Using bidding statistics to predict completed construction cost

Michael G. Wright, Trefor Williams

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The completed cost of a competitively bid construction project often exceeds the original low bid. This paper presents two models to predict completed construction cost based upon characteristics of the submitted bids. Data on completed projects were obtained from New Jersey Department of Transportation for 298 highway construction projects. Median bid and normalized median absolute deviation (NMAD) were selected from various bid characteristics as the best predictors of completed construction cost. Regression and neural network models were developed from the data. Both models have similar utility to predict completed costs. Due to ease of use, the regression model is preferred over the neural network model.

Original languageEnglish (US)
Pages (from-to)114-128
Number of pages15
JournalEngineering Economist
Volume46
Issue number2
DOIs
StatePublished - Dec 1 2001

All Science Journal Classification (ASJC) codes

  • Education
  • Engineering(all)
  • Economics and Econometrics

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