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Empirical tests for identifying leading indicators of ENR Construction Cost Index

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  • Baabak Ashuri
  • Seyed Mohsen Shahandashti
  • Jian Lu

Abstract

Engineering News-Record (ENR) publishes its Construction Cost Index (CCI) monthly. CCI is the weighted average price of construction activities in 20 United States (US) cities. CCI has widely been used for cost estimation, bid preparation and investment planning. Cost estimators and investment planners are not only interested in the current CCI, but also are interested in forecasting changes in CCI trends. However, CCI is subject to significant variations that are difficult to predict. An important step towards forecasting CCI trends is to identify its leading indicators. The research objective is to identify the leading indicators of CCI using empirical tests. The results of Granger causality tests show that consumer price index, crude oil price, producer price index, GDP, employment levels in construction, number of building permits, number of housing starts and money supply are the leading indicators of CCI. The results of Johansen’s cointegration tests show that money supply and crude oil price are the leading indicators with long-term relationships with CCI. These findings contribute to the body of knowledge in CCI forecasting. CCI can be predicted more accurately using its leading indicators. Cost estimators and capital project planners can benefit from better forecasting through reduction in uncertainty about future construction costs.

Suggested Citation

  • Baabak Ashuri & Seyed Mohsen Shahandashti & Jian Lu, 2012. "Empirical tests for identifying leading indicators of ENR Construction Cost Index," Construction Management and Economics, Taylor & Francis Journals, vol. 30(11), pages 917-927, November.
    • Handle: RePEc:taf:conmgt:v:30:y:2012:i:11:p:917-927
    DOI: 10.1080/01446193.2012.728709
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    Cited by:

    1. Zhang, Rong & Ashuri, Baabak & Shyr, Yu & Deng, Yong, 2018. "Forecasting Construction Cost Index based on visibility graph: A network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 239-252.
    2. Qadan, Mahmoud & Nama, Hazar, 2018. "Investor sentiment and the price of oil," Energy Economics, Elsevier, vol. 69(C), pages 42-58.

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