Predicting City-Level Construction Cost Index Using Linear Forecasting Models

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.