GAO-01-516R RD Loan Validation

United States General Accounting Office

Washington, DC 20548

April 10, 2001

The Honorable Ann Veneman

The Secretary of Agriculture

Subject: Rural Development: Assessment of Data Used to Support Non-Housing

Direct Loan Programs Subsidy Cost Estimates

Dear Madam Secretary:

Rural Development’s (RD) long-standing problems with estimating the cost of its

credit programs in accordance with the Federal Credit Reform Act of 1990 and

federal accounting standards—credit reform implementation—continues to be a

major factor preventing the Department of Agriculture (USDA) from achieving an

unqualified opinion on its consolidated financial statements. In addition, these

problems materially affect USDA’s budget submissions because the same cost

estimates are generally used for both budget preparation and financial reporting.

Since April 1999, we have been assessing RD’s credit reform implementation efforts

in such areas as (1) identifying key cash flow assumptions, (2) improving cash flow

models, (3) assessing cash flow model data, and (4) implementing other procedures

to enhance the credit subsidy estimation process. RD has divided its credit programs

into three areas: housing direct loans, non-housing direct loans, and guaranteed

loans. Our efforts to date have primarily focused on the non-housing and guaranteed

loan programs. This letter is part of a series of status reports1

 on RD’s efforts to

improve its credit program cost estimates, and focuses solely on RD’s major nonhousing direct loan programs,2

 which RD reported at $40.6 billion in loans

outstanding as of September 30, 2000.3

 1

See Credit Reform: Improving Rural Development’s Credit Program Cost Estimates (GAO/AIMD-00-

286R, August 22, 2000) and two related correspondences: Credit Reform: Rural Development’s Efforts

to Improve Loan Cost Estimates, December 17, 1999, and Credit Reform: Improving Rural

Development’s Loan Cost Estimates, June 25, 1999.

2

The major non-housing direct loan programs were Water and Waste Disposal, Federal Financing Bank

Electric, Municipal Electric, Telecommunications Hardship, and Electric Hardship. The criteria used

to identify these major programs included outstanding loan balances, obligation trends, and subsidy

cost.

3

Rural Development’s Consolidated Financial Statements for Fiscal Year 2000.
Page 2 GAO-01-516R RD Loan Validation

The reasonableness of RD’s loan program cost estimates is affected by the quality of

its cash flow assumptions, which are calculated based on data recorded in RD’s loan

accounting systems. As part of our ongoing work, this letter provides our assessment

of the accuracy of the data that RD uses to calculate key cash flow assumptions—the

assumptions that have the greatest impact on the program’s estimated subsidy cost.

RD accounts for the major non-housing direct loan programs in the Program Loan

Accounting System (PLAS), the Rural Electrification Administration system (REA),

and the Federal Financing Bank system (FFB).

Results in Brief

For RD to prepare reasonable subsidy cost estimates, being able to draw on reliable

data is an important first step. Our testing determined that the data included in RD’s

PLAS, REA, and FFB loan accounting systems that are used to calculate key cash

flow assumptions for the major non-housing direct loan programs are generally

accurate. The assumptions that RD has determined to be key for calculating the

subsidy cost estimates for these programs are the average borrower interest rate and

average loan term, except for the FFB electric program.4

 For this program, RD staff

identified the average borrower interest rate as the key cash flow assumption.

In commenting on a draft of this letter, RD officials agreed with our finding. We have

incorporated their comments as appropriate.

Background

The Federal Credit Reform Act of 1990, the related accounting standard, Statement of

Federal Financial Accounting Standards (SFFAS) No. 2,5

Accounting for Direct Loans

and Loan Guarantees, and various budget guidance,6

 together known as credit reform,

were established to more accurately measure the government’s costs of federal credit

programs. As part of implementing credit reform, agencies are required to estimate

the net cost of extending or guaranteeing credit—generally referred to as the subsidy

cost—based on the present value7 of estimated net cash flows for the life of the loan,

excluding administrative costs.

 4

Based on our previous work to assess RD’s credit reform implementation efforts, we agree with the

key cash flow assumptions RD has identified for its major non-housing direct loan programs.

5

SFFAS No. 2 was amended by SFFAS No. 18, Amendments to Accounting Standards for Direct Loans

and Loan Guarantees. The objective of the amendments was to improve financial reporting related to

subsidy costs and performance of federal credit programs.

6

Office of Management and Budget circulars A-11 and A-34 include guidance for implementing credit

reform including estimating credit subsidy costs.

7

Present value is the worth of a future stream of returns or costs in terms of money paid immediately.

In calculating present value, prevailing interest rates provide the basis for converting future amounts

into their “money now” equivalents.
Page 3 GAO-01-516R RD Loan Validation

RD management is responsible for accumulating sufficient, relevant, and reliable data

on which to base its estimated net cash flows. RD’s process for estimating its net

cash flows and calculating its subsidy costs are shown in figure 1.

Figure 1: Rural Development’s Process for Calculating Subsidy Costs for Its Non-Housing Direct Loan

Programs

Notes:

1. Except where indicated, these processes are automated.

2. Model B is the cash flow model for RD’s non-housing direct loan programs.

3. SAS, a statistical analysis system, is an integrated suite of software designed to perform various functions such as data

access, data management, data analysis, and data presentation. RD uses this SAS program to summarize and analyze loan

accounting system data related to the key cash flow assumptions.

4. In conjunction with the Model B calculations, RD uses the Credit Subsidy Calculator, a computer software program

developed by the Office of Management and Budget (OMB), to calculate the subsidy costs for the non-housing direct loan

programs. The OMB Credit Subsidy Calculator was developed to provide a consistent approach to calculating the present

values of credit program costs.

Source: Rural Development.

As RD makes loans to borrowers, data related to each loan—including the applicable

term and interest rate—are entered in RD’s loan accounting systems, and the

associated loan documents are filed in headquarters or regional offices. From the

loan accounting systems, key data such as loan amount or borrower interest rate are

captured by an automated program that calculates RD’s average borrower interest

rate and average loan term assumptions. These assumptions are then manually

entered into RD’s cash flow model, a computer-based spreadsheet referred to as

Model B,8

 which calculates an estimate for the subsidy cost of the program.

In response to prior audit findings, USDA organized a credit reform implementation

task force and developed a detailed implementation plan. The task force has made

 8

RD has three cash flow models: Model A for direct housing loan programs, Model B for non-housing

direct loan programs, and Model C for guaranteed loan programs.

Rural

Development

Loan

Accounting

System

Subsidy

costs

Model B

estimates net

cash flows

Manual process

Manual

process

Loan

Files

 SAS report on

key cash flow

assumptions

SAS Program

reads and

extracts data
Page 4 GAO-01-516R RD Loan Validation

progress in several areas, including completing and documenting sensitivity analyses9

for the non-housing direct loan program’s cost estimates. As reported in our

August 22, 2000 letter,10 we assessed RD’s approach to performing sensitivity analyses

in order to identify key cash flow assumptions. RD determined that there were two

key cash flow assumptions for the major non-housing direct loan programs: average

borrower interest rate and average loan term.11 We agreed with RD’s determination

that these were the most significant cash flow assumptions based on our prior

review. As a result, variations in these assumptions have the greatest impact on

subsidy cost estimates. Therefore, it is critical that these assumptions be based on

reliable data and be correctly calculated.

Scope and Methodology

In order to assess the reliability of the data in the loan accounting systems that

support the calculation of key cash flow assumptions, which are entered in RD’s cash

flow model, we selected a random sample of loans (Water and Waste Disposal

Program) or advances (all other programs) from the appropriate systems—PLAS,

REA, and FFB—as of June 30, 2000. Prior to selecting these sample transactions, we

determined that the unpaid principal balances recorded in the loan accounting

systems agreed with the June 30 general ledger trial balances for each loan program.

For the FFB electric loan program, our population comprised 631 loans. For the

REA loan program (Municipal Electric, Telecommunications Hardship, and Electric

Hardship), our population comprised 4,052 loans. For the Water and Waste Disposal

loan program, our population comprised 5,844 loans.

For sampled loans, we obtained documentation from the RD loan files supporting the

loan accounting systems’ data that are used to calculate the average borrower

interest rate and loan term assumptions used in the cash flow model. The

documentation obtained included loan or bond agreements, Financial Requirement

and Expenditure Statements, Voucher and Schedule of Payments, FFB Interest Rate

Confirmation Notices, and Quarterly Federal Register Municipal Interest Rate

Schedules. We then compared the data in the loan accounting systems to the

supporting documents to verify that the recorded interest rates and components of

the loan terms documented in the automated records agreed with the supporting

documents.

We conducted our work in Washington, D.C., and at selected RD field offices from

August 2000 through February 2001 in accordance with generally accepted

government auditing standards. We received oral comments on a draft of our letter

from the Office of the Chief Financial Officer, Rural Development.

 9

Sensitivity analysis is a process used to identify the assumptions that, when adjusted, have the

greatest impact on the credit subsidy estimate.

10GAO/AIMD-00-286R.

11However, for the FFB electric program, RD staff identified the average borrower interest rate as the

key cash flow assumption.
Page 5 GAO-01-516R RD Loan Validation

Loan Accounting Systems Data Are Generally Accurate

Our testing determined that the data included in the PLAS, REA, and FFB systems

that are used to calculate key cash flow assumptions for RD’s major non-housing

direct loan programs are generally accurate. For the PLAS and REA programs, RD

has determined that the average borrower interest rate and average loan term are the

key assumptions used in their calculation of subsidy costs. For the FFB electric

program, RD has determined that the average borrower interest rate is the key

assumption.

RD uses the borrower interest rates recorded in the three loan accounting systems to

calculate the average interest rate assumption. In 323 of the 325 total sample loans12

tested, the borrower interest rate recorded in the loan accounting systems agreed to

the supporting documentation in the loan files. For two sample loans, RD could not

locate the documentation to support the borrowers’ interest rates. As a result, we

counted those two items as errors because RD had no assurance that the rates for

those loans were correct. However, these errors were not material, as shown in

enclosure I, and therefore, we concluded that the interest rate data in RD’s loan

accounting systems for the major non-housing direct loan programs were generally

accurate.

RD uses the loan maturity date and the loan advance date13 recorded in the three loan

accounting systems to calculate the average loan term assumption. In 323 of the 325

total sample loans tested, the advance date agreed to supporting documentation in

the loan files. As with testing the accuracy of the interest rate data, the two errors

related to missing documentation. In 320 of the 325 total sample loans tested, the

maturity dates in the systems agreed to the supporting documentation. Of the five

cases in error, the missing documentation mentioned above accounted for two, and

there were three cases in which the maturity dates in the system did not agree with

those in the loan files. These errors related to the Water and Waste Disposal loans

included in RD’s PLAS system. In two of the three cases, the loan system’s maturity

dates reflected 1 year more than the loan file documentation. In the remaining case,

the loan system’s maturity date reflected 5 years more than the loan file

documentation. However, these errors were not material, as shown in enclosure I,

and therefore, we concluded that the loan term data for the major non-housing direct

loan programs were generally accurate.

Conclusion

Having reliable non-housing direct loan programs’ data to prepare subsidy cost

estimates represents important progress in achieving more reasonable estimates for

the cost of these programs. Our testing showed that the information used to

 12The 325 total sample loan population consisted of 114 REA loans, 97 FFB loans, and 114 Water and

Waste Disposal loans.

13The advance date reflects the date the agency releases a portion of the total loan amount to the

borrower.
Page 6 GAO-01-516R RD Loan Validation

calculate key cash flow assumptions for the major non-housing direct loan programs

was reliable.

Agency Comments

In commenting on a draft of this letter, RD officials agreed with our finding. We have

incorporated their comments as appropriate.

We are sending copies of this letter to Patricia Healy, Acting Chief Financial Officer,

Department of Agriculture, and R. Mack Gray, Acting Deputy Under Secretary for

Natural Resources and Environment. This letter will also be available on GAO’s

home page at http://www.gao.gov.

If you have any questions about this letter, please contact me at (202) 512-9508 or

McCoy Williams, Acting Director, at (202) 512-6906. Key contributors to this

assignment are listed in enclosure II.

Sincerely yours,

Linda M. Calbom

Director, Financial Management and Assurance

Enclosures
Enclosure I

Page 7 GAO-01-516R RD Loan Validation

Sample Results

We selected a random sample of loans from RD’s major non-housing direct loan

programs in the Program Loan Accounting System (PLAS), the Rural Electrification

Administration (REA) system, and the Federal Financing Bank (FFB) system. Table

1 and the accompanying notes identify the random samples selected, the number of

errors found, and a projection of these errors to the population of the major nonhousing direct loan programs in the three loan accounting systems.

Table 1: Summary of Sample Results for Rural Development’s Major Non-Housing Direct Loan Programs

Loan

accounting

system

Major non-housing

direct loan program

Population

size

Sample

size

Errors

found in

the

interest

rate data

Errors

found in the

loan term

data

Conclusion

PLAS Water and Waste

Disposal

5,844 114 0 3 maturity

date errorsa

Not material

REA Municipal Electric

Telecommunications

Hardship

Electric Hardship

4,052 114 2b

2 interest

rate,

advance

date, and

maturity date

errorsb

Not material

FFB Federal Financing

Bank

631 97 0 0 No errors

found

a

For three of the sampled loans, the maturity dates did not agree to those in the loan files. When projecting these three errors

to the population of 5,844, we are 95 percent confident that the errors in the population are between 42 and 389 loans. Our

best estimate is that 154 loans have maturity dates that did not agree to those in the loan files. This fell below our tolerable

amount of errors, which was 526 loans (a 9 percent tolerable error rate) for the Water and Waste Disposal sample.

b

For two of the sample loans, RD could not locate the documentation to support the borrowers’ interest rates, advance dates,

and maturity dates. As a result, we counted those two items as errors because RD had no assurance that the system data for

those loans were correct. When projecting these two errors to the population of 4,052, we are 95 percent confident that the

errors in the population are between 13 and 220 loans. Our best estimate is that 71 loans had no documentation. This fell

below our tolerable amount of errors, which was 365 loans (a 9 percent tolerable error rate) for the REA sample.
Enclosure II

Page 8 GAO-01-516R RD Loan Validation

Staff Acknowledgments

Dan Blair, Marcia Carlsen, Carla Lewis, Jerry Pennington, and Ronda Price made key

contributions to this report.

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