Mapping the landscape: A systematic literature review on automated valuation models and strategic applications in real estate
Abstract
In the rapidly evolving real estate industry, integrating automated valuation models (AVMs) has become critical for improving property assessment accuracy and transparency. Although there is some research on the subject, no thorough qualitative systematic review has been done in this field. This paper aims to provide an up-to-date and systematic understanding of the strategic applications of AVMs across various real estate subsectors (i.e., real estate development, real estate investment, land administration, and taxation), shedding light on their broad contributions to value enhancement, decision-making, and market insights. The systematic review is based on 97 papers selected out of 652 search results with an application of the PRISMA-based method. The findings highlight the transformative role of AVMs approaches in streamlining valuation processes, enhancing market efficiency, and supporting data-driven decision-making in the real estate industry, along with developing an original conceptual framework. Key areas of future research, including data integration, ethical implications, and the development of hybrid AVMs approaches are identified to advance the field and address emerging challenges. Ultimately, stakeholders can create new avenues for real estate valuation efficiency, accuracy, and transparency by judiciously utilizing AVMs approaches, leading to more educated real estate investment decisions.
Keyword : real estate, automated valuation models, strategic applications, systematic literature review, PRISMA, conceptual framework
How to Cite
El Jaouhari, A., Samadhiya, A., Kumar, A., Šešplaukis, A., & Raslanas, S. (2024). Mapping the landscape: A systematic literature review on automated valuation models and strategic applications in real estate. International Journal of Strategic Property Management, 28(5), 286–301. https://doi.org/10.3846/ijspm.2024.22251
This work is licensed under a Creative Commons Attribution 4.0 International License.
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