Research On Spatial Differentiation And Influencing Factors Of Shenzhen Housing Price Based On GWR Model

Housing is a necessity for residents' lives,and its primary function is to live.The architectural characteristics of the house itself determine the basic value of the house,but the price of the house of the same building size varies greatly under different location conditions.In addition to residential functions,houses are often tied to resources such as education,medical care,transportation,and convenience of life.Therefore,it is crucial to explore the impact of relevant factors on housing prices.This article takes Shenzhen as an example,using the 2018 second-hand housing transaction records that can be obtained by reptiles on the chain home network,and sorting out the first-rate and one-price data of second-hand housing transactions in Shenzhen,which is used to comprehensively study the factors affecting Shenzhen's housing prices.This paper uses the map of Amap to obtain the data of Shenzhen's interest points,including 8 external categories such as subway stations,bus stations,shopping malls,hospitals,primary schools,middle schools,parks and hospitals.The ARCGIS10.6 software was used to import the housing data points and POI data points into the Shenzhen map,and the Shenzhen city price map database was established.Combined with the analysis of existing literature,the hypothesis is put forward to consider the impact of transportation,education,medical,commercial and environmental factors on house prices,and the spatial interpolation of the above factors can be demonstrated by the spatial interpolation of spline functions.Combine the actual analysis of the reasons.The spatial differentiation of house prices is the difference in the spatial distribution of house prices in different regions,also known as spatial heterogeneity.Empirical studies show that housing prices in Shenzhen are mainly affected by the distance of transportation infrastructure such as subway stations,the distance to hospitals and other medical infrastructure,the distance to primary and secondary education infrastructure,the construction area,and the age of housing.On the whole,the main factors negatively related to housing prices are the distance from the middle school,the distance to the primary school and the distance to the subway.The other influencing factors are positively related to the distance of the shopping mall.The spatial distribution of the distance to the hospital is quite different,about 50% respectively.The coefficients are positively or negatively correlated.The results of GWR(Geographically Weighted Regression)model show that among the above explanatory variables,the influence of building area and house age on housing price is consistent in direction and different in size.The influence of other variables on housing prices is in the direction and There is a spatial difference in size.