Estimating Performance and Payment Bond Premiums in Construction Projects Using Machine Learning

Contract performance and payment bonds are essential components that protect project stakeholders financially. For contractors, bond premiums represent a significant financial commitment; thus, misestimating these costs can lead to financial strain, unbalanced bids, or lost opportunities. This study develops machine learning algorithms to predict bid ranges for bond premiums, including minimum and maximum estimates. Data from the Ohio Department of Transportation was used to develop and tune 21 regressive machine learning models. Ridge regression and orthogonal matching pursuit emerged as the best-performing models for predicting bond premium bid values. These predictions provide contractors with valuable insights for strategic decision-making, enabling them to secure favorable terms and improve competitiveness. The findings enhance risk mitigation strategies by aligning bond values with project-specific characteristics, reducing financial disputes and delays. By addressing the economic challenges of bond premium estimation, the study contributes to the financial stability and successful delivery of construction projects.