Predicting construction cost growth in ODOT's paving projects using information available at the bidding time

Scope and method of study. The purpose of this study was to identify factors that may cause cost growth in Oklahoma Department of Transportation (ODOT) paving projects, determine the relationship among the factors, and develop a statistical model to predict the amount of cost growth based on the factors. Two questionnaires were developed and distributed to ODOT engineers and highway contractors to collect data to identify factors that may cause cost growth. An analysis was performed on the collected data to determine a final list of cost factors. Project information related to the cost factors was collected and analyzed to determine the relationship among the factors. A statistical model was then developed to predict the amount of cost growth for ODOT paving projects based on the information available at the bidding time. Findings and conclusions. The analysis of the participants' responses to the questionnaires identified 33 factors that may lead to cost growth in ODOT paving projects. Of the 33 factors, 22 were unique to ODOT engineers, 28 unique to contractors, and 17 related to both groups. “Project size” was found to be the most influencing factor according to engineers, whereas “availability of labor” was found to be the most influencing factor according to contractors. Information related to 22 factors was found in ODOT files on each of the 266 paving projects involved in the study. These 22 cost factors were used in the analysis as independent variables. Principal components analysis, factor analysis, and regression analysis were performed. The analysis indicated that 8 of the 22 factors were found to be significant in predicting the cost growth of ODOT paving projects. The factors were ratio of projects with overrun, average overrun, average cost growth, contract amount, project duration, number of bids, contract difference ratio, and resident engineer. The first 3 factors apply to “contractor”, the fourth and fifth apply to “project size”, the sixth and seventh apply to “bidding environment”, and the eighth factor applies to “resident engineer”. The factors related to the “contractor” were found to have the most impact on cost growth followed by “project size”, “bidding environment”, and “resident engineer” respectively. A numerical model was then developed based on the 8 predictor variables to predict the amount of cost growth in ODOT paving projects using information available at the bidding time.