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Metrics for evaluating the performance of machine learning based automated valuation models

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  • Miriam Steurer
  • Robert J. Hill
  • Norbert Pfeifer

Abstract

Automated Valuation Models (AVMs) based on Machine Learning (ML) algorithms are widely used for predicting house prices. While there is consensus in the literature that cross-validation (CV) should be used for model selection in this context, the interdisciplinary nature of the subject has made it hard to reach consensus over which metrics to use at each stage of the CV exercise. We collect 48 metrics (from the AVM literature and elsewhere) and classify them into seven groups according to their structure. Each of these groups focuses on a particular aspect of the error distribution. Depending on the type of data and the purpose of the AVM, the needs of users may be met by some classes, but not by others. In addition, we show in an empirical application how the choice of metric can influence the choice of model, by applying each metric to evaluate five commonly used AVM models. Finally – since it is not always practicable to produce 48 different performance metrics – we provide a short list of 7 metrics that are well suited to evaluate AVMs. These metrics satisfy a symmetry condition that we find is important for AVM performance, and can provide a good overall model performance ranking.

Suggested Citation

  • Miriam Steurer & Robert J. Hill & Norbert Pfeifer, 2021. "Metrics for evaluating the performance of machine learning based automated valuation models," Journal of Property Research, Taylor & Francis Journals, vol. 38(2), pages 99-129, April.
  • Handle: RePEc:taf:jpropr:v:38:y:2021:i:2:p:99-129
    DOI: 10.1080/09599916.2020.1858937
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    Cited by:

    1. Robert J. Hill & Norbert Pfeifer & Miriam Steurer & Radoslaw Trojanek, 2021. "Warning: Some Transaction Prices can be Detrimental to your House Price Index," Graz Economics Papers 2021-11, University of Graz, Department of Economics.
    2. Hill, Robert J. & Trojanek, Radoslaw, 2022. "An evaluation of competing methods for constructing house price indexes: The case of Warsaw," Land Use Policy, Elsevier, vol. 120(C).
    3. Oğuz Mısır & Mehmet Akar, 2022. "Efficiency and Core Loss Map Estimation with Machine Learning Based Multivariate Polynomial Regression Model," Mathematics, MDPI, vol. 10(19), pages 1-18, October.
    4. Battiston, Pietro & Gamba, Simona & Santoro, Alessandro, 2024. "Machine learning and the optimization of prediction-based policies," Technological Forecasting and Social Change, Elsevier, vol. 199(C).

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