Quantifying Uncertainty of Construction Material Price Volatility Using Monte Carlo

This study uses Monte Carlo simulation to derive a quantitative measure of construction price volatility for two case study projects in Oklahoma. In the past four years, the impact of events like Hurricane Katrina and the economic growth in China have induced unprecedented volatility in US construction prices. This bas caused a huge number of public construction project bids to exceed the owners' estimates, making it difficult for public construction agencies to be able to predict construction budgets. Construction prices are a function of many factors beyond pure material costs. This study broke down the pay items from a typical transportation project down into associated-conimodities costs. It then modeled the price volatility in each of the fundamental commodities as a stochastic function and used that output to develop a probabilistic cost model for the project. Monte Carlo simulations were run and the relative sensitivity to valatility for each commodity group was measured. The study finds that diesel is the most volatile commodity group for an equipment intensive highway paving project. But when the same volatility model was applied to a specific bridge project, volatility in Portland cement prices became the commodity with the greatest potential impact on the project's bid price. Finally, this study furnishes a methodology with which a public agency can test the validity of its estimates using commodity price data that is available in the public domain.