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Project cost estimation using principal component regression

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  • Swee Lean Chan
  • Moonseo Park

Abstract

Factors affecting construction project cost include project-specific factors and those reflecting the characteristics of the project team. Multiple regression is often used to estimate a project's cost, but independent variables with a high degree of correlation are likely be left out of such a model. As a result, only a limited number of factors are included in the estimate of project cost and predictions from such models will not be accurate. To overcome this technical inefficiency, the aims of this study are: to identify factors that contribute to project cost, to construct a predictive project cost model using the principal component technique and to assess the relative importance of determining factors. The data are obtained from a random sample survey comprised of Singapore building projects completed after 1992 costing more than US$5 million in value. Three main groups of variables are identified, pertaining to characteristics of the project, contractors and owner/consultants. Special project requirements such as high technological level; contractor's specialized skills; and public administered contract have significant effects on cost. Other factors include contractor's technical expertise; owner's level of construction sophistication and contractor's financial management ability. The model assesses the impact of individual factors on project cost and provides a decision support tool to estimate cost more accurately.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:conmgt:v:23:y:2005:i:3:p:295-304
    DOI: 10.1080/01446190500039812
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    References listed on IDEAS

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    1. Peter Kaming & Paul Olomolaiye & Gary Holt & Frank Harris, 1997. "Factors influencing construction time and cost overruns on high-rise projects in Indonesia," Construction Management and Economics, Taylor & Francis Journals, vol. 15(1), pages 83-94.
    2. Akintola Akintoye, 2000. "Analysis of factors influencing project cost estimating practice," Construction Management and Economics, Taylor & Francis Journals, vol. 18(1), pages 77-89.
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    2. Nyoni, Thabani, 2019. "Cost overrun factors in construction industry: a case of Zimbabwe," MPRA Paper 96788, University Library of Munich, Germany.
    3. Smiritee Srivastava & Smiritee Srivastava, 2020. "Analysis of Various Major Contributing Factors of Cost Overrun in Construction Projects," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 7(9), pages 28-33, September.
    4. Yu Yu & Nita Umashankar & Vithala R. Rao, 2016. "Choosing the right target: Relative preferences for resource similarity and complementarity in acquisition choice," Strategic Management Journal, Wiley Blackwell, vol. 37(8), pages 1808-1825, August.
    5. 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.
    6. C.G. Hart & Z. He & R. Sbragio & N. Vlahopoulos, 2012. "An advanced cost estimation methodology for engineering systems," Systems Engineering, John Wiley & Sons, vol. 15(1), pages 28-40, March.
    7. Minhyuk Jung & Shira You & Seokho Chi & Ilhan Yu & Bon-Gang Hwang, 2018. "The Relationship between Unbilled Accounts Receivable and Financial Performance of Construction Contractors," Sustainability, MDPI, vol. 10(8), pages 1-14, July.
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    10. Qiao, Yu & Fricker, Jon D. & Labi, Samuel, 2019. "Effects of bundling policy on project cost under market uncertainty: A comparison across different highway project types," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 606-625.

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