Draft Analysis Plan

App. E.-Draft Analysis Plan FY2010.pdf

Quality Control for Rental Assistance Subsidy Determination

Draft Analysis Plan

OMB: 2528-0203

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Analysis Plan
for the FY 2010 HUDQC Study
Quality Control for Rental Assistance
Subsidy Determinations Study

Prepared for:
Office of Policy Development and Research
Department of Housing and Urban Development
Washington, DC 20410

Contract #: GS-23F-0777H
Task Order #: C-CHI-01026, CHI-T0001

Prepared by:
ICF Macro
11785 Beltsville Drive
Calverton, MD 20705-3119

May 3, 2010

TABLE OF CONTENTS
INTRODUCTION......................................................................................................................... 1
Rent Error—The Dependent Variable .................................................................................3
Preparation of Analytic Data Files.......................................................................................5
ANALYSIS PLAN BY STUDY OBJECTIVE ........................................................................... 6
Objective 1: ................................................................................................................................................. 6
Exhibit 1 Percent of Households with Proper Payments (FY 2009 and FY 2010) ............ 6
Exhibit 2 Rent Error: Percent of Households in Error, Average Gross Dollars in Error,
and Error Rate (FY 2009 and FY 2010) ............................................................................ 7
Exhibit 3a Underpayment Households: Percent of Households and Average Monthly
Dollar Amount of Error (FY 2009 and FY 2010) .............................................................. 7
Exhibit 3b Overpayment Households: Percent of Households and Average Monthly
Dollar Amount of Error (FY 2009 and FY 2010) .............................................................. 8
Objective 2: ................................................................................................................................................. 8
Exhibit 4 Percent of Households with Calculation and Consistency Errors (FY 2010) ..... 9
Exhibit 5 Timeliness of Certification Status (FY 2009 and FY 2010) ............................... 9
Exhibit 6 Average Monthly Underpayment and Overpayment Dollar Amount
Averaged across All Households (FY 2009 and FY 2010) ............................................. 10
Exhibit 7 Procedural Error: Percent of Households, Average Dollars in Error,
All Households with 50058/59 Recalculated Rent (FY 2010) ......................................... 10
Exhibit 8 50058/50059 Procedural Error: Percent of Households, Average Dollars in
Error (FY 2010) ................................................................................................................ 12
Objective 3: ............................................................................................................................................... 13
Exhibit 9 Gross and Net Dollar Rent Error (Monthly) for All Households
(FY 2009 and FY 2010) .................................................................................................... 13
Objective 4: ............................................................................................................................................... 13
Exhibit 10 50058/59 Rent Calculation Error Compared to QC Rent Error
(FY2008 and FY2009) ...................................................................................................... 14
Exhibit 11 Percent of Households in Error and Dollar Error by Error Basis
(FY 2009 and FY 2010) .................................................................................................... 14
Objective 5: ............................................................................................................................................... 14
Exhibit 12 The Impact of Program Type on Gross and Net Dollar Error (FY 2010) ....... 15
Objective 6: ............................................................................................................................................... 15
Exhibit 13 Percent of Households with Verification of 50058/50059 Rent Components
(FY 2009 and FY 2010) .................................................................................................... 16
Exhibit 14 Verification of 50058/50059 Rent Components (FY 2010) ........................... 17

Analysis Plan

May 3, 2010

Exhibit 15 QC Error Households with Missing Verification in the Tenant File
(FY 2009 and FY 2010) .................................................................................................... 17
Exhibit 16 Rent Components Responsible for the Largest Dollar Error Households
with Rent Error (listed by amount of dollar error) (FY 2009 and FY 2010) ................... 18
Exhibit 17 Income and Expense Component Error by Payment Type for All
Households (FY 2010) ...................................................................................................... 19
Exhibit 18 Percent of Households and Standard Error by Rent Component and
Payment Type (FY 2010).................................................................................................. 19
Exhibit 19 Annual Gross Dollar Error by Largest Component Error for Households
with Rent Error (FY 2010)................................................................................................ 20
Exhibit 20 Percent of Households with Elderly/Disabled Allowances and Dependent
Allowances (FY 2010) ...................................................................................................... 20
Objective 7: ............................................................................................................................................... 21
Exhibit 21 PHA Section 8 Unit Size Standards ................................................................ 21
Exhibit 22 Percent of Households in Units with Correct Number of Bedrooms
(According to Study Guidelines) (FY 2009 and FY 2010) .............................................. 22
Exhibit 23 Percent of All Households by Number of Bedrooms and Number of
Household Members (in thousands) (FY 2010) ............................................................... 22
Objective 8: ............................................................................................................................................... 22
Objective 9: ............................................................................................................................................... 23
Exhibit 24 Percent of Newly Certified Households Meeting Certification Criteria
(FY 2010) .......................................................................................................................... 23
Exhibit 25 Percent of Newly Certified Households Meeting Certification Criteria
(FY 2010) .......................................................................................................................... 24
Objective 10: ............................................................................................................................................. 24
Rent Reasonableness Analysis.................................................................................................24
Exhibit 26 Rent Reasonableness Determination Methods (FY 2010) .............................. 24
Exhibit 27 Rent Reasonableness Documents in Files for New Admissions
(FY 2009 and FY 2010) .................................................................................................... 25
Exhibit 28 Timing of Most Recent Rent Reasonableness Determination—
New Admissions (FY 2009 and FY 2010) ....................................................................... 25
Exhibit 29 Rent Reasonableness Documents for Annual Recertifications
(FY 2009 and FY 2010) .................................................................................................... 26
Exhibit 30 Timing of Most Recent Rent Reasonableness Determination—
Annual Recertifications (FY 2009 and FY 2010) ............................................................. 26
Payment Standards Analysis ....................................................................................................27
Exhibit 31 Number and Percent of Households with Payment Standard Discrepancies
(FY 2010) .......................................................................................................................... 27
Exhibit 32 Percent of Households by Fair Market Rent Category after Comparing
Payment Standard to Fair Market Rent (FMR; FY 2010) ................................................ 27

Analysis Plan

May 3, 2010

Exhibit 33 Percent of Households Meeting Payment Standard Requirements
(FY 2010) .......................................................................................................................... 28
Utility Schedules ......................................................................................................................28
Exhibit 34 Type of Document Used by the PHA to Calculate the Utility Allowance
Value (FY 2010) ............................................................................................................... 28
Exhibit 35 QC Utility Allowance Comparison Findings (FY 2010) ................................ 29
Objective 11: ............................................................................................................................................. 29
Exhibit 36 Negative Subsidy Households (Under-subsidies) Percent of Households
and Average Monthly Dollar Amount of Error (FY 2009 and FY 2010)........................ 29
Exhibit 37 Positive Subsidy Households (Over-Subsidies) Percent of Households
and Average Monthly Dollar Amount of Error (FY 2009 and FY 2010)........................ 30
Exhibit 38 Average Monthly Dollar Amounts of Error for Negative (Under-) and
Positive (Over-) Subsidies Averaged Across All Households (FY 2009 and FY 2010) . 30
Objective 12: ............................................................................................................................................. 30
Exhibit 39 Percent of Projects Using Computer Software for Administrative Tasks
in the Past 12 Months (FY 2010) ...................................................................................... 31
Exhibit 40 Percent of Projects Using Computer Software Uses in the Past 12 Months,
by Project Size (FY 2010) ................................................................................................ 32
Objective 13: ............................................................................................................................................. 32
Objective 14: ............................................................................................................................................. 33
Exhibit 41 Average Dollars in Error by Program Type and TRACS/PIC Data (FY 2010)
........................................................................................................................................... 33
Exhibit 42 Average Dollars in Error by Payment Type and TRACS/PIC Data (FY 2010)
........................................................................................................................................... 34
Exhibit 43 Percent of Matched and Non-Matched Dollar Amounts for Key Variables
Matching Variables from the 50058/50059 Form and TRACS/PIC Data Files (FY 2010)
........................................................................................................................................... 34
Exhibit 44 Percent of Gross Dollar Rent Errors for Cases Where Key Variables Did Not
Match (FY 2010) .............................................................................................................. 35
Exhibit 45 Percent of Procedural Errors for Cases Where Key Variables Did Not Match
(FY 2010) .......................................................................................................................... 35
Objective 15: ............................................................................................................................................ 35
Exhibit 46 Categorization of Earned Income for Each Household by Program Type
(FY 2010) .......................................................................................................................... 36
Exhibit 47 Categorization of Unemployment Compensation for Each Household by
Program Type (FY 2010) .................................................................................................. 36
Exhibit 48 Results of Verification Attempts (FY 2010) ................................................... 36
Exhibit 49 Income Match Case Dispositions (FY 2010) .................................................. 37
Exhibit 50 Summary of Subsidy Cost Estimates for both Earned Income and
Unemployment Compensation.......................................................................................... 38

Analysis Plan

iii

May 3, 2010

Exhibit 51 Comparison of FY 2008 and FY 2009 Findings Using Nationally
Weighted Values ............................................................................................................... 39
Exhibit 52 Comparison of FY 2009 and FY 2010 Summary of Potential New Sources
of Income and Verification Requests................................................................................ 40
Exhibit 53 Gross Erroneous Payments by Source (FY 2010) .......................................... 41
Objective 16: ............................................................................................................................................ 41
Objective 17: ............................................................................................................................................ 41
Final Report Outline ......................................................................................................................42
APPENDICES
Appendix A: Definitions of Key Terms
Appendix B: Source Tables Responding to Each Objective
Appendix C: National Estimate Source Tables

Analysis Plan

May 3, 2010

INTRODUCTION
The purpose of this document is to describe how analyses will be conducted for the FY 2010
HUDQC Study: Quality Control for Rental Assistance Subsidy Determinations. The Department of
Housing and Urban Development (HUD) provides housing assistance through several rental
assistance programs. Subsidies are based on HUD regulations defining financial need, eligibility
requirements, and subsidy amounts. Generally, eligibility for a HUD-assisted housing unit requires a
total income equal to or below the very-low-income standard (50% of the median family income of
the area). The tenant payment is set at the higher of two amounts: 10 percent of total income, or
30 percent of adjusted income, based on certain types of deductions.
This study examines the following rent subsidy programs:
•

PIH-administered Public Housing (i.e., Public Housing)

•

PIH-administered Section 8 projects
- Moderate Rehabilitation
- Vouchers

•

Office of Housing-administered projects (i.e., owner-administered)
- Section 8 New Construction/Substantial Rehabilitation
- Section 8 Loan Management
- Section 8 Property Disposition
- Section 202 Project Rental Assistance Contracts (PRAC)
- Section 202/162 Project Assistance Contracts (PAC)
- Section 811 PRAC

The HUDQC Study focuses on the nature and extent of errors in rental assistance subsidies in the
assisted housing programs listed above. The overall purpose of the study is to determine the type,
severity, and cost of errors associated with income certification and rent calculations. This study will
produce national estimates of error in each program. A nationally representative sample of 2,400
households in approximately 600 projects nationwide will be selected for review and verification of
information used to determine rental assistance subsidies in their most recent (re)certification.1
In order to conduct this review and verification, we will execute the following steps:

The timing of the verification information is a key aspect of the study. This study seeks to verify information as of the
most recent (re)certification, or in the absence of a (re)certification, to verify information when the (re)certification was
due. If the (re)certification is more than one year overdue, verification will be obtained for the month the recertification
would have been effective if it had been completed on time. The fact that the study is being conducted after the
(re)certification has occurred, requires more attention to obtaining accurate reports and verifications than would be
needed if the study was done at the time of (re)certification. In order for the study to represent the population of assisted
households, it is necessary to select all households with equal probability, even if it means that their most recent
(re)certifications were performed up to a year before.

Analysis Plan

May 3, 2010

1. Review Household File. ICF Macro staff will use computer-assisted data collection
technology to review and extract information contained in each sampled household’s file.
The focus of the review is HUD’s forms 50058 and 50059 which are used by housing
managers to record information required for determining rental assistance eligibility and
subsidy amount; and the specific pieces of information contained in the file that are used by
management to verify the figures used in the 50058 or 50059. The 50058/59 forms also
contain the rent calculated by management.
2. Determine Procedural Errors. Using the information in the household file, ICF Macro will
re-calculate the rent on the basis of verification documentation and information contained in
the file. Discrepancies between the rent recorded on the 50058/59 and this recalculation will
indicate procedural errors.
3. Interview Households. Each household will participate in a detailed item by item interview,
capturing each element in the rent calculation. This interview will probe on all financial
resources and household circumstances, including those that may not be contained in the
tenant file. Household members will be asked to sign releases permitting ICF Macro to
obtain verification from relevant third parties for items lacking verification documentation in
the household file.
4. Conduct Enhanced Verification. Based on new or more accurate information provided by
the household, ICF Macro will independently obtain verification from third parties regarding
this new information. In addition, verification of benefits and earned income will be obtained
directly from official Federal-level sources by matching household member identifying
information (name, Social Security number, date of birth) with Social Security
Administration files and the National Directory of New Hires.
5. Calculate QC Rent. A rent calculation will be performed on the basis of verified
information, including that contained in the original household file and that obtained through
the interview process and third party verification.
6. Determine Error. Errors are defined as the difference between the rent calculation on the
50058/59 and rent determined by the QC rent calculation.
Using the data collected in the above steps and the error determinations, the data analysis will
proceed to address the study’s objectives.

Analysis Plan

May 3, 2010

RENT ERROR—THE DEPENDENT VARIABLE
Rent error in this study has several dimensions and definitions. At a very basic level, an error
pertains to the condition in which a tenant is receiving an incorrect amount of subsidy, based on
verified information.
Rent Used in Error Determinations. Error is determined by the difference between the rent
actually paid by the household and the rent that should have been paid, based on verified information
obtained by the HUDQC study:
•
•

Actual Rent—the monthly rent indicated on the 50058/59 forms or, if this item is missing,
this information is obtained from other sources in the household file. This is the monthly rent
for the year to follow the most recent (re)certification.
Quality Control (QC) Rent—the monthly rent calculated by ICF Macro using the
information reported by the household and verified, if possible, as well as the verified
information contained in the tenant file.

Calculation of Quality Control Rent. HUD specifies the formulas for determining assisted
household rent for each of its programs. These formulas generally consider adjusted annual income,
which is the total of household members’ earned and unearned income, less specific allowances.
There are several different calculation formulas, depending on the program and the specifics of each
household’s situation. These formulas are defined in the HUDQC Study Standards document
delivered under separate cover.
Error Definitions. Study objectives require that several different types of errors be estimated on the
basis of data collected in this study. The two primary distinctions are total errors and error rates.
Total Errors
•

Dollar Rent Error—the dollar amount of Actual Rent minus QC Rent for an individual
household. A negative number indicates an underpayment, meaning the household paid less
than it should and HUD’s subsidy was higher than it should have been. A positive number
indicates a household overpayment, meaning HUD’s contribution was less than it should
have been.

•

Total Gross Rent Error—the weighted sum of the absolute values of positive and negative
individual household Rent Dollar Errors.

•

Total Net Rent Error—the arithmetic value of the weighted sum of individual household
Rent Dollar Errors.

Error Rates
•

Dollar Error Rate—the quotient of Total Gross Rent Error divided by the weighted sum of
individual household QC rents.

•

Case Error Rate—the quotient of the weighted sum of Dollar Rent Errors in excess of $5
per month divided by the total weighted number of households.

Analysis Plan

May 3, 2010

Errors in rental assistance subsidies relate to both eligibility and amount of subsidy:
•

Eligibility Error—a household may not be eligible for rental assistance, which places the
entire subsidy in error.2

•

Subsidy Error—the amount of subsidy may be too high or too low.

Error sources are classified into two broad types:
•

Rent Error—any of the components used to determine rent (e.g., earned income, household
size, medical expenses) could be in error. These are often attributed to tenant misreporting,
but they can also be due to tenant misunderstanding.

•

Procedural Error—local housing administrative staff may make mistakes (e.g., calculation
errors, transcription errors, improper application of income or allowances) or they may fail to
follow HUD requirements (e.g., fail to recertify on time). Some procedural errors (e.g., not
requesting a Social Security number) do not produce rent errors.

Errors may be made in either the determination of initial eligibility or in the determination of the
correct household payment. Two types of payment errors may occur:3
•

Overpayment—household payment is above the correct amount, and HUD’s subsidy is too
low.

•

Underpayment—household payment is below the correct amount, and HUD’s subsidy is
too high.

Appendix A contains the definitions of all key terms used in this analysis plan.

Eligibility is determined at the time of initial certification; therefore, eligibility errors will be assessed only for
certifications, not recertifications.
3
It is possible that rent or procedural errors may produce no error in rent payment or subsidy amount. Some errors may
“cancel” others out, or the individual items may not be of sufficient magnitude to have an effect on rents or subsidies.

Analysis Plan

May 3, 2010

PREPARATION OF ANALYTIC DATA FILES
The main analytic data files will be based on the results of household file reviews, household
interviews, and third party verification. While we will be using the third party verified information to
determine errors, the analytic files must also contain the information collected from the household
files and household interviews to address the study objectives pertaining to error sources and causes.
The household file information is needed to identify the incidence of procedural errors; the
household interview data is needed to determine the incidence of household misreporting; both files
and the verification file are needed to determine the extent that various types of resources contribute
to error.
Our core master analytic file will consist of a household record containing:
•

Household Record Review Data—all information collected from the 50058/59, the items
that are verified and the type of verification observed; and the tenant rent.

•

Household Interview Data—all information collected during the household interview
pertaining to items needed to calculate rent and determine eligibility.

•

QC Verification Data—all information used to calculate the QC rent, consisting of verified
information obtained from third parties, information provided by the household, and verified
information obtained from the household file.

We will construct a series of analytic files to address the research questions, using the data in the
master analytic file. Error values (as defined by the methods described above) will be calculated and
appended to the main analytic file, and identify discrepancies and dollar differences between the
three sources of household data listed above. Additional variables will be constructed, including
error type (e.g., transcription, calculation). Weights equal to the inverse of the sampling fractions
will be appended so that national estimates can be produced. Variance estimates will be produced
using a replication procedure.
We will use two additional data sources. One of the study objectives is to determine whether
50058/59 data entered into TRACS/PIC has associated QC errors. Another objective is to determine
whether errors can be predicted from household and project characteristics. To obtain information on
housing project characteristics, we conduct a survey of local housing managers (i.e., Project Staff
Questionnaire, PSQ) from which we obtain information on characteristics of the housing project and
management practices. We will create separate analytic files to conduct the analyses associated with
the PSQ. Relevant household information will be appended to the project survey file. The study
sample will be matched with TRACS/PIC, and the 50058/59 data from TRACS/PIC will be
appended to the household data for analysis.

Analysis Plan

May 3, 2010

ANALYSIS PLAN BY STUDY OBJECTIVE
This section of the Analysis Plan discusses the study objectives and describes the analysis that will
address each objective. Appendix B contains a summary of the objectives and the source tables that
address each objective. Appendix C contains shells for the source tables. Source tables will be used
to produce the analytic exhibits displayed in the body of the report. We describe specific analytic
exhibits and provide shells for these in the discussion below.
Objective 1:

Identify the various types of rent errors and error rates and related
estimation variances.

This objective requires us to identify types of errors and produce national estimates of the proportion
of household cases with errors, along with associated variance estimates. These errors include the
percent of households paying correct and incorrect rent, average dollar rent error, and dollar error
rate. Analyses will cross-tabulate national estimates to produce a series of tables as described below.
To assure comparability with prior studies, the tabular displays will follow the previously used
formats and will include FY 2009 study results alongside the FY 2010 study results. Variance
estimates are displayed in tables discussed under Objective 3.
Exhibit 1 illustrates how we will display the percent of households with proper payments. It provides
the national estimate of the proportion of households whose QC rent is exactly equal to the Actual
Rent, and the proportion within $5 of an exact match. This exhibit also provides a comparison
between FY 2009 and FY 2010 results, and a comparison of results by program type.
Exhibit 1
Percent of Households with Proper Payments (FY 2009 and FY 2010)
Percent Matched Within $5

Percent Matched Exactly

Program Type
FY 2009

FY 2010

FY 2009

FY 2010

Public Housing
PHA-administered Section 8
Total PHA-administered
Total Owner-administered
Total
Source Tables 1a and 1b

Exhibit 2 provides further information on the tenant error rate, displaying the average dollars in error
and gross dollar error rate for the total population in PHA-administered and owner-administered
projects. It compares the FY 2009 results with the FY 2010 results.

Analysis Plan

May 3, 2010

Exhibit 2
Rent Error: Percent of Households in Error, Average Gross Dollars in Error, and Error Rate
(FY 2009 and FY 2010)

Program Type

Percent of Households in
Error
FY 2009

FY 2010

Average Gross Dollars in
Error
FY 2009

Gross Dollar Error Rate

FY 2010

FY 2009

FY 2010

Public Housing
PHA-administered Section 8
Total PHA-administered
Total Owner-administered
Total
Source Tables 1a and 2

Exhibits 3a and 3b display the dollar amount of error associated with tenant over- and underpayments. Exhibit 3a displays the percent of households paying less than the proper amount and the
average dollar underpayment error. Exhibit 3b displays the same information for households paying
more than the proper amount.
Exhibit 3a
Underpayment Households: Percent of Households and Average Monthly Dollar Amount of Error
(FY 2009 and FY 2010)

Program Type

Percent of Households with
Underpayment
FY 2009

FY 2010

Average Dollar Error for Households
with Underpayment
FY 2009

FY 2010

Public Housing
PHA-administered Section 8
Total PHA-administered
Total Owner-administered
Total
Source Tables 1b and 3

Analysis Plan

May 3, 2010

Exhibit 3b
Overpayment Households: Percent of Households and Average Monthly Dollar Amount of Error
(FY 2009 and FY 2010)
Percent of Households with
Overpayment

Program Type

FY 2009

FY 2010

Average Dollar Error for Households
with Overpayment
FY 2009

FY 2010

Public Housing
PHA-administered Section 8
Total PHA-administered
Total Owner-administered
Total
Source Tables 1b and 3

Objective 2:

Identify the dollar costs of the various types of error.

Previous QC studies identified several types of error that can be detected using information in the
household file. These errors are identified using data obtained from the 50058/59 directly as it
appears on the 50058/59 form, and other information from files used to determine which information
should be recorded on the 50058/59. Procedural errors are detectable through the analysis of the
household file data, and may or may not result in rent errors. This analysis will not use QC rent error
as a standard because the QC rent will be based on information obtained during the household
interview as well as verification obtained from third parties.
Calculation errors are detected by recalculating section subtotals and final rent based on the exact
information in the 50058/59 forms. The rent will be calculated using the detailed information on the
50058/59 and compared to the tenant rent on the 50058/59. If the two rents differ, this indicates a
calculation error.
Consistency errors are identified by assessing the logical conformity between elements within the
50058 or 50059 forms. For example, the yearly child care cost that is not reimbursed should only be
completed if any family member is less than 13 years old. Elderly status must be consistent with the
age of the head of household or spouse. If two items within the 50058/59 form contradict one
another, a consistency error exists.
Transcription errors are detected by comparing 50058/59 data with information obtained from the
household file. Each type of income and expense listed on the 50058/59 form is compared to the
supporting information found in the household file. If the 50058/59 data do not match the household
file data, a transcription error occurs.
The improper application of allowances and incorrect calculation of income are a subset of
transcription errors. Failure to apply allowances correctly and identify income correctly will be
identified by comparing household file information to 50058/59 data. Allowance errors will be
detected by calculating the allowances based on the household file and comparing this QC allowance
to the Actual Allowance on the 50058/59. Similarly, income will be calculated based on the types
and amounts of income reported in the household file.

Analysis Plan

May 3, 2010

A series of exhibits will display errors detected in household file data. Exhibit 4 presents the percent
of households with calculation and consistency errors in different sections of the 50058 and 50059
forms. More detailed data will be presented in Source Tables 4 (calculation errors) and 5
(consistency errors). Note that the 50058 form is formatted differently and in some sections provides
more line items of information than the 50059 form. Consequently, the number and types of
calculation and consistency errors on the forms will be different, and the findings from the two
forms will not be comparable.
Exhibit 4
Percent of Households with Calculation and Consistency Errors (FY 2010)
Percent of Households
50058/50059 Item

Calculation Errors

General Information

Consistency Errors

50058

50059

Total

n/a

50058

50059

Total

Household Composition
Net Family Assets and Income
Allowances and Adjusted Income
Family Rent and Subsidy Information
Total
Source Tables 4 and 5

Overdue Recertifications also produce errors in rents because rents are calculated using old
information. We will calculate the error amount due to overdue recertification, based on the
difference between Actual and QC Rent. Exhibit 5 will display the percent of cases with overdue
recertifications, timely recertifications, and new certifications. This exhibit will provide this
information by program type.
Exhibit 5
Timeliness of Certification Status (FY 2009 and FY 2010)
New Certifications

Timely Recertifications

Overdue Recertifications

FY 2009

Rent Component
FY 2010

FY 2010

Public Housing
PHA-administered Section 8
Total PHA-administered
Total Owner-administered
Total
Source Table 7

Analysis Plan

May 3, 2010

Our analysis will also graphically represent the proportion of cases that are new certifications, timely
recertifications, and overdue recertifications. Exhibit 6 illustrates the error associated with overdue
recertifications compared to errors from certification and timely recertifications. In cases with
overdue recertifications, the information at the time the recertification was due will be used to
determine rent, as it was in the previous studies.
Exhibit 6
Average Monthly Underpayment and Overpayment Dollar Amount Averaged across All Households
(FY 2009 and FY 2010)
Action Type

Underpayment
Average Dollar Amount
FY 2009

Overpayment Average
Dollar Amount

FY 2010

FY 2009

FY 2010

New Certification
Timely Recertification
Overdue Recertification
Total
Source Table 6

As in FY 2009, we will conduct additional analyses to summarize the information that addresses this
objective. Exhibit 7 provides the proportion of cases with procedural error, the estimated average
cost of each type of error, and the standard error of the estimated average (i.e., the variance estimate
of the average). That cost will be the difference between the actual rent and the recalculated rent,
using the household file information and correcting transcription and calculation errors.
Exhibit 7
Procedural Error: Percent of Households, Average Dollars in Error, All Households with 50058/59
Recalculated Rent (FY 2010)
Gross Rent Error
Error Type

Percent of
Households in
Error

Average
Dollars in
Error

Net Rent Error
Standard
Error of
Mean

Percent of
Households in
Error

Average
Dollars in
Error

Standard
Error of
Mean

Transcription Error
Calculation Error – Allowances
Calculation Error – Income
Calculation Error – Other
Overdue Recertifications
Any Procedural Error
All Households with Procedural
Errors
Source Table 8

Analysis Plan

May 3, 2010

Exhibit 8 provides a summary of the errors identified from the 50058/59 forms. These data are
produced using cross-tabulations and show the error rates and costs for households with each type of
procedural error, for households without procedural errors, and for the total weighted sample. The
exhibit presents the percent of households in error, the average dollar error, and the standard errors
for both households with recalculated 50058/59 error (error determined using only the 50058/59
form), and households with QC Rent error.

Analysis Plan

May 3, 2010

Exhibit 8
50058/50059 Procedural Error: Percent of Households, Average Dollars in Error (FY 2010)
Households with Recalculated 50058/59 Error
Error Type Based on 50058/59 Recalculation

(Standard
(Standard
Percent of
Error of
Average
Households in Error of
Mean)
Percent) Dollar Error
Error

Households with QC Rent Error
Percent of
Households in
Error

(Standard
Error of
Percent)

Average
Dollar
Error

(Standard
Error of
Mean)

Households with Consistency Error
Households without Consistency Error
Households with Allowance Calculation Error
Households without Allowance Calculation Error
Households with Income Calculation Error
Households without Income Calculation Error
Households with Other Calculation Error
Households without Other Calculation Error
Overdue Recertifications
On-time Recertifications
Certifications
Unduplicated Count, Any Type of 50058/50059 Error
Unduplicated Count, No 50058/50059 Error
Total
Source Tables 4–8

Analysis Plan

May 3, 2010

Incorrectly applied flat rent schedule will be identified by obtaining flat rent schedules from
PHAs and comparing them to the actual rent amount recorded on the 50058. This examination only
applies to public housing program tenants. HUD policy requires that “for families who choose flat
rents, the PHA must conduct a reexamination of family composition at least annually, and must
conduct a reexamination of family income at least once every three years.” [24 CFR 960.257 (a)(2)].
Therefore, multiple flat rent schedules to cover the three year period prior to the data collection
effort must be obtained and documented as to when they became effective. The correct flat rent for a
particular case will vary depending on when the last annual recertification was conducted. The
examination of flat rents and schedules will be accompanied by a discussion of the issues identified
during the analysis.
Objective 3:

Estimate national-level net costs for total errors and major error types.

This analysis will replicate the cross-tabulations developed in the previous studies that address error
dollars. Results from FY 2009 and FY 2010 will be presented for comparison. The gross rent error is
obtained by adding together the absolute values of the dollar amount of overpayments to the dollar
amount of underpayments. The net cost for total errors is an arithmetic calculation of the sum of
positive and negative nationally weighted error costs. This sum represents the net amount of tenant
payments in error and will be displayed by program type. Exhibit 9 provides this information with its
associated standard error.
Exhibit 9
Gross and Net Dollar Rent Error (Monthly) for All Households
(FY 2009 and FY 2010)
Average Dollars in Error
Gross Rent Error

Program Type
FY 2009

(Standard
Error)

FY 2010

Net Rent Error
(Standard
FY 2009
Error)

(Standard
Error)

FY 2010

(Standard
Error)

Public Housing
PHA-administered Section 8
Total PHA-administered
Owner-administered
Total
Source Table 9

Objective 4:

Determine the relationship between errors detectable by using the HUD50058 and HUD-50059 forms and total errors found in the study.

Objective 2 estimates procedural error that can be attributed to mistakes made by the housing
management staff. Except for overdue recertifications, it does not estimate QC error detected
through the verification process. The purpose of Objective 4 is to determine the relationship between
those procedural errors detected from the 50058/59 forms and the total error found after all
information was verified in the study. Exhibit 10 illustrates this analysis.

Analysis Plan

May 3, 2010

Exhibit 10
50058/59 Rent Calculation Error Compared to QC Rent Error
(FY2008 and FY2009)
Percent of Households with
Correctly Calculated Rent

Rent Calculation Method

FY 2009

FY 2010

Percent of Households with
Incorrectly Calculated Rent
FY 2009

FY 2010

Using Information on the 50058/50059 Form
According to the QC Rent Calculation
Both 50058/50059 calculation and QC Rent calculation
Source Table 1

Since HUD collects 50058/59 forms centrally on the TRACS/PIC System, it may be beneficial for
the agency to re-calculate information on the 50058/59 forms to help identify cases likely to be in
error. This decision could be made on the basis of the results of the descriptive analysis, or HUD
may choose to use more sophisticated techniques. Additional discussion of the use of PIC and
TRACS data to predict error is found under Objective 14.
Exhibit 11 presents the percent of households in error and the total annual program dollar errors,
comparing error obtained from household file information alone to error obtained from household
file information plus household interview information.
Exhibit 11
Percent of Households in Error and Dollar Error by Error Basis
(FY 2009 and FY 2010)
Percent of Households in Error

Total Annual Dollar Errors

Error Basis
FY 2009

FY 2010

FY 2009

FY 2010

Error based on household file and interview
information
Error based on household file information
only
Source Table 3

Objective 5:

Determine whether error rates and error costs have statistically
significant differences from program to program.

We plan to tabulate the household/tenant data to generate mean error rates and mean dollar costs for
each program type (Public Housing, PHA-administered Section 8, and owner administered) and
perform two-tailed t-tests to determine statistical significance of the differences across programs.
Specifically, we will compare program means of gross error rate, gross dollar error, net error rate,
and net dollar error. The gross error rate is the sum dollar amount of gross error divided by the sum
dollar amount of QC Rent, and the net error rate, which is the sum dollar amount of net error divided
again by the sum dollar amount of QC Rent. We will also aggregate the data to generate total gross
and net dollar errors for each program type by summing up, respectively, the two measures for the

Analysis Plan

May 3, 2010

sampled projects under each program type. Again, statistical significance of program differences
will be tested by two-tailed t-tests. Sampling weights and replicate weights will be used in variance
estimation for program differences in both means and aggregated measures. Exhibit 12 illustrates
how these results might be displayed.
Exhibit 12
The Impact of Program Type on Gross and Net Dollar Error (FY 2010)
Program Type

Average Gross Error

Gross Error Rate

Average Net Error

Net Error Rate

Public Housing
PHA-administered Section 8
Owner-administered

Objective 6:

Determine the apparent cause of significant rent errors to provide HUD
with information on whether the error was caused primarily by the tenant
or by program administrator staff.

Understanding the sources and causes of significant rent errors is important for determining
corrective actions. First, it is necessary to have an understanding of which items in the rent
calculation formula contribute most to error. Second, it is important to understand whether this error
is caused by the tenant or by the project’s administrative staff. However, it is sometimes difficult to
disentangle the source and cause of errors. Transcription and calculation errors, improper application
of allowances, and failure to recertify on time are procedural errors. These are clear responsibilities
of the project’s management and administrative staff. The cause of discrepancies between the
information used to calculate rent by the project and that obtained through the QC verification
process is not always clear. Tenants may have failed to report an income item because they
intentionally withheld the information to pay less rent; they may not have been asked to report an
item during the interview; or they may have misunderstood the requirement. For that reason, we
prefer not to ascribe to the tenant all errors attributed to discrepancies between information in the
project files and the QC verification process. It may often be the case that the error is due to the
tenant, but this study will not be able to make that determination. Therefore, we consider
discrepancies between information used to determine rent and verified information as sources of
error, rather than ascribing cause to tenants or project staff.
For the purposes of analysis and corrective action, it is useful to learn which elements in the rent
computation formula contribute to QC errors. Even if we don’t know why items such as income or
medical expenses were inaccurate, HUD will know that these items should be given more careful
attention by local project staff when they obtain information from tenants and/or verify information
from third parties.
We propose two levels of analysis to address this issue. First, we will provide descriptive
information on the sources of discrepancies between housing file information and verified
information, and describe the incidence of procedural errors and their impacts. Exhibit 7, already
presented in our discussion of objective 4, describes the proportion of cases with procedural errors

Analysis Plan

May 3, 2010

(i.e., calculation, transcription, improper application of allowances, improper calculation of income,
and overdue certification), and their corresponding QC rent error.
It shows the relationship between these procedural errors and QC errors. Second, we will produce
exhibits that illustrate another type of procedural error—failure to verify information or
inappropriate application of verification information, as shown in Exhibits 13, 14, and 15 below.
Source Table 11 will provide the data for these exhibits.
Exhibit 13 presents the number of households by rent component where verification was not
obtained, where it was obtained but the verification amount did not match the amount used on the
50058/59, and where verification was obtained and it did match the amount used on the 50058/59.
Exhibit 13
Percent of Households with Verification of 50058/50059 Rent Components
(FY 2009 and FY 2010)

Rent Component

No Project Verification
FY 2009

Item Verified by Project

FY 2010

FY 2009

FY 2010

Verification Matched
50058/59
FY 2009

FY 2010

Earned Income
Pensions
Public Assistance
Other Income
Asset Income
Dependent Allowance
Elderly Allowance
Child Care Allowance
Disability Allowance
Medical Allowance
Source Table 11

Analysis Plan

May 3, 2010

Exhibit 14 provides case file verification information by program type. It displays the percent of
households where the rent component was verified, as well as the percent of the verification for each
rent component found in the tenant file that matched the data on the 50058/50059 form within $100.
Exhibit 14
Verification of 50058/50059 Rent Components (FY 2010)
PHA-administered Section 8
Rent Component

Verified

Owner-administered

Matched*

Verified

Public Housing

Matched*

Verified

Matched*

Earned Income
Pensions
Public Assistance
Other Income
Asset Income
Child Care Expense
Disability Expense
Medical Expense
Source Table 13. * Matched within $100

Exhibit 15 takes the analysis a step further. It provides data on whether failure to verify sources of
income and expenses was a contributor to QC error. It displays the percent of households with QC
error for which verification was missing in the household file. Each error is presented by rent
component.
Exhibit 15
QC Error Households with Missing Verification in the Tenant File
(FY 2009 and FY 2010)
50058
Rent Component

50059

Households with QC
Error

Households with QC
Errors and Missing
Verification

Households with QC
Error

Households with QC
Errors and Missing
Verification

FY 2009

FY 2010

Earned Income
Pensions
Public Assistance
Other Income
Asset Income
Child Care Expense
Disability Expense
Medical Expense
No Component Error
Source Table 11

Analysis Plan

May 3, 2010

Rent components—the elements used to calculate rent—are another source of error, so we will
conduct analyses of rent component error. Exhibit 16 shows the relationship between errors in each
rent component and the average dollar amount for cases in error.
Exhibit 16
Rent Components Responsible for the Largest Dollar Error
Households with Rent Error (listed by amount of dollar error)
(FY 2009 and FY 2010)
Rent Component

Percent of Households
in Error
FY 2009

FY 2010

Average Dollar Amount
FY 2009

FY 2010

Earned Income
Other Income
Pensions
Asset Income
Public Assistance
Child Care Expenses
Medical Expenses
Dependent Allowance
Disability Expenses
Elderly/Disabled Allowance
No Rent Component Error
Source Table 12

Analysis Plan

May 3, 2010

Exhibit 17 compares the percent of total households with and without component error by
component type and payment type. It also compares this information for households in different
housing program types.
Exhibit 18 presents the standard errors for the total number of households with and without
component error by component type and payment type.
Exhibit 17
Income and Expense Component Error by Payment Type for All Households (FY 2010)
Income/Expense Component

Underpayment
PHA

Owner

Proper Payment

Total

PHA

Owner

Total

Overpayment
PHA

Owner

Total

Earned Income
Pension Income
Public Assistance Income
Other Income
Asset Income
Dependent Allowance
Elderly Household Allowance
Child Care Allowance
Disability Assistance Expense
Medical Expense
No Rent Component Error
Source Table 13

Exhibit 18
Percent of Households and Standard Error by Rent Component and Payment Type (FY 2010)
Underpayment
Component

Percent of
Total
Households

Proper Payment

Standard
Error

Percent of
Total
Households

Standard
Error

Overpayment
Percent of
Total
Households

Standard
Error

Analysis Plan

May 3, 2010

Exhibit 19 will provide the annual gross dollar error and the percent of dollar error attributed to each
component.
Exhibit 19
Annual Gross Dollar Error by Largest Component Error for Households with Rent Error (FY 2010)
Largest Component
Error

Annual Gross
Dollar Error

Col % of Dollar
Error

Number of Cases
in Error (in 1,000)

Col % of Cases in
Error

Earned Income
Pensions
Medical Allowance
Child Care Allowance
Dependent Allowance
Asset Income
Elderly/Disabled
Allowance
Other Income
Public Assistance
No Rent Component
Error
Total
This table presents the sum of gross dollar error for cases categorized by their largest component error. Many individual cases have errors in
multiple components.

Exhibit 20 will explore whether elderly/disabled and dependent allowances4 are applied correctly.
Exhibit 20
Percent of Households with Elderly/Disabled Allowances and Dependent Allowances (FY 2010)
Elderly Allowance
Non-Elderly/
Non-Disabled
Households

Elderly/
Disabled
Households

Dependent Allowance

All
Households

Households
without
Dependents

Households
with
Dependents

All
Households

No Allowance
Incorrect Allowance
Correct Allowance
Source Table 15

Households with an elderly or disabled head or spouse are entitled to one $400 allowance (i.e., deduction from gross
annual income) in calculating rent. Households are entitled to a $480 allowance for each dependent (defined as children
under 18, full-time students, and disabled members other than the head or spouse).

Analysis Plan

May 3, 2010

Objective 7:

Determine the extent to which households are overhoused relative to
HUD’s occupancy standards.

This objective addresses whether households reside in units with the correct number of bedrooms.
Generally acceptable standards5 specifying the appropriate size unit for PHA-administered Section 8
households are shown in Exhibit 21 below.
Exhibit 21
PHA Section 8 Unit Size Standards
Number of Persons in Household
Number of Bedrooms
Minimum

Maximum

There are exceptions to these guidelines. If a tenant is elderly, disabled, pregnant, or meets other
criteria, they may be allowed a larger bedroom unit. There are also circumstances when households
are allowed smaller bedroom units. The determination of appropriate bedroom size is locally based.
For this study it will be based on the Data Collection Standards, delivered under separate cover,
which specify rules for bedroom size.
Overhousing refers to tenants occupying units that exceed the bedroom size allowed by HUD
regulation for their actual household size. This study will replicate the analysis completed in
previous studies, identifying by bedroom size and program, the proportion of households in
compliance with and in violation of occupancy standards. This analysis will be conducted with
national estimates of proportions in tabular displays showing the results for FY 2009 and FY 2010.

Local projects have discretion in determining unit size, and may determine unit size differently than shown.

Analysis Plan

May 3, 2010

Exhibit 22 presents the percent of households in units with the correct number of bedrooms by
program type with information for both the FY 2009 and FY 2010 study. Exhibit 23 presents the
overall findings. The shaded cells generally indicate incorrect unit assignments.
Exhibit 22
Percent of Households in Units with Correct Number of Bedrooms
(According to Study Guidelines)
(FY 2009 and FY 2010)
PHA-administered
Number of
Bedrooms

Public Housing
FY 2009

Owner-Administered

Section 8

FY 2010

FY 2009

FY 2010

FY 2009

FY 2010

Total
FY 2009

FY 2010

0
1
2
3
4
5
All Units
Source Table 16

Exhibit 23
Percent of All Households by
Number of Bedrooms and Number of Household Members (in thousands) (FY 2010)
Number of
Bedrooms

Number of Household Members
1

0
1
2
3
4
5
Source Table 16

Objective 8:

Provide information on the extent to which errors are concentrated in
projects and programs.

We will determine the degree to which errors are concentrated in certain projects, as opposed to
randomly distributed across the sample. On the one hand, if most errors are caused by the project
staff, we would expect to find errors clustered in certain projects. On the other hand, if errors are
mostly caused by the tenant, we would expect to find errors randomly distributed among projects.
We will explore the application of the hierarchical linear modeling (HLM) technique to partition the
variance of rent error and estimate the proportion of variance at the project level. Given the nested

Analysis Plan

May 3, 2010

data structure (household/tenants within the project), HLM allows us to formally estimate the
variance at the two levels and model the variance with predictor variables if the project level
variance is substantially large.
Using information obtained from the Project Staff Questionnaire in combination with
household/tenant data, we will conduct multivariate analyses to explore the association between
project characteristics (e.g., program type, staff training practices, percent of elderly tenants,
management practices) and error rates. This analysis will identify how each of these variables
contributes to differences in error. The results will provide HUD with information to guide the
management of error rates, and will elaborate relationships between management practices and
project/tenant characteristics associated with error rates.
Objective 9:

Estimate the percentage of newly certified tenants who were incorrectly
determined eligible for program admission.

Incorrect initial eligibility determinations create long-term problems for assisted-housing programs.
It is key to prudent housing management practices to correctly determine initial eligibility criteria.
Eligibility for housing assistance is based on five certification criteria: family composition,
citizenship, verification of Social Security numbers, signed consent forms, and low and very low
income limits. This study will examine eligibility criteria and verify the accuracy of collected
information. We will examine citizenship, Social Security number, consent form and low income
criteria, and present results as shown in Exhibit 24, and by program type, as in Exhibit 25.
Exhibit 24
Percent of Newly Certified Households Meeting Certification Criteria (FY 2010)
Percent of Households
Certification Criteria
Met Criterion

Did Not Meet
Criterion

Unable to Determine

Citizenship
Social Security Number
Consent Form
Low and Very Low Income
Meets All Eligibility Criteria
Source Table 17

Analysis Plan

May 3, 2010

Exhibit 25
Percent of Newly Certified Households Meeting Certification Criteria (FY 2010)
Percent of Households Meeting the Criteria
Certification Criteria

PHA-administered
Section 8

Public Housing

Owner-administered
Section 8

Citizenship
Social Security Number
Consent Form
Low and Very Low Income
Meets All Eligibility Criteria
Source Table 18

Objective 10: Determine the extent to which Section 8 Voucher rent comparability
determinations are found in the tenant file, and indicate the method used
to support the determination. Determine whether Voucher payment
standards are within 90–110 percent of fair market rents, and determine
whether the correct utility allowances are being applied.

Objective 10 examines several issues related to the Section 8 Voucher program that have important
but indirect influences on rent errors.

RENT REASONABLENESS ANALYSIS
To comply with the rent reasonableness requirement, housing authorities must determine that
Section 8 Voucher rents are reasonable in comparison to rents for similar housing in the private,
unassisted market. We will determine, based on information obtained from PHAs, their usual
method for assessing rent reasonableness. Exhibit 26 illustrates these results.
Exhibit 26
Rent Reasonableness Determination Methods (FY 2010)
PHAs Using Method
Method for Assessing Rent Reasonableness
Number

Percent

Unit-to-Unit Comparison
Unit-to-Market Comparison
Point System
Other or Rent Control
No Information Provided
Total

Analysis Plan

May 3, 2010

Using information collected from household files, we will estimate the proportion of new admission
Section 8 Voucher recipients with rent reasonableness documentation. We will also determine the
timing of their most recent determination, and compare this to the results from FY 2009. Exhibits 27
and 28 illustrate these results.
Exhibit 27
Rent Reasonableness Documents in Files for New Admissions
(FY 2009 and FY 2010)
FY 2009
Status

Units in
1000s

Percent

FY 2010
Units in
1000s

Percent

Determination documented
A signed statement certifying that the rent is reasonable
Comparable units documented by the property owner in
section 12a of HUD 52517
Comparable units documented on other documents
Any other reference to rent reasonableness
Missing reference
No determination documented
Total

Exhibit 28
Timing of Most Recent Rent Reasonableness Determination—New Admissions
(FY 2009 and FY 2010)
FY 2009
Determination-Certification Chronology

Units in
1000s

Percent

FY 2010
Units in
1000s

Percent

More than 4 months before lease date
Up to 4 months before lease date
After lease date—up to 2 months
After lease date—greater than 2 months
Date missing
Total

Analysis Plan

May 3, 2010

Annual recertifications require rent reasonableness documents only when owners increased rental
rates. We will examine case files to determine when the current rent first became effective, and
whether rent reasonableness documentation is present in the files. This analysis is displayed in
Exhibit 29. We will also compare timing of determinations from FY 2009 and FY 2010, as Exhibit
30 illustrates.
Exhibit 29
Rent Reasonableness Documents for Annual Recertifications
(FY 2009 and FY 2010)
FY 2009
Status

Units in
1000s

Percent

FY 2010
Units in
1000s

Percent

Exhibit 30
Timing of Most Recent Rent Reasonableness Determination—Annual Recertifications
(FY 2009 and FY 2010)
FY 2009
Determination-Certification Chronology

Units in
1000s

Percent

FY 2010

Units in 1000s

Percent

More than 4 months before lease date
Up to 4 months before lease date
After lease date—up to 2 months
After lease date—greater than 2 months
Date missing
Total

Analysis Plan

May 3, 2010

PAYMENT STANDARDS ANALYSIS
HUD will supply the published Fair Market Rents (FMR) to ICF Macro. This information will be
compared to payment standard data from the 50058 form, which will be captured during the data
collection process. As Exhibit 31 indicates, payment standard discrepancies will be tabulated by
reason for the discrepancy. Household rents outside of the 90–110 percent band of the FMR will be
appropriately flagged and this information will be sent to HUD. The comparison of FMRs and
payment standard data will result in a table that summarizes the number and percent of households
below, in, and above the 90–110 percent band. Exhibit 32 displays this.
Exhibit 31
Number and Percent of Households with Payment Standard Discrepancies (FY 2010)
Number of
Households
(Elderly/
Disabled)

Reason

Number of
Households
(Non-Elderly/
Disabled)

Total Percent
of Households
with
Discrepancies

Wrong Number of Bedrooms was Used
Gross Rent instead of the Payment Standard was Used
Old Payment Standard Amount was Used
Other Reasons; Decrease in Payment Standard, Typos, Used the
FMR, Limitation of the Computer Software System
Total
Data provided in this table are not weighted.

Exhibit 32
Percent of Households by Fair Market Rent Category
after Comparing Payment Standard to Fair Market Rent (FMR; FY 2010)
Percent of Households

Fair Market Rent Category
Under 90% FMR

90–110% FMR

Over 110% FMR

Less than $500
$500–$599
$600–$799
$800–$999
$1,000–$1,199
$1,200–or Higher
All Voucher Households

Analysis Plan

May 3, 2010

For households that fall outside the 90–110 band, we will determine whether they received an
exemption. Exhibit 33 illustrates this analysis.
Exhibit 33
Percent of Households Meeting Payment Standard Requirements (FY 2010)
Percent of Households
Under 90% 90–110%
FMR
FMR

Over
110%
FMR

Total
Percent
Outside the
90–110%
Band

Payment Standard Compared with Fair Market Rent
Households Granted an Exemption
Households (without exemptions) with Elderly or Disabled Members
Households Not Meeting Requirements

ICF Macro will also obtain payment standard schedules from the PHAs included in the study. We
will determine the correct payment standard for each household, using the PHA schedules, and
compare this amount to the payment standard data from the 50058. Where discrepancies are found,
we will attempt to determine the reason for the discrepancy. This analysis will be summarized and
presented with the above analysis.

UTILITY SCHEDULES
The types of documents used by PHAs to calculate utility allowance values will be tabulated.
Voucher utility allowances will also be evaluated by comparing the utility allowance amount
recorded in the household file utility worksheet to the utility allowance recorded on the 50058/59
form, and to the amount calculated using the PHA utility allowance schedule. ICF Macro will obtain
utility schedules in use by the PHAs and the utility allowance worksheet from the household file. We
will compare the total utility allowance amount, the number of bedrooms, and the address. Exhibits
34 and 35 illustrate this analysis.
Exhibit 34
Type of Document Used by the PHA to Calculate the Utility Allowance Value (FY 2010)
Type of Document

Number of PHAs

Percent of PHAs

HUD Form 52667
HUD Form 52641—HAP contract
PHA Created Form
HUD Form 52617—Tenancy Approval
Combination of Above
Total
Data in this table are not weighted.

Analysis Plan

May 3, 2010

Exhibit 35
QC Utility Allowance Comparison Findings (FY 2010)
Number

Percent

Outcome
No Worksheet Was Available
QC UA Matched Amount on 50058
Worksheet Was Missing Critical Information
Discrepancy in Number of Bedrooms
Discrepancy Due to Math Error
Discrepancy—Incorrect Schedule Used
Discrepancy—Unable to Determine Reasons
Total

Data in this table are not weighted.

Objective 11:

Estimate total positive and negative errors in terms of HUD subsidies.

The actual cost of errors to HUD is expressed in terms of subsidy payments. HUD subsidies for
assisted housing programs equal the allowed expense level or payment standard minus the tenant
rent. In the previous study, proper payments were defined as those in which the Actual Rent equals
the QC Rent (i.e., there is no dollar error in the tenant payment). Errors can be either overpayments
(Actual Rent greater than QC Rent) or tenant underpayments (Actual Rent less than QC Rent).
Overpayment error rates are computed by dividing the total amount of overpayment by the total
Actual Rent; underpayment error rates are calculated by dividing the total amount of underpayments
by the total Actual Rent. Tenant overpayments are negative subsidy errors; tenant underpayments
are positive subsidy errors. Tables as shown in Exhibits 36, 37 and 38 below will illustrate the
results of these comparisons.
Exhibit 36
Negative Subsidy Households (Under-subsidies)
Percent of Households and Average Monthly Dollar Amount of Error
(FY 2009 and FY 2010)
Average Dollar Amount of Error
Program Type

Negative Subsidy
Households
(with errors > $5)

Percent of Households
in Error
FY 2009

FY 2010

FY 2009

FY 2010

All Households
FY 2009

FY 2010

Public Housing
PHA-administered Section 8
Total PHA-administered
Total Owner-administered
Total
Source Tables 1b and 3

Analysis Plan

May 3, 2010

Exhibit 37
Positive Subsidy Households (Over-Subsidies)
Percent of Households and Average Monthly Dollar Amount of Error
(FY 2009 and FY 2010)
Average Dollar Amount of Error

Administration Type

Positive Subsidy
Households
(with errors > $5)

Percent of Households
in Error
FY 2009

FY 2010

FY 2009

All Households

FY 2010

FY 2009

FY 2010

Public Housing
PHA-administered Section 8
Total PHA-administered
Total Owner-administered
Total
Source Tables 1b and 3

Exhibit 38
Average Monthly Dollar Amounts of Error for Negative (Under-) and Positive (Over-) Subsidies
Averaged Across All Households
(FY 2009 and FY 2010)

Household Type

Negative Subsidy Average Dollar
Amount of Error
FY 2009

FY 2010

Positive Subsidy Average Dollar
Amount of Error
FY 2009

FY 2010

Certifications
Non-overdue Recertifications
Overdue Recertifications
Total
Source Tables 1b and 3

Objective 12: Determine the extent to which error rates in projects that use an
automated rent calculation system differ from errors in those that do not.

In previous studies we found that the vast majority of projects used computers for various
administrative processes. For the FY 2010 study, we will augment these findings by examining the
data to measure the sophistication of computer and information technology use by projects. We will
build a scale to gauge the extent to which project personnel use computer technologies in
information collection/integration, rent calculation, verification, and database management. Exhibit
39 displays the possible administrative tasks for which projects may use computer technology.

Analysis Plan

May 3, 2010

Exhibit 39
Percent of Projects Using Computer Software for Administrative Tasks in the Past 12 Months (FY 2010)
Percent Using Computer Software
Administrative Tasks

Public Housing
Projects

PHA-Administered Owner-Administered
All Projects
Section 8 Projects
Projects

Interview tenants and record answers
Keep track of pending verifications
Input verified information
Calculate rent
Print the 50058/50059 form
Conduct accounting tasks
Track maintenance activities
Print letters to the tenants
Assign recertification dates/appointments
Print checks
Submit tenant information to HUD
Conduct rent reasonableness comparisons
Maintain demographics on the population
Keep other types of statistics
Do not use computers
Total Number of PHA/Projects

We will also examine use of computers by project size, as illustrated by Exhibit 40.

Analysis Plan

May 3, 2010

Exhibit 40
Percent of Projects Using Computer Software Uses in the Past 12 Months, by Project Size (FY 2010)
Percent Using Computer Software
Administrative Tasks

Projects with
<150 Units

Projects with
150 to 500 Units

Projects with
>500 Units

Objective 13: Determine whether other tenant or project characteristics on which data
are available are correlated with higher or low error rates.

Prior HUDQC studies have identified a number of tenant and project variables that accounted for
rent errors. We will build upon the information to further examine household/tenant and project
characteristics that are potentially related to errors. Multiple regression with combined project and
household data will be conducted to examine this issue.
Many Federal and state agencies use error-prone modeling techniques to identify cases with a high
probability of being in error. These techniques are often used in welfare, Medicaid, student aid, food
assistance, and tax compliance programs. A variety of tools have been used, including regression
analysis, sequential search techniques, discriminant analysis, correlation and regression trees
(CART), and other statistical methods, depending on the nature of the available data. Ideally, these
methods are used to develop equations that predict the likelihood a case is in error or an
administrative unit is making errors.
Error prone models provide a cost-effective means to target quality control monitoring efforts by
identifying specific types of households and projects likely to exhibit high error rates. We will use
multivariate regression techniques, path analysis, and CART to develop error-prone models.
The dependent variable in these analyses will be rent errors.

Analysis Plan

May 3, 2010

Project characteristics (e.g., PHA/project size; staff training methods) and tenant characteristics
(e.g., number of sources of income; type of expenses) will be used as independent variables. Where
possible, we will incorporate data from TRACS/PIC into the models to provide HUD with more
information for identifying projects and households likely to exhibit high error rates. Although the
explanatory findings of error-prone models are important, we believe that such models will be most
useful to HUD if its analysts can combine the findings from program data (e.g., TRACS/PIC) to
target projects and households likely to exhibit high error rates. In this proposed study, our errorprone modeling efforts will focus on producing practical tools that HUD analysts can use in ongoing
quality control efforts.
Objective 14: Determine whether cases for which 50058/59 data had been submitted to
HUD were more or less likely to have errors than those for which data
had not been submitted.

A national database of tenant 50058/59s is maintained by HUD on the TRACS/PIC system.
However, not all tenants are on the system. There are concerns about projects that fail to routinely
transmit information to TRACS/PIC, and it is hypothesized that a reason for this failure is that
recertifications are not performed on a timely basis. The existence of TRACS/PIC in concert with
the QC study provides the opportunity to investigate the relationship between TRACS/PIC reporting
and rent accuracy.
ICF Macro will compare QC error rates for sampled tenants who appear on TRACS/PIC with those
who do not. Any difference that is greater than sampling error would be considered significant.
The results will be presented, as shown in Exhibits 41 and 42, for program type and payment type.
The total population will be used to determine the average dollars in error.
Exhibit 41
Average Dollars in Error by Program Type and TRACS/PIC Data (FY 2010)
TRACS/PIC Present
Administration Type

Percent of All
Households in
Error

Average Dollars in
Error

TRACS/PIC Absent
Percent of All
Households in
Error

Average Dollars in
Error

Public Housing
PHA-administered Section 8
Total PHA-administered
Total Owner-administered
Total
Source Table 19

Analysis Plan

May 3, 2010

Exhibit 42
Average Dollars in Error by Payment Type and TRACS/PIC Data (FY 2010)
TRACS/PIC Present
Payment Type

Percent of Households
in TRACS/PIC

TRACS/PIC Absent

Average Dollars in
Error

Percent of Households
Not in TRACS/PIC

Average Dollars in
Error

Overpayment
Underpayment
Proper Payment
Total
Source Table 20

Analyses will identify the number of households where the effective date of action on the
50058/50059 used in the study matches the effective date of action in the TRACS/PIC file. For those
households that match on effective date of action, we will determine whether certain key variables
match. Variables included in this analysis will be gross income, net income, tenant rent, and total
tenant payment (TTP). Exhibit 43 provides the percent of households where key variables on the
50058/59 forms matched the TRACS/PIC data.
Exhibit 43
Percent of Matched and Non-Matched Dollar Amounts for Key Variables
Matching Variables from the 50058/50059 Form and TRACS/PIC Data Files (FY 2010)
Gross Income
PIC

TRACS

Net Income
PIC

TRACS

Total Tenant Payment
PIC

TRACS

Tenant Rent
PIC

No Match
Match
Subtotal
Missing
Total
Source Table 20

The households which included variables where the 50058/50059 data did not match the
TRACS/PIC data will be reviewed to determine if these households’ rent was calculated in error.
Exhibit 44 displays the cases with discrepancies in gross income, net income, total tenant payment,
and tenant rent, and the percents that also have rent errors.

Analysis Plan

May 3, 2010

Exhibit 44
Percent of Gross Dollar Rent Errors for Cases Where Key Variables Did Not Match (FY 2010)
Gross Income

Net Income

Total Tenant Payment

Tenant Rent

Rent Error Status
PIC

TRACS

PIC

TRACS

PIC

TRACS

PIC

Rent Error
No Rent Error
Total

Analysis will also be conducted to determine whether non-matching households had consistency,
transcription or calculation errors within the 50058/50059. Exhibit 45 presents these households by
type of error.
Exhibit 45
Percent of Procedural Errors for Cases Where Key Variables Did Not Match (FY 2010)
Calculation and
Consistency Error
Status

Gross Income
PIC

TRACS

Net Income
PIC

TRACS

Total Tenant Payment
PIC

TRACS

Tenant
Rent
PIC

Consistency Error
Allowance Calculation
Error
Income Calculation
Error
Other Calculation Error
Transcription Error

Objective 15: Determine the extent of errors that were due to unreporting of income by
tenants

All household members in the QC study will be matched with the National Directory of New Hires
(NDNH) database to identify sources of earnings and unemployment compensation benefits
received, but not reported, by tenants. Following the guidelines provided in the HUD Income
Matching Procedures for Analyzing Income Match Data, unreported sources of income will be
identified and the subsidy overpayment dollars associated with those unreported sources of income
will be identified.
Analysis will be conducted to categorize the information obtained via the NDNH match with that
collected in the QC Study. Categorization will be done separately for earned income and
unemployment compensation as Exhibits 46 and 47 illustrate.

Analysis Plan

May 3, 2010

Exhibit 46
Categorization of Earned Income for Each Household by Program Type (FY 2010)

Categories

PHA-administered
Section 8

Owner-administered
Section 8

Number

Percent

Total
Number

Percent

NDNH and QC Employer Are the Same
NDNH Earnings Are Not Considered to be New
Unclear Whether NDNH and QC Are the Same
Total

For those income match items where it is unclear whether they match with the QC Study
information, third party verification requests will be made. Our analysis will produce a table such as
the one in Exhibit 48 to summarize earned income verification requests by program type.
Exhibit 48
Results of Verification Attempts (FY 2010)
PHA-administered Section 8

Owner-administered
Section 8

Total

Third Party Verification Requests
Number
Requested

Number/
Percent

Number
Requested

Number/
Percent

Number
Requested

Number/
Percent

Directly to the Employer
The Work Number
Total Number of Requests

Analysis Plan

May 3, 2010

After the third party verification has been obtained and reviewed, each case will be given a final
disposition regarding the match of QC Study and NDNH match data. As depicted in Exhibit 49,
information will be presented by program type for both earned income and unemployment
compensation separately.
Exhibit 49
Income Match Case Dispositions (FY 2010)
PHA-administered
Third Party Verification Requests
Public Housing

Section 8
Vouchers

Owner-administered
Section 8

Total

QC Household Sample
QC Households Reporting Earnings or
Unemployment Compensation
Households with NDNH Identified
Income Sources Unmatched with QC
Study Sources
Earned Income
Unemployment Compensation
QC Households with Countable
Unreported Income
Earned Income
Unemployment Compensation
Total Countable Unreported Income that
Affected Subsidy Determinations for QC
Households

Analysis Plan

May 3, 2010

Further analysis will provide the subsidy cost estimates by program type for both earned income
and unemployment compensation. Both unweighted and weighted values will be provided as
indicated in Exhibit 50.
Exhibit 50
Summary of Subsidy Cost Estimates for both Earned Income
and Unemployment Compensation
Unweighted Values
Cases w/ Unreported Income

Program Type

Nationally Weighted Values
Cases w/ Unreported Income

EARNED INCOME
PIH-administered—Public Housing
Households in Error
Unreported Income
Subsidy Cost
PIH-administered—Section 8 Vouchers
Households in Error
Unreported Income
Subsidy Cost
Owner-administered
Households in Error
Unreported Income
Subsidy Cost
UNEMPLOYMENT COMPENSATION
PIH-administered—Public Housing
Households in Error
Unreported Income
Subsidy Cost
PIH-administered—Section 8 Vouchers
Households in Error
Unreported Income
Subsidy Cost
Owner-administered
Households in Error
Unreported Income
Subsidy Cost
TOTAL
Household in Error
Unreported Income
Subsidy Cost

Analysis Plan

May 3, 2010

Finally, income match findings from FY 2009 and FY 2010 will be compared. The first comparison
will focus on weighted subsidy costs while the second will provide a summary of potential new
sources of income and verification requests (Exhibits 51 and 52).
Exhibit 51
Comparison of FY 2008 and FY 2009 Findings Using Nationally Weighted Values
FY 2009
Cases w/ Unreported Income

Program Type

FY 2010
Cases w/ Unreported Income

Analysis Plan

May 3, 2010

Exhibit 52
Comparison of FY 2009 and FY 2010
Summary of Potential New Sources of Income and Verification Requests
FY 2009
Owner-admin

PIH-admin

FY 2010
Total

Owner-admin

PIHadmin

Total

Total Households With Potential
New Sources of Income
Employers to Whom Third Party
Requests Were Sent
Employers from Whom Third
Party Verification Was Received

In recent years HUD has requested an additional analysis that depicts gross erroneous payments
associated with income sources covered by EIV. Specifically, the focus of this information is to
present findings related to unreported income. Exhibit 53 will be created using the FY 2010 data and
findings will be listed by program type and for all assisted housing programs together.

Analysis Plan

May 3, 2010

Exhibit 53
Gross Erroneous Payments by Source (FY 2010)
A. Total Gross Erroneous Payments. Calculated by adding together erroneous payments identified in the QC
study with erroneous payments identified through the Income Match study.
Public
Housing
1

Gross Erroneous Payments from the
FY2007 HUDQC Report

Erroneous Payments for Households with
Unreported Unemployment Compensation
from Income Match

4
C.
5

6
7

PHA-Admin
Section 8

OwnerAdministered

Total

Erroneous Payments for Households with
Unreported Earned Income from Income
Match
TOTAL Gross Erroneous Payments
Erroneous Payments Associated with Unreported SSA/SSI benefits
Erroneous Payments for Households with
Unreported SSA/SSI Benefits (Included in
Gross Erroneous Payments from the
HUDQC Report above)
Percentage of Payment Error Attributable to the Income Sources Covered by EIV
Percent of Payment Error Attributable to
Unreported SSA/SSI
Percent of Payment Error Attributable to
Unreported Unemployment
Compensation
Percent of Payment Error Attributable to
Unreported Earned Income

Objective 16: Determine the extent of program administrator rent and income
determination errors

This objective is essentially a summary of objectives 1 through 3. The percent of households in error
and the dollars associated with those households will be determined analytically and reported
accordingly. Refer to Exhibits 1–9 in this document for how we will fulfill objective 16
(i.e., objectives 1–3).
Objective 17: Determine the extent of errors due to Multifamily Housing Program
administrators billing for subsidy that did not correspond to the subsidy
reported on the HUD-50019/HUD-50059A for a tenant household.

A separate deliverable if being created that details all aspects of the Billing Study. The analysis
plans for the Billing Study will be presented in this document.

Analysis Plan

May 3, 2010

FINAL REPORT OUTLINE
The final report will communicate all study findings and recommendations to HUD, the assisted
housing community, the Congress, and other interested parties. As such, it must provide accurate
and clear findings in a fashion that is easy to read and understand. While many of the overall goals
of the project are straightforward, the processes for addressing them may be analytically complex.
The challenge in preparing the report is to present important findings without burdening the reader
with all of the complexity that went into conducting the analysis. Our approach to report preparation
is to use simple tabular and graphical displays that illustrate key findings.
The final report outline is presented below.
Executive Summary
I.

Introduction (Purpose, background, and organization of the report)

II. Methodology (Requirements and study standards, sample description, data collection
process, data sources, and analysis processes)
III. Study Objectives (Discussion of each of the study’s analytic objectives)
IV. Findings (Narrative, tabular, and graphical presentations of the findings)
A.
B.
C.
D.
E.
F.
G.
H.
I.
J.
K.

Overview
Rent Error
Sources of Error
Errors Detected Using Information Obtained from Project Files
Occupancy Standards Analysis
Rent Reasonableness Analysis
Utility Allowance Analysis
Payment Standards Analysis
PIC/TRACS Analysis
Project Staff Questionnaire Analysis
Multivariate Analysis

V. Recommendations (Policy implications, and a discussion of how study methodologies can
be improved)
VI. Appendices
A.
B.
C.
D.
E.
F.

Rent Calculations
Weighting Procedures
Source Tables
Consistency and Calculation Errors
Project Staff Questionnaire Analysis
Multivariate Analysis

Analysis Plan

May 3, 2010

Appendix A
Definitions of Key Terms

DEFINITIONS
Actual TTP—actual Total Tenant Payment obtained from the 50058/50059.
Administration Type—PHA or Owner.
Aggregate Error—the difference between the actual rental payment and the QC rental payment.
Case Type—certification, recertification, and overdue recertification.
Dollar Error Rate—the quotient of dividing the Total Gross Rent Error by the weighted sum of the
QC rents.
Case Error Rate—the quotient of dividing the sum of the weights of tenant cases with dollar error
rates in excess of $5 per month by the total sum of the weights of tenant cases.
Gross Rent Error—the sum of the absolute values of under- and overpayments.
Largest Dollar Error—the annual dollar amount of error in the component with the largest error.
Overpayment—results when the tenant paid more than he/she should have paid; HUD’s
contribution was less than it should have been.
Payment Type—underpayment, proper payment, and overpayment.
Program Type—Public Housing, Section 8 Vouchers, Section 8 Moderate Rehabilitation, Section 8
Substantial Rehabilitation and New Construction, Section 8 Loan Management, Section 8 Property
Disposition, Section 202 PRAC/PAC, and Section 811 PRAC/PAC.
Quality Control Month (QCM)—the effective date of the most recent action in the file.
Quality Control (QC) Total Tenant Payment (TTP)—calculated value using both household
interview and verification data.
Rent Component—the five sources of income (earned, pensions, public assistance, other income,
and assets) and the five types of deductions (medical, child care, disability, dependent allowance,
and elderly/disabled family allowance).
Rent Dollar Error—the dollar amount of the Actual Rent minus the QC Rent for an individual
household. A negative number indicates an underpayment, meaning the household paid less than it
should and HUD’s subsidy was higher than it should have been. A positive number indicates a
household overpayment, meaning HUD’s contribution was less than it should have been.
Total Gross Rent Error—the weighted sum of the absolute values of positive and negative Dollar
Rent Errors.

Analysis Plan

A-1

May 3, 2010

Total Net Rent Error—the arithmetic value of the weighted sum of individual tenant Rent Dollar
Errors.
Underpayment—results when the tenant paid less than he/she should have paid; HUD’s
contribution was higher than it should have been.

Analysis Plan

A-2

May 3, 2010

Appendix B
Source Tables Responding
to Each Objective

Tables Responding to Objective(s)
OBJECTIVE
Objective 1: Identify the various types of errors and error rates and
related estimated variances.

SOURCE TABLE
1.
2.
3.

Percent of Households by Payment Type and Program Type
1a. Proper payment based on exact match of actual and QC rent
1b. Proper payment based on a match of actual and QC rent within $5
Dollar Rent Error by Program Type
Dollar Error Amount by Payment Type and Program Type

Objective 2: Identify the dollar costs of the various types of errors.

4.
5.
6.
7.
8.

Calculation Errors on Form 50058/59
Consistency Errors on Form 50058/59
Dollar Error Amount by Payment Type and Case Type
Case Type by Program Type
Administrative Error: percent of Households, Average Dollars in Error

Objective 3: Estimate national-level net costs for total errors and
major error types.

Gross and Net Rent Error by Program Type

Objective 4: Determine the relationship between errors detectable
using the HUD 50058 and HUD 50059 forms and total
errors found in the study.

1.
3.

Percent of Households by Payment Type and Program Type
Dollar Error Amount by Payment Type and Program Type

Objective 5: Determine whether error rates and error costs have
statistically significant differences from program to
program.

9.
10.

Gross and Net Rent Error by Program Type
Total and Largest Dollar Error by Program Type for Households with
Rent Error

Objective 6: Determine the apparent cause of significant rent errors.

11.
12.
13.
14.

Verification of Form 50058/59 Rent Component
Largest Component Error for Households with Rent Error
QC Rent Components by Payment Type and Administrative Type
Percent of Cases and Standard Error by Rent Component and Payment
Type
Allowances

15.

Multivariate regression analysis with error sources and error causes as
independent variables, and QC error as the dependent variable.
Objective 7: Determine the extent to which households are
overhoused relative to HUD’s occupancy standards.

Analysis Plan

16.

B-1

Occupancy Standards

May 3, 2010

OBJECTIVE
Objective 8: Provide information on the extent to which errors are
concentrated in projects and programs.

SOURCE TABLE
2.

Dollar Rent Error by Program Type

These data are from the Project Staff Questionnaire
Objective 9: Estimate the percentage of newly certified tenants who
were incorrectly determined eligible for program
admission.

17.
18.

Objective 10: Determine the extent to which Section 8 voucher rent
comparability determinations are found in the tenant
file, and indicate the method used to support the
determination.

Source tables are not used for rent comparability reporting.

Objective 11: Estimate total positive and negative errors in terms of
HUD subsidies.

1.
3.

Percent of Households by Payment Type and Program Type
Dollar Error Amount by Program Type and Program Type

Objective 12: Determine the extent to which error rates in projects that
use an automated rent calculation system differ from
error rates in those that do not.

2.
3.

Dollar Rent Error by Program Type
Dollar Error Amount by Payment Type and Program Type

Objective 13: Determine whether other tenant or project characteristics
on which data are available are correlated with high or
low error rates.

Multivariate error prone analysis using tenant and project characteristics as
independent variables and QC error as the dependent variable.

Objective 14: Determine whether cases for which 50058/59 data had
been submitted to HUD were more or less likely to have
errors than those for which data had not been submitted.

19.
20.

Objective 15: Determine the extent of errors that were due to unreporting of income by tenants

Source tables are not used for income match reporting.

Analysis Plan

B-2

Percent of Newly Certified Households Meeting Certification Criteria
Percent of Newly Certified Households Meeting Certification Criteria by
Program Type

QC Errors by Match with TRACS/PIC and Program
Payment Type by Program and Match with TRACS/PIC

May 3, 2010

OBJECTIVE

SOURCE TABLE

Objective 16: Determine the extent of program administrator rent and
income determination errors

1.
1a.
1b.
2.
3.
4.
5.
6.
7.
8.
9.

Objective 17: Determine the extent of errors due to Multifamily
Housing Program administrators billing for subsidy that
did not correspond to the subsidy reported on the HUD50019/HUD-50059A for a tenant household.

Analytic tables associated with the Billing Study will be provided under separate
cover.

Analysis Plan

B-3

Percent of Households by Payment Type and Program Type
Proper payment based on exact match of actual and QC rent
Proper payment based on a match of actual and QC rent within $5
Dollar Rent Error by Program Type
Dollar Error Amount by Payment Type and Program Type
Calculation Errors on Form 50058/59
Consistency Errors on Form 50058/59
Dollar Error Amount by Payment Type and Case Type
Case Type by Program Type
Administrative Error: percent of Households, Average Dollars in Error
Gross and Net Rent Error by Program Type

May 3, 2010

Appendix C
National Estimate Source Tables

National Estimate Source Tables
Table 1a. Percent of Households by Payment Type by Program Type
Proper Payment Based on Exact Match of Actual and QC Rent
UNDERPAYMENT
# of
Cases*

Row % of
Cases

Col %of
Cases

PROPER PAYMENT
# of
Cases*

Row % of
Cases

Col %of
Cases

OVERPAYMENT
# of
Cases*

Row % of
Cases

Col %of
Cases

# of
Cases*

TOTAL
Row % of
Cases

Col %of
Cases

PHA-Administered
Public Housing
PHA-Administered Sec. 8
Group Total
Owner-Administered
Group Total
Table Total
Note: * denotes values in the thousands

Analysis Plan

C-1

May 3, 2010

National Estimate Source Tables
Table 1b.

Percent of Households by Payment Type and Program Type
Proper Payment Based on a Match of Actual and QC Rent within $5
UNDERPAYMENT
# of
Cases*

Row %
of Cases

Col % of
Cases

PROPER PAYMENT
# of
Cases*

Row %
of Cases

Col % of
Cases

OVERPAYMENT
# of
Cases*

Row %
of Cases

Col % of
Cases

TOTAL
# of
Cases*

Row %
of Cases

Col % of
Cases

PHA-Administered
Public Housing
PHA-Administered Sec. 8
Group Total
Owner-Administered
Group Total
Table Total
Note: * denotes values in the thousands

Analysis Plan

C-2

May 3, 2010

National Estimate Source Tables
Table 2. Dollar Rent Error by Program Type
ACTUAL RENT
(MONTHLY)

# of
Cases* (1)

Col %
of Cases

Sum
Dollar
Amount*
(2)

QC RENT
(MONTHLY)
Ave.
Dollar
Amount
(2)/(1)

# of
Cases*

Col % of
Cases

Sum
Dollar
Amount
* (3)

GROSS RENT ERROR
(MONTHLY)
Ave.
Dollar
Amount
(3)/(1)

Sum
Dollar
Amount*
(4)

Ave.
Dollar
Amount*
(4)/(1)

Error Rate

PHA-Administered
Public Housing
PHA-Administered Sec. 8
Group Total
Owner-Administered
Group Total
Table Total
Note: * denotes values in the thousands

Analysis Plan

C-3

May 3, 2010

National Estimate Source Tables
Table 3. Dollar Error Amounts by Payment Type and Program Type
UNDERPAYMENT (MONTHLY)

# of Cases*

Col % of
Cases

Sum
Dollar
Amount*
(1)

Ave.
Dollar
Amount
(1)/(3)

OVERPAYMENT (MONTHLY)

# of Cases *

Col % of
Cases

Sum
Dollar
Amount*
(2)

Ave.
Dollar
Amount
(2)/(3)

QC RENT (MONTHLY)
# of
Cases*
(3)

Col % of
Cases

Sum
Dollar
Amount*
(4)

Ave.
Dollar
Amount
(4)/(3)

PHA-Administered
Public Housing
PHA-Administered Sec. 8

Group Total
Owner-Administered
Group Total
Table Total

Analysis Plan

C-4

May 3, 2010

National Estimate Source Tables
Table 4. Calculation Errors on Form 50058/59
FORM
50058
50059
# of Cases*
Col %of Cases
# of Cases*
Col %of Cases

Total Number of Cases
# of Cases*

Col % of
Cases

Age
Number of Family Members
Number of Foster Children & Live-in
Number of Dependents
Total Assets
Imputed Asset Income
Earned Income Sum
Pension, Etc., Income Sum
Public-Assistance Income Sum
Asset Income Sum
Other Income Sum
Total Non-asset Income
Income From Asset
Total Annual Income
Elderly/Disabled Allowance
Dependent Allowance
3% of Annual Income
Medical Allowance
Disability Allowance
Child Care Allowance
Total Allowance
Adjusted Annual Income
Gross Rent
Total Tenant Payment
Tenant Rent
Table Total
Note: * denotes values in the thousands

Analysis Plan

C-5

May 3, 2010

National Estimate Source Tables
Table 5. Consistency Errors on Form 50058/59
50058
ITEM

# of
Errors*

# of Cases*

50059
# of
Errors*

# of Cases*

Total
# of
Errors*

# of Cases*

General Information
Household Composition
Net Family Assets and Income
Allowances & Adjusted Income
Family Rent and Subsidy Information
Flat Rent Schedule Information (PH only)
Note: * denotes values in the thousands

Analysis Plan

C-6

May 3, 2010

National Estimate Source Tables
Table 6. Dollar Error Amount by Payment Type and Case Type
UNDERPAYMENT (MONTHLY)
# of
Cases*

Sum
Col % of Dollar
Cases
Amount*

Ave.
Dollar
Amount

OVERPAYMENT (MONTHLY)
# of
Cases*

Sum
Col % of Dollar
Cases
Amount*

Ave.
Dollar
Amount

QC RENT (MONTHLY)
# of
Cases*

Sum
Col % of Dollar
Cases
Amount*

Ave.
Dollar
Amount

Certification
Group Total
Recertification
Non-Overdue
Overdue
Group Total
Table Total
Note: * denotes values in the thousands

Analysis Plan

C-7

May 3, 2010

National Estimate Source Tables
Table 7. Case Type by Program Type
CERTIFICATIONS
# of
Cases*

Row %
of Cases

Col %
of Cases

RECERTIFICATIONS/
NON-OVERDUE
# of
Cases*

Row %
of Cases

Col %
of Cases

RECERTIFICATIONS/OVERDUE
# of
Cases*

Row %
of Cases

Col % of
Cases

TOTAL
# of
Cases*

Row %
of Cases

Col % of
Cases

PHA-Administered
Public Housing
PHA-Administered Sec. 8
Group Total
Owner-Administered
Group Total
Table Total
Note: * denotes values in the thousands

Analysis Plan

C-8

May 3, 2010

National Estimate Source Tables
Table 8. Administrative Error: Percent of Households, Average Dollars in Error
GROSS RENT ERROR (MONTHLY)
Error Type

# of
Cases*
(1)

Sum
Ave.
Dollar
Dollar
Col % of Amount * Amount
Cases
(2)
(2)/(1)

NET RENT ERROR (MONTHLY)
# of
Cases *
(3)

Col %
of
Cases

Sum
Ave.
Dollar
Dollar
Amount * Amount
(4)
(4)/(3)

QC RENT (MONTHLY)
# of
Cases *
(1)

Sum
Ave.
Col %
Dollar
Dollar
of
Amount* Amount
Cases
(5)
(4)/(3)

Transcription Errors
Calculation Errors - Allowances
Calculation Errors - Income
Calculation Errors - Other
Overdue Recertifications
Any Administrative Errors
Note: * denotes values in the thousands

Analysis Plan

C-9

May 3, 2010

National Estimate Source Tables
Table 9. Gross and Net Rent Error by Program Type
GROSS RENT ERROR (MONTHLY)
Sum
# of
Dollar
Cases * Col % Amount *
(1)
of Cases
(2)

NET RENT ERROR (MONTHLY)

Ave.
Dollar
Amount
(2)/(1)

Sum
Ave.
# of
Dollar
Dollar
Cases* Col % of Amount * Amount
(3)
Cases
(4)
(4)/(3)

QC RENT (MONTHLY)
# of
Cases*
(5)

Col %
of
Cases

Sum Dollar
Amount *
(6)

Ave.
Dollar
Amount
(6)/(5)

PHA-Administered
Public Housing
PHA-Administered Sec. 8

Group Total
Owner-Administered
Group Total
Table Total
Note: * denotes values in the thousands

Analysis Plan

C-10

May 3, 2010

National Estimate Source Tables
Table 10. Total and Largest Dollar Error by Program Type for Households with Rent Errors
TOTAL DOLLAR IN ERROR
# of
Cases*

Col % of Cases

Sum Dollar
Amount*

LARGEST DOLLAR ERROR
Ave. Dollar
Amount

# of Cases*

Col % of Cases

Sum Dollar
Amount*

Ave. Dollar
Amount

PHA-Administered
Public Housing
PHA-Administered Sec. 8
Group Total
Owner-Administered
Group Total
Total
Note: * denotes values in the thousands

Analysis Plan

C-11

May 3, 2010

National Estimate Source Tables
Table 11.

Verification of Form 50058/59 Rent Component
Provided for Each Major Program Type
Third Party Verbal or in Writing, or Documentation
NO VERIFICATION

RENT COMPONENT
# of Cases*

Row %of
Cases

VERIFICATION
Dollar Amt. Not Matched
# of Cases*

Row %of
Cases

Dollar Amt. Matched
# of Cases*

Row %of
Cases

TOTAL
# of Cases*

Row %of
Cases

Earned Income
Pension, Etc.
Public Assistance
Other Income
Asset Income
Elderly/Disabled
Allowance
Child Care Allowance
Disability Allowance
Medical Allowance
Note: * denotes values in the thousands

Analysis Plan

C-12

May 3, 2010

National Estimate Source Tables
Table 12. Largest Component Error for Households with Rent Error (Annual Dollars)
RENT COMPONENT

# of Cases*

Col %of Cases

Sum Dollar Amount*

Ave. Dollar Amount

Earned Income
Pension, Etc.
Public Assistance
Other Income
Asset Income
Dependent Allowance
Elderly Allowance
Child Care Allowance
Disability Allowance
Medical Allowance
No Rent Component Error
Total
Note: * denotes values in the thousands

Analysis Plan

C-13

May 3, 2010

National Estimate Source Tables
Table 13. QC Rent Components by Payment Type and Administration Type
PHA-ADMINISTERED

RENT COMPONENT
# of Cases*

Col %of Cases

Row % of
Cases

OWNER-ADMINISTERED
Row % of
# of Cases*
Col %of Cases
Cases

TOTAL
# of Cases*

Col %of Cases

Row % of
Cases

Underpayment
Earned Income
Pension, Etc.
Public Assistance
Other Income
Asset Income
Dependent Allowance
Elderly/Disabled Allowance
Child Care Allowance
Disability Allowance
Medical Allowance
No Error
Proper Payment
Earned Income
Pension, Etc.
Public Assistance
Other Income
Asset Income
Dependent Allowance
Elderly/Disabled Allowance
Child Care Allowance
Disability Allowance
Medical Allowance
No Error
Overpayment
Earned Income
Pension, Etc.
Public Assistance
Other Income
Asset Income
Dependent Allowance
Elderly/Disabled Allowance
Child Care Allowance
Disability Allowance
Medical Allowance
No Error
Total w/Rent Error Calc
Note: * denotes values in the thousands

Analysis Plan

C-14

May 3, 2010

National Estimate Source Tables
Table 14. Percent of Cases and Standard Error by Rent Component and Payment Type
RENT COMPONENT

% PHA-ADMINISTERED

% OWNER-ADMINISTERED

TOTAL

% of Total
Cases

# of Cases*

SE (%)

# of Cases*

SE (%)

# of Cases*

SE (%)

Analysis Plan

C-15

May 3, 2010

National Estimate Source Tables
Table 15. Allowances
ELDERLY/DISABLED ALLOWANCE

NONELDERLY/DISABLED HH
# of Cases*

Col %of
Cases

Row % of
Cases

ELDERLY/DISABLED HH
# of Cases*

Col %of
Cases

Row % of
Cases

TABLE TOTAL
# of Cases*

Col %of Cases

Row % of
Cases

No Allowance
Incorrect Allowance
Correct Allowance
Table Total
Note: * denotes values in the thousands
DEPENDENT ALLOWANCE

HH W/OUT DEPENDENT
# of Cases*

Col %of Cases

Row % of
Cases

HH W/DEPENDENT
# of Cases*

Col %of
Cases

TABLE TOTAL
Row % of
Cases

# of Cases*

Col %of
Cases

No Allowance
Incorrect Allowance
Correct Allowance
Table Total
Note: * denotes values in the thousands

Analysis Plan

C-16

May 3, 2010

Row % of
Cases

National Estimate Source Tables
Table 16. Occupancy Standards
Public Housing
Number of
Bedrooms

PHA-Administered Section 8

Table
Total

Owner-Administered

Underhoused

Correct

Overhoused

Group
Total

Underhoused

Correct

Overhoused

Group
Total

Underhoused

Correct

Overhoused

Group
Total

# of
Cases*

0
1
2
3
4
5
Table Total
Note: * denotes values in the thousands

Analysis Plan

C-17

May 3, 2010

National Estimate Source Tables
Table 16. Occupancy Standards (cont’d)
Percent of Cases
Number of
Bedrooms

Public Housing
Underhoused

Correct

PHA-Administered Section 8
Overhoused

Underhoused

Correct

Overhoused

Owner-Administered
Underhoused

Correct

Overhoused

0
1
2
3
4
5
Table Total

Analysis Plan

C-18

May 3, 2010

National Estimate Source Tables
Table 17. Percent of Newly Certified Households Meeting Certification Criteria
MET CRITERION
# of Cases
(in 1,000)

% of Cases

DID NOT MEET CRITERION
# of Cases
(in 1,000)

% of Cases

Citizenship
Social Security Number
Consent Form
Low and Very Low Income
Meets All Eligibility Criteria

Analysis Plan

C-19

May 3, 2010

Table 18. Percent of Newly Certified Households Meeting Certification Criteria by Program Type
MET CRITERION
# of Cases
(in 1,000)
PUBLIC HOUSING

% of Cases

DID NOT MEET CRITERION
# of Cases
(in 1,000)

% of Cases

Citizenship
Social Security Number
Consent Form
Low and Very Low Income
Meets All Eligibility Criteria

PHA-ADMINISTERED SECTION 8

Citizenship
Social Security Number
Consent Form
Low and Very Low Income
Meets All Eligibility Criteria

OWNER-ADMINISTERED

Citizenship
Social Security Number
Consent Form
Low and Very Low Income
Meets All Eligibility Criteria

Analysis Plan

C-20

May 3, 2010

National Estimate Source Tables
Table 19. QC Errors by Match with TRACS/PIC and Program
PERCENT OF CASES
50058/59 DATA ON
TRACS/PIC

HOUSING AUTHORITY MANAGED

Public Housing

Section 8

Subtotal
PHA
Administered

TOTAL
OWNER
ADMINISTERED

All Projects

Matched With TRACS/PIC
% Cases in Error
Average Payment Error
Std. Error of Mean
Nonmatch with TRACS/PIC
% Cases in Error
Average Payment Error
Std. Error of Mean
Table Total
% Cases in Error
Average Payment Error
Std. Error of Mean

Analysis Plan

C-21

May 3, 2010

National Estimate Source Tables
Table 20. Payment Type by Program and Match with TRACS/PIC
UNDERPAYMENT
# of
Cases
(in 1,000)

Row
% of
Cases

Col %
of
Cases

PROPER PAYMENT
# of
Cases
(in 1,000)

Row
% of
Cases

Col %
of Cases

OVERPAYMENT
# of
Cases
(in 1,000)

Row %
of Cases

Col %
of Cases

TOTAL
# of
Cases
(in 1,000)

Row %
of Cases

Col % of
Cases

PHA-Administered
TRACS/PIC Present
TRACS/PIC Absent
Public Housing
TRACS/PIC Present
TRACS/PIC Absent
Section 8
TRACS/PIC Present
TRACS/PIC Absent
Owner-Administered
TRACS/PIC Present
TRACS/PIC Absent
Total
TRACS/PIC Present
TRACS/PIC Absent
Table Total

Analysis Plan

C-22

May 3, 2010

File Type	application/pdf
File Title	Microsoft Word - Draft Analysis Plan FY2010 -1 _gr_
Author	gabriela.c.romeri
File Modified	2010-05-03
File Created	2010-05-03