Unveiling Location-Specific Price Drivers: A Two-Stage Cluster Analysis for Interpretable House Price Predictions

• URL: http://arxiv.org/abs/2508.03156v1
• Date: Tue, 05 Aug 2025 07:00:42 GMT
• Title: Unveiling Location-Specific Price Drivers: A Two-Stage Cluster Analysis for Interpretable House Price Predictions
• Authors: Paul Gümmer, Julian Rosenberger, Mathias Kraus, Patrick Zschech, Nico Hambauer,
• Abstract summary: House price valuation remains challenging due to localized market variations.<n>Existing approaches often rely on black-box machine learning models, which lack interpretability.<n>We propose a machine learning approach that applies two-stage clustering, first grouping properties based on minimal location-based features.
• Score: 3.738325076054202
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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