Examining the nonlinear and threshold effects of the 5Ds built environment to land values using interpretable machine learning models

Previous studies have extensively explored the critical influence of the built environment on land values, but the non-linear relationship has yet to be fully revealed. This study aims to uncover the non-linear relationship between land values and the five built environment dimensions using machine learning algorithms and Shapley Additive exPlanation (SHAP). The results highlight that the Gradient Boost Decision Tree (GBDT) outperforms eXtreme Gradient Boosting (XGBoost), Ordinary Least Squares (OLS), and Multiscale Geographically Weighted Regression (MGWR) in land value estimation, exhibiting higher R² and lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results illustrate that density and destination accessibility are the dominant factors, contributing 32.48% and 37.38% to land value variation, respectively. We observed that the top three factors affecting land values are the built-floor area ratio, the number of floors and the number of restaurants. Additionally, the results revealed the non-linear relationship between the built environment and land values, suggesting that maintaining built environment features at optimal thresholds may increase land values. Neglecting interaction effects may lead to bias in determining relationships between land values and the built environment. This study contributes to the literature by providing non-linear and threshold identification evidence in land value determinants, offering valuable insights for urban planners and real estate managers.