Unraveling nonlinear relationship of built environment on pre-sale and second-hand housing prices using multi-source big data and machine learning

Abstract

Pre-sale and second-hand housing transaction modes dominate China's real estate market. However, many existing studies tend to treat the housing market as a homogeneous entity, overlooking the heterogeneity in core influencing factors across different transaction types. Thoroughly understanding the factors affecting various housing types can assist policy-makers in formulating differentiated regulatory decisions through environmental intervention. Therefore, this study utilized multi-source big data and compared the performance of multiple machine learning models to evaluate the relative importance and nonlinear effects of building-level, neighborhood-level, and street-level built environment factors on pre-sale and second-hand housing prices. The empirical study of Chengdu, China revealed that distance to city center was the most significant explanatory factor influencing pre-sale and second-hand housing prices among all factors. Significant differences existed between neighborhood-level and street-level built environment factors' nonlinear and threshold effects on pre-sale and second-hand housing prices. Notably, subway accessibility showed a U-shaped impact on pre-sale housing prices. To the best of our knowledge, our study is one of the early studies systematically investigating the influencing differences between pre-sale housing prices and second-hand housing prices, providing robust evidence for regulating housing prices through environmental interventions and offering critical references for policymakers and market participants.

Keywords

Real estate market / Machine learning / Random forest / Semantic segmentation / Street view image

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