making the case talmage

1 SocialCompact| www.socialcompact.org

September 2009

2009 FLORIDA HOUSING COALITION22nd Annual Affordable Housing Conference

Making the Case for Investment in Low Income Neighborhoods


A tale of two cities …

Neighborhood A

Source: U.S. Census Bureau, 2000, Source: Social Compact Miami DrillDown 2008/2009

MARKET SIZE MARKET SIZE

Population: 367,426 Population: 504,226

Households: 137,577 Households: 179,471

MARKET BUYING POWER MARKET BUYING POWER

Median Household Income: $27,344 Median Household Income: $31,990

Average Household Income: $39,308 Average Household Income: $50,637

Aggregate Income: $5.4 Billion Aggregate Income: $9.1 Billion

Income per Acre: $386,074

Aggregate Informal Economy: (9.3%)

Income of New Home Buyers: $114,972


20 cities completed

350 Underserved Neighborhoods

1.2 Million Additional Residents

$36 Billion Additional Buying Power

* 50 cities have approached Social Compact for DrillDown analyses

DrillDowns CompletedSocial Compact’s Capacity


What do we do with better data?


Census Challenge


Over 170 federal programs allocate $300 billion annually using census estimate data

For every person not captured in census estimates, the city loses $2,263 of state and federal funding

80% of retail investment deals use data derived from the census to determine where and when to invest

Inaccurate census estimates greatly contribute to the perception of the city

Census Challenge ProgramWhy Are Census Estimates Important?


Detroit, MI, (+47,000)

New Orleans, LA, (+50,000)

San Francisco, CA (+34,000)

Toledo, OH (+21,000)

Miami, FL (+15,000)

Together, Social Compact’s census challenges will result in an additional $420 million state and federal funding to the cities.

Census Challenge ProgramSocial Compact Successes


Food Security


Distribution of grocery providers overlaid with grocery store sales demand.

2007 Houston DrillDown FindingsGrocery Provider Distribution


2007 Houston DrillDown FindingsGrocery Customer Attraction


Food Desert to Food OasisProviding East Access to Information: The Finder


Small BusinessDevelopment


Using a large number of public and private business data providers including INFO USA, NETS, and ESRI, Social Compact is creating detailed business environment profiles citywide and for tailored geographies (i.e. neighborhoods, business districts, main streets).

The profiles contain an analysis of business health and performance, taking into consideration the following business characteristics:

Size SMBE – Small, Minority Owned Business Enterprise Age SWBE – Small, Women Owned Business Enterprises Industry Local vs. National Relocation Performance

Additional information includes:

Detailed information is provided through a series of business profiles and descriptive maps and charts.

Small BusinessBusiness Environment Assessments

Businesses headquartered in the city Average rental price by location

Top performing industries Top most prevalent industries


Small BusinessBusiness Environment Assessments

Social Compact is currently working with WDCEP and DC government on a scan of the District’s business environment, informing:

Business Development Strategies Enterprise Zone location/impact Attraction and retention strategies Buy Local campaigns

Industry Change Analysis Job growth/loss Business Openings/Closings/Relocation

Automated process for tracking change

NEXT STEPS: Expand this model to evaluate partnerships with federal agencies

and other CDFIs to explore small business development models World Bank: Johannesburg, Bogota, Buenos Aires, Manila, Recife, Hanoi


Establish areas with high business density for an industry.

Determine the overall demographic and market characteristics of these areas

Search for areas with similar demographic and market characteristics that do not have businesses belonging to the industry in question (potential profitable sites).

Overlay the information on possible profitable sites with land codes and other relevant site information (i.e. nearby developments, vacant properties).

This pilot analysis was possible thanks to ACCION USA’s loan data.

Uncover possible profitable sites per industry


Trade areas’ properties and store performanceUncover trade area characteristics that are likelyto determine store performance by industry

Determine which are the stores that are performing the best per industry (i.e. barber shops, mini markets, restaurants, etc.)

Determine if there are any common demographic and market properties in the stores’ trade areas

Establish, per industry, trade area demographic and market properties (indicators) that are likely to support high performance stores.

This pilot analysis was possible thanks to ACCION USA’s loan data.


Financial Services


Surveys of unbanked populations and consumer expenditure patterns (Los Angeles, Miami)

Financial services finder (National): Financial advice/counselors, CDCs, NHSA

Center for Financial Empowerment (New York City)

• Financial behavior analysis: household’s financial practices, products usage and access to services.

• Program will leverage consumer credit bureau data as well as rich data from organized local initiatives in selected cities

Financial Diaries (Louisville)

Financial Services


Financial Behavior Analysis (New York City) Address level variables Presence of traditional and non traditional financial institutions Block group level variables Underbanked proxy: range from 1 (unbanked) to 20 (most likely

banked) Discretionary spending index: range from 0 to 100 (households

rates as top spenders) Credit card usage and number of credit lines Revolving bankcard balances Bank card households and bank card holders Collateral risk score Tract level variables Home loan approvals Average income and ethnicity of new homebuyers

Financial Services


Financial Services Provision2007 Miami DrillDown Findings

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

Liberty City Little Haiti Overtown Wynwood

Cash 83.5% 76.6% 79.9% 87.0%

Pay Day Loans 16.6% 42.9% 9.8% 10.8%

Credit Cards 27.0% 56.2% 20.9% 37.3%

Check cashing facilities 46.3% 63.2% 16.7% 29.9%

Personal Checks 26.8% 50.0% 29.8% 43.9%

Bill Payment Methods

Survey Results: Consumer Purchasing PatternsBill Payment Methods


0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

PERSONAL LOANS

MONEY TRANSFERS

HEALTH INSURANCE

BUSINESS INSURANCE

CHECKING ACCOUNT

SMALL BUSINESS LOAN S

PREPAID CARDS

White 15.7% 9.8% 49.0% 21.6% 53.0% 16.0% 7.9%

African American 32.3% 11.9% 69.6% 34.3% 53.5% 27.7% 10.0%

Asian 31.3% 43.8% 56.3% 50.0% 75.0% 62.5% 0.0%

Hispanic/Latino (non indigenous) 24.5% 39.2% 49.5% 18.9% 38.3% 22.8% 13.2%

Hispanic/Latino (indigenous) 18.8% 43.6% 44.1% 17.3% 36.3% 15.0% 20.0%

Other 35.7% 21.4% 57.1% 21.4% 50.0% 14.3% 21.4%

Individual’s Interest in Financial ProductsLos Angeles: Financial Behavior Study


Capturing Urban Market PotentialNew Tools: Risk Mitigation Profiles


Homeownership


Number of foreclosures (2005-2008) by month/quarter/ year/cumulative Number of notices (2005-2008) by month/quarter/

year/cumulative Number of market rate, arms-length transactions (2005-

2008) by month/quarter/year/cumulative Average/Median foreclosure sale price (2005-2008) by

month/quarter/year/cumulative Average/Median market sale price (2005-2008) by

month/quarter/year/cumulative Average/Median current assessed value (2008) Number of ARM resets forthcoming over next 36 months

(as of June 2008) Number of properties with tax liens (as of June 2008) Average/Median value of tax liens outstanding (as of June

2008) Top 10 institutions holding REOs (2005-2008) by

month/quarter/year/cumulative Address-level Property Sale Prices and Dates (as of 1995) Automated Value Models (non-distressed and distressed) Number of new loans originated by year (2003-2008; by

census tract) Number of high cost loans originated by year (2003-2008;

by census tract) Number of high cost refinances originated by year (2003-

2008; by census tract) Number of loans originated intended for primary

occupancy by year (2003-2008; by census tract)

Number of loans originated not intended for primary occupancy by year (2003-2008; by census tract) Average/Aggregate loan value by year (2003-2008; by

census tract) Top 10 high cost loan originators by year (2003-2008; by

census tract) Top 10 high cost loan purchasers by year (2003-2008; by

census tract) Average income of new home buyers by year (2003-2008;

by census tract) Ethnicity of new home buyers by year (2003-2008; by

census tract) Total population (2008; by block group) Population density (2008; by block group) Number of households (2008; by block group) Average/Median/Aggregate household income (2008; by

block group) Income density (2008; by block group) Number of owner-occupied units (2008; by block group) Number of renter-occupied units (2008; by block group)) New construction permits (2008; by block group) Additions/Alterations/Repairs permits (2008; by block group) Banks per capita (Banks per 10,000 people) (2008; by block

group) Nontraditional Financial Institutions per capita (Pay Day

Loans/Pawn Shops, etc per 10,000 people) (2008; by block group) Owner Occupied Buildings (2008; by block group)

Foreclosure Tool Indicators


Property Characteristics (Detailed Characteristics, eg. 1-car Garage, 2-Car Garage, # of Bedrooms, Age, Masonry, Central Air, Fire Place, two Story, Attic Finished, Basement Finished, etc). Short Sales/Distressed Sales Loan Performance (by zipcode) Debt Information on Loan Characteristics Loan-to-Value Current FICO scores Employment Data: Population employed/Population unemployed (granularity

of the data not clear yet)Migration Data (this data tracks where residents have moved to/from we only

have data on Detroit residents so it covers to/from movement of residents of Detroit but not of persons migrating to Detroit – say from Chicago). A. Where Detroiters have moved within Detroit, tri-county, MI, out of state B. Resident “Churn” within Detroit (moved to/from) C. Ethnicity of this population D. Income of this population

Additional indicators


CityDNAComplete intellectual capacity of datasets


CityDNATrend analysis: temporal aspects of datasets


CityDNANext-generation reporting


CityDNAInteractive comparisons - Variables


CityDNAInteractive comparisons – geographic areas


CityDNAInteractive comparisons – address specific


CityDNAInteractive comparisons – by industry


CityDNAInteractive comparisons – reporting


John Talmage, President and CEOSocial Compact

738 7th St., SE, Washington, DC [email protected]

Making the Case for Investment in Low Income Neighborhoods

September 2009

2009 FLORIDA HOUSING COALITION22nd Annual Affordable Housing Conference