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An Exploration of Neural Networks and Its Application to Real Estate Valuation

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Abstract

This research applies neural network (NN) technology to real estate appraisal and compares the performance of two NN models in estimating the sales price of residential properties with a traditional multiple regression model. The study is based on 288 sales of homes in Fort Collins, Colorado. Results do not support previous findings that NNs are a superior tool for appraisal analysis. Furthermore, significant problems were encountered with the NN models: inconsistent results between packages, inconsistent results between runs of the same package, and long run times. Any appraiser who plans on using this new technology would do so with caution.

Suggested Citation

  • Elaine M. Worzala & Margarita Lenk & Ana Silva, 1995. "An Exploration of Neural Networks and Its Application to Real Estate Valuation," Journal of Real Estate Research, American Real Estate Society, vol. 10(2), pages 185-202.
  • Handle: RePEc:jre:issued:v:10:n:2:1995:p:185-202
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    Cited by:

    1. Camilo Serrano & Martin Hoesli, 2010. "Are Securitized Real Estate Returns more Predictable than Stock Returns?," The Journal of Real Estate Finance and Economics, Springer, vol. 41(2), pages 170-192, August.
    2. José-María Montero-Lorenzo & Beatriz Larraz-Iribas, 2012. "Space-time approach to commercial property prices valuation," Applied Economics, Taylor & Francis Journals, vol. 44(28), pages 3705-3715, October.
    3. Beatriz Larraz, 2011. "An Expert System for Online Residential Properties Valuation," Review of Economics & Finance, Better Advances Press, Canada, vol. 1, pages 69-82, April.
    4. Maurizio d’Amato, 2007. "Comparing Rough Set Theory with Multiple Regression Analysis as Automated Valuation Methodologies," International Real Estate Review, Asian Real Estate Society, vol. 10(2), pages 42-65.
    5. Renigier-Biłozor Małgorzata & Wiśniewski Radosław, 2012. "The Impact of Macroeconomic Factors on Residential Property Price Indices in Europe," Folia Oeconomica Stetinensia, De Gruyter Open, vol. 12(2), pages 103-125, December.
    6. Ali Azadeh & Mohammad Sheikhalishahi & Ali Boostani, 2014. "A Flexible Neuro-Fuzzy Approach for Improvement of Seasonal Housing Price Estimation in Uncertain and Non-Linear Environments," South African Journal of Economics, Economic Society of South Africa, vol. 82(4), pages 567-582, December.
    7. Steven Peterson & Albert B. Flanagan, 2009. "Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal," Journal of Real Estate Research, American Real Estate Society, vol. 31(2), pages 147-164.
    8. Yang, Z. R. & Platt, Marjorie B. & Platt, Harlan D., 1999. "Probabilistic Neural Networks in Bankruptcy Prediction," Journal of Business Research, Elsevier, vol. 44(2), pages 67-74, February.
    9. 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.
    10. 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.
    11. George H. Lentz & Ko Wang, 1998. "Residential Appraisal and the Lending Process: A Survey of Issues," Journal of Real Estate Research, American Real Estate Society, vol. 15(1), pages 11-40.
    12. Baker, Bruce D. & Richards, Craig E., 1999. "A comparison of conventional linear regression methods and neural networks for forecasting educational spending," Economics of Education Review, Elsevier, vol. 18(4), pages 405-415, October.
    13. Elena B. Pokryshevskaya & Evgeny A. Antipov, 2011. "Applying a CART-based approach for the diagnostics of mass appraisal models," Economics Bulletin, AccessEcon, vol. 31(3), pages 2521-2528.
    14. Manuel Landajo & Celia Bilbao & Amelia Bilbao, 2012. "Nonparametric neural network modeling of hedonic prices in the housing market," Empirical Economics, Springer, vol. 42(3), pages 987-1009, June.
    15. Baker, Bruce D., 2001. "Can flexible non-linear modeling tell us anything new about educational productivity?," Economics of Education Review, Elsevier, vol. 20(1), pages 81-92, February.
    16. Shuofen Hsu & Chaohsin Lin & Yaling Yang, 2008. "Integrating Neural Networks for Risk-Adjustment Models," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(3), pages 617-642.
    17. Vinci Chow, 2017. "Predicting Auction Price of Vehicle License Plate with Deep Recurrent Neural Network," Papers 1701.08711, arXiv.org, revised Feb 2017.
    18. Núñez Tabales, Julia M. & Caridad y Ocerin, José María & Rey Carmona, Francisco J., 2013. "Artificial Neural Networks for Predicting Real Estate Prices || Redes neuronales artificiales para la predicción de precios inmobiliarios," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 15(1), pages 29-44, June.

    More about this item

    JEL classification:

    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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