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Testing machine learning systems in real estate

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  • Wayne Xinwei Wan
  • Thies Lindenthal

Abstract

Uncertainty about the inner workings of machine learning (ML) models holds back the application of ML‐enabled systems in real estate markets. How do ML models arrive at their estimates? Given the lack of model transparency, how can practitioners guarantee that ML systems do not run afoul of the law? This article first advocates a dedicated software testing framework for applied ML systems, as commonly found in computer science. Second, it demonstrates how system testing can verify that applied ML models indeed perform as intended. Two system‐testing procedures developed for ML image classifiers used in automated valuation models (AVMs) illustrate the approach.

Suggested Citation

  • Wayne Xinwei Wan & Thies Lindenthal, 2023. "Testing machine learning systems in real estate," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(3), pages 754-778, May.
    • Handle: RePEc:bla:reesec:v:51:y:2023:i:3:p:754-778
    DOI: 10.1111/1540-6229.12416
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    References listed on IDEAS

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    Cited by:

    1. Lin Deng & Xueqing Zhang, 2025. "Boosting the accuracy of property valuation with ensemble learning and explainable artificial intelligence: The case of Hong Kong," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 74(1), pages 1-28, March.
    2. Elias Bjørgve & Are Oust & Arne Johan Pollestad & Cato Sandnes & Ole Jakob Sønstebø, 2026. "Comparing Housing Valuation Techniques and Stacked Generalization: Exploiting Explainable AI," The Journal of Real Estate Finance and Economics, Springer, vol. 72(3), pages 640-665, April.
    3. Timothy L. Hamilton & Erik B. Johnson, 2023. "The amenity value of natural views," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(5), pages 1079-1107, September.
    4. Hu, Maggie Rong & Kuang, Weida & Li, Xiaoyang & Shi, Yang, 2025. "Is the more the merrier? Buyers’ onsite viewing activities and housing search outcomes," Journal of Banking & Finance, Elsevier, vol. 180(C).
    5. Arne Johan Pollestad & Arild Brandrud Næss & Are Oust, 2026. "Towards a Better Uncertainty Quantification in Automated Valuation Models," The Journal of Real Estate Finance and Economics, Springer, vol. 72(3), pages 530-566, April.
    6. Dieudonné Tchuente, 2026. "Real Estate Automated Valuation Model with Explainable Artificial Intelligence Based on Shapley Values," The Journal of Real Estate Finance and Economics, Springer, vol. 72(3), pages 567-605, April.

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