Research On Spatial Spillover Effect Of Housing Price Based On Panel Data

The housing problem is always attracting much attention, supported by the government, and playing an important role in the stability and development of national economy. With the development of economy and the significant inflation, the problem of high housing price has become another serious economic problem. Many scholars at home and abroad researched the volatility of housing price, mostly on the economic reasons in the dimension of time. The paper attempts to put spatial autocorrelation into the study of housing price, research the spatial dependence and spatial spillover effect of housing price in China on the spatial dimension.On the basis of literature research, the paper explores spatial housing price in data analysis, and analyzes the mechanism of the spatial spillover effect of housing price to find out the direct factors and the indirect factors of spatial spillovers. Then the paper uses global Moran’s I and local Moran’s I, to test the spatial correlation of housing price from two angles of: the global and local perspective. after that the paper uses panel data of 31 provinces from 1998 to 2013, to explore the relationship between loan rate, housing price-income ratio, real estate investment plan, real estate completion cost, population growth rate and housing price with general panel model, and then add space factor to established the spatial panel model, researching the spatial spillover effect of housing price using empirical research method. In the end, based on the conclusions, the paper makes the recommendations for the regulation of housing price.Empirical results of this study display: Firstly, the results illustrate the presence of significant positive spatial autocorrelation, showing rising tendency, housing price correlated each other evidently in some areas; Secondly, the Spatial Error Model shows that there is a significant and positive spatial spillovers, reflecting coefficient of 0.3487 the whole county, housing price-income ratio, real estate investment plan, real estate completion cost and population growth rate push the housing price rising positively, while loan rate in reverse; Thirdly, after adding space factor, interpretability of the independent variables change in varying degrees, with respect to traditional econometrics model, interpretability of loan rate, housing price-income ratio and population growth rate improve, which affect the real estate demand, while the interpreting ability of real estate investment plan and real estate completion cost decrease.