1. Impact of development on VMT  WOC-Low VMT: 338873 new households averaging 0.9266 * 2 miles of non-work travel per day  LIB-Low VMT: 297156 new households averaging 1.2559 * 2 miles of non-work travel per day  LIB-Random: 297160 new households averaging 1.4024 * 2 miles of non-work travel per day

2. Impact of development on VMT Big picture…  LIB vs. WOC: 1.2559/0.9266 = 1.355  LIB is 36% worse than WOC even if you locate development in the most accessible areas  LIB-random vs. WOC: 1.4024/0.9266 = 1.5134  With no preference for accessible land, LIB-random is 51% “worse” than WOC

3. Impact of development on VMT Big picture…  Compared with baseline mileage, WOC, LIB and LIB-random non-work VMT increases are:  WOC: 17.8%  LIB: 23.8%  LIB-random: 26.6%  Assuming 20% growth and 20mpg average, we estimate 4-6 million extra gallons of gas from LIB vs. WOC.

4. Prediction or prescription?  Bottom line: Accessibility is important! (big differences between algorithms)  It matters how you calculate it—e.g., VMT vs. EUDIST, both are imperfect  Also matters how you measure its impacts on future growth  Two roles: both a real-world driver of development and something we want to optimize for  Models are a two-way street

5. Building an Algorithm: A few philosophical issues that matter  Do different housing types “want” different things in the real world?  Accessibility vs. open space  Simulate the market, or seek smart growth?  Prediction vs. prescription  LIB and WOC allocation strategies—same or different?  Ease of comparison vs. faithfulness to model

6. Prediction or prescription?  What use are housing algorithms like ours for the public dialogue? A few possibilities:  A smart-growth ideal that can be used to influence zoning debates  A way to engage local residents in the issue  Hey, there’s my neighborhood  Environmental impact  A two-way dialogue: Can they be used to get both planners and residents involved in improving the models?

7. Local vs. Regional: Where the rubber meets the road  Model looks at effects over a huge region  Zoning decisions happen at a local— even hyperlocal—level  Even if most people “buy” the goals of MetroFuture, there will still be fierce local battles  Winds of Change or Patchwork of Change?

8. How can finer resolution help?  Visualizing the kind of change needed at the local—and hyperlocal—level  Town planners may feel they have a handle on their own towns—but what’s happening next door?  Finer-resolution models inevitably introduce some error—but they can also help get the conversation down to the level where decisions get made