PROPERTY VALUE ASSESSMENT USING ARTIFICIAL NEURAL NETWORKS, HEDONIC REGRESSION AND NEAREST NEIGHBORS REGRESSION METHODS

In this paper, hedonic regression, nearest neighbors regression and artificial neural networks methods are applied to the real and up to date estate data set belongs to Adana province of Turkey. Traditionally, hedonic regression methods have been used to predict house prices. Because of the nature of the relationships between the factors affecting house prices are generally being nonlinear; some alternative methods have been needed. Nearest neighbors regression (k-nn) and artificial neural networks (ANN) present both flexible and nonlinear fittings. Classical hedonic approach and its nonlinear alternatives have been employed on a mixed types data set and compared based on some performance measures including root mean squared error, the coefficient of determination (R squared), the coefficient of determination, and mean absolute error. Cross validation method has been used to determine the appropriate model parameters for nearest neighbors and ANN. According to the results, ANN is found better when compared to other methods in terms of all measures. Besides, k-nn regression method provides reasonable results despite of lower performance than hedonic regression method. It has been seen that ANN is a powerful tool for predicting house prices.

Yapay Sinir Ağları, Hedonik Regresyon Ve En Yakın Komşuluk Regresyon Metotlarını Kullanarak Emlak Fiyatlarının Belirlenmesi

Bu çalışmada hedonik regresyon, en yakın komşu regresyon ve yapay sinir ağları metotları Türkiye’nin Adana iline ait gerçek ve güncel bir veri seti üzerinde uygulanmıştır. Geleneksel olarak, ev fiyatlarının tahmininde hedonic regresyon metotları kullanılmaktadır. Ev fiyatlarını etkileyen faktörler arasındaki ilişkilerin yapısının genel olarak doğrusal olmaması nedeniyle bazı alternatif metotlara ihtiyaç duyulmaktadır. En yakın komşuluk regresyon ve yapay sinir ağları hem esnek hem de doğrusal olmayan uyumlar sunmaktadır. Klasik hedonic regresyon yaklaşımı ve doğrusal olmayan alternatifleri karma yapıda ki bir veri kümesine uygulanmış ve hata kareler ortalaması, belirleyicilik katsayısı (R kare) ve ortalama mutlak hatayı içeren bazı performans ölçütlerine dayanarak karşılaştırılmıştır.En yakın komşu ve yapay sinir ağları için uygun model parametrelerini belirlemek için çapraz geçerlilik metodu kullanılmıştır.Sonuçlara göre, yapay sinir ağları diğer metotlarla karşılaştırıldığında tüm ölçülere göre daha iyi bulunmuştur.Ayrıca en yakın komşu metodu hedonik regresyon metodundan daha düşük performanslı olmasına rağmen makul sonuçlar sağlamaktadır. Yapay sinir ağlarının ev fiyatlarının tahmininde güçlü bir araç olduğu görülmüştür.

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Abidoye, R. B.,& Chan, A. P., 2017, “Modelling Property Values in Nigeria Using Artificial Neural Network”, Journal of Property Research, 34(1), 36-53.

Bin, O., 2004, “A Prediction Comparison of Housing Sales Prices by Parametric versus Semi-Parametric Regressions”, Journal of Housing Economics, 13(1), 68-84.

Bishop C.,Neural Networks for Pattern Recognition, Oxford University Press, Oxford, 1995.

Borst, R. A., 1991, “Artificial Neural Networks: The Next Modelling/Calibration Technology for the Assessment Community”, Property Tax Journal, 10(1), 69-94.

Box, G.,& Cox, D., 1964, “An Analysis of Transformations”, Journal of the Royal Statistical Society B, 26, 211–252.

Cechin, A., Souto, A. & Gonzalez, M.A., “Real Estate Value at Porto Alegre City Using Ann”, Proceedings 6th Brazilian Symposium On Neural Networks, November, 2000.

Demuth, H. B., Beale, M. H., De Jess, O., & Hagan, M. T., Neural Network Design, Martin Hagan, 2014.

Frew, J.,& G. D. Jud., 2003, “Estimating The Value of Apartment Buildings”, The J. Real Estate Res., 25: 77 - 86.

Goodman, A. C., 1998, “Andrew Court and the Invention of Hedonic Price Analysis”, Journal of Urban Economics, 44, 291–298.

Halvorsen, R.,& Palmquist, R., 1980, “The Interpretation of Dummy Variables in Semilogrithmic Regressions”, American Economic Review, 70(June), 474–475.

Iacoviello, M., 2000, “House Prices and the Macroeconomy in Europe: Results from a Structural Var Analysis”, Working Paper Series 0018, European Central Bank.

IBM Corp. Released, 2017, IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp.

James, G., Witten, D., Hastie, T., & Tibshirani, R., 2013, An Introduction to Statistical Learning (Vol. 112). New York: Springer.

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), 342-360.

Khalafallah, A., 2008, “Neural Network Based Model for Predicting Housing Market Performance”, Tsinghua Science & Technology, Vol. 13, Pp. 325-328.

Kontrimas, V.,& Verikas, A., 2011, “The Mass Appraisal of the Real Estate by Computational Intelligence”, Applied Soft Computing, 11(1), 443-448.

Kuhn, M.,& Johnson, K., Applied Predictive Modeling (Vol. 26), New York: Springer, 2013.

Lancaster, K. J., 1966, “A new approach to consumer theory”, J .Political Economy, 74:132- 157.

Limsombunchai, V. & Samarasinghe, S., 2004, “House Price Prediction Using Artificial Neural Network: A Comparative Study with Hedonic Price Model”, Applied Economics Journal, Vol. 9-2, Pp. 65- 74.

Liu B.,Web Data Mining, Springer, Berlin, Heidelberg, 2017.

Miles, D., 1992, “Housing Markets, Consumption and Financial Liberalisation in the Major Economies”, European Economic Review, 36, 5, 1093- 1127.

Montgomery, D. C., Peck, E. A., & Vining, G. G., Introduction to Linear Regression Analysis (Vol. 821), John Wiley & Sons, 2012.

Mousa, A. A.,& Saadeh, M., 2010, “Automatic Valuation of Jordanian Estates Using A GeneticallyOptimised Artificial Neural Network Approach”, WSEAS Transactions on Systems, 9, 905-916.

Nguyen, N. & Cripps, A., 2001, “Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks”, The Journal of Real Estate Research, Vol 22 (3): 313-336.

Pagourtzi, E., Assimakopoulos, V., Hatzichristos, T., & French, N., 2003, “Real Estate Appraisal: A Review of Valuation Methods”, Journal of Property Investment & Finance, 21(4), 383-401.

Quigley, J. M., 1992, Housing Markets in J. Eatwell, M. Milgate and P. Newman (eds.), The New Palgrave: A Dictionary of Economics, 3-20, London, Macmillan Press.

R Core Team, 2018, R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Rosen, S., 1974, “Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition”, Journal of Political Economy, 82, 34–55.

Rossini, P.A., 1997, “Artificial Neural Networks versus Multiple Regression in the Valuation of Residential Property”, Australian Land Economics Review, November Vol 3(1).

Rumelhart D., Hinton G., & Williams R., Learning Internal Representations by Error Propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition, The MIT Press, 1986.

Sampathkumar, V., Santhi, M. H., & Vanjinathan, J., 2015, “Forecasting the Land Price Using Statistical and Neural Network Software”, Procedia Computer Science, 57, 112-121.

Selim, H., 2009, “Determinants of House Prices in Turkey: Hedonic Regression versus Artificial Neural Network”. Expert Systems with Applications, 36(2), 2843-2852. StataCorp., 2015, Stata Statistical Software: Release 14, College Station, TX: StataCorp LP.

Tay, D. P.,& Ho, D. K., 1992, “Artificial Intelligence and the Mass Appraisal of Residential Apartments”, Journal of Property Valuation and Investment, 10(2), 525-540.

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), 185-201.

Zurada, J. M., Levitan, A. S. & Guan, J., 2006, “Non-Conventional Approaches to Property Value Assessment”, Journal of Applied Business Research, Vol. 22(3).