Quality Control for Rental Assistance Subsidy Determinations - Final Report for FY 2007

by HUD USER

Quality Control for
Rental Assistance
Subsidy Determinations
Final Report for FY 2007
U.S. Department of Housing and Urban Development
Oﬃce of Policy Development and Research

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Quality Control for

Rental Assistance

Subsidies Determinations

Final Report for FY 2007
Prepared for:
U.S. Department of Housing and Urban Development
Office of Policy Development and Research
Prepared by:
Macro International
Calverton, MD

October 15, 2008
Acknowledgments
This report was prepared by Macro International of Calverton, MD and could not have been done
without the deep expertise and hard work of the following: JoAnn Kuchak, Officer in Charge;
Mary K. Dent Sistik, Project Manager; Sophia Zanakos, PhD., Deputy Project Manager;
Analysts and Programmers Pedro Saavedra, Ph.D., Francine Barrington, Gary Huang, Ph.D.,
Michelle Wilson. Cecille Witt, and Martin Wulfe; Expert Consultants Judy Lemeshewsky, Judy
Payne and Frank Wann; Survey Managers Jagruti Patel, Karen Smith and Laura Webb; Field
Supervisors, Data Quality Specialists and Support Staff Meagan Canali, Brian Connolly, Ebele
Ebonwu, Safal Khatri, Helene Mulligan, Diana Oresky, Wilma Samuels-Reeves, Rebecca
Villagomez, Andrew Walton, and Caren Williams.

The contents of this report are the views of the contractor and do not necessarily reflect the views
or policies of the U.S. Department of Housing and Urban Development or the U.S. Government.

Table of Contents
Executive Summary ................................................................................................................. ES-i

I. Introduction.....................................................................................................................I-1

A. Purpose of the Quality Control for Rental Assistance Subsidy

Determinations Study for FY2007..............................................................................I-1

B. Background of the Study ............................................................................................I-1

C. Organization of This Report .......................................................................................I-2

D. Definitions of Key Terms ...........................................................................................I-2

II. Methodology .................................................................................................................. II-1

A. HUD Requirements and Study Standards.................................... .............................II-1

B. The Sample....................................................................... .........................................II-1

C. Data Collection.................................................................. ...................................... .II-2

D. Field Data Collection Time Periods......................................................................... .II-5

E. Constructing the Analysis Files.................................................... ........................... .II-6

F. Rent Formulae.......................................................................................................... .II-7

G. Calculation of Rent Error......................................................................................... .II-8

H. Quality Control Rent................................................................................................ .II-8

I. HUD Requirements Complicating the Analysis....................... .............................. II-10

III. Study Objectives and Analytic Methods................................................................... III-1

IV. Findings.........................................................................................................................IV-1

A. Overview.................................................................................................................. IV-1

B. Rent Error................................................................................................................. IV-2

C. Sources of Error ..................................................................................................... IV-12

D. Errors Detected Using Information Obtained From Project Files ......................... IV-15

E. Occupancy Standards............................................................................................. IV-23

F. Project Staff Questionnaire Analysis ..................................................................... IV-25

G. The Relationship between Characteristics and Practices, and Error...................... IV-25

H. Rent Reasonableness.............................................................................................. IV-25

I. Utility Allowance Analysis.................................................................................... IV-31

J. Payment Standard Analysis ................................................................................... IV-34

K. PIC/TRACS Analysis ............................................................................................ IV-39

V. Recommendations……………………………………………………………………..V-1
A. Modifying the Quality Control Process…………………………………………….V-1
B. Policy Actions………………………………………………………………………V-3
iii

Table of Contents
Appendices
Appendix A: Rent Calculations
Appendix B: Weighting Procedures
Appendix C: Source Tables
Appendix D: Consistency and Calculation Errors
Appendix E: Project Staff Questionnaire Descriptive Analysis
Appendix F: Multivariate Analysis
List of Exhibits
Exhibit ES-1 Frequency of Rent Error by Program Type ............................................... ES-iv

Exhibit ES-2 Subsidy Dollar Error ....................................................................................ES-v

Exhibit ES-3 Estimates of Error in Program Administrator Income
and Rent Determinations (in 1,000’s)..........................................................ES-v
Exhibit ES-4 Comparative 2000 and FY 2003 through FY 2007 Gross
Erroneous Payments................................................................................... ES-vi
Exhibit ES-5 Rent Components Responsible for the Largest Dollar Error
for Households with Rent Error............................................................... ES-viii
Exhibit III-1 PHA-Administered Section 8 Unit Size Standards...................................... III-4

Exhibit IV-1 Percent of Households Fully Verified by Either the
PHA/Owner or Macro.................................................................................. IV-3
Exhibit IV-2 Percent of Households with Proper Payments ............................................. IV-4

Exhibit IV-3 Percent of Households with Error, Average Dollars in Error,
and Dollar Error Rate for All Households with Error.................................. IV-5
Exhibit IV-4a Underpayment Households Percent of Households
and Average Monthly Dollar Amount of Error ........................................... IV-5
Exhibit IV-4b Overpayment Households Percent of Households
and Average Monthly Dollar Amount of Error ........................................... IV-6
Exhibit IV-5 Gross and Net Dollar Rent Error (Monthly) for
All Households............................................................................................. IV-7
Exhibit IV-6 Gross and Net Dollar Error Rates (Monthly) for
All Households............................................................................................. IV-7
Exhibit IV-7 Certifications and Recertifications by Administration Type ....................... IV-8

Exhibit IV-8a Percent of Newly Certified Households Meeting
Certification Criteria .................................................................................... IV-9
Exhibit IV-8b Percent of Newly Certified Households Meeting
Certification Criteria by Program Type ..................................................... IV-10
Exhibit IV-9 Average Monthly Underpayment and Overpayment
Dollar Amount Averaged Across All Households..................................... IV-10
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Table of Contents
Exhibit IV-10a Negative Subsidy Households (Tenant Overpayment)

Percent of Households and Average Monthly Dollar

Amount of Error ......................................................................................... IV-11

Exhibit IV-10b Positive Subsidy Households (Tenant Underpayment)

Percent of Households and Average Monthly Dollar

Amount of Error ......................................................................................... IV-11

Exhibit IV-11 Average Monthly Dollar Amounts of Error for Negative

(Tenant Overpayment) and Positive (Tenant Underpayment)

Subsidies Averaged Across All Households.............................................. IV-12

Exhibit IV-12 Rent Components Responsible for the Largest Dollar

Error for Households with Rent Error ....................................................... IV-13

Exhibit IV-13 Total and Largest Component Dollars in Error

for Households with Rent Error................................................................. IV-14

Exhibit IV-14 Rent Component Error by Payment Type for All Households .................. IV-14

Exhibit IV-15 Elderly/Disabled Allowances and Dependent Allowances........................ IV-15

Exhibit IV-16 Findings With and Without Information Obtained

from Sources Other Than the Tenant File.................................................. IV-16

Exhibit IV-17 Percentage of Households with Calculation

and Consistency Errors .............................................................................. IV-17

Exhibit IV-18 50058/50059 Rent Calculation Error Compared

with QC Rent Error........................................................... .........................IV-18
Exhibit IV-19 Verification of 50058/50059 Rent Components by

PHA/Owners.......................... .................................................................... IV-19

Exhibit IV-20 Verification of 50058/50059 Rent Components

by PHA/Owner Staff by Program .............................................................. IV-20

Exhibit IV-21 QC Error Households with Missing Verification

in the Tenant File ............................................................ ...........................IV-21
Exhibit IV-22 50058/50059 Administrative Error: Percent of

Households, Average Dollars in Error....................................................... IV-22

Exhibit IV-23 Administrative Error: Percent of Households

Average Dollars in Error For All Households ........................................... IV-23

Exhibit IV-24 Percentage of Households in Units with the Correct Number

of Bedrooms According to Study Guidelines ............................................ IV-24

Exhibit IV-24a Percentage of All Households in FY 2006 by Number of

Bedrooms and Number of Household Members ....................................... IV-24

Exhibit IV-24b Percentage of All Households in FY 2007 by Number of

Bedrooms and Number of Household Members ....................................... IV-25

Exhibit IV-25a PHAs by Predominant Rent Reasonableness Method (unweighted) ......... IV-30

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Table of Contents
Exhibit IV-25b PHAs by Predominant Rent Reasonableness Method

Combinations (unweighted)....................................................................... IV-31

Exhibit IV-26a Rent Reasonableness Documentation for New Admissions ...................... IV-32

Exhibit IV-26b Type of Rent Reasonableness Documentation for

New Admissions ........................................................................................ IV-32

Exhibit IV-27 Timing of Most Recent Rent Reasonableness

Determination—New Admissions ............................................................. IV-33

Exhibit IV-28a Rent Reasonableness Documentation for

Annual Recertifications ............................................................................. IV-33

Exhibit IV-28b Type of Rent Reasonableness Documentation for Annual

Recertifications Where Documentation of the Rent Reasonableness

Determination Was Found ......................................................................... IV-34

Exhibit IV-29 Timing of Most Recent Rent Reasonableness

Determination—Annual Recertifications .................................................. IV-34

Exhibit IV-30a Type of Document Used by the PHA to Calculate

the Utility Allowance Value ...................................................................... IV-35

Exhibit IV-30b Utility Allowance Worksheet (WS) vs. 50058 (AC) Utility Allowance

Comparison Findings ................................................................................. IV-36

Exhibit IV-30c Availability of All Information to Enable QC Utility

Allowance Calculation............................................................................... IV-37
Exhibit IV-30d QC vs. AC Utility Allowance Comparison Findings................................. IV-37

Exhibit IV-31 Number and Percent of Households with Payment

Standard Discrepancies.............................................................................. IV-40

Exhibit IV-32 Percentage of Households Meeting Payment Standard Requirements

When Comparing Payment Standard to Fair Market Rent ........................ IV-40

Exhibit IV-33 Details of Cases Falling Outside the 90-110% of FMR Band ................... IV-41

Exhibit IV-34 Comparison of the FY 2006 to FY 2007 Payment Standard

Analysis...................................................................................................... IV-42
List of Figures
Figure IV-1 Payment by Program Type........................................................................... IV-6

Figure IV-2 Case Type..................................................................................................... IV-8

vi

Executive Summary
The Department of Housing and Urban Development (HUD) Quality Control for Rental Assistance
Subsidy Determinations studies provide national estimates of the extent, severity, costs, and sources
of rent errors in tenant subsidies for the PHA-administered Public Housing, Section 8 Housing
Choice Voucher, and Moderate Rehabilitation programs; and the owner-administered Section 8, and
Section 202 and Section 811 programs with Project Rental Assistance Contracts (PRAC) or Project
Assistance Contracts (PAC). These programs account for nearly all of HUD&rsquo;s current housing
assistance outlays administered by the Offices of Housing and Public and Indian Housing, as well as
the large majority of units assisted by HUD. This study was designed to measure the extent of
administrator income and rent determination error by housing providers. This study does not involve
an audit of individual PHAs or projects; nor does it monitor the implementation of housing programs.
Its focus is on identifying households where an error was made when calculating the amount of the
household’s rent; and providing nationally representative findings related to those errors.
The errors we evaluated in this study affect the rent contributions tenants should have been charged.
The findings presented in this report are a result of data collected from February through June 2008
for actions taken by Public Housing Authority (PHA) and project staff during FY 2007 (October
2006 through September 2007). These findings show that the percent of errors, the average dollars in
error, and the gross dollar error rate in the Public Housing, Section 8 Housing Choice Voucher,
Moderate Rehabilitation, owner-administered Section 8, and Section 202 and Section 811 programs
with PRAC or PAC tenant subsidies continues to remain stable when compared with results from
previous studies.
HUD’s rental housing assistance programs are administered on HUD’s behalf by third-party program
administrators, including PHAs, public and private project owners, and contracted management
agents. In the programs examined, eligible tenants are generally required to pay 30 percent of their
income toward shelter costs (rent plus utilities), with HUD providing the balance of the rental
payment. New program applicants are required to provide certain information on household
characteristics, income, assets, and expenses that is used to determine what rent they should pay.
Existing tenants are required to recertify this information annually and also, in some circumstances,
when there are significant changes in household income or composition. Applicant or tenant failure
to correctly report income may result in HUD’s over- or underpayment of housing assistance. The
failure of the responsible program administrator to correctly interview the tenant or process,
calculate, and bill the tenant’s rental assistance may also result in HUD’s over- or underpayment of
housing assistance.
In 2000, HUD began to establish a baseline error measurement to cover the three major types of
rental housing assistance payment errors: 1) program administrator income and rent determination
error, 2) intentional tenant misreporting of income, and 3) errors in program administrator billings for
assistance payments. Six studies have been conducted to identify program administrator income and
rent determination error. In addition to the 2000 study, studies were conducted in 2003/2004,
2004/2005, 2005/2006 and 2006/2007 covering (re)certifications conducted in FY 2003, FY 2004,
FY 2005 FY 2006 and FY 2007 respectively. The study referenced in this report covers FY 2007,
and is being used to update the FY 2006 measurement of errors in program administrator income and
rent determinations. The tenant data collected for this study were also used to provide the sample for
the income match to measure the extent of intentionally unreported tenant income. The findings from
this income match study will be published as a separate report. The balance of this report relates
solely to program administrator income and rent determination error.
ES-i

Executive Summary
For purposes of this study, “error” is defined as any rent calculation or eligibility determination
that differs from what would have occurred if the PHA or other program administrator had
followed all HUD income certification and rent calculation requirements during the income
certification or annual recertification conducted in FY 2007. When appropriate, study findings 
are compared with findings from the previous studies.
Financial Impact of Identifying Rent Error. Reduction in the rent error associated with the
programs included in this study does not mean there will be an overall savings in the costs
associated with administering these programs. Given there are large numbers of eligible
households on waiting lists, if a household leaves the program because it is no longer eligible for
a subsidy, another household will take its place. The replacement household may be entitled to a
smaller or a larger subsidy than the household that left the program. Therefore, the most direct
benefit of identifying households with rent error is making sure those households who are
eligible for the program are receiving the correct subsidy, rather than reducing the amount of
funds needed to administer the programs. The most appropriate use of this study is as a tool for
strengthening HUD’s procedures for ensuring administrative compliance with regulations. The
recommendations presented in this report may require greater rather than fewer resources to
provide HUD, PHAs, and owners with the written policy guidelines, training, standardized
forms, and on-going monitoring needed to assure the programs are administered correctly.
HUD’s objective of providing the right subsidies to the right families is a worthy one that this
study can assist in achieving.
A. Methodology
HUD Requirements and Study Standards. Using the Code of Federal Regulations and official
HUD handbooks and notices, all HUD requirements relevant to the determination of rent were
consolidated into a set of HUD requirements. Nationally recognized experts were involved in
establishing and reviewing the standards used in this study.
The Sample. A nationally representative sample of 600 projects in the United States and Puerto
Rico was selected for this study. These projects were selected from the universe of the three
program types covered by the study—
♦ Public Housing
♦ PHA-administered Section 8 (Vouchers and Moderate Rehabilitation)
♦ Owner-administered Section 8, Section 202 PRAC, Section 811 PRAC, Section 202/162
PAC
A random sample of four households was selected for most projects, but more tenants were
selected from unusually large projects. The final study data set includes responses from 2,404
households.
The Data Collection Process. The data collection effort included creating and automating more
than 30 data collection instruments, contacting and obtaining information from PHA/owner staff,
hiring and training more than 60 field interviewers, and selecting the project and tenant sample.
Field interviewers obtained data from tenant files, and interviewed tenants using computer
ES-ii

Executive Summary
assisted personal interviewing software developed for this study. The automated data collection
process included built-in consistency and edit checks that prompted interviewers to probe
inconsistent and anomalous responses. Collected data were electronically transferred daily to
Macro headquarters for review. Requested third-party verifications related to income, assets and
expenses were also processed at Macro headquarters.
Calculation of Rent Error. A quality control (QC) rent was calculated for each household in
the sample using the information reported by the PHA/project and household. Rent error was
calculated by subtracting the QC rent from the actual tenant rent (the rent from HUD Forms
50058 or 50059 that had been calculated by the project staff). A discrepancy of $5 or less
between the actual and QC rent was not counted as an error. This $5 differential was used to
eliminate rounding differences and minor calculation discrepancies that have little effect on
program-wide subsidy errors.
B. Major Rent Error Findings
National Rent Error Estimates. The analysis of the FY 2007 tenant files, tenant interview, and
income verification data indicates that—
♦ 64 percent of all households paid the correct amount of rent within $5 (51 percent paid
exactly the right amount)
♦ 18 percent of all households paid in excess of $5 less than they should have (with an average
error of $58 per month)
♦ 18 percent of all households paid in excess of $5 more than they should (with an average
error of $30 per month)
Rent Error Estimates by Program Type. Both the owner-administered and PHA–
administered Section 8 programs had an underpayment of rent rate of 19 percent compared to 16
percent in the Public Housing program. The rate of overpayment was highest in the PHA –
administered Section 8 programs with 20 percent overpayments of rent compared to 17 percent
of the owner-administered program, and 14 percent in the Public Housing program. The exhibit
that follows summarizes this information.
Exhibit ES-1
Frequency of Rent Error by Program Type
Rent Underpayment (Subsidy Rent Overpayment (Subsidy
Program
Overpayment) Underpayment)
Public Housing 16% 14%
PHA-Administered Section 8 19% 20%
Owner-Administered 19% 17%
Total 18% 18%
Dollar Error Effect of Rent Errors. All summary error estimates represent the summation of
net case-level errors. That is, a case is determined to have a net overpayment error, no error, or a
net underpayment error. Major findings were—
ES-iii

Executive Summary
♦
♦
Multiplying the $63 by
♦ gross
The net
The
study found that the net
1
.
This data responds to
Exhibit ES-2

l i
$57 $30

l lds $11 $5

1
Rent Underpayments of Approximately $524 Million Annually (down from $648 Million in
FY 2006). For tenants who paid less monthly rent than they should pay (18.46 percent), the
average monthly underpayment was $57. For purposes of generalization, total underpayment
errors were spread across all households (including those with no error and overpayment
error) to produce a program-wide average monthly underpayment error of $10.56 ($127
annually). Multiplying the $127 by the approximately 4.1 million units represented by the
study sample results in an overall annual underpayment dollar error of approximately $524
million per year.
Rent Overpayments of Approximately $260 Million Annually (down from $306 Million in
FY 2006). For tenants who paid more monthly rent than they should pay (17.52 percent), the
average monthly overpayment was $30. When this error was spread across all households, it
produced an average monthly overpayment of $5.24 ($63 annually).
the approximately 4.1 million assisted housing units represented by the study sample results
in an overall annual overpayment dollar error of approximately $260 million per year.
Aggregate Net Rent Error of $264 Million Annually. When combined, the average rent
error per case is $16 ($11 + $5). Over- and underpayment errors partly offset each other.
overall average monthly rent error is $6 ($11-$5). HUD subsidies for Public
Housing and Section 8 programs equal the allowed expense level or payment standard minus
the tenant rent, which means that rent errors have a dollar-for-dollar correspondence with
subsidy payment errors, except in the Public Housing program in years in which it is not
fully funded (in which case errors have slightly less than a dollar-for-dollar effect).
subsidy cost of the under- and overpayments was approximately
$264 million per year ($524 million - $260 million)
Subsidy over- and underpayment dollars are summarized in Exhibit ES-2.
study Objective 1 (identify the various types of errors and error rates and related estimated
variances).
Subsidy Dollar Error
Subsidy Subsidy
Type Dollar Error Overpayment Underpayment
Average Month y Per Tenant Error for Households w th Errors
(18.46% of cases) (17.52% of cases)
Average Month y Per Tenant Error Across All Househo
Total Annual Program Errors $524 million $260 million
Total Annual Errors—95% Confidence Interval $370 - 677 million $215 – 305 million
Exhibit ES-3 provides estimates of program administrator error by program type. This data
responds to study Objectives 3 (estimate national-level net costs for total errors and major error
The actual estimate of annual rent underpayments is $523.70 million. The actual estimate of annual rent
overpayments is $259.79 million. Therefore the actual estimate of net rent error is $263.90 million ($523.70 -
259.79 = $263.91).
ES-iv

Executive Summary
types), 8 (provide information on the extent to which errors are concentrated in projects and
programs), and 11 (estimate total positive and negative errors in terms of HUD subsidies).
Exhibit ES-3
)
Net
Estimates of Error in Program Administrator Income
and Rent Determinations (in $1,000’s
Administration Type
Subsidy
Overpayments
Subsidy
Underpayments
Erroneous
Payments
Gross Erroneous
Payments
Public Housing $106,392 $42,972 $63,420 $149,364
PHA-Administered Section 8 $282,840 $152,172 $130,668 $435,012
Total PHA-Administered $389,232 $195,144 $194,088 $584,376
Owner-Administered $134,460 $64,644 $69,816 $199,104
Total $523,692 $259,788 $263,904 $783,480
95% Confidence Interval +/-$153,438 +/-$44,799 +/-$162,357 +/-$157,292
In response to study Objective 5 (determine whether error rates and error costs have statistically
significant differences from program to program), multiple regression analyses with design effect
adjustment were conducted to compare the three program types included in the study on mean
gross dollar error, mean gross dollar error rate, mean net dollar error, and mean gross dollar error
rate. No significant differences in net dollar error or net dollar error rate were found, and no
significant differences between owner-administered and Public Housing households were found.
However, PHA-administered Section 8 households have a greater gross dollar error than
households in either of the other two programs, and this result is statistically significant at the .05
level (two-tailed) for both gross error rate and gross error cost.
Comparison with Prior Studies. Five prior studies, the 2000 baseline, the FY 2003 study, the
FY 2004 study, the FY 2005 and the FY 2006 study estimated erroneous payments attributed to
program administrator rent calculation and processing errors, using the same methodology,
sampling procedures, and sample sizes as this FY 2007 study. The 2000 “Quality Control for
Rental Assistance Subsidy Determinations” study was published as a final report in June 2001.
The FY 2003 final report—“Quality Control for Rental Assistance Subsidies Determinations”—
was completed in August 2004. The FY 2004 final report was completed in July 2005. The FY
2005 final report was completed in October 2006 and the FY 2006 final report was completed in
October 2007. While the FY 2003 and FY 2004 studies demonstrated significant reductions in
erroneous payments attributed to program administrator income and rent determinations, the FY
2005 findings indicated a smaller reduction in the gross dollars in erroneous payments that did
not represent a statistically significant decrease from FY 2004. The FY 2006 study indicated a
small increase in the gross dollars in erroneous payments which also did not represent a
statistically significant difference. The FY 2007 study, however, indicates the lowest level of
gross dollars in erroneous payments in study history with significant reductions in PHA
administered programs. Exhibit ES-4 presents a comparison of the gross erroneous payments for
2000, FY 2003, FY 2004, FY 2005, FY 2006 and FY 2007.
ES-v

Executive Summary
Exhibit ES-4
Comparative 2000 through FY 2007 Gross Erroneous Payments*
Percent
Reduction in
2007 Gross 2006 Gross 2005 Gross 2004 Gross 2003 Gross 2000 Gross Gross
Erroneous Erroneous Erroneous Erroneous Erroneous Erroneous Erroneous
Payments Payments Payments Payments Payments Payments Payments from
Administration Type (in $1000’s) (in $1,000’s) (in $1,000’s) (in $1,000’s) (in $1,000’s) (in $1,000’s) 2000 to 2007
Public Housing $149,364 $172,824 $220,464 $242,076 $316,116 $602,556 75.21%
PHA-Administered Section 8 $435,012 $520,020 $456,240 $521,220 $730,956 $1,096,524 60.33%
Total PHA-Administered $584,376 $692,844 $676,704 $763,292 $1,047,072 $1,699,092 65.61%
Owner-Administered $199,104 $261,324 $248,580 $224,460 $368,796 $539,160 63.07%
Total $783,480 $954,168 $925,232^ $987,744^ $1,415,844^ $2,238,252^ 65.00%
+/-$157,292 +/-$192,000 +/- $164,000 (+/-$131,000) (+/-$163,000) (+/-$275,000)
* Gross Rent Error is the sum of the absolute value of positive and negative rent error.

^ Numbers do not add exactly due to rounding.

C. Sources of Errors
Rent errors are often a result of a mix of different types of errors. This study also examined
administrative and component errors. For purposes of this study, administrative errors are
analyzed separately from specific component errors. Administrative errors are errors that result
from administrative mistakes. They consist of—
♦ Consistency errors—errors in logical conformity between elements within the 50058 or
50059 Forms
♦ Calculation errors—arithmetic errors within subsections of the 50058 or 50059 Forms
♦ Transcription errors—errors in transferring information from documentation in the tenant file
to the 50058 or 50059 Forms
♦ Failure to conduct a recertification in a timely manner
♦ Failure to verify information
Component errors are related to the income and expense components used to calculate rent. The
income components are employment income, Social Security benefits and pensions, public
assistance, other income, and asset income. The expense/allowance components are
elderly/disabled allowance, dependent allowance, medical allowance, child care allowance, and
disability allowance. Component errors often occur when project staff do not conduct a
thorough tenant interview or do not verify the information obtained during the interview.
However, component error may also occur when the tenant supplies incorrect information, either
intentionally or unintentionally. The discussion below responds to study Objectives 2 (identify
the dollar costs of the various types of errors), and 6 (determine the apparent cause of significant
rent errors).
ES-vi

Executive Summary
Administrative Errors. The two most common administrative errors are calculation errors and
transcription errors. The HUD PIC and TRACS data systems check the rent calculations on
Forms 50058 and 50059. For tenants for whom data are submitted (and corrected if required),
these systems virtually eliminate rent determination calculation errors for the items included on
the forms. However, not all cases are reported and some cases returned to program sponsors for
correction are ignored or are changed in HUD systems but not actually implemented.
PIC/TRACS data system matches were made (to respond to Objective 14) for the 2404
households in the study. Ninety-five percent of these households (97 percent of owner-
administered households, and 94 percent of PHA-administered households) were found in the
PIC/TRACS databases. Interestingly enough, there was very little difference in the percent of
households with rent error for households for which PIC/TRACS data were or were not
available. However, the average gross dollars in error were higher for households where
PIC/TRACS data were absent.
Verification Errors. The percentage of income and expense items verified by PHA/owner staff
in FY 2007 showed little change compared to FY 2006. Income items were verified at least 75
percent of the time (compared to 74 percent in FY 2006). While in FY 2005, the percentage of
items verified remained similar to the percentage verified in FY 2004, there was a downward
trend in FY 2006. In FY 2007 the percentage of items verified increased for most rent
components. Failure to use verified income and expense amounts continues to be a problem.
The percent of items where the verified amounts matched the amount reported on the 50058 and
50059 Forms increased slightly for three rent components, pensions, public assistance and
medical expenses. The degree to which programs correctly incorporate verified data for earned
income is unchanged in FY 2007 (68 percent in both FY 2007 and FY 2006).
Obtaining income verification is often difficult. Even when repeated requests are made,
employers sometimes do not respond to requests for verification. Some program sponsors do a
much better job than others in achieving third-party compliance with written verification. The
QC study shows that it is reasonable to expect all program sponsors to have as high a success
rate as the current high performers. The study also shows that there is significant room for
improvement in using the verification data obtained, which are often collected consistent with
procedures but then filed and never used.
Overdue Recertifications. HUD requires that every household be recertified annually.
Recertifications for 2 percent of the households were overdue, compared to 3 percent in FY
2006.
Component Errors. Incorrect income and allowance amounts were by far the most significant
sources of error in determining rents. Less than one percent of households with rent errors did
not have an income or expense component error. Earned income (24 percent), pension income
(21 percent), and medical allowances (23 percent) continued to have the greatest percent of
households in error. The following exhibit shows the frequency of the most serious component
errors and the average dollar amount for each type. The Percentage of Households represents the
households with any rent component error where the specified rent component was responsible
for the largest error. The Average Dollar Amount represents the average dollar amount for the
specified rent component for households where the specified component was responsible for the
largest error. For comparison purposes, findings from FY 2006 are provided in parentheses.
ES-vii

Executive Summary
Note that both the percentage of households with component errors and the average dollar
amount of error have tended to decrease.
Exhibit ES-5
Rent Components Responsible for the Largest Dollar Error
for Households with Rent Error
(FY 2006 Findings Are Provided in Parentheses)
Percentage of Average Dollar
Rent Component Households Amount
Earned Income
24% (26%) $2892 ($4,544)
Pensions
21% (25%) $2065 ($2,246)
Other Income
11% (10%) $2437 ($2,488)
Public Assistance
6% (9%) $2492 ($1,823)
Asset Income
4% ( 2%) $1502 ($1,733)
Medical Allowance
23% (17%) $972 ($1,099 )
Child Care Allowance
4% (4%) $2259 ($2,128)
Dependent Allowance
3% (3%) $622 ($703)
Elderly/Disabled Allowance
3% (2%) $400 ($400)
No Rent Component Error
1% (1%) $0
Total 100% $1957 ($2,513)*
* The sum of the dollars associated with the largest component in error divided by the
number of households with error.
D. Additional Findings
Eligibility of Newly Certified Households. A separate analysis of newly certified households
(16 percent) was conducted to determine if these households were eligible for HUD housing
assistance. Ninety one percent of these households met all the eligibility criteria (the same as in
FY 2006). There were no newly certified households in the sample who were not income-
eligible on the basis of the QC income determination.
Two percent of the newly certified households failed to document Social Security numbers (or
certify non-assignment of a number) for one or more family members (at least 6 years of age),
and 14 percent lacked the signed consent forms needed to authorize verification of income and
assets (for each member of the household at least 18 years of age). Only 2 percent lacked the
signed declaration forms or evidence accepted as proof of citizenship. These findings respond to
study Objective 9 (estimate the percentage of newly certified tenants who were incorrectly
determined eligible for program admission.
Occupancy Standards. Study Objective 7 asks for the extent to which households are
overhoused relative to HUD’s occupancy standards. Fifteen percent of all households occupied a
unit with too many or too few bedrooms in FY 2007, according to the guidelines used for this
study. This number is up slightly from FY 2006, where fourteen percent of all households
occupied a unit with an incorrect number of bedrooms
ES-viii

Executive Summary
Rent Reasonableness. Study Objective 10 asks for the extent to which Section 8 Voucher rent
comparability (reasonableness) determinations are found in the tenant file, and the method used
to support the determinations. Seventy-four percent of new admission files contained rent
reasonableness documents, as did 66 percent of the files for households for which data were
collected for an annual recertification. However, the absence of documentation does not
necessarily indicate a determination was not completed; only that it was not properly
documented. Information was also collected at the PHA level to understand the method used to
determine rent reasonableness. About 91 percent of the PHAs in the study used unit-to-unit rent
comparison, unit-to-market rent comparison, or a point system when determining if the rent was
reasonable. For the remaining 9 percent there was either no information available, the PHA used
some other method of determining rent reasonableness, or the units were subject to rent control.
Utility Allowances. For PHA-administered Section 8 Voucher households, the utility allowances
found on the 50058 forms were compared to the utility allowance worksheets found in the tenant
file, and to the utility allowance values calculated using the utility allowance schedules provided
by the PHAs. For the first comparison, 90 percent of the utility allowance values matched. For
the second comparison, 88 percent of the values matched. However, the fact that the values did
not match, does not necessarily mean the utility allowance found on the 50058 form was
incorrect. For at least 5 percent of the households, the information needed to determine the
accuracy of the utility allowance value was not available.
Payment Standards. A special analysis was conducted to determine if the correct payment
standards were used for Section 8 Voucher households. The payment standard found on the
50058 form was compared to the payment standard schedules provided by the PHA, and to the
Fair Market Rent (FMR) for the appropriate geographical area. For the first comparison, 92
percent of the payment standards matched. For the second comparison, 96 percent of the
payment standards found on the 50058 form fell within the 90 to 110 percent FMR band. As with
the utility allowance analysis, the information needed to conduct the analysis was not always
available. Therefore, the fact that the payment standards did not match does not necessarily mean
the incorrect payment standard was used when calculating the amount of the tenant rent.
50058/50059 Rent Calculation Error. The tenant rent was calculated using only data on the
50058/50059 to determine the relationship between errors detected using the 50058/50059 forms
and total rent errors found in the study (in response to study Objective 4). When using only the
50058/50059 data to calculate rent, errors were found in 7 percent of the households. This is
clearly different then the QC error calculation where errors were found in 36 percent of the
households. In addition, error was found in both the 50058/50059 and QC calculation in only 3
percent of the households.
Automated Rent Calculation Systems. Study Objective 12 asks whether error rates in projects
that use an automated rent calculation system differ from errors in those that do not. We did not
find a difference between PHA/projects that use automated rent calculation systems and those
that do not. This is not surprising because nearly 97 percent of all projects use an automated rent
calculation system.
Tenant Characteristics, and Project Characteristics and Practices. In response to study
Objective 8 (provide information on the extent to which errors are concentrated in projects and
programs), data were collected from PHA/project staff via a structured mail survey. Multivariate
ES-ix

Executive Summary
analyses were conducted to explore whether project characteristics or practices contributed to
administrative or rent errors. It was found that projects that retained experienced certification
staff (over 5 years) had lower average rent error. This finding is compatible with what was
found in the FY 2006 analysis where staff educational requirements were related to lower rent
error, namely, staff quality is a critical factor in reducing errors.
In response to study Objective 13 (determine whether other tenant or project characteristics on
which data are available are correlated with high or low error rates), additional multivariate
analyses were conducted. The analysis found that projects whose survey respondents reported
perceived error causes tended to have a lower chance of certain types of staff error. It is arguable
that staff’s ability to report difficulties causing mistakes is equivalent to knowledge of the
problems and hence a potential way to deal with them. However, far more information is needed
to learn why and how project staff perceive error.
E. 2000–2007 Progress
In response to the findings and recommendations of the 2000 Assisted Housing Quality Control
Study, HUD initiated a series of aggressive actions to address the causes of erroneous assistance
payments, including extensive onsite monitoring. Actions taken by HUD included the
following—
♦ A Rental Housing Integrity Improvement Program (RHIIP) committee headed by the Office
of the Chief Financial Officer with representatives from the other affected Offices was
formed to coordinate and monitor corrective actions. The committee meets to review
progress, and identify and resolve impediments to progress in reducing errors.
♦ The Offices of Housing and Public and Indian Housing developed and issued new handbooks
and instructional material that detailed all current HUD program requirements and
standardized them to the extent possible without regulatory or statutory change. These
handbooks cover nearly all aspects of occupancy policy, from the point of tenant application
for admission and rent calculations through ongoing occupancy to lease termination. For
Public Housing, the issuance of a Public Housing Occupancy Guidebook represented the first
such effort in more than 20 years, and provided a defined methodology for calculating a
number of complex requirements (e.g., the Earned Income Disallowance).
♦ The Offices of Housing and Public and Indian Housing substantially increased training
efforts, and have held a number of national and regional training sessions. This contrasts
with a less activist role in the 1980’s and 1990’s.
♦ The Offices of Housing and Public and Indian Housing initiated comprehensive, large-scale,
and onsite occupancy and management reviews, which also represented a major procedural
change from the previous two decades for most HUD offices—
• The Office of Housing primarily used new agreements with Contract Administrators,
which are usually State agencies, to perform this function. Contract Administrators
provide technical support in adhering to HUD program requirements and routinely
perform detailed monitoring on agency compliance.
ES-x

Executive Summary
• The Office of Public and Indian Housing initiated a system of Rental Integrity
Monitoring (RIM) reviews to detect and reduce errors in income and rent calculations
at targeted PHAs, reduce rent under- and/or overpayments by residents, and ensure
that HUD’s limited housing resources were being used to serve eligible families in a
fair and equitable manner as intended by Congress.
♦ HUD initiated a legislative change that gives it access to the Department of Health and
Human Service’s New Hires income and wage database for income matching purposes. It
will use these data to compare tenant-reported income with state wage data to better ensure
that the right subsidy payments are made to the right households in accordance with program
statutory and regulatory requirements. This legislation was passed in late 2003 and required
implementation of agreements and data systems. HUD also negotiated agreements with
some states to obtain access to the same information. Some local agencies have already
initiated income-matching systems, and it seems that this has made some contribution to
error reductions.
HUD’s performance goals, which were developed in consultation with the Office of
Management and Budget, called for reducing the 2000 benchmark assisted housing error levels
by 50 percent by the end of 2005. The study of program administrator error for FY 2007 shows
that HUD exceeded this goal. It should be noted, however, that the reduction of errors and
improper payments is unlikely to have an equivalent effect on budget outlays. HUD’s
experience indicates that its program integrity improvement efforts are likely to result in some
higher income tenants leaving assisted housing and being replaced with lower income tenants
requiring increased outlays. Nevertheless, HUD’s goal remains to ensure that the right benefits
go to the right people.
F. Recommendations
The progress when comparing the 2000 findings to the FY 2007 results is impressive. However,
the percent of errors has remained stable since the FY 2004 study and the average dollars in error
and the gross dollar error rate have only decreased slightly. On the basis of the current study’s
results, the following approaches to further reduce program administrator income and rent
determination error rates are recommended:
♦ HUD should continue its plans to use the Department of Health and Human Service’s
New Hires income matching database. However, access to the New Hires income
matching database by itself will not result in a reduction in error. PHA/project staff must
use this information to assist them in resolving discrepancies between the database and
the tenant’s declaration.
♦ HUD should continue to provide PHAs and owners with the forms, training, and other
tools required to determine rent correctly. Changes in policy should be reported to PHAs
and owners in a timely fashion with the guidance needed to implement those changes in
an accurate manner.
♦ HUD should continue to implement its onsite monitoring program, and PHAs and owners
should be held accountable for implementing HUD regulations and calculating rent
accurately.
ES-xi

Executive Summary
♦ Federal laws, regulations, and HUD requirements should be simplified to the extent
possible.
♦ HUD should consider implementing policy that allows reexaminations, for selected
populations, to be completed less often than annually.
In addition, the quality control studies could be modified to supplement the findings from this
study and identify options for reducing error in the future. The following are possible methods
to achieve this goal:
♦ Collect more information regarding PHA/project policies and practices. Each PHA
establishes its own policies, procedures, and forms for collecting the information that is
ultimately used to calculate tenant rent. The differentiation in these practices may have
some (possibly major) impact on the rent error, yet the analysis of the project practices
and characteristics collected in the Project Staff Questionnaire designed for this study do
not demonstrate the expected impact. Focus groups and cognitive interviewing could be
used to identify additional PHA/project level factors that may impact error. This
additional information could be used to revise the Project Staff Questionnaire to include
questions focused on the specific practices expected to influence errors.
♦ Gather information to document the outcome of the HUD quality control studies.
Overall, the HUDQC studies indicate that both the percent of errors and dollars
associated with those errors have decreased in the last seven years. However, there is no
information on changes in tenant behavior related to the identification and reduction of
error. To really understand the overall impact of the quality control studies on subsidy
funding, additional information is needed regarding both the tenants receiving the
subsidies and the PHA/projects administering the housing benefits.
♦ Expand contractor access to verification obtained through Social Security Administration
and National Directory of New Hires data. Despite increasing rates of third-party
verification, a large proportion of tenant income and expenses are not being verified.
This is especially important given the study results indicate a significant relationship
between third-party verification of certain types of income and rent errors. Expanded
access to Federal databases would allow the contractor to investigate discrepancies in the
information obtained from multiple sources.
♦ Continue to investigate PIC/TRACS data for sampling and other purposes. Ideally
PIC/TRACS data would be used to select the quality control sample, and provide the
actual data used by the PHA/project staff when calculating rent (in place of abstracting
50058/50059 Form data from the tenant file). However, to do this the data must be
available for the specific period of time covered by the study.
♦ Continue to expand existing computer systems and processes that further automate data
collection, processing, and reporting functions. Expanding and investing in better
automated systems will yield large dividends in terms of costs, time required to collect
and process data, as well as the breadth, depth, and quality of data.
ES-xii

Executive Summary
♦ Continue the HUD quality control studies as a regular, ongoing effort to monitor and
manage HUD rent determination processes. Ongoing evaluation of the subsidy programs
administered by HUD is essential to the management of those programs. Although the
primary goal of these studies is to measure rent errors, the studies also give HUD the
opportunity to learn more about alternatives to reducing rent errors, and better
management of current and changing conditions at PHAs/projects.
ES-xiii

I. Introduction
A. Purpose of the Quality Control for Rental Assistance Subsidies
Determinations Study for FY 2006
The purpose of this study is to provide national estimates of rent subsidy errors for the
U.S. Department of Housing and Urban Development’s (HUD’s) Public and Indian Housing
(PIH)-administered Public Housing (Public Housing), PIH-administered Section 8 Housing
Choice Voucher and Moderate Rehabilitation programs (PHA-administered Section 8); and
Housing-administered Section 8, Section 202 PRAC, Section 811 PRAC, and Section 202/162
PAC programs (owner-administered). Rent subsidy errors occur during the tenant certification
and annual recertification processes, and this study examines the extent, costs, and sources of
these subsidy errors.
1
For the purpose of this study, “error” is defined as any rent calculation or
eligibility determination that differs from what would have occurred if the PHA/owner had
followed all of HUD’s income certification and rent calculation requirements. This study
focuses on (re)certifications conducted during Federal FY 2007. HUD identified 14 study
objectives related to types of errors and cost issues; this report addresses each of these objectives.
The analysis also identifies errors in assigning appropriate size units to households and certain
procedural errors in the eligibility and rent determination process. In addition, a special analysis
was conducted of Utility Allowances, Payment Standards and Rent Reasonableness practices
used by the PHAs administering the Section 8 voucher program.
B. Background of the Study
This study is the seventh in a series of studies designed to identify current HUD eligibility,
income, and rent determination regulations, translate these regulations into survey instruments,
develop an error detection system, and provide nationally representative estimates of rent
subsidy errors. In the past three studies, an additional income match of Social Security benefit
data was conducted. The results of previous studies were published as follows:
♦ The final report for the first study, conducted by Macro International Inc., (Macro), and
KRA Corporation (KRA) was published in April 1996 (data were collected in 1992).
♦ The final report for the second study, conducted by Macro
2
, was published in June 2001
(data were collected in 2000).
♦ The final report for the third study, also conducted by Macro and which covered the first
half of FY 2003, was published in April 2004. Following the collection of data for the
second half of FY 2003 a follow-up report was written and published in August 2004.
1
PHAs and owners of HUD-assisted housing are required to make an initial determination of eligibility (a
“certification”) and thereafter an annual recertification of each household’s rent (a “recertification”). In this report,
the term (re)certification refers to certifications and annual recertifications. Interim recertifications were not
included in this study.
2
From May, 1999 through December, 2006 Macro International was a wholly owned subsidiary of Opinion
Research Corporation (ORC) and conducted business under the name ORC Macro.
I-1

I. Introduction
♦ The final report for the fourth study, conducted by Macro was published in July, 2005
(data were collected in 2004).
♦ The final report for the fifth study, conducted by Macro was published in October, 2006
(data were collected in 2006).
♦ The final report of the sixth study, conducted by Macro was published in October, 2007
(data were collected in 2007).
Work on the current project began in May 2007. Tasks completed before data collection
included designing the research and survey methodology, compiling HUD’s regulations for the
programs included in the study (Public Housing, PHA-administered Section 8, and owner-
administered), and automating the data collection process. Data were collected from a nationally
representative sample of HUD-assisted housing projects and project residents whose
(re)certifications were conducted from November 2006 through October 2007.
C. Organization of This Report
This report is organized as follows:
♦ Section I: Introduction
♦ Section II: Methodology
♦ Section III: Study Objectives and Analytic Methods
♦ Section IV: Findings
♦ Section V: Recommendations
♦ Appendices
A. Rent Calculations
B. Weighting Procedures
C. Source Tables
D. Consistency and Calculation Errors
E. Project Staff Questionnaire Analysis

F. Multivariate Analysis

D. Definitions of Key Terms
Definitions of key terms used throughout this report are listed below:
Actual Rent—the tenant rent from the 50058 or 50059 Form.
I-2

I. Introduction
Administration Type—PHA or owner.
Abstract Month—the month in which the data collection process for any given household was

initiated.

Calculation errors—arithmetic errors within subsections of the 50058 or 50059 Form.

Case Type—certification, recertification, and overdue recertification.

Component errors—the income and expense components used to calculate rent. The income

components are employment income, Social Security and pensions, public assistance, other

income, and asset income. The expense/allowance components are elderly/disabled allowance,

dependent allowance, medical expenses, child care expenses, and disability expenses.

Consistency errors—errors in logical conformity between elements within the 50058 or 50059

Form.

Dollar Rent Error—is calculated at the household level by subtracting the household’s QC

Rent from the Actual Rent.

Error Rate—the sum of the dollar amount of Gross Rent Error divided by the sum of the dollar

amount of the QC Rent.

Gross Rent Error—the sum of the absolute values of under- and overpayments.

Largest Component Dollar Error—the annual dollar amount of error in the component with

the largest error.

Net Rent Error—the arithmetic sum of over- and underpayments.

(Rent) Overpayment—results when the household paid more than it 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 Housing Choice Voucher, Section 8 Moderate

Rehabilitation, Section 8 project-based, Section 202 PRAC, Section 811 PRAC, and Section

202/162 PAC.

Quality Control Month—the month in which the PHA/owner completed the rent calculation.

Quality Control (QC) Rent—calculated by Macro using the tenant file, 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, and disability

assistance expenses, dependent allowance, and elderly/disabled allowance).

Rent Error—the difference between the monthly Actual Rent and the monthly QC Rent.

I-3

I. Introduction
Total Component Dollars in Error—the absolute sum (i.e., the sum of the positive and
negative amounts, ignoring the plus or minus signs) of all individual income and expense
component errors. These errors are combined to provide an overall Total Dollars in Error and
are presented as an annual amount.
Transcription errors—errors in transferring information from documentation in the tenant file
to the 50058 or 50059 Form.
(Rent) Underpayment—results when the household paid less than it should have paid; HUD’s
contribution was higher than it should have been.
I-4

II. Methodology
A. HUD Requirements and Study Standards
Using the Code of Federal Regulations and official HUD handbooks and notices, all HUD rules
relevant to the determination of rent were consolidated into a set of HUD requirements. These
requirements were used to create a uniform set of rules that could identify errors in eligibility
determination, rent calculation, and unit assignment for the housing programs in the study. In
general this uniform set of rules, known as the standards, follows the official HUD requirements.
However, for some complex requirements, standardized procedures had to be developed so the
data could be collected in a uniform manner. A complete list of standards used in this study can
be found in the Data Collection Standards for the FY2007 HUDQC Study, Quality Control for
Rental Assistance Subsidies Studies: 2007.
1
B. The Sample
The initial sampling design called for a nationally representative sample of 600 projects with
four households randomly selected from each project, or 2,400 households. Projects were
selected with probabilities proportional to size (PPS), but projects whose size exceeded the
sampling interval were selected for eight, twelve, or more households in the project, and were
counted as more than one project for purposes of determining the sample size. The sampling
design required approximately equal allocations for the three assisted program types: Public
Housing, PHA-administered Section 8 (Vouchers and Moderate Rehabilitation), and owner-
administered (Section 8, Section 202 PRAC/PAC, and Section 811 PRAC/PAC). PHAs that
participated in the Move to Work block grant demonstration program through Public Housing or
Section 8 Vouchers were removed from the project-level sample. Because some large projects
were selected multiple times, the study sample included 549 distinct projects in 59 geographic
areas across the United States and Puerto Rico. We sampled 200 projects from each major
program type. In addition, data were collected for four households in one additional owner-
administered project. This additional project was added to the sample to ensure, that given any
unexpected circumstances, the sample would included a minimum of 2400 households. Since
no unexpected issues were encountered, the final data set includes responses from 2,404
households in the 549 projects.
The tenant sample was selected from all households that were receiving assistance in Federal FY
2007. A random sample of four households was selected from most projects. An equal number
of potential “replacement” households were identified as potential substitutes when selected
households did not meet the study requirements or were unavailable to be interviewed.
However, as noted above, some large projects had additional households. For example, the
Columbus Metropolitan Housing Authority Section 8 Voucher program had a household sample
size of 16, and eleven other Housing Authorities’ Section 8 Voucher programs had household
sample sizes of 12, including those of New York City and Los Angeles. For additional
information on the sampling procedures, see the Sampling Report: FY2007, Quality Control for
Rental Assistance Subsidies.
2
1
Macro unpublished report to HUD dated July 27, 2007.
2
Macro unpublished report to HUD dated July 2, 2007.
II-1

II. Methodology
C. Data Collection
This study used a multi-stage data collection process to obtain all required information. Mail
surveys provided project-level information from PHA/project staff. Tenant-level information
was obtained by field interviewers who abstracted data from the household file, interviewed the
tenant, and requested verification for income, expense, and household composition items from
third parties.
3
Tenant income, expense, allowance, and third-party verification information were
collected using HUD-sanctioned data collection procedures. Macro field interviewers strictly
adhered to these procedures to avoid misclassifying errors caused by PHAs/projects that did not
follow HUD requirements.
The initial collection of project level data began in November 2007. Field data collection began
in February 2008 and ended in June 2008. Because PHA/projects have varying practices, data
collection forms and guidelines for data collection were designed to be flexible enough to obtain
data from circumstances as found in the PHA/project. The major tasks accomplished during data
collection and the forms used to accomplish them are discussed below.
Creating the Data Collection Instruments. More than 35 data collection forms were used for
this study to collect data on both the project and tenant levels. These forms were similar to those
used for the previous data collection efforts, though modifications were made to many forms to
improve the data collection process. Project-level forms were used to gather information to
facilitate data collection, collect data elements necessary to calculate Quality Control (QC) rent,
and gather information about certification and recertification practices. The tenant-level data
collection forms were created to collect data and determine whether: 1) there were errors in the
eligibility determination, 2) the household rent was calculated correctly, and 3) units were
correctly assigned according to the study standards. Each form was created by a survey research
specialist and reviewed by a HUD policy expert. The Office of Management and Budget (OMB)
approved all data collection forms.
Automating the Data Collection Process. This study used an enhanced version of the data
collection system used in previous studies. While project-level data were collected on paper and
the data entered upon receipt at Macro, data from tenant files were entered directly into laptop
computers, and a computer-assisted personal interviewing (CAPI) system was used to interview
tenants. This system, referred to as the HUDQC Data Collection Software (HDCS) system, was
developed by a special team of Macro survey specialists and computer systems experts.
4
As
sections of the instruments were collected by field interviewers, the HDCS system compared the
data with a range of acceptable responses and data previously entered, allowing data entry errors
to be corrected in the field. The system required that the data be collected in the correct order,
and that all the appropriate skip patterns be followed.
The automated system also alerted the field interviewer if key pieces of information used to
calculate rent were missing and needed to be located and documented. This structured,
automated process greatly reduced the need to edit, code, and clean the data after data collection
3
Verification is a process of obtaining information about income or expenses from a third party who can attest to the
accuracy of the information provided by the household. HUD requires that most information provided by the
household be verified by a third party or substantiated from documents (e.g., print-outs from EIV system).
4
The base of HDCS is the CSPRO software system used to collect demographic and health information in many
countries, in conjunction with the U.S. Agency for International Development (USAID).
II-2

II. Methodology
was completed. HDCS data were transferred to Macro electronically on a daily basis. The
incoming data were reviewed in an ongoing quality control process. This continual review of
data during data collection ensured the accuracy of the data and permitted headquarters staff to
resolve issues or request further clarifying documents while the field interviewers were still in
the field.
Contacting the PHA/Project. PHA/project contact names were obtained from HUD
headquarters staff. Letters were sent to PHA/project staff advising them of the study and
requesting their participation. Prior to field interviewer training and data collection, each project
in the study was sent a form requesting background information essential to the data collection
process and specific data used in the calculation of QC rent. The rent calculation information
requested varied by program but included such items as passbook rate, utility allowance
schedules, payment standards, minimum rent and flat rent. PHA/project staff verified the project
type and size, and the location of project offices and files. Projects were also requested to
indicate if the selected project had been designated a “special demonstration project” by HUD.
If a project answered in the affirmative to this question, the status was confirmed and the project
was replaced in the study. Public Housing projects were also requested to identify any income
exclusions that had been adopted in addition to those specified by HUD. The data requested
from the PHA/project were essential in preparation for interviewers to begin the process of
collecting data and for the calculation of the QC rent. For these reasons, a 100 percent response
rate to our request for information was necessary. Rigorous strategies were employed to ensure
compliance and completeness of requested information prior to field data collection.
As the data collection in the field began, a second mail survey was sent to a PHA/project staff
person knowledgeable about certification and recertification procedures. This survey requested
information about local policies and procedures that might help explain the rent error findings.
Questions included staff training practices, verification procedures, workload of staff who
conduct certifications and recertifications, quality control practices used to review the work of
this staff, and, for PHAs, optional questions regarding their policies on interim reviews.
Hiring and Training Field Interviewers. Fifty-nine field interviewers were hired to complete
the field data collection. Each field interviewer was assigned a group of projects. Field
interviewers typically lived in the same general area as the projects selected for the study. Ten-
day training sessions were held for 34 field interviewers who had not worked in the FY 2006
study, and a three-day training was conducted for 25 interviewers who had completed the FY
2006 study. The ten-day training covered:
♦ Project background
♦ HUD programs and requirements
♦ Survey procedures
♦ Automated data collection
♦ Administrative procedures
The three day training covered a review of the background and procedures and focused
particularly on changes implemented for the 2007 study.
II-3

II. Methodology
Abstracting from Tenant Files. At certification and recertification, PHAs/projects must
complete a HUD Form 50058 for each household in Public Housing and PHA-administered
Section 8 programs. A HUD Form 50059 is required for all other programs in the study. Data
from the HUD Forms 50058/50059 (50058/50059 Form) were entered directly into the HUD
Data Collection Software (HDCS) on each field interviewer’s laptop computer. As the data were
entered, the system identified potential data entry errors, such as incorrect codes or numbers, on
the basis of internal calculations and consistency checks. If key data used in the rent calculation
formula were missing from the 50058/50059 Form, the system alerted the interviewer and the
interviewer obtained the information from another document in the tenant file or project office.
These electronic checking procedures enabled field interviewers to make immediate corrections
and updates.
HDCS was designed to collect data in the same formats as the official 50058 and 50059 Forms
published by HUD. New York City Public Housing Authority uses a format for the 50058 that
differs from this standard format. However, due to the large number of NYC Public Housing
cases in the study, copies of the corresponding PIC 50058 forms for these cases were requested
and used for data collection when available.
5
In other projects where the 50058 forms differed
from the official HUD format, paper crosswalks were developed by Macro. Specially trained
Macro staff examined the data elements on the atypical form and developed a plan that
illustrated which fields corresponded to the standard 50058 form reflected in HDCS. There were
no crosswalks needed for the 50059 Form, since the newest format seems to be widely in use. A
paper crosswalk for the 50058 form was developed for less than one percent of projects in the
study.
In addition to the data collected from the 50058/50059 Form, field interviewers collected data
from the tenant files to document the determination of tenant eligibility and the calculation of
rent. A series of Documentation Forms were created for this purpose. The Documentation Form
data were entered directly into the HDCS system. The Documentation Form module also
collected information indicating whether the income, asset, household composition, or expense
information used by the PHA/owner was verified. HDCS compared data from the 50058/50059
Form with that entered into the Documentation Forms module and alerted the field interviewer to
possible data entry errors so that data could be reviewed and any necessary corrections made
immediately, while the file documents were easily accessible.
During the Documentation Form data entry phase, documents from the file were photocopied
when appropriate and sent to Macro weekly. Always copied were the 50058/50059 Forms, any
earned income documentation, utility allowance calculation worksheets, and the most recent
9886/9887 Tenant Consent form from the file. Field interviewers were also required to
photocopy file documents that provided information that was missing from the 50058/50059
Form, if that information was necessary to calculate QC rent (i.e., number of bedrooms), and any
Earned Income Disregard documentation in the file, as well as documents to support Flat Rent
selection. The photocopies were used to insure the accuracy of QC rent.
Interviewing Tenants. An adult household member (preferably the head of the household) was
interviewed in person using CAPI for this study. Interview questions focused on family
5
This was the first study where obtaining copies of the standard 50058 form for NYC Public Housing cases was
applied universally. This improvement to the study process enhanced the ability to collect accurate information in a
timely manner.
II-4

II. Methodology
composition, sources and amounts of income, assets, and applicable expenses. Data were
collected for the same point in time as when the (re)certification was conducted. HDCS
compared data from the 50058/50059 Form with that entered during the interview to alert the
interviewer to possible errors.
Requesting Verification from Third-Party Sources. When there was no evidence in the tenant
file that the PHA/owner verified the information used for calculating rent, or the existing
verification information did not meet requirements agreed to for this study,
6
Macro requested
verification from the appropriate third-party sources. Verification was also requested from third
parties when household interviews resulted in the identification of sources of income that were
not shown in the tenant files. Tenants signed release forms during the household interview so
that third-party verification of income and expenses could be obtained. In addition, release form
cover letters were also signed by all adult members of the household to ensure that the third
parties would be satisfied with the validity of the requests for verification. Third-parties
completed the forms and returned them to Macro.
Matching Social Security Data. Sample household members were matched with Social
Security Administration (SSA) files by HUD. Using the output from this match, the Social
Security and SSI benefit, and Medicare premium data for all household members were identified.
These data were considered third-party verification during the final QC rent determination.
D. Field Data Collection Time Periods
Data were collected in the field between January 2008 and June 2008 for the certification or
annual recertification that occurred during FY 2007 (October 2006 through September 2007)
7
.
Field interviewers collected data related to actions that may have occurred up to 20 months prior
to the file abstraction and household interview. One of the challenges of collecting data to
document actions taken in the past is developing methodologies to ensure data are collected for
the situation that existed at the selected point in time. For the respondent in the household
interview, recalling details of life situations at a past point in time presents difficulties. This may
be complicated by the fact that some respondents in this population may have unstable situations
resulting from inconsistent income or changing numbers of household members. In light of this,
strategies were developed to ensure consistent and accurate collection of data across program
types, projects, and households in the study. Two of the strategies developed that were of
primary importance to the data collection are described in this section.
Quality Control Month. The month for which data were collected is referred to as the Quality
Control Month (QCM). This month represents the date the rent calculation for the certification
or annual recertification (conducted in FY 2007) was completed. For most households in the
owner-administered programs, the QCM is the month in which the project manager (or other
authorized housing project staff member) signed the 50059 Form, certifying that the information
contained on the form was correct. The rent calculation date on the 50058 form was the “date
modified” printed on the form. If these pieces of information were not available on the
6
For purposes of this study, verification was acceptable if it was in writing, received from the third party, and dated
60 days before or 30 days after the (re)certification was completed.
7
To account for delays between the time the work is completed by the PHA/project staff and the effective date of
the (re)certification, actions effective in October 2007were included in the FY 2007 study.
II-5

II. Methodology
50058/50059 Form, the field interviewer used other documentation in the tenant file to determine
when the action was taken.
After the QCM was established, the data from the 50058/50059 Form corresponding to the QCM
was entered into HDCS. The data from the documents used by the project staff to verify
information on the 50058/50059 Form in the QCM were also entered in a separate HDCS
module. The household interview was conducted with frequent reminders to the respondent that
questions were being asked as of the QCM.
Note: If the recertification was overdue by more than 12 months, the QCM was moved forward
in 12-month intervals to a point in time within FY 2007. In this situation, during the household
interview, the respondent was questioned about circumstances for the month in which the
recertification would have been completed had the housing project staff completed it on time. In
rare situations, when the rent was calculated after the effective date of the action (because of
retroactive adjustments) the QCM is the earlier of the two dates—the rent calculation or the
effective date of the action.
Third-Party Verification Rules. Occasionally the verifications found in the file for household
composition, income, asset, and expense items were different than those required by HUD. In
addition, files were likely to contain verification documents other than those intended to support
the (re)certification corresponding to the QCM. To ensure that the data from the right documents
(those that had been gathered to verify the information on the 50058/50059 Form being
reviewed) were entered in to HDCS, and to apply rules fairly and consistently across all
households in the study, a set of rules defining acceptable verification were developed. For
purposes of this study, verification was considered acceptable if it was in writing, was received
from a third party, and was dated 60 days before or 30 days after the date the (re)certification
was completed. Field interviewers were given detailed instructions on the various types of
documents they were likely to find in the file and how to classify them. The date and type of
verification for each household, income, and expense item was entered in to HDCS during file
abstraction. The HDCS system informed the interviewer if any items did not meet the
verification requirements of the study. For the items that did not meet the requirements, the field
interviewer requested written verification from the appropriate third party.
E. Constructing the Analysis Files
The initial database consisted of five separate files that included abstracted 50058 and 50059
Forms, tenant file information from the Documentation Form module, information from the
household interview, and the third-party release forms. Data fields were at both the member and
household levels, with income and expense items in hourly, weekly, monthly, or annual amounts.
Macro constructed an analysis file that annualized all income and expense data at the household
level. For some items, such as stable income from Social Security, this calculation was relatively
easy. For other items, such as sporadic employment or medical expenses, annualizing income or
deductions was more complicated. A unique linking variable was created to compare
information abstracted from the 50058/50059 Form and other file documentation with
information obtained in the household interview and received from third-party verification. This
variable specifically identified the income/asset/expense and household member to which it
belonged.
II-6

II. Methodology
For the calculation of rent error, the final analysis files contained income and expense/allowance
data aggregated at the household level in annual amounts. Rent data were in monthly amounts.
Separate files were created for the analysis of issues such as verification, internal 50058/50059
Form errors, and occupancy standards.
F. Rent Formulae
HUD uses specific formulae for determining tenant rents for each of its programs. The formula
for determining the Total Tenant Payment (TTP) is the same for all programs except Sections
202 PRAC, 811 PRAC, and 202/162 PAC. The TTP is the greater of:
1) 30 percent of a household’s adjusted monthly income, which is one-twelfth of the total of all
household members’ earned and unearned income (other than those amounts specifically
excluded by HUD or PHA policy), less allowances for elderly/disabled households and for
household dependents, and deductions for disability, medical, and child care expenses.
2) 10 percent of a household’s gross monthly income with no allowances or expense
deductions.
3) The welfare rent in as-paid states (New York was the only as-paid state in this study).
4) The minimum rent ($25 for owner-administered projects, or an amount established by the
PHA, not to exceed $50).
The formula for determining the TTP for the Sections 202 PRAC, 811 PRAC, and 202/162 PAC
programs includes steps (1) through (3) above, but there is no minimum rent requirement for
these programs.
There are five different rent calculations used to calculate the actual amount of the household’s
rent depending on the program type. For the Section 8 Voucher program, household-specific
characteristics also affect the calculation. These five rent calculations include:
♦ Public Housing
♦ Section 8 Project-Based (including Moderate Rehabilitation), Sections 202 PRAC, 811
PRAC, and Section 202/162 PAC
♦ Section 8 Vouchers
♦ Section 8 Enhanced Vouchers (there were no Enhanced Voucher households in the study)
♦ Manufactured Home Space Rental for Section 8 Vouchers (there were no households in
the study sample that met this criterion)
The household rent was calculated after data from all sources were collected. When calculating
rent, a cap was placed on the maximum amount of rent the tenant was required to pay. For all
Section 8 programs, this is the Gross Rent. In the Public Housing program, this is the Flat Rent.
If the Flat Rent was not available, the Ceiling Rent was used to cap the rent. The rent is not
capped for the Section 202 PRAC or Section 811 PRAC programs.
II-7

II. Methodology
Additional rent calculations were necessary for households with ineligible noncitizens.
Determining the correct rent for these households is a multi-part process that first determines
whether the household is entitled to continued assistance, or temporary deferral of termination of
assistance, and then prorating the rent if appropriate. Two proration formulae were used—one
for Public Housing and one for all Section 8 programs.
The algorithms for the rent calculation formulae can be found in Appendix A.
G. Calculation of Rent Error
The monthly rent algorithms used by Macro to calculate the national estimates of error are the
following:
♦ Actual Rent: The monthly rent indicated on the 50058/50059 Form. If this item was
missing on the 50058/50059 Form, the Actual Rent was taken from another official
document in the file.
8
♦ Quality Control Rent: The monthly rent calculated by Macro using all of the verified
household information.
8
Rent error was calculated by subtracting the QC Rent from the Actual Rent. A discrepancy of
$5 or less between the monthly Actual and QC Rent was not considered to be an error. The $5
window was used to allow for minor calculation and rounding errors, and to focus the data
analysis on major sources of error.
H. Quality Control Rent
Macro calculated QC Rents using the best available information. Every effort was made to use
data that would have been available to the PHA/project when determining which data to use in
the QC rent calculation. Each income and expense item was processed individually. For each
item, Macro first used available verification from the project files. If acceptable verification was
not available from the tenant file, verification was requested from an appropriate third party (see
Section II-D for a discussion of acceptable verification). If the verification was not returned by
the third party and the tenant file did not include verification, information obtained during the
household interview was used. The following special procedures were followed when
calculating the QC Rent as appropriate:
♦ Income that started after the QCM was not counted when calculating the QC Rent.
♦ Income that ended after the QCM was counted for the full year unless it was clear that the
PHA/owner knew that this income was going to end.
8
Rent Roll data was not used as a substitute for Actual Rent because a previous study found that the Rent Roll
sometimes included amounts to make up for previous unpaid rent, fines, or damages, etc.
8
Attempts were made to verify items that were not verified by PHA/owner staff; however, verification was not
always obtained. If verification was not available, other information from the tenant file or information obtained
during the household interview was used to calculate the QC rent. When calculating QC rents, codes were assigned
to indicate which rents were based on verified information and those for which the income/expense information was
only partially or not verified.
II-8

II. Methodology
♦ Earned income bonuses were not counted unless it was clear that the bonus was paid on a
regular basis.
♦ Temporary Assistance to Needy Families (TANF) and Other Welfare income were
treated as the same source of income so that income listed as TANF on one form (e.g.,
the household questionnaire), and Other Welfare on another form (e.g., the
Documentation Forms) would not be counted twice.
♦ Welfare (TANF and Other Welfare) income, Child Support income, and Child Care
expenses were treated at the household level instead of the member level so that the same
source of income associated with one member (e.g., the head of household) on one form,
and another member (e.g., a child) on another form would not be counted twice.
♦ Disability status identified in the Social Security match data for household members
receiving Social Security or Supplemental Security Income (SSI) benefits was used to
determine the disability status for the recipient of the Social Security benefit.
♦ Passbook rates (for determining the imputed income from assets) for PHA-administered
programs were taken from the project-level information provided by PHA/owner staff.
The passbook rate for owner-administered programs is 2 percent.
♦ For new certifications, the low and very low income limits were obtained from HUD’s
Web site.
♦ When determining the prorated rent for Public Housing households with ineligible
noncitizens, if the Maximum Rent was not present on the 50058 form, the Fair Market
Rent (FMR) was used instead of the 95th percentile of Gross Rent because the 95th
percentile of Gross Rent was not available.
♦ The values from the 50058 form were used for minimum rent, gross rent, payment
standard, and flat rent unless the value was missing, in which case the missing value was
taken from the PHA/project-level information provided by PHA staff.
♦ The values from the 50059 form were used for gross rent and contract rent unless the
value was missing, in which case the missing value was taken from the project-level
information provided by owner staff.
♦ Welfare rent for the State of New York was taken from the project-level information
provided by PHA staff.
♦ A separate verification code was used to identify verification obtained from the
Enterprise Income Verification (EIV) system. When Social Security, SSI, or Black Lung
benefits were verified with EIV, the verification was considered third party in writing. If
EIV information was in the file for earned income or unemployment benefits, the dates
associated with the form were examined to determine if the PHA/project staff had access
to the EIV information at the time of the (re)certification. Copies of EIV (as well as other
types of verification of earned income found in the tenant file) were sent to Macro
headquarters and reviewed by data quality specialists to prevent mistakes in calculating
the QC earned income value.
II-9

II. Methodology
I. HUD Requirements Complicating the Analysis
Several HUD requirements affected the data collection methodology and subsequent analysis.
As noted in Section II-A, relevant HUD requirements were incorporated in the study standards
used to determine error. All data collection procedures and analyses were developed on the basis
of these study standards. Though most standards were easily implemented, several were more
problematic and they complicated the data collection or analysis, as discussed below.
Anticipated Income. The amount of rent a household will pay is determined on the basis of
anticipated household income and deductions for the 12 months following (re)certification. For
households with a stable income source like Social Security or steady employment, annual
income estimates for the next 12 months are relatively accurate. However, many assisted
households have members with sporadic employment or members who move in and out of the
household. Also, certain expenses such as medical expenses (for elderly/disabled households)
and child care costs may be very difficult to anticipate. Determining whether such income and
expense amounts were figured correctly at the time of recertification is very difficult when data
are collected after the changes occurred. Every effort was made to treat questionable income or
expenses in the same manner as PHA/project staff treated them. Several of the special
procedures described in Section II-H were created for this purpose.
Third-Party Verification. HUD regulations require that the information supplied by residents
at (re)certification be verified by third parties (e.g., employers, the Social Security
Administration, banks, medical personnel). Field interviewers obtained release forms from the
households when evidence of verification was not present in the tenant’s file and they then
requested verification from the appropriate third parties. However, some third parties did not
respond, others returned information for incorrect time periods, others required payment for the
information requested, and other problems were encountered in obtaining the correct
verification. Follow-up requests for missing verification were not made in all cases due to time
constraints.
Macro and HUD established a set of verification rules to determine whether an item was verified.
Section II-D shows the rules used to determine if verification was acceptable and for each
matched item used in the rent calculation. Verification rates for different rent components are in
Tables 1a–1f (in Appendix C) and Exhibit IV-1 in Section IV-B.
Earned Income Disregard. The regulations governing the Public Housing and the Section 8
Voucher programs require PHAs to exclude a portion of earned income for households meeting
certain criteria. Only participants in these programs—not applicants entering the programs—are
eligible for this income exclusion.
To identify households eligible for the earned income disregard, tenants were asked about
training and self-sufficiency programs during the household interview. Fifty-seven household
members were identified as possibly being entitled to an earned income disregard.
For these household members, we examined the tenant file information on the 50058 and the
Documentation Forms. We compared the QC calculated earned income exclusion (using the
household questionnaire information) with the earned income used by the PHA when calculating
the total annual income. When determining whether a household member was entitled to an
II-10

II. Methodology
earned income disregard because of unemployment, we reviewed income match data available
from the NDNH.
In 36 (of the 57) cases, the PHA/project did not give an earned income disregard. In 33 of these
cases, the QC calculated earned income disregard also indicated that the disregard was not
applicable. In 21 (of the 57) cases the PHA/project did give an earned income disregard. In 17
of these cases, the QC calculated earned income disregard also indicated that the disregard was
applicable. When both the PHA and the QC calculation indicated an EID was appropriate, the
actual amount of the EID matched in 13 of the 17 cases. These differences in the amount of the
earned income disregard were considered as errors in this study. In previous studies no error was
attributed to differences in the EID calculations.
Training Programs. The regulations governing all housing programs included in this study
require PHA/owners to exclude all amounts received under training programs funded by HUD,
as well as the incremental earnings and benefits resulting to any family member from
participation in qualifying State or local employment training programs.
To identify households eligible for the training program exclusions, the field interviewers
documented training program information found in the tenant file and provided during the tenant
interview. Between documentation in the tenant file and information from the household
interview, eight household members had indications of involvement in training programs. Only
two of these eight were found to be eligible for a training program income exclusion. In the first
case, the income exclusion was applied by both the PHA and during the QC process. In the
second of the two cases the income exclusion was not given by the PHA.
Permissible Deductions. Public Housing programs may adopt deductions from annual income
in addition to HUD’s required deductions. To make sure that the appropriate additional
permissible deductions were taken into consideration when determining the adjusted annual
income, we looked at two sources. First, we looked at items 8b through 8e on the 50058 form
where the type and amount of permissible deductions were recorded. Second, we asked a
question in the Project Specific Information request to identify additional exclusions adopted by
the Public Housing PHAs. We found that many PHAs use the Permissible Deduction section
(items 8b through 8e) of the 50058 form to record all kinds of information that have nothing to
do with permissible deductions. Therefore, we had to rely on the Project Specific Information
request to determine whether the items listed on the 50058 form were in fact additional
permissible deductions. On the basis of the information obtained through the Project Specific
Information requests and the 50058 forms, twelve households representing six PHAs were
entitled to permissible deductions. In six cases the percent of FICA tax (7.65%) was deducted
from gross earned income, and in two cases the percent of FICA tax (7.65%) plus the federal
income tax were deducted from the gross earned income. In one case there was a $1000 flat
deduction from the gross earned income; in one case child support payments were deducted from
the gross earned income, and in one case $1300 for transportation costs was subtracted from
earned income. In one other case thirty percent of the earned income was deducted from the
gross earned income. The permissible deduction applied for QC purposes was exactly the same
as the permissible deduction allowed by the PHA.
Flat Rent. Households that elected to pay a flat rent rather than an income-based rent were
included in the study. For these households there is no rent error. The QC rent is the same as the
Flat Rent used by the PHA. There are 71 flat rent cases in the study sample. It should be noted
II-11

II. Methodology
that determining if a household is paying the flat rent is not always easy because of contradicting
data within the 50058 form. For most cases, items 2a-Flat Rent Annual Update, and 10u-Type of
Rent Selected could be used to identify whether the household is paying the flat rent instead of
income-based rent. However, if these two items contradicted one another, notations from other
documents in the file were taken into consideration.
Ineligible Noncitizens. HUD regulations require that rent be prorated for households with
ineligible noncitizens unless the household meets certain criteria that allow continuation of full
assistance. Macro reviewed all households with ineligible noncitizens to ensure that the rent was
calculated correctly. No households with ineligible noncitizens were entitled to continuation of
full assistance. Less than one percent of the households in the study included an ineligible
noncitizen.
Reduced or Terminated TANF Benefits. The regulations governing Public Housing and PHA-
administered Section 8 programs included in the study require using the amount of the TANF
benefit before reduction or termination, resulting from fraud or failure to cooperate with the
welfare family self sufficiency program. To identify households with reduced or terminated
TANF benefits, tenants were asked during the household interview about previous receipt of
TANF and whether their TANF benefits were reduced during the household interview.
If the TANF benefits were reduced or terminated due to fraud or failure to comply with the
welfare family self sufficiency requirements, the value of the TANF benefit before the reduction
or termination was used in the QC Rent calculation.
9
The TANF benefits in 51 households were
reviewed and in all cases the PHA/project was accounting for TANF correctly.
Students. The regulations governing PHA-administered programs included in the study require
that students age 18 or over but under age 24 meet certain criteria. If these criteria are not met,
the student’s parent’s income must be included when determining if the student meets the
program’s financial requirements. For households with students, field interviewers documented
student enrollment and member characteristics found in the tenant file and provided during the
tenant interview. These households were reviewed to determine if the student met the special
student criteria as defined by HUD regulations. Fifteen cases were reviewed and all were
correctly receiving housing assistance.
9
The value of this reduced or terminated TANF is offset by the amount of additional income the family received
that started after the time the sanction was imposed.
II-12

III. Study Objectives and Analytic Methods
This section presents the 14 study objectives and a brief description of the methodology used to
meet them.
1
At the end of this section, Exhibit III-2 presents a chart summarizing the objectives
and providing information on where each objective is addressed within the report.
Objective 1: Identify the various types of rent errors, rent error rates, and
calculate their variance estimates.
The types of errors and error rates in the 2000 through FY 2006 studies are replicated in the FY
2007 analyses. These errors include percent of households paying correct and incorrect rent,
dollar error amounts, and dollar error rates. Variance estimates (standard errors) are provided for
selected error rates. Errors are determined by recalculating the tenant rent on the basis of
verified QC information and subtracting this amount from the tenant rent indicated on the
50058/50059 Form (Actual Rent). The following three types of dollar rent error estimates were
calculated:
Dollar Rent Error—The difference between the monthly Actual Rent and the monthly QC Rent
(i.e., Actual Rent minus QC Rent). A household rent is found to be in error if the difference
between the Actual Rent and QC Rent is greater than $5, while “proper” rent payments reflect
differences of $5 or less. Rates of exactly matching Actual and QC rents (within $1) are also
presented. Simple percentages of the number of households paying the proper and exact rents
are reported, as well as the percentage of households in error per program, the average gross
dollars in error, and the percentage of rent dollars in error. For households who were ineligible
when initially certified, the QC Rent is the flat rent for Public Housing households, or the
Housing Assistance Payment (HAP) for Section 8 programs. The dollar error is this amount
minus the Actual Rent.
Total Component Dollars in Error—The absolute sum (i.e., the sum of the positive and
negative amounts, ignoring the plus or minus signs) of all individual income and expense
component errors. These errors are combined to provide an overall Total Dollars in Error and
are presented as annual amounts
2
. A dollar amount of rent overpayment and underpayment was
calculated for each component with identified error; however, some of these errors were
overlapping or offsetting. For example, earned income may have been underreported while—
perhaps because of a calculation error—Supplemental Security Income may have been
overstated. The net difference could be zero, or a positive or negative amount.
Largest Component Dollar Error—The annual dollar amount of error for the income or
expense components with the largest error. Income and expense components include the five
sources of income (earned, pension, public assistance, other income, and assets) and the five
types of deductions (medical, child care, and disability assistance expenses, dependent
1
See Analysis Plan for the FY2007 HUDQC Study, Quality Control for Rental Assistance Subsidy Determinations,
an unpublished Macro report to HUD, dated August 20, 2007 for a more detailed description of the methodology.
2
Because dollar component errors (CE) are reported on an annual basis while dollar rent errors (RE) are reported
on a monthly basis, and rents are generally set at 30 percent of adjusted income, component errors are usually 40
times the corresponding rent error (.30 * CE = 12 * RE, or CE = (12/.30) * RE = (120/3) x RE = 40 * RE).
III-1

III. Study Objectives and Analytic Methods
allowance, and elderly/disabled allowance). If the component with the largest error is earned
income, the largest dollar error would reflect the difference between the earned income used by
the PHA/project, and the earned income used in the QC rent calculation.
The dollar error rate is used for other error calculations, including the National Rent Error Rate
and Net and Gross Error Rates. The latter error calculations link errors in the rent determination
process to dollar error rates, sparking new oversight practices to better manage HUD subsidies.
Objective 2: Identify the dollar costs of the various types of errors.
Five types of administrative errors are linked to rent errors. Data obtained directly from the
50058/50059 Form as well as project and tenant information from the tenant file are used to
identify and measure each of the following error types:
♦ Calculation errors
♦ Consistency errors
♦ Transcription errors
♦ Incorrect determination of allowances and income sources
♦ Overdue recertifications
Calculation errors are detected by recalculating section subtotals and the final rent based on the
exact information on the 50058/50059 Form. The tenant rent is calculated using the detailed
information on the 50058/50059 Form and compared to the actual tenant rent on the
50058/50059 Form. If the two rents differ, there is a calculation error.
Consistency errors are determined when there is a lack of logical conformity between elements
within the 50058/50059 Form. For example, the Effective Date of Action must be on or after the
Date of Admission. Elderly status information must be consistent with information about the age
of the head of household or spouse.
Transcription errors are detected by comparing 50058/50059 Form data with information in
the tenant file. If the 50058/50059 Form data for a specific income or expense item does not
match the tenant file data, a transcription error exists.
Incorrect determination of allowances and income sources are identified by taking tenant file
information and comparing it with the 50058/50059 Form data. Allowance errors are detected
by calculating the allowances based on the tenant file information and comparing this QC
allowance with the Actual Allowance on the 50058/50059 Form. Similarly, income is calculated
based on the types and amounts of income reported in the tenant file. The improper application
of allowances and incorrect calculation of income are a subset of transcription errors.
Overdue Recertifications produce rent errors because rents are based on out-of-date
information. For households with overdue recertifications, the QC information is based on the
month the recertification should have been completed rather than when it was completed.
III-2

III. Study Objectives and Analytic Methods
Objective 3: Estimate the national-level costs for total error and major error types.
This analysis includes determining the National Rent Error Rate, the numbers and proportions of
households found to be in error, and the dollar amount of rent error and the proportion of total
dollars found to be in error. Sample data are weighted to provide national estimates.
Objective 4: Determine the relationship between errors detectable using the HUD
50058 and HUD 50059 Forms and total errors found in the study.
As discussed under Objective 2, calculation and consistency errors identify mistakes made by the
housing project staff. Under Objective 4, households with calculation and consistency errors are
compared to households with QC errors to determine if error found within the 50058/50059
Form can be used to predict QC error.
Objective 5: Determine whether error rates and error costs have statistically
significant differences from program to program.
This analysis presents differences in error rates by program type. Data are provided for three
program groups: Public Housing, PHA-administered Section 8 (Section 8 Vouchers and
Moderate Rehabilitation programs), and owner-administered (Section 8, Section 202 PRAC,
Section 811 PRAC, and Section 202/162 PAC). The gross and net error rates are provided for
each of these program types. 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 is the sum dollar amount of net error
divided again by the sum dollar amount of QC Rent.
Objective 6: Determine the apparent cause of significant rent errors, either on a
sample or a comprehensive basis, to provide HUD with information on whether
the error was caused primarily by the tenant or by program sponsor staff.
As was done in the previous studies, we provide descriptive information on the sources of
discrepancies between housing file information and verified information, and describe the
incidence of administrative errors and their impacts. We also examine whether failure to verify
sources of income and expenses contributes to QC error. Multivariate analyses using
administrative errors and income components as independent variables are performed to identify
how these errors affect the QC Dollar Rent Error.
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 HUD guidelines specifying the appropriate size unit for assisted
households are shown in Exhibit III-1.
3
For most programs, the rules are not based solely on household size and allow discretion on the
part of the project staff. All programs allow exceptions to these rules. This study replicates the
3
Local projects have discretion in determining unit size, and may determine unit size differently than shown.
III-3

III. Study Objectives and Analytic Methods
analyses in the previous studies that identified bedroom size and program, and the proportion of
households in compliance with and in violation of occupancy standards according to the
guidelines in the table below.
Exhibit III-1
PHA-Administered Section 8 Unit Size Standards
Number of Bedrooms Number of Persons in Household
Minimum Maximum
0 1 1
1 1 2
2 2 4
3 3 6
4 4 8
5 5 10
Objective 8: Provide information on the extent to which errors are concentrated in
projects and programs.
Further descriptive analyses are conducted to examine whether errors are concentrated within or
are randomly distributed across PHAs/projects. Multivariate analyses are conducted with the
tenant as the unit of analysis. Tenant and PHA/project characteristics were analyzed as
independent variables predicting error rates. This analysis identified how each of these variables
contributes to rent error. The results will help guide HUD’s management of error rates and
elaborate relationships between management practices and project/tenant characteristics that
affect error rates.
Objective 9: Identify 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. Newly certified households are reviewed to determine whether they met the eligibility
requirements for assisted housing.
Five eligibility requirements reviewed at initial certification are not a part of the recertification
process (and thus not confirmed on an ongoing basis): definition of family, citizenship,
verification of Social Security numbers, signing consent forms, and low and very low income
limits. This study did not investigate the definition of family because it is determined by the
PHA or owner. Therefore, findings are provided on four of the five initial certification criteria.
This study also did not include suitability factors that PHA/owners may use in selecting
tenants—factors such as tenant histories, histories of drug use or criminal activity.
III-4

III. Study Objectives and Analytic Methods
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 used in Section 8 voucher households.
To comply with the rent reasonableness requirement, housing authorities must determine that
Section 8 voucher rents are reasonable in comparison with rents for similar housing in the
private, unassisted market. Using information collected from tenant files, we estimated the
proportion of Section 8 voucher recipients with comparable documentation. For those with
documentation, we classified the type of evidence cited in the tenant file documentation (e.g., no
evidence, cited market estimates for comparable units, or the rents of one or more units
considered to be comparable). We present weighted proportions of voucher recipients with rent
comparability data.
Additionally, payment standard data from the 50058 Form are compared with FMR data to
identify the households whose payment standards fall outside the 90–110 percent FMR band.
Utility allowance schedules are likewise matched to tenant files to evaluate the issues associated
with independently evaluating utility allowances as a potential component of rent error.
Objective 11: Estimate the total positive and negative errors in terms of
HUD subsidies.
Proper payments are those in which the Actual Rent equals the QC Rent. Errors can be either
overpayments (Actual Rent greater than QC Rent) or tenant underpayments (Actual Rent less
than QC Rent). Overpayment error rates were calculated by dividing the total amount of
overpayment by the total QC Rent; underpayment error rates were calculated similarly by
dividing the total amount of underpayments by the total QC Rent.
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.
We investigated the relationship between using an automated rent calculation system and
project-level gross error rate using an Analysis of Variance. We also examined whether gross
rent error differed significantly by computer use between programs.
Objective 13: Determine whether other tenant or project characteristics on which
data are available are correlated with higher or lower error rates.
To respond to this objective, we use multivariate analysis to conduct more detailed analyses of
differences among PHA/projects and provide HUD with more information for identifying
projects and tenants likely to exhibit high error rates.
III-5

III. Study Objectives and Analytic Methods
Objective 14: Determine whether cases for which 50058/50059 Form data had
been submitted to HUD were more or less likely to have errors than those for
which data had not been submitted.
The QC sample was matched to the TRACS/PIC data. Analysis was conducted to compare the
average dollars in error for households included in TRACS/PIC with those that are not.
III-6

III. Study Objectives and Analytic Methods
Exhibit III-2
Summary of Study Objectives
Objective: Where Objective is Addressed
Exec. Summary Section IV
1
Identify the various types of rent errors, rent error rates, and calculate
their variance estimates
• Dollar Rent Error
• Total Component Dollars in Error
• Largest Component Dollar Error
p. iv – vii
Exhibits 2 & 5
p. 4 -7;
Exhibits 3 - 5
p. 13–14;
Exhibits 13 - 14
2
Identify the dollar costs of the various types of errors.
• Calculation errors
• Consistency errors
• Transcription errors
• Incorrect determination of allowances and income sources
• Overdue recertifications
p. vi - viii p. 21 -23; Exhibits 22
- 23
p. 13 – 14;
Exhibits 12 - 13
p. 10; Exhibit 9
3 Estimate the national-level costs for total error and major error types. p. v; Exhibit 3 p. 5 - 7; Exhibits 3 - 6
4
Determine the relationship between errors detectable using the HUD
50058 and HUD 50059 Forms and total errors found in the study.
p. x p. 17 – 18; Exhibit 18
5
Determine whether error rates and error costs have statistically significant
differences from program to program.
p. v p. 5; Exhibit IV-3
6
Determine the apparent cause of significant rent errors, either on a
sample or a comprehensive basis, to provide HUD with information on
whether the error was caused primarily by the tenant or by program
sponsor staff.
p. vi - viii p. 12 – 23;
Exhibits 12 - 23
7
Determine the extent to which households are over-housed relative to
HUD's occupancy standards.
p. ix p. 23 – 25;
Exhibits 24
8
Provide information on the extent to which errors are concentrated in
projects and programs.
p. x p. 28 - 29
9
Identify the percentage of newly certified tenants who were incorrectly
determined eligible for program admission.
p. ix p. 9 – 10; Exhibits 8
10
For Section 8 voucher households, determine:
• the extent to which rent comparability determinations are found
in the tenant file, and indicate the method used to support the
determination.
• whether payment standards are within 90-110 percent of fair
market rents
• whether the correct utility allowances are being used.
p. ix p. 29 – 42;
Exhibits 25 - 34
11 Estimate the total positive and negative errors in terms of HUD subsidies p. v; Exhibit 3 p. 10 – 12;
Exhibits 10 - 11
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.
p. x p. 27
13
Determine whether other tenant or project characteristics on which data
are available are correlated with higher or lower error rates.
p. x p. 28 - 29
14
Determine whether cases for which 50058/50059 Form data had been
submitted to HUD were more or less likely to have errors than those for
which data had not been submitted.
p. vii p. 42 – 45
Exhibits 35 - 37
III-7

IV. Findings
A. Overview
Analyses were conducted using weighted sample data for 2,404 households.
1
Data are presented
by the three program types that were the basis for the sampling design—Public and Indian
Housing (PIH)-administered (Public Housing), PIH-administered Section 8 Housing Choice
Voucher, and Moderate Rehabilitation programs (PHA-administered Section 8); and Housing-
administered Section 8, Section 202 PRAC, Section 811 PRAC, and Section 202/162 PAC
programs (owner-administered). Each of the major study findings, the reasons for the errors, and
other background information concerning these errors are discussed below. In many of the
exhibits throughout the report, the data collected during the current study (referred to as the FY
2007 data) are compared with the data collected in a previous study. The data were collected and
the analysis was completed for this earlier study (referred to as the FY 2006 data) in 2007.
This discussion is divided into ten parts: the errors in the rent amount based on the QC data (rent
error), the errors in sources of income and expenses (component errors), the errors found using
only project file data (administrative error), occupancy standards, analysis of the responses
received from PHA/project staff regarding PHA/project practices (based on the Project Staff
Questionnaire), multivariate analysis, findings related to rent reasonableness determinations,
utility allowance analysis, payment standard analysis, and comparisons with PIC/TRACS data.
The first three parts present different types of error.
Rent error is error that results in an actual dollar error. A dollar error means the household paid
too much rent (an overpayment) or the household paid less rent than it should have paid (an
underpayment).
Component errors are the income and expense components used to calculate rent. The income
components are employment income, Social Security and pensions, public assistance, other
income, and asset income. The expense/allowance components are elderly/disabled allowance,
dependent allowance, medical expenses, child care expenses, and disability expenses.
Administrative Errors are errors that result from administrative mistakes. They consist of the
following:
♦ Consistency errors—errors in logical conformity between elements within the 50058 or
50059 Form
♦ Calculation errors—arithmetic errors within subsections of the 50058 or 50059 Form
♦ Transcription errors—errors in transferring information from documentation in the tenant
file to the 50058 or 50059 Form.
♦ Failure to conduct a recertification in a timely manner
♦ Failure to verify information.
1
Appendix B presents the procedure used in weighting the data.
IV-1

IV. Findings
Component and administrative errors may or may not result in rent errors. Administrative errors
tell us at what point during the rent determination process an error occurred, while the
component errors tell us which income or expense caused the error. Data supporting the
discussion are presented in the source tables found in Appendix C.
B. Rent Error
Overview. Rent errors were identified by subtracting the QC Rent from the Actual Rent.
2
The
QC Rent was calculated using third-party verification whenever possible. If third-party
verification was not available, information from the Documentation Forms or Household
Questionnaire was used. The Actual Rent is the Tenant Rent from the 50058/50059 Form. As
noted above, a household was considered to be correct (proper payment) if the QC Rent and the
Actual Rent matched within $5. All exhibits included in this report (except IV-2) and all tables
in Appendix C define households whose Actual and QC Rents matched within $5 as proper
payments, except for the supplemental tables (designated by the letter “S”), which are based on
exact matches between these two rents.
Definitions of Rent Errors. Dollar error can be determined by comparing the rent the
household should have paid with what it was paying, or by identifying the percentage of the
Federal subsidy that was paid in error. In this study, error was determined by the first method.
The rent errors presented throughout this report were calculated in the following manner:
♦ Dollar Rent Error was calculated at the household level by subtracting the household’s
QC Rent from the Actual Rent. Note that these are monthly rents. A negative number
indicates an underpayment, meaning the household paid less than it should have paid, and
that HUD’s contribution 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.
♦ Gross Rent Error is the absolute value (i.e., the sum of the absolute value of positive
and negative Rent Error) of the Dollar Rent Error for the sample as a whole or a specified
group of households. The Gross Rent Error functions simply as a measure of the
magnitude of the errors. The dollar amounts presented in the tables are Gross Rent Error
values, unless otherwise indicated.
♦ Net Rent Error is the arithmetic value (i.e., the sum of the negative and positive values
of over- and underpayments) of the rent error.
♦ Error Rate is calculated by dividing the sum of the Gross Rent Error by the sum of the
QC Rent, for the entire sample or a specified group of households.
2
Rent error is determined on the basis of Tenant Rent, not TTP. Error based on TTP may differ from Tenant Rent
because of the program specific rent formulas applied when calculating Tenant Rent. These rent formulas are listed
in Section II-F and presented in detail in Appendix A.
IV-2

IV. Findings
Financial Impact of Identifying Rent Error. Reduction in the rent error associated with the
programs included in this study does not mean there will be an overall savings in the costs
associated with administering these programs. Given there are large numbers of eligible
households on waiting lists, if a household leaves the program because it is no longer eligible for
a subsidy, another household will take its place. The replacement household may be entitled to a
smaller or a larger subsidy than the household that left the program. Therefore, the most direct
benefit of identifying households with rent error is making sure those households who are
eligible for the program are receiving the correct subsidy, rather than reducing the amount of
funds needed to administer the programs. The most appropriate use of this study is as a tool for
strengthening HUD’s procedures for ensuring administrative compliance with regulations. The
recommendations presented in this report may require greater rather than fewer resources to
provide HUD, PHAs, and owners with the written policy guidelines and training, standardized
forms, and on-going monitoring needed to assure the programs are administered correctly.
Verification Used in Determining the QC Rent. As indicated above, a set of rules was
established for third-party verification (see Section II-D). If an income or expense component
was used for a rent calculation and was not verified by the PHA/owner, Macro staff sought third-
party verification. However, Macro verification could not be obtained for all PHA/owner
unverified items despite considerable effort and expense
3
.
Exhibit IV-1 shows the percentage of each rent component that was verified by either the
PHA/owner or Macro.
Exhibit IV-1
Percent of Households Fully Verified by Either the PHA/Owner or Macro
Third-Party Verbal or In-Writing,
Documentation, or EIV Third-Party In-writing
Rent Component 2006 2007 2006 2007
Earned Income 89% 90% 73% 76%
Pensions 99% 98% 91% 90%
Public Assistance 86% 94% 65% 73%
Other Income 83% 81% 65% 58%
Asset Income 86% 86% 66% 65%
Child Care Expense 75% 70% 68% 59%
Medical Expense 74% 75% 54% 48%

Source: Tables 1a and 1b, Appendix C

The first two columns present the percentage of rent components that were verified with third-
party in-writing, third-party verbal, documentation
4
or Enterprise Income Verification (EIV).
The remaining two columns present the percentage of rent components that were verified with
the more stringent verification requirements for this study (i.e., third-party in-writing). As the
3
If third-party verification was not available, documentation from the tenant file was used to calculate the QC rent.

If neither third party verification nor file documentation was available, information collected during the household

interview was used to calculate the QC rent.

4
Documentation means documents submitted by the family such as pay stubs or bank statements, or a statement in

the file indicating the project staff viewed an acceptable verification (but there was no copy in the file).

IV-3

IV. Findings
exhibit indicates, while primarily the percentage of households where the rent component was
fully verified remained the same, for the Public Assistance rent component, the percentage of
households where the rent component was fully verified increased. It should be noted that since
the sample size for Disability Expenses is so small, the findings are not reliable national
estimates and not included in Exhibit IV-1.
Tables C-1c, C-1d, and C-1e in Appendix C provide additional verification information by rent
component. They present the number of households for which the income or expense
component was not verified (i.e., no component items verified), partially verified (i.e., some
component items verified), or fully verified (i.e., all component items verified). Table C-1c
includes items that were verified verbally by a third party. Table C-1d provides data for items
verified by file documentation, and Table C-1e provides data for items verified through the EIV
system.
Proper Payments. Exhibit IV-2 shows the percentage of households with proper payments by
program, for households where the Actual and QC Rents matched within $5 and where the
Actual and QC Rents matched exactly. At (re)certification, the rent was calculated correctly
(within $5) in 64 percent of the households, the same percentage as in FY 2006. There was an
exact match of rent payment in 51 percent of households in FY 2007, again the same as FY
2006.
Exhibit IV-2
Percent of Households with Proper Payments
Percent of Households Standard Percent of Households Standard
Administration Type Within $5 Error Matched Exactly Error
2005 2006 2007 2007 2005 2006 2007 2007
Public Housing 66% 65% 69% 1.8% 53% 54% 57% 2.2%
PHA-Administered Section 8 63% 61% 62% 2.3% 51% 49% 50% 2.2%
Total PHA-Administered 64% 62% 64% 1.6% 51% 51% 53% 1.8%
Owner-Administered 63% 66% 64% 1.9% 46% 53% 48% 2.4%
Total 64% 64% 64% 1.4% 50% 51% 51% 1.8%

Source: Table 2 and 2S, Appendix C

Households with QC Rent Error. Exhibit IV-3 shows the percentage of households in error,
the average dollar amount in error, and error rate by program. Thirty-six percent of the
households have a rent error greater than $5, compared to 37 percent in FY 2006. The average
gross dollars in error, calculated by dividing the sum of the dollar amount of gross error (i.e., the
sum of the absolute values of under- and overpayments) by the total number of households is $16
in FY 2007 lower than the $19 average gross dollar error in FY 2006. The gross dollar error
rate, calculated by dividing the sum of the dollar amount of Gross Rent Error by the sum of the
dollar amount of the QC Rent, was 7 percent in FY 2007 compared to 10 percent in FY 2006.
IV-4

IV. Findings
Exhibit IV-3
Percent of Households with Error, Average Dollars in Error, and Dollar Error Rate
for All Households with Error
Percent of
Dollars Gross Dollar Error
Error in Error Rate
35% 31% $15 $13 7% 6%
39% 39% $23 $20 11% 9%
Total PHA-Administered 38% 36% $21 $17 10% 8%
i 34% 36% $17 $13 9% 6%
37% 36% $19 $16 10% 7%
and IV-4b show the
This finding is the same as in FY
Exhibit IV-4a
Percent of
In Error
( )
17% 19% 16% $59 $54 $57 $10 $10 $9
20% 22% 19% $70 $73 $67 $14 $16 $13
Total PHA-Administered 19% 21% 18% $67 $67 $64 $13 $14 $12
i 18% 16% 19% $55 $68 $44 $10 $11 $8
19% 19% 18% $63 $67 $57 $12 $13 $11
Households with
Average Gross
Administration Type
2006 2007 2006 2007 2006 2007
Public Housing
PHA-Administered Section 8
Owner-Admin stered
Total
Source: Table 3, Appendix C
Underpayment and Overpayment Households. Exhibits IV-4a
percentage of households and average dollar amount of error for all households when errors of
$5 or less are excluded from calculations. Exhibit IV-4a and IV-4b present the error for
underpayment and overpayment households, respectively. Nineteen percent of all households
paid in excess of $5 less than they should have in FY 2007.
2006 and FY 2005. For the FY 2007 households, the average monthly payment error was $57,
lower than the mean of $67 in FY 2006 and the mean of $63 in FY 2005.
Underpayment Households
Percent of Households and Average Monthly Dollar Amount of Error
Average Dollar Amount of Error
Households
For Underpayment
Households
Administration Type
with errors > $5 For All Households
2005 2006 2007 2005 2006 2007 2005 2006 2007
Public Housing
PHA-Administered Section 8
Owner-Admin stered
Total
Source: Table 2 and 4, Appendix C
As shown in Exhibit IV-4b, 18 percent of all households paid in excess of $5 more than they
should have in FY 2007, compared to 17 percent in FY 2006, and 18 percent in FY 2005. The
average monthly overpayment for households with overpayment error was $30 in FY 2007,
down from $36 in FY 2006 and $39 in FY 2005.
IV-5

IV. Findings
Exhibit IV-4b
Overpayment Households
Percent of Households and Average Monthly Dollar Amount of Error
( )
Average Dollar Amount of Error
Percent of Households

Administration Type In Error

For Overpayment
Households
with errors > $5 For All Households
2005 2006 2007 2005 2006 2007 2005 2006 2007
Public Housing 17% 15% 14% $53 $31 $26 $9 $5 $4
PHA-Administered Section 8 17% 18% 20% $38 $42 $35 $7 $7 $7
Total PHA-Administered 17% 17% 18% $43 $39 $32 $7 $6 $6
Owner-Administered 19% 17% 17% $31 $31 $24 $6 $5 $4
Total 18% 17% 18% $39 $36 $30 $7 $6 $5

Source: Table 3 and 4. Appendix C

Figure IV-1 shows the percentage of underpayments, proper payments, and overpayments by
program type. Programs were grouped into three categories—Public Housing, PHA-
administered Section 8, and owner-administered. Note that the majority of cases fall in the
proper payment category for all program types. As indicated above, a household was considered
to be correct (proper payment) if the Actual Rent and the QC Rent matched within $5.
Figure IV-1: Payment by Program Type

20

10

0
70

60

50

40

30

-
P
er
cen
t
o
f
C
as
es
Public Housing PHA- Ow ne r
Underpayment
Proper Payment
Overpayment
Adm inistered Adm inistered
Section 8
Program Type
Gross and Net Dollars in Error. Exhibit IV-5 presents the gross and net average dollars in
error and their associated standard error. To obtain the Gross and Net Rent Error, the dollar
amount of overpayments is added to the dollar amount of underpayments, first using the absolute
values for gross error, and then the arithmetic values for the net error. The net error measures the
dollar cost of the errors and is -$5 (indicating a tenant underpayment) for FY 2007; the average
gross dollar error is $16 for FY 2007 and represents the dollars associated with the errors (the
magnitude of the errors).
IV-6

IV. Findings
Exhibit IV-5

Gross and Net Dollar Rent Error (Monthly) for All Households

Gross Rent Error Net Rent Error

Average Dollars
Average Dollars

in Error Standard Error
in Error Standard Error

Administration Type
2006 2007 2006 2007 2006 2007 2006 2007

Public Housing
$15 $13 $1.54 $1.89 -$6 -$6 $1.94 $2.04
PHA-Administered Section 8 $23 $20 $2.85 $2.51 -$8 -$6 $2.86 $2.04
Total PHA-Administered $21 $17 $1.88 $1.86 -$8 -$6 $1.97 $1.72
Owner-Administered $17 $13 $2.69 $1.71 -$6 -$4 $2.71 $1.82
l $19 $16 $1.79 $1.52 -$7 -$5 $1.79 $1.57
Error Rates by Program.
Exhibit IV-6
7.4% 5.6% -2.8% -2.4%
PHA-Admini 11.2% 9.0% -4.1% -2.7%
Total PHA-Administered 9.9% 7.8% -3.6% -2.6%
i 8.7% 5.9% -2.9% -2.1%
9.6% 7.2% -3.4% -2.4%
Tota
Source: Table 5, Appendix C
* Difference at significance p < .05
Differences in error rates by program were investigated and the
results are summarized in Exhibit IV-6. Differences include Gross Error Rate, which 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. The Gross Error Rate is slightly higher for PHA-administered Section 8 programs than for
either Public Housing or owner-administered programs. Both the Gross Error Rate and the Net
Error Rate for all programs are lower in FY 2007 than in FY 2006.
Gross and Net Dollar Error Rates (Monthly) for All Households
Error Rates
Gross Error Rate Net Error Rate
Administration Type
2006 2007 2006 2007
Public Housing
stered Section 8
Owner-Admin stered
Total
Source: Table 5, Appendix C
Certifications/Recertifications. The sample households included both certifications (i.e., newly
admitted households) and recertifications. Certifications were analyzed to determine if these
households were eligible for HUD housing assistance and recertifications were analyzed to
determine if they were overdue. Figure IV-2 presents the breakdown of cases by case type—
certifications, recertifications, and overdue recertifications.
IV-7

IV. Findings
Figure IV-2: Case Type
Overdue
Recertifications
2%
Certifications 16%
Recertifications

83%

Source: Table 6, Appendix C
Exhibit IV-7 shows the breakdown of the percentage of certifications, recertifications not
overdue, and recertifications overdue, by program type. The exhibit indicates that in FY 2007
16 percent of the households were certifications, and 2 percent of the households were overdue
recertifications. The findings indicate that there was no change in the percentage of
certifications from FY 2006, but a slight decrease in the percentage of overdue certifications
(from 3% to 2%).
Exhibit IV-7
Certifications and Recertifications by Administration Type
By Year* Certifications
Timely
Recertifications
Overdue
Recertifications
Row Total
Administration Type
2006 2007 2006 2007 2006 2007
Public Housing 14% 16% 81% 82% 5% 3% 100%
PHA-Administered Section 8 15% 15% 81% 83% 4% 2% 100%
Total PHA-Administered 15% 15% 81% 83% 4% 2% 100%
i 17% 16% 82% 84% 1%
16% 16% 81% 83% 3% 2%
Owner-Admin stered 100%
Total 100
Source: Table 6, Appendix C *Rounding error may result in totals not equal to 100%.
Certifications. Exhibit IV-8a presents a summary of the findings related to eligibility criteria
and Exhibit IV-8b shows the percentage of newly certified households meeting the certification
criteria by program type.
The reviewed criteria included citizenship, Social Security number, signing the appropriate
consent form, and qualifying as low income or very low income households. However, only
those households that do not meet the appropriate low or very low income limit are ineligible for
assistance. One hundred percent of the households (according to the QC Rent calculation) fell
within the low-income limit for total gross income.
IV-8

IV. Findings
A household met the citizenship or Social Security number criteria if there was evidence in the
tenant file that the citizenship or Social Security number was verified. The data indicate that a
citizenship code (indicating whether each household member was a citizen, eligible noncitizen,
or ineligible noncitizen) and a Social Security number was available (from either the tenant file
or the household interview) for each household member. According to the citizenship codes,
only two percent of the households had at least one household member for whom there was no
verification of citizenship. This finding was the same in FY 2007 as in the FY 2006 study. To
meet the citizenship verification requirement, the file must have contained (for each household
member) a signed declaration of U.S. citizenship or eligible immigration status; proof of age
documentation; an INS card; or INS system verification of citizenship status, or documentation
that the member was in process for verification or an INS hearing.
Two percent of the households had at least one member age six or over for whom there was no
verification of their Social Security number. To meet the Social Security number verification
requirements the file must have contained (for each household member six years of age or older)
a copy of the Social Security card, or statement from the Social Security Administration
verifying the Social Security number or a certification indicating the member does not have a
Social Security number.
In 96 percent of the households, there was a signed consent form, dated within 15 months of the
QCM (the date for which data were collected), for all members age 18 or over. Note that not
meeting the Social Security number, citizenship, and consent form criteria may not mean the
household was not eligible for assistance; rather, the project did not follow the HUD
requirements in documenting the information.
Exhibit IV-8a
Percent of Newly Certified Households
Meeting Certification Criteria
Certification Criteria Met Criterion
2006 2007
Citizenship 98% 98%
Social Security Number 97% 98%
Consent Form 95% 96%
Low and Very Low Income 100% 100%
Meets All Eligibility Criteria 91% 91%
Source: Table 7, Appendix C
IV-9

IV. Findings
Exhibit IV-8b
Certification Criteria
99% 99% 94%
95% 99% 98%
98% 94% 97%
Percent of Newly Certified Households
Meeting Certification Criteria by Program Type
Percent of Households Meeting the Criteria
PHA-Administered
Public Housing Section 8 Owner-Administered
Citizenship
Social Security Number
Consent Form
99%
Meets All Eligi 92% 92% 88%
recertification.
).
The result is an
Exhibit IV-9
Low and Very Low Income 100% 100%
bility Criteria
Source: Table 7b, Appendix C
Underpayments and Overpayments for Certifications, Recertifications, and Overdue
Recertifications. Exhibit IV-9 presents a summary of the households with overpayments and
underpayments by the type of case—certification, timely recertification, and overdue
The Average Dollar Amounts are based on the sum of the dollar amounts for
payment errors (either underpayment or overpayment) for the type of household (certification,
overdue recertification, or timely recertification) divided by the number of households with that
payment type (for whom a QC Rent could be calculated For example, the sum of the dollar
amounts for new certifications with monthly underpayments ($6M) was divided by the total
number of certifications for whom QC Rent could be calculated (.64M).
underpayment average dollar amount of $9.
The data indicate that the amount of underpayment and overpayment dollar error in new
certifications in FY 2007 is about the same as the amount for recertifications. As might be
expected, there is a very large difference in the underpayment error for overdue and timely
recertifications ($53 and $10, respectively).
$11 $9 $6 $6
$13 $10 $6 $5
$34 $53 $20 $6
$13 $11 $6 $5
Subsidies. For
For Public Housing, the
Average Monthly Underpayment and Overpayment Dollar Amount
Averaged Across All Households
Household Type
Underpayment
Average Dollar Amount
Overpayment
Average Dollar Amount
2006 2007 2006 2007
Certifications
Timely Recertifications
Overdue Recertifications
Total
Source: Table 8, Appendix C
The actual cost of errors to HUD is expressed in terms of subsidy payments.
purposes of this study, HUD subsidies for the Section 8 voucher program equal the lower of the
Gross Rent or the applicable payment standard minus the Tenant Share.
subsidy is the applicable payment standard minus the TTP, and for Housing programs, the
subsidy is the Gross Rent minus the TTP. The subsidy is correct if the Actual Rent equals the
IV-10

IV. Findings
QC Rent (within $5). A negative subsidy error occurs when the tenant pays too much rent
(QC Rent < Actual Rent). A positive subsidy error occurs when the tenant pays too little rent
(QC Rent > Actual Rent). These subsidy errors by program type are summarized in
Exhibit IV-10a and 10b, below. The subsidy errors by certification status are summarized in
Exhibit IV-11.
Exhibit IV-10a

Negative Subsidy Households (Tenant Overpayment)

Percent of Households and Average Monthly Dollar Amount of Error

Average Dollar Amount of Error
Percent of

Households in

Administration Type Error

For Negative
Subsidy
Households
(with errors > $5) For All Households
2006 2007 2006 2007 2006 2007
Public Housing 15% 14% $31 $26 $5 $4
PHA-Administered Section 8 18% 20% $42 $35 $7 $7
Total PHA-Administered 17% 18% $39 $32 $6 $6
Owner-Administered 17% 17% $31 $24 $5 $4
Total 17% 18% $36 $30 $6 $5
Source: Tables 3 and 4. Appendix C
Note: Table results replicate Exhibit IV-4b for the convenience of the reader.
Exhibit IV-10b

Positive Subsidy Households (Tenant Underpayment)

Percent of Households and Average Monthly Dollar Amount of Error

Percent of
Error
( )
19% 16% $54 $57 $10 $9
22% 19% $73 $67 $16 $13
Total PHA-Administered 21% 18% $67 $64 $14 $12
i 16% 19% $68 $44 $11 $8
19% 18% $67 $57 $13 $11
Average Dollar Amount of Error
Households in
For Positive Subsidy
Households
Administration Type
with errors > $5 For All Households
2006 2007 2006 2007 2006 2007
Public Housing
PHA-Administered Section 8
Owner-Admin stered
Total
Source: Tables 3 and 4, Appendix C
Note: Table results replicate Exhibit IV-4a for the convenience of the reader.
IV-11

IV. Findings
Exhibit IV-11
Average Monthly Dollar Amounts of Error for Negative (Tenant Overpayment) and
Positive (Tenant Underpayment) Subsidies Averaged Across All Households
Household Type
Negative Subsidy Average
Dollar Amount of Error 

Positive Subsidy Average Dollar
Amount of Error
2006 2007 2006 2007
Certifications $6 $6 $11 $9
Timely Recertifications $6 $5 $13 $10
Overdue Recertifications $20 $6 $34 $53
Total $6 $5 $13 $11
Source: Table 8, Appendix C

Note: Table results replicate Exhibit IV-9 for the convenience of the reader.

C. Sources of Error
Additional analyses examined which income and expense components contributed the most to
rent error. It should be noted that the component dollar amounts are annual income and expense
dollars, rather than the monthly figures used to present rent error data, and that rents are
generally computed at 30 percent of adjusted income. Therefore, every $100 of income or
expense error generally translates into $2.50 of rent error. In addition, the sum of the component
errors is greater than net rent errors because of off-setting errors. For example, the household
presented in the chart below has earned income and child care costs with errors in both
components. The total component error is $1000 ($800 + $200); however, the adjusted net
income error (the amount used to determine the household’s rent) is only $600.
Example:
Component File Data QC Data Dollar Error
Earned Income $2,200 $3,000 $800
Child Care Expense $400 $600 $200
Adjusted Income $1,800 $2,400 $600
Exhibit IV-12 presents each income and expense component included in the rent calculation and
the percent of the households in error
5
where this component contributed the most to the gross
error. The exhibit indicates that the largest average dollar error continues to be in earned
income, with an average error of $2,887, in the 24 percent of households in error where earned
income is the largest component error. Medical expense was the next most frequent component,
23 percent of the errors with an average dollar error of $972. Pensions were a component of
error 22 percent of the time, with an average associated dollar error of $2,075. Other income
was the largest component of error in 11 percent of households in error, with the associated
average dollar amount being $2,437.
5
The denominator in the percentage is the number of households with any component error, which was 36 percent
of total households in FY 2007.
IV-12

IV. Findings
Between FY 2005 and FY 2006, average dollar error amounts had increased for some income
Rent components and decreased for others. In FY 2007, average dollar error amounts have
shown modest decreases in all components except childcare and public assistance. The largest
component increase was in public assistance which went up by over $600 in FY 2007 to an
average dollar error amount of $2,492 compared to $1,823 in FY 2006. The largest component
decrease was seen in earned income. In the 24 percent of households where earned income was
the largest component in error, the average dollar amount of error was $2,887 in FY 2007
compared to $4,544 in FY 2006, a decrease of over $1600.
Exhibit IV-12
Rent Components Responsible for the Largest Dollar Error
for Households with Rent Error
Rent Component
Percent of Households in Error Average Dollar Amount
2006 2007 2006 2007
Earned Income 26% 24% $4,544 $2,887
Other Income 10% 11% $2,488 $2,437
Pensions 25% 21% $2,246 $2,075
Asset Income 2% 4% $1,733 $1,502
Public Assistance 9% 6% $1,823 $2,492
Child Care Allowance 4% 4% $2,128 $2,259
Medical Allowance 17% 23% $1,099 $972
Dependent Allowance 3% 3% $703 $622
Elderly/Disabled Allowance 2% 3% $400 $400
No Rent Component Error 1% 1% $0 $0
Total 100%* 100%* $2,513 $1957
Source: Table 9, Appendix C *Numbers do not add up to 100% due to rounding.
Note that for some households the rent error is not caused by one of the ten components listed.
Rather, it is caused by other arithmetic errors or using the wrong rent calculation formula. The
percent of households in error stayed the same or changed slightly for most Rent components,
with the largest increase in percent of households with medical allowances, from 17 percent in
FY 2006 to 23 percent in FY 2007.
Total and Largest Component Dollar Error by Program Type. Exhibit IV-13 shows the
dollar amounts associated with the total dollars in error (the sum of the absolute value of errors
in all Rent components) and the largest dollars in error (the largest error attributable to a specific
source for each household), by program type. There were notable decreases in Average Total
Dollars in Error for all program types from FY 2006 to FY 2007, with Owner-administered
Housing showing the largest decrease of $929. There were also notable decreases in the Average
Largest Dollars in Error for all program types in FY 2007. On average, the total for all programs
decreased by $556 between FY 2006 and FY 2007.
IV-13

-- --
-- --
IV. Findings
Allowances. Elderly/disabled and dependent allowances were examined to determine whether
these allowances were being applied correctly.
6
The findings are summarized in Exhibit IV-15.
The exhibit shows the percentage of elderly/disabled and nonelderly/disabled households for
which allowances were correctly or incorrectly applied. Elderly/disabled allowances were
incorrectly used in five percent of the households in FY 2007. Five percent of the
elderly/disabled households received an incorrect allowance, while less than one percent of non-
elderly/disabled households received an allowance.
The exhibit also shows the percentage of households with and without dependents for which a
dependent allowance was correctly or incorrectly applied. The dependent allowances were
incorrect in eight percent of the households. In less than one percent of the households, a
dependent allowance was given to a household that did not have dependents. For the remainder
of the households with dependents in error (8 percent), either a dependent allowance was not
given when it should have been or the wrong allowance amount was given.
Exhibit IV-15
Elderly/Disabled Allowances and Dependent Allowances
Elderly Allowance
Non-Elderly/
Elderly/
Disabled Disabled
Dependent Allowance
Households Households
All Without With All
Allowance Households Households Households Dependents Dependents Households
No Allowance 100% 42% 100% 56%
Incorrect Allowance <1% 5% 3% <1% 8% 4%
Correct Allowance 95% 55% 92% 40%
lTota 100% 100% 100% 100% 100% 100%
Source: Tables 12a and 12b, Appendix C
D. Errors Detected Using Information Obtained From Project Files
To respond to HUD’s interest in understanding the cause of errors, tenant rent was recalculated
using only income and expense items documented in the tenant file. The source of information
used for this analysis only included items that were clearly documented in the tenant file in a
location other than the 50058/50059 form worksheet. If an item was recorded on the
50058/50059 Form worksheet but not documented elsewhere in the tenant file, it was not
included when the tenant file tenant rent was calculated for this analysis. Therefore, it is possible
that some of the discrepancies identified between 50058/50059 rents and rents calculated solely
based on file data were not, in fact, due to incorrect determinations but rather due to program
sponsor failure to maintain information supporting income or expense items.
The outcome is that relying solely on information in tenant files may result in misstating the
basis for the program sponsor income and rent determination and could lead to a determination
that an error existed when the determination was actually correct. The fact remains that, even if
6
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).
IV-15

IV. Findings
a program sponsor made the correct income determination, failure to document the determination
is and should be treated as a serious administrative problem. Also, in practice, it appears that
these types of discrepancies are often suggestive of subsidy determination errors even if they
cannot be assumed to prove the existence of such errors.
The findings from this analysis were compared to the quality control findings where tenant rent
was calculated based on all the information collected during the study (including household
interview data, and verification obtained by Macro through third party sources). Exhibit IV-16
shows the percent of households in error and the average dollar error with and without income
and expense items identified during the household interview and verified by Macro through third
party sources.
The data indicate that the income and expense items documented in the tenant file identify just
over half of the cases with tenant underpayments (subsidy overpayments) and subsidy
underpayments (tenant overpayments). The data regarding average dollar error indicate the
tenant file more closely predicts the subsidy overpayments, but overestimates subsidy
underpayments.
Exhibit IV-16
Findings With and Without Information Obtained from Sources Other Than the Tenant File
Percent of Households in Error Average Dollar Error
Error Source
Subsidy Subsidy Subsidy Subsidy
Overpayment Underpayment Overpayment Underpayment
Error Based on All Income and
18% $57 $30
Expense Items Identified during
18%

the Study

Error Without Income and
11% $56 $70
Expense Items Identified during
11%

the Household Interview

Source: QC Tables 2 and 4, and Tenant File Table 2 and 4, Appendix C
Analysis of the errors on the 50058/50059 Form examined whether the errors identified using the
50058/50059 Form as a sole source of information are representative of the total errors in the
program. The analyses focused on calculation and consistency errors:
Calculation error was identified from income, expenses, and allowances used to calculate the
rent amount and recorded on the 50058/50059 Form. This calculation did not take into account
whether dollar amounts were verified or whether the recertification was conducted on time. This
analysis identified errors due to arithmetic mistakes, the incorrect use of a formula, and items
that were not completed but should have been. This analysis did not identify households where
items were recorded in the wrong place on the 50058/50059 Form, although improper use of a
field on the 50058/50059 Form can result in a calculation error. Table C-13 in Appendix C
presents the number of households with 50058/50059 Form that contained calculation errors by
the rent component contributing to the error. The items considered when determining calculation
error, are listed in Appendix D.
IV-16

IV. Findings
Consistency errors were based on the logical conformity of elements in the 50058/50059 Form.
For example, the effective date of action must be on or after the date of admission, elderly status
information should be consistent with household head and spouse ages, and number of
dependents should not exceed the number of household members. Table C-14 in Appendix C
shows the number of households with consistency errors on the 50058/50059 Form, summarized
by form subsections. Appendix D lists the data items by subsection that were included in this
analysis.
Exhibit IV-17 shows the percentage of households with calculation and consistency errors by
50058/50059 Form subsections. It is important to emphasize that the 50058 form is formatted
differently and has more line items of information than the 50059 Form. Consequently, the
number and types of calculation and consistency errors on the forms differ, and findings from the
two forms are not directly comparable. In addition, the Office of Housing implemented a new
version of the 50059 form in FY 2006. The large number of calculation errors (particularly on
the 50058 forms) may be a contributing factor to QC errors, though a calculation or consistency
error does not necessarily lead to a rent error. The PHA/owner may make an error when
completing one section of the form, and still calculate the rent correctly.
Exhibit IV-17
Percentage of Households with Calculation and Consistency Errors
Percentage of Households
50058/50059 Item
Calculation Errors Consistency Errors
50058 50059 Total 50058 50059 Total
General Information n/a n/a n/a 2% 5% 3%
Household Composition 8% 3% 6% 6% 3% 5%
Net Family Assets and Income 8% 6% 7% 10% <1% 7%
Allowances and Adjusted Income 43% 5% 31% 10% 1% 7%
Family Rent and Subsidy Information 10% 3% 8% 3% 2% 3%
Source: Tables 13 and 14, Appendix C
Comparison of 50058/50059 Errors to QC Error. A comparison was made between the rent
calculation errors on the 50058/50059 Form and errors identified through the QC Rent
calculation process. The purpose of this comparison was to determine if errors identified using
only the 50058/50059 Form data could predict the rent errors found in a QC review. When using
only the 50058/50059 Form data to calculate the Actual Rent, errors were found in 7 percent of
the households in FY 2007, a small increase from FY 2006’s figure of 5 percent. The QC error
calculation found errors in 36 percent of the households in FY 2007, essentially the same as FY
2006’s 37 percent. The results are quite different from the individual and joint comparison
methods. Error was found in both the 50058/50059 Form calculation and QC rent calculation in
only 3 percent of the households. In 40 percent of the households, rent calculation error was
found in either the 50058/50059 Form or the QC rent calculation, but not in both. This
emphasizes that data from the 50058/50059 Form alone cannot accurately identify rent error.
Exhibit IV-18 summarizes these results for FY 2006 and FY 2007.
IV-17

IV. Findings
Exhibit IV-18
50058/50059 Rent Calculation Error Compared with QC Rent Error
Percentage of Percentage of
Households Households
Rent Calculation
Correct Incorrect
2006 2007 2006 2007
Using Information on the 50058/50059 Form 95% 93% 5% 7%
According to the QC Rent Calculation 64% 64% 37% 36%
Both 50058/50059 Form Calculation and QC Rent Calculation 61% 60% 3% 3%
Verification errors were identified by whether an item was verified by the project and, if it was,
whether the correct information was transferred to the 50058/50059 Form. An error occurs when
the verified amount obtained by the project is not recorded properly on the 50058/50059 Form
(and, presumably, not used in the rent calculation). When determining whether a verified
income or expense item matched the amount used on the 50058/50059 Form, we assumed a
variance of $100 to accommodate potential rounding errors when annualizing data.
Table C-15a in Appendix C shows the number of households where verification (of any type)
was not obtained, where it was obtained but did not match the amount used on the 50058/50059
Form, and where the verified amount did match the 50058/50059 Form. Table C-15b provides
the same information but only includes the number of households where verification was
obtained from third parties in-writing (as required by the study). Tables C-15e and C-15f
provide the same data by program type.
Exhibit IV-19 summarizes the findings in Table C-15a. The percentage of items verified by the
PHA/owner increased in six of the seven Rent components between FY 2006 and FY 2007 with
the largest percentage increase of verification seen in Public Assistance (77 percent verified in
FY 2006 compared to 89 percent in FY 2007), Pensions (91 percent verified in FY 2006
compared to 96 percent in FY 2007), and Medical Expenses (89 percent verified in FY 2006
compared to 93 percent in FY 2007). The percentage of items where the verification matched
within $100 tended to be slightly higher in FY 2007 compared to FY 2006, with only Other
Income and Child Care Expense verification showing decreases in percent of cases matching the
50058/50059 within $100. In FY 2007, the number of households where verification was not
obtained by the PHA/owner decreased by a few percent in most Rent components. For example,
Public Assistance showed the largest decrease (23 percent in FY 2006 compared to 11 percent in
FY 2007). Pension Income verification showed the next highest decrease (9 percent in FY 2006
compared to 4 percent FY 2007), followed by Medical Expenses (11 percent in FY 2006
compared to 7 percent in FY 2007).
IV-18

IV. Findings
Exhibit IV-19
Verification of 50058/50059 Rent Components by PHA/Owners
No Project Item Verified by Verification Matched
Rent Component
Verification Project 50058/50059 Within $100
2006 2007 2006 2007 2006 2007
Earned Income 12%

Pensions 9%

Public Assistance 23%

Other Income 26%

Asset Income 9%

Child Care Expense 12%

Medical Expense 11%

10% 88% 90% 68% 68%
4% 91% 96% 78% 84%
11% 77% 89% 65% 75%
25% 74% 75% 63% 61%
7% 91% 93% 84% 84%
12% 88% 88% 71% 67%
7% 89% 93% 67% 75%
Source: Table 15a, Appendix C
Exhibit IV-20 shows verification results by program type, again showing the verification rate for
each rent component and the proportion that matched within $100 of the 50058/50059 Form
amounts. When comparing the FY 2007 results to the FY 2006 findings, the following changes
are of note:
♦ In the Public Housing program, there were increases in the verification rate for five out of the
seven Rent components in FY 2007 when compared to FY 2006 with the largest rise
occurring in public assistance verification (89 percent in FY 2007 compared to 75 percent in
FY 2006). Percentage verified for medical expenses increased slightly (92 percent in FY
2007 compared to 90 percent in FY 2006). Verification rate for earned income increased
from 85 percent in FY 2006 to 91 percent in FY 2007. Pension income was verified more
often in FY 2007, 95 percent of the time compared to 88 percent in FY 2006. There was an
increase in the percentage of verification rates in child care expenses to 81 percent in FY
2007 compared to 74 percent in FY 2006. The percentage to which Rent components
declined were in asset income (85 percent in FY 2007 compared to 94 percent in FY 2006),
and other income (65 percent in FY 2007 compared to 70 percent in FY 2006).
♦ In the PHA-administered Section 8 programs, there were increases in percentages of all Rent
components verified comparing FY 2007 and FY 2006 The greatest gains were seen in public
assistance, from 78 percent verified in FY 2006 to 89 percent verified in FY 2007, asset
income, with 95 percent verified in FY 2007 compared to 89 percent verified in FY 2006,
and medical expense from 74 percent verified in FY 2007 compared to 90 percent in FY
2006. Not only was there an increase in percentages verified, but verifications were used
more often in most cases in FY 2007 than in FY 2006. One exception was a drop in child
care expense verifications which fell from 90 percent verified in FY 2006 to 89 percent in FY
2007. In most cases verification was used more often when comparing FY 2007 to FY 2006.
The most notable increase was in medical expense verifications which increased from 65
percent in FY 2006 to 79 percent in FY 2007. Other income and child care expenses were the
only two components that verification was used less often, 63 percent in FY 2007 compared
IV-19

IV. Findings
with 65 percent in FY 2006, and 68 percent in FY 2006 compared to 66 percent in FY 2007,
respectively.
♦ In the owner-administered programs, increases in percentages verified were seen in all Rent
components in FY 2007, except for child care expenses which remained at 92 percent. Public
assistance verifications increased the most, 79 percent in FY 2006 compared to 90 percent in
FY 2007 and asset income showed the next greatest increase, 96 percent in FY 2007
compared to 91 percent in FY 2006. However, the percentage of verifications actually used
in rent calculation increased slightly for all components, except earned income and child care
expenses, with the greatest increase in verification use occurring in pension income from 81
percent matching in FY 2006 to 86 percent matching in FY 2006. Earned income was
verified 93 percent of the time in FY 2006 compared to 90 percent in FY 2005, however
verification use decreased by 4 percentage points in FY 2007, 70 percent compared to 74
percent in FY 2006.
Exhibit IV-20
Verification of 50058/50059 Rent Components by PHA/Owner Staff by Program*
PHA-Administered
Public Housing
Section 8
Owner-Administered
Rent Component
Verified Matched** Verified Matched** Verified Matched**
Earned Income 91% (85%) 62% (64%) 89% (88%) 70% (67%) 93% (90%) 70% (74%)
Pensions 95% (88%) 83% (71%) 94% (90%) 82% (80%) 97% (93%) 86% (81%)
Public Assistance 89% (75%) 69% (65%) 89% (78%) 74% (60%) 90% (79%) 82% (76%)
Other Income 65% (70%) 53% (54%) 78% (75%) 63% (65%) 76% (75%) 64% (63%)
Asset Income 85% (94%) 77% (86%) 95% (89%) 87% (81%) 96% (91%) 86% (85%)
Child Care Expense 81% (74%) 66% (59%) 89% (90%) 66% (68%) 92% (92%) 69% (81%)
Medical Expense 92% (90%) 74% (62%) 96% (90%) 79% (65%) 92% (88%) 74% (69%)
Source: Table 15g, Appendix C * Findings from FY 2006 are in parentheses.
** Matched within $100
Tenant File Verification Compared with QC Error. Errors identified through the QC process
were investigated to determine whether they were associated with sources of income and
expenses. Exhibit IV-21 presents the percentage of households with QC error for which
verification was missing in the tenant file. Each error is presented by rent component. The data
indicate that missing verification does have a major impact on error. Verification for most Rent
components was missing in at least 71 percent of all households with QC error. Missing
verification for Public Assistance was an exception with 59 percent of households with missing
Public Assistance verification also having QC errors. Data from PHA-administered programs
show that 13 percent had a QC error in earned income with 74 percent of the cases in error
missing verification for earned income. There were minor changes in these findings when
compared to 2006 data. These changes were most evident in the following rent component:
Public assistance across all program types showed a significant decrease in errors with missing
verification, (PHA-administered, 73 percent in FY 2006 compared to 59 percent in FY 2007,
owner-administered, 96 percent in FY 2006 compared to 85 percent in FY 2007). However,
asset income showed an increase in missing verification, (73 percent in FY 2006 compared to 81
percent in FY 2007). Owner-administered programs showed significant changes in two of the
IV-20

-- --
-- -- -- --
IV. Findings
rent component, child care expenses decreased significantly, (82 percent in FY 2006 compared
to 71 percent in FY 2007). However, there was a large increase in missing verifications between
FY 2006 and FY 2007 for medical expense from 80 percent to 89 percent.
Exhibit IV-21
QC Error QC Error
14% 13% 68% 74%
15% 12% 86% 85%
5% 3% 73% 59%
7% 7% 80% 75%
5% 5% 73% 81%
3% 2% 81% 82%
<1% 1%
Medical 10% 11% 90% 90%
64% 66%
8% 6% 69% 71%
15% 15% 84% 81%
3% 2% 96% 85%
6% 4% 83% 81%
6% 11% 81% 77%
2% 2% 82% 71%
0% 0%
15% 20% 80% 89%
68% 65%
QC Error Households with Missing Verification in the Tenant File
50058
Households with Households with
Households with
QC Errors and
Missing
Verification
Households with
QC Errors and
Missing
Verification
Rent Component
2006 2007 2006 2007 2006 2007 2006 2007
Earned Income
Pensions
Public Assistance
Other Income
Asset Income
Child Care Expense
Disability Expense 100% 100%
Expense
No Component Error
50059
Source: Tables 16a and 16b, Appendix C
Summary of 50058/50059 Form Errors. Exhibit IV-22 provides a summary of the errors
identified from the 50058/50059 Form. These include consistency errors, calculation errors, and
overdue recertifications. The exhibit shows the percentage of households in error, the average
dollar error, and the standard errors for both households with recalculated 50058/50059 Form
error (error determined using only the 50058/50059 Form), and households with QC Rent error.
This information is provided for households with error for each error type. Beginning with the
FY 2005 study, transcription error for any household was added to this exhibit and the data that
was described as an unduplicated count of 50058/50059 Form error has been revised to an
unduplicated count of any type of administrative error. The exhibit shows that most individual
types of 50058/50059 Form errors are not closely associated with QC rent error. However,
50058/50059 Forms with transcription error are associated with QC rent error in 62 percent of
households and any type of administrative error (transcription, consistency, calculation, or
overdue recertifications) are associated with QC Rent Error in 72 percent of the households.
When the findings in this exhibit are compared with the FY 2006 findings, there are significant
decreases in percentage of households with QC rent for overdue recertifications and households
with other calculation error. There were also significant decreases in the households for
recalculated 50058/50059 error, households with transcription error (53% in FY 2006 compared
to 40% in FY 2007), households with allowance calculation error (12% in FY 2006 compared to
5% in FY 2007), households with other calculation error (19% in FY 2006 compared to 12% in
FY 2007), and overdue recertifications (8% in FY 2006 compared to 2% in FY 2007).
IV-21

IV. Findings
In addition, both the average dollar error for households with QC rent error and the average
dollar error for recalculated 50058/50059 error show some declines relative to FY 2006 figures.
This is true for households with recalculated 50058/50059 error, with income calculation ($189
in FY 2006 and $94 in FY 2007. There were no significant increases in average dollar error
amounts or percent of households in error.
To understand the reason for the change in the average dollar error for households with
recalculated 50058/50059 error, it is important to review how this number is calculated. It is the
average dollar rent error for all cases (based on recalculated 50058/50059 Form rent error—not
QC rent error) that have error in the category identified in the row header. So, for example,
although the average rent error dollars for households with income calculation errors is $94,
because many of these cases have a large rent error (which may have nothing to do with the
income) and the number of cases with income calculation error is small (4 percent of households
in error), the average dollar error is large.
Exhibit IV-22
50058/50059 Administrative Error: Percent of Households, Average Dollars in Error
Households with Recalculated Households with
50058/9 Error QC Rent Error
Error Type Based
Percent of Standard Average Standard Percent of Standard Average Standard
on 50058/50059
Households Error of Dollar Error of Households Error of Dollar Error of
Recalculation
in Error Percent Error Mean in Error Percent Error Mean
Households with
Transcription error
40% 4.5% $24 $8.75 62% 3.0% $48 $3.99
Households with
Consistency Error
32% 4.9% $33 $9.54 23% 2.3% $43 $6.20
Households with
Allowance
1.9% $88 $20.23 5% 1.0% $43 $14.16
Calculation Error
5%
Households with
Income
1.7% $94 $32.93 4% 0.8% $34 $10.94
Calculation Error
4%
Households with
Other Calculation
Error
12% 4.2% $44 $18.94 8% 1.3% $68 $10.39
Overdue
Recertifications
2% 1.3% $78 $93.84 3% 0.8% $78 $23.95
Unduplicated
Count, Any Type
of Administrative
Error
59% 4.3% $24 $7.30 72% 2.3% $46 $3.62
l $19 $5.52 $44 $3.12Total Househo ds 100% 100%
Source: Table 17, Appendix C
Summary of Administrative Errors. As outlined in the study objectives, calculation errors,
consistency errors, transcription errors, failure to recertify on time, and failure to apply
allowances appropriately produce administrative errors. Exhibit IV-23 shows the Gross and Net
IV-22

IV. Findings
Rent Errors for households with each type of administrative error. Starting in FY 2005, two
major changes were made to this exhibit. First, the category of consistency errors was added to
illustrate inconsistencies found within the 50058/50059 Form. Second, the findings are based on
QC error rather than recalculated 50058/50059 error. Percent of households in error remained
stable when compared to FY 2006 for all error types. Gross average dollars in error for
consistency errors decreased from $25 in FY 2006 to $17 in FY 2007; otherwise the gross
average amounts by error type are comparable to FY 2006. Net average dollars in error remained
comparable for income calculation errors and any administrative errors; decreased for
consistency errors and allowance calculation errors; and increased for transcription errors and
overdue recertification errors.
Exhibit IV-23
Administrative Error: Percent of Households, Average Dollars in Error
for All Households
Gross Rent Error Net Rent Error
Percent of Average Standard Average Standard
Households Dollars Error of Dollars Error of
Error Type
in Error in Error Mean in Error Mean
Transcription Errors 38% $28 $2.65 -$10 $3.25
Consistency Errors 20% $17 $3.09 -$3 $3.05
Calculation Errors—Allowances 3% $27 $9.15 -$0 $10.12
Calculation Errors—Income 3% $16 $4.66 -$4 $6.96
Calculation Errors—Other 6% $34 $5.66 -$15 $5.22
Overdue Recertifications 2% $59 $21.36 -$47 $21.22
Any Administrative Errors 51% $24 $2.33 -$8 $2.78
l
$16 $1.63 -$5 $1.68
Tota
100%
Source: Table 18, Appendix C
E. Occupancy Standards
Exhibit IV-24 presents a summary of the analysis that determined whether households are
assigned units with the correct number of bedrooms. It shows the percentage of households by
actual number of bedrooms and correct number of bedrooms according to the guidelines used in
the study. Note that the guidelines used in this study are generally acceptable HUD guidelines.
All programs allow exceptions to HUD’s rules. The Section 8 Voucher program sometimes
allows households to rent units with fewer or more bedrooms than specified by the guidelines.
Fifteen percent of all households occupied a unit with too many or too few bedrooms in FY
2007, according to the guidelines used for this study. This number is up slightly from FY 2005,
where fourteen percent of all households occupied a unit with an incorrect number of bedrooms.
Seventeen percent of Public Housing households, seven percent of households, and nineteen
percent of Housing Choice voucher program households were over- or under-housed in FY 2007.
IV-23

-- -- -- -- --
-- -- -- --
-- -- --
--
IV. Findings
Exhibit IV-24
Percentage of Households in Units with the Correct Number of Bedrooms
According to Study Guidelines
Owner-
PHA-Administered Administered Total
Number of
Public Housing HCVP

Bedrooms

2006 2007 2006 2007 2006 2007 2006 2007

0
98% 96% 77% 90% 98% 100% 94% 96%
1
100% 99% 100% 100% 99% 100% 99% 100%
2
80% 71% 70% 74% 85% 75% 77% 74%
3
83% 81% 85% 77% 84% 93% 84% 81%
4
55% 55% 45% 58% 53% 44% 48% 56%
5+
35% 19% 34% 58% -- -- 31% 43%
All Units
87% 83% 81% 81% 93% 93% 86% 85%
The shaded cells indicate the
percentage of households that fall within study guidelines.
Number of
Source: Table 19, Appendix C
Exhibits IV-24a and IV-24b show the percentage of households that met these guidelines for
each bedroom size for FY 2006 and FY 2007, respectively.
Exhibit IV-24a
Percentage of All Households in FY 2006 by
Number of Bedrooms and Number of Household Members
FY 2006
Number of Household Members
Bedrooms
1 2 3 4 5 6 7 8+
0 94% 3% 3%
1 91% 9% 1% <1%
2 22% 47% 23% 7% 1%
3 5% 10% 37% 31% 13% 4% 1%
4 1% 5% 17% 24% 19% 19% 8% 6%
5 4% 10% 14% 16% 10% 16% 31%
<1%
Source: Table 19a, Appendix C
IV-24

-- -- -- -- -- --
-- -- -- --
-- -- --
--
IV. Findings
Exhibit IV-24b
Percentage of All Households in FY 2007 by
Number of Bedrooms and Number of Household Members
FY 2007
Number of
Number of Household Members
Bedrooms
1 2 3 4 5 6 7 8+
0 96% 4%
1 90% 10% <1% <1%
2 25% 45% 20% 8% 1%
3 6% 11% 29% 30% 17% 5% 2% 1%
4 1% 2% 6% 32% 27% 12% 13% 5%
5 8% 6% 10% 16% 5% 9% 11%
F. Project Staff Questionnaire Analysis
The purpose of the Project Staff Questionnaire (PSQ) was to obtain information on project and
PHA practices and procedures, in order to better understand how work is carried out in projects
and PHAs, and to identify difficulties and potential areas for improvement. The executive
directors or managers of the PHA/projects
7
in the FY 2007 study were surveyed, using a self-
administered, paper questionnaire that examined in detail such topics as the number and type of
PHA/project staff, training received by staff on how to conduct (re)certifications, communicating
information about changes in HUD policies to the staff, quality control monitoring of work done
by (re)certification staff, methods of obtaining household information, automation use when
processing (re)certifications, various verification procedures employed in the process of
(re)certifications, and difficulties in verifying tenants’ information. The results were analyzed
separately for three major program types: Public Housing, PHA-administered Section 8, and
owner-administered.
A brief summary of the key findings from this analysis are presented below. A more detailed
summary of the PSQ information is found in Appendix E.
♦ Number and Type of Staff. Overall, PHA/projects indicated an average of 56 units per
staff member and 145 units per each full-time (re)certification staff member. However,
there was a wide diversity of responses with respect to the ratio of staff per unit within, as
well as between, different types of PHA/projects. PHA Section 8 projects reported the
highest number of units per staff (106 units per staff member, on average) and highest
number of units per full-time (re)certification staff (234 on average). Owner-administered
projects had the lowest number of units per staff (30) and units per full-time
(re)certification staff (80). Overall, 87 percent of PHA/projects (re)certification staff had
over one year of experience, compared to 72 percent who had over 5 years of experience.
7
For purposes of this study, a PHA-administered Section 8 project is defined as a PHA/county combination
representing the Section 8 Voucher households living in a particular county.
IV-25

IV. Findings
PHA/projects typically required at least a high school diploma/GED for new employees,
with only 7 percent stating that no minimum education was required. Owner-
administered projects were most likely not to require any education (11%), and PHA
Section 8 projects were least likely (3%).
♦ New (Re)Certification Staff. About 38 percent of PHA/projects had new staff assigned to
conduct (re)certifications in the past 12 months. These PHA/projects reported 2 new staff
members being assigned to conduct (re)certifications in the past 12 months, on average.
More PHA Section 8 projects assigned new staff to (re)certifications compared to Public
Housing and owner-administered projects (54% versus 34% and 30%, respectively).
PHA-administered Section 8 projects also assigned the most new staff to conduct
(re)certifications (4 new staff, on average). Fewer owner-administered projects assigned
new staff members to (re)certifications, compared to projects in the other programs.
They also assigned the fewest new staff to (re)certifications (1 staff member, on average).
♦ New (Re)Certification Staff Training. PHA/projects provided on average 92 training
hours to new (re)certification staff in the past 12 months. Three methods of training new
staff were most prevalent – working one one-on-one with experienced staff; reading
manuals, watching videos, or asking questions; and attending training sessions conducted
by the supervisor. PHA-administered Section 8 projects provided the most hours of
training (146 hours, on average). Owner-administered projects provided the fewest hours
of training (60 hours, on average).
♦ Training of Experienced (Re)Certification Staff. About 77 percent of PHA/projects
trained experienced staff in the past 12 months. This year, owner-administered projects
provided more training to experienced staff, compared to projects in the other two
programs. Among all projects, an average of 5 experienced staff members received an
average of 33 training hours. Most PHA/projects usually or always trained using self-
training, training sessions conducted by the supervisor, and training conducted by other
experienced staff.
♦ Communicating Information about Changes in HUD Policies. PHA/projects used a
variety of methods to communicate with staff about changes in HUD PHA/Owner
policies affecting eligibility or rent calculations. One-on-one discussions between the
managers and the staff was used most frequently, followed by staff meetings, distributing
copies of HUD announcements to staff, and distributing a memo that described the
changes and provided instructions for implementation. PHA/projects found answers to
staff questions by referring to HUD PHA/owner memos or manuals, asking HUD field
offices or other HUD staff, and asking questions at a HUD training session. Many
PHA/projects reported that they had to figure out the answers themselves by conducting
internal meetings, talks, or training with supervisors, directors, or other senior staff. An
increasing number of PHA/projects used Internet and web-based resources and training to
obtain answers to questions.
♦ Quality Control via Work Monitoring. Most PHA/projects conduct quality control
monitoring of (re)certification work. PHA/projects typically have the supervisor conduct
work monitoring, although an increasing number are turning to outside auditors to
monitor their work. PHA/projects most frequently randomly spot checked a percent of
IV-26

IV. Findings
all cases, but other methods were also used, such as reviewing cases of new staff and
checking cases on certain dates or times of the year. During the review process
PHA/projects often or occasionally (71%) found mistakes in calculating rent, and missing
or incomplete verifications of income (62%). The most commonly stated reason for
errors was tenants providing inaccurate or incomplete information (87%).
♦ Issues in Conducting Tenant Interviews. The average duration of the typical initial
certification interview was 36 minutes, while the average duration of a typical
recertification interview was 30 minutes. PHA-administered Section 8 and owner-
administered projects reported longer initial and recertification interviews, while Public
Housing projects reported the shortest. PHA/projects overall were more likely to start the
annual recertification process 3 to 6 months before the effective date (56%). Owner-
administered projects were most likely. Fifty-six percent of PHA/projects overall were
likely at 72 percent, compared to Public Housing projects and owner-administered
projects.
♦ Using Computers and Software Programs. Almost all PHA/projects are using computers
to support processing (re)certifications, as well as a wide variety of purposes. The
number of PHA/projects using computers and software has been increasing. The most
frequently reported uses for the computers were to calculate rent, print 50058/50059
forms, input verified information, and print letters to the tenants. Interestingly, one of the
least frequently reported use of computers was to interview tenants and record answers.
♦ Use of PIC/TRACS. Ninety-seven percent of PHA/projects transmit 50058/50059 data
via PIC/TRACS, and about 81 percent of all 50058/50059 data were transmitted to HUD
via PIC/TRACS. Owner-administered projects transmitted only about a half of their
50058/50059 data to HUD directly and slightly less than a half through another agency or
using other methods. Almost all PHA/projects kept copies of all 50058/50059 data in the
household file, not just the most recent ones.
♦ Verification Procedures. More than 88 percent of PHA/projects (compared with 82% in
FY 2006 and 85% in FY 2005) verify the components of tenant information at least
occasionally, and more than 73 percent (compared with 72% in FY 2006 and 75% in FY
2005) always verify tenant information. (Re)certification staff are usually responsible for
keeping track of verification requests and returns. Most PHA/projects keep track of
outstanding verification in the tenant file. PHA/projects reported that it was often or
sometimes difficult to verify sporadic, infrequent, or seasonal employment; sources of
income other than employment; income from employment; and medical expenses. Most
PHA/projects use various procedures to get verification information, including calling the
third-party, sending letters to the third-party, calling the tenants, and sending letters to the
tenants. When none of these procedures produced the verification information, most
PHA/projects resorted to accepting other, less preferred verification information. When
asked to name the causes of problems that emerged when obtaining complete
verifications, the two major causes reported by PHA/projects were tenants providing
incomplete or inaccurate information, and employers not responding to requests in a
timely manner. TASS and EIV were most frequently used to verify Social Security/SSI
benefits, employment income, and disability status and dual entitlement benefits. Most
IV-27

IV. Findings
PHA/projects also used other methods such as pay stubs, third party verification, and
employer information to supplement EIV information.
G. The Relationship between Characteristics and Practices, and Error
Multivariate analysis was conducted to further examine gross rent error and different types of
error that occurred in the certification/recertification process. The goal was to identify and
estimate the relationship between tenant characteristics, project characteristics, and project
practices and gross rent error. These analyses address study objectives 5, 6, 8, 12, and 13. A
brief summary of the findings is provided below. A more detailed description of the analyses is
found in Appendix F.
To address Objective 5 (differences in error by program type), results from both bivariate
tabulations and multiple regression analyses indicated that the Public Housing program had
lower average gross rent error than the owner-administered program. In the multiple regression
analysis, this difference remained statistically significant even after other project-level variables
and tenant-level variables were held constant. The PHA-administered Section 8 program was
found to have significantly higher gross error than the owner-administered program in bivariate
tabulations, but this difference was not found to be statistically significant in the multiple
regression analysis. The implication being that the descriptive differences identified in bivariate
tabulations are further accounted for by tenant and project factors. Given similar tenant and
project characteristics, in comparison with the owner-administered program, the Public Housing
program’s lower gross error persisted; whereas the PHA-administered Section 8 program’s
higher gross error disappeared. The latter finding is important as it suggests that tenant
characteristics for the Section 8 program may have imposed greater difficulty in quality control
processes as they apply to rent calculation.
Note that the finding from the FY 2006 multiple regression analysis was somewhat different,
where Public Housing program did not show a significant difference from the owner-
administered program. The PHA-administered Section 8 program did have significantly higher
gross error than the owner-administered program. The shifting pattern of error difference cross
administrative type requires continued research to understand the underlying factors that lead to
the differences.
To address Objective 6 (impact on error by tenants vs. project/staff), this analysis found that,
consistent with previous results, the impact of tenant characteristics on rent error was apparently
greater than project characteristics and practices. As shown in variance partitioning with HLM
unconditional model and the sequential OLS models, tenants’ high income and multiple sources
of income and allowance items were related to both higher gross rent error and different types of
error. These tenant variables should be seen as important indicators of risk for rent errors and be
targeted by program intervention.
To address Objective 8 (error concentration in program/project), this analysis found that, to a
varying extent, a number of program/project features did relate to rent error and different types
of error. Specifically, projects that retained experienced certification staff (over five years) had
lower average rent error. This finding is compatible with what was found in the FY 2006
analysis where staff educational requirements were related to lower rent error, namely, staff
quality is a critical factor in reducing errors. We suggest focusing more on staff quality data
IV-28

IV. Findings
collection and analysis in the future. One possibility is to collect additional staff information and
job descriptions. The information would allow accurate comparison of staff quality across local
providers in connection to the quality control results.
To address Objective 12 (rent errors differentiated by using automated systems), the analysis
provided evidence that computer application in certification operation helped reduce different
types of processing error. Use of TASS or EIV, however, was not yet clear in consistently
relating to rent error or different types of error. Again, more systematic data collection and
analysis on computer application may help assess the impact of automation. For example, we
may consider collecting data items on the respondents’ knowledge and skills specific to
computer use in certification/verification.
To address Objective 13 (other tenant or project characteristics related to errors), the analysis
found that projects whose survey respondents reported perceived error causes tended to have
lower chance of certain types of staff error. It is arguable that staff’s ability to report difficulties
causing mistakes is equivalent to knowledge of the problems and hence potential ways to deal
with them. However, far more information is needed to learn how project staff acquires
information on different types of error in daily practice and the ways they identify and address
risks of improper certification and payment determination.
So far, we have revealed a very limited impact of project factors on rent error. It is vexing that,
as organized activities of managing housing assistance, project variation did not seem highly
meaningful with the statistical analysis. As suggested in earlier analyses, we will continue to
improve our data collection tools to gather more relevant information on quality control. In depth
understanding of the actual operation of the local providers is needed to more accurately
conceptualize the organization structures differentiated by program types. For instance, we
ponder collecting data to cover issues that are decision-maker specific (e.g., the owner, the
managing contractor, or the PHA), rather than data in reference to a generic project.
Furthermore, we note the limitation of statistical analysis, which typically can only describe
broad patterns and identify strong, clear-cut relationships between causal and effect factors. To
learn the complex and subtle differences in housing program operation that involve vastly
different individuals and groups, qualitative research is needed. Quantitative data collection and
analysis, in fact, can be benefited tremendously by rich, deep, personalized qualitative
observations of the local housing offices daily activities.
H. Rent Reasonableness
The Housing Choice Voucher Program (HCVP) assists low-income families in obtaining
housing in the private market. Public housing authorities are responsible for administering the
program and ensuring that the rents paid for dwellings leased by participants in the HCVP are
reasonable in comparison with rental units in the private, unassisted local market. High rents can
waste government funds and inadvertently raise private market rents. HUD regulations require
PHAs to conduct a rent reasonableness determination before units are leased, before rent
increases are granted to owners, and when Fair Market Rents decrease by at least 5 percent. This
analysis examines whether PHAs fulfilled the requirement for documenting rent reasonableness
determinations, but does not investigate whether rents were in fact reasonable.
IV-29

IV. Findings
Methodology. Each of the 146 PHAs, administering the Section 8 Voucher program for
households participating in the study, were asked to describe their standard rent reasonableness
processes and provide copies of the forms used when determining rent reasonableness. This
information was used to classify the methods used by PHAs to determine rent reasonableness.
In addition, field interviewers were instructed to search the tenant files for each of the 789
voucher households in the tenant sample to locate the documents supporting the rent
reasonableness certification. For new certifications (there were 115)
8
field interviewers searched
the file for the initial rent reasonableness certification and recorded its date. For annual
recertifications (674), field interviewers examined case files for evidence of when the current
rent to owner became effective. If the rent became effective within the past two years, the case
file was searched for a rent reasonableness certification and the date of certification. The
owner’s rent certification on the Request for Tenancy Approval (RTA) form was considered a
rent reasonableness certificate.
Findings Pertaining To Rent Reasonableness Methods Used By PHAs. The most common
method of determining rent reasonableness is the unit-to-unit comparison (see Exhibit IV-25).
Fifty three percent of the housing authorities reported using this method as either the only
method used or the predominant method. Twenty four percent reported using unit-to-unit
methodology as a component in combination with other methods. The unit-to-unit method is
similar to the standard real estate appraisal technique of comparing a unit to similar private,
unassisted units. Rent amounts are sometimes modified for differences in unit characteristics,
such as size, age, amenities, housing services, maintenance, and utilities.
Exhibit IV-25a
PHAs by Predominant Rent Reasonableness Method (unweighted)
2006 2006 2007 2007
Method
Number Percent Number Percent
Unit-to-Unit Comparison 74 56% 78 53%
Unit-to-Market Comparison
11 8% 12 8%
Point System
19 14% 17 12%
Other or Rent Control 9 7% 9 6%
No Single Predominant Method 17 13% 26 18%
No information 2 2% 4 3%
Total 132 100% 146 100%
The unit-to-market comparison approach estimates the average and/or range of “market” rents
for units with similar characteristics in the private, unassisted market. Valuation adjustments are
based on typical units in the private market. Eight percent of housing authorities reported using
this method solely or primarily. However, 27 percent of the PHAs used the unit-to-market
method in combination with other methods. Twelve percent of housing authorities indicated that
their primary method of making rent reasonableness determinations was based on a point system;
8
In FY 2007, portability move-ins (19 cases) were classified as annual recertifications. In FY 2006 they were
categorized as new admissions.
IV-30

IV. Findings
Thirteen percent reported using the point system in combination with other methods. Using this
system, units are assigned points based on their condition and attributes and comparisons are
made to unassisted units.
Eighteen percent of the PHAs used a combination of methods equally, meaning no predominant
method was identified. Six percent of PHAs used some other method to determine rent
reasonableness or the rents for their properties were restricted by rent control.
The frequency of various combinations of rent reasonableness methodologies are addressed in
Exhibit IV-25b.
Exhibit IV-25b
PHAs using Rent Reasonableness Method Combinations (unweighted)
100 %
Method
Predominant
Method
Equivalent
Method
Lesser
Component
Unit-to-Unit
Total times cited = 113 49 29 14 21
Unit-to-Unit and Unit-to-Market 12 5 11
Unit-to-Unit and Point Ranking 8 5 6
Unit-to-Unit and Professional Judgment 20 4 15
Unit-to-Market
Total times cited = 51 10 2 7 32
Unit –to-Market and Unit-to-Unit 1 6 27
Unit-to-Market and Point Ranking 0 1 0
Unit-to-Market and Professional Judgment 1 0 18
Point Ranking
Total times cited = 36 15 2 2 17
Point Ranking and Unit-to-Unit 1 1 17
Point Ranking and Unit-to-Market 0 1 12
Point Ranking and Professional Judgment 1 0 11
Other and Rent Control
Total times cited = 12 0 9 0 3
Other and Unit-to-Unit 5 0 3
Other and Unit-to-Market 5 0 3
IV-31

IV. Findings
Each methodology is considered based on its proportion in the mix of methods. The total times
cited refers to the number of PHA’s which used the rent reasonableness method to any extent.
When the mix of methods sum to a number higher than the times cited in the column subheading,
it indicates that there were more than two methods involved. For example, unit-to-unit
methodology is cited by one hundred thirteen PHA’s and is used as a sole method of rent
reasonableness determination in forty nine Section 8 voucher programs. It is the predominant
method in twenty nine voucher programs. In twenty of the twenty nine programs, project staff
report using professional judgment as a component of their rent reasonableness methodology.
Twelve of these twenty nine voucher programs include unit-to-market analysis and four include
point ranking. Without identifying specific mixes and proportions, one can see that the twenty
nine voucher programs which primarily use the unit-to-unit method often include a mix of two or
more other methods for determining rent reasonableness with professional judgment being the
most frequent associated method.
Findings Pertaining To Rent Reasonableness Documentation Found In Tenant Files for
New Admissions. In FY 2007, 71 percent of new admission files contained rent reasonableness
documents compared to 88 percent in FY 2006 (see Exhibit IV-26a)
9
. The absence of
documentation does not necessarily indicate a determination was not completed, only that it was
not properly documented. Of those files that had documentation, 61 percent contained a
statement signed by the PHA staff certifying that the rent is reasonable (see Exhibit IV-26b).
Exhibit IV-26a
Rent Reasonableness Documentation for New Admissions
Status
80% 88% 71%
20% 12% 29%
i 81% 68% 61%
4% 10% 11%
11% 16% 24%
4% 6% 4%
2005 2006 2007
Determination Documented
No Determination Documented
Total 100% 100% 100%
Exhibit IV-26b
Type of Rent Reasonableness Documentation for New Admissions
Type
A signed statement certify ng 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
2005 2006 2007
Total 100% 100% 100%
9
As noted previously, in prior studies portability move-ins were categorized as new admissions rather than annual
recertifications. Therefore we calculated the percent of households with rent reasonableness documents when new
admissions and portability move-ins households are classed together. The result indicates 76 percent of the files
contained rent reasonableness documents, a significant decrease from FY 2006.
IV-32

IV. Findings
HUD requires that rent reasonableness determinations be conducted before signing the contract
and lease. The timeliness of the rent reasonableness determination was evaluated by comparing
the lease date with the rent reasonable certification date in the case file. Exhibit IV-27 provides a
summary of how the date of the rent reasonableness determination relates to the initial lease date
for those households where reference to the rent reasonableness determination was found in the
file. If the lease effective date occurred before the determination, the rent reasonableness
determination had no impact on the rent charged. The percent of rent reasonable determinations
made after the rent had been established as part of the initial lease agreement increased from FY
2006 (11 percent) to FY 2007 (15 percent).
Exhibit IV-27
Timing of Most Recent Rent Reasonableness Determination—New Admissions
2% 3% 5%
Determination-Certification Chronology
2005 2007
More than 4 months before lease date
Up to 4 months before lease date 85% 75% 77%
After lease date—up to 2 months 3% 9% 10%
After lease date—greater than 2 months 4% 2% 5%
Date missing 6% 12% 3%
Total 100% 100% 100%
Findings Pertaining To Rent Reasonableness Documentation Found In Tenant Files for
Annual Recertifications. Annual recertifications require rent reasonableness documents only
when owners increase rental rates. We examined case files to determine when the current rent to
owner first became effective. The case file was searched for the rent reasonableness
determination when rent reasonableness determinations were performed in the previous two
years. In FY 2007, about 65 percent of these case files had certified rent reasonableness
documents within the past two years compared to 69 percent in FY 2006 (see Exhibit IV-28a).
Exhibit IV-28a
Rent Reasonableness Documentation for Annual Recertifications
Status
2005 2006 2007
Determination Documented 65% 69% 65%
No Determination Documented 35% 31% 35%
Total 100% 100 100%
Type of reference to rent reasonableness documentation was recorded for households where
documentation of the rent reasonableness determination was found. Of the files that had
documentation within the last two years, 63 percent contained a statement signed by the PHA
staff certifying that the rent is reasonable (see Exhibit IV-28b).
IV-33

IV. Findings
Exhibit IV-28b
Type of Rent Reasonableness Documentation for Annual Recertifications
Where Documentation of the Rent Reasonableness Determination Was Found
Type 2006 2007
A signed statement certifying that the rent is reasonable
68% 63%
Comparable units documented by the property owner in section
12a of HUD 52517
4% 10%
Comparable units documented on other documents
16% 22%
12% 5%
Any other reference to rent reasonableness
Total 100%
The current rents to owner in the lease agreements were compared with the dates of the rent
reasonable documents. If the lease effective date occurred before the determination, the rent
reasonableness determination had no impact on the rent charged. In FY 2007, about 9 percent of
the rent reasonable determinations were made after rents had been established, compared with 22
percent in FY 2006 (see Exhibit IV-29).
Exhibit IV-29
11% 8% 15%
Timing of Most Recent Rent Reasonableness Determination—Annual Recertifications
Determination-Certification Chronology
2005 2006 2007
More than 4 months before lease date
Up to 4 months before lease date 42% 47% 42%
After lease date—up to 2 months 3% 3% 2%
After lease date—greater than 2 months 13% 19% 7%
Date missing 31% 23% 34%
Total 100% 100% 100%
Conclusion. PHAs are not fully documenting rent reasonableness determinations as required by
HUD regulations, and a large percentage of existing rent determinations have been made on the
basis of less formal means of evaluating rents. These findings may be partially attributable to the
PIH notice issued May 16, 2003 (notice PIH 2003-12) that supports a more streamlined rent
reasonable process. For example, a PHA need not consider all nine criteria cited in 24 CFR
982.507(b) to fully comply with the regulation. PIH 2003-12 also asserts that “each PHA should
use appropriate and practical procedures for determining rental values in the local market.” This
statement may also be intended to justify less formal methods of rent determination.
I. Utility Allowance Analysis
As part of the FY 2007 HUDQC study, two separate analyses were conducted of the utility
allowances (UA) provided to households assisted through PHA-administered Section 8 Voucher
programs. The first analysis focused on whether there was documentation in the tenant file
indicating how the utility allowance amount used in rent determination was calculated, and
whether those documents were used correctly in calculating the utility allowance amounts. The
IV-34

IV. Findings
second analysis focused on identifying discrepancies between the utility allowance on the 50058
form (AC utility allowance), and the utility allowance determined by using the appropriate
Utility Allowance Schedule provided by the PHA staff (QC utility allowance) that often varied
by unit type, effective date of (re)certification and location within a county.
To support these analyses, PHAs were asked to provide utility allowance schedules used for
actions effective in FY 2007 as well as details regarding how the utility allowance was
calculated. Then the field interviewers were asked to copy documents showing calculation of
utility allowances found in tenant files at the PHA office.
Documentation of Utilities and Utility Allowance Values. One-hundred and forty-six PHA
administered Section 8 Housing Choice Voucher programs, administered by 133 housing
authorities (several of which administered multiple Voucher programs in different counties)
participated in the 2007 HUDQC study. According to information provided by those PHAs more
than half (51%) of the programs used HUD Form 52517 (Request for Tenancy Approval) as the
official source for identifying the utilities for which the households were responsible; and
majority (73%) of the programs used HUD Form 52667 (Schedule of Allowances for Tenant
Furnished Utilities) to calculate the value of the utilities paid by the tenants. Exhibit IV-30a
provides the information on the type of documents used as the official source for identifying
utilities for which the households were responsible, as well as the type of documents used to
calculate the value of the utilities paid by the tenants.
Exhibit IV-30a
Types of Documents Used by PHA to Identify Utilities and Calculate the Utility Allowance Value
Type of Document Used
For Identifying Utilities
For Calculating the Utility
Allowance Value
Number of Percent of
Section 8 Section 8
Programs Programs
Number of Percent of
Section 8 Section 8
Programs programs
HUD Form 52517 (Tenancy Approval) 75 51% 8 6%
HUD Form 52641 (HAP Contract) 13 9% 2 1%
HUD Form 52667 (Allowances Schedule) 17 12% 107 73%
Other (Lease, Reports, Comparisons etc) 20 14% 25 17%
Various combinations of above 21 14% 4 3%
Total 146 100% 146 100%
Data in this exhibit are not weighted
Seven hundred and eighty-nine households, assisted through PHA-administered Section 8
Housing Choice Voucher Programs, participated in this study. Field interviewers were able to
locate worksheets or other documents indicating how the utility allowance was calculated for
749 households (95%).
Discrepancies between the AC and QC Utility Allowances. The analysis related to
discrepancies between the AC and QC utility allowance was conducted in two steps. First the
utility allowance value from the 50058 (AC) was compared to the utility allowance amount on
the worksheet found in the household files (WS). Second, the utility allowance value from the
50058 (AC) was compared to the QC allowance amount.
IV-35

IV. Findings
The QC utility allowance was calculated also in two steps. In the first step, the utilities for which
the tenants were responsible were identified by using documents – usually PHA utility allowance
Worksheets – found in tenant files that indicated those specific utilities. In the second step, the
identified household specific utilities were mapped onto the utility allowance schedule, and the
total summed up to determine the QC allowance amount.
Comparison of AC and WS Utility Allowances. The utility allowance amount for the 749
households from the 50058 (AC) was matched with amount on the utility allowance worksheet
obtained from the tenant files (WS). For 90 percent of the households (672 units) the 50058
utility allowance amount (AC) matched with the Worksheet amount (WS). This included 71
households that did not have any utility expenses because either they were included in the rent or
the owner paid all utilities. For three percent of the households, the worksheet provided was for
the incorrect period of time. Hence, we could not determine whether the utility allowance
amount used in the rent calculation was correct. In the remaining 7 percent of the households
there were discrepancies between the amount on the worksheet (WS) and the AC amount.
Exhibit IV- 30b lists the description of the findings from the comparison between the utility
allowance listed on the 50058 (AC) and the amount on the worksheets (WS) found in tenant
files.
Exhibit IV-30b
Utility Allowance Worksheet (WS) vs. 50058 (AC) Utility Allowance Comparison Findings
Number Percent Outcome
672 90% 50058 (AC) amount matched with Worksheet (WS) amount
25 3% Worksheet in file for incorrect period of time
25 3% Discrepancy due to math error or other clerical errors
27 4% Discrepancy – Unable to determine reasons
749 100% Total
Data in this exhibit are not weighted.
Comparison of AC and QC Utility Allowances. The utility allowance amount on the 50058
form (AC) was matched with the QC utility allowance amount. As in the previous comparison,
we were unable to calculate the QC utility allowance in 5 percent (41 households) of the cases
because their worksheet was not available and consequently the specific utilities the household
paid for could not be identified. Furthermore, we were unable to calculate the QC utility
allowance in 2 percent of the cases because the worksheets in the files did not include specific
utilities or other critical information needed for QC allowance calculation; and in another 2
percent because the appropriate Utility Allowance Schedule was not available. Exhibit IV-30c
below differentiates between the cases whose QC allowance amount was able to be calculated and
lists the reasons and number of cases whose QC utility allowance amount was not able to be
calculated.
IV-36

______________________
IV. Findings
Number Percent Outcome
Exhibit IV-30c
Availability of all Information to Enable QC Utility Allowance Calculation
QC UA amount
calculated
721 91 % Appropriate Worksheet and Schedule available
Yes
41 5% UA Worksheet or other comparable document not available
No
15 2% Appropriate UA Schedule not available
No
12 2% Worksheet was missing critical information
No
789
Data in this exhibi
100% Total
t are not weighted.
For the 721 cases whose QC utility allowance amounts were calculated, the QC utility allowance
was compared to the AC utility allowance amounts. In 88 percent of those households, the AC
and QC utility allowance values matched. The remaining (discrepant) 12 percent were
categorized into 4 broad categories. Three of the 4 categories fall under administrative errors,
whereas for the remaining cases, we were unable to determine the reason for the discrepancy.
Exhibit IV-30d below presents the findings from this analysis.
Exhibit IV-30d
QC vs. AC Utility Allowance Comparison Findings
Number Percent Outcome
639 88% QC UA matched amount on 50058
14 2% Discrepancy due to difference in number of bedrooms used
20 3% Discrepancy due to math error / transfer error
Discrepancy – incorrect schedule used (schedule for improper effective date
13 2% or locality rates used)
35 5% Discrepancy – Unable to determine reasons
721 100% Total
Data in this exhibit are not weighted.
Note: When calculating the QC rent, the AC utility allowance amount, and not the QC allowance
amount was used.
J. Payment Standard Analysis
As part of the FY 2007 HUDQC study, a special analysis was conducted to determine if PHAs
are using the correct payment standards. This special analysis was conducted independently of
the rent calculation error findings presented elsewhere in this chapter, and the payment standard
Analysis did not affect the rent calculation determinations. This analysis consisted of two parts.
First, the payment standard on the 50058 form was compared to the payment standard schedules
IV-37


IV. Findings
provided by the PHA. Second, the payment standard on the 50058 form was compared to the
Fair Market Rent for the appropriate geographical area. The findings from these two
comparisons are presented below.
Background. Payment Standards are used in the Section 8 Voucher Program when determining
the tenant’s portion of the rent-to-owner. They must be kept current and set between 90 and 110
percent of the Fair Market Rent (FMR). If a PHA does not ensure that their payment standards
are within this range, or they misunderstand how new FMRs affect their payment standards,
errors in tenant rent determinations will result.
PHAs may apply payment standards incorrectly resulting in errors in tenant rents. A PHA may
have several payment standard areas with complex borders, sometimes making it difficult to
select the correct payment standard for any given address within the jurisdiction. PHAs may also
err by applying the family-size payment standard (the size authorized for the family as shown on
the voucher) in lieu of the lesser of either the family-size payment standard or the payment
standard for the unit size (number of bedrooms in the unit).
Other potential areas for error include whether a PHA has been authorized to use FMRs based on
the 50
th
percentile of the rents in the area; whether the PHA has been authorized to use Success
Rate payment standards based on the 50
th
percentile of rents; and whether the PHA continues to
be eligible for these higher subsidy standards. Another complication allows PHAs to change the
payment standard only at the time of the annual recertification or before moving to a new
address. Thus, even if a change in the family composition requires an interim recertification
with several family members moving in or out, the payment standard used in determining the
rent should not be changed at the interim recertification. The complexity of the payment
standard guidelines increases errors, but most of the errors found were not due to these complex
guidelines.
Comparison of the Payment Standard on the 50058 Form to the Payment Standard
Schedules Provided by the PHA. The first analysis consisted of comparing the payment
standard on the 50058 form (AC) to the payment standard schedule (QC) provided by the PHA.
For all voucher households in the study, the appropriate QC payment standard was selected and
compared to the AC payment standard. The selection of the QC payment standard from the
schedules provided by the PHA was based on:
♦ the lower of either the number of bedrooms in the unit, or the number of authorized
bedrooms for the household on the voucher,
♦ the effective date of action, and
♦ the determination and application of any exception listed on the information provided by
the PHA staff.
For every household where the AC and QC payment standard did not match, a call was placed to
the PHA staff for clarification and, if appropriate, to gather payment standard schedules for
previous years. Through the calls, often other complications were discovered and taken into
consideration when selecting the QC payment standard. The types of complications included:
♦ A decrease in the payment standards for units, requiring the PHA and Macro to use the
previous (higher) payment standard for the first recertification after the decrease. Many
IV-38

IV. Findings
PHAs only sent the payment standards for a specific time period. Calls were made to get
the historic payment standard Schedules.
♦ Households that were granted exceptions for special circumstances such as living in a
house with additional amenities or setting the payment standard to the Gross Rent for
Enhanced Vouchers.
♦ Housing Authorities using higher payment standards for Exception Rent Areas.
♦ Housing Authorities using payment standards from a previous Housing Authority for
port-in households understanding the rates would be adjusted at the next annual re
examination.
There were 789 Housing Choice Voucher households in the study. For the majority (92%) of the
households, the AC payment standard matched the QC payment standard. However, there were
64 households (8%) with discrepant payment standards. Forty (63%) of these discrepant
households were elderly or disabled households. Elderly and disabled households are identified
separately because they are often entitled to individual exemptions to the payment standard rules.
Fifty-one (80%) of the total discrepancies were attributable to one of six common reasons.
The most typical reason for a discrepancy between the AC and QC payment standard was that
the Gross Rent was used in lieu of the payment standard. Also, the use of either the incorrect
number of bedrooms or the use of the incorrect payment standard schedule (i.e., schedule was for
a different geographic area or time period) accounted for a cumulative 32 percent of the
discrepancies found. In addition, the payment standard used by the PHA was not available for 8
households (12%). Section 12 of the 50058 was incomplete or missing from the tenant file, and
the PHA staff were not able to provide the payment standard for that case. These households
were considered as having a discrepant payment standard and categorized as having a payment
standard that was below 90 percent of the Fair Market Rent. Exhibit IV-31 below summarizes
the number and percent of households where the QC and AC payment standard did not match by
reason.
IV-39

IV. Findings
Exhibit IV-31
Number and Percent of Households with Payment Standard Discrepancies
Number of
(Elderly
/Disabled)
Number of
Percent of
/Disabled)
1 0 1 %
13 6 30 %
7 3 16 %
5 5 16 %
0 3 5 %
7 1 12 %
7 6 20 %
40 24
The
FMR.
Exhibit IV-32
Fair Market Rent
%
3 405 5 1.0 %
16 353 7 2.9 %
i 19 758 12 3.9 %
Households
Reason
Households
(Non-Elderly
Households
Fair Market Rent was Used Instead of the Payment Standard
Gross Rent was Used Instead of the Payment Standard
Incorrect Number of Bedrooms was Used
Incorrect Payment Standard Schedule was Used
Overdue Recertification; Payment Standard Amount Did Not
Match the FY 2007 Payment Standard
Section 12 of the 50058 was Incomplete or Missing
Other Reasons – Typo, Enhanced Rate Incorrectly Applied
Total 100 %
Data provided in this exhibit are not weighted.
Comparison of the Payment Standard on the 50058 Form to the Fair Market Rent for the
Appropriate Geographic Area. The second analysis consisted of comparing the payment
standard on the 50058 form (AC) to the Fair Market Rents (FMR) for the appropriate area.
payment standard for 758 of the households (96%) fell within the 90 to 110 percent FMR band;
19 households (2.4%) used an amount that was less than 90 percent of the FMR; and 12
households (1.5%) exceeded 110 percent of the FMR. Exhibit IV-32 below summarizes the
number and percent of households by the relationship of the payment standard to the acceptable
Percentage of Households Meeting Payment Standard Requirements
Under
90%
90–110
Over 110%
Cases Outside the 90–
110% Band
Non-Elderly or Disabled
Elderly or Disabled
Payment Standard Compared w th Fair Market Rent
The analysis of the households that fell outside the 90 to 110 percent FMR band indicated that 27
households (87%) fell outside of the 90 to 110 percent band of the FMR for six general reasons;
the household’s voucher was administered by a PHA that was granted an exemption by HUD;
the household received an exemption because of elderly or disability status; Gross Rent was used
instead of the established payment standard; the incorrect number of bedrooms was used when
determining the appropriate payment standard, the Recertification was overdue and the AC
payment standard fell outside of the updated FMR 90 to 110 percent band; or Section 12 of the
IV-40

IV. Findings
50058 was incomplete or missing. Exhibit IV-33 summarizes the number and percent of
households that fall outside the 90 to 110 percent FMR band by categories.
With 26 households not ratified by any exemptions, two percent of all Housing Choice Voucher
households had a payment standard less than 90 percent of the FMR, and one percent had a
payment standard exceeding 110 percent of the FMR. Therefore, a total of three percent of
households did not meet HUD’s payment standard requirements.
Exhibit IV-33
Details of Cases Falling outside the 90-110% of FMR Band
Fair Market Rent Percent of
Cases
Outside the
90–110%
Under 90% Over 110%
Band
Households Assisted by a PHA Granted an Exemption by

HUD 0 1 3 %

Households with Elderly or Disabled Members Receiving

an Exemption 2 2 13 %

Discrepancy Due to Incorrect Number of Bedrooms 4 4 26 %
Discrepancy Due to Use of Gross Rent Instead of

Payment Standard 2 3 16 %

Discrepancy Due to Overdue Recertification 1 0 3 %
Section 12 of the 50058 form was Incomplete or Missing 8 0 26 %
Other Reason – No Explanation Provided by PHA Staff

during Follow-up or Typo 2 2 13 %

Total Number of Cases outside 90-110% FMR Band 19 12 100 %
Data provided in this exhibit are not weighted.
Comparison of the FY 2006 to the FY 2007 Payment Standard Analysis Results. The same
payment standard Analysis was conducted for the FY 2006 study. Of the 795 Housing Choice
Voucher households in the FY 2006 study, the AC and the QC payment standard matched for
609 (77%) households. Additionally, 121 (15.2%) households had payment standards that did
not fall within the 90 to 110 percent Fair Market Rent band. Of those 121 households, 86 cases
were not granted any exemptions. Therefore, a total of 11 percent of the Housing Choice
Voucher households included in the FY 2006 did not meet HUD’s payment standard
requirements.
For cases in the FY 2006 payment standard Analysis where the AC and the QC payment
standard did not match, 42 cases (23%) incorrectly applied a new, lower payment standard at the
first recertification after a decrease as opposed to using the lower payment standard at the second
recertification of the decrease, as outlined in the HUD Policy Form 24 CFR 982.505. For the FY
IV-41

IV. Findings
2007 analysis, no cases were discovered where a new, lower payment standard was incorrectly
applied in the first recertification after a decrease.
Exhibit IV-34 below summarizes the results from the FY 2006 and FY 2007 Payment Standard
Analysis.
Exhibit IV-34
Comparison of the FY 2006 to FY 2007 Payment Standard Analysis
FY 2006 FY 2007
Number Percent Number Percent
Housing Choice Voucher Sample 795 789
Households where the AC and QC Payment Standard Did

Not Match 186 23 % 64 8 %

Households where the AC Payment Standard Did Not Meet

the 90 to 110 Percent of FMR Threshold 121 15 % 31 4 %

Households that Were Not Exempt from the 90 to 110

Percent of FMR Threshold – Did Not Meet HUD’s Payment

Standard Requirements 86 11 % 26 3 %

Data provided in this exhibit are not weighted.
K. PIC/TRACS Analysis
The households included in this study were matched against the PIC/TRACS data files using
identifying information (a combination of the Social Security Number, name, and date of birth)
for the head of each household. Because this study covers FY 2007, an attempt was made to use
historical PIC/TRACS files to identify the 50058/50059 data for the specific effective date and
type of action for which study data were collected.
PIC/TRACS data were received for any household (in the study sample) that were in the
historical databases used by HUD analysts even if the specific study effective date and type of
action did not match. When matching on the specific study effective date and type of action, only
1613 of the 2404 households in the study were represented. Therefore, most of the PIC/TRACS
analysis for this report was based on the broader match (PIC/TRACS data received for any
household in the study sample). Using these criteria, PIC records were found for 94 percent of
the households in PHA-administered projects; TRACS records were found for 97 percent of the
households in owner-administered projects. Of the 2404 households sampled, 2289 households
(or 95%) were matched against PIC/TRACS.
Analysis was conducted to compare the average dollars in gross rent error for households that
matched PIC/TRACS with those that did not. Exhibit IV-34a provides the percentage of
households in each of the three program types by presence or absence in PIC/TRACS, and the
average dollars in error based on all households in the study. Exhibit IV-34b provides the same
information, but uses only households with rent error as its base. These exhibits demonstrate
that proportionally an equal number of households in error matched against PIC/TRACS data.
IV-42

IV. Findings
Exhibit IV-34a
PIC/TRACS Data by Program Type and Average Gross Dollars in Error for all Households
PIC/TRACS PRESENT PIC/TRACS ABSENT
Percent of Average Dollars Percent of Average Dollars
Administration Type Households in Error Households in Error
Public Housing
94% $12 6% $32
$19 6% $23
PHA-Administered Section 8
94%

$17 6% $26

Total PHA-Administered
94%
$13 3% $12
Total Owner-Administered
97%
95% $15 5% $23
Percent of
in Error
Percent of
in Error
93% $39 7% $87
94% $50 6% $59
Total PHA-Administered 94% $47 6% $68
98% $35 3% $37
95% $43 5% $63
Total
As presented in Exhibit IV-34b the average dollars in error for households in error is higher for
households when PIC/TRACS data is absent in PHA-administered programs, but about the same
in owner-administered programs.
Exhibit IV-34b
PIC/TRACS Data by Program Type and Average Gross Dollars in Error for Households in Error
PIC/TRACS PRESENT PIC/TRACS ABSENT
Administration Type
Households
Average Dollars
Households
Average Dollars
Public Housing
PHA-Administered Section 8
Total Owner-Administered
Total
IV-43

IV. Findings
Exhibit IV-35 presents the percentage of households and average dollars in error for households
matched/not-matched with PIC/TRACS by payment type. Essentially the same proportion of
households with and without matched PIC/TRACS data had proper payments.
Exhibit IV-35
Average Gross Dollars in Error by Payment Type and PIC/TRACS Data
PIC/TRACS PRESENT PIC/TRACS ABSENT
Percent of Average Dollars Percent of Average Dollars
Payment Type Households in Error
1
Households in Error
1
18% $56 21% $80
18% $29 16% $39
64% n/a 63% n/a
$15 $23
1
l
Exhibit IV-36
-$5 -$9 $12 $32
PHA-Administered Section 8
-$5 -$15 $19 $23
Total PHA-Administered
-$5 -$13 $17 $26
-$4 -$3 $13 $12
-$5 -$11 $15 $23
Underpayment
Overpayment
Proper Payment
Total 100% 100%
Average dol ar error per under- and overpayment subgroups.
Exhibit IV-36 examines net and gross errors by program type and matched PIC/TRACS data.
This table provides no new insights about the impact of matching PIC/TRACS data but
highlights the importance of reviewing both gross and net rent errors.
Average Net and Gross Dollars in Error by Administration Type
and PIC/TRACS Data for all Households
Average Net Rent Error Average Gross Rent Error
PIC/TRACS PIC/TRACS PIC/TRACS PIC/TRACS
Administration Type Present Absent Present Absent
Public Housing
Total Owner-Administered
Total
For households where PIC/TRACS data matched on specific study effective date and type of
action, further analysis was conducted to determine if certain key variables matched. The key
variables included gross income, net income, total tenant payment, and tenant rent
.
Exhibit IV-37
provides the percentage of households where the data gathered through the QC process matched
that in PIC/TRACS.
IV-44

IV. Findings
Exhibit IV-37
PIC PIC PIC PIC
No Match 1.2% 2.2% 2.0% 2.6% 8.4% 12.8% 27.1%
Match 98.8% 97.8% 98.0% 97.4% 97.1% 91.6% 87.2% 72.9%
Percentage of Matched and Non-Matched Dollar Amounts for Key Variables
Matching Variables from the 50058/50059 Form and PIC/TRACS
Total Tenant
Gross Income Net Income Tenant Rent
Payment
Match Status TRACS TRACS TRACS TRACS
2.9%
Total 100% 100% 100% 100% 100% 100% 100% 100%
IV-45

V. Recommendations
This section discusses recommended changes to the study that will improve the data collection
process or the quality of the data used in the analysis, as well as policy actions that could be
taken to reduce error. Section A discusses changes to the quality control process itself.
Section B addresses policy recommendations. Note that these recommendations have not
changed significantly from recommendations made in previous final reports. However, if further
reduction in error is desired, it continues to be important to learn more about local policies and
procedures that impact error, and methods of changing those processes to reduce error.
A. Modifying the Quality Control Process
The current methodology used by Macro to conduct its quality control study is based on the
successes and failures of previous studies, and meets the established objectives. However, there
are some recommendations that would be helpful for expanding the utility of data products as
well as improving the overall efficiency of ongoing quality control studies. These include the
following:
1) Continue the HUD quality control studies as a regular, ongoing effort to monitor and
manage HUD rent determination processes. Ongoing evaluation of the subsidy programs
administered by HUD is essential to the management of those programs. The primary goal
of the quality control studies is to measure rent errors. However, these studies also give
HUD the opportunity to learn more about alternatives to reducing rent errors, and better
management of current and changing conditions at PHAs/projects. Annual evaluations
facilitate more accurate cross-year comparisons of rent errors. They also allow for data
collection and analysis staff to develop specific expertise with HUD policy areas, and
develop tailored solutions for improving data quality. Further, other HUD-related topics
could be investigated (e.g., the changing demographics of HUD tenants) and piggybacked
on to the rent error data collection processes.
Data collected through the quality control studies provides detail not available through
other HUD sources (e.g., PIC/TRACS) that could be used to track such trends as the extent
to which income and expense items are verified, or the number of sources of employment
income received by a particular household or household member.
2) Gather information to document the outcome of the HUD quality control studies. Overall,
the HUDQC studies indicate that both the percent of errors and dollars associated with
those errors have decreased in the last six years. However, there is no information on
changes in tenant behavior related to the identification and reduction of error. One might
want to assume that reducing error should save HUD money. However, because the
housing programs managed by HUD are not entitlement programs (meaning not everyone
who is eligible for the program is entitled to benefits), as soon as an ineligible household is
removed from the roles, another household takes that household’s place.
The subsidy for the replacement household could be even higher than the subsidy for the
previously subsidized household. The existing quality control studies identify the dollars
associated with error, but do not identify an overall reduction in subsidy dollars. To really
understand the overall impact of the quality control studies on subsidy funding, additional
V-1

V. Recommendations
information is needed regarding both the tenants receiving the subsidies and the
PHA/projects administering the housing benefits.
3) Expand contractor access to verification obtained through inter-agency agreements.
Despite increasing rates of third-party verification, a large proportion of tenant income and
expenses are not being verified. This is especially important given the study results
indicate a significant relationship between third-party verification of certain types of
income and rent errors.
During the current study, household-level information was used to match sample household
members with Social Security data files through the Enterprise Income Verification (EIV)
system. Through this electronic match, verification was obtained for most sample
household members’ Social Security and Supplemental Security Income (SSA/SSI)
benefits. However, there were many household members where a match between the study
electronic files and the SSA/SSI electronic files was not found when expected and other
situations where irresolvable discrepancies were identified. If Macro as the contractor for
the HUDQC study could have access to the SSA/SSI database, these mismatches and
discrepancies could be investigated further.
4) Collect more information regarding PHA/project policies and practices. Each PHA
establishes its own policies, procedures, and forms for collecting the information that is
ultimately used to calculate tenant rent. The differentiation in these practices should have
some (possibly major) impact on the rent error, yet the analysis of the project practices and
characteristics collected in the Project Staff Questionnaire designed for this study does not
demonstrate the expected impact. Therefore, we recommend that focus groups and
cognitive interviewing be used to identify additional PHA/project level factors that may
impact error. This additional information could be used to revise the Project Staff
Questionnaire to include questions focused on the specific practices expected to influence
errors. As the data are already starting to reflect, as rent error decreases it will become
increasingly difficult for HUD and PHA/project staff to continue to make changes that will
reduce the error. Analysis of more detailed project-level data will assist in this process.
5) Continue to investigate PIC/TRACS data for sampling and other purposes. Ideally
PIC/TRACS data would be used to select the quality control sample, and provide the actual
data used by the PHA/project staff when calculating rent (in place of abstracting
50058/50059 Form data from the tenant file). However, the most recent match of the study
sample households with PIC/TRACS data indicated that only 95 percent of the sample
households are included in the PIC/TRACS databases. This is an improvement over the
findings from the match using the FY 2006 study sample households which indicated that
83 percent of the sample households were included in the PIC/TRACS databases.
However, this information continues to confirm that consideration should not be given to
using these data for selecting the household sample until there is some assurance that the
databases are all inclusive. Even if it is determined that PIC/TRACS data include all
households receiving assistance, using the PIC/TRACS data for selecting the household
sample may not be appropriate unless the data are available for the specific period of time
covered by the study.
V-2

V. Recommendations
6) Continue to expand existing computer systems and processes that further automate data
collection, processing, and reporting functions. Most of the data for the current study were
collected using an automated data collection system. This system continues to be enhanced
for each study so it now, not only simplifies the data collection process and reduces the
number of data collection errors, but also allows for review of the data at Macro
headquarters as the data are being collected. While the existing systems work well, there
are additional improvements that can be made to the data collection software, the field
monitoring software, and the processing and tracking of third-party verifications. The next
series of improvements should be aimed at increasing the amount of third-party verification
obtained by the contractor. Expanding and investing in better automated systems will yield
large dividends in terms of costs, time required to collect and process data, as well as the
breadth, depth, and quality of data.
B. Policy Actions
This study was not designed to provide recommendations regarding basic program objectives
and policies. However, the findings from this study suggest that some major procedural changes
should be considered when establishing and revising policy. Again, the recommendations in this
section remain essentially the same. While HUD has initiated several initiatives in the last few
years, the errors associated with the programs included in this study are no longer decreasing.
Additional action is needed. The suggestions below are examples of the type of actions that need
to be taken. Overall PHA/projects must be held accountable for their work, but HUD must
provide the tools needed to accomplish the work accurately.
1) HUD should continue to require both PHAs and owners to use the information available
through the Department of Health and Human Services’ “New Hires” income matching
database. The majority of subsidy overpayment errors are associated with earned income,
and a large majority of tenant income underreporting also relates to earned income. The
“New Hires” income matching database provides the opportunity to correct errors
associated with reported and unreported income. However, our experience working with
the “New Hires” data indicates that caution needs to be taken when using the information
provided by the database. The data are extremely helpful in identifying unreported sources
of income. However, the data are not current and often contain errors. Great care needs to
be taken when using these data to insure that income is only counted when it is clear that it
is received by the tenant and not simply because it is identified through the New Hires
database.
2) HUD should continue expanding support of the occupancy function and conducting
outreach campaigns to PHAs and owners informing them of the Department's occupancy-
related resources. Provision of detailed, current occupancy handbooks is essential in
addition to providing a mechanism for answering questions as they surface. Specifically,
HUD should develop a nationwide, consistent, reliable approach for providing guidance
and support to both PHAs and owners.
It is also critical that there be a close link between the team that responds to field concerns
and the staff responsible for writing HUD notices and guidance documents. The team
responding to field questions and concerns knows what the problems are that face the field.
These problems should be the subject of the guidance that comes from HUD.
V-3

V. Recommendations
3) HUD should provide the PHA/owners with the forms, training, and other tools needed to
determine rent correctly. Rent calculation error could be reduced if HUD would provide
structured forms for interviewing tenants, obtaining verifications, and calculating rent.
Ideally, these tools would be provided in the form of computer-assisted interview software
that minimizes the number of questions that need to be asked. Such systems would ensure
that tenants are asked about all income sources and expenses that affect their rent. Manuals
and training materials explaining how to implement requirements correctly and calculate
rent accurately should be provided. To the extent that HUD program rules can be
simplified, provision of automated and manual tools would be easier.
HUD experts and local housing staff should be given an opportunity to work together to
develop these tools and systems needed to reduce rent error. Many local PHA/owners have
already developed forms, training materials, manuals, automated systems, and monitoring
processes that have enabled them to provide accurate, efficient service to the tenants they
serve. HUD should learn from these PHA/owners and develop materials that will help
those PHA/owners who for one reason or another have not been as successful.
4) HUD should continue to implement its on-site monitoring program, and PHA/owners
should be held accountable for implementing HUD regulations and calculating rent
accurately. An on-site monitoring system that includes reviews at both the local and
Federal level is essential to improving accountability. PHA/owners with excessive errors
should be required to develop corrective action plans and show improvement within
specified time periods. HUD has initiated extensive on-site monitoring efforts since the
2000 QC study, in contrast with its policies of most of the previous two decades. The most
obvious explanation for the magnitude of error reductions in subsidy determinations
between 2000 and 2007 is improved HUD monitoring and the expectation of such
monitoring. However, as the dollars associated with rent error stop declining further action
will be needed to help the PHAs and owners focus on policies and procedures that lead to
error.
Monitoring can be conducted at a variety of different levels. We recommend that HUD
require PHA/owners to perform their own quality control reviews on a percentage of
income determinations and rent calculations. Agencies that have aggressively sought to
improve performance of their programs have had some significant successes, and one of
the most frequently used error reduction strategies includes the establishment of internal
quality control review procedures.
In addition to agency monitoring, HUD Field Offices and/or other national-level well-
trained staff should conduct a re-review of a percentage of the cases reviewed at the local
level to ensure that the quality control reviews are being conducted correctly, or select their
own random sample of files for review. This type of oversight not only identifies errors,
but also prevents them. In addition, it demonstrates HUD’s concern and focuses
PHA/owner attention on tenant income and rent.
5) Federal laws, regulations, and HUD requirements should be simplified to the extent
possible. The current statutory environment poses substantial obstacles to efficient,
accurate income and rent calculations. It contains dozens of requirements that may all be
well-intentioned and have potentially desirable impacts but which, taken as a whole, make
V-4

V. Recommendations
the income and rent determination process extremely complex. HUD has sought to issue
guidance on virtually all aspects of current income and rent determination requirements,
but some of the legislative provisions were written without any thought as to implications
for their administrative complexity. While determining which income to count, which
expenses to allow, and annualizing that information in a program with multiple objectives
may always be complicated, the various specialized provisions that relate to small subparts
of the population could be eliminated or simplified.
The policy related to students is the most recent example of such complex policies. PHA
and project staff are required to gather a series of information to determine whether
students continue to be eligible to receive assisted housing. For students who do not meet
certain criteria, PHA/project staff are required to determine the eligibility of the student’s
parents. This new policy, while well intentioned, just adds to the complex rules
PHA/project staff are required to implement when determining eligibility and calculating
rent for assisted households.
6) HUD should consider requiring some reexaminations to be completed less often than
annually. Many years ago, the reexaminations for elderly and disabled families were
conducted biannually rather than annually. HUD should consider implementing this policy
again or possibly conducting reexaminations for selected populations every three years. To
remove the issues related to incorrect subsidies because of the annual increase in Social
Security benefits, the policy could require adding the annual SSA cost of living adjustment
(COLA) to the total annual income for the households included in this group. With the
time-savings made available by this change in policy, PHA/project staff could spend more
time conducting required reexaminations, following up on suspected cases of fraud, and
conducting more internal monitoring of tenant files.
V-5

Appendix A—Rent Calculations

Appendix A—Rent Calculations
1. Public Housing
a. Obtain the Total Tenant Payment (TTP).
b. Determine if the family includes any ineligible noncitizens. IF YES, continue.

If NO, go to d.

c. Determine if the family includes any citizens or eligible noncitizens. IF YES, go to #3
(continuation). IF NO, go to #4 (temporary deferral).
MARKER (marks the return point after determining continuation of assistance status)
d. Obtain the Utility Allowance.
e. Determine if the tenant selected the Flat Rent. IF NO, go to f. IF YES, the QC RENT
equals the Flat Rent. Go to g.
f. The amount of the tenant’s rent (QC RENT) is the lower of: a. (TTP), minus d. (Utility
Allowance), or the Flat Rent*.
g. Determine if the QC RENT equals the ACTUAL RENT. IF YES, no error. IF NO,
dollar error.
*Note: If there is no Flat Rent, the QC rent will be capped with the Ceiling Rent to determine
the dollar amount of error.
2. Section 8 Vouchers
a. Obtain TTP.
b. Obtain the Gross Rent.
c. Obtain Utility Allowance.
d. If TTP is greater than Gross Rent, then set TTP to Gross Rent.
e. Obtain Payment Standard
1
(the Payment Standard is based on the lower of the Unit
(actual) Bedroom Size, and Family (eligible) Bedroom Size).

f. Obtain the household’s Adjusted Monthly Income.

1
For Project Based Vouchers, the Payment Standard equals the Gross Rent.
A-1

Appendix A—Rent Calculations
g. Subtract e. (Payment Standard) from b. (Gross Rent). If the Payment Standard is higher
than the Gross Rent, use 0.
h. Add a. (TTP) to g. (Gross Rent minus Payment Standard).
i. Determine if this is the initial occupancy for this dwelling unit. (Item 12b on the 50058 is
yes). IF YES, continue. IF NO, the Family Share = h. Go to l.
j. Calculate 40 percent of the household’s Adjusted Monthly Income (f.).
k. Determine if j. (40 percent of Adjusted Monthly Income) is equal to or greater than h.
(TTP plus Gross Rent minus Payment Standard). IF YES, the Family Share = h. Go
to l. IF NO, procedural error. Family Share = h. Go to l.
l. Determine if the family includes any ineligible noncitizens. IF YES, continue. If NO,
go to n.
m. Determine if the family includes any citizens or eligible noncitizens. IF YES, go to #3
(continuation). IF NO, go to #4 (temporary deferral).
MARKER (marks the return point after determining continuation of assistance status)
n. Subtract c. (Utility Allowance) from the Family Share (h.). This is the QC RENT.
o. Determine if the QC RENT equals the ACTUAL RENT. IF YES, no error. IF NO,
dollar error.
3. Section 8 Enhanced Voucher
a. Determine if household is receiving an Enhanced Voucher. If YES, continue. If NO,
use regular Voucher formula.
b. Obtain the Total Tenant Payment.

c. Obtain the Gross Rent.

d. Determine the lesser of b. (TTP) or c. (Gross Rent).
e. Determine if the family includes any ineligible noncitizens. IF YES, continue. If NO,
go to g.
f. Determine if the family includes any citizens or eligible noncitizens. IF YES, go to #3
(continuation). IF NO, go to #4 (temporary deferral).
MARKER (marks the return point after determining continuation of assistance status)
g. Obtain the Utility Allowance.
h. Subtract g. (Utility Allowance) from d. (the lesser of TTP or Gross Rent). This is the
Family Rent to Owner (QC RENT).
A-2

Appendix A—Rent Calculations
i. Determine if the QC RENT equals the ACTUAL RENT. IF YES, no error. IF NO,
dollar error.
4. Section 8 Project-Based, Section 202, Section 811, Section 8 Moderate Rehabilitation
a. Obtain the Gross Rent (Gross Rent equals the Contract Rent plus the Utility Allowance).
b. Obtain the TTP.
c. Determine if the family includes any ineligible noncitizens. IF YES, continue. If NO,
go to e.
d. Determine if the family includes any citizens or eligible noncitizens. IF YES, go to #3
(continuation). IF NO, go to #4 (temporary deferral).
MARKER (marks the return point after determining continuation of assistance status)
e. Obtain the Utility Allowance.
f. If Subsidy Type on 50059 = 7 or 8 (PRAC), go to h.
g. Subtract e. (Utility Allowance) from b. (TTP) or a. (Gross Rent) whichever is lower.
This is the QC RENT. Go to i.
h. Subtract e. (Utility Allowance) from b. (TTP). This is the QC RENT.
i. Determine if the QC RENT equals the ACTUAL RENT. IF YES, no error. IF NO,
dollar error.
5. Manufactured Home Space Rental for Section 8 Vouchers
a. Obtain the Rent to Owner.
b. Obtain the owner maintenance and management charges for the space.
c. Obtain the Utility Allowance
d. Add together a. (Rent to Owner), b. (owner maintenance and management charges), and
c. (utility allowance). This is the Space Rent.
e. Obtain the TTP.
f. Obtain the Payment Standard.
g. Subtract f. (Payment Standard) from d. (Space Rent).
h. Add e. (TTP) to g. (the amount by which the Space Rent exceeds the Payment Standard).
This is the Family Share.
A-3

Appendix A—Rent Calculations
i. Determine if this is the initial occupancy for this dwelling unit. (Item 12b on the 50058 is
yes). IF YES, continue. IF NO, the Family Share = h. Go to m.
j. Obtain the household’s Adjusted Monthly Income.
k. Calculate 40 percent of the household’s Adjusted Monthly Income.
l. Determine if k. (40 percent of Adjusted Monthly Income) is equal to or greater than h.
(TTP plus Space Rent minus Payment Standard). If YES, the Family Share = h.; go to
m. If NO, Procedural Error. The family is not entitled to assistance in this unit.
m. Determine if the family includes any ineligible noncitizens. IF YES, continue. If NO,
go to o.
n. Determine if the family includes any citizens or eligible noncitizens. IF YES, go to #3
(continuation). IF NO, go to #4 (temporary deferral).
MARKER
o. Subtract c. (Utility Allowance) from h. (Family Share) to determine QC Rent (Family
Rent to Owner).
p. Determine if the QC RENT equals the ACTUAL RENT. IF YES, no error. IF NO,
dollar error.
Special Calculations for Household with Ineligible Noncitizens
1. Continuation of Assistance
a. Determine if the family was receiving assistance on June 19, 1995. IF YES, continue.
IF NO, the FAMILY is eligible for prorated assistance; go to #5 (proration formula).
b. Determine if the FAMILY head or spouse is a citizen or eligible noncitizen. IF YES,
continue. IF NO, the FAMILY is eligible for prorated assistance; go to #5 (proration
formula).
c. Determine if the FAMILY includes any ineligible members other than the head, spouse,
and child or parent of the head or spouse. IF NO, continue. IF YES, the FAMILY is
eligible for prorated assistance; go to #5 (proration formula).
d. Determine if the FAMILY was granted continuation of assistance before November 29,
1996. IF YES, the FAMILY is eligible for full continuation of assistance. Return to
MARKER. IF NO, the FAMILY is eligible for prorated assistance; go to #5 (proration
formula)
2. Temporary Deferral of Termination of Assistance
a. Determine if Temporary Deferral of Termination of Assistance has been granted. If
YES, continue. If NO, go to d.
A-4

Appendix A—Rent Calculations
b. Determine the date Temporary Deferral of Assistance was granted.
c. Determine if more than 18 months have passed since Temporary Deferral of Termination
of Assistance was granted. IF YES, go to d. IF No, the FAMILY is entitled to
Temporary Deferral of Termination of Assistance; go to MARKER.
d. Determine if the FAMILY includes a refugee under Section 207 of the Immigration and
Naturalization Act or an individual seeking asylum under Section 208 of that Act. IF
YES, the Family is entitled to ongoing Deferral of Termination of Assistance; go to
MARKER. IF NO, continue.
e. Determine if the FAMILY was receiving assistance on June 19, 1995. If YES, the
Family is eligible for Temporary Deferral of Termination of Assistance; go to
MARKER.
f. Determine if the FAMILY is exercising its hearing rights (waiting for a decision from an
INS or PHA/owner appeal). If YES, go to MARKER. IF NO, continue.
g. Determine if the PHA is making reasonable efforts to evict. IF YES, go to MARKER.
IF NO, Procedural Error, HOUSEHOLD IS INELIGIBLE.
3. Proration Formula for Public Housing
a. Determine if this is a Public Housing case? IF YES, continue. IF NO, go to #4.

b. Determine the number of FAMILY members.

c. Determine the number of eligible FAMILY members.

d. Obtain the TTP.

e. Obtain the 95
th
percentile of Gross Rents for similarly sized public housing units in order

to determine the public housing maximum rent.
f. Determine if the Family pays a Flat Rent. IF NO, go to i. IF YES, continue.
g. Obtain the Flat Rent.
h. If g. (Flat Rent) is greater than or equal to e. (Maximum Rent), there is no prorated rent.
Use the Flat Rent; go to n. If g. (Flat Rent) is less than the e. (Maximum Rent), subtract
the Flat Rent from the Maximum Rent. This is the Family’s Maximum Subsidy. Go to j.
i. Subtract d. (TTP) from e. (Maximum Rent) to determine Maximum Subsidy.
j. Divide h. or i. (Maximum Subsidy) by b. (number of FAMILY members) and multiply
by c. (number of eligible members) to determine the Eligible Subsidy for the FAMILY.
k. Subtract j. (Eligible Subsidy) from e. (Maximum Rent) to obtain the prorated TTP.
l. Obtain the Utility Allowance.
A-5

Appendix A—Rent Calculations
m. The amount of the tenant’s rent (QC RENT) is k. (prorated TTP) minus l. (Utility

Allowance). Did the Family accept the prorated rent? Y/N. IF NO, go to #4.

n. Determine if the QC RENT equals the ACTUAL RENT. IF YES, no error. IF NO,
dollar error
4. Proration Formula for All Section 8 Programs
a. Obtain the Rent to Owner (voucher).
b. Obtain the Utility Allowance
c. Obtain the Gross Rent.

Voucher Gross Rent = Rent to Owner plus the Utility Allowance.

d. Obtain the TTP.
e. Obtain the Payment Standard (Voucher).
f. Obtain the HAP.
Owner Administered: HAP = Gross Rent minus TTP.
Voucher: HAP = Gross Rent or Payment Standard (whichever is less) minus the TTP.
Enhanced Voucher: HAP = Gross Rent minus the Payment Standard.
g. Record the number of FAMILY members.

h. Record the number of eligible FAMILY members.

i. Divide f. (HAP) by g. (total number of FAMILY members), and then multiply the result

by h. (number of eligible FAMILY members) to obtain the prorated HAP.
j. If Manufactured Home Space Rental, return to MARKER.
k. Subtract i. (prorated HAP) from c. (Gross Rent) to obtain the prorated Family Share.
l. Subtract b. (Utility Allowance) from k. (Prorated Family Share) to determine the prorated
QC RENT.
m. Determine if the QC RENT equals the ACTUAL RENT. IF YES, no error. IF NO,
dollar error.
A-6

Appendix B—Weighting Procedure

Appendix B—Weighting Procedure
This appendix describes the procedures followed in weighting the sample data.
Study Population. The universe under study includes all projects and tenants located in the
continental United States, Alaska, Hawaii, and Puerto Rico.
The following programs are included in the sample:
♦ PIH-administered Public Housing (Public Housing)
♦ PIH-administered Section 8 (PHA-administered Section 8)

ƒ Moderate Rehabilitation

ƒ Housing Choice Voucher Program

♦ Office of Housing-administered projects (owner-administered)

ƒ Section 8 New Construction/Substantial Rehabilitation

ƒ Section 8 Loan Management

ƒ Section 8 Property Disposition

ƒ Section 202 Project Rental Assistance Contracts (PRAC)

ƒ Section 202/162 Project Assistance Contracts (PAC)

ƒ Section 811 PRAC

The frames used to draw the sample include many out-of-scope projects such as projects in the
Move-to-Work program and projects that have been demolished or that are no longer assisted
housing. Many of these projects were identified before the sample was drawn, but others were
not and had to be replaced. In addition, at times projects resulting from a merger of two or more
projects or that were split into two or more were identified, resulting in difficult sampling
decisions.
Weighting Strategy. The weighting procedure usually begins with the determination of the
probability of selection of every unit in the sample. The use of purposive replacement for out-of-
scope projects for any of several reasons makes the sample weight calculations complicated.
The determination of an actual probability of selection for a replacement is impossible to make.
A sampling weight proportional to what the probability would have been if the project had been
selected originally is a reasonable estimate.
The probability of selection of a tenant was thus the product of the following combinations:
1) The probability of selection of the Primary Sampling Unit (PSU)
2) The probability of selection of a sub-PSU if the PSU was split
3) The probability of selection of the project from the PSU
4) The probability of selection of the tenant from the project.
The four probabilities were multiplied together and formed the preliminary weights. The
weights were then adjusted to be added to estimates of the national total of tenants in each
program. The weights summed to 1,320,000 for the owner-administered programs, 955,000 for
Public Housing, and 1,858,000 for the PHA-administered Section 8 Program.
B-2

Appendix B—Weighting Procedure
Primary Sampling Unit Probabilities. Each PSU was sampled with probabilities proportional
to size. The size measure used was the number of tenants adjusted to obtain equal expectation
for the three major types of programs in the study. The number of tenants of each kind in a PSU
was multiplied by an inflation factor to make all three numbers equal. The size measures were
then added; the PSU probability of selection was its size measure divided by the sum of the size
measures nationwide, multiplied by the number of PSUs to be selected (60). PSUs with
probabilities greater than one could be selected more than once (Sampling with Minimal
Replacement). For weighting purposes, probabilities greater than one were set to 1.0. Some
PSUs were divided into multiple geographic areas and one of these smaller geographic areas was
selected with probabilities proportional to size. This resulted in the same probability that would
have ensued had the division taken place before the sample was drawn.
Project Probabilities. This was defined as the minimum of kt/T and one, where k is the number
of projects in the program selected from the PSU, t is the number of tenants in the project and T
is the number of tenants in the program that are in the PSU. The PHA-administered Section 8
projects could have a probability greater than one for sampling purposes (meaning they could be
sampled more than once) but for the other two major program types, if the calculated probability
exceeded one, it was set to one and all the other probabilities were readjusted so they added to
the allocation for the program in the PSU. For weighting purposes probabilities greater than one
among PHA-administered Section 8 projects were set to one.
Tenant Probabilities. This is the total number of tenants sampled from the project divided by
the estimated number of tenants whose annual recertifications were conducted during the study
period. The estimate was obtained by multiplying the total number of tenants by the proportion
of tenants selected who were in scope for the study (i.e., who were subsidized by one of the
programs). For example, if six tenants were reviewed to find four tenants who were both in
scope and available for interviewing, one who was out of town, and one who was not subsidized,
from a list of 120 tenants, then the estimate would be 120 x (5/6) = 100 tenants.
Post-Stratification. The sample was designed to obtain similar numbers of tenants in each of
the following three categories of projects:
♦ Public Housing projects
♦ PHA-administered Section 8 projects
♦ Owner-administered projects
HUD provided approximate totals for each of the three categories. The sampling frame totals did
not correspond exactly to these numbers and required extensive adjustments. This was in part
because the numbers were approximations; but also in part because the geographic areas affected
by the 2005 hurricanes were excluded from the frame, but included during the weighting process.
To recapitulate, the weights were adjusted so that they add up to the totals provided by the
external source, so the sum of the weights would have been the same had a different sample been
selected.
Trimming the Weights. The final step was the trimming of the weights. Weights more than
three times the median weight were set to three times the median weight and all the weights were
readjusted. Large weights usually resulted from incorrect frame information.
B-3

Appendix B—Weighting Procedure
Effective Sample Size. The weights led to an effective sample size (because of the weighting)
of 759 (down from an actual size of 804) for the Office of Housing-administered projects, 726
for the Public Housing projects (down from 800), and 738 for the PHA-administered Section 8
projects (down from 800).
Variance Estimation. Standard errors were obtained for a number of estimates using a delete-a-
group Jackknife procedure (JK1 in WESVAR). This was implemented using twenty replicate
groups and creating twenty sets of replicate weights. This procedure is considered more robust
with respect to design characteristics than the Taylor Series method used in the previous cycle
(Kott, 1998
1
).
1
Kott, P. S. (1998), “Using the Delete-a-Group Jacknife Variance Estimator in Practice,” Proceedings of the Annual

Meeting of the American Statistical Association, Section on Survey Research Methods, pp. 763-768.

Alexandria, VA: American Statistical Association.

B-4

Appendix C—Source Tables Based on Quality Control Data

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 1a. Verification of QC Rent Components
Third Party Verbal or In Writing, or Documentation, or EIV
FULLY VERIFIED
# of Cases # of Cases # of Cases
Cases Cases Cases
Earned Income
74 (5.7%) 56 (4.2%) 1,181 (90.1%)
3 (.1%) 36 (1.4%) 2,436 (98.4%)
Public Assistance
33 (6.5%) 478 (93.5%)
Other Income
122 (16.1%) 22 (2.9%) 615 (81.0%)
Asset Income
13 (1.6%) 101 (12.5%) 697 (85.9%)
46 (25.9%) 8 (4.5%) 123 (69.6%)
2 )
118 (8.9%) 216 (16.3%) 988 (74.7%)
2008.10.05
HUD QC FY 2007
FULLY VERIFIED
# of Cases # of Cases # of Cases
Cases Cases Cases
Earned Income
235 (17.5%) 90 (6.7%) 1,020 (75.8%)
92 (3.7%) 163 (6.6%) 2,224 (89.7%)
Public Assistance
142 (26.1%) 4 (.8%) 398 (73.1%)
Other Income
330 (38.8%) 25 (2.9%) 496 (58.3%)
Asset Income
92 (11.3%) 190 (23.4%) 530 (65.3%)
66 (37.3%) 6 (3.4%) 104 (59.3%)
2 )
240 (18.1%) 446 (33.7%) 636 (48.1%)
NOT VERIFIED PARTIALLY VERIFIED
Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000)
Pension, Etc.
Child Care Expense
Disability Expense
(100.0%
Medical Expense
Table 1b. Verification of QC Rent Components
Third Party In Writing
NOT VERIFIED PARTIALLY VERIFIED
Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000)
Pension, Etc.
Child Care Expense
Disability Expense
(100.0%
Medical Expense
2008.10.05
C-1

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 1c. Verification of QC Rent Components
Third Party In Writing, or EIV
FULLY VERIFIED
# of Cases # of Cases # of Cases
Cases Cases Cases
Earned Income
203 (15.1%) 86 (6.4%) 1,056 (78.5%)
63 (2.6%) 141 (5.7%) 2,274 (91.7%)
Public Assistance
134 (24.6%) 4 (.8%) 406 (74.6%)
Other Income
330 (38.8%) 22 (2.6%) 499 (58.6%)
Asset Income
92 (11.3%) 190 (23.4%) 530 (65.3%)
66 (37.3%) 6 (3.4%) 104 (59.3%)
2 )
224 (17.0%) 426 (32.2%) 672 (50.8%)
2008.10.05
HUD QC FY 2007
FULLY VERIFIED
# of Cases # of Cases # of Cases
Cases Cases Cases
Earned Income
1,329 (98.8%) 8 (.6%) 8 (.6%)
2,471 (99.7%) 7 (.3%)
Public Assistance
538 (98.8%) 6 (1.2%)
Other Income
842 (99.0%) 3 (.3%) 6 (.6%)
Asset Income
811 (99.8%) 1 (.2%)
175 (99.3%) 1 (.7%)
2 )
1,305 (98.7%) 12 (.9%) 4 (.3%)
NOT VERIFIED PARTIALLY VERIFIED
Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000)
Pension, Etc.
Child Care Expense
Disability Expense
(100.0%
Medical Expense
Table 1d. Verification of QC Rent Components
Third Party - Verbal
NOT VERIFIED PARTIALLY VERIFIED
Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000)
Pension, Etc.
Child Care Expense
Disability Expense
(100.0%
Medical Expense
2008.10.05
C-2

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 1e. Verification of QC Rent Components
Documentation
FULLY VERIFIED
# of Cases # of Cases # of Cases
Cases Cases Cases
Earned Income
1,231 (91.5%) 31 (2.3%) 83 (6.2%)
2,314 (93.4%) 113 (4.5%) 52 (2.1%)
Public Assistance
478 (87.9%) 4 (.8%) 61 (11.3%)
Other Income
732 (86.1%) 17 (2.1%) 101 (11.8%)
Asset Income
622 (76.5%) 117 (14.4%) 74 (9.1%)
157 (89.3%) 2 (1.1%) 17 (9.6%)
2 )
936 (70.8%) 300 (22.7%) 86 (6.5%)
2008.10.05
HUD QC FY 2007
)
FULLY VERIFIED
# of Cases # of Cases # of Cases
Cases Cases Cases
Earned Income 1,302 (96.8%) 14 (1.0%) 29 (2.2%)
2,420 (97.6%) 32 (1.3%) 27 (1.1%)
Public Assistance 536 (98.6%) 8 (1.4%)
Other Income 848 (99.7%) 3 (.3%)
Asset Income 812 )
176 )
2 )
1,279 (96.8%) 31 (2.3%) 11 (.9%)
NOT VERIFIED PARTIALLY VERIFIED
Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000)
Pension, Etc.
Child Care Expense
Disability Expense
(100.0%
Medical Expense
Table 1f. Verification of QC Rent Components
EIV (Enterprise Income Verification
NOT VERIFIED PARTIALLY VERIFIED
Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000)
Pension, Etc.
(100.0%
Child Care Expense
(100.0%
Disability Expense
(100.0%
Medical Expense
2008.10.05
C-3

2008.10.05
Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 2. Percent of Households by Payment Type and Program Type
UNDERPAYMENT PROPER PAYMENT OVERPAYMENT Total
# of
# of Row % Row % Row % Cases
Cases of Col % of # of Cases of Col % of # of Cases of Col % of (in Row % of Col % of
(in 1,000) Cases Cases (in 1,000) Cases Cases (in 1,000) Cases Cases 1,000) Cases Cases
Public Housing 156 (16.4%) (20.5%) 663 (69.4%) (25.0%) 136 (14.2%) (18.8%) 955 (100.0%) (23.1%)
Section 8
PHA
l
OWNER
350
506
257
(18.8%)
(18.0%)
(19.5%)
(45.8%)
(66.3%)
(33.7%)
1,142
1,805
841
(61.5%)
(64.2%)
(63.7%)
(43.2%)
(68.2%)
(31.8%)
366
502
222
(19.7%)
(17.8%)
(16.8%)
(50.6%)
(69.3%)
(30.7%)
1,858
2,813
1,320
)
)
)
(45.0%)
(68.1%)
(31.9%)
l
l
257
763
(19.5%)
(18.5%)
(33.7%)
)
841
2,646
(63.7%)
(64.0%)
(31.8%)
)
222
724
(16.8%)
(17.5%)
(30.7%)
)
1,320
4,133
)
)
(31.9%)
)
ADMINISTERED
Tota
Owner-
Administered ADMINISTERED
(100.0%
(100.0%
(100.0%
Tota
Tota
(100.0% (100.0% (100.0%
(100.0%
(100.0% (100.0%
HUD QC FY 2007
Table 2(S). Percent of Households by Payment Type and Program Type
(Proper Payment based on exact match of Actual and QC Rent)
UNDERPAYMENT PROPER PAYMENT OVERPAYMENT Total
# of Cases Row % Col % of # of Cases Row % Col % of # of Cases Row % Col % of # of Cases Row % of Col % of
(in 1,000) of Cases Cases (in 1,000) of Cases Cases (in 1,000) of Cases Cases (in 1,000) Cases Cases
Section 8
PHA
l
OWNER
(45.0%)
(68.1%)
(31.9%)
l
l
356
968
(27.0%)
(23.4%)
(36.8%)
)
636
2,113
(48.2%)
(51.1%)
(30.1%)
)
327
1,052
(24.8%)
(25.5%)
(31.1%)
)
1,320
4,133
)
)
(31.9%)
)
ADMINISTERED
Tota
Owner-
Administered ADMINISTERED
Tota
Tota
(100.0% (100.0% (100.0%
(100.0%
(100.0% (100.0%
Public Housing
191 (20.0%) (19.7%) 544 (57.0%) (25.8%) 220 (23.1%) (20.9%) 955 (100.0%) (23.1%)
421 (22.6%) (43.5%) 933 (50.2%) (44.1%) 505 (27.2%) (48.0%) 1,858 (100.0%)
611 (21.7%) (63.2%) 1,477 (52.5%) (69.9%) 725 (25.8%) (68.9%) 2,813 (100.0%)
356 (27.0%) (36.8%) 636 (48.2%) (30.1%) 327 (24.8%) (31.1%) 1,320 (100.0%)
C-4
2008.10.05

2008.10.05
Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 3. Dollar Rent Error by Program Type
ACTUAL RENT (MONTHLY) QC RENT (MONTHLY) GROSS RENT ERROR (MONTHLY)
Sum
# of Dollar Ave.
Cases Col % of Amount Dollar
(in 1,000) Cases (in 1,000) Amount
Public Housing
955 (23.1%) 218,505 228.80
Sum
Sum Dollar
# of Dollar Ave. # of Amount Ave.
Cases Col % of Amount Dollar Cases Col % of (in Dollar
(in 1,000) Cases (in 1,000) Amount (in 1,000) Cases 1,000) Amount
955 (23.1%) 224,044 234.60 955 (23.1%) 12,447 13.03
PHA
OWNER
Section 8
l
1,858
2,813
1,320
(45.0%)
(68.1%)
(31.9%)
1,858
2,813
1,320
(45.0%)
(68.1%)
(31.9%)
1,858
2,813
1,320
(45.0%)
(68.1%)
(31.9%)
19.51
17.31
12.57
l
l
1,320
4,133
(31.9%)
)
1,320
4,133
(31.9%)
)
1,320
4,133
(31.9%)
)
12.57
15.80
ADMINISTERED
ADMINISTERED
Tota
Owner-
Administered
392,125
610,631
276,013
211.05
217.07
209.10
402,903
626,947
281,784
216.85
222.87
213.47
36,251
48,698
16,593
Tota
Tota
(100.0%
276,013
886,643
209.10
214.53 (100.0%
281,784
908,731
213.47
219.87 (100.0%
16,593
65,291
C-5

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007

Table 4. Dollar Error Amount by Payment Type and Program Type

Sum
Sum Sum
# of Amount Ave. # of Ave. # of Ave.
Cases Col % of (in Cases Col % of Amount Cases Col % of Amount
Cases 1,000) Amount Cases Amount (in 1,000) Cases Amount
PHA
OWNER
Section 8
l
156
350
506
257
(20.5%)
(45.8%)
(66.3%)
(33.7%)
8,866 56.72
67.41
64.11
43.64
136
366
502
222
(18.8%)
(50.6%)
(69.3%)
(30.7%)
3,581
5,387
26.33
34.64
32.39
24.28
955
1,858
2,813
1,320
(23.1%)
(45.0%)
(68.1%)
(31.9%)
l
l
257
763
(33.7%)
)
43.64
57.22
222
724
(30.7%)
)
5,387 24.28
29.90
1,320
4,133
(31.9%)
)
HUD QC FY 2007
(
Sum Sum Sum
# of Ave. # of Ave. # of Ave.
Cases Col % of Amount Cases Col % of Amount Cases Col % of Amount
(in 1,000) Cases Amount (in 1,000) Cases Amount Cases Amount
PHA
OWNER
Section 8
l
191
421
611
356
(19.7%)
(43.5%)
(63.2%)
(36.8%)
8,943 46.93
56.49
53.51
32.16
220
505
725
327
(20.9%)
(48.0%)
(68.9%)
(31.1%)
3,739
5,690
16.98
25.72
23.07
17.38
955
1,858
2,813
1,320
(23.1%)
(45.0%)
(68.1%)
(31.9%)
l
l
356
968
(36.8%)
)
32.16
45.64
327
1,052
(31.1%)
)
5,690 17.38
21.30
1,320
4,133
(31.9%)
)
UNDERPAYMENT (MONTHLY) OVERPAYMENT (MONTHLY) QC RENT (MONTHLY)
Dollar
Dollar Dollar
Dollar Dollar Dollar
(in 1,000) (in 1,000) (in 1,000) (in 1,000)
ADMINISTERED
ADMINISTERED
Public Housing
Tota
Owner-
Administered
23,570
32,436
11,205
12,681
16,262
224,044
402,903
626,947
281,784
234.60
216.85
222.87
213.47
Tota
Tota
(100.0%
11,205
43,641 (100.0% 21,649 (100.0%
281,784
908,731
213.47
219.87
2008.10.05
Table 4 S). Dollar Error Amount by Payment Type and Program Type
(Proper Payment based on exact match of Actual and QC Rent)
UNDERPAYMENT (MONTHLY) OVERPAYMENT (MONTHLY) QC RENT (MONTHLY)
Dollar Dollar Dollar
Dollar Dollar Dollar
(in 1,000) (in 1,000) (in 1,000) (in 1,000)
ADMINISTERED
ADMINISTERED
Public Housing
Tota
Owner-
Administered
23,757
32,700
11,461
12,979
16,718
224,044
402,903
626,947
281,784
234.60
216.85
222.87
213.47
Tota
Tota
(100.0%
11,461
44,161 (100.0% 22,408 (100.0%
281,784
908,731
213.47
219.87
C-6

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 5. Gross and Net Rent Error by Program Type
GROSS RENT ERROR (MONTHLY) NET RENT ERROR (MONTHLY) QC RENT (MONTHLY)
Sum
Dollar Sum Sum
# of Amount Ave. # of Dollar Ave. # of Dollar Ave.
Cases Col % of (in Dollar Cases Col % of Amount Dollar Cases Col % of Amount Dollar
(in 1,000) Cases 1,000) Amount (in 1,000) Cases (in 1,000) Amount (in 1,000) Cases (in 1,000) Amount
PHA
OWNER
Section 8
l
955
1,858
2,813
1,320
(23.1%)
(45.0%)
(68.1%)
(31.9%)
13.03
19.51
17.31
12.57
955
1,858
2,813
1,320
(23.1%)
(45.0%)
(68.1%)
(31.9%)
-5,284
-5,818
-5.53
-5.86
-5.75
-4.41
955
1,858
2,813
1,320
(23.1%)
(45.0%)
(68.1%)
(31.9%)
l
l
1,320
4,133
(31.9%)
)
12.57
15.80
1,320
4,133
(31.9%)
)
-5,818 -4.41
-5.32
1,320
4,133
(31.9%)
)
HUD QC FY 2007
(
Sum Sum Sum
# of Ave. # of Ave. # of Ave.
Cases Col % of Amount Cases Col % of Amount Cases Col % of Amount
Cases Amount (in 1,000) Cases Amount (in 1,000) Cases Amount
PHA
OWNER
Section 8
955
1,858
2,813
1,320
(23.1%)
(45.0%)
(68.1%)
(31.9%)
13.28
19.77
17.57
12.99
955
1,858
2,813
1,320
(23.1%)
(45.0%)
(68.1%)
(31.9%)
-5,204
-5,771
-5.45
-5.80
-5.68
-4.37
955
1,858
2,813
1,320
(23.1%)
(45.0%)
(68.1%)
(31.9%)
l
1,320
4,133
(31.9%)
)
12.99
16.11
1,320
4,133
(31.9%)
)
-5,771 -4.37
-5.26
1,320
4,133
(31.9%)
)
ADMINISTERED
ADMINISTERED
Public Housing
Tota
Owner-
Administered
12,447
36,251
48,698
16,593
-10,890
-16,174
224,044
402,903
626,947
281,784
234.60
216.85
222.87
213.47
Tota
Tota
(100.0%
16,593
65,291 (100.0% -21,992 (100.0%
281,784
908,731
213.47
219.87
2008.10.05
Table 5 S). Gross and Net Rent Error by Program Type
(Proper Payment based on exact match of Actual and QC Rent)
GROSS RENT ERROR (MONTHLY) NET RENT ERROR (MONTHLY) QC RENT (MONTHLY)
Dollar Dollar Dollar
Dollar Dollar Dollar
(in 1,000) (in 1,000) (in 1,000) (in 1,000)
ADMINISTERED
ADMINISTERED
Public Housing
Group Total
Owner-
Administered
12,681
36,737
49,418
17,151
-10,778
-15,982
224,044
402,903
626,947
281,784
234.60
216.85
222.87
213.47
Tota
Group Total
(100.0%
17,151
66,569 (100.0% -21,753 (100.0%
281,784
908,731
213.47
219.87
C-7

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 6. Case Type by Program Type
OVERDUE l
# of # of # of
# of Cases Col % of Cases Col % of Cases Col % of Cases (in Col % of
of Cases Cases of Cases Cases of Cases Cases 1,000) Cases Cases
OWNER
Section 8
l
148
280
428
212
(15.5%)
(15.1%)
(15.2%)
(16.1%)
(23.1%)
(43.8%)
(66.9%)
(33.1%)
783
1,538
2,321
1,108
(82.0%)
(82.8%)
(82.5%)
(83.9%)
(22.8%)
(44.9%)
(67.7%)
(32.3%)
24
39
64
(2.5%)
(2.1%)
(2.3%)
(38.2%)
(61.8%)
)
955
1,858
2,813
1,320
)
)
)
)
(23.1%)
(45.0%)
(68.1%)
(31.9%)
l
l
212
641
(16.1%)
(15.5%)
(33.1%)
)
1,108
3,429
(83.9%)
(83.0%)
(32.3%)
) 64 (1.5%) )
1,320
4,133
)
)
(31.9%)
)
CERTIFICATIONS
RECERTIFICATIONS/NON-
RECERTIFICATIONS/OVERDUE Tota
Row % Row % Row % Row % of
(in 1,000) (in 1,000) (in 1,000)
PHA ADMINISTERED
ADMINISTERED
Public Housing
Tota
Owner-
Administered
(100.0%
(100.0%
(100.0%
(100.0%
(100.0%
Tota
Tota
(100.0% (100.0% (100.0%
(100.0%
(100.0% (100.0%
2008.10.05
C-8

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 7. Percent of Newly Certified Households Meeting Certification Criteria
# of Cases # of Cases
% of Cases % of Cases
624 (97.5%) 16 (2.5%)
625 (97.6%) 15 (2.4%)
612 (95.6%) 28 (4.4%)
Income
639 (99.7%) 2 (.3%)
i
Criteria
581 (90.6%) 60 (9.4%)
Met Criterion Did Not Meet Criterion
(in 1,000) (in 1,000)
Citizenship
Social Security Number
Consent Form
Low and Very Low
Meets All Elig bility
2008.10.05
C-9

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 7b. Percent of Newly Certified Households Meeting Certification Criteria by Program Type
# of Cases # of Cases
% of Cases % of Cases
PUBLIC HOUSING
Income
146
140
144
148
(99.0%)
(94.6%)
(97.5%)
)
2
8
4
(1.0%)
(5.4%)
(2.5%)
i
Criteria
136 (92.0%) 12 (8.0%)
Income
278
278
263
280
(99.1%)
(99.1%)
(93.7%)
)
2
2
18
(.9%)
(.9%)
(6.3%)
i
Criteria
258 (92.0%) 22 (8.0%)
Income
200
208
205
210
(94.3%)
(97.8%)
(96.7%)
(99.1%)
12
5
7
2
(5.7%)
(2.2%)
(3.3%)
(.9%)
i
Criteria
187 (87.9%) 26 (12.1%)
Met Criterion Did Not Meet Criterion
(in 1,000) (in 1,000)
Citizenship
Social Security Number
Consent Form
Low and Very Low
(100.0%
Meets All Elig bility
PHA-ADMINISTERED
SECTION 8
Citizenship
Social Security Number
Consent Form
Low and Very Low
(100.0%
Meets All Elig bility
OWNER-ADMINISTERED Citizenship
Social Security Number
Consent Form
Low and Very Low
Meets All Elig bility
2008.10.05
C-10

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 8. Dollar Error Amount by Payment Type and Case Type
UNDERPAYMENT (MONTHLY) OVERPAYMENT (MONTHLY) QC RENT (MONTHLY)
Sum Dollar Ave. Sum Dollar Ave. Sum Dollar Ave.
# of Cases Col % of Amount (in Dollar # of Cases Col % of Amount (in Dollar # of Cases Col % of Amount (in Dollar
(in 1,000) Cases 1,000) Amount (in 1,000) Cases 1,000) Amount (in 1,000) Cases 1,000) Amount
ON
.00
l
Non-
Overdue
100
100
627
(13.1%)
(13.1%)
(82.2%)
5,450
5,450
54.56
54.56
55.55
110
110
602
(15.2%)
(15.2%)
(83.2%)
4,042
4,042
36.69
36.69
28.64
641
641
3,429
(15.5%)
(15.5%)
(83.0%)
l
Overdue
l
36
663
763
(4.7%)
(86.9%)
)
3,362 93.72
57.62
57.22
12
614
724
(1.6%)
(84.8%)
)
357 30.88
28.68
29.90
64
3,492
4,133
(1.5%)
(84.5%)
)
HUD QC FY 2007
Sum
Ave. Ave. Ave.
# of Cases Col % of Amount # of Cases Col % of Amount # of Cases Col % of Amount
Cases (in 1,000) Amount Cases Amount Cases Amount
ON
.00
l
Non-
Overdue
137
137
790
(14.2%)
(14.2%)
(81.7%)
5,534
5,534
40.30
40.30
44.62
148
148
889
(14.1%)
(14.1%)
(84.5%)
4,135
4,135
27.92
27.92
20.14
641
641
3,429
(15.5%)
(15.5%)
(83.0%)
l
Overdue
l
40
830
968
(4.1%)
(85.8%)
)
3,375 84.09
46.53
45.64
15
904
1,052
(1.4%)
(85.9%)
)
367 24.62
20.21
21.30
64
3,492
4,133
(1.5%)
(84.5%)
)
CERTIFICATION
RECERTIFICATI
Tota
34,829 17,250
112,621
112,621
777,696
175.83
175.83
226.80
Tota
Tota
(100.0%
38,192
43,641 (100.0%
17,608
21,649 (100.0%
18,413
796,109
908,731
289.91
227.95
219.87
2008.10.05
Table 8(S). Dollar Error Amount by Payment Type and Case Type
(Proper Payment based on exact match of Actual and QC Rent)
UNDERPAYMENT (MONTHLY) OVERPAYMENT (MONTHLY) QC RENT (MONTHLY)
Sum Dollar Sum Dollar Dollar
Dollar Dollar Dollar
(in 1,000) (in 1,000) (in 1,000) (in 1,000) (in 1,000)
CERTIFICATION
RECERTIFICATI
Tota
35,252 17,906
112,621
112,621
777,696
175.83
175.83
226.80
Tota
Tota
(100.0%
38,627
44,161 (100.0%
18,273
22,408 (100.0%
18,413
796,109
908,731
289.91
227.95
219.87
2008.10.05
C-11

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
TABLE 9. Largest Component Error for Households with Rent Error (Annual Dollars)
# of Cases Col % of Amount
Cases Amount
Earned Income
362 (24.3%) 2,887
316 (21.2%) 2,075
Public Assistance
87 (5.8%) 2,492
Other Income
166 (11.2%) 2,437
Asset Income
53 (3.6%) 1,502
51 (3.4%) 622
40 (2.7%) 400
57 (3.8%) 2,259
342 (23.0%) 972
No Error
13 (.9%) 0 0
l
1,487 ) 1,957
HUD QC FY 2007
# of Cases Col % of Amount # of Cases Col % of Amount
Cases (in 1,000) Amount Cases (in 1,000) Amount
292 (19.7%) 292 (19.7%)
Section 8
716 (48.1%) 716 (48.1%)
l
1,008 (67.8%) 1,008 (67.8%)
i
479 (32.2%) 479 (32.2%)
l
479 (32.2%) 479 (32.2%)
l
1,487 ) 1,487 )
Sum Dollar
Ave. Dollar
(in 1,000) (in 1,000)
1,044,257
Pension, Etc.
654,980
216,730
405,306
79,432
Dependent Allowance
31,917
Elderly HH Allowance
15,927
Child Care Allowance
129,267
Medical Allowance
332,330
Tota
(100.0% 2,910,147
2008.10.05
Table 10. Total and Largest Dollar Error by Program Type for Households with Rent Errors
TOTAL DOLLAR IN ERROR LARGEST DOLLAR ERROR
Sum Dollar Sum Dollar
Ave. Dollar Ave. Dollar
(in 1,000) (in 1,000)
PHA ADMINISTERED Public Housing
621,578 2,126.47 519,627 1,777.69
1,923,679 2,687.57 1,632,911 2,281.34
Tota
2,545,258 2,524.87 2,152,538 2,135.30
OWNER ADMINISTERED Owner-Admin stered
912,898 1,907.03 757,610 1,582.64
Tota
912,898 1,907.03 757,610 1,582.64
Tota
(100.0% 3,458,156 2,325.94 (100.0% 2,910,147 1,957.35
2008.10.05
C-12

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 11. QC Rent Components by Payment Type and Administration Type
PHA ADMINISTERED OWNER ADMINISTERED
# of Cases Col % of
(in 1,000) Cases
Earned Income 199
Total
Row % of # of Cases Col % of Row % of # of Cases Col % of Row % of
Cases (in 1,000) Cases Cases (in 1,000) Cases Cases
51 250(7.1%) (79.5%) (3.9%) (20.5%) (6.0%) )
198 (7.1%) (66.7%) 99 (7.5%) (33.3%) 297 (7.2%) )
Public Assistance 43 (1.5%) (81.2%) 10 (.7%) (18.8%) 52 (1.3%) )
Other Income 113 (4.0%) (74.6%) 39 (2.9%) (25.4%) 152 (3.7%) )
Asset Income 61 (2.2%) (41.0%) 88 (6.6%) (59.0%) 148 (3.6%) )
23 (.8%) (62.1%) 14 (1.0%) (37.9%) 36 (.9%) )
21 (.8%) (85.9%) 4 (.3%) (14.1%) 25 (.6%) )
15 (.5%) (50.7%) 14 (1.1%) (49.3%) 29 (.7%) )
4 (.1%) ) 4 (.1%) )
109 (3.9%) (45.9%) 128 (9.7%) (54.1%) 237 (5.7%) )
No Error 2 (.1%) (23.7%) 6 (.4%) (76.3%) 8 (.2%) )
Earned IncomePROPER PAYMENT 164 (5.8%) (82.6%) 34 (2.6%) (17.4%) 198 (4.8%) )
205 (7.3%) (57.5%) 152 (11.5%) (42.5%) 356 (8.6%) )
Public Assistance 65 (2.3%) (91.5%) 6 (.5%) (8.5%) 71 (1.7%) )
Other Income 103 (3.7%) (82.0%) 23 (1.7%) (18.0%) 126 (3.0%) )
Asset Income 152 (5.4%) (62.7%) 90 (6.8%) (37.3%) 242 (5.9%) )
34 (1.2%) (85.8%) 6 (.4%) (14.2%) 39 (1.0%) )
15 (.5%) (92.0%) 1 (.1%) (8.0%) 16 (.4%) )
10 (.4%) (65.5%) 5 (.4%) (34.5%) 15 (.4%) )
124 (4.4%) (44.6%) 154 (11.7%) (55.4%) 278 (6.7%) )
No Error 1,216 (43.2%) (70.2%) 515 (39.0%) (29.8%) 1,731 (41.9%) )
Earned IncomeOVERPAYMENT 154 (5.5%) (82.5%) 33 (2.5%) (17.5%) 186 (4.5%) )
147 (5.2%) (59.0%) 102 (7.7%) (41.0%) 249 (6.0%) )
Public Assistance 37 (1.3%) (74.7%) 13 (1.0%) (25.3%) 50 (1.2%) )
Other Income 76 (2.7%) (81.9%) 17 (1.3%) (18.1%) 93 (2.3%) )
Asset Income 66 (2.3%) (54.1%) 56 (4.2%) (45.9%) 122 (2.9%) )
68 (2.4%) (89.4%) 8 (.6%) (10.6%) 76 (1.8%) )
61 (2.2%) (86.4%) 10 (.7%) (13.6%) 70 (1.7%) )
45 (1.6%) (84.9%) 8 (.6%) (15.1%) 53 (1.3%) )
1 (.0%) ) 1 (.0%) )
171 (6.1%) (55.0%) 140 (10.6%) (45.0%) 311 (7.5%) )
No Error 6 (.2%) ) 6 (.1%) )
/ 2,813 ) (68.1%) 1,320 ) (31.9%) 4,133 ) )
UNDERPAYMENT (100.0%
Pension, Etc. (100.0%
(100.0%
(100.0%
(100.0%
Dependent Allowance (100.0%
Elderly HH Allowance (100.0%
Child Care Allowance (100.0%
Disability Allowance (100.0% (100.0%
Medical Allowance (100.0%
(100.0%
(100.0%
Pension, Etc. (100.0%
(100.0%
(100.0%
(100.0%
Dependent Allowance (100.0%
Elderly HH Allowance (100.0%
Child Care Allowance (100.0%
Disability Allowance
Medical Allowance (100.0%
(100.0%
(100.0%
Pension, Etc. (100.0%
(100.0%
(100.0%
(100.0%
Dependent Allowance (100.0%
Elderly HH Allowance (100.0%
Child Care Allowance (100.0%
Disability Allowance (100.0% (100.0%
Medical Allowance (100.0%
(100.0% (100.0%
TOTAL w Rent Error Calc (100.0% (100.0% (100.0% (100.0%
C-13

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 12a. Elderly/Disabled Allowances
NON-ELDERLY/DISABLED HH ELDERLY/DISABLED HH l
# of Cases Col % of # of Cases Col % of # of Cases Col % of
Cases Cases Cases Cases Cases Cases
1,748 (99.8%) ) 1,748 (42.3%) )
3 (.2%) (2.7%) 108 (4.6%) (97.3%) 111 (2.7%) )
2,273 (95.4%) ) 2,273 (55.0%) )
l
1,751 ) (42.4%) 2,382 ) (57.6%) 4,133 ) )
HUD QC FY 2007
HH W/DEPENDENT l
# of Cases Col % of # of Cases Col % of # of Cases Col % of
Cases Cases Cases Cases Cases Cases
2,315 (99.7%) ) 2,315 (56.0%) )
7 (.3%) (4.5%) 145 (8.0%) (95.5%) 151 (3.7%) )
1,667 (92.0%) ) 1,667 (40.3%) )
l
2,322 ) (56.2%) 1,811 ) (43.8%) 4,133 ) )
Tota
Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000)
No Allowance
(100.0% (100.0%
Incorrect Allowance
(100.0%
Correct Allowance
(100.0% (100.0%
Tota
(100.0% (100.0% (100.0% (100.0%
2008.10.05
Table 12b. Dependent Allowances
HH W/OUT DEPENDENT Tota
Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000)
No Allowance
(100.0% (100.0%
Incorrect Allowance
(100.0%
Correct Allowance
(100.0% (100.0%
Tota
(100.0% (100.0% (100.0% (100.0%
2008.10.05
C-14

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 13. Calculation Errors on Form 50058/59
58 59 l
# of Errors # of Cases # of Errors # of Cases # of Errors # of Cases
HOUSEHOLD
223 212 62 44 284 255
AND INCOME
362 220 163 78 525 298
1,537 1,213 144 61 1,681 1,274
FAMILY RENT AND
SUBSIDY INFORMATION
491 283 65 38 556 322
Tota
(in 1,000) (in 1,000) (in 1,000) (in 1,000) (in 1,000) (in 1,000)
COMPOSITION
NET FAMILY ASSETS
ALLOWANCES AND
ADJUSTED INCOME
2008.10.05
C-15

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 14. Consistency Errors on Form 50058/59
58 59 l
# of Errors # of Cases # of Errors # of Cases # of Errors # of Cases
GENERAL INFORMATION
48 48 89 71 137 119
HOUSEHOLD
630 156 47 39 677 195
AND INCOME
275 272 4 3 279 275
273 272 14 14 287 286
FAMILY RENT AND
SUBSIDY INFORMATION
96 96 26 22 122 118
Tota
(in 1,000) (in 1,000) (in 1,000) (in 1,000) (in 1,000) (in 1,000)
COMPOSITION
NET FAMILY ASSETS
ALLOWANCES AND
ADJUSTED INCOME
2008.10.05
C-16

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 15a. Verification of Form 50058/59 Rent Components
Third Party Verbal or In Writing, or Documentation, or EIV
l
Matched
# of Cases # of Cases # of Cases # of Cases
Cases Cases Cases Cases
Earned Income
128 (9.7%) 295 (22.5%) 890 (67.8%) 1,312 )
108 (4.4%) 286 (11.7%) 2,062 (84.0%) 2,456 )
Public Assistance
53 (10.7%) 73 (14.7%) 372 (74.6%) 498 )
Other Income
204 (25.3%) 108 (13.3%) 494 (61.3%) 806 )
Asset Income
46 (6.6%) 65 (9.4%) 584 (84.0%) 694 )
15 (11.7%) 28 (21.6%) 85 (66.6%) 127 )
68 (7.1%) 171 (17.7%) 727 (75.2%) 966 )
HUD QC FY 2007
/
Matched
# of Cases # of Cases # of Cases # of Cases
Cases Cases Cases Cases
Earned Income
429 (32.7%) 175 (13.3%) 708 (53.9%) 1,312 )
1,316 (53.6%) 110 (4.5%) 1,031 (42.0%) 2,456 )
Public Assistance
153 (30.8%) 56 (11.3%) 288 (57.9%) 498 )
Other Income
385 (47.8%) 64 (8.0%) 356 (44.2%) 806 )
Asset Income
250 (36.0%) 38 (5.4%) 407 (58.6%) 694 )
40 (31.3%) 19 (14.6%) 69 (54.1%) 127 )
581 (60.1%) 64 (6.7%) 321 (33.2%) 966 )
NO VERIFICATION VERIFICATION Tota
Dollar Amount Not
Dollar Amount Matched
Row % of Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000) (in 1,000)
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0%
Child Care Expense
(100.0%
Medical Expense
(100.0%
2008.10.05
Table 15b. Verification of Form 50058 59 Rent Components
Third Party In Writing
NO VERIFICATION VERIFICATION TOTAL
Dollar Amount Not
Dollar Amount Matched
Row % of Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000) (in 1,000)
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0%
Child Care Expense
(100.0%
Medical Expense
(100.0%
C-17

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 15c. Verification of Form 50058/59 Rent Components
Third Party In Writing or EIV
Matched
# of Cases # of Cases # of Cases # of Cases
Cases Cases Cases Cases
Earned Income
363 (27.7%) 226 (17.2%) 724 (55.1%) 1,312 )
760 (30.9%) 186 (7.6%) 1,511 (61.5%) 2,456 )
Public Assistance
146 (29.2%) 56 (11.3%) 296 (59.5%) 498 )
Other Income
383 (47.5%) 64 (8.0%) 359 (44.5%) 806 )
Asset Income
250 (36.0%) 38 (5.4%) 407 (58.6%) 694 )
40 (31.3%) 19 (14.6%) 69 (54.1%) 127 )
508 (52.6%) 78 (8.1%) 380 (39.3%) 966 )
HUD QC FY 2007
/
Matched
# of Cases # of Cases # of Cases # of Cases
Cases Cases Cases Cases
Earned Income 1,295 (98.7%) 3 (.2%) 15 (1.1%) 1,312 )
2,452 (99.8%) 1 (.0%) 3 (.1%) 2,456 )
Public Assistance
488 (97.9%) 10 (2.1%) 498 )
Other Income 795 (98.7%) 11 (1.3%) 806 )
Asset Income 693 (99.8%) 1 (.2%) 694 )
126 (99.0%) 1 (1.0%) 127 )
961 (99.5%) 5 (.5%) 966 )
NO VERIFICATION VERIFICATION TOTAL
Dollar Amount Not
Dollar Amount Matched
Row % of Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000) (in 1,000)
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0%
Child Care Expense
(100.0%
Medical Expense
(100.0%
2008.10.05
Table 15d. Verification of Form 50058 59 Rent Components
Third Party - Verbal
NO VERIFICATION VERIFICATION TOTAL
Dollar Amount Not
Dollar Amount Matched
Row % of Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000) (in 1,000)
(100.0%
Pension, Etc. (100.0%
(100.0%
(100.0%
(100.0%
Child Care Expense (100.0%
Medical Expense
(100.0%
C-18

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 15e. Verification of Form 50058/59 Rent Components
Documentation
Matched
# of Cases # of Cases # of Cases # of Cases
Cases Cases Cases Cases
Earned Income
1,123 (85.6%) 61 (4.7%) 128 (9.7%) 1,312 )
1,941 (79.0%) 73 (3.0%) 442 (18.0%) 2,456 )
Public Assistance
416 (83.5%) 17 (3.4%) 65 (13.1%) 498 )
Other Income
648 (80.4%) 41 (5.1%) 117 (14.5%) 806 )
Asset Income
553 (79.7%) 17 (2.5%) 124 (17.8%) 694 )
104 (81.4%) 9 (7.0%) 15 (11.5%) 127 )
792 (82.0%) 33 (3.4%) 141 (14.6%) 966 )
HUD QC FY 2007
/
)
Matched
# of Cases # of Cases # of Cases # of Cases
Cases Cases Cases Cases
Earned Income 1,256 (95.7%) 41 (3.1%) 16 (1.2%) 1,312 )
1,975 (80.4%) 64 (2.6%) 418 (17.0%) 2,456 )
Public Assistance 490 (98.4%) 8 (1.6%) 498 )
Other Income
803 (99.7%) 3 (.3%) 806 )
Asset Income
694 ) 694 )
127 ) 127 )
930 (96.2%) 7 (.7%) 30 (3.1%) 966 )
NO VERIFICATION VERIFICATION TOTAL
Dollar Amount Not
Dollar Amount Matched
Row % of Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000) (in 1,000)
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0%
Child Care Expense
(100.0%
Medical Expense
(100.0%
2008.10.05
Table 15f. Verification of Form 50058 59 Rent Components
EIV (Enterprise Income Verification
NO VERIFICATION VERIFICATION TOTAL
Dollar Amount Not
Dollar Amount Matched
Row % of Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000) (in 1,000)
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0% (100.0%
Child Care Expense (100.0% (100.0%
Medical Expense
(100.0%
C-19

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 15g. Verification of Form 50058/59 Rent Components
Third Party Verbal or In Writing, or Documentation, or EIV
l
Matched
# of Cases # of Cases # of Cases # of Cases
Cases Cases Cases Cases
Earned Income
31 (9.1%) 98 (29.0%) 209 (61.9%) 338 )
25 (4.6%) 67 (12.3%) 456 (83.2%) 548 )
Public Assistance
13 (11.0%) 23 (19.6%) 81 (69.4%) 116 )
Other Income
59 (34.7%) 21 (12.1%) 91 (53.2%) 171 )
Asset Income
21 (15.5%) 11 (8.0%) 103 (76.6%) 134 )
4 (18.7%) 3 (15.6%) 14 (65.7%) 22 )
15 (7.9%) 34 (18.3%) 136 (73.8%) 185 )
PHA-Administered
Section 8
Earned Income
78
56
(11.0%)
(5.7%)
135
120
(19.2%)
(12.3%)
491
804
(69.8%)
(82.0%)
703
980
)
)
Public Assistance
28 (10.6%) 41 (15.5%) 196 (73.8%) 266 )
Other Income
102 (22.3%) 66 (14.3%) 290 (63.3%) 458 )
Asset Income
10 (5.0%) 16 (8.5%) 167 (86.5%) 193 )
8 (11.5%) 15 (23.0%) 44 (65.5%) 67 )
9 (4.3%) 37 (16.8%) 174 (78.9%) 220 )
i Earned Income
20 (7.3%) 62 (22.9%) 189 (69.8%) 271 )
27 (2.9%) 99 (10.6%) 802 (86.4%) 928 )
Public Assistance
12 (10.4%) 9 (8.0%) 94 (81.6%) 116 )
Other Income
43 (24.0%) 21 (12.0%) 113 (64.0%) 177 )
Asset Income
15 (4.1%) 38 (10.4%) 314 (85.5%) 367 )
3 (8.2%) 9 (22.7%) 27 (69.1%) 39 )
44 (7.9%) 100 (17.9%) 417 (74.2%) 561 )
NO VERIFICATION VERIFICATION Tota
Dollar Amount Not
Dollar Amount Matched
Row % of Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000) (in 1,000)
Public Housing
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0%
Child Care Expense
(100.0%
Medical Expense
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0%
(100.0%
Child Care Expense
(100.0%
Medical Expense
(100.0%
Owner-Admin stered
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0%
Child Care Expense
(100.0%
Medical Expense
(100.0%
2008.10.05
C-20

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 15h. Verification of Form 50058/59 Rent Components
Third Party In Writing
Matched
# of Cases # of Cases # of Cases # of Cases
Cases Cases Cases Cases
Earned Income
112 (33.1%) 58 (17.0%) 168 (49.9%) 338 )
322 (58.7%) 25 (4.6%) 202 (36.8%) 548 )
Public Assistance
37 (31.9%) 18 (15.5%) 61 (52.6%) 116 )
Other Income
96 (56.0%) 12 (7.2%) 63 (36.8%) 171 )
Asset Income
49 (36.6%) 7 (5.2%) 78 (58.2%) 134 )
7 (31.9%) 2 (10.1%) 13 (58.0%) 22 )
110 (59.5%) 15 (7.9%) 60 (32.6%) 185 )
PHA-Administered
Section 8
Earned Income
247
550
(35.1%)
(56.1%)
75
36
(10.6%)
(3.7%)
382
394
(54.3%)
(40.2%)
703
980
)
)
Public Assistance
73 (27.6%) 31 (11.5%) 162 (60.8%) 266 )
Other Income
192 (41.9%) 39 (8.6%) 227 (49.6%) 458 )
Asset Income
80 (41.3%) 6 (3.2%) 107 (55.5%) 193 )
17 (25.8%) 11 (15.8%) 39 (58.4%) 67 )
120 (54.4%) 11 (4.9%) 90 (40.7%) 220 )
i Earned Income
71 (26.1%) 43 (15.8%) 157 (58.1%) 271 )
445 (47.9%) 48 (5.2%) 435 (46.9%) 928 )
Public Assistance
43 (37.0%) 8 (6.5%) 65 (56.4%) 116 )
Other Income
98 (55.4%) 13 (7.2%) 66 (37.4%) 177 )
Asset Income
121 (32.9%) 24 (6.6%) 222 (60.5%) 367 )
16 (40.5%) 6 (15.0%) 17 (44.5%) 39 )
351 (62.6%) 39 (7.0%) 171 (30.5%) 561 )
NO VERIFICATION VERIFICATION TOTAL
Dollar Amount Not
Dollar Amount Matched
Row % of Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000) (in 1,000)
Public Housing
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0%
Child Care Expense
(100.0%
Medical Expense
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0%
(100.0%
Child Care Expense
(100.0%
Medical Expense
(100.0%
Owner-Admin stered
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0%
Child Care Expense
(100.0%
Medical Expense
(100.0%
C-21

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 15i. Verification of Form 50058/59 Rent Components
Third Party In Writing or EIV
Matched
# of Cases # of Cases # of Cases # of Cases
Cases Cases Cases Cases
Earned Income
89 (26.4%) 75 (22.3%) 173 (51.3%) 338 )
140 (25.6%) 51 (9.3%) 357 (65.1%) 548 )
Public Assistance
35 (30.4%) 18 (15.5%) 63 (54.2%) 116 )
Other Income
96 (56.0%) 12 (7.2%) 63 (36.8%) 171 )
Asset Income
49 (36.6%) 7 (5.2%) 78 (58.2%) 134 )
7 (31.9%) 2 (10.1%) 13 (58.0%) 22 )
84 (45.2%) 20 (11.0%) 81 (43.7%) 185 )
PHA-Administered
Section 8
Earned Income
204
277
(29.1%)
(28.2%)
107
79
(15.3%)
(8.0%)
392
625
(55.7%)
(63.8%)
703
980
)
)
Public Assistance
73 (27.6%) 31 (11.5%) 162 (60.8%) 266 )
Other Income
189 (41.3%) 39 (8.6%) 230 (50.1%) 458 )
Asset Income
80 (41.3%) 6 (3.2%) 107 (55.5%) 193 )
17 (25.8%) 11 (15.8%) 39 (58.4%) 67 )
93 (42.5%) 18 (8.4%) 108 (49.1%) 220 )
i Earned Income
69 (25.6%) 43 (15.8%) 159 (58.6%) 271 )
343 (37.0%) 56 (6.1%) 529 (57.0%) 928 )
Public Assistance
37 (31.8%) 8 (6.5%) 71 (61.7%) 116 )
Other Income
98 (55.4%) 13 (7.2%) 66 (37.4%) 177 )
Asset Income
121 (32.9%) 24 (6.6%) 222 (60.5%) 367 )
16 (40.5%) 6 (15.0%) 17 (44.5%) 39 )
331 (59.0%) 39 (7.0%) 191 (34.1%) 561 )
NO VERIFICATION VERIFICATION TOTAL
Dollar Amount Not
Dollar Amount Matched
Row % of Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000) (in 1,000)
Public Housing
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0%
Child Care Expense
(100.0%
Medical Expense
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0%
(100.0%
Child Care Expense
(100.0%
Medical Expense
(100.0%
Owner-Admin stered
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0%
Child Care Expense
(100.0%
Medical Expense
(100.0%
2008.10.05
C-22

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 15j. Verification of Form 50058/59 Rent Components
Third Party - Verbal
Matched
# of Cases # of Cases # of Cases # of Cases
Cases Cases Cases Cases
Earned Income
335 (99.1%) 3 (.9%) 338 )
547 (99.8%) 1 (.2%) 548 )
Public Assistance
116 ) 116 )
Other Income
167 (97.7%) 4 (2.3%) 171 )
Asset Income
134 ) 134 )
22 ) 22 )
184 (99.5%) 1 (.5%) 185 )
PHA-Administered
Section 8
Earned Income
692
978
(98.4%)
(99.8%)
3 (.4%) 8
2
(1.2%)
(.2%)
703
980
)
)
Public Assistance
257 (96.7%) 9 (3.3%) 266 )
Other Income
455 (99.3%) 3 (.7%) 458 )
Asset Income
191 (99.2%) 1 (.8%) 193 )
67 ) 67 )
218 (99.0%) 2 (1.0%) 220 )
i Earned Income
268 (98.9%) 3 (1.1%) 271 )
927 (99.9%) 1 (.1%) 928 )
Public Assistance
114 (98.6%) 2 (1.4%) 116 )
Other Income
173 (97.9%) 4 (2.1%) 177 )
Asset Income
367 ) 367 )
37 (96.8%) 1 (3.2%) 39 )
559 (99.7%) 2 (.3%) 561 )
NO VERIFICATION VERIFICATION TOTAL
Dollar Amount Not
Dollar Amount Matched
Row % of Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000) (in 1,000)
Public Housing
(100.0%
Pension, Etc.
(100.0%
(100.0% (100.0%
(100.0%
(100.0% (100.0%
Child Care Expense
(100.0% (100.0%
Medical Expense
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0%
(100.0%
Child Care Expense
(100.0% (100.0%
Medical Expense
(100.0%
Owner-Admin stered
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0% (100.0%
Child Care Expense
(100.0%
Medical Expense
(100.0%
2008.10.05
C-23

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 15k. Verification of Form 50058/59 Rent Components
Documentation
Matched
# of Cases # of Cases # of Cases # of Cases
Cases Cases Cases Cases
Earned Income
290 (85.8%) 20 (5.8%) 28 (8.4%) 338 )
473 (86.2%) 4 (.7%) 72 (13.1%) 548 )
Public Assistance
94 (80.6%) 5 (4.1%) 18 (15.2%) 116 )
Other Income
140 (82.0%) 8 (4.9%) 22 (13.1%) 171 )
Asset Income
111 (82.3%) 4 (2.7%) 20 (14.9%) 134 )
19 (86.8%) 1 (5.5%) 2 (7.7%) 22 )
165 (89.5%) 3 (1.8%) 16 (8.7%) 185 )
PHA-Administered
Section 8
Earned Income
609
788
(86.6%)
(80.4%)
22
37
(3.2%)
(3.8%)
72
155
(10.2%)
(15.8%)
703
980
)
)
Public Assistance
229 (86.3%) 11 (4.0%) 26 (9.7%) 266 )
Other Income
380 (82.9%) 24 (5.2%) 54 (11.8%) 458 )
Asset Income
141 (73.1%) 7 (3.7%) 45 (23.2%) 193 )
57 (85.7%) 5 (7.1%) 5 (7.1%) 67 )
168 (76.5%) 11 (4.8%) 41 (18.7%) 220 )
i Earned Income
224 (82.8%) 19 (7.1%) 27 (10.1%) 271 )
680 (73.3%) 32 (3.5%) 216 (23.2%) 928 )
Public Assistance
93 (80.0%) 2 (1.5%) 21 (18.5%) 116 )
Other Income
128 (72.5%) 8 (4.8%) 40 (22.8%) 177 )
Asset Income
301 (82.1%) 6 (1.8%) 59 (16.1%) 367 )
27 (71.0%) 3 (7.7%) 8 (21.4%) 39 )
459 (81.7%) 19 (3.4%) 84 (14.9%) 561 )
NO VERIFICATION VERIFICATION TOTAL
Dollar Amount Not
Dollar Amount Matched
Row % of Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000) (in 1,000)
Public Housing
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0%
Child Care Expense
(100.0%
Medical Expense
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0%
(100.0%
Child Care Expense
(100.0%
Medical Expense
(100.0%
Owner-Admin stered
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0%
(100.0%
Child Care Expense
(100.0%
Medical Expense
(100.0%
2008.10.05
C-24

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 15l. Verification of Form 50058/59 Rent Components
EIV (Enterprise Income Verification)
Matched
# of Cases # of Cases # of Cases # of Cases
Cases Cases Cases Cases
Earned Income
320 (94.6%) 13 (4.0%) 5 (1.4%) 338 )
401 (73.2%) 22 (3.9%) 126 (22.9%) 548 )
Public Assistance
114 (98.5%) 2 (1.5%) 116 )
Other Income
171 ) 171 )
Asset Income
134 ) 134 )
22 ) 22 )
166 (89.8%) 3 (1.8%) 16 (8.4%) 185 )
PHA-Administered
Section 8
Earned Income
667
735
(94.8%)
(75.0%)
27
37
(3.9%)
(3.8%)
10
208
(1.4%)
(21.2%)
703
980
)
)
Public Assistance
266 ) 266 )
Other Income
456 (99.4%) 3 (.6%) 458 )
Asset Income
193 ) 193 )
67 ) 67 )
206 (93.8%) 3 (1.5%) 10 (4.6%) 220 )
i Earned Income
269 (99.4%) 2 (.6%) 271 )
838 (90.3%) 5 (.5%) 85 (9.1%) 928 )
Public Assistance
110 (94.8%) 6 (5.2%) 116 )
Other Income
177 ) 177 )
Asset Income
367 ) 367 )
39 ) 39 )
557 (99.3%) 4 (.7%) 561 )
NO VERIFICATION VERIFICATION TOTAL
Dollar Amount Not
Dollar Amount Matched
Row % of Row % of Row % of Row % of
(in 1,000) (in 1,000) (in 1,000) (in 1,000)
Public Housing
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0% (100.0%
(100.0% (100.0%
Child Care Expense
(100.0% (100.0%
Medical Expense
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0% (100.0%
(100.0%
(100.0% (100.0%
Child Care Expense
(100.0% (100.0%
Medical Expense
(100.0%
Owner-Admin stered
(100.0%
Pension, Etc.
(100.0%
(100.0%
(100.0% (100.0%
(100.0% (100.0%
Child Care Expense
(100.0% (100.0%
Medical Expense
(100.0%
2008.10.05
C-25

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 16a. QC Rent Component for Household with QC Rent Error (>$5)
l
# of Cases # of Cases # of Cases
% of Cases % of Cases % of Cases
Earned Income No Error
2,460 (87.5%) 1,236 (93.7%) 3,697 (89.4%)
w/Error
353 (12.5%) 84 (6.3%) 436 (10.6%)
No Error
2,468 (87.7%) 1,119 (84.8%) 3,587 (86.8%)
w/Error
345 (12.3%) 201 (15.2%) 546 (13.2%)
Public Assistance No Error
2,733 (97.2%) 1,298 (98.3%) 4,031 (97.5%)
w/Error
80 (2.8%) 22 (1.7%) 102 (2.5%)
Other Income No Error
2,623 (93.3%) 1,264 (95.8%) 3,888 (94.1%)
w/Error
190 (6.7%) 56 (4.2%) 245 (5.9%)
Asset Income No Error
2,686 (95.5%) 1,177 (89.1%) 3,863 (93.5%)
w/Error
127 (4.5%) 143 (10.9%) 270 (6.5%)
No Error
w/Error
2,751
62
(97.8%)
(2.2%)
1,298
22
(98.3%)
(1.7%)
4,049
84
(98.0%)
(2.0%)
No Error
2,811 (99.9%) 1,320 ) 4,131 (99.9%)
w/Error
2 (.1%) 2 (.1%)
No Error
2,517 (89.5%) 1,061 (80.4%) 3,579 (86.6%)
w/Error
296 (10.5%) 259 (19.6%) 554 (13.4%)
No Error
1,856 (66.0%) 864 (65.4%) 2,720 (65.8%)
w/Error
957 (34.0%) 456 (34.6%) 1,413 (34.2%)
l
2,813 ) 1,320 ) 4,133 )
50058 50059 Tota
(in 1,000) (in 1,000) (in 1,000)
Pensions, Etc.
Child Care
Expense
Disability Expense
(100.0%
Medical Expense
All Components
Tota
(100.0% (100.0% (100.0%
2008.10.05
C-26

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007

Table 16b. QC Error Cases with Missing Verification in Tenant File

l
# of Cases # of Cases # of Cases
% of Cases % of Cases % of Cases
Earned Income Verified
90 (25.6%) 24 (28.8%) 114 (26.2%)
Not Verified
262 (74.4%) 60 (71.2%) 322 (73.8%)
Verified
51 (14.7%) 38 (19.1%) 89 (16.3%)
Not Verified
294 (85.3%) 163 (80.9%) 457 (83.7%)
Public Assistance Verified
33 (41.5%) 3 (15.3%) 37 (35.7%)
Not Verified
47 (58.5%) 19 (84.7%) 66 (64.3%)
Other Income Verified
47 (25.0%) 10 (18.6%) 58 (23.5%)
Not Verified
142 (75.0%) 45 (81.4%) 187 (76.5%)
Asset Income Verified
24 (19.3%) 33 (23.3%) 58 (21.4%)
Not Verified
102 (80.7%) 110 (76.7%) 212 (78.6%)
Verified
Not Verified
11
51
(17.8%)
(82.2%)
6
16
(29.0%)
(71.0%)
17
67
(20.8%)
(79.2%)
Not Verified
2 ) 2 )
Verified
28 (9.6%) 29 (11.1%) 57 (10.3%)
Not Verified
267 (90.4%) 230 (88.9%) 497 (89.7%)
50058 50059 Tota
(in 1,000) (in 1,000) (in 1,000)
Pension, Etc.
Child Care
Expense
Disability Expense
(100.0% (100.0%
Medical Expense
C-27

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 17. Administrative Error: Number & Percent of Households, Average Dollars in Error
For Households with Recalculated 50058/59 Rent Error and Households with QC Rent Error by Administrative Error Type
Households wi Households wi
%
110 (39.6%) 23.61 927 (62.3%) 47.58
i
167 (60.4%) 16.39 560 (37.7%) 37.84
88 (31.7%) 32.90 338 (22.7%) 42.57
189 (68.3%) 12.91 1,149 (77.3%) 44.31
13 (4.7%) 88.50 77 (5.1%) 43.43
Error
264 (95.3%) 15.81 1,410 (94.9%) 43.94
11 (3.9%) 93.69 53 (3.6%) 33.83
No Income Calculation Error
266 (96.1%) 16.21 1,434 (96.4%) 44.29
32 (11.7%) 44.07 124 (8.4%) 68.12
244 (88.3%) 15.95 1,362 (91.6%) 41.70
7 (2.5%) 78.39 47 (3.2%) 78.40
226 (81.6%) 18.27 1,229 (82.7%) 42.37
44 (16.0%) 15.15 210 (14.1%) 45.19
162 (58.5%) 23.62 1,065 (71.7%) 46.32
115 (41.5%) 13.08 421 (28.3%) 37.83
277 ) 19.25 1,487 ) 43.91
th Recalculated 50058/59 Rent Error th QC Rent Error
# of Households in Error # of Households in Error
(in 1,000) of Households in Error Average Gross Dollar Error (in 1,000) % of Households in Error Average Gross Dollar Error
Transcription Error
No Transcr ption Error
Consistency Error
No Consistency Error
Allowances Calculation Error
No Allowances Calculation
Income Calculation Error
Other Calculation Error
No Othere Calculation Error
Overdue Recertification
On-time Recertification
Certification
Any Admin/proc Error
No Admin/proc Error
Total Households
(100.0% (100.0%
2008.10.05
C-28

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 18. Administrative Error: Number & Percent of Households, Average Dollars in Error
For All Households by Administrative Error Type
1,579 (38.2%) 28.35 1,579 (38.2%) -9.95
i
2,554 (61.8%) 8.54 2,554 (61.8%) -2.37
838 (20.3%) 17.42 838 (20.3%) -2.64
3,295 (79.7%) 15.77 3,295 (79.7%) -5.93
127 (3.1%) 26.53 127 (3.1%) -.23
Error
4,006 (96.9%) 15.78 4,006 (96.9%) -5.42
113 (2.7%) 16.41 113 (2.7%) -3.80
No Income Calculation Error
4,020 (97.3%) 16.10 4,020 (97.3%) -5.30
252 (6.1%) 33.90 252 (6.1%) -14.60
3,881 (93.9%) 14.95 3,881 (93.9%) -4.66
64 (1.5%) 58.93 64 (1.5%) -47.36
3,429 (83.0%) 15.50 3,429 (83.0%) -5.06
641 (15.5%) 15.10 641 (15.5%) -2.18
2,092 (50.6%) 23.97 2,092 (50.6%) -8.31
2,041 (49.4%) 8.04 2,041 (49.4%) -2.14
Total
4,133 ) 16.11 4,133 ) -5.26
Gross QC Rent Error Net QC Rent Error
# of Households
(in 1,000) % of Households Average Dollar Error
# of Households
(in 1,000) % of Households Average Dollar Error
Transcription Error
No Transcr ption Error
Consistency Error
No Consistency Error
Allowances Calculation Error
No Allowances Calculation
Income Calculation Error
Other Calculation Error
No Othere Calculation Error
Overdue Recertification
On-time Recertification
Certification
Any Admin/proc Error
No Admin/proc Error
(100.0% (100.0%
2008.10.05
C-29

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 19. Occupancy Standards on Form 50058/59
PUBLIC HOUSING l
# of Cases # of Cases # of Cases # of Cases
% of Cases % of Cases % of Cases % of Cases
0
2 (3.2%) 4 (10.1%) 7 (3.7%)
1
2 (.7%) 1 (.3%) 2 (.2%) 5 (.3%)
2
4 (1.4%) 8 (1.1%) 3 (1.0%) 14 (1.1%)
3
2 (.7%) 18 (3.3%) 20 (2.2%)
4
2 (4.5%) 4 (3.9%) 7 (3.8%)
5+
1 (9.4%) 1 (2.6%)
All Units
13 (1.4%) 35 (1.9%) 4 (.3%) 53 (1.3%)
CORRECT 0
65 (96.8%) 40 (89.9%) 66 ) 171 (96.3%)
1
327 (99.3%) 442 (99.7%) 817 (99.8%) 1,586 (99.7%)
2
200 (71.1%) 509 (74.1%) 211 (75.4%) 920 (73.7%)
3
171 (80.5%) 430 (77.4%) 129 (92.6%) 731 (80.5%)
4
30 (55.2%) 60 (58.1%) 6 (43.8%) 96 (56.1%)
5+
2 (19.2%) 14 (57.6%) 16 (43.4%)
All Units
795 (83.3%) 1,495 (80.5%) 1,229 (93.1%) 3,520 (85.2%)
OVER-HOUSED 2
77 (27.5%) 170 (24.8%) 66 (23.6%) 314 (25.1%)
3
40 (18.8%) 107 (19.3%) 10 (7.4%) 158 (17.4%)
4
22 (40.3%) 39 (38.0%) 7 (56.2%) 69 (40.1%)
5+
7 (71.4%) 10 (42.4%) 2 ) 20 (54.0%)
All Units
146 (15.3%) 327 (17.6%) 86 (6.5%) 560 (13.5%)
PHA-ADMINISTERED
SECTION 8 OWNER-ADMINISTERED Tota
(in 1,000) (in 1,000) (in 1,000) (in 1,000)
UNDER-HOUSED
(100.0%
(100.0%
2008.10.05
C-30

Appendix C—Source Tables Based on Quality Control Data
HUD QC FY 2007
Table 19a. Frequency & Percent of All Households
by Number of Bedrooms and Number of Household Members
1 2 3 4 5 6 7 8 9 10 12
N % N % N % N % N % N % N % N % N % N % N %
0
171 96.3% 7 3.7%
1
90.0% 154 9.7% 4 .2% 1 .1%
2
314 25.1% 566 45.4% 254 20.4% 100 8.0% 14 1.1%
3
55 6.1% 103 11.3% 260 28.6% 276 30.4% 152 16.8% 42 4.7% 14 1.5% 5 .6% 1 .1%
4
1 .6% 3 2.0% 10 6.0% 54 31.5% 46 27.0% 20 11.8% 22
12.7
%
8 4.5% 1 .7% 5 3.1%
5+
3 8.2% 2 6.0% 4 10.0% 6 15.8% 2 5.3% 3 8.6% 4
11.0
%
12
32.4
%
1 2.6%
Number of Household Members (in 1,000)
1432
2008.10.05
C-31

Appendix C—Source Tables Based on Tenant File Data

Appendix C—Source Tables Based on Tenant File Data
HUD QC FY 2007 [Tenant File]

Table 2. Percent of Households by Payment Type and Program Type

PROPER PAYMENT OVERPAYMENT l
# of # of # of # of
Cases of Col % of Cases of Col % of Cases (in of Col % of Cases (in Col % of
Cases Cases Cases Cases 1,000) Cases Cases 1,000) Cases Cases
OWNER
Section 8
l
116
199
315
132
(12.1%)
(10.7%)
(11.2%)
(10.0%)
(25.9%)
(44.6%)
(70.5%)
(29.5%)
775
1,387
2,162
1,066
(81.1%)
(74.7%)
(76.9%)
(80.8%)
(24.0%)
(43.0%)
(67.0%)
(33.0%)
64
272
336
122
(6.8%)
(14.6%)
(11.9%)
(9.2%)
(14.1%)
(59.3%)
(73.4%)
(26.6%)
955
1,858
2,813
1,320
)
)
)
)
(23.1%)
(45.0%)
(68.1%)
(31.9%)
l
132 (10.0%) (29.5%) 1,066 (80.8%) (33.0%) 122 (9.2%) (26.6%) 1,320 ) (31.9%)
l
446 (10.8%) ) 3,229 (78.1%) ) 458 (11.1%) ) 4,133 ) )
HUD QC FY 2007 [Tenant File]
(
PROPER PAYMENT OVERPAYMENT l
# of # of # of
# of Cases Col % of Cases Col % of Cases Col % of Cases Col % of
of Cases Cases of Cases Cases of Cases Cases Cases Cases
PHA
OWNER
Section 8
l
148
250
398
207
(15.5%)
(13.5%)
(14.2%)
(15.7%)
(24.4%)
(41.4%)
(65.8%)
(34.2%)
683
1,199
1,883
911
(71.6%)
(64.5%)
(66.9%)
(69.0%)
(24.5%)
(42.9%)
(67.4%)
(32.6%)
124
408
532
202
(13.0%)
(22.0%)
(18.9%)
(15.3%)
(16.9%)
(55.7%)
(72.5%)
(27.5%)
955
1,858
2,813
1,320
)
)
)
)
(23.1%)
(45.0%)
(68.1%)
(31.9%)
l
l
207
605
(15.7%)
(14.6%)
(34.2%)
)
911
2,794
(69.0%)
(67.6%)
(32.6%)
)
202
734
(15.3%)
(17.8%)
(27.5%)
)
1,320
4,133
)
)
(31.9%)
)
UNDERPAYMENT Tota
Row % Row % Row %
Row % of
(in 1,000) (in 1,000)
PHA ADMINISTERED
ADMINISTERED
Public Housing
Tota
Owner-
Administered
(100.0%
(100.0%
(100.0%
(100.0%
Tota
(100.0%
Tota
(100.0% (100.0% (100.0% (100.0% (100.0%
2008.10.05
Table 2 S). Percent of Households by Payment Type and Program Type
(Proper Payment based on exact match of Actual and QC Rent)
UNDERPAYMENT Tota
Row % Row % Row % Row % of
(in 1,000) (in 1,000) (in 1,000) (in 1,000)
ADMINISTERED
ADMINISTERED
Public Housing
Tota
Owner-
Administered
(100.0%
(100.0%
(100.0%
(100.0%
Tota
Tota
(100.0% (100.0% (100.0%
(100.0%
(100.0% (100.0%
2008.10.05
C-1Tenant File

Appendix C—Source Tables Based on Tenant File Data
HUD QC FY 2007 [Tenant File]

Table 3. Dollar Rent Error by Program Type

ACTUAL RENT (MONTHLY) DC RENT (MONTHLY) GROSS RENT ERROR (MONTHLY)
Sum Sum Sum
Dollar Ave. # of Dollar Ave. Dollar Ave.
# of Cases Col % of Amount Dollar Cases Col % of Amount Dollar # of Cases Col % of Amount Dollar
(in 1,000) Cases (in 1,000) Amount (in 1,000) Cases (in 1,000) Amount (in 1,000) Cases (in 1,000) Amount
Public
955 (23.1%) 218,505 228.80 955 (23.1%) 222,809 233.31 955 (23.1%) 13,656 14.30
Section 8
PHA
1,858 (45.0%) 1,858 (45.0%) 1,858 (45.0%) 17.04
l
2,813 (68.1%) 2,813 (68.1%) 2,813 (68.1%) 16.11
OWNER
1,320 (31.9%) 1,320 (31.9%) 1,320 (31.9%) 9.08
l
1,320 (31.9%) 1,320 (31.9%) 1,320 (31.9%) 9.08
l
4,133 ) 4,133 ) 4,133 ) 13.86
Housing ADMINISTERED
392,125 211.05 379,798 204.41 31,657
Tota
610,631 217.07 602,607 214.22 45,313
Owner-
Administered ADMINISTERED
276,013 209.10 277,228 210.02 11,990
Tota
276,013 209.10 277,228 210.02 11,990
Tota
(100.0% 886,643 214.53 (100.0% 879,835 212.88 (100.0% 57,302
2008.10.05
C-2Tenant File

Appendix C—Source Tables Based on Tenant File Data
HUD QC FY 2007 [Tenant File]

Table 4. Dollar Error Amount by Payment Type and Program Type

UNDERPAYMENT (MONTHLY) OVERPAYMENT (MONTHLY) DC RENT (MONTHLY)
Sum Sum Sum
# of Dollar Ave. Dollar Ave. Dollar Ave.
Cases Col % of Amount Dollar # of Cases Col % of Amount Dollar # of Cases Col % of Amount Dollar
(in 1,000) Cases (in 1,000) Amount (in 1,000) Cases (in 1,000) Amount (in 1,000) Cases (in 1,000) Amount
PHA
OWNER
Section 8
l
116
199
315
132
(25.9%)
(44.6%)
(70.5%)
(29.5%)
8,824
9,734
6,593
76.36
48.86
58.96
50.08
64
272
336
122
(14.1%)
(59.3%)
(73.4%)
(26.6%)
4,832
5,397
74.94
80.74
79.62
44.26
955
1,858
2,813
1,320
(23.1%)
(45.0%)
(68.1%)
(31.9%)
l
l
132
446
(29.5%)
)
6,593 50.08
56.34
122
458
(26.6%)
)
5,397 44.26
70.21
1,320
4,133
(31.9%)
)
HUD QC FY 2007 [Tenant File]
(
Sum
Sum Sum
# of Amount Ave. # of Ave. # of Ave.
Cases Col % of (in Cases Col % of Amount Cases (in Col % of Amount
(in 1,000) Cases 1,000) Amount (in 1,000) Cases Amount 1,000) Cases Amount
PHA
OWNER
Section 8
l
148
250
398
207
(24.4%)
(41.4%)
(65.8%)
(34.2%)
8,891
9,838
6,789
60.15
39.30
47.04
32.79
124
408
532
202
(16.9%)
(55.7%)
(72.5%)
(27.5%)
4,923
5,574
39.78
54.28
50.91
27.65
955
1,858
2,813
1,320
(23.1%)
(45.0%)
(68.1%)
(31.9%)
l
l
207
605
(34.2%)
)
6,789 32.79
42.16
202
734
(27.5%)
)
5,574 27.65
44.52
1,320
4,133
(31.9%)
)
ADMINISTERED
ADMINISTERED
Public Housing
Tota
Owner-
Administered
18,558
21,923
26,755
222,809
379,798
602,607
277,228
233.31
204.41
214.22
210.02
Tota
Tota
(100.0% 25,150 (100.0% 32,152 (100.0%
277,228
879,835
210.02
212.88
2008.10.05
Table 4 S). Dollar Error Amount by Payment Type and Program Type
(Proper Payment based on exact match of Actual and QC Rent)
UNDERPAYMENT (MONTHLY) OVERPAYMENT (MONTHLY) DC RENT (MONTHLY)
Dollar
Dollar Dollar
Dollar Dollar Dollar
(in 1,000) (in 1,000)
ADMINISTERED
ADMINISTERED
Public Housing
Tota
Owner-
Administered
18,729
22,165
27,088
222,809
379,798
602,607
277,228
233.31
204.41
214.22
210.02
Tota
Tota
(100.0% 25,519 (100.0% 32,662 (100.0%
277,228
879,835
210.02
212.88
2008.10.05
C-3Tenant File

Appendix C—Source Tables Based on Tenant File Data
HUD QC FY 2007 [Tenant File]

Table 5. Gross and Net Rent Error by Program Type

Sum Sum
# of Ave. # of Ave. Ave.
Cases Col % of Amount Cases Col % of Amount # of Cases Col % of Amount
Cases Amount Cases Amount Cases Amount
Section 8
PHA
l
OWNER
955
1,858
2,813
1,320
(23.1%)
(45.0%)
(68.1%)
(31.9%)
14.30
17.04
16.11
9.08
955
1,858
2,813
1,320
(23.1%)
(45.0%)
(68.1%)
(31.9%)
-3,992
8,197
-1,195
-4.18
6.56
2.91
-.91
955
1,858
2,813
1,320
(23.1%)
(45.0%)
(68.1%)
(31.9%)
l
l
1,320
4,133
(31.9%)
)
9.08
13.86
1,320
4,133
(31.9%)
)
-1,195
7,002
-.91
1.69
1,320
4,133
(31.9%)
)
HUD QC FY 2007 [Tenant File]
(
Sum Sum Sum
Ave. Ave. Ave.
# of Cases Col % of Amount # of Cases Col % of Amount # of Cases Col % of Amount
Cases Amount Cases Amount Cases Amount
Section 8
PHA
l
OWNER
955
1,858
2,813
1,320
(23.1%)
(45.0%)
(68.1%)
(31.9%)
14.46
17.22
16.29
9.37
955
1,858
2,813
1,320
(23.1%)
(45.0%)
(68.1%)
(31.9%)
-3,968
8,359
-1,216
-4.16
6.63
2.97
-.92
955
1,858
2,813
1,320
(23.1%)
(45.0%)
(68.1%)
(31.9%)
l
l
1,320
4,133
(31.9%)
)
9.37
14.08
1,320
4,133
(31.9%)
)
-1,216
7,143
-.92
1.73
1,320
4,133
(31.9%)
)
GROSS RENT ERROR (MONTHLY) NET RENT ERROR (MONTHLY) DC RENT (MONTHLY)
Dollar Sum Dollar Dollar
Dollar Dollar Dollar
(in 1,000) (in 1,000) (in 1,000) (in 1,000) (in 1,000) (in 1,000)
Public Housing
ADMINISTERED
Tota
Owner-
Administered ADMINISTERED
13,656
31,657
45,313
11,990
12,189
222,809
379,798
602,607
277,228
233.31
204.41
214.22
210.02
Tota
Tota
(100.0%
11,990
57,302 (100.0% (100.0%
277,228
879,835
210.02
212.88
2008.10.05
Table 5 S). Gross and Net Rent Error by Program Type
(Proper Payment based on exact match of Actual and QC Rent)
GROSS RENT ERROR (MONTHLY) NET RENT ERROR (MONTHLY) DC RENT (MONTHLY)
Dollar Dollar Dollar
Dollar Dollar Dollar
(in 1,000) (in 1,000) (in 1,000) (in 1,000) (in 1,000) (in 1,000)
Public Housing
ADMINISTERED
Tota
Owner-
Administered ADMINISTERED
13,814
32,003
45,817
12,363
12,327
222,809
379,798
602,607
277,228
233.31
204.41
214.22
210.02
Tota
Tota
(100.0%
12,363
58,180 (100.0% (100.0%
277,228
879,835
210.02
212.88
2008.10.05
C-4Tenant File

Appendix D—Consistency and Calculation Errors

Appendix D––Consistency and Calculation Errors

50058—Consistency Errors
50058 ITEM ERROR
General Information:
1c. Program Must equal P, CE, VO, or MR

2a. Type of Action Must equal 1 through 15

2b. Effective Date of Action
Cannot be earlier than Date of Admission to the Program

(2h)

Household Composition:
3g. Sex
3h. Relationship
3i. Citizenship
3k. Race
3m. Ethnicity
3u. Family Subsidy Status
3v. Effective Date
Must equal M or F

Must equal H, S, K, F, Y, E, L, or A

Must equal EC, EN, IN, PV, or XX

Must equal 1 through 4

Must equal 1 or 2

Must equal C, E, F, P, or blank

Should not be blank if 3u equals C

Net Family Assets and Income
6a. Family Member No.
7a. Family Member No.
7b. Income Code
8a. Total Annual Income
8i. Earnings Made Possible by
Disability Assistance Expense
Must equal a number used in Section 3. Household.

Must equal a number used in Section 3. Household.

Must equal B, F, HA, M, W, G, IW, T, P, S, SS, C, E, I, N,

or U

Must equal Total Annual Income recorded in 7i

Must be <= the sum of Dollars per Year (7d) for Income

Codes (7b) HA, F, W, B, or M

Allowances and Adjusted Income
8h. Maximum Disability Allowance
8j. Allowable Disability Assistance
Expense
Should only be completed if any member is disabled

• Should be <= Maximum Disability Allowance (8h)
• Should be 0 if Medical/Disability Threshold (8f) is >
Maximum Disability Allowance (8h)
• Should be 0 or blank if Maximum Disability Allowance
(8h) is 0 or blank
8k. Total Medical Expenses Should only be completed if the head, spouse, or co-head
is 62 or over, or disabled; otherwise it should be blank
D-1

Appendix D––Consistency and Calculation Errors
50058 ITEM ERROR
8n. Medical/Disability Assistance • Should equal Total Annual Disability Assistance and
Allowance Medical Expense (8m) minus Medical/disability
Threshold (8f) if Allowable Disability Expense (8j) is
blank or Total Annual Unreimbursed Disability
Assistance Expense (8g) is less than Medical
/disability Threshold (8f)
• Should equal Total Annual Disability Assistance and
Medical Expense (8m) if 8 Total Annual
Unreimbursed Disability Assistance Expense (8g)
and Allowable Disability Expense (8j) is >=
Medical/disability Threshold (8f)
8p. Elderly/Disabled Allowance Should be $400 if head, spouse or co-head is 62 or over,
or disabled; otherwise it should be 0 or blank
8s. Dependent Allowance Must be completed if the household contains a member

under age 18, disabled, or a full-time student (excluding

the head, spouse, foster child or adult, or live-in

attendant)

8t. Yearly Child Care Cost That Is Not Should only be completed if any member is less than 13

Reimbursed (Child Care Allowance) years old

Family Rent and Subsidy Information
10a. 11q, 12r, 13j, 14s TTP Must equal TTP (9j) or blank
10a. through 14ag. Rent Calculations • If Program (1c) = P:
- TTP (10a), must be completed;
- Flat Rent (10b), or Tenant Rent (10f), or Mixed
Family Tenant Rent (10s) must be completed;
- Section 11 through 14 must be blank.
• If Program (1c) = VO or C:
- Section 11, or 12 must be completed
- Tenant Rent (11s or 12k), or Mixed Family
Tenant Rent (11ak, or 12 ai) must be completed;
- Section 10, 13, and 14 must be blank
• If Program (1c) = MR:
- Contract Rent to Owner must be completed
- Tenant Rent (13k), or Mixed Family Tenant Rent
(13x) must be completed;
- Sections 10, 11, 12, and 14 must be blank.
D-2

Appendix D––Consistency/Calculations

50059 - Consistency Errors
50059 ITEM ERROR
General Information:
2. Subsidy Type Must equal 1 through 9
13. Effective Date Cannot be earlier than Date Tenant Moved into Project (16.)
18. Certification Type Must equal 1 through 5
19. Action Processed Must equal 1 through 4, or blank
44. Race of Head of Household Must equal 1 through 4
45. Ethnicity of Head of Household Must equal 1 or 2
Household Composition
43. Sex Must equal M or F
47. Special Status Code Must equal E, S, H, F, I, J, or blank; should be E if Age > 61
49. Eligibility Code (Citizenship) Must equal EC, EN, IC, IN, IP, PV, or XX
Net Family Assets and Income
69. Member No. – Income Info Should not be greater than the total number of members
78. Member No. – Asset Info
listed in item 38 (Family Member Number)
Allowances and Adjusted Income
100. Dependent Allowance
101. Child Care Expense (work)
102 Child Care Expense (school)
105. Disability Allowance
Must be completed if Number of Dependents (58) is greater
than 0
Should only be completed if any member is less than 13
years old
• Should be <= Disability Expenses (104)
• Should be 0 if 3% of Annual Income (103) is > Total
Disability Assistance Expenses (104)
• Should be 0 or blank if Total Disability Assistance
Expenses (104) is 0 or blank
106. Total Medical Expenses Should only be completed if the Special Status Code (47) for
the head or spouse or co-head = H or E, or if the head,
spouse, or co-head is age 62 years old or older
108. Elderly Household Allowance Should be $400 if the Special Status Code (47) for the head
or spouse or co-head = H or E; otherwise it should be 0 or
blank
Family Rent and Subsidy Information:
112. Tenant Rent Should equal the maximum of TTP (111) minus Utility
Allowance (33) or 0; or be blank if Utility Reimbursement
(113) is greater than 0
113. Utility Reimbursement Should be blank if Item 33 < Item 111
D-3

Appendix D––Consistency/Calculations
50058 - Calculation Errors
50058 ITEM ERROR CALCULATION
Household Composition:
3f. Age
8q. Number of Dependents
Net Family Assets and Income
6f. Total Asset Value
6i. Imputed Asset Income
6j. Income from Asset
7g. Total Non Asset Income
7i. Total Annual Income
Allowances and Adjusted Income
8e. Total Permissible Deductions
8f. 3% of Annual Income
8h. Disability Allowance
8n. Medical Allowance
8p. Elderly/Disabled
8s. Dependent Allowance
8t. Child Care Costs
Must equal the age calculated based on Date of Birth (3e)
and Effective Date of Action (2b)
Must equal the number of household members under 18,
with a disability, or a full-time student (other than head,
spouse co-head, foster child/adult, or live-in aide)
Must equal the sum of all values in Cash Value of Asset (6d)
Must equal Total Cash Value of Asset (6f) * Passbook Rate
(6h) if Total Value of Assets (6f) is > $5,000. If Total Value
of Assets (6f) is <= $5,000 Imputed Asset Income (6i) = 0
Must equal the larger of Total Anticipated Income (6g) or
Imputed Asset Income (6i)
Must equal the sum of all values in Income After Exclusions
(7f)
Must equal (Final Asset Income (6j) + Total Income Other
Than Assets (7g)
Must equal the sum of all values in Amount of Permissible
deduction (8d)
Must equal 3% * Total Annual Income (8a)
Must equal Total Annual Unreimbursed Disability Assistance
Expense (8g) minus Medical/Disability Threshold (8f) if there
is a disabled household member, and if there is earned
income greater than or equal to the disability expense
Must equal: Total Annual Disability Assistance and Medical
Expense (8m) minus Medical/disability Threshold (8f) if
Allowable Disability Assistance Expense (8j) is blank or
Total Annual Unreimbursed Disability Assistance Expense
(8g) is less than Medical/disability Threshold (8f); or equal
Total Annual Disability Assistance and Medical Expense
(8m) if Total Annual Unreimbursed Disability Assistance
Expense (8g) and Allowable Disability Assistance Expense
(8j) is >= Medical/Disability Threshold (8f); if the head,
spouse, or co-head is elderly or disabled
Must equal $400 if head, spouse, or co-head is elderly or
disabled
Must equal Number of Dependents (8q) * $480
Must be 0 or blank, if no household member under age 13
D-4

Appendix D––Consistency/Calculations
50058 ITEM ERROR CALCULATION

8x. Total Allowance Must equal Total Permissible Deductions (8e) + Medical
/Disability Assistance Allowance (8n) + Elderly/Disability
Allowance (8p) + Dependent Allowance (8s) + Total Annual
Unreimbursed Childcare Costs (8t) + Total Annual Travel
Cost to Work/School (8u)
8y. Adjusted Annual Income Must equal Total Annual Income (8a) minus Total

Allowances (8x)

Family Rent and Subsidy Information
9j. Total Tenant Payment Must equal the highest of TTP if Based on Annual Income
(9c), TTP if Based on Adjusted Annual Income (9f), Welfare
Rent (9g), Minimum Rent (9h), or Enhanced Voucher
Minimum Rent (9i).
12p. Gross Rent Must equal Rent to Owner (12k) + Utility Allowance (12m)
Tenant Rent (item number varies by Tenant Rent must equal the recalculated tenant rent based
program) on the Rent Calculation rules provided in Appendix A
Note: With the exception of tenant rent, negative numbers are always converted to 0
D-5

Appendix D––Consistency/Calculations
50059 - Calculation Errors
50059 ITEM ERROR CALCULATION
Household Composition:
51. Age
56. Number of Family Members
57. Number of Non-family Members
58. Number of Dependents
Net Family Assets and Income
84. Total Asset Value
85. Actual Income From Asset
87. Imputed Asset Income
73. Earned Income Sum
74. Pension Income Sum
75. Public Assistance Income Sum
76. Other Income Sum
77. Total Non Asset Income
88. Asset Income
89. Total Annual Income
Allowances and Adjusted Income
100. Dependent Allowance
101. Child Care Expense (work)
102. Child Care Expense (school)
103. 3% of Annual Income
105. Disability Allowance
Must equal age calculated based on Date of Birth (46) and
Effective Date of Action (13)
Must equal the number of family members listed
Must equal the number of family members listed with a
relationship code of “L” or “F”
Must equal the number of household members under 18,
with a disability, or a full-time student (other than head,
spouse co-head, foster child/adult, or live-in aide)
Must equal the sum of the asset values in Cash Value of
Assets (81)
Must equal the sum of the income values in Actual Yearly
Income From Assets (82)
Must equal Total Asset Value (84) * 2%, if Total Value of
Assets is > $5,000
Must equal the sum of income values (in item 71) for items
with codes B, F, M, or W in Income Type Code (70)
Must equal the sum of the income values (in item 71) for
items with codes PE, SI, or SS in Income Type Code (70)
Must equal the sum of the income values (in item 71) for
items with codes TA or G in Income Type Code (70)
Must equal the sum of the income values (in item 71) for
items with codes CS, I, N, or U in Income Type Code (70)
Must equal Earned Income Sum (73) + Pension Income
Sum (74) + Public Assistance Income Sum (75) + Other
Income Sum (76)
Must equal the greater of Imputed Asset Income (87) or
Actual Income from Asset (85)
Must equal Total Non Asset Income (77) + Income from
Asset (88)
Must equal Number of Dependents (58) * $480
Must be 0 or blank, if no household member under age 13
Must equal Total Annual Income (89) * .03
Must equal Total Disability Expenses (104) minus 3% of
Annual Income (103) if there is a disabled household
member, and if there is earned income greater than or equal
to the disability expense
D-6

Appendix D––Consistency/Calculations
50059 ITEM ERROR CALCULATION

107. Medical Allowance Must equal Total Medical Expenses (106) minus 3% of
Annual Income (103) if Total Handicapped Assistance
Expense (107a) = 0; or if (Disability Allowance (105) = 0,
then Medical Allowance (106) = Total Medical Expenses
(106) + Total Handicapped Assistance Expenses (104) –3%
of Annual Income (89), if the head, spouse, or co-head is
elderly or disabled
108 Elderly Household Allowance Must equal $400 if head, spouse, or co-head is elderly or

disabled

109.Total Allowance Must equal Allowance for Dependents (100) + Child Care

Allowance (101+102) + Allowance for Disability Expenses

(105) + Allowance for Medical Expenses (107) + Elderly

Household Allowance (108)

110. Adjusted Annual Income Must equal Total Annual Income (89) minus Total

Allowances (109)

Family Rent and Subsidy Information
34. Gross Rent Must equal Contract Rent (32) + Utility Allowance (33)
111. Total Tenant Payment Must equal the higher of 30% of Adjusted Income (110),

10% of Total Annual Income (89), Welfare Rent (115), or

$50 (Minimum Rent).

112. Tenant Rent Tenant Rent must equal the recalculated tenant rent based
on the Rent Calculation rules provided in Appendix A
Note: With the exception of tenant rent, negative numbers are always converted to 0
D-7

Appendix E—Project Staff Questionnaire
Descriptive Analysis

Appendix E – Project Staff Questionnaire Descriptive Analysis
The Project Staff Questionnaire (PSQ) is a self-administered survey given to project managers
and executive directors of PHA/projects
1
included in the FY 2007 study. The objective of the
questionnaire was to obtain information regarding 1) project characteristics and practices that
promote accurate (re)certifications, and 2) identify difficulties experienced by PHAs/projects in
order to identify areas of potential improvement. Topics covered the number and types of
PHA/project staff, training received for new (re)certification staff and those with experience,
procedures for communicating information from HUD, quality control measures for verifying
work done by (re)certification staff, methods and difficulties in obtaining household information,
utilization of computer software to automate the (re)certification process, and procedures and
difficulties in verifying tenant information.
A. Methodology
The PSQ, a self-administered survey, was mailed in February 2008 to the executive director or
manager of each PHA/Project, and respondents mailed their completed questionnaires back to
Macro headquarters. Data were entered into an electronic data base via an automated tool that
programmed in skip patterns, missing items, and range of valid responses. PSQs with
questionable responses or skip patterns were individually investigated and all of the data issues
were resolved. Of the 555 PSQs that were originally mailed, 546 were completed and returned,
for a response rate of 98.5 percent.
This year’s questionnaire expanded and modified several sections. New open-ended questions
relating to changes in policy and procedures were added, as well as questions relating to
procedures used to complete the annual recertification process. A new optional section was
included asking about PHAs’ policy regarding interim recertifications.
B. Results
Number and Type of Staff. (Re) certification staffs are those who interview the tenants, gather
information from them, calculate rents, track verifications, and supervise other staff in
performing move-in certifications and annual recertification’s. In FY 2007, PHA/projects had on
average 145 units per staff member responsible for certifying and recertifying households.
Owner-administered projects had the lowest unit to staff ratio (79 units per staff member), while
PHA-administered Section 8 projects had the highest unit to staff ratio (234 units per staff
member). Public Housing projects were in the middle with a ratio of 146 units per staff member.
Exhibit E-1a shows the average and median number of units per type of staff member, by
program type. Exhibit E-1a also shows the ratio of households to all staff members at the
PHA/Project (e.g., administrative staff, maintenance staff).
1
j For purposes of this study, a PHA-administered Section 8 pro ect is defined as a PHA/county combination
representing the Section 8 Voucher households living in a particular county.
E-1

Appendix E – Project Staff Questionnaire Descriptive Analysis
Exhibit E-1a.
Number of Units per Staff Member, by Program Type
Program Type
PHA-
Administered Owner-
Public Housing Section 8 Administered
Total
Units per (re)certification staff 146.1 234.3 79.2 145.2
Average ratio
Median ratio 112.8 209.9 65.0 103.5
Units per staff member 44.2 105.7 30.2 55.5
Average ratio
Median ratio 27.9 96.7 26.0 32.9
200 146 199 545
on (re)certifications. j
have
( )
Exhibit E-1b breaks
Exhibit E-1b.

(Re)

i ience 88.3% 82.4% 89.4% 87.1%
i 75.4% 56.5% 80.2% 71.7%
Total Number of PHA/projects
Of those staff members, a majority of them worked on a variety of tasks in addition to working
PHA-administered Section 8 pro ects were most likely to have staff work
primarily on recertifications, with 25 percent of staff working mostly on (re)certifications, and
owner-administered projects were most likely to their staff multi-task on other
responsibilities, with only 7 percent working mostly on recertifications.
Most PHA/projects had staff with more than one year of experience working with
(re)certifications. All three program types had similar percentages of re certification staff with
over one year of experience. However, when looking at the percentage of staff with over 5 years
of experience, PHA-administered Section 8 projects drop down to 56.5 percent, while both
Public Housing and owner-Administered projects stay above 75 percent.
down the percentages by program type.
Percentage of Staff Who Have Worked with
certifications for Over 1 Year and 5 Years, by Program Type
Program Type
PHA-
Administered Owner-
Public Housing Section 8 Administered
Total
(Re) certification staff w th over 1 year exper
(Re) certification staff w th over 5 years experience
When hiring new staff who will be working with (re)certifications, most PHAs/projects had
some minimum education requirements. Overall, only about 7 percent of PHAs/projects did not
require some education. Owner-administered projects were most likely not to require any
education at about 11 percent, while PHA-administered Section 8 projects were least likely at 3
percent.
E-2

Appendix E – Project Staff Questionnaire Descriptive Analysis
In addition to minimum education requirements, PHAs/projects also in general had other
minimum requirements when hiring new staff to work with (re)certifications. Seventy-nine
percent of PHAs/projects overall required some computer skills, 71 percent required background
checks, 68 percent required administrative or clerical experience, and 62 percent required math
or logic skills. However, only 32 percent required special housing related training or
certification, and 51 percent required other housing related experience. This shows an emphasis
on general office skills and experience over more specific housing related experience. Despite
being less likely to have minimum education requirements, owner-administered projects were
more likely to have other minimum requirements in most categories than their Public Housing
and PHA-administered Section 8 counterparts. Exhibit E-1c and E-1d describe the minimum
requirements reported by the PHA/projects for education and other requirements, by program
type.
Exhibit E-1c.

Minimum Education Requirements for New Employees

5.5% 2.8% 10.6% 6.6%
60.5% 66.0% 63.3% 63.0%
l 8.5% 9.0% 7.5% 8.3%
Bachel 14.5% 13.2% 7.5% 11.6%
Exhibit E-1d.
75.0% 77.4% 83.0% 78.6%
Background Checks 66.5% 61.0% 83.0% 71.1%
65.0% 63.0% 73.5% 67.6%
65.5% 58.2% 61.5% 62.1%
48.5% 45.2% 56.5% 50.5%
23.5% 24.0% 45.0% 31.5%
Working with (Re)certifications, by Program Type
Program Type
PHA-
Administered Owner-
Public Housing Section 8 Administered
Total
No Minimum Requirements
High School/GED
Associates/2 years col ege/some college
or's
Other Minimum Requirements for New Employees
Working with (Re)certifications, by Program Type
Program Type
PHA-
Administered Owner-
Public Housing Section 8 Administered
Total
Computer Skills
Administrative or Clerical Experience
Math or Logic Sills
Other Housing Related Experience
Special Housing Related Training or Certification
E-3

Appendix E – Project Staff Questionnaire Descriptive Analysis
Training of New (Re)Certification Staff. PHA/projects train both new and experienced
(re)certification staff. The Project Staff Questionnaire collected information about the number of
new staff assigned to conduct (re)certifications, as well as the number of hours of training
received and the types of training activities used. New staff was defined as staff who were newly
assigned to conduct (re)certifications in the past 12 months. In this year’s study, 38 percent of
PHA/projects assigned new staff to conduct (re)certifications in the past 12 months. Among the
three program types, the PHA-administered Section 8 programs reported the largest proportion
of new (re)certification staff members (54.1%), the highest number of new staff assigned to
conduct (re)certifications (4 staff, on average), and the highest training hours, on average, for
new (re)certification staff (146 hours).
By comparison, owner-administered projects had only 30 percent new (re)certification staff,
assigned the fewest number of new staff (1), and had 60 hours of training, on average. Public
Housing projects had only a slightly higher percentage of new staff and more new staff assigned,
and had slightly fewer hours of training than owner-administered projects as shown in Exhibit E-2a.
Exhibit 2a.
New Recertification Staff Training, by Program Type
Program Type
PHA-
Administered Owner-
Section 8 Administered
Total
Public Housing
Average number of new staff assigned to conduct
1.4
(re)certifications
3.7 1.2 2.2
Average number of training hours received by each 56.7 145.9 60.1 92.4
new (re)certification staff
Percent of PHA/projects with new(Re)Certification 34.0% 54.1% 29.5% 37.7%
staff
Note 1: Averages were calculated for PHA/projects that assigned new staff to conduct (re)certifications in the past 12 months.
Of the various types of training used for new (re)certification staff, the three most frequently
used were: working with experienced staff one-on-one while conducting (re)certifications (90%
of PHA/projects), training sessions with the supervisor (85% of PHA/projects), and self training
through manuals, videos, or informal questions (62% of PHA/projects). Interestingly in the FY
2006 study, 88 percent of PHAs/projects reported that they used self-training tools such as
manuals, videos or informal questioning compared with 62 percent in FY 2007. This represents
a substantial drop in the use of these methods.
Exhibit E-2b.

Three Most Frequently Used Trainings Types

For New (Re)Certification Staff, by Program Type

Training Methods Usually or Always Used by
PHA/projects:
PHA-
Administered Owner-
Section 8 Administered
Total
New staff worked one-on-one with experienced
88.3% 93.7% 88.1% 90.3%

staff during the conduct of (re)certifications

Supervisor/senior staff held training sessions with
82.4%

new staff explaining procedures
88.6% 84.7% 85.4%

Read HUD/PHA/owner manual, watched videos, or
63.2%

asked informal questions
64.5% 57.6% 62.1%

Note 1: Percentages were calculated for PHA/projects that assigned new staff to conduct (re)certifications in the past 12 months.

Program Type
Public Housing
E-4

Appendix E – Project Staff Questionnaire Descriptive Analysis
Training for Experienced (Re)Certification Staff. For experienced (re)certification staff,
about 77 percent of PHA/projects provided training of some form in the past 12 months. On
average, PHA/projects trained 5 experienced staff members for an average of 33 hours during the
year. Owner-administered projects and PHA-administered Section 8 projects trained the largest
percentage of experienced (re)certification staff (81% and 80%, respectively). However, PHA-
administered Section 8 projects trained the most number of experienced staff (13 on average),
and provided the most hours of training (36 hours, on average). Figures for average number of
staff, average number of hours, and percentage of PHA/projects that trained (re)certification
staff, by program type are shown in Exhibit E-3a.
Exhibit E-3a.
PHA-
Administered Owner-
Section 8 Administered
Total
Experienced Staff Training, by Program Type
Program Type
Public Housing
Average number of experienced staff receiving training 3.3 12.6 1.5 5.2
Average number of training hours received by each
32.0 36.0 31.7 33.0
experienced (re)certification staff
70.0% 80.1% 81.0% 76.7%Percent of PHA/projects that trained experienced
(Re)Certification staff
Note 1: Averages were calculated for PHA/projects that provided training to experienced staff.
The same three methods that were most commonly used to train new (re)certification staff were
also used most commonly to train experienced (re)certification staff, but to less of a degree than
with new (re)certification staff. On average, 70 percent of PHA/projects used self-training
through manuals, videos, and informal questions to train (re)certification staff, 62 percent used
training sessions conducted by a supervisor/senior staff, and 55 percent used experienced staff to
work one-on-one with other experienced staff. The use of self-training was more prevalent for
training experienced staff than for new staff, and PHA/projects also were less likely to work one-
on-one with experienced staff as well. For more detailed figures by individual program type,
please refer to Exhibit E-3b.
Exhibit E-3b.

Methods for Training Experienced

PHA/projects Usually or Always:
PHA-
Administered Owner-
Section 8 Administered
Total
Read HUD/PHA/owner manual, watched videos, or asked
71.5%

informal questions
75.2% 66.0% 70.4%

Had supervisor/senior staff hold training sessions with
56.5% 69.3% 62.4% 62.3%

new staff explaining procedures

Had experienced staff work one-on-one with other
45.7% 53.0% 64.2% 54.9%

experienced staff to conduct (re)certifications

Note 1: Percentages were calculated for PHA/projects that provided training to experienced staff.

(Re)Certification Staff, by Program Type
Program Type
Public Housing
E-5

Appendix E – Project Staff Questionnaire Descriptive Analysis
The top three topics most frequently covered in training for experienced staff were covered in
training over 90 percent of the time. Training related to general HUD policies and rules for
conducting (re)certifications was covered about 94 percent of the time, and training related to
HUD or PHA/project changes in polices or procedures relating to (re)certifications were covered
about 92 percent of the time. Training topics covering tools available in the PHA/project were
covered about 90 percent of the time. Training topics did not differ consistently across
PHA/projects in different programs, as shown in Exhibit E-3c.
Exhibit E-3c.

Experienced Staff Training Topics

in the Past 12 Months, by Program Type

Program Type
PHA-
Administered Owner-
Public Housing Section 8 Administered
Total
HUD policies and rules for conducting (re)certifications 91.4% 94.9% 95.1% 93.8%
Changes in HUD or PHA/project policies or procedures 89.3% 90.6% 95.1% 91.9%
related to (re)certifications
Tools available in the PHA/project (e.g., software, forms) 90.7% 94.0% 87.0% 90.2%
to help in conducting (re)certifications
Note 1: Percentages were calculated for PHA/projects that provided training to experienced staff.
Transfer of Information about Changes in HUD Policies. PHA/projects used a variety of
methods to inform staff about changes in HUD eligibility and rent calculation policies. The most
commonly used method by PHA/projects was one-on-one discussions between supervisors and
staff at 78 percent. The second most used method was through staff meetings at 70 percent, and
distributing to the staff copies of HUD announcements was the third most commonly used
method at 65 percent. Distributing detailed staff memos describing the changes and providing
instructions for implementation was also used about 54 percent of the time.
Exhibit E-4a.

Methods to Communicate Changes in HUD/PHA/Owner

Policies to Staff in the Past 12 Months, by Program Type

Program Type
PHA-
Administered Owner-
Public Housing Section 8 Administered
Total
One-on-one discussions between supervisors/managers and
74.1% 72.8% 86.4% 77.7%
staff
Staff Meetings 71.8% 75.7% 61.9% 69.8%
Copies of HUD announcement distributed to staff 52.4% 65.4% 79.6% 65.1%
Detailed staff memo describing the changes and providing
43.5%
60.3% 61.2% 54.3%

instructions for implementation
E-6

Appendix E – Project Staff Questionnaire Descriptive Analysis
When PHA/projects had questions concerning HUD policies, they used a variety of methods to
seek answers. Eighty-three percent of PHA/projects referred to the HUD/PHA/owner-
administered manual, 72 percent asked their HUD field office or other HUD staff, and 51 percent
asked questions at a HUD training session. More detailed numbers by program type are shown in
Exhibit E-4b.
Exhibit E-4b.

Methods for Getting Answers to Questions about

HUD Policies in the Past 12 Months, by Program Type

Program Type
PHA-
Administered Owner-
Public Housing Section 8 Administered
Total
Referred to HUD/PHA/owner memo or manual 80.0% 80.8% 87.5% 83.0%
Asked HUD field office or other HUD staff 64.0% 87.7% 69.0% 72.2%
Used Internet/web-based information/training 45.0% 66.4% 52.6% 53.5%
Asked questions at a HUD training session 44.0% 57.5% 53.0% 50.9%
Quality Control via Work Monitoring. Around 72 percent of PHA/projects usually or always
have the team leader or supervisor monitor (re)certification work. More PHA/projects are
turning to outside auditors to monitor their work, increasing from 29 percent in FY 2006 to about
33 percent in FY 2007. In order to monitor the quality of work performed by (re)certification
staff, PHA/projects used various methods. The most used technique to monitor (re)certifications
was reviewing files after completion (76%), followed by using a pre-designed form to check key
steps at 70 percent. Other commonly used techniques were computer programs (66%), making
individualized notes for each case reviewed (52%), and discussing the (re)certification with staff
after completion (47%) as shown in Exhibit E-5a.
Exhibit E-5a.

Techniques Used to Monitor (Re)Certifications,

/
73.0% 85.0% 71.5% 75.7%
67.0% 78.1% 67.0% 70.0%
61.6% 72.5% 65.6%
49.0% 56.8% 50.0% 51.5%
i 44.0% 49.3% 48.5% 47.0%
in the Past 12 Months, by Program Type
Program Type
PHA-
Administered Owner-
PHA projects Usually or Always:
Public Housing Section 8 Administered
Total
Review files after completion
Use pre-designed form to check key steps
Use computer program 61.5%
Make individualized notes for each case reviewed
Discuss (re)certification w th staff after completion
In determining which cases to select for review, PHA/projects most frequently randomly spot
checked a percentage of all cases (65%). Overall, 33 percent of PHA/projects reported
reviewing all cases. Other methods commonly used to select cases for review were: checking
(re)certifications conducted by new staff (41%), checking cases on certain dates or times of the
year (34%), and checking certain cases completed within a given period (33%).
E-7

Appendix E – Project Staff Questionnaire Descriptive Analysis
In the past year, PHA/projects found various errors during the course of monitoring
(re)certifications. The error most reported was finding mistakes in calculating rent, with 71
percent of PHA/projects reporting the error as occurring often or occasionally. Sixty-two percent
of PHA/projects occasionally found missing or incomplete verifications of income and 56
percent often or occasionally found missing or incomplete verifications of expenses. Owner-
administered projects in general were less likely to find errors than Public Housing projects and
PHA-administered Section 8 projects.
Exhibit E-5b.
l 67.5% 83.6% 65.0% 70.9%
ificati 64.0% 77.4% 47.5% 61.5%
ificati 51.5% 57.5% 50.5% 56.2%
PHA-
Exhibit E-5c.

lete 89.5% 95.9% 79.0% 87.4%
information
l 41.5% 61.0% 34.5% 44.2%
36.0% 53.4% 14.5% 34.4%
27.5% 35.7% 40.0% 34.3%
Types of Errors Found in the Past 12 Months, by Program Type
Program Type
PHA-
Administered Owner-
Types of errors found often or occasionally:
Public Housing Section 8 Administered Total
Mistakes in Ca culating Rent
Missing or incomplete ver ons of income
Missing or incomplete ver on of expenses
In this year’s questionnaire, when asked about the cause of errors in eligibility determinations
and rent calculations, PHA/projects were asked to focus on the frequency of errors instead of the
seriousness of the error. The most commonly stated cause that occasionally or often resulted in
errors was tenants providing inaccurate or incomplete information (87%). Other frequently cited
reasons were: complex HUD regulations for rent calculations (44%), not having enough staff to
handle the workload (34%), and frequent changes in HUD regulations (34%).
administered Section 8 projects were most likely to cite tenants providing incomplete/inaccurate
information as occasionally or often causing errors at 96 percent. Exhibit E-5c details the most
frequently reported causes of some errors.
Underlying Causes of Errors in Eligibility Determinations
and Rent Calculations in the Past 12 Months, by Program Type
Program Type
PHA-
Administered Owner-
Issues Often or Occasionally Causing Errors:
Public Housing Section 8 Administered Total
Tenants providing inaccurate/incomp
Complex HUD regulations for rent calcu ations
Not having enough staff to handle the workload
Frequent changes in HUD regulations
A new question was included in this year’s questionnaire, asking PHA/projects whether HUD,
field staff, or outside contractors had reviewed their files in the past 2 years. In the last 2 years,
23 percent overall of PHA/projects had their files reviewed by a HUD related group such as
headquarters, field offices, and field staff. About just as many PHA/projects had their files
reviewed by outside auditors and contractors at 22 percent of PHA/projects. Only a few had
their files audited by a state housing authority at about 5 percent.
E-8

Appendix E – Project Staff Questionnaire Descriptive Analysis
Conducting Tenant Interviews. When conducting both initial certifications and annual
recertifications, the most common method of obtaining household information was by
conducting an in-person interview (92% and 90%, respectively). The second most common
method was having the tenant complete a form and return it via mail or in-person (62% and 61%,
respectively). A typical initial certification interview required about 36 minutes to complete, on
average, while a typical recertification interview required only 30 minutes. PHA-administered
Section 8 projects took the longest to conduct both initial certifications (40 minutes) and annual
recertifications (31 minutes), while Public Housing projects took the least amount of time to
conduct both initial certifications (32 minutes) and annual recertifications (28 minutes).
Exhibit E-6a.

/
i l 31.8 39.4 38.9 36.4
i l
27.8 31.0 31.6 30.1
PHA/projects were asked how
Most PHA/projects
date (28.8%).
year’s questionnaire.
Exhibit E-6b.

10.5% 5.5% 0% 5.9%
40.5% 39.0% 9.5% 28.8%
35.5% 43.2% 77.5% 52.9%
3.0% 0% 8.5% 4.1%
Minutes Spent on Typical Interviews, by Program Type
Program Type
PHA-
Administered Owner-
PHA projects Usually or Always:
Public Housing Section 8 Administered
Total
Average number of minutes spent on a typ ca
initial certification interview
Average number of minutes spent on a typ ca
annual recertification interview
In this year’s questionnaire, a new question was added regarding the procedures used by
PHA/projects to complete the annual recertification process.
many days prior to the effective date they started the recertification process.
started the process 120 days prior to the effective date (52.9%) or 90 days prior to the effective
Another new question asked PHA/projects if their annual recertification
procedures were the same for households with stable income as for households with more
volatile income. An overwhelming majority of PHA/projects stated that procedures were the
same for both (97%). In addition to adding questions, some questions were excluded from this
A question regarding tenant’s difficulty in responding to questions on
certain topics was excluded because its results did not vary significantly from year to year.
Number of Months Prior to Effective Date PHA/projects Start the Annual
Recertification Process, by Program Type
Program Type
PHA-
Administered Owner-
Number of Months
Public Housing Section 8 Administered
Total
Less than 90 days
90 days
120 days
Greater than 120 days
E-9

Appendix E – Project Staff Questionnaire Descriptive Analysis
Over 56 percent of PHA/projects have a proportion of tenants where less than 20 percent speak a
language other than English as their primary language. In these cases where a proportion of
tenants did speak a language other than English, PHA/projects used a combination of methods to
communicate with their tenants. On average, a majority of tenants brought their own translators
(76%). Sixty percent of PHA/projects had bilingual staff available, and 38 percent of
PHA/projects brought in translators to communicate with tenants. A smaller percentage of
PHA/projects used other methods, such as translating documents, and using Internet translating
services to communicate with their tenants (14%).
Computers and Software Use. Computer software is playing an increasingly integral part in
PHA/projects daily tasks. In the past 12 months, almost all PHA/projects utilized computers and
computer software when performing various (re)certification and other administrative tasks.
Over 90 percent overall of PHA/projects use computer software to calculate rent, income, or
allowances, print the 50058/50059 form, input verified information, print letters to tenants, and
record tenant demographics In addition, an increasing number of PHA/projects are using
computer software to submit tenant information to HUD. As has been the case, using computer
software to interview tenants and record answers was one of the least frequently reported uses.
For a more detailed look, by program type of computer utilization, refer to Exhibit E-7a.
Exhibit E-7a.

Calcul 96.5% 98.6% 96.0% 96.9%
91.5% 99.3% 94.5% 94.7%
91.5% 93.8% 92.0% 92.3%
91.5% 91.8% 91.5% 91.6%
90.5% 95.9% 85.5% 90.1%
81.0% 94.5% 91.0% 88.3%
29.5% 32.9% 25.5% 28.8%
Computer Software Uses in the Past 12 Months, by Program Type
Program Type
PHA-
Administered Owner-
Public Housing Section 8 Administered
Total
ate rent
Print the 50058/50059 form
Input Verified Information
Print letters to the tenants
Maintain demographic information about the residents
Submit tenant information to HUD
Interview tenants and record answers
In addition to asking about the different tasks performed by PHA/projects using computers and
computer software, the Project Staff Questionnaire also asked more specifically what percent of
a PHA/project’s 50058/50059 data was transferred to HUD via PIC/TRACS. Almost all
PHA/projects (97%, on average) reported doing so, an increase from 95 percent in 2006. The
percentage of PHA/projects who reported transferring all their 50058/50059 data through this
method increased from 74 percent in 2006 to 81 percent in 2007. This year we saw an increase
in Public Housing projects transmitting their information directly to HUD, as opposed to through
another agency or other methods.
E-10

Appendix E – Project Staff Questionnaire Descriptive Analysis
Exhibit E-7b.

Transmission of 50058/50059 Data to HUD via

PIC/TRACS in the Past 12 Months, by Program Type

Program Type
PHA-
Administered Owner-
Public Housing Section 8 Administered
Total
Transmitted directly
Transmitted through another agency
Transmitted by other methods
Average percentage of 50058/50059 data
transmitted via PIC/TRACS per
PHA/Project
Percent of PHA/projects using PIC/TRACS
to submit some or all 50058/50059 data
88.5% 88.4%
6.0% 6.2%
11.0% 7.5%
49.5% 74.2%
42.0% 19.2%
17.5% 12.5%
96.6% 96.0% 97.9% 96.9%
97.5% 96.6% 98.0% 97.4%
This year’s questionnaire posed a new question addressing the increasing trend towards a
paperless (or less paper) process of conducting annual recertifications. Most PHA/projects when
asked, still keep copies of all 50058/50059’s, including less recent ones, in the household file at
91 percent overall. Owner-administered projects were most likely to keep historical copies in the
household file at 94 percent, and PHA-administered Section 8 projects were least likely at 84
percent. Public Housing projects were similar to owner-administered projects at 93 percent. The
few PHA/projects who stated that they did not keep historical copies in the household file for the
most part printed them from the system when they needed historical information, or kept them
archived or on site.
Verification Procedures. The most frequently reported methods of keeping track of when
verification information was received were: keeping files with outstanding verification in a
separate location (70%, on average), making a notation in the tenant file (59%, on average), and
marking information using a paper list or tickler file (47%, on average).
Exhibit E-8a.

Methods for Keeping Track of

Verification Information, by Program Type

Program Type
PHA-
Administered Owner-
Public Housing Section 8 Administered Total
Kept files with outstanding verification in
74.5%
70.5% 64.5% 69.8%

separate location or folder

Kept record in tenant file 58.5% 69.9% 51.0% 58.8%

Marked on a paper list/tickler file (tracking
42.5%

sheet, monitoring form, checklist, or log)
52.7% 48.0% 47.3%

With respect to the person responsible for keeping track of verification requests and returns, a

majority of the time the project (re)certification staff were the ones responsible (85%, on

average). PHA-administered Section 8 projects were least likely to have a supervisor keep track

of verification status (22%), whereas owner-administered projects were most likely (47%).

However, this is most likely due to owner-administered projects relatively small number of staff

in comparison to the other program types.

E-11

Appendix E – Project Staff Questionnaire Descriptive Analysis
In last year’s questionnaire, PHA/projects were asked when they started using TASS and EIV to
verify income and benefits. This year, the question was modified to ask them if they had used
either TASS or EIV in the past year. Overall, 51 percent of PHA/projects had used TASS
between October 1, 2006 and September 30, 2007. Eighty-two percent of PHA/projects had
used EIV in the same period.
Of the PHA/projects who have ever used TASS or EIV, the most frequent uses were to verify:
Social Security/SSI benefits (89%), employment income (75%), disability status (64%) and dual
entitlement benefits (68%). Owner-administered projects were much less likely to frequently use
TASS or EIV to verify information. A breakdown of the frequency of use of TASS and EIV is
broken down in Exhibit E-8b.
Exhibit E-8b.
Social 94.5% 95.2% 74.5% 88.8%
88.4% 89.0% 41.8% 75.0%
75.3% 68.3% 43.2% 63.8%
73.2% 71.0% 48.2% 68.2%
71.0% 30.5% 61.2%
51.5% 49.6% 27.0% 43.8%
Frequency of Use: TASS, EIV to Verify Certain Factors, by Program Type
Program Type
PHA-Administered Owner-
Use TASS or EIV usually or always to verify:
Public Housing Section 8 Administered
Total
Security Benefits
Employment Income
Disability Status
Dual Entitlement Benefits
Unemployment Benefits 75.8%
Black Lung Benefits
PHA/projects who used EIV sometimes would sometimes supplement EIV’s information due to
discrepancies found between EIV and tenant data, because EIV’s information was out of date, or
to double check. Those that supplemented EIV data listed pay stubs, third party verification, and
employer information as other methods used to supplement.
More than 88 percent of PHA/projects verified all components of tenant information at least
occasionally, on average. More than 73 percent always verified tenant information. The types of
household information most likely to always be verified were: Social Security Benefits (97% of
PHA/projects, on average), Income from employment (96%, on average), Other Sources of
Income (94%, on average), and TANF/Welfare benefits (90%, on average). Citizenship was the
least likely to always be verified at 73 percent of PHA/projects, on average. Other types of
household information less likely to always be verified were: Age of Household Members and
Social Security Numbers (74%), Disability Status (79%), and full time student status (80%).
Verification rates in all four of the top items verified have varied from year to year, as shown in
Exhibit E-8c.
E-12

Appendix E – Project Staff Questionnaire Descriptive Analysis
Exhibit E-8c.

Yearly Comparison of Rates of Verification

PSQ 2005 PSQ 2006 PSQ 2007
Social 93.7% 89.5% 97.3%
91.3% 86.5% 96.2%
87.4% 82.0% 94.0%
85.4% 81.8% 89.9%
most difficult to verify.
On the other hand, Other
were also reported as being difficult.
Exhibit E-8d.

65.6% 78.8% 56.5% 65.8%
65.5% 71.9% 47.5% 60.6%
62.0% 65.1% 50.5% 58.6%
48.0% 62.4% 46.0% 51.1%
of Various Items, by Program Type
Targets of Verification Procedures
Always Verified:
Security Benefits
Income from Employment
Other Sources of Income
TANF/Welfare Benefits
In addition to identifying how often PHA/projects identified household income, the Project Staff
Questionnaire also asked PHA/projects to identify which types of household information were
In this year’s questionnaire, instead of asking PHA/projects how
difficult different types of household information were to verify, PHA/projects were asked how
often they had difficulty. Sporadic Income, in addition to being less likely to be always verified,
was listed as the most difficult to verify (66% of PHA/projects).
Sources of Income, which was listed as more likely to always be verified, was reported as being
more difficult to verify (61%). Income from Employment (59%) and Medical Expenses (51%)
Difficulties in Verifying Tenant Information
in the Past 12 Months, by Program Type
Program Type
PHA-
Tenant Information Often or Sometimes
Administered Owner-
Difficult to Verify:
Public Housing Section 8 Administered
Total
Sporadic/infrequent/seasonal employment
Other sources of income
Income from employment
Medical expenses
When asked to name causes of problems that emerged when obtaining complete verifications,
this year, PHA/projects were asked how often issues emerged, instead of how serious the issues
were. PHA/projects cited employers not responding to requests in a timely manner as often or
sometimes causing problems at 83 percent. Tenants providing incomplete or inaccurate
information (80%) and employers providing incomplete information (76%) were the other two
reasons most cited as causing problems. More detailed figures broken down by program type are
shown in Exhibit E-8e.
E-13

Appendix E – Project Staff Questionnaire Descriptive Analysis
Exhibit E-8e.

Causes of Problems in Obtaining Complete

Verifications, in the Past 12 Months, by Program Type

Program Type
PHA-
Administered Owner-
Issues Caused Serious or Some Problems:
Public Housing Section 8 Administered
Total
Employers not responding to requests in timely
87.5% 90.4% 73.5% 83.2%
manner
Tenants providing incomplete/inaccurate
82.0% 89.7% 72.0% 80.4%
information
Employers not providing all requested
76.0% 87.0% 67.5% 75.8%
information
With respect to the level of cooperation of various individuals and institutions when verifying
tenant information, insurance companies and health care providers were most likely to be usually
uncooperative. Twelve percent of PHA/projects reported insurance companies as being usually
uncooperative, 12 percent reported financial institutions and 9 percent of PHA/projects reported
health care providers as being so. Overall, the percentage of PHA/projects who viewed
insurance companies and health care providers as being usually uncooperative decreased from
last year.
Exhibit E-8f.

Uncooperativeness of People in Obtaining Verification

) 10.0% 17.8% 8.5% 11.5%
Fi 11.5% 14.4$ 10.0% 11.7%
6.5% 12.3% 8.5% 8.8%
Information, in the Past 12 Months, by Program Type
Program Type
PHA-
Administered Owner-
Usually Uncooperative:
Public Housing Section 8 Administered
Total
Insurance companies (e.g., health insurance
nancial institutions (e.g., banks, investment firms)
Health care providers (e.g., doctors, physicians,
pharmacies)
When problems and difficulties arose in verifying information, PHA/projects resolved these
issues though a variety of methods. Most prevalently, PHA/projects called third-parties to obtain
information (92%). PHA/projects also sent follow-up letters to third-parties (87%), called
tenants (86%), sent follow-up letters to tenants (80%), and used electronic verification or data
matching such as EIV (77%). On average, 69 percent of PHA/projects reported resorting to
accepting other/less preferred verification, up slightly from 63 percent in FY 2006. Owner-
administered programs were significantly less likely to resort to accepting less preferred
verification. The number of PHA/projects who used EIV when problems and difficulties arose
increased from 74 percent in FY 2006 to 77 percent.
E-14

Appendix E – Project Staff Questionnaire Descriptive Analysis
Exhibit E-8g.

Procedures Used When Verification Was Not Provided

As Requested in the Past 12 Months, by Program Type

Program Type
PHA-
Administered Owner-
Public Housing Section 8 Administered
Total
Called third-party 91.0% 94.5% 90.5% 91.8%
Sent follow-up letter to third-party 87.5% 87.0% 87.5% 87.4%
Called tenant 89.0% 87.7% 83.0% 86.4%
Sent follow-up letter to tenant 89.5% 85.6% 66.0% 79.9%
Used electronic verification or data matching
90.5% 92.5% 52.0% 76.9%
(e.g., EIV)
Accepted other/less preferred verification 66.0% 84.2% 62.0% 69.4%
C. Conclusion.
Overall the PSQ analyses portrayed a complex and interesting picture of PHA/Project practices
and procedures. Most PHA/projects train (re)certification staff, transfer information about
changes in HUD policies to their staff, monitor (re)certification work quality, use computer
software for various purposes, and verify most (re)certification information. Some findings
differed by program type. Owner-administered PHAs were more likely to differ from the other
two program types, perhaps due to their size. They had the fewest staff, fewest (re)certification
staff, and fewest units supported by the (re)certification staff, on average. Owner-administered
projects also trained the fewest staff for the fewest hours, and were the least likely to use TASS
or EIV systems to verify information. They are also much more likely to start the annual
recertification process 3 to 6 months before the effective date. Lastly, they seem to have fewer
difficulties verifying tenant information, which would explain why they are also the least likely
to resort to accepting less preferred verification information when difficulties arose in obtaining
that information.
This year’s study introduced new questions focusing on: the tenure of (re)certification staff,
outside reviews of PHA/project files, and the timing of procedures used to complete the annual
recertification process. The questionnaire also expanded the examination of PHA/projects use of
EIV and TASS. The results of these questions highlighted some new differences in procedures
between owner-administered projects and their counterparts, as mentioned above. For the future
studies, it would be helpful to develop and validate additional items specifically targeting
potential difficulties in conducting training, using computer software, and getting support from
various sources in verifying tenants’ information. Focus groups and cognitive interviewing might
aid in revision of the PSQ items by focusing attention on the specific circumstances and issues
faced by the PHA/projects. Having detailed indicators of the positive, as well as negative
aspects of the (re)certification process, defined by the PHA/Project staff, would provide a more
complete picture of the issues faced by the PHA/project, as well as may provide a better link
between PSQ information and the estimation of payment and income errors.
E-15

Appendix F—Multivariate Analysis

Appendix F: Multivariate Analysis
The multivariate analyses sought to further examine gross rent error and different types of error
in the certification/recertification process made by PHA/project personnel. We linked the error
measures to major project and household characteristics using multivariate analytic techniques.
The analysis allowed us to identify potential project or tenant characteristics that help account for
(or predict) each error measure, net of the effects of other variables. We attempted to meet the
specified study objectives by examining two analytic questions:
1. Holding other conditions constant, what project and household variables (e.g.,
training for certification staff, household financial characteristics) accounted for rent
error and errors made by staff?
2. What was the effect size (or relative strength) of a predictor variable in accounting for
rent error?
1
To inform HUD’s program improvement initiative, the focus of this analysis was project
variables and types of staff error in connection to rent error. It was necessary to take this focus to
address study Objectives 5, 6, 8, 12, and 13 regarding the influence on rent error by project
factors vis-à-vis tenant characteristics. Project variables and errors made by staff that contributed
to rent error are possibly controllable in future work. Knowledge about project features in
relation to rent error is thus more useful for program improvement.
Prior studies have found that tenant background and financial situations were predictive of rent
errors. Specifically, higher incomes from different sources and the complexity of
income/expenses and allowance sources were related to larger rent errors. These variables,
however, are beyond the control of staff and not subject to program remedy. In this study, tenant
variables were primarily considered as covariates for statistical control in the examination of the
effects of error and project operation variables. We did, however, examined joint effects or the
interaction between tenant variables and project variables.
Conceptual Framework
This analysis was largely guided by the conceptual framework used in the previous studies (see
Figure F-1). Rent error was considered as the outcome influenced by three sets of factors,
household characteristics, project characteristics/practices, and staff errors; whereas each type of
error made by staff is thought to relate to household and project factors. Project variables are
conceptualized in several subcategories: project organization and staffing, certification practice,
verification practice, system automation based on computer application, and project survey
respondents’ perception of causes to errors (e.g., staff shortage). The conceptual framework
recognizes that different process errors made by staff may be inadequate in typology and
measurement to fully explain rent error; and project characteristics and quality control processes
may have additional impact on rent error. For example, staff qualifications and the use of
computer automation may potentially reduce rent errors through improved quality control in
broad areas beyond coping with particular types of process errors.
Estimation of the effect size for predictor variables requires valid measurement of each variable, sensible model
specifications, and good model fit. In survey data analysis, however, it is always challenging to obtain accurate
measures of every variable and specify models that generate robust estimates of effect sizes.
F-1

1

Appendix F: Multivariate Analysis
Figure F-1
Conceptual Framework for Modeling Rent Error
Tenant factors
Demographics

Income sources

Allowance for expenses

Elderly or disabled tenant

Types of error
Overdue recertification error
Allowance calculation error
Income calculation error
Consistency error
Procedure error
Transcription error
Administrative error
Other calculation error
Project factors
Organization (size, staffing,

training)

Certification

Automation/IT use

Verification

Perceived error causes

Rent error
Analytic Approach
Project-level data were merged with tenant-level data for the analysis. We took tenant gross rent
error and types of process error as outcomes in two sets of models. For gross rent error models,
tenant variables, types of error, and project variables were used as predictor variables. For
process error types, tenant and project variables were used as predictor variables.
The analysis included three steps. First we constructed composite variables to measure key
concepts that hypothetically influence rent errors. Second we conducted preliminary analysis to
identify predictor variables that were important to account for gross error, test alternative
modeling approaches, and perform diagnostic analysis. Finally, we estimated models to address
objectives specified in the Analysis Plan and additional issues of interest.
All statistical analyses were run with SAS. To adjust the design effect resulting from the
clustered sampling design, we used the procedures of SURVEYREG for multiple regression
modeling of gross rent error, SURVEYLOGISTIC for modeling different types of error, and
SURVEYMEANS or SURVEYFREQ to examine weighted descriptive statistics. For initial
analysis we used PROC MIXED for estimating two level variance, and SAS conventional
procedures to examine raw data and residual scores of the predicted gross error.
Construction of Composite Variables
The large number of tenant and project data items required editing and rescaling to build
composite indicators of the concepts to explain rent errors. The process is summarized below.
General Strategy for Selecting Variables to Include in the Analysis. Predictor variables were
selected for analyses based on a mixture of hypothesis-driven and data-driven approaches. First,
F-2

Appendix F: Multivariate Analysis
variables predictive of rent errors in the multivariate analyses conducted in past HUDQC studies
(2000-FY 2006) were selected; this was the primary approach used to select tenant variables.
Second, variables were selected based on hypotheses that they would be associated with rent
errors; this was the primary approach used to select Project Staff Questionnaire (PSQ) variables.
Some variables that were initially selected were excluded based on preliminary analyses. For
example, variables were excluded if they were redundant with other variables included in the
analysis based on examination of variance inflation factors. Details of the process of constructing
and selecting variables for the analysis are explained below.
Tenant-Level Data. Tenant data are the source for outcome measures of the analysis (dollar
amount of rent errors and types of processing error) and household characteristics and financial
conditions. Largely following previous studies, we constructed composite variables from the
original data items and tested the variables’ bivariate and multivariate relationships with gross
rent error. Specifically, to the extent that such variables were available in the FY 2007 study, we
started with tenant variables that were predictive of gross rent error in past HUDQC multivariate
analyses (2000-FY 2006). In the process we examined descriptive statistics and missing values.
The goal was to determine what variables were related to the error and therefore to be included
into the multivariate equations. Exhibit F-1 lists all the modeled variables with descriptive
statistics.
Gross rent error is the sum of the absolute values of over- and underpayments for each tenant.
We took the logarithm of gross rent error to compact its skewed distribution (due to few cases
with large error and many with zero error). Tenant background is represented by a binary-coded
indicator with one for household heads who were elderly (age 62 or older) or disabled. Tenant
financial conditions were represented by a set of measures, each rescaled by the grand mean
(known as centered scaling) to make interpretation more straightforward
2
. Tenant financial
condition variables were:
♦ Households with elderly or disabled members
♦ Total earned income, in dollar value
♦ Total public assistance income, in dollar value
♦ Total pension income, in dollar value
♦ Number of allowances
♦ Total other income, in dollar value
♦ Number of income and expense sources
2
With such centered scaling, the intercept of the regression model is the log gross rent error for tenants who had
mean values on all the predictor variables; and each regression coefficient as the change in log gross rent error
associated with one unit change around the grand mean of the given predictor variable.
F-3

Appendix F: Multivariate Analysis
Exhibit F-1
Variable Mean Std Dev Minimum Maximum
Unweighted Descriptive Statistics
for Tenant-Level Variables Used in the Multiple Regression Analysis
Log gross error 1.293 1.603 0.000 6.919

N household members -0.058 1.496 -1.185 9.815

Gross adjusted income -0.196 8.985 -12.506 89.787

N bedrooms -0.042 1.125 -1.873 24.127

Earned income -0.006 0.727 -0.416 6.584

Other income -0.005 0.503 -0.219 3.781

Public assistance income -0.004 0.337 -0.127 1.873

Pension income -0.010 0.950 -0.936 6.064

Age of household head 0.227 19.567 -39.415 47.585

Total number of sources of

income/expenses
0.025 3.068 -3.116 22.884
Number of allowances -0.005 0.632 -1.286 1.714
Elderly or disabled household 0.578 0.494 0.000 1.000
Proportion of items with
0.001 0.263 -0.186 0.814
transcription errors
Proportion of item categories
0.002 0.209 -0.059

without written 3rd party ver
0.941

Overdue recertification error 0.015 0.123 0.000 1.000
Other calculation error 0.058 0.234 0.000 1.000
Allowance calculation error 0.032 0.175 0.000 1.000
Income calculation error 0.027
Consistency error 0.195

Procedure error 0.250

Transcription error 0.380
Administrative error 0.417

Public Housing 0.333

Section 8 0.333

0.161 0.000 1.000
0.396 0.000 1.000
0.433 0.000 1.000
0.486 0.000 1.000
0.493 0.000 1.000
0.471 0.000 1.000
0.471 0.000 1.000
Source: HUDQC FY 2007 tenant-level data

Note: Tenant background variables are centered by grand mean, with variable name start with c_.

The typology of error was defined and measured the same way as in prior studies (see
Introduction and Methodology for details), including:
♦ Overdue recertification error
♦ Allowance calculation error
F-4

Appendix F: Multivariate Analysis
♦ Income calculation error
♦ Consistency error
♦ Procedure error
♦ Transcription error
♦ Administrative error
♦ Other calculation error
Each type of error was indicated by dummy variables with one for error and zero for no error.
Three interval measures of error were constructed—transcription and verification error rates and
the total number of errors. Note that the verification error used in prior studies was removed
from the analysis. The measure was the proportion of the number of verified items over the
number of items that were available for verification. If a household did not have any item for
verification, then it would be coded as no verification error, though in reality this may not be
true. Thus the variable might not accurately indicate a mistake; it represents instead the extent of
the verification effort to the extent that items were available for verification.
Project-Level Data. The FY 2007 Project Staff Questionnaire (PSQ) survey collected new data
items and modified some of the questions in the instrument. The new items were, for example,
the proportion of certification staff with at least one year and five years experience, whether
certification staff worked on ongoing caseloads, and the proportion of tenants speaking
languages other than English. The raw data items were examined systematically through data
editing and univariate, bivariate, and initial multivariate analysis. Composite variables were built
with original items, giving preference to those with conceptual importance in accounting for the
outcomes (rent error and process error), reasonable variation (frequency distributions were not
concentrated in one category), and minimal missing cases. Exhibit F-2 lists all the modeled
variables with descriptive statistics.
Data recoding/rescaling entailed two approaches. One was to create accumulated counts of staff
activities (e.g., the number of items to review/monitor, the number of personnel and the number
of methods used in review and verification) from original data items that were expected to be
relevant to rent error. This approach generated composite variables of interval scale. The other
approach was recoding original data items to create dichotomous (dummy) variables. It required
examination of the raw data frequency distributions and missing values to ensure adequate
differentiation and available cases. For example, in creating dummy variables for perceived
difficulty in certification verification, we recoded cases to define “always” vs. others
(“somewhat” and “not difficult”). If few cases were in a given category, we combined two
categories that were meaningful in making a distinction relevant to rent error (e.g., combine
“usually verify” with “always verify”). We also examined the bivariate relationship between the
constructed variables with rent error and adjusted the coding to make the variable more relevant
to the outcome measures. For example, educational requirements for certification staff provided
categories ranging from “no requirement”, “high school graduation or GED”, “2 years of
F-5

Appendix F: Multivariate Analysis
college”, and “4 years of college”. Two dummy variables were created, with one representing
“no requirement” versus “high school or more” and the other contrasting “no college required”
against “2 years or more of college”. Testing the two variables, we decided to include only the
first variable in modeling due to its closer relationship with rent error.
Exhibit F-2 (Page 1 of 2)

Unweighted Descriptive Statistics

for Project-Level Variables Used in the Multiple Regression Analysis
Variable Mean Std Dev Minimum Maximum
N units supported
1143.76 3686.65 1 44083
Units per staff
62.674 63.241 0.0714 574
Units per cert staff
156.727 124.471 1 728.5
Certified staff total staff ratio
0.451 0.282 0.007 1
% cert staff only do certification
0.232 0.364 0 1
Ongoing caseload (yes=1, no=0)
0.587 0.492 0 1
% cert staff 5+ yr experience
0.606 0.356 0 1
Require hs grad+ (yes=1, no=0)
0.938 0.241 0 1
Other hiring requirement (y=1, n=0)
0.978 0.146 0 1
All experienced cert staff trained (y=1, n=0)
0.448 0.497 0 1
Any policy/procedure change (y=1, n=0)
0.388 0.487 0 1
HUD review files (y=1, n=0)
0.743 0.437 0 1
N months begin process before recert
3.285 1.238 0 5
Require interview for init cert (y=1, n=0)
0.921 0.270 0 1
Require interview for recert (y=1, n=0)
0.831 0.375 0 1
All info used PIC/TRACS transfer (y=1, n=0)
0.800 0.400 0 1
Direct transfer to HUD (y=1, n=0)
0.755 0.430 0 1
Used TASS last yr (y=1, n=0)
0.510 0.500 0 1
Use either TASS or EIV (y=1, n=0)
0.857 0.350 0 1
N activities using computer
9.666 2.443 0 14
Frequency of TASS or EIV last yr
3.656 2.254 0 6
N persons monitor cert
2.070 1.430 0 6
N methods used to monitor
4.008 2.113 0 9
F-6

Appendix F: Multivariate Analysis
Exhibit F-2 (Page 2 of 2)

Unweighted Descriptive Statistics

for Project-Level Variables Used in the Multiple Regression Analysis

Variable

Monitored by supervisor
0.718 0.450 0 1
1Monitored by auditor
0.336 0.472 0
Monitored by HUD
0.205 0.404 0 1
1
1
Monitor w computer
0.653 0.476 0
Monitor w pre-form
0.711 0.453 0
N methods used to review cases
2.539 1.557 0 7
16
10
N items always verified
12.990 3.096 0
N items never verified
1.592 1.486 0
Mean Std Dev Minimum Maximum
N items very difficult to verify 0.705 1.619 0 14

Supervisor keep track of ver requests 0.347 0.476 0 1

N methods used to follow up
4.998 1.303 0 7
1
5
1
1
Other less preferred verification accepted
0.710 0.454 0
N of perceived errors
0.393 0.820 0
Any perceived errors
0.257 0.437 0
Staff shortage
0.022 0.146 0
Source: HUDQC FY 2007 project-level data

Note: Of the 549 projects contacted, 3 did not respond, thus leaving 20 tenant records without project data.

The following composite/rescaled variables by conceptual groups were used in the final
modeling:
♦ Staffing and Workload
ƒ Organization size: the total number of households served
ƒ Workload: unit/all staff ratio, unit/certification staff ratio, certification staff/all staff
ratio, and the assignment of ongoing caseload of specified tenants
ƒ Certification staffing experience: percent of staff having more than one-year
experience and percent of staff having more than five-years experience
ƒ Certification staff educational hiring requirements; a dummy variable indicating
requirement of high school/GED or more versus no minimal requirement
F-7

Appendix F: Multivariate Analysis
ƒ Other credential requirements in hiring: a dummy variable indicating any requirement
of qualifications (including special housing-related training/certification,
administrative and clerk skills, computer skills, and other skills) versus no additional
requirements
ƒ Experienced certification staff training: a dummy variable indicating provision of
training last year versus no training for experienced certification staff
3
♦ Certification and Recertification Process
ƒ Timeline to start recertification: A number of data items measuring the length of time
for projects to start recertification activities before the effective date. Closely
correlated, these variables were tested in relation with rent error. The number of
months the recertification process started before expiration was used in the modeling
ƒ Personal interview requirement in certification/recertification: Two dummy
variables—respectively for certification and recertification--indicate a personal
interview requirement versus no such requirement
ƒ Method of reviewing certification: counts of different methods that were usually or
always used to review certification materials, ranging from sitting in on the interview,
reviewing files while the (re)certification was in process, discussing the
(re)certification with staff, to using a computer program to review
ƒ Change in policy and procedure: a dummy variable indicates any changes
implemented in policy/procedures regarding eligibility or rent calculation versus no
such changes in the past two years
ƒ HUD review certification: a dummy variable indicates whether HUD reviewed the
(re)certification work in the last year
♦ Automation with Computer/IT Application
ƒ PIC or TRACS use: a dummy variable indicates using either PIC or TRACS in
operation versus using neither
ƒ Direct transfer of data to HUD: a dummy variable indicates directly transferring data
to HUD versus transferring data via other organizations
ƒ TASS use: a dummy variable indicates using the TASS in process
ƒ EIV use: a dummy variable indicates using EIV
The measures of certification staff training are important but the data were available only for subgroups of the
sampled projects. Items on training of new staff were available for less than one-half of the project sample that hired
new staff and assigned them to conduct certifications last year. Therefore it was impossible to use the detailed
training measures for new staff (participation, length, methods and topics) in the analysis of the data for the total
sample. For experienced staff, less than three quarters of the sampled projects reported to have provided training last
year. The detailed data on training were again not available for analysis of the entire sample.
F-8

3

Appendix F: Multivariate Analysis
ƒ Frequency of automated activities: number of certification activities that were
performed by computer
ƒ Number of certification activities that were frequently processed via TASS or EIV
♦ (Re)certification Verification. Data on monitoring and case reviewing seemed important
to account for rent error. The interval scales were summed across the number of items
monitored/reviewed, different personnel categories involved, and various methods used.
Binary indicators were created for distinct methods and personnel categories that were
expected to impact error (monitor with computer, monitor with predetermined form,
monitor by supervisor, HUD/HUD contractor, outside auditor, etc.). Likewise, numbers
of items, methods, and personnel involved in verification of certification/recertification
were, respectively, summed up to gauge the extent of the verification efforts. These were
dummy variables that indicate:
ƒ Frequently verified items: counts of items that were usually or always verified, out of
16 items ranging from age of household members, social security numbers, income
from employment to full time student status.
ƒ Never verified items: counts of items that were never verified, out of the same16
items
ƒ Difficult verified items: counts of items that were reported often difficult to verify,
out of the same 16 items
ƒ Supervisor verification: a dummy variable indicates whether the supervisor kept track
of verification
ƒ Procedures to follow-up verification: counts of procedures used to follow verification
that was not provided, e.g., letters to the third party, call the third party, letters to the
tenant, and call the tenant, among others.
ƒ Acceptance of less preferred verification: a dummy variable indicates whether the
project accepted verifications that were less preferred verification.
♦ Staff perceived error causes. Six items were collected about staff perception of error
causes, including staff shortage with the workload, complexity of HUD regulations, and
frequent changes in HUD regulations, among others. Since these variables were
correlated, we used only two measures: One is a dummy variable indicating if any items
were reported to often cause errors versus none of the items reported. The other is a
dummy variable representing whether staff shortage was a frequent cause of error.
Initial Analyses
Initial analyses were conducted iteratively with model testing to check missing data, explore
alternative analytic approaches, select predictor variables, and examine residual values of the
predicted gross error. Based on such diagnostic analyses, we then specified and tested multiple
regression and logistic models of gross rent error and types of error.
F-9

Appendix F: Multivariate Analysis
Missing Data. There were 3 projects that did not respond to the PSQ survey, resulting in 20
tenant records without corresponding PSQ data (equivalent to 0.8% of the 2,404 tenants
sampled). Analysis revealed that these tenants had lower gross rent error than the rest of the
sample. For this analysis, these 20 cases were excluded. We believe that this small proportion of
unit non-response should cause little bias to our complete-case analysis, based on Harrell’s
(2001) rule-of-thumb. Item non-response occurred to a small number of variables. The largest
number of missing data was 29, occurring for the item of the number of certification staff with
five-years experience. We conducted regression analysis to impute the value, using the total units
served, total staff, and the staff with one-year experience as predictor variables. For items with
extremely low non-response (only one to three cases), we imputed the value with the mode or
mean.
Project-Level Variance: Hierarchical Linear Modeling. The HUDQC sampling design and
the resulting tenant and project data present a structure where tenants are nested within
organizations. With such a design, hierarchical linear modeling (HLM) is more effective than
ordinary least square (OLS) regression in estimating effects at the project and household levels if
a substantial proportion of the total variance exists between projects (Raudenbush & Bryk,
2002). We tested the unconditional HLM model to estimate PHA/project/owner level variance
(Exhibit F-3). Similar to prior studies, we found the project level variance was small, only 5.9
percent of the total variance. So we decided to use ordinary least square regression (OLS) to
analyze gross rent error and logistic regression to analyze likelihood of different types of error
made by staff.
Exhibit F-3
Standard Z Value /

Model and effect Estimate Error t Value

Unconditional model
Probability
(ANOVA)
Variance Estimates at Project and Tenant Levels: Unconditional HLM Modeling
Random effect
a
Project level variance, u
j 0.254 0.045 5.60 <.0001
Household variance, r
ij 4.032 0.128 31.60 <.0001

Total variance
4.286

Project-level proportion of variance
0.059
Fixed effect
a
Mean log gross rent error (Intercept), γ
00 1.298 0.039 33.140 <.0001
a

Random effects were tested in Z statistics and fixed effects in t statistics.
Source: HUD Quality Control Survey, FY 2007

Model Specification and Testing
To select from the large number of tenant and project variables for the analysis, separate
regression equations were specified. From each set of variables that represent a given construct,
we initially selected predictor variables that were relatively substantial and statistically
significant in bivariate relation with the gross rent error.
Next, we examined multicollinearity among the selected predictor variables. Multicollinearity
may generate unstable estimates of the regression coefficients such that trivial changes in
F-10

Appendix F: Multivariate Analysis
independent variables may substantially alter the regression coefficients. Variance inflation
factors (VIF) are measures of multicollinearity among independent variables in a regression
model. In a diagnostic analysis, we examined VIF in OLS regression analysis of selected
predictor variables to minimize collinearity in the final selection of predictor variables.
For the large set of project level variables, extensive examination was performed in an attempt to
identify variables that were predictive of rent errors and variables that were closely correlated.
We selected for each conceptual group (as listed above) variables that had relatively large
coefficients in relation to gross error or were conceptually more relevant to rent error, although
few project variables were found to have strong relationship with gross error.
Residual Analysis. Examining the distribution of residual scores of the predicted rent error is a
way to ensure reasonable model specification and model fit with the data. Residuals refer to the
discrepancies between the observed value of rent error and the predicted value of rent error
resulting from the multiple regression model. Residual distribution should be roughly normal and
without clear trend if the model fits well. We ran OLS regression with log gross rent error as the
dependent variable and the final set of independent variables. The results suggested that the
standardized residual values were distributed approximately normal. The trend was moderate:
cases with higher predicted rent errors tended to have slightly lower residual scores.
Effect Size. Estimating effect size is more informative and challenging relative to identifying
significant predictors. Hypothesis tests determine whether or not an effect exists; a “statistically
significant” predictor is one for which there is strong evidence that it is associated with rent
error. In contrast, effect size estimates indicate the size or degree of association between a
predictor and rent error, answering the question about the “relative importance” of a given
predictor variable or set of predictor variables in accounting for rent errors. To assess relative
effect size, we calculated the proportion of the total variance of gross error accounted for by each
group of predictor variables. In addition, we provided the effect size measured with Cohen’s f
2
for predictor groups, not individual predictor variables
4
. This approach was conservative since
many project variables were newly developed and their psychometric properties untested.
Findings
We first present the results of gross rent error modeling, where gross rent error was seen as a
function of staff errors as well as household and project characteristics. Then we show the
estimates of multiple logistic regression analysis of different types of error made by staff, again
from household and project variables. Finally, analysis of three interval measures of staff error,
i.e., counts of transcription errors, counts of verification errors, and the total counts of errors,
were presented.
4
The effect size for multiple regression analysis may be assessed by comparing the change of the R
2
. Given an R
2
A
value resulting from an equation with a set of independent variables A, and an R
2
AB
value generated from an
equation with the A and another set of independent variables B, Cohen's f
2
is commonly used in the context of
sequential (or nested) multiple regression analyses (Cohen, 1988). The f
2
effect size measure for multiple regression
is defined as:
F-11

Appendix F: Multivariate Analysis
Gross Rent Error. Seven models were tested to estimate the effects of different sets of predictor
variables in relation to gross rent error (see Exhibit F-4). Linear regression adjusted for design
effects (PROC SURVEYREG in SAS) was the modeling technique. Predictor variables
representing explanatory concepts were added into the equation in a sequence (known as
sequential modeling). Each model allowed us to estimate the effect of a particular set of variables
that were added into the equation, the changing effects of previously entered variables, and the
model fit statistics.
The final model (model 7) included the seven sets of variables representing as specified in the
conceptual model:
♦ Project characteristics,
♦ Certification practice,
♦ Verification practice,
♦ Automation with computer/IT application,
♦ Major types of error, and
♦ Tenant factor (characteristics and financial conditions).
The predictor variables entered into the sequential models incrementally accounted for the
variance of the gross rent error, with the largest share by indicators of the eight types of error
made by staff (i.e., overdue recertification error, allowance calculation error, income calculation
error, consistency error, procedure error, transcription error, administrative error and other
calculation error, 16.4%). Tenant characteristics and financial conditions accounted for 10.3
percent of the variance. The proportion of rent error variance explained by project variables
totaled only 3.1 percent (Figure F-2). Corresponding to variance partitioning, the effect size
estimates with Cohen’s f
2
also showed that staff errors represented the bulk of the effects on rent
error (.203); measures of household attributes also had a sizable effect (.147); and project
characteristic effects were relatively small to the prediction of gross rent error (.032).
The estimated intercept serves a contrast to interpret estimates from each model. For example, in
model 1, the intercept estimated in log scale 1.586, equivalent to $4.59.
5
This was the expected
average gross error of a “reference” group of households that had a zero values on each predictor
variable. Because of the centered rescaling, the “reference” households were characterized by
their projects’ average value on each interval predictor variables (e.g., average unit/staff ratio)
and zero value on each dummy coded predictor variables (e.g., zero as owner-administered for
Public Housing and Section 8 variables). Interpreting the estimates, we focus only on those that
were statistically significant (p<.05 or smaller) as these represent effects that are unlikely due to
chance.
5
e
l
e lDollar amount = , where is a constant approximately 2.72, is the estimated regression intercept in log scale. To
convert coefficients in log scale to dollar amount, we add the log-scale estimate to the intercept and use the sum to
perform the same calculation as above. The difference between the resulting dollar amount and the intercept-
equivalent dollar amount is the estimated effect in dollar amount.
F-12

Appendix F: Multivariate Analysis
Exhibit F-4 (Page 1 of 3)

Log Gross Rent Error Accounted For By Tenant, Process Error, and Project Variables:

Multiple Regression Coefficients, Design Effect Adjusted

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Variable Project background Certification Verification Automation Error types Perceived problem Tenant characteristics

Intercept 1.586

Project: Total number of units 0.000

Project: Unit per cert staff 0.000

Project: Unit per cert staff 0.000

Project: Cert staff as a % of total staff 0.126

Project:% cert staff only do cert 0.011

Project: Ongoing caseload 0.008

Project:% cert staff 5+ yr experience -0.300

Project: Require hs grad+ -0.067

Project: Other requirement -0.003

Project: All exp cert staff trained -0.091

Project: Public Housing -0.264

Project: Section 8 -0.051

Project: Months process before recert

Project: Require interview for init cert

Project: Require interview for recert

Project: N methods used to review cases

Project: Any policy/procedure change

Project: HUD review files

Project: N items always verified

Project: N items never verified

Project: N items never verified

Project: Supervisor keep track ver requests

Project: N methods used to follow up

1.390 1.780 1.995 1.410 1.351 1.779
0.000 0.000 0.000 0.000 0.000 0.000
0.000 -0.001 0.000 0.001 0.001 0.001
0.000 0.001 0.000 0.000 0.000 0.000
0.147 0.213 0.176 0.056 0.054 -0.006
0.004 -0.032 -0.025 0.099 0.104 0.104
0.008 0.002 -0.011 -0.039 -0.037 -0.042
** -0.269 ** -0.250 * -0.232 * -0.235 * -0.216 * -0.201
-0.054 -0.047 -0.040 -0.156 -0.167 -0.203
0.055 0.255 0.164 0.172 0.159 0.060
-0.097 -0.094 -0.083 -0.019 -0.022 -0.034
** -0.215 * -0.178 -0.269 * -0.271 * -0.261 * -0.285
-0.032 -0.010 -0.112 -0.048 -0.047 -0.124
0.017 0.019 0.028 0.026 0.026 0.018
-0.175 -0.158 -0.146 -0.122 -0.131 -0.060
0.147 0.130 0.130 0.143 0.156 0.131
-0.027 -0.010 -0.002 -0.006 -0.008 -0.008
0.047 0.041 0.001 -0.032 -0.035 -0.083
0.122 0.126 0.120 0.051 0.054 0.043
-0.023 -0.017 -0.011 -0.008 -0.006
-0.033 -0.031 -0.043 -0.037 -0.039
-0.009 -0.007 0.000 -0.007 0.003
-0.079 -0.103 -0.132 -0.119 -0.111
-0.074 * -0.057 -0.043 -0.044 -0.025
F-13

Appendix F: Multivariate Analysis
Exhibit F-4 (Page 2 of 3)

Log Gross Rent Error Accounted For By Tenant, Process Error, and Project Variables:

Multiple Regression Coefficients, Design Effect Adjusted

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Project
Variable background Certification Verification Automation Error types Perceived problem Tenant characteristics
Project: Other Less preferred verification accepted 0.097

Project: 100% used PIC/TRACS transfer

Project: Direct transfer to HUD

Project: Enterprise Income Verification (EIV)

Project: Used TASS last yr

Project: N activities using computer

Project: Frequency of TASS or EIV use last yr

Tenant: Overdue recertification error

Tenant: Other calculation error

Tenant: Allowance calculation error

Tenant: Income calculation error

Tenant: Consistency error

Tenant: Procedure error

Tenant: Transcription error

Tenant: Administrative error

Project: Any perceived error cause

Project: Staff shortage

Tenant: Elderly or disabled household

Tenant: Earned income

Tenant: Other income

Tenant: Public assistance income

Tenant: Pension income

Tenant: Total number of sources of income/expenses

Tenant: Number of allowances

R
2
0.122 0.047 0.048 -0.082
-0.208 * -0.157 -0.162 -0.108
-0.018 0.012 0.023 0.070
-0.029 -0.125 -0.125 -0.196
-0.283 *** -0.175 * -0.180 * -0.190
-0.004 0.005 0.003 -0.005
0.030 0.024 0.022 0.034
1.003 ** 0.975 ** 1.015
-0.069 -0.057 -0.159
0.234 0.239 0.125
-0.292 -0.288 -0.383
0.023 0.026 -0.114
-0.041 -0.052 0.001
0.931 *** 0.932 *** 0.679
0.412 0.412 0.333
0.144 0.192
-0.135 -0.138
-0.225
0.438
0.323
0.106
0.093
0.054
0.604
0.013 ** 0.017 ** 0.021 ** 0.030 ** 0.194 *** 0.195 *** 0.298
F-14

Appendix F: Multivariate Analysis
Exhibit F-4 (Page 3 of 3)

Log Gross Rent Error Accounted For By Tenant, Process Error, and Project Variables:

Multiple Regression Coefficients, Design Effect Adjusted

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Project
background Certification Verification Automation Error types Perceived problem Tenant characteristics
Cohen's f
2
0.014 0.003 0.004 0.009 0.203 0.001 0.147
% variance accounted for 0.013 0.003 0.004 0.009 0.164 0.001 0.103
Denominator DF 2324 2324 2324 2324 2324 2324 2324

Source: HUDQC FY 2007 PSQ data merged with the Tenant Survey

Note: Project represents Project-Level variables; Tenant represents Tenant-Level variables

p< 0.05 ** p< 0.01 (test with the null hypothesis that a coefficient [or R
2
]=0; a significant result indicates that the corresponding variable(s) is associated with gross error).

F-15

Appendix F: Multivariate Analysis
Figure F-2
Proportions of Variance of Gross Rent Error Accounted for by Tenant, Process Error, and
Project Variables: Multiple Regression Analysis with Design Effect Adjusted
Source: HUD Quality Control Survey, FY 2007
Public Housing, PHA-administered Section 8, and owner-administered projects differed in rent
error as found in the bivariate analysis. Regression modeling confirmed that Public Housing had a
lower average gross error than owner-administered households (-.264 in log scale, equivalent to a
reduction $.84), if other conditions equal. Note that this estimate changed little in the seven
models where the other predictor variables were held constant. The difference between households
under PHA-administered Section 8 projects versus owner-administered projects, however, was not
significant net of other effects. The finding may imply that the higher gross error for Section 8
projects identified in bivariate tabulations was attributable largely to the programs’ differences in
tenant characteristics. In other words, the Section 8 programs’ higher gross error may be caused
by more complex tenant situations.
Other variables equal, households served by projects with a higher proportion of experienced
certification staff tended to have lower gross errors (-.300 in log scale under Model 1 and -.201
under Model 7); and the effect was significant statistically albeit slightly reduced in magnitude
after controlling for increasingly more variables. The implication is that, other conditions on
project and tenant levels being equal, more experienced (and stable) staff helped reduce the gross
error. Another remarkable finding was with TASS use. Households managed by projects that used
TASS tended to have lower gross error, other things being equal. Again, the effect remained
significant in Models 4 through 7, after strong effects of staff error and household variables
entered.
Two types of error made by staff appeared to predict higher gross error. One was overdue
recertification, with estimates around 1.000 in log scale, suggesting that overdue recertifications
could increase the average gross error substantially, around $7.00, holding other variables
constant. Another type of staff error, transcription error, also related to higher gross error, with
F-16

Appendix F: Multivariate Analysis
estimated .931 under Model 5 and .679 under Model 7, equivalent approximately to an average
dollar amount of $6, other things being equal.
Model 7 generated estimates of household effects that were similar to those found in prior studies.
Other things being equal, household with elderly or disabled household heads had lower gross rent
error; higher earned and other incomes were also related to larger gross rent error; and complex
financial conditions were related to higher gross error as shown in the significant estimates for the
number of income and expense items and the number of allowances.
The results from modeling gross rent error in FY 2007 were largely consistent with the findings
from studies in prior years. Key points are:
♦ At the project-level, experienced staff and use of TASS in the certification operation were
associated with lower gross rent error, given similar tenant characteristics and project
conditions.
♦ Overdue certifications and transcription errors contributed substantially to gross error,
given other project and household conditions being similar.
♦ Tenants with complex financial conditions (more sources of income, expense and
allowance) and with relatively high incomes were likely to have larger gross rent error,
other project and household conditions being similar.
♦ Compared with owner-administered programs, the Public Housing program had lower
gross error, net of other factors on project and household levels.
Other Types of Error. Binary and interval measures of types of staff error were analyzed using,
respectively, multiple logistic regression and OLS regression. The logistic models specified the
same set of tenant and project characteristics as predictor variables. Exhibit F-5 presents the logit
estimates (log odds) and Max-rescaled R
2
from the logistic models of the eight types of error in
dummy coding.
6
Max-rescaled R
2
(roughly interpreted as the proportion of variance accounted for by the model)
fluctuated from 7 to 30 percent across models. Since effect size was not estimated for predictor
variables and no attempt was made to run sequential modeling, we interpret the estimates by only
stating whether or not a relationship between the predictor variable and the error existed based on
the significant test (with p < 0.01). We emphasize the tentativeness of the results and hope to
conduct further analysis to clarify the patterns. All else equal, the following relationships were
found in the analyses:
6
We choose to present logit estimates rather than odds ratio because logits can be understood in a similar way as
linear regression coefficients. The logistic regression models the relationship between the outcome Y=1 (a given error
in our analysis) and the predictor variables through the logit function, the natural logarithm of odds of Y=1. The
model assumes a linear relation between the log of odds and predictor variables, X
1
, X
2
, ... , X
k
, and can be written as:
Let p=P(Y=1), then log (p/(1-p)) = intercept + b
1
X
1
+ b
2
X
2
+ ... + b
k
X
k
. Max-rescaled R
2
allows the maximal value of
1 and is recommended as a better approximate of the variance explained by the logistic model, comparable with
generalized R
2
(Hosmer & Lemeshow, 2001).
F-17

Appendix F: Multivariate Analysis
♦ Overdue Recertification Error. Households served by projects that reported perceived error
causes were less likely to have overdue recertification error than those served by projects
that reported no perceived error causes.
♦ Calculation Error (other than income and allowance). Households with higher pension
income and more allowances were more likely to have error.
♦ Allowance Calculation Error.
ƒ Households under projects of Public Housing program were more likely than those
under owner-administered program to have allowance calculation error.
ƒ Households with a higher other income (other than earned, public assistance, pension
income) and greater number of allowance were more likely to have allowance
calculation error.
♦ Income Calculation Error.
ƒ Households served by project of large size were more likely to have income
calculation error.
ƒ Households served by project of Public Housing program were more likely than those
served by owner-administered program to have income calculation error.
ƒ Households served by projects where supervisor kept track of verification versus other
staff kept track of verification were more likely to have income calculation error.
♦ Procedure Error
ƒ Households served by projects that provided training to all experienced certification
staff were more likely to have procedure error than those served by projects that did not
provide training to all experienced certification staff.
ƒ Households served by projects that used more methods to review certification were
more likely to have procedure error.
ƒ Households served by Section 8 projects were more likely to have procedure error than
those served by owner-administered projects.
ƒ Households served by projects that reported perceived error causes were more likely to
have procedure error than those served by project that did not reported such perceived
causes.
ƒ The larger the number of households’ allowances, the more likely the procedure error
to occur.
F-18

Appendix F: Multivariate Analysis
Exhibit F-5 (Page 1 of 2)

Different Types of Process Error Accounted For by Tenant and

Variable
n
Error
i
Error Error Error Error Error
Procedure
Error Error
Project-Level Variables: Multiple Logistic Coefficients, Design Effect Adjusted
Allowance Income Other Overdue
Calculatio Admin strative Consistency Calculation Calculation Recertification Transcription
Intercept -6.666 0.285 -1.852 -13.384 -8.227 -42.391 -1.212 -0.181
Project: Total 0.000 0.000 0.000 0.000 ** 0.000 0.000 0.000 0.000
number of units
Project: Units per 0.005 -0.002 -0.002 0.001 0.003 -0.002 -0.001 -0.003
staff
Project: Units per
cert staff
-0.001 0.001 0.002 0.001 -0.001 0.001 0.001 0.001
Project: Cert staff as -0.276 0.238 0.615 0.579 -1.304 * -1.551 0.315 0.372
a % of total staff
Project:% cert staff -0.033 -0.408 ** 0.136 0.049 -0.466 -0.185 0.011 -0.361 *
only do cert
Project: Ongoing
caseload
-0.054 -0.033 0.067 -0.012 -0.270 -0.130 -0.067 0.052
Project:% cert staff 0.490 0.059 0.200 0.186 0.223 -1.569 * 0.224 0.069
5+ yr experience
Project: Require hs 0.026 -0.155 -0.224 0.138 -0.408 -0.634 -0.192 -0.145
grad+
Project: Other
requirement
Project: All exp cert
0.738
0.127
0.000
0.094
-0.027
0.250 ***
0.601
0.090
-6.634
0.096
-7.718
0.063
0.146
0.170 **
0.052
0.125 *
staff trained
Project: Public
Housing
Project: Section 8
1.504
0.396
*
*
0.123
-0.003
1.036
0.635 *
1.644
0.680
** 0.321
0.507
17.016
16.324
0.953
0.755 **
0.078
-0.039
Project: Months
process before
-0.097 -0.007 -0.261 -0.072 -0.029 0.397 -0.204 -0.008
recert
Project: Require
interview for init cert
0.352 0.027 0.543 * 0.036 0.228 1.596 0.423 * -0.003
Project: Require -0.110 -0.006 -0.117 0.019 0.350 -0.153 -0.093 -0.056
interview for recert
Project: N methods 0.234 * 0.024 0.162 *** -0.093 -0.075 -0.375 * 0.112 ** 0.024
used to review cases
Project: Any
policy/procedure
0.040 -0.023 0.012 -0.293 -0.117 -0.030 0.027 -0.012
change
Project: HUD review
files
-0.345 * -0.068 0.149 * -0.375 * 0.177 -0.221 0.065 -0.125 *
Project: N items 0.133 -0.023 0.021 0.045 0.028 0.086 0.018 -0.028
always verified
Project: N items 0.193 -0.010 -0.072 0.113 -0.069 -0.616 -0.028 0.001
never verified
F-19

Appendix F: Multivariate Analysis
Exhibit F-5 (Page 2 of 2)

Different Types of Process Error Accounted For by Tenant and

Project-Level Variables: Multiple Logistic Coefficients, Design Effect Adjusted

Allowance Income Other Overdue

Calculation Administrative Consistency Calculation Calculation Recertification Procedure Transcription

Variable Error Error Error Error Error Error Error Error

Project: N items never
verified 0.009 -0.010 0.036 -0.059 -0.055 0.106 0.034 -0.014
Project: N items never
verified 0.170 -0.039 -0.065 0.505 ** -0.110 0.055 -0.045 -0.059
Project: Supervisor
keep track ver requests 0.030 -0.028 -0.035 0.367 0.010 0.206 -0.018 -0.007
Project: N methods
used to follow up 0.038 0.004 -0.076 0.235 0.124 0.019 -0.022 -0.005
Project: 100% used
PIC/TRACS transfer -0.510 -0.078 -0.195 0.731 -0.297 -0.851 -0.170 -0.087
Project: Direct transfer
to HUD 0.402 * 0.029 -0.056 0.183 0.214 -0.492 0.000 0.033
Project: Enterprise
Income Verification
(EIV) 0.510 0.044 -0.469 -0.876 -0.936 13.337 -0.357 0.070
Project: Used TASS last *
yr 0.274 0.144 ** 0.050 -0.095 0.006 0.180 0.049 0.144 *
Project: N activities
using computer -0.116 -0.039 -0.085 * -0.108 0.001 -0.007 -0.062 * -0.045
Project: Frequency of
TASS or EIV use last yr 0.045 0.048 0.009 0.114 0.085 -0.158 0.005 0.055
Project: Any perceived *
errors -0.107 -0.042 -0.171 * -0.071 -0.148 -0.642 * -0.170 ** -0.024
Project: Staff shortage 0.747 -0.197 0.142 6.978 -0.459 * -0.484 0.098 0.036

Tenant: Elderly or

disabled household -0.449 -0.102 0.134 -0.118 -1.270 -0.825 -0.153 -0.028

Tenant: Earned income -0.201 0.479 -0.132 -0.406 0.310 * 0.153 -0.052 0.505
*

Tenant: Other income -0.935 * 0.082 -0.026 -0.516 0.194 0.361 -0.130 0.071

Tenant: Public

assistance income 0.467 0.357 * 0.229 -0.600 1.065 0.317 0.328 * 0.171

*
Tenant: Pension income -0.280 0.109 -0.053 -0.016 0.448 * 0.257 -0.040 0.102
Tenant: Total number of
sources of
income/expenses 0.211 0.209 0.095 0.272 -0.080 0.172 * 0.108 0.200
Tenant: Total number of
sources of * ** * **
income/expenses 0.728 * 0.373 * 0.420 *** -0.202 0.493 * -0.276 0.398 * 0.387 ***
* * *
* ** * * **
R
2
0.203 * 0.253 * 0.259 *** 0.204 ** 0.246 * 0.229 * 0.232 * 0.278 ***
Source: HUDQC 2007 PSQ Survey merged with the Tenant Survey *p< 0.05 ** p<0.01
F-20

Appendix F: Multivariate Analysis
♦ Transcription Error
ƒ Households served by projects that used TASS were more likely to have transcription
error than those served by projects that did not use TASS.
ƒ The larger the number of households’ allowances, the more likely the procedure error
to occur.
♦ Administrative Error
ƒ Households served by projects where certification staff did not work on tasks other
than certification were less likely to have administrative error compared with those
served by projects where certification staff also worked on other tasks.
ƒ Households served by projects that used TASS were more likely to have administrative
error than those served by projects that did not use TASS.
ƒ The larger the number of households’ allowances, the more likely the administrative
error to occur.
Internal Measures of Staff Error. Three interval measures of errors—the numbers of
transcription errors, of verification errors, and of all errors, were modeled in multiple linear
regression analysis (Exhibit F-6). The patterns revealed in the models of the transcription errors
and verification errors were largely consistent with the findings from logistic regression analyses
of the binary coded transcription error and verification error as described above.
In short, for most types of error, household higher incomes and complex financial conditions
contributed to greater chance of occurrence. The patterns were comparable with those found in the
gross rent error models. The estimates for using the TASS systems were confusing in relation to
different measures of error. It was positively associated with the likelihood of administrative error
and transcription error in binary code; but negatively associated with counts of transcription error,
verification error, and the total errors. Also, the significant positive estimates for training for
certification staff on consistence error and procedure error is perplexing. There may be unknown
confounding factors that require further analysis.
Summary
Multivariate analysis was conducted to further examine gross rent error and different types of error
that occurred in the certification/recertification process. The goal was to identify and estimate the
relationship between tenant characteristics, project characteristics, and project practices and gross
rent error. These analyses address study objectives 5, 6, 8, 12, and 13. A brief summary of the
findings is provided below. A more detailed description of the analyses is found in Appendix F.
To address Objective 5 (differences in error by program type), results from both bivariate
tabulations and multiple regression analyses indicated that the Public Housing program had lower
average gross rent error than the owner-administered program. In the multiple regression analysis,
this difference remained statistically significant even after other project-level variables and tenant-
level variables were held constant. The PHA-administered Section 8 program was found to have
significantly higher gross error than the owner-administered program in bivariate tabulations, but
F-21

Appendix F: Multivariate Analysis
this difference was not found to be statistically significant in the multiple regression analysis. The
implication being that the descriptive differences identified in bivariate tabulations are further
accounted for by tenant and project factors. Given similar tenant and project characteristics, in
comparison with the owner-administered program, the Public Housing program’s lower gross
error persisted; whereas the PHA-administered Section 8 program’s higher gross error
disappeared. The latter finding is important as it suggests that tenant characteristics for the Section
8 program may have imposed greater difficulty in quality control processes as they apply to rent
calculation.
Note that the finding from the FY 2006 multiple regression analysis was somewhat different,
where Public Housing program did not show a significant difference from the owner-administered
program. The PHA-administered Section 8 program did have significantly higher gross error than
the owner-administered program. The shifting pattern of error difference cross administrative type
requires continued research to understand the underlying factors that lead to the differences.
To address Objective 6 (impact on error by tenants vs. project/staff), this analysis found that,
consistent with previous results, the impact of tenant characteristics on rent error was apparently
greater than project characteristics and practices. As shown in variance partitioning with HLM
unconditional model and the sequential OLS models, tenants’ high income and multiple sources of
income and allowance items were related to both higher gross rent error and different types of
error. These tenant variables should be seen as important indicators of risk for rent errors and be
targeted by program intervention.
To address Objective 8 (error concentration in program/project), this analysis found that, to a
varying extent, a number of program/project features did relate to rent error and different types of
error. Specifically, projects that retained experienced certification staff (over five years) had lower
average rent error. This finding is compatible with what was found in the FY 2006 analysis where
staff educational requirements were related to lower rent error, namely, staff quality is a critical
factor in reducing errors. We suggest focusing more on staff quality data collection and analysis in
the future. One possibility is to collect additional staff information and job descriptions. The
information would allow accurate comparison of staff quality across local providers in connection
to the quality control results.
To address Objective 12 (rent errors differentiated by using automated systems), the analysis
provided evidence that computer application in certification operation helped reduce different
types of processing error. Use of TASS or EIV, however, was not yet clear in consistently relating
to rent error or different types of error. Again, more systematic data collection and analysis on
computer application may help assess the impact of automation. For example, we may consider
collecting data items on the respondents’ knowledge and skills specific to computer use in
certification/verification, perhaps in a form of mini quiz.
To address Objective 13 (other tenant or project characteristics related to errors), the analysis
found that projects whose survey respondents reported perceived error causes tended to have
lower chance of certain types of staff error. It is arguable that staff’s ability to report difficulties
causing mistakes is equivalent to knowledge of the problems and hence potential ways to deal
with them. However, far more information is needed to learn how project staff acquires
information on different types of error in daily practice and the ways they identify and address
risks of improper certification and payment determination.
F-22

Appendix F: Multivariate Analysis
Exhibit F-6 (Page 1 of 2)

Numbers of Transcription Errors, of Verification Errors, and of All Errors Accounted for by Tenant and Project-Level Variables:

Multiple Regression Coefficients, Design Effect Adjusted

N of transcription N of verification
Intercept
Project: Total number of units
Project: Units per staff
Project: Units per cert staff
Project: Cert staff as a % of total staff
Project:% cert staff only do cert
Project: Ongoing caseload
Project:% cert staff 5+ yr experience
Project: Require hs grad+
Project: Other requirement
Project: All exp cert staff trained
Project: Public Housing project
Project: Section 8 project
Project: Months process before recert
Project: Require interview for init cert
Project: Require interview for recert
Project: N methods used to review cases
Project: Any policy/procedure change
Project: HUD review files
Project: N items always verified
Project: N items never verified
Project: N items never verified
Project: N items never verified
Project: Supervisor keep track ver requests
Project: N methods used to follow up
Project: 100% used PIC/TRACS transfer
errors errors N of all errors
0.263 0.122 1.952
0.000 0.000 0.000 **
0.000 0.000 -0.001
0.000 0.000 0.001
-0.006 0.013 0.144
-0.041 ** -0.014 -0.180
0.005 -0.006 -0.014
0.018 0.006 0.122
0.022 0.022 0.257 *
-0.027 -0.047 -0.244
-0.021 -0.011 -0.233 ***
0.022 0.020 0.385 **
-0.004 0.010 0.162
-0.009 0.006 -0.115 **
0.012 0.017 0.217
-0.008 -0.015 -0.043
0.004 0.001 0.048
0.002 0.007 0.049
0.023 -0.016 0.034
-0.001 -0.001 -0.001
0.001 -0.003 -0.017
-0.001 0.003 0.013
0.033 * -0.004 0.097
0.002 -0.007 -0.010
0.001 0.012 0.011
-0.013 0.001 -0.156
F-23

Appendix F: Multivariate Analysis
Exhibit F-6 (Page 2 of 2)

Numbers of Transcription Errors, of Verification Errors, and of All Errors Accounted for by Tenant and Project-Level Variables:

Multiple Regression Coefficients, Design Effect Adjusted

N of transcription N of verification
errors errors N of all errors
Project: Direct transfer to HUD 0.000

Project: Enterprise Income Verification (EIV) 0.003

Project: Used TASS last yr -0.040

Project: N activities using computer -0.007

Project: Frequency of TASS or EIV use last yr 0.004

Project: Any perceived error 0.020

Project: Staff shortage -0.022

Tenant: Elderly or disabled household 0.002

Tenant: Earned income 0.063

Tenant: Other income 0.016

Tenant: Public assistance income 0.019

Tenant: Pension income 0.012

Tenant: Total number of sources of income/exp 0.017

Tenant: Total number of sources of income/exp -0.003

R
2
0.013 -0.041
-0.008 -0.116
** -0.026 ** -0.219 **
** 0.000 -0.046 **
0.004 0.036
-0.003 0.165
0.055 0.164
-0.026 -0.142
*** -0.002 0.197 **
0.059 *** 0.045
-0.013 0.299 **
0.001 0.039
*** 0.002 0.140 ***
0.008 0.263 ***
0.105 *** 0.051 *** 0.166
Denominator DF 2324 2324 2324
Source: HUD Quality Control Survey, FY 2007
*p< 0.05 ** p<0.01
F-24

Appendix F: Multivariate Analysis
So far, we have revealed a very limited impact of project factors on rent error. It is vexing that,
as organized activities of managing housing assistance, project variation did not seem highly
meaningful with the statistical analysis. As suggested in earlier analyses, we will continue to
improve our data collection tools to gather more relevant information on quality control. In depth
understanding of the actual operation of the local providers is needed to more accurately
conceptualize the organization structures differentiated by program types. For instance, we
ponder collecting data to cover issues that are decision-maker specific (e.g., the owner, the
managing contractor, or the PHA), rather than data in reference to a generic project.
Furthermore, we note the limitation of statistical analysis, which typically can only describe
broad patterns and identify strong, clear-cut relationships between causal and effect factors. To
learn the complex and subtle differences in housing program operation that involve vastly
different individuals and groups, qualitative research is needed. Quantitative data collection and
analysis, in fact, can be benefited tremendously by rich, deep, personalized qualitative
observations of the local housing offices daily activities.
References
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ:
Erlbaum
Harrell, F.E. (2001). Regression modeling strategies. New York: Springer.
Hosmer, D.W. & Lemeshow, S. (2001). Applied Logistic Regression, Textbook and Solutions
Manual. New York: Wiley & Sons.
Raudenbush, S. W. & Bryk, A. S. (2002). Hierarchical linear models: Applications and data

a