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

• Autorzy: Changro Lee
• Języki publikacji: EN

Abstrakty

EN

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 buildingfocused 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. (original abstract)

Słowa kluczowe

EN

Neural networks; Real estate valuation; Data interpretation

PL

Sieci neuronowe; Wycena nieruchomości; Interpretacja danych

• Czasopismo: Real Estate Management and Valuation
• Rocznik: 2022
• Numer: vol. 30, iss. 1
• Strony: 13--22

Twórcy

autor

Changro Lee

• Kangwon National University

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Identyfikatory

DOI

doi.org/10.2478/remav-2022-0002