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

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 policymakers 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.