Structuring Human-AI Collaboration in Residential Property Valuation Using Levels of Automation

The increasing adoption of artificial intelligence (AI) in residential property valuation has been driven largely by advances in automated valuation models and predictive analytics. However, existing research has focused predominantly on model performance and interpretability, offering limited guidance on how AI should be embedded within professional valuation practices that are judgement-intensive, regulated, and accountable. This study addresses this gap by reframing AI integration as a governance and workflow design problem rather than a purely technical challenge. Drawing on Levels of Automation (LOA) as an analytical lens, the study systematically maps human and AI roles across valuation workflows and identifies the limitations of conventional LOA frameworks in addressing professional authority and accountability. To overcome these limitations, the study introduces a Professional Governance Layer as a cross-cutting mechanism addressing decision authority, accountability ownership, override capability, and audit responsibility in AI-supported valuation. Using a systematic literature review and an expert-based Delphi approach, the study develops a Human-AI Hybrid Valuation Framework that structures human-AI collaboration while preserving non-transferable professional responsibility. The proposed framework contributes a process-oriented and professionally defensible approach to AI adoption in residential property valuation, offering practical implications for valuers, regulators, and system developers concerned with responsible and accountable AI integration.

residential property valuation; artificial intelligence

1. Antipov, E. A., & Pokryshevskaya, E. B. (2012). Mass appraisal of residential apartments: An application of random forest for valuation and a CART-based approach for model diagnostics. Expert Systems with Applications, 39(2), 1772–1778. https://doi.org/10.1016/j.eswa.2011.08.077 [Google Scholar] [Crossref]

2. Board of Valuers, Appraisers, Estate Agents and Property Managers Malaysia (BOVAEP). (2021). Malaysian valuation standards (6th ed.). BOVAEP. [Google Scholar] [Crossref]

3. Cheung KS (2024), "Real Estate Insights: Establishing transparency – setting AI standards in property valuation". Journal of Property Investment & Finance, Vol. 42 No. 4 pp. 406–408, doi: https://doi.org/10.1108/JPIF-04-2024-0050 [Google Scholar] [Crossref]

4. Deppner, S., Simon, A., & Wagner, J. (2023). Explainable machine learning in real estate valuation: Transparency, interpretability, and professional implications. Journal of Property Research, 40(3), 219–240. https://doi.org/10.1080/09599916.2022.2157784 [Google Scholar] [Crossref]

5. Dumas, J. S., Redish, J. C., & Lewis, C. (2012). User performance in human–computer interaction. In J. A. Jacko (Ed.), The human–computer interaction handbook (3rd ed., pp. 83–104). CRC Press. [Google Scholar] [Crossref]

6. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5 [Google Scholar] [Crossref]

7. Hsu, C. & Sandford, B. A., (2007) “The Delphi Technique: Making Sense of Consensus”, Practical Assessment, Research, and Evaluation 12(1): 10. doi: https://doi.org/10.7275/pdz9-th90 [Google Scholar] [Crossref]

8. Matthias, A. (2004). The responsibility gap: Ascribing responsibility for the actions of learning automata. Ethics and Information Technology, 6(3), 175–183. https://doi.org/10.1007/s10676-004-3422-1 [Google Scholar] [Crossref]

9. Matysiak, G. (2023). Automated valuation models and professional liability: Rethinking accountability in valuation practice. Journal of Property Research, 40(4), 321–338. https://doi.org/10.1080/09599916.2023.2231457 [Google Scholar] [Crossref]

10. Morgan, D. L. (2014). Pragmatism as a paradigm for social research. Qualitative Inquiry, 20(8), 1045–1053. https://doi.org/10.1177/1077800413513733 [Google Scholar] [Crossref]

11. Muaz Hafizuddin Ahmad Muzir, Sr. Dr. Maimunah Sapri, Dr. Norshaliza Kamaruddin, PMgr Sr. Dr. Mustafa Omar (2026). Reconciling Art, Science, and Prediction in Real Estate Valuation: Toward a Hybrid Epistemology for the Ai Era., 10(3), https://doi.org/10.47772/IJRISS.2026.100300057. [Google Scholar] [Crossref]

12. Okoli, C., & Pawlowski, S. D. (2004). The Delphi method as a research tool: An example, design considerations and applications. Information & Management, 42(1), 15–29. https://doi.org/10.1016/j.im.2003.11.002 [Google Scholar] [Crossref]

13. R. Parasuraman, T. B. Sheridan and C. D. Wickens, "A model for types and levels of human interaction with automation," in IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol. 30, no. 3, pp. 286-297, May 2000, doi: 10.1109/3468.844354. keywords: {Humans;Design automation;NASA;Hardware;Software;Man machine systems;Costs;Information analysis;Reliability engineering;Design engineering}, [Google Scholar] [Crossref]

14. Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072 [Google Scholar] [Crossref]

15. Royal Institution of Chartered Surveyors (RICS). (2022). RICS global valuation standards (Red Book Global Edition). RICS. [Google Scholar] [Crossref]

16. Sheridan, T. B., & Verplank, W. (1978). Human and Computer Control of Undersea Teleoperators. Man-Machine Systems Laboratory, Department of Mechanical Engineering. [Google Scholar] [Crossref]

17. Teikari, P., Jarrell, M., Azh, M., & Pesola, H. (2025). The Architecture of Trust: A Framework for AI-Augmented Real Estate Valuation in the Era of Structured Data. arXiv preprint arXiv:2508.02765. [Google Scholar] [Crossref]

18. Wyatt, P. (2013). Property valuation (2nd ed.). Wiley-Blackwell. [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet