Anatomy of an Overrun

Step 1 of 5

The point estimate

Picture a road scheme on the day it is approved. Its business case carries one number for the final cost, and every downstream decision treats that number as settled. The figure shows the estimate as it appears in the plan, a single point at 1.0 on a scale of final cost as a multiple of the estimate.

Step 2 of 5

The reference class

No road is the first of its kind. The curve is the roadways cost class from the tables in Hubbard, Budzier & Leed (2025), How to Measure Anything in Project Management, Wiley, covering 1,263 road projects with known outcomes. A quarter of them finished at or under their approved figure, half ended at 1.13 times it or more, and one in twenty reached 1.95 times or beyond. Underruns are shallow and overruns are deep, which is why the curve leans right.

Try this yourself: Reference Class Forecasting in the Lab.

Step 3 of 5

Selection

Projects compete for funding, and the ones that look cheapest tend to win. An estimate can look cheap because the project is cheap or because the estimate fell low, and the approval process cannot tell those apart, so funding the lowest numbers clips the favourable tail before construction starts. The funded portfolio then begins life tilted toward overrun even when every estimator was unbiased. In my working paper I find that noise plus selection can account for roughly 25 to 50% of observed overruns across most categories, with a central reading near a third.

Try this yourself: the Noise Audit in the Lab.

Step 4 of 5

The fat right tail

The right side of the curve is where budgets fail. In this class one road project in four ends beyond 1.32 times its estimate, and one in twenty ends beyond 1.95 times. A P90 budget is one that outcomes like these would have stayed inside nine times in ten, so choosing a percentile is choosing how often you are willing to go back for more money. That choice is invisible while the budget is a single number.

Try this yourself: the Monte Carlo Simulator in the Lab.

Step 5 of 5

What to do about it

The remedy is to budget from the distribution rather than the point. Reference class forecasting reads the budget off a chosen percentile of comparable projects, and where funding was competitive the uplift should also correct for selection, since the observed class is itself a set of winners. The quietest improvement is measurement, keeping records of estimates and outcomes so that next year's reference class includes your own projects.

Try this yourself: Selection-Adjusted RCF in the Lab.