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5B-Brown

Colby Brown
Colby Brown

This document discusses mesoscopic land use forecasting models. It begins by classifying land use models into four categories based on spatial resolution and segmentation: traditional, scenario planning, microsimulation, and input-output. It then introduces mesoscopic models as bridging these categories. The document provides examples of mesoscopic models used by regional planning agencies to evaluate policies around housing affordability, jobs-housing balance, and more. It emphasizes that while not perfectly accurate, equilibrium models are still useful policy analysis tools. The document concludes by outlining lessons learned about using mesoscopic models to engage stakeholders and allow for robust policy scenario testing.

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1 Mesoscopic Land Use Forecast Modeling for Scenario Planning, Policy Analysis, and Pricing Evaluation Colby M. Brown AICP PTP Simon Choi, Ph.D. AICP Timothy G. Reardon
Land use models can be classified in one of four categories based upon spatial resolution and segmentation 1. Traditional (e.g. gravity-based) 2. Scenario planning & visioning tools 3. Micro-simulation 4. Input-output New mesoscopic land use models bridge these categories Cube Land Issues of Scale In Land Use Modeling Segmentation Space MACRO MACRO micro 1 23 4
Answers to policy questions: Housing affordability (relationship of household income to housing price) Jobs-housing balance Environmental justice Gentrification Local economic development Taxes and subsidies Economic performance measures Rent Tenant income Effective subsidy Economics of Land Use
Although not always the most accurate depiction of reality, equilibrium models are still extremely useful policy analysis tools Example: what level of cost/taxation results in a desired level of housing supply in a particular zone or subarea of a region? The equilibrium framework allows us to select a performance goal and then solve for the policies that achieve this target, all else equal Equilibrium Models
Kyoto Protocol and Sustainable Cities Donoso et. al. TRR (2006)
Greater Los Angeles region with over 18.4 million population in study area 22.1 million in 2035 3.7 M added between 2013 and 2035 Strategic model 531 land use zones Aggregate accessibility (travel model) Test case Take two pre-established visions for 2035 (trend and TOD) and solve for the real estate costs that achieve theses scenarios what do they cost? Applications to housing affordability SCAG Cube Land Forecasting Model
SCAG Shadow Pricing Test Results
Land use forecasting model purpose-built for traffic and revenue study in Louisville Experts on the local area couldnt create the entire forecast by hand but knew that some things simply wouldnt happen Solution: shadow pricing approach used to apply adjustments and constrain forecast Louisville - Ohio River Bridges Project Image source: http://www.kyinbridges.com/maps.aspx
Design & specification: Five-step integrated land use and travel demand forecasting model with same-year feedback Residential 13 household lifecycle groups 5 housing unit types Non-residential 11 industry supersectors 7 land use types Dynamic calibration to match base year and regional housing demand projections Boston Region MPO Cube Land Model
Conclusions and Lessons Learned Potentially threatening information from outside the model will always creep into the planning processno forecaster can secure a monopoly on predictions and expectations for future development. Old methods of dealing with this: Fight (lawsuits, claim greater credibility, build more sophisticated models etc.) Flight (give up on prediction, use indicator models and visioning tools instead) New ways opened up by the mesoscopic economic LU-T models: Run the model in reverse to find out how much the outsider scenario costs shifts the debate from whose scenario is correct to the assumptions, conditions and policies that will make one become reality versus another Explicitly input local expertise and knowledge to the model as constraints for a forecast keeping what the experts do know and letting the model fill in the rest Use dynamic calibration techniques to chain the baseline to an a priori scenario while still allowing room for robust policy scenario testing and sensitivity to changes in transportation accessibility due to project phasing, etc. In short: use the models to engage in dialogue based upon a common language

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5B-Brown

  • 1. 1 Mesoscopic Land Use Forecast Modeling for Scenario Planning, Policy Analysis, and Pricing Evaluation Colby M. Brown AICP PTP Simon Choi, Ph.D. AICP Timothy G. Reardon
  • 2. Land use models can be classified in one of four categories based upon spatial resolution and segmentation 1. Traditional (e.g. gravity-based) 2. Scenario planning & visioning tools 3. Micro-simulation 4. Input-output New mesoscopic land use models bridge these categories Cube Land Issues of Scale In Land Use Modeling Segmentation Space MACRO MACRO micro 1 23 4
  • 3. Answers to policy questions: Housing affordability (relationship of household income to housing price) Jobs-housing balance Environmental justice Gentrification Local economic development Taxes and subsidies Economic performance measures Rent Tenant income Effective subsidy Economics of Land Use
  • 4. Although not always the most accurate depiction of reality, equilibrium models are still extremely useful policy analysis tools Example: what level of cost/taxation results in a desired level of housing supply in a particular zone or subarea of a region? The equilibrium framework allows us to select a performance goal and then solve for the policies that achieve this target, all else equal Equilibrium Models
  • 5. Kyoto Protocol and Sustainable Cities Donoso et. al. TRR (2006)
  • 6. Greater Los Angeles region with over 18.4 million population in study area 22.1 million in 2035 3.7 M added between 2013 and 2035 Strategic model 531 land use zones Aggregate accessibility (travel model) Test case Take two pre-established visions for 2035 (trend and TOD) and solve for the real estate costs that achieve theses scenarios what do they cost? Applications to housing affordability SCAG Cube Land Forecasting Model
  • 7. SCAG Shadow Pricing Test Results
  • 8. Land use forecasting model purpose-built for traffic and revenue study in Louisville Experts on the local area couldnt create the entire forecast by hand but knew that some things simply wouldnt happen Solution: shadow pricing approach used to apply adjustments and constrain forecast Louisville - Ohio River Bridges Project Image source: http://www.kyinbridges.com/maps.aspx
  • 9. Design & specification: Five-step integrated land use and travel demand forecasting model with same-year feedback Residential 13 household lifecycle groups 5 housing unit types Non-residential 11 industry supersectors 7 land use types Dynamic calibration to match base year and regional housing demand projections Boston Region MPO Cube Land Model
  • 10. Conclusions and Lessons Learned Potentially threatening information from outside the model will always creep into the planning processno forecaster can secure a monopoly on predictions and expectations for future development. Old methods of dealing with this: Fight (lawsuits, claim greater credibility, build more sophisticated models etc.) Flight (give up on prediction, use indicator models and visioning tools instead) New ways opened up by the mesoscopic economic LU-T models: Run the model in reverse to find out how much the outsider scenario costs shifts the debate from whose scenario is correct to the assumptions, conditions and policies that will make one become reality versus another Explicitly input local expertise and knowledge to the model as constraints for a forecast keeping what the experts do know and letting the model fill in the rest Use dynamic calibration techniques to chain the baseline to an a priori scenario while still allowing room for robust policy scenario testing and sensitivity to changes in transportation accessibility due to project phasing, etc. In short: use the models to engage in dialogue based upon a common language