eXplainable Machine Learning for Real Estate: XGBoost and Shapley Values in Price Prediction

Abstract

This study examines the application of eXplainable Artificial Intelligence (XAI) in property market research, utilizing housing transaction data from Quarry Bay, Hong Kong. The research employs the XGBoost algorithm to predict property prices and subsequently computes Shapley Additive Explanations (SHAP) values to quantify feature importance. A beeswarm plot is used to visualize the distribution of SHAP values, uncovering complex relationships between prices and property characteristics. The findings demonstrate how features such as square footage and property age contribute to average price predictions, offering valuable insights for urban planning and real estate decision-making. In contrast to the traditional black-box models, this study integrates XAI methodologies to enhance model interpretability, thereby fostering trust in AI-driven market analyses. The novelty of this research lies in its combination of machine learning and explainable techniques, bridging the gap between predictive accuracy and interpretability in property valuation. By advancing data-driven decision-making, this study underscores the potential of XAI in promoting transparency and facilitating informed policymaking in the property market.