An Empirical Analysis Of The Relationship Between Housing Price Spillover And Population Flow In China Based On Spatial Econometric Model

The Nineteenth National Congress of the Communist Party of China called for a steady progress in the construction of affordable housing,and focused on building an economic system in which the micro-subjects have vitality,macro-control,and market mechanisms,and strengthened domestic economic innovation and competitiveness.Although the State Council has issued a series of real estate stimulus policies since 2005,taking Beijing as an example,housing prices in Beijing in 2001-2015 increased by 7.5 times in ten years,and rents rose for 44 consecutive months.Faced with such high prices,high rents may allow some people to choose to abandon employment opportunities in economically developed cities and return to small city life.Taking the employment of real estate,construction,and building materials industries and wage levels as examples,there are data showing that the demand for real estate,construction,and building materials industry has increased by 55% year-on-year,and the increase has jumped to the second place in the industry,and the wages of employees on the job have also increased to varying degrees.However,cities such as Beijing,Shanghai,and Guangzhou have experienced relatively flat growth,which has contrasted sharply with the growth of over 80% in some cities.In the face of this situation,this paper measures the population inflow rate,tests the autocorrelation of house prices and population space,builds a spatial econometric model based on cross-sectional data and panel data,and analyzes the spatial spillover effect of house prices and the phenomenon of population flow.This article selects the relevant variable data of 31 provincial capitals and municipalities in China.First,we use the global Moran's I index,local Local Moran's I index,and Moran scatter plot to test the spatial correlation of housing prices and population in China.Then,using cross-sectional data of China's 2016 relevant variables,the OLS model of home prices,the spatial lag model(SLM),and the population OLS model and spatial error model(SEM)were established.Secondly,using panel data of relevant variables from 1999 to 2016 in China,we established a space panel model with individual fixed effects through stationarity test,F-test and Hausman test.Finally,we used spatial Doberman model(SDM)to analyze relevant variables for influences house price influences overflow.The main research conclusions obtained in this paper are as follows:First,there is significant spatial autocorrelation in housing prices and population in 31 cities in China;Second,using cross-sectional data and panel data to study found that there is a significant spatial spillover effect in China's housing prices,including population flow speed,population density has a significant positive role in promoting house price overflow;The proportion of the tertiary industry in GDP and high prices will,to some extent,curb the flow of people.Third,through the establishment of the spatial Doberman model(SDM),it is found that the wage level and the industrial structure have little effect on the housing prices in the surrounding areas;The increase in the inflow rate of the local population will lead to the increase in housing prices in the surrounding areas.Fourth,the rise in housing prices in a certain area has caused the flow of population to the surrounding areas,and the flow of population to surrounding areas has also led to the increase in housing prices in the surrounding areas,leading to the phenomenon of room prices overflowing.