Comparative Analysis of Cost Estimation Accuracy in Traditional vs. Building Information Modeling Methods

Thesis Abstract

Accurate cost estimation remains a fundamental challenge in construction project management, directly influencing project viability, budgeting, and stakeholder confidence. Despite advances in technology, discrepancies persist between estimated and actual costs, leading to financial overruns and compromised project success. This study investigates the comparative accuracy of cost estimation methods—traditional techniques and Building Information Modeling (BIM)—to determine which approach yields more precise forecasts and under what conditions. The primary aim is to assess the extent to which BIM enhances cost estimation accuracy over conventional procedures within the context of residential and commercial building projects. The specific objectives include (1) to evaluate the difference in cost estimation accuracy between traditional methods and BIM-supported approaches; (2) to identify key factors influencing estimation errors in both methodologies; (3) to analyze the relationship between project complexity and estimation accuracy within each method; and (4) to develop a predictive model of cost estimation accuracy based on selected variables. The study adopts a comparative cross-sectional research design integrated with quantitative analysis, focusing on construction projects completed within the last five years in an urban metropolitan region. The population comprises project managers, quantity surveyors, and BIM specialists involved in residential and commercial building projects, with a targeted sample size of 120 professionals selected through stratified random sampling to ensure representation across project scales and roles. Data collection instruments consist of structured questionnaires and cost estimation reports sourced from project documentation. The questionnaires are designed to capture respondents’ perceptions of estimation accuracy, factors influencing estimates, and methodological preferences. Cost data are collected from project records and compared to final project costs to quantify estimation errors. Data analysis employs descriptive statistics (mean, standard deviation) to summarize estimation errors, while inferential analysis utilizes paired sample t-tests to compare mean accuracy differences between methods. Multiple regression analysis is conducted using the Ordinary Least Squares (OLS) technique to identify significant predictors of estimation accuracy, guided by the Theory of Cost Planning and the Information Processing Theory. The models explore the influence of variables such as project complexity, experience level of estimators, and technological familiarity on estimation accuracy. Key expected findings include a statistically significant reduction in estimation errors when employing BIM-supported methods compared to traditional techniques. The analysis anticipates that project complexity and estimator expertise significantly moderate the accuracy outcomes. The study also aims to reveal that BIM’s integration facilitates better clash detection, improved quantity take-offs, and enhanced cost forecasting precision, supporting the hypothesis that technological integration improves estimation performance. This research contributes to the body of knowledge by providing empirical evidence on the efficacy of BIM in cost estimation, offering a rigorous basis for its wider adoption and integration within project management practices. The developed predictive models can guide practitioners in optimizing estimation processes, reducing financial risks, and improving project delivery outcomes. The main conclusion underscores the superiority of BIM-supported techniques in achieving more accurate cost forecasts, particularly for complex projects, and recommends targeted training for professionals to maximize BIM’s potential. Additionally, the study advocates for the development of organizational policies favoring the systematic adoption of BIM in cost management workflows, and suggests avenues for future research examining long-term impacts across different project types and geographic contexts.