConsumptionFunction: An Interpretation of Cross-section Data[A]. Kenneth Kurihara.Post-Keynesian Economics[C], New Brunswick: Rutgers University Press.1954.
[75] Albert Ando, Franco Modigliani. The “Life Cycle” Hypothesis of Saving:Aggregate Implications and Tests[J]. The American Economic Review,1963,53(1):55-84.
[76] John N. Muellbauer. Housing, Credit and Consumer Expenditure[A], JacksonHole Wyoming. Housing, Housing Finance and Monetary Policy[C], FederalReserve Bank of Kansas City:31st Economic Policy Symposium,2007.
[77] Alexander Ludwig, Torsten Sl k. The Impact of Changes in Stock Prices andHouse Prices on Consumption in OCED Countries[R]. International MonetaryFund Working Paper Series,2002.
[78] Riccardo Calcagno, Elsa Fornero, Mariacristina Rossi. The Effect of HousePrices on Household Consumption in Italy[J]. The Journal of Real EstateFinance and Economics,2009,39(3):284-300.
[79] Juan Contreras, Joseph Nichols. Consumption Responses to Permanent andTransitory Shocks to House Appreciation[R]. FEDS Working Paper,2010.
[80]丁攀，胡宗义.股价与房价波动对居民消费影响的动态研究[J].统计与决策，2008,(15):106-108.
[81]况伟大.房价变动与中国城市居民消费[J].世界经济，2011,(10):21-34.
[82] John Muellbauer, Keiko Murata, Consumption, Land Prices and The MonetaryTransmission Mechanism in Japan[R]. CEPR Discussion Papers,2009.
[83]朱新玲，黎鹏.我国房地产市场财富效应的实证分析[J].武汉科技大学学报（社会科学版）.2006,8(2):16-18.
[84]刘旦.中国城镇住宅市场财富效应分析——基于生命周期假说的宏观消费函数[J].首都经济贸易大学学报，2007,(4):108-116.
[85]戴颖杰，周奎省.房价变动对居民消费行为影响的实证分析[J].宏观经济研究，2012,(3):73-79.
[86] Khoon Lek Goh, Richard Downing. Modelling New Zealand ConsumptionExpenditure over the1990s[R]. New Zealand Treasury Working Paper,2002.
[87]李玉山，李晓嘉.对我国居民消费的财富效应计量分析[J].山西财经大学报，2006,28(2):39-43.
[88]张红.房地产经济学[M].清华大学出版社，2005.
[89]孔宪丽.房价波动对城镇居民消费影响效应的区域差异性分析[J].统计与决策，2011,(24):108-110.
[90]丹尼尔·迪帕斯奎尔，威廉·C·惠顿.城市经济学与房地产市场[M].北京：经济科学出版社，2002:40-55.
[91]曹振良.房地产经济学通论[M].北京：北京大学出版社，2004:364-369.
[92] Richard A. Meese, Nancy E. Wallace. Testing the present value relation forhousing prices: Should I leave my house in San Francisco[J]. Journal of UrbanEconomics,1994,35(3):245-266.
[93] Yan-leung Cheung, Shu-ki Tsang, Sui-choi Mak. The Causal Relationshipsbetween Residential Property Prices and Rentals in Hong Kong:1982-1992[J].Journal of Real Estate Finance and Economics,1995,10(1):23-35.
[94]周永宏.当前我国房价与租金关系的经济学分析——一个市场区隔理论的解释[J].当代财经，2005,(10):13-16.
[95]曾建颖，张金鹗，花敬群.不同空间、时间住宅租金与其房价关联性之研究——台北地区之实证现象分析[J].住宅学报，2005,14(2):27-49.
[96]方毅，赵石磊.房屋销售价格和租赁价格的关系研究[J].数理统计与管理，2007,26(6):951-957.
[97] Joshua Gallin. The Long-Run Relationship Between House Prices and Rents[J].Real Estate Economics,2008,36(4):635-658.
[98]杜红艳，马永开.我国房价与租金Granger因果关系的实证研究[J].管理评论，2009,21(1):94-99.
[99]王文莉，赵奉军.城市化进程与房价租金比高企问题研究——基于中国35个大中城市面板数据实证分析[J].财贸研究，2011,22(5):28-33,70.
[100] Kelvin J. Lancaster. A New Approach to Consumer Theory[J]. Journal ofPolitical Economy,1966,74(2):132-157.
[101] Sherwin Rosen. Hedonic Prices and Implicit Markets: Product Differentiationin Pure Competition[J]. Journal of Political Economy,1974,82(1):35-55.
[102]黄亚平，丁烈云.城市规划对地价的作用机制研究[J].城市发展研究，1998,5(4):25-28,63.
[103]赵华平.基于信息技术的房地产评价方法与应用研究[M].北京：经济管理出版社，2012.
[104]罗云启.数字化地理信息系统建设与MapInfo高级应用[M].北京：清华大学出版社，2003.
[105]赵文武.3S技术集成及其应用研究进展[J].山东农业大学学报（自然科学版），2001,32(2):236.
[106] Jay Gao. Integration of GPS with Remote Sensing and GIS: Reality andProspect[J]. Photogrammetric engineering&Remote Sensing,2002,68(5):447-453.
[107]张继贤，程烨.3S技术支持的土地利用现状图更新[J].中国土地科学，2002,16(1):20-25.
[108]赵华平，张所地.城市宜居性特征对商品住宅价格的影响分析——基于中国35个大中城市静态和动态空间面板模型的实证研究[J].数理统计与管理，已录用，稿件编号：12-0572.
[109]赵华平，张所地.商品住宅的城市宜居性特征空间评价研究[J].软科学，已录用，稿件编号：2013.03.16.0011.
[110] Randall Reback. House Prices and the Provision of Local Public Services:Capitalization under School Choice Programs[J]. Journal of Urban Economics,2005,57(2):275-301.
[111] David Brasington, Donald R. Haurin. Educational Outcomes and House Values:A Test of the value added Approach[J]. Journal of Regional Science,2006,46(2):245-268.
[112]梁云芳，高铁梅.中国房地产价格波动区域差异的实证分析[J].经济研究，2007,42(8):133-142.
[113]周京奎，吴晓燕.公共投资规模、居民支付意愿与住宅价格走势——基于中国30省市截面数据的实证检验[J].财贸研究，2008,19(6):53-61.
[114]何鸣，柯善咨，文嫣.城市环境特征品质与中国房地产价格的区域差异[J].财经理论与实践，2009,30(2):97-103.
[115]张娟锋，刘洪玉.住宅价格与土地价格的城市差异及其决定因素[J].统计研究，2010,27(3):37-44.
[116]方晓萍，丁四保.中国城市住房价格的地理扩散及其区域外部性问题[J].地理科学，2012,32(2):143-148.
[117]陈浪南，王鹤.我国房地产价格区域互动的实证研究[J].统计研究，2012,29(7):37-43.
[118]吴玉鸣.空间计量经济模型在省域研发与创新中的应用研究[J].数量经济技术经济研究，2006,23(5):74-85,130.
[119] Tadashi Higasa. Urban Planning[M].Tokyo: Kioritz Corporation Press,1977.
[120] Andrew D. Basiago. Economic, Social and Environmental Sustainability inDevelopment Theory and Urban Planning Practice[J]. The Environmentalist,1998,19(2):145-161.
[121]王坤鹏.城市人居环境宜居度评价——来自我国四大直辖市的对比与分析[J].经济地理，2010,30(12):1992-1997.
[122]叶青，鄢涛，李芬，余涵.城市生态宜居发展二维向量结构指标体系构建与测评[J].城市发展研究，2011,18(11):中彩页16-20.
[123]郑思齐.城市经济的空间结构：居住、就业及其衍生问题[M].北京：清华大学出版社，2012.
[124] Luc Anselin. Spatial Externalities, Spatial Multipliers, and SpatialEconometrics[J]. International Regional Science Review,2003,26(2):153-166.
[125] Luc Anselin, Julie Le Gallo, Hubert Jayet. Spatial Panel Econometrics[A],László Mátyás, Patrick Sevestre. The Econometrics of Panel Data:Fundamentals and Recent Developments in Theory and Practice[C]. Germany:Springer Berlin Heidelberg,2008:625-660.
[126] Badi H. Baltagi. Econometric Analysis of Panel Data(4th Edition)[M]. NewYork: Wiley,2008.
[127]陈彦斌，邱哲圣.高房价如何影响居民储蓄率和财产不平等[J].经济研究，2011,46(10):25-38.
[128]赵华平，张所地.居民收入异质预期对住房价格影响的实证研究[J].统计与决策，2012,(9):125-127.
[129]吴公樑，龙奋杰.中国城市住宅价格与居民收入关系的定量研究[J].土木工程学报，2005,38(6):132-136.
[130]刘丹，霍德明.基于时空模型的中国房价收入关系研[J].中国经济问题，2010,(6):3-10.
[131]黄瑜.土地价格、居民收入对商品住宅价格影响的动态分析——基于状态空间模型的实证[J].经济与管理研究，2010,(10):24-28.
[132]张亚丽，梁云芳，高铁梅.预期收入、收益率和房价波动——基于35个城市动态面板模型的研究[J].财贸研究，2011,22(1):122-129.
[133] Andrew Theo Levin, Chien-Fu Lin, Chia-Shang James Chu. Unit Root Tests inPanel Data: Asymptotic and Finite-sample Properties[J]. Journal ofEconometrics,2002,108(1):1-24.
[134] J rg Breitung. The Local Power of Some Unit Root Tests for Panel Data[A].Badi H. Baltagi. Advances in Econometrics: Nonstationary Panels, PanelCointegration, and Dynamic Panels[C]. United Kingdom: Emerald GroupPublishing Limited,2000:161-178.
[135] J rg Breitung, Samarjit Das. Panel Unit Root Tests under Cross-sectionalDependance[J]. Statistica Neerlandica,2005,59(4):414-433.
[136] Kyung So IM, M Hashem Pesaran, Yongcheol Shin. Testing for Unit Roots inHeterogeneous Panels[J]. Journal of Econometrics,2003,115(1):53-74.
[137] Gangadharrao Soundalyarao Maddala, Shaowen Wu. A Comparative Study ofUnit Root Tests with Panel Data and a New Simple Test[J]. Oxford Bullertin ofEconomics and Statistics, Special Issue,1999,61(S1):631-652.
[138] Peter Pedroni. Critical Values for Cointegration Tests in Heterogeneous Panelswith Multiple Regressors[J]. Oxford Bulletin of Economics and Statistics,1999,61(S1):653-670.
[139] Chihwa Kao. Spurious Regression and Residual-Based Tests for Cointegrationin Panel Data[J]. Journal of Econometrics,1999,90(1):1-44.
[140]潘丹，应瑞瑶.中国水资源与农业经济增长关系研究——基于面板VAR模型[J].中国人口·资源与环境，2012,22(1):161-166.
[141]高铁梅.计量经济分析方法与建模：Eviews应用及实例（第二版）[M].北京：清华大学出版社，2009.
[142]沈悦，张学峰，周奎省.住宅价格与居民收入均衡关系及住房支付能力稳定性[J].财经研究，2011,37(3):81-92.
[143] Harrison Hong, Jeremy C. Stein. A Unified Theory of Underreaction,Momentum Trading, and Overreaction in Asset Markets[J]. Journal of Finance,1999,54(6):2143-2184.
[144]张凌，温海珍，贾生华.中国沿海和内陆城市住房价格波动差异与动力因素[J].中国土地科学，2011,25(3):77-84.
[145]赵华平，张所地.城市房地产预期评估研究[J].城市问题，已录用.
[146]赵华平，张所地.房地产价格预期评估模型研究[J].中国房地产（学术版），2011,(5):33-36.
[147] Kimberley D. Edwards. Prospect theory: a literature review[J]. InternationalReview of Financial Analysis,1996,5(1):19-38.
[148]陈石清，朱玉林.中国城市化水平与房地产价格的实证分析[J].经济问题，2008,(1):47-49.
[149]吴淑莲.城市化与房地产业互动发展研究[D].华中农业大学博士学位论文，2006.
[150]徐建炜，徐奇渊，何帆.房价上涨背后的人口结构因素：国际经验与中国证据[J].世界经济，2012,(1):24-42.
[151] Charles Yuji Horioka. Tenure Choice and Housing Demand in Japan[J]. Journalof Urban Economics,1988,24(3):289-303.
[152] Barton A. Smith, Robert Ohsfeldt. Housing Price Inflation in Houston:1970To1976[J]. Policy Studies Journal,1982,8(2):257-276.
[153]毛腾飞.中国城市基础设施建设投融资模式创新研究[D].中南大学博士学位论文，2006.
[154]李祥，高波，李勇刚.房地产税收、公共服务供给与房价——基于省际面板数据的实证分析[J].财贸研究，2012,23(3):67-75.
[155]赵华平，张所地.住房价格变动对居民消费的影响效应研究——基于山西省的实证分析[J].未来与发展，2010,(10):56-61.
[156] Douglas Holtz-Eakin, Whitney Newey, Harvey S. Rosen. Estimating VectorAutoregressions with Panel Data[J]. Econometrica,1988,56(6):1371-1395.
[157] Michael Binder, Cheng Hsiao, M. Hashem Pesaran. Estimation and Inference inShort Panel Vector Autoregressions with Unit Roots and Cointegration[R].Cambridge Working Papers in Economics,2003.
[158]恩德斯.应用计量经济学：时间序列分析（第2版）[M].高等教育出版社，2006.
[159]苏梽芳，王祥，陈昌楠.中国粮食价格低频波动影响因素研究：基于面板VAR模型[J].农业技术经济，2012,(10):22-30.
[160]赵华平，张所地，李斌.房地产宏观调控影响下的房价与租金关系研究——基于中国35个大中城市面板数据的实证分析[J].数理统计与管理，已录用，稿件编号：12-0241.
[161]张所地.不动产静态与动态评估方法[M].北京：中国科学技术出版社，2005.
[162] Kamila Sommer, Paul Sullivan, Randal Verbrugge. The Equilibrium Effect ofFundamentals on House Prices and Rents[R]. Working paper,2012.
[163]梁云芳，高铁梅.我国商品住宅销售价格波动成因的实证分析[J].管理世界，2006,(8):76-82.