NCRC 2012 Annual Conference

1. Using Data to Support Advocacy and Increase
Lending in Your Community: How the “Paying More for
the American Dream” Collaborative Uses the HMDA Data
Barbara van Kerkhove
Empire Justice Center

2. Effective use of data in
advocacy
To make a point with the numbers,
hmm, let’s see
You can’t let the numbers just be
You gotta keep tweaking
Statistically speaking
Till they make sense to you and to me
(with thanks to Warren Wightman)

3. Using the Raw HMDA LAR Data
 What is it?
 What you need
 Understanding of the dataset and its
formatting
 A database program like MS Access
 Allows flexibility in what you want to
examine
 By loan, borrower and geography
 Free download from FFIEC
 All of New York State in an afternoon

4. Flexibility to focus on “hot” issues
Year(s) Hot Issues Report Number and Focus
2006-07 Subprime lending and #1: Disparities in high-cost loans across borrower
reverse redlining race/ethnicity
2007-08 Collapse of subprime #2: High cost lending by now defunct subprime lenders in
lending industry neighborhoods of color
2008-09 Did CRA cause the #3: Differences between CRA-covered banks and non-
foreclosure crisis? CRA lenders in high cost lending in neighborhoods of
color
2010-12 The foreclosure crisis #4: Changes by neighborhood in prime lending and
and redlining redux refinance lending and by top 4 bank holding companies—
TARP recipients
#5: Continuing changes in prime refinance lending by
neighborhood and denial disparities
#6: Disparities in the concentration of government-backed
lending by neighborhood and borrower

5. Telling your story with the data
 Determine overall message
 Key finding
 Recommendation(s) arising from it
 Balancing act between making your
point and simplicity or
understandability
 Get feedback from people not really familiar
with the issue or data analysis

6. Presenting your findings
 A picture tells a thousand words, but
try to make it simpler
 Charts, graphs
 Maps
 Avoid tables with lots of numbers
 Keep these for the appendix
 Present findings in more than one way
 Increases understanding by people with
different learning styles
 Reinforces the message

7. Tell the story with charts
70.0%
Chart II. Conventional Refinance Loan Denial Rates by City and Racial/Ethnic
Compostion of Community, 2009
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
11.7%
12.5%
18.1%
21.9%
34.9%
50.5%
18.6%
22.3%
27.3%
42.7%
60.0%
15.6%
15.9%
26.7%
35.1%
57.8%
17.3%
20.2%
27.3%
38.2%
14.1%
15.0%
18.3%
24.4%
38.9%
16.8%
17.8%
16.7%
21.0%
28.9%
24.4%
22.2%
25.6%
30.9%
39.8%
0.0%
Boston Charlotte Chicago Cleveland Los Angeles New York Rocheter
< 10% of color 10% - 19% 20% - 49% 50% - 79% 80% - 100% of color

8. Retell the story with maps

9. Government-Backed Loans (GBLs) as a Percentage of Home Purchase Loans, Differences
Between Predominantly White Neighborhoods (<10% residents of color) and Communities of
Color (80-100% residents of color)
86.4%
90.0%
75.1%
72.0%
69.6%
80.0%
66.6%
62.7%
70.0%
51.5%
48.8%
60.0%
46.1%
46.1%
50.0%
37.0%
32.8%
31.0%
40.0%
27.7%
30.0%
15.1%
12.4%
20.0%
10.0%
0.0%
Boston Charlotte Chicago Cleveland Los Angeles* New York Rochester 7-City
City Average
Disparity (how much more often borrowers in communities of color obtained GBLs than those in predominantly white communities)
ratios 1.9 1.9 2.3 1.5 5.1 3.2 1.9 2.1
<10% of color 80%-100% of color
* LA has too few tracts with <10% of color, so 10<20% of color is used as the baseline for LA rather than <10% of
color.

10. “Paying More for the American Dream”
Collaborators
Kevin Stein Charles Bromley
California Reinvestment Coalition Ohio Fair Lending Coalition
Barb van Kerkhove Adam Rust
Empire Justice Center Reinvestment Partners
Jim Campen Spencer Cowan, Katie
Massachusetts Affordable Housing Buitrago and Tom
Alliance
Feltner
Alexis Iwanisziw and Woodstock Institute
Sarah Ludwig
NEDAP

11. For more information
Barbara van Kerkhove, Ph.D.
Empire Justice Center
585-454-4060
bvankerkhove@empirejustice.org
www.empirejustice.org