Training and Interpreting Machine Learning Models: Application in Property Tax Assessment

Lee Changro ()
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Lee Changro: Department of Real Estate, Kangwon National University

Real Estate Management and Valuation, 2022, vol. 30, issue 1, 13-22

Abstract: In contrast to the outstanding performance of the machine learning approach, its adoption in industry appears to be relatively slow compared to the speed of its proliferation in a variety of business sectors. The low interpretability of a black-box-type model, such as a machine learning-based valuation model, is one reason for this. In this study, house prices in Seoul and Jeollanam Province, South Korea, were estimated using a neural network, a representative model to implement machine learning, and we attempted to interpret the resultant price estimations using an interpretability tool called a partial dependence plot. Partial dependence analysis indicated that locally optimized valuation models should be designed to enhance valuation accuracy: a land-oriented model for Seoul and a building-focused model for the Jeollanam Province. The interpretable machine learning approach is expected to catalyze the adoption of machine learning in the industry, including property valuation.

Keywords: machine learning; interpretability; neural network; partial dependence plot; house valuation (search for similar items in EconPapers)
JEL-codes: C13 C45 L85 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:remava:v:30:y:2022:i:1:p:13-22:n:4

DOI: 10.2478/remav-2022-0002

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