A House Price Valuation Based on the Random Forest Approach: The Mass Appraisal of Residential Property in South Korea

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.

Methodology for Calculating the Reproduction Value of Buildings

“…To provide a value estimate of a building, it is also possible to use the hedonic regression model, which has already been examined many times, or also for specific buildings, data on their repeated sales [5]. The random forest method has proven to be a useful complementary tool to capture the complexity and nonlinearity of the entire set of buildings that is the subject of the valuation [6]. Using an interoperable data model for the valuation of land intended for building, national geographic data infrastructures can be extended, which could lead to mass valuation when using machine learning as a numerical processor.…”

Real Estate Market Performance in Nigeria: Longitudinal Evidence from Residential and Commercial Property Sectors in Port Harcourt

“…Generic applications of real estate market performance appraisals highlight adoptions of weighted appraisal models, random forest approach, hedonic models, genetic algorithm models, artificial intelligence and multicriteria analysis (Abidoye & Chan, 2017;Braun et al, 2020;Hong et al, 2020;Morano et al, 2018;Tajani et al, 2017;Tajani et al, 2020). In light of the scope of the study which focused on the residential and commercial real estate market in Port Harcourt, Nigeria, a benchmarking of these performance appraisal frameworks may not be apposite in view of the data constructions deficit encumbering the market.…”

Estimation of Residential Property Market Price: Comparison of Artificial Neural Networks and Hedonic Pricing Model

“…Hong et al (2020) showed better predictive performance of machine learning-based predictor (property price predictor based on the Random Forest method) compared to the hedonic pricing model (ordinary-least square-based property price predictor) [12]. Moreno-Izquierdo et al (2018) compared the performance of the Artificial Neural Network (ANN) and hedonic regression model for the price optimization procedure [7].…”