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Spatial Effects of Public Service Facilities Accessibility on Housing Prices: A Case Study of Xi’an, China

Feng Lan (), Qi Wu (), Tao Zhou () and Huili Da ()
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Feng Lan: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Qi Wu: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Tao Zhou: School of Construction Management and Real Estate, Chongqing University, Chongqing 400044, China
Huili Da: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China

Sustainability, 2018, vol. 10, issue 12, 1-20

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.

Keywords: public service facilities; accessibility; housing prices; mixed geographical weighted regression; geographical detector; Xi’an (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2018
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