Reference Class Forecasting

Reference class forecasting is the method of predicting the future, through looking at similar past situations and their outcomes.

Reference class forecasting predicts the outcome of a planned action based on actual outcomes in a reference class of similar actions to that being forecast. The theories behind reference class forecasting were developed by Daniel Kahneman and Amos Tversky. The theoretical work helped Kahneman win the Nobel Prize in Economics.

Kahneman and Tversky found that human judgment is generally optimistic due to overconfidence and insufficient consideration of distributional information about outcomes. Therefore, people tend to underestimate the costs, completion times, and risks of planned actions, whereas they tend to overestimate the benefits of those same actions. Such error is caused by actors taking an "inside view," where focus is on the constituents of the specific planned action instead of on the actual outcomes of similar ventures that have already been completed.

Kahneman and Tversky concluded that disregard of distributional information, that is, risk, is perhaps the major source of error in forecasting. On that basis they recommended that forecasters "should therefore make every effort to frame the forecasting problem so as to facilitate utilizing all the distributional information that is available". Using distributional information from previous ventures similar to the one being forecast is called taking an "outside view". Reference class forecasting is a method for taking an outside view on planned actions.

Reference class forecasting for a specific project involves the following three steps:

1. Identify a reference class of past, similar projects.
2. Establish a probability distribution for the selected reference class for the parameter that is being forecast.
3. Compare the specific project with the reference class distribution, in order to establish the most likely outcome for the specific project.

Whereas Kahneman and Tversky developed the theories of reference class forecasting, Flyvbjerg and COWI (2004) developed the method for its practical use in policy and planning. The first instance of reference class forecasting in practice is described in Flyvbjerg (2006). This was a forecast carried out in 2004 by the UK government of the projected capital costs for an extension of Edinburgh Trams. The promoter's forecast estimated a cost of £255 million. Taking all available distributional information into account, based on a reference class of comparable rail projects, the reference class forecast estimated a cost of £320 million. A report issued in August 2011 estimated that the final cost of the yet unfinished project would be over £1 billion, for a shorter tram line than the proposed Line 2.

Since the Edinburgh forecast, reference class forecasting has been applied to numerous other projects in the UK, including the £15 (US$29) billion Crossrail project in London. After 2004, The Netherlands, Denmark, and Switzerland have also implemented various types of reference class forecasting.

Before this, in 2001 (updated in 2006), AACE International (the Association for the Advancement of Cost Engineering) included Estimate Validation as a distinct step in the recommended practice of Cost Estimating (Estimate Validation is equivalent to Reference class forecasting in that it calls for separate empirical-based evaluations to benchmark the base estimate):

The estimate should be benchmarked or validated against or compared to historical experience and/or past estimates of the enterprise and of competitive enterprises to check its appropriateness, competitiveness, and to identify improvement opportunities...Validation examines the estimate from a different perspective and using different metrics than are used in estimate preparation.

In the process industries (e.g., oil and gas, chemicals, mining, energy, etc. which tend to dominate AACE's membership), benchmarking (i.e., "outside view") of project cost estimates against the historical costs of completed projects of similar types, including probabilistic information, has a long history.