House Price Prediction in Nanjing Based on Machine Learning and SHAP Interpretability

Abstract

Accurate house price prediction is of great significance for homebuyers, developers, and policymakers, yet the complexity and non-linearity of housing markets pose substantial challenges to traditional linear models. This study addresses the dual goals of high predictive accuracy and high interpretability by systematically comparing five regression models—linear regression, random forest, XGBoost, LightGBM, and multilayer perceptron (MLP)—on a large-scale second-hand housing dataset from Nanjing, China. Model performance is evaluated using MAE, RMSE, and R². Results show that ensemble tree models significantly outperform linear regression and MLP, with LightGBM and XGBoost achieving the lowest MAE (6,200 RMB/m²) and RMSE (8,800 RMB/m²), and LightGBM attaining the highest R² (0.92). We further employ SHAP (SHapley Additive exPlanations) to open the "black box" of the optimal LightGBM model. Global feature importance reveals that house age, subway distance, and decoration condition are the three most influential drivers of Nanjing house prices. SHAP summary plots demonstrate that newer houses, closer subway proximity, and better decoration consistently raise prices, while area exhibits a non-monotonic effect—small units command higher unit prices under total-price constraints, whereas very large luxury properties show diminishing marginal value. Prediction diagnostics confirm robust performance in the mainstream price range (15,000–35,000 RMB/m²), though extreme high-end properties remain harder to predict due to unobserved idiosyncratic factors. This study not only provides a high-performance predictive tool for the Nanjing market but also offers transparent, actionable insights into the underlying price-formation mechanism. The integrated framework of machine learning plus SHAP interpretability is readily generalisable to other cities and real-estate contexts, supporting evidence-based decision-making and policy design.