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Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks

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  • Nghiep Nguyen

    ()
    (Middle Tennessee State University, Murfreesboro, TN)

  • Al Cripps

    ()
    (Middle Tennessee State University, Murfreesboro, TN)

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    Abstract

    This paper compares the predictive performance of artificial neural networks (ANN) and multiple regression analysis (MRA) for single family housing sales. Multiple comparisons are made between the two data models in which the data sample size is varied, the funcional specifications is varied, and the temporal prediction is varied. We conclude that ANN performs better than MRA when a moderate to large data sample size is used. For our application, this "moderate to large data sample size" varied from 13% to 39% of the total data sample (506 to 1506 observations out of 3906 total observations). Our results give a plausible explanation why previous papers have obtained varied results when comparing MRA and ANN predictive performance for housing values.

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    File URL: http://aux.zicklin.baruch.cuny.edu/jrer/papers/pdf/past/vol22n03/04.313_336.pdf
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    Bibliographic Info

    Article provided by American Real Estate Society in its journal Journal of Real Estate Research.

    Volume (Year): 22 (2001)
    Issue (Month): 3 ()
    Pages: 313-336

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    Handle: RePEc:jre:issued:v:22:n:3:2001:p:313-336

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    Postal: American Real Estate Society Clemson University School of Business & Behavioral Science Department of Finance 401 Sirrine Hall Clemson, SC 29634-1323
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    Postal: Diane Quarles American Real Estate Society Manager of Member Services Clemson University Box 341323 Clemson, SC 29634-1323
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    Web: http://aux.zicklin.baruch.cuny.edu/jrer/about/get.htm

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    1. Goodman, Allen C., 1978. "Hedonic prices, price indices and housing markets," Journal of Urban Economics, Elsevier, vol. 5(4), pages 471-484, October.
    2. Linneman, Peter, 1980. "Some empirical results on the nature of the hedonic price function for the urban housing market," Journal of Urban Economics, Elsevier, vol. 8(1), pages 47-68, July.
    3. Grether, David M. & Mieszkowski, P., . "Determinants of Real Estate Values," Working Papers 28, California Institute of Technology, Division of the Humanities and Social Sciences.
    4. A. Quang Do & G. Grudnitski, 1993. "A Neural Network Analysis of the Effect of Age on Housing Values," Journal of Real Estate Research, American Real Estate Society, vol. 8(2), pages 253-264.
    5. Halvorsen, Robert & Pollakowski, Henry O., 1981. "Choice of functional form for hedonic price equations," Journal of Urban Economics, Elsevier, vol. 10(1), pages 37-49, July.
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    Cited by:
    1. Craig Ellis & Patrick J. Wilson & Ralf Zurbruegg, 2007. "Real Estate ‘Value’ Stocks and International Diversification," Journal of Property Research, Taylor & Francis Journals, vol. 24(3), pages 265-287, September.
    2. Plakandaras, Vasilios & Gupta, Rangan & Papadimitriou, Theophilos & Gogas, Periklis, 2014. "Forecasting the U.S. Real House Price Index," DUTH Research Papers in Economics 10-2014, Democritus University of Thrace, Department of Economics.
    3. Antipov, Evgeny & Pokryshevskaya, Elena, 2010. "Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics," MPRA Paper 27645, University Library of Munich, Germany.
    4. Antipov, Evgeny & Pokryshevskaya, Elena, 2010. "Applying a CART-based approach for the diagnostics of mass appraisal models," MPRA Paper 27646, University Library of Munich, Germany.
    5. Camilo Serrano & Martin Hoesli, . "Are Securitized Real Estate Returns more Predictable than Stock Returns?," Swiss Finance Institute Research Paper Series 08-27, Swiss Finance Institute.
    6. Christian Pierdzioch, 2012. "Macroeconomic Factors and the German Real Estate Market: A Stock-Market-Based Forecasting Experiment," Review of Economics & Finance, Better Advances Press, Canada, vol. 2, pages 87-96, May.

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