Spatial Differentiation Of Urban Residential Prices In The Yangtze River Economic Belt And Its Influencing Factors

In recent years,with the acceleration of urbanization,China’s urban residential prices have continued to increase while there have been obvious regional differences.The Yangtze River economic belt spans the three major regions of east,middle and west of China.The urban development in the region is very different,and the real estate market development differences are more significant.China has been advocating coordinated regional development,and studying the spatial differentiation pattern and formation mechanism of residential prices in the Yangtze River Economic Belt is of great practical significance to coordinate regional coordinated development.In order to deeply understand the spatial changes of urban housing prices and the internal mechanism that affects the spatial differentiation of housing prices,this article comprehensively analyzes the effects of macro and micro factors on housing prices from different scales and dimensions.Taking the residential prices of 123 prefecture-level cities in the Yangtze River Economic Belt as the basic data for the study,the spatial differentiation pattern of urban house prices in the Yangtze River Economic Belt was studied with the help of spatial autocorrelation analysis and cold and hot spot analysis.Discuss the effect of macro factors such as population,economy,society,and administrative hierarchy on the housing price difference in cities along the Yangtze River Economic Belt.The price of urban housing is not only affected by macro factors,the price of urban internal housing is also affected by micro factors such as building characteristics,neighborhood characteristics,and location characteristics.Therefore,this paper selects three typical cities of Chongqing,Nanchang,and Nanjing on the Yangtze River Economic Belt for comparative research.Based on exploratory spatial data analysis,the three cities are analyzed for global trends.The overall change trend in space,and the kriging method is used to analyze the spatial distribution characteristics and change rules of residential prices in three cities of different sizes.By constructing a GWR model,a comparative analysis of the degree of contribution of the influencing factors to the urban internal housing price from a micro perspective is made.The research results show that the urban house prices in the Yangtze River Economic Belt generally show a decreasing trend from east to west,with a positive spatial correlation in space,and significant agglomeration characteristics.High-value areas are concentrated in provincial capitals and eastern cities,and low-value areas are concentrated in prefecture-level cities in Hunan Province and southeast Sichuan.Economic factors,demographic factors,and real estate development together affect the spatial distribution of urban residential prices in the Yangtze River Economic Belt.Among them,real estate development investment and the proportion of high-end practitioners have a value-added effect on urban housing prices;residential supply has a negative correlation with urban housing prices;and the impact of income levels and population size on urban housing prices has spatial heterogeneity,which is overall related to the Yangtze River Economic Belt.There is a positive correlation between house prices in eastern cities and a negative correlation with house prices in western cities.The spatial distribution of residential prices in cities is also uneven.On the whole,residential prices have shown a declining trend from the central urban area to the fringe suburbs.At the same time,Chongqing and Nanchang’s urban interior housing prices have a multi-center spatial distribution pattern,while Nanjing has a dual-core model.The spatial differentiation of urban housing prices is the result of a combination of various factors,and different factors have different degrees of impact on prices.Among them,the factors that have a greater effect on the price of second-hand housing in the city are the age of the house and the distance from the CBD.The impact of the same factor on housing prices is also different in different spaces.