Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach

• Published in: The journal of real estate finance and economics Vol. 68; no. 2; pp. 235 - 273
• Main Authors: Deppner, Juergen, Cajias, Marcelo
• Format: Journal Article
• Language: English
• Published: New York, NY Springer US 01.02.2024; Springer Nature B.V
• Subjects: Algorithms; Automated valuation models; Bias; Credit risk; Decision making; Economics; Economics and Finance; Financial Services; Generalizability; Hedonic modeling; Housing prices; Machine learning; Mass appraisal; Optimism; Regional/Spatial Science; Risk management; Spatial autocorrelation; Spatial cross-validation; Valuation; Valuation methods; Spatial cross-validation; Hedonic modeling; Spatial autocorrelation; Automated valuation models; Mass appraisal; Machine learning
• ISSN: 1573-045X; 0895-5638; 1573-045X
• DOI: 10.1007/s11146-022-09915-y

Data-driven machine learning algorithms have initiated a paradigm shift in hedonic house price and rent modeling through their ability to capture highly complex and non-monotonic relationships. Their superior accuracy compared to parametric model alternatives has been demonstrated repeatedly in the literature. However, the statistical independence of the data implicitly assumed by resampling-based error estimates is unlikely to hold in a real estate context as price-formation processes in property markets are inherently spatial, which leads to spatial dependence structures in the data. When performing conventional cross-validation techniques for model selection and model assessment, spatial dependence between training and test data may lead to undetected overfitting and overoptimistic perception of predictive power. This study sheds light on the bias in cross-validation errors of tree-based algorithms induced by spatial autocorrelation and proposes a bias-reduced spatial cross-validation strategy. The findings confirm that error estimates from non-spatial resampling methods are overly optimistic, whereas spatially conscious techniques are more dependable and can increase generalizability. As accurate and unbiased error estimates are crucial to automated valuation methods, our results prove helpful for applications including, but not limited to, mass appraisal, credit risk management, portfolio allocation and investment decision making.