Which estimation method uses historical data to predict future costs?

Analogous estimating is a method that relies on historical data and experience from previous projects to forecast the costs associated with future projects. This approach utilizes the costs from similar projects to create a cost estimate for the new project, assuming that those past projects will have relevant similarities in terms of scope, complexity, and context.

This method is particularly useful early in the project lifecycle when detailed project information may not yet be fully developed, but historical data can provide a solid foundation for making reasonable assumptions. When project teams draw from this type of historical information, they leverage collective experience, which can lead to more informed and reliable cost predictions. Analogous estimating is often seen as a quick and efficient way to provide a rough order of magnitude for project costs, allowing stakeholders to make decisions based on past performance metrics.

Moreover, while other estimation methods, such as parametric estimating or bottom-up estimating, also use data and can involve historical information, they do so in more specific or detailed contexts. Parametric estimating relies on statistical relationships between variables, while bottom-up estimating breaks down project components and provides more granular detail. Thus, when specifically focusing on using historical data to predict future costs, analogous estimating stands out as the most relevant method.