Uncovering drivers of community-level house price dynamics through multiscale geographically weighted regression: A case study of Wuhan, China

Abstract

For buyers, investors and urban policy, understanding drivers of community-level house prices across space and across time, are important for urban management and economic planning. In this study, we interrogated two housing market datasets, one from 2015, the other from 2019, for Wuhan, China, in order to uncover diversities and similarities in the spatial relationships between house price and contextual data; and in the context of increasingly volatile markets. A non-stationary approach was adopted with basic geographically weighted regression (GWR) and multiscale GWR (MGWR), where only the latter enables relationships to vary at their own spatial scale. In terms of model fit, both MGWR (adj. R2: 0.94 and 0.97, for 2015 and 2019, respectively) and GWR (adj. R2: 0.87 and 0.81) represented an improvement over the usual linear regression (adj. R2: 0.11 and 0.09) and the spatial lag mode (adj. R2: 0.21 and 0.27). Similarly marked improvements for GWR and for MGWR were found using corrected Akaike Information Criterion (AICc) based fit diagnostics. However, of more importance and via MGWR, the spatially varying drivers of house price were found to operate at a range of spatial scales, that in turn changed in strength and significance between the two study years. Such insights allow for spatially- and temporally-aware decision- and policy-making for housing price control and urban planning, given China’s housing markets can be increasing prone to strong growth coupled with severe depressions.