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Valuating residential real estate using parametric programming

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

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.

Suggested Citation

  • Narula, Subhash C. & Wellington, John F. & Lewis, Stephen A., 2012. "Valuating residential real estate using parametric programming," European Journal of Operational Research, Elsevier, vol. 217(1), pages 120-128.
    • Handle: RePEc:eee:ejores:v:217:y:2012:i:1:p:120-128
    DOI: 10.1016/j.ejor.2011.08.014
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    Cited by:

    1. Chica-Olmo, Jorge & Cano-Guervos, Rafael, 2020. "Does my house have a premium or discount in relation to my neighbors? A regression-kriging approach," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    2. Dieudonné Tchuente & Serge Nyawa, 2022. "Real estate price estimation in French cities using geocoding and machine learning," Annals of Operations Research, Springer, vol. 308(1), pages 571-608, January.
    3. Michalis Doumpos & Dimitrios Papastamos & Dimitrios Andritsos & Constantin Zopounidis, 2021. "Developing automated valuation models for estimating property values: a comparison of global and locally weighted approaches," Annals of Operations Research, Springer, vol. 306(1), pages 415-433, November.
    4. Xiangqian Li & Keke Li & Yaxin Tian & Siqi Shen & Yue Yu & Liwei Jin & Pengyu Meng & Jingjing Cao & Xiaoxiao Zhang, 2024. "Decision Support for Carbon Emission Reduction Strategies in China’s Cement Industry: Prediction and Identification of Influencing Factors," Sustainability, MDPI, vol. 16(13), pages 1-17, June.
    5. Daikun Wang & Victor Jing Li, 2019. "Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review," Sustainability, MDPI, vol. 11(24), pages 1-14, December.
    6. William Suley Menges & Kevin Getii Moranga, 2020. "Indirect investment and financial performance of the real estate sector in Nairobi County Kenya," Bussecon Review of Finance & Banking (2687-2501), Bussecon International, vol. 2(1), pages 25-34, January.
    7. Lessmann, Stefan & Voß, Stefan, 2017. "Car resale price forecasting: The impact of regression method, private information, and heterogeneity on forecast accuracy," International Journal of Forecasting, Elsevier, vol. 33(4), pages 864-877.
    8. William Suley Menges & Kevin Getii Moranga, 2019. "Indirect investment and financial performance of the real estate sector in Nairobi county Kenya," International Journal of Business Ecosystem & Strategy (2687-2293), Bussecon International Academy, vol. 1(4), pages 09-18, October.
    9. Shawn L. Robey & Mark A McKnight & Misty R. Price & Rachel N. Coleman, 2019. "Considerations for a Regression-Based Real Estate Valuation and Appraisal Model: A Pilot Study," Accounting and Finance Research, Sciedu Press, vol. 8(2), pages 1-99, May.

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