Integrating Artificial Neural Network and Taguchi Method on Constructing the Real Estate Appraisal Model

Authors: Mu-Yen Chen, Min-Hsuan Fan, Chia-Chen Chen, Siang-Yu Jhong

Abstract:

In recent years, real estate prediction or valuation has been a topic of discussion in many developed countries. Improper hype created by investors leads to fluctuating prices of real estate, affecting many consumers to purchase their own homes. Therefore, scholars from various countries have conducted research in real estate valuation and prediction. With the back-propagation neural network that has been popular in recent years and the orthogonal array in the Taguchi method, this study aimed to find the optimal parameter combination at different levels of orthogonal array after the system presented different parameter combinations, so that the artificial neural network obtained the most accurate results. The experimental results also demonstrated that the method presented in the study had a better result than traditional machine learning. Finally, it also showed that the model proposed in this study had the optimal predictive effect, and could significantly reduce the cost of time in simulation operation. The best predictive results could be found with a fewer number of experiments more efficiently. Thus users could predict a real estate transaction price that is not far from the current actual prices.

Keywords: Artificial Neural Network, Taguchi Method, Real Estate Valuation Model.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1096141

References:

[1] J.F.C. Khaw, B.S. Lim, and L.E.N. Lim, "Optimal design of neural networks using the Taguchi method,” Neurocomputing, vol.7, no.3, pp. 225-245, 1995.
[2] A. Tortum, N. Yayla, C. Çelik, and M. Gökdağ, "The investigation of model selection criteria in artificial neural networks by the Taguchi method,”Physica A: Statistical Mechanics and its Applications, vol.386, no.1,pp. 446-468, 2007.
[3] W.C. Chen, Y.Y. Hsu, L.F. Hsieh, and P.H. Tai, "A systematic optimization approach for assembly sequence planning using Taguchi method, DOE, and BPNN,” Expert Systems with Applications, vol.37, no.1, pp. 716-726, 2010.
[4] K.Y. Chang, "The optimal design for PEMFC modeling based on Taguchi method and genetic algorithm neural networks,” International Journal of Hydrogen Energy, vol.36, no.21, pp. 13683-13694, 2011.
[5] J.R. Jung, and B.J. Yum, "Artificial neural network based approach for dynamic parameter design,” Expert Systems with Applications, vol.38, no.1,pp. 504-510, 2011.
[6] T.B. Asafa, N. Tabet, and S.A.M. Said, "Taguchi method–ANN integration for predictive model of intrinsic stress in hydrogenated amorphous silicon film deposited by plasma enhanced chemical vapour deposition,” Neurocomputing, vol. 106, pp. 86-94, 2013.
[7] C.O. Chang. Real estate in the world: trade, investment, agency, policy, Yuan-Liou Publishing, 1990.
[8] W.J. McCluskey, and A.S. Adair, Computer Assisted Mass Appraisal. 1997.
[9] F. Rosenblatt, "The perceptron - a perceiving and recognizing automaton,” Cornell Aeronautical Laboratory report, pp.85-460-1, 1957.
[10] W.S. McCulloch, and W. Pitts, "A logical calculus of the ideas immanent in nervous activity,” The bulletin of mathematical biophysics, vol.5, no.4, pp. 115-133, 1943.
[11] D.O. Hebb, The organization of behavior: a neuropsychological theory. John Wiley and Sons, 1949.
[12] M. Minsky, and S.Papert, Perceptrons. MIT Press. Cambridge, Ma, 1969.
[13] S. Grossberg, "Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors,” Biological Cybernetics, vol.23, no.3,pp. 121-134, 1976.
[14] T. Kohonen, "Self-organized formation of topologically correct feature maps,” Biological Cybernetics, vol.43, no.1,pp. 59-69, 1982.
[15] J.J. Hopfield, "Neurons with graded response have collective computational properties like those of two-state neurons,” Proceedings of the National Academy of Sciences, vol.81, no.10, pp. 3088-3092, 1984.
[16] D.E. Rumelhart, G.E. Hinton, and R.J. Williams, "Learning representations by back-propagating errors,” in Neurocomputing: foundations of research, A.A. James and R. Edward, Editors. MIT Press. pp. 696-699, 1986.
[17] H.H. Lee, Taguchi Methods: Principles and Practices of Quality Design. GauLih Book Publishing (4nd), 2013.
[18] Ministry of the Interior of R.O.C., Monthly Bulletin of Interior Statistics, 2014. Available at: http://sowf.moi.gov.tw/stat/month/list.htm.
[19] J.R. Quinlan, "Discovering rules by induction from large collections of examples,” Expert Systems in the Microelectronic Age, pp. 168-201, 1979.
[20] J.R. Quinlan, "Induction of decision trees,” Machine Learning, vol. 1, no.1, pp. 81-106, 1986.
[21] I.C. Yeh, Neural Network Model: Application and Implementation (9rd), Scholars Book Publishing, 2009.
[22] C. Cortes, and V. Vapnik, "Support-Vector Networks,” Machine Learning, vol.20, no.3, pp. 273-297, 1995.
[23] S.S. Keerthi, and C.-J. Lin, "Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel,” Neural Computation, vol.15, no.7, pp. 1667-1689, 2003.
[24] C.W. Hsu, C.C. Chang, and C.J. Lin, A practical guide to support vector classification. 2003.
[25] R.S. Pindyck, and D.L. Rubinfeld, Econometric models and economic forecasts. Boston, Mass.: Irwin/McGraw-Hill, 1998.
[26] J. Mingers, "An empirical comparison of selection measures for decision-tree induction,” Machine Learning, 1vol.3, no.4, pp. 319-342, 1989.
[27] U.M. Fayyad, and K.B. Irani, "On the handling of continuous-valued attributes in decision tree generation,” Machine Learning, vol.8, no.1, pp. 87-102, 1992.
[28] T.G. Dietterich, "Ensemble Methods in Machine Learning, in Multiple Classifier Systems,” Lecture Notes in Computer Science, vol. 1857, pp. 1-15, 2000.