Forecasting Construction Material Prices Using Macroeconomic Indicators of Trading Partners
Publication: Journal of Management in Engineering
Volume 40, Issue 5
Abstract
Supply chain instabilities and inflated material prices have had a disruptive impact on cost estimating of construction projects. While several research efforts used national macroeconomic indicators to forecast the prices of domestically produced construction materials, none of the existing studies investigated whether the lagged macroeconomic indicators of the main trading partners could enhance the predictability of the prices of cement, steel, and lumber in the US construction sector. This paper fills this knowledge gap. The authors adopted a multi-step methodology that included: (1) collecting data on the target variables and the candidate leading indicators; (2) identifying the structural breaks in the collected data sets; (3) conducting causality tests to identify short-term associations and cointegration tests to examine long-term relationships; (4) developing vector error correction (VEC) models to forecast the prices in the short and long terms; and (5) evaluating the performance of the proposed models against existing forecasting models in the literature. Results of the Granger test and Johansen test indicate that Canada’s overall producer price index (PPI) is a consistent leading indicator of the prices of cement, and Mexico’s overall PPI is a consistent leading indicator of the prices of steel. Findings indicate no statistical evidence to suggest that neither Canada’s PPI nor Mexico’s PPI can be leading indicators of lumber prices. Over an 18-month ahead of sample horizon, the presented VEC models of cement and steel prices outperformed existing models, particularly beyond the 1-year-ahead forecasts. Utilization of the proposed forecasting models can significantly enhance the accuracy of cost estimates and feasibility studies of construction projects. This provides proactive financial planning for construction contractors and project owners through improved short- and long-term forecasting of the prices of main construction materials.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
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Journal of Management in Engineering
Volume 40 • Issue 5 • September 2024
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© 2024 American Society of Civil Engineers.
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Received: Dec 20, 2023
Accepted: Mar 27, 2024
Published online: Jun 12, 2024
Published in print: Sep 1, 2024
Discussion open until: Nov 12, 2024
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