A house price valuation based on the random forest approach: the mass appraisal of residential property in South Korea
Mass appraisal is the standardized procedure of valuing a large number of properties at the same time and is commonly used to compute real estate tax. While a hedonic pricing model based on the ordinary least squares (OLS) linear regression has been employed as the traditional method in this process, the stability and accuracy of the model remain questionable. This paper investigates the features of a house price predictor based on the Random Forest (RF) method by comparing it with that of a conventional hedonic pricing model. We used apartment transaction data from the period of 2006 to 2017 in the district of Gangnam, one of the most developed areas in South Korea. Using a data set covering 40% of all transactions in the sample area, we demonstrate that the accuracy of a machine learning-based predictor can be surprisingly high. The average of percentage deviations between the predicted and the actual market price was found to be only around 5.5% in the RF predictor, whereas it was almost 20% in the OLS-based predictor. With the RF predictor, the probability of the predicted price being within 5% of its actual market price was 72%, while only about 17.5% of the regression-based predictions fell within the same range. These results show that, in the practice of mass appraisal, the RF method may be a useful complement to the hedonic models, as it more adequately captures the complexity or non-linearity of actual housing markets.
First published online 03 February 2020
This work is licensed under a Creative Commons Attribution 4.0 International License.
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