Parametric cost estimation builds a statistical model of cost as a function of one or more cost drivers — measurable project characteristics that historically predict cost well. The simplest form is a linear regression:
where are the cost drivers (size, throughput, weight, square footage, headcount, complexity score) and the ‘s are fitted from a historical dataset.
For a software project, the classic example is COCOMO (Constructive Cost Model): cost is estimated from lines of code, team experience, schedule pressure, and similar drivers, with the coefficients fitted from decades of historical project data. For chemical plants, parametric models use throughput, pressure, and process type as drivers. For buildings: floor area, number of storeys, building class.
Parametric models sit between unit cost estimation (one driver, simple ratio) and definitive estimates (bottom-up bills of materials). They’re appropriate for budgetary-level accuracy when:
- You have enough historical data to fit a reasonable model.
- The new project falls within the range of the historical projects on each driver. Extrapolating outside the fitted range is risky.
- The drivers actually capture the variation in cost — adding more drivers helps only if they explain variance in the historical data.
Parametric estimates are usually middle-of-accuracy: better than pure unit-cost scaling because they account for several factors, but worse than detailed bottom-up estimates because they’re still aggregate-level. The accuracy depends heavily on how well the historical fit predicts new projects.
The closely related technique Power-sizing model is a one-driver parametric model with a specific non-linear functional form (cost scales as size to a fractional power). Power-sizing is the workhorse for engineering economies of scale.
For broader context, see Cost estimate classes and Cost index.