Making The Case Talmage

Making the Case for Investment in
Low Income Neighborhoods

2009 FLORIDA HOUSING COALITION
22nd Annual Affordable Housing Conference
September 2009

1 | www.socialcompact.org SocialCompact

A tale of two cities …
MARKET SIZE MARKET SIZE
Neighborhood A
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

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


Social Compact’s Capacity
DrillDowns Completed
 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


What do we do with
better data?

Census Challenge


Census Challenge Program
Why Are Census Estimates Important?
 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 Program
Social Compact Successes
 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.


Food Security


2007 Houston DrillDown Findings
Grocery Provider Distribution

Distribution of
grocery providers
overlaid with
grocery store sales
demand.


2007 Houston DrillDown Findings
Grocery Customer Attraction


Food Desert to Food Oasis
Providing East Access to Information: The Finder


Small Business
Development


Small Business
Business Environment Assessments
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:
 Businesses headquartered in the city  Top performing industries
 Average rental price by location  Top most prevalent industries

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


Small Business
Business 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

Uncover possible profitable sites per industry
 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.


Trade areas’ properties and store performance
Uncover trade area characteristics that are likely
to 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


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 Provision
2007 Miami DrillDown Findings
Bill Payment Methods
Survey Results: Consumer Purchasing Patterns
100.0% Bill Payment Methods
90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
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%


Individual’s Interest in Financial Products
Los Angeles: Financial Behavior Study
80.0%

70.0%

60.0%

50.0%

40.0%

30.0%

20.0%

10.0%

0.0%
PERSONAL MONEY HEALTH BUSINESS CHECKING SMALL BUSINESS
PREPAID CARDS
LOANS TRANSFERS INSURANCE INSURANCE ACCOUNT LOAN S
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%


Capturing Urban Market Potential
New Tools: Risk Mitigation Profiles


Homeownership


Foreclosure Tool Indicators
 Number of foreclosures (2005-2008) by month/quarter/  Number of loans originated not intended for primary
year/cumulative occupancy by year (2003-2008; by census tract)
 Number of notices (2005-2008) by month/quarter/  Average/Aggregate loan value by year (2003-2008; by
year/cumulative census tract)
 Number of market rate, arms-length transactions (2005-  Top 10 high cost loan originators by year (2003-2008; by
2008) by month/quarter/year/cumulative census tract)
 Average/Median foreclosure sale price (2005-2008) by  Top 10 high cost loan purchasers by year (2003-2008; by
month/quarter/year/cumulative census tract)
 Average/Median market sale price (2005-2008) by  Average income of new home buyers by year (2003-2008;
month/quarter/year/cumulative by census tract)
 Average/Median current assessed value (2008)  Ethnicity of new home buyers by year (2003-2008; by
 Number of ARM resets forthcoming over next 36 months census tract)
(as of June 2008)  Total population (2008; by block group)
 Number of properties with tax liens (as of June 2008)  Population density (2008; by block group)
 Average/Median value of tax liens outstanding (as of June  Number of households (2008; by block group)
2008)  Average/Median/Aggregate household income (2008; by
 Top 10 institutions holding REOs (2005-2008) by block group)
month/quarter/year/cumulative  Income density (2008; by block group)
 Address-level Property Sale Prices and Dates (as of 1995)  Number of owner-occupied units (2008; by block group)
 Automated Value Models (non-distressed and distressed)  Number of renter-occupied units (2008; by block group))
 Number of new loans originated by year (2003-2008; by  New construction permits (2008; by block group)
census tract)  Additions/Alterations/Repairs permits (2008; by block group)
 Number of high cost loans originated by year (2003-2008;  Banks per capita (Banks per 10,000 people) (2008; by block
by census tract) group)
 Number of high cost refinances originated by year (2003-  Nontraditional Financial Institutions per capita (Pay Day
2008; by census tract) Loans/Pawn Shops, etc per 10,000 people) (2008; by block
 Number of loans originated intended for primary group)
occupancy by year (2003-2008; by census tract)  Owner Occupied Buildings (2008; by block group)


Additional 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


CityDNA
Complete intellectual capacity of datasets


CityDNA
Trend analysis: temporal aspects of datasets


CityDNA
Next-generation reporting


CityDNA
Interactive comparisons - Variables


CityDNA
Interactive comparisons – geographic areas


CityDNA
Interactive comparisons – address specific


CityDNA
Interactive comparisons – by industry


CityDNA
Interactive comparisons – reporting


Making the Case for Investment in
Low Income Neighborhoods
John Talmage, President and CEO
Social Compact
738 7th St., SE, Washington, DC 20003
jtalmage@socialcompact.org

2009 FLORIDA HOUSING COALITION
22nd Annual Affordable Housing Conference
September 2009


Making The Case Talmage

Recommandé

Recommandé

Contenu connexe

En vedette

En vedette (19)

Similaire à Making The Case Talmage

Similaire à Making The Case Talmage (20)

Plus de Florida Housing Coalition

Plus de Florida Housing Coalition (20)

Dernier

Dernier (20)

Making The Case Talmage