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Towards robust and speculation-reduction real estate pricing models based on a data-driven strategy

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  • Vladimir Vargas-Calderón
  • Jorge E. Camargo

Abstract

In many countries, real estate appraisal is based on conventional methods that rely on appraisers’ abilities to collect data, interpret it and model the price of a real estate property. With the increasing use of real estate online platforms and the large amount of information found therein, there exists the possibility of overcoming many drawbacks of conventional pricing models such as subjectivity, cost, unfairness, among others. In this paper we propose a data-driven real estate pricing model based on machine learning methods to estimate prices reducing human bias. We test the model with 178,865 flats listings from Bogotá, collected from 2016 to 2020. Results show that the proposed state-of-the-art model is robust and accurate in estimating real estate prices. This case study serves as an incentive for local governments from developing countries to discuss and build real estate pricing models based on large data sets that increases fairness for all the real estate market stakeholders and reduces price speculation.

Suggested Citation

  • Vladimir Vargas-Calderón & Jorge E. Camargo, 2022. "Towards robust and speculation-reduction real estate pricing models based on a data-driven strategy," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(12), pages 2794-2807, December.
    • Handle: RePEc:taf:tjorxx:v:73:y:2022:i:12:p:2794-2807
    DOI: 10.1080/01605682.2021.2023672
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    File URL: http://hdl.handle.net/10.1080/01605682.2021.2023672
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    Cited by:

    1. Takahiro Yoshida & Daisuke Murakami & Hajime Seya, 2024. "Spatial Prediction of Apartment Rent using Regression-Based and Machine Learning-Based Approaches with a Large Dataset," The Journal of Real Estate Finance and Economics, Springer, vol. 69(1), pages 1-28, July.

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