Forecasters are often confronted with exogenous development scenarios based upon assumed or expected growth and asked to force their models to conform to these expectations. The scope of this tension is not limited to land use models, but also extends to public transit ridership as well as toll traffic and revenue forecasting. Faced with this challenge, they may either deny the validity of the externally generated development "vision" and fight for the use of their model, or abandon the model fully or partially, in some cases "dumbing down" or simplifying the logic substantially. We propose a "third way" solution to this problem based upon shadow pricing. A shadow pricing algorithm can be used to measure the difference between a purely market-driven forecast and one or more scenario visions defined by stakeholders; quantifying the amount of policy intervention required to achieve a certain level of development, ridership, or traffic, which can then be used as an economic performance measure in the scenario analysis portion of a performance-based planning process. This algorithm may be applied with any model that has properties of economic equilibrium; however we demonstrate the application of the algorithm using a recently developed Cube Land model.