Analysis On The Spatial Differentiation Of Commodity Housing Prices And Its Influencing Factors In The Main Urban Area Of Guiyan

This paper investigates the spatial distribution characteristics and influencing factors of housing prices in Guiyang.Firstly,using spatial autocorrelation tools to verify the spatial correlation between housing prices in each urban area of Guiyang City,and then using Kriging interpolation to describe the spatial variability of housing prices.Secondly,variables are selected from neighborhood characteristics,educational resources,medical facilities,malls,transportation infrastructure,leisure and tourism to construct an index system,and after passing unit root test,F-test and Hausman test,constructing a fixed effects model to study the influences of housing prices.Thirdly,the geographical coordinates of the sample are added to the GWR model to analyze the spatially heterogeneous changes in the effects of each explanatory variable on housing prices,compare the results with those of the Fixed-Effect Model.Finally,make recommendations and research outlook based on the findings.The main findings and conclusions of this paper are as follows:(1)Housing prices in the central city and suburban areas of Guiyang show an inverted "U" shape in the east-west and south-north directions.(2)According to the kriging interpolation results,it can be seen that the high prices in Guiyang are located in the central area and the low prices are mainly distributed in the suburban fringe areas;the spatial distribution shows a circle effect,with housing prices gradually decreasing from the center to the fringe.(3)The Fixed-Effect Model shows that the age of the house,property costs,educational resources,hospitals and recreational tourism all show a1% level of significance on housing prices,and the nearest shopping mall and nearest bus stop do not show a significant effect on housing prices.(4)The GWR results show that after considering the geographic coordinates of housing neighborhoods,the standard deviation coefficients of hospitals,nearest universities,nearest shopping malls,and nearest subways are the largest,indicating the strongest contribution to housing prices in terms of spatial location,while the standard deviation coefficients of greening rate and housing age are the smallest,indicating the weakest contribution to housing prices in terms of spatial location.(5)Different variables have different degrees of spatial heterogeneity in their effects on housing prices.