Analyzing bidding statistics to predict completed project cost

Trefor P. Williams, Sudha Lakshminarayanan, Harold Sackrowitz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationComputing in Civil Engineering - Proceedings of the 2005 International Conference
EditorsL. Soibelman, F. Pena-Mora
Pages1671-1680
Number of pages10
StatePublished - 2005
Event2005 ASCE International Conference on Computing in Civil Engineering - Cancun, Mexico
Duration: Jul 12 2005Jul 15 2005

Publication series

NameProceedings of the 2005 ASCE International Conference on Computing in Civil Engineering

Other

Other2005 ASCE International Conference on Computing in Civil Engineering
Country/TerritoryMexico
CityCancun
Period7/12/057/15/05

All Science Journal Classification (ASJC) codes

  • General Engineering

Keywords

  • Bidding
  • Construction Costs
  • Highways
  • Neural Networks
  • Statistical Analysis

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