Predicting City-Level Construction Cost Index Using Linear Forecasting Models
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 2
Abstract
Because of the importance of budget planning and contract bidding, accurate prediction of future movements of the construction cost index (CCI) has been a crucial part of construction cost management. Despite the fact that construction costs can vary widely across locations with different market conditions and environments, the national CCI, the simple average of construction costs for 20 US metropolitan areas, has often been used to forecast CCIs across the nation. This study finds considerable differences across US cities in both the level and growth rates of CCIs and shows that using the national CCI for predicting city-level CCIs can cause nonnegligible forecast errors. Comparing four popular linear forecasting models, including standard autoregressive integrated moving average (ARIMA) models and a multivariate vector error correction model (VECM) drawn on monthly CCI data from January 1995 to December 2019, this study reveals that no single model is dominant in terms of the out-of-sample performance. If any leading indicator is available at the city level, however, it is recommended to choose between the ARIMA model based on the Bayesian information criterion (BIC) rule and the multivariate VECM augmented with the leading indicators. For the leading indicator in multivariate VECM, city consumer price index (CPI) works better than national CPI. In the absence of such leading indicators, it is recommended to use a parsimonious ARIMA model based on the BIC rule. The proposed approach is expected to help decision makers in the construction industry prepare more accurate budgets and biddings for regional construction projects.
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Data Availability Statement
Acknowledgments
The authors are grateful to the three anonymous reviewers for their helpful comments and suggestions. The authors also thank Ms. Sooin Kim (Ph.D. student at the University of Texas at Arlington) for her assistance, which contributed to this study. The research reported in this paper was conducted with the aid of a College of Business Impactful Research Grant from the University of Texas at Arlington, which Choi gratefully acknowledges.
References
Ashuri, B., and J. Lu. 2010. “Time series analysis of ENR construction cost index.” J. Constr. Eng. Manage. 136 (11): 1227–1237. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000231.
Ashuri, B., S. M. Shahandashti, and J. Lu. 2012. “Empirical tests for identifying leading indicators of ENR construction cost index.” Constr. Manage. Econ. 30 (11): 917–927. https://doi.org/10.1080/01446193.2012.728709.
Ballesteros-Pérez, P., M. L. del Campo-Hitschfeld, D. Mora-Melià, and D. Domínguez. 2015. “Modeling bidding competitiveness and position performance in multi–attribute construction auctions.” Oper. Res. Perspect. 2 (Dec): 24–35. https://doi.org/10.1016/j.orp.2015.02.001.
Cao, M.-T., M.-Y. Cheng, and Y.-W. Wu. 2015. “Hybrid computational model for forecasting Taiwan construction cost index.” J. Constr. Eng. Manage. 141 (4): 04014089. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000948.
Carr, P. G. 2005. “Investigation of bid price competition measured through prebid project estimates, actual bid prices, and number of bidders.” J. Constr. Eng. Manage. 131 (11): 1165–1172. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:11(1165).
Choi, C.-Y., and A. Hansz. 2020. From banking integration to housing market integration—Evidence from U.S. metropolitan housing prices. Arlington, TX: The Univ. of Texas at Arlington.
Choi, C.-Y., L. Hu, and M. Ogaki. 2008. “Robust estimation for structural spurious regressions and a Hausman—Type cointegration test.” J. Econom. 142 (1): 327–351. https://doi.org/10.1016/j.jeconom.2007.06.003.
Choi, K., and E.-B. Lee. 2008. Innovative contracting methods implementation studies. Berkeley, CA: Univ. of California.
Enders, W. 2014. Applied econometric time series. Hoboken, NJ: Wiley.
ENR (Engineering News-Record). 2020. “Construction economics.” Accessed February 11, 2020. https://enr.construction.com/economics.
Faghih, S. A. M., and H. Kashani. 2018. “Forecasting construction material prices using vector error correction model.” J. Constr. Eng. Manage. 144 (8): 04018075. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001528.
Forcada, N., M. Gangolells, M. Casals, and M. Macarulla. 2017. “Factors affecting rework costs in construction.” J. Constr. Eng. Manage. 143 (8): 04017032. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001324.
Greenaway-McGrevy, R., C. Han, and D. Sul. 2012. “Estimating the number of common factors in serially dependent approximate factor models.” Econ. Lett. 116 (3): 531–534. https://doi.org/10.1016/j.econlet.2012.03.031.
Hwang, S. 2011. “Time series models for forecasting construction costs using time series indexes.” J. Constr. Eng. Manage. 137 (9): 656–662. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000350.
Hwang, S., S. Park, H.-S. Lee, and H. Kim. 2012. “Automated time-series cost forecasting system for construction materials.” J. Constr. Eng. Manage. 138 (11): 1259–1269. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000536.
Joukar, A., and I. Nahmens. 2016. “Volatility forecast of construction cost index using general autoregressive conditional heteroskedastic method.” J. Constr. Eng. Manage. 142 (1): 04015051. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001020.
KPMG (Klynveld Peat Marwick Goerdeler). 2015. “2015 global construction project owner’s survey. “Accessed January 16, 2020. https://assets.kpmg/content/dam/kpmg/pdf/2015/05/construction-survey-201502.pdf.
McCracken, M. W., and S. Ng. 2016. “FRED–MD: A monthly database for macroeconomic research.” J. Bus. Econ. Stat. 34 (4): 574–589. https://doi.org/10.1080/07350015.2015.1086655.
McEniry, G., and W. Ibbs. 2007. “The cumulative effect of change orders on labour productivity: The Leonard study ‘reloaded’.” Revay Rep 26 (1): 1–8. https://doi.org/10.1.1.515.1728&rep=rep1&type=pdf.
Shahandashti, S., and B. Ashuri. 2016. “Highway construction cost forecasting using vector error correction models.” J. Manage. Eng. 32 (2): 04015040. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000404.
Shane, J. S., K. R. Molenaar, S. Anderson, and C. Schexnayder. 2009. “Construction project cost escalation factors.” J. Manage. Eng. 25 (4): 221–229. https://doi.org/10.1061/(ASCE)0742-597X(2009)25:4(221).
Shiha, A., E. M. Dorra, and K. Nassar. 2020. “Neural networks model for prediction of construction material prices in Egypt using macroeconomic indicators.” J. Constr. Eng. Manage. 146 (3): 04020010. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001785.
St. Louis Fed. 2020. “FRED-MD and FRED-QD: Monthly and quarterly databases for macroeconomic research.” Accessed February 23, 2020. https://research.stlouisfed.org/econ/mccracken/fred-databases/.
Sul, D. 2019. Panel data econometrics: Common factor analysis for empirical researchers. London: Taylor & Routledge.
Xu, J.-W., and S. Moon. 2013. “Stochastic forecast of construction cost index using a cointegrated vector autoregression model.” J. Manage. Eng. 29 (1): 10–18. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000112.
Information & Authors
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Published In
Journal of Construction Engineering and Management
Volume 147 • Issue 2 • February 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: May 21, 2020
Accepted: Aug 24, 2020
Published online: Nov 19, 2020
Published in print: Feb 1, 2021
Discussion open until: Apr 19, 2021
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