Predicting Construction Cost Using Multiple Regression Techniques
Publication: Journal of Construction Engineering and Management
Volume 132, Issue 7
Abstract
This paper describes the development of linear regression models to predict the construction cost of buildings, based on 286 sets of data collected in the United Kingdom. Raw cost is rejected as a suitable dependent variable and models are developed for , log of cost, and log of . Both forward and backward stepwise analyses were performed, giving a total of six models. Forty-one potential independent variables were identified. Five variables appeared in each of the six models: gross internal floor area (GIFA), function, duration, mechanical installations, and piling, suggesting that they are the key linear cost drivers in the data. The best regression model is the log of cost backward model which gives an of 0.661 and a mean absolute percentage error (MAPE) of 19.3%; these results compare favorably with past research which has shown that traditional methods of cost estimation have values of MAPE typically in the order of 25%.
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Acknowledgments
The writers gratefully acknowledge the support of the Engineering and Physical Sciences Research Council (EPSRC), who funded the research through two grants; our industrial collaborators: Paul Moore—EC Harris, the late Chris Powell—formerly with Tweeds (then Faithful and Gould), Alun Williams—Symonds, and Joe Martin—Building Cost Information Service (BCIS); and the contribution made by the research assistants: Mick Gregory and Adam Hickson and by Dr. Roy Duff, former senior lecturer, Manchester Centre for Civil and Construction Engineering, UMIST.
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Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 132 • Issue 7 • July 2006
Pages: 750 - 758
Copyright
© 2006 ASCE.
History
Received: Mar 23, 2004
Accepted: Apr 29, 2005
Published online: Jul 1, 2006
Published in print: Jul 2006
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