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Identifying non-competitive bids in construction contract auctions

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  • Skitmore, Martin

Abstract

Construction contract auctions are characterised by (1) anticipated high outliers due to the presence of non-competitive bids, (2) very small samples and (3) uncertainty of the appropriate underlying density function model of the bids. This paper describes the simultaneous identification of high outliers and density function by systematically identifying and removing candidate (high) outliers and examining the composite goodness-of-fit of the resulting reduced samples with the normal and lognormal density functions. Six different identification strategies are tested empirically by application, both independently and in pooled form, to several sets of auction data gathered from around the world. The results indicate the normal density to be the most appropriate model and a multiple of the auction standard deviation to be the best identification strategy.

Suggested Citation

  • Skitmore, Martin, 2002. "Identifying non-competitive bids in construction contract auctions," Omega, Elsevier, vol. 30(6), pages 443-449, December.
    • Handle: RePEc:eee:jomega:v:30:y:2002:i:6:p:443-449
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    References listed on IDEAS

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    1. Shmuel S. Oren & Michael H. Rothkopf, 1975. "Optimal Bidding in Sequential Auctions," Operations Research, INFORMS, vol. 23(6), pages 1080-1090, December.
    2. M. S. Mitchell, 1977. "The Probability of Being the Lowest Bidder," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 26(2), pages 191-194, June.
    3. Lawrence Friedman, 1956. "A Competitive-Bidding Strategy," Operations Research, INFORMS, vol. 4(1), pages 104-112, February.
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    Cited by:

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    2. Banaitiene, Nerija & Banaitis, Audrius & Kaklauskas, Arturas & Zavadskas, Edmundas Kazimieras, 2008. "Evaluating the life cycle of a building: A multivariant and multiple criteria approach," Omega, Elsevier, vol. 36(3), pages 429-441, June.
    3. Tomáš Hanák & Ivan Marović & Nikša Jajac, 2020. "Challenges of Electronic Reverse Auctions in Construction Industry—A Review," Economies, MDPI, vol. 8(1), pages 1-14, February.
    4. Odey Alshboul & Ali Shehadeh & Ghassan Almasabha & Ali Saeed Almuflih, 2022. "Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction," Sustainability, MDPI, vol. 14(11), pages 1-20, May.

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