Rural Housing Service
Opportunities Exist to Strengthen Farm Labor Housing Program Management and Oversight Gao ID: GAO-11-329 March 30, 2011Congress created the Farm Labor Housing (FLH) Loan and Grant Program in the early 1960s to support the development of affordable housing for farm workers. In 2010, Congress appropriated $19.7 million for this program, which is administered by the Rural Housing Service (RHS) in the U.S. Department of Agriculture (USDA). GAO was asked to examine (1) demand for the FLH program; (2) RHS's processes for ensuring that the program is providing decent housing for eligible farmworkers; and (3) the financial status and financial management of FLH properties. To do this work, GAO analyzed agency data, regulations, and FLH program documentation; convened a group of experts with assistance of The National Academies; selected and inspected 20 properties in five states; and interviewed RHS staff and various stakeholders.
Available RHS occupancy data indicate that overall demand for FLH units has remained stable in recent years--with the vacancy rate ranging from 12 to 16 percent from 2007 through 2010. But the data showed, and stakeholders GAO interviewed and other experts agreed, that rates varied significantly across states. For example, RHS occupancy data show that the vacancy rates ranged from 0 percent in South Carolina to 64 percent in Wisconsin in 2010. However, stakeholders and experts offered divergent perspectives on trends in the demand for FLH units, with some citing instances of declining demand and others suggesting that demand was still high. The experts frequently cited housing costs and eligibility requirements among the factors having the greatest impact on demand. RHS management processes have hindered the agency's ability to assure farmworkers access to decent and safe housing and compliance with program requirements. For example, RHS cannot readily determine the severity of occurrences of noncompliance among FLH borrowers because the program information it uses to track borrower performance lacks specificity. Moreover, the enforcement mechanisms RHS uses may not be effective in bringing borrowers back into compliance in a timely manner, because some are too mild (servicing letters), and others too severe (acceleration of the loan payments) to have the intended effect. Additionally, the processes RHS has used for verifying tenant eligibility were inconsistent among states. For example, some states used third-party income and residency verification systems, while other states did not have access to or were unaware of these verification tools. Further, RHS has not analyzed all available program data to best target program funds to areas of greatest need. For example, information from program applicants has not been summarized to assess demand in a local area or state. Most FLH program borrowers were able to make timely loan payments; however, more could be done to ensure that FLH funds are used efficiently. For example, according to GAO's analysis, RHS overestimated its credit subsidy costs for fiscal year 2010 by $3 million, and another $11.8 million in low-interest financing could have been available to loan applicants. An investigation of unusual fluctuations in the credit subsidy cost components and a greater degree of coordination by budget and program staff could have helped ensure that key assumptions, namely the predicted default rates, used in the credit subsidy model more closely reflected portfolio performance and would have allowed RHS to optimize funding use. In addition, more than $184 million in loans and grant obligations were unliquidated, or unpaid, as of September 2010 and the balance of unliquidated obligations has not changed significantly over the past 6 years. However, RHS had no guidelines in place on when to recapture these funds, making it difficult to ensure that limited program funds are used effectively by being made available to other projects in a timely manner. GAO recommends that the Secretary of Agriculture take steps to strengthen oversight and management of the FLH program by, among other things, improving performance and financial information, increasing borrower compliance, and ensuring the efficient use of resources for the FLH program. The agency generally agreed with GAO's recommendations.
RecommendationsOur recommendations from this work are listed below with a Contact for more information. Status will change from "In process" to "Open," "Closed - implemented," or "Closed - not implemented" based on our follow up work.
Director: Angela N. Clowers Team: Government Accountability Office: Financial Markets and Community Investment Phone: (202) 512-4010GAO-11-329, Rural Housing Service: Opportunities Exist to Strengthen Farm Labor Housing Program Management and Oversight
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United States Government Accountability Office:
GAO:
Report to Congressional Committees:
March 2011:
Rural Housing Service:
Opportunities Exist to Strengthen Farm Labor Housing Program
Management and Oversight:
GAO-11-329:
GAO Highlights:
Highlights of GAO-11-329, a report to congressional committees.
Why GAO Did This Study:
Congress created the Farm Labor Housing (FLH) Loan and Grant Program
in the early 1960s to support the development of affordable housing
for farm workers. In 2010, Congress appropriated $19.7 million for
this program, which is administered by the Rural Housing Service (RHS)
in the U.S. Department of Agriculture (USDA). GAO was asked to examine
(1) demand for the FLH program; (2) RHS‘s processes for ensuring that
the program is providing decent housing for eligible farmworkers; and
(3) the financial status and financial management of FLH properties.
To do this work, GAO analyzed agency data, regulations, and FLH
program documentation; convened a group of experts with assistance of
The National Academies; selected and inspected 20 properties in five
states; and interviewed RHS staff and various stakeholders.
What GAO Found:
Available RHS occupancy data indicate that overall demand for FLH
units has remained stable in recent years”with the vacancy rate
ranging from 12 to 16 percent from 2007 through 2010. But the data
showed, and stakeholders GAO interviewed and other experts agreed,
that rates varied significantly across states. For example, RHS
occupancy data show that the vacancy rates ranged from 0 percent in
South Carolina to 64 percent in Wisconsin in 2010. However,
stakeholders and experts offered divergent perspectives on trends in
the demand for FLH units, with some citing instances of declining
demand and others suggesting that demand was still high. The experts
frequently cited housing costs and eligibility requirements among the
factors having the greatest impact on demand.
RHS management processes have hindered the agency‘s ability to assure
farmworkers access to decent and safe housing and compliance with
program requirements. For example, RHS cannot readily determine the
severity of occurrences of noncompliance among FLH borrowers because
the program information it uses to track borrower performance lacks
specificity. Moreover, the enforcement mechanisms RHS uses may not be
effective in bringing borrowers back into compliance in a timely
manner, because some are too mild (servicing letters), and others too
severe (acceleration of the loan payments) to have the intended
effect. Additionally, the processes RHS has used for verifying tenant
eligibility were inconsistent among states. For example, some states
used third-party income and residency verification systems, while
other states did not have access to or were unaware of these
verification tools. Further, RHS has not analyzed all available
program data to best target program funds to areas of greatest need.
For example, information from program applicants has not been
summarized to assess demand in a local area or state.
Most FLH program borrowers were able to make timely loan payments;
however, more could be done to ensure that FLH funds are used
efficiently. For example, according to GAO‘s analysis, RHS
overestimated its credit subsidy costs for fiscal year 2010 by $3
million, and another $11.8 million in low-interest financing could
have been available to loan applicants. An investigation of unusual
fluctuations in the credit subsidy cost components and a greater
degree of coordination by budget and program staff could have helped
ensure that key assumptions, namely the predicted default rates, used
in the credit subsidy model more closely reflected portfolio
performance and would have allowed RHS to optimize funding use. In
addition, more than $184 million in loans and grant obligations were
unliquidated, or unpaid, as of September 2010 and the balance of
unliquidated obligations has not changed significantly over the past 6
years. However, RHS had no guidelines in place on when to recapture
these funds, making it difficult to ensure that limited program funds
are used effectively by being made available to other projects in a
timely manner.
What GAO Recommends:
GAO recommends that the Secretary of Agriculture take steps to
strengthen oversight and management of the FLH program by, among other
things, improving performance and financial information, increasing
borrower compliance, and ensuring the efficient use of resources for
the FLH program. The agency generally agreed with GAO‘s
recommendations.
View [hyperlink, http://www.gao.gov/products/GAO-11-329] or key
components. For more information, contact A. Nicole Clowers at (202)
512-8678 or clowersa@gao.gov.
[End of section]
Contents:
Letter:
Background:
Limited Available Data Suggest Varying Levels of Demand for FLH Units
and Funds, and Experts and Stakeholders Identified Options to Improve
Program:
Improvements in RHS Processes Needed to Better Manage FLH Program and
Enforce Requirements:
Most FLH Program Borrowers Are Not Delinquent or in Default on Their
Payments, but Additional Management Attention Needed to Help Ensure
Efficient Use of Funds:
Conclusions:
Recommendations for Executive Action:
Agency Comments and Our Evaluation:
Appendix I: Objectives, Scope, and Methodology:
Appendix II: Experts Convened by GAO with the Assistance of The
National Academies on Demand for Farm Labor Housing:
Appendix III: Federal Data on Farmworkers:
Appendix IV: Age of the FLH Property Portfolio and Condition of FLH
Properties We Visited:
Appendix V: FLH Credit Subsidy Rate Calculation:
Appendix VI: Comments from the U.S. Department of Agriculture:
Appendix VII: GAO Contact and Staff Acknowledgments:
Tables:
Table 1: RHS Performance Classification System for FLH Properties:
Table 2: Age of Low Grades for All Properties in Site Visit States as
of September 30, 2010:
Table 3: 2010 FLH Program Characteristics for Site Visit States:
Table 4: Estimated Credit Subsidy Rate for FLH Program:
Figures:
Figure 1: Structure of the Farm Labor Housing Loan and Grant Program:
Figure 2: Number of Properties and Units by State and Housing Type, as
of September 2010:
Figure 3: Factors That Very Greatly Influence Demand for FLH Units,
Based on Questionnaire Responses from Our Group of 11 Experts:
Figure 4: Factors That Very Greatly Influence Demand for Funds to
Develop Farm Labor Housing, Based on Questionnaire Responses from Our
Group of 11 Experts:
Figure 5: Performance Grades for FLH Borrowers from Fiscal Years 2006
through 2010:
Figure 6: Percentage and Number of Properties with Delinquent
Borrowers by State and Housing Type, as of September 2010:
Figure 7: Open Financial Findings by Type, as of September 2010:
Figure 8: Loans and Grants Obligated but Unliquidated, by Age, as of
September 30, 2010:
Figure 9: Age of Farm Labor Housing Program Portfolio, as of September
30, 2010:
Figure 10: FLH Property in California with Reflective Roof Materials,
Energy-efficient Appliances, On-demand Water Heater, and Artificial
Turf:
Figure 11: Newly Developed Florida Properties with Low Levels of
Disrepair:
Figure 12: Windows Replaced with Wooden Boards and a Kitchen in Need
of Repair at an Older Florida FLH Property:
Figure 13: FLH Unit in Michigan with Water Damage to the Exterior:
Figure 14: Window Covered by a Board in an FLH Unit in New York:
Figure 15: FLH Unit Undergoing Rehabilitation in Texas:
Abbreviations:
AMAS: Automated Multi-Family Housing Accounting System:
CPS: Current Population Survey:
FASAB: Federal Accounting Standards Advisory Board:
FCRA: Federal Credit Reform Act of 1990:
FLH: Farm Labor Housing:
FLS: Farm Labor Survey:
HUD: Department of Housing and Urban Development:
MFIS: Multi-Family Housing Information System:
NASS: National Agricultural Statistics Service:
NAWS: National Agricultural Workers Survey:
NOFA: Notice of Funding Availability:
OMB: Office of Management and Budget:
PHAS: Public Housing Assessment System:
RD: Rural Development:
RHS: Rural Housing Service:
SAVE: Systematic Alien Verification for Entitlements Program:
USDA: U.S. Department of Agriculture:
[End of section]
United States Government Accountability Office:
Washington, DC 20548:
March 30, 2011:
The Honorable Herb Kohl
Chairman:
The Honorable Roy Blunt:
Ranking Member:
Subcommittee on Agriculture, Rural Development, Food and Drug
Administration, and Related Agencies:
Committee on Appropriations:
United States Senate:
The Honorable Jack Kingston:
Chairman:
The Honorable Samuel Farr:
Ranking Member:
Subcommittee on Agriculture, Rural Development, Food and Drug
Administration, and Related Agencies:
Committee on Appropriations:
House of Representatives:
Farmworkers play a critical role in the nation's agricultural sector.
However, according to the U.S. Department of Agriculture (USDA),
farmworkers frequently are among the most poorly housed people in the
United States, sometimes living in tents, shacks without running
water, or crowded, poorly built dormitories. To support the
development of adequate, affordable housing for farmworkers, Congress
enacted the Farm Labor Housing (FLH) Loan and Grant Program in the
early 1960s--the loan program was enacted in 1961 and the grant
program in 1964--which the Rural Housing Service (RHS) in USDA
administers. This program provides capital financing to buy, develop,
improve, or repair housing for domestic farmworkers employed on farms
or in agricultural or processing industries off-farm. The FLH program
is the only federally assisted source of housing dedicated to farm
labor, which is defined as services associated with the spectrum of
farming activities, from cultivating the soil to delivering
commodities to market.
In 2010, the FLH program had about 731 active properties with more
than 16,800 units in 40 states, and a loan portfolio balance of around
$300 million.[Footnote 1] Thirty-seven percent of properties and 93
percent of units were located off-farm; 61 percent of properties and 4
percent of units were located on-farm.[Footnote 2] Off-farm properties
generally are developed by nonprofit organizations and public entities
that assist farmworkers at off-farm locations with no requirements
that workers be employed on a particular farm. On-farm properties are
located on the farm where laborers work or in the nearby community. In
general, on-farm housing occupants do not pay rent and utilities
unless such charges are approved by the USDA. About half of all on-
farm properties (51 percent) are more than 21 years old, and about
half of all off-farm properties (53 percent) are from 11 to 30 years
old. Thirteen states (Arizona, Arkansas, California, Colorado,
Florida, Idaho, Michigan, New Mexico, New York, North Carolina,
Oregon, Texas, and Washington) had more than 100 housing units.
Arkansas, Michigan, and Vermont had the largest number of on-farm
properties; while California, Florida, Oregon, and Washington had the
largest number of off-farm properties.
However, with an aging portfolio, changing labor needs in the
agriculture industry, and changes in the demographics of farmworkers,
the continued demand for these properties is unclear. In the
conference report to the Omnibus Appropriations Act, 2009 (Public Law
111-8), Congress directed us to conduct an assessment of the
properties financed under the FLH program, provide information on the
physical condition, occupancy status, and financial status of the
units, and discuss other management and compliance issues confronting
FLH management entities. To respond to this mandate and your
additional interests, we examined (1) how demand for the FLH program
has changed over time, key factors that influence demand for such
housing, and whether the program model addresses demand; (2) the
extent to which RHS management processes assure farmworkers access to
decent and safe housing and compliance with program requirements; and
(3) the financial status of properties in the FLH portfolio and the
extent to which RHS processes ensure the sound financial management of
the program.
To address these objectives, we reviewed literature and reports on
farm labor housing demand; held a 1-day discussion with a group of
experts with the assistance of The National Academies; and conducted
interviews with academics, government entities, nonprofit
organizations, and farm labor housing developers to gather
perspectives on trends in farm labor housing and factors that
contribute to demand.[Footnote 3] We completed site visits to five
states that included interviews with USDA state and local office
staff, walkthroughs of 20 properties (4 properties in each of the five
states) that included inspecting the interior and exterior of the
properties and speaking to the borrower or property manager, and
reviews of tenant files, the results of which cannot be generalized
across the portfolio.[Footnote 4] The five states were California,
Florida, Michigan, New York, and Texas, which were selected to obtain
regional diversity and a range in type (on-farm, off-farm and seasonal
housing) and number of properties/units per state. We selected
properties to include both property types (on-farm and off-farm
properties) and a range of property sizes and performance grades. We
present information about the age and condition of the properties we
visited in appendix IV. We also obtained and analyzed electronic
program data from RHS's Automated Multi-Family Housing Accounting
System (AMAS) and its Multi-Family Housing Information System (MFIS).
RHS uses AMAS to identify delinquencies and financially delinquent
borrowers, and MFIS to identify program compliance and assess overall
program needs using information on budgets, operating costs,
nonfinancial defaults, insurance, reserve account funding, management
plans, supervisory visits, taxes, and tenant changes. We assessed the
reliability of these data by (1) performing electronic testing, (2)
reviewing existing information about the data and the system that
produced them, and (3) interviewing agency officials knowledgeable
about the data and related management controls. Based on this
assessment, we determined the data to be sufficiently reliable for the
purposes of this report. We reviewed USDA and RHS handbooks, reports,
and documentation of national FLH stakeholder meetings convened by
USDA in 2008 and 2009. We interviewed headquarters, state, and local
RHS staff knowledgeable about financial underwriting, the oversight of
FLH loans and the program's credit subsidy model. Finally, we examined
federal budget documentation for the program, our Standards for
Internal Control in the Federal Government, and support for the
program's credit subsidy calculation.[Footnote 5]
We conducted this performance audit from March 2010 to March 2011 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:
RHS administers the FLH Loan and Grant program under Section 514 and
516 of the Housing Act of 1949, as amended, which provides direct
loans and grants for the development of on-farm and off-farm housing.
[Footnote 6] Through the FLH program, the agency distributes capital
financing annually to buy, develop, improve, or repair housing for
laborers employed on farms or in associated handling or processing
industries off-farm.[Footnote 7] In 2010, Congress appropriated
$19,746,000 for this program. Grants for up to 90 percent of the
development cost of the properties are made to farmworker
associations, nonprofit organizations, Indian tribes, and public
agencies. Direct loans are made for 33 years at 1 percent interest to
these entities, individual farmers, associations of farmers, family
farm corporations, or partnerships. Loan and grant recipients may
manage the properties or contract with management agents.
The RHS national office reviews state office funding requests,
implements, and monitors performance measures to ensure program
objectives are met, and provides authority and direction to field
offices on customer service and program delivery. Although state
office responsibilities may vary according to state, these offices
typically accept, review, and service loans; monitor budgets; conduct
fiscal and physical inspections; and engage in limited policy-making
and oversight of local field offices.[Footnote 8] The state office
also ranks, scores, and forwards eligible applications it receives for
funding to the national office. Regional and local field offices
provide day-to-day loan oversight and conduct reviews of FLH
properties to ensure compliance with program rules. (See figure 1 for
the organizational structure of the program.)
Figure 1: Structure of the Farm Labor Housing Loan and Grant Program:
[Refer to PDF for image: illustration]
Top level:
USDA RHS National Office:
Program oversight, policy-making.
Second level, reporting to USDA RHS National Office:
USDA RHS State Offices:
Oversight of local offices and loan servicing.
Third level, reporting to USDA RHS State Offices:
USDA RHS Local Offices:
Day-to-day loan servicing and grant oversight.
Fourth level, reporting to: USDA RHS Local Offices:
On-farm properties (Section 514 loans):
Eligible borrowers: Individual farmers, associations of farmers,
family farm corporations or partnerships;
* Seasonal units: Occupied 8 months or less per year;
* Year-round units: Occupied more than 8 months per year.
Off-farm properties (Section 514 loans and Section 516 grants):
Eligible borrowers and grantees: Nonprofit organizations; farm labor
associations; state, local or public agencies; Indian tribes;
* Seasonal units: Occupied 8 months or less per year;
* Year-round units: Occupied more than 8 months per year.
Tenants:
* Farm laborers and their families;
* Must receive a substantial portion of income from primary
production, processing, and transport of agricultural or aquacultural
commodities;
* Must be a citizen of the United States or a person legally admitted
for permanent residence.
Source: GAO analysis; Art Explosion (images).
[End of figure]
The number and type of FLH properties and units vary across states
(see figure 2). States with many off-farm properties--such as
California, Florida, and Texas--are often referred to as "home base
states" where farmworkers live and work throughout the year. States
with more on-farm housing--such as Michigan and Arkansas--tend to
house workers seasonally and are often called "stream" states.
Figure 2: Number of Properties and Units by State and Housing Type, as
of September 2010:
[Refer to PDF for image: 2 illustrated U.S. maps]
Properties on-farm/properties off-farm:
Total: 449 on-farm; 272 off-farm.
Alabama: 3/0;
Alaska: 1/0;
Arizona: 5/5;
Arkansas: 155/0;
California: 4/91;
Colorado: 0/12;
Connecticut: none;
Delaware: 0/2;
Florida: 0/40;
Georgia: 1/2;
Hawaii: 1/2;
Idaho: 0/10;
Illinois: 3/1;
Indiana: none;
Iowa: 4/1;
Kansas: none;
Kentucky: 0/2;
Louisiana: 9/1;
Maine: 4/0;
Maryland: 0/2;
Massachusetts: 3/2;
Michigan: 82/2;
Minnesota: 0/3;
Mississippi: 28/0;
Missouri: none;
Montana: 1/0;
Nebraska: 1/2;
Nevada: 2/0;
New Hampshire: 3/0;
New Jersey: 18/1;
New Mexico: 0/8;
New York: 17/2;
North Carolina: 6/4;
North Dakota: none;
Ohio: 2/1;
Oklahoma: 0/2;
Oregon: 1/23;
Pennsylvania: 0/2;
Puerto Rico: 0/1;
Rhode Island: none;
South Carolina: 9/0;
South Dakota: none;
Tennessee: 9/1;
Texas: 0/19;
Utah: 0/2;
Vermont: 71/0;
Virginia: 0/1;
Washington: 2/23;
West Virginia: none;
Wisconsin: 4/4
Wyoming: none.
Units on-farm/units off-farm:
Total: 1,081 on-farm; 15,772 off-farm.
Alabama: 7/0;
Alaska: 1/0;
Arizona: 49/141;
Arkansas: 233/0;
California: 20/5,559;
Colorado: 0/626;
Connecticut: none;
Delaware: 0/50;
Florida: 0/4,647;
Georgia: 20/48;
Hawaii: 1/44;
Idaho: 0/572;
Illinois: 27/36;
Indiana: none;
Iowa: 7/4;
Kansas: none;
Kentucky: 0/42;
Louisiana: 12/40;
Maine: 4/0;
Maryland: 0/90;
Massachusetts: 3/48;
Michigan: 320/44;
Minnesota: 0/78;
Mississippi: 75/0;
Missouri: none;
Montana: 1/0;
Nebraska: 2/24;
Nevada: 2/0;
New Hampshire: 3/0;
New Jersey: 23/24;
New Mexico: 0/241;
New York: 86/24;
North Carolina: 32/109;
North Dakota: none;
Ohio: 8/24;
Oklahoma: 0/42;
Oregon: 1/800;
Pennsylvania: 0/12;
Puerto Rico: 0/24;
Rhode Island: none;
South Carolina: 9/0;
South Dakota: none;
Tennessee: 11/24;
Texas: 0/1,426;
Utah: 0/25;
Vermont: 79/0;
Virginia: 0/34;
Washington: 41/799;
West Virginia: none;
Wisconsin: 4/63;
Wyoming: none.
Sources: GAO analysis of MFIS and AMAS data; map (MapInfo).
Note: On-farm borrowers are not required to annually report the number
of units on their properties, which may result in an underestimation
of on-farm units shown above. However, RHS is required to conduct on-
site supervisory reviews every 3 years of properties and units. Total
includes 10 properties and 14 units that were of an unknown
classification (e.g., on-farm or off-farm) or nonlabor housing
properties.
[End of figure]
Under the FLH program, eligible tenants must receive a substantial
portion of their income through the primary production of agricultural
or aquacultural commodities, or those involved in off-farm handling or
processing of such commodities. In addition, eligible tenants must be
U.S. citizens or noncitizens with permanent residency status and
program-eligible employment. According to USDA officials, applicants
in off-farm properties must show documentation of their legal
residency status or declare U.S. citizenship.[Footnote 9] In addition,
off-farm FLH tenants must qualify as a very low-, low-, or moderate
income household based on the Department of Housing and Urban
Development's (HUD) income eligibility standards and provide borrowers
documentation to verify their income eligibility.[Footnote 10]
Although estimates of the domestic farm labor population have varied
widely, according to the USDA's National Agricultural Statistics
Service, the United States had more than 1 million hired farmworkers
in 2010.[Footnote 11] In addition, the most recent Census of
Agriculture in 2007, which provides state specific data on
farmworkers, shows that six states--California, Florida, North
Carolina, Oregon, Texas, and Washington--account for about 43 percent
of all hired farmworkers. These data show that in the aggregate the
number of hired farmworkers has remained relatively stable over the
last 10 years and the geographic distribution of farmworkers has not
changed significantly in the past decade. The labor market for
farmwork typically includes a large population of relatively poor
workers, a portion of whom migrate to, and within, the United States.
Hired farmworkers also are typically young, more likely to be foreign-
born, less likely to speak English, have lower levels of education,
and are less likely to be U.S. citizens or to have a legally
authorized work permit. According to National Agricultural Workers
Survey performed by the U.S. Department of Labor, about half of all
hired crop farmworkers lack legal authorization to work in the United
States.
Application process for off-farm and on-farm housing:
Each year nonprofit organizations, state and local entities, community
organizations, and federally recognized Indian tribes may submit
proposals to develop off-farm labor housing. RHS state offices rank,
score and forward all eligible applications to the national office for
selection for funding, in accordance with national requirements
outlined in an annual Notice of Funding Availability (NOFA). Off-Farm
Labor Housing applications are ranked on a national basis according to
the scoring awarded to each application on the state and local level.
Funds are then awarded to the top-scoring applications. Once a
proposal is selected for funding, applicants must submit a final
application. Every year the national office notifies state offices of
the deadline for submitting applications for consideration in the
national funding selection process. Application deadlines, the type of
funding available (such as loans versus grants) and the selection and
scoring processes may vary from year to year as outlined in the NOFA.
For example, in 2007, 2008, and 2010 applications received additional
scoring points for participation in sustainable development and energy
efficiency programs; whereas, such incentives did not previously
exist. In addition, the deadline for applications has varied over the
past 10 years from early May to mid-August. The NOFA may vary on
whether or not funds are available that year for construction of new
units only, or both for new construction and rehabilitation.
Farm owners and associations of farmers who wish to provide housing to
the farmworkers they employ may apply for loans for on-farm housing.
Each year RHS establishes a specific allocation of funding for the
development of on-farm labor housing. Interested applicants may submit
relevant information to their local RHS field office to determine
their likelihood for funding. Local and state offices then forward
applications to the national office, which processes them on a first-
come, first-served basis.
Underwriting and Oversight of FLH Loans:
RHS underwriting and loan oversight processes include financial
analyses of applicants, annual budget reviews, the setting of reserve
fund requirements, and other loan servicing activities. An RHS loan
originator in a state or local office first assesses the financial
feasibility of a proposed project and the financial condition of the
applicant during the initial application stage.[Footnote 12] As part
of these reviews, the loan originator reviews current credit reports,
analyzes projected cash flows, and also confirms that the applicant
has the ability to provide the required financial resources to the
project--either 3 or 5 percent of the loan as equity and, if a for-
profit entity, up to 2 percent initial operating capital. Finally,
though RHS does not pre-determine the amount of loan versus grant
funding it will obligate each year, when property cash flows are
negative, the agency will consider various methods to help address the
financial distress, including loan restructuring and consolidation.
[Footnote 13]
Once a project is approved by an RHS state director or loan approving
official designated by a state director, borrowers must establish a
replacement reserve account with funding levels sufficient to meet the
major capital needs of a project over its life, such as replacing the
roof or windows, doing major exterior work, and adding new kitchen
fixtures. The aggregate, fully funded reserve amount must equal at
least 10 percent of the greater of the total development cost or
appraised value, and annual contributions must be a minimum of 1
percent of the total development cost. The agency requires that
borrowers submit annual property budgets for approval, identify major
maintenance and replacement needs during the annual budget cycle, and
develop a schedule for making withdrawals from the reserve account. In
the case of larger properties that have 24 units or more, borrowers
must submit annual audited financial statements.
RHS's Performance Management System:
RHS's performance management system involves multiple monitoring
activities. While local RHS loan servicers monitor FLH properties for
program compliance, RHS state offices are responsible for oversight of
these efforts. Local office loan servicers conduct a variety of off-
site monitoring activities, or desk reviews and on-site supervisory
reviews to assess whether the property is managed in accordance with
FLH program objectives, the housing is decent, safe, sanitary, and
affordable, and occupancy requirements are being met. The state office
uses MFIS and AMAS to monitor the performance of FLH properties
reported by local loan servicers. Based on these monitoring
activities, RHS assigns all properties a performance grade in MFIS
from A to D. See table 1 for more information on the classification
system.
Table 1: RHS Performance Classification System for FLH Properties:
Classification designation: Class A;
Description of classification: Properties have no unresolved findings
or program violations.
Classification designation: Class B;
Description of classification: Properties for which RHS has taken
servicing steps and the borrower is cooperating and has a plan to
resolve identified findings or violations.
Classification designation: Class C;
Description of classification: Properties with identified findings or
violations for which no plan has been developed to resolve the problem.
Classification designation: Class D;
Description of classification: Properties in monetary or nonmonetary
default of the program. Properties in monetary default have mortgage
payments that are 60 days overdue. Properties in nonmonetary default
are those for which a loan servicer has notified the borrower of a
program violation using at least three servicing letters and the
borrower has not addressed the violation.
Source: USDA.
[End of table]
Subsidy Cost of FLH Program:
The Federal Credit Reform Act of 1990 (FCRA), enacted as part of the
Omnibus Budget Reconciliation Act of 1990, reformed budgeting methods
for federal credit programs, including RHS's farm labor housing direct
loan program. As a result of FCRA, the Office of Management and Budget
(OMB) requires federal agencies with credit programs to report the
actual cost and estimated lifetime cost to the government of their
programs in their annual budgets. Similarly, federal accounting
standards require agencies to recognize the estimated lifetime cost of
their programs in their financial statements. To determine the
expected cost of credit programs, agencies predict or estimate the
future performance of the programs on a cohort basis.[Footnote 14]
This cost, known as the credit subsidy cost, is the net present value
of estimated payments the government makes less estimated amounts it
receives over the life of the direct loan or loan guarantee, excluding
administrative costs.[Footnote 15] OMB also requires federal agencies
with credit programs to reestimate subsidy costs annually to reflect
actual loan performance and expected changes in estimates of future
loan performance. Annual estimates of a program's expected lifetime
subsidy cost changes from year to year. Each additional year provides
more historical data on loan performance that may influence estimates
of the amount and timing of future claims and prepayments, as well as
changes in estimation methodology may cause changes in subsidy cost.
Limited Available Data Suggest Varying Levels of Demand for FLH Units
and Funds, and Experts and Stakeholders Identified Options to Improve
Program:
Available RHS Data and Stakeholder Perspectives Suggest Demand for
Farm Labor Housing Has Varied:
Although RHS maintains some program data through AMAS and MFIS--the
agency's accounting system and program management information system--
it does not maintain or collect comprehensive data that could be used
to better assess demand for farm labor housing. Demand can be measured
from two perspectives: (1) demand from prospective tenants to occupy
farm labor housing units and (2) demand for the funds to develop the
units. Information needed to fully understand demand for FLH units
could include occupancy rates, tenant applications, and waitlists for
units. However, RHS's national office does not retain electronic data
on tenant applications for units or waitlists for the units. Demand
for funds can be measured by the number of applications submitted by
potential borrowers, but RHS's national office does not retain
electronic data on borrowers' applications after a funding round is
completed. Therefore, it is difficult to assess demand for program
funds on a state, regional, or nationwide basis.[Footnote 16] Given
these limitations, we reviewed RHS's data on occupancy and anecdotal
information from stakeholders and experts to describe demand for FLH
units and funds.
Available data on occupancy suggest that demand from tenants to occupy
units has remained stable in recent years but varies significantly
among states. RHS collects data on occupancy for each property in the
portfolio and uses vacancy rates to track occupancy levels of
properties. RHS occupancy data show that vacancy rates ranged from 0
percent in South Carolina to 64 percent in Wisconsin in 2010. These
data also show that from 2007 to 2010, vacancy rates have remained
relatively stable, with the average vacancy rate ranging from 12 to 16
percent during this time. In 2010, among the 13 states with more than
100 units, 8 states had average vacancy rates of under 10 percent and
11 of the 12 had rates under 20 percent. Although overall demand
appears to have remained stable in recent years, the occupancy data
also suggest that demand varies regionally and by state. Vacancy rates
in states with the most FLH units, such as California, Florida, and
Texas, ranged from 14 to 18 percent in 2010. Most states to the south,
such as Arkansas, Arizona, Georgia, Louisiana, Mississippi, North
Carolina, New Mexico, South Carolina, and Virginia, also had low
vacancy rates--below 10 percent in 2010. In contrast, some states with
shorter growing seasons such as Colorado, Minnesota, and Wisconsin had
FLH vacancy rates above 50 percent in 2010. However, some states to
the north, such as Massachusetts, Michigan, New York, Oregon, and
Washington, had vacancy rates under 10 percent in 2010. RHS officials
noted that seasonal units often have higher average vacancy rates,
because these units are unoccupied for part of the year, compared to
year-round units, which may impact a state's overall vacancy rate.
Stakeholders we interviewed in the course of our audit work and the
group of experts who participated in our 1-day group discussion,
convened with the assistance of The National Academies in October 2010
also indicated that demand for units varies significantly across the
country. They noted that farmworker populations have heavier
concentrations in the home base states of California, Florida, and
Texas, which have longer growing seasons and thus have a more
consistent need for housing.[Footnote 17] However, they also offered
contrasting examples of demand even among states with heavy
concentrations of farmworkers. For example, stakeholders we
interviewed and an expert indicated that tenant demand for housing
among farmworkers has remained high, with the expert noting that
farmworkers in California increasingly have been living in informal
dwellings such as garages, sheds, and trailers because they lack other
options. In contrast, a nonprofit organization, property managers, and
RHS officials cited declining demand for units in Florida. For
example, 7 of the 40 FLH properties in Florida have obtained waivers
to rent to otherwise ineligible tenants (for example, tenants who are
not employed in domestic farm labor) due to diminished demand.
Stakeholders we interviewed and the experts selected for our 1-day
group discussion offered divergent perspectives on trends in the
demand for FLH units. RHS officials and experts said they have
witnessed a decline in demand, anecdotally reporting that waitlists
for FLH units have declined in recent years in several states. For
example, during our expert group discussion, one expert stated that
over the past 10 years waitlists at his FLH properties had declined,
whereas in the past his FLH properties were consistently fully
occupied and regularly had waitlists that were 100 households long. He
also noted that waitlists continue to exist for non-FLH housing.
Another expert stated that it often takes up to 6 months to fully rent
FLH housing in his state, while housing that is not financed through
the FLH program can be fully occupied within 1 day. In addition, one
FLH property manager noted that six FLH properties have requested that
RHS allow otherwise ineligible tenants who do not meet FLH
requirements to rent the units due to a high level of vacancies in the
FLH housing.
In contrast, other experts in our group and stakeholders we
interviewed suggested that there is a high demand for tenants to
occupy FLH units and that the program does not fully meet this demand.
In response to a questionnaire at our 1-day group discussion, all of
the experts responded that overall the program did not meet demand
among current and prospective tenants. For example, one expert in our
group noted that in her state, changes in agriculture have had no
significant impact on demand since the unmet need for housing among
farmworkers far outstrips supply. Such differences in perspectives on
trends in demand for FLH units may be explained by the experts'
comments that sustained funding now and in the future is critical to
meet the housing needs in the agriculture sector. Participants at a
nationwide FLH stakeholder meeting convened by USDA in November 2009
also cited a critical need for housing to support farmwork.
Overall demand for funds to develop or remodel FLH housing also
varies. According to experts in our group, stakeholders we
interviewed, and a study by the Housing Assistance Council, demand for
funds to develop on-farm properties had decreased, while demand for
funds to develop off-farm properties had increased.[Footnote 18]
Experts attributed the lower demand for funds for on-farm properties
to reluctance among individual growers, particularly those with small
operations, to provide housing for workers because of uncertain
economic conditions and being subject to program requirements and
restrictions. In addition, demand for funds may vary according to
geographic region. For example, stakeholders from nonprofits and
developers and an expert reported that demand for FLH funds in
California has increased as indicated by a high number of
applications. In contrast, RHS officials, a nonprofit organization,
and a developer in Florida indicated demand for FLH funds has
decreased in certain areas, with fewer applications submitted for new
property development. Furthermore, demand for the type of funds--off-
farm versus on-farm--vary according to region or state. For example,
almost all FLH properties in Michigan are on-farm, while states such
as Florida and Texas currently have no on-farm properties.
Housing Costs, Eligibility Requirements, and Availability of Rental
Assistance Funding Frequently Cited among Other Factors Influencing
Demand for Units and Funds:
Experts in our group cited a number of factors that influence demand
for FLH units. Among the various factors, experts identified the cost
of the housing, program requirements for legal residency, and the
availability of employment opportunities as having the greatest impact
on tenant demand (see figure 3).
Figure 3: Factors That Very Greatly Influence Demand for FLH Units,
Based on Questionnaire Responses from Our Group of 11 Experts:
[Refer to PDF for image: illustrated table]
Factor: Cost of housing (e.g., monthly rent, weekly rent, and/or
utility cost);
Number of very great influence responses: 10.
Factor: FLH program eligibility requirements for citizenship or
permanent residency status;
Number of very great influence responses: 9.
Factor: Availability of employment opportunities in agriculture and
processing;
Number of very great influence responses: 6.
Factor: Availability of affordable housing among farm worker
populations;
Number of very great influence responses: 4.
Factor: Changes in agriculture (including increasing mechanization,
changes in technology, or changes in types of crops/commodities
produced);
Number of very great influence responses: 2.
Factor: Physical condition of housing available (i.e., property
maintenance, sanitation, age of properties, amenities);
Number of very great influence responses: 2.
Factor: Factors associated with geographic region (including length of
growing seasons, migration patterns, or weather patterns);
Number of very great influence responses: 1.
Factor: Type of housing available (seasonal or year-round);
Number of very great influence responses: 1.
Factor: FLH program eligibility requirements for income derived from
farm labor;
Number of very great influence responses: 1.
Factor: Proximity of housing to work site;
Number of very great influence responses: 0.
Factor: Proximity of housing to area services (such as schools,
shopping, childcare, etc.);
Number of very great influence responses: 0.
Factor: Availability of transportation from housing to work site;
Number of very great influence responses: 0.
Source: GAO analysis of experts‘ questionnaire responses.
[End of figure]
* Cost of housing: Ten of the 11 experts in our group identified the
cost of housing--monthly or weekly rents and utility costs--as very
greatly influencing the demand for FLH units among prospective
tenants. According to a USDA report, farmworkers tend to earn very low
wages; therefore, prospective tenants may choose cheaper options, such
as sharing a room with other workers, over FLH units.[Footnote 19]
* Requirements for legal residency: According to nine experts in our
group the program requirement that prospective tenants be U.S.
citizens or document their permanent residency status limits the
number of applicants for FLH units in properties around the country.
Stakeholders and experts also cited the FLH residency requirement as a
cause of declining waitlists to occupy FLH units. According to one
borrower, farmworkers may not feel safe or comfortable living in FLH
properties, even when the leaseholders can show legal residency
status, because other members of the household or extended family
members may not have documented legal residency. During our 1-day
expert group discussion, many experts agreed that FLH demand was lower
than it could be due to the program's residency requirements.
Stakeholders we interviewed noted that other federally assisted
housing programs--including RHS's Section 515 Rural Rental Housing
program and low-income housing tax credits--do not require
documentation of legal residency status.
* Availability of employment opportunities: In addition, six experts
in our group indicated that the availability of employment
opportunities in agriculture and food processing very greatly
influences the demand for FLH units among prospective tenants. Since
many farmworkers travel to find employment, housing needs often are
determined by the prevalence and length of available work. Changing
patterns of agricultural production or the amount of work available in
a given location may change the patterns of demand among farmworkers
to occupy FLH units. For example, RHS officials noted that FLH
properties around Orlando, Florida, at one time were located near
orange groves. But due to increased housing development and
urbanization, agriculture now plays a diminished role in the area and
demand for farm labor housing decreased.
Experts in our group discussion also cited a number of factors that
influence demand for funds to develop FLH units. The factors they most
frequently identified as having the greatest impact include the
availability of Section 521 Rental Assistance, the level of community
support for properties, and opportunities to leverage other sources of
funds (see figure 4).
Figure 4: Factors That Very Greatly Influence Demand for Funds to
Develop Farm Labor Housing, Based on Questionnaire Responses from Our
Group of 11 Experts:
[Refer to PDF for image: illustrated table]
Factor: Availability of Section 521 Rural Rental Assistance funding to
subsidize rent costs in FLH program units;
Number of very great influence responses: 8.
Factor: Level of community support for developing FLH program units,
including NIMBY issues[A];
Number of very great influence responses: 6.
Factor: Availability of other funds or ability to leverage other
funding with FLH program funds;
Number of very great influence responses: 5.
Factor: Availability of FLH program funding (i.e., amount of funds
available and level of competition for funds during an award year);
Number of very great influence responses: 3.
Factor: Financial challenges associated with maintaining seasonal
migrant properties year-round (financial challenges may include
maintaining needed cash flow to operate the property);
Number of very great influence responses: 3.
Factor: Level of responsiveness, efficiency, and ease of working with
USDA Rural Development staff;
Number of very great influence responses: 3.
Factor: Preference among nonprofits and borrowers/developers to devote
resources to other affordable housing programs instead of the FLH
program (e.g., tax credits, HUD‘s Home Ownership Investment
Partnership Program (HOME), Community Development Block Grants (CDBG),
Section 515 Rural Rental Housing loans);
Number of very great influence responses: 3.
Factor: FLH program management challenges (including complexity of
program requirements, regulations or application process, or
requirements of managing FLH program properties in compliance with
program requirements);
Number of very great influence responses: 3.
Factor: Management challenges associated with maintaining seasonal
migrant properties year-round (management challenges may include
maintenance, annual rental process sometimes called ’rent up“, or
other management issues associated with seasonal housing management);
Number of very great influence responses: 2.
Factor: Level of eligible tenant demand–size of farmworker population
in need of housing and eligible to live in FLH program units;
Number of very great influence responses: 2.
Factor: Level of USDA RHS outreach and availability of education on
FLH program to developers and growers;
Number of very great influence responses: 2.
Factor: Availability of support, technical expertise, or capacity
among non-profit organizations to assist with the development of
properties;
Number of very great influence responses: 2.
Factor: Cost to assemble an application, including pre-application
development costs;
Number of very great influence responses: 1.
Factor: General economic conditions or concerns;
Number of very great influence responses: 1.
Factor: Changes in agriculture (including increasing mechanization,
changes in technology, or changes in types of crops/commodities
produced);
Number of very great influence responses: 0.
Factor: Housing site proximity to area services (such as schools,
shopping, childcare, etc.);
Number of very great influence responses: 0.
Factor: Housing site proximity to work site(s);
Number of very great influence responses: 0.
Source: GAO analysis of experts‘ questionnaire responses.
[A] NIMBY means not in my backyard and is a term commonly used to
describe community objections to projects such as low-income housing.
[End of figure]
* Availability of Section 521 Rental Assistance: Eight of 11 experts
in our group identified the availability of Section 521 Rental
Assistance funding to subsidize the cost of rent as a factor that very
greatly influences the demand for FLH funds.[Footnote 20] USDA
generally has limited funds for rental assistance that can be
allocated to new or existing FLH properties. An expert in our group
noted that rental assistance has not always been available, and it was
not available during fiscal year 2008 for newly constructed FLH units.
In addition, while approximately 64 percent of off-farm properties
receive this subsidy from RHS, seasonal properties for migrant workers
may not use rental assistance as an operating subsidy to help fund
units when they are not occupied by the workers. However, RHS
officials noted that two demonstration programs are currently underway
that allow a select number of FLH seasonal properties to use rental
assistance as an operating subsidy. Participants at a nationwide
stakeholder meeting convened by USDA in 2009 and several experts in
our group suggested that RHS commit to using rental assistance for
operating assistance in seasonally operated housing facilities for
migrant farmworkers. In addition, if a tenant who receives rental
assistance becomes ineligible through an increase in income, that
rental assistance subsidy may be recaptured. Since rental assistance
subsidies are sometimes tied to individual units, future tenants may
no longer receive rental assistance in that unit and property owners
report difficulties renting out units without rental assistance. One
USDA official who participated in the 2009 stakeholders meeting noted
that, when rental assistance is taken from a property, it can cause
long-term financial hardship.
* Level of community support: Six experts in our group identified the
level of community support for developing FLH units as very greatly
influencing the demand for funds to develop farm labor housing.
Experts noted the presence of organized community objections to FLH
developments, often referred to as "not in my back yard" or NIMBY.
Experts from our group and stakeholders we interviewed noted that
community bias toward migrant farmworkers can pose major roadblocks to
the success of development properties. One expert actively encouraged
USDA to proactively take a stance against NIMBY and consider making
its other agency services or funding contingent upon its ability to
meet housing needs locally.
* Opportunities to leverage other sources of funds: Five experts
indicated that the ability to leverage other sources of funding very
greatly influences demand for funds to develop FLH housing. Because
housing developers often leverage multiple sources of funding for
property development, such as combining federal low-income housing tax
credits with FLH funds, the financial viability of a project may rest
on the ability to obtain multiple funding sources. During a 2009 FLH
nationwide stakeholder meeting convened by USDA, staff from nonprofit
organizations and farm labor housing developers emphasized the need
for increased assistance from RHS and more RHS staff experienced with
leveraging other sources of funding with FLH program monies.
Experts and Stakeholders Noted Existing Program Was Useful and
Necessary but Provided Opinions on Changes to the Program:
Stakeholders we interviewed and experts in our group noted that they
believe the existing program is useful and should not be eliminated;
however, many provided opinions on possible changes to the program
that would better meet demand. When asked whether to eliminate,
radically redesign, or relocate the program's oversight to another
agency such as HUD, almost all members of our group of experts agreed
that the program should be preserved in its current form as a loan and
grant mechanism run out of USDA but undergo significant reform to
better meet demand. However, one expert disagreed with the rest of the
group, arguing that the program was incapable of meeting demand for
farm labor housing and should be replaced with an alternative model.
During the expert group discussion, experts suggested many program
changes, which included reforming RHS's overall management approach to
the program; conducting a comprehensive needs assessment of the
current program to inform policy and regulatory changes; better
targeting program funds to areas of greatest need; and considering
innovative housing designs to lower the cost of housing units. For
example, many experts discussed the importance of increasing RHS
management capacity, such as the number of staff and years of
experience, at the national and state level, as well as prioritizing
and emphasizing the program within USDA by increasing commitment to
and awareness of the program among staff. During the expert group
discussion, several experts noted that the FLH program does not
receive appropriate care or attention from the national office.
Experts noted shortcomings in leadership from headquarters, staff
capacity, and training. Currently, the national RHS office has five
specialists, who help track servicing efforts for the entire
multifamily housing portfolio, of which FLH properties account for
approximately 5 percent. Each specialist is responsible for oversight
and guidance of 9 to 10 state offices and each state's local offices.
In addition, one financial and loan analyst in the national office
works on FLH loan and grant making activities, and each of the three
team leaders reviews the underwriting of all multifamily housing loans
and grants in 14 to 21 states, which may include FLH loans and grants.
Experts in our group also discussed the importance of conducting a
comprehensive needs assessment of the current program to inform policy
and regulatory changes. Specifically, experts noted that a lack of
information on farmworkers, housing needs of farmworkers, and
agricultural patterns of production inhibits RHS's ability to
effectively assess demand for the program and target resources.
Stakeholders we interviewed and studies from our literature review
also described a lack of available data on farm labor housing demand.
Similarly, during stakeholder meetings for organizations and
individuals involved with the FLH program that USDA convened in 2008
and 2009, participants recommended that USDA conduct a needs
assessment of the year-round, seasonal, and migrant workforce and
travel patterns, which would help USDA set policy by utilizing
information on trends in agricultural production needs and
concentrations of the agricultural workforce. During the 2009
stakeholders meeting, officials underscored the importance of better
understanding the target population of the FLH program to set policy
by obtaining data on the industry and utilizing state RHS directors as
a resource for information. Stakeholders we interviewed and experts in
our group also noted the importance of targeting program funds to
areas of greatest need.
Further, several experts in our group discussed the potential benefits
of considering innovative housing designs to lower housing costs. For
example, experts noted that modular and manufactured housing may
expand production and decrease costs per unit. Others suggested that
USDA make available innovative pre-approved housing designs plans to
borrowers. However, two experts expressed skepticism about
manufactured housing and pre-approved housing designs, noting that
these methods have not lowered costs in other programs. Participants
at a national stakeholder meeting convened by USDA in 2008 had a
similar discussion on alternative housing models to better serve
farmworkers and lower costs. For example, participants at this
conference discussed the costs and benefits of using temporary housing
standards, dormitory housing, and temporary emergency housing.
Participants noted that providing housing for the migrant agricultural
workforce that is employed from 2 to 3 months of the year in a given
location may require a different approach. Such housing has limited
use during the off season and may not be cost-effective to maintain or
develop.
Improvements in RHS Processes Needed to Better Manage FLH Program and
Enforce Requirements:
Deficiencies in RHS management processes have limited the extent to
which the agency can determine whether farmworkers have access to
decent and safe housing and ensure compliance with program
requirements. Numerous occurrences of noncompliance among FLH
borrowers can be identified in RHS's performance management system,
MFIS, and the proportion of compliance problems has grown over the
last 5 years. However, RHS cannot readily determine the severity of
noncompliance among FLH borrowers because the program information it
uses to track borrower performance lacks specificity. Moreover, RHS
has used limited enforcement mechanisms to address the full range of
performance problems among its borrowers because its penalties either
are too mild or too severe to be applied to many types of
noncompliance. Additionally, the processes RHS uses for verifying
tenant eligibility are inconsistent across states, as some states have
access to third-party income and residency verification while others
do not or are unaware of the verification tools. Finally, RHS does not
analyze all available program data to best target program funds to
areas of greatest need. For example, information from program
applicants is not summarized to assess demand in a local area or state.
Low Performance Grades Increased in the Past 5 Years, but RHS Did Not
Always Resolve Problems in a Timely Manner and Could Not Readily
Assess Their Severity:
In the past 5 years, the proportion of FLH borrowers with low
performance grades has grown to half of the overall borrower
portfolio. RHS uses multiple methods to measure program performance
including assigning a performance grade (grade of A to D) to each
borrower based on documented physical, financial or management
problems with the FLH property. From 2006 through 2009, RHS had more
class A and B grades associated with FLH properties than C and D
grades. However, the proportion of class C and D properties grew from
40 percent in 2009 to 50 percent of the portfolio in 2010 (see figure
5).[Footnote 21]
Figure 5: Performance Grades for FLH Borrowers from Fiscal Years 2006
through 2010:
[Refer to PDF for image: vertical bar graph]
Year: 2006;
Grade A/B: 377;
Grade C/D: 260;
Total projects: 637.
Year: 2007;
Grade A/B: 383;
Grade C/D: 280;
Total projects: 663.
Year: 2008;
Grade A/B: 419;
Grade C/D: 267;
Total projects: 686.
Year: 2009;
Grade A/B: 427;
Grade C/D: 281;
Total projects: 708.
Year: 2010;
Grade A/B: 365;
Grade C/D: 366;
Total projects: 731.
Source: GAO analysis of MFIS data.
[End of figure]
From 2006 through 2010, performance grades varied among 13 states with
larger FLH portfolios--states with more than 100 units.[Footnote 22]
For example, three states (California, New Mexico, and North Carolina)
reflected the national average, with about half of FLH borrower or
grantees in those states receiving Cs. In contrast, the proportion of
Michigan and Texas borrowers to receive a C or D grade was above 80
percent in 2010. Furthermore, in Colorado, the proportion of borrowers
with C and D grades was 58 percent in 2006 but grew dramatically to
100 percent with a C grade in 2010.
Within the FLH program, a number of properties maintained C or D
grades with unresolved findings and violations for multiple years (see
table 2). For example, in Michigan, 19 out of 85 properties have been
graded as a C or D for more than 4 years. In one case, a Michigan
borrower received a C grade 10 years ago, which has yet to be
resolved. According to loan servicers in two states, borrowers
(particularly for on-farm properties) have disregarded notices from
RHS that they are out of compliance for multiple years.
Table 2: Age of Low Grades for All Properties in Site Visit States as
of September 30, 2010:
Site visit state: California (95 total properties);
Properties by number of years of C or D performance classification:
2 years or less: 34;
3 or 4 years: 5;
5 to 8 years: 4;
9 years or more: 0.
Site visit state: Florida (40 total properties);
Properties by number of years of C or D performance classification:
2 years or less: 22;
3 or 4 years: 1;
5 to 8 years: 0;
9 years or more: 1.
Site visit state: Michigan (85 total properties);
Properties by number of years of C or D performance classification:
2 years or less: 48;
3 or 4 years: 6;
5 to 8 years: 18;
9 years or more: 1.
Site visit state: New York (20 total properties);
Properties by number of years of C or D performance classification:
2 years or less: 9;
3 or 4 years: 1;
5 to 8 years: 2;
9 years or more: 0.
Site visit state: Texas (19 total properties);
Properties by number of years of C or D performance classification:
2 years or less: 12;
3 or 4 years: 4;
5 to 8 years: 1;
9 years or more: 0.
Site visit state: Total;
Properties by number of years of C or D performance classification:
2 years or less: 125;
3 or 4 years: 17;
5 to 8 years: 25;
9 years or more: 2.
Source: GAO analysis of MFIS data.
Note: Total row does not refer to the total number of properties or
units we visited. Properties include multiple units.
[End of table]
According to our standards for internal control, an important aspect
of a program's internal control includes monitoring the results of
reviews.[Footnote 23] Monitoring of internal control should include
policies and procedures for ensuring that the findings of audits and
other reviews are promptly resolved.[Footnote 24] However, the Multi-
Family Housing Asset Management Handbook does not designate a length
of time to resolve underlying findings to improve a C or D grade. The
handbook states that the loan servicer, state office, and national
office should be available to provide further oversight of a borrower
with a D grade and that loan servicers should be concerned when
findings or violations resulting in a C grade continue for an extended
period of time with no indication of resolution efforts.
Furthermore, although RHS assigns a performance grade to each
property, the agency's performance classification and information
systems do not readily provide agency officials with information
necessary to assess the causes of low performance by borrowers or the
severity of findings. Specifically, RHS's classification system does
not provide specific information on performance problems, such as
severity or type, associated with FLH properties. Performance
classifications are listed in the RHS system as a grade, but the grade
does not specifically indicate its cause. For example, a D grade could
be due to a long-standing failure of the borrower to submit required
paperwork or a more severe finding or violation such as a health and
safety problem with the physical condition of the property. An RHS
official noted that the causes of C grades, in particular, can vary
widely in severity. According to this official, RHS is considering
methods to better specify of the severity of the grade for properties
graded C. In 2010, 46 percent of FLH properties received a C grade.
During site visits, local office staff explained that they submit
servicing information into MFIS--such as the results of RHS visits to
the property or a finding that required documentation from the
borrower is missing. Although they document physical, financial, or
management problems with the property as supervisory findings, it is
not readily clear which specific findings yielded poor performance
grades and the extent to which each finding affected the final
performance grade. Moreover, performance grades may not always match
the most current information gathered by local office loan servicers.
According to loan servicers in two states we visited, documentation of
monitoring activities was not always entered into MFIS in a timely
manner due to reportedly large workloads. For example, according to
one local office the completion and results of supervisory visits
often were not entered into MFIS for 6 or more months following the
review. Furthermore, in 2009, RHS determined through an internal
review that MFIS had not been maintained or updated in a timely
manner.[Footnote 25] Therefore, current information is not always
available on individual property or overall portfolio performance.
According to our standards for internal control, program information
should be recorded and communicated to management in a form and within
a time frame that enables them to carry out their internal control and
other responsibilities.[Footnote 26] Relevant, reliable, and timely
information should be available for management decision-making and to
identify risks and problem areas in a program.
To supplement the performance grades, RHS officials told us that they
use performance information that local staff enter into MFIS.
Specifically, RHS national office staff told us that they use the
findings report in MFIS to track existing and ongoing findings related
to physical or management problems within the portfolio and coordinate
with state and local offices to resolve existing findings. As of the
end of fiscal year 2010, RHS reported 306 out of 1,460 total open
findings in the FLH portfolio, or 21 percent of all open findings, as
physical condition findings. However, the findings report generated
from MFIS, often does not detail the type and severity of these
problems. The report lists the property and borrower identification
numbers, type of housing, RHS loan servicer in charge of the property,
and finding type, among other details. It also includes a category
that provides a brief description of the finding titled "Finding
Name." In the finding name field, short descriptions such as
"lighting," "flooring," "foundation," "smoke alarms," and "windows,
doors, and external structure" are listed. For some findings, loan
servicers have used the comment field to better document the specific
physical condition problem and its severity. For other physical
findings, the comment fields do not contain detailed descriptions of
the finding and, therefore, limit the extent to which management can
understand of the severity of the problem. Thirty-eight percent of all
open physical findings as of July 2010 had no additional comments to
expand on the limited description of the physical condition problem at
the property.
Our site visits to 20 FLH properties in five states that included on-
farm, off-farm, and year-round and seasonally occupied properties
illustrate the limitations of using performance data from RHS's
classification and information systems. We selected properties, in
part, based on their performance grades--with the goal of selecting
properties with varying performance, including some that were in poor
physical condition. However, most of these properties selected and
visited generally met the standards of decent, safe, and sanitary
housing--even some properties classified as Cs or Ds--with low levels
of disrepair often observed on interiors and exteriors of the units.
Using RHS's regulations--which outline physical condition standards
FLH properties--as the baseline, we evaluated the physical condition
of each property visited. We found differences in the quality of the
units but such differences were often related to the age of the units,
age of renovation, or maintenance of the unit by the tenant. For
example, in two newly renovated properties in Texas the units had
central air conditioning, while one property that had not been
renovated lacked this amenity. For 17 of the 20 properties we visited,
we noted low or medium levels of disrepair or deficiencies; for the
other remaining 3 properties, we noted high levels of disrepair.
[Footnote 27]
RHS's performance classification system differs from HUD's Public
Housing Assessment System (PHAS), which applies performance scores to
housing agencies that own and manage public housing properties.
According to the regulation that governs PHAS, the purpose of the
system is to score four aspects of a housing agency's performance: the
physical condition of public housing properties, the financial
condition of the housing agency, the management operations of the
housing agency, and the housing agency's performance with respect to
the obligation and expenditure of Capital Fund program grants.
[Footnote 28] To measure management performance, HUD assigns a score
for each of these four aspects of a housing agency's
operations.[Footnote 29] In contrast to RHS's performance
classification system, HUD's PHAS and performance score, on its face,
specifies performance areas of concern. Because the methods RHS uses
to measure and oversee FHL borrower performance do not provide
sufficiently specific information, RHS cannot readily determine the
severity of occurrences of noncompliance among borrowers using its
classification system and findings report and it has been limited in
the extent to which it can understand overall portfolio performance.
FLH Program Uses Only Mild or Severe Enforcement Mechanisms to Address
a Range of Noncompliance Issues:
Throughout the FLH program, RHS loan servicers have used limited
enforcement mechanisms to compel noncompliant borrowers to address
program findings or violations. Federal regulations and program
guidance set forth specific enforcement actions available for use in
the FLH program to address borrower noncompliance. These include the
following:
1. issuing servicing letters to notify borrowers that they are out of
program compliance,
2. accelerating a borrowers mortgage to make all outstanding borrower
debts to the agency immediately payable,
3. transferring ownership of an FLH property from one borrower to
another,
4. suspending a borrower from participation in federal programs,
5. debarring a borrower from participation in federal programs,
6. assessing civil money penalties, and:
7. civil or criminal sanctions.
However, loan servicers told us that the primary enforcement
mechanisms they used included sending servicing letters to the
borrower to serve notice of noncompliance or accelerating the
borrower's mortgage payments, which could lead to foreclosure. In the
past 5 years, RHS has issued 145 servicing letters and accelerated 17
mortgages. The enforcement mechanisms used by loan servicers are
either too mild or too severe responses to noncompliance. Civil money
penalties, which RHS has the authority to use, can be tailored to the
level of severity of noncompliance, but the agency does not use this
enforcement mechanism. According to RHS officials, USDA's general
counsel determined that there was not sufficient detail in the
regulation to enforce civil money penalties, and recommended that the
regulation section be revised to specify step-by-step procedures for
enforcement and identify who would arbitrate cases involving civil
money penalties. However, to date, the regulations have not been
revised, and RHS does not currently use its authority to impose civil
money penalties.
Forms of borrower noncompliance vary in severity. More severe forms of
noncompliance include mortgage default or health and safety violations
on a property. However, other forms of borrower noncompliance are less
severe. For example, a common form of noncompliance among borrowers is
failure to submit annual budget documentation. Although local
servicers we spoke to said that borrowers should submit annual
budgets, as required, they did not believe failure to do so warranted
a severe enforcement mechanism such as the acceleration of mortgage
payments. On the other hand, loans servicers noted sending servicing
letters does not often result in borrower compliance because there are
no appropriately tailored penalties associated with such letters.
Consequently, FLH properties often retain low performance
classifications for multiple years because they are out of program
compliance for an extended period of time. For example, loan servicers
in two states stated borrowers in their portfolios have remained out
of compliance for multiple years because of limited enforcement
mechanisms.
We have previously reported that penalties in federal award programs
should correspond to performance.[Footnote 30] Specifically, penalties
can sometimes be too mild to discourage violations or perceived as too
severe to invoke, as is the case with the primary enforcement
mechanisms used in the FLH program.[Footnote 31] In the past, we have
also reported that penalties may lose their effectiveness and
credibility over time if they are not executed consistently.[Footnote
32]
Inconsistencies Exist among Methods for Verifying Legal Residency and
Income:
As part of the application process, borrowers must verify the legal
residency of tenants, as only U.S. citizens or permanent residents are
eligible for FLH units. The methods RHS uses for ensuring that
borrowers (or their designated management agents) verify the legal
residency status of tenants differ across states. In our review of 111
tenant files in five states, we found 63 files without legal permanent
residency documentation. However, according to some loan servicers
with whom we spoke, documentation of legal status is only required of
tenants who are not U.S. citizens. Therefore, missing permanent
residency documentation does not necessarily indicate that the tenant
is ineligible to rent in FLH housing. For files with permanent
residency documentation, we examined the documentation to determine if
it appeared to be questionable or inconsistent with legally issued
permanent resident cards. Five of the 43 permanent resident cards that
we reviewed either were expired or appeared questionable.[Footnote 33]
The U.S. Citizenship and Immigration Services division of the
Department of Homeland Security provides an online process for
verifying the legitimacy of legal residency documentation called the
Systematic Alien Verification for Entitlements (SAVE) program. This
system is available, upon request, to all federal, state, and local
benefit-granting government agencies. One local RHS loan servicer in
California described using this system when conducting FLH tenant file
reviews in preparation for triennial supervisory visits. Staff from
two other RHS offices also described using SAVE. However, when asked,
officials from the other seven offices that service FLH loans we
visited around the country stated that they either did not use the
SAVE system or were not aware that the system existed. An RHS official
in the national office stated that access to the SAVE system would
help RHS verify that tenants have eligible residency status and be
useful tool for local offices. According to this official, SAVE use
has not yet become an FLH requirement because RHS is not certain that
all offices have the technical capacity to access it.
In addition to verifying the legal residency of prospective tenants,
loan servicers must ensure that borrowers (or their management agents)
verify tenants' income levels during the application process. Some
loan servicers have access to and use state wage matching systems to
verify income levels of FLH tenants, but loan servicers in other
states do not have access to these systems. For example, the loan
servicers with whom we spoke in California described using a wage
matching system administered by the California Employment Development
Department when completing tenant file reviews. In New York, loan
servicers stated that they did not have access to a state wage
matching tool but would like to have such a method to verify the
income amounts tenants report. In 2004, we recommended that Congress
consider giving RHS access to the Department of Health and Human
Services' national wage matching system, or the National Directory of
New Hires.[Footnote 34] RHS has stated that access to a national wage
matching database would help the agency with tenant income
verification.
According to RHS's asset management handbook, loan servicers in field
offices are to provide consistent, effective oversight of properties
financed by RHS to ensure that they are operated in accordance with
applicable regulatory and administrative requirements.
Because some loans servicers either do not have access or are unaware
of resources to verify the legal residency and income information
provided by tenants, the level of oversight applied across the FLH
portfolio varies. In cases where verification systems are not used,
RHS cannot be assured that borrowers and management companies are
properly implementing tenant legal residency and income requirements,
and ineligible tenants may reside in FLH units.
RHS Does Not Take Advantage of Existing Data Sources to Assess Program
Demand, Making It Difficult to Target Available Funding:
RHS has several sources of information collected within the FLH
program that would help estimate trends in demand to occupy FLH units
and demand for funds but it does not analyze the information for these
purposes. These data include the number, geographic location, and type
(that is, for on-farm or off-farm properties) of applications
submitted by potential borrowers for FLH funding each year; local
market studies submitted as part of an FLH application that document
supply and demand for FLH in a given area; and data on properties'
vacancy rates that are stored in MFIS.[Footnote 35]
RHS uses application information as part of its FLH loan and grant
award process. However, it does not analyze borrower applications or
data associated with application submission to assess trends in demand
in a local area or state. The number of applications or the
information in the application package could serve as indicators of
demand for funds in particular states or regions. For example, market
studies are submitted with each application to assess the need for the
project. These studies are used to identify the supply and demand for
farm labor housing in a specific market area as part of the loan and
grant award process. The market analysis must include information on
the annual income level of farmworker families in the area; an
estimate of the number of farmworkers who remain in the area where
they work and the number of workers who migrate into the region;
general information concerning the types of labor-intensive crops in
the region and prospects for continued demand for farmworkers; and the
condition, number and adequacy of housing currently available to
farmworkers in that area, among other information. However, according
to the RHS national office, because applicants that were not
successful do not become program participants, the national office
does not retain data associated with their applications, including the
market studies of the applicants' area. According to the RHS national
office, state offices do retain application data but do not
systematically analyze these data in later funding cycles to estimate
trends in demand in a given local area or state.
RHS also does not analyze vacancy data to assess trends in demand in a
local area or state. RHS routinely collects vacancy data on each
property in the portfolio and calculates 3-year average vacancy rates
to track the occupancy level of individual properties. While RHS uses
information on unit vacancies to identify issues with individual
properties, according to RHS, partly due to limited resources, it does
not systematically assess overall demand or trends in demand for the
FLH program on a local, statewide, or national level. According to
RHS, program directors also meet at least on an annual basis to
discuss program needs with RHS's Administrator. However, according to
RHS, it does not conduct a regional comparison or national review of
available data to help manage their program and determine demand for
units or funds to develop and rehabilitate units.
Some in our group of experts noted that the lack of detailed
information on demand for the program hinders RHS's ability to target
funds to areas of greatest need. Furthermore, our standards for
internal control state that relevant, reliable, and timely information
should be available for management decision-making.[Footnote 36]
Without analyzing available information--such as application data,
market studies including the population of eligible farmworkers, and
occupancy data--it is difficult for RHS to estimate demand for FLH
units and changes in the needs of its target population, and best
allocate FLH resources during its loan and grant award process.
However, in February 2011, RHS's Administrator told us that the agency
is committed to modifying the program, as necessary, to meet changes
in demand trends. For example, she noted that her office had recently
discussed conducting a systematic review of the need for the FLH
program with USDA's Economic Research Service.
Most FLH Program Borrowers Are Not Delinquent or in Default on Their
Payments, but Additional Management Attention Needed to Help Ensure
Efficient Use of Funds:
Most Borrowers in the FLH Portfolio Were Able to Make Timely Payments
on Their Properties:
According to RHS data and interviews with RHS officials, most
borrowers were able to meet the financial needs of their properties.
About 6 percent, or 37 out of 731, of farm labor housing borrowers
were delinquent on their loans in September 2010. In terms of the age
of delinquencies, an estimated 4 percent of properties had delinquent
borrowers who were from 91 to 365 days delinquent, and 2 percent were
a year or more delinquent.[Footnote 37] About half, or 19, of
delinquent loans were associated with properties having fewer than 5
units, and total delinquent properties accounted for 718 units, or 4
percent, of the total FLH portfolio of 16,032 units. Of the 40 states
with farm labor housing properties, 15 had borrowers with delinquent
FLH accounts. Delinquency rates by state ranged from 0 to 50 percent,
though many of the higher delinquency rates were in states with few
FLH properties, as shown in figure 6. In interviews with agency
officials, staff reported that delinquencies and defaults generally
were not a problem with farm labor housing properties and that few
properties in the portfolio were in poor financial condition.
Figure 6: Percentage and Number of Properties with Delinquent
Borrowers by State and Housing Type, as of September 2010:
[Refer to PDF for image: illustrated table]
Total[A]:
On-farm percentage of properties: 5.12%;
On-farm number of properties: 23;
Off-farm percentage of properties: 4.78%;
Off-farm number of properties: 13;
All percentage of properties: 5.06%;
All number of properties: 37.
Arkansas:
On-farm percentage of properties: 4.52%;
On-farm number of properties: 7;
Off-farm percentage of properties: 0%;
Off-farm number of properties: 0;
All percentage of properties: 4.32%;
All number of properties: 7.
California:
On-farm percentage of properties: 25.00%;
On-farm number of properties: 1;
Off-farm percentage of properties: 1.10%;
Off-farm number of properties: 1;
All percentage of properties: 2.11%;
All number of properties: 2.
Colorado:
On-farm percentage of properties: 0%;
On-farm number of properties: 0;
Off-farm percentage of properties: 8.33%;
Off-farm number of properties: 1;
All percentage of properties: 8.33%;
All number of properties: 1.
Florida:
On-farm percentage of properties: 0%;
On-farm number of properties: 0;
Off-farm percentage of properties: 10.00%;
Off-farm number of properties: 4;
All percentage of properties: 10.00%;
All number of properties: 4.
Idaho:
On-farm percentage of properties: 0%;
On-farm number of properties: 0;
Off-farm percentage of properties: 50.00%;
Off-farm number of properties: 5;
All percentage of properties: 50.00%;
All number of properties: 5.
Maine:
On-farm percentage of properties: 50.00%;
On-farm number of properties: 2;
Off-farm percentage of properties: 0%;
Off-farm number of properties: 0;
All percentage of properties: 50.00%;
All number of properties: 2.
Massachusetts:
On-farm percentage of properties: 33.33%;
On-farm number of properties: 1;
Off-farm percentage of properties: 0%;
Off-farm number of properties: 0;
All percentage of properties: 20.00%;
All number of properties: 1.
Michigan:
On-farm percentage of properties: 3.66%;
On-farm number of properties: 3;
Off-farm percentage of properties: 0%;
Off-farm number of properties: 0;
All percentage of properties: 3.45%;
All number of properties: 1.
Mississippi:
On-farm percentage of properties: 3.57%;
On-farm number of properties: 1;
Off-farm percentage of properties: 0%;
Off-farm number of properties: 0;
All percentage of properties: 3.45%;
All number of properties: 1.
New Jersey:
On-farm percentage of properties: 11.11%;
On-farm number of properties: 2;
Off-farm percentage of properties: 0%;
Off-farm number of properties: 0;
All percentage of properties: 10.53%;
All number of properties: 2.
New York:
On-farm percentage of properties: 0%;
On-farm number of properties: 0;
Off-farm percentage of properties: 0%;
Off-farm number of properties: 0;
All percentage of properties: 5.00%;
All number of properties: 1.
Pennsylvania:
On-farm percentage of properties: 0%;
On-farm number of properties: 0;
Off-farm percentage of properties: 50.00%;
Off-farm number of properties: 1;
All percentage of properties: 50.00%;
All number of properties: 1.
Texas:
On-farm percentage of properties: 0%;
On-farm number of properties: 0;
Off-farm percentage of properties: 5.26%;
Off-farm number of properties: 1;
All percentage of properties: 5.26%;
All number of properties: 1
Vermont:
On-farm percentage of properties: 7.04%;
On-farm number of properties: 5;
Off-farm percentage of properties: 0%;
Off-farm number of properties: 0;
All percentage of properties: 7.04%;
All number of properties: 5.
Remaining states:
On-farm percentage of properties: 0%;
On-farm number of properties: 0;
Off-farm percentage of properties: 0%;
Off-farm number of properties: 0;
All percentage of properties: 0%;
All number of properties: 0.
Source: GAO analysis of MFIS data.
[A] Total includes one property in New York that is of an unknown
classification (e.g., on-farm or off-farm) or is a nonlabor housing
property.
[End of figure]
RHS data show that the majority of financial noncompliance among FLH
properties was related to the late submission of required paperwork.
Loan servicers track borrower compliance with financial requirements
in the multifamily housing information system, or MFIS.[Footnote 38]
We found that about 26 percent of all properties in the FLH portfolio
had one or more unresolved financial findings associated with them.
However, as shown in figure 7, most open financial findings--89
percent--were related to the late submission of financial
documentation, which would include management certificates, budgets,
and audit reports. Some loan servicers we interviewed suggested that
properties that do not submit all the required financial documentation
are not necessarily in poor financial condition, as measured by their
ability to make loan repayments and fund their reserve account. For
example, an RHS official in Michigan and New York stated that on-farm
borrowers may be resistant to sending financial documentation because
they consider it an unnecessary bureaucratic burden, particularly if
they are making timely payments.
Figure 7: Open Financial Findings by Type, as of September 2010:
[Refer to PDF for image: pie-chart]
Information not received from borrower: 89%;
Delinquency or unpaid financial obligation: 9%;
Unacceptable submissions or other: 2%.
Source: GAO analysis of MFIS data.
Note: Values in this figure represent the percentage of open financial
findings among FLH properties that are delinquent, as opposed to the
percentage of all FLH properties that are delinquent.
[End of figure]
The majority of properties were able to fund ongoing maintenance
needs, as measured by data on reserve account balances and
observations made during site visits. Specifically, about 85 percent
of the 269 FLH properties with reserve requirements had reserve
account balances that met or exceeded the requirement, and about 94
percent of properties had accounts that were at least 75 percent
funded, according to RHS data.[Footnote 39] Although we analyzed the
extent to which reserve account balances met RHS requirements, we did
not assess the extent to which these requirements enabled borrowers to
make substantial long-term capital investments in their properties.
While the majority of the 20 properties we visited appeared to meet
ongoing maintenance needs, we also noted that many of those that
recently had engaged in major rehabilitation efforts had sought
additional financing for these improvements.
Certain characteristics of the FLH program and its borrowers may
contribute to the relatively lower delinquency rates in the portfolio.
Specifically, about 64 percent of all off-farm FLH units receive RHS
Section 521 Rental Assistance funding, which subsidizes tenant rent
payments, and according to RHS staff newer properties generally have
an even higher proportion of units receiving the subsidy.[Footnote 40]
Some RHS officials cited the number of FLH units receiving rental
assistance as one likely reason for timely payments from borrowers,
since full rental assistance may cover a borrower's loan payment. In
addition, the loan program provides a fixed 1 percent loan for a term
of 33 years. Therefore, the borrowers' monthly interest expenses are
relatively small.
High Default Rates Used in Credit Subsidy Calculations Led to
Overestimation of Program Costs and Less Low Interest Financing
Available for Applicants:
Rural Development (RD) overestimated its credit subsidy costs for the
fiscal year 2010 FLH loan cohort (that is, the year the loans were
obligated), based on supporting documentation the agency provided us
and our analysis, which resulted in $11.8 million less available for
low-interest financing for program applicants.[Footnote 41] As
required by OMB budget guidance, RD officials prepare a credit subsidy
estimate during the annual budget formulation process. They estimate
the lifetime costs of any direct loans obligated through the FLH
program in the applicable budget year using the latest OMB-approved
cash flow model and cost components.[Footnote 42] This budget
formulation credit subsidy estimate is used to determine the maximum
amount of loans that can be obligated under the budget authority
available. Then, when RD assembles end-of-year financial statements
and subsequent budget submissions, it annually reestimates the credit
subsidy cost of each cohort year to include actual performance of
individual loan cohorts, expected changes in future loan performance,
and changes to the estimation methodology. Four cost components
comprise the credit subsidy estimate for the FLH program: defaults,
net of recoveries; interest; fees; and a component labeled "all
other," which includes prepayments. Appendix V provides additional
information on the requirement for agencies to develop credit subsidy
rate estimates and reestimates, and information on RD's processes for
doing so.
During the fiscal year 2010 budget formulation process, in January
2009, RD estimated the credit subsidy rate for the 2010 cohort of the
FLH program at 36.14 percent, comprising primarily of interest and
default cost components. With a credit subsidy rate of 36.14 percent,
for every $100 of direct loans obligated RD estimated that it would
incur a cost of $36.14. However, in October 2010, when RD reestimated
the credit subsidy rate for fiscal year 2010, the original estimate
was shown to be too high and the agency decreased the rate to 25.83
percentage points--or more than 10 percentage points lower than the
original credit subsidy rate. This credit subsidy reestimate indicated
that the cost of loans obligated in that year decreased by about $3
million.[Footnote 43] According to RD, when it developed the 2010
credit subsidy reestimate it used actual 2010 program data and updated
economic assumptions, as required by OMB. It also changed its
estimation methodology. RD noted that these changes to the credit
subsidy estimation model may also have affected its reestimated rate.
However, we found that the primary driver of the change from the
fiscal year 2010 credit subsidy estimate to the reestimate was the
default cost component and, more specifically, how this cost component
was calculated. Specifically, when the fiscal year 2010 budget
formulation credit subsidy estimate was calculated, the estimated
default cost component was inflated by a prepayment estimate. That is,
RD overstated the estimated default cost component to reflect the
effect of prepayment. RD, includes the impact of prepayment estimates
in the all other cost component. However, RD also included its
prepayment estimate to calculate its overall default cost component in
2010. To determine the impact of including the prepayment estimate in
both cost components, we recalculated the subsidy rate by removing the
prepayment estimate from the default cost component. We found that the
inclusion of the prepayment estimate in the default assumptions
overstated the cost of the program by about $3 million.[Footnote 44]
When we removed the prepayment estimate from the default cost
component, the credit subsidy rate was 25.25 percent. According to our
analysis, at a credit subsidy rate of 25.25 percent another $11.8
million dollars would have been available in low-interest financing
for program applicants for the fiscal year 2010 FLH loan program
cohort.
RD's supporting documentation for the fiscal year 2010 credit subsidy
rate calculations showed that the prepayment estimate was included in
the default cost component, but the supporting documentation did not
describe the rationale for the inclusion. In response to our questions
about the inclusion, an USDA official said that the inclusion of the
prepayment estimate in the default cost component was suggested by a
USDA Office of Inspector General official to help adjust future cash
flows for the impact of partial prepayments. However, this does not
explain why partial prepayments would significantly increase default
costs. For the 2011 budget formulation process, RD changed its
methodology for calculating the credit subsidy rate to consider only
the prepayment estimate in the "all other" component as opposed to
also including this factor in the default cost component.
A more thorough review comparing the key assumptions used in the
credit subsidy rate calculations with actual program characteristics
may have helped identify the overstated costs earlier, because the
default assumptions used in the cash flow model did not reflect actual
program performance. For example, including the prepayment estimate in
the default cost component resulted in a predicted borrower default
rate of 59 percent before recoveries for the 2010 cohort.[Footnote 45]
However, this rate is inconsistent with historical actual default
performance data. Specifically, for the fiscal years 1992 through 2009
cohorts, actual defaults have been less than 1 percent.
According to Federal Accounting Standards Advisory Board (FASAB)
guidance, preparing reliable and timely direct loan subsidy estimates
must be a joint effort between the budget, accounting, and program
offices at each agency, and these offices should coordinate all key
assumptions used.[Footnote 46] The FASAB guidance also directs
agencies to perform a trend analysis of the credit subsidy cost
components, including interest, defaults, and fees, and investigate or
explain any unusual fluctuations that are identified. RD documentation
shows that the default cost component decreased from $6 million to $72
thousand in the 2010 credit subsidy rate reestimate but there is no
explanation of this change. Agency officials' responses to our
questioning about the high default rate suggests that RD had not have
been closely monitoring unusual fluctuations in credit subsidy cost
components, which in 2010 resulted in less money being available for
low-interest financing for program applicants.
More Than $184 Million in FLH Loan and Grant Obligations Were
Unliquidated in 2010 and No Guidelines for De-obligation Were in Force:
RHS had more than $184 million in loans and grant obligations for the
FLH program that were unliquidated--that is unused--as of September
2010 and the balance of unliquidated obligations has been over $125
million for the past 6 years.[Footnote 47] As shown in figure 8, about
$184 million in loans and grants obligated were unliquidated at the
end of fiscal year 2010 (about $71 million of this was in grant
funding and $112 million was in direct loans). About $24 million of
the loans and grants were obligated at least 5 years prior and the
oldest unliquidated obligations dated to fiscal year 2001.[Footnote 48]
Figure 8: Loans and Grants Obligated but Unliquidated, by Age, as of
September 30, 2010:
[Refer to PDF for image: stacked vertical bar graph]
Obligations in millions:
Years: 0 to 0.9;
Grants: $13 million;
Direct loans: $10 million.
Years: 1 to 1.9;
Grants: $29 million;
Direct loans: $14 million.
Years: 2 to 2.9;
Grants: $27 million;
Direct loans: $13 million.
Years: 3 to 3.9;
Grants: $19 million;
Direct loans: $11 million.
Years: 4 to 4.9;
Grants: $8 million;
Direct loans: $16 million.
Years: 5 to 5.9;
Grants: $1 million;
Direct loans: $5 million.
Years: 6 to 6.9;
Grants: $1 million;
Direct loans: $1 million.
Years: 7 to 7.9;
Grants: $ million;
Direct loans: $0 million.
Years: 8 to 8.9;
Grants: $4 million;
Direct loans: $2.9 million.
Years: 9 or more;
Grants: $0;
Direct loans: $1 million.
Total obligations 5 or more years old: $24,175,785.
Source: GAO analysis of MFIS data.
[End of figure]
Reasons may exist for loans and grants obligated when the FLH award
was made to remain unliquidated for extended periods until
construction or rehabilitation begins. RHS officials described several
reasons why a property could experience long periods of inactivity
between loan or grant obligation and liquidation. Officials explained
that developers can experience difficulties securing funding from
multiple federal and state sources. For example, RHS officials in
California told us that many developers have been financing FLH
properties with multiple funding sources--a mix of federal, state, and
private loans, credits, and grants. Many developers also obtain
federal low-income housing tax credits to complement FLH program
funding during each funding cycle, according to multiple developers
with whom we spoke. Some developers with whom we spoke noted that if
they do not qualify for tax credits in the first year of applying,
they may re-apply in a subsequent year, thus extending the time
between agency obligation and liquidation of the funds.
According to one agency official, the unliquidated balances also are
attributable to existing FLH properties intended for rehabilitation.
Funds may remain unliquidated when a property is unable to secure the
full amount of financing necessary to complete all needed
improvements. The official cited the example of a property in Florida
that received about $9 million in loan and grant obligations over 3
years for rehabilitation. According to the official, the property had
about $60 million in rehabilitation needs. The official noted that the
entire $9 million remained unliquidated as of June 2010. RHS plans to
end the practice of using the FLH program as an ongoing source for
rehabilitation funding, and anticipated that the balance of
unliquidated funds should decline as funding from the agency's Multi-
Family Housing Revitalization Demonstration Program was made available
for rehabilitation work on older properties.[Footnote 49]
Although about $24 million in FLH funds have remained unliquidated for
more than 5 years there are no guidelines on de-obligation time
frames. RHS issued guidance in 2008 on setting obligation expiration
dates after an internal review identified the liquidation of obligated
FLH funds as a weakness. The review found that all states visited
during the course of the review had loans or grants that were
obligated and not closed after 2 to 5 years. The review also found
that funds obligated and not closed after 5 years were likely to be
insufficient to complete the property, due to increased construction
costs, thereby increasing the likelihood that additional agency funds
would be needed. The review recommended that RHS issue guidance
requesting that all states with loans or grants obligated and not
closed after 5 years de-obligate those funds to allow for more
immediate program use.[Footnote 50] To address the weakness identified
in the review, RHS issued program guidance in the form of an
unnumbered letter in 2008 to establish de-obligation time frames for
FLH loans and grants.[Footnote 51] In the unnumbered letter, RHS
stipulated that obligations for off-farm housing should expire 5 years
from the date of obligation and on-farm housing 2 years from the date
of obligation.[Footnote 52] The unnumbered letter expired on March 31,
2009, and officials from state and local offices that we visited did
not appear to be familiar with it. In particular, none of the RHS
staff in five state offices we visited referenced the unnumbered
letter or guidance for obligation expiration dates when we asked about
their processes for ensuring that obligations do not remain
outstanding for more than 5 years. Although no current guidance or
requirement setting forth timelines to de-obligate unliquidated loans
and grants exists, RHS officials stated that the guidance set forth in
the expired unnumbered letter are still warranted.
Conclusions:
As the only federally assisted source of housing for farmworkers, the
FLH program plays an important role in constructing and rehabilitating
housing for residents that support the national agricultural sector.
However, in several areas RHS could strengthen its management
processes to more effectively implement and oversee the FLH program.
For instance, RHS performance information indicates a decrease in
performance grades among borrowers in recent years. However, low
performance grades can stem not only from serious safety and soundness
concerns but also from late paperwork. The grade alone does not
indicate the severity or type of the problems and on a findings report
more than a third of open MFIS entries on the physical condition of
properties do not contain additional, descriptive information that
could do so. Agency managers require readily usable information. RHS
could improve both the functionality and content of its information
systems and reporting by considering methods to improve the
specificity its performance grades and comments related to performance
findings in MFIS. By undertaking such actions, RHS managers could more
readily use performance information to plan and conduct its oversight.
RHS not only faces some constraints in effectively monitoring FLH
performance, but also in enforcing compliance. Due, in part, RHS's use
of only mild or severe penalties for noncompliance, some findings and
violations leveled against borrowers remain unresolved for extended
periods. That is, the enforcement actions RHS uses often may not be
applicable or effective against the range of noncompliance that occurs
because they are either too mild to be effective or too severe to be
invoked. We previously have reported that penalties in federal award
programs should correspond to performance. By putting in place more
tailored enforcement actions, such as the civil money penalty provided
for in program regulations for which RHS has not developed procedures
to use, RHS could appropriately and more effectively ensure that FLH
program requirements are met.
RHS must ensure that borrowers (or their management agents) verify
that tenants meet eligibility requirements. However, RHS did not
consistently do so because its staff could not access or were unaware
of electronic third-party verification systems for tenant legal
residency or income documentation. For example, some local offices
used the SAVE program to verify residency, but others were unaware of
it. To verify income, some RHS offices have access to state wage
matching systems, while others do not. We previously recommended that
Congress consider giving RHS access to the National Directory of New
Hires. RHS has stated that such access would help with income
verification and that access to SAVE would help verify residency
status. By consistently applying oversight methods--and being able to
leverage the information in third-party verification systems--RHS can
help assure that only eligible tenants reside in FLH units.
RHS's financial management and cost estimation of the FLH program also
needs attention because weaknesses could impede achievement of a key
program goal--to increase housing for farmworkers. For example, RHS
must prepare reliable estimates of program costs to ensure the
efficient use of appropriated funds. However, for fiscal year 2010, we
found that the agency overestimated the cost of the FLH program by $3
million. As a result, according to our analysis, another $11.8 million
could have been available to loan applicants. Reasons for the
overestimate include a change to the credit subsidy model and apparent
inattention to unusual fluctuations in credit subsidy cost components.
A more thorough investigation of unusual fluctuations in key
assumptions, namely the predicted default rates, used in the credit
subsidy model could help ensure that these assumptions more closely
reflect portfolio performance and would allow RHS to optimize funding
use. Additionally, RHS had more than $184 million in unliquidated
obligations for the FLH program as of September 2010. RHS state and
local offices must report and certify the ongoing need for
unliquidated obligations semiannually, but no agency guidance to state
and local offices on when to recapture these funds is currently in
place. Although there may be legitimate reasons why it could take
multiple years to liquidate FLH obligations, the lack of agency
guidance makes it difficult for management to ensure that limited
program funds are timely and efficiently used. Issuing guidance to all
RHS staff in the state and local offices about how and when to
recapture program funds would help ensure greater utilization of these
limited funds for the development and rehabilitation of farm labor
housing.
Finally, RHS has an opportunity to leverage existing data to
strengthen program management. RHS uses application data and market
studies to manage individual applications and properties, but it does
not analyze these data sources to identify trends or patterns in
demand over time in local areas or states. By utilizing existing data
sources for these purposes, RHS could better estimate the extent of
demand for farm labor housing and funding and more effectively target
funds to areas of greatest need.
Recommendations for Executive Action:
We recommend that the Secretary of Agriculture direct the
Administrator of RHS to take the following seven actions:
* To better determine and track compliance across the portfolio, RHS
should implement mechanisms to improve the specificity and timely
reporting of its compliance review information--such as findings data
and performance grade data in MFIS.
* To help resolve identified borrower noncompliance in a timely
manner, RHS should implement enforcement mechanisms that can be
tailored to the severity of the borrower noncompliance, such as the
civil money penalty enforcement provision in its program regulations.
* To better ensure that requirements for tenant eligibility are met
across the FLH portfolio, RHS should (1) require its loan servicers to
use the Systematic Alien Verification and Entitlements (SAVE) program
administered by the Department of Homeland Security to verify tenant's
residency status during supervisory reviews; and (2) seek legislative
authority to gain access to the Department of Health and Human
Services' National Directory of New Hires and make this information
available to RHS so that they can assess the accuracy of tenant income
documentation during supervisory reviews and other oversight
activities.
* To help ensure that reliable program costs are estimated in future
years, program officials should, on an annual basis, work with budget
staff to investigate key assumptions, including comparing these
assumptions to actual program performance, in order to explain unusual
fluctuations impacting the credit subsidy rate used in budget
formulation.
* To better ensure that FLH funds obligated but unliquidated are
efficiently used to provide farm labor housing, RHS should issue
guidance on obligation expiration dates and make all RHS staff in the
state and local offices aware of the guidance and how to implement it.
* RHS should also better utilize available data on demand for the FLH
program--such as systematically reviewing local market analyses,
further analyzing occupancy data on a statewide, regional, or national
level, and retaining and analyzing application information--to help
target available funding to areas of greatest need.
Agency Comments and Our Evaluation:
We provided a draft of this report to USDA for review and comment.
USDA's Under Secretary for Rural Development provided written comments
that are discussed below and presented in appendix VI.
USDA generally agreed with all of our recommendations, noting that the
recommendations will help make the FLH program better. In its letter,
however, the agency provided some additional information and disagreed
with certain statements in the report. For example, USDA stated it
disagreed with a comment from "an expert" who noted that the FLH
program does not receive appropriate care or attention from the
national office. However, as noted in the report, this statement
reflected the opinions of multiple experts who participated in our 1-
day discussion on demand for farm labor housing and the extent to
which the FLH program is positioned to meet demand. In addition, as
USDA commented in its letter, we noted in the report that the national
RHS office has specialists, team leaders, and a financial and loan
analyst who work on multifamily housing loans and grants, including
FLH loans and grants, across multiple states.
Although USDA also generally agreed with our recommendation to improve
the specificity of its compliance review information, the agency
offered additional explanation for observed decreases in performance
scores (grades) in the FLH portfolio. The agency noted that in 2008 it
automated performance scoring, a change that, according to USDA,
increased the number of low grades by identifying previously
unaddressed open findings. We also reported this change to the
performance classification system as a potential cause for the
observed increase in low grades. However, other deficiencies in FLH
program management processes drove our recommendation to improve the
specificity and timely reporting of compliance review information. For
instance, USDA notes that the performance classification system
monitors the quality of its FLH properties as determined by a
property's physical, financial, and management operations. However, a
grade in the classification system of A through D does not yield
information on whether the problem is related to physical, financial,
or management operations. In the report, we also discuss the FLH
performance classification system and a similar system that the HUD
uses and state that the FLH system differs from HUD's in that it does
not specify the performance problem causing the grade. USDA commented
that further examination of its full electronic data system--the MFIS--
would yield specific information on these performance areas. We agree
that additional information is available in MFIS. But, the
classification system itself lacks specificity and does not readily
provide agency officials with information necessary to assess the
causes of low grades. Furthermore, we noted deficiencies in MFIS
findings information--as reflected in the finding reports generated
from MFIS, which often do not detail the type and the severity of the
findings. In the report, we also discuss reported deficiencies in the
timely submission of compliance information into the database that
underlies the performance classification system, which could impact
the accuracy of the information available to FLH management.
Therefore, we believe our findings and recommendation on improving the
program's compliance review information remain valid--and USDA also
commented that more detail on specific physical condition issues from
its performance management processes would be beneficial to all users.
In its letter, USDA acknowledged that the default cost component in
the 2010 estimate of the credit subsidy rate was overstated, as
described in this report. However, the agency notes that subsidy
estimates are routinely revised and identifies several factors that
may have contributed to the downward reestimate. For example,
according to USDA, the original estimate was based on an "interim data
solution" until a new model could be developed. According to USDA,
when it developed the reestimate, the agency changed its estimation
methodology, used actual 2010 program data, and updated economic
assumptions, as required by OMB. We discussed these changes with the
Deputy Director of Rural Development's Budget Division while the draft
was with USDA for comment and made technical changes to the report as
a result. However, during this meeting, the Deputy Director also noted
that, while the additional factors may have affected the downward
credit subsidy reestimate rate, these factors likely had a lesser
influence on the overall reestimate than did the corrected default
cost component--which is what our analysis of the credit subsidy model
and supporting materials indicated was the primary cause for downward
adjustment. In the letter, the Undersecretary further describes recent
efforts to review and revise credit subsidy rate assumption data and
calculations, and agrees that increased cooperation among program,
financial, and budget staff would improve the FLH program.
We are sending copies of this report to interested congressional
committees and the Secretary of Agriculture. In addition, the report
will be available at no charge on GAO's Web site at [hyperlink,
http://www.gao.gov].
If you or your staffs have any questions about this report, please
contact me at (202) 512-8678 or clowersa@gao.gov. Contact points for
our Offices of Congressional Relations and Public Affairs are listed
on the last page of this report. GAO staff who made major
contributions to this report are listed in appendix VII.
Signed by:
A. Nicole Clowers, Acting Director:
Financial Markets and Community Investment:
[End of section]
Appendix I: Objectives, Scope, and Methodology:
The objectives of this report were to examine: (1) how demand for the
Farm Labor Housing (FLH) program has changed over time, key factors
that influence demand for such housing, and whether the program model
addresses demand; (2) the extent to which Rural Housing Service (RHS)
management processes assure farmworkers access to decent and safe
housing and compliance with program requirements; and (3) the
financial status of properties in the FLH portfolio and the extent to
which RHS processes ensure the sound financial management of the
program.
To address the first objective, we contracted with The National
Academies to convene a diverse group of experts, to discuss trends in
demand for farm labor housing, factors that influence demand, and the
extent to which the FLH program is positioned to meet demand. To
select the experts, we and The National Academies identified 12
individuals for the group through interviews on the basis of their
extensive knowledge of the FLH program and trends in demand for farm
labor housing and to obtain regional diversity and a range of types of
organizations.[Footnote 53] While we attempted to select experts who
provide a range of experience and views, the group of experts selected
may not represent all perspectives on demand for FLH, including that
of RHS, as no RHS staff were invited to the group discussion in order
to encourage openness among other participants who use FLH program
funds.
The final group of 11 experts who convened at The National Academies
in Washington, D.C., on October 13, 2010, represented housing
developers; borrowers of FLH funds; researchers who conduct research
or are involved in the study of issues related to farmworker housing;
staff of nonprofit organizations who are knowledgeable about and
advocate for issues related to farmworker housing; and USDA
contractors who provide technical assistance to FLH developers. A
contractor recorded and transcribed the meeting to ensure that we had
accurately captured the group's statements. The day was divided into
three discussion sessions which were structured to focus on the
aspects of demand noted above. A moderator and an assistant moderator
helped guide the discussions in each session. To help elicit
additional information relevant to our three-part focus on demand, we
administered a questionnaire to the experts to collect their responses
on factors that most influence demand for units and for FLH funds, the
extent to which the FLH program meets demand for units and for FLH
funds, and the extent to which the FLH program could be changed to
better meet demand. In addition, to systematically analyze information
experts suggested for changes to the FLH program, we conducted a
content coding review of the transcripts by coding relevant quotes and
grouping them into categories. An analyst identified, coded, and
entered relevant text into a spreadsheet, while another verified these
entries.
To balance and augment the perspectives of our group of experts, we
also reviewed relevant studies and reports to identify research
studies that examined how demand for farm labor housing in general and
the FLH program in particular had changed over time, as well as key
factors that influenced these changes and influence demand for such
housing. We used various Internet search databases to identify
studies, including ProQuest, ABI Inform, SIRS Researcher, and
Agricola. We sought to identify additional studies by consulting with
government officials, researchers, and staff from nonprofit
organizations throughout the course of research and by reviewing the
bibliographies of the previously identified studies. As part of this
effort, we also reviewed documentation of national FLH stakeholder
meetings convened by USDA in 2008 and 2009. The studies and reports we
reviewed primarily focused on specific states or regions, and also
indicated that demand may vary across states and agricultural regions.
Many of the studies noted limitations in the data available on
farmworkers. We present additional information about federal data
sources on farmworkers in appendix III.
To address the second objective, we conducted site visits to RHS local
and state offices, and FLH properties to determine the extent to which
RHS management processes assure farmworkers access to decent and safe
housing and assure compliance with program requirements. To address
this objective we also conducted tenant file reviews, analyzed
electronic program data, reviewed Multifamily Housing program
regulations and handbooks, and interviewed program staff at all levels
as well as program borrowers.
To obtain more in-depth information about the oversight of the FLH
program in individual states, and the servicing of FLH properties, we
completed multi-day site visits to five states including California,
Florida, Michigan, New York, and Texas that each included interviews
with the state office and two local offices with the exception of
Michigan. In Michigan we met with only one local office because the
state's on-farm labor housing program was serviced by the state
office, which was not the case in the other four states. The five site
visit states were selected to obtain regional diversity and a range in
type (on-farm and off-farm) and number of properties and units per
state (see table 3). We completed walkthroughs of 20 properties (4
properties in each of the five states), which included a tour and
inspection of the interior and exterior of the properties and
conversations with the borrower or property manager, and a tenant file
review for each off-farm property.[Footnote 54] We selected properties
to include both property types (on-farm and off-farm) and a range of
property sizes and performance grades. An expert in facilities and
construction management accompanied us to Texas. On-farm borrowers are
not required to maintain tenant files for each unit. Prior to each
site visit we received a list of tenants from each off-farm property
scheduled for a walkthrough, randomly selected 10 tenant files per
property for 10 of the 13 off-farm properties visited, and requested
copies of the files, which were sent to us in hard copy or
electronically prior to the site visit.[Footnote 55] We developed a
data collection instrument to review and summarize contents of each
tenant file, including documents to assess income and residency
eligibility. However, the contents of these tenant files are not
necessarily representative of the contents of all other FLH tenant
files.
Table 3: 2010 FLH Program Characteristics for Site Visit States:
State: California;
U.S. region: West;
Total properties: 95;
On-farm properties: 4;
Off-farm properties: 91;
Total units: 5,490;
On-farm units: 16;
Off-farm units: 5,474.
State: Florida;
U.S. region: South;
Total properties: 40;
On-farm properties: 0;
Off-farm properties: 40;
Total units: 4,547;
On-farm units: 0;
Off-farm units: 4,547.
State: Michigan;
U.S. region: Midwest;
Total properties: 85;
On-farm properties: 82;
Off-farm properties: 2;
Total units: 353;
On-farm units: 309;
Off-farm units: 44.
State: New York;
U.S. region: Northeast;
Total properties: 20;
On-farm properties: 17;
Off-farm properties: 2;
Total units: 91;
On-farm units: 67;
Off-farm units: 24.
State: Texas;
U.S. region: South;
Total properties: 19;
On-farm properties: 0;
Off-farm properties: 19;
Total units: 1,320;
On-farm units: 0;
Off-farm units: 1,320.
Source: GAO analysis of MFIS data.
Note: According to MFIS data, two properties, one in Michigan and one
in New York, are of an unknown type and are not listed under on-farm
or off-farm properties in those states.
[End of table]
To analyze portfolio-wide data on compliance with FLH program
requirements, we obtained extracts from the agency's Multi-Family
Housing Information System (MFIS). To assess the performance of FLH
properties over time, we reviewed performance classification data in
MFIS from fiscal year-end 2006 through fiscal year-end 2010. To assess
the types of findings assigned to FLH properties over time, we
reviewed the number of open and resolved findings by type,
specifically financial and physical finding. We assessed the
reliability of these data by (1) performing electronic testing, (2)
reviewing existing information about the data and the system that
produced them, and (3) interviewing agency officials knowledgeable
about the data and related management controls. Based on this
assessment, we determined the data to be sufficiently reliable for the
purposes of this report.
We also consulted our Standards for Internal Control in the Federal
Government to review control activities that apply to RHS's
performance management and servicing activities. We interviewed the
RHS national office to determine its role in monitoring the FLH
program and state and local offices with FLH oversight and servicing
responsibilities. Lastly, we interviewed nonprofit organizations in
each state we visited that were current or past FLH borrowers, had
received funds from the FLH program to provide technical assistance to
other developers, or provided services to farmworkers to help them
find safe and decent housing.
To address the third objective, we analyzed delinquency, reserve
account, and financial findings data from extracts of RHS's MFIS to
assess the financial status of properties in the FLH portfolio. To
identify program compliance and assess overall program needs, MFIS
contains information on budgets, operating costs, non-financial
defaults, insurance, reserve account funding, management plans,
supervisory visits, taxes, and tenant changes. We also obtained and
analyzed electronic program data from RHS's Automated Multi-Family
Housing Accounting System (AMAS), which contains accounting
information that is used to identify delinquencies and financially
delinquent borrowers. For both AMAS and MFIS, we received data that
were current as of the end of fiscal 2010. According to RHS's
officials these data systems contain only the last 3 years of data for
each property. RHS underwriting and servicing processes include
financial analyses of applicants, annual budget reviews, and the
setting of reserve fund requirements.[Footnote 56] We assessed the
reliability of these data by (1) performing electronic testing, (2)
reviewing existing information about the data and the system that
produced them, and (3) interviewing agency officials knowledgeable
about the data. Based on this assessment, we determined the data to be
sufficiently reliable for the purposes of this report. We examined
documents and reports, such as financial statement audits, which RHS
officials use to monitor the performance of the loan portfolio. We
also reviewed agency handbooks that contained guidance on asset
management and project servicing, and interviewed headquarters, state,
and local staff knowledgeable about financial underwriting and
servicing efforts.
To specifically assess the extent to which RHS processes ensure the
sound financial management of the program, we studied the credit
subsidy estimation process and RHS's management of its balance of
unliquidated loan and grant obligations. For our credit subsidy work,
we examined the fiscal year 2010 and 2011 credit subsidy cash flow
models for the FLH program, reestimate data, and supporting
documentation. To verify the validity of the fiscal year 2010 model,
we entered data that RHS provided into OMB's Credit Subsidy Calculator
2 and confirmed that the resulting estimates matched the figures
provided in federal budget documents. Based on these results, we
determined that the information was sufficiently reliable for our
analysis. We also interviewed program and budget staff about the
default assumptions used in and recent changes to the model. For our
examination of program obligations, we examined end-of-fiscal-year
unliquidated obligations reports for 2003 through 2010 and obligation
data from AMAS. We compared agency documents with obligation data in
federal budget appendixes and confirmed that these figures were
sufficiently reliable for our analysis. We also interviewed RHS staff
from the national, state, and local offices about their management of
unliquidated loan and grant obligations and reasons for extended
obligation periods.
We conducted this performance audit from March 2010 to March 2011 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.
[End of section]
Appendix II: Experts Convened by GAO with the Assistance of The
National Academies on Demand for Farm Labor Housing:
This appendix provides the names and affiliation of individuals who
participated in our 1-day expert group discussion convened by GAO,
with the assistance of The National Academies on October 13, 2010.
The following experts discussed topics related to the demand for farm
labor housing:
* Gideon Anders, Senior Attorney, National Housing Law Project, San
Francisco, Calif.
* Pamela Borton, President, Southwind Management Services, Inc.,
Clearwater, Fla.
* Peter Carey, President and Chief Executive Officer, Self-Help
Enterprises, Visalia, Calif.
* Dennis Harris, Housing Director, Telamon Corporation, Raleigh, N.C.
* Moises Loza, Executive Director, Housing Assistance Council,
Washington, D.C.
* Joe Myer, Executive Director, National Council on Agricultural Life
and Labor Research Fund, Inc., Dover, Del.
* Brien Thane, Executive Director, Washington State Farmworker
Housing, Seattle, Wash.
* Kathy Tyler, Director of Housing, Motivation Education and Training,
Inc., New Caney, Tex.
* Don Villarejo, Founder and Director Emeritus, California Institute
for Rural Studies, Davis, Calif.
* Rob Williams, Director, Florida Legal Services, Inc., Tallahassee,
Fla.
* John Wiltse, Senior Operations Director, PathStone Corporation,
Rochester, N.Y.
[End of section]
Appendix III: Federal Data on Farmworkers:
This appendix provides information about nationwide, federal data
available on farmworker populations. Both the terms farmworker and
farm laborer are used by researchers, government entities, and
nonprofit organization in reference to individuals who work in
agriculture, aquaculture, and processing activities. The U.S. Census
Bureau considers migrant and seasonal farmworkers to be a "hard to
count" population for reasons such as language barriers, mobility,
unconventional housing arrangements (such as dormitories, cabins, or
trailers in labor camps), and mistrust of formal government efforts to
collect data.
Available sources of federal data on farmworker populations include,
but are not limited to:
* The U.S. Department of Agriculture's (USDA) National Agricultural
Statistics Service (NASS) conducts the Farm Labor Survey (FLS), which
provides quarterly estimates of the number of hired farmworkers, the
percentage of workers who are migrants, and average weekly hours
worked. Four times a year, USDA surveys about 14,500 farms in all
states except Alaska and provides total numbers of farmworkers
obtained from farm establishments. The FLS also provides average wage
rates for hired workers by type (field, livestock, supervisor, and
other) for 16 states and 15 regions. Its data on hired farmworkers
refer to all types of workers on the farm, including bookkeepers,
secretaries, and mechanics, as well as persons who pay themselves
regular salaries, such as partners or corporate shareholders.
* NASS also conducts the Census of Agriculture on nationwide
farmworker employment data every 5 years with the last survey
conducted in 2007. The Census of Agriculture offers comprehensive
geographic coverage of hired and contract farm labor use as measured
by labor expenditures, and currently is the only national level data
source that offers consistent farm labor information at the county and
state level. The Census of Agriculture also reports the number of
hired workers, separated by whether they worked less than 150 days or
150 days or more. As with the FLS, the data refer to all hired workers
on the farm, including those not generally considered farmworkers.
* The Department of Labor (Labor) sponsors the National Agricultural
Workers Survey (NAWS), which is an employment-based, random sample
survey that collects detailed information on individual farmworkers,
including their legal residency status. NAWS data are limited to hired
crop farmworkers and excludes hired livestock farmworkers and
processing workers. NAWS collects data from personal interviews of
between 1,518 and 3,600 randomly selected crop field workers.
According to a 2008 Economic Research Service report, NAWS data are
collected at the worksite and, therefore, are more likely to capture
persons who have less stable living arrangements and who tend to avoid
participation in more formal data collection efforts.
* The Bureau of the Census for the Bureau of Labor Statistics conducts
the Current Population Survey (CPS), which provides employment and
demographic information on the entire U.S. workforce. It is conducted
each month using a probability sample of households over 16 months and
is designed to represent the U.S. civilian non-institutional
population. Since the survey is conducted for the same households over
an extended period, it may undercount unauthorized and foreign-born
persons who migrate frequently and are reluctant to participate in
formal government questionnaires.
Although estimates of the domestic farm labor population have varied
widely depending on the survey, according to USDA's NASS, the United
States had an average of 1,041,250 hired farmworkers in 2010.[Footnote
57] These data show that the total number of farmworkers has remained
relatively stable over the past decade.
However, available nationwide data sources have limitations,
especially for determining characteristics related to tenant
eligibility in USDA's Farm Labor Housing (FLH) program, such as
residency status and type of farmworker. No single source of data is
available to provide all the necessary detail for understanding farm
labor supply, demand, and characteristics that relate to eligibility
criteria for the FLH program. The data sets mentioned above provide
information for different subgroups within the entire population of
persons employed in agriculture and (1) may exclude a portion of FLH-
eligible program participants such as processing workers, (2) may
include a population not eligible for the FLH program, or (3) may not
collect information on characteristics that determine program
eligibility such as residency status. For example, the Census of
Agriculture and FLS provide numbers of farmworkers nationwide;
however, they lack information on residency or housing status, and the
data do not include processing workers. NAWS collects information on
residency status, but excludes farmworkers who work on ranches. The
FLS defines hired workers on farms to include bookkeepers,
secretaries, and mechanics, as well as persons who pay themselves
regular salaries, such as partners or corporate shareholders. This
population is not eligible to reside in FLH program units.
[End of section]
Appendix IV: Age of the FLH Property Portfolio and Condition of FLH
Properties We Visited:
The overall FLH program portfolio is aging, with 46 percent of the
properties more than 20 years old according to U.S. Department of
Agriculture (USDA) data (see figure 9). Nearly three quarters, 73
percent, of properties were more than 10 years old. States with the
highest number of units also have high proportions of aging
properties. In Texas, with the third highest number of units, 79
percent of FLH program properties are more than 20 years old. In
Florida, 35 percent of the properties is more than 20 years old. In
California, 39 percent of properties are more than 20 years old.
Properties in California and Florida have received revitalization
funds in recent years. In 2009, revitalization funds became available
for FLH properties through Multi-Family Housing Revitalization
Demonstration Program administered by the RHS. In 2010, RHS obligated
$2.4 million in funds to repair and rehabilitate three FLH properties.
Figure 9: Age of Farm Labor Housing Program Portfolio, as of September
30, 2010:
[Refer to PDF for image: vertical bar graph]
Age of properties (in years): 0 to 5;
On farm (449): 71;
Off farm (272): 54;
Total projects(731): 125
Age of properties (in years): 6 to 10;
On farm (449): 30;
Off farm (272): 45;
Total projects(731): 75
Age of properties (in years): 11 to 20;
On farm (449): 120;
Off farm (272): 72;
Total projects(731): 192
Age of properties (in years): 21 to 30;
On farm (449): 209;
Off farm (272): 73;
Total projects(731): 291
Age of properties (in years): More than 30;
On farm (449): 19;
Off farm (272): 28;
Total projects(731): 48
Source: GAO analysis of MFIS data.
[End of figure]
We completed site visits to five states that included walkthroughs of
four properties in each state. A brief description of some of the
findings from our site visits to assess FLH properties in five states
are as follows:
* California: All four properties visited had low levels of disrepair
with few visible minor deficiencies and no major deficiencies. We
noticed some vermin infestation in an unoccupied, seasonal property.
Some recently developed properties met energy-efficient standards. For
example, one property in California exceeds California Title 24 energy
standards according to the borrower. This property includes energy-
efficient appliances, solar reflective roof materials that decrease
heat absorption, on-demand water heaters, and artificial turf (see
figure 10).
Figure 10: FLH Property in California with Reflective Roof Materials,
Energy-efficient Appliances, On-demand Water Heater, and Artificial
Turf:
[Refer to PDF for image: 3 photographs]
Source: GAO.
[End of figure]
* Florida: Two properties that we visited had been newly developed and
had low levels of disrepair. (See figure 11.) However, one large
property with more than 700 units had not undergone rehabilitation
since 1968 and exhibited deficiencies on the exterior and interior of
the units visited. For example, windows in some units were blocked or
replaced with wooden boards, and, in some cases, kitchen appliances,
including ovens and refrigerators, were not provided by the landlord
and had to be provided by farmworkers. None of the units had central
air conditioning and some of the kitchen appliances were in need of
repair (see figure 12).
Figure 11: Newly Developed Florida Properties with Low Levels of
Disrepair:
[Refer to PDF for image: 2 photographs]
Source: GAO.
[End of figure]
Figure 12: Windows Replaced with Wooden Boards and a Kitchen in Need
of Repair at an Older Florida FLH Property:
[Refer to PDF for image: 2 photographs]
Source: GAO.
[End of figure]
* Michigan: Two on-farm properties that we visited had a number of
deficiencies such as rotting and unstable porch steps, water damage to
the exterior, and an open crawl space (see figure 13). Two other
properties we visited in Michigan were well maintained, with few
visible minor deficiencies.
Figure 13: FLH Unit in Michigan with Water Damage to the Exterior:
[Refer to PDF for image: photograph]
Photograph highlights water damage and moss.
Source: GAO.
[End of figure]
* New York: The properties we visited in New York exhibited both high
and low levels of disrepair. Tenant living standards partly
contributed to the observed deficiencies. For example, grease covered
the surfaces in one kitchen we observed. However, we also observed
deficiencies, such a window covered by a board and severely damaged
carpeting, which the owner is required to address (see figure 14).
Figure 14: Window Covered by a Board in an FLH Unit in New York:
[Refer to PDF for image: photograph]
Source: GAO.
[End of figure]
* Texas: The FLH properties we visited in Texas exhibited low to
medium levels of disrepair. Some units had newer sinks, countertops,
and ovens, while some units had kitchen appliances in need of repair.
Three of the properties have received recent or ongoing rehabilitation
(see figure 15).
Figure 15: FLH Unit Undergoing Rehabilitation in Texas:
[Refer to PDF for image: 2 photographs]
Source: GAO.
[End of figure]
[End of section]
Appendix V: FLH Credit Subsidy Rate Calculation:
Under the Federal Credit Reform Act of 1990 (FCRA), USDA and other
federal agencies must estimate the net lifetime cost--known as credit
subsidy cost--of their direct loan programs and include the costs to
the government in their annual budgets. Credit subsidy cost represents
the net present value of expected lifetime cash flows, excluding
administrative costs. Generally, agencies must produce annual updates
of their credit subsidy cost estimates--known as reestimates--for each
cohort on the basis of information on actual performance and estimated
changes in future loan performance. Agencies may makes changes in
their estimation methodology, which can effect reestimates, and each
additional year provides more historical data on loan performance that
may influence future year estimates. Economic assumptions (such as
interest rates) also can change from year to year. The credit subsidy
cost is frequently presented as a credit subsidy rate. For example, RD
estimated that the loans obligated during 2010, would have a credit
subsidy rate of 36.14 percent meaning that for every $100 of direct
loans obligated, RD estimated that it would incur a cost of $36.14.
[Footnote 58] Agencies estimate four cost components that account for
total program costs: defaults, net of recoveries; interest; fees; and
all other, which includes an estimate of prepayments, both during the
budget formulation process and again when assembling year-end
financial statements. RD's fiscal year 2006 to 2011 estimated credit
subsidy rates and estimated subsidy rate components are shown in table
16.
Table 4: Estimated Credit Subsidy Rate for FLH Program:
Cohort year: 2006;
Subsidy rate components: Defaults, net of recoveries: 0;
Subsidy rate components: Interest: 44.9;
Subsidy rate components: All other: -0.3;
Total subsidy rate: 44.6.
Cohort year: 2007;
Subsidy rate components: Defaults, net of recoveries: 0.2;
Subsidy rate components: Interest: 45.5;
Subsidy rate components: All other: 2.2;
Total subsidy rate: 48.0.
Cohort year: 2008;
Subsidy rate components: Defaults, net of recoveries: 8.9;
Subsidy rate components: Interest: 44.5;
Subsidy rate components: All other: -10.1;
Total subsidy rate: 43.3.
Cohort year: 2009;
Subsidy rate components: Defaults, net of recoveries: 9.5;
Subsidy rate components: Interest: 41.0;
Subsidy rate components: All other: -8.4;
Total subsidy rate: 42.1.
Cohort year: 2010;
Subsidy rate components: Defaults, net of recoveries: 11.5;
Subsidy rate components: Interest: 25.5;
Subsidy rate components: All other: -0.8;
Total subsidy rate: 36.1.
Cohort year: 2011;
Subsidy rate components: Defaults, net of recoveries: 0.1;
Subsidy rate components: Interest: 39.1;
Subsidy rate components: All other: -0.8;
Total subsidy rate: 38.4.
Source: Federal budget credit supplements.
Note: The subsidy rate component of defaults net of recoveries
includes the estimated cost of defaults less recoveries of defaults.
The interest component reflects the cost associated with the interest
payments from the borrower based on the borrower interest rate of 1
percent and the interest cost to the government to provide the loans,
which for the 2010 cohort was estimated to be 2.92 percent. "All
other" includes the effect of prepayments, losses other than defaults,
and any forecasted subsidy reduction due to program fees.
[End of table]
[End of section]
Appendix VI: Comments from the U.S. Department of Agriculture:
USDA:
United States Department of Agriculture:
Rural Development:
Office of the Under Secretary:
1400 Independence Ave, SW:
Washington, DC 20250-0700:
[hyperlink, http://www.rurdev.usda.gov]
Committed to the future of rural communities.
"USDA is an equal opportunity provider, employer and lender."
To file a complaint of discrimination write:
USDA, Director, Office of Civil Rights, 1400 Independence Avenue, S.W.,
Washington, DC 20250-9410 or call (800) 795-3272 (voice) or (202) 720-
6382 (TDD).
March 15, 2011:
A. Nicole Clowers:
Acting Director:
Financial Markets and Community Investment:
United States Government Accountability Office:
441 G Street, NW:
Washington, DC 20548:
Dear Ms. Clowers:
Thank you for providing the Department of Agriculture (USDA) Rural
Development and the Rural Housing Service (RHS) with your Government
Accountability Office (GAO) draft report entitled, "Opportunities
Exist to Strengthen Farm Labor Housing Program Management and
Oversight," Report Number GA0-11-329. We appreciate the opportunity to
respond to GAO's comprehensive study of the Farm Labor Housing (FLH)
program, and the agency generally agrees with the report's
recommendations. For your consideration, USDA offers the following
comments to the draft report and requests that a copy of these
comments be included in your final report.
USDA's FLH program is vital to providing safe, decent, and affordable
housing for farm workers throughout America. Currently more than
13,000 seasonal and non-seasonal farm workers and their families live
in more than 730 on-farm and off-farm housing developments made
possible through USDA's Section 514 and Section 516 FLH loan and grant
programs.
USDA takes its management and oversight of the FLH program very
seriously, although it recognizes that there are opportunities for
improvement, and the recommendations provided by GAO will help make
the FLEA program better. However, the agency disagrees with the comment
"does of an expert cited by GAO that the program not receive
appropriate care or attention from the national office." As GAO notes
in the report, several members of the national office staff are
involved in FLH loan making, servicing, and oversight activities. At
least one-third of the national office multi-family housing staff work
on FLH projects and issues, even though FLH accounts for only 5
percent of the overall multi-family housing portfolio.
The report also notes that there was a substantial increase in the
rate of low performance grades in the agency's FLH portfolio. This was
a result of improved reporting capabilities and does not necessarily
indicate deterioration in the condition of USDA's FLH stock. The
agency uses a performance classification system that monitors the
quality of its FLH as determined by the property's physical,
financial, and management operations.
In 2008, the agency found that its existing classification scoring
standards were inadequately accounting for problems with borrower
plans to work out certain open findings. To address the issues,
changes were made to automate project classification scoring to
identify all open findings addressed in the borrower work out plans.
As a result of the automation process, unaddressed open findings were
identified, which caused projects' classification designations to be
changed from B to C. Although agency loan servicers had already been
working with owners of B classified properties, the automation
enhancement provided servicers with more comprehensive information to
monitor the progress of the borrower work out plan.
GAO compares RHS' Multi-Family Information System (MFIS) to HUD's
Public Housing Assessment System (PHAS), and states that MFIS does not
specify performance areas of concern, unlike PHAS. RES disagrees. MFIS
does include information on specific deficiencies in each of its three
categories of operations, so that a user of the system can quickly
identify the type of performance concern. A user proficient in MFIS,
and familiar with the specific project, would be able to understand
the areas of concern based on the information in the system.
However, we agree that more detail on specific physical conditions
issues would be beneficial for all users, such as those not familiar
with the specific project or new to the system.
We appreciate GAO's recommendation that RHS obtain access to the
Department of Health and Human Services' (HHS) National Directory of
New Hires, because we agree that access to the system would provide
significant benefits, and savings, in the FLH program. RHS has met with
HHS about system access for both RHS multi-family and single-family
staff, and the legislative authority to gain access to the New Hires
database is under review with the Administration.
In the report, GAO indicates that the need for further analysis of
available data on local, state, and national markets could help RHS
better determine markets most in need of FLH assistance, and allow the
agency to better target its funding to areas of greatest need. RHS is
interested in using its FLH application data to better understand
seasonal and non-seasonal migrant patterns and improve our
understanding of current migrant housing needs. Although the agency
still needs to determine whether the quantity of available FLH
application data is sufficient to completely meet its needs, we are
optimistic that it will enable RHS to better target its FLH awards to
the markets that can benefit the most.
RHS agrees with GAO that the market studies provided with each FLH
loan or grant application may be a valuable tool in better targeting
loans and grants toward projects with the greatest chance of success.
The market studies, which are submitted with every application
package, must support the need for the FLH housing in that market
area. Clearly, there are cases where the demand as demonstrated in the
market study does not materialize, causing high vacancy rates and
potentially a change in the use of the project to accept tenants
eligible under the Section 515 program. RHS believes that a review of
the market studies of such projects may help us identify patterns
indicative of future problems, thereby improving our analysis of
future FLH loan and grant applications.
RHS also agrees that it needs to be more aggressive in pursuing the de-
obligation of transactions that were never closed. However, it should
be recognized that many of the transactions involve funding from
multiple sources, which increases the complexity of project
transactions and may cause delays in the timing of the transactions as
all parties work to finalize a complete funding package. The delays in
funding may have been exacerbated by the recent weakness in financial
markets, including the Low Income Housing Tax Credit market as well as
funding from state sources. Therefore, while RIIS will review its
unliquidated FLH transactions for potential de-obligation, it
anticipates that its guidance to the field would include allowing
reasonable time frames for funding packages to be assembled before
requiring the funding de-obligation.
Finally, GAO recommends that RD staff review key assumptions that
impact the FLU credit subsidy rate.
During reestimates execution, agencies routinely revise their original
subsidy estimates using the latest available information. The purpose
of reestimates is to make the original estimate more accurate by
including actual data, updating assumptions, and improving estimation
methodology.
At the time of 2010 budget formulation Rural Development (RD) was
aware of the old model's limitations. An interim data solution was
implemented to support assumptions until a new model could be
developed to properly forecast out-years assumptions. With this
interim solution and a minor model revision, there was a misallocation
of cash flows into the wrong component which had nothing to do with
the actual defaults. However, the fact that the default cost component
was overstated does not automatically imply that the overall subsidy
rate was overstated by that amount. Historically, formulation subsidy
rates for the Farm Labor Direct Loan Program range from 34.15% (FY
2012) to as high as 56.80% (FY 1996). The formulation rate for FY 2010
was 36.14% which was comparable with the historical rate pattern for
this model even though the default rate was overstated. A new model,
described below, was used to formulate subsidy rates for FY 2011 and
FY 2012.
The agency has learned much and improved many Credit Reform processes
since the development of this program's original model. RD initiated a
process of developing a new model to accurately calculate the subsidy
rate for this program. In the course of this process, RD re-evaluated
the old model methodology, reviewed the program's historical
performance, built a new cash flow model, and updated the assumption
calculation methodology. As a result, the new cash flow model used in
2010 reestimates produced a subsidy rate that estimated the program's
cost more accurately and correctly allocated cash flows among various
cost components.
The assumption curves for this program have been completely revised to
be consistent with the new model format and calculations. The
prepayments and default curves have been separated and the
calculations for these curves are independent of one another.
Additionally, surviving principal is no longer used as the basis for
the curve calculations and the computation of these curves are now
based on the obligation. Actual program data are used to calculate the
prepayment and default curves. The actual data is verified to the
account and/or financial data. Additionally, significant enhancements
have been made to the review process of the assumption data to ensure
its accuracy. The Office of the Deputy Chief Financial Officer (DCFO)
and Budget Division (BD) have implemented a process in which both
offices now review the assumption data and calculations. Finally, the
new assumption data and curve for this model has been thoroughly
tested and audited, and no inconsistencies between the model and input
assumption calculations have been noted.
Even though DCFO, BD and mission program have been working together
during the budget formulation process, we agree that increased
coordination among the three groups would benefit the program.
Once again, we appreciate the opportunity to respond to GAO's report
on FLH, and we hope that our comments will help GAO in the preparation
of its final report. If you have any questions, please contact John
Purcell, Director, Financial Management Division, at (202) 692-0328.
Sincerely,
Signed by:
Cheryl L. Cook, for:
Dallas Tonsager:
Under Secretary:
Rural Development:
[End of section]
Appendix VII: GAO Contact and Staff Acknowledgments:
GAO Contact:
A. Nicole Clowers (202) 512-8678 or clowersa@gao.gov:
Staff Acknowledgments:
In addition to the individual named above, Andy Finkel, Assistant
Director; Michael Armes; Marcia Carlsen; Kimberly Cutright; Terence
Lam; John McGrail; John Mingus, Jr.; Marc Molino; Luann Moy; Amy
Radovich; Barbara Roesmann; Julie Trinder; and Michelle Wong made
major contributions to this report.
[End of section]
Footnotes:
[1] Throughout the report, we refer to FLH "properties" and "units."
An FLH property is a piece of real estate (land and buildings) with
one or more rental units and related facilities operated under one
management plan and financed with FLH funds. "Units" are rented
individual dwellings on a property, such as an apartment.
[2] Percentages do not add up to 100 because RHS data included 10
properties that were of an unknown classification (e.g., on-farm or
off-farm) or nonlabor housing properties.
[3] Experts in our group consisted of FLH borrowers and property
managers, FLH property developers, staff from nonprofit organizations,
researchers, and USDA contractors that provide technical assistance to
developers. For more information on the selection of experts, please
see appendix I.
[4] RHS may award both loan and grant funds in the FLH program, and it
may award both types of funds to one recipient. Therefore, we refer to
recipients as borrowers throughout this report, as RHS does in its
management handbooks and FLH regulation.
[5] GAO, Standards for Internal Control in the Federal Government,
[hyperlink, http://www.gao.gov/products/GAO/AIMD-00-21.3.1]
(Washington, D.C.: November 1999).
[6] 42 U.S.C. §§ 1484, 1486. RHS is a mission area in USDA's Office of
Rural Development and administers most federal rural housing programs.
The FLH program is the only program in RHS that does not have to meet
rural eligibility criteria--that is, it funds properties in both urban
and rural areas.
[7] For the FLH program, farm labor is defined as a service or
services in connection with cultivating the soil or raising or
harvesting any agriculture or aquaculture commodity; or in catching,
netting, handling, planting, drying, packing, grading, storing, or
preserving in the unprocessed stage, without respect to the source of
employment (but not self-employed), any agriculture or aquaculture
commodity; or delivering to storage, market, or a carrier for
transportation to market or to processing any agricultural or
aquaculture commodity in its unprocessed stage.
[8] RHS loan servicers, who generally operate in local offices,
conduct a variety of off-site monitoring activities, or desk reviews,
and on-site supervisory reviews to assess whether a property is
managed in accordance with the goals and objectives of the FLH program.
[9] Occupancy requirements and income restrictions for off-farm
properties do not apply to on-farm properties, as they are owned by
farmers with the purpose of providing housing for their specific
employees only. On-farm FLH borrowers are expected to manage their own
properties and are required to maintain a lease or employment contract
with each tenant specifying employment with the borrower as a
condition for continued occupancy.
[10] Three different income limits are used to establish eligibility
for the FLH program: (1) the very low-income limit is established at
approximately 50 percent of the median income for the area, adjusted
for household size; (2) the low-income limit is established at
approximately 80 percent of the median income for the area, adjusted
for household size; and (3) the moderate-income limit is established
by adding $5,500 to the low-income limit for each household size.
[11] This number does not include hired laborers employed in
agricultural processing activities, such as canning fruits and
vegetables. For more information on data that relate to farmworkers,
see appendix III.
[12] RHS requirements and procedures for originating FLH loans are
often similar to those of the Section 515 loan program.
[13] In general, an obligation is a definite commitment that creates a
legal liability of the government for the payment of goods and
services ordered or received. An agency makes an obligation, for
example, when it places an order, signs a contract, awards a grant,
purchases a service, or takes other actions that require the
government to make payments to the public or from one government
account to another. An unliquidated obligation is the amount of
outstanding liability for goods and services ordered and obligated but
not yet received.
[14] A cohort is defined as all direct loans or loan guarantees of a
program for which a subsidy appropriation is provided for a given
fiscal year. For direct loans for which multi-year or no-year
appropriations are provided, such as the Section 514 FLH Loan Program,
the cohort is defined by the year of obligations.
[15] Present value is the worth of the future stream of cash inflows
and outflows, as if they had occurred immediately. In calculating
present value, prevailing interest rates provide the basis for
converting future amounts into their "money now" equivalents. Net
present value is the present value of estimated future cash inflows
minus the present value of estimated future cash outflows.
[16] We discuss RHS management of program information, (including
information related to demand for housing and funding, monitoring of
property condition and program compliance, and tenant eligibility) in
greater detail in the next section of this report.
[17] According to USDA data, approximately 35 percent of farm workers
hired directly by farm operators lived in California, Florida,
Oklahoma, and Texas throughout the year in 2010.
[18] Housing Assistance Council, USDA Section 514/516 Farmworker
Housing: Existing Stock and Changing Needs (Washington, D.C.: October
2006).
[19] William Kandel, Profile of Hired Farmworkers, a 2008 Update,
Economic Research Report No. 60, Economic Research Service, U.S.
Department of Agriculture (June 2008).
[20] Rental subsidies, which are funded through the Section 521 Rental
Assistance program and provided to property owners through multiyear
contracts, are intended to limit rent payments to 30 percent of the
household's adjusted monthly income. Only off-farm FLH properties are
eligible for rental assistance subsidies.
[21] According to RHS, C grades increased as the result of a recent
change to its classification system. RHS added a process whereby
certain instances of noncompliance, such as a missed loan payment,
would trigger an automatic C grade in the classification system.
[22] The states with more than 100 units were Arizona, Arkansas,
California, Colorado, Florida, Idaho, Michigan, New Mexico, New York,
North Carolina, Oregon, Texas, and Washington.
[23] See [hyperlink, http://www.gao.gov/products/GAO/AIMD-00-21.3.1].
[24] The resolution process begins when audit or other review results
are reported to management and is completed only after action has been
taken that corrects identified deficiencies, produces improvements, or
demonstrates the findings and recommendations do not warrant
management action. See [hyperlink,
http://www.gao.gov/products/GAO/AIMD-00-21.3.1].
[25] In response to the results of its internal review, in January
2010 RHS's national office issued guidance to state and local offices
to evaluate findings in MFIS to ensure up-to-date, accurate
information, and review a missing data report on each property. RHS
also held four Web-based trainings in 2009 to improve the integrity of
MFIS.
[26] [hyperlink, http://www.gao.gov/products/GAO/AIMD-00-21.3.1].
[27] During our inspections of FLH properties, we rated aspects of
property exteriors and interiors by the level of disrepair we observed
(that is, no, low, medium, or high levels of apparent disrepair). We
assigned a rating of zero (no disrepair) when no physical condition
problems were noted with an aspect of a property's interior or
exterior. We assigned a rating of one (low) to properties with minor
physical condition problems such as worn or older aspects of a unit's
interior or exterior. We assigned a rating of two (medium) to
properties with more advanced physical condition problems that
appeared to have deteriorated over time. We assigned a rating of three
(high) to properties with physical condition problems that could
affect the health and safety of residents.
[28] Housing agencies are the type of entity eligible for federal
public housing funds administered by HUD, whereas FLH borrowers may be
public agencies, nonprofit entities, individual farmers, or other
types of organizations. The Capital Fund provides funds to housing
agencies for the development, financing, and modernization of public
housing developments and for management improvements. On February 23,
2011, HUD released an interim rule to make changes to PHAS. These
changes became effective on March 25, 2011. 24 CFR Parts 902 and 907.
[29] For more information on PHAS, see GAO, Public Housing: New
Assessment System Holds Potential for Evaluating Performance,
[hyperlink, http://www.gao.gov/products/GAO-02-282] (Washington, D.C.:
Mar. 15, 2002); and Public Housing: HUD's Oversight of Housing
Agencies Should Focus More on Inappropriate Use of Program Funds,
[hyperlink, http://www.gao.gov/products/GAO-09-33] (Washington, D.C.:
June 11, 2009).
[30] GAO, Grants Management: Enhancing Performance Accountability
Provisions Could Lead to Better Results, [hyperlink,
http://www.gao.gov/products/GAO-06-1046] (Washington, D.C.: Sept. 29,
2006).
[31] For more information on graduated penalties, see GAO, Federal
User Fees: Key Aspects of International Air Passenger Inspection Fees
Should Be Addressed Regardless of Whether Fees Are Consolidated,
[hyperlink, http://www.gao.gov/products/GAO-07-1131] (Washington,
D.C.: Sept. 24, 2007).
[32] [hyperlink, http://www.gao.gov/products/GAO-06-1046].
[33] We considered permanent residency cards that were expired or were
deemed invalid through third party verification questionable.
[34] GAO, Rural Housing Service: Updated Guidance and Additional
Monitoring Needed for Rental Assistance Distribution Process,
[hyperlink, http://www.gao.gov/products/GAO-04-937] (Washington, D.C.:
Sept. 13, 2004).
[35] There may be limitations with these data sources and these data
may not fully capture the extent and dynamics of tenant demand. For
example, in one state we visited, according to the local RHS office, a
market study submitted with an application documented a sufficient
number of eligible residents; however, once the property opened, there
were few farm workers to fill the units.
[36] [hyperlink, http://www.gao.gov/products/GAO/AIMD-00-21.3.1].
[37] No properties were delinquent for fewer than 90 days. In
comparison, about 8.5 percent of Federal Housing Administration single
family loans were seriously delinquent, or 90 or more days delinquent
as of September 2010, according to a report published by HUD. The
overall delinquency rate for properties in RHS's Section 515 Rural
Rental Housing Program as of the end of fiscal year 2010 was about 3
percent.
[38] A finding is recorded in MFIS when the agency finds that a
borrower is not operating in accordance with the loan or grant
agreement, with agency regulations, or with applicable local, state,
or federal laws.
[39] The reserve account requirement excludes on-farm properties with
fewer than 12 units.
[40] Rental assistance owed to the borrower can either offset the
payment owed on the loan, or even exceed the loan payment amount,
resulting in RHS remitting payment to the borrower.
[41] RHS is located within USDA's RD mission area. The FLH credit
subsidy estimates and reestimates are prepared by RD's budget
division. The $11.8 million was calculated, in part, using the FLH
loan program allocation amount as described in the conference report
for the fiscal year 2010 Department of Agriculture appropriation.
[42] Federal agencies use OMB's credit subsidy calculator to calculate
the subsidy cost of direct loan and loan guarantee programs for budget
and financial reporting purposes. The subsidy cost is the net present
value of estimated payments the government makes less estimated
amounts it receives over the life of the direct loan or loan
guarantee, excluding administrative costs, as described in the
background section of this report.
[43] When the reestimate is reflected in the financial statements and
budget, the reestimate amount will be adjusted for the obligations
that were not disbursed by the end of fiscal year 2010. As a result,
the recorded reestimate will be less than $3 million. The remaining
impact of the reestimate will be recorded as the remaining obligations
are disbursed in the future.
[44] Congress may place specific limits on the total obligations that
can be made by a program. The appropriated subsidy level and the
estimated subsidy rate combine to produce the loan level.
Specifically, subsidy budget authority divided by subsidy rate equals
supportable loan level. For example, $9,873,000 in budget authority
for FLH loan obligations was described in the conference report for
the fiscal year 2010 Department of Agriculture appropriation. Based on
the estimated credit subsidy rate of 36.14 percent, RD would be
allowed to obligate $27.3 million of direct loans ($9,873,000/0.3614).
Given the same amount of budget authority for subsidy costs, an agency
would be able to obligate more funding for direct loans when the
credit subsidy rate is lower. As a result, had RD correctly considered
defaults in its credit subsidy estimate for the loans obligated in
2010, it would have used $3 million less of its budget authority for
subsidy costs and could have obligated an additional $11.8 million of
direct loans.
[45] The fiscal year 2010 predicted recovery rate was 67 percent. The
fiscal year 2008 and 2009 cohorts both had predicted default rates of
56 percent and predicted recovery rates of 61 and 59 percent,
respectively, which contributed to relatively high predicted default
costs for these years (see appendix V). Our audit scope did not
include an assessment of the fiscal year 2008 and 2009 credit subsidy
models, and since these cohorts report a negative "all other" expense
that offsets the default costs, we are not reporting on the accuracy
of the estimates in these years.
[46] FASAB Federal Financial Accounting and Auditing Technical Release
No. 6 (January 2004).
[47] Unliquidated obligations are outstanding obligations or
liabilities that have not yet been paid.
[48] According to agency obligation reports, between the fiscal years
2004 and 2010, the balance of unliquidated loans and grants ranged
from a low of $127 million in 2004 to a high of $184 million in 2010.
[49] The Multi-Family Housing Revitalization Demonstration Program is
intended to restructure selected existing RHS FLH and Rural Rental
Housing loans and grants to ensure that sufficient resources are
available to revitalize these properties.
[50] Fund appropriated in 1994 or later were "no-year" funds and do
not expire after 1 or multiple years. For funds appropriated to the
FLH program in 1993 or earlier, the appropriations for the costs of
the Section 514 direct loan program were 1-year appropriations.
Because the appropriation was available only for that fixed period
under the terms of 31 U.S.C. 1552(a), RHS had 5 years from the end of
the 1-year period of availability to liquidate the obligations.
[51] Generally unnumbered letters issued by RHS's national office only
clarify existing rules or regulations and do not set new guidelines
regarding policies and procedures.
[52] Although the previously established de-obligation time frames are
not currently in force, FLH obligations are still monitored by program
staff semiannually, as part of an agencywide requirement. USDA directs
its chief financial officer to have program staff review and certify
unliquidated obligations quarterly in order to properly document
obligation balances and deobligate any unliquidated obligations found
to be either unnecessary, or for which a bona fide purpose for the
obligation and justification for the period of inactivity do not
exist. According to RHS, to comply with the regulation, on a
semiannual basis finance staff furnish a list of all unliquidated
obligations more than 6 months old to RHS state offices and state
directors then review the list and certify that the listed obligations
are valid, including those obligations associated with the FLH
program. See USDA Departmental Regulation 2230-001 (Apr. 21, 2009).
According to RHS, it obtained a waiver to the regulation in 2009 to
perform the reviews of unliquidated obligations semiannually instead
of quarterly.
[53] We invited 12 experts, but 1 invitee was unable to attend.
[54] Findings from site visits and tenant file reviews cannot be
generalized across the FLH portfolio.
[55] RHS allowed one off-property in Texas to rent to tenants who are
normally ineligible under the FLH program. Therefore, no tenant files
were selected from this property. A second property in Texas had only
five FLH tenants; therefore, we reviewed all five tenant files.
Finally, a third property in New York had only six FLH tenants and all
six tenant files were reviewed.
[56] Once an FLH project is approved, borrowers must establish a
replacement reserve account with funding levels sufficient to meet the
major capital needs of a property over its life, such as replacing the
roof or windows, doing major exterior work, and adding new kitchen
fixtures. The aggregate, fully funded reserve amount must equal at
least 10 percent of the greater of the total development cost or
appraised value, and annual contributions must be a minimum of 1
percent of the total development cost. RHS requires that borrowers
submit annual property budgets to the agency for approval, identify
major maintenance and replacement needs during the annual budget
cycle, and develop a schedule for making withdrawals from the reserve
account, and, in the case of larger properties, submit annual audited
financial statements.
[57] This figure is a rounded average of the four quarterly FLS report
figures for hired farm workers in 2010.
[58] A total of $19,746,000 was appropriated to the program for the
2010 fiscal year, and according the Conference Report for the 2010
Appropriations Act, about $9.9 million of which was available for
Section 514 loan subsidies and $9.9 million of which was available for
Section 516 grants. Subject to the availability of funding, RHS has
the ability to adjust loan and grant levels.
[End of section]
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