1. bookVolume 30 (2022): Issue 1 (March 2022)
Journal Details
License
Format
Journal
eISSN
2300-5289
First Published
16 May 2013
Publication timeframe
4 times per year
Languages
English
access type Open Access

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

Published Online: 17 Mar 2022
Volume & Issue: Volume 30 (2022) - Issue 1 (March 2022)
Page range: 13 - 22
Journal Details
License
Format
Journal
eISSN
2300-5289
First Published
16 May 2013
Publication timeframe
4 times per year
Languages
English
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

JEL Classification

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