Abstract

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.

Highlights

  • Mass appraisal, called automatic valuation of real estate assets, is the introduction of mathematical statistics, computer technology, and geographic information technology to establish a mathematical model that serves as a systematic appraisal of a group of real estate properties and reveals its market value (Zhou, Ji, Chen, & Zhang, 2018)

  • The condition is based on impurity, which is Gini impurity in case of classification problems, while mean squared error (MSE) and its variance are used for regression trees

  • We discussed the features of the Random Forest (RF) predictor in comparison to the conventional ordinary least squares (OLS)-based predictor

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Summary

Introduction

Called automatic valuation of real estate assets, is the introduction of mathematical statistics, computer technology, and geographic information technology to establish a mathematical model that serves as a systematic appraisal of a group of real estate properties and reveals its market value (Zhou, Ji, Chen, & Zhang, 2018). A qualified professional must evaluate the property when information indicates that the Traditionally, the hedonic pricing model, originating from Lancaster’s consumer theory, has been one of. Rosen (1974) defines the theory as “a model of product differentiation based on the hedonic hypothesis that goods are valued for their utility-bearing attributes or characteristics.”. A consumer who purchases a good acquires a collection of the characteristics embodied in it, and these attributes can be converted into utility. The advantage of the hedonic pricing models is that the marginal implicit values of the characteristics can be obtained by differentiating the price function with respect to each attribute (McMillan, Reid, & Gillen, 1980)

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