UCLA

Employing over 10 million people, with an annual expenditure averaging over $1.2 trillion, the U.S. construction industry is massive in both scale and reach. Every day, thousands of construction builds are underway across the country, each with a unique set of costs contributing to its overall budget. For many, the cost of commodities, or materials used to build a project, is a huge variable that changes constantly and can negatively impact a project both financially and temporally. Being able to better understand and predict the way the prices these commodities change over time could lead to huge savings of time and money for anyone at the planning stages of a construction project. Statistical time series modeling is one effective way to increase this understanding and prediction abilities. This thesis models and forecasts the price indices of three construction commodities integral to the industry: lumber, iron/steel, and concrete. For each commodity, the month-to-month percentage changes of nearly 100 years of index values are modeled using Autoregressive Moving Average (ARMA) models, which are selected based on multiple model selection criteria. The models are then used to forecast an 8-month period directly following the end of the data series, which corresponds to the first 8 months of 2022. In doing so, it is found that the lumber model using ARMA methods makes forecasts indicative of what has happened in reality in the first half of 2022, where as the iron/steel and concrete models have much more difficulty in doing so.