Dynamic Regression Models for Prediction of Construction Costs
Publication: Journal of Construction Engineering and Management
Volume 135, Issue 5
Abstract
Accurate prediction of construction costs in the market is essential to effectively estimate costs for construction projects. In the construction industry, cost indexes that are reported in series are often used to explain the change of construction costs. By tracking the trend of such quantitative contemporaneous cost index and making frequent and regular forecasts of the future values of the index, one can develop a deeper understanding of prices of resources used for construction. Incorporating such an understanding and prediction into estimating will help practitioners manage construction costs. This paper proposes two dynamic regression models for the prediction of construction cost index. Comparison of the proposed models with the existing methods proves that the new models provide several advantages and improvements.
Get full access to this article
View all available purchase options and get full access to this article.
References
Abraham, B., and Ledolter, J. (1983). Statistical methods for forecasting, Wiley, New York.
Dawood, N., and Molson, A. (1997). “An integrated approach to cost forecasting and construction planning for the construction industry.” Proc., 4th Congress on Computing in Civil Engineering, ASCE, New York, 535–542.
Diekmann, J. E. (1983). “Probabilistic estimating: Mathematics and applications.” J. Constr. Eng. Manage., 109(3), 297–308.
Federal Reserve Board. (2006). “Federal Reserve statistical release—Selected interest rates.” ⟨http://www.federalreserve.gov/⟩ (Feb. 2006).
Flood, I. (1997). “Modeling uncertainty in cost estimates: A universal extension of the central limit theorem.” Proc., 4th Congress on Computing in Civil Engineering, ASCE, New York, 551–558.
Grogan, T. (1992). “Keeping track of a moving target.” ENR, 228(13), 42–47.
Hegazy, T., and Petzold, K. (2003). “Genetic optimization for dynamic project control.” J. Constr. Eng. Manage., 129(4), 396–404.
Isidore, L. J., and Back, W. E. (2001). “Probabilistic optimal-cost scheduling.” J. Constr. Eng. Manage., 127(6), 431–437.
Koppula, S. D. (1981). “Forecasting engineering costs: Two case studies.” J. Constr. Div., 107(CO4), 733–743.
Ostwald, P. E. (1984). Cost estimating, Prentice-Hall, Englewood Cliffs, N.J.
Pankratz, A. (1991). Forecasting with dynamic regression models, Wiley, New York.
Rao, G. N., and Grobler, F. (1997). “Integrated analysis of cost risk and schedule risk.” Proc., 4th Congress on Computing in Civil Engineering, ASCE, New York, 1404–1411.
U.S. Census Bureau. (2006). “Housing starts for regions and states.” ⟨http://www.census.gov/const/www/newresconstindex.html⟩ (Dec. 2006).
U.S. Department of Labor. (2006). “Consumer price indexes.” ⟨http://www.bls.gov/cpi/home.htm⟩ (Dec. 2006).
Weisberg, S. (1985). Applied linear regression, Wiley, New York.
Williams, T. P. (1994). “Predicting changes in construction cost indexes using neural networks.” J. Constr. Eng. Manage., 120(2), 306–320.
Wilmot, C. G., and Cheng, G. (2003). “Estimating future highway construction costs.” J. Constr. Eng. Manage., 129(3), 272–279.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 135 • Issue 5 • May 2009
Pages: 360 - 367
Copyright
© 2009 ASCE.
History
Received: Feb 27, 2008
Accepted: Aug 23, 2008
Published online: May 1, 2009
Published in print: May 2009
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.