Machine Learning Applications in Real Estate: Critical Review of Recent Development

Jamal Al-Qawasmi*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Machine learning (ML) and deep learning (DL) methods have recently become a hot topic in the real estate discipline. They have contributed to the advancement of various domains in real estate sector. This paper provides a critical review of recent trends in applying machine learning and deep learning (ML/DL) techniques in various domains of real estate and investigate their potential for the real estate sector. Recent advances in model development, testing and areas of application in real estate in the past 4 years (2017–2020) are presented. Findings reveal that 20 different ML and DL algorithms were utilized to examine various aspects of real estate development and valuation, and that the most commonly used algorithms are neural networks, regression models, random forest, booting, support vector machine and cubist/pruned model tree.

Original languageEnglish
Title of host publicationArtificial Intelligence Applications and Innovations - 18th IFIP WG 12.5 International Conference, AIAI 2022, Proceedings
EditorsIlias Maglogiannis, Lazaros Iliadis, John Macintyre, Paulo Cortez
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages19
ISBN (Print)9783031083365
StatePublished - 2022

Publication series

NameIFIP Advances in Information and Communication Technology
Volume647 IFIP
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X

Bibliographical note

Funding Information:
Acknowledgment. The author acknowledges the support of the King Fahd University of Petroleum and Minerals for the research project.

Publisher Copyright:
© 2022, IFIP International Federation for Information Processing.


  • Deep learning
  • Machine learning
  • Properties
  • Real estate

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management


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