Bidding ratios to predict highway project costs

Abstract

Purpose

Ratios were constructed using bidding data for highway construction projects in Texas to study whether there are useful patterns in project bids that are indicators of the project completion cost. The use of the ratios to improve predictions of completed project cost was studied.

Design/methodology/approach

Ratios were calculated relating the second lowest bid, mean bid, and maximum bid to the low bid for the highway construction projects. Regression and neural network models were developed to predict the completed cost of the highway projects using bidding data. Models including the bidding ratios, low bid, second lowest bid, mean bid and maximum bid were developed. Natural log transformations were applied to the data to improve model performance.

Findings

Analysis of the bidding ratios indicates some relationship between high values of the bidding ratios and final project costs that deviate significantly from the low bid amount. Addition of the ratios to neural network and regression models to predict the completed project cost were not found to enhance the predictions. The best performing regression model used only the low bid as input. The best performing neural network model used the low bid and second lowest bid as inputs.

Originality/value

The nature of bid ratios that can describe the pattern of bids submitted for a project and the relationship of the ratios to project outcomes were studied. The ratio values may be useful indicators of project outcome that can be used by construction managers.

Citation

Williams, T.P. (2005), "Bidding ratios to predict highway project costs", Engineering, Construction and Architectural Management, Vol. 12 No. 1, pp. 38-51. https://doi.org/10.1108/09699980510576880