Author(s) Itedal Sabri Hashim Bahia^*

Affiliation(s)
Ministry of Higher Education and Scientific Research, Baghdad, Iraq.

ABSTRACT

This paper aims to demonstrate the importance and possible value of housing predictive power which provides independent real estate market forecasts on home prices by using data mining tasks. A (FFBP) network model and (CFBP) network model are one of these tasks used in this research to compare results of them. We estimate the median value of owner occupied homes in Boston suburbs given 13 neighborhood attributes. An estimator can be found by fitting the inputs and targets. This data set has 506 samples. “ousing inputs” is a 13 × 506 matrix. The “housing targets” is a 1 × 506 matrix of median values of owner-occupied homes in $1000’s. The result in this paper concludes that which one of the two networks appears to be a better indicator of the output data to target data network structure than maximizing predict. The CFBP network which is the best result from the Output_network for all samples are found from the equation output = 0.95 * Target + 1.2. The regression value is approximately 1, (R = 0.964). That means the Output_network is matching to the target data set (Median value of owner-occupied homes in $1000’s), and the percent correctly predict in the simulation sample is 96%.

KEYWORDS
Cascade Forward Back Propagation (CFBP); Feed Forward Back Propagation (FFBP); Data Mining; House Price

Cite this paper
I. Bahia, "A Data Mining Model by Using ANN for Predicting Real Estate Market: Comparative Study," International Journal of Intelligence Science, Vol. 3 No. 4, 2013, pp. 162-169. doi: 10.4236/ijis.2013.34017.

References
[1]   “An Introduction to Real Estate Futures,” 2011. http://www.investopedia.com/articles/optioninvestor/11/an-introduction-to-real-estate-futures.html

[2]   C. A. Calhoun, “Property Valuation Models and House Price Indexes for The Provinces of Thailand: 2000,” Housing Finance International, Vol. 18, No. 3, 2003, pp. 31-41.

[3]   A. Araque, E. D. Martin, G. Perea, J. I. Arellano and W. Buno, “Synaptically Released Acetylcholine Evokes Ca2+ Elevations in Astrocytes in Hippocampal Slices,” Journal of Neuroscience, Vol. 22, No. 7, 2002, pp. 2443-2450.

[4]   P. D. Wasserman, “Advanced Methods in Neural Computting,” Van Nostrand Reinhold, New York, 1993, p. 255.

[5]   R. S. Parmer, R. W. McClendon, G. Hoogenboom, P. D. Blankenship, R. J. Cole and J. W. Dorner, “Estimation of Aflatoxin Contamination in Preharvest Peanuts Using Neural Networks,” Transaction ASAE, Vol. 40, No. 3, 1997, pp. 809-813.

[6]   H. Demuth and M. Beale, “Neural Network Toolbox for Matlab-Users Guide Version 4.1,” The Mathworks Inc., Natrick, 2003.

[7]   G. Grudnitski, A. Quang Do and J. D. Shilling, “A Neural Network Analysis of Mortgage Choice,” International Journal of Intelligent Systems in Accounting, Finance and Management, Vol. 4, No. 2, 1995, pp. 127-135.

[8]   R. M. Heristev, “The ANN Book, GNU Public License,” 1998. ftp://ftp.funet.fi/pub/sci/neural/books/

[9]   M. Stanley, A. Alastair, M. Dylan and D. Patterson, “Neural Networks: The Prediction of Residential Values,” Journal of Property Valuation & Investment, Vol. 16, No. 1, 1998, pp. 57-70. http://dx.doi.org/10.1108/14635789810205128

[10]   U. Fayyad, G. Piatetsky-Shapiro and P. Smyth, “From Data Mining to Knowledge Discovery in Databases,” American Association for Artificial Intelligence ( AA AI), AI Magazine, Vol. 17 No. 3, 1996, pp. 37-54.

[11]   D. S. Zhang and L. N. Zhou, “Discovering Golden Nuggets: Data Mining in Financial Application,” IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, Vol. 34, No. 4, 2004, pp. 513-522. http://dx.doi.org/10.1109/TSMCC.2004.829279

[12]   P. Kaihla, M. V. Copeland, C. Hawn, T. Lappin, M. LevRam and P. Sloan, “The New Rules of Real Estate,” Business 2.0 The New Rules of Real Estate Survival Tips for a Sluggish Market how to Buy how to Sell the 10 Best Places to Invest, Vol. 7, No. 10, 2006, p. 80.

[13]   V. Kontrimas and A. Verikas, “The Mass Appraisal of the Real Estate by Computational Intelligence,” Applied Soft Computing Journal, Vol. 11, No. 1, 2011, pp. 443-448. http://dx.doi.org/10.1016/j.asoc.2009.12.003

[14]   J. Landers, “Market Forecasts See Mixed Conditions for Nonresidential Construction,” Civil Engineering, Vol. 78 No. 4, 2008, pp. 14-16.

[15]   R. D. Jaen, “Data Mining: An Empirical Application in Real Estate Valuation, FLAIRS-02 Proceedings,” American Association for Artificial Intelligence, 2002.

[16]   S. Zdenek, “Artificial Intelligence as a Discursive Practice: The Case of Embodied Software Agent Systems,” Springer-Verlag London Limited, AI & Society, 2003, pp. 340-363.

[17]   L. Huan and M. Hiroshi, “Feature Selection for Knowledge Discovery and Data Mining,” The Springer International Series in Engineering and Computer Science, 1998.

[18]   J. Coakley and C. Brown, “Artificial Neural Networks in Accounting and Finance: Modeling Issues,” International Journal of Intelligent Systems in Accounting, Finance and Management, Vol. 9, No. 2, 2000, pp. 119-144.

[19]   G. Papadourakis, “Introduction to Neural Networks,” Technological Educational Institute of Crete, Department of Applied Informatics and Multimedia, 2004.

[20]   P. B. Joseph, “Data Mining with Neural Networks Solving Business Problems,” McGraw-Hill Companies, Inc., 1996.

[21]   A. Dave and M. George, “Artificial Neural Networks Technology,” Kaman Sciences Corporation, Utica, 1992

[22]   R. A. Chayjan and M. Esna-Ashari, “Comparison between Artificial Neural Networks and Mathematical Models for Equilibrium Moisture Characteristics Estimation in Raisin,” Agricultural Engineering International: The CIGR E-Journal, Vol. 12, 2010.

[23]   M. H. Beale, M. T. Hagan and H. B. Demuth, “Neural Network Toolbox? 7 User’s Guide,” Math Works, Inc., Natick, 2010.