The Space Difference Research Of Real Estate Price In The View Of Floating Population

What is related to the people’s livelihood、social harmony and stability is to ensure and improve people’s wellbeing. Since ancient times, the home ownership is the people’s expectation, housing problem is not only the problem of people’s livelihood, but also the social development, which is related to economic development, social harmony and stability and people live within a house. Housing system reform has been carried out for nearly twenty years, although it has made great achievements, but the existing problems and difficult works cannot be ignored. The purpose of this paper is to research the problem of spatial difference of housing price from the perspective of population flow, in the hope of to provide theoretical and practical basis for improving the livelihood of the people, solving the problem of housing development.This paper analyzed the current situation of China’s regional housing price changes and the migration rule of floating population, in addition, this article established a theoretical model of housing price spatial difference in the perspective of population flow based on the housing price spatial heterogeneity and features of population flow, and expounded the effects of the floating population on the price fluctuation in different regions. And then, this article measured and calculated the floating population index of 235 prefecture-level cities in China from 2005 to 2013, so that obtained the most important explanatory variables in the theoretical model of this paper. Next, this article studied the problem of housing price spatial heterogeneity in different regions by using the classical linear regression model, spatial expansion model and geographically weighted regression model, and sort out 35 medium and large cities to test the result of the stability by using the panel vector auto regression model and dynamic panel model. and next, this article studied the problem of housing price spatial heterogeneity in 159 regions of the United Kingdom and Welsh based on the view of population flow, and compared with the domestic research results. At last, combined with the domestic situation, this article put forward related policy recommendations for housing reform and the livelihood’s improvement from real estate, population and regional economy.Through the research, this paper mainly draws the following conclusions:(1) The floating population is consistent with the change of house price in space. From the analysis of the situation, in the eastern coastal city of more population inflows, prices rose significantly larger, while the vast majority of the three or four cities in the western part of the population are net outflow cities.(2) The floating population is the most important factor on affect the price of house price, and the marginal utility of the floating population is more and more important. The linear regression result shows that the floating population is the most important factor, the number of floating population in the city increases every 10 thousand, will make 100 square meters of housing price rises 857 yuan.(3) The influence of floating population, real estate investment, per capita income and educational level on housing price has obvious spatial heterogeneity. The result of the spatial expansion model shows that, the heterogeneity of the floating population is mainly manifested in the East and West after 2010, and the spatial heterogeneity of the north and South has not been reflected, which is generally consistent with the migration rule of the floating population.(4) The impact of floating population on housing price in the eastern coastal region is obvious. The result of geographical weighted regression shows that the influence of floating population on housing prices in the Pearl River Delta, Southwest China, Northwest China and central China is the maximum from 2005 to 2007; Affected by the subprime crisis, the influence of the floating population in the Pearl River Delta region on the price of the house is negative in 2008 and 2009; Pearl River Delta region, the Yangtze River Delta region, Southern Fujian and the southwest region has become such area again that the floating population has the maximum impact on the housing price from 2010 to 2013, the number of floating population in the city increases every 10 thousand, will make 100 square meters of housing price rises nearly 600-1100 yuan.(5) The higher the level of urban economic development, the greater the impact of the floating population on housing price. The result of the panel vector auto regression model and dynamic panel model shows that the population agglomeration of higher level of economic development or second-tier city has a positive effect on housing price, general secondtier city in the level of economic development is flat effect, and third-tier/fourth-tier city in the lower level economic development is negative effect.(6) The education level of floating population determines the degree of social integration, thus affecting the volatility of housing price. The result of the comparative analysis of international experience shows that the education level of floating population affects the determination of the migration of local residents, thus affecting the volatility of housing price, especially in the lower education level area, when the number of floating population increases to 1% compared with the total initial population, housing prices will decline by about 1.23%-1.85%.Compared with the existing research, the innovation of this paper lies in that:(1) This article proposed that the floating population is the most important reason to affect the spatial difference of housing price, and five kinds of regression models are used to confirm this view.(2) A theoretical model of housing price spatial difference is constructed in the perspective of population flow. According to the educational level, the floating population is divided into two kinds of high and low income, combined with local residents’ acceptance attitude to the floating population, and the complementarity and exclusion of the floating population and the local population in the process of production, this article deduced the housing price spatial difference theory model by using maximization of utility function.(3) This article constructed the floating population database of 235 cities in China during 2005-2013. Three different processing methods are adopted in different time interval, and calculated the floating population index of 235 cities, which provides the data support for the future research about the floating population.