Machine Learning Applications for Sustainable Housing Policy: Understanding Price Determinants to Inform Affordable Housing Strategies

Understanding how housing attributes are capitalized into prices is central to addressing urban affordability challenges. Using 2799 second-hand housing transactions from Wenzhou, China, this study examines residential price formation under pronounced spatial and structural heterogeneity. Multiple predictive models are evaluated within a unified 10-fold cross-validation framework. Results indicate that Random Forest delivers the strongest predictive performance, achieving a normalized mean squared error below 0.10 and explaining over 90% of out-of-sample price variation, substantially outperforming hedonic regression, regression trees, bagging, boosting, and support vector models. Permutation-based importance analysis identifies district location, building scale, and floor area as the dominant price determinants, while the influence of renovation quality, transportation access, and educational amenities varies across districts and dwelling types. These findings reveal strong nonlinearities and heterogeneous valuation mechanisms in rapidly urbanizing housing markets. Methodologically, the study demonstrates how interpretable machine learning complements traditional hedonic analysis, while providing policy-relevant insights into housing affordability dynamics in medium-sized Chinese cities.