Dissertation > Economic > Economic planning and management > Urban and municipal economy > Urban Economics and Management > The real estate economy

Public rental housing development in Chongqing City

Author LuoLingJun
Tutor LiuShengQun
School Jiangxi University of Technology
Course Management Science and Engineering
Keywords Public-rent-house GM (1, 1) Multiple regression model Predict
CLC F293.3
Type Master's thesis
Year 2011
Downloads 354
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According to Maslow’s theory of hierarchy of human needs, physiological need ranked first, and habitation is an important part and basis of physiological needs. Therefore, we can say that residence is the most fundamental human needs. It has stepped into an accelerated development stage of urbanization in our country today, and the rapid growth of urban population has brought a great demand for housing. As the executor of macroregulation under market economy, Government’s has the responsibility to resolve the housing needs problems of middle-low income class through social welfare and social security and other means of income redistribution of civil income, which also can maintain social peace and stability. The introduction of public-rent-house policy is an important measure for government solves the "housing" problem, which related to people’s well-being. The Chongqing Model whose constructing program is mainly in public-rent-house, and with applicability to the whole country, is gradually becoming the main trend of government’s indemnificatory housing constructions.The first part of paper is introduction. In this part, the background and significance of this study were explained, domestic and foreign predictive research results were summarized and evaluated. Also, the study contents and ideas were described too.In the second part of paper, we contrastive analyzed the advantages of the public-rent-house relative to other kinds of indemnificatory housing, and obtained its advantages compared to other kinds of indemnificatory housing. Finally, we illustrated the inevitability of the development of public-rent-house.The third part of paper introduced the gray system theory analysis, explained the applicable condition and advantages of GM (1, 1) model, and then briefly described modeling steps. At last we listed of test methods of the model accuracy and the gray correlation factors analysis. Hided the foreshadowing for the short-term prediction with the GM (1, 1) model and correlative degree analysis of the influence factors of medium-term prediction behind.The fourth part of paper forecasted the short-term demands of public-rent-house in Chongqing with GM (1, 1) model, referenced to official data in Chongqing Statistical Yearbook. We carried on posterior difference testing analysis on gray model predictive value and calculated the fitting precision value, thus we judged the fitting precision as high degree. Therefore, our model prediction was credible and was better applicable to predict and analyze the short-term market supply volume of Chongqing City.In the fifth part of paper, the factors that influence the demand for public-rent-house were listed, and some quantifiable factors were selected, and their correlations were analyzed by SPSS19.0. The medium-term demands model for public-rent-house was build based on multiple regression equation. After the advantages and disadvantages of forward method, backward method, and stepwise regression method were analyzed, stepwise regression method was selected to build the model. The SPSS19.0 screened the influence factors, analyzed of variance, estimated regression coefficient and analyzed the residual. Then medium-term demands forecasting model for public-rent-house in Chongqing was fixed and the demands for public-rent-house in the coming six years were forecasted.The sixth part of paper described the problems which the public-rent-house faced, and provided personal recommendations about settling those problems.The seventh part summarized conclusions, innovations and insufficiencies of this paper, and clearly showed further research directions.

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