Analysis of spatial differentiation of merchandise residence prices in Shenzhen

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Analysis of spatial differentiation of merchandise residence prices in Shenzhen

 

Author: Qiu, Yue
Title: Analysis of spatial differentiation of merchandise residence prices in Shenzhen
Degree: M.Sc.
Year: 2013
Subject: Housing -- Prices -- China -- Shenzhen (Guangdong Sheng : East)
Real property -- China -- Shenzhen (Guangdong Sheng : East)
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Land Surveying and Geo-Informatics
Pages: ix, 91 leaves : ill. (chiefly col.), col. maps ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2627099
URI: http://theses.lib.polyu.edu.hk/handle/200/7046
Abstract: Real estate industry is one of the pillar industries in China. From the 1998, the price of merchandise residence continued to rise, which become the focus of attention of the government, developers and individual residents. Due the spatial distribution differences of housing prices, it must study the spatial distribution characteristics of urban merchandise residence price to analyze whether the phenomenon is reasonable and reveal the implicated characteristics. Therefore, it has important theoretical and practical significance to carry out spatial principle research about urban merchandise residence price. Based on the Geographical Information System (GIS), the theories and methods about methods of spatial analysis, such as exploratory spatial data analysis, spatial correlation analysis, spatial interpolation, Hedonic and Geographically weighted were be introduced and carried out in the study about the real estate in Shenzhen, a modern cityscape in the south of Southern China's Guangdong Province, situated immediately north of Hong Kong.
The study has achieved initial research in the following areas: - Reviewing from the larger number of domestic and foreign literature, the study gives a comprehensive overview of the basic theory about merchandise residence price formation and differences, spatial analysis methods and GIS software technology, which provides a theoretical basis and technical support for empirical research. - The study collected 1083 primary residential property samples from 2011 to 2012, and through statistical analysis, data correction and standardized, it is proved that the trading samples of property are reliability. On the basis, the average prices of projects are calculated by the construction area weighted, which is also considered as the basic object of the spatial analysis. - Acquisition of spatial information of property samples is the prerequisite for spatial analysis; this study established the indicators system of the spatial characteristics selecting, acquiring and quantifiable. Based on the geocoding technology, the study also established the spatial database of residential property samples and projects. - Using GIS spatial analysis and exploratory spatial data analysis, the study comprehensive analyzed the integrity spatial characteristics of the prices of commodity housing, and also analyzed the spatial differences and autocorrelation of the prices about merchandise residence. - In this study, the Hedonic model is constructed of assessed value for residential properties with independent variable are included structure, location, neighborhood attributes to carry on a residence characteristic price analysis. The hedonic model is specified in the functional form of liner regression model using OLS (ordinary least square) estimator, which is referred to the global statistics model. And the global regression results demonstrate that the model is reasonable and with good performance in global analysis. - Due to the spatial non-stationary of the houses prices, and the hedonic price model have some limitations, the Geographically regression model is used to explore the effects of the series of locational and neighborhood characteristics of housing on the merchandise residence price. After comparing the four weighted functions, the GWR model based on the adaptive Gaussian weighted function makes the best performance, which is decided by the distribution of houses price in Shenzhen. Besides the GWR model also identify the non-stationary of prices by interpolating the local estimates parameters of associated variables. Overall, the GWR is an effective model to present the distribution of the merchandise residence and serve as a tool for house price appraisal during the property tax levy process. - Compared with the traditional regression model (the global model), not only does the GWR model usually have greater explanatory power than OLS, but it also enables in-depth local analysis to be conducted because it uses a locally adjusted regression equation for each observation. Besides, The GWR model has considered the geographic features, through the spatial weight method, which could eliminate the spatial dependence among the residuals.

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