Explainable AI-based mass appraisal: Insights from machine learning applications in Korea’s residential property market

Abstract

Mass appraisal plays a pivotal role in real estate management, facilitating property tax assessments, mortgage evaluations, and urban planning across large geographical areas. In regions like Korea, where real estate markets are rapidly evolving, valuation models based on multiple linear regression are valued for their simplicity and interpretability but often fall short in capturing complex market dynamics. In contrast, machine learning (ML) models, while addressing non-linear relationships between property characteristics and market values and offering superior predictive performance, are often criticized for their “black-box” nature, which raises concerns over interpretability in transparency-critical domains like property tax assessments and policy planning. To address these concerns, this study investigates the application of Explainable AI (XAI) techniques in the mass appraisal of residential properties in Korea, integrating XAI methods with both multiple linear regression and random forest models. Using SHAP (SHapley Additive exPlanations) and PFI (Permutation Feature Importance) values, the study analyzes feature importance and predictive contributions, offering insights into the factors driving property valuations. Additionally, a temporal analysis was conducted by segmenting the data into time intervals to examine how feature importance and predictive contributions evolve over time. By combining high predictive performance with transparent and interpretable insights, the findings demonstrate that XAI can enhance the usability of both traditional and advanced automated valuation models (AVMs) for real-world decision-making in the Korean real estate sector.