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Valuating residential real estate using parametric programming

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  • Narula, Subhash C.
  • Wellington, John F.
  • Lewis, Stephen A.
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    Abstract

    When the estimation of the single equation multiple linear regression model is looked upon as an optimization problem, we show how the principles and methods of optimization can assist the analyst in finding an attractive prediction model. We illustrate this with the estimation of a linear prediction model for valuating residential property using regression quantiles. We make use of the linear parametric programming formulation to obtain the family of regression quantile models associated with a data set. We use the principle of dominance to reduce the number of models for consideration in the search for the most preferred property valuation model (s). We also provide useful displays that assist the analyst and the decision maker in selecting the final model (s). The approach is an interface between data analysis and operations research.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0377221711007557
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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 217 (2012)
    Issue (Month): 1 ()
    Pages: 120-128

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    Handle: RePEc:eee:ejores:v:217:y:2012:i:1:p:120-128

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    Web page: http://www.elsevier.com/locate/eor

    Related research

    Keywords: Linear programming; Parametric programming; Real estate valuation; Regression; Regression quantiles;

    References

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    1. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521608275, October.
    2. Ball, J'Noel & Srinivasan, Venkat C, 1994. "Using the Analytic Hierarchy Process in House Selection," The Journal of Real Estate Finance and Economics, Springer, vol. 9(1), pages 69-85, July.
    3. Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
    4. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    5. Narula, Subhash C. & Wellington, John F., 2007. "Multiple criteria linear regression," European Journal of Operational Research, Elsevier, vol. 181(2), pages 767-772, September.
    6. Aznar, Jeronimo & Guijarro, Francisco, 2007. "Estimating regression parameters with imprecise input data in an appraisal context," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1896-1907, February.
    7. Ihlanfeldt, Keith R. & Martinez-Vazquez, Jorge, 1986. "Alternative value estimates of owner-occupied housing: Evidence on sample selection bias and systematic errors," Journal of Urban Economics, Elsevier, vol. 20(3), pages 356-369, November.
    8. Jorge Chica-Olmo, 2007. "Prediction of Housing Location Price by a Multivariate Spatial Method: Cokriging," Journal of Real Estate Research, American Real Estate Society, vol. 29(1), pages 95-114.
    9. 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.
    10. Timothy P. Cronan & Donald R. Epley & Larry G. Perry, 1986. "The Use of Rank Transformation and Multiple Regression Analysis in Estimating Residential Property Values With A Small Sample," Journal of Real Estate Research, American Real Estate Society, vol. 1(1), pages 19-31.
    11. Aragones-Beltran, P. & Aznar, J. & Ferris-Onate, J. & Garcia-Melon, M., 2008. "Valuation of urban industrial land: An analytic network process approach," European Journal of Operational Research, Elsevier, vol. 185(1), pages 322-339, February.
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