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AbouRizk, S and Shi, J (1994) Automated Construction‐Simulation Optimization. Journal of Construction Engineering and Management, 120(02), 374–85.

AbouRizk, S M, Halpin, D W and Wilson, J R (1994) Fitting Beta Distributions Based on Sample Data. Journal of Construction Engineering and Management, 120(02), 288–305.

Bai, Y and Amirkhanian, S N (1994) Knowledge‐Based Expert System for Concrete Mix Design. Journal of Construction Engineering and Management, 120(02), 357–73.

Everett, J G and Slocum, A H (1994) Automation and Robotics Opportunities: Construction versus Manufacturing. Journal of Construction Engineering and Management, 120(02), 443–52.

Farid, F and Koning, T L (1994) Simulation Verifies Queuing Program for Selecting Loader‐Truck Fleets. Journal of Construction Engineering and Management, 120(02), 386–404.

Furuya, N, Yamaoka, R and Paulson, B C (1994) Construction of Akashi‐Kaikyo Bridge West Anchorage. Journal of Construction Engineering and Management, 120(02), 337–56.

Hinze, J and Tracey, A (1994) The Contractor‐Subcontractor Relationship: The Subcontractor's View. Journal of Construction Engineering and Management, 120(02), 274–87.

Ndekugri, I and Turner, A (1994) Building Procurement by Design and Build Approach. Journal of Construction Engineering and Management, 120(02), 243–56.

Nishigaki, S, Vavrin, J, Kano, N, Haga, T, Kunz, J C and Law, K (1994) Humanware, Human Error, and Hiyari‐Hat: A Template of Unsafe Symptoms. Journal of Construction Engineering and Management, 120(02), 421–42.

Pin, T H and Scott, W F (1994) Bidding Model for Refurbishment Work. Journal of Construction Engineering and Management, 120(02), 257–73.

  • Type: Journal Article
  • Keywords: Statistical models; Bids; Distribution functions; Renovation; Contracts;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(1994)120:2(257)
  • Abstract:
    This paper develops a simple statistical model for competitive bidding in the building industry. We take a statistical approach, using a large set of actual bids (1,350 renovation contracts) collected by the Builders' Conference in London, United Kingdom. The distribution of bids is fitted to a normal curve, from which one may estimate the distribution of the lowest of n bids (representing a given contractor's competitors). Part of the study involves the estimation of various parameters, such as the coefficient of variation, which is a measure of the relative spread of bids. A simple formula is obtained for the bid that has a specified chance of success (e.g. 20%, 50%, or 90%), and the theory is tested on data from five contractors. A likely consequence of the adoption of the proposed models by the industry in general would be a tendency toward tighter bidding, i.e. the difference between the winning bid and the next lowest (which is, in a sense, a loss to the construction industry caused by variations in bids) would be reduced.

Severson, G D, Russell, J S and Jaselskis, E J (1994) Predicting Contract Surety Bond Claims Using Contractor Financial Data. Journal of Construction Engineering and Management, 120(02), 405–20.

Thomas, H R, Smith, G R and Mellott, R E (1994) Interpretation of Construction Contracts. Journal of Construction Engineering and Management, 120(02), 321–36.

Williams, T P (1994) Predicting Changes in Construction Cost Indexes Using Neural Networks. Journal of Construction Engineering and Management, 120(02), 306–20.