Increasing the Transparency of Pricing Dynamics in the US Commercial Real Estate Market with Interpretable Machine Learning Algorithms
研究提出一个兼顾准确性和可解释性的自动估值模型框架，用深度神经网络分析40多万条商业地产数据，并用SHAP方法解释模型预测规则，帮助理解机构投资市场的定价过程。
This study proposes a holistic framework for the practical use of automated valuation models (AVMs) in a commercial real estate context that considers both accuracy and interpretability. The authors train a deep neural network (DNN) on a unique sample of more than 400,000 property-quarter observations from the NCREIF Property Index and perform model-agnostic analysis using Shapley Additive exPlanations (SHAP) to provide ex post comprehensibility of the algorithm’s prediction rules. They further assess the extent to which the inner workings of the DNN follow an economic rationale and set out how the proposed methods can add to the understanding of pricing processes in institutional investment markets. By addressing the caveats and illustrating the potential of machine learning in the field of commercial real estate, this article represents another important pillar in the practical use of AVMs.