Big Data Analytics in Real Estate Valuation: A systematic literature review

Authors

  • Nur Edrina Muhammad Nazrin Faculty of Built Environment, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Abdul Hadi Nawawi Faculty of Built Environment, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Aida Wati Zainan Abidin Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Edie Ezwan Mohd Safian Faculty of Technology Management and Business, Universiti Tun Hussein Onn, 86400 Parit Raja, Johor, Malaysia

Keywords:

Big data analytics, real estate, valuation, systematic

Abstract

This systematic literature review (SLR) assesses the applicability of big data analytics in real estate valuation by examining 77 studies published between 2018 and 2023. The findings reveal that this is an emerging research area with a growing body of literature. Key application contexts identified are property price prediction, property rental prediction, land value prediction, and property price indexing, with takeaways suggesting that big data analytics has the potential to enhance the accuracy of property valuation. Building on these insights, this study develops a comprehensive framework to synthesise the existing literature and serves as a foundation for future researchers.

References

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Published

2025-12-08

How to Cite

Muhammad Nazrin, N. E., Nawawi, A. H., Zainan Abidin, A. W., & Mohd Safian, E. E. (2025). Big Data Analytics in Real Estate Valuation: A systematic literature review. Environment-Behaviour Proceedings Journal, 10(SI40). Retrieved from https://ebpj.e-iph.co.uk/index.php/EBProceedings/article/view/7700

Issue

Vol. 10 No. SI40 (2025): Dec. 10th International Conference on Science & Social Research Virtual Conference, CSSR 2023, 6-7 Nov 2023 (DRAFT)

Section

Big Data Environment

License

Copyright (c) 2026 Nur Edrina Muhammad Nazrin, Abdul Hadi Nawawi, Aida Wati Zainan Abidin, Edie Ezwan Mohd Safian

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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