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

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.

Modeling the Spatial Dimensions of Warehouse Rent Determinants: A Case Study of Seoul Metropolitan Area, South Korea

“…Housing has been the most dominant research subject among all types of real estate properties in the urban and transportation literature [17,24,29,30]. Studies exclusively on warehouse rent were rare in the past, but several studies on industrial properties do exist.…”

“…They identified four major influence factors on housing price with significant spatial variabilities: Distance to the inner ring, distance to hospitals, bus density, and distance to subway stations. Lan et al [24] applied the MGWR and the geographical detector (GD) to analyze the spatial effect of accessibility to public service facilities on housing prices based on the case of Xi'an, China.…”

“…Therefore, applying OLS regression to warehouse rent data with significant spatial autocorrelation results in the biased and unreliable estimation of regression coefficients [23]. Moreover, the conventional OLS regression models assume stationary relationships between the rental price and its explanatory variables over the entire study area [24]. However, such a homogeneous warehouse rental market may not exist.…”

Does the Difference in Urban Public Facility Allocation Cause Spatial Inequality in Housing Prices? Evidence from Chongqing, China

“…For facilities in case II that are laid out according to large administrative units, their capitalisation effects are considerable. Similarly, cultural facilities [43,57] and sports facilities [43] as well as shopping malls [60] were found that they have notable value-added effects on surrounding houses.…”

“…Municipal parks and river-views [26,46] as high-quality and scarce landscape endowments, make an obvious value-added effect on houses in the vicinity. Lan et al [57] also deemed that basic education is a basic necessity, and culture, sports and finance are a higher-level of needs. The current study responds to the conclusions of previous researches, which partially explain the correctness of the current study.…”