Abstract
The premium effect of public service facilities on the housing market is a critical determinant of housing prices, leading to the competition of different social groups in the housing market and fueling spatial inequality. Taking Xi’an, China as a case in point, this study uses Geographic Information System (GIS) to describe the spatial distribution pattern of housing prices and urban public service facilities. Using the mixed geographically weighted regression model (MGWR) and the geographical detector model (GD), this study reveals the spatial effects of these facilities’ accessibility on housing prices. The results show that commercial and leisure facilities are spatially stationary, whereas a non-stationary effect is observed among those providing educational, medical, cultural, sport, and financial services. From the urban spatial resource allocation perspective, facilities meeting people’s basic needs, such as medical care and education, constitute the basic elements of housing price differentiation. When any two of these interact, a bivariate-enhanced effect emerges. The decisive interactive elements of housing price differentiation involve the facilities meeting people’s higher-level needs, such as leisure, culture, sports, and finance. When these interactive elements interact with other facilities, a non-linear enhancement effect is induced. This research is of practical value for improving people’s living quality, optimizing the spatial distribution of public service facilities, and eliminating urban spatial inequality.
Highlights
The housing market is characterized by residents increasingly emphasizing the accessibility of public service facilities, such as educational and medical facilities and parks [1,2]
Note: FSR is floor space ratio, GREEN is the ratio of green space, FEE is management fee, AGE is the age of the building, EDUCATION is educational facilities, MEDICAL is medical facilities, COMMERCE is commercial facilities, LEISURE is leisure facilities, C&S is cultural and sports facilities, BANK is financial facilities, CBD is traditional central business district, new CBD (NCBD) is newly central business district
Taking Xi’an, China as a case study, we used Geographic Information System (GIS) to describe the spatial distribution of housing prices and urban public service facilities
Summary
The housing market is characterized by residents increasingly emphasizing the accessibility of public service facilities, such as educational and medical facilities and parks [1,2]. A substantial number of studies have been published on the external effects of urban public service resources on housing prices Their focus has been schools, parks, subways, and other public service facilities that are closely related to the lives of residents [3,11,12,13,14,15]. Few studies have explored the influence of public service facilities on housing prices from the perspective of the whole spatial allocation of urban resources. After analyzing the public service facilities’ accessibility and housing price differentiation in Xi’an, China, we used a mixed geographical weighted regression model to explore the direction of influence of public service facilities on housing price changes in different locations. Compared with the existing literature, we attempted to provide the following insights: (i) housing price differentiation based on urban resource spatial allocation, revealing the influence of the facilities’ accessibility on housing prices.
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