Analysis Of Spatial Differentiation And Influencing Factors Of Urban Housing Rent

With the continuous advancement and acceleration of my country’s urban modernization process,Beijing,Shanghai,Guangzhou,Shenzhen and other domestic first-tier cities attract many foreigners with their abundant employment opportunities,perfect urban infrastructure and rich material and cultural life.Since the housing prices in these first-tier cities are all at a high level,far exceeding the salary level of ordinary working class,housing problems also arise.The housing issue is not only an economic issue,but also an important livelihood issue that affects social stability and concerns people’s well-being.Under such circumstances,in order to meet their own housing needs,renting has become the main way of living for more and more people,and the development and management of the domestic housing rental market is not perfect enough to cope with the strong population influx.rental needs.In order to realize the optimization and service of the development and management of the urban housing rental market,and to be able to effectively deal with the problem of rental housing,the research on the spatial differentiation characteristics and influencing factors of urban housing rental is also worthy of in-depth discussion.This paper takes Guangzhou as the research area and the housing rent as the research object.Using the rental housing data in August 2021 in the Shell Rentals website,combined with other geospatial data such as POI data of public service facilities and traffic data,this paper studies the spatial distribution of housing rent.Different characteristics and influencing factors.The main research contents and conclusions of this paper are as follows:(1)Exploration of housing rent and analysis of spatial differentiation characteristics.Using the ESDA analysis method in the geostatistical theory to conduct an exploratory analysis on housing rents,it is found that housing rents are basically"normal"distribution,and the east-west and north-south directions both show an"inverted U-shaped"trend,that is,housing rents increase from urban to urban areas.The distribution characteristics of the core area gradually decreasing to the outer areas of the city;the spatial differentiation characteristics of housing rents are studied from the overall and regional aspects,and it is found that the overall distribution of housing rents presents a“fan-shaped”urban spatial structure,and the housing rents in the eastern area The rent is higher than that in the western region,and the housing rent in the southern region is higher than that in the northern region;the housing rent in the regional distribution from high to low is Yuexiu District,Tianhe District,Haizhu District,Liwan District,Huangpu District,and Baiyun District.The rent levels are 75.93 yuan/m~2,70.80 yuan/m~2,62.20 yuan/m~2,53.78 yuan/m~2,52.32 yuan/m~2,and 44.59yuan/m~2;It is manifested in the unbalanced structure of rent levels and the spatial stratification of rental groups.(2)Based on the hedonic price model,an empirical analysis is carried out on the influencing factors of housing rent.Following the basic principle of factor selection,a system of factors affecting housing rent is constructed,and then based on the three functional models of the hedonic price model,the fitting operation of housing rent and the influencing factors is carried out.The model fitting effect of the function is the best;according to the regression results of the logarithmic function model,it is found that there are 10 factors that are significantly related to the housing rent,among which the distance to the railway station,the distance to the employment center,the distance to the market,and the distance to the factory are positively correlated with the housing rent.,CBD distance,subway station distance,middle school distance,shopping center distance,dining distance,park distance and housing rent are negatively correlated;there are 5 factors that have no significant correlation with housing rent,namely bus distance,primary school distance,university Distance,sight distance and distance from top 3 hospitals.(3)Based on the geographically weighted model,an empirical analysis is carried out on the influencing factors of housing rent.Using the spatial autocorrelation analysis method to study the spatial correlation of housing rents,it is found that the housing rents have a positive spatial autocorrelation,showing an aggregation state.For the hedonic price model of the square method,the fitting effect of the geographically weighted regression model considering the spatial non-stationarity is better,indicating that there are spatial differences in the influencing factors of housing rent in Guangzhou;according to the results of the geographically weighted regression model,it can be seen that It can be seen that the influence intensity of each influencing factor varies in space,which can better explain the spatial differentiation of housing rents in Guangzhou.