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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/7993

Title: Contractors' bidding behaviour and tender price prediction
Authors: McCaffer, Ron
Issue Date: 1976
Publisher: © Ronald McCaffer
Abstract: Data relating to the bids for 384 roads contracts and 190 buildings contracts and a library of individual unit prices were obtained. The normality or near normality of the distribution of bids for buildings and roads contracts is established. This allows the relationship between mean and lowest bids to be defined using normal order statistics. It also permits the application of outlier tests to be used in identifying unrealistically low bids. The average mean standardised bids of contractors have a strong negative correlation with the contractor's success ratio. This allows contractors to predict success ratios of others using their mean-standardised bids. The data required for this is not limited to the competitions in which the contractor himself enters. Contractors have different behaviour patterns, some with disproportionate numbers of high or low bids and others behave randomly. These behaviour features correlate well with the average mean-standardised bids. Graphs of the cumulative sum of (bid-mean bid)/mean bid are useful in identifying contractors who are seeking work and those who are not. These can be used to identify serious rivals for particular contracts. Contractors have different sensitivity of success ratio to changes in bid value thus indicating different market judgements. Contractors also have different trends within their standardised bids to contract value. This only affects success ratios in extreme cases. Designers have accuracies of standard deviations of 16.63% and 20.14% for predicting the lowest bid of buildings and roads contracts respectively. Price models based on multiple regression analysis produce similar accuracies for comparable construction works. The tender price prediction system developed, based on a library of, untt prices and inflation indices achieved a standard deviation of 8,30% in predicting the mean bid and 11.08% in predicting the lowest bid for roads contracts. This could be improved with more data in the price library but nevertheless is a substantial improvement on the results achieved by designer's estimating.
Description: A Doctoral Thesis. Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Loughborough University.
URI: https://dspace.lboro.ac.uk/2134/7993
Appears in Collections:PhD Theses (Civil and Building Engineering)

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