16
Adding Underserved Census Tracts as Criterion on CRA Exams Bruce Mitchell, PhD, Senior Analyst, Research & Evaluation, NCRC Josh Silver, Senior Advisor, NCRC
Adding Underserved Census
Tracts as Criterion on
CRA Exams
Bruce Mitchell, PhD, Senior Analyst, Research & Evaluation, NCRCJosh Silver , Senior Advisor, NCRC
2
NCRC
RESEARCHAdding Underserved Census Tracts as Criterion on CRA exams
ABOUT NCRCNCRC and its grassroots member organizations create opportunities for people
to build wealth. We work with community leaders, policymakers and financial
institutions to champion fairness in banking, housing and business.
Our members include community reinvestment organizations, community
development corporations, local and state government agencies, faith-based
institutions, community organizing and civil rights groups, minority and women-
owned business associations, and social service providers from across the nation.
For more information about NCRC’s work, please contact:
John Taylor
Founder and President
(202) 688-8866
Jesse Van Tol
Chief Executive Officer
(202) 464-2709
Bruce C. Mitchell, PhD
Senior Analyst, Research & Evaluation
202-464-2739
Josh Silver
Senior Advisor
202-464-2733
www.ncrc.org
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NCRC
RESEARCHAdding Underserved Census Tracts as Criterion on CRA exams
ADDING UNDERSERVED CENSUS TRACTS AS CRITERION ON CRA EXAMSIntroduction
The Community Reinvestment Act (CRA) is an income-based law. Accordingly, the
regulations and exams focus on evaluating bank lending, investing and services to low-
and moderate-income (LMI) borrowers and tracts. However, one of the major motivations
prompting the passage of CRA in 1977 was reversing redlining and disinvestment from
minority as well as working class neighborhoods. Community advocates and local public
sector officials testified during the hearings before the vote on CRA that redlining was
pervasive in communities of color.1
Over the years, the National Community Reinvestment Coalition (NCRC) and our members
have advocated for the inclusion of race on CRA exams since people of color and
communities of color continue to experience a shortage of affordably priced banking
products and a disproportionate amount of high-cost and abusive products. The financial
crisis stemmed in large part from abusive lenders targeting communities of color that were
starved of mainstream banking products.2 Currently, communities of color still encounter
shortages of loans.
The federal bank agencies have not added race as an explicit part of CRA exams,
maintaining that Congress would need to amend the statute in order to allow the agencies
to do so. CRA exams indirectly address racial disparities in lending with an accompanying
fair lending review that is designed to ensure that banks are not engaging in discrimination
based on race or other prohibited classes. The public information in the fair lending review
on CRA exams has been cursory and has usually consisted of a few sentences stating that
no discrimination was found.
In addition to the fair lending review, exams ensure that there are no “conspicuous gaps” in
lending activity. Examiners scrutinize lending data to make sure that groupings of contiguous
census tracts that are in the bank’s service area are not arbitrarily excluded from a bank’s
1 For a review of the CRA hearings and passage of CRA in 1977, see Josh Silver, The Purpose And Design Of The Community
Reinvestment Act (CRA): An Examination Of The 1977 Hearings And Passage Of The CRA, https://ncrc.org/the-purpose-and-design-
of-the-community-reinvestment-act-cra-an-examination-of-the-1977-hearings-and-passage-of-the-cra/
2 For an overview of continued racial disparities in lending, see Aaron Glantz and Emmanuel Martinez, For People of Color, Banks
are Shutting the Door on Homeownership, February 15, 2018, https://www.revealnews.org/article/for-people-of-color-banks-
are-shutting-the-door-to-homeownership//. For documentation of high cost lending targeting communities of color, see NCRC,
Foreclosure in the Nation’s Capital: How Unfair and Reckless Lending Undermines Homeownership, April 2010, https://ncrc.org/
foreclosure-in-the-nations-capital-how-unfair-and-reckless-lending-undermines-homeownership/. Also, see NCRC, Income is No
Shield Against Racial Disparities in Lending II: A Comparison of High-Cost Lending In America’s Metropolitan and Rural Areas, July
2008, https://ncrc.org/wp-content/uploads/2008/07/income%20is%20no%20shield%20ii.pdf
www.ncrc.org
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RESEARCHAdding Underserved Census Tracts as Criterion on CRA exams
lending activity.3 If examiners detect gaps and possible redlining, they refer cases to the
Department of Justice, which has resulted in redlining settlements over the years.
While conspicuous gap analysis is a commendable aspect of CRA exams, it is not a
comprehensive overview of lending in communities of color, which means it cannot spot
overall low lending levels in communities of color. Some CRA exams look at “economically
disadvantaged areas.” While this could be the start of a more systematic analysis of lending
and service in communities with chronically low levels of lending, it is generally not a well-
developed analysis. Exams occasionally have a few short paragraphs describing the numbers
of loans in low-income tracts or distressed/underserved middle-income rural tracts which
seems to be a proxy for economically disadvantaged areas.4
The lending shortfalls facing communities of color and other underserved communities have
received increased attention over the years. These issues made their way into the Office
of the Comptroller of the Currency’s (OCC’s) Advanced Notice of Proposed Rulemaking
(ANPR) issued in September 2018. The OCC included a question of whether community
development activities should receive consideration if they benefited “specified underserved
populations or areas.”5 NCRC responded to this question by suggesting that the agencies
identify census tracts that exhibit low levels of lending. NCRC surmised that a high number
of these tracts would be communities of color and many, but not all of these tracts, would
be LMI. Then CRA exams could scrutinize activity in these underserved tracts, not only
evaluating the level of community development activity but also lending and basic banking
services.
Another benefit of adding underserved tracts on CRA exams would be to relieve pressure
on LMI tracts that are gentrifying. Gentrification, typified by an influx of middle- and upper-
income people into a neighborhood, can be accompanied by displacement of longer-term
residents that are people of color or lower-income. Gentrification is most intense on the
East and West coasts but is increasing in other parts of the country.6 Banks can sometimes
find it too easy to satisfy their requirements to lend in LMI areas by focusing on gentrifying
neighborhoods. Carolina Reid, for example, documents that more than 90% of the lending in
LMI neighborhoods in San Francisco is for middle- and upper-income people.7 If CRA exams
3 Comptroller’s Handbook, Community Reinvestment Act Examination Procedures, October 1997, p. 39, https://www.occ.gov/
publications-and-resources/publications/comptrollers-handbook/files/cra-exam-procedures/pub-ch-cra-exam-procedures.pdf,
Also, see Large Institution CRA Examination Procedures OCC, FRB, and FDIC, April 2014, p. 7, https://www.federalreserve.gov/
supervisionreg/caletters/CA_14-2_attachment_1_Revised_Large_Institution_CRA_Examination_Procedures.pdf
4 See FDIC Bank of the West CRA exam, August 2017, p. 37, https://www7.fdic.gov/CRAPES/2017/03514_170821.PDF
5 OCC, ANPR see Question 17, https://www.regulations.gov/document?D=OCC-2018-0008-0001
6 Jason Richardson, Bruce Mitchell, Juan Franco, NCRC, Gentrification And Cultural Displacement Most Intense In America’s Largest
Cities, And Absent From Many Others, March 2019, https://ncrc.org/study-gentrification-and-cultural-displacement-most-intense-in-
americas-largest-cities-and-absent-from-many-others/
7 Carolina Reid, Quantitative Performance Metrics for CRA: How Much “Reinvestment” is Enough? in Penn Institute for Urban Research,
September 2019, p. 12, https://penniur.upenn.edu/uploads/media/Quantitative_Performance.pdf
www.ncrc.org
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RESEARCHAdding Underserved Census Tracts as Criterion on CRA exams
added a criteria of lending to and service in underserved tracts, some of this overheated
market activity would be diverted from gentrifying LMI tracts and would be channeled to
underserved tracts that need more lending.
This analysis discusses whether the concept of identifying underserved tracts would be
helpful in increasing banking, investments and services to communities of color and whether
it would ease the market pressures on rapidly gentrifying LMI tracts.
Methodology
NCRC used Core Based Statistical Areas (CBSAs) as the unit of geography in which to
conduct the census tract analysis. CBSAs are urban areas with populations greater than
10,000. These are broken down into two types of MSAs: Micropolitan Statistical Area
(population 10,000 -50,000) and Metropolitan Statistical Areas (population greater than
50,000).
NCRC created a lending index by categorizing census tracts based on levels of lending
activity. Specifically, NCRC calculated home loans per housing units and small business
loans per operating businesses. The resulting values of mortgages and small business loans
were then standardized at the Core Based Statistical Area (CBSA) level utilizing z-scores.
A simple average of z-scores combined the two measures in an index score for each tract.
Census tracts for each CBSA were then ranked by the combined lending index and sorted
into quintiles. NCRC used the most recent year for both the Home Mortgage Disclosure Act
(HMDA) and CRA small business loan data, which was 2017. Additional data is described in
a table in the appendix.
Calculate
Loans
Sort by
Quintiles
Create
Index
• Census tract level calculation
• Small business loans/Number of businesses
• Mortgage originations/Number of housing units
• Calculate z-scores at CBSA level for small business and mortgage loans per unit
• Calculate average (z-score SB+ z-score Mortgages)/2
• Rank all census tracts for the CBSA by lending index score
• Sort into quintiles
Methodology for creating lending index
www.ncrc.org
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RESEARCHAdding Underserved Census Tracts as Criterion on CRA exams
Results
NCRC calculated quintiles of census tracts based on levels of lending activity for all CBSAs
in the nation. We also focused on two CBSAs; the Washington, D.C., MSA and Milwaukee
MSA; since these two are from different regions of the country and have distinct economic
and demographic characteristics.
On a national level, the concept of dividing census tracts into quintiles based on lending
activity levels appeared to achieve the objective of identifying underserved census tracts that
merit more attention on CRA exams. Quintile 1 tracts, which had the lowest level of lending,
had an average of 2.8 home loans per 100 housing units. In contrast, Quintile 5 tracts,
which had the highest level of lending, had an average of 5.3 loans per 100 housing units.
The results for small business lending were even more pronounced. Quintile 1 had almost 82
small business loans per 100 businesses while Quintile 5 had 218 small business loans per
100 businesses (as shown below in the tables in the appendix).
It may appear unusual that there were more small business loans than there were small
businesses in all the quintiles except Quintile 1. The CRA small business loan data includes
data on credit card lending, which significantly increases the volume of lending. Credit card
lending is higher cost than non-credit card lending and usually of much lower amounts,
averaging $10,000 or less. Researchers do not have the ability to separate credit card from
non-credit card lending from the CRA small business loan data. Hence, the loans per small
business often appear high. Nevertheless, it still revealed significant differences in access to
lending across groups of census tracts.
The lower lending levels in Quintiles 1 and 2 corresponded to historical patterns of redlining.
In a report last year, NCRC applied the Federal Home Owners’ Loan Corporation (HOLC)
1930s’ classifications of so-called definitely declining or hazardous neighborhoods to
census tracts.8 These two lowest classifications often corresponded to communities
of color and neighborhoods with recent immigrants. As NCRC found last year, these
pernicious characterizations continue to have lingering impacts. This exercise reconfirmed
the harmful impact of the HOLC characterizations. Quintiles 1 and 2 had the highest
numbers of redlined tracts (definitely declining or hazardous) at 1,850 tracts and 1,174
tracts, respectively. Quintiles 4 and 5 had 556 and 468 tracts, respectively, that were
redlined. Tracts in Quintiles 4 and 5 were more successful in combating the stigma of an
unfortunate HOLC classification and climbing economically. Since fewer of them had low
HOLC classifications and they were most likely contiguous, it was probably easier for these
communities to advance economically.
8 Jason Richardson, Bruce Mitchell, Juan Franco, NCRC, HOLC “redlining” maps: The persistent structure of segregation and
economic inequality, March 2018, https://ncrc.org/holc/
www.ncrc.org
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RESEARCHAdding Underserved Census Tracts as Criterion on CRA exams
Tracts with lower levels of lending activity corresponded to communities of color. Tracts in
Quintile 1 were, on average, 56.8% minority and tracts in Quintile 2 were on average, 43.3%
minority. In contrast, tracts in Quintile 4 were, on average, 32.2% minority and tracts in
Quintile 5 were, on average, 28.7% minority. The average percentage of African Americans
and Hispanics were much higher in Quintiles 1 and 2 than in Quintiles 4 and 5. In contrast,
the percentage of Asians was similar across all quintiles.
Average income levels were also significantly different across tract quintiles. The average
income in Quintile 1 and 2 was $41,798 and $55,380, respectively. In contrast, the average
tract income in Quintile 5 was almost twice as high as Quintile 1 at $74,899. Likewise,
the average poverty rate was more than twice as high in Quintile 1 (24.7%) than Quintile
5 (10.5%). Unemployment rates were also consistent with this pattern; average tract
unemployment in Quintile 1 was 9.9%, almost twice the average tract rate of 5.7% in
Quintile 5.
Additional socio-economic indicators also favored Quintiles 4 and 5. The average tract
percentage of people with college degrees was between 32% and 33% in Quintiles 4 and 5.
In Quintile 1, it was just 20.4% and in Quintile 2, it was 26.4%. Similarly, average tract home
values were about $266,000 and $283,000 in Quintiles 4 and 5, respectively. It was much
lower in Quintile 1 at about $170,000.
Most of the LMI tracts were in Quintiles 1 and 2, but the percentage of tracts that were
LMI in both of these quintiles was less than 50%. Forty percent of the tracts in Quintile 1
were LMI and 24.9% of the tracts in Quintile 2 were LMI. On the other end of the spectrum,
11.3% and 7.4% of the tracts in Quintiles 4 and 5, respectively, were LMI (how these LMI
tracts had higher lending levels than tracts in Quintiles 1 and 2 is an important question for
future research).
Most pertinent for our purposes is that this exercise has shown that it is possible and
desirable to classify tracts by lending levels into quintiles and to further categorize the
quintiles of tracts by demographic and economic characteristics. The exercise has identified
not only LMI tracts but a significant number of non-LMI tracts that are majority people of
color. A significant number of tracts, particularly in Quintile 1, were economically distressed
with high levels of poverty and unemployment in addition to low levels of lending. It would
seem that including a criterion of underserved tracts on the current lending, investment and
service tests of CRA exams would help target CRA activity to tracts in need of it.
In order to further test the robustness of this classification system, NCRC has highlighted
two MSAs with significantly different economic and demographic conditions. The results
for Milwaukee and Washington, D.C., confirmed that the classification system based on
levels of lending work for different MSAs. For example, while the Washington, D.C., area
had overall higher levels of lending than Milwaukee, the quintiles for both areas showed
www.ncrc.org
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NCRC
RESEARCHAdding Underserved Census Tracts as Criterion on CRA exams
significantly lower levels of lending per housing unit or business in Quintiles 1 and 2 than
for Quintiles 4 and 5. The demographic characteristics were also consistent across both
MSAs with significantly higher percentages of communities of color in the lower quintiles
and higher percentages of LMI tracts in the lower quintiles. The economic characteristics
were also consistent across quintiles. While overall poverty levels were higher in Milwaukee
and income levels were lower, the quintiles in both MSAs displayed higher percentages of
poverty and lower average income in the lower quintiles (as shown below in the tables in
the appendix).
The Federal government did not make HOLC maps for the Washington, D.C., area. Hence,
the column in the table that showed the number of redlined tracts had a notation of “Not
Applicable.” For the Milwaukee area, most of the tracts that were redlined were in Quintiles
1 and 2, just like the national sample discussed above.
The columns in the far right of the table show the numbers of tracts that gentrified during
two time periods (the first from 2000-2012 and the second from 2012-2017).9 NCRC’s
previous report, Shifting Neighborhoods: Gentrification and Cultural Displacement
in American Cities, used a methodology for classifying tracts that had undergone
gentrification. Since gentrification was a phenomena that impacted LMI tracts and since a
disproportionate amount of LMI tracts were in Quintiles 1 and 2, a substantial number of
tracts that gentrified were in Quintiles 1 and 2.
CRA policymakers may want to classify underserved tracts as those that have not
gentrified in order to focus CRA activity on underserved tracts that are more likely to have
low market levels of economic activity. Excluding gentrified tracts is unlikely to delete most
underserved tracts in a MSA. For example, eliminating recently gentrifying tracts during the
2012 and 2017 time periods would result in a subtraction of two tracts from Quintile 1 in
Milwaukee and eight in Washington, D.C.
NCRC had also produced maps for the Milwaukee and Washington, D.C., MSAs that
illustrate the spatial distribution of quintiles. In the Washington, D.C., area, the maps show
Quintiles 1 and 2 clustered in predominantly communities of color in the eastern part of the
District of Columbia and in Prince George’s County, which is to the north and east of the
District of Columbia. Also, a disproportionate number of tracts in the lower two quintiles
were LMI (as shown in the appendix).
9 Jason Richardson, Bruce Mitchell, Ph.D., Juan Franco, Shifting Neighborhoods: Gentrification and Cultural displacement in
American Cities, NCRC, March 2019, https://ncrc.org/gentrification/. This report describes a methodology of how to categorize
tracts as gentrified.
www.ncrc.org
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RESEARCHAdding Underserved Census Tracts as Criterion on CRA exams
Conclusion
This study illustrated that it was feasible and desirable to create a classification of census
tracts for CRA called “underserved” that were defined as those with low levels of lending per
housing unit and business. When tracts were grouped into quintiles based on lending levels,
the tracts in the lowest two quintiles were disproportionately LMI, though not entirely so. They
were also predominantly communities of color. A subset of these tracts exhibited indicators of
economic distress as shown by high poverty and unemployment levels.
In addition, the tracts were disproportionately redlined in that the HOLC had classified them
as definitely declining or hazardous in the 1930s. The approach in this paper, therefore,
successfully targeted redlined neighborhoods that were predominantly minority; Quercia and
Park documented a lack of bank CRA lending in these neighborhoods.10
CRA has focused its attention on LMI communities, as it should. However, communities of
color remain underserved because of decades of redlining and discrimination. The proposal
of adding underserved tracts as a criterion on the lending, investment and service tests
would direct needed lending and investments to these underserved communities. It would
also help alleviate pressure on LMI tracts that are gentrifying by giving banks additional
geographical areas in which to serve and receive favorable CRA consideration.
The OCC and the Federal Deposit Insurance Corporation (FDIC) recently proposed to add
distressed and underserved tracts as geographical areas in which banks could receive
CRA credit for lending, investing and the provision of branches. They did not explain their
methodology or estimate the impact of their criteria in terms of how many tracts would
become CRA eligible or where they were located. The agencies removed the constraint that
distressed and underserved middle-income tracts must be in rural areas, and they added a
qualification that underserved tracts have no branches.11 This paper, however, showed that
there are a number of LMI and predominantly minority tracts, in addition to middle-income
tracts, that remain underserved and should be a subject of focus on CRA exams. The lack of
analysis and explanation in the agencies’ proposal renders their choice unsatisfactory.
Some operational considerations should guide the selection of underserved tracts. First, this
exercise has considered just CBSAs. A similar approach can identify underserved tracts in
rural communities. In fact, the agencies update a list of distressed and underserved middle-
income tracts in rural counties to be considered on CRA exams on an annual basis.12
10 Kevin A. Park and Roberto G. Quercia, Who Lends Beyond the Red Line? The Community Reinvestment Act and the Legacy of
Redlining, a Penn Institute for Urban Research working paper, September 2019, https://penniur.upenn.edu/uploads/media/Park_
Quercia.pdf
11 Office of the Comptroller of the Currency and Federal Deposit Insurance Corporation, Notice of Proposed Rulemaking, Community
Reinvestment Act Regulations, https://www.fdic.gov/news/board/2019/2019-12-12-notice-dis-a-fr.pdf, pp. 126 & 131.
12 For a description of underserved and distressed tracts, see the FFIEC website, https://www.ffiec.gov/cra/pdf/Regulatory%20
Background%20-%20Distressed%20and%20Underserved%20Tracts%20FINAL.pdf
www.ncrc.org
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NCRC
RESEARCHAdding Underserved Census Tracts as Criterion on CRA exams
Our study used additional economic and demographic variables that the agencies should
consider in designating census tracts as underserved. Second, the agencies could select
either the first and/or second quintiles in our study as underserved tracts. Our preference is
using the lowest quintile since including the lowest two quintiles will include 40% of the tracts
in an area as underserved, which seems to be on the high side. Further research could inform
a decision regarding which quintiles to use. Lastly, to avoid overheating gentrified tracts and
possibly causing displacement, NCRC recommends not including tracts as underserved that
have recently gentrified.
Policymakers and stakeholders have struggled for several years regarding how to consider
communities of color and other underserved communities in addition to LMI communities as
part of CRA’s focus. The data driven approach presented in this paper hopefully provides a
road map for targeting CRA to truly underserved communities, including but not limited to
communities of color. It is a dynamic approach; if the list of underserved tracts is updated
periodically (every five years to correspond with new American Housing Survey data),
some tracts will be removed from the list because they have been revitalized while other
underserved tracts will receive more CRA focus and attention.
Appendix
CATEGORY VARIABLE SOURCE
Lending Lending Index Composite CRA SB & HMDA 2017
Lending # Mortgage Loans HMDA 2017
Lending # SB loans FFIEC SB 2017
Socioeconomic CRA Tract Income Classification CRA classifications 2019
Socioeconomic % Poverty FFIEC Census 2019
Socioeconomic % Unemployment FFIEC Census 2019
Socioeconomic Median Income $ FFIEC Census 2019
Demographic % Minority FFIEC Census 2019
Demographic % Black and % Hispanic (any race) FFIEC Census 2019
Neighborhood Gentrified 2000-2017 NCRC derived 2000-2017
Neighborhood Low classified HOLC (C&D classes) HOLC maps 1936-1940
Data sources used in this paper:
www.ncrc.org
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NCRC
RESEARCHAdding Underserved Census Tracts as Criterion on CRA exams
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www.ncrc.org
12
NCRC
RESEARCHAdding Underserved Census Tracts as Criterion on CRA exams
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G
AS
IAN
%
QU
INT
ILE
11
98
2$
96
,98
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31
,99
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3.3
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7.6
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QU
INT
ILE
22
76
6$
13
4,6
80
$4
4,4
84
25
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23
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8.3
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3.6
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0.8
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6.5
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QU
INT
ILE
33
35
2$
19
3,8
35
$6
2,5
98
32
.8%
14
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5.8
%3
0.9
%1
3.3
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1.4
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QU
INT
ILE
44
35
4$
20
6,7
05
$7
0,1
57
38
.4%
10
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4.9
%2
4.6
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0.4
%9
.0%
3.2
%
QU
INT
ILE
55
63
2$
24
8,4
16
$7
9,8
27
38
.8%
6.9
%3
.9%
16
.6%
6.7
%5
.0%
3.1
%
AV
G/T
OTA
L3
61
7$
17
6,0
98
$5
7,8
09
30
.6%
17
.6%
6.9
%3
8.6
%2
1.6
%1
1.2
%3
.5%
LE
ND
ING
LE
VE
LLM
I #
LM
I %
MO
RT
GA
GE
S #
BU
SIN
ES
S
LO
AN
S #
MO
RT
GA
GE
S/1
00
HO
ME
S
BU
SIN
ES
S
LO
AN
S/1
00
RE
DLIN
ED
#G
EN
TR
IFIE
D
20
00
-20
12
GE
NT
RIF
IED
20
12
-20
17
QU
INT
ILE
16
33
8.0
%9
04
2,2
03
1.9
49
.15
07
2
QU
INT
ILE
25
43
2.5
%1
,87
92
,84
02
.55
6.1
27
20
QU
INT
ILE
32
41
4.5
%3
,29
34
,80
43
.71
05
.41
72
5
QU
INT
ILE
41
71
0.2
%5
,10
26
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43
.98
5.4
70
0
QU
INT
ILE
58
4.8
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19
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27
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4.8
80
0
AV
G/T
OTA
L1
66
10
0.0
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8,1
39
25
,50
33
.87
8.1
10
91
17
www.ncrc.org
13
NCRC
RESEARCHAdding Underserved Census Tracts as Criterion on CRA exams
Lend
ing
ind
ex b
y q
uin
tile
s fo
r census t
racts
in t
he W
ashin
gto
n D
.C., M
SA
with d
em
og
rap
hic
and
so
cio
eco
no
mic
data
by n
um
ber
of lo
ans a
nd
typ
e
LE
ND
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LE
VE
L
AV
G P
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20
17
AV
G H
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20
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NC
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E
20
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AV
G C
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GE
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EE
AV
G
PO
VE
RT
Y
AV
G
UN
EM
PLO
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T
AV
G
MIN
OR
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AV
G
BLA
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HIS
PA
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13
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QU
INT
ILE
23
45
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52
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16
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8.3
%
QU
INT
ILE
33
93
4$
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05
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QU
INT
ILE
45
01
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42
4,8
85
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QU
INT
ILE
56
42
5$
43
2,1
55
$1
16
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04
5.9
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6.7
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0.4
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1.4
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1.4
%
AV
G/T
OTA
L4
44
5$
41
3,9
21
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9,5
20
44
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8.6
%5
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53
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26
.6%
14
.4%
9.3
%
LE
ND
ING
LE
VE
LLM
I #
LM
I %
MO
RT
GA
GE
S #
BU
SIN
ES
S
LO
AN
S #
MO
RT
GA
GE
S/1
00
HO
ME
S
BU
SIN
ES
S
LO
AN
S/1
00
RE
DLIN
ED
#G
EN
TR
IFIE
D
20
00
-20
12
GE
NT
RIF
IED
20
12
-20
17
QU
INT
ILE
11
68
39
.0%
8,2
68
16
,85
57
.71
33
.9N
/A2
28
QU
INT
ILE
21
03
23
.9%
11
,70
82
3,7
22
6.2
27
9.5
N/A
16
3
QU
INT
ILE
36
71
5.5
%1
5,0
83
21
,78
15
.44
02
.7N
/A1
43
QU
INT
ILE
45
41
2.5
%2
0,1
85
30
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25
.53
70
.9N
/A6
0
QU
INT
ILE
53
99
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30
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73
9,6
58
10
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67
.7N
/A4
0
AV
G/T
OTA
L4
31
10
0.0
%8
5,7
31
13
2,2
98
7.0
31
1.1
N/A
62
14
www.ncrc.org
14
NCRC
RESEARCHAdding Underserved Census Tracts as Criterion on CRA exams
L a k e
M i c h i g a n
¯
2017 Lending Activity
QUINTILE
HOLC GRADED"HAZARDOUS"
VERY LOW (1)
LOW (2)
AVERAGE (3)
ABOVE AVERAGE (4)
HIGH (5)
Lending index by quntiles with formerly redlined
areas of Milwaukee
www.ncrc.org
15
NCRC
RESEARCHAdding Underserved Census Tracts as Criterion on CRA exams
¯
2017 Lending Activity
QUINTILE
VERY LOW (1)
LOW (2)
AVERAGE (3)
ABOVE AVERAGE (4)
HIGH (5)
LOW TO MODERATE
INCOME
L a k e
M i c h i g a n
Lending index by quintiles with low- to moderate-
income census tracts in Milwaukee
www.ncrc.org
16
NCRC
RESEARCHAdding Underserved Census Tracts as Criterion on CRA exams
¯
2017 Lending Activity
QUINTILE
VERY LOW (1)
LOW (2)
AVERAGE (3)
ABOVE AVERAGE (4)
HIGH (5)
LOW TO MODERATE
INCOME
Lending index by quintiles with low- to moderate-
income census tracts in the Washington D.C. MSA
www.ncrc.org
