Spatial And Expected Appraial On Urban Real Estate

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.