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• W4285087234 abstract "Abstract 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." @default.
• W4285087234 created "2022-07-14" @default.
• W4285087234 creator A5010230567 @default.
• W4285087234 creator A5045615504 @default.
• W4285087234 date "2022-07-13" @default.
• W4285087234 modified "2023-09-25" @default.
• W4285087234 title "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach" @default.
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• W4285087234 doi "https://doi.org/10.1007/s11146-022-09915-y" @default.
• W4285087234 hasPublicationYear "2022" @default.
• W4285087234 type Work @default.
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• W4285087234 hasAuthorship W4285087234A5045615504 @default.

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