Ratios were constructed relating the second lowest bid, mean bid, median bid, maximum bid to the low bid for highway construction projects in Texas to study if there are useful patterns in project bids that are indicators of the project completion cost. It was found that the value of the ratios tend to be larger for projects where the completed cost deviates significantly from the original low bid. Larger ratio values were found for projects that were completed with a greater than 20% cost increase and for projects that were completed with a reduction in cost from the original bid amount of 10% or more. Regression and neural network models were developed to predict the completed cost of Texas highway projects using the bidding ratio data. The input data used were transformed using the natural logarithm. Various combinations of the calculated ratios, as well as the project low bid were used as input to the models. The models tested produced predictions of varying accuracy. The models produced predictions that varied between having 41.02% and 54.01% of their test cases within 5% of the actual completed project cost. The model with the highest percentage of closely predicted cases was a regression model using the mean bid ratio, the coefficient of variation, and the low bid as inputs.