Impact of BIM on Cost Forecasting Accuracy in Public Construction Projects

Thesis Abstract

The study investigates how Building Information Modeling (BIM) adoption influences the accuracy of cost forecasting in public construction projects within a metropolitan region, addressing persistent cost overruns and limited integration between design and cost management. The aim is to determine the extent to which BIM-based Cost Estimation, Clash Detection, and 5D BIM integration improve forecast accuracy compared with traditional 3D/2D approaches, and to identify organizational and project-level factors that modulate this effect. Specific objectives include (1) to quantify the relationship between BIM maturity and cost forecast error (variance between forecasted and actual final costs) across 60 publicly procured projects completed in the last five years; (2) to examine how BIM-enabled quantity take-off precision, schedule-cost integration, and data interoperability influence forecast accuracy; (3) to assess the moderating roles of project size, procurement method, and team readiness on BIM effectiveness; and (4) to propose a domain-specific framework for benchmarking BIM-driven cost estimation practices in the public sector. The study adopts an explanatory sequential mixed-methods design. A quantitative phase analyzes secondary project data and survey responses from 120 professionals (cost estimators, project managers, BIM coordinators) drawn from 30 agencies and 60 completed public projects, employing multiple linear regression and hierarchical modeling to test hypotheses about BIM maturity, estimation accuracy, and variance components. A qualitative phase uses semi-structured interviews with 18 senior practitioners and 6 BIM program managers to explore underlying mechanisms and contextual factors, analyzed through thematic analysis guided by the Technology-Organization-Environment (TOE) framework and the Contingency Theory perspective. Data collection combines project documentation (budgets, final accounts, BIM execution plans), survey instruments validated for construct reliability (Cronbach’s alpha > 0.70), and interview transcripts subjected to blind coding and triangulation with documentary evidence. Validity and reliability are enhanced through pilot testing of instruments, triangulation across data sources, and sensitivity analyses addressing outliers and multicollinearity. The analytical strategy integrates regression diagnostics, effect size estimation, and model comparison (AIC/BIC) to identify the strongest predictors of forecast accuracy, while the qualitative data provide explanatory depth for observed empirical patterns. Anticipated findings include a statistically significant negative association between BIM maturity levels and cost forecast error, with 5D BIM and integrated cost-management workflows showing the largest improvements; the magnitude of improvement is expected to be moderated by project complexity and organizational readiness, and amplified in agencies with standardized BIM protocols and centralized data repositories. The study contributes to knowledge by offering empirical evidence on the cost-management benefits of BIM in the public sector, refining the theoretical understanding of technology-driven value creation in built-environment projects, and articulating a pragmatic framework for benchmarking BIM-enabled cost estimation practices. Methodologically, it advances empirical mixed-methods research in construction management by integrating robust quantitative estimation with theory-driven qualitative inquiry to unpack the causal mechanisms linking BIM to forecasting performance. The main conclusion is that mature, interoperable BIM ecosystems embedded within standardized processes materially enhance the accuracy of cost forecasts in public construction, thereby reducing contingency reliance and improving public value. Recommendations include (1) fostering BIM maturity through formal training, standardized data schemas, and centralized data governance; (2) integrating 5D BIM with rigorous cost-estimation protocols and early involvement of quantity surveyors in BIM planning; (3) developing sector-specific BIM maturity benchmarks and continuous improvement dashboards; and (4) encouraging procurement policies that incentivize accurate early-stage cost forecasting and BIM-enabled transparency.