The Impact of Public Service Facilities on Housing Prices from the Perspective of Spatiotemporal Accessibility:A Study Based on Multiscale Geographically Weighted Regression Model(MGWR)
A combination of factors determines urban house prices.The architectural characteristics of a residence can determine its basic value,but different zoning conditions can also make a difference in housing prices.Differences in transportation,education,medical care,leisure,and recreation services brought about by differences in location affect the convenience of living,and thus housing prices.Therefore,studying the impact of the convenience of arriving at public service facilities on housing prices can reveal the inherent patterns of residential price fluctuations and provide a basis for urban planning and policy formulation.This paper investigates the spatial stratified heterogeneity of housing prices and their influencing factors in six old and two new urban areas in Nanjing.By collecting the average housing prices of each neighborhood in January 2024,analyzing the spatial characteristics of housing prices by using the Kriging method,and calculating the minimum time from residential to various public service facilities by using the network analysis,the residential attributes and the cost of passage time are incorporated into the MGWR model to explore the impact of accessibility to public service facilities on the spatial distribution of housing prices.The results indicate the following:(1)Housing prices in Nanjing decrease from the city center to the suburbs,showing a circle-like distribution,with peaks occurring in Gulou,Xuanwu,and Jianye districts.(2)Residential floor area ratios,greening rates of neighborhoods,and the time to reach the nearest parks,bus stops,shopping malls,train stations,and Tertiary A-level hospitals have a significant impact on housing prices.(3)There are variations in the impacts of the same factors on housing prices in different urban spaces.
house pricesspatiotemporal accessibilityspatial differentiationmultiscale geographically weighted regression
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