Methodological and Behavioural Drivers of Valuation Decision-Making in Property Markets: A Systematic Literature Review

Abstract

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 datadriven property markets.

KEYWORDS property valuation; real estate appraisal; managerial decision-making; automated valuation model; professional judgement; systematic literature review.

Introduction

Property valuation constitutes a foundational element of organizational asset management, providing essential inputs for a broad range of managerial and strategic decisions. In contemporary organizations, valuation results directly inform decisions related to property investment, mortgage financing, portfolio allocation, financial reporting, and strategic resource management. For financial institutions, real estate firms, and public sector organizations, reliable valuation outcomes are prerequisites for sound managerial decision-making and effective governance of asset portfolios (Hoesli, M.; MacGregor, 2014; McAllister, 2019). Accordingly, the credibility and reliability of the valuation process are critical for organizational performance and stakeholder confidence. From a theoretical perspective, property valuation has traditionally been conceptualized as an analytically grounded discipline. Classical valuation theory emphasizes structured approaches such as the sales comparison approach, income capitalization approach, and cost approach (Pagourtzi et al., 2003a). These methods rely on economic principles, market evidence, and systematic analysis to generate value estimates that reflect prevailing market conditions (French & Gabrielli, 2004). Over time, developments in econometrics and real estate analytics have further strengthened the scientific orientation of valuation through the application of hedonic pricing models, spatial econometrics, and mass appraisal systems (; (Glumac & Des Rosiers, 2018; McCluskey et al., 2013). Technological innovation has increasingly transformed valuation in recent years. The emergence of automated valuation models (AVMs), machine learning techniques, and artificial intelligence has enabled analysts to process large volumes of property data with greater efficiency (Baldominos et al., 2018; Kok Koponen, Eija-Leena, Martínez-Barbosa, Carmen Adriana, 2017). These developments have created new analytical capabilities in real estate management, supporting financial institutions and property platforms seeking to enhance both analytical efficiency and decision speed (Deng & Zhang, 2025; Dimopoulos et al., 2025; Ho et al., 2021; Yuan, Jian; Li, Ming; Zheng, 2022). Consequently, contemporary valuation increasingly emphasizes data-driven methods and technological integration. Despite these advancements, property valuation in organizational contexts cannot be fully understood as a purely technical or algorithmic process. Real estate markets are inherently heterogeneous and shaped by contextual factors including local economic conditions, regulatory environments, and market behaviour ; (French & Gabrielli, 2004; Patrick McAllister, 2019). In practice, information is frequently incomplete or ambiguous, requiring managers and professional valuers to interpret available evidence and exercise judgement. A careful examination of the existing literature reveals that prior reviews have addressed these dimensions in isolation. In terms of methodological reviews, (Pagourtzi et al., 2003b) synthesized classical valuation approaches; (Abidoye & Chan, 2017) conducted an SLR of 31 studies on artificial neural network-based valuation models; and (Glumac & Des Rosiers, 2018) reviewed big data applications in real estate research. On the behavioural side, while individual empirical studies have examined cognitive biases—(Gallimore, 1996) on confirmation bias, (Diaz & Hansz, 1997) on anchoring, and (A. Amidu & Tajudeen Aluko, 2007) on client influence—these have not been synthesized in a standalone systematic review, nor have they been connected systematically to organizational governance frameworks. The rapid adoption of automated valuation technologies further raises new questions about the evolving role of human judgement in organizational contexts that none of these prior streams has addressed comprehensively. The integrative gap this manuscript addresses is therefore as follows: no prior systematic review has combined the methodological evolution stream, the behavioural bias stream, and the institutional governance stream within a unified management decision-making framework. Addressing this gap is the central contribution of the present study. Given these research gaps, this study conducts a Systematic Literature Review (SLR) to synthesize existing research on property valuation, integrating methodological, behavioural, and institutional perspectives. The review addresses three research questions: 1. How has the relationship between analytical methods and professional judgement been conceptualized in property valuation literature? 2. What methodological approaches dominate contemporary property valuation research? 3. What behavioural, organizational, and institutional factors influence managerial decision-making in property valuation? By addressing these questions, this study contributes to the management literature by conceptualizing property valuation as a hybrid managerial decision-making process and proposing a conceptual framework that integrates methodological and behavioural drivers of valuation.

Literature Review and Conceptual Framework

Property Valuation as a Managerial Activity Property valuation is inherently embedded in organizational management. Valuation results provide the informational basis for managerial decisions in investment analysis, financial reporting, credit risk assessment, and portfolio management. Because real estate assets often represent significant proportions of organizational balance sheets, reliable valuation is essential for organizational performance, regulatory compliance, and stakeholder confidence (Hoesli, M.; MacGregor, 2014; Pagourtzi et al., 2003b). The management perspective on property valuation extends beyond technical methods to encompass organizational processes, governance structures, and decisionmaking frameworks. Asset managers must coordinate valuation processes, manage professional valuers, and integrate valuation outputs into broader strategic decisions. This organizational dimension distinguishes valuation from purely technical appraisal practice, emphasizing the importance of process design, quality assurance, and institutional accountability (Baur, Christoph; Braun, Thomas; Wimschulte, 2023; McAllister, 2019). Methodological Foundations of Property Valuation The analytical foundation of property valuation has evolved substantially over time. Early valuation practice relied primarily on the sales comparison, income capitalization, and cost approaches, each grounded in economic theory and market evidence (Pagourtzi et al., 2003b). These approaches remain central to professional valuation practice because they provide systematic frameworks for interpreting market data. Advances in econometrics and data analytics have introduced more sophisticated methodological approaches, including hedonic pricing models, spatial econometric methods, and mass appraisal systems. Hedonic models decompose property values into the contributions of individual attributes, enabling researchers and managers to quantify the impact of location, size, and neighbourhood characteristics on property prices (McCluskey et al., 2013); (Morano et al., 2018). The increasing availability of large-scale property datasets has further encouraged the adoption of machine learning and AI techniques. Automated valuation models (AVMs) apply statistical algorithms to transaction records, enabling rapid, scalable value estimation (Kok Koponen, Eija-Leena, Martínez-Barbosa, Carmen Adriana, 2017); (Abidoye & Chan, 2017). Recent advances in ensemble learning and explainable AI further enhance model accuracy while maintaining interpretability (Deng & Zhang, 2025). Professional Judgement in Valuation Professional judgement constitutes a critical dimension of valuation, particularly in complex and uncertain market environments. Even when sophisticated analytical models are employed, managers and professional valuers must interpret model outputs, select relevant market comparables, and apply contextual adjustments (A.-R. Amidu & Boyd, 2018; Gallimore, 1996). The concept of tacit knowledge is particularly relevant: experienced valuers develop contextual expertise through extensive practice, enabling them to interpret subtle market signals and property-specific characteristics that cannot be fully captured in quantitative models Figure 1. Conceptual Framework: Balancing Art and Science in Property Valuation Source: Processed by the authors (derived inductively from thematic coding of 42 included studies)

(French & Gabrielli, 2004; Gilbertson & Preston, 2005). Behavioural research has further demonstrated that professional judgement in valuation is susceptible to cognitive biases, including anchoring effects and confirmation bias ; (Diaz & Hansz, 1997; Gallimore, 1996).

Valuation Accuracy and Managerial Implications Valuation accuracy is a central concern because inaccurate valuations can lead to suboptimal organizational decisions, including mispriced assets, inefficient capital allocation, and increased financial risk. Empirical studies have documented systematic discrepancies between appraised values and actual transaction prices, commonly referred to as appraisal bias or valuation error (Crosby et al., 2010; Geiger & Fuerst, 2018; Tzioumis, 2017). Appraisal smoothing—whereby valuers adjust estimates conservatively in response to market volatility—has been identified as a significant source of valuation inaccuracy (Crosby et al., 2010). Client pressure and institutional incentives can also influence valuation outcomes, raising governance concerns (A. Amidu & Tajudeen Aluko, 2007; Fuerst, Franz; McAllister, 2016). Technological Transformation in Valuation Technological innovation has fundamentally altered the operational landscape of property valuation. Digital property databases, geographic information systems, and automated valuation platforms have expanded analytical capabilities while reducing per-unit valuation costs (Glumac & Des Rosiers, 2018); (A.-R. Amidu et al., 2025). Comparative studies confirm that modern machine learning models, including gradient boosting and neural network approaches, consistently outperform traditional hedonic specifications in prediction accuracy (Yuan, Jian; Li, Ming; Zheng, 2022); (Zhang, 2021). However, the managerial literature consistently emphasizes that automated models should not substitute for professional judgement in complex valuation contexts, and leading organizations have adopted human-inthe-loop approaches (Glumac & Des Rosiers, 2018; Glumac, Boris; Hervé-Bazin, Marie; Des Rosiers, 2021). Institutional Governance and Valuation Standards Institutional frameworks and professional standards exert a profound influence on how property valuation is structured and governed. International standards developed by the IVSC and national frameworks established by RICS provide methodological guidance, reporting requirements, and ethical principles (Lorenz & Lützkendorf, 2011; Patrick McAllister, 2019). Adherence to professional standards serves both quality assurance and legitimacy functions—technically reducing methodological variability, and institutionally signalling credibility to investors and regulators (Cheloti & Mooya, 2023; Patrick McAllister, 2019). The integration of sustainability considerations into valuation represents an emerging governance challenge requiring new methodological capabilities (Lorenz & Lützkendorf, 2011). Conceptual Framework Based on the preceding literature synthesis, property valuation can be conceptualized as a hybrid decision-making process shaped by two primary dimensions operating within a broader organizational and institutional context. Crucially, the framework components were derived through the three-stage thematic coding process (open, axial, and selective coding) applied to the 42 included studies, rather than being imposed a priori. The Scientific/Methodological Drivers dimension emerged from the cluster of studies focusing on quantitative modelling, AVMs, and machine learning (e.g.(Deng & Zhang, 2025; Kok Koponen, Eija-Leena, Martínez-Barbosa, Carmen Adriana, 2017; McCluskey et al., 2013; Pagourtzi et al., 2003b), representing 18 of the 42 included articles. The ManagerialBehavioural Drivers dimension was derived from studies examining professional judgement, cognitive bias, and tacit knowledge (e.g.,(A.-R. Amidu & Boyd, 2018; Diaz & Hansz, 1997; Gallimore, 1996), representing 14 studies. The Contextual Environment layer was informed by studies addressing market conditions, regulatory frameworks, and technology adoption (e.g., (A.-R. Amidu et al., 2025; Cheloti & Mooya, 2023; Lorenz & Lützkendorf, 2011), accounting for 10 studies. Note that several studies informed multiple dimensions (as shown in Table 3 below). The framework goes beyond a narrative summary of the literature by proposing directional interactions among these components: specifically, that methodological and behavioural drivers jointly shape valuation outcomes, and that this interaction is moderated by the contextual environment (market conditions, regulatory frameworks, and technological developments). This interaction produces the final valuation decision, which reflects both the outputs of analytical models and the judgements of managerial professionals (see figure 1).

Methods

Research Design This study employs a Systematic Literature Review (SLR) to synthesize existing research on property valuation, integrating methodological, behavioural, and institutional perspectives. The SLR approach was selected because it allows researchers to systematically identify, evaluate, and synthesize relevant studies using transparent and replicable procedures. Compared with traditional narrative reviews, SLR provides stronger methodological rigour through explicit criteria for literature selection, data extraction, and thematic analysis ; Table 1. Literature Search Keywords Primary Search Terms Management Context Terms "property valuation" "asset management decision" "real estate appraisal" "professional judgement valuation" "valuation accuracy" "valuation governance" "appraisal bias" "portfolio management real estate" "automated valuation model" "valuation decision-making" "machine learning valuation" "property investment management" Table 2. Inclusion and Exclusion Criteria Inclusion Criteria Exclusion Criteria • Articles related to property • Articles not directly valuation or asset management decisionmaking related to property valuation or real estate decision-making • Studies discussing valuation methodologies, accuracy, or behavioural aspects • Non-academic publications (reports, industry blogs, news articles) • Published in peerreviewed academic journals indexed in Scopus or Web of Science (SSCI/ESCI) • Conference papers without peer review • Journals meeting SJR ≥ • Duplicate records identified during screening • Articles published between 2014 and 2024 (seminal pre-2014 studies included where warranted on theoretical grounds) 0.20 or Scopus CiteScore ≥ 0.50 at time of search • Dissertations and grey literature • Articles written in English • Articles from journals below the stated SJR/CiteScore threshold (Moher et al., 2015; Tranfield et al., 2003). The review process follows the PRISMA 2020 framework (Page et al., 2021), structured into four stages: identification, screening, eligibility, and inclusion.

Literature Search Strategy All database searches were conducted in March 2024. The primary database was Scopus, selected for its extensive coverage of international peer-reviewed journals in real estate, finance, management, and related disciplines. Additional searches were performed using Web of Science (SSCI/ESCI) and Emerald Insight to broaden coverage in property management Table 1 summarizes the keyword categories used across all databases.and organizational research. The following full Boolean search strings were applied per database: Scopus: TITLE-ABS-KEY ("property valuation" OR "real estate appraisal" OR "automated valuation model" OR "appraisal bias" OR "professional judgement valuation") AND ("decision-making" OR "asset management" OR "organizational" OR "governance") AND PUBYEAR > 2013 Web of Science: TS= ("property valuation" OR "real estate appraisal" OR "valuation accuracy" OR "appraisal bias") AND TS= ("decision-making" OR "management" OR "behavioural") AND PY= (2014-2024) Emerald Insight: "property valuation" OR "valuation decision-making" OR "professional judgement valuation" [fulltext search, filtered: peer-reviewed journals, 2014–2024] Google Scholar was not used as a primary search database. It was employed solely to verify the availability of seminal pre2014 theoretical works (e.g., (Diaz & Hansz, 1997; Gallimore, 1996; Kahneman, 2011; Simon, 1957) that were included on explicit theoretical grounds as stated in the inclusion criteria. These supplementary works are clearly identified as exceptions and are not included in the primary retrieval count. The full search protocol is provided in Appendix B. Inclusion and Exclusion Criteria Predefined inclusion and exclusion criteria were applied to ensure the relevance and quality of selected studies. The inclusion criteria required that articles: (1) addressed property valuation or real estate appraisal management; (2) discussed valuation methodologies, accuracy, behavioural factors, or organizational governance of valuation; (3) were published in peer-reviewed academic journals indexed in Scopus or Web of Science (SSCI/ESCI); (4) journals must meet a Scimago Journal Rank (SJR) score ≥ 0.20 or a Scopus CiteScore ≥ 0.50 at the time of the search; (5) were published between 2014 and 2024, with explicit exceptions for seminal theoretical works (pre-2014) foundational to the field regardless of current indexing status; and (6) were written in English. The exclusion criteria eliminated articles that: (1) did not directly address property valuation; (2) constituted nonacademic publications, reports, or unpublished manuscripts; (3) were conference papers without peer review; (4) were identified as duplicates during the screening process; (5) were dissertations and grey literature; or (6) were published in journals below the stated SJR/CiteScore threshold. Table 2 presents the complete criteria. Article Selection Process (PRISMA) The article selection process followed the four-stage PRISMA 2020 framework. Stage 1 – Identification (Search date: March 2024): The initial database search identified 812 records: Scopus (n = 524), Web of Science (n = 198), and Emerald Insight (n = 90). Twelve seminal pre-2014 works identified through reference tracking were included separately on theoretical grounds and are not counted in this retrieval total. After removing 77 duplicate records, 735 articles remained for screening. Stage 2 – Screening: Titles and abstracts of 735 articles were reviewed for relevance to property valuation and managerial decision-making. Articles not meeting the topic relevance criteria were excluded, removing 537 records and leaving 198 articles for full-text evaluation. Stage 3 – Eligibility: Full texts of 198 articles were assessed against the inclusion and exclusion criteria. Articles lacking sufficient focus on valuation decision-making or management relevance were excluded, removing 156 studies. Stage 4 – Inclusion: A total of 42 peer-reviewed articles met all inclusion criteria and were included in the systematic review. The PRISMA flow diagram is presented in Figure 2.

Data Extraction and Analysis Data were systematically extracted from each selected article, including: author and year of publication, journal and indexing source, research methodology, thematic focus, and key findings related to valuation. Extracted data were organized into a structured literature matrix to facilitate comparative analysis. Thematic analysis was conducted following (Braun & Clarke, 2006) three-stage coding process. In the open coding stage, all three authors independently read and coded each of the 42 articles, identifying key concepts, theoretical constructs, and empirical findings. In the axial coding stage, the authors convened to group related concepts into preliminary thematic categories. Where disagreements arose in code assignment— Figure 2. PRISMA 2020 Flow Diagram Source: Processed by the authors (adapted from PRISMA 2020) occurring in 11 of the 42 articles, primarily in the overlap between the behavioural bias and institutional governance themes—resolution was achieved through structured discussion until consensus was reached. No formal inter-rater reliability statistic was calculated, as the study used interpretive rather than content-analytic coding, consistent with (Braun & Clarke, 2006) reflexive approach. In the selective coding stage, dominant themes were identified by frequency, theoretical salience, and alignment with the research questions. The five final themes were validated through peer debriefing: each author independently reviewed the final theme structure and confirmed it accurately represented the included literature before it was used as the basis for synthesis.

Result and Discussion

The systematic literature review identified 42 relevant articles examining property valuation from methodological, behavioural, and organizational perspectives. Thematic analysis of these studies yielded five major themes: (1) methodological evolution in property valuation, (2) professional judgement and managerial decision-making, (3) behavioural bias in appraisal management, (4) technological transformation in valuation practice, and (5) institutional governance and regulatory influences. Table 3 below presents a cross-theme frequency matrix mapping each of the 42 included studies to its primary theme(s), research method, and geographic context, enabling readers to see how the literature is distributed across the five thematic areas.

Methodological Evolution in Property Valuation The reviewed literature demonstrates a significant evolution in the analytical methods applied to property valuation. Traditional approaches based on sales comparison, income capitalization, and cost methods remain foundational in professional practice because they provide transparent, theoretically grounded frameworks for interpreting market evidence. Hedonic pricing models have emerged as one of the most widely applied analytical tools, enabling managers to conduct systematic, evidence-based valuation analysis (Morano et al., 2018; Pagourtzi et al., 2003b)). Spatial econometric methods have further extended this capability by accounting for spatial dependencies in property markets (Glumac, Boris; Hervé-Bazin, Marie; Des Rosiers, 2021; McCluskey et al., 2013). The adoption of automated valuation models and machine learning techniques represents the most recent phase of methodological evolution, with recent advances in ensemble learning and explainable AI further enhancing model accuracy while maintaining transparency (Deng & Zhang, 2025; Zhang, 2021). However, the literature consistently emphasizes that advanced analytical models do not eliminate the need for managerial interpretation, reinforcing the hybrid nature of valuation.

Professional Judgement and Managerial Decision-Making Professional judgement represents an indispensable dimension of property valuation. Even in organizations with sophisticated analytical systems, managers and professional valuers must exercise interpretative judgement when selecting comparable transactions, applying valuation adjustments, and contextualizing model outputs within specific market conditions (A.-R. Amidu & Boyd, 2018; Gallimore, 1996). The role of tacit knowledge is particularly significant: experienced valuers and asset managers develop contextual expertise through years of professional practice (French & Gabrielli, 2004; Gilbertson & Preston, 2005). The concept of bounded rationality (Simon, 1957) provides an important theoretical lens, and (Kahneman, 2011) dual-process theory further illuminates how both analytical (System 2) and intuitive (System 1) cognitive processes contribute to valuation decisions in organizational settings.

Behavioural Bias in Appraisal Management The behavioural literature reveals several cognitive biases that can systematically affect managerial decision-making in organizational valuation processes. Anchoring bias is the most extensively documented: (Diaz & Hansz, 1997) demonstrated experimentally that valuers may anchor their estimates on Table 3. Cross-Theme Frequency Matrix of Included Studies (n = 42) Author (Year) T1: Methodolo T2: Professional T3: Behavioural gical Judgement Pagourtzi et al. (2003) ✓

Lit. review / Global Gallimore (1996)

✓ ✓

Experimental / UK Diaz & Hansz (1997)

✓ ✓

Experimental / USA French & Gabrielli (2004) ✓ ✓

Conceptual / UK Gilbertson & Preston (2005) ✓

✓ Conceptual / UK Amidu & Aluko (2007)

✓

✓ Survey / Nigeria Peterson & Flanagan (2009) ✓

✓

Modelling / USA Crosby et al. (2010) ✓

✓

Empirical / UK Lorenz & Lützendorf (2011) ✓

✓ Conceptual / Europe McCluskey et al. (2013) ✓

Statistical / UK Hoesli & MacGregor (2014) ✓ ✓

Analytical / Global Schulz et al. (2014) ✓

✓

Empirical / Germany Rianne et al. (2015) ✓

✓

Bayesian / Netherlands Geiger et al. (2016) ✓

✓ CVaR / Global Fuerst & McAllister (2016) ✓

✓ Empirical / UK Abidoye & Chan (2017) ✓

✓

SLR / Global Kok et al. (2017) ✓

✓

Conceptual / Global Baldominos et al. (2018) ✓

✓

ML / Spain Glumac & Des Rosiers (2018) ✓

✓

Lit. review / Global Geiger & Fuerst (2018)

✓

Empirical / UK Amidu & Boyd (2018)

✓ ✓

Qualitative / Ghana Morano et al. (2018) ✓

✓

Case study / Italy McAllister (2019)

✓

✓ Conceptual / Global Ho et al. (2021) ✓

✓

ML / Hong Kong Zhang (2021) ✓

✓

Econometric / China Glumac et al. (2021) ✓

✓

Econometric / Global McAllister (2021)

✓ Conceptual / Global Nanda & Xu (2022)

✓ ✓ Empirical / China Zhou & Kockelman (2018) ✓

✓

Econometric / USA Kauko (2022) ✓

✓

Conceptual / Global Yuan et al. (2022) ✓

✓

Comparative / China Cheloti & Mooya (2023)

✓

✓ Empirical / Kenya Baur et al. (2023)

✓ ✓

Empirical / Europe Deng & Zhang (2025) ✓

✓

ML / Hong Kong Amidu et al. (2025)

✓ ✓ Survey / New Zealand Tranfield et al. (2003) ✓

Methodological / Global Moher et al. (2015) ✓

Methodological / Global Page et al. (2021) ✓

Methodological / Global Braun & Clarke (2006)

✓

Methodological / Global Simon (1957)

✓ ✓

Theoretical / Global Kahneman (2011)

✓ ✓

Theoretical / Global Hoesli & MacGregor (2014b) ✓ ✓

Analytical / Global FREQUENCY (n=42) 18 (43%) 9 (21%) 8 (19%) 11 (26%) 7 (17%) Note: studies may span multiple themes Note: T1 = Methodological Evolution; T2 = Professional Judgement; T3 = Behavioural Bias; T4 = Technological Transformation; T5 = Institutional Governance. Studies may be coded under multiple themes; percentages sum to more than 100%.

T4: Technology T5: Institu Method / Context Bias tional Table 4. Characteristics of Reviewed Literature Author Year Journal Focus Method Key Findings Pagourtzi et al. 2003 J. Property Invest. & Finance Valuation methods review Lit. review Synthesizes traditional valuation approaches Gallimore 1996 J. Property Research Confirmation bias Experimental Demonstrates confirmation bias in appraisal Diaz & Hansz 1997 J. Property Valuation & Investment Anchoring in valuation Experimental Prior estimates anchor valuers' judgements French & Gabrielli 2004 J. Property Invest. & Finance Valuation uncertainty Conceptual Valuation inherently involves uncertainty Gilbertson & Preston 2005 J. Property Invest. & Finance Valuation profession Conceptual Professional standards shape practice Amidu & Aluko 2007 Property Management Client influence Survey Client expectations affect valuation outcomes Peterson & Flanagan 2009 J. Real Estate Research Neural network hedonic Modelling ANN improves hedonic price estimation Appraisal smoothing deviates from market prices Lorenz & Lützendorf 2011 J. Property Invest. & Finance Crosby et al. 2010 J. Property Research Valuation accuracy & smoothing Empirical Sustainability & valuation Conceptual Sustainability factors influence property values McCluskey et al. 2013 J. Property Research Mass appraisal modelling Statistical Spatial econometrics improve mass appraisal Hoesli & MacGregor 2014 Routledge Property investment mgmt Analytical Valuation central to portfolio decisions Schulz et al. 2014 J. Property Research AVM specification Empirical modelling Rianne et al. 2015 J. Property Invest. & Finance CRE decision support Bayesian Network Bayesian models support CRE management Geiger et al. 2016 J. Sustainable Real Estate Sustainable RE portfolios Sustainable REITs add diversification benefits Fuerst & McAllister 2016 J. Property Research Valuation accuracy Empirical Appraisal values may diverge from prices Abidoye & Chan 2017 Property Management ANN in valuation SLR (31 studies) ANN improves accuracy; mainly developed markets Kok et al. 2017 J. Portfolio Management Big data in real estate Conceptual Big data enables datadriven valuation Baldominos et al. 2018 Applied Sciences ML valuation ML modelling AI-based approaches enhance accuracy Glumac & Des Rosiers 2018 J. Property Research Big data in RE Lit. review Big data expands property market analysis Geiger & Fuerst 2018 J. Property Research Valuation bias & market cycles Empirical Detects systematic appraisal bias in cycles Amidu & Boyd 2018 J. Property Invest. & Finance Expert problemsolving Qualitative Need for structured valuation approaches Morano et al. 2018 Intl J. Housing Markets & Analysis Mass appraisal methods Case study Multicriteria algorithms enhance accuracy McAllister 2019 J. Property Invest. & Finance Valuation reliability Conceptual Transparency improves valuation credibility Ho et al. 2021 J. Property Research ML price prediction ML modelling ML models improve prediction accuracy Zhang 2021 Scientific Programming Housing price prediction Econometric Accuracy improves with ML approaches Glumac et al. 2021 J. Property Research Spatial price modelling Econometric Spatial econometrics improves reliability McAllister 2021 J. Property Invest. & Finance Valuation transparency Conceptual Transparency improves trust in valuation Nanda & Xu 2022 J. Urban Management Real estate digitalisation Empirical Digital platforms reshape market transparency Zhou & Kockelman 2018 J. RE Finance & Economics ML housing prices Econometric ML improves housing price prediction Kauko 2022 J. Property Research Integrated spatialAI valuation Conceptual Spatial & AI integration improves modelling Yuan et al. 2022 J. Real Estate Research ML valuation models Cheloti & Mooya 2023 J. Property Research Valuation problems – Kenya Empirical Institutional context shapes appraisal reliability AVM performance depends on model specification CVaR optimisation Comparative modelling Hybrid ML models outperform traditional Author Year Journal Focus Method Key Findings Baur et al. 2023 J. Property Invest. & Finance Valuation uncertainty Empirical Market volatility increases valuation dispersion Deng & Zhang 2025 Annals of Regional Science Ensemble learning & XAI Amidu et al. 2025 J. European RE Research Digital tech adoption Tranfield et al. 2003 British J. Management SLR methodology Methodological SLR provides rigour for knowledge synthesis Moher et al. 2015 PLOS Medicine PRISMA reporting Methodological PRISMA enhances transparency in reviews Page et al. 2021 BMJ PRISMA 2020 update Methodological Updated standards improve SLR reporting Braun & Clarke 2006 Qualitative Research in Psychology Thematic analysis method Methodological Flexible framework for qualitative analysis Simon 1957 Wiley Bounded rationality Theoretical Managers operate under bounded rationality Kahneman 2011 Farrar Straus & Giroux Dual-process theory Theoretical System 1 and 2 processes shape decisions Pagourtzi et al. (2003) [ref in Hoesli] 2014 Routledge Property investment mgmt Analytical Portfolio management reference Source: Processed by the authors

initial reference values and subsequently fail to adjust sufficiently away from these anchors. Confirmation bias further complicates decision-making: (Gallimore, 1996) found that valuers may selectively interpret market evidence to confirm pre-existing value expectations. Client pressure and institutional incentives represent additional sources of bias: (A. Amidu & Tajudeen Aluko, 2007) documented that client expectations can materially influence residential property valuations, while (Fuerst, Franz; McAllister, 2016) demonstrated that appraisal bias can arise from institutional contexts. Valuation uncertainty is further exacerbated during periods of market volatility (Baur, Christoph; Braun, Thomas; Wimschulte, 2023).

Technological Transformation in Valuation Technological innovation has fundamentally reshaped the operational and managerial landscape of property valuation. Automated valuation models represent the most transformative technological development. By applying machine learning algorithms to large transaction datasets, AVMplatforms can generate value estimates rapidly and at scale (Ho et al., 2021; Kok Koponen, Eija-Leena, MartínezBarbosa, Carmen Adriana, 2017). Comparative studies confirm that modern machine learning models consistently outperform traditional hedonic specifications in prediction accuracy (Yuan, Jian; Li, Ming; Zheng, 2022; Zhang, 2021). However, the managerial literature consistently emphasizes that automated models should not be treated as substitutes for professional judgement in complex valuation contexts. Research on digital technology adoption in valuation practice reveals that organizational implementation is shaped by managers' perceptions of technological value, available resources, and institutional constraints, with (A.-R. Amidu et al., 2025) finding 60% non-adoption rates among valuers surveyed in New Zealand. Institutional Governance and Regulatory Influences Institutional frameworks and professional standards exert a profound influence on how property valuation is structured, governed, and accountable. International standards developed by the IVSC and national frameworks established by RICS provide methodological guidance, reporting requirements, and ethical principles (Lorenz & Lützkendorf, 2011; McAllister, 2019). Adherence to professional standards serves both technical and legitimacy functions: technically reducing methodological variability and ML (MRMR, Bayesian) Higher accuracy; building age & location key drivers Survey (131 valuers) 60% non-adoption; driven by perceived opportunities institutionally signalling organizational credibility (Cheloti & Mooya, 2023; McAllister, 2019). The relationship between institutional governance and professional judgement in valuation is not without tension, as different institutional environments may produce different interpretations of standardized guidelines (Cheloti & Mooya, 2023). The integration of sustainability considerations into valuation represents an emerging governance challenge (Lorenz & Lützkendorf, 2011). Integrating the Findings: A Management Synthesis The thematic analysis collectively demonstrates that effective property valuation requires the integration of scientific methodology, professional judgement, and institutional governance. These dimensions are not mutually exclusive but represent complementary components of a hybrid management process. The cross-theme frequency analysis (Table 3) reveals that methodological studies are the most numerous (18 studies, 43%), followed by technology-focused studies (11 studies, 26%), with professional judgement (9 studies, 21%), behavioural bias (8 studies, 19%), and institutional governance (7 studies, 17%) receiving comparatively less systematic attention—confirming that the behavioural and governance streams have been underserved in the existing review literature and supporting the integrative contribution of the present study. Novelty And Contribution This study makes several contributions to the property valuation literature (see table 4). First, it provides the first comprehensive systematic synthesis integrating methodological, behavioural, and institutional perspectives within a management framework. While prior reviews (e.g., (Abidoye & Chan, 2017; Glumac & Des Rosiers, 2018) examined valuation models or big data applications, and empirical studies examined behavioural biases in isolation (e.g., (Gallimore, 1996); (Diaz & Hansz, 1997), no systematic review had combined these streams within a management decision-making framework. Second, this study introduces a management-oriented conceptual framework derived inductively from the thematic coding of the 42 included studies. The framework advances the theoretical understanding of valuation as a management process rather than purely a technical activity, and goes beyond a narrative summary by proposing directional interactions among the methodological, behavioural, and contextual dimensions. Third, by synthesizing recent developments in automated valuation technologies, machine learning methods, and digital transformation, this study provides timely insights for managers and organizations navigating the evolving landscape of valuation practice. Finally, this study highlights underexplored connections between management theories—including bounded rationality, dual-process theory, and organizational learning— and property valuation practice, identifying productive avenues for future interdisciplinary research.

Conclusion

This study has conducted a systematic literature review to examine how property valuation research conceptualizes the interaction between analytical methods and professional judgement within organizational management contexts. Using the PRISMA 2020 framework, 42 peer-reviewed articles were analyzed through three-stage thematic analysis (open, axial, and selective coding), yielding five major themes: methodological evolution, professional judgement and managerial decision-making, behavioural bias in appraisal management, technological transformation, and institutional governance. The findings demonstrate that property valuation is shaped by a dynamic interplay between the scientific dimension of analytical modelling and the managerialbehavioural dimension of professional judgement and organizational expertise. The cross-theme frequency analysis confirms that methodological advances have received the greatest research attention, while the integration of behavioural and governance perspectives remains underserved—validating the integrative contribution of this study. The proposed conceptual framework conceptualizes property valuation as a hybrid managerial decision-making process shaped by the interaction between scientific methods and behavioural expertise within an organizational and institutional context. This framework was derived inductively from the thematic coding of the 42 included studies and contributes to the management literature by providing a theoretically grounded model for understanding and improving valuation practice. Managerial Implications The findings carry several implications for practitioners and policymakers. Organizations should invest in training programs that develop both technical and judgement capabilities in valuation professionals. Quality assurance frameworks that mitigate cognitive biases—including blind reviews, peer assessment, and standardized presentation protocols—should be integrated into organizational valuation systems. Financial institutions and real estate organizations adopting automated valuation technologies should implement human-in-the-loop governance structures where References Abidoye, R. B., & Chan, A. P. C. (2017). Artificial neural network in property valuation: application framework and research trend. Property Management, 35(5), 554–571. https://doi.org/10.1108/PM-06-20160027 Amidu, A., & Tajudeen Aluko, B. (2007). Client influence in residential property valuations: an empirical study. Property Management, 25(5), 447–461. https://doi.org/10.1108/02637470710824720 Amidu, A.-R., & Boyd, D. (2018). Expert problem solving practice in commercial property valuation: an exploratory study. Journal of Property Investment & Finance, 36(4), 366–382. https://doi.org/10.1108/JPIF-05-2017-0037 Amidu, A.-R., Levy, D. S., Kasim, I., & Liman, H. S. (2025). Adoption of digital technologies in valuation practice: a survey of valuers’ perspectives in automated models serve as decision-support tools rather than autonomous valuation systems. Professional development programs should be expanded to include data literacy and technology management competencies alongside traditional appraisal skills. Policymakers and professional bodies should update valuation standards to address the governance challenges posed by artificial intelligence and automated valuation systems. Limitations And Future Research Directions This study has several limitations. As a systematic literature review, it is subject to publication bias, and findings are contingent on the quality and scope of studies included. The review focused primarily on English-language publications, which may limit coverage of important non-English research traditions. Additionally, the conceptual framework proposed has not been empirically validated, representing an important avenue for future research. Future research should empirically examine the interaction between automated valuation technologies and professional judgement in diverse organizational contexts using primary data. Longitudinal studies tracking how valuation practices evolve with technological change would provide important insights for both theory and practice. Comparative research across different institutional environments and regulatory regimes could illuminate how governance contexts shape valuation outcomes. Finally, interdisciplinary research integrating management theory, behavioural economics, and real estate science represents a productive direction for advancing the theoretical foundations of valuation.

Author Contributions

All authors contributed equally to this manuscript. Specifically, all authors participated jointly in: (1) conceptualization of the study design and systematic review protocol; (2) development of the research methodology, including the PRISMA framework, search strategy, and inclusion/exclusion criteria; (3) literature search, screening, and data extraction; (4) thematic analysis, coding, and interpretation of findings—including independent open coding, axial coding discussions, and peer debriefing for theme validation; (5) construction of the conceptual framework and derivation of research propositions; (6) writing, reviewing, and editing of all sections of the manuscript; and (7) approval of the final version for submission. No single author claims exclusive credit for any individual contribution.

Acknowledgements

The authors would like to express their sincere gratitude to Politeknik Keuangan Negara STAN (PKN STAN) for institutional support throughout the preparation of this manuscript. The authors also thank the editors and anonymous reviewers of the Ilomata International Journal of Management for their constructive feedback, which substantially improved the quality and rigour of this work.

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