IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i4p740-d1107160.html
   My bibliography  Save this article

The Use of Machine Learning in Real Estate Research

Author

Listed:
  • Lennon H. T. Choy

    (Department of Real Estate and Construction, University of Hong Kong, Hong Kong SAR, China)

  • Winky K. O. Ho

    (Independent Researcher, Hong Kong SAR, China)

Abstract

This research seeks to demonstrate how machine learning, a branch of artificial intelligence, is able to deliver more accurate pricing predictions, using the real estate market as an example. Utilizing 24,936 housing transaction records, this paper employs Extra Trees (ET), k –Nearest Neighbors (KNN), and Random Forest (RF) to predict property prices and then compares their results with those of a hedonic price model. In particular, this paper uses a feature (property age x square footage) instead of property age in order to isolate the effect of land depreciation on property prices. Our results suggest that these three algorithms markedly outperform the traditional statistical techniques in terms of explanatory power and error minimization. Machine learning is expected to play an increasing role in shaping our future. However, it may raise questions about the privacy, fairness, and job displacement issues. It is therefore important to pay close attention to the ethical implications of machine learning and ensure that the technology is used responsibly and ethically. Researchers, legislators, and industry players must work together to create appropriate standards and legislation to govern the use of machine learning.

Suggested Citation

  • Lennon H. T. Choy & Winky K. O. Ho, 2023. "The Use of Machine Learning in Real Estate Research," Land, MDPI, vol. 12(4), pages 1-15, March.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:4:p:740-:d:1107160
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/4/740/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/12/4/740/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. W. Erwin Diewert & Jan de Haan & Rens Hendriks, 2015. "Hedonic Regressions and the Decomposition of a House Price Index into Land and Structure Components," Econometric Reviews, Taylor & Francis Journals, vol. 34(1-2), pages 106-126, February.
    2. Raul-Tomas Mora-Garcia & Maria-Francisca Cespedes-Lopez & V. Raul Perez-Sanchez, 2022. "Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times," Land, MDPI, vol. 11(11), pages 1-32, November.
    3. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    4. Trond-Arne Borgersen, 2022. "A Housing Market with Cournot Competition and a Third Housing Sector," International Journal of Economic Sciences, European Research Center, vol. 11(2), pages 13-27, November.
    5. Fumihiko Isada, 2022. "The impact of inter-organisational network structures on research outcomes for artificial intelligence technologies," International Journal of Economic Sciences, European Research Center, vol. 11(1), pages 1-18, April.
    6. Georgios Tsertekidis, 2022. "Migrating from Greece to Germany after 2010: a qualitative approach," International Journal of Social Sciences, European Research Center, vol. 11(1), pages 73-92, March.
    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. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    2. Patrick Bajari & Victor Chernozhukov & Ali Hortaçsu & Junichi Suzuki, 2019. "The Impact of Big Data on Firm Performance: An Empirical Investigation," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 33-37, May.
    3. Nathan, Max & Rosso, Anna, 2014. "Mapping information economy businesses with big data: findings from the UK," LSE Research Online Documents on Economics 60615, London School of Economics and Political Science, LSE Library.
    4. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    5. Nicodemo, Catia & Satorra, Albert, 2020. "Exploratory Data Analysis on Large Data Sets: The Example of Salary Variation in Spanish Social Security Data," IZA Discussion Papers 13459, Institute of Labor Economics (IZA).
    6. Duffy, David & FitzGerald, John & Timoney, Kevin & Byrne, David, 2013. "Quarterly Economic Commentary, Autumn 2013," Forecasting Report, Economic and Social Research Institute (ESRI), number QEC20133, march.
    7. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
    8. Evangelista, Rui & Ramalho, Esmeralda A. & Andrade e Silva, João, 2020. "On the use of hedonic regression models to measure the effect of energy efficiency on residential property transaction prices: Evidence for Portugal and selected data issues," Energy Economics, Elsevier, vol. 86(C).
    9. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    10. Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.
    11. Lidia Ceriani & Sergio Olivieri & Marco Ranzani, 2023. "Housing, imputed rent, and household welfare," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(1), pages 131-168, March.
    12. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    13. Leif Anders Thorsrud, 2016. "Nowcasting using news topics Big Data versus big bank," Working Papers No 6/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    14. Matteo Iacopini & Carlo R.M.A. Santagiustina, 2021. "Filtering the intensity of public concern from social media count data with jumps," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1283-1302, October.
    15. Lopez Cordova,Jose Ernesto, 2020. "Digital Platforms and the Demand for International Tourism Services," Policy Research Working Paper Series 9147, The World Bank.
    16. Barzin,Samira & Avner,Paolo & Maruyama Rentschler,Jun Erik & O’Clery,Neave, 2022. "Where Are All the Jobs ? A Machine Learning Approach for High Resolution Urban Employment Prediction inDeveloping Countries," Policy Research Working Paper Series 9979, The World Bank.
    17. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    18. Larson, William D. & Shui, Jessica, 2022. "Land valuation using public records and kriging: Implications for land versus property taxation in cities," Journal of Housing Economics, Elsevier, vol. 58(PA).
    19. Jens Ludwig & Sendhil Mullainathan, 2021. "Fragile Algorithms and Fallible Decision-Makers: Lessons from the Justice System," Journal of Economic Perspectives, American Economic Association, vol. 35(4), pages 71-96, Fall.
    20. Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2018. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Sustainability, MDPI, vol. 10(5), pages 1-18, May.

    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:gam:jlands:v:12:y:2023:i:4:p:740-:d:1107160. See general information about how to correct material in RePEc.

    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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.