通过两阶段聚类分析，让房价预测既准又看得懂。
Unveiling Location-Specific Price Drivers: A Two-Stage Cluster Analysis for Interpretable House Price Predictions
- 先按地理位置分群，再分别建模，兼顾准确与解释性。
- 相比不聚类模型，误差降低36%（GAM）和58%（LR）。
- 适合想理解房价驱动因素的购房者、卖家和分析师。
房价评估因市场地域差异而复杂。现有方法多依赖难以解释的黑箱模型，或过于简化的线性回归（LR），无法捕捉市场异质性。为此，我们提出一种两阶段聚类方法：首先基于最少位置特征对房产进行分组，再引入其他特征建模。每个簇采用线性回归（LR）或广义加性模型（GAM），在预测性能与可解释性间取得平衡。在2023年德国43,309条房产数据上验证，相较于无聚类模型，GAM的平均绝对误差降低36%，LR降低58%。图形分析揭示了不同簇间模式变化。结果表明，针对簇的洞察对提升可解释性具有关键作用，为购房者、卖家及房地产分析师提供更可靠的估值参考。
原文摘要 · Abstract (English)
House price valuation remains challenging due to localized market variations. Existing approaches often rely on black-box machine learning models, which lack interpretability, or simplistic methods like linear regression (LR), which fail to capture market heterogeneity. To address this, we propose a machine learning approach that applies two-stage clustering, first grouping properties based on minimal location-based features before incorporating additional features. Each cluster is then modeled using either LR or a generalized additive model (GAM), balancing predictive performance with interpretability. Constructing and evaluating our models on 43,309 German house property listings from 2023, we achieve a 36% improvement for the GAM and 58% for LR in mean absolute error compared to models without clustering. Additionally, graphical analyses unveil pattern shifts between clusters. These findings emphasize the importance of cluster-specific insights, enhancing interpretability and offering practical value for buyers, sellers, and real estate analysts seeking more reliable property valuations.
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