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

Author

Listed:
  • Lee Changro

    (Department of Real Estate, Kangwon National University)

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.

Suggested Citation

  • Lee Changro, 2022. "Training and Interpreting Machine Learning Models: Application in Property Tax Assessment," Real Estate Management and Valuation, Sciendo, vol. 30(1), pages 13-22, March.
    • Handle: RePEc:vrs:remava:v:30:y:2022:i:1:p:13-22:n:4
    DOI: 10.2478/remav-2022-0002
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    References listed on IDEAS

    as
    1. Mukund Sundararajan & Amir Najmi, 2019. "The many Shapley values for model explanation," Papers 1908.08474, arXiv.org, revised Feb 2020.
    2. Rosen, Sherwin, 1974. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, University of Chicago Press, vol. 82(1), pages 34-55, Jan.-Feb..
    3. John M. Clapp, 2004. "A Semiparametric Method for Estimating Local House Price Indices," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 32(1), pages 127-160, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    machine learning; interpretability; neural network; partial dependence plot; house valuation;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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