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<p>The nonlinearity of hedonic datasets demands flexible automated valuation models to appraise housing prices accurately, and artificial intelligence models have been employed in mass appraisal to this end. However, they have been referred to as “black-box” models owing to difficulties associated with interpretation. In this study, we compared the results of traditional hedonic pricing models with those of machine learning algorithms, e.g., random forest and deep neural network models. Commonly implemented measures, e.g., Gini importance and permutation importance, provide only the magnitude of each explanatory variable’s importance, which results in ambiguous interpretability. To address this issue, we employed the SHapley Additive exPlanation (SHAP) method and explored its effectiveness through comparisons with traditionally explainable measures in hedonic pricing models. The results demonstrated that (1) the random forest model with the SHAP method could be a reliable instrument for appraising housing prices with high accuracy and sufficient interpretability, (2) the interpretable results retrieved from the SHAP method can be consolidated by the support of statistical evidence, and (3) housing characteristics and local amenities are primary contributors in property valuation, which is consistent with the findings of previous studies. Thus, our novel methodological framework and robust findings provide informative insights into the use of machine learning methods in property valuation based on the comparative analysis.</p>

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Toward transparent and accurate housing price appraisal: Hedonic price models versus machine learning algorithms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月31日

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  • Sihyun An,
  • Yena Song,
  • Hanwool Jang,
  • Kwangwon Ahn

摘要

The nonlinearity of hedonic datasets demands flexible automated valuation models to appraise housing prices accurately, and artificial intelligence models have been employed in mass appraisal to this end. However, they have been referred to as “black-box” models owing to difficulties associated with interpretation. In this study, we compared the results of traditional hedonic pricing models with those of machine learning algorithms, e.g., random forest and deep neural network models. Commonly implemented measures, e.g., Gini importance and permutation importance, provide only the magnitude of each explanatory variable’s importance, which results in ambiguous interpretability. To address this issue, we employed the SHapley Additive exPlanation (SHAP) method and explored its effectiveness through comparisons with traditionally explainable measures in hedonic pricing models. The results demonstrated that (1) the random forest model with the SHAP method could be a reliable instrument for appraising housing prices with high accuracy and sufficient interpretability, (2) the interpretable results retrieved from the SHAP method can be consolidated by the support of statistical evidence, and (3) housing characteristics and local amenities are primary contributors in property valuation, which is consistent with the findings of previous studies. Thus, our novel methodological framework and robust findings provide informative insights into the use of machine learning methods in property valuation based on the comparative analysis.