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Construction cost estimation: A parametric approach for better estimates of expected cost and variation

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  • Swei, Omar
  • Gregory, Jeremy
  • Kirchain, Randolph

Abstract

As project planners continue to move towards frameworks such as probabilistic life-cycle cost analysis to evaluate competing transportation investments, there is a need to enhance the current cost-estimation approaches that underlie these models to enable improved project selection. This paper presents an approach for cost estimation that combines a maximum likelihood estimator for data transformations with least angle regression for dimensionality reduction. The authors apply the proposed method for 15 different pavement bid items across five states in the United States. The results from the study demonstrate that the proposed approach frequently leads to consistent parametric estimates that address the structural bias and heteroscedasticity that plague the current cost-estimation procedures. Both of these aspects are particularly important for large-scale construction projects, where traditional methods tend to systematically underestimate expected construction costs and overestimate the associated variance.

Suggested Citation

  • Swei, Omar & Gregory, Jeremy & Kirchain, Randolph, 2017. "Construction cost estimation: A parametric approach for better estimates of expected cost and variation," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 295-305.
    • Handle: RePEc:eee:transb:v:101:y:2017:i:c:p:295-305
    DOI: 10.1016/j.trb.2017.04.013
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    References listed on IDEAS

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    1. Congressional Budget Office, 2015. "Public Spending on Transportation and Water Infrastructure, 1956 to 2014," Reports 49910, Congressional Budget Office.
    2. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
    3. Swee Lean Chan & Moonseo Park, 2005. "Project cost estimation using principal component regression," Construction Management and Economics, Taylor & Francis Journals, vol. 23(3), pages 295-304.
    4. Margaret Emsley & David Lowe & A. Roy Duff & Anthony Harding & Adam Hickson, 2002. "Data modelling and the application of a neural network approach to the prediction of total construction costs," Construction Management and Economics, Taylor & Francis Journals, vol. 20(6), pages 465-472.
    5. Eliasson, Jonas & Fosgerau, Mogens, 2013. "Cost overruns and demand shortfalls – Deception or selection?," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 105-113.
    6. Congressional Budget Office, 2015. "Public Spending on Transportation and Water Infrastructure, 1956 to 2014," Reports 49910, Congressional Budget Office.
    7. Congressional Budget Office, 2015. "Public Spending on Transportation and Water Infrastructure, 1956 to 2014," Reports 49910, Congressional Budget Office.
    8. Congressional Budget Office, 2015. "Public Spending on Transportation and Water Infrastructure, 1956 to 2014," Reports 49910, Congressional Budget Office.
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    Cited by:

    1. Peyman Babashamsi & Shabir Hussain Khahro & Hend Ali Omar & Sri Atmaja P. Rosyidi & Abdulnaser M Al-Sabaeei & Abdalrhman Milad & Munder Bilema & Muslich Hartadi Sutanto & Nur Izzi Md Yusoff, 2022. "A Comparative Study of Probabilistic and Deterministic Methods for the Direct and Indirect Costs in Life-Cycle Cost Analysis for Airport Pavements," Sustainability, MDPI, vol. 14(7), pages 1-20, March.

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