Exploring the Spillover Effects of Urban Renewal on Local House Prices Using Multi-Source Data and Machine Learning: The Case of Shenzhen, China

Abstract

Urban renewal is a current hotspot for research in the field of urban geography and urban planning. However, few studies have been able to quantify the impact of urban renewal on local house prices. Taking Shenzhen as an example, this paper measures the added premium effect of urban renewal on local house prices through econometric models and multi-source data and explores the spillover effect of urban renewal on house prices using an integrated model based on machine learning and Geo-detector analysis. The main findings are: (1) Shenzhen’s urban renewal had a significant positive premium effect on the unit transaction price of local housing. (2) The population characteristics and the accessibility to transport in the context of urban renewal are the main drivers for premiums on house prices. (3) There is spatial heterogeneity with respect to the housing premium effect due to urban renewal, among which optimization of the density of the road network is most closely associated with the premium effects. The interaction of the road density network and the population density of particular streets drives the medium premium effect. Our findings have important implications for refinements in management practices for urban renewal in the context of the housing market.