Chapter One

1. Federal Highway Administration. Highway Statistics Series 1998, Table HF-2. Federal Transit Administration. National Transit Summaries and Trends. 1998

2. Unless otherwise noted, spending figures in this report are from the 1998 Consumer Expenditure Survey conducted annually by the U.S. Bureau of Labor Statistics, an agency of the U.S. Department of Labor. Metropolitan area spending figures are averaged over a two-year time period, 1997-98.

3. AAA. Your Driving Costs: Figuring It Out. 2000.

4. U.S. Dept of Housing and Urban Development. The State of the Cities 2000: Megaforces Shaping the Future of the Nation’s Cities. 2000.

Chapter Two

1. All data in this section are derived from the research of Peter Newman, Jeffrey R. Kenworthy, and Felix B. Laube, as published in Sustainability in Cities. Island Press. 1999, and An International Sourcebook of Automobile Dependence in Cities 1960-1999 University Press of Colorado. 1999.

2. We avoided using raw dollar figures, because these figures are skewed by varying income levels in the different metro areas. We also chose not to compare the percentage of income spent on transportation because these figures are distorted by differing tax rates. By looking at the percent of expenditures we can see how much of a household’s actual spending must go to transportation.

3. Characteristics include demographic (income and household size), land use (households per residential acre and households per total acre), pedestrian friendliness (existence of a block grid, access to amenities), and transit service (location and frequency).

4. CNT, NRDC and STPP established a Research Review Committee to review and guide the development of the modeling protocol and variable definition. Review Committee members, whom hail from a variety of sectors and fields of expertise. A complete list of members is available from Ryan Tracey-Mooney at the Center For Neighborhood Technology. In addition, the Transportation Planning and Technology journal is conducting a peer review of the final LEV explanatory paper which will be published in a forthcoming report.

Chapter Three

1. Because of data limitations, our analysis was limited to 27 of the 28 major metropolitan areas in the U.S. for which the Department of Labor reports household expenditure data (Anchorage, Alaska is excluded).

2. The development of the composite sprawl measure was a collaborative effort of Professor Reid Ewing, Rutgers University; Professor John Ottensmann, Indiana University; and Professor Rolf Pendall, Cornell University. While this analysis gives us a way to compare metro areas, it is not intended as an absolute measure of sprawl. The research team continues to add variables to the database, and continues to refine the measure.

3. Reid Ewing and Robert Cervero, "Travel and the Built Environment," literature review prepared for the 2001 Annual Meeting, Transportation Research Board, Washington, D.C., 2000.

4. Transit service miles are from the National Transit Database and are measured by how many miles all the buses and trains in a region travel in one day of service. Roadway capacity was supplied by the Federal Highway Administration. While this analysis gives us a way to compare metro areas, it is not intended to be used as a measure of the correct balance of transit to roads. Such a judgement is beyond the scope of this paper.

5. A bivariate correlation analysis shows that more than 76 percent of the variation in the transportation choice ratio can be explained by the composite sprawl measure (R2 = 0.762).

6. This transportation balance differs slightly from the Transportation Choice Ratio and uses data from Newman and Kenworthy’s database.

7. Bureau of Labor Statistics. Consumer Price Index. "Average Price Data for Unleaded Regular Gasoline per Gallon." 1998. National Association of Insurance Commissioners. State Average Expenditures & Premiums for Personal Automotive Insurance in 1998. 2000.

8. David Lewis and Fred Laurence Williams, Policy and Planning as Public Choice: Mass Transit in the United States. Brookfield, VT: Ashgate Publishing Company, 1999.

Chapter Four

1. U.S. Department of Housing and Urban Development. U.S. Housing Market Conditions Summary. August 2000.

2. Analysis performed using software developed by FinanCenter.

3. The authors use an econometric model and the results from the 1989 Survey of Consumer Finance to evaluate the influence of income and wealth on home tenure decisions and homeownership rates. They conclude that wealth constraints have a larger effect on both outcomes. Megbolugbe Linneman, Wachter and Cho. "Do Borrowing Constraints Change U.S. Homeownership Rates?" Journal of Housing Economics 6, 318-333 (1997).

4. "Recent Changes in U.S. Family Finances: Results from the 1998 Survey of Consumer Finances." Federal Reserve Bulletin. January 2000, 15, 24.

5. Seasonally Adjusted. Council of Economic Advisers. Economic Indicators. June 1999. Source of data: Federal Reserve Board of Governors. In 1995, total automobile consumer credit outstanding was $364.2 billion, while total personal consumption expenditures for user-operated transportation were $514.2 billion. Council of Economic Advisors. Economic Indicators. June 1999. Bureau of Economic Analysis data cited in American Automobile Manufacturers Association’s Motor Vehicle Facts & Figures. 1996.

6. Analysis done by Peter Haas and Scott Bernstein of the Center for Neighborhood Technology, 1999.

7. In Los Angeles and San Francisco, the unit of measurement was Traffic Analysis Zones. TAZs are often equivalent to census tracts: they are based on population and so are smaller in dense cities and larger in suburban areas.

8. For conventional mortgages, the qualifying ratio, (PITI+debt)/monthly income, must be less than or equal to 35 percent of the home purchase price. The LEM increases the approvable ratio to 45 percent and accounts for the LEV: (PITI+debt-LEV)/monthly income, must be less than or equal to 45 percent of the purchase price. The LEM program also decreases the down payment to three percent of the purchase price.

9. LEM calculator available at http://www.locationefficiency.com

10.Estimates of homeownership potential based on GIS analysis by Scott Bernstein, Peter Haas, and James Hoeveler at the Center for Neighborhood Technology, 1999.

11.For more information, visit http://www.luc.edu/info/walktowork.html  or http://www.uc.edu/info-services/walkwork.htm

12.Luann Lanke, "Select Milwaukee joins MGIC, Freddie Mac to boost home ownership," Milwaukee Business Journal, July 28, 1997. Article available at http://www.bizjournals.com/milwaukee/stories/1997/07/28/focus1.html

Chapter Five

1. Surface Transportation Policy Project, Changing Direction: Federal Transportation Spending in the 1990s. March 2000. Report available at http://www.transact.org

2. Neal Peirce. "Designing a Transit Future — Is the Light Green?" September 24, 2000.

Methodology

 1. The data for the regression analyses was collected from the Census Bureau, local transit agencies and metropolitan planning associations.

2. FHWA. "Cost of Owning and Operating Automobiles, Vans and Light Trucks." 1991. In 1991 dollars, does not include expenses for parking or tickets.

3. The development of the composite sprawl measure was a collaborative effort of Professor Reid Ewing, Rutgers University; Professor John Ottensmann, Indiana University; and Professor Rolf Pendall, Cornell University.