Using shadow pricing to mediate between visions and forecasts revised
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Transcript of Using shadow pricing to mediate between visions and forecasts revised
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Using Shadow Pricing to Mediate Between Models and
Visions in Forecasting Troy Hightower, Kern COG
Colby Brown, AICP PTP
Pedro Donoso
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MINNEAPOLIS, MN
We model real estate markets for transportation forecasters.
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LOS ANGELES, CA
We model real estate markets for transportation forecasters.
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BOSTON, MA
We model real estate markets for transportation forecasters.
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The “Vision Thing”
• It’s incredibly important for stakeholders to agree on goals and objectives
• This may or may not align with a forecast, however
• A vision represents the desired outcome whereas forecast is a likely outcome
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Land Use Forecasting Example
• California MPOs are required to set targets for greenhouse gas reductions and develop sustainable community strategies (SCS) that are demonstrated to achieve these targets
• Kern COG, like many, used a “scenario planning” tool (UPlan) to develop their SCS
• Also has an econometric forecasting model (Cube Land)
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Comparing the Land Use “Vision” to an Economic Land Use “Forecast”
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Building A Better Black Box With Shadow Pricing
The Model Assumptions Forecast
Compare Vision Revise prices
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Shadow Pricing Outputs Supplier Cost Adjustments – Residential Market
Disclaimer: not an official KernCOG model result!
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Applications Outside Land Use
• Managed Lanes – Pricing strategy & operational policy
• Transit Planning – Fare structure & mode share
• Parking Policy – Impacts on trip distribution
• Travel Demand Management – Gas taxes and VMT pricing
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SHARE YOUR STORY
Have you ever been asked to evaluate a “vision” that differed markedly from your forecast? How did you respond? What did you do?