Rebound Neighborhoods in St. Louis: Causes and Consequences

Todd Swanstrom,

University of Missouri-St. Louis

Hank Webber,

Washington University in St. Louis

With the assistance of Laura Jenks, Dean Obermark, Leslie Duling & Derrick Redhead National Neighborhood Indicators Partnership Meeting St. Louis, Missouri April 2-4, 2014

Research Focus: Dynamics of Neighborhood Change Two primary questions: 1. Why do some neighborhoods rebound in the wake of urban decline while others continue to decline or stagnate?

2. Do rebound neighborhoods in St. Louis fit the pattern of gentrification or do they vary in significant ways both in the path to revitalization and the impact on previous residents of the neighborhood and surrounding areas?

Plan for Today 1. Quantitative analysis identifying rebound neighborhoods and some of their effects (Todd) 2. Case studies of the drivers of neighborhood revitalization with a focus on one neighborhood, the Central West End (Hank)

50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% -5% 1970-1980 St. Louis: A Slow-Growth Region 1980-1990 1990-2000 2000-2010 St. Louis MSA Los Angeles MSA Chicago MSA Miami MSA Boston MSA San Francisco MSA

St. Louis: A Thinning Out Region

Decentralized Job Clusters

1,60 1,40 1,20 1,00 0,80 0,60 0,40 0,20 0,00 Overbuilt Housing Ratio of New Housing Units to New Households for St. Louis MSA 1,080 1970-1980 1,274 1980-1990 0,991 1990-2000 1,437 2000-2011

Study Area • 218 census tracts in “urbanized area” of St. Louis as defined by the U.S. Census Bureau in 1950 • Total population of study area in 1970: 1.3 million

Regional Sprawl

140.00% Population Decentralization

Change in Population of MSA and Study Area by Decade

120.00% 100.00%

103.43% 110.18% 118.17% 99.72% 79.59%

80.00% 60.00% 40.00%

71.38% 64.88% 60.83%

MSA Study Area 20.00% 0.00%

1970 1980 1990 2000 2010 Source(s): U.S. Decennial Census 1970-2000; American Community Survey 2006-2010

Falling Occupancy Rates, Especially in Older Areas

Percentage of Occupied Housing Units in St. Louis, MO-IL MSA and Study Area by Decade

96.00% 94.00% 92.00% 90.00% 88.00% 86.00% 84.00% 82.00% 80.00%

93.90% 93.23% 93.60% 91.93% 91.90% 89.12% 92.60% 88.71% 90.2% 84.97%

MSA Study Area 1970 1980 1990 2000 2010

Source(s): U.S. Decennial Census 1970-2000; American Community Survey 2006-2010

Older Neighborhoods – Running Up the Down Escalator

Neighborhood Vitality Index (NVI) Three variables: 1. Economic (Per Capita Income) 2. Social (Poverty Rate) 3. Physical (Vacancy Rate) NVI measures the performance of each census tract relative to the mean for the study area in each decade.

Neighborhood Vitality Index Tract Rankings by Decade, 1970-2010

Identifying Rebound Neighborhoods • Basic idea: neighborhoods that bounced back from decline (U-shaped) • We define a “rebound tract” as any census tract that moved up at least 10 percentile points in the rankings from 1990-2000 or 2000-2010 • Eliminated tracts that were never in the bottom half of the distribution at some point between 1970 and 2000 • Of the 218 tracts in our study area, 38 (17%) are rebound tracts

Rebound Census Tracts Fit the Demographic Profile of Gentrifying Neighborhoods

Surprising Finding In contrast to conventional wisdom on gentrifying neighborhoods, rebounding tracts, overall, had significantly higher levels of racial/ethnic and economic diversity than non-rebounding tracts.

Racial/Ethnic Diversity Diversity Index = 1 - %white 2 + %black 2 + %Hispanic 2 + %other 2

Economic Diversity

What Are the Drivers of Rebound Neighborhoods?

1. Economic Theory 2. Sociological Theory 3. Political/Institutional Theory

Five Case Studies: Exploring the Drivers of Success

250 Performance of Case Study Neighborhoods 200 150 100 50 Central West End Botanical Heights Shaw Maplewood Mark Twain Study Area Mean 0 1970 1980 1990 2000 2010

100 Racial Diversity of Case Study Neighborhoods 80 20 0 60 40 Central West End Botanical Heights Shaw Maplewood Mark Twain Study Area Mean 1970 1990 2010

Case Studies: Key Success Factors

Success Factor Central West End Botanical Heights Shaw Mark Twain Strong Anchor Institutions Excellent Housing Stock Thoughtful Commercial Development Thoughtful Residential Development Resident Civic Engagement X X X X X X X X X X X X X Maplewood X X X X X X Good Location Successful Public Policy Strong Public Schools X X X

Central West End – Location St. Louis City, County, and Region

Central West End – Location

Central West End – Borders

Central West End – Housing

Central West End – Housing

Central West End – Housing

Central West End – Apartments

Central West End – Chase Park Plaza

Central West End – Euclid Avenue

Central West End – Euclid Avenue

Central West End – Weak Housing

Central West End – 1970-2010

Population Poverty Rate Per Capita Income* Occupancy % Under 18 % 18-34 % White Index Score 1970

25,859 24% $23,078 85% 20% 28% 54% 100.84

1990

17,282 22% $38,690 86% 10% 35% 59% 164.15

2010

15,589 24% $43,406 86% 7% 44% 58% 192.12

*in 2012 dollars

Central West End – The 1970s • Assets – Excellent housing stock – Great location • Threats – Housing and commercial areas in state of disrepair – Weakening public schools – Exodus of families

Key Success Factor – Growth in Anchor Institutions Washington University Medical Campus, 1970 2.0 million square feet

Key Success Factor – Growth in Anchor Institutions Washington University Medical Campus, 2008 5.6 million square feet

Central West End – Case Study: 4388 Waterman

Central West End – Success Factors • Strong anchor institutions in growing industries • Excellent housing stock • Supportive public policy • Resident civic engagement • Thoughtful and contextual commercial and residential development

Central West End – What Did Not Happen • No great transformative change in low-income and minority population • No significant change in building stock • Limited spill-over effects north of Delmar

Central West End – Fountain Park

Central West End – Fountain Park

Central West End – Fountain Park

Conclusions • Neighborhoods with strong anchor institutions and high levels of civic capacity are better able to utilize the public policy tools for revitalization (tax credits, special taxing districts, overlay zoning districts, etc.) • Location, Location, Location – Proximity to growing job centers is key – In the central corridor or well-located suburbs considerable success is possible – It is very difficult for all-black neighborhoods to rebound; in North St. Louis, stability is a victory • Diversity is now an asset to community revitalization

Remaining Questions • Is St. Louis an outlier, or can neighborhoods in other weak market cities rebound without significantly displacing low-income and minority residents?

• Are rebound neighborhoods in St. Louis simply the first stage on route to classic gentrification as found in strong market cities?

• Are diverse rebound neighborhoods the result of a significant attitudinal change, especially by Millennials? If so, will this preference for diversity endure as Millennials age or will they revert back to attitudes of earlier generations?

We would like to thank the many people and organizations that shared their experiences and history with us. We commend the hard work and dedication of those who have contributed to the revitalization of neighborhoods in St. Louis and elsewhere.

Questions?