Detecting the Spatially Non-Stationary Relationships between Housing Price and Its Determinants in China: Guide for Housing Market Sustainability

Abstract

Given the rapidly developing processes in the housing market of China, the significant regional difference in housing prices has become a serious issue that requires a further understanding of the underlying mechanisms. Most of the extant regression models are standard global modeling techniques that do not take spatial non-stationarity into consideration, thereby making them unable to reflect the spatial nature of the data and introducing significant bias into the prediction results. In this study, the geographically weighted regression model (GWR) was applied to examine the local association between housing price and its potential determinants, which were selected in view of the housing supply and demand in 338 cities across mainland China. Non-stationary relationships were obtained, and such observation could be summarized as follows: (1) the associations between land price and housing price are all significant and positive yet having different magnitudes; (2) the relationship between supplied amount of residential land and housing price is not statistically significant for 272 of the 338 cities, thereby indicating that the adjustment of supplied land has a slight effect on housing price for most cities; and (3) the significance, direction, and magnitude of the relationships between the other three factors (i.e., urbanization rate, average wage of urban employees, proportion of renters) and housing price vary across the 338 cities. Based on these findings, this paper discusses some key issues relating to the spatial variations, combined with local economic conditions and suggests housing regulation policies that could facilitate the sustainable development of the Chinese housing market.