International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020


IJSTR >> Volume 3- Issue 12, December 2014 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Comparative Study On Estimate House Price Using Statistical And Neural Network Model

[Full Text]



Azme Bin Khamis, Nur Khalidah Khalilah Binti Kamarudin



Index Terms: multiple linear regression, artificial neural networks, estimate, house price, mean square error, R2, model performance



Abstract: This study was conducted to compare the performance between Multiple Linear Regression (MLR) model and Neural Network model on estimate house prices in New York. A sample of 1047 houses is randomly selected and retrieved from the Math10 website. The factors in prediction house prices including living area, number of bedrooms, number of bathrooms, lot size and age of house. The methods used in this study are MLR and Artificial Neural Network. It was found that, the value of R2 in Neural Network model is higher than MLR model by 26.475%. The value of Mean Squared Error (MSE) in Neural Network model also lower compared to MLR model. Therefore, Neural Network model is prefered to be used as alternative model in estimating house price compared to MLR model.



[1] M. H. Beale, M. T. Hagan, & H. B. Demuth, “Neural Network Toolbox™ User’s Guide”. The MathWorks, Inc., 2013.

[2] C. A. Calhoun, “Property valuation models and house price indexes for The Provinces of Thailand: 1992–2000”. Housing Finance International, 17: 31 – 41, 2003.

[3] Creative Research Systems. (n.d.). Retrieved December 22, 2013, from Survey System: http://www.surveysystem.com/correlation.htm., 2013.

[4] S. Das, R. Gupta, & A. Kabundi, “Could we have predicted the recent downturn in the South African housing market?” Journal of Housing Economics 4:325-335, 2009.

[5] M. Forni, M. Hallin, M. Lippi, & L. Reichlin, “Do financial variables help forecasting inflation and real activity in the euro area?” Journal of Monetary Economics 6: 1243-1255, 2003.

[6] J. Frew, & G. D. Jud, “Estimating the value of apartment buildings”, The J. Real Estate Res., 25: 77 – 86, 2003).

[7] J. Gallego, & Mora-Esperanza (2004). “Artificial intelligence applied to real estate valuation: An example for the appraisal of Madrid”. Catastro: 255-265.

[8] L. Gattini, & P. Hiebert, “Forecasting and assessing euro area house prices through the lens of key fundamentals”, Working Paper Series, No.124/ October, 2010, 2010

[9] N. Girouard, , M. Kennedy, P. van den Noord, & C. Andr´e, “Recent house price developments: The role of fundamentals”, OECD Economics Department Working Paper No. 475, 2006

[10] R. Gupta, S.M. Miller, & D.V. Wyk, “Financial market liberalization, monetary policy, and housing price dynamics”. Working paper No. 201009, Dept. of Econ., University of Pretonia. 2010.

[11] H. Hossein, A.Khairil, , H. T. Huam, , K. Naser, , & R. Mohsen, “Artificial neural networks: Applications in management”. World Applied Sciences Journal , 14 (7), 1008-1019, 2011.

[12] A. Khalafallah, “Neural network based model for predicting housing market performance”. Tsinghua Science and Technology, 13(1): 325-328, 2008.

[13] M. Khashei, & M. Bijari, “An artificial neural network (p, d, q) model for time series forecasting”, Expert Systems with Applications, 37: 479-489, 2010.

[14] S. V. Kunwar, & K. B. Ashutosh, “An Analysis of the Performance of Artificial Neural Network Technique for Stock Market Forecasting”. International Journal On Computer Science and Engineering , 2 (6), 2104-2109, 2010.

[15] F. Laurene, “Fundamentals of Neural Networks : Architectures, Algorithm, and Applications”. United States: Florida Institute of Technology, 1994.

[16] Y. Li, & D. J. Leatham, “Forecasting housing prices: Dynamic factor model versus LBVAR model”. Paper for presentation at the Agricultural & Applied Economics Association’s 2011 AAEA & NAREA Joint Annual Meeting, Pittsburgh, Pennsylvania, July 24-26, 2011, 2011.

[17] V. Limsombunchai, C. Gan, & M. Lee, “House price prediction : Hedonic price model”. American Journal of Applied Sciences , 1 (3), 193-20, 2004.

[18] P. Linneman, “An empirical test of the efficiency of the housing market”. Journal of Urban Economics 20(1986): 140-154, 1986.

[19] K. McQuinn, & G. OReilly, “A model of cross-country house prices, Central Bank and Financial Services Authority of Ireland”, Research Technical Paper 5 (July), 2007.

[20] M. S. Mohd Radzi, , C. Muthuveerappan, N. Kamarudin, & I. S. Mohammad, “Forecasting house price index using artificial neural network”, International Journal of Real Estate Studies, Volume 7, Number 1, 2012

[21] Mymat. “Probabilities and Statistics : Guidance to solve House Prices Data Set Statistical Analysis”. Retrieved May 28, 2014, from Math10.com., 2013.

[22] S. T. Ng, , & M. Skitmoreb, “Using genetic algorithms and linear regression analysis for private housing demand forecast”. Building and Environment, 43: 1171-1184, 2008.

[23] N. Nguyen & Al Cripps, “Predicting housing value: a comparison of multiple regression analysis and artificial neural networks”, Journal of Real Estate Research, Vol . 22, No.3. 2001.

[24] J.M. Quigley, “Real estate prices and economic cycles”. International Real Estate Reviews 2: 1-20. 1999.

[25] H. Simon, “Neural Networks : A Comprehensive Foundation”, 2nd Edition. Hoboken, New Jersy, Prentice-Hall, 1999.

[26] S. N.Sivanandam, S. Sumathi, , & S. N. Deepa, “Introduction To Neural Networks using MATLAB 6.0”. New Delhi: The McGraw-Hill Companies, 2006.

[27] J.H. Stock, & M.W. Watson, “Macroeconomic forecasting using diffusion indexes”. Journal of Business and Economic Statistics 2: 147-162, 2002.

[28] K.Tsatasaronis, & H. Zhu, “What drives housing price dynamics: Cross-country evidence?” BIS Quarterly Review March,