Spatial heterogeneity in implicit housing prices: evidence from Hangzhou, China

Abstract

Estimated coefficients in hedonic price models are generally assumed to be constant throughout the entire study area. However, increasing evidence reveals that the marginal prices of housing characteristics may vary over space and that the spatial heterogeneity problem in implicit housing prices should be given attention. Taking Hangzhou, China, as an example, this study uses the micro data of 603 residential communities in 2014 to examine spatial heterogeneity in implicit housing prices. On the basis of the traditional hedonic price model, we establish spatial expansion and geographically weighted regression (GWR) models for comparative analysis. Results show that the spatial expansion and GWR models have excellent goodness of fit and can improve the traditional hedonic price model. The mixed geographically weighted regression (MGWR) model further reveals that the implicit prices of nine housing characteristics vary significantly over space and that the impacts of the four remaining housing characteristics on housing prices are fixed throughout the entire study area. Unlike the traditional hedonic price model and spatial expansion model, the GWR/MGWR model has the unique advantage of visually providing the spatial distribution of implicit housing prices and accurately describing spatial heterogeneity.

Subway Opening, Traffic Accessibility, and Housing Prices: A Quantile Hedonic Analysis in Hangzhou, China

“…Distance to West Lake, distance to Wulin Square, distance to the Qiantang River, interior environment quality, surrounding environment quality, and nearby universities also significantly influence housing prices. This finding is coherent with the conclusion of early studies on the real estate market in Hangzhou [72][73][74]76]. Since the regression coefficients represent the price elasticity and semi-elasticity of housing characteristics, the influence degree of each characteristic on housing prices cannot be compared directly.…”

“…Explanatory variables can also be classified into three categories [31,71], namely, location, neighborhood, and structural variables. On the basis of previous studies on the Hangzhou housing market conducted by Wen, Bu, and Qin [72], Wen, Jin, and Zhang [73], and Wen, Xiao, and Zhang [74], three location variables, six neighborhood variables, and one structural variable are chosen as control variables.…”

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

“…Helbich et al [38] confirmed that stationarity and non-stationarity spatial relationships coexist in the same housing market based on data from Austria. Wen et al [39] found that, in the Hangzhou housing market, the marginal price of property costs, sports facilities, internal environment, and surrounding universities are stable, whereas the influence of coefficients of other factors vary within the spatial locations.…”

Exploring the Influence of an Urban Water System on Housing Prices: Case Study of Zhengzhou

“…It is a common research method to use a spatial econometric model to explore the heterogeneity of influence from local perspective. There have been a number of studies involving individual heterogeneity [23], distance heterogeneity [17], regional heterogeneity [24], and direction heterogeneity [7] that have reported the heterogeneity effects of water systems on housing prices. While the main research method adopted by most studies is just to divide the regions artificially and analyze the heterogeneity by taking them as dummy variables.…”