IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2012.09115.html
   My bibliography  Save this paper

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

Author

Listed:
  • Vladimir Vargas-Calder'on
  • 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 Bogot\'a, 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-Calder'on & Jorge E. Camargo, 2020. "Towards robust and speculation-reduction real estate pricing models based on a data-driven strategy," Papers 2012.09115, arXiv.org.
  • Handle: RePEc:arx:papers:2012.09115
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2012.09115
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. George W. Gau & Tsong‐Yue Lai & Ko Wang, 1992. "Optimal Comparable Selection and Weighting in Real Property Valuation: An Extension," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 20(1), pages 107-123, March.
    2. Allan Din & Martin Hoesli & Andre Bender, 2001. "Environmental Variables and Real Estate Prices," Urban Studies, Urban Studies Journal Limited, vol. 38(11), pages 1989-2000, October.
    3. Antonios K. Alexandridis & Dimitrios Karlis & Dimitrios Papastamos & Dimitrios Andritsos, 2019. "Real Estate valuation and forecasting in non-homogeneous markets: A case study in Greece during the financial crisis," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1769-1783, October.
    4. Jorge Iván Pérez-Rave & Juan Carlos Correa-Morales & Favián González-Echavarría, 2019. "A machine learning approach to big data regression analysis of real estate prices for inferential and predictive purposes," Journal of Property Research, Taylor & Francis Journals, vol. 36(1), pages 59-96, January.
    5. Kerry D. Vandell, 1991. "Optimal Comparable Selection and Weighting in Real Property Valuation," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 19(2), pages 213-239, June.
    6. 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.
    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. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Helga Flavia Tothăzan & Adela Deaconu, 2020. "Neuronal Network Artificial Model for Real Estate Appraisal: Logic, controversies, and utility for the Romanian context," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 1093-1100, December.
    3. 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.
    4. Kristoffer B. Birkeland & Allan D. D'Silva & Roland Füss & Are Oust, 2021. "The Predictability of House Prices: "Human Against Machine"," International Real Estate Review, Global Social Science Institute, vol. 24(2), pages 139-183.
    5. 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.
    6. James A. Bryant & Donald R. Epley, 1998. "Cancerphobia: Electromagnetic Fields and Their Impact on Residential Loan Values," Journal of Real Estate Research, American Real Estate Society, vol. 15(1), pages 115-129.
    7. R. Kelley Pace, 1998. "Total Grid Estimation," Journal of Real Estate Research, American Real Estate Society, vol. 15(1), pages 101-114.
    8. Jos魍ar, 2012. "Space-time approach to commercial property prices valuation," Applied Economics, Taylor & Francis Journals, vol. 44(28), pages 3705-3715, October.
    9. 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.
    10. Hyunsoo Kim & Youngwoo Kwon & Yeol Choi, 2020. "Assessing the Impact of Public Rental Housing on the Housing Prices in Proximity: Based on the Regional and Local Level of Price Prediction Models Using Long Short-Term Memory (LSTM)," Sustainability, MDPI, Open Access Journal, vol. 12(18), pages 1-25, September.
    11. Horvath, Sabine & Soot, Matthias & Zaddach, Sebastian & Neuner, Hans & Weitkamp, Alexandra, 2021. "Deriving adequate sample sizes for ANN-based modelling of real estate valuation tasks by complexity analysis," Land Use Policy, Elsevier, vol. 107(C).
    12. Mahdieh Yazdani, 2021. "Machine Learning, Deep Learning, and Hedonic Methods for Real Estate Price Prediction," Papers 2110.07151, arXiv.org.
    13. Pierluigi Morano & Paolo Rosato & Francesco Tajani & Benedetto Manganelli & Felicia Di Liddo, 2019. "Contextualized Property Market Models vs. Generalized Mass Appraisals: An Innovative Approach," Sustainability, MDPI, Open Access Journal, vol. 11(18), pages 1-28, September.
    14. Donald R. Epley, 1997. "A Note on the Optimal Selection and Weighting of Comparable Properties," Journal of Real Estate Research, American Real Estate Society, vol. 14(2), pages 175-182.
    15. Sebastian Gnat, 2021. "Property Mass Valuation on Small Markets," Land, MDPI, Open Access Journal, vol. 10(4), pages 1-14, April.
    16. 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.
    17. 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.
    18. Kuan-Lun Pan & Hsiao Jung Teng & Shih-Yuan Lin & Yu En Cheng, 2021. "An Empirical Method for Decomposing the Contributions of Land and Building Values to Housing Value," International Real Estate Review, Global Social Science Institute, vol. 24(3), pages 385-403.
    19. 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.
    20. Ünsal Özdilek, 2020. "Land and building separation based on Shapley values," Palgrave Communications, Palgrave Macmillan, vol. 6(1), pages 1-13, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2012.09115. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://arxiv.org/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.