Assessing the Impact of Building Information Modeling on Cost Estimation Accuracy

Thesis Abstract

Accurate cost estimation remains a critical challenge in construction project management, directly influencing project feasibility, stakeholder satisfaction, and overall financial performance. Despite advances in project delivery methods, traditional estimation techniques often exhibit variability and inaccuracies, leading to cost overruns and project delays. Building Information Modeling (BIM) has emerged as a transformative technology promising enhanced visualization, data integration, and process efficiencies that could significantly improve cost estimation accuracy. However, empirical assessments of BIM's impact in this domain are limited, particularly in contexts where BIM adoption is still evolving. This study aims to systematically evaluate the extent to which BIM influences the accuracy of cost estimates in construction projects, specifically within large-scale residential developments in metropolitan regions. The research objectives include (1) determining the comparative accuracy of cost estimates produced with and without BIM; (2) identifying critical factors that mediate BIM's effectiveness in cost estimation; (3) examining the relationship between BIM maturity levels and estimation precision; and (4) providing actionable recommendations for practitioners and policymakers to optimize BIM implementation for cost management. The study is guided by the hypothesis that BIM use significantly improves cost estimation accuracy, with the relationship moderated by factors such as BIM expertise, data quality, and project complexity. The research adopts a mixed-methods approach, integrating quantitative and qualitative analyses to ensure comprehensive insights. Quantitatively, the study utilizes a cross-sectional design comprising data from 56 recent large-scale residential projects, of which 28 employed BIM-based estimating techniques while 28 relied on traditional methods. Data sources include detailed project documentation, cost estimates, and actual project expenditure records obtained through collaboration with construction firms and industry associations. The primary instrument for data collection is a structured data extraction template, validated through expert review and pilot testing for internal consistency and construct validity. Descriptive statistics summarize cost estimate deviations, while inferential analyses—specifically multiple regression models and Analysis of Variance (ANOVA)—test the significance of differences in accuracy between BIM and non-BIM projects, controlling for confounding variables such as project size and complexity. Qualitative data are gathered through semi-structured interviews with 15 project managers, cost estimators, and BIM specialists. Thematic analysis, following Braun and Clarke’s methodology, explores contextual factors influencing BIM efficacy. The integration of these findings enables a nuanced understanding of technological and organizational determinants affecting estimation results. Expected results suggest that projects utilizing BIM demonstrate statistically significant reductions in cost estimation errors, with the most notable improvements observed at higher BIM maturity levels. The analysis is anticipated to reveal that BIM’s positive impact is mediated by organizational factors such as staff expertise, software interoperability, and process standardization. These outcomes will extend existing knowledge by empirically validating the benefits of BIM in cost estimation, contributing to the theoretical understanding of technology-organization interactions aligned with the Technology-Organization-Environment (TOE) framework and Diffusion of Innovations theory. The study’s findings will contribute practical guidance for construction professionals aiming to enhance cost accuracy through BIM adoption and will inform policy directives on industry standards and training programs. The research concludes with recommendations emphasizing targeted investments in staff training, BIM process integration, and software interoperability enhancements. Furthermore, it advocates for longitudinal investigations to assess the long-term impact of BIM maturity on project outcomes. Overall, this research underscores the strategic importance of BIM as a transformative tool in achieving reliable and efficient cost management in construction projects, thereby supporting sustainable industry practices and improved project performance.