GAO-24-106682: Disaster Loan Program: SBA Should Include Key Issues in Its Review of How the Program Affects Underserved Communities
Summary
GAO-24-106682, Disaster Loan Program: SBA Should Include Key Issues in Its Review of How the Program Affects Underserved Communities, a November 2023 Government Accountability Office report to congressional committees. It states that in its 2022 Equity Action Plan SBA identified lending criteria, such as satisfactory credit score, as a barrier for underserved communities, but does not plan to assess those criteria in its planned review. GAO reports that SBA declined about 40 percent of disaster loan applications for 13 hurricanes in fiscal years 2018–2022. Of the 312,916 applications SBA accepted in the first stage of review, SBA approved 42 percent and declined 38.6 percent.
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“GAO-24-106682: Disaster Loan Program: SBA Should Include Key Issues in Its Review of How the Program Affects Underserved Communities.”
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United States Government Accountability Office
Report to Congressional Committees
DISASTER LOAN
November 2023
PROGRAM
SBA Should Include
Key Issues in Its
Review of How the
Program Affects
Underserved
Communities
GAO-24-106682
November 2023
DISASTER LOAN PROGRAM
SBA Should Include Key Issues in Its Review of How
the Program Affects Underserved Communities
Highlights of GAO-24-106682, a report to
congressional committees
Why GAO Did This Study What GAO Found
Natural disasters cause billions of The Small Business Administration (SBA) has taken initial steps to improve
dollars of damage each year. access to the Disaster Loan Program for historically underserved communities
Underserved communities with low (such as those with high poverty rates or high percentages of Black or African
levels of community resilience may be American, Asian, and Hispanic or Latino residents). In its 2022 Equity Action
disproportionately affected. SBA’s Plan, SBA identified its lending criteria, such as satisfactory credit score, as a
Disaster Loan Program assists barrier that underserved communities face in accessing the program. SBA plans
homeowners, businesses, and others to conduct a congressionally requested review to determine if any current
affected by natural and other disasters. practices provide unequal outcomes for certain underserved communities and, if
The Explanatory Statement to the so, develop recommendations to improve outcomes. However, SBA does not
Consolidated Appropriations Act, 2023 plan to assess its lending criteria as part of this review. Doing so would help it to
includes a provision for GAO to report better determine if changes to the criteria are needed to reduce unequal
on delivery of SBA disaster recovery outcomes for historically underserved communities, consistent with Congress’s
support and how the needs of certain direction.
communities are addressed. This
report examines (1) steps SBA has SBA declined about 40 percent of disaster loan applications for 13 hurricanes in
taken to improve equitable access to fiscal years 2018–2022. Loan outcomes tended to be worse in communities with
the Disaster Loan Program and (2) higher percentages of low-income and Black or African American individuals. Of
loan outcomes in and characteristics of the 312,916 applications SBA accepted in the first stage of its application review,
communities in which program SBA approved 42 percent and declined 38.6 percent. Applications were declined
applicants and recipients are located. primarily due to insufficient credit score or lack of repayment ability. The average
GAO reviewed SBA policies and approval rate in communities with the lowest median household incomes was 23
procedures and interviewed SBA percentage points lower than in communities with the highest median household
officials. GAO also analyzed disaster incomes. The average approval rate in communities with the highest percentages
loan application data for all 17 of Black or African American residents was 22 percentage points lower than in
hurricanes in fiscal years 2017–2022 communities with the lowest percentages of Black or African American residents.
that caused at least $1 billion in Results were similar for areas affected by four hurricanes in fiscal year 2017 and
damage. This included calculating loan for Puerto Rico.
outcomes and using census data to
determine the demographic and Disaster Loan Approval Rates by Socioeconomic and Demographic Characteristic in
socioeconomic characteristics of Communities Affected by 13 Hurricanes, Fiscal Years 2018–2022
communities in which applicants were
located. Because of the magnitude of
the four fiscal year 2017 hurricanes
and Puerto Rico’s unique
demographics, GAO analyzed those
applications separately.
What GAO Recommends
GAO recommends that SBA assess its
lending criteria as part of its internal
review of the Disaster Loan Program.
SBA agreed, stating it would assess its
lending criteria and evaluate any
relevant gaps in loan outcomes.
Note: For each characteristic, GAO ordered the census tracts within the disaster areas from the
View GAO-24-106682. For more information, lowest to the highest prevalence of that characteristic and divided the ordered distribution into four
contact William B. Shear at (202) 512-8678 or equal groups (quartiles). The tracts with the lowest prevalence of that characteristic fall into the lowest
shearw@gao.gov. quartile (or <=25th percentile), while those with the highest prevalence fall into the highest quartile (or
>75th percentile).
United States Government Accountability Office
Contents
Letter 1
Background 4
SBA Has Taken Steps to Address Barriers to Accessing the
Disaster Loan Program but Does Not Plan to Assess Its
Lending Criteria 9
SBA Disaster Loan Outcomes Were Less Favorable in Some
Historically Underserved Communities 21
Conclusions 31
Recommendation for Executive Action 32
Agency Comments 32
Appendix I Objectives, Scope, and Methodology 34
Appendix II Disaster Loan Outcomes in and Characteristics of Communities
Affected by Four Hurricanes in Fiscal Year 2017 40
Appendix III Socioeconomic and Demographic Characteristics of Communities in
Puerto Rico Affected by Four Hurricanes in Fiscal Years 2017–2022 51
Appendix IV Outcomes of Disaster Loan Applications the Small Business
Administration Accepted for 17 Hurricanes in Fiscal Years 2017–2022 59
Appendix V Comments from the Small Business Administration 61
Appendix VI GAO Contact and Staff Acknowledgments 63
Tables
Table 1: Disaster Loan Applications Accepted by SBA from
Survivors of 13 Hurricanes in Fiscal Years 2018–2022, by
Loan Type 22
Page i GAO-24-106682 Disaster Loan Program
Table 2: Disaster Loan Applications Accepted by SBA from
Survivors of Four Hurricanes in Fiscal Year 2017, by Loan
Type 41
Table 3: Outcomes of Disaster Loan Applications SBA Accepted
in Fiscal Years 2017–2022, by Hurricane 59
Figures
Figure 1: SBA Disaster Loan Processing 7
Figure 2: SBA Disaster Loan Program Outreach Flyer in English
and Spanish 19
Figure 3: Outcomes of Applications SBA Accepted for 13
Hurricanes in Fiscal Years 2018–2022, as of June 2023 21
Figure 4: Top Reasons for SBA Declining Disaster Loan
Applications from Survivors of 13 Hurricanes in Fiscal
Years 2018–2022 23
Figure 5: Average Acceptance Rates for SBA Disaster Loans by
Socioeconomic and Demographic Characteristic for 13
Hurricanes, Fiscal Years 2018–2022 26
Figure 6: Average Automatic-Decline Rates for SBA Disaster
Loans by Socioeconomic and Demographic
Characteristic for 13 Hurricanes, Fiscal Years 2018–2022 28
Figure 7: Average Approval Rates for SBA Disaster Loans by
Socioeconomic and Demographic Characteristic for 13
Hurricanes, Fiscal Years 2018–2022 30
Figure 8: Outcomes of Applications SBA Accepted for the Disaster
Loan Program for Four Hurricanes in Fiscal Year 2017 40
Figure 9: Top Reasons for SBA Declining Disaster Loan
Applications from Survivors of Four Hurricanes in Fiscal
Year 2017 42
Figure 10: Average Acceptance Rates for SBA Disaster Loans by
Socioeconomic and Demographic Characteristic for Four
Hurricanes, Fiscal Year 2017 45
Figure 11: Average Automatic-Decline Rates for SBA Disaster
Loans by Socioeconomic and Demographic
Characteristic for Four Hurricanes, Fiscal Year 2017 47
Figure 12: Average Approval Rates for SBA Disaster Loans by
Socioeconomic and Demographic Characteristic for Four
Hurricanes, Fiscal Year 2017 49
Figure 13: Average Acceptance Rates for SBA Disaster Loans by
Socioeconomic and Demographic Characteristic for Two
Hurricanes That Affected Puerto Rico, Fiscal Year 2017 53
Page ii GAO-24-106682 Disaster Loan Program
Figure 14: Average Acceptance Rates for SBA Disaster Loans by
Socioeconomic and Demographic Characteristic for Two
Hurricanes That Affected Puerto Rico, Fiscal Years
2018–2022 55
Figure 15: Average Automatic-Decline Rates for SBA Disaster
Loans by Percentage of Black or African American
Residents for Three Hurricanes that Affected Puerto
Rico, Fiscal Years 2018–2022 56
Figure 16: Average Approval Rates for SBA Disaster Loans by
Socioeconomic and Demographic Characteristic for Four
Hurricanes That Affected Puerto Rico, Fiscal Years
2017–2022 57
Abbreviations
ACS American Community Survey
EIDL Economic Injury Disaster Loan
FEMA Federal Emergency Management Agency
HUD Department of Housing and Urban Development
LGBTQ+ lesbian, gay, bisexual, transgender, and queer
PDL physical disaster loan
SBA Small Business Administration
SBDC Small Business Development Center
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Page iii GAO-24-106682 Disaster Loan Program
Letter
441 G St. N.W.
Washington, DC 20548
November 30, 2023
The Honorable Chris Van Hollen
Chair
The Honorable Bill Hagerty
Ranking Member
Subcommittee on Financial Services and General Government
Committee on Appropriations
United States Senate
The Honorable Steve Womack
Chair
The Honorable Steny Hoyer
Ranking Member
Subcommittee on Financial Services and General Government
Committee on Appropriations
House of Representatives
Natural disasters affect numerous American communities and cause
billions of dollars of damage each year. From 2016 through 2022, the
U.S. sustained 122 weather and climate disasters, with overall damage
reaching or exceeding $1 billion and cumulative costs exceeding $1
trillion. Historically underserved communities with low levels of community
resilience may be disproportionately affected by damage caused by
natural disasters. 1
The Small Business Administration (SBA), along with more than 30 other
federal agencies and departments, provides assistance to help
businesses, nonprofits, homeowners, and renters recover from federally
1Definitions of underserved communities vary. In an executive order on advancing racial
equity and support for underserved communities, the Biden administration defined
underserved communities as populations sharing a particular characteristic, as well as
geographic communities, that have been systematically denied a full opportunity to
participate in aspects of economic, social, and civic life. The executive order provided
examples of these populations, including Black, Latino, and Indigenous and Native
American persons, Asian Americans and Pacific Islanders and other persons of color;
members of religious minorities; lesbian, gay, bisexual, transgender, and queer (LGBTQ+)
persons; persons with disabilities; persons who live in rural areas; and persons otherwise
adversely affected by persistent poverty or inequality. See White House, Executive Order
on Advancing Racial Equity and Support for Underserved Communities Through the
Federal Government, Exec. Order No. 13985, 86 Fed. Reg. 7009 (Jan. 25, 2021).
Community resilience is the ability to prepare for anticipated hazards, adapt to changing
conditions, and withstand and recover rapidly from disruptions.
Page 1 GAO-24-106682 Disaster Loan Program
declared natural disasters. Although SBA is known primarily for its
financial support of small businesses, its Disaster Loan Program assists
most types of businesses, private nonprofit organizations, homeowners,
and renters affected by declared disasters. Through this program, SBA
offers low-interest loans for physical and economic damage to
businesses, nonprofits, homeowners, and renters to help repair, rebuild,
and recover from economic losses after a declared disaster. The majority
of the agency’s disaster loans are made to individuals and households to
help repair and replace homes and personal property.
The Explanatory Statement to the Consolidated Appropriations Act, 2023
includes a provision for us to report on recovery outcomes in communities
that received SBA disaster recovery support and identify how SBA
programs address the needs of economically disadvantaged communities
or communities of color. 2 This report examines (1) steps SBA has taken
to improve equitable access to the Disaster Loan Program and (2) loan
outcomes in and characteristics of communities in which program
applicants and recipients are located. Consistent with the Explanatory
Statement, we focused on the following communities that may be
underserved: economically disadvantaged communities as indicated by
characteristics such as median household income and poverty rate and
communities with high percentages of Black or African American, Asian,
and Hispanic or Latino residents.
To address the first objective, we reviewed Disaster Loan Program
policies and procedures related to the loan process and equitable access,
including lending criteria outlined in the program’s standard operating
procedure, and SBA’s outreach materials to communities affected by a
disaster. We also reviewed SBA’s 2022 Equity Action Plan and compared
it against the requirements for federal agencies to advance racial equity
and support for underserved communities in Executive Order 13985. 3 In
addition, we compared SBA’s plans to evaluate its current program
practices against Executive Order 14091 and the provision in the
Explanatory Statement to the Consolidated Appropriations Act, 2023 that
2For the Explanatory Statement, see CONG. REC. S7819, S8492 (Dec. 20, 2022). Pub. L.
No. 117-249, § 2, 136 Stat. 2350, 2351 (2022) includes a provision for us to report on any
unique challenges that communities in rural areas face compared to communities in
urbanized areas when seeking to obtain disaster assistance. We plan to report on this
topic separately in early 2024.
3Small Business Administration, Equity Action Plan (Washington, D.C.: April 2022) and
Exec. Order No. 13985, 86 Fed. Reg. 7009 (Jan. 25, 2021).
Page 2 GAO-24-106682 Disaster Loan Program
requires the evaluation. 4 We interviewed officials in SBA’s Offices of
Capital Access and Disaster Recovery and Resilience and in SBA’s two
Field Operations Centers.
To address the second objective, we analyzed SBA application data for
all 17 hurricanes on the National Oceanic and Atmospheric
Administration’s list of billion-dollar disaster incidents in fiscal years 2017–
2022. We focused on hurricanes, as opposed to other types of disasters,
because they affect large stretches of land and often affect economically,
racially, and ethnically diverse communities. Three of the top five costliest
hurricanes on record occurred in fiscal year 2017, and fiscal year 2022
was the most recent complete fiscal year at the beginning of our review.
Using the disaster loan application data, we determined loan outcomes in
affected communities, including the numbers and percentages of
applications accepted, approved, declined, and withdrawn. 5
We also geocoded the SBA disaster loan application data and combined
the geocoded data with census-tract data from the Census Bureau’s
American Community Survey (ACS) to examine the demographic and
socioeconomic characteristics of the census tracts in which applicants
and recipients of SBA disaster loans were located. 6 Appendix I describes
our objectives, scope, and methodology in greater detail.
The body of this report focuses on aggregate results for the 13 hurricanes
in our review that occurred in fiscal years 2018–2022. In appendixes II
and III, respectively, we present (1) our aggregate results for the four
fiscal year 2017 hurricanes in our review and (2) our findings on the
characteristics of communities in Puerto Rico affected by the hurricanes
in our review. We present these analyses separately because the large
number of applications for fiscal year 2017 disasters and the unique
4White House, Executive Order on Further Advancing Racial Equity and Support for
Underserved Communities Through the Federal Government, Exec. Order No. 14091, 88
Fed. Reg. 10825 (Feb. 22, 2023).
5We assessed the reliability of the application data by reviewing relevant documentation,
interviewing knowledgeable agency officials, and performing electronic testing. We
determined these data were sufficiently reliable for the purpose of describing the
outcomes of disaster loan applications.
6The ACS is conducted by the Census Bureau to help local officials, community leaders,
and businesses understand the changes taking place in their communities. It is the
premier source for detailed population and housing information. The ACS releases new
data every year in the form of estimates. To assess the reliability of the census data, we
reviewed technical information. We determined the data were sufficiently reliable for the
purposes of reporting on community socioeconomic and demographic characteristics.
Page 3 GAO-24-106682 Disaster Loan Program
demographic composition of Puerto Rico’s communities may obscure
trends in the 2018–2022 period. Appendix IV provides the loan outcomes
for each of the 17 hurricanes in our review individually.
We conducted this performance audit from March 2023 to November
2023 in accordance with generally accepted government auditing
standards. Those standards require that we plan and perform the audit to
obtain sufficient, appropriate evidence to provide a reasonable basis for
our findings and conclusions based on our audit objectives. We believe
that the evidence obtained provides a reasonable basis for our findings
and conclusions based on our audit objectives.
Background
Federal Disaster Federal agencies can respond to a disaster when effective response and
Assistance recovery are beyond the capabilities of affected state and local
governments. The SBA Disaster Loan Program is activated through
federal disaster declarations. Declarations can be issued by the President
or the SBA Administrator if certain criteria are met. 7
The National Disaster Recovery Framework outlines a strategy for the
delivery of federal disaster recovery assistance. This framework identifies
guiding principles that enable recovery partners to provide a
comprehensive national recovery strategy facilitating the effective
coordination of all levels of federal, state, and local government with the
private sector and other community recovery entities. It charges federal
recovery partners with identifying and removing social and institutional
barriers to program participation, whether intentional or unintentional.
According to the framework, one critical aspect of federal assistance that
contributes to successful individual and community outcomes is the
extent to which individuals and communities can access the assistance
they most need.
The Federal Emergency Management Agency (FEMA) has lead
responsibility for and offers disaster assistance. At least 30 other federal
agencies, including SBA through its Disaster Loan Program, also
administer disaster assistance programs and activities. One FEMA
disaster assistance program is the Individuals and Households Program,
which provides financial assistance and direct services to eligible
7See 13 C.F.R. § 123.3. See also 42 U.S.C. § 5170 for further details on the criteria for
issuing a major disaster declaration.
Page 4 GAO-24-106682 Disaster Loan Program
individuals and households. To be considered for certain categories of
this assistance, such as personal property and transportation assistance,
applicants must first apply to SBA for a disaster loan. If SBA denies these
applicants, they may then be referred to FEMA. 8
SBA Disaster Loan Through its Disaster Loan Program, SBA offers low-interest loans for
Program physical and economic damage to businesses, nonprofits, homeowners,
and renters to help repair, rebuild, and recover from economic losses
after a declared disaster. The majority of the agency’s disaster loans are
made to homeowners and renters to help repair and replace homes and
personal property. For these types of loans, loan eligibility is restricted to
uninsured or otherwise uncompensated disaster losses. There are three
main types of loans for disaster-related losses: 9
• Home and personal property loans. Homeowners may apply for up
to $500,000 to repair or replace their primary residences, and both
homeowners and renters can apply for up to $100,000 to replace or
repair personal property. 10
• Business physical disaster loans. Qualified businesses and private
nonprofit organizations can apply for up to $2 million to cover disaster
losses. 11
• Economic Injury Disaster Loans (EIDL). Eligible small businesses,
certain small agricultural businesses, and eligible private nonprofit
organizations can apply for up to $2 million in financial assistance for
8Assistance may also be available if an applicant can demonstrate that the assistance
received from SBA does not satisfy their total necessary expenses or serious needs
arising out of the major disaster. 44 C.F.R. § 206.119(a)(3).
9Eligible SBA disaster loan borrowers may apply to receive an increase in disaster loan
funding for their home or business for mitigation measures implemented after a disaster
has occurred. To assist borrowers in making these upgrades, SBA disaster loans can be
increased by an amount equal to the lesser of the cost of the mitigation measure or up to
20 percent of the verified loss. For home loans, a maximum increase of $500,000 applies.
13 C.F.R. §§ 123.107, 123.204.
10These loan limits are applicable to disasters declared on or after July 31, 2023. For the
period of our data analysis (fiscal years 2017–2022), the loan limits were lower.
Homeowners could apply for up to $200,000 to repair or replace their primary residences,
and both homeowners and renters could apply for up to $40,000 to replace or repair
personal property.
11In certain circumstances, SBA may waive the $2 million limitation. 13 C.F.R. § 123.202.
Page 5 GAO-24-106682 Disaster Loan Program
economic injury suffered as a result of a declared disaster (regardless
if there has been physical damage to the business). 12
SBA administers the Disaster Loan Program through its Office of Disaster
Recovery and Resilience and Office of Capital Access. The Office of
Disaster Recovery and Resilience is responsible for declaring disasters
and coordinating the agency’s field response and outreach. Within that
office, two Field Operations Centers establish staff and maintain field
operations in declared disaster areas, including Disaster Recovery
Centers, Business Recovery Centers, and Disaster Loan Outreach
Centers. 13 The Office of Capital Access is responsible for loan origination,
processing, closing and disbursement, and servicing.
Processing disaster loan applications involves several stages, including
application acceptance and application processing (see fig. 1). The first
step in application processing is for SBA to accept the application.
Accepted applications can be approved, declined, or withdrawn.
12Disaster business loans, including both physical disaster loans and EIDL loans, to the
same borrower, together with its affiliates, cannot exceed up to $2 million. 13 C.F.R. §
123.202. In certain circumstances, SBA may waive the $2 million limit. 13 C.F.R. §
123.202. SBA also offers a Military Reservist EIDL loan in addition to the three main types
of disaster loans. 13 C.F.R. § 123.5.
13FEMA coordinates Disaster Recovery Centers at which FEMA, SBA, volunteer groups,
and other agencies answer questions and help survivors apply for federal disaster
assistance. SBA operates Business Recovery Centers, which are created specifically to
help business owners with recovery. SBA also may assist homeowners or renters seeking
assistance at a Business Recovery Center. Other organizations and SBA’s resource
partners usually help staff these centers. A Disaster Loan Outreach Center is a public
facility established and staffed by SBA to help disaster loan applicants obtain applications,
return completed applications, complete disaster loan application forms, and close loans,
and to assist them with loan modifications, reconsiderations, and late application requests.
SBA is usually the only agency present at a Disaster Loan Outreach Center.
Page 6 GAO-24-106682 Disaster Loan Program
Figure 1: SBA Disaster Loan Processing
SBA processes disaster loan applications through its Disaster Credit
Management System. Homeowner and renter applicants who are
declined for SBA disaster loan assistance are referred to FEMA for
possible assistance through the component of its Individuals and
Households Program that provides assistance to repair or replace a
vehicle damaged by a disaster or to repair or replace personal property.
According to SBA officials, applicants’ unsatisfactory credit scores or
inability to repay SBA loans are identified early in the application process
Page 7 GAO-24-106682 Disaster Loan Program
to allow SBA to refer them to alternative forms of assistance as quickly as
possible. Officials also said that applicants with strong credit—credit
scores of 625 or greater and adjusted gross income of $50,000 or
greater—are processed using an expedited method.
Federal Efforts to Improve According to FEMA, too many disaster survivors face barriers in
Equitable Access to accessing disaster assistance programs and resources to support their
recovery. The federal government continues to acknowledge a need for
Disaster Assistance
focus on resource equity. The Biden administration recently issued two
executive orders directing agencies to advance equity in federal
programs:
• Executive Order 13985. In January 2021, the President issued an
executive order that calls for the federal government to “pursue a
comprehensive approach to advancing equity for all, including people
of color and others who have been historically underserved,
marginalized, and adversely affected by persistent poverty and
inequality.” 14 Further, the executive order directs federal agencies to
assess whether underserved groups face systemic barriers in
accessing opportunities and benefits available pursuant to certain
programs and produce a plan for addressing any identified barriers to
full and equal participation in the programs.
• Executive Order 14091. In February 2023, the President signed an
executive order that builds on Executive Order 13985 by extending
and strengthening equity-advancing requirements for agencies. 15 This
order requires that agencies create equity teams to coordinate and
implement equity initiatives and outcomes; develop and annually
submit an “equity action plan” concurrent with the budget submission
process; and increase engagement with underserved communities
through new tools and resources.
14Exec. Order No. 13985, 86 Fed. Reg. 7009 (Jan. 25, 2021).
15Exec. Order No. 14091, 88 Fed. Reg. 10825 (Feb. 22, 2023). The executive order
defines “‘equity’’ as the consistent and systematic treatment of all individuals in a fair, just,
and impartial manner, including individuals who belong to communities that often have
been denied such treatment.
Page 8 GAO-24-106682 Disaster Loan Program
SBA Has Taken Steps
to Address Barriers to
Accessing the
Disaster Loan
Program but Does
Not Plan to Assess Its
Lending Criteria
SBA Has Taken Some SBA has taken initial steps to identify and address barriers to accessing
Initial Steps to Address the Disaster Loan Program for underserved communities. In response to
the requirements of Executive Order 13985 to advance racial equity and
Equity Concerns and
support for underserved communities, SBA released an Equity Action
Barriers Plan in April 2022. 16 This plan outlines how SBA intends to reduce
barriers and improve access to agency resources, including disaster
assistance, for underserved communities. 17 The plan also highlights
SBA’s commitment to advance civil rights and to improve its gathering of
quantitative and qualitative data to help inform ongoing analyses. The
section of the plan that addresses disaster assistance meets the
requirements of Executive Order 13985, including the requirement to
identify potential barriers that underserved communities and individuals
face when accessing the Disaster Loan Program. 18
16Executive Order 14091 requires agencies to update their equity action plans annually.
SBA officials told us they submitted an updated plan to the Office of Management and
Budget in September 2023.
17In the plan, SBA defines underserved communities as including persons who are Asian
American, Black and African American, Hispanic and Latino, Middle Eastern, North
African, other persons of color, Alaska Native, Native American, Native Hawaiian, Pacific
Islander, LGBTQ+, religious minorities, military service members, military spouses,
veterans, and women; tribal communities; persons with disabilities; persons living in rural
or underserved urban areas; and persons otherwise adversely affected by persistent
poverty or systemic inequality.
18The 2022 SBA Equity Action Plan outlines five SBA Equity Actions. Other than Equity
Action #4, “Improve Access to Disaster Assistance,” the actions are not relevant to the
SBA Disaster Loan Program.
Page 9 GAO-24-106682 Disaster Loan Program
In the plan, SBA identifies two barriers to equitable access to the Disaster
Loan Program for underserved communities: lending criteria and lack of
resources for financial counseling. 19
• Lending criteria. SBA’s plan states that the lending criteria for
disaster loans could make it difficult for disaster survivors in
underserved communities to obtain assistance from disaster
assistance programs because of financial disparities. 20 SBA’s lending
criteria for disaster loans require that applicants have a satisfactory
credit history, generally showing payments to creditors as agreed
unless otherwise justified. In addition, an applicant must demonstrate
an ability to repay a disaster loan within the maximum term. 21
Prior to accepting a loan application, SBA will summarily decline a
homeowner or renter application if the household income is below
SBA’s income test-table threshold for the family size or if it determines
the applicant is unlikely to have repayment ability based on the stated
income and stated debts from the application. 22 If summarily declined,
a homeowner or renter is referred to FEMA for additional disaster
assistance through a component of its Individuals and Household
Program.
After SBA accepts a loan application and obtains a credit report, the
agency may automatically decline homeowner, renter, and some
19In February 2020, we identified several challenges disaster survivors experienced in
accessing the Disaster Loan Program after the 2017 hurricanes, which could be additional
barriers for underserved communities. For example, we reported that disaster survivors
found it challenging to provide required loan documentation and that they experienced
frequent changes in loan officers or case managers, lack of responsiveness from SBA,
and translation issues. GAO, Small Business Administration: Disaster Loan Processing
Was Timelier, but Planning Improvements and Pilot Program Evaluation Needed,
GAO-20-168 (Washington, D.C.: Feb. 7, 2020).
20The plan cites a 2015 Consumer Financial Protection Bureau report that stated that
about 20 percent of U.S. adults were credit invisible or unscorable because of a limited or
older credit history, including nearly 30 percent of Black or African American and Hispanic
or Latino adults. Report authors also found that almost 30 percent of consumers in low-
income communities were credit invisible and an additional 15 percent had unscored
records. Percentages were notably lower in higher-income communities. See Consumer
Financial Protection Bureau, Data Point: Credit Invisibles (Washington, D.C.: May 2015).
21Additional SBA disaster loan underwriting criteria apply. These include collateral
requirements and other federal underwriting requirements. See, e.g., 13 C.F.R. § 123.6
and § 123.17.
22The income test threshold is updated annually by numbered memorandum. The current
income levels went into effect on February 17, 2023.
Page 10 GAO-24-106682 Disaster Loan Program
business applications because of unsatisfactory credit. 23 Although
SBA officials told us there is no minimum credit score requirement for
loan approval, an application will be automatically declined if the
applicant has a credit score lower than 570. 24 This benchmark is
below the benchmarks historically used by federal mortgage
programs. For example, our September 2016 report on single-family
home purchase loans guaranteed by the Rural Housing Service and
the Federal Housing Administration described the qualifying
benchmarks for these loans in fiscal years 2010–2014. 25 The Rural
Housing Service had a borrower credit score requirement of 640 or
above, and the Federal Housing Administration had one of 580 or
above. Qualified borrowers with credit scores as low as 581 could
obtain a Rural Housing Service-guaranteed loan if the loan was
manually underwritten and the lender provided additional
documentation of borrower creditworthiness. Qualified borrowers with
credit scores of 500 to 579 could obtain a Federal Housing
Administration-guaranteed loan if they made a down payment of 10
percent.
According to SBA officials, the policy of automatically declining
applications with applicant credit scores below 570 is a long-
established policy based on SBA’s analysis of historic loan processing
data and applicant credit scores. 26 SBA automatically declines
applicants so they can seek assistance from FEMA faster. SBA
officials also said they plan to start using the FICO Small Business
Scoring Service, which is used in other SBA loan programs and
assesses small business credit risk by taking into account both
personal and business credit scores. In addition, homeowner and
23Based on established criteria, SBA’s system separates those applicants with
unsatisfactory credit (based on the credit score) from those with satisfactory credit. For a
business that operates as a sole proprietorship or is 100 percent owned by one individual,
the automatic-decline process makes a determination on the creditworthiness of the
owner.
24All applicants declined for credit have the right to request reconsideration of declination.
25GAO, Home Mortgage Guarantees: Issues to Consider in Evaluating Opportunities to
Consolidate Two Overlapping Single-Family Programs, GAO-16-801 (Washington, D.C.:
Sept. 29, 2016).
26SBA established automatic declination for applicants with poor credit in 2005. SBA
officials told us that the cut-off credit score can be, and at times has been, adjusted.
Page 11 GAO-24-106682 Disaster Loan Program
renter applications may be automatically declined for lack of
repayment ability. 27
Accepted loans that SBA does not automatically decline or expedite
because of strong credit are subject to full processing. 28 SBA uses a
fixed debt method to calculate applicants’ ability to repay a disaster
loan by determining the applicant’s debt level as a percentage of
gross income, or an applicant’s maximum acceptable fixed debt. 29 For
an application to be approved, SBA generally requires a maximum
acceptable fixed debt of less than or equal to 75 percent. 30
• Lack of resources for financial counseling. SBA’s Equity Action
Plan, as well as SBA representatives, indicated that the agency does
not regularly have resources in position to assist applicants who are
declined and who need financial counseling. If a homeowner or renter
is ineligible to receive an SBA disaster loan, SBA refers the applicant
to FEMA for individual disaster assistance or other federal and local
resources.
As required by Executive Order 13985, SBA identified three actions to
address these barriers and advance equitable access to the Disaster
Loan Program, and it has begun to implement these actions.
• Working with resource partners. SBA plans to work with its field
and program offices to develop a systematic and formal process with
SBA resource partners—Small Business Development Centers
(SBDC), SCORE business mentors, Women’s Business Centers, and
Veterans Business Outreach Centers—to assist businesses with
27SBA performs a repayment calculation using income from the application and debts
obtained from a credit bureau report.
28As noted earlier, according to SBA officials, applicants with strong credit scores of 625
or greater and an adjusted gross income of $50,000 or greater are processed using an
expedited method.
29If the maximum debt level is not exceeded, SBA assumes that an applicant is able to
pay taxes, loan debts, and other living expenses. Once the maximum debt level is
exceeded, the risk of disaster loan default increases.
30According to SBA officials, SBA can waive this acceptable fixed debt standard and can
consider factors such as whether an applicant has historically carried a debt load of more
than 75 percent, has obtained a license or certification that will result in a significant pay
increase, or works in a rapidly growing field or industry.
Page 12 GAO-24-106682 Disaster Loan Program
services such as application completion and credit repair. 31 According
to SBA, these partnerships foster direct engagement with disaster
survivors and will help them obtain financial and credit counseling
services for activities such as preparing income statements and
balance sheets, managing debt to improve credit scores, and applying
for disaster loans. Further, SBA officials told us they chose to work
with these entities to reach underserved communities because they
share SBA’s overall mission to start and grow resilient businesses and
its goal to equitably support small businesses that need SBA’s help.
In February 2023, SBA began a collaborative initiative with the Florida
SBDC Network to assist businesses that were affected by Hurricane
Ian but were automatically declined for an SBA disaster loan because
of their credit history. After Hurricane Ian, 2,998 businesses that
applied were automatically declined for not meeting the minimum
credit score of 570. This initiative targeted 93 businesses that were
within two points of the minimum credit score and 299 businesses that
were within 10 points of this score. SBA’s outreach team contacted
these businesses by telephone and referred the 174 businesses
interested in reconsideration to the SBDC Network for credit
counseling and other services, such as business and strategic
planning. After these credit deficiencies were addressed, an SBDC
within the network discussed reconsideration for an SBA disaster
loan. However, none of SBA’s referrals resulted in a business
submitting a reconsideration request.
In addition, in May 2023 SBA targeted businesses in Arkansas,
California, Oklahoma, and Texas that did not meet the minimum credit
score of 570. SBA’s outreach team contacted 71 businesses that
were within 10 points of the minimum credit score and 12 businesses
that were within two points of this score. Twenty-nine of these
businesses interested in having their loan applications reconsidered
were referred to the SBDC network for credit counseling and other
services, such as business and strategic planning. After these
deficiencies were addressed, an SBDC within the network discussed
reconsideration for an SBA disaster loan. As of early September
31SBDCs provide counseling and training to small businesses. SCORE business mentors
offer advice on financing, human resources, and business planning. Women’s Business
Centers provide counseling and training and focus on women who want to start or expand
their small business. Veterans Business Outreach Centers provide entrepreneurial
development services such as business training, counseling, and resource partner
referrals to veterans, service members, and military spouses.
Page 13 GAO-24-106682 Disaster Loan Program
2023, eight of these businesses had submitted reconsideration
requests.
• Establishing relationships with financial institutions and
community groups. SBA plans to identify and develop formal and
informal relationships with financial institutions and local community
support groups to arrange for additional assistance for disaster
survivors. Officials told us that SBA is actively working with financial
institutions and local community support groups to improve
awareness of the Disaster Loan Program, particularly in historically
marginalized communities. When requested at the local or state level,
SBA supports community meetings, roundtables, seminars, and other
outreach events led by local leaders to help answer questions about
the Disaster Loan Program and explain how to access disaster
assistance resources.
• Conducting listening sessions. SBA plans to conduct targeted
outreach and “listening sessions” to obtain feedback from new and
existing stakeholders on improvements to disaster loan processing.
Officials told us that SBA has made progress in engaging with new
and existing stakeholders through these listening sessions. They said
the sessions are coordinated with SBA district offices and resource
partners to ensure that there remains a continuity of relationship
between community stakeholders and SBA. As of August 2023, SBA
had conducted listening sessions in 10 metropolitan areas frequently
impacted by natural disasters, including New Orleans, Houston, and
Miami. According to SBA officials, the agency includes a wide range
of stakeholders and perspectives in these sessions, including
emergency management agencies, nonprofit organizations, social
services agencies, and voluntary organizations active in disasters.
SBA’s Planned Review of SBA also has developed plans to evaluate how the Disaster Loan
the Disaster Loan Program is assisting disaster survivors in underserved communities. As
we reported in December 2021, little is known about the relationship
Program Will Not Review
between participation in federal disaster recovery programs (including
Lending Criteria SBA’s Disaster Loan Program) and recovery outcomes. 32 We reported
that federal recovery programs lack quality information to allow agencies
to identify potential access barriers and disparate outcomes. Therefore,
we recommended that SBA, in coordination with FEMA and the
Department of Housing and Urban Development (HUD), (1) develop and
32GAO, Disaster Recovery: Additional Actions Needed to Identify and Address Potential
Barriers, GAO-22-104039 (Washington, D.C.: Dec. 21, 2021).
Page 14 GAO-24-106682 Disaster Loan Program
implement an interagency plan to help ensure the availability and use of
quality information and (2) design and establish routine processes to be
used within and across federal recovery programs to address identified
access barriers and disparate outcomes on an ongoing basis.
SBA agreed with these recommendations and has begun to implement
them. According to SBA officials, the agency has begun communicating
regularly with FEMA and HUD through biweekly operational meetings and
monthly interagency coordination meetings with agency management.
Through these coordination sessions, the agencies have shared
information on available data and how these data can be used to identify
equity concerns, including barriers to access. Each agency has
committed to providing technical assistance to the other agencies to build
tools that allow the use of data and data sharing to support these efforts.
SBA officials also said SBA shares loan application data with federal
partners, such as FEMA and HUD, to reduce the likelihood of duplication
of benefits. They noted that data-sharing agreements with FEMA should
allow SBA to more quickly refer applicants who do not qualify for a
disaster loan to FEMA for assistance. For example, SBA worked with
FEMA to modify the income tables used at the beginning of the disaster
loan application process in order to provide faster and more targeted
disaster assistance to survivors. These income tables allow SBA to
quickly determine which applicants should be declined loan assistance
and, as a result, be immediately referred to FEMA for assistance.
SBA has taken some initial steps to collect data that may be used to
improve equity in the Disaster Loan Program for underserved
communities. For example, SBA released new disaster loan applications
in 2022 that collect optional demographic information, including
applicants’ race and ethnicity. According to SBA officials, this information
will increase SBA’s understanding of the population applying for loans
and allow SBA to focus on outcomes in specific communities that may
experience additional barriers. Officials told us that SBA is not currently
tracking the extent to which applicants are providing demographic
information but is working with a contractor to evaluate how these data
might eventually be used to inform SBA’s efforts to improve equity. For
example, SBA is exploring the possibility of geocoding the data and
conducting a demographic analysis.
In addition to collecting demographic data, SBA is planning three efforts
to evaluate the effects of the Disaster Loan Program on small businesses
Page 15 GAO-24-106682 Disaster Loan Program
and SBA’s efforts to advance equity. These evaluations are in the
planning stages or are ongoing.
• Evaluation of the effects of the Disaster Loan Program. SBA’s
fiscal year 2023 Annual Evaluation Plan includes a planned evaluation
of the effects of the Disaster Loan Program on individuals, small
businesses, and communities affected by disasters. Although the
evaluation plan does not mention underserved communities
specifically, it states that SBA plans to match administrative data from
the Disaster Loan Program for fiscal years 2005–2020 to census data
to compare businesses that received disaster loans to those that did
not. SBA plans to use the evaluation to determine the effects of the
program on small business outcomes and recovery. Officials told us
that SBA is working with the Census Bureau to publish the results of
this evaluation by the end of calendar year 2023.
• Assessment of efforts to advance equity. SBA’s fiscal year 2024
Annual Evaluation Plan includes a planned assessment of SBA’s
efforts to advance equity, including the effectiveness of SBA’s efforts
to reduce barriers, improve access, and create an equitable
entrepreneurial ecosystem. This assessment is to focus on SBA’s
2022 Equity Action Plan, including the section on disaster assistance.
The plan states that SBA will evaluate how it can refine key actions,
goals, and objectives to meet the objective of reducing barriers and
improving access to disaster assistance, counseling, training, and
service programs for underserved communities. The evaluation will
analyze SBA administrative data, and SBA plans to use the results to
improve the design of its Equity Action Plan and develop a model for
its use across the agency. According to officials, SBA is working to
identify a third-party contractor to design and conduct this evaluation
with the aim of completing it by the third quarter of fiscal year 2024.
• Internal review of disaster recovery program implementation. The
Explanatory Statement to the Consolidated Appropriations Act, 2023
directs SBA to conduct an internal review of its disaster recovery
program implementation practices to determine if any current
practices leave out or provide unequal outcomes for communities of
color and economically disadvantaged communities and, if so,
develop recommendations to improve outcomes. 33 According to SBA
officials, SBA has not yet formalized a plan for this internal review,
including determining whether to hire contract support, because it
recently made changes to the Disaster Loan Program that it wants to
33The Explanatory Statement does not include a time frame for this review. See CONG.
REC. S7819, S8492 (Dec. 20, 2022).
Page 16 GAO-24-106682 Disaster Loan Program
consider as part of the review. In June 2023, SBA issued a final rule
that, effective July 31, 2023, made changes such as permanently
authorizing a 1-year deferment period on disaster loan payments and
increasing the maximum loan amounts for physical property damage
for homeowners and renters. 34 SBA officials told us that SBA is
planning to continue its policy of also charging zero interest on loans
for 1 year.
SBA has not yet determined the full scope of work for this internal review.
As noted earlier, Executive Order 14091 requires agencies to reevaluate
and reissue their Equity Action Plans annually. In these annual plans,
agencies are to include strategies, including new or revised policies and
programs, to address identified barriers and to ensure equitable access
and opportunity for underserved communities. However, SBA has no
plans to assess its lending criteria, which it identified as a barrier to
accessing disaster assistance in its 2022 Equity Action Plan. According to
SBA officials, SBA has no plans to do so because regulations require it to
have reasonable assurance that borrowers can repay the loan. However,
this requirement does not preclude SBA from reassessing its lending
criteria within the context of this recognized barrier. For example, SBA
could consider an alternative means of evaluating homeowner and renter
creditworthiness.
Further, SBA’s efforts to help applicants meet its current lending criteria
generally have focused on businesses rather than homeowners and
renters. For example, SBA’s collaborative initiative with the Florida SBDC
Network assists businesses with poor credit. Yet the vast majority of SBA
disaster loans (almost 80 percent) are made to homeowners and renters.
In addition, SBA has not yet determined how it plans to analyze the
demographic data it recently started collecting in its internal review.
Considering ways to help homeowners and renters with poor credit and
determining how to analyze the demographic data could help it to identify
and understand differences in the lending outcomes among communities
that may experience additional barriers. These steps would also provide
valuable information needed to help SBA balance advancing equity in
agency programs and managing the credit risks associated with providing
disaster loans. Reviewing the agency’s lending criteria and specifically
considering homeowners and renters in its congressionally requested
internal review would enable SBA to better identify any changes needed
3488 Fed. Reg. 39335 (June 16, 2023), codified at 13 C.F.R. part 123.
Page 17 GAO-24-106682 Disaster Loan Program
to program practices to reduce unequal outcomes for historically
underserved communities.
SBA Plans to Increase In March 2023, SBA released a new outreach and marketing plan for the
Outreach to Underserved Disaster Loan Program. 35 The plan announced an outreach campaign for
Communities the program designed to build new and stronger relationships with
federal, state, local, and tribal leadership; emergency managers; and the
private sector. As part of this campaign, SBA plans to streamline
messaging to all stakeholders, establish the Office of Disaster Recovery
and Resilience as a primary resource for disaster preparedness, increase
awareness of SBA disaster-related products and services, and use SBA’s
nationwide infrastructure for short- and long-term recovery. For example,
SBA plans to develop and package outreach materials specifically for
state, local, tribal, and emergency management personnel to help them
better understand the Disaster Loan Program.
According to SBA, the agency uses census data and the Centers for
Disease Control and Prevention’s Social Vulnerability Index data to guide
its on-the-ground outreach before and after disasters. 36 This outreach is
conducted through SBA’s Disaster Field Operations Centers and District
Offices. Predisaster outreach activities include coordinating with state
emergency management agencies; other federal, state, local, territorial,
and tribal officials; and SBA’s resource partners to promote their disaster
preparedness. SBA also works with media outlets to publicize the
Disaster Loan Program in communities commonly affected by natural
disasters.
Postdisaster outreach activities include the establishment of Disaster
Recovery Centers, Business Recovery Centers, and Disaster Loan
Outreach Centers. SBA officials told us that Disaster Field Operations
staff coordinate with state and local community partners to determine
where to locate Business Recovery Centers based on damage
assessments and accessibility to the community. SBA also operates
Mobile Business Recovery Centers that allow staff to visit more
communities in the disaster area. In addition, SBA translates its disaster
35Small Business Administration, Outreach and Marketing Plan (Washington, D.C.: Mar. 6,
2023).
36The Centers for Disease Control and Prevention’s Social Vulnerability Index uses ACS
variables to determine the vulnerability of communities by socioeconomic status and race
and ethnicity. The index helps local officials identify communities that may need support
before, during, or after disasters.
Page 18 GAO-24-106682 Disaster Loan Program
outreach materials into multiple languages depending on the needs of the
community (see fig. 2).
Figure 2: SBA Disaster Loan Program Outreach Flyer in English and Spanish
SBA also plans to increase its outreach to underserved communities
specifically. SBA’s fiscal year 2022–2026 Strategic Plan includes an
equity measure to increase the number of outreach activities to
underserved communities. According to the strategic plan, SBA intends to
work with its Office of Diversity, Inclusion, and Civil Rights to enhance
communication through groups such as the Council for Underserved
Communities and through chambers of commerce in underserved
Page 19 GAO-24-106682 Disaster Loan Program
communities. 37 SBA plans to work with these organizations to develop
and conduct effective communication and program implementation
strategies. SBA’s fiscal year 2024 congressional budget justification
quantifies the equity measure in the strategic plan, with targets of 2,025
and 2,120 outreach activities to underserved communities in fiscal years
2023 and 2024, respectively. (In fiscal year 2022, SBA completed more
than 1,900 such outreach activities, which is the baseline.)
In prior work, we have stressed the importance of assessing the quality of
outreach activities, not just measuring their number. In February 2020, we
found that SBA had not established metrics to measure the success of its
outreach efforts. 38 Therefore, we recommended that SBA establish
metrics to measure the success of its outreach efforts during the
response to a disaster. SBA implemented this recommendation by
establishing such metrics and incorporating them into its 2020 annual
customer satisfaction survey. For example, the survey asked about the
outreach efforts that the respondent was exposed to, such as in-person
meetings and online media. The results showed that in-person meetings
were the most popular method of outreach.
However, in its fiscal year 2024 congressional budget justification, SBA
states that it is transitioning away from its current customer satisfaction
methodology and plans to evaluate new customer satisfaction areas. SBA
officials told us they plan to revise this methodology after the Disaster
Loan Program is included in SBA’s new unified lending platform. Officials
stated this platform would expedite the application process and decrease
applicant wait time by providing a platform for communication with SBA
staff throughout the loan application process. SBA plans to use customer
feedback on the new unified lending platform to develop new customer
satisfaction metrics in fiscal year 2024.
37The SBA Council for Underserved Communities consists of 20 stakeholders from
around the country and is tasked with advising SBA on strengthening and improving its
strategies to help underserved communities. We reviewed meeting minutes from two
council sessions that occurred in January and October 2022. Although SBA officials from
the Office of Disaster Recovery and Resilience, formerly the Office of Disaster Assistance,
attended these sessions, there was no substantive policy discussion of changes needed
to improve equitable access to the program.
38GAO-20-168.
Page 20 GAO-24-106682 Disaster Loan Program
SBA Disaster Loan
Outcomes Were Less
Favorable in Some
Historically
Underserved
Communities
For Selected 2018–2022 SBA received 430,158 applications for the Disaster Loan Program from
Hurricanes, SBA Declined survivors in areas affected by the 13 hurricanes that caused at least $1
billion in damage in fiscal years 2018–2022. 39 Of the 312,916 applications
About 40 Percent of
SBA accepted, SBA approved 42.0 percent and declined 38.6 percent
Applications, Primarily (see fig. 3). The remaining applications were withdrawn (19.1 percent) or
Because of Credit History were in progress as of June 2023 (0.3 percent).
and Repayment Ability
Figure 3: Outcomes of Applications SBA Accepted for 13 Hurricanes in Fiscal Years
2018–2022, as of June 2023
Notes: Automatic decline occurs at the initial stage of application processing and depends on an
applicant’s credit history and repayment ability. Automatic decline is intended to reject applicants who
will not qualify for an SBA loan and refer them to the Federal Emergency Management Agency for
additional assistance as quickly as possible.
39Our analysis excludes 10,857 duplicate applications. A duplicate application occurs
when an applicant submits more than one application of the same type for the same
event.
Page 21 GAO-24-106682 Disaster Loan Program
As shown in table 1, the majority of the disaster loan applications that
SBA accepted were from individuals and households to help repair and
replace homes and personal property. As noted earlier, the three main
types of SBA disaster loans are (1) home and personal property loans, (2)
business physical disaster loans, and (3) Economic Injury Disaster Loans
(EIDL). 40
Table 1: Disaster Loan Applications Accepted by SBA from Survivors of 13
Hurricanes in Fiscal Years 2018–2022, by Loan Type
Percentage
Type of loan Number of loans of loans
Business physical disaster loan and Economic 48,437 15.48
Injury Disaster Loan
Economic Injury Disaster Loan only 16,592 5.30
Home and personal property loan 247,887 79.22
Total 312,916 100.00
Source: GAO analysis of Small Business Administration (SBA) data. | GAO-24-106682
Note: Businesses can apply for both a physical disaster loan and an Economic Injury Disaster Loan
or just an Economic Injury Disaster Loan. Nonprofit loans are counted as business loans.
Acceptance. As its first step, SBA accepted 72.7 percent (or 312,916) of
the total applications submitted. Most applications (95 percent) that were
not accepted were summarily declined. SBA takes this action if the
household income is below the minimum income threshold for the family
size or after it compares the stated debts listed by the applicant with
household income. A summary decline action usually results in an
immediate referral to FEMA or other organizations for disaster financing.
Other reasons applications were not accepted included a withdrawn
application or a late application filing by the applicant. 41
Approval. SBA approved 42 percent (or 131,378) of the applications it
accepted. Of approved loans, 28 percent (or 36,721) were later canceled.
The top three reasons for loan cancellation were decisions made by the
borrower (58 percent), failure to complete and return all loan closing
documents (23 percent), and adverse changes in the borrower’s
economic circumstances (5 percent). SBA refers borrowers with adverse
40SBA also offers a Military Reservist EIDL loan. 13 C.F.R. § 123.5.
41According to the SBA Disaster Loan Program standard operating procedure,
applications are considered to be late if they are not received or postmarked within 15
days of the filing deadline and the applicant does not provide written justification for
missing the deadline.
Page 22 GAO-24-106682 Disaster Loan Program
changes in their economic circumstances to FEMA’s Individuals and
Households Program.
Declination. In total, SBA declined 38.6 percent (or 120,924) of the
applications it accepted. Of the accepted applications, 20.5 percent (or
64,036) were automatically declined because of credit score, repayment
ability, or both. An automatic-decline decision means the application was
declined by the system upon receipt. According to SBA officials, SBA
established automatic declination for applicants with poor credit instead of
moving forward with the full processing of all applications. SBA refers
homeowners and renters who are automatically declined to FEMA for a
potential grant and refers such businesses to other resource partners for
assistance. The average credit score for accepted applications that were
automatically declined for credit was 533, compared to average credit
scores of 647 for all other accepted applications and 697 for all approved
applications. 42
SBA declined 18.2 percent (or 56,888) of accepted applications after
further review. As shown in figure 4, the top three reasons for declining
these applications were unsatisfactory credit history (27.2 percent), lack
of repayment ability (16.9 percent), and ineligible business enterprises
(5.7 percent). 43 SBA can cite more than one reason for declining a loan
application.
Figure 4: Top Reasons for SBA Declining Disaster Loan Applications from
Survivors of 13 Hurricanes in Fiscal Years 2018–2022
Note: SBA can cite more than one reason for declining a loan application.
42Because we did not have complete data for two hurricanes, we calculated these
average credit scores using data for 11 of the 13 hurricanes included in our scope.
43Ineligible businesses that were declined included agricultural enterprises, members of a
fishing crew, and a property located in a Coastal Barrier Resource Area.
Page 23 GAO-24-106682 Disaster Loan Program
Withdrawal. A total of 19.1 percent (or 59,703) of accepted applications
were withdrawn. Applications can be withdrawn either at the applicant’s
request at any time during loan processing or at SBA’s discretion
because of the inability to verify losses, a lack of tax transcript, or an
incomplete response to a loan processing request. The top three reasons
for withdrawing accepted applications were an inability to verify losses
(32.0 percent), applicant reporting a change of plans (25.9 percent), and
the Internal Revenue Service having no applicant record (25.2 percent).
Loan Outcomes Were Our analysis showed that applicants from some underserved
Generally Less Favorable communities—such as communities with lower median household
incomes and higher percentages of Black or African American
in Low-Income and Black
residents—often experienced worse loan outcomes, including lower
or African American approval rates, than other communities. In contrast, applicants from
Communities and More communities with higher percentages of Asian residents experienced
Favorable in Asian more favorable loan outcomes. We used census data to analyze the
Communities socioeconomic and demographic characteristics of the census tracts in
which applicants’ damaged properties were located. 44 Specifically, we
looked at outcomes in various communities based on the following
characteristics: median household income, poverty rate, unemployment
rate, percentage of residents 25 years of age or older with no high school
diploma, percentage of residents with no health insurance, percentage of
44SBA began collecting voluntary demographic information from disaster loan program
applicants in April 2022. Because the scope of most of our analysis predates this period
and also includes an assessment of socioeconomic characteristics, we focused on the
characteristics of communities in which applicants’ damaged properties were located, not
the characteristics of the applicants themselves. We present the results of our analysis of
applications from Puerto Rico separately in app. III because of the unique demographic
composition of Puerto Rico’s communities.
Page 24 GAO-24-106682 Disaster Loan Program
rent-burdened households, and percentage of the population by race and
ethnicity. 45
We analyzed each characteristic separately without controlling for other
factors that can affect loan outcomes. For example, when we analyzed
differences in race among communities, we did not control for income
differences. As a result, our analysis indicates correlation between these
characteristics and loan outcomes rather than causation.
Acceptance. We found that applications from communities with lower
median household incomes, higher poverty rates, higher percentages of
residents with no health insurance, and higher percentages of Black or
African American residents were accepted at lower rates than other
communities (see fig. 5). 46 According to SBA data, the primary reason for
applications not being accepted was failure to meet SBA’s income
threshold requirement.
45The percentage of rent-burdened households in a census tract is the percentage of
households paying more than 30 percent of their income for housing. We determined
which community characteristics to evaluate with the help of the Centers for Disease
Control and Prevention’s Social Vulnerability Index, which uses ACS variables to
determine the vulnerability of communities by socioeconomic status and race and
ethnicity, among other things. For each characteristic, we ordered the census tracts within
the hurricane disaster areas from the lowest to the highest value. We then divided the
ordered distribution into four equally sized groups (quartiles). We discuss the results of our
analysis only if they were statistically significant, meaning that the 95 percent confidence
intervals for the top and bottom quartile for each ACS characteristic did not overlap. We
used the ACS categories of White, Black or African American, and Asian to describe the
race characteristic. The ACS racial categories of “American Indian and Alaska Native” and
“Native Hawaiian and Other Pacific Islander” had a small sample size that would not allow
us to create unique quartiles for analysis. We created a racial category “Other Races” to
include American Indians, Alaska Natives, Native Hawaiians, and Other Pacific Islanders
and those that fell under the “Two or More Races” category in the ACS data. See app. I
for more details on our scope and methodology.
46The acceptance rate is calculated by dividing the total number of accepted applications
by the total number of applications received, excluding duplicate applications. We used
the following quartiles to analyze the socioeconomic and demographic characteristics of
census tracts within which applicants’ damaged properties were located: less than or
equal to the 25th percentile, greater than the 25th percentile to the 50th percentile, greater
than the 50th percentile to the 75th percentile, and greater than the 75th percentile. See
app. I for more details on our scope and methodology.
Page 25 GAO-24-106682 Disaster Loan Program
Figure 5: Average Acceptance Rates for SBA Disaster Loans by Socioeconomic and Demographic Characteristic for 13
Hurricanes, Fiscal Years 2018–2022
Notes: To calculate the acceptance rate for each quartile by American Community Survey
characteristic, we divided the total number of accepted applications by the total number of
applications received, excluding duplicate applications. For each characteristic, we ordered the
census tracts within the hurricane disaster areas from the lowest to the highest value. We then
divided the ordered distribution into four equally sized groups (quartiles). The census tracts with the
lowest prevalence of that characteristic fall into the lowest quartile (or <=25th percentile), while those
with the highest prevalence fall into the highest quartile (or >75th percentile).
Of the community characteristics we reviewed, applicants from the
following communities had applications accepted at the lowest average
rates: those in the quartiles with the lowest median household incomes
(66 percent accepted), highest poverty rates (67 percent accepted),
Page 26 GAO-24-106682 Disaster Loan Program
highest percentages of residents with no health insurance (68 percent
accepted), and highest percentages of Black or African American
residents (69 percent accepted). 47 Applications were accepted at the
highest average rates from communities with the highest median
household incomes (78 percent accepted), lowest percentages of
residents with no health insurance (78 percent accepted), lowest poverty
rates (77 percent accepted), and highest percentages of Asian residents
(76 percent accepted).
We found that acceptance rates were more favorable in communities with
high percentages of Asian residents. Applicants from communities in the
quartile with the highest percentages of Asian residents had a higher
average acceptance rate (76 percent accepted) than applicants from
communities in the quartile with the lowest percentages of Asian
residents (70 percent accepted).
The largest differences in average acceptance rates between
communities were as follows:
• 12 percentage points between communities with the highest and
lowest median household incomes;
• 10 percentage points between communities with the highest and
lowest poverty rates; and
• 6 percentage points between communities with the highest and lowest
percentages of Black or African American residents.
Automatic decline. We found that applications from communities with
lower median household incomes, higher percentages of Black or African
American residents, and higher poverty rates were automatically declined
at higher rates than other communities (see fig. 6). 48 The two reasons for
an application to be automatically declined were unsatisfactory credit
score and lack of repayment ability. Of the community characteristics we
reviewed, applications from the following communities were automatically
declined at the highest average rates: those in the quartiles with the
lowest median household incomes (39 percent automatically declined),
highest percentages of Black or African American residents (39 percent
47Of the ACS variables we reviewed to determine community characteristics, all were
statistically significant, meaning the 95 percent confidence intervals for the highest and
lowest quartiles did not overlap.
48Automatic-decline rates were calculated by dividing the total number of automatically
declined applications by the sum of both approved and declined applications.
Page 27 GAO-24-106682 Disaster Loan Program
automatically declined), and highest poverty rates (37 percent
automatically declined). 49
Figure 6: Average Automatic-Decline Rates for SBA Disaster Loans by Socioeconomic and Demographic Characteristic for 13
Hurricanes, Fiscal Years 2018–2022
Notes: To calculate the automatic-decline rate for each quartile by American Community Survey
characteristic, we divided the total number of automatically declined applications by the sum of both
approved and declined applications. For each characteristic, we ordered the census tracts within the
hurricane disaster areas from the lowest to the highest value. We then divided the ordered distribution
49Of the ACS variables we reviewed to determine community characteristics, all were
statistically significant except the percentage of “Other Race” residents. For the variable
that was not statistically significant, the 95 percent confidence intervals for the highest and
lowest quartiles overlapped.
Page 28 GAO-24-106682 Disaster Loan Program
into four equally sized groups (quartiles). The census tracts with the lowest prevalence of that
characteristic fall into the lowest quartile (or <=25th percentile), while those with the highest
prevalence fall into the highest quartile (or >75th percentile).
a
This characteristic was not statistically significant, meaning the 95 percent confidence intervals for
the highest and lowest quartiles overlapped.
We found that automatic-decline rates were more favorable in
communities with high percentages of Asian and Hispanic or Latino
residents. Applicants from communities in the quartile with the lowest
percentages of Asian residents were automatically declined at a higher
rate (34 percent automatically declined) than communities in the quartile
with the highest percentages of Asian residents (23 percent automatically
declined). Applicants from communities in the quartile with the lowest
percentages of Hispanic or Latino residents were automatically declined
at a higher rate (32 percent automatically declined) than communities in
the quartile with the highest percentages of Hispanic or Latino residents
(30 percent automatically declined).
The largest differences in average automatic-decline rates between
communities were as follows:
• 19 percentage points between communities with the highest and
lowest median household incomes;
• 18 percentage points between communities with the highest and
lowest percentages of Black or African American residents;
• 16 percentage points between communities with the highest and
lowest poverty rates; and
• 11 percentage points between communities with the highest and
lowest percentages of Asian residents. 50
Approval. According to our analysis, applications from communities with
higher percentages of Black or African American residents, lower median
household incomes, and higher poverty rates were approved at lower
rates than other communities (see fig. 7). 51
50The percentage-point difference for poverty rate does not match figure 6 because of
rounding.
51We calculated approval rates by dividing the total number of approved applications by
the sum of both approved and declined applications. SBA uses this same calculation for
approval rates.
Page 29 GAO-24-106682 Disaster Loan Program
Figure 7: Average Approval Rates for SBA Disaster Loans by Socioeconomic and Demographic Characteristic for 13
Hurricanes, Fiscal Years 2018–2022
Notes: To calculate the approval rate for each quartile by American Community Survey characteristic,
we divided the total number of approved applications by the sum of both approved and declined
applications. For each characteristic, we ordered the census tracts within the hurricane disaster areas
from the lowest to the highest value. We then divided the ordered distribution into four equally sized
groups (quartiles). The census tracts with the lowest prevalence of that characteristic fall into the
lowest quartile (or <=25th percentile), while those with the highest prevalence fall into the highest
quartile (or >75th percentile).
Of the community characteristics we reviewed, applicants from the
following communities had the lowest approval rates: those in the
quartiles with the highest percentages of Black or African American
residents (33 percent approved), lowest median household incomes (33
Page 30 GAO-24-106682 Disaster Loan Program
percent approved), and highest poverty rates (34 percent approved). 52
Applications were approved at the highest average rates from
communities in quartiles with the highest median household incomes (56
percent approved), lowest poverty rates (55 percent approved), and
lowest percentages of Black or African American residents (55 percent
approved).
We found that approval rates were more favorable in communities with
high percentages of Asian residents. Applicants from communities in the
quartile with the highest percentages of Asian residents had a higher
average approval rate (51 percent approved) than communities in the
quartile with the lowest percentages of Asian residents (39 percent
approved).
Differences in average approval rates between communities were larger
than differences in average acceptance and automatic-decline rates. The
largest differences in average approval rates between communities were
as follows:
• 23 percentage points between communities with the highest and
lowest median household incomes;
• 22 percentage points between communities with the highest and
lowest percentages of Black or African American residents, and
• 21 percentage points between communities with the highest and
lowest poverty rates.
As costly disasters continue to affect numerous American communities,
Conclusions SBA’s Disaster Loan Program is a key federal program available to help
homeowners and businesses in underserved communities recover. SBA
has taken steps to evaluate equitable access to the Disaster Loan
Program and set a goal to increase outreach to underserved
communities.
Among these steps is SBA’s congressionally requested internal review of
whether its current practices for the SBA Disaster Loan Program lead to
unequal outcomes for certain historically underserved communities. SBA
has identified lending criteria as a barrier to accessing the program. We
found that applicants from some underserved communities often
52Of the ACS variables we reviewed to determine community characteristics, all were
statistically significant, meaning the 95 percent confidence intervals for the highest and
lowest quartiles did not overlap.
Page 31 GAO-24-106682 Disaster Loan Program
experienced lower approval rates than other communities. SBA’s internal
review is an opportunity for the agency to assess its lending criteria for
both businesses and homeowners and further analyze its disaster loan
data, including new demographic data, to identify and understand
differences in loan outcomes. Doing so would enable SBA to better
identify any changes needed to program practices to reduce unequal
outcomes for historically underserved communities. It would also provide
valuable information to help SBA balance advancing equity in agency
programs and managing the credit risks associated with providing
disaster loans.
The Administrator of SBA should ensure that the Associate Administrator
Recommendation for of the Office of Capital Access includes in the scope of SBA’s internal
Executive Action review of the Disaster Loan Program an assessment of its lending criteria
for both businesses and homeowners and an analysis of the gaps in loan
outcomes among program applicants, including those in underserved
communities. (Recommendation 1)
We provided a draft of this report to SBA for review and comment. In its
Agency Comments comments, which are reproduced in appendix V, SBA agreed with our
recommendation, stating that it would assess its lending criteria and
evaluate any relevant gaps in loan outcomes. SBA also stated that the
recommendation should be made to the Associate Administrator of the
Office of Capital Access rather than the Associate Administrator of the
Office of Disaster Recovery and Resilience. Accordingly, we made this
change. SBA also provided technical comments, which we incorporated
as appropriate.
We are sending copies of this report to the appropriate congressional
committees, the SBA Administrator, and other interested parties. In
addition, the report is available at no charge on the GAO website at
http://www.gao.gov.
Page 32 GAO-24-106682 Disaster Loan Program
If you or your staff have any questions about this report, please contact
me at (202) 512-8678 or shearw@gao.gov. Contact points for our Offices
of Congressional Relations and Public Affairs may be found on the last
page of this report. GAO staff who made key contributions to this report
are listed in appendix VI.
William B. Shear
Director, Financial Markets
and Community Investment
Page 33 GAO-24-106682 Disaster Loan Program
Appendix I: Objectives, Scope, and
Appendix I: Objectives, Scope, and
Methodology
Methodology
This report examines (1) steps the Small Business Administration (SBA)
has taken to improve equitable access to the Disaster Loan Program and
(2) loan outcomes in and characteristics of communities in which program
applicants and recipients are located.
For our first objective, we reviewed SBA policies and procedures related
to the Disaster Loan Program, including the program’s standard operating
procedure, the Disaster Preparedness and Recovery Plan, and the
Disaster Loan Quick Reference Guide. 1 In particular, we reviewed the
standard operating procedure to identify disaster loan lending criteria that
affect access to the program.
We also reviewed SBA’s 2022 Equity Action Plan, which outlines how
SBA plans to reduce barriers for underserved communities and improve
access to assistance, including the Disaster Loan Program. 2 We
compared the SBA Equity Action Plan against requirements for federal
agencies to advance racial equity and support for underserved
communities in Executive Order 13985. 3 This Executive Order includes
requirements to identify potential barriers that underserved communities
and individuals face when accessing federal programs and actions to
address these barriers.
To identify SBA’s plans to evaluate equity in the Disaster Loan Program,
we reviewed SBA’s program planning documentation, including its Fiscal
Year 2022–2026 Strategic Plan, Fiscal Year 2023 Annual Evaluation
1Small Business Administration, Standard Operating Procedure 50 30 9 (Washington,
D.C.: May 31, 2018); Disaster Preparedness and Recovery Plan (Washington, D.C.:
2019); and A Reference Guide to the SBA Disaster Loan Program (Washington, D.C.:
May 2015).
2Small Business Administration, Equity Action Plan (Washington, D.C.: Apr. 14, 2022). In
the plan, SBA defines underserved communities as including persons who are Asian
American, Black and African American, Hispanic and Latino, Middle Eastern, North
African, other persons of color, Alaska Native, Native American, Native Hawaiian, Pacific
Islander, LGBTQ+, religious minorities, military service members, military spouses,
veterans, and women; tribal communities; persons with disabilities; persons living in rural
or underserved urban areas; and persons otherwise adversely affected by persistent
poverty or systemic inequality.
3White House, Executive Order on Advancing Racial Equity and Support for Underserved
Communities Through the Federal Government, Exec. Order No. 13985, 86 Fed. Reg.
7009 (Jan. 25, 2021).
Page 34 GAO-24-106682 Disaster Loan Program
Appendix I: Objectives, Scope, and
Methodology
Plan, and Fiscal Year 2024 Annual Evaluation Plan. 4 We also reviewed
previous GAO reports and recommendations related to the Disaster Loan
Program and equity in federal disaster assistance programs and
determined the progress SBA has made in response. 5 We interviewed
SBA headquarters officials from the Office of Capital Access and the
Office of Disaster Recovery and Resilience to discuss SBA’s progress in
implementing the Equity Action Plan and the status of its efforts to
evaluate equity in the Disaster Loan Program. In addition, we compared
SBA’s plans to evaluate its current program practices against Executive
Order 14091 and the provision in the Explanatory Statement to the
Consolidated Appropriations Act, 2023 that requires the evaluation. 6
To examine SBA’s outreach on disaster assistance, we reviewed SBA’s
most recent Outreach and Marking Plan and a sample of recent outreach
materials that SBA distributes to communities before and after disaster
events. 7 We also interviewed SBA Field Operations Center staff to
discuss SBA’s efforts to improve equity in the Disaster Loan Program
during disaster response efforts, including how SBA conducts outreach
about the program in affected communities.
For our second objective, we focused our review on communities affected
by all 17 hurricanes that occurred in fiscal years 2017–2022 that caused
at least $1 billion in damage. To identify these hurricanes, we used the
National Oceanic and Atmospheric Administration’s list of billion-dollar
weather and climate disasters. 8 We focused on hurricanes, as opposed to
other types of disasters, because they affect large stretches of land and
4Small Business Administration, Strategic Plan: Fiscal Years 2022–2026 (Washington,
D.C.: Mar. 28, 2022); FY 2023 Annual Evaluation Plan (Washington, D.C.: Mar. 28, 2022);
and FY 2024 Annual Evaluation Plan (Washington, D.C.: Mar. 13, 2023).
5GAO, Small Business Administration: Disaster Loan Processing Was Timelier, but
Planning Improvements and Pilot Program Evaluation Needed, GAO-20-168 (Washington,
D.C.: Feb. 7, 2020), and Disaster Recovery: Additional Actions Needed to Identify and
Address Potential Barriers, GAO-22-104039 (Washington, D.C.: Dec. 21, 2021).
6White House, Executive Order on Further Advancing Racial Equity and Support for
Underserved Communities Through the Federal Government, Exec. Order No. 14091, 88
Fed. Reg. 10825 (Feb. 22, 2023). For the Explanatory Statement, see CONG. REC. S7819,
S8492 (Dec. 20, 2022).
7Small Business Administration, Outreach and Marketing Plan (Washington, D.C.: Mar. 6,
2023). We requested outreach materials that SBA developed for its response to Hurricane
Ian because that was the most recent hurricane included in our review.
8This list includes the name of the disaster, beginning and ending dates, total Consumer
Price Index-adjusted cost, and number of deaths.
Page 35 GAO-24-106682 Disaster Loan Program
Appendix I: Objectives, Scope, and
Methodology
often affect economically, racially, and ethnically diverse communities.
We selected the time frame 2017–2022 because three of the top five
costliest hurricanes on record occurred in fiscal year 2017, and fiscal year
2022 was the most recent complete fiscal year at the beginning of our
review.
For all 17 hurricanes, we analyzed SBA disaster loan application data as
of June 23, 2023, from SBA’s Disaster Credit Management System. 9
Applications from the four hurricanes that occurred in fiscal year 2017
accounted for 57 percent of the total number of applications. Therefore, to
avoid potentially overweighting the data from fiscal year 2017, we
reported the application data for the fiscal year 2017 hurricanes
separately from the aggregated data for the hurricanes that occurred in
fiscal years 2018–2022. For the four hurricanes in fiscal year 2017 and for
the 13 hurricanes in fiscal years 2018–2022, we calculated the number of
SBA disaster loan applications submitted; number of applications
accepted; percentage of applications accepted, approved, declined, and
withdrawn; percentage of approved loans that were canceled; and top
reasons for declines, cancellations, and withdrawals. 10 For 11 of the 13
hurricanes in fiscal years 2018–2022, we also calculated the average
credit scores of accepted, automatically declined, and approved
applications. 11
We assessed the reliability of the application data by reviewing relevant
documentation, interviewing knowledgeable agency officials, and
performing electronic testing. We determined these data were sufficiently
reliable for the purpose of describing the outcomes of the disaster loan
applications for our 17 hurricanes.
We used census data to analyze the socioeconomic and demographic
characteristics of the census tracts in which applicants’ damaged
9The Disaster Credit Management System supports SBA’s disaster loan operations,
beginning with the processing of disaster declaration information until final disbursement
of disaster loan funds. The system allows SBA to gather information about disaster loan
applicants, along with information about damage to the property or business that is used
in making disaster loan application decisions.
10The four hurricanes in fiscal year 2017 were Hurricanes Matthew, Harvey, Irma, and
Maria. The 13 hurricanes in fiscal years 2018–2022 were Hurricanes Florence, Michael,
Dorian, Hanna, Laura, Isaias, Sally, Zeta, Delta, Ida, Nicholas, Fiona, and Ian.
11We did not have complete credit score data for the other two hurricanes in fiscal years
2018–2022 and the four hurricanes in fiscal year 2017.
Page 36 GAO-24-106682 Disaster Loan Program
Appendix I: Objectives, Scope, and
Methodology
properties were located. 12 First, we geocoded SBA disaster loan
application data using the damaged property address. Second, we
matched the geocoded application data to U.S. census tracts. 13 Third, we
combined the SBA application data with data from the Census Bureau’s
2017–2021 American Community Survey (ACS) 5-year estimates, the
most recent available data at the time of our review, to examine the
socioeconomic and demographic characteristics of census tracts within
which applicants’ damaged properties were located. 14 Specifically, we
analyzed SBA disaster loan outcomes across census tracts based on the
following characteristics: median household income, poverty rate,
unemployment rate, percentage of residents 25 years of age or older with
no high school diploma, percentage of residents with no health insurance,
percentage of rent-burdened households, and percentage of the
population by race and ethnicity. 15
12SBA began collecting voluntary demographic information from disaster loan program
applicants in April 2022. Because the scope of our analysis predates this period and also
includes an assessment of socioeconomic characteristics, we focused on the
characteristics of communities in which applicants’ damaged properties were located, not
the characteristics of the applicants themselves.
13Census tracts are small, relatively permanent statistical subdivisions of a county or
statistically equivalent entity that generally have a population size between 1,200 and
8,000 people, with an optimum size of 4,000 people.
14The American Community Survey uses a series of monthly samples to produce annually
updated estimates for different geographic units, including census tracts, across the U.S.
The survey collects data on the economic, social, housing, and demographic
characteristics of communities at various geographic levels, including metropolitan areas,
states, and counties.
15The percentage of rent-burdened households in a census tract is the percentage of
households paying more than 30 percent of their income for housing. We determined
which community characteristics to evaluate with the help of the Centers for Disease
Control and Prevention’s Social Vulnerability Index, which uses ACS characteristics to
determine the vulnerability of communities by socioeconomic status and race and
ethnicity, among other things. We used the ACS categories of White, Black or African
American, and Asian to describe the race characteristic. The ACS racial categories of
“American Indian and Alaska Native” and “Native Hawaiian and Other Pacific Islander”
had a small sample size that would not allow us to create unique quartiles for analysis. We
created a racial category “Other Races” to include American Indians, Alaska Natives,
Native Hawaiians, and Other Pacific Islanders and those that fell under the “Two or More
Races” category in the ACS data.
Page 37 GAO-24-106682 Disaster Loan Program
Appendix I: Objectives, Scope, and
Methodology
We examined the relationship between SBA loan outcomes and each
characteristic identified above. 16 To identify the hurricane disaster area,
we restricted our analysis to applications within a state where there was a
Federal Emergency Management Agency disaster declaration. Any
census tracts within those states from which SBA received applications
were included in our analysis.
We divided each census-tract characteristic into four percentiles, or
quartiles, for the set of census tracts within the hurricane disaster area. 17
For those census tracts, we then calculated the average for each SBA
loan outcome by quartile. For example, to examine the relationship
between loan approval rates and poverty rates, we calculated the
average loan approval rates for census tracts with (1) poverty rates less
than or equal to the 25th percentile, (2) poverty rates greater than the
25th percentile to the 50th percentile, (3) poverty rates greater than the
50th percentile to the 75th percentile, and (4) poverty rates greater than
the 75th percentile.
We then compared the average loan outcomes across quartiles to
determine whether there was a statistically significant difference between
the 75th percentile and 25th percentile of each characteristic at the 5
percent level.
Our analysis has limitations, and our results should be interpreted
accordingly. Differences reported in loan outcomes by community
characteristic are uncontrolled and do not account for other factors or
characteristics across communities. Our analysis does not identify the
causes of any difference between loan outcomes for communities with
different characteristics. Further, the results are not generalizable to other
hurricanes, geographic areas, or time periods.
16To calculate acceptance rates, we divided the number of accepted applications by the
number of total applications received, excluding duplicate applications. To calculate
automatic-decline rates, we divided the total number of automatically declined applications
by the sum of both approved and declined applications. To calculate approval rates, we
divided the number of approved applications by the sum of approved and declined
applications. This is the same way that SBA calculates approval rates.
17We ordered each census tract characteristic from the lowest to the highest value. We
then divided the ordered distribution into four equally sized groups (quartiles). The census
tracts with the lowest prevalence of a characteristic fall into the lowest quartile (or <=25th
percentile) while those with the highest prevalence fall into the highest quartile (or >75th
percentile).
Page 38 GAO-24-106682 Disaster Loan Program
Appendix I: Objectives, Scope, and
Methodology
To assess the reliability of the census data, we reviewed technical
information and determined the data were sufficiently reliable for the
purpose of reporting on socioeconomic and demographic characteristics
at the census tract-level.
We reported results on the relationship between SBA loan outcomes and
census tract characteristics for the hurricanes that occurred in fiscal year
2017 separately from hurricanes that occurred in fiscal years 2018–
2022. 18 Because of Puerto Rico’s unique demographic composition
compared to the contiguous United States, we conducted a separate
analysis of census tracts affected by the four hurricanes that struck
Puerto Rico in fiscal years 2017–2022 for which SBA received disaster
loan applications. Because ACS data are not collected for the U.S. Virgin
Islands, we were unable to analyze the characteristics of the communities
in this territory that were affected by the 2017 hurricanes.
We conducted this performance audit from March 2023 to November
2023 in accordance with generally accepted government auditing
standards. Those standards require that we plan and perform the audit to
obtain sufficient, appropriate evidence to provide a reasonable basis for
our findings and conclusions based on our audit objectives. We believe
that the evidence obtained provides a reasonable basis for our findings
and conclusions based on our audit objectives.
18As mentioned, applications from the four hurricanes that occurred in fiscal year 2017
accounted for 57 percent of the total number of applications.
Page 39 GAO-24-106682 Disaster Loan Program
Appendix II: Disaster Loan Outcomes in and
Appendix II: Disaster Loan Outcomes in and
Characteristics of Communities Affected by
Characteristics of Communities Affected by
Four Hurricanes in Fiscal Year 2017
Four Hurricanes in Fiscal Year 2017
This appendix covers applications for the Small Business Administration’s
(SBA) Disaster Loan Program from survivors of four hurricanes in fiscal
year 2017. 1 We present our analysis of these applications separately from
the applications for the 13 hurricanes included in our review that occurred
in fiscal years 2018–2022 because the large number of applications SBA
received for these hurricanes may obscure outcomes in the 2018–2022
period.
SBA received 578,122 applications for the Disaster Loan Program from
Outcomes of Loan survivors in areas affected by the four hurricanes that caused at least $1
Applications billion in damage in fiscal year 2017. 2 Of the 381,064 applications SBA
accepted, SBA approved 42.0 percent and declined 42.1 percent (see fig.
8). The remaining applications were withdrawn (15.3 percent) or were in
progress (0.6 percent) as of June 2023.
Figure 8: Outcomes of Applications SBA Accepted for the Disaster Loan Program
for Four Hurricanes in Fiscal Year 2017
Notes: The four hurricanes were all of the hurricanes that caused more than $1 billion in damage in
fiscal year 2017. Automatic decline occurs at the initial stage of application processing and depends
on an applicant’s credit history and repayment ability. Automatic decline is intended to reject
applicants who will not qualify for an SBA loan and refer them to the Federal Emergency
Management Agency for additional assistance as quickly as possible.
1We selected these hurricanes because they were all of the hurricanes that occurred in
fiscal year 2017 that caused at least $1 billion in damage. See app. I for more information
on our scope and methodology.
2Our analysis excludes one duplicate application. A duplicate application occurs when an
applicant submits more than one application of the same type for the same event.
Page 40 GAO-24-106682 Disaster Loan Program
Appendix II: Disaster Loan Outcomes in and
Characteristics of Communities Affected by
Four Hurricanes in Fiscal Year 2017
As shown in table 2, the majority of the disaster loan applications that
SBA accepted were from individuals and households to help repair and
replace homes and personal property. The three main types of SBA
disaster loans are (1) home and personal property loans, (2) business
physical disaster loans, and (3) Economic Injury Disaster Loans (EIDL). 3
Table 2: Disaster Loan Applications Accepted by SBA from Survivors of Four
Hurricanes in Fiscal Year 2017, by Loan Type
Type of loan Number of loans Percentage of
loans
Business physical disaster loans and 50,980 13.38
Economic Injury Disaster Loans
Economic Injury Disaster Loans only 9,308 2.44
Home and personal property loans 320,776 84.18
Total 381,064 100.00
Source: GAO analysis of Small Business Administration (SBA) data. | GAO-24-106682
Note: The four hurricanes were all of the hurricanes that caused at least $1 billion in damage in fiscal
year 2017. Businesses can apply for both a physical disaster loan and an Economic Injury Disaster
Loan or just an Economic Injury Disaster Loan. Nonprofit loans are counted as business loans.
Acceptance. As its first step, SBA accepted 65.9 percent (or 381,064) of
the total applications submitted. Many of the applications that were not
accepted (56 percent) were summarily declined. The agency takes this
action if the household income is below the minimum income threshold
for the family size or after it compares the stated debts listed by the
applicant with household income. A summary decline action usually
results in an immediate referral to the Federal Emergency Management
Agency (FEMA) or other organizations for disaster financing.
Approval. SBA approved 42.0 percent (or 159,910) of the applications it
accepted. Of approved loans, 18.7 percent (or 29,828) were later
canceled. The top three reasons for loan cancellation were decisions
made by the borrower (48 percent), failure to complete and return all loan
closing documents (19 percent), and adverse changes in the borrower’s
economic circumstances (18 percent). SBA refers borrowers with adverse
changes in their economic circumstances to FEMA’s Individuals and
Households Program.
3SBA also offers a Military Reservist EIDL loan in addition to the three main types of
disaster loans. 13 C.F.R. § 123.5.
Page 41 GAO-24-106682 Disaster Loan Program
Appendix II: Disaster Loan Outcomes in and
Characteristics of Communities Affected by
Four Hurricanes in Fiscal Year 2017
Declination. In total, SBA declined 42.1 (or 160,279) percent of the
applications it accepted. Of the accepted applications, 19.7 percent (or
74,904) were automatically declined because of credit score, repayment
ability, or both. An automatic-decline decision means the application was
declined by the system upon receipt. According to SBA officials, SBA
established automatic declination for applicants with poor credit instead of
moving forward with the full processing of all applications. 4 SBA refers
homeowners and renters who are automatically declined to FEMA for a
potential grant and refers such businesses to other resource partners for
assistance.
SBA declined 22.4 percent (or 85,375) of accepted applications after
further review. As shown in figure 9, the top three reasons for declining
these applications were unsatisfactory credit history (35 percent), lack of
repayment ability (20 percent), and failure to pass a minimum income test
(6 percent). SBA can cite more than one reason for declining a loan
application.
Figure 9: Top Reasons for SBA Declining Disaster Loan Applications from
Survivors of Four Hurricanes in Fiscal Year 2017
Note: The four hurricanes were all of the hurricanes that caused at least $1 billion in damage in fiscal
year 2017. SBA can cite more than one reason for declining a loan application.
Withdrawal. A total of 15.3 percent (or 58,487) of accepted applications
were withdrawn. Applications can be withdrawn either at the applicant’s
request at any time during loan processing or at SBA’s discretion
because of the inability to verify losses, a lack of a tax transcript, or an
incomplete response to a loan processing request. The top three reasons
for withdrawing accepted applications were the applicant reporting a
4According to SBA officials, applicants with strong credit also do not go through full
processing. Specifically, applicants with credit scores of 625 or greater and an adjusted
gross income of $50,000 or greater are processed using an expedited method.
Page 42 GAO-24-106682 Disaster Loan Program
Appendix II: Disaster Loan Outcomes in and
Characteristics of Communities Affected by
Four Hurricanes in Fiscal Year 2017
change of plans (38 percent), inability to verify losses (29 percent), or the
requested information not being provided (17 percent).
Our analysis showed that applicants from some underserved
Socioeconomic and communities—such as communities with lower median household
Demographic incomes and higher percentages of Black or African American
residents—often experienced worse loan outcomes, including lower
Characteristics of approval rates, than other communities. We used census data to analyze
Communities in the socioeconomic and demographic characteristics of the census tracts
in which applicants’ damaged properties were located. 5 Specifically, we
Which Applicants looked at outcomes in various communities based on the following
Were Located characteristics: median household income, poverty rate, unemployment
rate, percentage of residents 25 years of age or older with no high school
diploma, percentage of residents with no health insurance, percentage of
rent-burdened households, and percentage of the population by race and
ethnicity. 6
Acceptance. We found that applications from communities with lower
percentages of residents with no high school diploma, higher median
household incomes, and higher percentages of Asian residents were
5SBA began collecting voluntary demographic information from disaster loan program
applicants in April 2022. Because the scope of most of our analysis predates this period
and also includes an assessment of socioeconomic characteristics, we focused on the
characteristics of communities in which applicants’ damaged properties were located, not
the characteristics of the applicants themselves. We present the results of our analysis of
applications from Puerto Rico separately in app. III because of the unique demographic
composition of Puerto Rico’s communities. Although SBA accepted applications from the
U.S. Virgin Islands, the American Community Survey does not collect data within the
territory. As a result, we were unable to analyze the characteristics of the communities in
this territory that were affected by the 2017 hurricanes.
6The percentage of rent-burdened households in a census tract is the percentage of
households paying more than 30 percent of their income for housing. We identified which
community characteristics to evaluate with the help of the Centers for Disease Control and
Prevention’s Social Vulnerability Index, which uses American Community Survey (ACS)
variables to determine the vulnerability of communities by socioeconomic status and race
and ethnicity, among other things. For each characteristic, we ordered the census tracts
within the hurricane disaster areas from the lowest to the highest value. We then divided
the ordered distribution into four equally sized groups (quartiles). We discuss the results of
our analysis only if they were statistically significant, meaning that the 95 percent
confidence intervals for the top and bottom quartile for each ACS characteristic did not
overlap. We used the ACS categories of White, Black or African American, and Asian to
describe the race characteristic. The ACS racial categories of “American Indian and
Alaska Native” and “Native Hawaiian and Other Pacific Islanders” had a small sample size
that would not allow us to create quartiles for analysis. We created a racial category
“Other Races” to include American Indians, Alaska Natives, Native Hawaiians, and Other
Pacific Islanders and those that fell under the “Two or More Races” category in ACS data.
See app. I for more details on our scope and methodology.
Page 43 GAO-24-106682 Disaster Loan Program
Appendix II: Disaster Loan Outcomes in and
Characteristics of Communities Affected by
Four Hurricanes in Fiscal Year 2017
accepted at lower rates than other communities (see fig. 10). 7 These
results differ from our findings for the 13 hurricanes in fiscal years 2018–
2022. For those hurricanes, we found that applications from communities
with lower median household incomes, higher poverty rates, higher
percentages of residents with no health insurance, and higher
percentages of Black or African American residents were accepted at
lower rates than other communities.
7Acceptance rate is calculated by dividing the total number of accepted applications by the
total number of applications received, excluding duplicate applications. We used the
following quartiles to analyze the socioeconomic and demographic characteristics of
communities affected by the hurricanes in our review: less than or equal to the 25th
percentile, greater than the 25th percentile to the 50th percentile, greater than the 50th
percentile to the 75th percentile, and greater than the 75th percentile.
Page 44 GAO-24-106682 Disaster Loan Program
Appendix II: Disaster Loan Outcomes in and
Characteristics of Communities Affected by
Four Hurricanes in Fiscal Year 2017
Figure 10: Average Acceptance Rates for SBA Disaster Loans by Socioeconomic and Demographic Characteristic for Four
Hurricanes, Fiscal Year 2017
Notes: The four hurricanes were all of the hurricanes that caused at least $1 billion in damage in
fiscal year 2017. To calculate the acceptance rate for each quartile by American Community Survey
characteristic, we divided the total number of accepted applications by the total number of
applications received, excluding duplicate applications. For each characteristic, we ordered the
census tracts within the hurricane disaster areas from the lowest to the highest value. We then
divided the ordered distribution into four equally sized groups (quartiles). The census tracts with the
lowest prevalence of that characteristic fall into the lowest quartile (or <=25th percentile), while those
with the highest prevalence fall into the highest quartile (or >75th percentile).
a
This characteristic was not statistically significant, meaning the 95 percent confidence intervals for
the highest and lowest quartiles overlapped.
Page 45 GAO-24-106682 Disaster Loan Program
Appendix II: Disaster Loan Outcomes in and
Characteristics of Communities Affected by
Four Hurricanes in Fiscal Year 2017
Of the community characteristics we reviewed, applicants from the
following communities had applications accepted at the lowest average
rates: those with the lowest percentages of residents 25 years of age or
older with no high school diploma (54 percent accepted), highest median
household incomes (56 percent accepted), and highest percentages of
Asian residents (57 percent accepted). 8 Applications were accepted at
the highest average rates from communities in quartiles with the lowest
percentages of Hispanic or Latino residents (64 percent accepted), lowest
percentages of Asian residents (64 percent accepted), and highest
percentages of Black or African American residents (63 percent
accepted).
The largest differences in average acceptance rates between
communities were as follows:
• 8 percentage points between communities with the highest and lowest
percentages of residents 25 years of age or older with no high school
diploma;
• 7 percentage points between communities with the highest and lowest
percentages of Asian residents; and
• 6 percentage points between communities with the highest and lowest
percentages of Black or African American residents.
Automatic decline. We found that applications from communities with
lower median household incomes, higher poverty rates, and higher
percentages of residents with no health insurance were automatically
declined at higher rates than other communities (see fig. 11). 9 The two
reasons for an application to be automatically declined were
unsatisfactory credit score and lack of repayment ability. Of the
community characteristics we reviewed, applicants from the following
communities were automatically declined at the highest average rates:
those in quartiles with the lowest median household incomes (38 percent
automatically declined), highest percentages of residents with no health
8Of the ACS variables we reviewed to determine community characteristics, all were
statistically significant except unemployment rate and “Other Races.” For those variables
that were not statistically significant, the 95 percent confidence intervals for the highest
and lowest quartiles overlapped.
9Automatic-decline rates were calculated by dividing the total number of automatically
declined applications by the sum of both approved and declined applications.
Page 46 GAO-24-106682 Disaster Loan Program
Appendix II: Disaster Loan Outcomes in and
Characteristics of Communities Affected by
Four Hurricanes in Fiscal Year 2017
insurance (38 percent automatically declined), and highest poverty rates
(37 percent automatically declined). 10
Figure 11: Average Automatic-Decline Rates for SBA Disaster Loans by Socioeconomic and Demographic Characteristic for
Four Hurricanes, Fiscal Year 2017
Notes: The four hurricanes were all of the hurricanes that caused at least $1 billion in damage in
fiscal year 2017. To calculate the automatic-decline rate for each quartile by American Community
Survey characteristic, we divided the total number of automatically declined applications by the sum
of both approved and declined applications. For each characteristic, we ordered the census tracts
10Of the ACS variables we reviewed to determine community characteristics, all were
statistically significant except the percentage of Asian residents. For the variable that was
not statistically significant, the 95 percent confidence intervals for the highest and lowest
quartiles overlapped.
Page 47 GAO-24-106682 Disaster Loan Program
Appendix II: Disaster Loan Outcomes in and
Characteristics of Communities Affected by
Four Hurricanes in Fiscal Year 2017
within the hurricane disaster areas from the lowest to the highest value. We then divided the ordered
distribution into four equally sized groups (quartiles). The census tracts with the lowest prevalence of
that characteristic fall into the lowest quartile (or <=25th percentile), while those with the highest
prevalence fall into the highest quartile (or >75th percentile).
a
This characteristic was not statistically significant, meaning the 95 percent confidence intervals for
the highest and lowest quartiles overlapped.
The largest differences in average automatic-decline rates between
communities were as follows:
• 14 percentage points between communities with the highest and
lowest median household incomes;
• 13 percentage points between communities with the highest and
lowest percentages of residents with no health insurance;
• 12 percentage points between communities with the highest and
lowest poverty rates; and
• 12 percentage points between communities with the highest and
lowest percentages of White residents. 11
Approval. According to our analysis, applications from communities with
lower median household incomes, higher poverty rates, and higher
percentages of Black or African American residents were approved at
lower rates (see fig. 12). 12
11The percentage-point difference for percentage of White residents does not match figure
11 because of rounding.
12We calculated approval rates by dividing the total number of approved applications by
the sum of both approved and declined applications. SBA uses this same calculation for
approval rates.
Page 48 GAO-24-106682 Disaster Loan Program
Appendix II: Disaster Loan Outcomes in and
Characteristics of Communities Affected by
Four Hurricanes in Fiscal Year 2017
Figure 12: Average Approval Rates for SBA Disaster Loans by Socioeconomic and Demographic Characteristic for Four
Hurricanes, Fiscal Year 2017
Notes: The four hurricanes were all of the hurricanes that caused at least $1 billion in damage in
fiscal year 2017. To calculate the approval rate for each quartile by American Community Survey
characteristic, we divided the total number of approved applications by the sum of both approved and
declined applications. For each characteristic, we ordered the census tracts within the hurricane
disaster areas from the lowest to the highest value. We then divided the ordered distribution into four
equally sized groups (quartiles). The census tracts with the lowest prevalence of that characteristic
fall into the lowest quartile (or <=25th percentile), while those with the highest prevalence fall into the
highest quartile (or >75th percentile).
Of the community characteristics we reviewed, applicants from the
following communities had applications approved at the lowest average
rates: those in the quartiles with the lowest median household incomes
(31 percent approved), highest poverty rates (31 percent approved), and
Page 49 GAO-24-106682 Disaster Loan Program
Appendix II: Disaster Loan Outcomes in and
Characteristics of Communities Affected by
Four Hurricanes in Fiscal Year 2017
highest percentages of Black or African American residents (32 percent
approved). 13 Applications were approved at the highest average rates
from communities in quartiles with the highest median household incomes
(46 percent approved), lowest poverty rates (46 percent approved), and
lowest percentages of residents 25 years of age or older with no high
school diploma (45 percent).
We found that approval rates were more favorable in communities with
high percentages of Asian residents. Applicants from communities in the
quartile with the highest percentages of Asian residents (40 percent
approved) were approved at higher rates than applicants from
communities in the quartile with the lowest percentages of Asian
residents (37 percent approved).
The largest differences in average approval rates between communities
were as follows:
• 16 percentage points between communities with the highest and
lowest percentages of White residents;
• 15 percentage points between communities with the highest and
lowest median household incomes;
• 15 percentage points between communities with the highest and
lowest poverty rates; and
• 14 percentage points between communities with the highest and
lowest percentages of residents 25 years of age or older with no high
school diploma. 14
13Of the ACS variables we reviewed to determine community characteristics, all were
statistically significant, meaning the 95 percent confidence intervals for the highest and
lowest quartiles did not overlap.
14The percentage-point difference for percentage of residents 25 years of age or older
with no high school diploma does not match figure 12 because of rounding.
Page 50 GAO-24-106682 Disaster Loan Program
Appendix III: Socioeconomic and
Appendix III: Socioeconomic and Demographic
Characteristics of Communities in Puerto Rico
Demographic Characteristics of
Affected by Four Hurricanes in Fiscal Years
2017–2022
Communities in Puerto Rico Affected by Four
Hurricanes in Fiscal Years 2017–2022
Survivors of four hurricanes that affected communities in Puerto Rico in
fiscal years 2017–2022 applied for Small Business Administration (SBA)
disaster loans. 1 We used census data to analyze the socioeconomic and
demographic characteristics of the Puerto Rican census tracts in which
these applicants’ damaged properties were located. 2 Specifically, we
examined the relationship between disaster loan outcomes—acceptance
rates, automatic-decline rates, and approval rates—and seven
characteristics. These characteristics were median household income,
poverty rate, unemployment rate, percentage of residents 25 years of age
and older with no high school diploma, percentage of residents with no
health insurance, percentage of rent-burdened households, and
percentage of population by race.
We were not able to evaluate loan outcomes for Hispanic or Latino
residents. Because 98 percent of Puerto Rico’s population is Hispanic or
Latino, it was not possible to separate the data into quartiles as we did for
the other characteristics. 3 We also were not able to evaluate loan
outcomes for Asian residents. Because there were so few Asian residents
in Puerto Rico, it was not possible to separate the data into quartiles as
we did for the other characteristics. We did not report on many of the
community characteristics we analyzed because the results were not
statistically significant. 4 In addition, we conducted separate analyses for
1For our review, we selected all 17 hurricanes that occurred in fiscal years 2017–2022 that
caused at least $1 billion in damage. Four of these 17 hurricanes affected Puerto Rico and
led to applications for SBA’s Disaster Loan Program. These four hurricanes were
Hurricanes Irma and Maria in fiscal year 2017, Hurricane Isaias in fiscal year 2020, and
Hurricane Fiona in fiscal year 2022. We analyzed the socioeconomic and demographic
characteristics of communities in Puerto Rico separately because of the unique
demographic composition of Puerto Rico compared to the contiguous United States. See
app. I for more details on our scope and methodology.
2According to the Census Bureau, Puerto Rico has 945 census tracts. In the raw
American Community Survey data we evaluated for the purposes of this report, the
census tracts are numbered, as in the data for the contiguous United States; however,
they also include the title of the municipality or “municipio.” A municipio is a large,
aggregate unit of geography similar to a U.S. county, and Puerto Rico has 78 of them.
3We ordered each census tract characteristic from the lowest to the highest value. We
then divided the ordered distribution into four equally sized groups (quartiles). The census
tracts with the lowest prevalence of a characteristic fall into the lowest quartile (or <=25th
percentile), while those with the highest prevalence fall into the highest quartile (or >75th
percentile). For example, census tracts with the lowest poverty rates are ranked in the
lowest quartile (or <=25th percentile).
4For the purpose of this report, we determined statistical significance using 95 percent
confidence intervals. A characteristic was deemed to be statistically insignificant if the 95
percent confidence intervals for the highest and lowest quartiles overlapped.
Page 51 GAO-24-106682 Disaster Loan Program
Appendix III: Socioeconomic and Demographic
Characteristics of Communities in Puerto Rico
Affected by Four Hurricanes in Fiscal Years
2017–2022
fiscal year 2017 and fiscal years 2018–2022 because the large number of
applications SBA received for the fiscal year 2017 hurricanes may
obscure outcomes in the 2018–2022 period.
The acceptance rate is the percentage of submitted applications that SBA
Acceptance Rate accepted for review. 5 Most applications that were not accepted were
summarily declined. SBA takes this action if the household income is
below the minimum income threshold for the family size or after it
compares the stated debts listed by the applicant with household
income. 6
Acceptance Rates for Two For the two fiscal year 2017 hurricanes, only three characteristics—(1)
Hurricanes in Fiscal Year median household income, (2) poverty rate, and (3) percentage of
residents 25 years of age and older with no high school diploma—had
2017 average acceptance rates that were statistically significant. 7 Out of these
community characteristics, applicants from census tracts with the highest
poverty rates and lowest median household incomes had the lowest
average acceptance rates (see fig. 13).
• Median household income. The average acceptance rate for
applicants in census tracts with the lowest median household incomes
was 60 percent. There was a difference of 10 percentage points in the
average acceptance rate between communities with the highest and
lowest median household incomes. 8
• Poverty rate. The average acceptance rate for applicants in census
tracts with the highest poverty rates was 60 percent. There was a
difference of 10 percentage points in the average acceptance rate
between communities with the highest and lowest poverty rates.
• No high school diploma. The average acceptance rate for
applicants in census tracts with the highest percentages of residents
5Acceptance rate is calculated by dividing the total number of accepted applications by the
total number of applications received, excluding duplicate applications.
6A summary decline action usually results in an immediate referral to the Federal
Emergency Management Agency or other organizations for disaster financing.
7The following characteristics were not statistically significant: unemployment rate;
percentage of rent-burdened households; percentage of residents with no health
insurance; and percentage of White, Black or African American, and Other Race
residents. For the purpose of this report, we determined statistical significance using 95
percent confidence intervals. A characteristic was deemed to be statistically insignificant if
the 95 percent confidence intervals for the highest and lowest quartiles overlapped.
8The percentage-point difference does not match figure 13 because of rounding.
Page 52 GAO-24-106682 Disaster Loan Program
Appendix III: Socioeconomic and Demographic
Characteristics of Communities in Puerto Rico
Affected by Four Hurricanes in Fiscal Years
2017–2022
25 years of age or older with no high school diploma was 58 percent.
There was a difference of 12 percentage points in the average
acceptance rate between communities with the highest and lowest
percentages of residents 25 years of age or older with no high school
diploma. 9
Figure 13: Average Acceptance Rates for SBA Disaster Loans by Socioeconomic
and Demographic Characteristic for Two Hurricanes That Affected Puerto Rico,
Fiscal Year 2017
Notes: To calculate the acceptance rate for each quartile by American Community Survey
characteristic, we divided the total number of accepted applications by the total number of
applications received, excluding duplicate applications. For each characteristic, we ordered the
census tracts within the hurricane disaster areas from the lowest to the highest value. We then
divided the ordered distribution into four equally sized groups (quartiles). The census tracts with the
lowest prevalence of that characteristic fall into the lowest quartile (or <=25th percentile), while those
with the highest prevalence fall into the highest quartile (or >75th percentile).
Acceptance Rates for Two For the two hurricanes that affected Puerto Rico in fiscal years 2018–
Hurricanes in Fiscal Years 2022, we found that applications from communities with higher
percentages of residents with no high school diploma, higher poverty
2018–2022
rates, and lower median household incomes were accepted at lower rates
than other communities (see fig. 14). 10
9The percentage-point difference does not match figure 13 because of rounding.
10The following characteristics were not statistically significant: percentage of rent-
burdened households and residents with no health insurance. This means the 95 percent
confidence intervals for the highest and lowest quartiles overlapped.
Page 53 GAO-24-106682 Disaster Loan Program
Appendix III: Socioeconomic and Demographic
Characteristics of Communities in Puerto Rico
Affected by Four Hurricanes in Fiscal Years
2017–2022
• No high school diploma. The average acceptance rate for
applicants in census tracts with the highest percentages of residents
25 years of age or older with no high school diploma was 56 percent.
There was a difference of 12 percentage points in the average
acceptance rate between communities with the highest and lowest
percentages of residents 25 years of age or older with no high school
diploma.
• Poverty rate. The average acceptance rate for applicants in census
tracts with the highest poverty rates was 59 percent. There was a
difference of 10 percentage points in the average acceptance rate
between communities with the highest and lowest poverty rates. 11
• Median household income. The average acceptance rate for
applicants in census tracts with the lowest median household incomes
was 59 percent. There was a difference of 9 percentage points in the
average acceptance rate between communities with the highest and
lowest median household incomes. 12
11The percentage-point difference does not match figure 14 because of rounding.
12The percentage-point difference does not match figure 14 because of rounding.
Page 54 GAO-24-106682 Disaster Loan Program
Appendix III: Socioeconomic and Demographic
Characteristics of Communities in Puerto Rico
Affected by Four Hurricanes in Fiscal Years
2017–2022
Figure 14: Average Acceptance Rates for SBA Disaster Loans by Socioeconomic and Demographic Characteristic for Two
Hurricanes That Affected Puerto Rico, Fiscal Years 2018–2022
Notes: To calculate the acceptance rate for each quartile by American Community Survey
characteristic, we divided the total number of accepted applications by the total number of
applications received, excluding duplicate applications. For each characteristic, we ordered the
census tracts within the hurricane disaster areas from the lowest to the highest value. We then
divided the ordered distribution into four equally sized groups (quartiles). The census tracts with the
lowest prevalence of that characteristic fall into the lowest quartile (or <=25th percentile) while those
with the highest prevalence fall into the highest quartile (or >75th percentile).
An automatic-decline decision means the application was declined by the
Automatic-Decline system upon receipt. 13 According to SBA officials, SBA established
Rate automatic declination for applicants with poor credit instead of moving
forward with the full processing of all applications. 14 SBA refers
homeowners and renters who are automatically declined to the Federal
Emergency Management Agency for a potential grant and refers such
13Automatic-decline rates were calculated by dividing the total number of automatically
declined applications by the sum of both approved and declined applications.
14According to SBA officials, applicants with strong credit also do not go through full
processing. Specifically, applicants with credit scores of 625 or greater and an adjusted
gross income of $50,000 or greater are processed using an expedited method.
Page 55 GAO-24-106682 Disaster Loan Program
Appendix III: Socioeconomic and Demographic
Characteristics of Communities in Puerto Rico
Affected by Four Hurricanes in Fiscal Years
2017–2022
businesses to other resource partners for assistance. The two reasons for
an application to be automatically declined were unsatisfactory credit
score and lack of repayment ability.
Our analysis of automatic-decline rates by community characteristic was
not statistically significant for the two hurricanes that affected Puerto Rico
in fiscal year 2017, meaning we cannot report on any differences. For the
three hurricanes that affected Puerto Rico in fiscal years 2018–2022, only
percentage of Black or African American residents had average
automatic-decline rates that were statistically significant (see fig. 15). 15
The average automatic-decline rate for applications from census tracts
with the highest percentages of Black or African American residents was
14 percent. There was a difference of 4 percentage points in the average
automatic-decline rate between communities with the highest and lowest
percentages of Black or African American residents.
Figure 15: Average Automatic-Decline Rates for SBA Disaster Loans by Percentage
of Black or African American Residents for Three Hurricanes that Affected Puerto
Rico, Fiscal Years 2018–2022
Notes: To calculate the automatic-decline rate for each quartile, we divided the total number of
automatically declined applications by the sum of both approved and declined applications. We
ordered the census tracts within the hurricane disaster areas from the lowest to the highest value. We
then divided the ordered distribution into four equally sized groups (quartiles). The census tracts with
the lowest prevalence of that characteristic fall into the lowest quartile (or <=25th percentile), while
those with the highest prevalence fall into the highest quartile (or >75th percentile).
For the two fiscal year 2017 hurricanes that affected Puerto Rico, only
Approval Rate two characteristics—(1) median household income and (2) poverty rate—
15The following characteristics were not statistically significant: poverty rate; median
household income; percentage of residents 25 years of age or older with no high school
diploma; unemployment rate; percentage of rent-burdened households; percentage of
residents with no health insurance; and percentage of White and Other Race residents.
This means the 95 percent confidence intervals for the highest and lowest quartiles
overlapped.
Page 56 GAO-24-106682 Disaster Loan Program
Appendix III: Socioeconomic and Demographic
Characteristics of Communities in Puerto Rico
Affected by Four Hurricanes in Fiscal Years
2017–2022
had average approval rates that were statistically significant (see fig.
16). 16 For the two hurricanes that affected Puerto Rico in fiscal years
2018–2022, only three characteristics—(1) percentage of residents with
no health insurance, (2) percentage of residents 25 years of age and
older with no high school diploma, and (3) percentage of White
residents—had approval rates that were statistically significant. 17
Figure 16: Average Approval Rates for SBA Disaster Loans by Socioeconomic and Demographic Characteristic for Four
Hurricanes That Affected Puerto Rico, Fiscal Years 2017–2022
Notes: To calculate the approval rate for each quartile by American Community Survey characteristic,
we divided the total number of approved applications by the sum of both approved and declined
applications, excluding duplicate applications. For each characteristic, we ordered the census tracts
within the hurricane disaster areas from the lowest to the highest value. We then divided the ordered
distribution into four equally sized groups (quartiles). The census tracts with the lowest prevalence of
that characteristic fall into the lowest quartile (or <=25th percentile), while those with the highest
prevalence fall into the highest quartile (or >75th percentile).
16The following characteristics were not statistically significant: unemployment rate;
percentage of rent-burdened households; percentage of residents with no health
insurance; percentage of residents 25 years of age and older with no high school diploma;
and percentage of White, Black or African American, and Other Race residents. This
means the 95 percent confidence intervals for the highest and lowest quartiles
overlapped.
17The following characteristics were not statistically significant: poverty rate; median
household income; unemployment rate; percentage of rent-burdened households; and
percentage of Black or African American and Other Race residents. This means the 95
percent confidence intervals for the highest and lowest quartiles overlapped.
Page 57 GAO-24-106682 Disaster Loan Program
Appendix III: Socioeconomic and Demographic
Characteristics of Communities in Puerto Rico
Affected by Four Hurricanes in Fiscal Years
2017–2022
Approval Rates for Two Median household income. The average approval rate for applications
Hurricanes in Fiscal Year from census tracts with the lowest median household incomes was 56
percent. There was a difference of 9 percentage points in the average
2017
approval rate between communities with the highest and lowest median
household incomes.
Poverty rate. The average approval rate for applications from census
tracts with the highest poverty rates was 56 percent. There was a
difference of 8 percentage points in the average approval rate between
communities with the highest and lowest poverty rates.
Approval Rates for Two No health insurance. The average approval rate for applications from
Hurricanes in Fiscal Years census tracts with the highest percentages of residents with no health
insurance was 54 percent. There was a difference of 7 percentage points
2018–2022
in the average approval rate between communities with the highest and
lowest percentage of residents with no health insurance.
No high school diploma. The average approval rate for applications
from census tracts with the highest percentages of residents 25 years of
age and older with no high school diploma was 55 percent. There was a
difference of 5 percentage points in the average approval rate between
communities with the highest and lowest percentages of residents 25
years of age and older with no high school diploma. 18
White. The average approval rate for applications from census tracts with
the lowest percentages of White residents was 56 percent. There was a
difference of 5 percentage points in the average approval rate between
communities with the highest and lowest percentages of White residents.
18The percentage-point difference does not match figure 16 because of rounding.
Page 58 GAO-24-106682 Disaster Loan Program
Appendix IV: Outcomes of Disaster Loan Appendix IV: Outcomes of Disaster Loan
Applications the Small Business
Applications the Small Business
Administration Accepted for 17 Hurricanes in
Fiscal Years 2017–2022
Administration Accepted for 17 Hurricanes in
Fiscal Years 2017–2022
This appendix presents the outcomes of disaster loan applications that
the Small Business Administration accepted for 17 hurricanes that
occurred in fiscal years 2017–2022. 1 We analyzed the number and
percentage of accepted applications that were approved, automatically
declined, and declined after full processing (see table 3).
Table 3: Outcomes of Disaster Loan Applications SBA Accepted in Fiscal Years 2017–2022, by Hurricane
Number of applications Number of applications
Number of applications automatically declined after full
Hurricane Date approved (percentage) declined (percentage) processing (percentage)
Matthew October 2016 8,190 6,605 2,835
(46.5%) (37.5%) (16.2%)
Harvey August 2017 46,339 26,911 26,272
(46.6%) (27.0%) (26.4%)
Irma September 2017 43,849 27,278 33,854
(41.8%) (26.0%) (32.3%)
Maria September 2017 61,532 24,581 11,943
(62.8%) (25.1%) (12.2%)
Florence September 2018 11,874 4,015 6,027
(54.2%) (18.3%) (27.5%)
Michael October 2018 32,528 10,113 13,337
(58.1%) (18.1%) (23.8%)
Dorian August 2019 153 142 233
(29%) (26.9%) (44.1%)
Hanna July 2020 197 112 117
(46.2%) (26.3%) (27.5%)
Laura August 2020 10,709 4,213 6,900
(49.1%) (19.3%) (31.6%)
Isaias August 2020 128 81 112
(39.9%) (25.2%) (34.9%)
Sally September 2020 5,867 2,412 4,309
(46.6%) (19.2%) (34.2%)
Zeta October 2020 2,097 1,100 1,809
(41.9%) (22.0%) (36.1%)
Delta October 2020 1,248 613 1,633
(35.7%) (17.5%) (46.7%)
Ida August 2021 38,072 17,481 18,207
(51.6%) (23.7%) (24.7%)
Nicholas September 2021 0 11 9
(0.0%) (55.0%) (45.5%)
1We selected these hurricanes because they were all of the hurricanes that occurred in
fiscal years 2017–2022 that caused at least $1 billion in damage. See app. I for more
information on our scope and methodology.
Page 59 GAO-24-106682 Disaster Loan Program
Appendix IV: Outcomes of Disaster Loan
Applications the Small Business
Administration Accepted for 17 Hurricanes in
Fiscal Years 2017–2022
Number of applications Number of applications
Number of applications automatically declined after full
Hurricane Date approved (percentage) declined (percentage) processing (percentage)
Fiona September 2022 3,645 2,983 856
(48.7%) (39.9%) (11.4%)
Ian September 2022 24,886 13,700 10,602
(50.6%) (27.9%) (21.6%)
Source: GAO analysis of Small Business Administration (SBA) data. | GAO-24-106682
Note: To calculate approval rates, we divided the number of approved applications by the sum of
approved and declined applications. To calculate automatic-decline rates, we divided the total
number of automatically declined applications by the sum of both approved and declined applications.
To calculate decline rates after full processing, we divided the number of declined applications by the
sum of approved and declined applications.
Page 60 GAO-24-106682 Disaster Loan Program
Appendix V: Comments from the Small
Appendix V: Comments from the Small
Business Administration
Business Administration
Page 61 GAO-24-106682 Disaster Loan Program
Appendix V: Comments from the Small
Business Administration
Page 62 GAO-24-106682 Disaster Loan Program
Appendix VI: GAO Contact and Staff
Appendix VI: GAO Contact and Staff
Acknowledgments
Acknowledgments
William B. Shear, (202) 512-8678 or shearw@gao.gov
GAO Contact
In addition to the contact named above, Paige Smith (Assistant Director),
Staff Laura Gibbons (Analyst in Charge), Lauren Capitini, Chelsea Carter,
Acknowledgments Lorraine Ettaro, Courtney LaFountain, Jennifer Schwartz, Sara Shore,
and Jena Sinkfield made key contributions to this report.
Page 63 GAO-24-106682 Disaster Loan Program
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