Methodological and Behavioural Drivers of Valuation Decision-Making in Property Markets: A Systematic Literature Review
DOI:
https://doi.org/10.61194/ijjm.v7i3.2274Keywords:
Decision-making, Professional judgement, Property valuation, Real estate appraisal, Automated valuation modelAbstract
Property valuation is a core function in organizational asset management, directly informing decisions related to investment, financing, and strategic resource allocation. Despite growing adoption of analytical models and automated valuation technologies, outcomes remain substantially influenced by professional judgement, organizational processes, and institutional constraints. Prior reviews have examined either methodological advances—such as Abidoye and Chan's (2017) review of ANN-based valuation models and Glumac and Des Rosiers' (2018) examination of big data applications—or behavioural factors in isolation (e.g., Gallimore, 1996; Diaz & Hansz, 1997), leaving an integrative gap this study addresses. Following the PRISMA 2020 framework, 42 peer-reviewed articles were selected from Scopus, Web of Science, and Emerald Insight (March 2024) and analyzed through three-stage thematic analysis. Five themes emerged: (1) methodological evolution in valuation practice, (2) professional judgement and organizational decision-making, (3) behavioural bias in appraisal management, (4) technological transformation through automated valuation systems, and (5) institutional governance of valuation standards. The resulting conceptual framework, derived inductively from thematic coding of the 42 included studies, conceptualizes valuation as a hybrid decision-making process where scientific and managerial-behavioural dimensions—moderated by institutional and technological context—jointly shape valuation outcomes. This study contributes by providing the first systematic integration of methodological and behavioural perspectives in property valuation, offering actionable insights for asset managers, financial institutions, and policymakers seeking to enhance valuation reliability in data-driven property markets.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
