Data-driven identification of key pricing factors in highway construction cost estimation during economic volatility

Traditional construction estimation relies on historical unit bid prices, which may not reflect actual construction costs due to site-specific conditions, market dynamics, and contractor strategies. However, few studies have systematically examined the full range of factors driving unit price variability, particularly under changing economic conditions. This research thus aims to identify key pricing factors in highway construction, including economic and regional variables, along with the impacts of the COVID-19 pandemic and high inflation. A two-step methodology was used. First, a literature review identified 80 pricing factors spanning macroeconomics, market, regional, client, and project categories. This was followed by a quantitative analysis employing Multivariate Regression, Random Forest, and Ensemble Learning models, applied to 14 years of bid data from the Michigan Department of Transportation. Key findings show that ensemble learning models outperformed other methods in predicting contract-level unit bid prices, achieving a higher explanatory power with an R2 of 0.63. Item quantity and regional spending patterns strongly influence bid prices. For example, larger quantities lower unit prices due to economies of scale. The study highlights the impact of the COVID-19 pandemic on construction costs, driven by supply chain disruptions, labor shortages, and material price inflation.