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.
First published online 03 February 2020
Keyword : housing price forecasting, hedonic pricing model, random forest approach, mass appraisal, apartment, machine learning technique
How to Cite
Hong, J., Choi, H., & Kim, W.- sung. (2020). A house price valuation based on the random forest approach: the mass appraisal of residential property in South Korea. International Journal of Strategic Property Management, 24(3), 140-152. https://doi.org/10.3846/ijspm.2020.11544
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Cannon, S. E., & Cole, R. A. (2011). How accurate are commercial real estate appraisals? Evidence from 25 years of NCREIF sales data. Journal of Portfolio Management, 37(5), 68-88. https://doi.org/10.3905/jpm.2011.37.5.068
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Čeh, M., Kilibarda, M., Lisec, A., & Bajat, B. (2018). Estimating the performance of random forest versus multiple regression for predicting prices of the apartments. ISPRS International Journal of Geo-Information, 7(5), 168. https://doi.org/10.3390/ijgi7050168
Chau, K. W., & Chin, T. L. (2003). A critical review of literature on the hedonic price model. International Journal for Housing Science and its Applications, 27(2), 145-165.
Chen, J. H., Ong, C. F., Zheng, L., & Hsu, S. C. (2017). Forecasting spatial dynamics of the housing market using Support Vector Machine. International Journal of Strategic Property Management, 21(3), 273-283. https://doi.org/10.3846/1648715X.2016.1259190
Clauretie, T. M., & Neill, H. R. (2000). Year-round school schedules and residential property values. Journal of Real Estate Finance and Economics, 20(3), 311-322. https://doi.org/10.1023/A:1007841326833
Darling, A. H. (1973). Measuring benefits generated by urban water parks. Land Economics, 49(1), 22-34. https://doi.org/10.2307/3145326
Debrezion, G., Pels, E., & Rietveld, P. (2007). The impact of railway stations on residential and commercial property value: a meta-analysis. Journal of Real Estate Finance and Economics, 35(2), 161-180. https://doi.org/10.1007/s11146-007-9032-z
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Dubin, R. A., & Sung, C. H. (1990). Specification of hedonic regressions: non-nested tests on measures of neighborhood quality. Journal of Urban Economics, 27(1), 97-110. https://doi.org/10.1016/0094-1190(90)90027-K
Espey, M., & Lopez, H. (2000). The impact of airport noise and proximity on residential property values. Growth and Change, 31(3), 408-419. https://doi.org/10.1111/0017-4815.00135
Fan, G. Z., Ong, S. E., & Koh, H. C. (2006). Determinants of house price: a decision tree approach. Urban Studies, 43(12), 2301-2315. https://doi.org/10.1080/00420980600990928
Fletcher, M., Gallimore, P., & Mangan, J. (2000). Heteroscedasticity in hedonic house price models. Journal of Property Research, 17(2), 93-108. https://doi.org/10.1080/095999100367930
Garrod, G. D., & Willis, K. G. (1992). Valuing goods’ characteristics: an application of the hedonic price method to environmental attributes. Journal of Environmental Management, 34(1), 59-76. https://doi.org/10.1016/S0301-4797(05)80110-0
Gillard, Q. (1981). The effect of environmental amenities on house values: the example of a view lot. The Professional Geographer, 33(2), 216-220. https://doi.org/10.1111/j.0033-0124.1981.00216.x
Goodman, A. C. (1989). Topics in empirical urban housing research. In R. Muth, & A. Goodman (Eds.), The economics of housing markets (pp. 49-146). Chur, Switzerland: Harwood Academic.
Gu, J., Zhu, M., & Jiang, L. (2011). Housing price forecasting based on genetic algorithm and support vector machine. Expert Systems with Applications, 38(4), 3383-3386. https://doi.org/10.1016/j.eswa.2010.08.123
Hanson, S. (2004). The context of urban travel: concepts and recent trends. In S. Hanson, & G. Giuliano (Eds.), The geography of urban transportation (pp. 3-29). New York: The Guilford Press.
Harrison Jr, D., & Rubinfeld, D. L. (1978). Hedonic housing prices and the demand for clean air. Journal of Environmental Economics and Management, 5(1), 81-102. https://doi.org/10.1016/0095-0696(78)90006-2
Hayes, K. J., & Taylor, L. L. (1996). Neighborhood school characteristics: what signals quality to homebuyers? Economic Review-Federal Reserve Bank of Dallas, 2-9.
Huh, S., & Kwak, S. J. (1997). The choice of functional form and variables in the hedonic price model in Seoul. Urban Studies, 34(7), 989-998. https://doi.org/10.1080/0042098975691
Jud, G. D., & Watts, J. M. (1981). Schools and housing values. Land Economics, 57(3), 459-470. https://doi.org/10.2307/3146025
Kain, J. F., & Quigley, J. M. (1970). Measuring the value of housing quality. Journal of the American Statistical Association, 65(330), 532-548. https://doi.org/10.1080/01621459.1970.10481102
Lancaster, K. J. (1966). A new approach to consumer theory. Journal of Political Economy, 74(2), 132-157. https://doi.org/10.1086/259131
Li, M. M., & Brown, H. J. (1980). Micro-neighborhood externalities and hedonic housing prices. Land Economics, 56(2), 125-141. https://doi.org/10.2307/3145857
Limsombunchai, V. (2004, June). House price prediction: hedonic price model vs. artificial neural network. In New Zealand Agricultural and Resource Economics Society Conference (pp. 25-26), New Zealand.
Malpezzi, S. (2002). Hedonic pricing models: a selective and applied review. Housing Economics and Public Policy, 67-89. https://doi.org/10.1002/9780470690680.ch5
McCluskey, W., & Anand, S. (1999). The application of intelligent hybrid techniques for the mass appraisal of residential properties. Journal of Property Investment & Finance, 17(3), 218-239. https://doi.org/10.1108/14635789910270495
McMillan, D., Jarmin, R., & Thorsnes, P. (1992). Selection bias and land development in the monocentric model. Journal of Urban Economics, 31, 273-284. https://doi.org/10.1016/0094-1190(92)90056-Q
McMillan, M. L., Reid, B. G., & Gillen, D. W. (1980). An extension of the hedonic approach for estimating the value of quiet. Land Economics, 56(3), 315-328. https://doi.org/10.2307/3146034
Miller, N., Peng, L., & Sklarz, M. (2011). House prices and economic growth. Journal of Real Estate Finance and Economics, 42(4), 522-541. https://doi.org/10.1007/s11146-009-9197-8
Mu, J., Wu, F., & Zhang, A. (2014). Housing value forecasting based on machine learning methods. Abstract and Applied Analysis, 2014, Article ID 648047. https://doi.org/10.1155/2014/648047
Palmquist, R. B. (1992). Valuing localized externalities. Journal of Urban Economics, 31, 59-68. https://doi.org/10.1016/0094-1190(92)90032-G
Park, B., & Bae, J. K. (2015). Using machine learning algorithms for housing price prediction: the case of Fairfax County, Virginia housing data. Expert Systems with Applications, 42(6), 2928-2934. https://doi.org/10.1016/j.eswa.2014.11.040
Richardson, H. W., Vipond, J., & Furbey, R. A. (1974). Determinants of urban house prices. Urban Studies, 11(2), 189-199. https://doi.org/10.1080/00420987420080341
Ridker, R. G., & Henning, J. A. (1967). The determinants of residential property values with special reference to air pollution. Review of Economics and Statistics, 49(2), 246-257. https://doi.org/10.2307/1928231
Rodriguez, M., & Sirmans, C. F. (1994). Quantifying the value of a view in single-family housing markets. Appraisal Journal, 62, 600-603.
Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. Journal of Political Economy, 82(1), 34-55. https://doi.org/10.1086/260169
Selim, H. (2009). Determinants of house prices in Turkey: hedonic regression versus artificial neural network. Expert Systems with Applications, 36(2), 2843-2852. https://doi.org/10.1016/j.eswa.2008.01.044
Sheppard, S. (1999). Hedonic analysis of housing markets. Handbook of Regional and Urban Economics, 3, 1595-1635. https://doi.org/10.1016/S1574-0080(99)80010-8
So, H. M., Tse, R. Y., & Ganesan, S. (1997). Estimating the influence of transport on house prices: evidence from Hong Kong. Journal of Property Valuation and Investment, 15(1), 40-47. https://doi.org/10.1108/14635789710163793
Song, Y., & Sohn, J. (2007). Valuing spatial accessibility to retailing: a case study of the single family housing market in Hillsboro, Oregon. Journal of Retailing and Consumer Services, 14(4), 279-288. https://doi.org/10.1016/j.jretconser.2006.07.002
Thaler, R. (1978). A note on the value of crime control: evidence from the property market. Journal of Urban Economics, 5(1), 137-145. https://doi.org/10.1016/0094-1190(78)90042-6
Verikas, A., Lipnickas, A., & Malmqvist, K. (2002). Selecting neural networks for a committee decision. International Journal of Neural Systems, 12(05), 351-361. https://doi.org/10.1142/S0129065702001229
Wilhelmsson, M. (2000). The impact of traffic noise on the values of single-family houses. Journal of Environmental Planning and Management, 43(6), 799-815. https://doi.org/10.1080/09640560020001692
Williams, A. W. (1991). A guide to valuing transport externalities by hedonic means. Transport Reviews, 11(4), 311-324. https://doi.org/10.1080/01441649108716793
Zhou, G., Ji, Y., Chen, X., & Zhang, F. (2018). Artificial neural networks and the mass appraisal of real estate. International Journal of Online Engineering, 14(3), 180-187. https://doi.org/10.3991/ijoe.v14i03.8420
Zurada, J., Levitan, A., & Guan, J. (2011). A comparison of regression and artificial intelligence methods in a mass appraisal context. Journal of Real Estate Research, 33(3), 349-387.
Antipov, E. A., & Pokryshevskaya, E. B. (2012). Mass appraisal of residential apartments: an application of Random forest for valuation and a CART-based approach for model diagnostics. Expert Systems with Applications, 39(2), 1772-1778. https://doi.org/10.1016/j.eswa.2011.08.077
Benson, E. D., Hansen, J. L., Schwartz, A. L., & Smersh, G. T. (1998). Pricing residential amenities: the value of a view. Journal of Real Estate Finance and Economics, 16(1), 55-73. https://doi.org/10.1023/A:1007785315925
Cannon, S. E., & Cole, R. A. (2011). How accurate are commercial real estate appraisals? Evidence from 25 years of NCREIF sales data. Journal of Portfolio Management, 37(5), 68-88. https://doi.org/10.3905/jpm.2011.37.5.068
Case, K. E., Quigley, J. M., & Shiller, R. J. (2005). Comparing wealth effects: the stock market versus the housing market. Advances in Macroeconomics, 5(1). https://doi.org/10.2202/1534-6013.1235
Čeh, M., Kilibarda, M., Lisec, A., & Bajat, B. (2018). Estimating the performance of random forest versus multiple regression for predicting prices of the apartments. ISPRS International Journal of Geo-Information, 7(5), 168. https://doi.org/10.3390/ijgi7050168
Chau, K. W., & Chin, T. L. (2003). A critical review of literature on the hedonic price model. International Journal for Housing Science and its Applications, 27(2), 145-165.
Chen, J. H., Ong, C. F., Zheng, L., & Hsu, S. C. (2017). Forecasting spatial dynamics of the housing market using Support Vector Machine. International Journal of Strategic Property Management, 21(3), 273-283. https://doi.org/10.3846/1648715X.2016.1259190
Clauretie, T. M., & Neill, H. R. (2000). Year-round school schedules and residential property values. Journal of Real Estate Finance and Economics, 20(3), 311-322. https://doi.org/10.1023/A:1007841326833
Darling, A. H. (1973). Measuring benefits generated by urban water parks. Land Economics, 49(1), 22-34. https://doi.org/10.2307/3145326
Debrezion, G., Pels, E., & Rietveld, P. (2007). The impact of railway stations on residential and commercial property value: a meta-analysis. Journal of Real Estate Finance and Economics, 35(2), 161-180. https://doi.org/10.1007/s11146-007-9032-z
Downes, T. A., & Zabel, J. E. (2002). The impact of school characteristics on house prices: Chicago 1987–1991. Journal of Urban Economics, 52(1), 1-25. https://doi.org/10.1016/S0094-1190(02)00010-4
Dubin, R. A., & Sung, C. H. (1990). Specification of hedonic regressions: non-nested tests on measures of neighborhood quality. Journal of Urban Economics, 27(1), 97-110. https://doi.org/10.1016/0094-1190(90)90027-K
Espey, M., & Lopez, H. (2000). The impact of airport noise and proximity on residential property values. Growth and Change, 31(3), 408-419. https://doi.org/10.1111/0017-4815.00135
Fan, G. Z., Ong, S. E., & Koh, H. C. (2006). Determinants of house price: a decision tree approach. Urban Studies, 43(12), 2301-2315. https://doi.org/10.1080/00420980600990928
Fletcher, M., Gallimore, P., & Mangan, J. (2000). Heteroscedasticity in hedonic house price models. Journal of Property Research, 17(2), 93-108. https://doi.org/10.1080/095999100367930
Garrod, G. D., & Willis, K. G. (1992). Valuing goods’ characteristics: an application of the hedonic price method to environmental attributes. Journal of Environmental Management, 34(1), 59-76. https://doi.org/10.1016/S0301-4797(05)80110-0
Gillard, Q. (1981). The effect of environmental amenities on house values: the example of a view lot. The Professional Geographer, 33(2), 216-220. https://doi.org/10.1111/j.0033-0124.1981.00216.x
Goodman, A. C. (1989). Topics in empirical urban housing research. In R. Muth, & A. Goodman (Eds.), The economics of housing markets (pp. 49-146). Chur, Switzerland: Harwood Academic.
Gu, J., Zhu, M., & Jiang, L. (2011). Housing price forecasting based on genetic algorithm and support vector machine. Expert Systems with Applications, 38(4), 3383-3386. https://doi.org/10.1016/j.eswa.2010.08.123
Hanson, S. (2004). The context of urban travel: concepts and recent trends. In S. Hanson, & G. Giuliano (Eds.), The geography of urban transportation (pp. 3-29). New York: The Guilford Press.
Harrison Jr, D., & Rubinfeld, D. L. (1978). Hedonic housing prices and the demand for clean air. Journal of Environmental Economics and Management, 5(1), 81-102. https://doi.org/10.1016/0095-0696(78)90006-2
Hayes, K. J., & Taylor, L. L. (1996). Neighborhood school characteristics: what signals quality to homebuyers? Economic Review-Federal Reserve Bank of Dallas, 2-9.
Huh, S., & Kwak, S. J. (1997). The choice of functional form and variables in the hedonic price model in Seoul. Urban Studies, 34(7), 989-998. https://doi.org/10.1080/0042098975691
Jud, G. D., & Watts, J. M. (1981). Schools and housing values. Land Economics, 57(3), 459-470. https://doi.org/10.2307/3146025
Kain, J. F., & Quigley, J. M. (1970). Measuring the value of housing quality. Journal of the American Statistical Association, 65(330), 532-548. https://doi.org/10.1080/01621459.1970.10481102
Lancaster, K. J. (1966). A new approach to consumer theory. Journal of Political Economy, 74(2), 132-157. https://doi.org/10.1086/259131
Li, M. M., & Brown, H. J. (1980). Micro-neighborhood externalities and hedonic housing prices. Land Economics, 56(2), 125-141. https://doi.org/10.2307/3145857
Limsombunchai, V. (2004, June). House price prediction: hedonic price model vs. artificial neural network. In New Zealand Agricultural and Resource Economics Society Conference (pp. 25-26), New Zealand.
Malpezzi, S. (2002). Hedonic pricing models: a selective and applied review. Housing Economics and Public Policy, 67-89. https://doi.org/10.1002/9780470690680.ch5
McCluskey, W., & Anand, S. (1999). The application of intelligent hybrid techniques for the mass appraisal of residential properties. Journal of Property Investment & Finance, 17(3), 218-239. https://doi.org/10.1108/14635789910270495
McMillan, D., Jarmin, R., & Thorsnes, P. (1992). Selection bias and land development in the monocentric model. Journal of Urban Economics, 31, 273-284. https://doi.org/10.1016/0094-1190(92)90056-Q
McMillan, M. L., Reid, B. G., & Gillen, D. W. (1980). An extension of the hedonic approach for estimating the value of quiet. Land Economics, 56(3), 315-328. https://doi.org/10.2307/3146034
Miller, N., Peng, L., & Sklarz, M. (2011). House prices and economic growth. Journal of Real Estate Finance and Economics, 42(4), 522-541. https://doi.org/10.1007/s11146-009-9197-8
Mu, J., Wu, F., & Zhang, A. (2014). Housing value forecasting based on machine learning methods. Abstract and Applied Analysis, 2014, Article ID 648047. https://doi.org/10.1155/2014/648047
Palmquist, R. B. (1992). Valuing localized externalities. Journal of Urban Economics, 31, 59-68. https://doi.org/10.1016/0094-1190(92)90032-G
Park, B., & Bae, J. K. (2015). Using machine learning algorithms for housing price prediction: the case of Fairfax County, Virginia housing data. Expert Systems with Applications, 42(6), 2928-2934. https://doi.org/10.1016/j.eswa.2014.11.040
Richardson, H. W., Vipond, J., & Furbey, R. A. (1974). Determinants of urban house prices. Urban Studies, 11(2), 189-199. https://doi.org/10.1080/00420987420080341
Ridker, R. G., & Henning, J. A. (1967). The determinants of residential property values with special reference to air pollution. Review of Economics and Statistics, 49(2), 246-257. https://doi.org/10.2307/1928231
Rodriguez, M., & Sirmans, C. F. (1994). Quantifying the value of a view in single-family housing markets. Appraisal Journal, 62, 600-603.
Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. Journal of Political Economy, 82(1), 34-55. https://doi.org/10.1086/260169
Selim, H. (2009). Determinants of house prices in Turkey: hedonic regression versus artificial neural network. Expert Systems with Applications, 36(2), 2843-2852. https://doi.org/10.1016/j.eswa.2008.01.044
Sheppard, S. (1999). Hedonic analysis of housing markets. Handbook of Regional and Urban Economics, 3, 1595-1635. https://doi.org/10.1016/S1574-0080(99)80010-8
So, H. M., Tse, R. Y., & Ganesan, S. (1997). Estimating the influence of transport on house prices: evidence from Hong Kong. Journal of Property Valuation and Investment, 15(1), 40-47. https://doi.org/10.1108/14635789710163793
Song, Y., & Sohn, J. (2007). Valuing spatial accessibility to retailing: a case study of the single family housing market in Hillsboro, Oregon. Journal of Retailing and Consumer Services, 14(4), 279-288. https://doi.org/10.1016/j.jretconser.2006.07.002
Thaler, R. (1978). A note on the value of crime control: evidence from the property market. Journal of Urban Economics, 5(1), 137-145. https://doi.org/10.1016/0094-1190(78)90042-6
Verikas, A., Lipnickas, A., & Malmqvist, K. (2002). Selecting neural networks for a committee decision. International Journal of Neural Systems, 12(05), 351-361. https://doi.org/10.1142/S0129065702001229
Wilhelmsson, M. (2000). The impact of traffic noise on the values of single-family houses. Journal of Environmental Planning and Management, 43(6), 799-815. https://doi.org/10.1080/09640560020001692
Williams, A. W. (1991). A guide to valuing transport externalities by hedonic means. Transport Reviews, 11(4), 311-324. https://doi.org/10.1080/01441649108716793
Zhou, G., Ji, Y., Chen, X., & Zhang, F. (2018). Artificial neural networks and the mass appraisal of real estate. International Journal of Online Engineering, 14(3), 180-187. https://doi.org/10.3991/ijoe.v14i03.8420
Zurada, J., Levitan, A., & Guan, J. (2011). A comparison of regression and artificial intelligence methods in a mass appraisal context. Journal of Real Estate Research, 33(3), 349-387.