Uncertainty in socioeconomic forecasts

1. Uncertainty in socioeconomic forecasts Todd Graham <todd.graham@metc.state.mn.us> Metropolitan Council Research

2. Why forecast? • Provides a reasonable basis for planning • local comprehensive planning • regional system planning • Engages stakeholders in addressing growth issues • Helps us understand trends and forces • Forces us to articulate our expectations

3. Forecast certainty is not possible DF = Development Framework SD = State Demographer

4. Many futures are possible • Many scenarios are possible • What do we imagine is the end-state? • What path takes us there? • Starting assumptions that will constrain the range of possibilities • Narrowing from the possible to the probable

5. Where does forecasting come in? • Forecast modeling is a system analysis • To represent a set of variables over time • And to represent the dynamics and relationships that move those variables • Probable range of futures • Or the most probable future… • Given a basket of system dynamics, trends, policies, other assumptions

6. Are multiple forecasts possible? • Probable range of futures • Or the most probable future… • Given a basket of system dynamics, trends, policies, other assumptions

7. All High Hi Fert. Hi Migr. Hi Life Exp. Lo Life Exp. All Mid Lo Fert. Lo Migr. All Low Twin Cities Population Possibilities Range Thousands

8. The most probable future(s)? • System dynamics and trends • Can be tweaked as appropriate by forecaster • Or trends can be endogenously modeled, or loaded in from other related models • Policies are variable • Different scenarios to explore policy options • Policymakers decide; forecasters assist • Result is a policy-based forecast – the desired future

9. Challenges and opportunities • Improvement of modeling practices • Integration or coordination of parallel forecast efforts • Engagement of policymakers, planners and publics

10. The Future of Forecasts at Met Council Todd Graham <todd.graham@metc.state.mn.us> Metropolitan Council Research

11. Metropolitan Council’s current model • REGIONAL • Jobs • Households • Population LOCAL Land use, current and planned • Current model does not consider spatial interactions • Currently, no feedback between land use and transportation dynamics ??? accessibility trip generation Transportation System Demand distribution  Mode choice  Network assignment

12. Complex Metro & Urban Dynamics: Elements and Interactions REGIONAL Economy and labor market dynamics ___________ LOCAL • REGIONAL • Jobs • Population • Households production & consumption LOCAL development & occupancy Land and floorspace price signals Spatial interaction Social & environmental outcomes accessibility trip generation Transportation System Demand distribution  Mode choice  Network assignment Acknowledgment: Modified from JD Hunt, et al. (2005) Acknowledgment: Modified from JD Hunt, et al. (2005)

13. Expected forecast models workflow • A regional economic model for economic activity, employment, and population • Preferred model: Regional Dynamics (ReDyn.com) • A demographic model for parsing population into households • Preferred model: ProFamy (ProFamy.com) • A land use model for allocating future land use, households and employment to the local level • Preferred model: Citilabs Cube Land • Travel demand model • Currently in use: Citilabs Cube Voyager

14. Program Objectives • Land economics and geographic science validity • Platform for the prediction of likely distributions of development and activity – given a set of rules, or given a set of represented behaviors or dynamics • Coordination/integration with Travel Demand Modeling (TDM) and ES capital planning • Model land use dynamics and transport network together – to better represent trends

15. Goals developed via Needs Assessment Workshops • A model that balances the need for transparency with the need for realism • Able to test a range of policy scenarios • A model that provides information on the interaction of the physical environment and development dynamics interact • Geographic scope and level of detail necessary for regional systems planning • Flexibility to forecast short-term, long-term, and “build-out”

16. 2010 Cube Land

17. Market-based integrated models evaluated against Met Council Needs Assessment

18. Evaluated against Hunt, Kriger, Miller (2005) review of best practices

19. Cube Land – a market based model • Equilibrium represented by simultaneous solution of three inter-dependent problems: • Location of real estate consumers • Supply of real estate • Rents and values at market-clearing equilibrium

20. Background on Martinez’s Modelo de Uso de Suelo de Santiago Martinez, Franisco; and Pedro Donoso. “MUSSA 2: A Land Use Equilibrium Model Based on Constrained Idiosyncratic Behavior of Agents in an Auction Market.” Paper at TRB Annual Meeting, January 2007. 16 pages. “MUSSA – Land Use Equilibrium Model.” February 2009 presentation at http://transp-or2.epfl.ch/ presentationsSeminaires/MUSSA_Martinez09.pdf “MUSSA – Its Basis.” 4 pages. Website at www.mussa.cl/E_fundamentos.html

21. Cube Land – a market based model • On demand side, households (h) buy or rent real estate type (v) at certain locations (i) • Neighborhood choice (location i) determined by income and willingness to pay: • Bhvi = Ih – {f(Uh–zvi)} • Where Uh is typical housing utility for an “h” household • Where zvi represents package of amenities, neighborhood characteristics • Better package  greater willingness to pay • Max (Bhvi – rvi) • Subject to available budget of “h” household

22. Cube Land – a market based model • On supply side, developers (j) will offer housing & built space in quantities (S) of certain type (v) at certain locations (i) in order to maximize profit • Max {SviJ* (rvi – cviJ)} • Subject to regulations at location “i” • And all households in region are matched with housing • Predicted location choices and predicted supply are calculated with MNL equations (i.e. choice probabilities)

24. Travel times, accessibility and networks are updated and inform socioeconomic/land modeling at each 5-year step Integrated modeling Base Transport Model Base SE-LU Updated Network & Access 2010-15 SE-LU 2010 Updated Network & Access 2015-20 SE-LU 2015 SE-LU 20## Updated Transport Model

25. Policy and regulation constraints • Permissible land uses • Housing unit density min/max • Building height max or FAR max • Protected land and planned parks/reserves • GIS coverage of aquifer depletion • Wastewater system capacity constraints?

26. Cube Land – a market based model • Equilibrium represented by simultaneous solution of three inter-dependent problems: • Location of real estate consumers • Supply of real estate • Rents and values at market-clearing equilibrium

27. Cube Land – a market based model • Cube Land outputs not only what land will be developed – but also what types of housing – and prices for real estate zones

28. Integrated modeling preferred Source: Johnston, R; and M McCoy. (2006): Assessment of Integrated Transportation-Land Use Models: Final Report. Online at www.ice.ucdavis.edu/um/

29. Complex Metro & Urban Dynamics: Elements and Interactions REGIONAL Economy and labor market dynamics ___________ LOCAL • REGIONAL • Jobs • Population • Households production & consumption LOCAL development & occupancy Land and floorspace price signals Spatial interaction Social & environmental outcomes accessibility trip generation Transportation System Demand distribution  Mode choice  Network assignment Acknowledgment: Modified from JD Hunt, et al. (2005) Acknowledgment: Modified from JD Hunt, et al. (2005)

30. Challenges and questions • Are the forecasts responsive to economics, market conditions, and urban dynamics? • Are the forecasts responsive to – or realistic considering – policies and plans? • If so, how? • Are the transportation forecasts responsive to future land use and socioeconomics? • And vice verse?

31. Travel times, accessibility and networks are updated and inform socioeconomic/land modeling at each 5-year step Integrated modeling Base Transport Model Base SE-LU Updated Network & Access 2010-15 SE-LU 2010 Updated Network & Access 2015-20 SE-LU 2015 SE-LU 20## Updated Transport Model

32. Integrated Models - Paths of Advancement Travel Demand No Transit Transit Advanced Aggregate Land Activity-based Model No Mode Split Logit Model Split Use Model Land Capacity, Trends, Judgment Met Council in 2008 Non-market-based land allocation Land allocation with price signals Fully integrated market-based model Met Council in 2010 Ideal Model Path of advancement Source: Miller, EJ, et al (1999): Integrated Urban Models for Simulation of Transit and Land Use Policies. http://onlinepubs.trb.org/Onlinepubs/tcrp/tcrp_rpt_48.pdf

33. Integrated modeling as a policy ideal • Transportation Policy: SAFETEA-LU and ISTEA • Coordination of land use and transportation planning • NEPA and Clean Air Act • Land development patterns must be consistent with regional transportation plan

34. Uncertainty in socioeconomic forecasts Todd Graham <todd.graham@metc.state.mn.us> Metropolitan Council Research