Quick abnormal-bid-detection method for construction contract auctions

Noncompetitive bids have recently become a major concern in both public and private sector construction contract auctions. Consequently, several models have been developed to help identify bidders potentially involved in collusive practices. However, most of these models require complex calculations and extensive information that is difficult to obtain. The aim of this paper is to utilize recent developments for detecting abnormal bids in capped auctions (auctions with an upper bid limit set by the auctioner) and extend them to the more conventional uncapped auctions (where no such limits are set). To accomplish this, a new method is developed for estimating the values of bid distribution supports by using the solution to what has become known as the German Tank problem. The model is then demonstrated and tested on a sample of real construction bid data, and shown to detect cover bids with high accuracy. This paper contributes to an improved understanding of abnormal bid behavior as an aid to detecting and monitoring potential collusive bid practices

Estimating the number of new and repeated bidders in construction auctions

The number of new bidders – bidders from whom there is no previous registered participation – is an important variable in most bid tender forecasting models, since the unknown competitive profile of the former strongly limits the predictive accuracy of the latter. Analogously, when a bidder considers entering a bid or when an auctioneer is handling a procurement auction, assessing the likely proportion of experienced bidders is considered an important aspect, as some strategic decisions or even the awarding criteria might differ. However, estimating the number of bidders in a future auction that have not submitted a single bid yet is difficult, since there is no data at all linking their potential participation, an essential requirement for the implementation of any forecasting or estimation method. A practical approach is derived for determining the expected proportion of new bidders to frequent bidders as a function of the population of potential bidders. A multinomial model useful for selective and open tendering is proposed and its performance is validated with a dataset of actual construction auctions. Final remarks concern the valuable information provided by the model to an enduring unsolved bidding problem and the prospects for new research continuations

On the distribution of bids for construction contract auctions

The statistical distribution representing bid values constitutes an essential part of many auction models and has involved a wide range of assumptions, including the Uniform, Normal, Lognormal and Weibull densities. From a modelling point of view, its goodness is defined by how well it enables the probability of a particular bid value to be estimated – a past bid for ex-post analysis and a future bid for ex-ante (forecasting) analysis. However, there is no agreement to date of what is the most appropriate form and empirical work is sparse. Twelve extant construction datasets from four continents over different time periods are analysed in this paper for their fit to a variety of candidate statistical distributions assuming homogeneity of bidders (ID not known). The results show there is no one single fit-all distribution, but that the 3p Log-Normal, Fréchet/2p Log-Normal, Normal, Gamma and Gumbel generally rank the best ex-post, and the 2p Log-Normal, Normal, Gamma and Gumbel the best ex-ante – with ex-ante having around three to four times worse fit than ex-post. Final comments focus on the results relating to the third and fourth standardised moments of the bids and a post-hoc rationalisation of the empirical outcome of the analysis

Generalised gamma bidding model

A generalised gamma bidding model is presented, which incorporates many previous models. The log likelihood equations are provided. Using a new method of testing, variants of the model are fitted to some real data for construction contract auctions to find the best fitting models for groupings of bidders. The results are examined for simplifying assumptions, including all those in the main literature. These indicate no one model to be best for all datasets. However, some models do appear to perform significantly better than others and it is suggested that future research would benefit from a closer examination of these