Project cost estimation using principal component regression

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.

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