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

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.

• Mu-Yen Chen
• Min-Hsuan Fan
• Chia-Chen Chen
• Siang-Yu Jhong

• Artificial Neural Network
• Taguchi Method
• Real Estate Valuation Model.

[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.