Is the Information Available from Historical Time Series Data on Economic, Energy, and Construction Market Variables Useful to Explain Variations in ENR Construction Cost Index?

Abstract

Engineering News Record (ENR) publishes Construction Cost Index (CCI) monthly. CCI is widely used by cost engineers for preparing cost estimates, bids, and budgets for capital projects. CCI is subject to significant variations. The information available from historical time series data on some economic variables (federal funds rate, consumer price index, gross domestic product and unemployment rate), energy-related variable (crude oil price), and construction market variables (housing starts, employment level in construction, and construction spending) may be useful for explaining CCI variations. The research objective of this paper is to empirically examine whether the time series information on the above economic, energy, and market variables is useful to explain CCI variations. We devise time series tests like Pearson correlation test and Granger causality test to statistically examine the impact of each variable on CCI variations over time. The results show that the information available from historical data on consumer price index, gross domestic product, crude oil price, housing starts and employment level in construction is useful to explain variations of CCI. It is concluded that the information available from historical data on federal funds rate, unemployment rate, and construction spending do not have power to explain CCI variations. This empirical study is expected to increase our understanding about the impact of various economic, energy, and construction market variables on CCI variations. It is anticipated that this knowledge will be useful for cost estimators and capital project planners to explain changes in construction cost over time.