| With the process of global economic integration,the domestic real estate market is gradually opening up,people's increasing demand for housing,real estate has gradually become one of the pillar industries of the national economy.In recent years,the housing price(house price)in China's major cities keeps going up,which makes the spatial and temporal distribution pattern of housing price and the mechanism of influencing factors become the focus of the real estate market research.From a macro perspective,housing prices are affected by policy regulation,national financial situation,population flow and land supply,and there are differences among provinces or cities in housing prices,influenced by the distribution difference of location,traffic,neighborhood and building property,the house price has the characteristic of spatial heterogeneity.This research takes the 2016-2019 commercial housing price in the main city of Xi'an as the research object,and uses the spatial autocorrelation and the geostatistical analysis method,obtains the spatial autocorrelation and the spatial heterogeneity characteristic of the Xi'an housing price,the spatial and temporal change pattern of housing price in Xi'an is obtained by using the visualization expression of housing price and its change rate with grid method.Finally,based on the community characteristics,location traffic,peripheral facilities and value-added attributes,this paper describes the contribution degree of different factors to the spatial differentiation of housing prices based on geographic weighted regression(GWR)model.It is expected to explore the past and present situation of commercial housing prices in Xi'an from the perspective of time and space,and to explore the main driving factors of the dynamic changes of housing prices,so as to pave the way for the positive and sustainable development of housing prices in Xi'an in the future.The main conclusions are as follows:(1)According to the law of spatial differentiation,there is a positive spatial correlation in the spatial distribution of housing prices in Xi'an,among which the high value agglomeration areas mainly appear in the areas of Keji road and Mutazhai,Qujiangchi and Giant Wild Goose Pagoda,the low value agglomeration area is located in the border area of Weiyang District,Baqiao District and Gaoling area and the edge of the main city area.According to the results of variance analysis,the price anisotropy of Xi'an housing is significant,and the region beyond 3-6 km has no spatial autocorrelation.After 2017,the spatial Heterogeneity of housing prices in Xi'an has been increasing.(2)According to the characteristics of time change,Xi'an housing prices are sTab or even slightly lower before 2016.From 2016 to 2019,Xi'an housing prices experienced three stages:a small increase,a large increase and a moderate correction.Since 2017,the ratio of homes rented to sold in Xi'an has risen sharply,to more than 500.In 2018,the ratio of house price to income in Xi'an exceeded 8 and continued to rise,which indicates that the real estate market in Xi'an has become a bubble,among them,high-tech,Qujiang,city sports park three groups of housing average price the highest and the largest increase,the overall high-value areas "three feet tripod",the basic pattern of low-value distribution.(3)From the perspective of driving factors,the spatial heterogeneity of housing prices in Xi'an is most affected by such factors as the distance from the city center,the nearest subway,the planning of new districts,the nearest third-class hospitals,the number of primary and junior middle schools,and property costs.Through the horizontal comparison of four dimensions,we find that the order of influence degree is:Location Traffic>Value-added attribute>Neighborhood facilities>Community characteristics.In addition,property fees,greening rate and the nearest park distance have the greatest impact on the housing prices of high-tech groups,while housing age has the strongest impact in areas around Daming Palace;The most influential area of Metro distance is the northern section of the central axis,the most influential area of the commercial center is the new district of Qujiang,and the influence of the urban center distance increases with the increase of the distance The influence of mall and bank was not significant;the most influential areas of the number of primary and junior middle schools were the east and southwest corner of the city,which indicated that the distribution of compulsory education resources was unbalanced and uneven.The innovation of this article mainly includes:1)This study uses a more authoritative data source(Xi Tai real estate big data),focusing on the most significant changes in Xi'an housing prices from 2016 to 2019,to get the microscopic scale of housing prices and its driving mechanism.To a certain extent,it alleviates the problem that the previous research data quantity is relatively few and the housing type is not comprehensive.2)Paying attention to the heterogeneity law of housing price spatial distribution,using 500m*500m Grids for visualization expression,minimizing the error of housing price caused by spatial interpolation smoothing effect,and analyzing the spatial and temporal pattern of housing price by calculating the rate of housing price change over the years. |
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