Construction Economics

Advancing Forecasting Models for Estimating Construction Cost Escalation

Since 2009, we have worked to create multivariate time series models for improving the accuracy of construction cost escalation estimation through utilizing information available from several indicators of macroeconomic condition, energy price, and construction market. Recently, the accuracy of construction cost estimates has been significantly affected by fluctuations in construction costs in the United States. Construction cost fluctuations have been larger and less predictable than was typical in the past. Construction cost variations are problematic for cost estimation, bid preparation and investment planning. Inaccurate cost estimation can result in bid loss or profit loss for contractors and hidden price contingencies, delayed or cancelled projects, inconsistency in budgets and unsteady flow of projects for owners. The major problem is that construction cost is subject to significant variations that are difficult to estimate.

Our research has made significant scholarly impacts in the academic domain of construction cost analysis and forecasting. The findings of these new prediction models were presented in 3 journal articles, 1 research report, and several peer-reviewed conference papers. “Empirical Tests for Identifying Leading Indicators of ENR Construction Cost Index” was published in Construction Management and Economics. The results of unit root and Granger causality tests presented in this paper show that consumer price index, crude oil price, producer price index, housing starts, and building permits are explanatory variables (i.e., leading indicators) of construction cost variations. “Forecasting ENR Construction Cost Index using Multivariate Time Series Models” and “Time Series Analysis of ENR Construction Cost Index” were published in the ASCE Journal of Construction Engineering and Management. Several univariate and multivariate time series models were created in these articles to estimate variations in cost escalation. These proposed models are more accurate than the previous models for predicting construction cost fluctuations.

This scholarly work in construction cost forecasting was recognized by a £4,700 award from the Royal Institution of Chartered Surveyors (RICS), the world’s leading professional body for qualifications and standards in land, property and construction based in London, UK. The findings of this research were published in the RICS Findings in Built and Rural Environment (FiBRE) Series report “Is it possible to forecast the construction costs index in the USA?”. We were invited by the Construction Industry Institute (CII) to present our findings to the member companies (CII owners and contractors). Over the past five years, we have been able to engage several undergraduate students into this research. Four of these undergraduate researchers were the recipients of President’s Undergraduate Research Award (PURA). One of these undergraduate researchers, Seungho Shin, won Outstanding Poster Presentation Award at the Georgia Tech’s Undergraduate Research Spring 2010 Symposium for the research “Regression Analysis Model for Predicting Construction Cost Index”.

This scholarly and research work has significant impact on the state of practice of forecasting in construction cost escalation. Owners and contractors of major capital projects deal with cost escalation as one of the most important risks impacting their bottom lines. Proposed cost escalation estimation models can help cost engineers and capital planners prepare more accurate bids, cost estimates and budgets for capital projects.  Currently, Dr. Baabak Ashuri is the PI on $169,045 research project “Development of Risk Management Strategies for State DOTs to Effectively Deal with Volatile Prices of Transportation Construction Materials” funded by the US Department of Transportation/Research and Innovative Technology Administration through the Georgia Tech University Transportation Center (UTC). The major objective of this research is to create predictive models to capture uncertainty about material prices and develop proper risk mitigation strategies to cope with material price volatility in different types of projects at various phases of project development. Ultimately, the public will be the ultimate beneficiaries of this research since their taxes will be spent more efficiently and effectively in major capital building and infrastructure projects. Advancing the state of knowledge in cost forecasting in both vertical and horizontal construction will remain the trajectory of his research over the next several years.