Testing machine learning systems in real estate

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?...

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Bibliographic Details
Published in:Real estate economics 2023-05, Vol.51 (3), p.754-778
Main Authors: Wan, Wayne Xinwei, Lindenthal, Thies
Format: Article
Language:English
Subjects:
accountability gap
computer vision
explainable machine learning
Machine learning
Real estate
Software testing
system testing
Test procedures
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Summary: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.
ISSN:1080-8620
1540-6229
DOI:10.1111/1540-6229.12416