<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

Real Estate Management and Valuation

This work is licensed under the Creative Commons Attribution 4.0 International License.

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...

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

Online veröffentlicht: 17. März 2022

Seitenbereich: 13 - 22

DOI: https://doi.org/10.2478/remav-2022-0002

Schlüsselwörter
machine learning, interpretability, neural network, partial dependence plot, house valuation

© 2022 Changro Lee, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Online veröffentlicht: 17. März 2022

Seitenbereich: 13 - 22

DOI: https://doi.org/10.2478/remav-2022-0002

Schlüsselwörtermachine learning, interpretability, neural network, partial dependence plot, house valuation

© 2022 Changro Lee, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
machine learning, interpretability, neural network, partial dependence plot, house valuation