Automated Time-Series Cost Forecasting System for Construction Materials
Publication: Journal of Construction Engineering and Management
Volume 138, Issue 11
Abstract
As large-scale building projects increase in frequency, their construction costs become a matter of great concern, especially because of their lengthy construction periods. In particular, recent volatile fluctuations of construction material prices have fueled problems like cost forecasting. Many researchers try to accurately estimate cost escalations, but price forecasting for numerous construction materials requires a simplified and automated process. The research in this paper develops an automated time-series material cost forecasting (ATMF) system including both autoselected procedures for determining a best-fitting model and an autoextracting module for forecasting values using the Box-Jenkins approach. If the modeling process is simplified and iterative arbitrary decisions for the modeler eliminated, each future prices of a large number of materials can be forecast differently. Thus, the ATMF system can be utilized for predicting future trends in construction material costs. Further, an out-of-sample forecast applying several material price data confirms that this system can be effectively applied to material cost estimation at a more detailed level in object-based cost planning. The proposed system can thus help decision makers in the construction industry deal with changes in economic conditions and design by estimating cost escalations caused by volatile factors such as inflation.
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Acknowledgments
This research was supported by a grant (Code No. 09 R&D A01) from Super-Tall Building R&D Project funded by the Ministry of Land, Transport and Maritime Affairs of the Korean government.
References
Akintoye, A., Bowen, P., and Hardcastle, C. (1998). “Macro-economic leading indicators of construction contract prices.” Constr. Manage. Econ., 16(2),159–175.
Akpan, E. O., and Igwe, O. (2001). “Methodology for determining price variation in project execution.” J. Constr. Eng. Manage., 127(5), 367–373.
Ashuri, B., and Lu, J. (2010). “Forecasting ENR construction cost index: A time series analysis approach.” Proc., Construction Research Congress 2010, ASCE, Reston, VA, 1345–1355.
Bowerman, B. L., O’Connell, R. T., and Koehler, A. B. (2005). Forecasting, time series, and regression: An applied approach, 4th Ed., Duxbury Press, Pacific Grove, CA, 401–590.
Box, G., and Jenkins, G. M. (1994). Time series analysis: Forecasting and control, 3rd Ed., Prentice Hall, Upper Saddle River, NJ.
Construction Association of Korea (CAK). (2008). “Statistics in production cost of completed construction projects.” 〈http://www.cak.or.kr〉 (Dec. 21, 2010).
Fan, R., Ng, T., and Wong, J. (2010). “Reliability of the Box-Jenkins model for forecasting construction demand covering times of economic austerity.” Constr. Manage. Econ., 28(3), 241–254.
Fellows, R. F. (1991). “Escalation management: Forecasting the effects of inflation on building projects.” Constr. Manage. Econ., 9(2), 187–204.
Hwang, S. (2009). “Dynamic regression models for prediction of construction costs.” J. Constr. Eng. Manage., 135(5), 360–367.
Hwang, S. (2010). “Cross-validation of short-term productivity forecasting methodologies.” J. Constr. Eng. Manage., 136(9), 1037–1046.
Hwang, S., and Liu, L. Y. (2010). “Contemporaneous time series and forecasting methodologies for predicting short-term productivity.” J. Constr. Eng. Manage., 136(9), 1047–1055.
Khosrowshahi, F., and Alani, A. M. (2003). “A model for smoothing time-series data in construction.” Constr. Manage. Econ., 21(5), 483–494.
Korea Price Information (KPI). (2011). “Construction material prices services.” 〈http://www.kpi.or.kr〉 (May 31, 2011) (in Korean).
Lu, T., and AbouRizk, S. M. (2009). “Automated Box-Jenkins forecasting modelling.” Autom. Constr., 18(5), 547–558.
Ng, S. T., Cheung, S. O., Skitmore, M., and Wong, T. C. (2004). “An integrated regression analysis and time series model for construction tender price index forecasting.” Constr. Manage. Econ., 22(5), 483–493.
Ranasinghe, M. (1996). “Total project cost: A simplified model for decision makers.” Constr. Manage. Econ., 14(6), 497–505.
Shane, J. S., Molenaar, K. R., Anderson, S., and Schexnayder, C. (2009). “Construction project cost escalation factors.” J. Manage. Eng., 25(4), 221–229.
Sonmez, R. (2008). “Parametric range estimating of building costs using regression models and bootstrap.” J. Constr. Eng. Manage., 134(12), 1011–1016.
Touran, A. (2003). “Probabilistic model for cost contingency.” J. Constr. Eng. Manage., 129(3), 280–284.
Trost, S., and Oberlender, G. (2003). “Predicting accuracy of early cost estimates using factor analysis and multivariate regression.” J. Constr. Eng. Manage., 129(2), 198–204.
Williams, T. P. (1994). “Predicting changes in construction cost indexes using neural networks.” J. Constr. Eng. Manage., 120(2), 306–320.
Wong, J., Chan, A., and Chiang, Y. (2005). “Time series forecasts of construction labour market in Hong Kong: The Box-Jenkins approach.” Constr. Manage. Econ., 23(9), 979–991.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 138 • Issue 11 • November 2012
Pages: 1259 - 1269
Copyright
© 2012 American Society of Civil Engineers.
History
Received: Sep 2, 2011
Accepted: Jan 31, 2012
Published online: Feb 2, 2012
Published in print: Nov 1, 2012
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