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Improving property valuation accuracy: a comparison of hedonic pricing model and artificial neural network

Author/s: Rotimi Boluwatife Abidoye, Albert P. C. Chan

Date Published: 2/01/2018

Published in: Volume 24 - 2018 Issue 1 (pages 71 - 83)

Abstract

Inaccuracies in property valuation is a global problem. This could be attributed to the adoption of valuation approaches, with the hedonic pricing model (HPM) being an example, that are inaccurate and unreliable. As evidenced in the literature, the HPM approach has gained wide acceptance among real estate researchers, despite its shortcomings. Therefore, the present study set out to evaluate the predictive accuracy of HPM in comparison with the artificial neural network (ANN) technique in property valuation. Residential property transaction data were collected from registered real estate firms domiciled in the Lagos metropolis, Nigeria, and were fitted into the ANN model and HPM. The results showed that the ANN technique outperformed the HPM approach, in terms of accuracy in predicting property values with mean absolute percentage error (MAPE) values of 15.94 and 38.23%, respectively. The findings demonstrate the efficacy of the ANN technique in property valuation, and if all the preconditions of property value modeling are met, the ANN technique is a reliable valuation approach that could be used by both real estate researchers and professionals.

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Keywords

Artificial Neural Network - Hedonic Pricing Model - Predictive Accuracy - Property Valuation - Valuation Accuracy

References

  • Abidoye, R. B.Chan, A. P. C. (2016a) A review of the application of hedonic pricing model in the Nigerian real estate market. Paper presented at the CRIOCM 21st International Conference on Advancement of Construction Management and Real Estate. A review of the application of hedonic pricing model in the Nigerian real estate market.
  • Abidoye, R. B.Chan, A. P. C. (2016b) A survey of property valuation approaches in Nigeria, Property Management. 34(5), p:364-380. Property Management.
  • Abidoye, R. B.Chan, A. P. C. (2017) Artificial neural network in property valuation: Application framework and research trend, Property Management. 35(5), p:554-571. Property Management.
  • Adair, A. S.Berry, J. N.McGreal, W. S. (1996) Hedonic modelling, housing submarkets and residential valuation, Journal of Property Research. 13(1), p:67-83. Journal of Property Research.
  • Adegoke, O.Olaleye, A.Oloyede, S. (2013) A study of valuation clients perception on mortgage valuation reliability, African Journal of Environmental Science and Technology. 7(7), p:585-590. African Journal of Environmental Science and Technology.
  • Aluko, B. T. (2007) Examining valuer’s judgement in residential property valuations in metropolitan Lagos, Nigeria, Property Management. 25(1), –.p:98-107. Property Management.
  • Amri, S.Tularam, G. A. (2012) Performance of mulitple linear regression and nonlinear neural networks and fuzzy logic techniques in modelling house prices, Journal of Mathematics and Statistics. 8(4), p:419-434. Journal of Mathematics and Statistics.
  • Babawale, G. K.Ajayi, C. A. (2011) Variance in residential property valuation in Lagos, Nigeria, Property Management. 29(3), p:222-237. Property Management.
  • Borst, R. A. (1991) Artificial neural networks: The next modelling/calibration technology for the assessment community, Property Tax Journal. 10(1), –.p:69-94. Property Tax Journal.
  • Borst, R. A. (1995) Artificial neural networks in mass appraisal, Journal of Property Tax Assessment & Administration. 1(2), –.p:5-15. Journal of Property Tax Assessment & Administration.
  • Brown, G. R.Matysiak, G. A.Shepherd, M. (1998) Valuation uncertainty and the Mallinson Report, Journal of Property Research. 15(1), p:1-13. Journal of Property Research.
  • Canavarro, C.Caridad, J. M.Ceular, N. (2010) Hedonic methodologies in the real estate valuation. Paper presented at the Mathematical Methods in Engineering International Symposium. Hedonic methodologies in the real estate valuation.
  • Cebula, R. J. (2009) The hedonic pricing model applied to the housing market of the city of Savannah and its Savannah historic landmark district, The Review of Regional Studies. 39(1), –.p:9-22. The Review of Regional Studies.
  • Cechin, A.Souto, A.Gonzalez, A. M. (2000) Real estate value at Porto Alegre city using artificial neural networks. Paper presented at the 6th Brazilian Symposium on Neural Networks, Rio de Janeiro, Brazil. Real estate value at Porto Alegre city using artificial neural networks.
  • Chiang, Y.Tao, L.Wong, F. K. (2015) Causal relationship between construction activities, employment and GDP: The case of Hong Kong, Habitat International. 46(1), p:1-12. Habitat International.
  • Chin, T. L.Chau, K. W. (2002) A critical review of literature on the hedonic price model, International Journal for Housing Science and Its Applications. 27(2), p:145-165. International Journal for Housing Science and Its Applications.
  • Cortez, P.Cerdeira, A.Almeida, F.Matos, T.Reis, J. (2009) Modeling wine preferences by data mining from physicochemical properties, Decision Support Systems. 47(4), p:547-553. Decision Support Systems.
  • Do, A. Q.Grudnitski, G. (1992) A neural network approach to residential property appraisal, The Real Estate Appraiser. 58(3), p:38-45. The Real Estate Appraiser.
  • Dugeri, T. T. (2011) An evaluation of the maturity of the Nigerian property market. (Doctoral dissertation). Retrieved fromUniversity of Lagos, Lagos, Nigeria. An evaluation of the maturity of the Nigerian property market. Retrieved from http://www.afrer.org/docs/pdf/dugeri.pdf
  • Famuyiwa, F.Babawale, G. K. (2014) Hedonic values of physical infrastructure in house rentals, Journal of Facilities Management. 12(3), p:211-230. Journal of Facilities Management.
  • Gilbertson, B.Preston, D. (2005) A vision for valuation, Journal of Property Investment & Finance. 23(2), p:123-140. Journal of Property Investment & Finance.
  • Grover, R. (2016) Mass valuations, Journal of Property Investment & Finance. 34(2), –.p:191-204. Journal of Property Investment & Finance.
  • Hofmann, B. (2003) Bank lending and property prices: Some international evidence, Working Paper No. 22. Hong Kong: (pp. 1–20)Hong Kong Institute for Monetary Research. Working Paper No. 22.
  • Hu, T.Lam, K.Ng, S. T. (2005) A modified neural network for improving river flow prediction, Hydrological Sciences Journal. 50(2), p:299-318. Hydrological Sciences Journal.
  • Hui, E. C.Chau, C.Pun, L.Law, M. (2007) Measuring the neighboring and environmental effects on residential property value: Using spatial weighting matrix, Building and Environment. 42(6), p:2333-2343. Building and Environment.
  • Janssen, C.Söderberg, B.Zhou, J. (2001) Robust estimation of hedonic models of price and income for investment property, Journal of Property Investment & Finance. 19(4), p:342-360. Journal of Property Investment & Finance.
  • Jiang, H.Jin, X.-H.Liu, C. (2013) The effects of the late 2000s global financial crisis on Australia’s construction demand, Australasian Journal of Construction Economics and Building. 13(3), p:65-79. Australasian Journal of Construction Economics and Building.
  • Jim, C.Chen, W. Y. (2006) Impacts of urban environmental elements on residential housing prices in Guangzhou (China), Landscape and Urban Planning. 78(4), p:422-434. Landscape and Urban Planning.
  • Kaastra, I.Boyd, M. (1996) Designing a neural network for forecasting financial and economic time series, Neurocomputing. 10(3), p:215-236. Neurocomputing.
  • Kutasi, D.Badics, M. C. (2016) Valuation methods for the housing market: Evidence from Budapest, Acta Oeconomica. 66(3), p:527-546. Acta Oeconomica.
  • Lam, K. C.Yu, C. Y.Lam, K. Y. (2008) An artificial neural network and entropy model for residential property price forecasting in Hong Kong, Journal of Property Research. 25(4), p:321-342. Journal of Property Research.
  • Lenk, M. M.Worzala, E. M.Silva, A. (1997) High?tech valuation: Should artificial neural networks bypass the human valuer?, Journal of Property Valuation and Investment. 15(1), p:8-26. Journal of Property Valuation and Investment.
  • Limsombunchai, V.Gan, C.Lee, M. (2004) House price prediction: Hedonic price model vs. artificial neural network, American Journal of Applied Sciences. 1(3), p:193-201. American Journal of Applied Sciences.
  • Lin, C. C.Mohan, S. B. (2011) Effectiveness comparison of the residential property mass appraisal methodologies in the USA, International Journal of Housing Markets and Analysis. 4(3), p:224-243. International Journal of Housing Markets and Analysis.
  • Lisboa, P. J.Taktak, A. F. (2006) The use of artificial neural networks in decision support in cancer: A systematic review, Neural Networks. 19(4), p:408-415. Neural Networks.
  • Maurer, R.Pitzer, M.Sebastian, S. (2004) Hedonic price indices for the Paris housing market, Allgemeines Statistisches Archiv. 88(3), p:303-326. Allgemeines Statistisches Archiv.
  • McCluskey, W. (1996) Predictive accuracy of machine learning models for the mass appraisal of residential property, New Zealand Valuers Journal. 16(1), –.p:41-47. New Zealand Valuers Journal.
  • McCluskey, W.Davis, P.Haran, M.McCord, M.McIlhatton, D. (2012) The potential of artificial neural networks in mass appraisal: The case revisited, Journal of Financial Management of Property and Construction. 17(3), p:274-292. Journal of Financial Management of Property and Construction.
  • McCluskey, W. J.McCord, M.Davis, P.Haran, M.McIlhatton, D. (2013) Prediction accuracy in mass appraisal: A comparison of modern approaches, Journal of Property Research. 30(4), p:239-265. Journal of Property Research.
  • McGreal, S.Adair, A.McBurney, D.Patterson, D. (1998) Neural networks: The prediction of residential values, Journal of Property Valuation and Investment. 16(1), p:57-70. Journal of Property Valuation and Investment.
  • Montgomery, D. C.Peck, E. A.Vining, G. G. (2015) Introduction to linear regression analysis. (5th ed. ed.). Hoboken, NJ: Wiley. Introduction to linear regression analysis.
  • Mora-Esperanza, J. G. (2004) Artificial intelligence applied to real estate valuation: An example for the appraisal of Madrid, CATASTRO, April. (1), –.p:255-265. CATASTRO, April.
  • Morano, P.Tajani, F.Torre, C. M. (2015) Artificial intelligence in property valuations an application of artificial neural networks to housing appraisal. Retrieved from http://www.wseas.us/e-library/conferences/2015/Tenerife/ENVIR/ENVIR-02.pdf
  • Ogunba, O. (2004) The demand for accuracy in valuations: The case of Nigeria. Paper presented at the CIB International Symposium on Globalization and Construction, Bangkok, Thailand. (.). ,.The demand for accuracy in valuations: The case of Nigeria.
  • Ogunba, O.Ajayi, C. (1998) An assessment of the accuracy of valuations in the residential property market of Lagos, The Estate Surveyor and Valuer. 21(2), p:19-23. The Estate Surveyor and Valuer.
  • Olden, J. D.Jackson, D. A. (2002) Illuminating the “black box”: A randomization approach for understanding variable contributions in artificial neural networks, Ecological Modelling. 154(1), p:135-150. Ecological Modelling.
  • Owusu-Ansah, A. (2012) Examination of the determinants of housing values in urban Ghana and implications for policy makers, Journal of African Real Estate Research. 2(1), –.p:58-85. Journal of African Real Estate Research.
  • Özkan, G.Yalp?r, ?.Uygunol, O. (2007) An investigation on the price estimation of residable real estates by using artificial neural network and regression methods. - Paper presented at the 12th Applied Stochastic Models and Data Analysis International conference (ASMDA), Crete, Greece. An investigation on the price estimation of residable real estates by using artificial neural network and regression methods.
  • Pagourtzi, E.Assimakopoulos, V.Hatzichristos, T.French, N. (2003) Real estate appraisal: A review of valuation methods, Journal of Property Investment & Finance. 21(4), p:383-401. Journal of Property Investment & Finance.
  • Pagourtzi, E.Metaxiotis, K.Nikolopoulos, K.Giannelos, K.Assimakopoulos, V. (2007) Real estate valuation with artificial intelligence approaches, International Journal of Intelligent Systems Technologies and Applications. 2(1), p:50-57. International Journal of Intelligent Systems Technologies and Applications.
  • R CoreTeam. (2016) R: A language and environment for statistical computing. Retrieved July 19, 2016, from R Foundation for Statistical Computing,Retrieved from https://www.R-project.org/
  • Rosen, S. (1974) Hedonic prices and implicit markets: Product differentiation in pure competition, Journal of Political Economy. 82(1), –.p:34-55. Journal of Political Economy.
  • Sampathkumar, V.Santhi, M. H.Vanjinathan, J. (2015) Evaluation of the trend of land price using regression and neural network models, Asian Journal of Scientific Research. 8(2), p:182-194. Asian Journal of Scientific Research.
  • Selim, H. (2009) Determinants of house prices in Turkey: Hedonic regression versus artificial neural network, Expert Systems with Applications. 36(2), –.p:2843-2852. Expert Systems with Applications.
  • Selim, S. (2008) Determinants of house prices in Turkey: A hedonic regression model, Do?u? Üniversitesi Dergisi. 9(1), –.p:65-76. Do?u? Üniversitesi Dergisi.
  • Sincich, T. (1996) Business statistics by example. (5th ed. ed.). Upper Saddle River, NJ.Prentice Hall Englewood. Business statistics by example.
  • Tabales, J. N. M.Ocerin, C. J. M.Carmona, F. J. R. (2013) Artificial neural networks for predicting real estate prices, Revista de Metodos Cuantitativos para la Economia y la Empresa. 15, p:29-44. Revista de Metodos Cuantitativos para la Economia y la Empresa.
  • Taffese, W. Z. (2006) A survey on application of artificial intelligence in real estate industry. 3rd International Conference on Artificial Intelligence in Engineering and Technology. Paper presented at the ,.A survey on application of artificial intelligence in real estate industry.
  • Tay, D. P.Ho, D. K. (1992) Artificial intelligence and the mass appraisal of residential apartments, Journal of Property Valuation and Investment. 10(2), p:525-540. Journal of Property Valuation and Investment.
  • Thanasi, M. (2016) Hedonic appraisal of apartments in Tirana, International Journal of Housing Markets and Analysis. 9(2), –.p:239-255. International Journal of Housing Markets and Analysis.
  • White, H. (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity, Econometrica. 48(4), –.p:817-838. Econometrica.
  • Willmott, C. J. (1981) On the validation of models, Physical geography. 2(2), –.p:184-194. Physical geography.
  • Wilson, I. D.Paris, S. D.Ware, J. A.Jenkins, D. H. (2002) Residential property price time series forecasting with neural networks, Knowledge-Based Systems. 15(5), p:335-341. Knowledge-Based Systems.
  • Wong, K.So, A. T.Hung, Y. (2002) Neural network vs. hedonic price model: Appraisal of high-density condominiums, Real estate valuation theory. New York, NY: p:181-198. Springer. Real estate valuation theory.
  • Worzala, E.Lenk, M.Silva, A. (1995) An exploration of neural networks and its application to real estate valuation, Journal of Real Estate Research. 10(2), p:185-201. Journal of Real Estate Research.
  • Yalpir, S. (2014) Paper presented at the People, Buildings and Environment Conference, an International Scientific Conference, Forecasting residential real estate values with AHP method and integrated GIS. (.). , Krom??íž, Czech Republic.Forecasting residential real estate values with AHP method and integrated GIS.
  • 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), p:349-387. Journal of Real Estate Research.
  • Zurada, J. M.Levitan, A. S.Guan, J. (2006) Non-conventional approaches to property value assessment, Journal of Applied Business Research. 22(3), p:1-14. Journal of Applied Business Research.