Final Report

FY 2015 Improper Payment for Quality Control

for Rental Subsidy Determination Study

FINAL
FY 2015 Final Report

Improper Payment for Quality Control 

for Rental Subsidy Determination Study

Prepared for:

Office of Policy Development and Research

Department of Housing and Urban Development

Washington, DC 20410

Prepared by:

ICF International

August 31, 2016
HUDQC Final Report for FY 2015 TOC-i 

Table of Contents

Table of Contents

Executive Summary................................................................................................................... ES-i

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

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

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

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

V. Recommendations ...................................................................................................................V-1

Appendix A: Rent Calculations .................................................................................................. A-1

Appendix B: Weighting Procedure ..............................................................................................B-1

Appendix C: Source Tables

Source Tables Based on Quality Control Data.................................................................C-1

Source Tables Based on Tenant File Data......................................................................C-34

Appendix D: Consistency and Calculation Errors...................................................................... D-1

Appendix E: Project Staff Questionnaire Descriptive Analysis...................................................E-1

Appendix F: Multivariate Analysis..............................................................................................F-1

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 $1,000s). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-v

Exhibit ES-4 Comparative 2000 Through FY 2015 Gross Erroneous Payments*. . . . . ES-vii

Exhibit ES-5 Rent Components Responsible for the Largest Dollar Error for 

Households With Rent Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-x

Exhibit ES-6 Impact of Changes in the Error Threshold on Frequency and Estimates 

of Error (in $1,000s). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-xi

Exhibit II-1 Change in Frame Population Totals Used to Pull the Study Sample Over 

Time. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II-3

Exhibit III-1 PHA-administered Section 8 Unit Size Standards. . . . . . . . . . . . . . . . . . . III-4

Exhibit III-2 Summary of Study Objectives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III-7
TOC-ii August 31, 2016

Table of Contents

Exhibit IV-1 Percentage of Households With Proper Payments. . . . . . . . . . . . . . . . . . . IV-3

Exhibit IV-2 Percentage of Households With Error, Average Dollars in Error, and 

Dollar Error Rate for All Households With Error. . . . . . . . . . . . . . . . . . . . IV-4

Exhibit IV-3a Underpayment Households: Percentage of Households and Average 

Monthly Dollar Amount of Error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-5

Exhibit IV-3b Overpayment Households: Percentage of Households and Average 

Monthly Dollar Amount of Error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-5

Exhibit IV-4 Gross and Net Dollar Rent Error (Monthly) for All Households. . . . . . . . IV-6

Exhibit IV-5 Gross and Net Dollar Error Rates (Monthly) for All Households. . . . . . . IV-7

Exhibit IV-6 Certifications and Recertifications by Administration Type*. . . . . . . . . . . IV-8

Exhibit IV-7a Percentage of Newly Certified Households Meeting 

Certification Criteria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-10

Exhibit IV-7b Percentage of Newly Certified Households Meeting Certification 

Criteria by Program Type. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-10

Exhibit IV-8 Average Monthly Underpayment and Overpayment: Dollar Amounts 

Averaged Across All Households. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-11

Exhibit IV-9a Positive Subsidy Households (Tenant Underpayment) Percentage of 

Households and Average Monthly Dollar Amount of Error. . . . . . . . . . . IV-11

Exhibit IV-9b Negative Subsidy Households (Tenant Overpayment) Percentage of 

Households and Average Monthly Dollar Amount of Error. . . . . . . . . . . IV-12

Exhibit IV-10 Average Monthly Underpayment and Overpayment: Dollar Amount 

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

Exhibit IV-11 Rent Components Responsible for the Average Annual Largest 

Dollar Error for Households With Rent Error. . . . . . . . . . . . . . . . . . . . . . IV-14

Exhibit IV-12 Total and Annual Component Dollars in Error for Households 

With Rent Error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-14

Exhibit IV-13 Percentage of Households With Rent Component Error by 

Payment Type. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-15

Exhibit IV-14 Percentage of Households With Elderly/Disabled Allowances 

and Dependent Allowances*. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-16

Exhibit IV-15 Findings With and Without Information Obtained From Sources 

Other Than the Tenant File. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-17

Exhibit IV-16 Percentage of Households With Calculation and Consistency Errors. . . IV-18

Exhibit IV-17 Form HUD-50058/50059 Rent Calculation Error Compared With 

QC Rent Error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-19

Exhibit IV-18 Percentage of Households Fully Verified by Either the 

PHA/Owner or ICF. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-20

Exhibit IV-19 Verification of Form HUD-50058/50059 Rent Components by 

PHA/Owners**. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-22
HUDQC Final Report for FY 2015 TOC-iii 

Exhibit IV-20 Verification of Form HUD-50058/50059 Rent Components by 

PHA/Owner Staff, by Form Type. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-23

Exhibit IV-21 QC Error Households With Missing Verification in the Tenant File. . . . IV-24

Exhibit IV-22a Form HUD-50058/50059 Administrative Error: Percentage of 

Households, Average Dollars in Error (Non-MTW Households). . . . . . . IV-25

Exhibit IV-22b Form HUD-50058/50059 Administrative Error: Percentage of 

Households, 

Average Dollars in Error (Households With QC Rent Error). . . . . . . . . . IV-26

Exhibit IV-23 Administrative Error: Percentage of Households Average Dollars in 

Error for All Households. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-27

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

Bedrooms (According to Study Guidelines). . . . . . . . . . . . . . . . . . . . . . . IV-28

Exhibit IV-24b Percentage of All Households in FY 2014 by Number of Bedrooms 

and Number of Household Members*. . . . . . . . . . . . . . . . . . . . . . . . . . . IV-28

Exhibit IV-24c Percentage of All Households in FY 2015 by Number of Bedrooms 

and Number of Household Members*. . . . . . . . . . . . . . . . . . . . . . . . . . . IV-29

Exhibit IV-25 PHAs by Predominant Rent Reasonableness Method*. . . . . . . . . . . . . . IV-30

Exhibit IV-26a Rent Reasonableness Documentation for New Admissions and 

Annual Recertifications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-31

Exhibit IV-26b Type of Rent Reasonableness Documentation for New Admissions 

and Annual Recertifications*. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-32

Exhibit IV-27 Timing of Most Recent Rent Reasonableness Determination: New 

Admissions and Annual Recertifications*. . . . . . . . . . . . . . . . . . . . . . . . IV-32

Exhibit IV-28a Types of Documents Used by PHAs to Identify Utilities and Calculate the 

Utility Allowance Value*. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-34

Exhibit IV-28b Comparison of Utility Allowance on the Form HUD-50058 to the 

Utility Allowance Worksheet. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-35

Exhibit IV-28c Availability of All Information to Enable QC Utility Allowance (UA) 

Calculation*. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-35

Exhibit IV-28d QC Utility Allowance Compared to Form HUD-50058 

Utility Allowance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-36

Exhibit IV-29a Number and Percentage of Households With Payment Standard 

Discrepancies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-38

Exhibit IV-29b Payment Standard Compared With the Fair Market Rent. . . . . . . . . . . . IV-39

Exhibit IV-29c Details of Cases Falling Outside 90%–110% of the Fair 

Market Rent*. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-39

Exhibit IV-29d Comparison of the FY 2014 to FY 2015 Payment Standard Analysis. . . IV-40

Exhibit IV-30a PIC/TRACS Data by Program Type and Average Gross Dollars in 

Error for All Households. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-42
TOC-iv August 31, 2016

Table of Contents

Exhibit IV-30b PIC/TRACS Data by Program Type and Average Gross Dollars in 

Error for Households in Error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-42

Exhibit IV-30c Average Gross Dollars in Error by Payment Type and 

PIC/TRACS Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-43

Exhibit IV-30d Average Net and Gross Dollars in Error by Administration Type 

and PIC/TRACS Data for All Households. . . . . . . . . . . . . . . . . . . . . . . . IV-43

Exhibit IV-30e Percentage of Matched and Non-Matched Dollar Amounts for Key 

Variables Matching Variables From the Form HUD-50058/50059 

and PIC/TRACS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-44

Exhibit IV-31a Administrative Errors in the 20 Largest PHAs. . . . . . . . . . . . . . . . . . . . . IV-51

Exhibit IV-31b Dollar Rent Errors in the 20 Largest PHAs*. . . . . . . . . . . . . . . . . . . . . . IV-52

List of Figures

Figure ES-1 Comparative 2000 through FY 2015 Gross Erroneous Payments 

Over Time. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-viii

Figure IV-1 Payment by Program Type. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-6

Figure IV-2 Percentage of Cases by Case Type. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-8

Figure IV-3 Example of the Impact of Component Errors. . . . . . . . . . . . . . . . . . . . . . IV-13

Figure IV-4 PIC/TRACS Data Present by Program Type for All Households 

Over Time. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV-41

Figure IV-5 Conceptual Model Used in Multivariate Analysis. . . . . . . . . . . . . . . . . . IV-48
HUDQC Final Report for FY 2015 ES-i 

Executive Summary

The U.S. Department of Housing and Urban Development (HUD) Quality Control for Rental 

Assistance Subsidy Determinations (HUDQC) Study provides national estimates of the extent, 

severity, costs, and sources of rent errors in tenant subsidies for the largest housing programs 

administered by the Office of Housing and the Office of Public and Indian Housing (PIH). These 

programs account for nearly all of HUD’s current housing assistance outlays administered by 

the Office of Housing and PIH, as well as the majority of rental units assisted by HUD. This 

study was designed to measure the extent of administrator income and rent determination error by 

housing providers. It does not involve an audit of individual Public Housing Authorities (PHAs) 

or projects, nor does it monitor the implementation of housing programs. Its singular focus is to 

identify households for which an error was made in the calculation of the amount of the household’s 

rent and to provide nationally representative findings related to those errors. 

The errors evaluated by ICF in this study affect the rent contributions that tenants should have been 

charged. The findings presented in this report are derived from data collected from December 2015 

through May 2016 for actions taken by PHA and project staff during Federal fiscal year (FY) 2015 

(October 2014 through September 2015). These findings show that 73 percent of households 

nationally paid the correct amount of rent in FY 2015. In 13 percent of cases, households paid too 

much rent, and in 14 percent of cases, households paid too little.

HUD administers its rental housing assistance programs through 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 adjusted 

income toward shelter costs (i.e., contract rent plus utilities), with HUD providing the balance of 

the rental payment. New program applicants are required to provide information on household 

characteristics, income, assets, and expenses, and this information is used to determine the amount 

of rent they need to pay. In most instances, current tenants must certify this information annually 

and, in some circumstances, they must recertify the information when there are significant changes 

in the household’s 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 and calculate the tenant’s rental 

assistance may also result in an improper payment.

In 2000, HUD established 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 (the Income Match Study), and (3) errors in program 

administrator billings for assistance payments (the HUDQC billing studies). Fourteen studies have 

been conducted to identify program administrator income and rent determination error. In addition 

to the 2000 study, studies were conducted in FY 2003 through FY 2015. The study referenced in this 

report covers FY 2015 and updates the FY 2014 measurement of errors in program administrator 

income and rent determinations. Separate reports will be provided for the other studies conducted 

in FY 2015.

For the 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 initial certification or 
Executive Summary

ES-ii August 31, 2016 

annual recertification conducted in FY 2015. 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 the large number 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, and 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 ensuring that the households eligible for the program are receiving the correct 

subsidy, rather than reducing the 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 implementation of the recommendations presented in this report may require 

greater resources in order to provide HUD, PHAs, and owners with the written policy guidelines, 

training, standardized forms, and ongoing monitoring needed to ensure program compliance. The 

HUDQC Study assists the agency’s objective of providing the right subsidies to the right families 

to sustain and support quality rental assistance programs for communities.

A. Methodology

HUD Requirements and Study Standards. Using the Code of Federal Regulations and official 

HUD handbooks and notices, we consolidated all HUD rules relevant to the determination of rent 

into a set of HUD requirements. We invited program experts to participate in establishing and 

reviewing the standards used in this study.

The Sample. A nationally representative sample of 583 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 Project Rental Assistance Contract (PRAC), 

Section 811 PRAC, and Section 202/162 Project Assistance Contract

A random sample of four households was selected for most projects, but more households 

were selected from unusually large projects. The final study data set includes responses from 

2,400 households.

Out-of-Scope Projects. Certain programs were excluded from the study because their eligibility 

and rent calculation rules differed from the standards, including the Owner-administered Rental 

Assistance Payment, Rental Supplement Program, Section 236, and Below Market Interest Rate 

programs. Since the FY 2012 study, Moving to Work (MTW) agencies have been included in 

the sampling frame and the HUDQC Study sample. Owner-administered Rental Assistance 

Demonstration properties were excluded from the FY 2015 sample. Universe files requested from 

HUD either excluded out-of-scope projects, or the projects were identified by HUD for easy removal.
HUDQC Final Report for FY 2015 ES-iii 

Executive Summary

Weighting. Updated population counts per program were calculated based on the assisted housing 

universe files provided by HUD in June 2015 to compile weights for the study. The same population 

totals per program, provided by HUD in the FY 2005 statement of work, were used from FY 2006 

through FY 2010. Starting in FY 2011, the population totals were updated based on the FY 2012 

HUDQC sample universe to better reflect the current population. The same population totals were 

used from FY 2012 to FY 2014, and were then updated for the FY 2015 study. In general, when the 

population totals are adjusted, the changes seen in total gross dollar error may be due to a change in 

the assisted housing population, not necessarily an increase in average dollar error. When comparing 

dollar error between years in which the population size has been adjusted, it is appropriate to 

compare average dollar error, as it is not affected by changes in population size. 

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 73 field interviewers, and selecting the project and household sample. Field 

interviewers obtained data from tenant files and interviewed tenants using computer-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 ICF headquarters 

for review. Requested third-party verifications related to income, assets, and expenses were also 

processed at ICF’s office in Rockville, MD.

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, household, Social Security match, 

and third-party verification. Rent error was calculated by subtracting the QC rent from the actual 

paid tenant rent (the rent from Forms HUD-50058/50059 that was calculated by the project staff). 

A discrepancy of $5 or less between the actual and QC rents 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 2015 tenant files, household interviews, 

and income verification data indicates that:1

● Seventy-three percent of all households paid the correct amount of rent within $5 (61 percent 

paid the exact amount).

● Fourteen percent of all households paid at least $5 less than they should have (with an 

average error of $66 per month).

● Thirteen percent of all households paid at least $5 more than they should have (with an 

average error of $35 per month).

1 Note that results may not total to 100 percent due to rounding.
ES-iv August 31, 2016

Rent Error Estimates by Program Type. The rate of rent underpayments was highest, at 15 percent, 

in the PHA-administered Section 8 program, followed by the Public Housing program at 13 percent 

and the Owner-administered program at 12 percent. The PHA-administered Section 8 program had 

the highest overpayment rate, at 15 percent, followed by Public Housing programs at 12 percent 

and Owner-administered programs with 11 percent. Exhibit ES-1 summarizes this information.

Exhibit ES-1 

Frequency of Rent Error by Program Type

Program

Rent Underpayment 

(Subsidy Overpayment)

Rent Overpayment 

(Subsidy Underpayment)

Public Housing 13% 12%

PHA-administered Section 8 15% 15%

Owner-administered 12% 11%

Total 14% 13%

Dollar Error Effect of Rent Errors. All summary error estimates represent the summation of net 

case-level errors, meaning that a case was determined to have a net overpayment error, no error, or 

a net underpayment error. Major findings are as follows:2

● Rent underpayments of approximately $499.3 million annually (up from $458.4 million 

in FY 2014). For tenants who paid less monthly rent than they should have paid 

(14 percent), the average monthly underpayment was $66. For purposes of generalization, 

spreading total underpayment errors across all households (including those with no error 

and overpayment error) produces a program-wide average monthly underpayment error of 

$8.94 ($107 annually). Multiplying and weighting the $107 by the approximately 4.7million 

units represented by the study sample resulted in an overall annual underpayment dollar 

error of approximately $499.3 million per year.

● Rent overpayments of approximately $248.5 million annually (down from 

$260.3 million in FY 2014). For tenants who paid more monthly rent than they should have 

paid (13 percent), the average monthly overpayment was $35. When this error is spread 

across all households, it produces an average monthly overpayment of $4.45 ($53 annually). 

Multiplying and weighting the $53 by the approximately 4.7 million units represented by 

the study sample resulted in an overall annual overpayment dollar error of approximately 

$248.5 million per year.

● Aggregate net rent error of $250.9 million annually. When combined, the average Gross 

Rent Error per case was $13.39 ($8.94 + $4. 45). Overpayment and underpayment errors 

partly offset each other; the net overall average monthly rent error was -$4.49 (-$8.94 

+ $4.45). HUD subsidies for Public Housing and PHA-administered Section 8 programs 

equal the allowed expense level or payment standard minus the tenant rent, which means 

2 National annual totals in the text and exhibits were calculated using exact values and were weighted. 

Although household-level numbers are presented below, using them to calculate national annual totals 

will result in different amounts due to both rounding and weighting. Similarly, the source tables in 

Appendix C were rounded to the nearest integer for formatting purposes.
HUDQC Final Report for FY 2015 ES-v 

Executive Summary

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). The study found that the net 

subsidy cost of the under- and overpayments was approximately $250.86 million per year 

($499.34 million – $248.48 million).

Subsidy overpayment and underpayment dollars are summarized in Exhibit ES-2.3 This information 

identifies the various types of errors, error rates, and related estimated variances.

Exhibit ES-2 

Subsidy Dollar Error 

Type of Dollar Error

Subsidy

Overpayment

Subsidy

Underpayment

Average Monthly per Tenant Error for Households with Errors

$66

(14% of cases)

$35

(13% of cases)

Average Monthly per Tenant Error Across All Households $8.94 $4.45

Total Annual Program Errors $499.3 million $248.5 million

Total Annual Errors (95% Confidence Interval) $374.2 – 624.5 million $178.3 – 318.7 million

Exhibit ES-3 provides estimates of program administrator error by program type. These data provide 

estimates of national-level net costs for total errors and major error types, provide information on 

the extent to which errors are concentrated in projects and programs, and estimate total positive 

and negative errors in terms of HUD subsidies.

Exhibit ES-3 

Estimates of Error in Program Administrator Income and Rent Determinations (in $1,000s)

Program

Subsidy 

Overpayment

Subsidy 

Underpayment

Net Erroneous 

Payment

Gross Erroneous 

Payment

Public Housing $98,861 $84,426 $14,435 $183,286

PHA-administered Section 8 $287,452 $113,326 $174,126 $400,778

Total PHA-administered $386,313 $197,752 $188,561 $584,064

Owner-administered $113,025 $50,732 $62,293 $163,758

Total $499,338 $248,484 $250,854 $747,822

95% Confidence Interval ±$125,137 ±$70,208 ±$136,035 ±$150,569

Comparison with Prior Studies. Thirteen prior studies (the 2000 baseline study and the annual 

studies since FY 2003) estimated erroneous payments attributed to program administrator rent 

calculation and processing errors. The current FY 2015 study used similar methodology, sampling 

procedures, and sample sizes as all previous studies. Although the FY 2003 and FY 2004 studies 

demonstrated significant reductions in erroneous payments attributed to program administrator 

income and rent determinations, the studies since that time have shown less dramatic changes in 

gross error.

3 Estimates should be viewed in conjunction with 95 percent confidence intervals. Based on the sample, 

estimates may vary from year to year. Variations in estimates may not be statistically significant.
ES-vi August 31, 2016

The total gross erroneous payments decreased from FY 2014 to FY 2015, but this change was not 

statistically significant. There were also no statistically significant overall changes or significant 

changes within program type. The average dollar error decreased from $13.55 in FY 2014 to 

$13.39 in FY 2015. The decrease in total gross dollar error may have been caused by the change 

in population size. Additionally, in a departure from previous years, newfound sources of Social 

Security income were treated as intentional tenant misreporting of income in FY 2015, and was not 

included in the QC rent calculation.4 This income source was instead included in the Income Match 

Study. Finally, sampling error contributes to the variation of estimates from year to year, as a new 

sample is drawn for each study in order to best represent the housing population at that point in time. 

Exhibit ES-4 presents a review of the gross erroneous payments for the QC studies from 2000 to 

FY 2015. Figure ES-1 shows the progression of gross erroneous payments over time.

4 For further details on intentional tenant misreporting of income, please refer to the FY 2015 Income 

Match Draft Report, delivered to HUD on August 20, 2016. 
HUDQC Final Report for FY 2015 ES-vii 

Executive Summary

Exhibit ES-4 

Comparative 2000 Through FY 2015 Gross Erroneous Payments*

Gross Erroneous 

Payments

(in $1,000s)

Administration Type

Total

Public 

Housing

PHAadministered 

Section 8

Total PHAadministeredOwneradministered

FY 2015† $183,286 $400,778 $584,064 $163,758 $747,822

±$150,569

FY 2014 $247,580 $392,317 $639,897 $129,452 $769,349

±$167,657

FY 2013 $177,908 $324,293 $502,201 $105,628 $607,829

±$112,660

FY 2012‡ $190,849 $430,716 $621,566 $177,234 $798,800

±$148,415

FY 2011§ $139,885 $436,156 $576,041 $119,168 $695,209

±$108,728

FY 2010 $141,033 $341,515 $482,548 $167,719 $650,266

±$137,235

FY 2009 $130,268 $440,288 $570,556 $209,455 $780,011

±$162,116

FY 2008 $183,305 $400,248 $583,553 $191,723 $775,276

±$153,447

FY 2007 $149,364 $435,012 $584,376 $199,104 $783,480

±$157,292

FY 2006 $172,824 $520,020 $692,844 $261,324 $954,168

±$192,264

FY 2005 $220,464 $456,240 $676,704 $248,580 $925,232

±$164,206

FY 2004 $242,076 $521,220 $763,292 $224,460 $987,744

±$131,201

FY 2003 $316,116 $730,956 $1,047,072 $368,796 $1,415,844

±$163,000

2000 $602,556 $1,096,524 $1,699,092 $539,160 $2,238,252

±$275,000

Percent Reduction

from 2000 to FY 2015 69.58% 63.45% 65.62% 69.48% 66.55%

* Gross Rent Error is the sum of the absolute value of positive and negative rent error. † For FY 2015, the population totals were updated to reflect the population in FY 2015. 

‡ For FY 2012, the population totals were updated to reflect the population in FY 2012. In addition, the MTW program was included 

in the HUDQC study for the first time. 

§ For FY 2011, the population totals were updated to reflect the population in FY 2011. 

Note: Numbers may not add exactly due to rounding.
ES-viii August 31, 2016

Figure ES-1 

Comparative 2000 through FY 2015 Gross Erroneous Payments Over Time

A sharp decline in erroneous payments occurred from 2000 to FY 2004, from $2.2 billion to 

$988 million. From FY 2004 through FY 2015, the Gross Rent Error was relatively less varied and 

showed a general plateau.

C. Sources of Errors

Rent errors are often a result of a mix of different types of errors. In addition to dollar errors, 

this study also examined administrative and component errors. For the purposes of this study, 

administrative errors are analyzed separately from specific component errors. 

Administrative Errors. Administrative errors are errors that result from administrative mistakes. 

They consist of the following:

● Consistency errors, which are errors in logical conformity between elements within Form 

HUD-50058/50059

● Calculation errors, which are arithmetic errors within subsections of Form HUD50058/50059 

● Transcription errors, which are errors in transferring information from documentation in 

the tenant file to Form HUD-50058/50059

● Failure to conduct a recertification in a timely manner

● Failure to verify information
HUDQC Final Report for FY 2015 ES-ix 

Executive Summary

Component Errors. 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 and allowance components 

are the 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 errors may also occur when the tenant supplies incorrect information, either intentionally 

or unintentionally. The discussion explains methods used to identify the various types of errors, 

error rates, and related estimated variances and the dollar costs of the various types of errors. 

Consistency and Transcription Errors. The two most common administrative errors are 

transcription and consistency errors. The HUD Public and Indian Housing Information Center 

(PIC) and Tenant Rental Assistance Certification System (TRACS) data systems check the rent 

calculations on Form HUD-50058/50059. 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 that are returned to 

program administrators for correction may either be ignored or changed in HUD systems without 

the changes actually being implemented.

Overdue Recertifications. In general, HUD requires that every household be recertified annually. 

About 2 percent of households had overdue recertifications in FY 2015, which was the same rate 

found in FY 2014. 

Verification Errors. Recognizing the issues associated with verifying tenant information, HUD 

program staff have taken steps to clarify and—to some extent—simplify verification guidelines. 

PIH Notice 2010-19, dated May 2010, and Housing Notice H 2010-10, dated July 2010, delineated 

guidelines for new procedures for obtaining and using verification. The new HUD verification 

guidelines were implemented at the end of FY 2010, and FY 2011 was the first fiscal year in which 

they were applied. For the HUDQC Study, methodology was changed to reflect these new HUD 

guidelines. In FY 2012, the acceptable verification date range for documents used by PHA/project 

staff was extended by approximately 2 months so that more documents in the tenant file met 

the HUDQC Study requirements. In FY 2013, the study’s verification date range was revised to 

provide a more accurate time frame for acceptable verification of documents. The changes included 

new criteria for selecting the Quality Control Month and narrowing the time frame for acceptable 

verification documents found in the tenant file to exclude any that were dated after the effective 

date of the transaction being reviewed. 

Obtaining income verification is often difficult. Even when repeated requests are made, employers 

sometimes do not respond to requests for verification, or they require payment for the information. 

Some program sponsors do a much better job than others of achieving third-party compliance 

with written verification. The HUDQC Study shows that it is reasonable to expect all program 

administrators 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.

Component Error Findings. Incorrect income and allowance amounts were by far the most 

significant sources of error when determining rents; only about 4 percent of households with rent 

errors did not have an income or expense component error. Earned income (33 percent), medical 
ES-x August 31, 2016

allowances (16 percent), pensions (15 percent), and other income (13 percent) continued to have 

the highest percentage of households in error. Exhibit ES-5 shows the frequency of the most serious 

component errors and the average dollar amount for each type. The percentage of households 

represents households with any rent component error in which the specified rent component was 

responsible for the largest error. The average dollar amount reported represents the average dollar 

amount for the specified rent component for households in which the specified component was 

responsible for the largest error. For comparison purposes, findings from FY 2014 are provided 

in parentheses. While the percentage of households with specific rent component errors remained 

relatively consistent from FY 2014 to FY 2015, there are often large differences in the annual 

average dollar error from year to year.

Exhibit ES-5

Rent Components Responsible for the Largest Dollar Error for Households With Rent Error 

Rent Component Percent of Households Annual Average Dollar Amount

Earned Income 33% (33%) $3,860 ($4,528)

Medical Allowance 16% (17%) $905 ($1,813)

Pensions 15% (15%) $2,572 ($1,945)

Other Income 13% (13%) $2,453 ($3,122)

Elderly/Disabled Allowance 6% (4%) $400 ($422)

Dependent Allowance 5% (4%) $522 ($566)

Public Assistance 4% (3%) $2,133 ($1,519)

Asset Income 2% (3%) $596 ($808)

Child Care Allowance 2% (2%) $2,148 ($1,256)

No Rent Component Error 4% (7%) $0

Total 100% $2,326 ($2,625)*

* Refers to the sum of the dollars associated with the largest component in error divided by the number of households with that error.

Note: FY 2014 findings are provided in parentheses. Rounding may result in totals not equal to 100 percent. The cell size for elderly/

disabled allowance is small; therefore, estimates may not be reliable.

Exhibit ES-6 displays the impact of changes in the error threshold on the case error rate and gross 

dollar error. As noted above, a monthly error of less than $5 is currently ignored due to rounding. 

An increase in the error threshold of $5 to $10 would result in an increase in proper payments by 

about 6 percent, as well as a decrease in the estimate for gross dollar error by about $28.8 million. 

Based on the distribution of household error, most rent errors are within $100 per month, or 

$1,200 per year. At the individual household level, the gross error may seem insignificant; however, 

these errors can result in a substantial amount of gross dollar error for the assisted housing programs 

in aggregate. Although an increase in the error threshold to $100 per month would result in 

97 percent of cases being proper payments, the increased error threshold would not capture most 

errors associated with improper payments.
HUDQC Final Report for FY 2015 ES-xi 

Executive Summary

Exhibit ES-6 

Impact of Changes in the Error Threshold on Frequency and Estimates of Error (in $1,000s)

Monthly

Error

Percentage of Households Dollar Error Amount

Rent 

UnderpaymentProper 

Payment

Rent 

Overpayment

Rent 

Underpayment

Rent 

Overpayment Gross ErrorNet

Error

Exact Match 19.0% 61.4% 19.7% $506.972 $256,933 $763,905 $250,039

Within $5 13.5% 73.6% 12.8% $499,338 $248,484 $747,822 $250,854

Within $10 11.2% 79.8% 9.0% $488,896 $230,173 $719,069 $258,723

Within $15 9.6% 83.5% 6.9% $477,702 $215,132 $692,834 $262,570

Within $25 7.3% 87.8% 4.9% $451,810 $191,948 $643,758 $259,863

Within $50 3.8% 94.5% 1.7% $379,778 $129,017 $508,795 $250,761

Within $100 2.1% 97.3% 0.7% $314,614 $90,526 $405,140 $224,088

*Row totals may not add up 100 percent to due to rounding.

D. Additional Findings

Eligibility of Newly Certified Households. A separate analysis of newly certified households 

(13 percent) was conducted to determine whether the households were eligible for HUD housing 

assistance. Ninety-six percent of these households met all of the eligibility criteria—the same rate 

found in new certifications in FY 2014. All certified households in the sample were income-eligible 

on the basis of the QC income determination.

One percent of newly certified households failed to document Social Security numbers for one 

or more household members, and 3 percent lacked the signed consent forms needed to authorize 

verification of income and assets (for each member of the household who is at least 18 years old). 

All households had the signed declaration forms or evidence accepted as proof of citizenship. These 

findings estimate the percentage of newly certified tenants who were incorrectly determined eligible 

for program admission.

Occupancy Standards. The study also determined the extent to which households are under- or 

over-housed, relative to HUD’s occupancy standards. Seventeen percent of all households occupied 

a unit with too many or too few bedrooms in FY 2015, according to the guidelines used for this 

study. Historically, the percentage of households in units with the correct number of bedrooms 

according to study guidelines has fluctuated between 83 percent and 88 percent since FY 2004.

Rent Reasonableness. The extent to which PHA-administered Section 8 Voucher rent comparability 

(reasonableness) determinations are found in the tenant file was also investigated, along with the 

method used to support the determinations. Ninety-six percent of new admission files contained rent 

reasonableness documents, as did 89 percent of the files for households for which data were collected 

for an annual recertification. However, the absence of documentation does not necessarily indicate 

that 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. To 

determine whether the rent was reasonable, all PHAs in the study used unit-to-unit rent comparison, 

unit-to-market rent comparison, or a point system. 

Utility Allowances. For PHA-administered Section 8 Voucher households, the utility allowances 

found on Form HUD-50058 were compared to the utility allowance worksheets found in the tenant 
ES-xii August 31, 2016

file and to the utility allowance values calculated using the utility allowance schedules provided 

by the PHAs. For the first comparison, 83 percent of the utility allowance values matched. For the 

second comparison, 87 percent of the values matched between the QC utility allowance amount 

and that on the Form HUD-50058. However, nonmatching values may not necessarily mean the 

utility allowance found on Form HUD-50058 was incorrect.

Payment Standards. A special analysis was conducted to determine whether the correct payment 

standards were used for PHA-administered Section 8 Voucher households. The payment standard 

found on Form HUD-50058 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, 

82 percent of the payment standards matched. For the second comparison, 89 percent of the payment 

standards found on Form HUD-50058 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.

Form HUD-50058/50059 Rent Calculation Error Compared to QC Rent Error. The tenant 

rent was calculated using only the data on Forms HUD-50058/50059 to determine the relationship 

between errors detected using Forms HUD-50058/50059 and total rent errors found in the study. 

When using only Form HUD-50058/50059 data to calculate rent, errors were found in 9 percent of 

households. This is clearly different from the QC error calculation, in which calculation errors were 

found in 26 percent of households. Calculation error was found in both Forms HUD-50058/50059 

and the QC calculation in only 4 percent of households.

Automated Rent Calculation Systems. The study examined whether error rates in projects that use 

an automated rent calculation system differ from errors in those using other calculation methods. 

We did not find a difference between PHAs/projects that use automated rent calculation systems 

and those that do not. This is not surprising because nearly all PHAs/projects use an automated rent 

calculation system of some kind.

Tenant Characteristics and Project Characteristics and Practices. The conceptual approach 

used for the multivariate modeling was updated to be a single combined model, but the analytical 

approach used was the same used in FY 2014. The analysis identified patterns in which rent errors 

related to project and household variables, particularly involving project-caused errors such as 

transcription error and overdue recertification error and their association with Gross Rent Error. 

These findings were essentially similar to those reported in prior years’ analyses—differences 

among program types were not found to be statistically significant with regard to Gross Rent Error, 

or subsidy overpayment, subsidy underpayment, net of other project and household effects. (See 

Appendix F for more information on the Multivariate Analysis.)

Project-caused errors accounted for a large proportion of Gross Rent Error, controlling for 

other effects. Of the project-caused errors, administrative errors, transcription errors, overdue 

recertification errors, and the rate of items without third-party written verification predicted a higher 

gross error, which has been consistently found in prior analyses. Transcription error was a driver 

of overpayment, while the percentage of items without verification was a driver of underpayment.

Household background variables were strong predictors of Gross Rent Error, subsidy overpayment, 

and subsidy underpayment. Variables indicative of complex financial conditions and income strongly 
HUDQC Final Report for FY 2015 ES-xiii 

Executive Summary

predicted higher rent errors. The relationship between household financial or sociodemographic 

variables and rent error is highly consistent across models and years, a finding which indicates that 

PHA/project staff should emphasize quality control of these cases.

E. HUD Initiatives: 2000–2015

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:

1. A Rental Housing Integrity Improvement Program committee, headed by the Office of the 

Chief Financial Officer with representatives from other affected offices, was formed to 

coordinate and monitor corrective actions. The committee meets to review progress and to 

identify and resolve impediments to progress in reducing errors.

2. The Office of Housing and PIH developed and issued new handbooks and instructional 

materials that detailed all current HUD program requirements and standardized them to 

the extent possible without regulatory or statutory change. The 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).

3. The Office of Housing and PIH substantially increased training efforts and held a number 

of national and regional training sessions. This contrasts with a less educational approach 

in the 1980s and 1990s.

4. The Office of Housing and PIH 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 of agency compliance.

● PIH 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 

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.

5. HUD initiated a legislative change that granted it access to the U.S. Department of Health 

and Human Services National Directory of New Hires (NDNH) income and wage database 

for income matching purposes. HUD uses 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 the implementation of agreements and data 

systems. HUD also negotiated agreements with some States to obtain access to the same 
ES-xiv August 31, 2016

information. Access to the NDNH database is available through the Enterprise Income 

Verification (EIV) system.

6. The Office of Housing and PIH initiated a computer matching program with the Social 

Security Administration (SSA) that provides SSA data for tenants receiving assisted housing. 

SSA electronically provides HUD with benefit information on all active household members 

who have disclosed a valid Social Security number. HUD makes this information available 

to administrators of the Public Housing and Section 8 programs through the EIV system. 

This information allows PHAs to validate Social Security numbers and SSA benefits quickly 

and efficiently.

7. In 2010, HUD issued the Implementation of Refinement of Income and Rent Rule, which 

mandated the use of the EIV system (discussed in the previous two bullets) as a third-party 

source to verify tenant employment and income information during mandatory recertification 

of household composition and income. The use of EIV minimizes the need for traditional 

third-party verification forms. To make the EIV system as effective as possible, the rule was 

also revised to require all applicants and participants to disclose a Social Security number, 

no longer exempting children younger than age 6.

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 2005 showed that HUD exceeded 

this goal, and HUD has further decreased error since. 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 has been that 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

HUD’s progress in decreasing improper payment since 2000 is impressive. However, findings from 

the study suggest general actions that should be continued or policies that should be considered 

to maintain or improve PHA/project performance in rent determination. We present the following 

recommendations that may improve administrative error rates in HUD programs, based on insights 

we have gathered during this and other studies: 

1. Continue Requiring the Use of EIV Reports. HUD should continue requiring the use 

of EIV information in the process of rent determination. Data systematically collected 

from the NDNH and SSA provide a strong method of identifying specific sources of 

income information. The study shows that the majority of subsidy errors are associated 

with earned income. HUD may also want to consider forming relationships with State 

programs, organizations, and companies to collect other data not currently captured by the 

EIV system. Although EIV provides a uniform and efficient method of verifying income 

sources that lessens the burden on program administrators, caution must be exercised when 

using information from the system. The data are extremely helpful in identifying unreported 

sources of income, but they are not current and sometimes contain errors (including 
HUDQC Final Report for FY 2015 ES-xv 

Executive Summary

instances of identity theft and incorrect identification of disability status). HUD’s EIV 

requirement should be coupled with policies aimed at addressing the challenges of using 

EIV for verification. 

2. Perform Onsite Review of Rent Calculation. HUD should continue onsite monitoring of 

program administration, and PHA/Owners should be held accountable for implementing HUD 

regulations and calculating rent accurately. Onsite monitoring that includes reviews at both the 

local and Federal levels 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. Improved HUD monitoring was likely a key factor in reducing subsidy 

error between the 2000 study and the current study. 

We recommend that HUD require PHA/Owners to perform their own QC reviews on income 

determinations and rent calculations. Agencies that have aggressively sought to improve 

the performance of their programs have demonstrated success in this area, and one of 

the most frequently used error-reduction strategies includes the establishment of internal 

QC review procedures. Based on the Project Staff Questionnaire (PSQ) survey, it appears 

that programs that conduct QC on all their transactions have a significantly lower rent 

determination error rate than programs that do not perform QC on all their files. Of course, 

a comprehensive approach may not be feasible, given limited staffing resources, but even a 

review of a small percentage of transactions may be beneficial in supporting the reduction 

of rent determination error. 

In addition to internal agency reviews, HUD regional offices can support field offices by 

conducting a secondary review of transactions. This review would provide HUD Federal 

staff with more on-the-ground insights into the issues and challenges faced by local program 

administrators. In addition, this approach would demonstrate HUD’s concern regarding 

program integrity and improper payments, thereby focusing PHA/Owner attention on 

accurately determining tenant income and rent.

3. Continue to Streamline the Program Requirements. Continue to simplify Federal laws, 

regulations, and HUD requirements to the extent possible. The new regulations outlined in 

Notice PIH 2016-05 provide much needed clarification on previous regulations, such as the 

time period associated with the Earned Income Disregard calculations. The implementation 

of triennial certification for fixed income households will save time for PHA staff, allowing 

them to spend more time conducting required reexaminations, following up on suspected 

cases of fraud, and conducting more internal reviews of tenant files. Office of Housing 

should implement similar changes.

4. Create an Online Community to Share Best Practices and Tools. HUD should provide 

PHA/Owners with an online venue to support the sharing of best practices for its assisted 

housing programs. A Web-based resource could facilitate communication between HUD 

and program administrators regarding identifying ways to improve and address challenges 

related to proposed policies. Comprehensive supporting documents, including forms for 

interviewing tenants, obtaining verification, and determining rent, could be posted to the site 

for downloading. Manuals and training materials describing how to implement requirements 
ES-xvi August 31, 2016

and accurately calculate rent could also be available electronically, with online webcasts 

providing an additional training resource for local program offices. 

HUD experts and local housing staff should be given the opportunity to work together to 

develop tools and systems that reduce rent error. Many local PHA/Owners have already 

developed forms, training materials, manuals, automated systems, and monitoring processes 

that enable them to provide accurate, efficient service to their tenants. HUD should create 

a platform for organizations to learn from each other. 

5. Develop a technical assistance platform for rent calculation staff. HUD should develop a 

technical assistance hotline for project/PHA staff to call about particularly difficult policies 

related to completing (re)certifications. The assistance could be a Web-based support system, 

where the PHA/project staff would be given a time estimate of when they will receive an 

answer after submitting a question, or a live telephone system staffed by experienced HUD 

policy advisors. This open dialogue would help avoid common errors that contribute to rent 

error. Additionally, HUD should keep detailed records for each call to determine if there 

were commonly asked questions, and provide written guidance on these issues to all PHA/

projects for future use. 

6. Develop a calculation worksheet for use in annualizing income. This electronic worksheet 

would be programmed to calculate annual income to be used for rent calculation. It would 

also serve as documentation of the methodology used by the PHA/project staff to annualize 

income. A physical copy could be kept in the tenant file after it is dated and signed by the 

certification staff. This would help to avoid calculation errors and thus reduce rent error. 

In addition to providing general program recommendations to decrease error rates, we seek to 

improve the HUDQC Study that provides the estimates of the error rates. The current methodology 

used by ICF to conduct the study is based on established study objectives and builds on insights from 

previous studies. The following recommendations serve to expand the utility of the data collected, 

support HUD’s research goals, and improve the overall efficiency of ongoing QC studies. 

7. Continue to Measure Improper Payment Associated With Billing Error. HUD should 

conduct billing error studies to obtain a more accurate assessment of improper payments. In 

the FY 2014 HUD Agency Financial Report, billing error estimates are based on FY 2004 

data for the Public Housing program and FY 2009 data for Owner-administered programs. In

FY 2015, ICF conducted billing studies for both the Office of Public and Indian Housing and 

the Office of Housing. This work should continue to be conducted. Current error estimates 

could be obtained by conducting primary data collection or by using statistical modeling 

to update the existing data. However, an updated study will provide HUD with a better 

assessment of billing error associated with rental assistance programs in order to understand 

one of the main contributors to improper payments. The information from these billing 

studies could strengthen financial management controls so that HUD can better detect and 

prevent improper payments.

8. Incorporate Additional Objectives in the HUDQC Study. Data collected through the 

HUDQC Study provide details that are not available through other HUD sources (e.g., PIC/

TRACS) that could be used to track trends, such as the extent to which income and expense 

items are verified or the number of sources of employment income for a particular household 
HUDQC Final Report for FY 2015 ES-xvii 

Executive Summary

or household member. Furthermore, because a statistically valid nationwide sample 

of projects and households is created for the study, other HUD-related topics could be 

investigated using the HUDQC Study’s research mechanisms and data collection processes. 

The rental integrity monitoring (RIM) review validation, identified in the July 2013 issuance 

of the HUD Research Roadmap for FY 2014–FY 2018, is a task that could be incorporated 

into the HUDQC Study’s data collection process. Additional topics could include a review 

of the changing demographics of HUD tenants, participant satisfaction surveys, and a more 

in-depth review and evaluation of MTW programs.

9. Continue to Conduct a Utility Allowance Comparison Study. In response to tightening 

budgets and overall concerns with energy efficiency, HUD should undertake a study to 

better understand utility costs and consumption in subsidized housing. ICF conducted this 

work in FY 2015, and this work should continue. HUD should consider conducting an 

in-depth quality control study of how utility allowance values are calculated and used in rent 

calculation. This study could involve collecting data from utility companies regarding utility 

use for a given fiscal year and comparing actual consumption with the utility allowance 

subsidy calculated by program administrators. 

The FY 2015 Utility Allowance Comparison Study found that current HUD Utility Schedule 

Model (HUSM) users produce incorrect allowances via the tool. HUD should consider 

making improvements to the HUSM tool to increase accuracy of data entry and calculated 

utility allowances. Additionally, performing project-level reviews and approvals of utility 

allowance levels would improve accountability in updating allowances in a timely manner 

and would likely reduce subsidy error in utility allowances. 

10. Learn More About PHA/Project Policies and Practices. Each PHA establishes its own 

policies, procedures, and forms for collecting information that is ultimately used to calculate 

tenant rent. The differences in these practices should have some (possibly major) impact 

on rent error, yet the analysis of the project practices and characteristics collected by the 

PSQ does not demonstrate the expected impact. We recommend conducting focus groups, 

interviews, and discussions with program administrators to identify additional PHA/

project-level factors that may impact error. This information could be used to revise the 

PSQ to include questions focused on the specific practices expected to influence errors. The 

analysis of more detailed, project-level data would assist in this process.

11. Continue to Perform the HUDQC Study. The HUDQC Study provides a consistent 

ongoing method of monitoring, managing, and improving HUD rent determination processes. 

The ongoing evaluation of HUD rental housing assistance programs is essential to program 

management and improvement, and rigorous research is important for understanding how 

well HUD programs are reaching their goals for the communities served. The primary 

objective of the HUDQC Study is to measure rent calculation and improper payment error; 

however, the study also gives HUD the opportunity to learn more about methods to reduce 

rent calculation errors and better manage current and changing conditions at PHAs/projects. 

Annual evaluations facilitate more accurate, cross-year comparisons of rent errors. They 

also allow data collection and data analysis staff to develop specific expertise in HUD policy 

areas, supporting the development of tailored solutions for improving data quality. 
HUDQC Final Report for FY 2015 I-1 

I. Introduction

The U.S. Department of Housing and Urban Development (HUD) provides housing subsidies 

to Multifamily project owners and Public Housing Authorities (PHAs) to administer housing 

assistance primarily to low-income households. The Office of Public and Indian Housing (PIH) and 

the Office of Multifamily Housing provide funding for rental subsidy through Public Housing, the 

PHA-administered Section 8 Voucher program, and the Owner-administered Section 8 project-based 

programs. Collectively, these programs are referred to as HUD’s Rental Housing Assistance 

Programs (RHAP). They are administered by more than 4,000 intermediary agencies and provide 

affordable housing for approximately 4.97 million households (i.e., 1.1 million though Public 

Housing, 2.2 million through the PHA-administered Section 8 program, and 1.6 million through 

project-based programs).5

Under the Improper Payments Elimination and Recovery Act (IPERA), signed into law in 2010, and 

the guidance of the Office of Management and Budget (OMB), agencies assess all programs they 

administer and identify those that may be susceptible to improper payments. An improper payment 

is any payment that should not have been made or that was made in an incorrect amount. In fiscal 

year (FY) 2014, $32 billion of HUD’s total payments were attributed to HUD’s rental assistance 

programs. These programs constitute a significant amount of HUD’s total payments and continue 

to be assessed as being at high risk of significant improper payments.6

 

During this challenging economic period, it is more important than ever to evaluate program 

administration and internal controls to maintain sustainable, quality programs that meet the needs 

of communities. The reduction of improper payments directly increases the efficacy of HUD’s 

housing programs and ensures that Federal dollars are being allocated fairly across the nation. This 

section outlines the purpose of the Quality Control for Rental Assistance Subsidy Determination 

Study (HUDQC Study), provides background information on the study, and explains how the report 

is organized.

A. Purpose of the Improper Payment for Quality Control for Rental 

Subsidy Determinations Study for FY 2015

ICF International was contracted to perform the HUDQC Study to support HUD’s continued 

dedication to reducing the amount of annual improper payments in its programs and to comply with 

the reporting and administrative requirements under IPERA. The HUDQC Study provides national 

estimates of the level of improper payments and rent calculation error in tenant subsidies for Public 

Housing; PHA-administered Section 8 Vouchers and Moderate Rehabilitation programs; and the 

Owner-administered Section 8, Section 202, and Section 811 Project Rental Assistance Contracts 

(PRAC) and Section 202/162 Project Assistance Contracts (PAC) programs. For the purpose of 

this study, error is defined as any rent calculation or eligibility decision that is determined based on 

methods discrepant from HUD’s income certification and rent calculation requirements. The study 

5 U.S. Department of Housing and Urban Development. Annual Report: FY 2015 Agency Financial 

Report. Washington, DC: U.S. Department of Housing and Urban Development, 2015. pg. 12

6 U.S. Department of Housing and Urban Development. Annual Report: FY 2015 Agency Financial 

Report. Washington, DC: U.S. Department of Housing and Urban Development, 2015. pg. 213
I-2 August 31, 2016 

I. Introduction

examines the sources, associated costs, and frequency of subsidy errors in tenant certification and 

annual recertification processes for recertification transactions conducted during Federal FY 2015.7

This report examines a total of 16 objectives and outlines them in more detail in Section III. The main 

focus of this work involved collecting and analyzing information that pertained to eligibility and 

rent determination processes to identify possible causes of error in rent calculation. Throughout this 

report, information is reported for the three major housing programs separately and in combination. 

As a separate analysis, key error estimates are also provided for the 20 largest PHAs included in the 

study sample. In addition, some special analyses were conducted regarding PHA utility allowances, 

payment standards, and rent reasonableness practices. As part of our review, we also compared unit 

size to household size to identify any errors in the determination of unit size.

B. Study Background

HUD defines potential rental assistance improper payment based on three major error types. These 

error types include the following:

1. Program administrator error, which is the program administrator’s failure to correctly 

determine eligibility and income and to apply all income exclusions and deductions when 

conducting the recertification.

2. Tenant income reporting error, which is a consequence of the tenant’s failure to disclose all 

employment income and unemployment compensation sources.

3. Billing error, which occurs when there is incorrect billing and payment of subsidies between 

HUD and third-party program administrators and/or housing providers.

The FY 2015 HUDQC Study is the 14th in a series of studies designed to:

● Identify potential metrics for improper payments error, including HUD eligibility 

determination, income calculation, and rent calculation.

● Translate regulations for HUD programs (i.e., Public Housing, PHA-administered Section 

8, and Owner-administered projects) into data collection and survey instruments.

● Develop an error detection system for flagging inconsistencies in household data and 

establishing an internal quality control process for data collectors.

● Provide nationally representative estimates of rent subsidy errors. 

Activities for the FY 2015 HUDQC Study commenced in December 2015, starting the review of 

recertification transactions effective November 1, 2014, to October 31, 2015. Tasks completed 

prior to data collection that have not been listed above included designing the research and survey 

methodology and automating the data collection process. Data were collected from a nationally 

representative sample of HUD-assisted housing projects, and participant household data were 

collected from tenant files, household interviews, and third-party verification when necessary. 

7 PHAs and owners of HUD-assisted housing are required to make an initial determination of eligibility 

and, thereafter, an annual recertification of each household’s rent. In this report, the term recertification 

refers to the initial certification and annual recertification. This study does not include interim 

recertification transactions.
HUDQC Final Report for FY 2015 I-3 

I. Introduction

C. Organization of This Report

This report is organized into the following sections:

● Section I: Introduction

● Section II: Methodology

● Section III: Study Objectives and Analytic Methods

● Section IV: Findings

● Section V: Recommendations

● Appendices

○ Appendix A: Rent Calculations

○ Appendix B: Weighting Procedure

○ Appendix C: Source Tables

○ Appendix D: Consistency and Calculation Errors

○ Appendix E: Project Staff Questionnaire Descriptive Analysis

○ Appendix F: Multivariate Analysis

D. Definitions of Key Terms

The HUDQC Study uses key terms for the study of RHAP rent calculation error and improper 

payments. These key terms are used throughout the report and can be referenced here:

● Abstract Month—The month in which the data collection process for any given household 

was initiated

● Actual Rent—The tenant rent listed on the Form HUD-50058/50059 

● Administration Type—PHA or owner

● Calculation Errors—Arithmetic errors within subsections of the Form HUD-50058/50059

● Case Type—Certification, recertification, and overdue recertification

● Component Errors—The income components (i.e., employment income, Social Security 

and pensions, public assistance, other income, and asset income) and expense components 

(i.e., elderly/disabled allowance, dependent allowance, medical expenses, child care 

expenses, and disability expenses) responsible for an error in rent calculation 

● Consistency Errors—Errors in logical conformity between elements within the Form 

HUD-50058/50059

● Dollar Rent Error—The difference between the household’s QC Rent (see definition below) 

and the Actual Rent

● Error Rate—The sum of the dollar amount of 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
I-4 August 31, 2016

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

with the largest error

● Net Rent Error—The arithmetic sum of underpayments and overpayments

● (Rent) Overpayment—Results when the household paid more than it should have paid, 

making HUD’s contribution less than it should have been

● Payment Type—Underpayment, proper payment, or overpayment

● Program Type—Public Housing, Section 8 Housing Choice Voucher, Section 8 Moderate 

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

202/162 PAC

● Quality Control Date—The day the tenant rent was calculated by the project staff; this date 

is used to determine whether verification is acceptable

● Quality Control Month—The month in which the PHA/owner completed the rent calculation; 

used during the household interview to obtain data for the correct time period

● Quality Control (QC) Rent—Calculated by ICF using the tenant file, household interview, 

and verification data

● Rent Component—One of the five sources of income (i.e., earned, pensions, public 

assistance, other income, and assets) or the five types of deductions (i.e., medical, child 

care, disability assistance expenses, dependent allowance, and elderly/disabled allowance)

● Rent Error—The difference between the monthly Actual Rent and the monthly QC 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, combined to provide an overall Total Dollars in Error and presented as 

an annual amount

● Transcription Errors—Errors in transferring information from documentation in the tenant 

file to the Form HUD-50058/50059

● (Rent) Underpayment—Results when the household paid less than it should have paid, 

making HUD’s contribution higher than it should have been
HUDQC Final Report for FY 2015 II-1 

II. Methodology

A. HUD Requirements and Study Standards

ICF used the Code of Federal Regulations and official HUD handbooks and notices to consolidate 

all HUD rules relevant to the determination of rent into a set of HUD requirements. We used these 

requirements 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 were developed to ensure a uniform 

manner of data collection. A complete list of the standards used in this study can be found in the 

Final FY 2015 Improper Payment for Quality Control for Rental Subsidy Determinations Data 

Collection Standards.

8

B. The Sample

The initial sampling design called for a nationally representative sample of 600 projects with 

4 households randomly selected from each project, equaling 2,400 households. We selected projects 

with probabilities proportional to size, but 8, 12, or more households were selected from larger 

projects whose size exceeded the sampling interval; these were counted as more than one project 

for the purpose 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, 

Section 202/162 PAC, and Section 811 PRAC). 

Project Sampling. Certain projects were excluded from the study because of their different 

eligibility and rent calculation rules, such as Owner-administered Rental Assistance Payment 

or Rental Supplement Program (RAP/SUP) projects. Based on a discussion with HUD, Owneradministered projects that were in the process of converting via the Rental Assistance Demonstration 

(RAD) program were excluded from the project frame files in FY 2015. This decision was due to 

the difficulty of determining rental subsidy error in units undergoing the transition. Universe files 

requested from HUD either excluded out-of-scope projects or those projects were identified for easy 

removal. Given that some large projects were selected multiple times, the study sample included 

583 distinct projects in 57 geographic areas in the United States and Puerto Rico. We sampled 200 

projects from each major program type9 and collected data for a multiple of 4 households from each 

project. An additional project was added to the sample to ensure that the sample would include a 

minimum of 2,400 households, even if unexpected circumstances were encountered. The final data 

set includes responses from 2,400 households in 583 projects. 

Household Sampling. For the second year in a row, ICF selected households using HUD-provided 

PIH Information Center/Tenant Rental Assistance Certification System (PIC/TRACS) data. 

Previously, field interviewers selected the sample of households while onsite at PHAs/projects 

8 ICF International unpublished report to HUD dated September 18, 2015.

9 For the purpose of this study, a “project” for the Section 8 Voucher program is defined as the 

administration of the program in one county/township. Therefore, if a PHA administers vouchers in more 

than one county/township, the PHA could be represented in this study by more than one “project.”
II-2 August 31, 2016 

II. Methodology

using PHA/project-supplied tenant rosters. ICF automated this process using PIC/TRACS data 

that included all active non-Moving to Work (MTW) tenants who had been certified or recertified 

in FY 2015. Due to nonstandard recertification cycles permitted by various MTW PHAs, ICF’s 

PIC/TRACS data request did not include MTW households. Certain MTW PHAs selected for the 

FY 2015 study allow tenants to have biennial or triennial recertifications, so those tenants would 

not be included in a list of all active and assisted tenants who were either certified or recertified 

in FY 2015. In order to not exclude eligible tenants from the study sample, ICF collected tenant 

rosters for MTW projects from the individual PHAs/projects and selected those samples using the 

previous method used for random sampling.

A random sample of 4 households was selected from most projects, with some larger projects having 

a larger random selection of 8, 12, or more households. For example, 16 PHA-administered Section 

8 Voucher projects, including those in New York City (NYC) and Los Angeles, had household 

sample sizes of 12 or more. An equal number of “replacement” households were identified at each 

selected project as potential substitutes in the event that a selected household did not meet the study 

requirements or was unavailable to be interviewed. 

Sampling for the 20 Largest PHAs. Once the sample for the HUDQC Study was identified, 

additional projects and households were selected for the 20 largest PHAs in the study sample. 

This additional sample allowed us to provide supplemental findings for these large PHAs. At least 

32 cases were sampled per PHA. If a PHA’s HUDQC Study sample size was sufficiently large, 

we did not supplement it; however, if only a few households were sampled from the PHA, we 

added substantially to the sample. As in the HUDQC Study, we allowed vouchers to be selected 

more than once. Since we selected households in groups of four, we aimed for eight projects per 

PHA, with possible multiple selections for the PHA-administered Section 8 Voucher and Moderate 

Rehabilitation projects. The resulting sample yielded 71 new projects that were not selected for the 

HUDQC Study and 268 new households. For additional information on the sampling procedures, 

see the Final FY 2015 Improper Payment for Quality Control for Rental Subsidy Determinations 

Sampling Plan.

10

Weighting. In studies from FY 2004 to FY 2011, Owner-administered RAP/SUP projects and MTW 

projects in Public Housing and Voucher programs were excluded from the population totals because 

of the differences in their eligibility and rent calculation rules. Beginning in FY 2012, however, 

MTW projects were included in the study, at HUD’s request. 

In studies from FY 2005 to FY 2010, the population totals from the June 13, 2005, request for 

proposal were used as the basis for the estimate of occupied units in each of the programs. In 

FY 2011, a comparison of the previous population totals to the frame population totals showed 

a change sufficient enough to warrant updating the population counts. In FY 2012, the inclusion 

of MTW projects led to an increase in the population. These population totals were used until 

FY 2014. For FY 2015, these population totals were updated per HUD’s request. Exhibit II-1 

displays the changes to frame population totals over time. 

10 ICF unpublished report to HUD dated September 18, 2015.
HUDQC Final Report for FY 2015 II-3 

II. Methodology

Exhibit II-1 

Change in Frame Population Totals Used to Pull the Study Sample Over Time

Program Type

FY 2005–

FY 2010 

Population 

Totals*

FY 2011 

Study Sample*

FY 2012–

FY 2014 

Study Sample** FY 2015**

Public Housing Total 955,000 1,052,503 1,154,796 1,061,690

Public Housing (non-MTW) 955,000 1,052,503 1,040,708 959,766

Public Housing (MTW) 0 0 114,088 101,924

PHA-administered Section 8 Total 1,858,000 1,912,467 2,198,722 2,209,296

PHA-administered Section 8 (non-MTW) 1,858,000 1,912,467 1,935,597 1,916,735

PHA-administered Section 8 (MTW) 0 0 263,125 292,561

Owner-administered 1,320,000 1,382,670 1,378,158 1,382,453

Total 4,133,000 4,347,640 4,731,676 4,653,439

* Excluding RAP/SUP and MTW populations

** Excluding RAP/SUP; including MTW

The use of the same population counts increases the comparability of data, so any change from year 

to year would not be due to a change in the number of households in the program but to an actual 

change in the average gross dollar error or percentage of households that are in error. However, 

maintaining constant population counts over time despite changes in the population itself may 

result in estimates for total dollar amounts and the proportion of the population represented by 

each program type not being representative of the current population. Based on these reasons and 

given the inclusion of MTW projects in the FY 2012 sample and with HUD’s agreement, ICF 

updated the population counts for the FY 2012 study and continued to use those counts for the 

FY 2013 and FY 2014 studies.11 In order to ensure accuracy with the current rental assistance 

population, these population counts were updated for FY 2015 based on the FY 2015 HUD sampling 

frame.

C. Data Collection

This study used a multistage data collection process to obtain all required information. Web 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 households, and 

requested tenant consent to verify income, expense, and household composition items from third 

parties.12 Tenant income, expense, allowance, and third-party verification information was collected 

using HUD-sanctioned data collection procedures. Field data collection began in December 2015 

and ended in May 2016.

Most data collection activities were categorized as either project level data collection or household 

level data collection, but select processes applied to both. 

11 For a more detailed discussion of population total updates, please reference Appendix B.

12 Verification is a process of obtaining information about income or expenses from a third party that 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 using documents (e.g., printouts 

from the Enterprise Income Verification [EIV] system).
II-4 August 31, 2016

Creating the Data Collection Instruments. For this study, more than 30 data collection instruments 

were used to collect data at both the project and tenant levels. These instruments were similar to 

those used for the previous data collection efforts, although instruments were modified to improve 

the data collection process. Project-level instruments were used to gather information to facilitate 

data collection, collect the data elements necessary to calculate QC rent, and gather information 

about certification and recertification practices. The tenant-level data collection instruments were 

created to collect data and determine whether:

1. There were errors in the eligibility determination

2. The household rent was calculated correctly

3. Units were correctly assigned according to the study standards

Each instrument was created by a survey research specialist and reviewed by a HUD policy expert. 

The Office of Management and Budget approved all data collection instruments.

Automating the Data Collection Process. This study used an enhanced version of the data collection 

system than was used in previous studies. Project-level data were collected through the PSI and 

PSQ Web surveys that were developed using Select Survey Software. Data from household 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 HUD Data Collection Software 

(HDCS) system, was developed by a special team of ICF survey specialists and computer systems 

experts.13

Project Level Data Collection 

Project Level Web Surveys. The initial collection of project-level data began in October 2015 

with the Web-based Project Specific Information (PSI) questionnaire. Using contact information 

obtained from HUD, this survey requested background information essential to the data collection 

process as well as specific data used for the calculation of QC Rent. The rent calculation information 

requested varied by program, but included questions relating to items such 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 asked to indicate 

whether the selected project had been designated a “special demonstration project” by HUD. If a 

project answered “yes” to this question, and this status was confirmed, the project was replaced in 

the study. In addition, PHA-administered Section 8 Voucher projects and Public Housing projects 

were asked to provide their Administrative Plan and their Admissions and Continued Occupancy 

Policy (ACOP) documents. Public Housing projects were asked to identify the location of any 

information on permissible deductions. Administrative Plans and ACOPs were thoroughly reviewed 

for local discretionary policies that would impact QC rent determination.

The data requested from the PHA/project were essential to the calculation of the QC Rent and to 

preparing interviewers to begin the process of data collection. For these reasons, a 100 percent 

13 The base of HDCS is the Census and Survey Processing System (CSPro) software package, which is 

used by the U.S. Agency for International Development to collect demographic and health information in 

many countries.
HUDQC Final Report for FY 2015 II-5 

response rate to our request for information was targeted. In FY 2015, 99 percent of the projects 

selected responded to the survey and supplied the necessary documents. Rigorous strategies were 

employed to ensure compliance and the completeness of requested information prior to field data 

collection.

Another Web-based survey, the Project Staff Questionnaire (PSQ), was sent to projects in February 

2016. This survey was designed to obtain information from PHA/projects about the processes that 

they use to calculate rent during certifications. 

Because PHAs/projects have varying practices, ICF designed data collection instruments 

and guidelines for data collection that were flexible enough to obtain data from the variety of 

circumstances found in PHAs/projects. The major tasks accomplished during data collection and 

the instruments used to accomplish those tasks are discussed as follows.

Contacting the PHA/Project. PHA/project contact names were obtained from HUD headquarters 

staff. Emails were sent to PHA/project staff advising them of the study and requesting their 

participation. When field data collection commenced, PHA/projects were sent communication 

introducing the field interviewer and requesting cooperation during their site visit. 

Household-Level Data Collection 

Hiring and Training Field Interviewers. Seventy-three field interviewers were hired to complete 

the field data collection, and each interviewer was assigned a group of projects. Field interviewers 

typically lived in the same general area as the projects selected for the study. Fifty field interviewers 

who had not worked on the previous year’s study (for FY 2014) attended a 7-day training; 

twenty-three experienced interviewers who completed the FY 2014 study attended a 

3-day training. The 7-day training covered the following topics:

● Project background

● HUD programs and requirements

● Survey procedures

● Automated data collection

● Administrative procedures

The 3-day training covered a review of the project background and data collection procedures and 

focused on changes implemented for the FY 2015 study.

Abstracting From Household Files. At certification and recertification, PHAs/projects must 

complete either Form HUD-50058 (for each household in Public Housing and PHA-administered 

Section 8 programs), Form HUD-50058 MTW (for each household in MTW projects), or Form 

HUD-50059 (for all other programs in the study). Data from Form HUD-50058/50059 were entered 

directly into 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. These electronic checking procedures enabled field interviewers 

to make immediate corrections and updates.
II-6 August 31, 2016

This structured, automated process greatly reduced the need to edit, code, and clean the data after the 

close of data collection. HDCS data were securely transferred to study headquarters electronically 

on a daily basis. The incoming data were reviewed in an ongoing QC process. This continual data 

review during the collection process ensured data accuracy and permitted study headquarters staff 

to resolve issues or request other clarifying documents while interviewers were still in the field.

HDCS was designed to collect data in the same format as the official Form HUD-50058 and Form 

HUD-50059 published by HUD. The NYC Housing Authority uses a Form HUD-50058 format that 

differs slightly from the standard. However, because of the large number of NYC Public Housing 

units and PHA-administered Section 8 Voucher cases in the study, copies of the corresponding PIH 

Information Center (PIC) 50058 data for these cases were requested and used for data collection 

when available. In previous study years, we encountered projects where Form HUD-50058 differed 

from the official HUD format. In those cases, ICF developed crosswalks by examining the data 

elements on the atypical forms and developing a plan that illustrated which fields corresponded 

to the standard Form HUD-50058. In the FY 2015 study, three nonstandard documents required 

crosswalks, compared to four in FY 2014. These three documents were used by three projects, two 

of which were MTW PHAs. 

In addition to the data collected from Form HUD-50058/50059, field interviewers collected data 

from the household files to document the determination of tenant eligibility and the calculation 

of rent. A series of documents that supported the certification action were copied by the field 

interviewer to verify income, assets, household composition, expenses, and other items needed 

for accurate rent calculations. The documents may have been supplied to the PHA/Project by the 

tenant or by a third-party agency. When these documents met various study verification criteria, 

they were used in the QC rent determination. 

Interviewing Tenants. For this study, an adult household member (preferably the head of household) 

was interviewed in person using CAPI. Interview questions focused on family composition as 

well as sources and amounts of income, assets, and applicable expenses. Data were collected for 

the same point in time that the recertification was conducted. HDCS compared data from Form 

HUD-50058/50059 with those entered during the interview to alert the interviewer to possible errors. 

While interviewing tenants, field interviewers also requested specific verification documents, which 

they then scanned and securely sent to study headquarters. Those documents were reviewed if the 

supporting documents from the tenant file did not meet study verification criteria. 

Requesting Verification From Third-Party Sources. When there was no evidence in the household 

file that the PHA/Owner verified the information used for calculating rent, or the existing verification 

information did not meet the requirements for this study,14 ICF requested verification from 

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 found in the household 

files. Tenants signed release forms during the household interview so that third-party verification 

14 For the purposes of this study, verification was acceptable if it was in writing, was received from a third 

party, and was dated with the Quality Control Month (QCM) date or within 119 days prior to that date. 

Acceptable verification could include documentation from a third party brought in by the tenant if the 

documents met specific date criteria. 
HUDQC Final Report for FY 2015 II-7 

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 third parties contacted for the verification of 

information would be satisfied with the validity of the request. Third-party entities completed the 

forms and returned them to study headquarters, where data were compared to other file information.

Matching Social Security Data. Sample household members were matched with Social Security 

Administration (SSA) files by HUD. The output from this match identified the Social Security 

and Supplemental Security Income (SSI) benefit, as well as the Medicare premium data, for all 

household members. 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 December 2015 and May 2016 for the certification or 

annual recertification that occurred during FY 2015 (November 2014 through October 2015). Field 

interviewers collected data related to actions that may have occurred up to 18 months prior to the 

file abstraction and household interview. In collecting data to document actions taken in the past, 

a major challenge was to develop methodologies to ensure that collected data reflect the situation 

that existed at the selected point in time. For the respondent in the household interview, it may be 

difficult to recall details of life situations at a past point in time. Some respondents in this population 

may have unstable situations resulting from inconsistent income or changes to household size, 

further complicating the collection of data from the past. In light of these challenges, ICF developed 

study constructs to ensure the consistent and accurate collection of data across program types, 

projects, and households in the study. The information below describes the two primary strategies 

developed for this purpose: the quality control month and third-party verification rules.

Quality Control Study Time Period. The month for which data were collected is referred to as 

the Quality Control Month (QCM) and was used during the household interview to obtain data 

for the correct time period. This time period refers to when an eligible action occurred during 

FY 2015. The Quality Control Date (QCD) refers to the day the tenant rent was calculated by the 

project staff. For most households in the Owner-administered program, the QCD is the date on 

which the project manager (or other authorized housing project staff member) or the tenant signed 

Form HUD-50059, certifying that the information on the form was correct. For most households 

in the PHA-administered programs, the QCD is the date on which the rent calculation worksheet 

was signed. If these pieces of information were not available, the field interviewer used other 

documentation in the household file to determine when the action was taken. 

After the QCD was established, the data from Form HUD-50058/50059 corresponding to the 

selected action was entered into HDCS. The data from the documents used by project staff to 

verify information on Form HUD-50058/50059 on the QCD were also entered in a separate HDCS 

module. The household interview included frequent reminders to the respondent that the questions 

being asked pertained to the QCM.

Note: If the recertification was overdue by more than 12 months, 12-month intervals were added 

to the QCM so that the QCD fell within the FY 2015 review period. 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 housing project staff completed it on time. In rare 
II-8 August 31, 2016

situations when the rent was calculated after the effective date of the action (because of retroactive 

adjustments), the QCM is the date of that action.

Third-Party Verification Rules. Occasionally the verification documents found in the file 

for household composition, income, assets, and expense items were different from those 

required by HUD. 

To ensure that data from the correct documents (i.e., those that were gathered to verify the 

information on the certification or recertification under review) were used to calculate QC Rent, 

and to apply rules fairly and consistently across all households in the study, ICF developed a 

set of guidelines defining acceptable verification. For the purpose of this study, verification was 

considered acceptable if it was either given verbally or in writing, was from the relevant third party, 

and was dated within the 4 months leading up to the time period of interest. These documents were 

considered acceptable whether they were received directly from the third party, provided by tenants 

during the recertification process, or submitted during the household interview.

ICF study headquarters staff classified these documents and determined whether each document met 

the acceptability criteria. For items that did not meet the requirements, verification was requested 

from the appropriate third-party entity. 

E. Construction of the Analysis Files

The initial data files consisted of information obtained from the tenant file at the project site, 

information from the household interview, and third-party verification data. Data items were collected 

at both the member and household levels. ICF 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 file documentation with information obtained in the 

household interview and received from third-party verification. This variable specifically identified 

the income, asset, or expense and the household member to which the item belonged.

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 Form HUD50058/50059 

errors, and occupancy standards.

F. Rent Formula

HUD uses a specific set of rules for determining tenant rents for each of its programs. The algorithm 

for determining the Total Tenant Payment (TTP) is the same for all programs except Sections 202 

PRAC, 811 PRAC, and 202/162 PAC and MTW. The TTP is the greater of the following:

● Thirty percent of a household’s adjusted monthly income, defined as 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 household dependents and deductions for disability, medical, and childcare 

expenses
HUDQC Final Report for FY 2015 II-9 

● Ten percent of a household’s gross monthly income, with no allowances or expense 

deductions

● The welfare rent in as-paid states (New York was the only as-paid state in this study)

● 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 202/16 

PAC programs includes the first three items above, but there is no minimum rent requirement 

for those programs.

MTW programs have the flexibility of modifying their TTP calculation process from the standard 

formulas if the modification was established in their ACOP or Administrative Plan. In order to 

ensure that the MTW projects were not found in error if modifications to rent calculation processes 

had been approved, ICF reviewed the ACOPs and Administrative Plans for all MTW projects. 

Based on the review, modifications to the standard TTP calculations were implemented for specific 

projects. Some common modifications used by MTW projects were:

● Using 28 percent, or some other set percentage, of a household’s adjusted monthly income 

to calculate TTP, instead of 30 percent

● Not deducting dependent or elderly/disability allowances from total annual income

● Using rent schedules for households within certain income bands

● Not counting income from assets if total assets were less than $50,000, or allowing for 

self‐certification of assets when assets totaled less than $50,000

● Using a tiered schedule to determine the amount of childcare, medical, or disability expense 

deductions

● Using non-standard or alternate disability, medical, or dependent allowances 

Five different rent calculations were used to calculate the actual amount of the household’s rent, 

depending on program type:

● Public Housing (MTW and non-MTW)

● Section 8 Project-based (including Moderate Rehabilitation) and Sections 202 PRAC, 

811 PRAC, and 202/162 PAC

● Section 8 Vouchers (MTW and non-MTW)

● Section 8 Enhanced Vouchers (there were 18 Enhanced Voucher households in the study)

● Manufactured Home Space Rental for Section 8 Vouchers (there were no households in the 

FY 2015 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 lesser of the gross rent or the payment standard; 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 was not capped for the Sections 202 PRAC or 811 PRAC programs.
II-10 August 31, 2016

Additional rent calculations were necessary for households with ineligible noncitizens. Determining 

the correct rent for these households was a multistep process that first determined whether the 

household is entitled to continued assistance or a temporary deferral of termination of assistance, 

and then prorated the rent, if appropriate. Two proration formulas were used, one for Public Housing 

and one for all Section 8 programs.

The algorithms for the rent calculation formulas can be found in Appendix A.

G. Calculation of Rent Error

The monthly rent algorithms used by ICF to calculate the national estimates of error are:

● Actual Rent—The Actual (AC) Rent is the monthly rent indicated on Form 

HUD50058/50059. If this item was missing on Form HUD-50058/50059, the AC Rent 

was taken from another official document in the file.15

● Quality Control Rent—The Quality Control (QC) Rent is the monthly rent calculated by 

ICF using all verified household information.16

Rent error was calculated by subtracting the QC Rent from the AC Rent. A discrepancy of $5 or 

less between the monthly AC and QC rents was not considered an error. The $5 increment 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

ICF 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, ICF 

first used available verification from the household files. If acceptable verification was not available 

from the household file, verification was requested during the household interview. If verification 

was not available during the household interview, verification was requested from an appropriate 

third party (see Section II-D for a discussion of acceptable verification). If verification was not 

returned by a third-party entity, data from certain documents in the household file were used, even 

if those documents did not meet the verification criteria. The only documents used when acceptable 

verification was not available were verification documents from third-party entities whose date fell 

outside the acceptable date range (when documents were present with other verification documents 

in the file for a particular transaction) and tenant self-certification documentation collected during

15 Rent Roll data were not used as a substitute for AC Rent because a previous study found that the Rent 

Roll sometimes included amounts to make up for previously unpaid rent, fines, or damages.

16 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 household file or 

documentation obtained during the household interview that met study requirements was used to calculate 

the QC Rent. 
HUDQC Final Report for FY 2015 II-11 

the household’s recertification process. 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.

● Earned income bonuses were not counted unless it was clear that the bonus was paid on a 

regular basis.

● Temporary Assistance for 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 income (TANF and other welfare), child support income, and childcare expenses 

were treated at the household level instead of the household member level so that the same 

source of income assigned to various household members would not be counted twice. For 

example, if one household member (e.g., the head of household) was assigned a source of 

income on one document and the same income was assigned to another household member 

(e.g., a child) on another form, the income would not be counted twice because it was 

assigned at the household level.

● Disability status was assigned to a household member based on Enterprise Income 

Verification (EIV) documentation if two items were evident on the EIV printout: (1) receipt 

of Social Security or SSI benefits and (2) a disability status of “yes.”

● Passbook rates (for determining the imputed income from assets) for PHA-administered 

programs were taken from the project-level data provided by PHA/Owner staff. The 

passbook rate for Owner-administered programs was 2 percent prior to February 2015, and 

0.06 percent for those certifications and recertifications starting in February 2015.

● For new certifications, the low and very low income limits were obtained from HUD’s 

website.

● When determining the prorated rent for Public Housing households with ineligible 

noncitizens, if the maximum rent was not present on Form HUD-50058, the Fair Market 

Rent was used instead of the 95th percentile of gross rent because the 95th percentile of 

gross rent was not available.

● The values from Form HUD-50058 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 data collection information provided by PHA staff.

● The values from Form HUD-50059 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 data 

collection information provided by owner staff.

● Welfare rent for the State of New York was taken from the project-level data collection 

information provided by PHA staff.
II-12 August 31, 2016

● A separate verification code was used to identify verification obtained from the EIV system. 

When Social Security, SSI, or Black Lung benefits were verified by EIV, the information 

was considered third-party in-writing verification. If EIV information was in the file 

for earned income or unemployment benefits, the dates associated with the form were 

examined to determine whether PHA/project staff had access to the EIV information at 

the time of recertification. Copies of EIV reports (as well as other types of verification of 

earned income found in the household file) were sent to study headquarters and reviewed 

by data quality specialists to prevent mistakes in calculating the QC earned income value. 

Note: EIV was not considered an acceptable verification source for earned income.

● When working with Social Security and SSI benefit information obtained through the SSA 

data match, discrepancies were sometimes found between those data and EIV printouts 

contained in the household file. If the two sources of information were contradictory, 

the information found on the EIV printout (from the household file) was used in the 

QC calculation.

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 into the study standards used 

to determine error. All data collection procedures and analyses were developed on the basis of 

these standards. Although most standards were easily implemented, several were more problematic, 

complicating the data collection process or the analysis. These complications are discussed below.

Anticipated Income. The amount of rent a household will pay is determined based on anticipated 

household income and deductions for the 12 months following recertification. For households with 

a stable income source, such as 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 

(e.g., medical expenses for elderly/disabled households, child care costs) are difficult to anticipate. 

Determining whether such income and expense amounts were calculated 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 manner they were treated by PHA/project 

staff. Several of the special procedures described in Section II-H were created for this purpose.

Third-Party Verification Requests. HUD regulations require the information supplied by residents 

at recertification to be verified by third parties (e.g., employers, SSA, banks, medical personnel). 

Field interviewers obtained release forms from the household when evidence of verification was not 

present in the tenant’s file; the release forms were used to request verification from the appropriate 

third parties. However, some third parties did not respond, returned information for incorrect time 

periods, required payment for the information requested, or presented other challenges that prevented 

ICF staff from obtaining the correct verification. Follow-up requests for missing verifications were 

not made in all cases because of time constraints.

ICF and HUD established a set of verification rules to determine whether an item was verified. 

Section II-C identifies the rules used to determine whether verification was acceptable for each 

matched item used in the rent calculation. Tables 1a to 1h (in Appendix C) and Exhibit IV-19 in 

Section IV-D present the verification rates for different rent components.
HUDQC Final Report for FY 2015 II-13 

Earned Income Disregard (EID). The regulations governing the Public Housing and PHAadministered 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 EID, tenants were asked during the household interview about 

training and self-sufficiency programs. Sixty household members were identified as possibly entitled 

to an EID.

For these household members, we examined information on Form HUD-50058 and other household 

file documentation. 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. 

From the original 60 cases identified from household interview data, QC calculations determined 

that 20 cases were possibly entitled to an EID. After investigating further for additional factors that 

affect EID, 16 of the 20 cases were eligible and 4 were not eligible for EID. In 4 of the 16 eligible 

cases, our QC calculation confirmed the PHA’s EID determination. In the remaining 12 cases, our 

QC review determined that an EID was appropriate, but the PHA did not provide the household 

with the income exclusion.

Training Programs. The regulations governing all housing programs included in this study require 

the PHA/Owners to exclude all amounts received under training programs funded by HUD as well 

as the incremental earnings and benefits resulting to any household member from participation in 

qualifying State or local employment training programs.

To identify households eligible for the training program exclusion, field interviewers documented 

training program information found in the household file and provided during the household 

interview. This process identified members of 13 households with indications of involvement in 

training programs, and 4 of those 13 households were found to be eligible for this income exclusion.

Permissible Deductions. Public Housing programs may adopt other deductions from annual income 

in addition to HUD’s required deductions. To ensure that the appropriate additional permissible 

deductions were taken into consideration when determining the adjusted annual income, two sources 

of information were examined. First, ICF reviewed items 8b through 8e on Form HUD-50058, 

which records the type and amount of permissible deductions. Second, a copy of local discretionary 

policies from all PHAs was requested to identify additional exclusions adopted in their Public 

Housing program. In the review of these documents, few unique permissible deductions were found 

across Public Housing programs. The few special deductions that we found are attributed to unique 

employment circumstances (i.e., union payments or individuals not utilizing the Mandatory Earned 

Income Deduction), and household members making childcare and alimony expenses. In FY 2015, 

this review found 5 households with permissible deductions using the local discretionary policies 

at the 2 PHAs. 

Moving to Work Exceptions. As mentioned in Section II-F, MTW programs have the flexibility 

of modifying their TTP calculation process from the standard formulas if the modification 

was established in their ACOP or Administrative Plan. To ensure that all modifications were 

incorporated into the QC Rent calculation, policies on the various exceptions were extrapolated 
II-14 August 31, 2016

from each project’s ACOP or Administrative Plan, and these policies were included in the 

QC Rent calculation.

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. In FY 2015, there were 79 flat rent cases in the study sample. Determining 

whether a household is paying a flat rent is not always easy because of contradictory data within 

Form HUD-50058. In most cases, items 2a (i.e., Flat Rent Annual Update) and 10u (i.e., Type of 

Rent Selected) could be used to determine whether the household is paying a flat rent instead of an 

income-based rent. However, if these two items contradicted one another, information from other 

documents in the file was used to determine the type of rent a selected tenant paid.

Ineligible Noncitizens. HUD regulations require that rents be prorated for households with ineligible 

noncitizens unless the household meets certain criteria that allow the continuation of full assistance. 

ICF reviewed all households with ineligible noncitizens to ensure that the rent was calculated 

correctly. Twelve households (less than 1 percent of households in this study) included an ineligible 

noncitizen and had a prorated rent amount per HUD regulations. 

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 when such changes to TANF benefits resulted 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.

If the TANF benefits were reduced or terminated because of 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.17 TANF benefits in 18 households were reviewed, 

and we identified no households for which TANF amounts should have been imputed.

Students. The regulations governing the PHA-administered Section 8 and Owner-administered 

programs included in the study require that students ages 17–24 meet certain criteria. If these criteria 

are not met, the parent’s income must be included when determining whether the student meets 

the program’s financial requirements. For households with students, field interviewers documented 

student enrollment and member characteristics found in the household file or provided during the 

household interview. These households were then reviewed to determine whether the student met 

the special student criteria as defined by HUD regulations. Nine cases were reviewed, and all cases 

were determined to be correctly receiving housing assistance.

17 The value of the reduced or terminated TANF benefit is offset by the amount of additional income that 

the family received starting after the sanction was imposed.
HUDQC Final Report for FY 2015 III-1 

III. Study Objectives and Analytic Methods

This section identifies the 16 HUDQC Study objectives and a brief description of the methodology 

that was used to fulfill these objectives.18 A summary of these objectives and the location of this 

information in the report is presented at the end of this section, in Exhibit III-2.

Objective 1: Identify the various types of rent errors and rent error rates and related 

estimation variances.

The FY 2015 study identified the error types and error rates that were previously analyzed and 

presented in the FY 2000 through FY 2014 studies. Using verified information as determined in 

the HUDQC Study standards, the tenant rent (QC Rent) was recalculated and subtracted from the 

tenant rent indicated on the Form HUD-50058/50059 (Actual Rent, or AC Rent) to determine error. 

Rent error was categorized and described in a number of ways, including providing the percentage 

of households paying correct and incorrect rent, total dollar error amounts, and dollar error rates. 

Variance estimates (standard errors) were provided for selected error rates. Errors were determined 

by recalculating the tenant rent on the basis of verified QC information and subtracting this amount 

from the tenant rent indicated on the Form HUD-50058/50059 (AC Rent). The following three types 

of dollar rent error estimates were calculated:

● Dollar Rent Error—The Dollar Rent Error is the difference between the monthly Actual 

Rent (AC Rent) and the monthly QC Rent (i.e., AC Rent minus QC Rent). A household 

rent was found to be in error if the difference between the AC Rent and QC Rent was 

greater than $5, while proper rent payments reflected differences of $5 or less. Rates of 

exactly matching AC and QC rents (within $1) were also presented. Simple percentages 

of the number of households paying the proper and exact rents were 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 that were ineligible when initially 

certified, the QC Rent was the flat rent for Public Housing households, or the Housing 

Assistance Payment (HAP) for Section 8 programs. The dollar error in these cases was also 

defined as the QC Rent amount minus the AC Rent.

● Total Component Dollars in Error—The Total Component Dollars in Error is 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 were combined to provide 

an overall Total Dollars in Error and were presented as annual amounts.19 Each component 

of identified error contributed to a dollar amount of rent overpayment or underpayment; 

however, some of these errors overlapped or were offset. For example, earned income 

may have been underreported because of a calculation error or the Supplemental Security 

18 For a more detailed description of the methodology, see the Analysis Plan for the FY 2015 HUDQC 

Study, an unpublished ICF report to HUD dated October 2, 2015.

19 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: 0.30 * CE = 12 * RE, or CE = (12/0.30) * RE = 

(120/3) x RE = 40 * RE.
III-2 August 31, 2016 

III. Study Objectives and Analytic Methods

Income (SSI) may have been overstated. Thus, the net difference could be zero or a positive 

or negative amount.

● Largest Component Dollar Error—The Largest Component Dollar Error is the annual 

dollar amount of error for the income or expense components with the largest error in a case. 

Income and expense components included the five sources of income (i.e., earned income, 

pension, public assistance, other income, and assets) and the five types of deductions 

(i.e., medical, childcare, and disability assistance expenses; and dependent allowance and 

elderly/disabled allowance). If, for example, the component with the largest error was earned 

income, the largest component dollar error was the difference between the earned income 

identified by the PHA/project and the earned income identified in the QC Rent calculation.

The dollar error rate was 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. This information is used to support policies that promote better oversight and 

administrative practices of HUD rental 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 Form 

HUD-50058/50059 as well as PHA/project and tenant information from the tenant file were 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 were detected by recalculating section subtotals and the final rent based on the 

exact information on Form HUD-50058/50059. The tenant rent was calculated using the detailed 

information on Form HUD-50058/50059, and the amount was compared to the actual tenant rent 

on Form HUD50058/50059. A difference in these two rents was identified as a calculation error.

Consistency errors identified a lack of logical conformity between elements within Form 

HUD-50058/50059. For example, the Effective Date of Action had to be on or after the Date of 

Admission; elderly status information had to be consistent with information about the age of the 

head of household or spouse.

Transcription errors are errors that were identified by comparing Form HUD-50058/50059 data 

with information in the tenant file. If the amount of a specific income or expense item on the Form 

HUD-50058/50059 did not match the tenant file data, a transcription error existed.

Incorrect determinations of allowances and income sources were also detected using tenant file 

information and comparing it to the Form HUD-50058/50059 data. Allowance errors were detected 

by calculating the allowances based on the tenant file information and comparing this QC allowance 

amount to the actual allowance on Form HUD-50058/50059. Similarly, income was calculated 
HUDQC Final Report for FY 2015 III-3 

III. Study Objectives and Analytic Methods

based on the types and amounts of income identified in the tenant file. The improper application of 

allowances and the incorrect calculation of income were a subset of transcription errors.

Overdue recertifications often produced rent errors because rents were based on out-of-date 

information. For households with overdue recertifications, the QC information was based on the 

month the recertification should have been completed rather than the month that the recertification 

was completed.

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

This analysis included determining the national Rent Error Rate, the numbers and proportions of 

households found to be in error, the dollar amount of rent error, and the proportion of total dollars 

found to be in error. Sample data were weighted to provide national estimates.

Objective 4: Determine the relationship between errors detectable using Form HUD50058/50059 and total errors found in the study.

As discussed under Objective 2, calculation and consistency errors identified mistakes made by 

the housing PHA/project staff. Under Objective 4, households with calculation and consistency 

errors were compared to households with QC errors to determine whether errors found within Form 

HUD-50058/50059 could be used to predict QC errors.

Objective 5: Determine whether error rates and error costs have statistically significant 

differences from program to program.

This analysis presented differences in error rates by program type. Data were provided for three 

program groups: Public Housing, PHA-administered Section 8 (Section 8 Vouchers and Moderate 

Rehabilitation programs), and Owner-administered programs (Section 8, Section 202 PRAC, Section 

811 PRAC, and Section 202/162 PAC). The Gross and Net Error Rates were provided for each 

of these program types. The Gross Error Rate was the sum dollar amount of gross error divided 

by the sum dollar amount of QC Rent, and the Net Error Rate was the sum dollar amount of net 

error divided again by the sum dollar amount of QC Rent. Multivariate analyses were performed 

to determine whether differences from program to program are statistically significant.

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 in the previous studies, ICF provided descriptive information on the sources of discrepancies 

between housing file information and verified information, and described the incidence of 

administrative errors and their impacts. We also examined whether failure to verify sources of 

income and expenses contributed to QC error. Multivariate analyses using administrative errors and 

income components as independent variables were performed to identify how these errors affected 

the QC Dollar Rent Error.
III-4 August 31, 2016

Objective 7: Determine the extent to which households are over-housed relative to HUD’s 

occupancy standards. 

This objective addressed whether households resided in units with the correct number of bedrooms. 

Exhibit III-1 shows HUD’s generally accepted guidelines specifying the appropriate size unit for 

assisted households.20

For most programs, unit-size rules are not based solely on household size but allow for discretion 

on the part of PHA/project staff. All programs allow exceptions to these rules. This study replicated 

the 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 

outlined in Exhibit III-1.

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 5 8

5 5 10

Objective 8: Provide information on the extent to which errors are concentrated in projects 

and programs.

ICF conducts further descriptive analyses to examine whether errors are concentrated within or 

randomly distributed across PHAs/projects. Multivariate analyses are conducted with the tenant as 

the unit of analysis. Tenant and PHA/project characteristics are 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 help HUD evaluate 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 were reviewed to determine whether they met the eligibility requirements 

for assisted housing.

Five eligibility requirements reviewed at initial/move-in certification are not a part of the 

recertification process (and thus are not confirmed on an ongoing basis): the definition of family, 

citizenship status, the verification of Social Security numbers, signed tenant consent forms, and 

20 Housing projects have discretion in determining unit size and may determine unit size differently than 

shown.
HUDQC Final Report for FY 2015 III-5 

low to very low income limits. This study did not investigate the definition of family because it 

is determined by the PHA or owner. Therefore, findings were 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 and history of drug use or criminal activity.

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 to 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 rent comparability 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 

presented weighted proportions of voucher recipients with rent comparability data.

Additionally, payment standard data from Form HUD-50058 were compared with Fair Market Rent 

(FMR) data to identify the households whose payment standards fall outside the 90 to 110 percent 

FMR band. Utility allowance schedules were 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 total positive and negative errors in terms of HUD subsidies.

Proper payments are those in which the AC Rent equals the QC Rent. Errors can be either tenant 

overpayments (i.e., AC Rent is greater than QC Rent) or tenant underpayments (i.e., AC Rent 

is 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 by 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 used multivariate analysis to conduct more detailed analyses 

of differences among PHAs/projects and to provide HUD with more information for identifying 

projects and tenants likely to exhibit high error rates. 
III-6 August 31, 2016

Objective 14: Determine whether cases for which Form HUD-50058/50059 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 Public and Indian Housing (PIH) Information Center/Tenant 

Rental Assistance Certification System (PIC/TRACS) data. Analysis was conducted to compare the 

average dollars in error for households included in PIC/TRACS with those that were not included.

Objective 15: Determine the extent of program administrator rent and income determination 

errors.

This objective is essentially a summary of Objectives 1–3. The percentage of households in error 

and the dollars associated with those households will be determined analytically and reported 

accordingly. 

Objective 16: Determine the error rate for the 20 Largest PHAs. 

For the 20 Largest PHAs sample, 32 households were selected per PHA. Since the desired error rate 

can use the households sampled in the HUDQC Study, we interpreted HUD’s needs in terms of the 

total sample needed from these PHAs. Thus, if a PHA already had 32 households selected for the 

HUDQC Study, no further cases would be sampled. However, if fewer than 32 households were 

selected in the HUDQC Study, additional tenant files would be selected to achieve 32 households 

per PHA. Analysis for the 20 Largest PHAs will include tables regarding administrative error and 

payment error. 

ICF addressed administrative error and provided the percentage of households with overdue 

recertification and transcription errors and the percentage of income and expense items verified 

by PHA staff, both with written third-party verification only and with verbal or written thirdparty verification or documentation. Totals for the 20 Largest PHAs were compared to the 

PHA-administered projects supplied for the HUDQC Study and the HUDQC Study as a whole. 

Additionally, payment error information was provided. This includes proper payments, under- and 

overpayments of tenant rents, and the mean gross rent errors by PHA. As with administrative error, 

totals for the 20 Largest PHAs were compared to the PHA-administered projects supplied for the 

HUDQC Study and the HUDQC Study as a whole.

Exhibit III-2 presents all study objectives and where they are addressed. 
HUDQC Final Report for FY 2015 III-7 

Exhibit III-2 

Summary of Study Objectives

# Objective Where Objective Is 

Addressed in Section IV

1

Identify the various types of rent errors and rent error rates and calculate their 

variance estimates. These include:

•	 Dollar Rent Error

•	 Total Component Dollars in Error

•	 Largest Component Dollar Error

Exhibit 1;

Exhibit 2;

Exhibits 3a & 3b

2

Identify the dollar costs of the various types of errors, including:

•	 Calculation errors

•	 Consistency errors

•	 Transcription errors

•	 Incorrect determination of allowances and income sources

•	 Overdue recertifications

Exhibit 6;

Exhibit 8;

Exhibit 16;

Exhibits 22a & 22b

3 Estimate the national-level costs for total error and major error types. Exhibit 4

4 Determine the relationship between errors detectable using the Form 

HUD-50058/50059 and total errors found in the study.

Exhibit 15;

Exhibit 17

5 Determine whether error rates and error costs have statistically significant 

differences from program to program. Exhibits 4-5

6

Determine the apparent cause of significant rent errors to provide HUD with 

information on whether the error was caused primarily by the tenant or by 

program administrator staff.

Exhibits 11 – 14

7 Determine the extent to which households are over-housed relative to HUD’s 

occupancy standards. Exhibits 24a – 24c

8 Provide information on the extent to which errors are concentrated in projects 

and programs. Exhibits 18 – 21

9 Identify the percentage of newly certified tenants who were incorrectly 

determined eligible for program admission. Exhibits 7a – 7b

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% of fair market rents

•	 Whether the correct utility allowances are being used. 

Exhibits 25 – 27;

Exhibits 29a – 29d;

Exhibits 28a – 28d

11 Estimate total positive and negative errors in terms of HUD subsidies. Exhibits 9a – 10

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 use an automated 

rent calculation system.

Appendix F

13 Determine whether other tenant or project characteristics on which data are 

available are correlated with higher or lower error rates. Appendix F

14

Determine whether cases for which Form HUD-50058/50059 data were 

submitted to HUD were more or less likely to have errors than those for which 

data were not submitted.

Exhibits 30a – 30e

15 Determine the extent of program administrator rent and income determination 

errors. Appendix E

16 Determine the error rate for the 20 Largest PHAs. Exhibits 31a–31b
HUDQC Final Report for FY 2015 IV-1 

IV. Findings

A. Overview

Analyses were conducted using nationally weighted sample data for 2,400 households.21 Data are 

presented by the three program types that were the basis for the sampling design: Public Housing; 

Public Housing Authority (PHA)-administered Section 8 Housing Choice Voucher and Moderate 

Rehabilitation programs (PHA-administered Section 8); and Office of Housing-administered Section 

8, Section 202 Project Rental Assistance Contracts (PRAC), Section 811 PRAC, and Section 

202/162 PAC programs (Owner-administered). The major study findings, the reasons for the errors, 

and other background information concerning these errors are discussed as follows. In many of 

the exhibits in this report, the data collected during the current study (referred to as the FY 2015 

data) are compared with the data collected in the previous study (referred to as the FY 2014 data). 

Our discussion is divided into 11 parts: 

1. The errors in the rent amount based on the QC data (rent errors)

2. The errors in sources of income and expenses (component errors)

3. The errors found using only project-file data (administrative errors)

4. Occupancy standards

5. Findings related to rent reasonableness determinations 

6. Utility allowance analysis 

7. Payment standard analysis 

8. Comparisons with PIH Information Center/Tenant Rental Assistance Certification System 

(PIC/TRACS) data

9. Analysis of the responses received from PHA/project staff regarding PHA/project practices 

(based on the Project Staff Questionnaire)

10. Multivariate analysis 

11. Errors in the 20 largest PHAs 

The first three parts present different types of errors, described as follows.

Rent errors are errors that result in an actual dollar error. A dollar error means that the household 

paid too much rent (an overpayment) or the household paid less rent than it should have paid (an 

underpayment).

Component errors are errors in 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.

21 Appendix B presents the procedure used in weighting the data.
IV. Findings

IV-2 August 31, 2016 

Administrative errors are errors that result from administrative mistakes. They include the following:

● Consistency errors, which are errors in logical conformity between elements within Form 

HUD-50058/50059

● Calculation errors, which are arithmetic errors within subsections of Form HUD-50058/ 

50059

● Transcription errors, which are errors in transferring information from documentation in 

the tenant file to Form HUD-50058/50059

● Failure to conduct a recertification in a timely manner

● Failure to verify information

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 component errors 

tell us which income or expense caused the error. Data supporting this 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 (AC) Rent.22 The 

QC Rent was calculated using data obtained from one of several sources, including the tenant file, 

the household interview, and third-party verification. If acceptable verification was present in the 

tenant file, that information was used. If acceptable verification was not present in the tenant file, 

other sources were used. The Actual Rent is the Tenant Rent from Form HUD-50058/50059. As 

noted previously, 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 Exhibit IV-1) and 

all tables in Appendix C (except the supplemental tables) define households in which AC Rents and 

QC Rents matched within $5. As noted, this is not the case for the supplemental tables in Appendix 

C (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 that 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 that the household paid more than it should have paid 

and that HUD’s contribution was less than it should have been.

22 Rent error is determined on the basis of Tenant Rent, not Total Tenant Payment (TTP). Error based on 

TTP may differ from error based on 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.
HUDQC Final Report for FY 2015 IV-3 

IV. Findings

● 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 

underpayments and overpayments) of the Rent Error.

● Error Rate is calculated by dividing the sum of the Rent Error (gross or net) by the sum of 

the QC Rent for the entire sample or a specified group of households.

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 that 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, and 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 ensuring that the households that are eligible for the 

program are receiving the correct subsidy, rather than reducing the 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 ongoing monitoring needed to 

ensure that the programs are administered correctly.

Proper Payments. Exhibit IV-1 shows the percentage of households with proper payments by 

program, for households in which the AC Rent and QC Rent matched within $5 and in which the 

AC Rent and QC Rent matched exactly.

● At certification/recertification, the rent was calculated correctly (within $5) in 73 percent of 

households, a slight drop from the 75 percent of households for which rent was calculated 

correctly in FY 2014. 

● There was an exact match of rent payment in 61 percent of households in FY 2015, which 

was a decrease from the 65 percent of the last two study years.

Exhibit IV-1 

Percentage of Households With Proper Payments

Administration Type

Percentage of Households 

Within $5

Standard 

Error

Percentage of Households 

That Matched Exactly

Standard 

Error

FY 

2013

FY 

2014

FY 

2015

FY 

2015

FY 

2013

FY 

2014

FY 

2015

FY 

2015

Public Housing 77% 73% 75% 2.4% 67% 64% 63% 2.3%

PHA-administered Section 8 77% 74% 71% 1.6% 62% 63% 58% 1.8%

Total PHA-administered 77% 74% 72% 1.5% 64% 63% 60% 1.5%

Owner-administered 80% 80% 77% 1.7% 68% 69% 66% 2.8%

Total 78% 75% 73% 1.3% 65% 65% 61% 1.5%

Source: Tables 2 and 2(S), Appendix C
IV-4 August 31, 2016

Households With QC Rent Error. Exhibit IV-2 shows the percentage of households in error, 

average dollar amount in error, and error rate by program. 

● Twenty-six percent of households had a rent error greater than $5, which is higher than the 

25 percent recorded in FY 2014.

The average gross dollars in error is calculated by dividing the sum of the dollar amount of gross 

error (i.e., the sum of the absolute values of underpayments and overpayments) by the total number 

of households.

● The average gross dollars in error was $13 in FY 2015, lower than the $14 average gross 

dollar error in FY 2014.

The total gross dollar error rate was calculated by dividing the sum of the dollar amount of Gross 

Rent Error by the sum of the dollar amount of the QC Rent. 

● The total gross dollar error rate decreased by 1 percent, from 6 percent in FY 2014 to 

5 percent in FY 2015.

Exhibit IV-2 

Percentage of Households With Error, Average Dollars in Error, 

and Dollar Error Rate for All Households With Error

Administration Type

Percentage of 

Households With Error

Average Gross Dollars 

in Error Gross Dollar Error Rate

FY 2014 FY 2015 FY 2014 FY 2015 FY 2014 FY 2015

Public Housing 27% 25% $18 $14 7% 5%

PHA-administered Section 8 26% 29% $15 $15 7% 6%

Total PHA-administered 26% 28% $16 $15 7% 5%

Owner-administered 20% 23% $8 $10 4% 4%

Total 25% 26% $14 $13 6% 5%

Source: Tables 2 and 5, Appendix C

Underpayment and Overpayment Households. Exhibits IV-3a and IV-3b show the percentage 

of households and average dollar amount of error for all households when errors of $5 or less are 

excluded from the calculations; these exhibits present the error for underpayment and overpayment 

households, respectively. 

● Fourteen percent of all households paid in excess of $5 less than they should have in 

FY 2015, which was the same percentage found in FY 2014. 

● For FY 2014 households, the average monthly underpayment error was $66, which is an 

increase from the average of $59 in FY 2014 and $52 in FY 2013.
HUDQC Final Report for FY 2015 IV-5 

Exhibit IV-3a 

Underpayment Households: Percentage of Households 

and Average Monthly Dollar Amount of Error

Administration Type

Percentage of 

Households in Error

Average Dollar Amount of Error

For Underpayment 

Households

(with errors > $5) For All Households

FY 

2013

FY 

2014

FY 

2015

FY 

2013

FY 

2014

FY 

2015

FY 

2013

FY 

2014

FY 

2015

Public Housing 11% 14% 13% $70 $63 $59 $7 $9 $8

PHA-administered Section 8 13% 14% 15% $53 $63 $73 $7 $9 $11

Total PHA-administered 12% 14% 14% $58 $63 $69 $7 $9 $10

Owner-administered 10% 12% 12% $36 $46 $57 $4 $6 $7

Total 12% 14% 14% $52 $59 $66 $6 $8 $9

Source: Tables 2 and 4, Appendix C

● Thirteen percent of all households paid in excess of $5 more than they should have in 

FY 2015, which is greater than the 11 percent found in FY 2014 and the 10 percent found 

in FY 2013. 

● The average monthly overpayment for households with overpayment error was $35 in 

FY 2015, a marked decrease from the past 2 years. This value was $51 in FY 2014 and $44 

in FY 2013.

Exhibit IV-3b 

Overpayment Households: Percentage of Households 

and Average Monthly Dollar Amount of Error

Administration Type

Percentage of 

Households in Error

Average Dollar Amount of Error

For Overpayment 

Households

(with errors > $5) For All Households

FY 

2013

FY 

2014

FY 

2015

FY 

2013

FY 

2014

FY 

2015

FY 

2013

FY 

2014

FY 

2015

Public Housing 13% 13% 12% $42 $70 $57 $5 $9 $7

PHA-administered Section 8 10% 11% 15% $53 $51 $29 $5 $6 $4

Total PHA-administered 11% 12% 14% $49 $58 $37 $5 $7 $5

Owner-administered 10% 8% 11% $29 $27 $28 $3 $2 $3

Total 10% 11% 13% $44 $51 $35 $5 $5 $4

Source: Tables 2, 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. For all program types, the majority of cases fall into the 

proper-payment category. 
IV-6 August 31, 2016

Figure IV-1 

Payment by Program Type

Gross and Net Dollars in Error. Exhibit IV-4 presents the gross and net average dollars in error 

and their associated standard error. To obtain the Gross Rent Error and the Net Rent Error, the 

dollar amount of overpayments was added to the dollar amount of underpayments, first using the 

absolute values for gross error and then the arithmetic values for the net error. 

● Gross average dollar error in FY 2015 decreased from FY 2014 in Public Housing ($18 to 

$14, respectively), remained steady in PHA-administered Section 8 at $15, and increased 

in the Owner-administered program from $8 in FY 2014 to $10 in FY 2015. 

● In FY 2015, there were no statistically significant results between either Gross Rent Error 

or Net Rent Error for any program types across the current and previous year’s results.

● The net error measures the dollar cost of the errors and was -$5 (indicating a tenant 

underpayment) for FY 2015; the average gross dollar error was $13 for FY 2015 and 

represents the dollars associated with the errors (the magnitude of the errors). 

Exhibit IV-4 

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

Administration Type

Gross Rent Error Net Rent Error

Average Dollars

in Error Standard Error

Average Dollars

in Error Standard Error

FY 

2014 FY 2015 FY 2014 FY 2015 FY 2014 FY 2015 FY 2014 FY 2015

Public Housing $18 $14 $2.09 $2.70 -$0.33 -$1 $2.44 $2.69

PHA-administered Section 8 $15 $15 $2.24 $1.95 -$3 -$7 $1.35 $1.91

Total PHA-administered $16 $15 $1.77 $1.88 -$2 -$5 $1.22 $1.65

Owner-administered $8 $10 $1.12 $1.33 $4 -$4 $1.26 $1.39

Total $14 $13 $1.42 $1.29 -$3 -$4 $0.97 $1.17

Source: Table 5, Appendix C
HUDQC Final Report for FY 2015 IV-7 

Error Rates by Program. Differences in error rates by program type were investigated, and the 

results are summarized in Exhibit IV-5. 

Differences include the Gross Error Rate (i.e., the sum dollar amount of gross error divided by the 

sum dollar amount of QC Rent) and the Net Error Rate (i.e., the sum dollar amount of net error 

divided again by the sum dollar amount of QC Rent). 

● The Gross Error Rate of 5.6 percent for PHA-administered Section 8 programs remained 

higher than the rate for either Public Housing or Owner-administered programs. In total, 

PHA-administered programs showed a slight decrease of 1.2 percent in their Gross Error 

Rate in FY 2015 compared to FY 2014. 

● The Net Error Rates for all programs increased slightly, from -1.1 percent in FY 2014 to 

-1.7 percent in FY 2015.

Exhibit IV-5 

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

Administration Type

Error Rates

Gross Error Rate Net Error Rate

FY 2014 FY 2015 FY 2014 FY 2015

Public Housing 6.6% 5.2% -0.1% -0.4%

PHA-administered Section 8 6.6% 5.6% -1.4% -2.4%

Total PHA-administered 6.6% 5.4% -0.9% -1.8%

Owner-administered 3.6% 3.9% -1.7% -1.5%

Total 5.8% 5.0% -1.1% -1.7%

Source: Table 5, Appendix C

Certifications/Recertifications. The sampled households included both certifications (i.e., newly 

admitted households) and recertifications. Certifications were analyzed to determine whether 

the households were eligible for HUD housing assistance, and recertifications were analyzed to 

determine whether they were overdue. 

Figure IV-2 presents the breakdown of cases by case type: timely certifications, recertifications, 

and overdue recertifications.
IV-8 August 31, 2016

Figure IV-2 

Percentage of Cases by Case Type

Source: Table 6, Appendix C

Exhibit IV-6 shows the percentage of certifications, timely recertifications, and overdue 

recertifications, by program type. 

● Eight-five percent of households had timely recertifications in FY 2015, a decrease for the 

third year in a row. In FY 2014, 88 percent of households received timely recertifications 

and in FY 2013, the rate was 90 percent.

● Two percent of households had overdue recertifications for the second year in a row.

● There was a slight increase in the total percentage of certifications, from 11 percent in 

FY 2014 to 13 percent in FY 2015.

Exhibit IV-6 

Certifications and Recertifications by Administration Type*

Administration Type

Certifications Timely Recertifications

Overdue 

Recertifications

FY 2014 FY 2015 FY 2014 FY 2015 FY 2014 FY 2015

Public Housing 11% 13% 87% 84% 2% 3%

PHA-administered Section 8 9% 12% 89% 86% 2% 2%

Total PHA-administered 10% 12% 88% 86% 2% 2%

Owner-administered 14% 14% 85% 86% < 1% < 1%

Total 11% 13% 88% 85% 2% 2%

Source: Table 6, Appendix C

*Totals may not add up 100 percent to due to rounding.
HUDQC Final Report for FY 2015 IV-9 

Certifications. Exhibit IV-7a presents a summary of the findings related to eligibility criteria, and 

Exhibit IV-7b shows the percentage of newly certified households meeting the certification criteria 

by program type.

The reviewed criteria included qualifying as low-income or very-low-income households, 

citizenship, social security number, and signing the appropriate consent form.

● However, only those households that do not meet the appropriate low-income or 

very-low-income limit are ineligible for assistance. All households (according to the QC 

Rent calculation) fell within the low-income limit for total gross income.

A household met the citizenship criteria if there was evidence in the tenant file that citizenship had 

been verified. 

● A citizenship code (indicating whether each household member was a citizen, eligible 

noncitizen, or ineligible noncitizen) was available from either the tenant file or the 

household interview for each household member.

● According to the citizenship codes, no households in FY 2015 had a household member for 

whom there was no verification of citizenship. This is unchanged since FY 2010.

To meet the citizenship verification requirement, the file must have contained one of the following 

for each household member: a signed declaration of U.S. citizenship or eligible immigration 

status, proof of age documentation, a U.S. Citizenship and Immigration Services (USCIS) card or 

USCIS system verification of citizenship status, or documentation that the household member was 

undergoing verification or an Immigration and Naturalization Service hearing.

To meet the social security number verification requirement, the file must have contained for 

each household member a copy of the social security card or statement from the Social Security 

Administration (SSA) verifying the social security number.

● One percent of households had at least one member for whom there was no verification of 

his or her social security number.

● In 97 percent of households, there was a signed consent form dated within 15 months of the 

Quality Control Month (the date for which data were collected) for all members age 18 or 

older, the same percentage found in FY 2014.

● A social security number was available for 99 percent of all Public Housing households, 100 

percent of PHA-administered Section 8 households, and 97 percent of Owner-administered 

households. 

Note: Not meeting the citizenship, social security number, or consent form criteria may not mean 

that the household was not eligible for assistance; rather, it may mean that project staff did not 

follow HUD requirements in documenting the information.
IV-10 August 31, 2016

Exhibit IV-7a 

Percentage of Newly Certified Households Meeting Certification Criteria

Certification Criteria

Met Criteria

FY 2014 FY 2015

Citizenship 100% 100%

Social Security Number 99% 99%

Consent Form 97% 97%

Low and Very Low Income 100% 100%

Meets All Eligibility Criteria 96% 96%

Source: Table 7, Appendix C

Exhibit IV-7b 

Percentage of Newly Certified Households Meeting Certification Criteria by Program Type

Certification Criteria

Percentage of Households Meeting the Criteria

Public Housing

PHA-administered 

Section 8

Owneradministered

Citizenship 100% 100% 100%

Social Security Number 99% 100% 97%

Consent Form 93% 97% 99%

Low and Very Low Income 100% 100% 100%

Meets All Eligibility Criteria 93% 97% 97%

Source: Table 7b, Appendix C

Underpayments and Overpayments for Certifications, Timely Recertifications, and Overdue 

Recertifications. Exhibit IV-8 presents a summary of the households with overpayments and 

underpayments by the type of case: certification, timely recertification, and overdue recertification. 

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 (i.e., certification, timely recertification, or 

overdue recertification) divided by the number of households with that payment type. 

● For example, the sum of monthly underpayment dollar amounts for new certifications 

($5.1 million) was divided by the total number of certifications (0.59 million). The result is 

an underpayment average dollar amount of $9.

● The amount of underpayment and overpayment average dollar error in new certifications 

and timely recertifications in FY 2015 ranged from $4 to $9 each month. 

● As might be expected, there is a large difference in the underpayment dollar error for overdue 

recertifications ($27) as well as the overpayment dollar error for overdue recertifications 

($50). 

It is important to note that the estimates for overdue recertifications can vary widely from year to 

year because of the small number of cases (in FY 2015, 2 percent of cases were overdue).
HUDQC Final Report for FY 2015 IV-11 

Exhibit IV-8 

Average Monthly Underpayment and Overpayment: 

Dollar Amounts Averaged Across All Households

Household Type

Underpayment

Average Dollar Amount

Overpayment

Average Dollar Amount

FY 2014 FY 2015 FY 2014 FY 2015

Certifications $6 $9 $3 $4

Timely Recertifications $8 $9 $5 $4

Overdue Recertifications $29 $27 $21 $50

Total $8 $9 $5 $4

Source: Table 8, Appendix C

Subsidies. The actual cost of errors to HUD is expressed in terms of subsidy payments. For the 

purpose of this study, HUD subsidies for the PHA-administered Section 8 Voucher program equal 

the lesser of the gross rent or the applicable payment standard minus the tenant share. For Public 

Housing, the subsidy is the applicable payment standard minus the TTP. For Owner-administered 

programs, the subsidy equals the gross rent minus the TTP. The subsidy is correct if the Actual Rent 

equals the QC Rent (within $5). A negative subsidy error occurs when the tenant paid too much 

rent (QC Rent < Actual Rent). A positive subsidy error occurs when the tenant paid too little rent 

(QC Rent > Actual Rent). 

These subsidy errors by program type are summarized in Exhibits IV-9a and IV-9b. The subsidy 

errors by certification status are summarized in Exhibit IV-10.

● As shown in Exhibit IV-9a, the percentage of households with a positive subsidy error 

remained steady for all program types between FY 2014 and FY 2015, at 14 percent. 

● The average dollar amount of error increased for all households between FY 2014 and 

FY 2015, from $8 to $9.

Exhibit IV-9a 

Positive Subsidy Households (Tenant Underpayment) 

Percentage of Households and Average Monthly Dollar Amount of Error

Administration Type

Percentage of 

Households

in Error

Average Dollar Amount of Error

For Positive Subsidy 

Households

(with errors > $5) For All Households

FY 2014 FY 2015 FY 2014 FY 2015 FY 2014 FY 2015

Public Housing 14% 13% $63 $59 $9 $8

PHA-administered Section 8 14% 15% $63 $73 $9 $11

Total PHA-administered 14% 14% $63 $69 $9 $10

Owner-administered 12% 12% $46 $57 $6 $7

Total 14% 14% $59 $66 $8 $9

Source: Tables 2 and 4, Appendix C

Note: Table results replicate Exhibit IV-3a for the convenience of the reader.
IV-12 August 31, 2016

● As seen in Exhibit IV-9b, the percentage of households in error due to a negative subsidy 

decreased for Public Housing households, from 13 percent to 12 percent. PHA-administered 

Section 8 households in error increased by 4 percent, and Owner-administered households 

increased from 8 percent to 11 percent. 

Exhibit IV-9b 

Negative Subsidy Households (Tenant Overpayment) 

Percentage of Households and Average Monthly Dollar Amount of Error

Administration Type

Percentage of 

Households 

in Error

Average Dollar Amount of Error

For Negative 

Subsidy Households

(with errors > $5) For All Households

FY 2014 FY 2015 FY 2014 FY 2015 FY 2014 FY 2015

Public Housing 13% 12% $70 $57 $9 $7

PHA-administered Section 8 11% 15% $51 $29 $6 $4

Total PHA-administered 12% 14% $58 $37 $7 $5

Owner-administered 8% 11% $27 $28 $2 $3

Total 11% 13% $51 $35 $5 $4

Source: Tables 2 and 4. Appendix C

Note: Table results replicate Exhibit IV-3b for the convenience of the reader.

● As seen in Exhibit IV-10, the average dollar amount of error for overpayment across 

program types decreased from $5 in FY 2014 to $4 in FY 2015. That amount increased for 

underpayment errors, from $8 in FY 2014 to $9 in FY 2015.

● Overdue recertifications remained the source of the highest average dollar amount for both 

underpayment and overpayment.

Exhibit IV-10 

Average Monthly Underpayment and Overpayment: 

Dollar Amount Averaged Across All Households 

Household Type

Underpayment

Average Dollar Amount

Overpayment

Average Dollar Amount

FY 2014 FY 2015 FY 2014 FY 2015

Certifications $6 $9 $3 $4

Timely Recertifications $8 $9 $5 $4

Overdue Recertifications $29 $27 $21 $50

Total $8 $9 $5 $4

Source: Table 8, Appendix C

Note: Table results replicate Exhibit IV-8 for the convenience of the reader.
HUDQC Final Report for FY 2015 IV-13 

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 annual 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 offsetting 

errors. For example, the household presented in Figure IV-3 has earned income and child care costs 

with errors in both components. The total component error is $1,000 ($800 + $200); however, the 

adjusted net income error (the amount used to determine the household’s rent) is only $600.

Figure IV-3 

Example of the Impact of Component Errors

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-11 presents each income and expense component included in the rent calculation and 

the percentage of households in error23 when each specific 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 $3,860. Thirty-three percent of households were in error when 

earned income was the largest component error. 

● Pensions, etc. were the next largest component error, with an average dollar error of $2,572 

found in 15 percent of households in error. 

● Other income was a component of error in 13 percent of households, with an average 

associated dollar error of $2,453. 

● The rent component with the greatest average dollar error increase was child care allowance, 

which experienced an increase from $1,256 in FY 2014 to $2,148 in FY 2015.

● For most rent components, the percentage of households in error remained relatively stable 

between years. 

● The rate of households in error without a component error decreased from 7 percent in 

FY 2014 to 4 percent in FY 2015.

Note: For some households, the rent error was not caused by any 1 of the 10 components listed. 

Rather, it was caused by other arithmetic errors or by the use of the wrong rent calculation formula. 

23 The denominator in the percentage is the number of households with any component error, which was 

the percentage of total households in FY 2015.
IV-14 August 31, 2016

Exhibit IV-11 

Rent Components Responsible for the Average Annual 

Largest Dollar Error for Households With Rent Error

Rent Component

Percentage of Households in Error Average Dollar Amount

FY 2014 FY 2015 FY 2014 FY 2015

Earned Income 33% 33% $4,528 $3,860

Medical Allowance 17% 16% $1,813 $905

Pensions, Etc. 15% 15% $1,945 $2,572

Other Income 13% 13% $3,122 $2,453

Elderly Household Allowance 4% 6% $422 $400

Dependent Allowance 4% 5% $566 $522

Public Assistance 3% 4% $1,519 $2,133

Asset Income 3% 2% $808 $596

Child Care Allowance 2% 2% $1,256 $2,148

No Rent Component Error 7% 4% $0 $0

Total 100% 100% $2,625 $2,326

Source: Table 9, Appendix C

Total and Largest Component Dollar Error by Program Type. Exhibit IV-12 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 decreases in the Average Total Dollars in Error for Public Housing and Owneradministered households in FY 2015, with the largest decrease evident in Public Housing 

programs. PHA-administered Section 8 households had a modest increase in FY 2015, 

with an increase of $108. 

● All program types decreased slightly in Average Largest Dollars in Error, with the largest 

decrease seen in Public Housing. 

Exhibit IV-12 

Total and Annual Component Dollars in Error for Households With Rent Error

Administration Type

Average Total Dollars in Error Average Largest Dollars in Error

FY 2014 FY 2015 FY 2014 FY 2015

Public Housing $4,222 $3,401 $3,523 $2,833

PHA-administered Section 8 $2,472 $2,580 $2,308 $2,270

Total PHA-administered $3,093 $2,817 $2,739 $2,432

Owner-administered $2,665 $2,428 $2,266 $2,019

Total $2,989 $2,717 $2,625 $2,326

Source: Table 10, Appendix C
HUDQC Final Report for FY 2015 IV-15 

QC Rent Components by Payment Type and Administration Type. Exhibit IV-13 shows the 

percentage of the total number of households with and without component error by component 

type and payment type. The exhibit also provides these data by PHA- and Owner-administered 

households. 

● For example, 4 percent of total households with overpayment rent error had errors in 

earned income, 10 percent of households with proper payment had errors in pensions, and 

3 percent of households with underpayment rent error had errors in medical allowances. 

● The exhibit indicates that when considering both underpayment and overpayment, pension 

income, medical allowance, other income, and earned income remain the rent components 

with the highest percentage of error leading to improper payment. 

Exhibit IV-13 also reflects component errors in proper payment households when the component 

dollar error resulted in a tenant payment error of $5 or less. 

● Considering all component errors, not just those that resulted in tenant payment error, the 

pensions (16 percent), earned income (16 percent), and medical allowance (12 percent) 

components had the highest rates of error.

Exhibit IV-13 

Percentage of Households With Rent Component Error by Payment Type

Rent Component

Underpayment Proper Payment Overpayment

PHA Owner Total PHA Owner Total PHA Owner Total

Earned Income 7% 4% 6% 7% 3% 6% 5% 2% 4%

Pensions 4% 3% 3% 9% 12% 10% 3% 3% 3%

Public Assistance 1% — 1% 2% 2% 2% 1% 1% 1%

Other Income 3% 2% 3% 5% 3% 5% 3% 2% 3%

Asset Income 1% 2% 1% 3% 4% 3% 1% 1% 1%

Dependent Allowance 1% < 1% 1% 1% < 1% 1% 1% 1% 1%

Elderly Household Allowance 1% < 1% < 1% 1% < 1% < 1% 1% 3% 2%

Child Care Allowance 1% < 1% 1% < 1% — < 1% 1% — 1%

Disability Allowance — — — — — — — — —

Medical Allowance 2% 5% 3% 5% 10% 6% 3% 5% 3 %

No Rent Component Error 1% 1% 1% 49% 53% 50% 1% — 1%

Source: Table 11, Appendix C

Allowances. Elderly/disabled and dependent allowances were examined to determine whether these 

allowances were applied correctly.24 The findings are summarized in Exhibit IV-14, which shows 

the percentage of elderly/disabled and non-elderly/disabled households for which allowances were 

correctly or incorrectly applied. 

24 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 age 18, full-time students, and disabled household members 

other than the head of household or spouse).
IV-16 August 31, 2016

● Elderly/disabled allowances were incorrectly used in 3 percent of all households in FY2015. 

● Four percent of elderly/disabled households received an incorrect allowance.

● One percent of non-elderly/disabled households received an allowance erroneously.

The exhibit also shows the percentage of households with and without dependents for which a 

dependent allowance was correctly or incorrectly applied. 

● The dependent allowance was incorrectly applied in 6 percent of all households with 

dependents. These households were given the wrong amount. 

● In less than 1 percent of households, a dependent allowance was given to a household that 

did not have dependents. 

● A total of 3 percent of all households had an incorrect dependent allowance in FY 2015.

Exhibit IV-14 

Percentage of Households With Elderly/Disabled Allowances and Dependent Allowances*

Allowance

Elderly/Disabled Allowance Dependent Allowance

Non-Elderly/

Disabled 

Households

Elderly/

Disabled 

Households

All 

Households

Households 

Without 

Dependents

Households 

With 

Dependents

All 

Households

No Allowance 99% — 42% 100% — 60%

Incorrect Allowance 1% 4% 3% < 1% 6% 3%

Correct Allowance — 96% 56% — 94% 38%

Total 100% 100% 100% 100% 100% 100%

Source: Tables 12a and 12b, Appendix C

*Totals may not add up 100 percent to due to rounding.

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 included only items that were documented clearly in the tenant file in a location 

other than Form HUD-50058/50059. If an item was recorded on Form HUD-50058/50059 but not 

documented elsewhere in the tenant file, it was not included when the tenant rent was calculated 

for this analysis. Therefore, it is possible that some of the discrepancies identified between Form 

HUD-50058/50059 tenant rents and tenant rents calculated solely on the basis of file data were not, 

in fact, due to incorrect determinations, but rather to program sponsor failure to maintain information 

supporting income or expense items.

Therefore, 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 a program 

sponsor made the correct income determination, failure to document the determination is 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.
HUDQC Final Report for FY 2015 IV-17 

The findings from this analysis were compared with the QC findings in which tenant rent was 

calculated based on all the information collected during the study (including household interview 

data and verification obtained by ICF through third-party sources). 

Errors Detected in the Tenant File Compared to QC Sources. Exhibit IV-15 shows the percentage 

of households in error and the average dollar error based on the tenant file, but without income and 

expense items identified during the household interview and verified by ICF through third-party 

sources.

The percentage of households with overpayment and underpayment errors are nearly identical if 

only the tenant file information or all sources of QC data were used. Findings varied considerably, 

however, for the average annual dollar error. 

● There was a difference of $68 in subsidy overpayment average dollar error between what 

was discovered in the tenant file and what the QC review determined. When calculated as 

a monthly average, this value is $6. 

● For subsidy underpayment, the difference in error between the QC determination and what 

was found in the file was $175 in FY 2015, or approximately $15 as a monthly average 

value.

The difference found in average dollar error using information other than the tenant file implies that 

there were income and expense items not listed in the file at the PHA/project.

Exhibit IV-15 

Findings With and Without Information Obtained From Sources Other Than the Tenant File

Error Source

Percentage of 

Households in Error

Average Annual 

Dollar Error

Subsidy 

Overpayment

Subsidy 

Underpayment

Subsidy 

Overpayment

Subsidy 

Underpayment

Error Based on All Income and Expense 

Items Identified During 

the Study

14% 13% $793 $416

Error Based on Tenant File Without 

Income and Expense Items Identified 

During the Household Interview and 

Verification Obtained by ICF Through 

Third-Party Sources

13% 13% $725 $591

Source: QC Tables 2 and 4 and Tenant File Tables 2 and 4, Appendix C

Calculation and Consistency Errors. Analysis of the errors on Form HUD-50058/50059 was 

completed to determine whether the errors identified using Form HUD-50058/50059 as the sole 

source of information are representative of the total errors in the program. These analyses focused 

on the following calculation and consistency errors.

Calculation errors were identified from income, expenses, and allowances used to calculate the 

rent amount and recorded on Form HUD-50058/50059. This calculation did not take into account 

whether dollar amounts were verified or whether the recertification was conducted on time. This 

analysis identified errors resulting from arithmetic mistakes, the incorrect use of a formula, and 
IV-18 August 31, 2016

items that were not completed but should have been. This analysis did not identify households in 

which items were recorded in the wrong place on Form HUD-50058/50059, although improper 

use of a field on Form HUD-50058/50059 can result in a calculation error. Table 13 in Appendix 

C presents the number of households with a Form HUD-50058/50059 that contained calculation 

errors by the rent component contributing to the error. The items considered when determining 

calculation error are listed in Appendix D.

Consistency errors were based on the logical conformity of elements in Form HUD-50058/50059. 

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 14 in Appendix C shows the number of 

households with consistency errors on Form HUD-50058/50059, summarized by form subsections. 

Appendix D lists the data items by subsection that were included in this analysis.

Exhibit IV-16 shows the percentage of households with calculation and consistency errors by 

Form HUD-50058/50059 subsections. It is important to emphasize that Form HUD-50058 is 

formatted differently from and contains more line items of information than Form HUD-50059. 

Consequently, the number and types of calculation and consistency errors on the forms differ, and 

findings from the two forms are not directly comparable. The Office of Housing has periodically 

implemented a new version of Form HUD-50059. A minor revision was made in FY 2011, and a 

new version was released during FY 2014. This further complicates the comparison between the 

Form HUD-50058/50059 elements.

● The large number of calculation errors (particularly in the allowances and adjusted income 

from Form HUD-50058) may be a contributing factor to QC errors.

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 yet still calculate the rent correctly.

Exhibit IV-16 

Percentage of Households With Calculation and Consistency Errors

Form HUD-50058/50059 Item

Percentage of Households

Calculation Errors Consistency Errors

Form

HUD50058 

Form

HUD50059 Total

Form

HUD50058 

Form

HUD50059 Total

General Information n/a n/a n/a 1% 12% 4%

Household Composition 5% — 4% 4% 23% 10%

Net Family Assets and Income 10% 5% 8% 2% — 1%

Allowances and Adjusted Income 42% — 30% 9% 1% 7%

Family Rent and Subsidy Information 9% — 7% 2% < 1% 2%

Source: Tables 13 and 14, Appendix C

Comparison of Form HUD-50058/50059 Errors to QC Error. A comparison was made between 

the rent calculation errors on Form HUD-50058/50059 and errors identified through the QC Rent 

calculation process. The purpose of this comparison was to determine whether errors identified 
HUDQC Final Report for FY 2015 IV-19 

using only Form HUD-50058/50059 data could predict the rent errors found in a QC review. Exhibit 

IV-17 summarizes these results for FY 2014 and FY 2015.

● When using only Form HUD-50058/50059 data to calculate the Actual Rent, errors were 

found in 9 percent of the households in FY 2015. This was a slight increase when compared 

with the findings in FY 2014, which had a rate of 8 percent. 

● The QC error calculation found errors in 26 percent of households in FY 2015, up from 

25 percent in FY 2014. The results are quite different from the individual and joint 

comparison methods (which were 3 percent in FY 2014 and 4 percent in FY 2015). 

This comparison emphasizes that data from Form HUD-50058/50059 alone cannot accurately 

identify rent error. 

Exhibit IV-17 

Form HUD-50058/50059 Rent Calculation Error Compared With QC Rent Error

Rent Calculation

Percentage of 

Households With 

Correctly 

Calculated Rent

Percentage of 

Households With 

Incorrectly 

Calculated Rent

FY 2014 FY 2015 FY 2014 FY 2015

Using Information on Form HUD-50058/50059 92% 91% 8% 9%

According to the QC Rent Calculation 75% 74% 25% 26%

Both Form HUD-50058/50059 Calculation and QC 

Rent Calculation 70% 69% 3% 4%

Source: QC Table 2 and Tenant File Table 2, Appendix C

Verification Used in Determining the QC Rent. Verification errors were identified by whether an 

item was verified by the project and, if it was, whether the correct information was transferred to 

Form HUD-50058/50059. An error occurs when the verified amount obtained by the project was 

not used or the incorrectly calculated amount was recorded on the Form HUD-50058/50059 (and, 

presumably, not used correctly in the rent calculation). When determining whether a verified income 

or expense item matched the amount used on Form HUD-50058/50059, we assumed a variance of 

$100 to accommodate potential rounding errors when annualizing data. 

In 2010, HUD issued the Implementation of Refinement of Income and Rent Rule, which mandated 

the use of Enterprise Income Verification (EIV) as a third-party source to verify tenant employment 

and income information during mandatory recertification of family composition and income. The 

use of EIV minimizes the need for traditional third-party verification forms. FY 2011, the first fiscal 

year affected by this rule, displayed significant verification rate decreases across the board when 

compared to FY 2010, as verification was required in fewer instances. In FY 2015, this trend was 

repeated, with modest decreases in items verified for the seven rent components.

The table series 15a through 15n in Appendix C shows the number of households with and without 

verification by type of verification (i.e., third-party in writing, third-party verbal, EIV, Upfront 

Income Verification (UIV), and documentation). These tables provide this information for each of 

the rent components and also by program type.
IV-20 August 31, 2016

As indicated above, a set of rules was established for the use of third-party verification (see Section 

II-C). If an income or expense component was used for a rent calculation and was not verified by 

the PHA/Owner, ICF staff sought third-party verification. However, ICF verification could not be 

obtained for all PHA/Owner unverified items, despite considerable effort.25 As of the FY 2011 study, 

ICF has accepted third-party documentation submitted by the tenant, based on new HUD guidelines. 

Exhibit IV-18 shows the percentage of each rent component that was verified by either the PHA/

Owner or ICF. Findings from FY 2015 are compared to findings from FY 2014.

The first two columns present the percentage of rent components that were verified using third-party 

in writing, third-party verbal, documentation, EIV, or Upfront Income Verification (UIV), which 

was counted as part of third-party in writing in studies prior to FY 2012. 

● Verification of most rent component categories remained relatively stable between FY 2014 

and FY 2015, with the exception of child care expenses, which saw a marked increase 

(from 79 percent in FY 2014 to 100 percent in FY 2015.

As of FY 2011, the category of third-party in writing only included written third-party verification 

forms, which are sent directly to the third party and completed by the third party. 

● Verification using third-party in writing increased slightly in FY 2015 compared to FY 2014 

for several rent components, with the most notable change seen in child care expense 

verification. This increased from 42 percent in FY 2014 to 57 percent in FY 2015.

Exhibit IV-18 

Percentage of Households Fully Verified by Either the PHA/Owner or ICF 

Rent Component

Third-Party Verbal or in 

Writing, Documentation, 

EIV, or UIV Third-Party in Writing Documentation

FY 2014 FY 2015 FY 2014 FY 2015 FY 2014 FY 2015

Earned Income 88% 90% 34% 31% 41% 47%

Pensions 97% 97% 9% 11% 24% 23%

Public Assistance 97% 96% 21% 22% 49% 28%

Other Income 80% 77% 25% 19% 37% 36%

Asset Income 93% 91% 41% 40% 24% 24%

Child Care Expense 79% 100% 42% 57% 34% 42%

Medical Expense 89% 96% 31% 42% 22% 19%

Source: Tables 1a, 1b, and 1e, Appendix C

25 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, documentation 

collected during the household interview that met study-specific date requirements was used to calculate 

the QC Rent. Information collected during the household interview that did not meet study-specific date 

requirements was not used.
HUDQC Final Report for FY 2015 IV-21 

Documentation of Verification. In FY 2011, HUD issued new guidelines regarding verification. 

These revised guidelines indicated that documentation from a third party submitted by the tenant 

is acceptable, requiring fewer instances of direct third-party requests. Such documentation would, 

for example, include paystubs or letters from benefits agencies. Since FY 2011, there was a general 

trend showing increased use of documentation for verification. Between FY 2014 and FY 2015, 

this trend generally stabilized, with the exception of public assistance verification. 

● The use of documentation decreased for public assistance documentation, from 49 percent 

in FY 2014 to 28 percent in FY 2015. 

Tables 1c, 1d, 1e, 1f, and 1g in Appendix C provide additional verification information by rent 

component, including 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) by different types of verification. 

● Table 1c includes items verified by a third party in writing or by EIV/UIV. 

● Table 1d provides data for items verified by verbal third-party information. 

● Table 1e provides data for items verified via tenant file documentation.

● Table 1f includes items verified by EIV.

● Table 1g includes items verified by UIV.

Exhibit IV-19 summarizes the findings in Table 15a in Appendix C. 

● In FY 2015, the number of households where verification was not obtained by the PHA/

Owner increased for three of the seven rent components. 

● Public assistance and other income showed an increase in lack of verification, at 

7 percent and 5 percent, respectively. 

● There was improvement in child care expense, which increased by 25 percent over FY 2014. 

The remainder of the rent components had a relatively stable rate of project verification.

● Child care expenses were the most commonly verified rent component item in FY 2015, 

with a rate of 98 percent, up from 73 percent in FY 2014. 

● Percentage of verifications found to match Form HUD-50058/50059 entries within $100 

decreased for three of the seven rent components in FY 2015, with the most marked 

decrease being within other income. This value was 56 percent in FY 2014 and decreased 

to 44 percent in FY 2015.
IV-22 August 31, 2016

Exhibit IV-19 

Verification of Form HUD-50058/50059 Rent Components by PHA/Owners**

Rent Component

Percentage of 

Households With No 

Project Verification

Percentage of 

Households With Item 

Verified by Project

Percentage of 

Households Where 

Verification Matched 

Form HUD50058/50059*

FY 2014 FY 2015 FY 2014 FY 2015 FY 2014 FY 2015

Earned Income 16% 14% 84% 86% 58% 60%

Pensions 4% 5% 96% 95% 86% 82%

Public Assistance 15% 22% 86% 79% 69% 65%

Other Income 30% 35% 71% 65% 56% 44%

Asset Income 12% 10% 88% 90% 76% 79%

Child Care Expense 28% 2% 73% 98% 60% 73%

Medical Expense 19% 9% 81% 91% 60% 68%

Source: Table 15a, Appendix C

* Within $100

** Row totals may not add up 100 percent to due to rounding.

Verification Found Within Forms HUD-50058/50059. Exhibit IV-20 shows verification results 

by form type, again presenting the verification rate for each rent component and the proportion that 

matched within $100 of Form HUD-50058/50059 amounts. 

Rates of verification found in the file for Form HUD-50058 remained relatively stable for six out 

of the seven rent components between FY 2014 and FY 2015. The following changes are notable:

● Within Owner-administered programs using Form HUD-50059, public assistance 

verification saw the largest decrease, falling from 78 percent in FY 2014 to 63 percent in 

FY 2015. 

● For all PHA-administered programs using Form HUD-50058, the most commonly verified 

rent components were pension income and child care expenses. The least verified rent 

components were other income items, with a rate of 67 percent. 

● Within Owner-administered programs using the Form HUD-50059, pension income and 

child care expenses were most commonly verified, while other income items were the least 

verified rent components.

● Other income had the lowest percentage of items that were matched within $100 based on 

file documents across all program types.
HUDQC Final Report for FY 2015 IV-23 

Exhibit IV-20 

Verification of Form HUD-50058/50059 Rent Components by PHA/Owner Staff, by Form Type

Rent Component

Form HUD-50058 Form HUD-50059

Verified

Matched 

Within $100 Verified

Matched 

Within $100

FY 2014 FY 2015 FY 2014 FY 2015 FY 2014 FY 2015 FY 2014 FY 2015

Earned Income 83% 85% 58% 59% 88% 93% 60% 66%

Pensions 96% 94% 85% 80% 97% 96% 89% 86%

Public Assistance 88% 83% 69% 67% 78% 63% 72% 55%

Other Income 71% 67% 56% 45% 68% 57% 58% 43%

Asset Income 79% 88% 64% 74% 97% 92% 86% 84%

Child Care Expense 66% 98% 55% 69% 93% 100% 75% 90%

Medical Expense 77% 89% 51% 61% 87% 93% 69% 76%

Source: Table 15h, Appendix C

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 in both PHA- and Owner-administered programs continues to be strongly associated 

with households that have QC error. This was observed for every rent component for both the 

PHA- and Owner-administered programs. 

● Within PHA-administered programs, the percentage of households in error with missing 

verification decreased the most for those with child care expense rent components, with 

a decrease of 44 percent, from 85 percent in FY 2014 to 41 percent in FY 2015. Public 

assistance components with missing verification increased from 47 percent in FY 2014 to 

66 percent in FY 2015. Rates across years stayed relatively stable for the remaining five of 

seven rent components. 

● Owner-administered households in error with missing verification were most common 

within those with public assistance income rent components, followed by other income 

rent components. Households with child care expense rent components showed the 

largest decrease in households in error with missing verification between FY 2014 and 

FY 2015 (53 percent to 31 percent, respectively).

Across all program types, the percentage of households in error with missing verification was lowest 

among households with pension income rent components, which was also the lowest percentage in 

FY 2014. However, for some of these components, the number of households in error was relatively 

small; therefore, the estimates may vary substantially from year to year and may not be reliable. 
IV-24 August 31, 2016

Exhibit IV-21 

QC Error Households With Missing Verification in the Tenant File

Rent Component

Form HUD-50058 Form HUD-50059

Percentage of 

Households With 

QC Error

Percentage of 

Households With 

QC Errors and 

Missing Verification

Percentage of 

Households With 

QC Error

Percentage of 

Households With 

QC Errors and 

Missing Verification

FY 2014 FY 2015 FY 2014 FY 2015 FY 2014 FY 2015 FY 2014 FY 2015

Earned Income 11% 12% 52% 48% 6% 6% 54% 38%

Pensions 6% 7% 33% 31% 5% 6% 36% 29%

Public Assistance 2% 2% 47% 66% 1% 1% 76% 100%

Other Income 5% 6% 52% 47% 3% 3% 73% 67%

Asset Income 2% 2% 62% 57% 3% 3% 39% 37%

Child Care Expense 1% 1% 85% 41% 1% < 1% 53% 31%

Medical Expense 6% 6% 69% 79% 8% 8% 62% 64%

No Component Error 77% 75% — — 82% 80% — —

Source: Tables 16a and 16b, Appendix C

Summary of Form HUD-50058/50059 Errors. Exhibits IV-22a and IV-22b provide a summary of 

the errors identified from Form HUD-50058/50059. These included consistency errors, calculation 

errors, and overdue recertifications. 

Note: Exhibit IV-22a excludes Moving to Work (MTW) cases, as they do not have Form 

HUD-50058/50059 recalculated rent error, and Exhibit IV-22b shows all cases with QC Rent error. 

Both exhibits show the percentage of households in error, the average dollar error, and the standard 

errors for both households with recalculated Form HUD-50058/50059 error (i.e., error determined 

using only Form HUD-50058/50059) and households with QC Rent error. This information is 

provided for households with error by each error type. 

Beginning with the FY 2005 study, transcription error for any household was added to the source 

table, and the data that were described as an unduplicated count of Form HUD-50058/50059 error 

was revised to indicate an unduplicated count of any type of administrative error. 

● Exhibit IV-22b shows that several individual types of Form HUD-50058/50059 errors 

were not closely associated with QC Rent error, such as allowance calculation, income 

calculation, and other calculation errors. 

● Form HUD-50058/50059 with only transcription errors were associated with QC Rent 

error in 49 percent of households.

● Any type of administrative error, which includes transcription, consistency, calculation, or 

overdue recertifications, was associated with QC Rent error in 57 percent of the households. 

This increase over those households in error with only transcription errors is primarily due 

to the small number of households with income calculation error, resulting in estimates 

with variances that are rather large from year to year.

● When compared to FY 2014, there is an increase in FY 2015 in the percentage of households 

in error for both recalculated Form HUD-50058/50059 and for households with QC Rent 
HUDQC Final Report for FY 2015 IV-25 

error, while the average dollars in error have decreased for households with recalculated 

Form HUD-50058/50059. 

● The highest average dollar error decrease for recalculated Form HUD-50058/50059 

pertains to income calculation error. This was $106 in FY 2014 and has decreased to $40 

in FY 2015. 

● In addition, the average dollar error for households with any recalculated Form 

HUD-50058/50059 error was $13. 

● In contrast, the average dollar error for households with QC Rent error was $51. 

These values support the assertion that an administrative error on Form HUD-50058/50059 is not 

necessarily associated with a QC Rent error.

To understand the reason for the change in the average dollar error for households with recalculated 

Form HUD-50058/50059 error, it is important to review how this number is calculated. The number 

is the average dollar rent error for all cases with error in the category (based on recalculated Form 

HUD-50058/50059 rent error, not QC Rent error). 

So, for example, although the average rent error dollars for households with allowance calculation 

error is $70, because many of these cases have a large rent error (which may have nothing to do 

with the allowances) and the number of cases with allowance calculation error is small (2 percent of 

households in error), the average dollar error is large. The combination of a small number of cases 

with allowance calculation errors and a large dollar value of rent error leads to a comparatively 

large average dollar error. 

Exhibit IV-22a 

Form HUD-50058/50059 Administrative Error: Percentage of Households, 

Average Dollars in Error (Non-MTW Households)

Error Type Based on Form HUD50058/50059 

Recalculation

Non-MTW Households With Recalculated 

Form HUD-50058/50059 Error

Percentage of 

Households 

in Error

Standard 

Error of 

Percent

Average 

Dollar Error

Standard 

Error of 

Mean

Households With Transcription Error 49% 4.7% $12 $3.27

Households With Consistency Error 16% 3.8% $15 $6.06

Households With Allowance Calculation Error 2% 0.7% $70 $43.72

Households With Income Calculation Error 3% 1.2% $40 $28.45

Households With Other Calculation Error 7% 2.8% $20 $14.31

Overdue Recertifications 2% 1.2% $5 $5.14

Unduplicated Count, Any Type of Administrative Error 57% 5.0% $11 $2.88

Total Households 100% — $13 $4.54

Note: Data exclude MTW households; MTW cases do not have Form HUD-50058/50059 recalculated rent error.

Source: Table 17a, Appendix C
IV-26 August 31, 2016

Exhibit IV-22b 

Form HUD-50058/50059 Administrative Error: Percentage of Households, 

Average Dollars in Error (Households With QC Rent Error)

Error Type Based on Form HUD50058/50059 

Recalculation

Households With QC Rent Error

Percentage of 

Households 

in Error

Standard 

Error of 

Percent

Average 

Dollar Error

Standard 

Error of 

Mean

Households With Transcription Error 82% 2.7% $51 $4.55

Households With Consistency Error 22% 2.4% $46 $7.47

Households With Allowance Calculation Error 2% 0.6% $68 $36.80

Households With Income Calculation Error 3% 0.9% $94 $29.81

Households With Other Calculation Error 6% 1.3% $89 $24.22

Overdue Recertifications 4% 1.0% $113 $35.28

Unduplicated Count, Any Type of 

Administrative Error 86% 2.8% $51 $4.26

Total Households 100% — $51 $4.09

Source: Table 17b, 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 Rent Error and Net Rent Error for households with each type of 

administrative error.

● In FY 2015, the percentage of households in error was generally very similar to the 

percentage in FY 2014 for all error types, though households with transcription error 

decreased by 2 percent between FY 2014 and FY 2015.

● Gross Rent Error calculated from specific types of error increased slightly in FY 2015 as 

compared to FY 2014 values for all seven categories. 

● Net Rent Error calculated from the various types of error increased in FY 2015 for the 

majority of error types as compared to FY 2014.

● Overdue recertifications had large differences in error amounts due to the small number of 

overdue cases.
HUDQC Final Report for FY 2015 IV-27 

Exhibit IV-23 

Administrative Error: Percentage of Households Average Dollars in Error for All Households

Error Type

Percentage of 

Households in 

Error

Gross Rent Error Net Rent Error

Average 

Dollars in 

Error

Standard 

Error of 

Mean

Average 

Dollars in 

Error

Standard 

Error of 

Mean

Households With Transcription Error 42% $26 $2.68 -$8 $2.69

Households With Consistency Error 21% $13 $2.65 -$5 $1.64

Households With Allowance Calculation Error 1% $32 $16.45 -$23 $16.60

Households With Income Calculation Error 2% $34 $11.72 -$15 $12.51

Households With Other Calculation Error 4% $39 $10.36 $9 $11.70

Overdue Recertifications 2% $77 $25.58 $23 $25.78

Unduplicated Count, Any Type of 

Administrative Error 53% $22 $2.12 -$7 $2.15

Total Households 100% $13 $1.29 -$4 $1.17

Source: Table 18, Appendix C 

E. Occupancy Standards

Exhibit IV-24a 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. For example, the PHA-administered Section 8 Voucher program 

sometimes allows households to rent units with fewer or more bedrooms than specified by the 

guidelines.

● Sixteen percent of all households occupied a unit with too many or too few bedrooms in 

FY 2015, compared to 17 percent in FY 2014.

● Sixteen percent of Public Housing households were over- or under-housed in 

FY 2015.

● Twenty-two percent of PHA-administered Section 8 program households were under- or 

over-housed in FY 2015, the same percentage found in FY 2014.

● Eight percent of Owner-administered households were under- or over-housed in FY 2015.
IV-28 August 31, 2016

Exhibit IV-24a 

Percentage of Households in Units With the Correct 

Number of Bedrooms (According to Study Guidelines)

Number of 

Bedrooms

PHA-administered

Public Housing Section 8 Owner-administered Total

FY 2014 FY 2015 FY 2014 FY 2015 FY 2014 FY 2015 FY 2014 FY 2015

0 98% 99% 100% 97% 96% 98% 98% 98%

1 98% 99% 100% 100% 99% 99% 99% 100%

2 73% 73% 63% 68% 78% 75% 69% 71%

3 78% 78% 83% 72% 79% 81% 81% 74%

4 68% 59% 46% 65% 57% 56% 53% 62%

5+ 24% 8% 47% 29% — — 53% 22%

All Units 83% 84% 78% 78% 91% 92% 83% 84%

Source: Table 19, Appendix C

Exhibits IV-24b and IV-24c show the percentage of households that met these guidelines for each 

bedroom size for FY 2014 and FY 2015, respectively. The shaded cells indicate the percentage of 

households that fell within study guidelines.

Exhibit IV-24b 

Percentage of All Households in FY 2014 by Number 

of Bedrooms and Number of Household Members*

Number of 

Bedrooms

FY 2014 Number of Household Members

1 2 3 4 5 6 7 8+

0 98% 2%

1 91% 8% 1%

2 29% 40% 24% 5% 2% 1%

3 7% 12% 35% 26% 13% 6% 1%

4 2% 5% 14% 24% 25% 15% 4% 11%

5+ 9% 26% 12% 6% 46%

Source: Table 19a, Appendix D of the HUDQC Final Report for FY 2014, delivered on September 25, 2015. 

*Row totals may not add up 100 percent to due to rounding.
HUDQC Final Report for FY 2015 IV-29 

Exhibit IV-24c 

Percentage of All Households in FY 2015 by Number 

of Bedrooms and Number of Household Members*

Number of 

Bedrooms

FY 2015 Number of Household Members

1 2 3 4 5 6 7 8+

0 98% 1% 1%

1 91% 9% < 1% < 1% < 1%

2 27% 43% 20% 7% 2% 1% < 1%

3 9% 13% 33% 24% 12% 6% 2% 1%

4 3% 5% 9% 22% 27% 18% 10% 9%

5+ 3% 6% 9% 36% 18% 13% 14%

Source: Table 19a, Appendix C 

*Row totals may not add up 100 percent to due to rounding.

F. Rent Reasonableness

The PHA-administered Section 8 program assists low-income families in obtaining housing in the 

private market. A PHA responsible for administering the program must not enter into a HAP contract 

until the housing authority has determined that the initial rent paid to the owner is a reasonable 

amount. The PHA must also determine whether the rent to the owner is reasonable in comparison 

to rent for other comparable unassisted units. 

Rent reasonableness is an important factor in determining participant subsidies and is critical for 

effective, PHA-administered Section 8 Voucher program operations. If PHAs approve rents that 

are low compared to the private market, landlords may only participate with their lowest cost, 

lowest quality units, or they may not rent out their units at all to program participants. 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. Our analysis examines whether PHAs fulfilled the requirement for documenting rent 

reasonableness determinations, but does not investigate whether rents were in fact reasonable.

Methodology. We surveyed PHAs administering the Section 8 Voucher program in our study. This 

year, 137 projects26 in our study fall into this category. The projects were asked about their standard 

rent reasonableness processes and file documentation from the project’s household sample were 

reviewed. 

We instructed field interviewers to review tenant files for 787 Voucher households in order to 

locate the documents supporting the rent reasonableness certification. For 113 new certifications,27

field interviewers reviewed the file for the initial rent reasonableness certification and recorded 

the date it was conducted. For the 674 annual recertifications we reviewed, field interviewers 

were asked to ascertain when the current rent to the owner became effective and to locate the 

26 For the purposes of this study, a project for the Section 8 Voucher program is defined as a PHA/county 

combination. Therefore, if a PHA administers vouchers in more than one county, that PHA could be 

represented in this study by more than one project.

27 

Beginning in FY 2007, portability move-ins were classified as annual recertifications. In FY 2006, they 

were categorized as new admissions.
IV-30 August 31, 2016

relevant supporting rent reasonableness documentation. If this documentation (relative to the 

date that the rent to the owner became effective) was not found, field interviewers were asked to 

search for any rent reasonableness certification in the file and enter the date of certification. The 

owner’s rent certification on the Request for Tenancy Approval form was considered a certification 

of rent reasonableness.

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). 

Sixty-two percent of the housing authorities that responded reported using unit-to-unit comparison 

as the predominant method for their rent reasonableness determination. The unit-to-unit method 

is similar to the standard real estate appraisal technique of comparing a unit to similar private, 

unassisted units in the same general location. Rent amounts are sometimes modified for differences 

in unit characteristics (e.g., size, age, amenities, housing services, maintenance, or utilities).

Exhibit IV-25 

PHAs by Predominant Rent Reasonableness Method*

Method

FY 2013 FY 2014 FY 2015

Number Percent Number Percent Number Percent

Unit-to-unit Comparison 96 69% 91 64% 85 62%

Unit-to-market Comparison 21 15% 25 17% 25 18%

Point System 17 12% 17 12% 20 15%

Other or Rent Control 6 4% 6 4% 5 4%

No Single Predominant Method 0 — 2 1% 2 1%

No Information 0 — 2 1% 0 1%

Total 140 100% 143 100% 143 100%

*Column totals may not add to 100 percent because of rounding.

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. Eighteen percent of housing 

authorities reported primarily using this method. Valuation adjustments are based on typical 

units in the private market. Fifteen percent of housing authorities indicated that their primary 

method of making rent reasonableness determinations was based on a point system. Using 

this system, units are assigned points based on their condition and attributes, and comparisons 

are made to unassisted units.

We asked PHA staff to identify only the primary method used to determine whether rents to owners 

were comparable to the private market, rather than enter a percentage use of various methods. When 

asked to identify a single predominant method, most PHAs selected only one. Two PHAs selected 

“no single method predominates,” the same number of PHAs as in FY 2014 as opposed to none in 

FY 2013. PHAs were also asked whether they used a software program and/or an outside contractor 

to determine whether the rent to owner was reasonable. Eighty-eight of the 137 voucher projects 

(64 percent) use rent reasonableness software. GoSection8.com remained the most commonly 

used software vendor, cited by 33 projects in FY 2015 and 37 projects in FY 2014, followed 

by Nelrod EZ Reasonable Rent Determination, used by 9 projects in FY 2015 and 10 projects 
HUDQC Final Report for FY 2015 IV-31 

in FY 2014. Three PHAs reported using software and systems developed in-house in FY 2015, 

compared with seven in FY 2014.

Findings Pertaining to Rent Reasonableness Documentation Found in Tenant Files for 

New Admissions and Annual Recertifications. In FY 2015, 96 percent of new admission files 

contained rent reasonableness documents, up from 90 percent in FY 2014 and 91 percent in 

FY 2013 (see Exhibit IV-26a). 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 and reviewed the file for the rent reasonableness documentation 

specific to that rent determination. If no rent reasonableness documentation was found within this 

specific timeframe, we reviewed any rent reasonableness documentation in the file. In FY 2015, 

89 percent of these case files had certified rent reasonableness documents, compared to 87 percent 

in FY 2014 and 82 percent in FY 2013 (see Exhibit IV-26a).

Exhibit IV-26a 

Rent Reasonableness Documentation for New Admissions and Annual Recertifications

Status

FY 2013 FY 2014 FY 2015

New AdmissionsRecertificationsNew AdmissionsRecertificationsNew AdmissionsRecertifications

Determination Documented 91% 82% 90% 87% 96% 89%

No Determination Documented 9% 18% 10% 13% 4% 11%

Total 100% 100% 100% 100% 100% 100%

The absence of rent reasonableness documentation does not necessarily indicate that a determination 

was not completed, only that it was not properly documented. Of new admission files that had 

documentation, 53 percent contained a statement signed by the PHA staff member certifying that 

the rent was reasonable. For recertifications with rent reasonableness documentation, 70 percent 

contained a statement signed by the PHA staff member certifying that the rent was reasonable (see 

Exhibit IV-26b). The increase in the use of Form HUD-52517 as a rent reasonableness source 

document among new admissions may be a result of study procedure that directed the data collector 

to locate the Request for Tenancy Approval as a utility document. Thirty-three percent of new 

admission rent reasonableness forms found were documented on the Form HUD-52517 in FY 2015, 

compared to 12 percent in FY 2014 and 14 percent in FY 2013.
IV-32 August 31, 2016

Exhibit IV-26b 

Type of Rent Reasonableness Documentation for New Admissions and Annual Recertifications*

Type of Documentation

FY 2013 FY 2014 FY 2015

New AdmissionsRecertificationsNew AdmissionsRecertificationsNew AdmissionsRecertifications

A signed statement certifying the rent is reasonable 50% 54% 61% 62% 53% 70%

Comparable units documented by the property 

owner in Section 12a of Form HUD-52517 14% 10% 12% 11% 33% 16%

Comparable units documented on other documents 33% 30% 22% 21% 9% 11%

Any other reference to rent reasonableness 3% 5% 6% 6% 6% 3%

Total 100% 100% 100% 100% 100% 100%

*Totals may not add up to 100% due to rounding.

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 (depending on the type of transaction, the lease date is the effective date of the current contract 

rent or the lease start date) with the rent reasonable certification date in the case file. Since the PHA 

is required to conduct a rent reasonableness assessment when the contract rent is increased by the 

owner, the current contract rent is compared with the previous rent amount to determine when and 

whether there was a change in the contract rent. This data is used to determine whether there was 

a timely rent reasonableness assignment. Exhibit IV-27 provides a summary of how the date of the 

rent reasonableness documentation relates to the initial lease date or contract rent change date for 

those households where a reference to the rent reasonableness determination was found in the file.

Exhibit IV-27 

Timing of Most Recent Rent Reasonableness Determination: 

New Admissions and Annual Recertifications*

FY 2013 FY 2014 FY 2015

sions

tions

sions

tions

sions

tions

Determination: Certification ChronologyNew AdmisRecertificaNew AdmisRecertificaNew AdmisRecertifica

More than 4 months before lease date 7% 40% 5% 28% 0% 22%

Up to 4 months before lease date 82% 50% 86% 47% 84% 60%

Up to 2 months after lease date 10% 3% 3% 3% 8% 4%

Greater than 2 months after lease date 0% 2% 1% 15% 1% 11%

Date missing 2% 5% 5% 7% 7% 3%

Total 100% 100% 100% 100% 100% 100%

*Totals may not add up to 100% due to rounding.
HUDQC Final Report for FY 2015 IV-33 

If the effective date of the lease with the current contract rent occurred prior to the date of the rent 

reasonableness documentation, rent reasonableness may not have been considered as a factor in 

approving the unit’s rent. In FY 2013, ICF changed the data collection methodology slightly, as 

discussed in Section II, and we collected the date the rent was in effect. This explains the greater 

number of cases with certifications occurring more than 4 months before the lease date in FY 2013, 

a trend that continued in FY 2014 and FY 2015. The percentage of rent reasonable determinations 

made after the rent had been established as part of the initial lease agreement increased for new 

admissions from 4 percent in FY 2014 to 9 percent in FY 2015. For annual recertifications in 

FY 2015, the percentage of rent reasonable documentation dated after the effective date of a lease 

decreased from the previous study year: 15 percent in FY 2015 compared to 18 percent in FY 2014.

Conclusion. PHAs are not fully documenting rent reasonableness determinations as required 

by HUD regulations, and a large number of existing rent determinations may have been made 

on the basis of less formal means of evaluating rents. Timely reviews decreased slightly in 

FY 2015 compared to FY 2014 for new admissions and increased for recertification transactions. 

The proportion of cases lacking any rent reasonableness decreased compared to FY 2014. 

Four percent of new admissions and 12 percent of annual recertification transactions lacked a rent 

reasonableness document in FY 2015, compared to 10 percent and 13 percent, respectively, in 

FY 2014. These findings may be attributed in part to PIH 2003-12, issued May 16, 2003, which 

supports a more simplified rent reasonableness determination process. PIH 2003-12 states that 

a PHA need not consider all nine criteria cited in 24 C.F.R. 982.507(b) to fully comply with the 

regulation. It justifies less formal methods of rent determination, stating that “each PHA should use 

appropriate and practical procedures for determining rental values in the local market.” 

G. Utility Allowance Analysis

As part of the FY 2015 HUDQC Study, ICF conducted two separate analyses on the utility 

allowances provided to households through the PHA-administered Section 8 Voucher program. 

For the first analysis, we focused on whether there was documentation in the tenant file indicating 

how the utility allowance amount used in the rent determination was calculated, and whether those 

documents were used correctly in calculating the utility allowance amounts. For the second analysis, 

we focused on identifying discrepancies between the utility allowance on the Form HUD-50058 and 

the appropriate utility allowance as listed on a PHA staff-provided utility allowance schedule. These 

schedules often varied within a county by unit type, effective date of recertification, and location.

Documentation of Utilities and Utility Allowance Values. We asked PHAs to provide information 

about the forms that were used to document and calculate the utility allowance and to provide the 

utility allowance schedules that were used for actions effective in FY 2015. In addition, we asked 

field interviewers to copy documents showing calculation of utility allowances found in tenant files 

at the PHA office.

ICF staff selected 137 distinct PHA-administered Section 8 Voucher projects for the study sample. 

These projects, administered by 120 housing authorities (several of which administered projects in 

multiple counties), participated in the FY 2015 HUDQC Study. According to information provided at 

the PHA level by 128 projects, 33 percent of the projects used Form HUD-52667 (Allowance Schedule) 

as the official source for identifying the utilities for which the households were responsible. This 
IV-34 August 31, 2016

was a change from FY 2014, when Form HUD-52517 (Request for Tenancy Approval) was the most

commonly used document. 

In FY 2015, 24 percent of projects used Form HUD-52641 (HAP Contract) for identification purposes, 

an increase from 14 percent in FY 2014. With respect to the document used to calculate utility allowance 

value, the most common document was Form HUD-52667 (Allowance Schedule) in FY 2015. In 

Exhibit IV-28a, we provide information on the type of documents used as the official source for 

identifying utilities for which the households were responsible.

Exhibit IV-28a 

Types of Documents Used by PHAs to Identify Utilities and Calculate the Utility Allowance Value*

Document Used

Identifying Utilities

FY 2014 FY 2015

Number Percent Number Percent

Form HUD-52517 (Request for Tenancy Approval) 46 34% 36 28%

Form HUD-52641(HAP Contract) 19 14% 31 24%

Form HUD-52667 (Allowance Schedule) 43 32% 42 33%

Other (e.g., lease, reports, comparisons) 11 8% 14 11%

Various combinations of documents above 15 11% 5 4%

Total 134 100% 128 100%

*Totals may not add up to 100% due to rounding.

Comparison of Form HUD-50058 Utility Allowance Values to Worksheets Found in the 

Household File. Seven hundred and ninety-nine (799) households from the PHA-administered 

Section 8 Voucher program were selected for this study. Field interviewers were able to 

locate worksheets or other documents indicating how the utility allowance was calculated for 

753 households (94 percent). 

For each household with utility allowance documentation available, we compared the utility 

allowance amount from the Form HUD-50058 to the amount on the utility allowance worksheet 

obtained from the tenant file. For 627 households (83 percent) the Form HUD-50058 utility 

allowance amount matched the worksheet amount. For 11 percent of the households, the worksheet 

provided was for the incorrect period of time or was missing critical information. For these 

79 households, we could not determine whether the utility allowance amount used in the rent 

calculation was correct. In the remaining 6 percent of the households, there were discrepancies 

between the amounts on the worksheet and on the Form HUD-50058. 

In FY 2015, there were slightly more households with matching values than in FY 2014 

(83 percent and 81 percent, respectively). There were fewer inadequate worksheets in FY 2015 

(11 percent) as compared to FY 2014 (17 percent). Exhibit IV-28b presents a summary comparing the 

utility allowance listed on the Form HUD-50058 and the amount on worksheets found in tenant files.
HUDQC Final Report for FY 2015 IV-35 

Exhibit IV-28b 

Comparison of Utility Allowance on the Form HUD-50058 to the Utility Allowance Worksheet

Outcome Number Percent

Form HUD-50058 (AC) amount matched with worksheet amount 627 83%

Worksheet in file for incorrect period of time or is missing critical information 79 11%

Discrepancy due to mathematical error or other clerical errors 25 3%

Discrepancy—Unable to determine reasons 22 3%

Total 753 100%

Comparison of Form HUD-50058 Utility Allowance Values to the Correct (QC) Utility Allowance 

Value. The ICF team calculated the QC utility allowance amount in two steps. In the first step, the 

utilities that the tenant was responsible for were identified by using documents—usually PHA utility 

allowance worksheets—found in tenant files that indicated those specific utilities. In the second 

step, we mapped the identified household’s specific utilities onto the utility allowance schedule and 

summed the total to determine the QC allowance amount.

We matched the utility allowance amount on Form HUD-50058 to the QC utility allowance 

amount. We were unable to calculate the QC utility allowance for 2 percent of the households 

(15 households) because worksheets were not available; consequently, the specific utilities paid 

by the household could not be identified. Furthermore, we were unable to calculate the QC utility 

allowance in an additional 1 percent of households because the worksheets in the files did not 

include specific utilities or other critical information needed for QC allowance calculation. Another 

1 percent could not be calculated because the appropriate utility allowance schedule was not 

available. Exhibit IV-28c differentiates between the households in which we were able to calculate 

the QC allowance amount and lists the reasons and number of households where we were unable 

to calculate the QC utility allowance amount.

Exhibit IV-28c 

Availability of All Information to Enable QC Utility Allowance (UA) Calculation*

Outcome

QC UA Amount 

Calculated Number Percent

Appropriate worksheet and schedule available Yes 771 97%

UA worksheet or other comparable document not available No 15 2%

Appropriate UA schedule not available No 4 1%

Worksheet was missing critical information No 9 1%

Total 799 100%

*Column totals may not add up 100 percent to due to rounding.

We calculated the QC utility allowance amounts for 771 households and then compared the QC utility allowance to the Form HUD-50058 utility allowance amount. In 87 percent of these households, 

Form HUD-50058 and the QC utility allowance values matched. We categorized the remaining 

13 percent of households where the values did not match into two broad categories: administrative 

error or unknown (i.e., ICF was unable to determine the reason for the discrepancy in utility allowance amounts). The majority of these errors were categorized as administrative. We present the 

findings from this analysis in Exhibit IV-28d.
IV-36 August 31, 2016

Exhibit IV-28d 

QC Utility Allowance Compared to Form HUD-50058 Utility Allowance

Outcome Number Percent

QC UA matched amount on Form HUD-50058 671 87%

Discrepancy due to math error/transfer error 84 11%

Discrepancy—Unable to determine reasons 16 2%

Total 771 100%

Note: The QC rent that is calculated for this study uses the utility allowance amount from Form HUD-50058 and not the QC 

allowance amount that was calculated for this comparison. 

H. Payment Standard Analysis 

As part of the FY 2015 HUDQC study, ICF conducted a special analysis to determine whether 

PHAs are using the correct payment standard amount. This special analysis was independent of the 

rent calculation error findings presented in another section in this report, and it did not affect rent 

calculation determinations. The payment standard analysis consisted of three parts: 

1. The payment standard on Form HUD-50058 was compared to the payment standard 

schedules provided by the PHA.

2. The payment standard on Form HUD-50058 was compared to the Fair Market Rent (FMR) 

for the appropriate geographical area.

3. The payment standards were compared to the FMRs to ensure that they fell between 

90 percent and 110 percent of FMR for each project. 

The findings from these comparisons are presented below.

Background. Payment standards are used in the PHA-administered Section 8 Voucher program to 

determine the tenant’s portion of the rent to owner. Payment standards must be kept current and set 

between 90 percent and 110 percent of the FMR. If a PHA does not ensure that its payment standards 

are within this range or if program administrators fail to apply the current payment standards, this 

will result in errors in tenant rent determinations.

PHAs can apply payment standards incorrectly in a variety of ways that will result in errors in tenant 

rent. A PHA may have several payment standards for different geographic areas with complex 

borders and/or based on groups of ZIP codes or municipalities that change every few years. This can 

make it difficult to select the correct payment standard for any given address within the jurisdiction. 

Additionally, a household can rent a unit whose number of bedrooms differs from the authorized 

number of bedrooms on the voucher, which is based on family size. In such a case, program 

administrators must remember to use the lower of the payment standard based on the voucher 

bedroom size or the payment standard for the actual size of the unit leased. Annual changes in 

payment standards can cause similar confusion; payment standard increases take effect immediately, 

but administrators may forget that there is a 1-year delay before decreases affect the determination 

of household rents or assume that the delay extends to increases as well.

Other potential areas for error include whether a PHA has been authorized to use FMRs based on 

the 50th percentile of the rents in the area, whether the PHA has been authorized to use Success 

Rate Payment Standards based on the 50th percentile of rents, and whether the PHA continues to be 
HUDQC Final Report for FY 2015 IV-37 

eligible for these higher subsidy standards. Moreover, PHAs are allowed to change a household’s 

payment standard only at the time of the annual recertification or before the household moves 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 to determine the rent 

should not be changed at the interim recertification. Yet, despite the complexity of payment standard 

guidelines, most of the errors found in this review were not due to this reason.

Comparison of the Payment Standard on Form HUD-50058 With the Payment Standard 

Schedules Provided by the PHA. For the first analysis, we compared the payment standard on 

Form HUD-50058 (the actual, or AC, payment standard) with the payment standard schedule (the 

quality control, or QC, payment standard) provided by the PHA. For all Voucher households in 

the study, we selected the appropriate QC payment standard when possible and compared it with 

the AC payment standard. We selected the QC payment standard from the schedules provided by 

the PHA on the basis of:

● 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.

● The determination and application of any special exception to payment standard guidelines 

provided by the PHA staff.

In previous years, for every household where the AC and QC payment standard did not match, 

we placed a call to the PHA staff for clarification and collected payment standard schedules when 

appropriate. Historical discussions with projects on the determination of the QC payment standard 

uncovered a host of other issues that required consideration when selecting the QC payment standard. 

While time restraints prevented us from conducting calls to PHA staff for FY 2015, types of past 

complications considered during the analysis included the following:

● Some PHAs use the previous (higher) payment standard amount for the first recertification 

after a decrease in the payment standard amount. 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, may be granted to some households.

● Exception Rent Areas may have higher payment standards.

● Some PHAs use payment standards from the initial housing authority for port-in households, 

with the understanding that the rates would be adjusted at the next annual recertification.

● Some PHAs use software systems that identify the lesser of gross rent or the payment 

standard to populate the payment standard field on Form HUD-50058.

The study included 791 PHA-administered households. Of these, ICF was able to determine a 

QC payment standard for 661 households, or 84 percent. For the majority of these households 

(82 percent), the AC payment standard matched the QC payment standard. There were 

118 households with discrepant payment standards; 74 (63 percent) of the households with 

discrepant payment standards 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. We attributed discrepancies to one of seven common reasons, as listed 
IV-38 August 31, 2016

in Exhibit IV-29a. The most typical reason for a discrepancy between the AC and QC payment 

standard was for reasons categorized as Other: overdue recertifications, a PHA using the FMR 

rather than the payment standard, and/or enhanced vouchers, etc. (32 percent). The use of incorrect 

payment standard schedules accounted for a cumulative 24 percent of the discrepancies found, 

while approximately a fifth of discrepancies (22 percent) were attributable to the use of a projectbased voucher. Exhibit IV-29a summarizes the number and percentage of households with payment 

standard discrepancies and indicates the reasons for these discrepancies.

Exhibit IV-29a 

Number and Percentage of Households With Payment Standard Discrepancies

Reason

Number of 

Households 

(Elderly/ 

Disabled)

Number of 

Households 

(Non-Elderly/ 

Non-Disabled)

Percent of 

Households 

With 

Discrepancies

Used incorrect number of bedrooms/household members 7 1 7%

Used incorrect payment standard schedule 15 13 24%

Used gross rent instead of the payment standard amount 4 2 5%

Project staff made a typographical error 4 7 9%

Section 12 of Form HUD-50058 was incomplete/missing 1 0 1%

Project-based voucher: No payment standard (Section 11 

filled out) 19 7 22%

Other reasons (e.g., overdue recertification, enhanced 

voucher) 24 14 32%

Total 44 74 100%

Comparison of the Payment Standard on Form HUD-50058 to the Fair Market Rent for the 

Appropriate Geographic Area. In the second analysis, we compared the payment standard on 

Form HUD-50058 to the FMRs for the appropriate geographic area. The payment standard for 

552 households (70 percent) fell within the 90 percent to 110 percent FMR band; 39 of the 

households (5 percent) that fell outside the 90 percent to 110 percent band used an amount that 

exceeded 110 percent of the FMR; and 34 of the households (4 percent) that fell outside the band 

used an amount that was less than 90 percent of the FMR. Exhibit IV-29b summarizes the number 

and percentage of households by the relationship of the payment standard to the acceptable FMR. The 

table is based on data for the 661 cases where we were able to determine correct paymentstandards.
HUDQC Final Report for FY 2015 IV-39 

Exhibit IV-29b 

Payment Standard Compared With the Fair Market Rent

Characteristic

Fair Market Rent Percent of Total 

Cases Outside the

90% to 110% Band Under 90% 90%–110% Over 110%

Non-elderly or Non-Disabled 10 292 17 3% 

Elderly or Disabled 24 260 22 6%

Total 34 552 39 9%

The analysis of cases that fell outside the 90 percent to 110 percent FMR band revealed that 

12 percent of cases fell outside the FMR band for four general reasons: (1) the incorrect number of 

bedrooms or household members was used; (2) the incorrect payment standard schedule was used; 

(3) project staff used gross rent instead of the payment standard; or other reasons. Exhibit IV-29c 

summarizes the number and percentage of households that fall outside the 90 percent to 110 percent 

FMR band by category.

Exhibit IV-29c 

Details of Cases Falling Outside 90%–110% of the Fair Market Rent*

Reason

Fair Market Rent Percent of Cases 

Outside the

90% to 110% Band Under 90% Over 110%

Used incorrect number of bedrooms or household members 6 0 8%

Used incorrect payment standard schedule 3 2 7%

Used gross rent instead of the payment standard amount 0 4 5%

Other reasons—overdue recertification, used 105% of FMR, 

software limitations, original payment standard over 110%, 

unable to determine a reason for the discrepancy

 25 33 79%

Total 34 39 100%

*Totals may not add up 100% to due to rounding.

Comparison of the FY 2014 to the FY 2015 Payment Standard Analysis Results. ICF conducted 

the same payment standard analysis for the FY 2014 study. Of the 791 PHA-administered Section 

8 Voucher households in FY 2015, 661 had a QC payment standard determination. Of these, the 

AC and the QC payment standard matched for 543 of the households (82 percent), compared to 

75 percent in FY 2014. Also, 73 (11 percent) of the households had payment standards that did 

not fall within the 90 percent to 110 percent FMR band. Therefore, a total of 11 percent of the 

PHA-administered Section 8 Voucher households included in the FY 2015 study did not meet 

HUD’s payment standard requirements, which indicates a 3 percent decrease from FY 2014. 

Exhibit IV29d summarizes the results from the FY 2014 and FY 2015 payment standard analysis.
IV-40 August 31, 2016

Exhibit IV-29d 

Comparison of the FY 2014 to FY 2015 Payment Standard Analysis

Characteristic

 FY 2014 FY 2015 

Number

Percent of 

Total Cases Number

Percent of 

Total Cases

PHA-administered Section 8 Voucher sample 782 100% 791 100%

Households where QC payment standard was determined 670 86% 661 82%

Households where the AC and QC payment standard 

did not match 171 26% 118 18%

Households where the AC payment standard did not 

meet the 90% to 110% FMR threshold 91 14% 73 11%

Households that were not exempt from the

90% to 110% FMR threshold and did not meet HUD’s 

payment standard requirements

91 14% 73 11%

I. PIC/TRACS Analysis 

A special analysis was conducted to determine whether cases for which Form HUD-50058/50059 

data had been submitted to HUD were more or less likely to have errors than those for which data 

had not been submitted. These households were compared using two methods. The first was based 

on whether household identifiers used by ICF were present in the PIC/TRACS data for the selected 

households (i.e., social security number, name, and date of birth). The second method of comparison 

examined whether the specific Form HUD-50058/50059 certification or recertification action used 

by ICF for the selected households was present in the PIC/TRACS data. 

In previous years, HUD provided PIC/TRACS head of household data for only those households in 

the HUDQC Study. For the second study in a row, ICF requested PIC/TRACS data for all assisted 

households with transaction data present within the FY 2015 study period in November 2015. These 

data were used for both household sampling and the PIC/TRACS analysis. Due to nonstandard 

recertification cycles permitted by various MTW PHAs, the PIC/TRACS data for MTW households 

were not requested. Certain MTW PHAs selected for the FY 2015 study allow tenants to have 

biennial or triennial recertifications, so those tenants would not have been included on the data file 

if included in the request. For this reason, 2,211 of the 2,400 households (92 percent) were included 

in the PIC/TRACS analysis (i.e., analytical sample). 

Matching the HUDQC Study to the PIC/TRACS Data. Head of household data were provided 

for all actions and updates from November 1, 2014, to October 31, 2015, resulting in multiple 

observations per household. The households included in the HUDQC Study were matched 

against these PIC/TRACS data using identifying information (a combination of the social security 

number, name, and date of birth) for each head of household in the analytical sample. Using these 

criteria, PIC records were found for just under 100 percent of households in PHA-administered 

projects (99.9 percent), while TRACS records were found for exactly 100 percent of households 

in Owner-administered projects. However, due to the use of PIC/TRACS data for household 

sample selection, a near-100-percent match rate was anticipated. Figure IV-4 shows the change in 

percentage of households in which PIC/TRACS was present over time, as identified in the HUDQC 
HUDQC Final Report for FY 2015 IV-41 

Study, beginning in FY 2005. PHA-administered percentages have increased since FY 2005, while 

Owner-administered percentages have remained fairly steady over time.

In addition to the match described above, ICF further compared household data obtained during 

the HUDQC study to PIC/TRACS data with the following certification information: program type, 

type of action, and effective date. If duplicate observations for each head of household remained, 

the transaction with the closest PIC/TRACS update date following the certification effective date 

was selected. Using this more explicit matching technique and including the additional certification 

elements, 2,124 of the 2,211 QC households in the PIC/TRACS analysis, or about 96 percent, 

were fully represented by both head of household identifying information and certification data. 

This matching rate was a slight increase from the previous rate of 95 percent in FY 2014 and a 

decrease from the 98 percent found in FY 2013 and FY 2012. The current match rate of 96 percent 

is still a substantial increase from rates in FY 2011 and FY 2010, in which 70 and 71 percent 

of households, respectively, were fully represented by a match on both identifying information 

and certification data. 

In past years, most of the PIC/TRACS analysis was based on the broader match using identifying 

information to maintain consistency with past years. However, with the new PIC/TRACS data request 

process and nearly all households matching on identifying information, the PIC/TRACS analysis 

presented in this report is based on the more explicit matching technique. Using these criteria, PIC 

action records were found for 95 percent of the households in PHA-administered projects, while 

TRACS action records were found for 97 percent of the households in Owner-administered projects. 

Of the 2,211 households in this analysis, 2,124 households (or 96 percent) were successfully matched 

with PIC/TRACS. 

Figure IV-4

PIC/TRACS Data Present by Program Type for 

All Households Over Time
IV-42 August 31, 2016

Analysis was conducted to compare the average dollars in Gross Rent Error for households that 

had the QC selected action in PIC/TRACS (i.e., matching action type and effective date) with those 

that did not. Exhibit IV-30a provides the percentage of households in each of the three program 

types by whether or not the action data for the household were available in PIC/TRACS (present or 

absent), and the average dollars in gross error based on all households in the study. Exhibit IV-30b 

provides the same information, but only for those households that had rent error. Exhibits IV-30a 

and IV-30b illustrate that the rate for which PIC/TRACS action data were present was reasonably 

comparable between all households and for only those households with rent error.

Exhibit IV-30a 

PIC/TRACS Data by Program Type and Average Gross Dollars in Error for All Households

Administration Type

PIC/TRACS Action Present PIC/TRACS Action Absent

Percentage of 

Households

Average Dollars

in Error

Percentage of 

Households

Average Dollars 

in Error

Public Housing 95.7% $11.83 4.3% $68.04

PHA-administered Section 8 95.5% $13.47 4.5% $31.15

Total PHA-administered 95.6% $12.93 4.4% $42.99

Total Owner-administered 97.3% $9.21 2.7% $7.89

Total 96.1% $11.74 3.9% $35.24

As presented in Exhibit IV-30b, the average dollars in Gross Rent Error for PHA-administered 

projects were higher for households in error when PIC/TRACS action data were absent ($90) 

than when PIC/TRACS action data were present ($47). More specifically, the largest difference in 

average gross error dollars was found for Public Housing households, with and without PIC/TRACS 

action data present ($51 and $192, respectively). However, because the number of households with 

actions absent from PIC/TRACS is relatively low, these estimates are less reliable and more volatile 

from year to year. In FY 2015, 96 percent of Owner-administered households in error had PIC/

TRACS data present. This rate is lower than in recent years, when the rate of Owner-administered 

households in error with PIC/TRACS data present ranged from 97 percent to 100 percent between 

FY 2010 and FY 2014. 

Exhibit IV-30b 

PIC/TRACS Data by Program Type and Average Gross Dollars in Error for Households in Error

Administration Type

PIC/TRACS Action Present PIC/TRACS Action Absent

Percentage of 

Households

Average Dollars 

in Error

Percentage of 

Households

Average Dollars 

in Error

Public Housing 93.6% $50.59 6.4% $192.07

PHA-administered Section 8 93.1% $48.89 6.9% $71.35

Total PHA-administered 93.2% $49.39 6.8% $104.84

Total Owner-administered 96.0% $41.61 4.0% $23.89

Total 94.0% $47.19 6.0% $89.79

Exhibit IV-30c presents the percentage of households and average dollars in error for households 

action matched/not action matched with PIC/TRACS by payment type. Although the percentages 

of underpayment, overpayment, and proper payment are similar, both where PIC/TRACS action 
HUDQC Final Report for FY 2015 IV-43 

was present and where it was absent, there was a large difference in overpayment amounts 

(e.g., $28 average overpayment error when PIC/TRACS action data could be matched, compared 

to $118 overpayment error when PIC/TRACS action data were not matched). Interestingly, for 

households with underpayments, average dollar in error was slightly less when PIC/TRACS action 

was absent than when it was present (e.g., $66 compared to $63). However, because there are fewer 

households in which PIC/TRACS data were absent, the average dollars in error amounts can vary 

substantially from year to year.

Exhibit IV-30c 

Average Gross Dollars in Error by Payment Type and PIC/TRACS Data

Payment Type

PIC/TRACS Action Present PIC/TRACS Action Absent

Percentage of 

Households

Average Dollars 

in Error*

Percentage of 

Households

Average Dollars 

in Error*

Underpayment 94.0% $65.54 6.0% $63.26

Overpayment 96.8% $27.67 3.2% $118.13

Proper payment 94.0% $0 6.0% $0

Total 96.1% $11.74 3.9% $35.24

*Average dollar error per under- and overpayment subgroups.

Exhibit IV-30d examines net and gross errors by program type and whether there was a PIC/

TRACS action match. This exhibit illustrates the importance of reviewing net error and gross error 

separately, as their average dollar errors are substantially different.

Exhibit IV-30d 

Average Net and Gross Dollars in Error by Administration Type and 

PIC/TRACS Data for All Households

Administration Type

Average Net Rent Error Average Gross Rent Error

PIC/TRACS 

Action Present

PIC/TRACS

Action Absent

PIC/TRACS 

Action Present

PIC/TRACS

Action Absent

Public Housing -$3.63 $47.89 $11.83 $68.04

PHA-administered Section 8 -$6.47 -$5.31 $13.47 $31.15

Total PHA-administered -$5.54 $11.78 $12.93 $42.99

Total Owner-administered -$4.06 $1.87 $9.21 $7.89

Total -$5.07 $9.59 $11.74 $35.24

To further illuminate details of those households in which PIC/TRACS data matched on specific 

study effective date and type of action (2,124 of 2,211 households), an additional analysis was 

conducted to determine whether certain key variables matched. The key variables included gross 

income, net income, total tenant payment, and tenant rent. Exhibit IV30e provides the percentage 

of households in which the data gathered through the QC process matched those in PIC/TRACS.
IV-44 August 31, 2016

Exhibit IV-30e 

Percentage of Matched and Non-Matched Dollar Amounts for Key Variables

Matching Variables From the Form HUD-50058/50059 and PIC/TRACS

Match Status

Gross Income Net Income Total Tenant Payment Tenant Rent

PIC TRACS PIC TRACS PIC TRACS PIC TRACS

No Match 1.7% 2.3% 2.0% 3.1% 2.6% 11.5% 10.9% 30.7%

Match 98.3% 97.7% 98.0% 96.9% 97.4% 88.5% 89.1% 69.3%

Total 100% 100% 100% 100% 100% 100% 100% 100%

J. Project Staff Questionnaire Analysis 

The purpose of the Project Staff Questionnaire (PSQ), a self-administered, Web-based questionnaire, 

was to obtain project-level information concerning the characteristics and processes that enable 

PHA/project staff to precisely and accurately calculate rent during certification transactions, 

including both initial/move-in certifications and annual recertifications. PHA/project staff identified 

as the point of contact for the FY 2015 study were surveyed on topics related to PHA/project 

characteristics, certification staff training and development, and performance management during 

the study period of November 1, 2014, to October 31, 2015. The results were analyzed separately 

for the three major program types: Public Housing, PHA-administered Section 8, and Owneradministered programs. 

A brief summary of the key findings from this analysis follows. A more detailed summary of the 

PSQ information can be found in Appendix E.

PHA/Project Characteristics. The PSQ surveyed respondents on PHA/project characteristics that 

may help explain differences in error rates. Questions in this section included questions about 

the number and types of staff, number of project units/tenants and certifications conducted, staff 

work experience, certification staff work assignments and workload, and staff use of software and 

computer technology. The findings of this section show the following:

● PHAs/projects that answered the survey based on project-specific information reported that 

they employed an average of 11 staff members and served 1,238 households during the 

study period. It is worth noting that each of the program types differed markedly in average 

size. PHA-administered Section 8 projects averaged 26 employees and administered rental 

assistance to 4,199 households, Public Housing projects had an average of 9 employees 

and served 470 units on average, and Owner-administered projects averaged 4 employees 

with 130 rental assistance units. Compared with FY 2014, the ratio of households to a 

single staff member decreased slightly.

● PHAs/projects reported an average of 8 certification staff members on site, with an average 

of 7 having more than 1 year of certification experience at the project. Thirty-one percent 

of PHAs/projects in the study had at least 1 certification staff member stop working on 

certification activities at the project during the study period. On average, PHAs/projects 

that experienced turnover had 3 certification staff leave the PHA/project during the study 

period. 
HUDQC Final Report for FY 2015 IV-45 

● The most frequently used methods for assigning cases to certification staff were by 

transaction type (e.g., initial certifications, annual recertifications, transfers, and interims) 

and by random assignment based on staff availability (19 percent and 16 percent, 

respectively). Compared with FY 2014, more PHAs/projects reported that their staff had 

a manageable workload, while fewer PHAs/projects reported a high workload. However, 

answers varied by program type. PHA-administered Section 8 projects were most likely to 

report a high workload (59 percent), while Owner-administered projects were most likely 

to report a steady workload (66 percent). Less than 1 percent of respondents in all program 

types reported that their certification staff had a low workload. 

● Eighty-nine percent of all PHAs/projects responded that they did not outsource certification 

activities to outside organizations during the study period. PHAs/projects were equally 

likely to contract out certification activities to private companies and nonprofit organizations 

(4 percent each), and somewhat less likely to choose a government organization (3 percent). 

● Automated systems and computer software continued to play an increasingly integral 

part in the daily tasks of PHAs/projects. PHAs/projects used computers most frequently 

to print letters to tenants (98 percent), print Form HUD-50058/50059 (97 percent), 

calculate income, expenses, and allowances (96 percent), and record tenant demographics 

(92 percent). Overall, rates of computer usage remained stable since FY 2014. The largest 

increase was found in keeping track of pending verification documents (from 59 percent to 

62 percent), and the largest decrease was found in keeping other types of statistics (from 

72 percent to 67 percent). 

● During the study period, almost all PHAs/projects used computer software when performing 

rent calculations (96 percent). Additionally, a majority of PHAs/projects used computer 

software for other administrative tasks, including storing household data from previous 

Form HUD-50058/50059 (96 percent), submitting data to the Public and Indian Housing 

(PIH) Information Center/Tenant Rental Assistance Certification System (PIC/TRACS) 

(92 percent), and annualizing income or expenses (91 percent).

Training and Development. The PSQ collected information on an array of topics regarding training 

and development provided to both new and experienced certification staff, including implementation 

of rent calculation policies, certification staff work practices, and the nature and extent of rent 

calculation training.

● Overall, 59 percent of PHAs/projects in FY 2015 had a training department or staff trainer 

for certification staff, a decrease from FY 2014 (67 percent). By program type, 53 percent 

of Public Housing sites reported having a training department or staff trainer, compared to 

50 percent of PHA-administered Section 8 projects and 71 percent of Owner-administered 

projects. On average, each new certification staff member received 49 hours of training 

prior to performing rent calculations unassisted (the lowest in the last 6 years). In addition, 

50 training hours were provided to new staff in FY 2014, 55 hours in FY 2013, 65 hours in 

FY 2012, and an average of 89 hours of training in FY 2011.28

28 Training hour averages for FY 2012 and FY 2011 were calculated as the average for new (re)

certification staff and reassigned staff because of a change in the question for FY 2013.
IV-46 August 31, 2016

● Most PHAs/projects trained new certification staff members by providing them with policies 

and procedural guides to read independently and by having them shadow or receive mentoring 

from more experienced staff members (96 percent and 94 percent, respectively). During 

the study period, PHAs/projects reported that a typical new certification employee spent 

an average of 36 hours self-training with manuals and 127 hours shadowing experienced 

staff. Additionally, the training hours dedicated to mentorship and classroom-style training 

administered by an outside organization increased substantially since FY 2014. 

● More than 90 percent of PHAs/projects trained new certification staff in calculating fixed 

income sources, earned income sources, deductions, and/or assets, as well as in Enterprise 

Income Verification (EIV) reports and EIV security. The average number of training hours 

spent on each topic area per new staff member during the study period was about evenly 

distributed among the topics listed in the PSQ, ranging from 24 to 36 hours for each topic 

area. This was a substantial increase from FY 2014, when between 16 and 25 training hours 

were spent on each topic area. 

● The vast majority of PHAs/projects trained experienced staff by providing policies 

and procedural guides to read (86 percent), while more than half of PHAs/projects had 

experienced staff attend classroom training administered by an outside organization 

(52 percent) and/or shadow more experienced staff (55 percent). A typical experienced 

certification staff member in the Public Housing program spent an average of 15 hours 

on training on the job with other experienced staff, compared to an average of 47 hours in 

Owner-administered projects and 79 hours in PHA-administered Section 8 projects. 

● PHAs/projects most frequently endorsed training an experienced certification staff 

member on the topics of EIV reports and EIV security as well as calculating deductions 

(81 percent and 66 percent, respectively). It was reported that the most training hours were 

spent on entering Form HUD-50058/50059 (10 hours), while the fewest training hours 

were spent on interviewing tenants (7 hours). On average, an experienced certification staff 

member received approximately 25 fewer training hours for each topic than a new certification 

staff member. This is a substantial increase from FY 2014, when experienced staff typically 

received 13 fewer hours of training per topic area than new staff. Furthermore, in FY 2013, 

experienced staff received only 7 fewer training hours than new certification staff.

● Twenty-seven percent of PHAs/projects typically implemented a new rent calculation 

policy immediately following the issuance of a PIH Notice or Housing Notice, and 

44 percent implemented a new rent calculation policy between 1 and 30 days after the 

issuance of the notice. In January 2013, PIH Notice 2013-03 (HA)29 was issued, affecting 

Public Housing, Moderate Rehabilitation, and Housing Choice Voucher programs. These 

programs reported that the primary methods used to notify certification staff about the 

policy change were discussing the policy with staff in a meeting and providing a paper 

29 The PIH Notice 2013-03 (HA) allows PHAs to verify income using actual past income and allows 

households with less than $5,000 in assets to self-certify their asset amount and asset income amount. It 

also allows PHAs to conduct streamlined re-examinations for elderly/disabled families with fixed incomes 

and to establish a payment standard of no more than 120 percent of Fair Market Rent as a reasonable 

accommodation.
HUDQC Final Report for FY 2015 IV-47 

or electronic copy of the notice (62 percent and 52 percent, respectively). Twenty-two 

percent of Public Housing respondents stated that there was no discussion of this policy 

with staff, and that staff did not receive a notice of the policy, compared to only 3 percent 

of PHA-administered Section 8 projects.

● PHAs/projects overwhelmingly rated their certification staff as having average or above 

average work behaviors during the study period. Ninety-eight percent of PHAs/projects 

rated their certification staff as either organized or very organized when working on 

certification activities in FY 2015. Similarly, 96 percent of PHAs/projects indicated that 

their staff had either good or very good time management skills, and 98 percent reported 

that their staff paid some or a lot of attention to detail while working on certification 

activities. Additionally, compared to FY 2014, PHAs/projects in FY 2015 were more likely 

to indicate that their staff was very organized, had very good time management skills, and 

paid a lot of attention to detail.

Performance Management. The PSQ also collected information regarding performance 

management of certification activities and QC reviews. Questions addressed the timing of reviews, 

methods used to select cases for review, type of information reviewed for QC, prevalence of various 

types of rent calculation errors, and performance feedback methods and timing.

● Eighty-nine percent of PHAs/projects reviewed move-in and annual certifications as a QC 

measure. On average, these PHAs/projects reviewed 47 percent of certifications. PHAs/

projects most frequently selected files randomly for QC review and conducted the reviews 

prior to Form HUD-50058/50059 approval (79 percent and 27 percent, respectively).

● Sixty-eight percent of PHAs/projects had a dedicated QC staff to monitor tenant files and 

certification activities, a decrease since FY 2014 (74 percent). Most often, PHAs/projects 

had a team leader or supervisor conducting QC checks (83 percent), who conducted an 

average of 266 file reviews during the study period. 

● Twenty-six percent of PHAs/projects reported that they reviewed not only move-in 

certifications and annual recertifications, but all tenant files during QC reviews. By program 

type, 23 percent of PHA-administered Section 8 projects selected all cases for QC review, 

compared to 27 percent for Public Housing and 29 percent of Owner-administered sites. 

● Among PHAs/projects that conducted dedicated QC reviews, at least 88 percent had 

reviewers check tenant files for correct income calculation, the presence of verification 

documents, and/or proper core household documentation. Five percent or less of PHAs/

projects reported that verification, income calculation, expense calculation, or other errors 

were made often or very often by certification staff, a decrease since FY 2014. Of all rent 

calculation errors, late annual recertifications were reported as occurring often or very 

often at the highest rate (5 percent of PHAs/projects).

● In order to provide performance feedback to staff regarding errors found during the QC 

process, PHAs/projects provided monthly rent calculation performance feedback to staff 

(25 percent) and had one-on-one conversations with staff to discuss QC findings 

(73 percent). Additionally, PHAs/projects overwhelmingly required that the certification 

staff member who made the error be responsible for making file corrections (83 percent).
IV-48 August 31, 2016

● Twenty-eight percent of PHAs/projects reported that they do not track any of the certification 

errors listed in the PSQ. PHAs/projects that did track errors most frequently monitored 

late annual recertification transactions (78 percent), errors in earned income calculations 

(57 percent), errors in calculating assets (56 percent), and errors in calculating medical 

expenses (54 percent).

● The majority of PHAs/projects had error mitigation strategies in place: 69 percent had 

a formal or informal goal-setting process related to rent calculations for certification 

staff, and 73 percent required that certification staff review a household’s previous Form 

HUD‐50058/50059 prior to beginning a new certification transaction.

K. Multivariate Analysis

The FY 2015 HUDQC multivariate modeling followed a similar conceptual approach to the one 

used in previous years by analyzing variables representing project characteristics, project practices, 

tenant characteristics, and project-caused errors. In previous years, we suggested a two-part 

modeling approach consisting of examining the relationships of the variables affecting (1) gross 

error, underpayment, and overpayment and (2) project-caused errors. However, in reality we were 

more interested in an overall conceptual model that would examine the impact of variables through 

the system in which we believe it interacts. As such, the conceptual model we currently propose is 

presented in Figure IV-5.

Figure IV-5

Conceptual Model Used in Multivariate Analysis

Our approach to constructing the analytic model followed a pattern that was similar to the model 

used in the past by initially examining the relationship between individual variables and the identified 

outcomes (i.e., project-caused errors, gross rent error, underpayment error, and overpayment error). 

The benefit in creating a single model is that we can examine the impacts of the project and household 

characteristics in conjunction with both the probability and direction of rent error and the intensity 

(amount) of gross rent error concurrently with the impact of the household and project characteristics 

on project-caused errors. (See Appendix F for more information on the multivariate analysis.)

Project-Caused Errors. Project-caused errors continue to represent the largest effects in measuring 

rent error. Overall, two indicators were salient to the gross rent error (continuous), subsidy 

underpayment (dichotomous) and subsidy overpayment (dichotomous). Applications that had a 

transcription error led to a higher amount of gross rent error and were more likely to demonstrate 
HUDQC Final Report for FY 2015 IV-49 

subsidy overpayment error and subsidy underpayment error. Additionally, overdue recertification 

errors showed an increase in both gross rent error and the likelihood of subsidy underpayment. 

Given the high correlation of project-caused errors with improper rent payment, high priority should 

be placed on reducing project-caused errors. We also found that transcription error was significantly 

affected by both financial and membership household characteristics. Higher annual income, more 

allowances, more income sources, and being an older head of household had an increased likelihood 

of transcription error, while fewer expenses and household characteristics (e.g., dependents, minor 

children, and adults over age 62) decreased the likelihood of a transcription error. Additionally, if the 

project was Owner-administered there was a lesser likelihood of a transcription error. Alternatively, 

overdue recertification errors increased slighted as the number of experienced, certified staff on a 

project increased. 

Household Characteristics. Household background variables were strong predictors of gross rent 

error and subsidy overpayment and underpayment. Significant relationships between a few of 

the financial indicators directly affected gross rent error and the underpayment and overpayment 

subsidies. Specifically, a higher household annual income increased both the amount of gross 

rent error and the likelihood of subsidy underpayment. Alternatively, our results indicated that 

an increase in the total amount received in allowances (e.g., medical, child care, and disability) 

reduced the amount of gross rent error. We found a more circumspect result with household rent. 

An increase in household rent indicated an increase in the amount of gross rent error; however, the 

same increase in household rent also demonstrated a lower likelihood of subsidy underpayment 

but did not have a significant effect on subsidy overpayment. This finding would suggest that as 

the amount of rent on applications increased, the likelihood of underpayment decreased, but when 

the subsidy underpayment did occur the amount of gross rent error increased with respect to the 

amount of rent. 

The results also demonstrated that a couple of the household membership variables were significantly 

related to the subsidy errors. Households with an individual over age 62 were less likely to have 

subsidy underpayment. Alternatively, households reporting any dependents were more likely to 

have received a subsidy overpayment. 

Project Characteristics and Practices. The impact of project characteristics and project practices 

on improper payments remained elusive within the current data analysis. As in previous years, 

most key indicators of project resources, staff capacity, training, certification procedures, computer 

application, and a broad array of quality control efforts were not found to be statistically significant, 

and no substantial relationships were found with rent error measures. 

Of the numerous project practices examined, two continued to be significant when examined using 

a multilevel modeling approach. Similar to FY 2014 results, FY 2015 results showed a relationship 

between EIV training for new staff and rent error. Additionally, we found that as the caseloads of 

certified staff increased so did the likelihood of subsidy overpayment. 

As was the case in FY 2013 and FY 2014, the project practices are much less predictive of rent error 

than project-caused errors and household characteristics. Further, the project practices that emerged 

from the modeling seem to be inconsistent in their relationship with rent errors (e.g., increased 

training associated with increased rent error). Investigating these counterintuitive effects could 
IV-50 August 31, 2016

help identify whether the effect is truly associated with rent error or whether there are underlying 

factors that are confounded with rent error. This will improve clarity on specific challenges facing 

certification staff that lead to processing errors and payment errors. 

This new conceptual and modeling approach to examining the impact of household characteristics, 

project practices and characteristics, and project-caused errors on the gross amount of rent error and 

overpayment and underpayment of rent subsidies continues to enhance our understanding of these 

rent errors. While our results are similar to prior studies, concurrent modeling of these outcomes 

and indicators has changed our understanding of their effects on amount and direction of rent error. 

However, our findings suggest that many of the household variables affect the amount and direction 

of the rent error through the project-caused transcription and overdue recertification errors. Using 

these characteristics (e.g., large sources of income, large households) to identify cases that could be 

at risk for improper payment in advance of the (re)certification process could allow for increased 

quality control of these cases, or the development of specific training around these at-risk units. 

L. The 20 Largest PHAs Study 

The 20 Largest PHAs Study aims to provide additional information about the 20 largest PHAs. 

Included in this study were the 18 largest PHAs and the 2 largest state PHAs in the project-level 

sample selected for the HUDQC Study. There were 32 households selected from most PHAs, but 

two PHAs had more. Specifically, NY005, which had 189 households selected, and IL002, which 

had 40 selected households. The study of the 20 largest PHAs ultimately included a total of 806 

households. 

Most PHAs represented both Public Housing and PHA-administered Section 8 households. MI901, 

NY110, CA063, and NY904 only represented PHA-administered Section 8 households. Weights 

for the 20 Largest PHAs Study were not calculated, and as a result all data presented in the exhibits 

in this section that pertain to the 20 largest PHAs are not weighted.30

Administrative Error in the 20 Largest PHAs. Exhibit IV-31a provides the percentage of 

households that had overdue recertification and transcription errors and the percentage of income and 

expense items that were verified by PHA staff using both written third-party verification only and 

verbal or written third-party verification, documentation, or EIV/UIV. These types of administrative 

errors were examined because they are typically associated with overall Gross Rent Error and Net 

Rent Error. 

● Compared to all HUDQC Study PHAs selected, the 20 largest PHAs had a higher percentage 

of overdue recertification errors (2 percent and 4 percent, respectively) and a slightly lower 

transcription error rate (43 percent and 37 percent, respectively). 

● Overdue recertification errors had low rates in most PHAs. 

● While most of the 20 largest PHAs had slightly lower transcription error percentages than 

the HUDQC Study mean of 37 percent, NY005 had a markedly higher transcription error 

rate (68 percent). 

30 For a more detailed discussion regarding weighting for the 20 Largest PHAs Study, please refer to 

Appendix B.
HUDQC Final Report for FY 2015 IV-51 

● The 20 largest PHAs verified items using third-party verbal or in writing, documentation, or 

EIV/UIV at a slightly higher rate than the HUDQC Study overall, at 88 percent compared 

to 87 percent. 

● The 20 largest PHAs verified items using only third-party in-writing verification, slightly 

less than the PHAs in the HUDQC Study overall (10 percent and 14 percent, respectively). 

● DC001 verified items using only third-party in-writing verification at the greatest rate 

(26 percent), while IL025 and PA002 used this method rarely (a rate of 2 percent for each). 

Exhibit IV-31a 

Administrative Errors in the 20 Largest PHAs

PHA

Number of 

Households

Percentage 

of Overdue 

Recertification 

Error*

Percentage of 

Transcription 

Error

Percentage of Verified Items

Third-Party Verbal 

or In Writing, 

Documentation, 

or EIV/UIV

Third-Party 

In Writing

CA002 32 — 50% 88% 0%

CA004 32 3% 34% 83% 6%

CA063 32 — 34% 91% 9%

DC001 32 6% 38% 92% 26%

IL002 40 5% 35% 89% 11%

IL025 32 — 47% 89% 2%

MA002 32 — 44% 88% 5%

MD002 32 6% 34% 95% 10%

MI901 32 3% 9% 85% 20%

NY005 189 5% 68% 83% 17%

NY110 32 6% 31% 96% 4%

NY904 32 3% 53% 88% 8%

OH001 32 6% 44% 92% 15%

OH003 32 3% 16% 78% 14%

OH004 32 — 31% 84% 11%

PA002 33 — 30% 87% 2%

TX005 32 3% 31% 93% 3%

TX009 32 3% 31% 89% 4%

WA001 32 — 38% 84% 14%

WA002 32 — 41% 76% 13%

Total 806 4% 37% 88% 10%

QC Study Total 2,400 2% 43% 87% 14%

Note: Data in this exhibit for the 20 largest PHAs are not weighted (see Appendix B), although HUDQC Study total data are 

weighted. 

*Those PHAs without a percentage in this column had no cases overdue for recertifications. 

Payment Error. Exhibit IV-31b provides payment error information. This exhibit includes proper 

payments, underpayments and overpayments of tenant rents, and the mean Gross Rent Errors 

by PHA. 
IV-52 August 31, 2016

● Compared to PHAs in the HUDQC Study as a whole, the 20 largest PHAs had a slightly 

lower percentage of households with proper payments (73 percent and 70 percent, 

respectively), as well as a higher average gross dollar error (about $20 for the 20 largest 

PHAs versus about $13 for the HUDQC Study). 

● The PHA with the highest percentage of proper payments was MI901, which had proper 

payments for 91 percent of households. 

● OH004 had the lowest average gross dollar error, at $2.03. 

Exhibit IV-31b 

Dollar Rent Errors in the 20 Largest PHAs*

PHA Underpayment Proper Payment Overpayment

Average Gross 

Dollar Error

CA002 25% 66% 9% $14.78

CA004 13% 72% 16% $22.81

CA063 16% 72% 13% $5.69

DC001 19% 69% 13% $8.66

IL002 23% 73% 5% $26.42

IL025 25% 66% 9% $14.62

MA002 13% 81% 6% $19.63

MD002 16% 72% 13% $24.94

MI901 9% 91% 0% $3.78

NY005 18% 62% 20% $28.96

NY110 13% 84% 3% $16.84

NY904 6% 69% 25% $57.34

OH001 13% 84% 3% $7.72

OH003 3% 88% 9% $7.87

OH004 16% 84% 0% $2.03

PA002 12% 36% 52% $10.91

TX005 13% 75% 13% $19.47

TX009 13% 81% 6% $21.19

WA001 19% 66% 16% $24.25

WA002 6% 50% 44% $18.50

Total/Average 15% 70% 15% $20.07

QC Study Total/Average 14% 73% 13% $13.39

Note: Data in this exhibit for the 20 largest PHAs are not weighted (see Appendix B), although HUDQC Study total data are 

weighted. 

*Row totals may not add up 100 percent to due to rounding.
HUDQC Final Report for FY 2015 IV-53 

In FY 2015, a smaller proportion of proper payments seemed to lead to a higher average gross 

dollar error within the PHA. 

● The PHA with the lowest percentage of proper payments was PA006, with a 33 percent rate 

of proper payment. PA002 had an Average Gross Dollar error rate of $11. This indicates 

that although there was a large rate of mispayment within PA002, the amount of dollars in 

errors was relatively low for most tenants.

● Alternatively, the PHA with the lowest average gross dollar amount, OH004 ($2.03), had a 

higher rate of proper payments than the average across the 20 largest PHAs, at 84 percent. 

These results imply that when the percentage of proper payments increases, the average gross dollar 

error may decrease. Consequently, policies that increase proper payment rates may have some effect 

on decreasing rent errors (and vice versa). These seemingly related problems may sometimes require 

different approaches targeted to specific PHAs.
HUDQC Final Report for FY 2015 V-1 

V. Recommendations

The HUDQC Study was originally conducted in 2000 and has been conducted on an annual basis 

since the FY 2003 review. A major goal of the study is to provide a snapshot of HUD national 

improper payment error. However, another important objective is to understand the causes of error 

and identify policies that reduce improper payments. As we executed the study, we identified 

general areas that could be improved in PHA/project rent calculation. We present these insights in 

this chapter of the report. Section A outlines general policy recommendations that could potentially 

reduce administrative error and tenant misreporting of income. 

In addition to program recommendations, we examined how the QC studies can be improved. Each 

year, changes and improvements are made in the execution of the study to achieve aims such as 

increased efficiency, reduced burden on project staff and households, and a better understanding of 

program practices and discretionary policies. Section B provides recommendations for improving 

the data collection process and the quality of the data used in the analysis of improper payments. 

A. Recommended Policy Actions

It should be noted that the study was not designed to provide recommendations regarding program 

policies and procedures. However, findings from the study suggest general actions that should be 

continued or policies that should be considered to maintain or improve PHA/project performance 

in rent determination. Below we present recommendations that may improve administrative error 

rates in HUD programs, based on insights we have gathered during this and previous studies. 

1. Continue Requiring the Use of EIV Reports. HUD should continue requiring the use of 

Enterprise Income Verification (EIV) information in the process of rent determination. Data 

systematically collected from the National Database of New Hires and the Social Security 

Administration provide a strong method of identifying specific sources of income information. 

The study shows that the majority of subsidy errors are associated with earned income. 

HUD may also want to consider forming relationships with State programs, organizations, 

and companies to collect other data not currently captured by the EIV system. Although 

EIV provides a uniform and efficient method of verifying income sources that lessens the 

burden on program administrators, caution must be exercised when using information from 

the system. The data are extremely helpful in identifying unreported sources of income, but 

they are not current and sometimes contain errors (including instances of identity theft and 

incorrect identification of disability status). HUD’s EIV requirement should be coupled with 

policies aimed at addressing the challenges of using EIV for verification. 

2. Perform Onsite Review of Rent Calculation. HUD should continue onsite monitoring of 

program administration, and PHA/Owners should be held accountable for implementing HUD 

regulations and calculating rent accurately. Onsite monitoring that includes reviews at both the 

local and Federal levels 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. Improved HUD monitoring was likely a key factor in reducing subsidy 

error between the 2000 study and the current study. 

We recommend that HUD require PHA/Owners to perform their own QC reviews on income 

determinations and rent calculations. Agencies that have aggressively sought to improve 
V. Recommendations

V-2 August 31, 2016 

the performance of their programs have demonstrated success in this area, and one of 

the most frequently used error-reduction strategies includes the establishment of internal 

QC review procedures. Based on the Project Staff Questionnaire (PSQ) survey, it appears 

that programs that conduct QC on all their transactions have a significantly lower rent 

determination error rate than programs that do not perform QC on all their files. Of course, 

a comprehensive approach may not be feasible, given limited staffing resources, but even a 

review of a small percentage of transactions may be beneficial in supporting the reduction 

of rent determination error. 

In addition to internal agency reviews, HUD regional offices can support field offices by 

conducting a secondary review of transactions. This review would provide HUD Federal 

staff with more on-the-ground insights into the issues and challenges faced by local program 

administrators. In addition, this approach would demonstrate HUD’s concern regarding 

program integrity and improper payments, thereby focusing PHA/Owner attention on 

accurately determining tenant income and rent.

3. Continue to Streamline the Program Requirements. Continue to simplify Federal laws, 

regulations, and HUD requirements to the extent possible. The new regulations outlined in 

Notice PIH 2016-05 provide much needed clarification on previous regulations, such as the 

time period associated with the Earned Income Disregard calculations. The implementation 

of triennial certification for fixed income households will save time for PHA staff, allowing 

them to spend more time conducting required reexaminations, following up on suspected 

cases of fraud, and conducting more internal reviews of tenant files. Office of Housing 

should implement similar changes.

4. Create an Online Community to Share Best Practices and Tools. HUD should provide 

PHA/Owners with an online venue to support the sharing of best practices for its assisted 

housing programs. A Web-based resource could facilitate communication between HUD 

and program administrators regarding identifying ways to improve and address challenges 

related to proposed policies. Comprehensive supporting documents, including forms for 

interviewing tenants, obtaining verification, and determining rent, could be posted to the site 

for downloading. Manuals and training materials describing how to implement requirements 

and accurately calculate rent could also be available electronically, with online webcasts 

providing an additional training resource for local program offices. 

HUD experts and local housing staff should be given the opportunity to work together to 

develop tools and systems that reduce rent error. Many local PHA/Owners have already 

developed forms, training materials, manuals, automated systems, and monitoring processes 

that enable them to provide accurate, efficient service to their tenants. HUD should create 

a platform for organizations to learn from each other. 

5. Develop a technical assistance platform for rent calculation staff. HUD should develop a 

technical assistance hotline for project/PHA staff to call about particularly difficult policies 

related to completing (re)certifications. The assistance could be a Web-based support system, 

where the PHA/project staff would be given a time estimate of when they will receive an 

answer after submitting a question, or a live telephone system staffed by experienced HUD 

policy advisors. This open dialogue would help avoid common errors that contribute to rent 

error. Additionally, HUD should keep detailed records for each call to determine if there 
HUDQC Final Report for FY 2015 V-3 

V. Recommendations

were commonly asked questions, and provide written guidance on these issues to all PHA/

projects for future use. 

6. Develop a calculation worksheet for use in annualizing income. This electronic worksheet 

would be programmed to calculate annual income to be used for rent calculation. It would 

also serve as documentation of the methodology used by the PHA/project staff to annualize 

income. A physical copy could be kept in the tenant file after it is dated and signed by the 

certification staff. This would help to avoid calculation errors and thus reduce rent error. 

B. Modifying the Quality Control Study

In addition to providing general program recommendations to improve error rates, we endeavored to 

improve the HUDQC Study that provides the estimates of the error rates. The current methodology 

used by ICF to conduct the HUDQC Study is based on meeting established study objectives and 

builds on insights from previous studies. The following recommendations serve to expand the 

utility of the data collected, support HUD’s research goals, and improve the overall efficiency of 

ongoing QC studies. 

7. Continue to Measure Improper Payment Associated With Billing Error. HUD should 

conduct billing error studies to obtain a more accurate assessment of improper payments. In 

the FY 2014 HUD Agency Financial Report, billing error estimates are based on FY 2004 

data for the Public Housing program and FY 2009 data for Owner-administered programs. In 

FY 2015, ICF conducted billing studies for both the Office of Public and Indian Housing and 

the Office of Housing. This work should continue to be conducted. Current error estimates 

could be obtained by conducting primary data collection or by using statistical modeling 

to update the existing data. However, an updated study will provide HUD with a better 

assessment of billing error associated with rental assistance programs in order to understand 

one of the main contributors to improper payments. The information from these billing 

studies could strengthen financial management controls so that HUD can better detect and 

prevent improper payments.

8. Incorporate Additional Objectives in the HUDQC Study. Data collected through the 

HUDQC Study provide details that are not available through other HUD sources (e.g., PIC/

TRACS) that could be used to track trends, such as the extent to which income and expense 

items are verified or the number of sources of employment income for a particular household 

or household member. Furthermore, because a statistically valid nationwide sample 

of projects and households is created for the study, other HUD-related topics could be 

investigated using the HUDQC Study’s research mechanisms and data collection processes. 

The rental integrity monitoring (RIM) review validation, identified in the July 2013 issuance 

of the HUD Research Roadmap for FY 2014–FY 2018, is a task that could be incorporated 

into the HUDQC Study’s data collection process. Additional topics could include a review 

of the changing demographics of HUD tenants, participant satisfaction surveys, and a more 

in-depth review and evaluation of MTW programs.

9. Continue to Conduct a Utility Allowance Comparison Study. In response to tightening 

budgets and overall concerns with energy efficiency, HUD should undertake a study to 

better understand utility costs and consumption in subsidized housing. ICF conducted this 

work in FY 2015, and this work should continue. HUD should consider conducting an 
V-4 August 31, 2016

in-depth quality control study of how utility allowance values are calculated and used in rent 

calculation. This study could involve collecting data from utility companies regarding utility 

use for a given fiscal year and comparing actual consumption with the utility allowance 

subsidy calculated by program administrators. 

The FY 2015 Utility Allowance Comparison Study found that current HUD Utility Schedule 

Model (HUSM) users produce incorrect allowances via the tool. HUD should consider 

making improvements to the HUSM tool to increase accuracy of data entry and calculated 

utility allowances. Additionally, performing project-level reviews and approvals of utility 

allowance levels would improve accountability in updating allowances in a timely manner 

and would likely reduce subsidy error in utility allowances. 

10. Learn More About PHA/Project Policies and Practices. Each PHA establishes its own 

policies, procedures, and forms for collecting information that is ultimately used to calculate 

tenant rent. The differences in these practices should have some (possibly major) impact 

on rent error, yet the analysis of the project practices and characteristics collected by the 

PSQ does not demonstrate the expected impact. We recommend conducting focus groups, 

interviews, and discussions with program administrators to identify additional PHA/projectlevel factors that may impact error. This information could be used to revise the PSQ to 

include questions focused on the specific practices expected to influence errors. The analysis 

of more detailed, project-level data would assist in this process.

11. Continue to Perform the HUDQC Study. The HUDQC Study provides a consistent 

ongoing method of monitoring, managing, and improving HUD rent determination processes. 

The ongoing evaluation of HUD rental housing assistance programs is essential to program 

management and improvement, and rigorous research is important for understanding how 

well HUD programs are reaching their goals for the communities served. The primary 

objective of the HUDQC Study is to measure rent calculation and improper payment error; 

however, the study also gives HUD the opportunity to learn more about methods to reduce 

rent calculation errors and better manage current and changing conditions at PHAs/projects. 

Annual evaluations facilitate more accurate, cross-year comparisons of rent errors. They 

also allow data collection and data analysis staff to develop specific expertise in HUD policy 

areas, supporting the development of tailored solutions for improving data quality. 
Appendix A: Rent Calculations
HUDQC Final Report for FY 2015 A-1 

Appendix A: Rent Calculations

1. Public Housing

a. Obtain the Total Tenant Payment (TTP).

b. Determine whether the family includes any ineligible noncitizens. IF YES, continue. 

IF NO, go to d.

c. Determine whether the family includes any citizens or eligible noncitizens. IF YES, 

go to #6 (continuation of assistance). IF NO, go to #7 (temporary deferral).

MARKER (marks the return point after determining continuation of assistance or temporary 

deferral status)

d. Determine whether the tenant selected Flat Rent. IF NO, go to e. IF YES, the QC RENT 

equals the Flat Rent. Go to g.

e. Obtain the Utility Allowance.

f. The amount of the tenant’s rent (QC RENT) is the lower of: a. (TTP) minus e. (Utility 

Allowance), or the Flat Rent.1

g. Determine whether any additional Moving to Work rent calculation policies apply. 

IF YES, recalculate the tenant’s rent (QC RENT) according to the policy outlined in 

the Public Housing Authority (PHA) Admissions and Continued Occupancy Policy 

(ACOP), Administrative Plan, or Other Policy Guidance Documents; continue. IF 

NO, continue.

h. Determine whether the QC RENT equals the ACTUAL RENT. IF YES, no error. 

IF NO, dollar error.

2. Section 8 Voucher Program

a. Obtain TTP.

b. Obtain the Gross Rent.

c. Obtain Utility Allowance.

d. Determine whether a. (TTP) is greater than b. (Gross Rent). IF YES, set TTP to Gross 

Rent. IF NO, TTP is equal to a.

e. Obtain Payment Standard2 (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.

g. Subtract e. (Payment Standard) from b. (Gross Rent). If the Payment Standard is higher 

than the Gross Rent, use 0.

1 If there is no Flat Rent, the QC RENT will be the lower of the Ceiling Rent or a. (TTP) minus e. (Utility 

Allowance) to determine the dollar amount of error. If there is also no Ceiling Rent, the QC RENT will be 

a. (TTP) minus e. (Utility Allowance).

2 For Project-Based Vouchers, the Payment Standard equals the Gross Rent.
A-2 August 31, 2016 

Appendix A: Rent Calculations

h. Add d. (TTP) to g. (Gross Rent minus Payment Standard).

i. Determine whether this is the initial occupancy for this dwelling unit. (Item 12b on the 

Form HUD-50058 is yes). IF YES, continue. IF NO, the Family Share is equal to h. 

Go to l.

j. Calculate 40 percent of f. (household’s Adjusted Monthly Income).

k. Determine whether 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 

equals h; continue. IF NO, procedural error. Family Share is equal to h; continue.

l. Determine whether the family includes any ineligible noncitizens. IF YES, continue.

IF NO, go to n.

m. Determine whether the family includes any citizens or eligible noncitizens. IF YES, 

go to #6 (continuation of assistance). IF NO, go to #7 (temporary deferral).

MARKER (marks the return point after determining continuation of assistance or temporary 

deferral status)

n. Subtract c. (Utility Allowance) from h. (Family Share). This is the QC RENT.

o. Determine whether any additional Moving to Work rent calculation policies apply. 

IF YES, recalculate the tenant’s rent (QC RENT) according to the policy outlined 

in the PHA ACOP, Administrative Plan, or Other Policy Guidance Documents; 

continue. IF NO, continue.

p. Determine whether the QC RENT equals the ACTUAL RENT. IF YES, no error. 

IF NO, dollar error.

3. Section 8 Enhanced Voucher

a. Determine whether household is receiving an Enhanced Voucher. IF YES, continue. 

IF NO, use #2 (the regular Section 8 Voucher formula).

b. Obtain the TTP.

c. Obtain the Gross Rent.

d. Determine the lesser of b. (TTP) or c. (Gross Rent).

e. Determine whether the family includes any ineligible noncitizens. IF YES, continue. 

IF NO, go to g.

f. Determine whether the family includes any citizens or eligible noncitizens. IF YES, 

go to #6 (continuation of assistance). IF NO, go to #7 (temporary deferral).

MARKER (marks the return point after determining continuation of assistance or temporary 

deferral 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).
HUDQC Final Report for FY 2015 A-3 

Appendix A: Rent Calculations

i. Determine whether any additional Moving to Work rent calculation policies apply. 

IF YES, recalculate the tenant’s rent (QC RENT) according to the policy outlined 

in the PHA ACOP, Administrative Plan, or Other Policy Guidance Documents; 

continue. IF NO, continue.

j. Determine whether the QC RENT equals the ACTUAL RENT. IF YES, no error. 

IF NO, dollar error.

4. Project-Based Section 8, 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 whether the family includes any ineligible noncitizens. IF YES, continue.

IF NO, go to e.

d. Determine whether the family includes any citizens or eligible noncitizens. IF YES, 

go to #6 (continuation of assistance). IF NO, go to #7 (temporary deferral).

MARKER (marks the return point after determining continuation of assistance or temporary 

deferral status)

e. Obtain the Utility Allowance.

f. Determine whether Subsidy Type on Form HUD-50059 is PRAC. IF NO, continue. 

IF YES, 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 whether 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). If Space Rent is less than the 

Payment Standard, use 0.

h. Add e. (TTP) to g. (the amount by which the Space Rent exceeds the Payment Standard). 

This is the Family Share.
A-4 August 31, 2016

i. Determine whether this is the initial occupancy for this dwelling unit. (Item 12b on 

Form HUD-50058). IF YES, continue. IF NO, the Family Share equals 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 whether 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 is equal to 

h.; go to m. IF NO, procedural error. The family is not entitled to assistance in this unit.

m. Determine whether the family includes any ineligible noncitizens. IF YES, continue. 

IF NO, go to o.

n. Determine whether the family includes any citizens or eligible noncitizens. IF YES, 

go to #6 (continuation of assistance). IF NO, go to #7 (temporary deferral).

MARKER (marks the return point after determining continuation of assistance or temporary 

deferral status)

o. Subtract c. (Utility Allowance) from h. (Family Share) to determine Tenant Rent to 

Owner (QC RENT).

p. Determine whether any additional Moving to Work rent calculation policies apply. 

IF YES, recalculate the tenant’s rent (QC RENT) according to the policy outlined 

in the PHA ACOP, Administrative Plan, or Other Policy Guidance Documents; 

continue. IF NO, continue.

q. Determine whether the QC RENT equals the ACTUAL RENT. IF YES, no error. 

IF NO, dollar error.

Special Calculations for Household With Ineligible Noncitizens

6. Continuation of Assistance

a. Determine whether the family was receiving assistance on June 19, 1995. IF YES, 

continue. IF NO, the FAMILY is eligible for prorated assistance; go to #8 (proration 

formula for Public Housing).

b. Determine whether 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 #8 

(proration formula for Public Housing).

c. Determine whether 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 #8 (proration formula for 

Public Housing).

d. Determine whether 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 for the appropriate program type. IF NO, the FAMILY is eligible for 

prorated assistance; go to #8 (proration formula for Public Housing). 
HUDQC Final Report for FY 2015 A-5 

7. Temporary Deferral of Termination of Assistance

a. Determine whether Temporary Deferral of Termination of Assistance has been granted. 

IF YES, continue. IF NO, go to c.

b. Determine whether 18 months have passed since Temporary Deferral was granted. 

IF YES, continue. IF NO, the Family continues to be eligible for Temporary Deferral 

of Termination of Assistance; return to MARKER for the appropriate program 

type.

c. Determine whether 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 NO, continue. IF YES, the Family is entitled to ongoing Deferral of Termination 

of Assistance; go to MARKER for the appropriate program type. 

d. Determine whether the FAMILY was receiving assistance on June 19, 1995. IF NO, 

continue. IF YES, the Family is eligible for Temporary Deferral of Termination of 

Assistance; go to MARKER for the appropriate program type. 

e. Determine whether the FAMILY is exercising its hearing rights (waiting for a decision 

from INS or PHA/Owner appeal). IF NO, continue. IF YES, go to MARKER for the 

appropriate program type. 

f. Determine whether the PHA is making reasonable efforts to evict. IF YES, go 

to MARKER for the appropriate program type. IF NO, procedural error,

HOUSEHOLD IS INELIGIBLE.

8. Proration Formula for Public Housing

a. Determine whether this is a Public Housing case. IF YES, continue. IF NO, go to #9 

(proration formula for all Section 8 programs).

b. Determine the number of FAMILY members.

c. Determine the number of eligible FAMILY members.

d. Obtain the TTP.

e. Obtain the 95th percentile of Gross Rents for similarly sized Public Housing units in 

order to determine the Public Housing maximum rent.3

f. Determine whether the Family pays a Flat Rent. IF YES, continue. IF NO, go to i. 

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.

3 If Maximum Rent is not available, Fair Market Rent is used as a substitution for Maximum Rent.
A-6 August 31, 2016

j. Divide h. or i. (Maximum Subsidy) by b. (number of FAMILY members) and multiply 

by c. (number of eligible FAMILY 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.

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

Allowance). 

n. Determine whether any additional Moving to Work rent calculation policies apply. 

IF YES, recalculate the tenant’s rent (QC RENT) according to the policy outlined 

in the PHA ACOP, Administrative Plan, or Other Policy Guidance Documents; 

continue. IF NO, continue.

o. Determine whether the QC RENT equals the ACTUAL RENT. IF YES, no error. 

IF NO, dollar error.

9. Proration Formula for All Section 8 Programs

a. For Voucher Projects Only: Obtain the Rent to Owner.

b. Obtain the Utility Allowance.

c. Obtain the Gross Rent.

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

Owner-administered: Gross Rent = Contract Rent plus the Utility Allowance.

d. Obtain the TTP.

e. For Voucher Projects Only: Obtain the Payment Standard.

f. Obtain the Housing Assistance Payment (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. Determine if Manufactured Home Space Rental. IF NO, continue. IF YES, return to 

MARKER for the appropriate program type. 

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.
HUDQC Final Report for FY 2015 A-7 

m. For PHA-administered Projects Only: Determine whether any additional Moving to 

Work rent calculation policies apply. IF YES, recalculate the tenant’s prorated rent 

(QC RENT) according to the policy outlined in the PHA ACOP, Administrative 

Plan, or Other Policy Guidance Documents; continue. IF NO, continue.

n. Determine whether the QC RENT equals the ACTUAL RENT. IF YES, no error.

IF NO, dollar error.
Appendix B: Weighting Procedure
HUDQC Final Report for FY 2015 B-1 

Appendix B: Weighting Procedure 

This appendix describes the procedures followed in weighting the project sample.

Study Population. The universe of the Improper Payment for Quality Control for Rental Subsidy 

Determination (HUDQC) Study included all projects and households located in the continental 

United States, Alaska, Hawaii, and Puerto Rico. In fiscal year (FY) 2015, Moving to Work (MTW) 

Public Housing Authorities (PHAs) were included in the study population.

The following programs were included in the sample:

● PHA-administered Public Housing (Public Housing)

● PHA-administered Section 8 (PHA-administered Section 8)

○ Moderate Rehabilitation

○ Housing Choice Voucher program

● Office of Housing-administered projects (Owner-administered)

○ Section 8 New Construction/Substantial Rehabilitation

○ Section 8 Loan Management

○ Section 8 Property Disposition

○ Section 202 Project Rental Assistance Contracts (PRAC)

○ Section 202/162 Project Assistance Contracts 

○ Section 811 PRAC

The initial universe files used to draw the sample occasionally reflected out-of-date or incorrect 

information, including out-of-scope projects such as demolished projects, projects undergoing 

renovation, projects that were no longer assisted, projects that had merged or split, and other 

special circumstances. Many of these projects were identified prior to drawing the sample. For 

example, in FY 2015, at the request of HUD, projects newly converted to Owner-administered 

assistance through the Rental Assistance Demonstration (RAD) program were excluded from the 

sampling frame, due to unique rent calculation rules while households phase into the new program 

type. However, other out-of-scope projects were identified later during data collection. Depending 

on the circumstance of those identified during data collection, sampling decisions were made to 

either replace the project, to subselect the project, or to make adjustments during weighting. The 

use of replacement projects for out-of-scope projects complicated the sample weight calculations. 

The determination of an actual probability of selection for these replacements was impossible to 

make. A sampling weight that is proportional to what the probability would have been, had the 

project been selected originally, was used as a reasonable estimate. 

Population Totals. The same population totals were used in studies for FY 2012–2014, and in order 

to provide accurate estimates based on the current assisted housing population, these population 

totals were updated for FY 2015. The use of the same population counts from year to year has 

had the advantage of increasing comparability of gross dollar estimates; any change from year to 

year would not have been due to a change in the number of households in the program, but to 
Appendix B: Weighting Procedure

B-2 August 31, 2016 

an actual change in the average gross dollar error or percentage of households. In FY 2012, the 

population counts used to produce the weights were updated from those used in FY 2011. As 

programs may grow or shrink over time, it is desirable to update population counts. Estimates of 

total dollar amounts and estimates of the proportion of the population represented by each program 

type run the risk of not being representative of the current population if the population counts are 

outdated or if the population changes significantly. Due to the inclusion of the MTW PHAs in 

FY 2012, the nature of the population itself had changed. Because the FY 2011 population totals 

and sample did not include the MTW population, using FY 2011 totals to produce FY 2012 weights 

would produce invalid estimates. 

Estimates of averages and percentages within program types have the advantage of being comparable 

regardless of changes in population counts from year to year. This approach of using the same 

population counts for multiple study years was used for FY 2005 through FY 2010 and again from 

FY 2012 to FY 2014 to allow for comparability across years. 

Exhibit B-1 provides the population totals by program type for the FY 2012 through 

FY 2015 studies. 

Exhibit B-1 

Population Totals Used for Weighting by Program Type

Administration Type FY 2012–FY 2014 

Population

FY 2015 

Population

Public Housing, non-MTW 1,040,708 959,766

Public Housing, MTW 114,088 101,924

PHA-administered Section 8, non-MTW 1,935,597 1,916,735

PHA-administered Section 8, MTW 263,125 292,561

Owner-administered 1,378,158 1,382,453

Total 4,731,676 4,653,439

Weighting Methodology. The procedure to determine the final weights involved several steps, 

including: 1) calculating the project weight (w1); 2) calculating the household weight (w3); 

3) accounting for nonresponding households (fn); 4) poststratifying (fp); and, 5) finally, trimming 

the weights. 

1. Calculating the Project Weight (w1). The first step to determine the final weights was calculating 

the project weight by compiling the sampling probabilities calculated during the cluster and project 

sampling and the initial data collection process. These probabilities were then used to calculate 

each project’s probability of selection. The probability of selection of a project was the product of 

the following:

1) The probability of selection of the cluster (p1)

2) The probability of selection of the subcluster if the cluster was divided (p2)

3) The probability of selection of the project from its respective cluster (p3) 
HUDQC Final Report for FY 2015 B-3 

Appendix B: Weighting Procedure

Each cluster was sampled with probabilities proportional to size. The measure of size used was the 

number of households adjusted to obtain equal expectation for the three major types of programs 

in the study. The number of households of each program in a cluster was multiplied by an inflation 

factor to make all three numbers equal. The probability of selection of the cluster (p1

) was calculated 

in three steps. First, the proportion of the households in each of the three programs in a particular 

cluster was obtained. These proportions were defined as the number of households in each program 

within a cluster divided by the number nationwide (program’s population count). Next, the three 

proportions in each cluster were averaged, and finally, the proportions were multiplied by 60, the 

number of clusters to be selected nationwide. 

In some instances, clusters were geographically too large to collect data in a cost-effective manner. 

To accommodate this logistical problem, clusters were divided into two or more subclusters or 

smaller geographic areas. A subcluster was then sampled from the group of subclusters using 

probabilities proportional to size. This resulted in the same probability that would have ensued had 

the division taken place before drawing the sample, or the probability of selection of the subcluster 

(p2). If the cluster was not divided into smaller clusters, then the subcluster probability of selection 

was one. The formula to calculate the project weight was: 

Clusters with probabilities greater than one could have been selected more than once (Sampling 

with Minimal Replacement). These clusters were certainty clusters, meaning that their selection 

into the sample was guaranteed. For the purposes of calculating the project weight, the certainty 

clusters’ probability of selection was set to one. 

The probability of selection of a project from its respective cluster (p3

) was calculated in two steps. 

First, the number of households in a program type within a project was divided by the total number 

of households in a program type within the project’s cluster. This proportion was then multiplied 

by the number of projects in a program type to be selected from the cluster. The PHA-administered 

Section 8 projects could have had a probability greater than one for sampling purposes (meaning 

they could be sampled more than once). However, 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 that they added to the allocation for the program in the cluster. For weighting purposes, 

probabilities greater than one among PHA-administered Section 8 projects were set to one.

2. Calculating the Household Weight (w3). The second step to determine the final weights was 

to calculate the household weight. To calculate the household weight, the number of households 

in the project (Np) and the number of households sampled from the project (np) were identified. 

The household probability of selection within the sampled project was the number of sampled 

households divided by the number of households in the project (p4

): 
Appendix B: Weighting Procedure

B-4 August 31, 2016

The household within project weight (w2) was the inverse of the probability of selecting the 

household within the sampled project: 

The household base weight (w3) was the product of the project weight and the household within 

project weight:

3. Account for Nonresponding Households (fn). The third step in the weighting process was 

to account for nonresponding households within the sampled project. To do this, the number of 

eligible sampled households (npe), the number of responding households (npr), and the eligibilityadjusted household weight was needed. The sum of the eligibility-adjusted household weights for 

all eligible households in the project and the sum of eligibility-adjusted household weights for only 

the responding households in a project was then calculated. A nonresponse adjustment factor (fn) 

was calculated as:

The nonresponse adjusted household weight (w4) was the eligibility-adjusted household weight 

multiplied by the nonresponse adjustment factor:

Poststratification (fp). The fourth step in the weighting process was poststratification. The sample 

was designed to obtain similar numbers of households in each of the following three program types:

1) Public Housing projects

2) PHA-administered Section 8 projects 

3) Owner-administered projects

Population totals for each of the programs were obtained from the FY 2012 sampling frame. The 

population estimates after weighting did not correspond exactly to these FY 2012 population totals 

and required adjustments. The weights were adjusted to sum to the known external population 

totals, so the sum of the weights would have been the same had a different sample been selected. 

In the past, this was due partially to special circumstances, such as the exclusion of geographic 

areas affected by the 2005 hurricanes and the Owner-administered projects from Alaska that were 

excluded from the frame but included during the weighting process. Alaska was excluded from 

the sample frame in the past because the state typically did not meet the parameters for creating 

a sample cluster. Prior to FY 2012, all Public Housing and PHA-administered Section 8 projects 

in the state were out of the scope of the study since they were part of the MTW program, leaving 

just Owner-administered projects. With the inclusion of MTW projects, more Alaska projects were 
HUDQC Final Report for FY 2015 B-5 

considered in the scope of the study, and, therefore, Alaska was once again included. In FY 2015, 

Alaska was included in the frame but was not selected. 

To poststratify the weights, the nonresponse adjusted household weights within program type 

were summed to estimate the population totals from the HUD sample. For example, the sum of 

weights for all Owner-administered households in the sample is an estimate of the total number 

of Owner‐administered households in the nation. A poststratification factor (fp) was calculated by 

dividing the known external population totals ( ) by the estimated population totals 

from the HUD sample ( ): 

A poststratification factor was calculated for each program type. This factor was then multiplied 

to the household weight within each program type, ensuring that the sum of the household weights 

by program type was the same as the external population totals. 

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.

Effective Sample Size Due to Weighting. In FY 2015, the weights led to an effective sample size 

(because of the weighting) of 757 (down from an actual size of 799) for the Owner-administered 

projects, 774 for the Public Housing projects (down from 805), and 777 for the PHA-administered 

Section 8 projects (down from 796). The effective sample size is the size of a random sample that 

would yield confidence intervals of the same size as the current sample. The design effect calculates 

what the sample size would be when the variance produced by weighting is accounted for, and 

this calculation yielded the same effective sample sizes as those cited above. The effective sample 

size will often be smaller than the actual sample, partly because of clustering and partly because 

of weighting. 

Variance Estimation. Standard errors were obtained for a number of estimates using a delete-a-group 

Jackknife procedure. This was implemented by using 20 replicate groups and creating 20 sets of 

replicate weights. This procedure is available starting with SAS 9.4, and is considered more robust 

with respect to design characteristics than the Taylor Series method.1

The 20 Largest PHAs Weighting. As in previous studies, the data for the 20 largest PHAs sample 

were not weighted. The sample is approximately a self-weighting sample. The term self-weighting

refers to a sample where all units being sampled (in this case households) have the same weight, 

assuming that the frame is accurate and that a 100 percent response is achieved.

1 Kott, P. S. (1998). Using the Delete-a-Group Jackknife 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.
Appendix B: Weighting Procedure

B-6 August 31, 2016

A self-weighting sample has several advantages, including:

● Permitting more precise estimates for the 20 largest PHAs. To the extent that the sample 

departs from equal weights, the design effect will increase, causing correspondingly less 

precise estimates.

● Facilitating reporting because unweighted means and proportions for the sample will be 

estimates of the same means and proportions for the weighted population so as not to 

confuse the reader.
Appendix C: Source Tables
Source Tables Based on Quality Control Data

Throughout these Source Tables, empty cells indicate that either the result was zero 

or the analysis was not applicable.

The following tables include all data collected for the Quality Control study.
C-2 August 31, 2016

Appendix C: Source Tables 

HUDQC FY 2015 

Table 1a. Verification of QC Rent Components

Third-Party Verbal or in Writing, Documentation, or EIV/UIV

Rent Component

Not Verified Partially Verified Fully Verified

# of Cases (in 1,000s) Row % of Cases # of Cases (in 1,000s) Row % of Cases # of Cases (in 1,000s) Row % of Cases

Earned Income 71 (4.6%) 84 (5.4%) 1,395 (90.0%)

Pension, Etc. 8 (0.3%) 86 (3.0%) 2,745 (96.7%)

Public Assistance 15 (4.1%) 2 (0.5%) 352 (95.5%)

Other Income 100 (12.6%) 81 (10.3%) 612 (77.1%)

Asset Income 5 (1.2%) 36 (8.2%) 395 (90.6%)

Child Care Expense 166 (100.0%)

Disability Expense 10 (100.0%)

Medical Expense 3 (0.3%) 43 (3.4%) 1,238 (96.4%)

2016.08.17 [Weighted]

HUDQC FY 2015 

Table 1b. Verification of QC Rent Components

Third Party in Writing

Rent Component

Not Verified Partially Verified Fully Verified

# of Cases (in 1,000s) Row % of Cases # of Cases (in 1,000s) Row % of Cases # of Cases (in 1,000s) Row % of Cases

Earned Income 969 (61.2%) 129 (8.2%) 485 (30.6%)

Pension, Etc. 2,237 (78.8%) 290 (10.2%) 312 (11.0%)

Public Assistance 342 (77.8%) 3 (0.7%) 94 (21.5%)

Other Income 682 (73.5%) 72 (7.7%) 174 (18.8%)

Asset Income 130 (29.5%) 135 (30.7%) 175 (39.8%)

Child Care Expense 71 (42.9%) 95 (57.1%)

Disability Expense 4 (38.5%) 6 (61.5%)

Medical Expense 355 (27.6%) 393 (30.6%) 536 (41.8%)

2016.08.17 [Weighted]
HUDQC Final Report for FY 2015 C-3 

Appendix C: Source Tables

HUDQC FY 2015 

Table 1c. Verification of QC Rent Components

Third Party in Writing or EIV/UIV

Rent Component

Not Verified Partially Verified Fully Verified

# of Cases (in 1,000s) Row % of Cases # of Cases (in 1,000s) Row % of Cases # of Cases (in 1,000s) Row % of Cases

Earned Income 898 (56.7%) 141 (8.9%) 545 (34.4%)

Pension, Etc. 690 (24.3%) 416 (14.6%) 1,735 (61.1%)

Public Assistance 211 (47.9%) 3 (0.7%) 226 (51.3%)

Other Income 621 (66.9%) 67 (7.3%) 240 (25.8%)

Asset Income 125 (28.5%) 136 (31.0%) 178 (40.5%)

Child Care Expense 71 (42.9%) 95 (57.1%)

Disability Expense 4 (38.5%) 6 (61.5%)

Medical Expense 250 (19.5%) 342 (26.6%) 692 (53.9%)

2016.08.17 [Weighted]

HUDQC FY 2015 

Table 1d. Verification of QC Rent Components

Third Party Verbal

Rent Component

Not Verified Partially Verified Fully Verified

# of Cases (in 1,000s) Row % of Cases # of Cases (in 1,000s) Row % of Cases # of Cases (in 1,000s) Row % of Cases

Earned Income 1,556 (98.2%) 4 (0.2%) 24 (1.5%)

Pension, Etc. 2,830 (99.7%) 6 (0.2%) 2 (0.1%)

Public Assistance 438 (99.6%) 2 (0.4%)

Other Income 907 (97.7%) 4 (0.5%) 17 (1.8%)

Asset Income 437 (99.6%) 2 (0.4%)

Child Care Expense 164 (98.9%) 2 (1.1%)

Disability Expense 10 (100.0%)

Medical Expense 1,267 (98.7%) 15 (1.2%) 2 (0.1%)

2016.08.17 [Weighted]
C-4 August 31, 2016

Appendix C: Source Tables

HUDQC FY 2015 

Table 1e. Verification of QC Rent Components

Documentation

Rent Component

Not Verified Partially Verified Fully Verified

# of Cases (in 1,000s) Row % of Cases # of Cases (in 1,000s) Row % of Cases # of Cases (in 1,000s) Row % of Cases

Earned Income 707 (44.7%) 136 (8.6%) 740 (46.8%)

Pension, Etc. 1,814 (63.9%) 363 (12.8%) 662 (23.3%)

Public Assistance 315 (71.6%) 1 (0.3%) 123 (28.0%)

Other Income 523 (56.4%) 68 (7.3%) 337 (36.3%)

Asset Income 200 (45.5%) 135 (30.6%) 105 (23.8%)

Child Care Expense 97 (58.1%) 70 (41.9%)

Disability Expense 6 (61.5%) 4 (38.5%)

Medical Expense 710 (55.3%) 334 (26.0%) 240 (18.7%)

2016.08.17 [Weighted]

HUDQC FY 2015 

Table 1f. Verification of QC Rent Components

EIV (Enterprise Income Verification)

Rent Component

Not Verified Partially Verified Fully Verified

# of Cases (in 1,000s) Row % of Cases # of Cases (in 1,000s) Row % of Cases # of Cases (in 1,000s) Row % of Cases

Earned Income 1,584 (100.0%)

Pension, Etc. 1,128 (39.7%) 471 (16.6%) 1,240 (43.7%)

Public Assistance 436 (99.2%) 4 (0.8%)

Other Income 928 (100.0%)

Asset Income 439 (100.0%)

Child Care Expense 166 (100.0%)

Disability Expense 10 (100.0%)

Medical Expense 1,030 (80.3%) 179 (13.9%) 75 (5.8%)

2016.08.17 [Weighted]
HUDQC Final Report for FY 2015 C-5 

Appendix C: Source Tables

HUDQC FY 2015 

Table 1g. Verification of QC Rent Components

UIV (Upfront Income Verification)

Rent Component

Not Verified Partially Verified Fully Verified

# of Cases (in 1,000s) Row % of Cases # of Cases (in 1,000s) Row % of Cases # of Cases (in 1,000s) Row % of Cases

Earned Income 1,471 (94.9%) 21 (1.3%) 58 (3.7%)

Pension, Etc. 2,804 (98.8%) 31 (1.1%) 4 (0.1%)

Public Assistance 241 (65.4%) 128 (34.6%)

Other Income 723 (91.1%) 14 (1.7%) 56 (7.1%)

Asset Income 13 (100.0%)

Medical Expense 15 (100.0%)

2016.08.17 [Weighted]

HUDQC FY 2015 

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

Program Type

Underpayment Proper Payment Overpayment Total

# of 

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

# of 

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

# of 

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

# of 

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

PHA-administered

Public Housing 139 (13.1%) (22.1%) 799 (75.2%) (23.3%) 124 (11.7%) (20.8%) 1,062 (100.0%) (22.8%)

Section 8 326 (14.8%) (51.8%) 1,560 (70.6%) (45.5%) 323 (14.6%) (54.0%) 2,209 (100.0%) (47.5%)

Total 465 (14.2%) (73.9%) 2,359 (72.1%) (68.8%) 447 (13.7%) (74.9%) 3,271 (100.0%) (70.3%)

Owner-administered

Owner-administered 165 (11.9%) (26.2%) 1,068 (77.2%) (31.2%) 150 (10.9%) (25.1%) 1,382 (100.0%) (29.7%)

Total 165 (11.9%) (26.2%) 1,068 (77.2%) (31.2%) 150 (10.9%) (25.1%) 1,382 (100.0%) (29.7%)

Total 630 (13.5%) (100.0%) 3,427 (73.6%) (100.0%) 597 (12.8%) (100.0%) 4,653 (100.0%) (100.0%)

2016.08.17 [Weighted]
C-6 August 31, 2016

Appendix C: Source Tables

HUDQC FY 2015 

Table 2(S). Percent of Households by Payment Type and Program Type

(Proper Payment Based on Exact Match of Actual and QC Rent)

Program Type

Underpayment Proper Payment Overpayment Total

# of 

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

# of

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

# of 

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

# of

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

PHA-administered

Public Housing 198 (18.6%) (22.4%) 671 (63.2%) (23.5%) 193 (18.2%) (21.1%) 1,062 (100.0%) (22.8%)

Section 8 445 (20.1%) (50.4%) 1,279 (57.9%) (44.8%) 485 (22.0%) (53.1%) 2,209 (100.0%) (47.5%)

Total 642 (19.6%) (72.8%) 1,950 (59.6%) (68.3%) 678 (20.7%) (74.2%) 3,271 (100.0%) (70.3%)

Owner-administered

Owner-administered 241 (17.4%) (27.3%) 905 (65.5%) (31.7%) 236 (17.1%) (25.8%) 1,382 (100.0%) (29.7%)

Total 241 (17.4%) (27.3%) 905 (65.5%) (31.7%) 236 (17.1%) (25.8%) 1,382 (100.0%) (29.7%)

Total 883 (19.0%) (100.0%) 2,856 (61.4%) (100.0%) 915 (19.7%) (100.0%) 4,653 (100.0%) (100.0%)

2016.08.17 [Weighted]

HUDQC FY 2015 

Table 3. Dollar Rent Error by Program Type

Program Type

Actual Rent (Monthly) QC Rent (Monthly) Gross Rent Error (Monthly)

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

PHA-administered

Public Housing 1,062 (22.8%) 292,018 275.05 1,062 (22.8%) 293,178 276.14 1,062 (22.8%) 15,274 14.39

Section 8 2,209 (47.5%) 586,239 265.35 2,209 (47.5%) 600,714 271.90 2,209 (47.5%) 33,398 15.12

Total 3,271 (70.3%) 878,258 268.50 3,271 (70.3%) 893,893 273.28 3,271 (70.3%) 48,672 14.88

Owner-administered

Owner-administered 1,382 (29.7%) 340,759 246.49 1,382 (29.7%) 345,912 250.22 1,382 (29.7%) 13,646 9.87

Total 1,382 (29.7%) 340,759 246.49 1,382 (29.7%) 345,912 250.22 1,382 (29.7%) 13,646 9.87

Total 4,653 (100.0%) 1,219,016 261.96 4,653 (100.0%) 1,239,804 266.43 4,653 (100.0%) 62,318 13.39

2016.08.17 [Weighted]
HUDQC Final Report for FY 2015 C-7 

Appendix C: Source Tables

HUDQC FY 2015 

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

Program Type

Underpayment (Monthly) Overpayment (Monthly) QC Rent (Monthly)

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

PHA-administered

Public Housing 139 (22.1%) 8,238 59.36 124 (20.8%) 7,035 56.57 1,062 (22.8%) 293,178 276.14

Section 8 326 (51.8%) 23,954 73.44 323 (54.0%) 9,444 29.26 2,209 (47.5%) 600,714 271.90

Total 465 (73.9%) 32,193 69.24 447 (74.9%) 16,479 36.86 3,271 (70.3%) 893,893 273.28

Owner-administered

Owner-administered 165 (26.2%) 9,419 57.21 150 (25.1%) 4,228 28.18 1,382 (29.7%) 345,912 250.22

Total 165 (26.2%) 9,419 57.21 150 (25.1%) 4,228 28.18 1,382 (29.7%) 345,912 250.22

Total 630 (100.0%) 41,612 66.10 597 (100.0%) 20,707 34.68 4,653 (100.0%) 1,239,804 266.43

2016.08.17 [Weighted]

HUDQC FY 2015 

Table 4(S). Dollar Error Amount by Payment Type and Program Type

(Proper Payment Based on Exact Match of Actual and QC Rent)

Program Type

Underpayment (Monthly) Overpayment (Monthly) QC Rent (Monthly)

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum Dollar 

Amount (in 

1,000)

Avg. 

Dollar 

Amount

PHA-administered

Public Housing 198 (22.4%) 8,387 42.43 193 (21.1%) 7,179 37.20 1,062 (22.8%) 293,178 276.14

Section 8 445 (50.4%) 24,255 54.56 485 (53.1%) 9,780 20.15 2,209 (47.5%) 600,714 271.90

Total 642 (72.8%) 32,642 50.83 678 (74.2%) 16,959 25.00 3,271 (70.3%) 893,893 273.28

Owner-administered

Owner-administered 241 (27.3%) 9,605 39.91 236 (25.8%) 4,452 18.84 1,382 (29.7%) 345,912 250.22

Total 241 (27.3%) 9,605 39.91 236 (25.8%) 4,452 18.84 1,382 (29.7%) 345,912 250.22

Total 883 (100.0%) 42,248 47.85 915 (100.0%) 21,411 23.40 4,653 (100.0%) 1,239,804 266.43

2016.08.17 [Weighted]
C-8 August 31, 2016

Appendix C: Source Tables

HUDQC FY 2015 

Table 5. Gross and Net Rent Error by Program Type

Program Type

Gross Rent Error (Monthly) Net Rent Error (Monthly) QC Rent (Monthly)

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

PHA-administered

Public Housing 1,062 (22.8%) 15,274 14.39 1,062 (22.8%) -1,203 -1.13 1,062 (22.8%) 293,178 276.14

Section 8 2,209 (47.5%) 33,398 15.12 2,209 (47.5%) -14,511 -6.57 2,209 (47.5%) 600,714 271.90

Total 3,271 (70.3%) 48,672 14.88 3,271 (70.3%) -15,713 -4.80 3,271 (70.3%) 893,893 273.28

Owner-administered

Owner-administered 1,382 (29.7%) 13,646 9.87 1,382 (29.7%) -5,191 -3.76 1,382 (29.7%) 345,912 250.22

Total 1,382 (29.7%) 13,646 9.87 1,382 (29.7%) -5,191 -3.76 1,382 (29.7%) 345,912 250.22

Total 4,653 (100.0%) 62,318 13.39 4,653 (100.0%) -20,905 -4.49 4,653 (100.0%) 1,239,804 266.43

2016.08.17 [Weighted]

HUDQC FY 2015 

Table 5(S). Gross and Net Rent Error by Program Type

(Proper Payment Based on Exact Match of Actual and QC Rent)

Program Type

Gross Rent Error (Monthly) Net Rent Error (Monthly) QC Rent (Monthly)

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

PHA-administered

Public Housing 1,062 (22.8%) 15,566 14.66 1,062 (22.8%) -1,209 -1.14 1,062 (22.8%) 293,178 276.14

Section 8 2,209 (47.5%) 34,035 15.41 2,209 (47.5%) -14,475 -6.55 2,209 (47.5%) 600,714 271.90

Total 3,271 (70.3%) 49,601 15.16 3,271 (70.3%) -15,684 -4.79 3,271 (70.3%) 893,893 273.28

Owner-administered

Owner-administered 1,382 (29.7%) 14,057 10.17 1,382 (29.7%) -5,153 -3.73 1,382 (29.7%) 345,912 250.22

Total 1,382 (29.7%) 14,057 10.17 1,382 (29.7%) -5,153 -3.73 1,382 (29.7%) 345,912 250.22

Total 4,653 (100.0%) 63,659 13.68 4,653 (100.0%) -20,837 -4.48 4,653 (100.0%) 1,239,804 266.43

2016.08.17 [Weighted]
HUDQC Final Report for FY 2015 C-9 

Appendix C: Source Tables

HUDQC FY 2015 

Table 6. Certifications and Recertifications by Administration Type

Program Type

Certifications Recertifications/Non-Overdue Recertifications/Overdue Total

# of 

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

# of 

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

# of 

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

# of 

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

PHA-administered

Public Housing 136 (12.8%) (22.9%) 895 (84.3%) (22.4%) 30 (2.9%) (42.3%) 1,062 (100.0%) (22.8%)

Section 8 262 (11.9%) (44.1%) 1,909 (86.4%) (47.9%) 38 (1.7%) (53.0%) 2,209 (100.0%) (47.5%)

Total 398 (12.2%) (67.1%) 2,805 (85.7%) (70.3%) 68 (2.1%) (95.3%) 3,271 (100.0%) (70.3%)

Owner-administered

Owner-administered 196 (14.1%) (32.9%) 1,184 (85.6%) (29.7%) 3 (.2%) (4.7%) 1,382 (100.0%) (29.7%)

Total 196 (14.1%) (32.9%) 1,184 (85.6%) (29.7%) 3 (.2%) (4.7%) 1,382 (100.0%) (29.7%)

Total 594 (12.8%) (100.0%) 3,988 (85.7%) (100.0%) 72 (1.5%) (100.0%) 4,653 (100.0%) (100.0%)

2016.08.17 [Weighted]

HUDQC FY 2015 

Table 7. Percent of Newly Certified Households Meeting Certification Criteria

Certification Criteria

Met Criterion Did Not Meet Criterion

# of Households (in 1,000s) % of Households # of Households (in 1,000s) % of Households

Citizenship 594 (100.0%)

Social Security Number 587 (98.9%) 7 (1.1%)

Consent Form 573 (96.6%) 20 (3.4%)

Low and Very Low Income 594 (100.0%)

Meets All Eligibility Criteria 568 (95.7%) 25 (4.3%)

2016.08.17 [Weighted]
C-10 August 31, 2016

Appendix C: Source Tables

HUDQC FY 2015 

Table 7b. Percent of Newly Certified Households Meeting Certification Criteria by Program Type

Certification Criteria

Met Criterion Did Not Meet Criterion

# of Households 

(in 1,000s) % of Households

# of Households 

(in 1,000s) % of Households

Public Housing

Citizenship 136 (100.0%)

Social Security Number 135 (98.9%) 1 (1.1%)

Consent Form 126 (92.7%) 10 (7.3%)

Low and Very Low Income 136 (100.0%)

Meets All Eligibility Criteria 126 (92.7%) 10 (7.3%)

PHA-administered Section 8

Citizenship 262 (100.0%)

Social Security Number 262 (100.0%)

Consent Form 253 (96.7%) 9 (3.3%)

Low and Very Low Income 262 (100.0%)

Meets All Eligibility Criteria 253 (96.7%) 9 (3.3%)

Owner-administered

Citizenship 196 (100.0%)

Social Security Number 190 (97.4%) 5 (2.6%)

Consent Form 194 (99.2%) 2 (.8%)

Low and Very Low Income 196 (100.0%)

Meets All Eligibility Criteria 189 (96.6%) 7 (3.4%)

2016.08.17 [Weighted]
HUDQC Final Report for FY 2015 C-11 

Appendix C: Source Tables

HUDQC FY 2015 

Table 8. Dollar Error Amount by Payment Type and Case Type

Case Type

Underpayment (Monthly) Overpayment (Monthly) QC Rent (Monthly)

# of Cases 

(in 1,000s)

Col. % of 

Cases

Sum Dollar 

Amount 

(in 1,000s)

Avg. Dollar 

Amount

# of Cases 

(in 1,000s)

Col. % of 

Cases

Sum Dollar 

Amount 

(in 1,000s)

Avg. Dollar 

Amount

# of Cases 

(in 1,000s)

Col. % of 

Cases

Sum Dollar 

Amount 

(in 1,000s)

Avg. Dollar 

Amount

Certification Total 83 (13.2%) 5,182 62.28 82 (13.7%) 2,327 28.42 594 (12.8%) 140,958 237.50

Recertification

Non-Overdue 520 (82.6%) 34,514 66.34 493 (82.5%) 14,800 30.05 3,988 (85.7%) 1,077,248 270.11

Overdue 26 (4.1%) 1,915 73.46 23 (3.8%) 3,580 157.71 72 (1.5%) 21,598 301.20

Total 546 (86.8%) 36,429 66.68 515 (86.3%) 18,380 35.68 4,060 (87.2%) 1,098,846 270.66

Total 630 (100.0%) 41,612 66.10 597 (100.0%) 20,707 34.68 4,653 (100.0%) 1,239,804 266.43

2016.08.17 [Weighted]

HUDQC FY 2015 

Table 8(S). Dollar Error Amount by Payment Type and Case Type

(Proper Payment Based on Exact Match of Actual and QC Rent)

Case Type

Underpayment (Monthly) Overpayment (Monthly) QC Rent (Monthly)

# of Cases 

(in 1,000s)

Col. % of 

Cases

Sum Dollar 

Amount 

(in 1,000s)

Avg. Dollar 

Amount

# of Cases 

(in 1,000s)

Col. % of 

Cases

Sum Dollar 

Amount 

(in 1,000s)

Avg. Dollar 

Amount

# of Cases 

(in 1,000s)

Col. % of 

Cases

Sum Dollar 

Amount 

(in 1,000s)

Avg. Dollar 

Amount

Certification Total 115 (13.0%) 5,251 45.86 121 (13.2%) 2,417 20.00 594 (12.8%) 140,958 237.50

Recertification

Non-Overdue 733 (83.0%) 35,051 47.81 770 (84.2%) 15,413 20.02 3,988 (85.7%) 1,077,248 270.11

Overdue 35 (4.0%) 1,946 55.29 24 (2.6%) 3,582 148.82 72 (1.5%) 21,598 301.20

Total 768 (87.0%) 36,997 48.15 794 (86.8%) 18,994 23.92 4,060 (87.2%) 1,098,846 270.66

Total 883 (100.0%) 42,248 47.85 915 (100.0%) 21,411 23.40 4,653 (100.0%) 1,239,804 266.43

2016.08.17 [Weighted]
C-12 August 31, 2016

Appendix C: Source Tables

HUDQC FY 2015 

Table 9. Largest Component Error for Households With Rent Error (Annual Dollars)

Rent Component # of Households (in 1,000s) Col. % of Households Sum Dollar Amount (in 1,000s) Avg. Dollar Amount

Earned Income 408 (33.3%) 1,575,799 3,860

Pension, Etc. 188 (15.4%) 484,639 2,572

Public Assistance 50 (4.0%) 105,865 2,133

Other Income 158 (12.9%) 387,683 2,453

Asset Income 30 (2.4%) 17,865 596

Dependent Allowance 57 (4.6%) 29,620 522

Elderly Household Allowance 69 (5.6%) 27,457 400

Child Care Allowance 24 (2.0%) 51,653 2,148

Disability Allowance

Medical Allowance 191 (15.6%) 173,009 905

No Error 52 (4.2%) 0 0

Total 1,227 (100.0%) 2,853,591 2,326

2016.08.17 [Weighted]

HUDQC FY 2015 

Table 10. Total and Largest Dollar Error by Program Type for Households With Rent Errors

Program Type

Total Dollar In Error Largest Dollar Error

# of Cases 

(in 1,000s)

Col. % of 

Cases

Sum Dollar 

Amount 

(in 1,000s)

Avg. Dollar 

Amount

# of Cases 

(in 1,000s)

Col. % of 

Cases

Sum Dollar 

Amount 

(in 1,000s)

Avg. Dollar 

Amount

PHA-administered

Public Housing 263 (21.5%) 895,080 3,401.41 263 (21.5%) 745,524 2,833.08

Section 8 649 (52.9%) 1,674,109 2,580.06 649 (52.9%) 1,472,832 2,269.86

Total 912 (74.4%) 2,569,189 2,817.05 912 (74.4%) 2,218,356 2,432.37

Owner-administered

Owner-administered 315 (25.7%) 763,830 2,427.56 315 (25.7%) 635,235 2,018.87

Total 315 (25.7%) 763,830 2,427.56 315 (25.7%) 635,235 2,018.87

Total 1,227 (100.0%) 3,333,019 2,717.15 1,227 (100.0%) 2,853,591 2,326.31

2016.08.17 [Weighted]
HUDQC Final Report for FY 2015 C-13 

Appendix C: Source Tables

HUDQC FY 2015 

Table 11. QC Rent Components by Payment Type and Administration Type

Rent Component

PHA-administered Owner-administered Total

# of Cases 

(in 1,000s)

Col. % of 

Cases

Row % of 

Cases

# of Cases 

(in 1,000s)

Col. % of 

Cases

Row % of 

Cases

# of Cases 

(in 1,000s)

Col. % of 

Cases

Row % of 

Cases

Underpayment

Earned Income 235 (7.2%) (81.6%) 53 (3.9%) (18.4%) 289 (6.2%) (100.0%)

Pension, Etc. 118 (3.6%) (73.9%) 42 (3.0%) (26.3%) 160 (3.4%) (100.0%)

Public Assistance 26 (.8%) (100.0%) 26 (.6%) (100.0%)

Other Income 109 (3.3%) (82.6%) 23 (1.7%) (17.4%) 131 (2.8%) (100.0%)

Asset Income 37 (1.1%) (57.3%) 28 (2.0%) (42.7%) 65 (1.4%) (100.0%)

Dependent Allowance 34 (1.0%) (88.0%) 5 (.3%) (12.0%) 38 (.8%) (100.0%)

Elderly Household 

Allowance 16 (.5%) (90.2%) 2 (.1%) (9.8%) 18 (.4%) (100.0%)

Child Care Allowance 16 (.5%) (76.6%) 5 (.4%) (23.4%) 21 (.5%) (100.0%)

Disability Allowance

Medical Allowance 66 (2.0%) (48.3%) 71 (5.1%) (51.7%) 137 (3.0%) (100.0%)

No Error 17 (.5%) (70.9%) 7 (.5%) (29.1%) 24 (.5%) (100.0%)

Proper Payment

Earned Income 228 (7.0%) (85.9%) 38 (2.7%) (14.1%) 266 (5.7%) (100.0%)

Pension, Etc. 285 (8.7%) (63.7%) 162 (11.7%) (36.3%) 447 (9.6%) (100.0%)

Public Assistance 58 (1.8%) (63.8%) 33 (2.4%) (36.2%) 91 (1.9%) (100.0%)

Other Income 171 (5.2%) (79.5%) 44 (3.2%) (20.5%) 215 (4.6%) (100.0%)

Asset Income 88 (2.7%) (61.3%) 56 (4.0%) (38.7%) 143 (3.1%) (100.0%)

Dependent Allowance 22 (.7%) (80.6%) 5 (.4%) (19.4%) 28 (.6%) (100.0%)

Elderly Household 

Allowance 16 (.5%) (83.5%) 3 (.2%) (16.5%) 20 (.4%) (100.0%)

Child Care Allowance 13 (.4%) (100.0%) 13 (.3%) (100.0%)

Disability Allowance

Medical Allowance 156 (4.8%) (53.1%) 138 (10.0%) (46.9%) 294 (6.3%) (100.0%)

No Error 1,617 (49.4%) (69.0%) 725 (52.5%) (31.0%) 2,342 (50.3%) (100.0%)
C-14 August 31, 2016

Appendix C: Source Tables

HUDQC FY 2015 

Table 11. QC Rent Components by Payment Type and Administration Type (continued)

Rent Component

PHA-administered Owner-administered Total

# of Cases 

(in 1,000s)

Col. % of 

Cases

Row % of 

Cases

# of Cases 

(in 1,000s)

Col. % of 

Cases

Row % of 

Cases

# of Cases 

(in 1,000s)

Col. % of 

Cases

Row % of 

Cases

Overpayment

Earned Income 154 (4.7%) (83.6%) 30 (2.2%) (16.4%) 184 (4.0%) (100.0%)

Pension, Etc. 108 (3.3%) (72.9%) 40 (2.9%) (27.1%) 149 (3.2%) (100.0%)

Public Assistance 34 (1.0%) (79.7%) 9 (.6%) (20.3%) 42 (.9%) (100.0%)

Other Income 102 (3.1%) (83.3%) 20 (1.5%) (16.7%) 123 (2.6%) (100.0%)

Asset Income 28 (.9%) (59.0%) 20 (1.4%) (41.0%) 48 (1.0%) (100.0%)

Dependent Allowance 34 (1.0%) (70.7%) 14 (1.0%) (29.3%) 48 (1.0%) (100.0%)

Elderly Household 

Allowance 46 (1.4%) (56.3%) 36 (2.6%) (43.7%) 82 (1.8%) (100.0%)

Child Care Allowance 22 (.7%) (100.0%) 22 (.5%) (100.0%)

Disability Allowance

Medical Allowance 85 (2.6%) (58.1%) 62 (4.5%) (41.9%) 147 (3.2%) (100.0%)

No Error 27 (.8%) (100.0%) 27 (.6%) (100.0%)

Total with Rent Error Calculation 3,271 (100.0%) (70.3%) 1,382 (100.0%) (29.7%) 4,653 (100.0%) (100.0%)

2016.08.17 [Weighted]

HUDQC FY 2015 

Table 12a. Elderly/Disabled Allowances

Allowances

Non-Elderly/Disabled Household Elderly/Disabled Household Total

# of Cases 

(in 1,000s)

Col. % of 

Cases

Row % of 

Cases

# of Cases 

(in 1,000s)

Col. % of 

Cases

Row % of 

Cases

# of Cases 

(in 1,000s)

Col. % of 

Cases

Row % of 

Cases

No Allowance 1,946 (99.2%) (100.0%) 1,946 (41.8%) (100.0%)

Incorrect Allowance 16 (.8%) (13.2%) 104 (3.9%) (86.8%) 120 (2.6%) (100.0%)

Correct Allowance 2,588 (96.1%) (100.0%) 2,588 (55.6%) (100.0%)

Total 1,961 (100.0%) (42.1%) 2,692 (100.0%) (57.9%) 4,653 (100.0%) (100.0%)

2016.08.17 [Weighted]
HUDQC Final Report for FY 2015 C-15 

Appendix C: Source Tables

HUDQC FY 2015 

Table 12b. Dependent Allowances

Allowances

Households Without Dependent(s) Households With Dependent(s) Total

# of Cases 

(in 1,000s)

Col. % of 

Cases

Row % of 

Cases

# of Cases 

(in 1,000s)

Col. % of 

Cases

Row % of 

Cases

# of Cases 

(in 1,000s)

Col. % of 

Cases

Row % of 

Cases

No Allowance 2,781 (99.8%) (100.0%) 2,781 (59.8%) (100.0%)

Incorrect Allowance 4 (.2%) (3.8%) 110 (5.9%) (96.2%) 114 (2.5%) (100.0%)

Correct Allowance 1,758 (94.1%) (100.0%) 1,758 (37.8%) (100.0%)

Total 2,785 (100.0%) (59.9%) 1,868 (100.0%) (40.1%) 4,653 (100.0%) (100.0%)

2016.08.17 [Weighted]

HUDQC FY 2015 

Table 13. Calculation Errors on Form HUD-50058/50059

Items

Form HUD-50058 Form HUD-50059 Total

# of Errors

# of Households 

(in 1,000s) # of Errors

# of Households

(in 1,000s) # of Errors

# of Households 

(in 1,000s)

Household Composition 173 171 0 173 171

Net Family Assets and Income 378 310 161 73 540 383

Allowances and Adjusted Income 1,542 1,380 0 1,542 1,380

Family Rent and Subsidy Information 545 302 0 545 302

2016.08.17 [Weighted]

HUDQC FY 2015 

Table 14. Consistency Errors on Form HUD-50058/50059

Items

Form HUD-50058 Form HUD-50059 Total

# of Errors

# of Households 

(in 1,000s) # of Errors

# of Households 

(in 1,000s) # of Errors

# of Households 

(in 1,000s)

General Information 46 46 203 159 250 205

Household Composition 313 139 344 316 657 454

Net Family Assets and Income 111 58 0 111 58

Allowances and Adjusted Income 323 307 7 7 330 314

Family Rent and Subsidy Information 69 69 5 5 74 74

2016.08.17 [Weighted]
C-16 August 31, 2016

Appendix C: Source Tables

HUDQC FY 2015 

Table 15a. Verification of Form HUD-50058/50059 Rent Components

Third-Party Verbal or in Writing, Documentation, or EIV

Rent Component

No Verification

Verification

Dollar Amount Not Matched Dollar Amount Matched Total

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases

(in 1,000s)

Row % of 

Cases

Earned Income 215 (13.7%) 410 (26.1%) 949 (60.3%) 1,575 (100.0%)

Pension, Etc. 143 (5.0%) 368 (13.0%) 2,328 (82.0%) 2,839 (100.0%)

Public Assistance 93 (21.5%) 60 (14.0%) 278 (64.5%) 432 (100.0%)

Other Income 323 (35.0%) 191 (20.7%) 410 (44.4%) 924 (100.0%)

Asset Income 41 (10.3%) 42 (10.5%) 316 (79.2%) 399 (100.0%)

Child Care Expense 3 (1.8%) 37 (24.8%) 110 (73.4%) 151 (100.0%)

Disability Expense 9 (80.3%) 2 (19.7%) 12 (100.0%)

Medical Expense 83 (8.8%) 213 (22.7%) 641 (68.4%) 937 (100.0%)

2016.08.17 [Weighted]

HUDQC FY 2015 

Table 15b. Verification of Form HUD-50058/50059 Rent Components

Third Party in Writing

Rent Component

No Verification

Verification

Dollar Amount Not Matched Dollar Amount Matched Total

# of Cases

(in 1,000s)

Row % of 

Cases

# of Cases

(in 1,000s)

Row % of 

Cases

# of Cases

(in 1,000s)

Row % of 

Cases

# of Cases

(in 1,000s)

Row % of 

Cases

Earned Income 1,253 (79.5%) 74 (4.7%) 248 (15.8%) 1,575 (100.0%)

Pension, Etc. 2,815 (99.2%) 4 (.2%) 19 (.7%) 2,839 (100.0%)

Public Assistance 362 (83.9%) 10 (2.2%) 60 (13.9%) 432 (100.0%)

Other Income 790 (85.5%) 31 (3.3%) 103 (11.2%) 924 (100.0%)

Asset Income 257 (64.5%) 14 (3.5%) 128 (32.1%) 399 (100.0%)

Child Care Expense 75 (49.8%) 14 (9.6%) 61 (40.6%) 151 (100.0%)

Disability Expense 5 (47.6%) 4 (32.7%) 2 (19.7%) 12 (100.0%)

Medical Expense 801 (85.5%) 20 (2.2%) 116 (12.3%) 937 (100.0%)

2016.08.17 [Weighted]
HUDQC Final Report for FY 2015 C-17 

Appendix C: Source Tables

HUDQC FY 2015 

Table 15c. Verification of Form HUD-50058/50059 Rent Components

Third Party in Writing or EIV/UIV

Rent Component

No Verification

Verification

Dollar Amount Not Matched Dollar Amount Matched Total

# of Cases

(in 1,000s)

Row % of 

Cases

# of Cases

(in 1,000s)

Row % of 

Cases

# of Cases

(in 1,000s)

Row % of 

Cases

# of Cases

(in 1,000s)

Row % of 

Cases

Earned Income 1,191 (75.6%) 101 (6.4%) 283 (18.0%) 1,575 (100.0%)

Pension, Etc. 1,317 (46.4%) 196 (6.9%) 1,325 (46.7%) 2,839 (100.0%)

Public Assistance 230 (53.2%) 34 (7.9%) 168 (38.9%) 432 (100.0%)

Other Income 724 (78.4%) 53 (5.8%) 146 (15.8%) 924 (100.0%)

Asset Income 256 (64.1%) 14 (3.5%) 129 (32.4%) 399 (100.0%)

Child Care Expense 75 (49.8%) 14 (9.6%) 61 (40.6%) 151 (100.0%)

Disability Expense 5 (47.6%) 4 (32.7%) 2 (19.7%) 12 (100.0%)

Medical Expense 642 (68.5%) 54 (5.8%) 241 (25.7%) 937 (100.0%)

2016.08.17 [Weighted]

HUDQC FY 2015 

Table 15d. Verification of Form HUD-50058/50059 Rent Components

Third Party Verbal

Rent Component

No Verification

Verification

Dollar Amount Not Matched Dollar Amount Matched Total

# of Cases

(in 1,000s)

Row % of 

Cases

# of Cases

(in 1,000s)

Row % of 

Cases

# of Cases

(in 1,000s)

Row % of 

Cases

# of Cases

(in 1,000s)

Row % of 

Cases

Earned Income 1,550 (98.5%) 3 (.2%) 22 (1.4%) 1,575 (100.0%)

Pension, Etc. 2,834 (99.8%) 4 (.2%) 2,839 (100.0%)

Public Assistance 430 (99.6%) 2 (.4%) 432 (100.0%)

Other Income 905 (98.0%) 18 (2.0%) 924 (100.0%)

Asset Income 399 (100.0%) 399 (100.0%)

Child Care Expense 149 (98.8%) 2 (1.2%) 151 (100.0%)

Disability Expense 12 (100.0%) 12 (100.0%)

Medical Expense 935 (99.8%) 2 (.2%) 937 (100.0%)

2016.08.17 [Weighted]
C-18 August 31, 2016

Appendix C: Source Tables

HUDQC FY 2015 

Table 15e. Verification of Form HUD-50058/50059 Rent Components

Documentation

Rent Component

No Verification

Verification

Dollar Amount Not Matched Dollar Amount Matched Total

# of Cases

(in 1,000s) Row % of Cases

# of Cases

(in 1,000s) Row % of Cases

# of Cases

(in 1,000s) Row % of Cases

# of Cases

(in 1,000s) Row % of Cases

Earned Income 669 (42.5%) 282 (17.9%) 624 (39.6%) 1,575 (100.0%)

Pension, Etc. 1,984 (69.9%) 92 (3.2%) 763 (26.9%) 2,839 (100.0%)

Public Assistance 297 (68.8%) 26 (6.1%) 108 (25.1%) 432 (100.0%)

Other Income 549 (59.5%) 132 (14.3%) 243 (26.3%) 924 (100.0%)

Asset Income 253 (63.3%) 13 (3.2%) 133 (33.4%) 399 (100.0%)

Child Care Expense 80 (53.2%) 23 (15.3%) 48 (31.6%) 151 (100.0%)

Disability Expense 6 (52.4%) 5 (47.6%) 12 (100.0%)

Medical Expense 568 (60.6%) 99 (10.6%) 270 (28.8%) 937 (100.0%)

2016.08.17 [Weighted]

HUDQC FY 2015 

Table 15f. Verification of Form HUD-50058/50059 Rent Components

EIV (Enterprise Income Verification)

Rent Component

No Verification

Verification

Dollar Amount Not Matched Dollar Amount Matched Total

# of Cases

(in 1,000s) Row % of Cases

# of Cases

(in 1,000s) Row % of Cases

# of Cases

(in 1,000s) Row % of Cases

# of Cases

(in 1,000s) Row % of Cases

Earned Income 1,575 (100.0%) 1,575 (100.0%)

Pension, Etc. 1,455 (51.3%) 147 (5.2%) 1,237 (43.6%) 2,839 (100.0%)

Public Assistance 428 (99.1%) 1 (.3%) 2 (.5%) 432 (100.0%)

Other Income 924 (100.0%) 924 (100.0%)

Asset Income 399 (100.0%) 399 (100.0%)

Child Care Expense 151 (100.0%) 151 (100.0%)

Disability Expense 12 (100.0%) 12 (100.0%)

Medical Expense 826 (88.1%) 25 (2.6%) 87 (9.3%) 937 (100.0%)

2016.08.17 [Weighted]
HUDQC Final Report for FY 2015 C-19 

Appendix C: Source Tables

HUDQC FY 2015 

Table 15g. Verification of Form HUD-50058/50059 Rent Components

UIV (Upfront Income Verification)

Rent Component

No Verification

Verification

Dollar Amount Not Matched Dollar Amount Matched Total

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

Earned Income 1,513 (96.1%) 27 (1.7%) 35 (2.2%) 1,575 (100.0%)

Pension, Etc. 2,835 (99.9%) 4 (.1%) 2,839 (100.0%)

Public Assistance 303 (70.1%) 23 (5.4%) 106 (24.5%) 432 (100.0%)

Other Income 863 (93.4%) 23 (2.5%) 38 (4.1%) 924 (100.0%)

Asset Income 388 (97.3%) 4 (.9%) 7 (1.8%) 399 (100.0%)

Child Care Expense 151 (100.0%) 151 (100.0%)

Disability Expense 12 (100.0%) 12 (100.0%)

Medical Expense 922 (98.4%) 4 (.4%) 12 (1.2%) 937 (100.0%)

2016.08.17 [Weighted]
C-20 August 31, 2016

Appendix C: Source Tables

HUDQC FY 2015 

Table 15h. Verification of Form HUD-50058/50059 Rent Components

Third-Party Verbal or in Writing, Documentation, or EIV

Rent Component by Program Type

No Verification

Verification

Dollar Amount Not Matched Dollar Amount Matched Total

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases (in 

1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

Public Housing

Earned Income 83 (21.7%) 104 (27.2%) 195 (51.1%) 381 (100.0%)

Pension, Etc. 37 (6.2%) 98 (16.7%) 454 (77.1%) 589 (100.0%)

Public Assistance 24 (20.6%) 19 (16.8%) 72 (62.6%) 115 (100.0%)

Other Income 86 (41.3%) 30 (14.6%) 91 (44.0%) 207 (100.0%)

Asset Income 8 (11.7%) 14 (20.0%) 48 (68.3%) 70 (100.0%)

Child Care Expense 11 (30.7%) 25 (69.3%) 36 (100.0%)

Disability Expense 2 (100.0%) 2 (100.0%)

Medical Expense 29 (15.1%) 48 (24.6%) 117 (60.3%) 193 (100.0%)

PHA-administered Section 8

Earned Income 110 (12.3%) 226 (25.3%) 557 (62.3%) 894 (100.0%)

Pension, Etc. 66 (5.3%) 167 (13.5%) 1,001 (81.2%) 1,233 (100.0%)

Public Assistance 35 (15.6%) 34 (15.1%) 155 (69.3%) 224 (100.0%)

Other Income 156 (29.5%) 134 (25.3%) 238 (45.2%) 528 (100.0%)

Asset Income 17 (12.4%) 13 (9.9%) 104 (77.7%) 134 (100.0%)

Child Care Expense 3 (3.3%) 23 (28.4%) 55 (68.3%) 81 (100.0%)

Disability Expense 4 (62.5%) 2 (37.5%) 6 (100.0%)

Medical Expense 23 (8.4%) 80 (29.8%) 165 (61.8%) 268 (100.0%)

Owner-administered

Earned Income 22 (7.4%) 80 (26.7%) 197 (65.9%) 300 (100.0%)

Pension, Etc. 40 (4.0%) 103 (10.1%) 873 (85.9%) 1,017 (100.0%)

Public Assistance 34 (37.2%) 7 (7.8%) 51 (55.0%) 92 (100.0%)

Other Income 81 (43.2%) 27 (14.3%) 80 (42.6%) 189 (100.0%)

Asset Income 16 (8.4%) 14 (7.4%) 164 (84.2%) 194 (100.0%)

Child Care Expense 3 (10.2%) 30 (89.8%) 34 (100.0%)

Disability Expense 4 (100.0%) 4 (100.0%)

Medical Expense 31 (6.6%) 86 (18.0%) 359 (75.5%) 476 (100.0%)

2016.08.17 [Weighted]
HUDQC Final Report for FY 2015 C-21 

Appendix C: Source Tables

HUDQC FY 2015 

Table 15i. Verification of Form HUD-50058/50059 Rent Components

Third Party in Writing

Rent Component by Program Type

No Verification

Verification

Dollar Amount Not Matched Dollar Amount Matched Total

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

Public Housing

Earned Income 264 (69.3%) 32 (8.4%) 85 (22.2%) 381 (100.0%)

Pension, Etc. 589 (100.0%) 589 (100.0%)

Public Assistance 93 (80.5%) 4 (3.5%) 18 (16.0%) 115 (100.0%)

Other Income 172 (83.0%) 11 (5.4%) 24 (11.7%) 207 (100.0%)

Asset Income 51 (72.3%) 5 (6.7%) 15 (21.0%) 70 (100.0%)

Child Care 

Expense 14 (37.9%) 5 (13.3%) 17 (48.8%) 36 (100.0%)

Disability Expense 2 (100.0%) 2 (100.0%)

Medical Expense 176 (91.1%) 4 (1.9%) 14 (7.0%) 193 (100.0%)

PHA-administered Section 8

Earned Income 797 (89.1%) 27 (3.0%) 70 (7.8%) 894 (100.0%)

Pension, Etc. 1,222 (99.1%) 3 (.2%) 9 (.7%) 1,233 (100.0%)

Public Assistance 194 (86.3%) 6 (2.5%) 25 (11.2%) 224 (100.0%)

Other Income 483 (91.5%) 12 (2.3%) 33 (6.2%) 528 (100.0%)

Asset Income 119 (88.4%) 7 (5.2%) 9 (6.4%) 134 (100.0%)

Child Care 

Expense 58 (71.5%) 8 (9.8%) 15 (18.6%) 81 (100.0%)

Disability Expense 4 (62.5%) 2 (37.5%) 6 (100.0%)

Medical Expense 258 (96.5%) 9 (3.5%) 268 (100.0%)

Owner-administered

Earned Income 192 (63.9%) 15 (4.9%) 93 (31.2%) 300 (100.0%)

Pension, Etc. 1,004 (98.8%) 2 (.2%) 11 (1.1%) 1,017 (100.0%)

Public Assistance 76 (82.1%) 16 (17.9%) 92 (100.0%)

Other Income 135 (71.4%) 7 (3.9%) 47 (24.7%) 189 (100.0%)

Asset Income 88 (45.1%) 2 (1.1%) 105 (53.8%) 194 (100.0%)

Child Care 

Expense 3 (10.3%) 2 (5.0%) 29 (84.7%) 34 (100.0%)

Disability Expense 4 (100.0%) 4 (100.0%)

Medical Expense 366 (77.0%) 17 (3.5%) 93 (19.5%) 476 (100.0%)

2016.08.17 [Weighted]
C-22 August 31, 2016

Appendix C: Source Tables

HUDQC FY 2015 

Table 15j. Verification of Form HUD-50058/50059 Rent Components

Third Party in Writing or EIV/UIV

Rent Component by Program Type

No Verification

Verification

Dollar Amount Not Matched Dollar Amount Matched Total

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases (in 

1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

Public Housing

Earned Income 258 (67.8%) 35 (9.2%) 87 (23.0%) 381 (100.0%)

Pension, Etc. 271 (46.0%) 58 (9.9%) 260 (44.1%) 589 (100.0%)

Public Assistance 42 (36.7%) 16 (14.1%) 57 (49.2%) 115 (100.0%)

Other Income 158 (76.4%) 15 (7.1%) 34 (16.6%) 207 (100.0%)

Asset Income 50 (70.6%) 5 (6.7%) 16 (22.8%) 70 (100.0%)

Child Care Expense 14 (37.9%) 5 (13.3%) 17 (48.8%) 36 (100.0%)

Disability Expense 2 (100.0%) 2 (100.0%)

Medical Expense 121 (62.6%) 16 (8.3%) 56 (29.2%) 193 (100.0%)

PHA-administered Section 8

Earned Income 758 (84.8%) 44 (4.9%) 92 (10.3%) 894 (100.0%)

Pension, Etc. 560 (45.4%) 83 (6.7%) 590 (47.9%) 1,233 (100.0%)

Public Assistance 126 (56.3%) 16 (7.2%) 82 (36.6%) 224 (100.0%)

Other Income 441 (83.6%) 28 (5.4%) 58 (11.0%) 528 (100.0%)

Asset Income 119 (88.4%) 7 (5.2%) 9 (6.4%) 134 (100.0%)

Child Care Expense 58 (71.5%) 8 (9.8%) 15 (18.6%) 81 (100.0%)

Disability Expense 4 (62.5%) 2 (37.5%) 6 (100.0%)

Medical Expense 204 (76.2%) 14 (5.1%) 50 (18.7%) 268 (100.0%)

Owner-administered

Earned Income 175 (58.2%) 22 (7.2%) 104 (34.5%) 300 (100.0%)

Pension, Etc. 486 (47.8%) 55 (5.4%) 475 (46.8%) 1,017 (100.0%)

Public Assistance 61 (66.2%) 2 (1.9%) 29 (31.9%) 92 (100.0%)

Other Income 125 (66.1%) 10 (5.5%) 54 (28.4%) 189 (100.0%)

Asset Income 88 (45.1%) 2 (1.1%) 105 (53.8%) 194 (100.0%)

Child Care Expense 3 (10.3%) 2 (5.0%) 29 (84.7%) 34 (100.0%)

Disability Expense 4 (100.0%) 4 (100.0%)

Medical Expense 317 (66.6%) 24 (5.1%) 135 (28.3%) 476 (100.0%)

2016.08.17 [Weighted]
HUDQC Final Report for FY 2015 C-23 

Appendix C: Source Tables

HUDQC FY 2015 

Table 15k. Verification of Form HUD-50058/50059 Rent Components

Third-Party Verbal

Rent Component by Program Type

No Verification Verification

Dollar Amount Not Matched Dollar Amount Matched Total

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

Public Housing

Earned Income 369 (96.8%) 3 (.7%) 10 (2.5%) 381 (100.0%)

Pension, Etc. 589 (100.0%) 589 (100.0%)

Public Assistance 115 (100.0%) 115 (100.0%)

Other Income 202 (97.5%) 5 (2.5%) 207 (100.0%)

Asset Income 70 (100.0%) 70 (100.0%)

Child Care Expense 34 (94.9%) 2 (5.1%) 36 (100.0%)

Disability Expense 2 (100.0%) 2 (100.0%)

Medical Expense 193 (100.0%) 193 (100.0%)

PHA-administered Section 8

Earned Income 889 (99.4%) 5 (.6%) 894 (100.0%)

Pension, Etc. 1,231 (99.8%) 2 (.2%) 1,233 (100.0%)

Public Assistance 224 (100.0%) 224 (100.0%)

Other Income 515 (97.5%) 13 (2.5%) 528 (100.0%)

Asset Income 134 (100.0%) 134 (100.0%)

Child Care Expense 81 (100.0%) 81 (100.0%)

Disability Expense 6 (100.0%) 6 (100.0%)

Medical Expense 268 (100.0%) 268 (100.0%)

Owner-administered

Earned Income 293 (97.7%) 7 (2.3%) 300 (100.0%)

Pension, Etc. 1,015 (99.8%) 2 (.2%) 1,017 (100.0%)

Public Assistance 90 (98.1%) 2 (1.9%) 92 (100.0%)

Other Income 189 (100.0%) 189 (100.0%)

Asset Income 194 (100.0%) 194 (100.0%)

Child Care Expense 34 (100.0%) 34 (100.0%)

Disability Expense 4 (100.0%) 4 (100.0%)

Medical Expense 475 (99.7%) 2 (.3%) 476 (100.0%)

2016.08.17 [Weighted]
C-24 August 31, 2016

Appendix C: Source Tables

HUDQC FY 2015 

Table 15l. Verification of Form HUD-50058/50059 Rent Components

Documentation

Rent Component by Program Type

No Verification

Verification

Dollar Amount Not Matched Dollar Amount Matched Total

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

Public Housing

Earned Income 236 (61.8%) 55 (14.3%) 91 (23.9%) 381 (100.0%)

Pension, Etc. 417 (70.8%) 22 (3.7%) 150 (25.5%) 589 (100.0%)

Public Assistance 97 (83.8%) 3 (2.7%) 16 (13.5%) 115 (100.0%)

Other Income 146 (70.3%) 12 (6.0%) 49 (23.7%) 207 (100.0%)

Asset Income 40 (57.2%) 5 (7.4%) 25 (35.4%) 70 (100.0%)

Child Care Expense 24 (67.1%) 6 (17.4%) 6 (15.5%) 36 (100.0%)

Disability Expense 2 (100.0%) 2 (100.0%)

Medical Expense 144 (74.7%) 15 (7.6%) 34 (17.7%) 193 (100.0%)

PHA-administered Section 8

Earned Income 270 (30.2%) 172 (19.3%) 452 (50.6%) 894 (100.0%)

Pension, Etc. 879 (71.3%) 51 (4.1%) 304 (24.6%) 1,233 (100.0%)

Public Assistance 133 (59.3%) 18 (7.9%) 73 (32.7%) 224 (100.0%)

Other Income 258 (48.9%) 103 (19.5%) 167 (31.6%) 528 (100.0%)

Asset Income 44 (32.5%) 6 (4.7%) 84 (62.8%) 134 (100.0%)

Child Care Expense 26 (31.8%) 15 (18.5%) 40 (49.7%) 81 (100.0%)

Disability Expense 2 (37.5%) 4 (62.5%) 6 (100.0%)

Medical Expense 102 (38.0%) 53 (19.8%) 113 (42.1%) 268 (100.0%)

Owner-administered

Earned Income 164 (54.8%) 55 (18.4%) 81 (26.9%) 300 (100.0%)

Pension, Etc. 689 (67.7%) 19 (1.9%) 309 (30.4%) 1,017 (100.0%)

Public Assistance 67 (72.9%) 5 (5.9%) 20 (21.2%) 92 (100.0%)

Other Income 145 (77.0%) 17 (8.8%) 27 (14.2%) 189 (100.0%)

Asset Income 169 (86.8%) 1 (.7%) 24 (12.5%) 194 (100.0%)

Child Care Expense 30 (89.7%) 2 (5.2%) 2 (5.1%) 34 (100.0%)

Disability Expense 4 (100.0%) 4 (100.0%)

Medical Expense 322 (67.6%) 32 (6.6%) 123 (25.8%) 476 (100.0%)

2016.08.17 [Weighted]
HUDQC Final Report for FY 2015 C-25 

Appendix C: Source Tables

HUDQC FY 2015 

Table 15m. Verification of Form HUD-50058/50059 Rent Components

EIV (Enterprise Income Verification)

Rent Component by Program Type

No Verification

Verification

Dollar Amount Not Matched Dollar Amount Matched Total

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

Public Housing

Earned Income 381 (100.0%) 381 (100.0%)

Pension, Etc. 286 (48.6%) 51 (8.7%) 251 (42.6%) 589 (100.0%)

Public Assistance 113 (98.0%) 2 (2.0%) 115 (100.0%)

Other Income 207 (100.0%) 207 (100.0%)

 Asset Income 70 (100.0%) 70 (100.0%)

Child Care Expense 36 (100.0%) 36 (100.0%)

Disability Expense 2 (100.0%) 2 (100.0%)

Medical Expense 144 (74.3%) 11 (5.9%) 38 (19.9%) 193 (100.0%)

PHA-administered Section 8

Earned Income 894 (100.0%) 894 (100.0%)

Pension, Etc. 615 (49.9%) 64 (5.2%) 554 (44.9%) 1,233 (100.0%)

Public Assistance 223 (99.4%) 1 (.6%) 224 (100.0%)

Other Income 528 (100.0%) 528 (100.0%)

Asset Income 134 (100.0%) 134 (100.0%)

Child Care Expense 81 (100.0%) 81 (100.0%)

Disability Expense 6 (100.0%) 6 (100.0%)

Medical Expense 224 (83.6%) 11 (4.3%) 33 (12.2%) 268 (100.0%)

Owner-administered

Earned Income 300 (100.0%) 300 (100.0%)

Pension, Etc. 554 (54.5%) 31 (3.0%) 432 (42.5%) 1,017 (100.0%)

Public Assistance 92 (100.0%) 92 (100.0%)

Other Income 189 (100.0%) 189 (100.0%)

Asset Income 194 (100.0%) 194 (100.0%)

Child Care Expense 34 (100.0%) 34 (100.0%)

Disability Expense 4 (100.0%) 4 (100.0%)

Medical Expense 459 (96.3%) 2 (.4%) 16 (3.3%) 476 (100.0%)

2016.08.17 [Weighted]
C-26 August 31, 2016

Appendix C: Source Tables

HUDQC FY 2015 

Table 15n. Verification of Form HUD-50058/50059 Rent Components

UIV (Upfront Income Verification)

Rent Component by Program Type

No Verification Verification Total Dollar Amount Not Matched Dollar Amount Matched

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

# of Cases 

(in 1,000s)

Row % of 

Cases

Public Housing

Earned Income 375 (98.5%) 3 (.8%) 3 (.8%) 381 (100.0%)

Pension, Etc. 589 (100.0%) 589 (100.0%)

Public Assistance 67 (58.2%) 12 (10.6%) 36 (31.2%) 115 (100.0%)

Other Income 195 (94.1%) 4 (1.7%) 9 (4.2%) 207 (100.0%)

Asset Income 68 (96.5%) 2 (3.5%) 70 (100.0%)

Child Care Expense 36 (100.0%) 36 (100.0%)

Disability Expense 2 (100.0%) 2 (100.0%)

Medical Expense 192 (99.2%) 1 (.8%) 193 (100.0%)

PHA-administered Section 8

Earned Income 855 (95.7%) 17 (1.9%) 22 (2.5%) 894 (100.0%)

Pension, Etc. 1,229 (99.7%) 4 (.3%) 1,233 (100.0%)

Public Assistance 158 (70.5%) 9 (4.1%) 57 (25.4%) 224 (100.0%)

Other Income 489 (92.6%) 16 (3.1%) 23 (4.3%) 528 (100.0%)

Asset Income 130 (96.6%) 5 (3.4%) 134 (100.0%)

Child Care Expense 81 (100.0%) 81 (100.0%)

Disability Expense 6 (100.0%) 6 (100.0%)

Medical Expense 260 (97.3%) 2 (.9%) 5 (1.9%) 268 (100.0%)

Owner-administered

Earned Income 283 (94.3%) 7 (2.3%) 10 (3.4%) 300 (100.0%)

Pension, Etc. 1,017 (100.0%) 1,017 (100.0%)

Public Assistance 78 (84.1%) 2 (1.9%) 13 (14.0%) 92 (100.0%)

Other Income 179 (94.7%) 3 (1.6%) 7 (3.7%) 189 (100.0%)

Asset Income 191 (98.1%) 4 (1.9%) 194 (100.0%)

Child Care Expense 34 (100.0%) 34 (100.0%)

Disability Expense 4 (100.0%) 4 (100.0%)

Medical Expense 470 (98.6%) 7 (1.4%) 476 (100.0%)

2016.08.17 [Weighted]
HUDQC Final Report for FY 2015 C-27 

Appendix C: Source Tables

HUDQC FY 2015 

Table 16a. QC Rent Component for Households With QC Rent Error (>$5)

Rent Component

Form HUD-50058 Form HUD-50059 Total

# of Households 

(in 1,000s) % of Households

# of Households 

(in 1,000s) % of Households

# of Households 

(in 1,000s) % of Households

Earned Income

No Error 2,882 (88.1%) 1,299 (94.0%) 4,181 (89.8%)

With Error 389 (11.9%) 83 (6.0%) 473 (10.2%)

Pension, Etc.

No Error 3,044 (93.1%) 1,300 (94.1%) 4,345 (93.4%)

With Error 227 (6.9%) 82 (5.9%) 309 (6.6%)

Public Assistance

No Error 3,211 (98.2%) 1,374 (99.4%) 4,585 (98.5%)

With Error 60 (1.8%) 9 (.6%) 68 (1.5%)

Other Income

No Error 3,060 (93.6%) 1,339 (96.9%) 4,400 (94.5%)

With Error 211 (6.4%) 43 (3.1%) 254 (5.5%)

Asset Income

No Error 3,205 (98.0%) 1,335 (96.6%) 4,540 (97.6%)

With Error 66 (2.0%) 47 (3.4%) 113 (2.4%)

Child Care Expense

No Error 3,231 (98.8%) 1,377 (99.6%) 4,608 (99.0%)

With Error 40 (1.2%) 5 (.4%) 45 (1.0%)

Disability Expense

No Error 3,271 (100.0%) 1,382 (100.0%) 4,653 (100.0%)

With Error

Medical Expense

No Error 3,067 (93.8%) 1,271 (92.0%) 4,338 (93.2%)

With Error 204 (6.2%) 111 (8.0%) 315 (6.8%)

All Components

No Error 2,446 (74.8%) 1,107 (80.1%) 3,553 (76.3%)

With Error 825 (25.2%) 276 (20.0%) 1,101 (23.7%)

Total 3,271 (100.0%) 1,382 (100.0%) 4,653 (100.0%)

2016.08.17 [Weighted]
C-28 August 31, 2016

Appendix C: Source Tables

HUDQC FY 2015 

Table 16b. QC Error Households With Missing Verification in Tenant File

Rent Component

Form HUD-50058 Form HUD-50059 Total

# of Households 

(in 1,000s) % of Households

# of Households 

(in 1,000s) % of Households

# of Households 

(in 1,000s) % of Households

Earned Income

Verified 202 (51.9%) 52 (62.5%) 254 (53.8%)

Not Verified 187 (48.1%) 31 (37.5%) 218 (46.2%)

Pension, Etc.

Verified 157 (69.4%) 59 (71.3%) 216 (69.9%)

Not Verified 69 (30.6%) 24 (28.7%) 93 (30.1%)

Public Assistance

Verified 20 (33.8%) 20 (29.6%)

Not Verified 40 (66.2%) 9 (100.0%) 48 (70.4%)

Other Income

Verified 113 (53.5%) 14 (33.0%) 127 (50.0%)

Not Verified 98 (46.5%) 29 (67.0%) 127 (50.0%)

Asset Income

Verified 28 (43.2%) 30 (63.1%) 58 (51.6%)

Not Verified 37 (56.8%) 17 (36.9%) 55 (48.4%)

Child Care Expense

Verified 24 (59.0%) 3 (69.0%) 27 (60.1%)

Not Verified 16 (41.0%) 2 (31.0%) 18 (39.9%)

Disability Expense

Verified

Not Verified

Medical Expense

Verified 44 (21.3%) 40 (36.1%) 84 (26.5%)

Not Verified 161 (78.7%) 71 (63.9%) 232 (73.5%)

2016.08.17 [Weighted]
HUDQC Final Report for FY 2015 C-29 

Appendix C: Source Tables

HUDQC FY 2015 

Table 17a. Administrative Error: Number and Percent of Households, Average Dollars in Error

For Non-MTW Households With Recalculated Form HUD-50058/50059 Rent Error by Administrative Error Type

Error Type

Non-MTW Households with Recalculated Form HUD-50058/50059 Rent Error

# of Households in Error % of Households in Error Average Gross Dollar Error

Transcription Error 144 (49.3%) 11.70

No Transcription Error 148 (50.7%) 14.50

Consistency Error 46 (15.6%) 15.42

No Consistency Error 247 (84.4%) 12.70

Allowances Calculation Error 5 (1.8%) 69.72

No Allowances Calculation Error 287 (98.2%) 12.10

Income Calculation Error 9 (3.2%) 40.06

No Income Calculation Error 283 (96.8%) 12.24

Other Calculation Error 21 (7.0%) 19.66

No Other Calculation Error 272 (93.0%) 12.63

Overdue Recertification 6 (2.1%) 5.37

On-time Recertification 229 (78.1%) 12.13

Certification 58 (19.8%) 17.88

Any Administrative/Procedural Error 166 (56.7%) 10.88

No Administrative/Procedural Error 127 (43.3%) 16.06

Total Households 293 (100.0%) 13.12

Note: Data presented above exclude Moving to Work households.

2016.08.17 [Weighted]
C-30 August 31, 2016

Appendix C: Source Tables

HUDQC FY 2015 

Table 17b. Administrative Error: Number and Percent of Households, Average Dollars in Error

For Households With QC Rent Error by Administrative Error Type

Error Type

Households with QC Rent Error

# of Households in Error % of Households in Error Average Gross Dollar Error

Transcription Error 1,002 (81.7%) 50.72

No Transcription Error 224 (18.3%) 51.16

Consistency Error 270 (22.0%) 45.95

No Consistency Error 956 (78.0%) 52.17

Allowances Calculation Error 24 (2.0%) 68.36

No Allowances Calculation Error 1,202 (98.0%) 50.45

Income Calculation Error 39 (3.2%) 93.81

No Income Calculation Error 1,188 (96.8%) 49.39

Other Calculation Error 75 (6.1%) 89.09

No Other Calculation Error 1,151 (93.9%) 48.30

Overdue Recertification 49 (4.0%) 112.67

On-time Recertification 1,013 (82.6%) 48.69

Certification 165 (13.5%) 45.48

Any Administrative/Procedural Error 1,047 (85.4%) 51.18

No Administrative/Procedural Error 179 (14.6%) 48.63

Total Households 1,227 (100.0%) 50.80

2016.08.17 [Weighted]
HUDQC Final Report for FY 2015 C-31 

Appendix C: Source Tables

HUDQC FY 2015 

Table 18. Administrative Error: Number and Percent of Households, Average Dollars in Error

For All Households by Administrative Error Type

Error Type

Gross QC Rent Error Net QC Rent Error

# of Households % of Households

Average Dollar 

Error # of Households % of Households

Average Dollar 

Error

Transcription Error 1,949 (41.9%) 26.08 1,949 (41.9%) -8.25

No Transcription Error 2,704 (58.1%) 4.25 2,704 (58.1%) -1.79

Consistency Error 976 (21.0%) 12.73 976 (21.0%) -4.69

No Consistency Error 3,677 (79.0%) 13.57 3,677 (79.0%) -4.44

Allowances Calculation Error 52 (1.1%) 31.69 52 (1.1%) -23.44

No Allowances Calculation Error 4,601 (98.9%) 13.18 4,601 (98.9%) -4.28

Income Calculation Error 108 (2.3%) 33.89 108 (2.3%) -15.49

No Income Calculation Error 4,545 (97.7%) 12.90 4,545 (97.7%) -4.23

Other Calculation Error 171 (3.7%) 39.38 171 (3.7%) 8.57

No Other Calculation Error 4,483 (96.3%) 12.40 4,483 (96.3%) -4.99

Overdue Recertification 72 (1.5%) 76.64 72 (1.5%) 23.22

On-time Recertification 3,988 (85.7%) 12.36 3,988 (85.7%) -4.94

Certification 594 (12.8%) 12.65 594 (12.8%) -4.81

Any Administrative/Procedural Error 2,479 (53.3%) 21.62 2,479 (53.3%) -7.04

No Administrative/Procedural Error 2,174 (46.7%) 4.01 2,174 (46.7%) -1.58

Total 4,653 (100.0%) 13.39 4,653 (100.0%) -4.49

2016.08.17 [Weighted]
C-32 August 31, 2016

Appendix C: Source Tables

HUDQC FY 2015 

Table 19. Occupancy Standards on Form HUD-50058/50059

Number of Bedrooms by 

Occupancy Standard

Public Housing PHA-administered Section 8 Owner-administered Total

# of 

Households

(in 1,000s)

% of 

Households

# of 

Households 

(in 1,000s)

% of 

Households

# of 

Households 

(in 1,000s)

% of 

Households

# of 

Households 

(in 1,000s)

% of 

Households

Under-Housed

0 1 (1.3%) 3 (3.5%) 2 (2.5%) 6 (2.4%)

1 2 (.6%) 1 (.2%) 5 (.6%) 9 (.5%)

2 6 (1.9%) 23 (2.9%) 5 (1.9%) 33 (2.5%)

3 5 (2.3%) 23 (3.7%) 2 (1.6%) 30 (3.1%)

4 2 (3.2%) 2 (.9%)

5+ 2 (7.6%) 2 (5.1%)

All Units 16 (1.5%) 52 (2.4%) 14 (1.0%) 82 (1.8%)

Correct

0 82 (98.7%) 70 (96.5%) 78 (97.5%) 230 (97.6%)

1 357 (99.4%) 592 (99.8%) 890 (99.4%) 1,838 (99.5%)

2 221 (73.2%) 536 (68.0%) 192 (74.9%) 950 (70.5%)

3 186 (78.3%) 440 (71.6%) 97 (81.3%) 722 (74.4%)

4 31 (58.9%) 66 (65.2%) 17 (56.3%) 115 (61.9%)

5+ 1 (8.1%) 8 (29.3%) 9 (22.1%)

All Units 878 (83.9%) 1,712 (77.9%) 1,274 (92.1%) 3,864 (83.5%)

Over-Housed

0

1

2 75 (24.8%) 229 (29.1%) 59 (23.2%) 364 (27.0%)

3 46 (19.4%) 152 (24.7%) 20 (17.1%) 218 (22.5%)

4 20 (37.9%) 35 (34.8%) 13 (43.7%) 69 (37.2%)

5+ 11 (91.9%) 18 (63.1%) 1 (100.0%) 31 (72.7%)

All Units 152 (14.6%) 434 (19.7%) 95 (6.8%) 681 (14.7%)

2016.08.17 [Weighted]
HUDQC Final Report for FY 2015 C-33 

Appendix C: Source Tables

HUDQC FY 2015 

Table 19a. Frequency and Percent of All Households

by Number of Bedrooms and Number of Household Members

Number of 

Bedrooms

Number of Household Members

1 2 3 4 5 6 7 8 9 10 11

N % N % N % N % N % N % N % N % N % N % N %

0 230 97.6% 3 1.3% 3 1.1%

1 1675 90.7% 163 8.8% 5 0.3% 2 0.1% 2 0.1%

2 364 27.0% 579 43.0% 273 20.3% 98 7.3% 24 1.8% 7 0.5% 2 0.2%

3 88 9.0% 130 13.4% 318 32.8% 237 24.4% 114 11.8% 53 5.5% 23 2.4% 6 0.6% 2 0.2%

4 5 2.5% 9 4.6% 16 8.5% 40 21.5% 50 27.0% 33 17.7% 18 9.5% 14 7.7% 2 0.9%

5+ 1 3.4% 2 5.9% 4 9.2% 15 35.8% 8 18.4% 6 13.2% 3 6.6% 1 2.4% 2 5.1%

2016.08.17 [Weighted]
Source Tables Based on Tenant File Data

Source Tables Based on Tenant File Data

Throughout these Source Tables, empty cells indicate that either the result was zero 

or the analysis was not applicable.

The following tables are based solely on data collected from tenant files.
HUDQC Final Report for FY 2015 C-35 

Appendix C: Source Tables

HUDQC FY 2015 [Tenant File]

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

Program Type

Underpayment Proper Payment Overpayment Total

# of 

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

# of 

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

# of 

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

# of 

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

PHA-administered

Public Housing 139 (13.1%) (23.9%) 811 (76.4%) (23.4%) 112 (10.5%) (18.6%) 1,062 (100.0%) (22.8%)

Section 8 305 (13.8%) (52.4%) 1,620 (73.3%) (46.7%) 284 (12.8%) (47.2%) 2,209 (100.0%) (47.5%)

Total 444 (13.6%) (76.2%) 2,431 (74.3%) (70.1%) 395 (12.1%) (65.8%) 3,271 (100.0%) (70.3%)

Owner-administered

Owner-administered 139 (10.0%) (23.8%) 1,038 (75.1%) (29.9%) 206 (14.9%) (34.2%) 1,382 (100.0%) (29.7%)

Total 139 (10.0%) (23.8%) 1,038 (75.1%) (29.9%) 206 (14.9%) (34.2%) 1,382 (100.0%) (29.7%)

Total 583 (12.5%) (100.0%) 3,469 (74.6%) (100.0%) 601 (12.9%) (100.0%) 4,653 (100.0%) (100.0%)

2016.08.17 [Weighted]

HUDQC FY 2015 [Tenant File]

Table 2(S). Percent of Households by Payment Type and Program Type

(Proper Payment Based on Exact Match of Actual and DC Rent)

Program Type

Underpayment Proper Payment Overpayment Total

# of 

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

# of 

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

# of 

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

# of 

Cases 

(in 1,000)

Row % of 

Cases

Col. % of 

Cases

PHA-administered

Public Housing 196 (18.5%) (24.0%) 690 (65.0%) (23.3%) 176 (16.6%) (20.0%) 1,062 (100.0%) (22.8%)

Section 8 405 (18.3%) (49.5%) 1,367 (61.9%) (46.3%) 437 (19.8%) (49.7%) 2,209 (100.0%) (47.5%)

Total 601 (18.4%) (73.5%) 2,056 (62.9%) (69.6%) 614 (18.8%) (69.7%) 3,271 (100.0%) (70.3%)

Owner-administered

Owner-administered 217 (15.7%) (26.5%) 899 (65.0%) (30.4%) 267 (19.3%) (30.3%) 1,382 (100.0%) (29.7%)

Total 217 (15.7%) (26.5%) 899 (65.0%) (30.4%) 267 (19.3%) (30.3%) 1,382 (100.0%) (29.7%)

Total 818 (17.6%) (100.0%) 2,955 (63.5%) (100.0%) 881 (18.9%) (100.0%) 4,653 (100.0%) (100.0%)

2016.08.17 [Weighted]
C-36 August 31, 2016

Appendix C: Source Tables

HUDQC FY 2015 [Tenant File]

Table 3. Dollar Rent Error by Program Type

Program Type

Actual Rent (Monthly) C Rent (Monthly) Gross Rent Error (Monthly)

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum Dollar 

Amount 

(in 1,000)

Avg. Dollar 

Amount

PHA-administered

Public Housing 1,062 (22.8%) 292,018 275.05 1,062 (22.8%) 293,477 276.42 1,062 (22.8%) 15,188 14.31

Section 8 2,209 (47.5%) 586,239 265.35 2,209 (47.5%) 592,732 268.29 2,209 (47.5%) 34,158 15.46

Total 3,271 (70.3%) 878,258 268.50 3,271 (70.3%) 886,209 270.93 3,271 (70.3%) 49,346 15.09

Owner-administered

Owner-administered 1,382 (29.7%) 340,759 246.49 1,382 (29.7%) 338,366 244.76 1,382 (29.7%) 15,480 11.20

Total 1,382 (29.7%) 340,759 246.49 1,382 (29.7%) 338,366 244.76 1,382 (29.7%) 15,480 11.20

Total 4,653 (100.0%) 1,219,016 261.96 4,653 (100.0%) 1,224,575 263.15 4,653 (100.0%) 64,826 13.93

2016.08.17 [Weighted]

HUDQC FY 2015 [Tenant File]

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

Program Type

Underpayment (Monthly) Overpayment (Monthly) DC Rent (Monthly)

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

PHA-administered

Public Housing 139 (23.9%) 8,346 60.02 112 (18.6%) 6,842 61.30 1,062 (22.8%) 293,477 276.42

Section 8 305 (52.4%) 20,338 66.61 284 (47.2%) 13,820 48.72 2,209 (47.5%) 592,732 268.29

Total 444 (76.2%) 28,684 64.55 395 (65.8%) 20,662 52.27 3,271 (70.3%) 886,209 270.93

Owner-administered

Owner-administered 139 (23.8%) 6,521 47.06 206 (34.2%) 8,959 43.51 1,382 (29.7%) 338,366 244.76

Total 139 (23.8%) 6,521 47.06 206 (34.2%) 8,959 43.51 1,382 (29.7%) 338,366 244.76

Total 583 (100.0%) 35,205 60.39 601 (100.0%) 29,621 49.27 4,653 (100.0%) 1,224,575 263.15

2016.08.17 [Weighted]
HUDQC Final Report for FY 2015 C-37 

Appendix C: Source Tables

HUDQC FY 2015[Tenant File]

Table 4(S). Dollar Error Amount by Payment Type and Program Type

(Proper Payment Based on Exact Match of Actual and DC Rent)

Program Type

Underpayment (Monthly) Overpayment (Monthly) DC Rent (Monthly)

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

PHA-administered

Public Housing 196 (24.0%) 8,483 43.30 176 (20.0%) 6,976 39.60 1,062 (22.8%) 293,477 276.42

Section 8 405 (49.5%) 20,596 50.84 437 (49.7%) 14,103 32.24 2,209 (47.5%) 592,732 268.29

Total 601 (73.5%) 29,078 48.38 614 (69.7%) 21,079 34.35 3,271 (70.3%) 886,209 270.93

Owner-administered

Owner-administered 217 (26.5%) 6,730 31.07 267 (30.3%) 9,122 34.14 1,382 (29.7%) 338,366 244.76

Total 217 (26.5%) 6,730 31.07 267 (30.3%) 9,122 34.14 1,382 (29.7%) 338,366 244.76

Total 818 (100.0%) 35,808 43.80 881 (100.0%) 30,201 34.29 4,653 (100.0%) 1,224,575 263.15

2016.08.17 [Weighted]

HUDQC FY 2015 [Tenant File]

Table 5. Gross and Net Rent Error by Program Type

Program Type

Gross Rent Error (Monthly) Net Rent Error (Monthly) DC Rent (Monthly)

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum Dollar 

Amount (in 

1,000)

Avg. 

Dollar 

Amount

PHA-administered

Public Housing 1,062 (22.8%) 15,188 14.31 1,062 (22.8%) -1,504 -1.42 1,062 (22.8%) 293,477 276.42

Section 8 2,209 (47.5%) 34,158 15.46 2,209 (47.5%) -6,518 -2.95 2,209 (47.5%) 592,732 268.29

Total 3,271 (70.3%) 49,346 15.09 3,271 (70.3%) -8,022 -2.45 3,271 (70.3%) 886,209 270.93

Owner-administered

Owner-administered 1,382 (29.7%) 15,480 11.20 1,382 (29.7%) 2,437 1.76 1,382 (29.7%) 338,366 244.76

Total 1,382 (29.7%) 15,480 11.20 1,382 (29.7%) 2,437 1.76 1,382 (29.7%) 338,366 244.76

Total 4,653 (100.0%) 64,826 13.93 4,653 (100.0%) -5,585 -1.20 4,653 (100.0%) 1,224,575 263.15

2016.08.17 [Weighted]
C-38 August 31, 2016

Appendix C: Source Tables

HUDQC FY 2015 [Tenant File]

Table 5(S). Gross and Net Rent Error by Program Type

(Proper Payment Based on Exact Match of Actual and DC Rent)

Program Type

Gross Rent Error (Monthly) Net Rent Error (Monthly) DC Rent (Monthly)

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

# of 

Cases 

(in 1,000)

Col. % of 

Cases

Sum 

Dollar 

Amount 

(in 1,000)

Avg. 

Dollar 

Amount

PHA-administered

Public Housing 1,062 (22.8%) 15,458 14.56 1,062 (22.8%) -1,507 -1.42 1,062 (22.8%) 293,477 276.42

Section 8 2,209 (47.5%) 34,699 15.71 2,209 (47.5%) -6,493 -2.94 2,209 (47.5%) 592,732 268.29

Total 3,271 (70.3%) 50,157 15.33 3,271 (70.3%) -8,000 -2.45 3,271 (70.3%) 886,209 270.93

Owner-administered

Owner-administered 1,382 (29.7%) 15,852 11.47 1,382 (29.7%) 2,392 1.73 1,382 (29.7%) 338,366 244.76

Total 1,382 (29.7%) 15,852 11.47 1,382 (29.7%) 2,392 1.73 1,382 (29.7%) 338,366 244.76

Total 4,653 (100.0%) 66,009 14.18 4,653 (100.0%) -5,607 -1.21 4,653 (100.0%) 1,224,575 263.15

2016.08.17 [Weighted]
Appendix D: Consistency and Calculation Errors
HUDQC Final Report for FY 2015 D-1 

Appendix D: Consistency and Calculation Errors

Exhibit D-1

Form HUD-50058—Consistency Errors

Form HUD-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 Must equal M or F

3h. Relationship Must equal H, S, K, F, Y, E, L, or A

3i. Citizenship Must equal EC, EN, IN, PV

3k. Race Must equal 1 through 5

3m. Ethnicity Must equal 1 or 2

3u. Family Subsidy Status Must equal C, E, F, P

3v. Effective Date Should not be blank if 3u equals C 

Net Family Assets and Income

6a. Family Member No. Must equal a number used in Section 3. Household

7a. Family Member No. Must equal a number used in Section 3. Household

7b. Income Code Must equal B, F, HA, M, W, G, IW, T, P, S, SS, C, E, I, N, or U

8a. Total Annual Income Must equal Total Annual Income recorded in 7i

8i. Earnings Made Possible by 

Disability Assistance Expense

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 Should only be completed if any member is disabled 

8j. Allowable Disability Assistance 

Expense

•	 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

8n. Medical/Disability Assistance 

Allowance

•	 Should equal Total Annual Disability Assistance and Medical 

Expense (8m) minus Medical/Disability Threshold (8f) if Allowable 

Disability Assistance Expense (8j) is blank or if the Total Annual 

Unreimbursed Disability Assistance Expense (8g) is less than the 

Medical/Disability Threshold (8f)

•	 Should 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)

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, 

a member who is disabled, or a full-time student (excluding the head, 

spouse, foster child, or adult, or live-in attendant)
Appendix D: Consistency and Calculation Errors

D-2 August 31, 2016 

Exhibit D-1

Form HUD-50058—Consistency Errors, continued

Form HUD-50058 Item Error

8t. Yearly Child Care Cost That Is 

Not Reimbursed (Child Care 

Allowance)

Should only be completed if any member is less than 13 years old

Family Rent and Subsidy Information

10a. TTP (Public Housing and Turnkey 

III)

11q. TTP (Section 8: Project Based 

Certificates and Vouchers)

12r. TTP (Housing Choice Vouchers: 

Tenant Based Vouchers)

13j. TTP (Section 8: Moderate Rehabilitation [Mod Rehab])

14s. TTP (Manufactured Home Owner 

Renting the Space)

Items 10a, 11q, 12r, 13j, or 14s must equal TTP (9j) or be blank

Rent Calculations (item numbers include 

10a through 14ag)

•	 If Program (1c) = P: 

o TTP (10a) must be completed

o Flat Rent (10b), Tenant Rent (10f), or Mixed Family Tenant Rent 

(10s) must be completed

o Sections 11 through 14 must be blank

•	 If Program (1c) = VO or C:

o Section 11 or 12 must be completed

o Tenant Rent (11s or 12k) or Mixed Family Tenant Rent (11ak or 

12ai) must be completed

o Sections 10, 13, and 14 must be blank

•	 If Program (1c) = MR:

o Contract Rent to Owner must be completed

o Tenant Rent (13k) or Mixed Family Tenant Rent (13x) must 

be completed

o Sections 10, 11, 12, and 14 must be blank
HUDQC Final Report for FY 2015 D-3 

Appendix D: Consistency and Calculation Errors

Exhibit D-2

Form HUD-50058 MTW*—Consistency Errors

Form HUD-50058 MTW Item Error

General Information

1c. Program Must equal P, PR, or T

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 Must equal M or F

3h. Relationship Must equal H, S, K, F, Y, E, L, or A

3i. Citizenship Must equal EC, EN, IN, PV

3k. Race Must equal 1 through 5

3m. Ethnicity Must equal 1 or 2

3u. Family Subsidy Status Must equal C, E, F, P

3v. Eligibility Effective Date Should not be blank if 3u equals C 

Net Family Assets and Income

18a. Family Member No. Must equal a number used in Section 3. MTW Household

19a. Family Member No. Must equal a number used in Section 3. MTW Household

19b. Income Code Must equal B, F, HA, M, W, G, IW, T, P, S, SS, C, E, I, N, U, or X

*For the purpose of the study, we implemented a Moving to Work (MTW) exception if a case was flagged as using Form 

HUD-50058 MTW. As a result, there were 112 MTW cases (representing 27 projects) that did not use Form HUD-50058 MTW, but 

did adhere to MTW policies. There were no non-MTW cases that used Form HUD‐50058 MTW.
Appendix D: Consistency and Calculation Errors

D-4 August 31, 2016

Exhibit D-3 

Form HUD-50059—Consistency Errors

Form HUD-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

40. Race of Head of Household Must equal 1 through 4

41. Ethnicity of Head of Household Must equal 1 or 2

Household Composition

39. Sex Must equal M or F

44. Special Status Code Must equal E, S, H, F, I, J, or blank; should be E if Age > 61

46. Eligibility Code (Citizenship) Must equal EC, EN, IC, IN, IP, PV, or XX

Net Family Assets and Income

66. Member No.—Income Info

75. Member No.—Asset Info

Should not be greater than the total number of members listed in item 

34 (Family Member Number)

Allowances and Adjusted Income

97. Deduction for Dependents Must be completed if Number of Dependents (55) is greater than 0 

98. Child Care Expense (work)

99. Child Care Expense (school) Should only be completed if any member is less than 13 years old

102. Disability Allowance

•	 Should be ≤ Disability Expenses (101)

•	 Should be 0 if 3% of Annual Income (100) is > Total Disability 

Assistance Expenses (101)

•	 Should be 0 or blank if Total Disability Expenses (101) is 0 or blank

103. Total Medical Expenses

Should only be completed if the Special Status Code (43) for the head, 

spouse, or co-head = H or E, or if the head, spouse, or co-head is 62 

years old or older

105. Elderly Household Allowance Should be $400 if the Special Status Code (43) for the head or spouse 

or co-head = H or E; otherwise it should be 0 or blank

Family Rent and Subsidy Information

109. Tenant Rent Should equal the maximum of TTP (108) minus the Utility Allowance 

(32) or 0, or be blank if the Utility Reimbursement (110) > 0

110. Utility Reimbursement Should be blank if Item 32 < Item 108
HUDQC Final Report for FY 2015 D-5 

Exhibit D-4 

Form HUD-50058—Calculation Errors

Form HUD-50058 Item Error Calculation

Household Composition

3f. Age Must equal the age calculated based on Date of Birth (3e) and Effective 

Date of Action (2b)

8q. Number of Dependents

Must equal the number of household members who are under age 18, 

have a disability, or are full-time students (other than head, spouse, 

co-head, foster child/adult, or live-in aide)

Net Family Assets and Income

6f. Total Asset Value Must equal the sum of all values in Cash Value of Asset (6d)

6i. Imputed Asset Income

Must equal Total Cash Value of Asset (6f) times the 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

6j. Income From Asset Must equal the larger of Total Anticipated Income (6g) or Imputed Asset 

Income (6i)

7g. Total Non-Asset Income Must equal the sum of all values in Income After Exclusions (7f)

7i. Total Annual Income Must equal Final Asset Income (6j) + Total Income Other Than 

Assets (7g)

Allowances and Adjusted Income

8e. Total Permissible Deductions Must equal the sum of all values in Amount of Permissible Deduction (8d)

8f. 3% of Annual Income Must equal 3% * Total Annual Income (8a)

8h. Disability Allowance

Must equal Total Annual Unreimbursed Disability Assistance Expense 

(8g) minus Medical/Disability Threshold (8f) if there is a disabled 

household member and an earned income greater than or equal to the 

disability expense

8n. Medical Allowance

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

8p. Elderly/Disabled Allowance Must equal $400 if head, spouse, or co-head is elderly or disabled

8s. Dependent Allowance Must equal Number of Dependents (8q) * $480

8t. Child Care Costs Must be 0 or blank if no household member is under age 13

8x. Total Allowance

Must equal Total Permissible Deductions (8e) + Medical/Disability 

Assistance Allowance (8n) + Elderly/Disability Allowance (8p) + 

Dependent Allowance (8s) + Total Annual Unreimbursed Child Care 

Costs (8t) + Total Annual Travel Cost to Work/School (8u)

8y. Adjusted Annual Income Must equal Total Annual Income (8a) minus Total Allowances (8x)
Appendix D: Consistency and Calculation Errors

D-6 August 31, 2016

Exhibit D-4 

Form HUD-50058—Calculation Errors, continued

Form HUD-50058 Item Error Calculation

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 program)

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.

Exhibit D-5

Form HUD-50058 MTW—Calculation Errors

Form HUD-50058 MTW Item Error Calculation

Household Composition

3f. Age Must equal the age calculated based on Date of Birth (3e) and Effective 

Date of Action (2b)

Net Family Assets and Income

18f. Total Asset Value Must equal the sum of all values in Cash Value of Asset (18d)

18i. Imputed Asset Income

Must equal Total Cash Value of Asset (18f) times the Passbook Rate 

(18h) if Total Value of Assets (18f) is > $5,000. If Total Value of Assets 

(18f) is ≤ $5,000 Imputed Asset Income (18i) = 0

18j. Income From Asset Must equal the larger of Total Anticipated Income (18g) or Imputed Asset 

Income (18i)

19h. Total Non-Asset Income Must equal the sum of all values in Income After Exclusions (19f)

19i. Total Annual Income Must equal Final Asset Income (18j) + Total Income Other Than 

Assets (19h)

Allowances and Adjusted Income

19k. Adjusted Annual Income Must equal Total Annual Income (19i) minus Total Deductions (19j)

Family Rent and Subsidy Information

21k. Gross Rent Must equal Rent to Owner (21i) + Utility Allowance/estimate (21j)

Note: With the exception of tenant rent, negative numbers are always converted to 0.

Exhibit D-6

Form HUD-50059—Calculation Errors

Form HUD-50059 Item Error Calculation

Household Composition

48. Age Must equal the age calculated based on Date of Birth (42) and Effective 

Date of Action (13)

53. Number of Family Members Must equal the number of family members listed

54. Number of Non-Family MembersMust equal the number of family members listed with a relationship code 

of “L” or “F”

55. Number of Dependents

Must equal the number of household members who are under age 18, 

have a disability, or are full-time students (other than head, spouse,

co-head, foster child/adult, or live-in aide)
HUDQC Final Report for FY 2015 D-7 

Exhibit D-6

Form HUD-50059—Calculation Errors, continued

Form HUD-50059 Item Error Calculation

Net Family Assets and Income

81. Total Asset Value Must equal the sum of the asset values in Cash Value of Assets (78)

82. Actual Income From Asset Must equal the sum of the income values in Actual Yearly Income From 

Assets (79)

84. Imputed Asset Income Must equal Total Asset Value (81) * 2%, if Total Value of Assets is > 

$5,000

70. Earned Income Sum Must equal the sum of income values (in item 68) for items with codes B, 

F, M, or W in Income Type Code (67)

71. Pension Income Sum Must equal the sum of the income values (in item 68) for items with codes 

PE, SI, or SS in Income Type Code (67)

72. Public Assistance Income Sum Must equal the sum of the income values (in item 68) for items with codes 

TA or G in Income Type Code (67)

73. Other Income Sum Must equal the sum of the income values (in item 68) for items with codes 

CS, I, N, or U in Income Type Code (67)

74. Total Non-Asset Income Must equal Earned Income Sum (70) + Pension Income Sum (71) + 

Public Assistance Income Sum (72) + Other Income Sum (73)

85. Asset Income Must equal the greater of Imputed Asset Income (84) or Actual Income 

from Asset (82)

86. Total Annual Income Must equal Total Non-Asset Income (74) + Income from Asset (85)

Allowances and Adjusted Income

97. Dependent Allowance Must equal Number of Dependents (55) * $480

98. Child Care Expense (work)

99. Child Care Expense (school) Must be 0 or blank if no household member is under age 13

100. 3% of Annual Income Must equal Total Annual Income (86) * .03

102. Disability Allowance

Must equal Total Disability Expenses (101) minus 3% of Annual Income 

(100) if there is a disabled household member and if there is earned 

income greater than or equal to the disability expense

104. Medical Allowance

Must equal Total Medical Expenses (103) minus 3% of Annual Income 

(100) if Total Disability Expense (101) = 0; or if (Disability Deduction (102) 

= 0, then Medical Deduction (104) = Total Medical Expenses (103) + 

Total Disability Expenses (101) - 3% of Annual Income (86), if the head, 

spouse, or co-head is elderly or disabled

105. Elderly Household Allowance Must equal $400 if head, spouse, or co-head is elderly or disabled

106. Total Allowance

Must equal Deduction for Dependents (97) + Child Care Expense 

Allowance (98 + 99) + Allowance for Disability Expenses (101) + 

Deduction for Medical Expenses (104) + Elderly Family Deduction (105)

107. Adjusted Annual Income Must equal Total Annual Income (86) minus Total Allowances (106)

Family Rent and Subsidy Information

33. Gross Rent Must equal Contract Rent (31) + Utility Allowance (32)

108. Total Tenant Payment Must equal the higher of 30% of Adjusted Income (107), 10% of Total 

Annual Income (86), Welfare Rent (112), or $25 (Minimum Rent)

109. 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.
Appendix E: Project Staff Questionnaire 

Descriptive Analysis
HUDQC Final Report for FY 2015 E-1 

Appendix E: Project Staff Questionnaire Descriptive Analysis

The Project Staff Questionnaire (PSQ) was designed to obtain project-level information about the 

characteristics and processes that support the Public Housing Authority (PHA) and project staff’s 

calculation of rent during certification transactions, including initial and annual certifications. The 

questionnaire aimed to identify structural procedures, standards, and policies that may hinder accurate 

rent determination to uncover potential areas of improvement. The PSQ is a self-administered online 

questionnaire sent to managers and executive directors of PHAs/projects included in the fiscal year 

(FY) 2015 Quality Control (QC) Study.

A. Methodology

The PSQ was administered as a Web questionnaire using a survey package called SelectSurvey. In 

February 2016, ICF staff contacted PHAs/projects via email with instructions on how to access and 

complete the survey. Follow-up emails and telephone calls were made through the end of May to 

PHAs/projects, reminding staff to complete the PSQ survey. Assistance was requested from HUD 

to encourage some of the nonresponsive PHAs/projects to complete the questionnaire. Overall, 

these efforts led to a response rate of 94 percent: 501 out of 531 PHAs/projects completed the PSQ. 

After data collection, we examined the data to confirm the completeness and validity of responses. 

PSQ surveys containing questionable responses or skip patterns were individually investigated 

and data issues were resolved. Three cases were removed due to missing data, which resulted in a 

total of 498 cases and a response rate of 94 percent.1

 The PSQ responses were analyzed separately 

for three major program types: Public Housing (179 projects), PHA-administered Section 8 (130 

projects), and Owner-administered Section 8 (189 projects). This analysis was conducted using 

SPSS, version 22.

The content of the FY 2015 PSQ was similar to the FY 2014 PSQ. It consisted of a combination of 

open-ended and closed-ended questions. Topics included project characteristics, software usage, 

training and development, performance management, and QC procedures. The results presented 

in this report reflect the project’s response for the study period of November 1, 2014, to October 

31, 2015.

B. Results

The results of the PSQ are presented in three sections that correspond to the three sections in the 

survey:

1. PHA/Project Characteristics. The PSQ surveyed respondents on PHA/project characteristics 

that may help to explain differences in error rates. This section included questions about 

the number and types of staff, number of project units/tenants and certifications conducted, 

staff work experience, staff work assignments and workload, and staff use of software and 

computer technology related to certification tasks.

2. Training and Development. The second section of the survey gathered information about 

the nature and extent of rent calculation training for new and experienced certification staff, 

1 Two of these projects only responded to the first question of the PSQ; the third project only responded 

to the first six questions.
Appendix E: Project Staff Questionnaire Descriptive Analysis

E-2 August 31, 2016 

procedures for implementing new policies related to rent calculation, and certification staff 

work practices. 

3. Performance Management. The PSQ also inquired about various aspects of performance 

management of certification activities and QC reviews, including the timing of reviews, 

methods used to select cases for review, the type of certification information reviewed for 

QC, the prevalence of various types of rent calculation errors, and performance feedback 

methods and timing. 

Our highlights of the survey findings are presented in the sections that follow.

1. PHA/Project Characteristics

Type and Number of Staff. Beginning in FY 2008, the PSQ has collected information from projects 

about whether or not they can provide information about the administration of rental assistance in the 

project specifically selected for the QC Study review. Some PHAs/projects cannot provide information 

about practices for a specific project because PHA/project staff work across multiple counties or across 

a number of assisted housing units beyond the site or county selected for QC review. In FY 2015, 

75 percent of PHAs/projects could provide information specific to the project selected for the QC 

Study review (see Exhibit E-1a). PHAs/projects that could not provide project-specific information, 

but offered information about their entire organization, indicated that they employed an average of 

22 staff members who supported an average total of 3,320 units/households.2

 These organizations 

reported an average ratio of 78 assisted units/households per total staff. PHA-administered Section 8 

projects had the highest average ratio of units per total staff at 129, Owner-administered projects had 

the smallest average ratio at 32, and Public Housing projects were in the middle, with an average of 

76 units per total staff in the organization.

In FY 2015, PHAs and management companies that could provide information on a 

specific project averaged 11 employees (see Exhibit E-1a). With respect to program type, 

PHA-administered Section 8 projects averaged 26 employees, followed by Public Housing with 

9 employees and Owner-administered projects with 4 employees. On average, PHA/project staff 

across all three program types supported 1,238 units over a 12-month period, with an average ratio 

of 88 units per total staff. As above, PHA-administered Section 8 projects had the highest ratio of 

units per total staff at 166, Owner-administered projects had the smallest ratio at 40, and Public 

Housing sites averaged 88 units per total staff. 

In addition to the general ratio of total employed staff to units/households served, the PSQ collected 

information about certification staff members who performed move-in and annual certifications. 

In FY 2015, PHAs/projects averaged 8 certification staff members who conducted an average of 

2 In the analysis tables, medians are presented alongside averages because, for this data, the medians 

may be a more accurate measure of central tendency. Since the data is consistently skewed to the right, 

the averages tend to be much larger than the medians. For example, the average total number of staff at 

entire organizations is 22.2, while the median is 5.0 staff members. In other words, while the average 

is 22.2 staff, half of PHAs/projects employ five staff members or less. While the medians are presented 

in the tables, only averages will be discussed in the analysis so that FY 2015 PSQ results can easily be 

compared with FY 2014 PSQ results.
HUDQC Final Report for FY 2015 E-3 

Appendix E: Project Staff Questionnaire Descriptive Analysis

164 move-in/initial certifications and 1,516 annual certifications over a 12-month period (see Exhibit 

E-1a). PHA-administered Section 8 projects had the highest average number of certification staff at 

23, but these sites also performed the most work overall, with an average of 531 initial certifications 

and 5,065 annual certifications completed during the study period. Owner-administered projects 

had the smallest average with 2 certification staff members who conducted an average of 20 initial 

certifications and 112 annual certifications. Public Housing sites had an average of 4 certification 

staff members and managed an average of 43 initial certifications and 422 annual certifications 

conducted in FY 2015.

Exhibit E-1a

 Number of Staff and Certifications, by Program Type

Number of Staff and

Number of Certifications Performed

Program Type

All 

Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

PHA/Project Can Provide Information About Staff and 

Certifications at Individual Projects 76.5% 68.5% 76.7% 74.5%

Entire Organizations

Total Number of Staff the Entire 

Organization Employs

Mean 7.4 55.4 5.4 22.2

Median 4.0 12.0 3.0 5.0

Total Number of Assisted Units 

Supported by These Staff 

Mean 380.1 9,802.8 85.8 3,320.1

Median 203.0 1,089.0 64.0 197.0

Units per Entire Organization Staff Ratio

Mean 76.1 128.6 31.6 77.7

Median 39.6 99.9 20.0 38.8

Individual Projects

Total Number of Staff the Individual 

Project Employs

Mean 8.8 25.8 3.9 11.0

Median 4.0 11.0 3.0 4.0

Total Number of Assisted Units 

Supported by These Staff

Mean 470.1 4,198.7 129.7 1,237.5

Median 256.0 1,908.0 100.0 200.0

Units per Individual Project Staff Ratio

Mean 87.6 166.0 39.6 87.8

Median 56.7 163.3 32.3 52.1

Entire Organizations and Individual Projects

Number of Certification Staff Who Work 

at the PHA/Project 

Mean 3.6 23.2 2.4 8.3

Median 3.0 9.0 2.0 3.0

Number of Initial/Move-in Certifications 

Conducted Over a 12-Month Period 

Mean 42.7 531.0 19.6 163.7

Median 33.5 189.0 11.5 31.0

Number of Annual Certifications 

Conducted Over a 12-Month Period

Mean 421.8 5,065.0 112.1 1,516.4

Median 207.0 1,379.0 89.0 176.0

Note: Averages and medians were calculated based on the number of PHAs/projects that responded to the specific items.

Experienced Certification Staff and Certification Staff Turnover. Additionally, the PSQ collected 

information specifically about the number of experienced certification staff at PHAs/projects. In the 

survey, “experienced certification staff” was defined as certification staff members with more than 

1 year of certification experience at the project. On average, PHA-administered Section 8 projects 
Appendix E: Project Staff Questionnaire Descriptive Analysis

E-4 August 31, 2016

reported having 18 experienced staff, while Public Housing had 3 experienced certification staff and 

Owner-administered projects had 2 experienced staff during the study period (see Exhibit E-1b).

PHAs/projects were also asked about the rate of certification staff turnover. Thirty-one percent of 

PHAs/projects in the study indicated that they had at least 1 certification employee stop working 

on certification activities at the specified project from November 1, 2014, to October 31, 2015 

(see Exhibit E-1b), the same rate as in FY 2014. Among those PHAs/projects that had turnover, 

an average of 3 certification staff stopped working on certification activities at the project during 

the study period. PHA-administered Section 8 projects were most likely to have certification staff 

turnover during the study period (44 percent) and reported the largest turnover (an average of 

5 certification staff). Public Housing sites were least likely to have certification staff turnover 

(26 percent) and reported turnover of 2 certification staff. Twenty-eight percent of Owner‐administered 

projects had some turnover, with an average of 1 certification staff member who stopped working 

on certification activities at the specified project in FY 2015.

Exhibit E-1b

 Experienced Certification Staff and Staff Turnover, by Program Type

Average Number of Certification Staff

Program Type

All 

Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

Experienced Certification Staff

Certification Staff With More Than 1 Year 

of Experience

Mean 3.1 18.2 2.2 6.7

Median 3.0 7.0 2.0 3.0

Certification Staff Turnover

PHAs/Projects With At Least One Certification Staff 

Member Who Stopped Working on Certification Activities 26.3% 43.8% 27.5% 31.3%

Number of Certification Staff Members 

Who Stopped Working on Certification 

Activities*

Mean 1.6 4.6 1.3 2.6

Median 1.0 2.0 1.0 1.0

Note: Averages, medians, and percentages were calculated based on the number of PHAs/projects that responded to the specific 

items.

* Averages and medians were calculated based on the PHAs/projects that had staff turnover.

Certification Staff Assignments and Workload. The FY 2015 PSQ asked PHAs/projects to indicate 

how work was assigned to certification staff. The most frequently employed methods for assigning 

cases were by certification transaction type (19 percent) and by random assignment of cases based 

on staff availability (16 percent) (see Exhibit E-1c). Methods with less frequently reported use 

included assignment of cases by certification activity type and by tenant last name (12 percent and 

10 percent, respectively), while almost no projects assigned cases by household characteristics (less 

than 1 percent). Although the question provided a variety of case assignment methods, nearly half 

of PHAs/projects reported that they use case assignment methods other than those provided in the 

survey (43 percent).

A look at program-specific breakdowns indicates that PHA-administered Section 8 projects were 

by far the most likely to assign work alphabetically by tenant last name (35 percent), while just 

1 percent of Owner-administered projects and Public Housing projects relied on this method 
HUDQC Final Report for FY 2015 E-5 

(see Exhibit E-1c). Owner-administered projects were most likely to assign work by certification 

transaction type (20 percent) and by certification activity type (18 percent). Public Housing projects 

were also most likely to assign cases by certification transaction type (22 percent), followed by 

random assignment based on staff availability (18 percent). 

Exhibit E-1c

 Certification Staff Case Assignment Methods, by Program Type

Certification Staff Case Assignment Methods

Program Type

All 

Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

By Transaction Type 

(e.g., some staff work on move-ins only, some staff 

work on annual certifications only)

22.3% 14.6% 19.6% 19.3%

Random Assignment Based on Staff Availability 18.4% 10.8% 16.4% 15.7%

By Activity Type 

(e.g., some certification staff perform interviews 

and send out initial third-party verifications, while 

other staff perform rent calculations and enter data 

from Form HUD-50058/50059)

8.4% 7.7% 18.0% 11.8%

Alphabetical by Tenant Last Name 

(e.g., households with a last name that starts with 

any letter between “A” through “E” belong to one 

certification staff member/staff team)

1.1% 34.6% 1.1% 9.8%

By Household Characteristic 

(e.g., more complicated cases go to particular staff) 0.6% 0.0% 1.6% 0.8%

Other Assignment Method Not Listed 49.2% 32.3% 43.4% 42.6%

Note: Percentages were calculated based on the number of PHAs/projects that responded to the specific items.

In addition to methods of assigning work, PHAs/projects were asked to comment on the average 

workload for certification staff at the specified project (see Exhibit E-1d). The results show that most 

certification staff had either a manageable workload (56 percent) (i.e., a workload that is neither too 

low nor too high) or a high workload (44 percent). In FY 2014, each category described 49 percent of 

PHAs/projects, suggesting that more certification staff had a manageable workload in FY 2015. With 

respect to program type, Owner-administered projects were most likely to report a manageable workload 

(66 percent) from November 1, 2014, to October 31, 2015, while PHA-administered Section 8 

projects were most susceptible to heavy workloads (59 percent). 
Appendix E: Project Staff Questionnaire Descriptive Analysis

E-6 August 31, 2016

Exhibit E-1d

Certification Staff Average Workload, by Program Type

Certification Staff Average Workload

Program Type

All Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

The Workload Was High 43.6% 58.5% 33.9% 43.8%

The Workload Was Not Too Low or Too High 55.9% 40.8% 65.6% 55.6%

The Workload Was Low 0.6% 0.8% 0.5% 0.6%

Note: Percentages were calculated based on the number of PHAs/projects that responded to the specific item.

Organizations Contracted to Perform Certification Activities. Despite the moderate to heavy 

workloads experienced by certification staff, a large majority of PHAs/projects (89 percent) indicated 

that they do not contract out certification activities to outside organizations (see Exhibit E-1e). PHAs/

projects that did have a certification contractor in the past year were more likely to hire a private 

company or nonprofit organization to handle certifications (4 percent each), as opposed to the 

government (3 percent). With respect to program type, Public Housing projects were least likely to 

use a contractor, with 92 percent of projects performing certifications themselves. PHA-administered 

Section 8 and Owner-administered projects reported similar numbers (88 percent and 86 percent, 

respectively). Owner-administered sites that did contract out certification activities were most 

likely to choose a nonprofit organization (9 percent), while an equal number of PHA-administered 

Section 8 projects contracted out certification activities to private companies and government 

agencies (5 percent each).

Exhibit E-1e

Organizations Contracted to Perform Certification Activities by Program Type

Organizations Contracted to Perform 

Certifications

Program Type

All Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

PHA/Project Does Not Contract Out Certification 

Activities 92.2% 87.7% 85.7% 88.6%

Private Company 4.5% 4.6% 3.7% 4.2%

Nonprofit Organization 0.0% 3.1% 8.5% 4.0%

Government 3.4% 4.6% 2.1% 3.2%

Note: Percentages were calculated for PHAs/projects that responded to the specific item.

Utilization and Capabilities of Computer Software in the Certification Process. Automated 

systems and computer software continued to play an integral part in daily tasks at the PHAs/

projects surveyed. PHAs/projects were asked to describe the tasks for which certification staff 

used computer systems. Virtually all of the PHAs/projects reported using computers to print 

letters to tenants (98 percent) and Form HUD-50058/50059 (97 percent); calculate income, 

expenses, and allowances (96 percent); and record tenant demographics (92 percent) (see Exhibit 

E-1f). The majority of PHAs/projects also acknowledged using computer software to input 

certification interview information (81 percent), determine certification dates/appointments 

(70 percent), record other types of statistics (67 percent), and keep track of pending verification 
HUDQC Final Report for FY 2015 E-7 

documents (62 percent). Less than half of PHAs/projects reported using computer systems to store 

electronic copies of verification documents (44 percent), conduct rent reasonableness comparisons 

(40 percent), and conduct computer-assisted interviews with tenants (36 percent). The largest 

increase from FY 2014 to FY 2015 was found in keeping track of pending verification documents 

(from 59 percent to 62 percent), and the largest decrease in keeping other types of statistics 

(72 percent to 67 percent). 

Exhibit E-1f

Use of Computer Systems for Key Tasks, by Program Type

Use of Computer Systems

Program Type

All Program 

Types

Public 

Housing

PHAadministered

Section 8

Owneradministered

Printing Letters to Tenants 96.1% 99.2% 97.9% 97.6%

Printing Form HUD-50058/50059 96.6% 93.8% 98.9% 96.8%

Calculating Income, Expenses, and Allowances 95.0% 96.2% 95.8% 95.6%

Recording Tenant Age, Ethnicity, Family Size, 

or Other Demographics 88.8% 97.7% 92.1% 92.4%

Inputting Certification Interview or Application 

Responses 77.7% 86.9% 78.8% 80.5%

Determining Certification Appointment Dates 73.7% 73.8% 63.5% 69.9%

Keeping Other Types of Statistics 60.3% 76.2% 67.2% 67.1%

Keeping Track of Pending Verification 

Documents 62.0% 60.8% 61.9% 61.6%

Storing Electronic Verification Documents 39.1% 53.8% 40.7% 43.6%

Conducting Rent Reasonableness 

Comparisons 34.6% 79.2% 16.9% 39.6%

Conducting Computer-Assisted Interviews with 

Tenants 39.1% 37.7% 32.3% 36.1%

Note: Percentages were calculated for PHAs/projects that responded to the specific items.

In addition to the tasks shown in Exhibit E-1f, 96 percent of PHAs/projects indicated that they 

use computer software to help calculate tenant rent, with PHA-administered Section 8 being most 

likely (99 percent), and Public Housing sites being the least likely (91 percent) (see Exhibit E-1g). 

Of that 96 percent, a large portion reported having software capable of assisting staff by storing 

household-specific information from previous Form HUD-50058/50059s (96 percent), submitting 

data to PIC/TRACS (92 percent), annualizing individual sources of income and/or expenses 

(91 percent), and containing pre-loaded information that identifies the appropriate payment 

standard or utility allowance (86 percent). However, 70 percent of PHAs/projects reported that 

their software was limited in its capabilities by requiring users to enter Form HUD-50058/50059 

after its manual completion.

Program-specific results show that PHA-administered Section 8 and Owner-administered projects 

were both very likely to report using software that is capable of submitting data to PIC/TRACS 

(93 percent and 97 percent, respectively), while only 85 percent of Public Housing projects 

had software with this capability (see Exhibit E-1g). Furthermore, PHA-administered Section 8 

and Owner-administered projects were about equally likely to report using software containing 
Appendix E: Project Staff Questionnaire Descriptive Analysis

E-8 August 31, 2016

pre-loaded information that identifies the appropriate payment standard or utility allowance 

(90 percent and 89 percent, respectively), while only 80 percent of Public Housing projects had 

software with this capability. 

Exhibit E-1g

Functionalities of Computer Software, by Program Type

Tasks Performed Using Computer Software

Program Type

All Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

Certification Staff Use Computer Software to 

Help Calculate Tenant Rent 91.1% 99.2% 98.4% 96.0%

Functionalities

Stores Household-Specific Information From 

Previous Form HUD-50058/50059s and Allows 

Updating With Current Information*

91.4% 98.4% 97.8% 95.8%

Submits Data to PIC/TRACS* 84.7% 93.0% 96.8% 91.6%

Annualizes Individual Sources of Income/

Expenses When Information Is Entered* 87.1% 94.6% 93.0% 91.4%

Contains Pre-Loaded Information that Identifies 

the Appropriate Payment Standard/Utility 

Allowance for Each Household Based on 

Information Entered*

80.4% 89.9% 88.7% 86.2%

User Must Enter Form HUD-50058/50059 Data 

After Its Manual Completion* 68.1% 67.4% 74.2% 70.3%

Note: Percentages were calculated for PHAs/projects that responded to the specific item.

* Percentages were calculated based on PHAs/projects that indicated using computer software to help calculate rent.

2. Training and Development

Certification Staff Training and Development. The PSQ collected information about the amount 

and type of training provided to both new and experienced certification staff during the study period. 

Fifty-nine percent of PHAs/projects reported having a training department or staff trainer that provided 

guidance to staff working on rent calculation activities, a decrease from 67 percent in FY 2014. 

Owner-administered programs were most likely to have a training department or staff trainer for 

certification staff (71 percent), followed by Public Housing (53 percent) and PHA-administered 

Section 8 projects (50 percent) (see Exhibit E-2a).

Training for New Staff. The average number of hours of training provided to each new certification 

staff member prior to performing rent calculations unassisted varied greatly by program type. On 

average, PHA-administered Section 8 projects provided the most training hours to new certification 

staff (69 hours), followed by Public Housing sites (60 hours), and Owner-administered programs 

(25-hours) (see Exhibit E-2a). The overall average for all program types was 49 hours of training, 

which is the lowest in the last 6 years (50 average hours were provided to staff in FY 2014, 
HUDQC Final Report for FY 2015 E-9 

55-average hours in FY 2013, 65 average hours in FY 2012, 89 hours on average in FY 2011, and 

about 85 training hours in FY 2010).3

Exhibit E-2a

Presence of Training Staff in PHA/Project and 

Average Training Hours for Certification Staff, by Program Type

Training Staff Present in PHA/Project and

Training Hours for Certification Staff

Program Type

All 

Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

Training Department

PHA/Project Has a Training Department or Staff 

Trainer for Certification Staff 52.5% 50.0% 71.4% 59.0%

Training Hours

Number of Training Hours Before 

Staff Can Perform Rent Calculations 

Unassisted

Mean 59.6 68.6 24.8 48.8

Median 8.0 33.5 16.0 16.0

Note: Percentages, averages, and medians were calculated for PHAs/projects that responded to the specific item.

PHAs/projects were asked to explain in further detail how the number of training hours provided 

to one certification staff member were typically allocated. PHAs/projects reported the approximate 

number of rent calculation training hours that a typical new staff member is provided via various 

training methods (see Exhibit E-2b). In FY 2012, FY 2011, and FY 2010, PHAs/projects were asked 

to rank the three most frequently used training methods so that percentages of training utilization 

within each PHA/project could be calculated. Beginning in FY 2013, any training hours reported for 

the methods indicated utilization. The reported hours were used to calculate the average percentage 

of PHAs/projects that have used the various methods during the study period, where zero hours 

indicated that the method was not used.

The most commonly used training method was reading policies and procedural guides 

on their own (96 percent), followed by shadowing or mentorship with experienced staff 

(94 percent) and classroom-style training administered in-house (70 percent) (see Exhibit E-2b). 

Web-based or recorded training video created in-house was the method least likely to be used 

(28 percent). The use of most of the training methods presented has remained stable since 

FY 2014. There was, however, a marked increase in the use of shadowing or mentorship with more 

experienced staff (91 percent to 94 percent) and in classroom-style training administered by an 

outside organization (56 percent to 63 percent) since FY 2014.

In FY 2015, all Public Housing projects prepared new certification employees by having them 

read policies and procedural guides on their own (100 percent). This method was also used by the 

vast majority of PHA-administered Section 8 and Owner-administered projects (96 percent and 

94 percent, respectively). PHA-administered Section 8 projects were most likely to prepare new 

certification staff by having them shadow experienced staff (99 percent). Owner-administered 

projects were the least likely to use a Web-based or recorded training video created by HUD 

3 Training hour averages for FY 2012, FY 2011, and FY 2010 were calculated as the average for new 

recertification staff and reassigned staff.
Appendix E: Project Staff Questionnaire Descriptive Analysis

E-10 August 31, 2016

(35 percent), while more than half of Public Housing and PHA-administered Section 8 projects 

employed this method (56 percent and 51 percent, respectively). Conversely, Owner-administered 

projects were more likely to use Web-based or recorded training videos created by an outside 

organization (48 percent) or in-house (39 percent) than both Public Housing projects (41 percent and 

25 percent, respectively) and PHA-administered Section 8 (37 percent and 16 percent, respectively).

Exhibit E-2b

Methods Used to Train New Certification Staff, by Program Type

Note: Percentages were calculated for those PHAs/projects that had new certification staff in FY 2015.

Although most PHAs/projects relied on new staff to self-train by reading policies and procedural 

guides, an average of only 36 total hours per staff member was spent on this training method (see 

Exhibit E-2c). PHAs/projects dedicated the most training hours to shadowing and mentorship with 

more experienced staff, with an average of approximately 127 hours for each new certification 

staff member from November 1, 2014, to October 31, 2015. While this was an increase from 

FY 2014 (98 hours), it is consistent with the results from the FY 2013 PSQ (120 hours). The fewest 

hours of training were spent using Web-based or recorded training videos created by HUD (3 hours) 

and created in-house (3 hours). 
HUDQC Final Report for FY 2015 E-11 

Exhibit E-2c

Training Hours per Training Method for New Certification Staff, by Program Type

Training Methods for New Staff

Program Type

All Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

Shadowing or Mentorship With More 

Experienced Staff

Mean 106.1 207.8 83.7 127.0

Median 25.0 70.0 24.0 36.0

Reading Policies and Procedural Guides 

on Their Own

Mean 26.7 77.3 12.8 36.1

Median 8.0 16.0 4.0 8.0

Classroom-Style Training Administered 

In-House

Mean 23.4 33.4 14.1 22.7

Median 6.0 12.0 8.0 8.0

Classroom-Style Training Administered 

by an Outside Organization

Mean 15.2 21.4 10.6 15.2

Median 4.5 24.0 8.0 8.0

Web-Based or Recorded Training Video 

Created by an Outside Organization

Mean 3.4 5.6 3.9 4.2

Median 0.0 0.0 0.0 0.0

Web-Based or Recorded Training Video 

Created In-House

Mean 1.5 0.7 5.5 2.8

Median 0.0 0.0 0.0 0.0

Web-Based or Recorded Training Video 

Created by HUD

Mean 3.5 3.9 1.4 2.8

Median 1.0 1.0 0.0 0.0

Other Type of Training Activity Not 

Mentioned Above

Mean 5.8 11.2 10.0 9.0

Median 0.0 0.0 0.0 0.0

Note: Averages and medians were calculated for those PHAs/projects that had new certification staff in FY 2014.

PHA/project training topics used to educate staff on conducting certifications are also important 

in determining activities that support proper rent calculation. PHAs/projects reported the 

approximate number of rent calculation training hours for a typical new staff member on various 

training topics. These hours were used to calculate the average percentage of PHAs/projects 

that provided various training topics related to certification activities from November 1, 2014, 

to October 31, 2015, where zero hours indicated that the training topic was not presented to 

certification staff. For each training topic area, most PHAs/projects (at least 88 percent) provided 

training (see Exhibit E-2d). The average number of hours spent on each topic per staff member 

was, for the most part, evenly distributed among the topic areas, with averages ranging from 

24 to 36 hours during the study period, with the most time spent on entering Form HUD-50058/50059 

and the least amount of time spent on Enterprise Income Verification (EIV) reports and security (see 

Exhibit E-2e). This was a substantial increase in training hours for each training topic area from 

FY 2014, when training hours ranged from 16 to 25 hours among the seven topics.
Appendix E: Project Staff Questionnaire Descriptive Analysis

E-12 August 31, 2016

Exhibit E-2d

Training Topic Areas for New Certification Staff, by Program Type

Note: Percentages were calculated for those PHAs/projects that had new certification staff in FY 2015. 

* Deductions refer to medical, disability, and childcare deductions.

Among the three program types, Owner-administered projects were the most likely to provide 

training in interviewing tenants and entering Form HUD-50058/50059 (92 percent each), while 

PHA-administered Section 8 projects were the most likely to provide training in calculating 

deductions, assets, and earned and fixed income sources, as well as in EIV reports and security 

(98 percent, 98 percent, 99 percent, 98 percent, and 97 percent, respectively) (see Exhibit E-2d). 

Furthermore, PHA-administered Section 8 projects spent, on average, the most training hours per 

training topic area, ranging from 36 to 52 hours, with the exception of EIV reports and security 

(see Exhibit E-2e).
HUDQC Final Report for FY 2015 E-13 

Exhibit E-2e

Average Training Hours per Training Topic for New Certification Staff, by Program Type

Training Topics for New Staff

Program Type

All 

Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

Entering Form HUD-50058/50059 

Information

Mean 28.6 51.8 29.7 35.8

Median 4.0 10.0 4.0 5.0

Calculating Earned Income 

Sources

Mean 31.6 47.7 29.1 35.3

Median 7.0 14.0 4.0 8.0

Calculating Deductions (Medical, 

Disability, Childcare)

Mean 32.2 38.9 29.0 32.9

Median 5.0 14.0 4.0 5.0

Calculating Fixed Income Sources

Mean 30.1 38.6 28.5 32.0

Median 5.0 12.0 4.0 5.0

Calculating Assets

Mean 28.9 37.0 28.7 31.2

Median 4.0 12.0 4.0 5.0

Interviewing Tenants

Mean 30.1 35.5 26.8 30.4

Median 4.3 10.0 4.0 5.0

EIV Reports and EIV Security

Mean 15.8 25.7 28.8 23.7

Median 4.8 6.0 4.0 5.0

Other

Mean 8.8 12.1 7.5 9.3

Median 0.0 4.0 0.0 1.0

Note: Averages and medians were calculated for those PHAs/projects that had new certification staff in FY 2014. 

Training for Experienced Staff. PHAs/projects were also asked to report the approximate number 

of rent calculation training hours for a typical experienced staff member for each training method. 

These reported hours were used to calculate the average percentage of PHAs/projects that have 

used the various methods, where zero hours indicated that the method was not used. As for new 

certification staff, PHAs/projects were most likely to have experienced staff read policies and 

procedural guides on their own (86 percent), shadow more experienced staff (55 percent), and attend 

classroom-style training administered by an outside organization (52 percent) (see Exhibit E-2f). 

Compared to FY 2014, the data showed a pronounced increase in the use of Web-based or recorded 

training videos created by HUD (22 percent to 38 percent) and by outside organizations (33 percent 

to 39 percent). However, there was a dramatic decrease in the use of Web-based or recorded training 

videos created in-house (34 percent to 20 percent). Additionally, there were slight decreases in the 

use of shadowing more experienced staff (58 percent to 55 percent), reading policies and procedural 

guides alone (88 percent to 86 percent), and classroom-style training administered in-house 

(53 percent to 50 percent) (see Exhibit E-2f). The use of classroom-style training administered 

in-house remained stable (52 percent).
Appendix E: Project Staff Questionnaire Descriptive Analysis

E-14 August 31, 2016

Exhibit E-2f

Methods Used to Train Experienced Certification Staff, by Program Type

Note: Percentages were calculated for PHAs/projects that responded to the specific items.

An analysis by program type found that PHA-administered Section 8 projects were most likely to 

provide policies and procedural guides to experienced staff for self-training (89 percent) and to use 

classroom-style training administered in-house (55 percent) (see Exhibit E-2f). Owner-administered 

projects were most likely to have an experienced employee attend classroom-style training 

administered by an outside organization (60 percent) and use Web-based or recorded training 

videos created by an outside organization (46 percent). All three program types were about 

equally likely to have experienced staff shadow more experienced staff members (ranging from 

54 percent to 55 percent). Additionally, Public Housing and PHA-administered Section 8 

projects were about equally likely to use Web-based or recorded training videos created by HUD 

(43 percent and 42 percent, respectively). In contrast, only 30 percent of Owner-administered 

projects used this method.
HUDQC Final Report for FY 2015 E-15 

Exhibit E-2g

Training Hours per Training Method for Experienced Certification Staff, by Program Type

Training Methods for Experienced Staff

Program Type

All Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

Shadowing or Mentorship With 

More Experienced Staff

Mean 15.2 78.7 47.3 44.0

Median 1.0 2.0 2.0 2.0

Reading Policies and Procedural 

Guides on Their Own

Mean 28.0 55.9 25.3 34.3

Median 5.0 9.5 4.0 5.0

Classroom-Style Training 

Administered by an Outside 

Organization

Mean 11.7 9.8 18.8 13.9

Median 0.0 0.5 4.0 2.0

Classroom-Style Training 

Administered In-House

Mean 8.2 10.1 7.3 8.3

Median 0.0 2.0 1.0 0.0

Web-Based or Recorded 

Training Video Created by an 

Outside Organization

Mean 2.5 3.5 3.3 3.1

Median 0.0 0.0 0.0 0.0

Web-Based or Recorded 

Training Video Created by HUD

Mean 3.1 2.5 1.8 2.4

Median 0.0 0.0 0.0 0.0

Web-Based or Recorded 

Training Video Created In-House

Mean 0.7 1.2 2.7 1.6

Median 0.0 0.0 0.0 0.0

Other (Activity Not Listed)

Mean 14.7 3.8 3.7 7.7

Median 0.0 0.0 0.0 0.0

Note: Averages were calculated for PHAs/projects that responded to the specific items.

The average number of training hours provided to an experienced certification staff member was 

considerably less than those provided to a new staff member. PHAs/projects dedicated the most 

training hours to shadowing and mentorship with more experienced staff, with an average of 

approximately 44 hours per experienced certification staff over the study period (see Exhibit E‐2g). 

This is an increase from an average of 29 hours in FY 2014, but is consistent with FY 2013 PSQ 

results (43 hours). The remaining training for experienced staff throughout the study period consisted 

of an average of 34 hours of self-training, 14 hours for classroom-style training administered by an 

outside organization, and less than 10 hours for each remaining training method. 

As mentioned above, PHA/project training topics used to educate staff on conducting certifications 

are also important in determining activities that support proper rent calculation. PHAs/projects 

reported the approximate number of rent calculation training hours for a typical experienced staff 

member on various training topics. These hours served to calculate the average percentage of PHAs/

projects that provided various training topics related to certification activities from November 1, 

2014, to October 31, 2015, where zero hours indicated that the training topic was not presented to 

certification staff. At least 53 percent of PHAs/projects provided training to experienced certification 

staff for each topic listed (see Exhibit E-2h). PHAs/projects were most likely to train experienced 

certification staff in EIV reports and security (81 percent) and calculating deductions (66 percent), 

earned income sources, and assets (65 percent each) (see Exhibit E-2h). Of the topics listed, PHAs/

projects were least likely to train on interviewing tenants (53 percent). With respect to program type, 
Appendix E: Project Staff Questionnaire Descriptive Analysis

E-16 August 31, 2016

Owner-administered projects were the most likely to train on EIV reports and security (86 percent), 

and PHA-administered Section 8 projects were the most likely to train on all other training topics.

Exhibit E-2h

Training Topic Areas for Experienced Certification Staff, by Program Type

Note: Percentages were calculated for PHAs/projects that responded to the specific items.

* Deductions refer to medical, disability, and childcare deductions.

The average number of hours spent on each topic listed was fairly evenly distributed among the topic 

areas (see Exhibit E-2i). On average, the most time was spent on entering Form HUD-50058/50059 

information (10 hours) and the least amount of time on interviewing tenants (7 hours). Interestingly, 

the most training time for experienced staff in FY 2014 was spent on interviewing tenants (12 hours).

In comparison to a new certification staff member, an experienced certification staff member 

received approximately 25 fewer hours of training for each topic (see Exhibit E-2i). This was a 

considerable increase from FY 2014, when experienced staff received 13 fewer hours of training 

than new staff per topic. Furthermore, in FY 2013, experienced staff only received 7 fewer training 

hours, on average, than new certification staff. This is primarily due to the substantial increase in 

training hours for new staff, though the number of training hours for experienced staff did slightly 

decrease as well.

With respect to program type, Public Housing sites provided the most training in calculating 

earned income sources, fixed income sources, deductions, assets, and in interviewing tenants 

(11 hours each) (see Exhibit E-2i). PHA-administered Section 8 projects provided the most training 

hours on entering Form HUD-50058/50059 (15 hours). Public Housing and PHA-administered 

Section 8 project offered, on average, the same number of training hours in EIV reports and security 

(8 hours). Owner-administered projects provided the least amount of training to experienced 

certification staff, with an average of 6 hours or less spent on each topic.
HUDQC Final Report for FY 2015 E-17 

Exhibit E-2i

Training Hours per Training Topic for

Experienced Certification Staff, by Program Type

Training Topics for Experienced Staff

Program Type

All Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

Entering Form 

HUD‐50058/50059 Information

Mean 10.9 14.5 5.5 9.8

Median 1.0 1.0 1.0 1.0

Calculating Earned Income 

Sources

Mean 11.4 10.6 5.1 8.8

Median 1.0 3.0 1.0 2.0

Calculating Fixed Income 

Sources

Mean 11.3 8.6 5.1 8.3

Median 1.0 2.0 1.0 1.0

Calculating Deductions 

(Medical, Disability, Childcare)

Mean 11.1 8.1 4.9 8.0

Median 1.0 3.0 1.0 2.0

Calculating Assets

Mean 11.3 7.3 4.8 7.8

Median 1.0 2.0 1.0 1.0

EIV Reports and EIV Security

Mean 7.7 7.7 6.2 7.1

Median 2.0 3.0 3.0 2.0

Interviewing Tenants

Mean 11.2 5.8 3.8 7.0

Median 0.0 1.0 1.0 1.0

Other 

Mean 4.2 6.2 4.8 5.0

Median 0.0 0.0 0.0 0.0

Note: Averages and medians were calculated for PHAs/projects that responded to the specific items.

Policy Implementation. In FY 2015, the PSQ aimed to capture information from PHAs/projects 

on the typical time it takes to implement a new policy related to rent calculation. An analysis of 

the qualitative data collected found that most PHAs/projects implement a new policy between 

1 and 30 days once an Office of Public and Indian Housing (PIH) Notice or Housing Notice is 

issued (44 percent) (see Exhibit E-2j). Many PHAs/projects also stated that implementation of a 

new policy related to rent calculation began immediately after issuance of a notice (27 percent), 

and only 4 percent of projects reported that they take longer than 90 days to implement a new rent 

calculation policy.

With respect to program type, Owner-administered projects were most likely to immediately 

implement a new policy and most likely to have full implementation of a new policy within 

30 days (34 percent and 89 percent, respectively) (see Exhibit E-2j). By comparison, 60 percent 

of PHA-administered Section 8 projects and 58 percent of Public Housing sites achieved full 

implementation of a new policy within 30 days. 
Appendix E: Project Staff Questionnaire Descriptive Analysis

E-18 August 31, 2016

Exhibit E-2j

Implementation Time for a New Rent Calculation Policy, by Program Type

Certification Staff Work Behaviors. Another goal of the FY 2015 PSQ was to gain insight 

into PHA/project assessments of the quality of certification staff work behaviors. Ninety-eight 

percent of PHAs/projects rated their certification staff as either organized or very organized when 

working on certification activities, an increase from FY 2014 (95 percent) (see Exhibit E-2k). 

Owner-administered projects were most likely to rate staff as very organized (59 percent), while 

more Public Housing and PHA-administered Section 8 projects rated their staff as organized 

(55 percent and 52 percent, respectively). Less than 1 percent of PHA-administered Section 8 

projects, as well as no Public Housing sites or Owner-administered projects, rated their staff as 

very unorganized.

Similarly, about 96 percent of PHAs/projects reported certification staff as having either good 

or very good time management (see Exhibit E-2k). As with organization, Owner-administered 

projects were most likely to select a rating of very good (57 percent), while Public Housing sites 

and PHA-administered Section 8 projects were more likely to report good time management 

(58 percent and 52 percent, respectively). Public Housing sites were most likely to report certification 

staff as having poor time management (7 percent). No PHAs/projects reported certification staff as 

having very poor time management. 

Eighty percent of PHAs/projects indicated that their certification staff had a lot of attention to 

detail (see Exhibit E-2k). This is an increase from FY 2014, when 72 percent of PHAs/projects 

rated their staff as having a lot of attention to detail. Following trends from previous questions, 

Owner-administered projects were the most likely to rate their staff as having a lot of attention to 

detail (86 percent) when compared with Public Housing sites and PHA-administered Section 8 

projects (74 percent and 78 percent, respectively). Overall, 19 percent of PHAs/projects rated staff 

as having some attention to detail, and just 2 percent as having little attention to detail. Less than 

1 percent of Public Housing sites, and no PHA-administered Section 8 or Owner-administered 

projects, indicated that certification staff had very little or no attention to detail.
HUDQC Final Report for FY 2015 E-19 

Exhibit E-2k

Certification Staff Work Behaviors, by Program Type

Certification Staff Organization, Attention to 

Detail, and Quality of Time Management

Program Type

All Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

Certification Staff Organization

Very Organized 41.3% 46.9% 58.7% 49.4%

Organized 55.3% 51.5% 39.7% 48.4%

Unorganized 3.4% 0.8% 1.1% 1.8%

Very Unorganized 0.0% 0.8% 0.0% 0.2%

Certification Staff Attention to Detail

A Lot 73.7% 77.7% 86.2% 79.5%

Some 23.5% 20.0% 12.7% 18.5%

Little 2.2% 2.3% 0.5% 1.6%

Very Little/None 0.6% 0.0% 0.0% 0.2%

Certification Staff Quality of Time Management

Very Good 35.2% 45.4% 56.6% 46.0%

Good 58.1% 52.3% 39.7% 49.6%

Poor 6.7% 2.3% 3.2% 4.2%

Very Poor 0.0% 0.0% 0.0% 0.0%

Note: Percentages were calculated for PHAs/projects that responded to the specific items.

3. Performance Management 

Quality Control Review and Timing of Reviews. The PSQ inquired about PHA/project practices on 

the review of tenant files as a QC measure. Most PHAs/projects indicated that they reviewed move‐in 

and annual recertifications as a QC measure (89 percent) (see Exhibit E-3a). PHA‐administered 

Section 8 projects were most likely to review tenant files (95 percent), compared with Public Housing 

sites and Owner-administered projects (87 percent each). The PHAs/projects that performed QC 

reviews reviewed an average of 47 percent of all move-in and annual certification transactions, 

and were most likely to conduct reviews prior to Form HUD-50058/50059 approval (27 percent) 

or within 30 calendar days of submitting Form HUD-50058/50059 to PIC/TRACS (25 percent).4

Public Housing sites and Owner-administered projects both reviewed the majority of all 

move-in and annual certification transactions (52 percent and 51 percent, respectively), while 

PHA-administered Section 8 projects reviewed an average of 36 percent of transactions (see Exhibit 

E-3a). Additionally, Owner-administered projects were most likely to conduct reviews prior to 

Form HUD-50059 approval (40 percent). By comparison, 21 percent of Public Housing sites and 

18 percent of PHA-administered Section 8 projects reviewed transactions prior to Form 

HUD-50058 approval. Public Housing sites were the most likely to conduct QC reviews within 

4 This percentage was calculated by combining the percentages of PHAs/projects that indicated that they 

conducted QC reviews within 7 calendar days and within 30 calendar days of Form HUD-50058/50059 

submission.
Appendix E: Project Staff Questionnaire Descriptive Analysis

E-20 August 31, 2016

30 calendar days (32 percent), followed closely by PHA-administered Section 8 projects 

(30 percent), while Owner-administered projects were least likely to conduct reviews within 

30 days (14 percent). Additionally, 22 percent of PHAs/projects indicated that QC reviews were 

typically conducted between 6 months and 1 year after submitting Form HUD-50058/50059.

Exhibit E-3a

Quality Control Reviews and Timing of Reviews, by Program Type

Percentage and Timing of Quality Control 

Reviews

Program Type

All Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

Percentage of PHAs/Projects That Perform QC 

Reviews of Move-in and Annual Certification 

transactions 

87.2% 95.4% 86.8% 89.2%

Average Percentage of Move-in and Annual 

Certification Transactions Reviewed for QC* 51.8% 36.3% 51.3% 47.3%

Primary Period When QC Review Was Conducted

Prior to Form HUD-50058/50059 approval* 21.4% 17.7% 39.9% 27.2%

Within 7 calendar days of Form 

HUD‐50058/50059 submission* 5.8% 1.6% 1.2% 2.9%

Within 30 calendar days of Form 

HUD‐50058/50059 submission* 26.0% 28.2% 12.9% 21.8%

Within 60 calendar days of Form 

HUD‐50058/50059 submission* 11.0% 12.9% 6.1% 9.8%

Within 3 months of Form HUD‐50058/50059 

submission* 7.8% 11.3% 10.4% 9.8%

Within 6 months of Form HUD-50058/50059 

submission* 5.8% 8.9% 6.1% 6.8%

Within 1 year of Form HUD-50058/50059 

submission* 22.1% 19.4% 23.3% 21.8%

Note: Percentages were calculated for PHAs/projects that responded to the specific items.

* Percentages were calculated for PHAs/projects that reviewed certification transactions as a QC measure. 

Selecting Cases for Review. Of PHAs/projects that conducted QC reviews, 26 percent reported 

that they reviewed not only move-in and annual certifications, but all tenant files as a QC measure 

(see Exhibit E-3b). This is consistent with FY 2014 results. As in FY 2014, FY 2013, and 

FY 2012, Owner-administered projects were most likely to review all cases (29 percent), and 

PHA-administered Section 8 projects were least likely to do so (23 percent). The percentage of 

Owner-administered projects that reviewed all tenant files decreased since FY 2014 (38 percent), 

while PHA-administered Section 8 projects were more likely to complete comprehensive reviews 

this year compared to FY 2014 (16 percent).

PHAs/projects that did not report performing QC reviews on all tenant files stated that they used 

the following methods most frequently to select cases for QC: randomly selecting cases for review 

(79 percent), selecting move-in transactions (50 percent), and selecting annual certification 

transactions (40 percent) (see Exhibit E-3b). With respect to program type, PHA-administered 

Section 8 projects were most likely to select tenant files randomly for QC review (89 percent). This 

is a slight decrease from FY 2014, when 92 percent of PHA-administered Section 8 projects selected 
HUDQC Final Report for FY 2015 E-21 

tenant files randomly. Additionally, PHA-administered Section 8 projects were the most likely of the 

three program types to select annual certification transactions, transfer/move transactions, interim 

transactions, files from new staff, files from staff with high error rates, and files based on household 

characteristics for review (49 percent, 33 percent, 32 percent, 39 percent, 33 percent, and 8 percent, 

respectively). Owner-administered projects were the most likely to select move-in transactions 

(61 percent), as well as the most likely to use another method of selecting cases for review that was 

not listed (10 percent).

Exhibit E-3b

Methods Used by PHAs/Projects to Select Cases for Review, by Program Type

Methods Used to Select Cases for Review

Program Type

All Program 

Types

Public 

Housing

PHAadministered

Section 8

Owneradministered

All Transactions That Were Processed Were 

Reviewed by Another Staff Member 26.6% 22.6% 28.8% 26.3%

All Transactions Were Not Reviewed

Files Were Randomly Sampled* 77.0% 88.5% 72.4% 78.8%

Move-In Transactions Were Chosen for Review* 35.4% 54.2% 61.2% 50.2%

Annual Certification Transactions Were Chosen 

for Review* 37.2% 49.0% 36.2% 40.3%

Transfer/Move Transactions Were Chosen for 

Review* 17.7% 33.3% 21.6% 23.7%

Interim Transactions Were Chosen for Review* 22.1% 32.3% 16.4% 23.1%

Files Were Chosen From New Staff* 13.3% 38.5% 6.9% 18.5%

Files Were Chosen From Staff With High Error 

Rates or Who Seem to Have More Trouble* 8.8% 33.3% 1.7% 13.5%

Files Were Chosen Based on Household Income, 

Asset, and Expense Characteristics* 4.4% 8.3% 0.9% 4.3%

PHA/Project Chose Another Method of Selecting 

Cases for Review That Is Not Listed* 6.2% 6.3% 10.3% 7.7%

Note: Percentages were calculated for PHAs/projects that indicated they review tenant files as a QC measure.

* Percentages were calculated based on PHAs/projects that indicated they did not review all (100%) tenant files.

File Reviewers. The majority of PHAs/projects indicated that they have a dedicated QC staff 

to review data submitted on Form HUD-50058/50059 (68 percent) (see Exhibit E-3c). This is, 

however, a decrease since FY 2014 (74 percent). PHA-administered Section 8 projects were 

most likely to have QC staff (80 percent), compared with Public Housing sites (62 percent) and 

Owner-administered projects (65 percent). PHAs/projects reported that, during the study period, 

the review or monitoring of tenant files was conducted primarily by the team leader or supervisor 

(83 percent) or internal QC staff (47 percent), who reviewed a yearly average of 266 and 233 tenant 

files, respectively (see Exhibits E-3d and E-3c).

Public Housing sites were the most likely to rely on a supervisor or HUD-affiliated auditor 

(87 percent and 26 percent, respectively) when compared to other program types. 

Owner‐administered projects were the most likely to use internal staff reviewers, other certification 

staff, contract administers, and Office of the Inspector General (OIG) auditors (55 percent, 
Appendix E: Project Staff Questionnaire Descriptive Analysis

E-22 August 31, 2016

39 percent, 22 percent, and 11 percent, respectively). Supervisors and internal staff reviewers 

at PHA-administered projects reviewed the most files, with an average of 681 and 643 files a 

year, respectively. In contrast, supervisors and internal staff reviewers at Public Housing sites 

reviewed an average of 165 and 117 files, respectively, and Owner-administered projects only 44 and 

30 files, respectively. However, as seen in Exhibit E-1a, PHA-administered Section 8 staff have 

a much larger workload of certifications to perform yearly, so it is reasonable that they also QC a 

larger number of files. 

Exhibit E-3c

Quality Control Staff and Average 

Number of Tenant Files Reviewed, by Program Type

Quality Control Staff and Files Reviewed

Program Type

All Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

Percentage of PHAs/projects With a Dedicated 

QC Staff 62.0% 80.0% 65.1% 67.9%

Number of Files Reviewed

Supervisors or Team Leaders*

Mean 164.9 680.7 44.0 265.8

Median 42.0 85.5 15.0 30.0

Internal Staff Reviewers/QC*

Mean 116.7 642.9 29.5 232.9

Median 0.0 3.0 3.5 0.0

Contract Administrators*

Mean 54.9 153.0 23.8 71.1

Median 0.0 0.0 0.0 0.0

Other Certification Staff*

Mean 3.6 96.6 7.2 31.2

Median 0.0 0.0 0.0 0.0

HUD-Affiliated Auditors*

Mean 3.4 8.3 4.2 5.1

Median 0.0 0.0 0.0 0.0

OIG Auditors*

Mean 0.6 1.5 2.5 1.6

Median 0.0 0.0 0.0 0.0

Other Type of Reviewers Not 

Listed Above*

Mean 5.2 22.0 8.5 11.1

Median 0.0 0.0 0.0 0.0

Note: Percentages, averages, and medians were calculated for PHAs/projects that responded to the specific items. 

* Averages and medians were calculated for the PHAs/projects that indicated that they review tenant files as a QC measure.
HUDQC Final Report for FY 2015 E-23 

Exhibit E-3d

Sources of Monitoring or Reviewing of Tenant Files, by Program Type

Note: Data presented in the figure were calculated for the PHAs/projects that indicated that they review tenant files as a QC 

measure.

File Information Reviewed. For those PHAs/projects that conducted dedicated QC reviews, the 

most common file information typically checked during QC reviews included correct income 

calculations (91 percent), presence of verification documents in the tenant file (90 percent), proper 

core household documentation in the tenant file (89 percent), correct medical expense calculations 

(88 percent), and accurate completion of documentation in the tenant file (88 percent) (see Exhibit 

E-3e). PHAs/projects were least likely to report that a general spot check of the file was performed 

(15 percent), indicating that specific file information was confirmed during QC checks. This is a 

decrease from FY 2014, when 19 percent of PHAs/projects performed general spot checks.

Exhibit E-3e provides a complete breakdown of documentation, verification, calculations, 

and other information typically checked during the QC process. The exhibit illustrates that 

PHA-administered Section 8 projects were most likely to review file information for most 

categories than other program types. However, Public Housing sites were most likely to review 

proper core household documentation in the tenant file (90 percent), and Owner-administered 

projects were most likely to review accurate completion of documentation in the tenant file 

(89 percent), the presence of verification documents (93 percent), that verification documents 

meet their program’s timeframe for acceptable documentation (81 percent), and that data on 

the Form HUD-50058/50059 was properly entered (88 percent). Compared to FY 2014, the 

percentage of PHAs/projects that reviewed items related to documentation increased for each 

category. Additionally, more PHAs/projects reviewed correct income calculations (91 percent), 

correct medical expense calculations (88 percent), and correct childcare expense calculations 

(77 percent) than in FY 2014 (88 percent, 84 percent, and 72 percent, respectively). 
Appendix E: Project Staff Questionnaire Descriptive Analysis

E-24 August 31, 2016

Exhibit E-3e

File Information Typically Reviewed During Quality Control, by Program Type

File Information Typically Reviewed 

During QC

Program Type

All Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

Documentation

Proper Core Household Documentation in the 

Tenant File 89.8% 87.2% 89.1% 88.8%

Accurate Completion Of Documentation in the 

Tenant File 87.9% 86.4% 88.5% 87.7%

Consistency of a Household’s Certification/

Interview Application to Tenant File 

Documentation

66.9% 80.8% 79.4% 75.4%

Proper Unit Documentation in the Tenant File 63.7% 83.2% 70.9% 71.8%

Verification

Presence of Verification Documents in the 

Tenant File 85.4% 92.0% 92.7% 89.9%

Appropriate Type of Verification 

Documentation (Follows Verification Policy) Is 

in the Tenant File

78.3% 89.6% 88.5% 85.2%

Verification Documents Meet Your Program’s 

Timeframe for Acceptable Documentation 70.7% 79.2% 81.2% 77.0%

Calculations

Correct Income Calculation 88.5% 93.6% 90.9% 90.8%

Correct Medical Expense Calculation 87.3% 88.8% 87.9% 87.9%

Correct Disability Expense Calculation 86.6% 92.8% 80.6% 86.1%

Correct Childcare Expense Calculation 84.7% 92.0% 58.8% 77.2%

Other Information

Properly Entered Data on the Form 

HUD‐50058/50059 77.1% 83.2% 87.9% 82.8%

Accuracy of the Rent Adjustment or HAP 

Amendment Letter in the Tenant File 51.6% 85.6% 72.1% 68.7%

General Spot Check of the File Is Performed 14.6% 12.8% 15.8% 14.5%

Correct Utility Allowance Amount Applied 

to Rent Calculation, if Applicable to Your 

Program Type

N/A 90.4% N/A 90.4%

Correct Payment Standard Amount Applied to 

Rent Calculation, if Applicable N/A 89.6% N/A 89.6%

Note: Percentages were calculated for PHAs/projects that indicated they review tenant files as a QC measure. 

Prevalence of Various Types of Errors. All PHAs/projects, regardless of whether they conduct 

quality reviews, were asked to rate the frequency of errors made by the staff as a whole for various 

types of rent calculation activities during the study period. They were asked whether errors in 

verifications, errors in calculations, and human errors were made very often, often, sometimes, 

or rarely. For each type of error related to verifications, at least 1 percent of the PHAs/projects 

reported that the error occurred often or very often (see Exhibit E-3f). Missing, incomplete, or 

incorrect verification of income was most likely to be reported as often or very often (4 percent). 

These findings are consistent with FY 2014 results. Additionally, errors in verification of income 
HUDQC Final Report for FY 2015 E-25 

were sometimes found (15 percent of PHAs/projects), as were errors in verification of deductions 

(16 percent), verification of assets (15 percent), and verification of allowances (11 percent). 

By program type, PHA-administered Section 8 projects were most likely to report income 

verification errors occurring often or very often (7 percent), while Public Housing projects were 

the most likely to report the often or very often occurrence of errors in verifying assets, allowances, 

and deductions (3 percent, 2 percent, and 3 percent, respectively) (see Exhibit E-3f). This is a shift 

from FY 2013 and FY 2014, when PHA-administered Section 8 projects were the most likely to 

report each type of verification error as occurring often or very often. Furthermore, in FY 2015, 

PHA-administered Section 8 projects were most likely to report that errors in verification of income, 

assets, allowances, and deductions were sometimes found (21 percent, 18 percent, 17 percent, and 

23 percent, respectively). Owner-administered projects were least likely to report errors occurring 

often or very often in each category; furthermore, they were least likely to report errors occasionally 

occurring in each category.

Exhibit E-3f

Frequency of Missing, Incomplete, or Incorrect Verifications, by Program Type

(PH = Public Housing, VO = PHA-administered Section 8, OA = Owner-administered)

Income Verifications Asset Verifications 
Appendix E: Project Staff Questionnaire Descriptive Analysis

E-26 August 31, 2016

Exhibit E-3f

Frequency of Missing, Incomplete, or Incorrect Verifications, by Program Type, cont.

(PH = Public Housing, VO = PHA-administered Section 8, OA = Owner-administered)

Allowance Verifications Deduction Verifications

Note: Percentages were calculated for PHAs/projects that responded to the specific items. 

Among errors related to calculations, PHAs/projects were most likely to report earned income 

calculation errors as occurring often or very often (3 percent), as well as occasionally (23 percent). 

This is a decrease from 5 percent and 26 percent in FY 2014, respectively. Errors were also found 

often or very often in calculating fixed income (1 percent), assets (2 percent), and student income 

or financial aid (2 percent) (see Exhibit E-3g). By program type, PHA-administered Section 8 

projects were most likely to report making earned income calculation errors often or very often 

(5 percent), while Public Housing sites were most likely to report making errors in calculating fixed 

income and assets often or very often (2 percent and 3 percent, respectively). Owner-administered 

projects were the most likely to report making errors often or very often in calculating student 

income or financial aid (2 percent). PHA-administered Section 8 projects were the most likely to 

report occasional errors for each type of calculation. 
HUDQC Final Report for FY 2015 E-27 

Exhibit E-3g

Frequency of Missing, Incomplete, or Incorrect Income Calculations, by Program Type

(PH = Public Housing, VO = PHA-administered Section 8, OA = Owner-administered)

Earned Income Calculations Fixed Income Calculations

Asset Calculations Student Income/Financial Aid Calculations

 Note: Percentages were calculated for PHAs/projects that responded to the specific items.

For errors related to expenses, 2 percent of PHAs/projects reported making medical expense 

calculation errors often or very often, while less than 1 percent reported making childcare expense 

calculation or disability expense calculation errors often or very often (see Exhibit E-3h). This 

is a decrease since FY 2014, when at least 1 percent of PHAs/projects reported finding errors in 

each category often or very often. Additionally, medical expense calculations were identified as 

having the highest occasional error rate (17 percent). PHA-administered Section 8 projects reported 

the highest rate of occasional errors in medical, childcare, and disability expense calculations 

(22 percent, 14 percent, and 13 percent, respectively) compared with other program types. 
Appendix E: Project Staff Questionnaire Descriptive Analysis

E-28 August 31, 2016

Exhibit E-3h

Frequency of Missing, Incomplete, or Incorrect Expense Calculations, by Program Type

(PH = Public Housing, VO = PHA-administered Section 8, OA = Owner-administered)

Medical Expense Calculations Childcare Expense Calculations

Disability Expense Calculations

Note: Percentages were calculated for PHAs/projects that responded to the specific items. 

Three percent of PHAs/projects reported that errors occurred due to poor attention to 

detail often or very often. With respect to program type, 3 percent of Public Housing and 

Owner-administered projects reported that these errors occurred often or very often, along 

with 4 percent of PHA-administered Section 8 projects (see Exhibit E-3i). This is a shift from 

FY 2014, when no Owner-administered sites reported this issue often or very often, while Public 

Housing and PHA-administered Section 8 sites reported relatively high numbers (7 percent 

and 5 percent, respectively). Furthermore, these errors were considerably more likely to occur 

occasionally compared to other types of calculation and verification errors (27 percent). Late 

annual certifications had the highest reported often or very often error rates of all rent calculation 
HUDQC Final Report for FY 2015 E-29 

activities among PHAs/projects (5 percent), with Public Housing projects being most likely to 

have certification staff frequently complete late annual certification transactions (6 percent). On the 

calculation of payment standards and utility allowances, errors were made very often or often at 

1 percent and 2 percent of PHAs/projects, respectively. Payment standard and utility 

allowance errors were identified as sometimes being made less than 10 percent of the time 

(5 percent and 8 percent, respectively). 

Exhibit E-3i

Frequency of Other Errors, by Program Type

(PH = Public Housing, VO = PHA-administered Section 8, OA = Owner-administered)

Errors Due to Poor Attention to Detail Late Annual Certification

Incorrect Payment Standard Incorrect Utility Allowance 

Note: Percentages were calculated for PHAs/projects that responded to the specific items.

Note: Traditional Payment Standards are not applicable to Public Housing or Owner-administered projects. However, a proportion of 

survey respondents within these programs responded to the question. 
Appendix E: Project Staff Questionnaire Descriptive Analysis

E-30 August 31, 2016

Performance Feedback to Staff. In order to provide performance feedback to staff on errors found 

during QC, PHAs/projects were most likely to have one-on-one conversations to discuss QC findings 

with staff (73 percent) (see Exhibit E-3j. This is an increase from FY 2014, when 65 percent of 

PHAs/projects provided feedback with staff in this way. Many PHAs/projects also recorded file 

errors and made that information available to certification staff (49 percent), conducted team/group 

meetings to discuss QC issues (39 percent), and provided general reports of QC findings to staff 

(17 percent). As in FY 2014, PHA-administered Section 8 projects were most likely to use each 

performance feedback method compared to other program types, while Owner-administered projects 

were most likely to use a feedback method not listed (15 percent).

Exhibit E-3j

Methods of Feedback to Staff Regarding Errors Found During QC, by Program Type

Methods of Feedback

Program Type

All 

Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

One-On-One Conversations to Discuss QC 

Findings (Phone or In-Person) 76.6% 78.0% 66.7% 73.4%

Specific Deficiencies for Each File Are Recorded 

and Made Available to Certification Staff 40.3% 56.1% 51.6% 48.9%

Team/Group Meetings to Discuss QC Issues 39.6% 63.4% 20.1% 39.2%

A General Report on QC Findings Is Provided to 

All Certification Staff 15.6% 22.8% 14.5% 17.2%

Other Feedback Method Not Mentioned Above 7.1% 8.1% 14.5% 10.1%

Note: Percentages were calculated for PHAs/projects that reviewed tenant files as a QC measure. 

PHAs/projects were asked to comment on the frequency with which they provided feedback to 

staff on their performance in calculating rent. Twenty-five percent of PHAs/projects reported 

they provided monthly feedback, followed by weekly (14 percent), annually (12 percent), daily 

(11 percent), quarterly (10 percent), and semi-annually (4 percent) (see Exhibit E-3k). Only 

7 percent of PHAs/projects reported that they did not provide any performance feedback to staff. 

With respect to program type, housing projects were most likely to provide daily and weekly 

feedback (12 percent and 19 percent, respectively), Owner-administered projects were most likely 

to provide feedback quarterly (12 percent) and annually (18 percent), and PHA-administered Section 

8 projects were most likely to provide monthly feedback (33 percent).
HUDQC Final Report for FY 2015 E-31 

Exhibit E-3k

Frequency of Rent Calculation Performance Feedback to Staff, by Program Type

Frequency of Feedback to Certification Staff

Program Type

All Program 

Types

Public 

Housing

PHAadministered

Section 8

Owneradministered

Daily 12.3% 10.5% 9.8% 10.9%

Weekly 18.8% 16.1% 6.7% 13.6%

Monthly 22.7% 33.1% 22.1% 25.4%

Quarterly 9.1% 8.1% 11.7% 9.8%

Semi-Annually 2.6% 3.2% 4.9% 3.6%

Annually 7.8% 9.7% 18.4% 12.2%

Other Time Period 17.5% 17.7% 16.6% 17.2%

Performance Feedback Is Not Provided to Staff 9.1% 1.6% 9.2% 7.0%

Note: Percentages were calculated for PHAs/projects that responded to the specific items.

PHAs/projects were asked which staff were responsible for file corrections when errors were 

found during QC review. Overwhelmingly, the certification staff member who made the error was 

responsible for making the file corrections (83 percent), while only 12 percent of PHAs/projects 

required the file reviewer to make the correction (see Exhibit E-3l). PHA-administered Section 8 

projects were most likely to have the certification staff member who made the error complete the file 

corrections (91 percent), compared to Owner-administered projects (78 percent) and Public Housing 

projects (82 percent). Owner-administered projects were most likely to have the QC reviewer correct 

file errors (17 percent), compared to other program types (7 percent for PHA-administered Section 

8 and 10 percent for Public Housing). 

Exhibit E-3l

Staff Members Responsible for File Corrections During Quality Control, by Program Type

Staff Responsible for File Corrections When 

Errors Were Found During Quality Control

Program Type

All Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

The Certification Staff Who Made the Error 82.4% 91.0% 78.2% 83.3%

The Person Who Performed QC of the File 10.1% 6.6% 17.3% 11.7%

Other Correction Staff Member Not Mentioned Above 7.4% 2.5% 4.5% 4.9%

Note: Percentages were calculated for PHAs/projects that reviewed certification transactions as a QC measure. 

Error Tracking and Mitigation. Twenty-eight percent of PHAs/projects reported that they do not 

track any of the listed certification errors (see Exhibit E-3m). PHA-administered Section 8 projects 

were least likely to report that they do not track certification errors (16 percent), compared to Owneradministered projects (37 percent) and Public Housing projects (29 percent). PHAs/projects that 

did track the types of certification errors monitored the following types of errors most frequently: 

late annual certification transactions (78 percent), errors in earned income calculations (57 percent), 

errors in calculating assets (56 percent), and errors in calculating medical expenses (54 percent). 
Appendix E: Project Staff Questionnaire Descriptive Analysis

E-32 August 31, 2016

Public Housing sites were most likely to track late annual certification transactions (86 percent), 

while PHA-administered Section 8 projects were most likely to track errors in all other categories, 

at considerably higher rates than both Public Housing and Owner-administered projects. 

Exhibit E-3m

Types of Rent Calculation Errors Tracked, by Program Type

Types of Certification Errors Tracked or 

Recorded

Program Type

All Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

None—The Project Does Not Track Any of the 

Listed Information 28.5% 16.2% 36.5% 28.3%

Projects That Tracked Rent Calculation Errors

Late Annual Certification Transactions* 85.9% 80.7% 66.7% 77.9%

Errors in Earned Income Calculation* 43.8% 81.7% 47.5% 56.6%

Errors in Asset Calculation* 38.3% 72.5% 59.2% 55.7%

Errors in Medical Expense Calculation* 39.8% 71.6% 54.2% 54.3%

Errors in Maintaining Accurate Tenant File 

Documentation That Is Not Related to Verification 

of Income, Assets, and Expenses*

44.5% 68.8% 40.0% 50.4%

Errors Related to Accurate Verification of Income, 

Assets, and Expenses* 39.8% 67.0% 46.7% 50.4%

Errors in Fixed Income Calculation* 31.3% 67.0% 45.8% 47.1%

Errors in Elderly/Disability Allowance 

Determination* 33.6% 66.1% 33.3% 43.4%

Errors in Childcare Expense Calculation* 32.0% 67.0% 30.0% 42.0%

Errors in Dependent Allowance Determination* 31.3% 63.3% 30.8% 40.9%

Errors in Disability Expense Calculation* 29.7% 59.6% 30.8% 39.2%

* Percentages were calculated based on PHAs/projects that indicated they track rent calculation errors.

Overall, the majority of PHAs/projects, regardless of whether they conducted quality checks, 

had a formal or informal goal-setting process in place during the study period (69 percent) (see 

Exhibit E-3n). Furthermore, 73 percent of PHAs/projects required certification staff to review the 

household’s previous Form HUD-50058/50059 before beginning a new certification transaction. 

With respect to program type, Owner-administered projects were least likely to have a goal-setting 

process in place, but most likely to require certification staff to review the previous Form 

HUD-50058/50059 (64 percent and 81 percent, respectively). While neither of these strategies are 

formal or thorough QC measures, implementing and enforcing these performance targets may help 

reduce rent calculation error.
HUDQC Final Report for FY 2015 E-33 

Exhibit E-3n

Percentage of PHAs/Projects With Certification Performance 

Goals and Form HUD-50058/50059 Review Requirements, by Program Type

Rent Calculation Error Mitigation Strategies

Program Type

All Program 

Types

Public 

Housing

PHAadministered 

Section 8

Owneradministered

Percentage of Certification Staff That Undergo a 

Formal or Informal Goal-Setting Process Related 

to Performing Certifications

70.9% 71.5% 64.0% 68.5%

PHA/Project Requires Certification Staff to 

Review Household’s Previous Form HUD50058/50059 Prior to Starting New Certification 

Transaction 

69.3% 66.9% 81.0% 73.1%

Note: Percentages were calculated for PHAs/projects that responded to the specific items.

C. Conclusion

Overall, PSQ questions on PHA/project characteristics, certification staff training and development, 

and performance management revealed a complex and interesting picture of PHAs/projects. 

Demographically, there was a decrease in the number of units/households from past years, 

with a slight increase in the average number of certification staff. This resulted in a declining 

ratio of units per individual staff member. Since FY 2014, there has been a decrease in projects 

reporting a too-high workload (49 percent to 44 percent), and an increase in projects reporting 

a manageable workload (49 percent to 56 percent). The percentage of PHAs/projects that had 

at least one staff member leave remained constant in FY 2015, though the average number of 

staff that stopped working on certification activities at projects with turnover increased slightly 

(2 staff to 3 staff). In addition, staff were much more likely than in previous years to be rated as 

very organized (43 percent to 50 percent), to have very good time management (36 percent to 

46 percent), and to pay a lot of attention to detail (72 percent to 80 percent). With respect to project 

characteristics, virtually all of the PHA/project respondents indicated that they did not contract out 

their certification activities. Additionally, almost all of the PHAs/projects indicated that they used 

computer software to help calculate tenant rents and reported that the software conducts a wide 

variety of tasks, with minimal limitations. 

PHAs/projects were less likely to have a dedicated training department or staff trainer for certification 

staff than in FY 2014 (67 percent to 59 percent). Additionally, there has been a pronounced 

downward trend in the average number of hours of training provided to each new certification 

staff member before performing rent calculations unassisted, with FY 2015 contributing the lowest 

average in the last 6 years. Self-training and mentorship with more experienced staff were still the 

most utilized training methods. For new certification staff, the training hours dedicated to mentorship 

and classroom-style training administered by an outside organization increased substantially since 

FY 2014, while the average number of training hours dedicated to other methods remained stable. 

Additionally, there was a notable increase in training hours spent on each training topic area listed. 

In contrast, for experienced certification staff, most training methods were used at fewer projects 

than in FY 2014. Interestingly, there was an increase in the average number of training hours 

allocated to mentoring with more experienced staff, self-training, and classroom-style training, 

and a decrease in hours spent watching training videos. The gap between the average number of 
Appendix E: Project Staff Questionnaire Descriptive Analysis

E-34 August 31, 2016

training hours on specific topics given to a new staff member and to an experienced staff member 

has further widened.

Fewer PHAs/projects in FY 2015 had a dedicated QC staff than in FY 2014 (74 percent to 

68 percent). With respect to implementing QC procedures, about the same percentage of PHA/

projects as found previously reported performing QC reviews, with the percentage of transactions 

reviewed increasing slightly. The PSQ also provided some specific information on the experiences 

and issues of PHAs/projects with the certification process. For instance, during the QC monitoring 

of certifications, the majority of PHAs/projects reported randomly sampling files for review 

and, furthermore, that the most effective QC technique was to review the files prior to Form 

HUD‐50058/50059 approval. As in FY 2014, most of these files were reviewed by supervisors 

or team leaders. These results, among others, suggest that QC has become more systematic; for 

example, only 15 percent reported that a general spot check was performed, compared to 19 percent 

in FY 2014 and 40 percent in FY 2013. Overall, rent calculation errors were not found very often. 

The most common errors made by certification staff were late annual certification transactions and 

incorrect calculations of earned income. Compared to FY 2014, projects were less likely to report 

errors occurring often or very often in most categories. When errors did occur, they were usually 

discussed monthly in one-on-one conversations and corrected by the staff member who made the 

error. 

For future HUDQC studies, it would be helpful to develop and validate additional items that 

specifically target potential difficulties or barriers to conducting training, managing staff 

performance, and collecting best practices for error mitigation. Specifically, a dual approach 

could be used for training methods and hours. Questions about the desired amount of training 

and the actual amount of training could determine the perceived importance of each topic and 

method. The discrepancy between the two would likely be due to barriers that would be discussed 

in an open-ended question. In addition, to provide a richer view of project practices to HUD, 

the development of questions that directly link staffing and staff performance to certification and 

QC procedures is desirable. Moreover, it may be useful to reinitiate questions that were asked in 

previous years and dropped, particularly for project characteristics. Currently, the survey is lacking 

a question on whether or not the PHA/project had new staff, which resulted in projects having to 

answer questions that may not have applied. Furthermore, it may be constructive to ask about the 

reasons behind staff turnover. While focus groups and cognitive interviewing may be optimal in 

supporting the revision of the PSQ items by focusing attention on the specific circumstances and 

issues faced by the PHAs/projects, open-ended questions also help identify and explain these issues. 

For example, given the number of projects reporting that another case assignment method is used 

(43 percent), transforming this question to an open-ended format may glean more insights to 

assignment methods. Gathering detailed descriptions of these aspects of the certification process 

would lead to a more complete and detailed picture of the issues faced by the PHAs/projects and 

would provide a better link between PHA/project practices and characteristics and the estimation 

of payment and income error.
Appendix F: Multivariate Analysis
HUDQC Final Report for FY 2015 F-1 

Appendix F: Multivariate Analysis

A. Introduction

ICF International conducted multivariate analyses to identify project and household factors related 

to rent error and project-caused errors in the certification/recertification process. The multivariate 

analyses also aimed to address the extent to which error rates in projects that use an automated rent 

calculation system differ from errors in those that do not and to determine whether error rates and 

error costs had statistically significant difference between program types. Multivariate analysis 

allows us to examine characteristics related to rent error and determine corrective actions that can 

be taken to lessen improper payment. 

Using measures of project characteristics and practices combined with household variables, the 

multiple regression analysis sought to systematically assess the net effects of project and household 

variables on the rent error and project-caused errors.

1

 We addressed two research questions:

1. Other things being equal, what project characteristics, project practices, and household 

variables accounted for rent error and project-caused errors?

2. What was the effect size (or relative strength) of project characteristics, project practice 

features, project-caused errors, and household characteristics in accounting for gross rent 

error?2

We developed a single conceptual model to address the stated research questions. Results from 

previous years have demonstrated the relationship between project factors and project-caused errors 

in connection to rent errors. In this year, we attempted to demonstrate the impact of the household 

and project factors on the project-caused errors and how these factors work through project-caused 

errors to impact rent error. It is hoped that these analyses will demonstrate specific areas on which 

projects and quality control staffs can reduce project-caused errors and overall reduce rent error. 

We used data obtained from our Project Staff Questionnaire (PSQ) and tenant data collection to 

conduct this analysis.

Modeling Rent Errors. We measured the dollar amount of rent error in terms of subsidy 

overpayment, subsidy underpayment, and gross rent error. Subsidy underpayment is the dollar 

error caused by the household paying more than it should have (tenant overpayment); thus, HUD’s 

contribution was less than it should have been. Subsidy overpayment, conversely, is the dollar error 

caused by the household’s contribution being less than it should have been (tenant underpayment), 

and HUD’s contribution being more. Gross error is the dollar amount of either overpayment or 

1 The term “net effect” refers to the relationship between a given independent variable and the outcome 

variable, statistically controlling for other independent variables in the model. A net effect is the estimated 

regression coefficient b or slope for a given predictor in multiple regression modeling. The term does not 

necessarily imply a causal effect, as this cross-sectional, survey-based design does not warrant causal 

conclusions. 

2 Estimation of the “effect size” for predictor variables requires valid measurement of each variable, 

sensible model specifications, and a 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.
Appendix F: Multivariate Analysis

F-2 August 31, 2016 

underpayment (in absolute value) for a given household. These three measures of rent error may 

relate to project and household factors in different patterns; therefore, it is necessary to model each 

rent error measure.

We modeled the rent error measures using a single multilevel, multivariate model that estimates both 

the probability of overpayment and underpayment subsidy concurrently with amount of gross rent 

error. In addition, we have also included in the model the relationships between household/project 

characteristics and project-caused errors. Combining these analyses will help us better understand 

the system of characteristics, practices, and project-caused errors that have either a direct or indirect 

relationship on rent error measures. 

Hypothetically, dollar amounts of rent errors are affected by four sets of factors: (1) project 

characteristics, (2) project practices, (3) project-caused errors, and (4) household characteristics 

(see Figure F-1). Project characteristics include organizational and staffing features (e.g., program 

type, caseload for all staff, certification staff, and staff experience). Project practices cover different 

ways to assign cases (i.e., by transaction type, certification activities, or complexity levels); training 

hours and methods/topics for new and experienced staff; information technology applications; 

contracting certification to outside entities; and a set of performance management or quality control 

(QC) measures. QC measures range from setting performance goals, dedicating a department or 

personnel to QC, or reviewing certification and recertification to a certain extent, to measuring 

staff-perceived frequency of making various errors in the process.

Figure F-1

Conceptual Framework for Modeling Rent Errors

The project-caused errors will be used as predictors of rent error and as the outcomes to be modeled 

through project variables and household characteristics. In previous years, we have examined 

individual project-caused errors and have also developed composite scales of these errors in an 

effort to understand the impact of these errors or types of errors on rent error measurement. This 

year, we have tried to maintain the independence of these variables so that we are better able to 

differentiate impact of these errors. 

Also, as we suggested earlier, we have chosen to combine the two conceptual models from last 

year to include project-caused errors not only as predictors of rent error but also as the intermediate 

outcome of household characteristics and project characteristics and practices. In doing so, we 

consider project-caused errors as an essential system within the model predicting rent error. 

Project‐caused errors were identified through investigation of household records conducted by 

the field work. By default, project-caused errors are related to rent errors to a varying extent. 
HUDQC Final Report for FY 2015 F-3 

Appendix F: Multivariate Analysis

Project‐caused errors occur due to limitations in organizational resources, insufficient staff skills, 

a lack of rigorous quality control, and complicated household financial situations, among other 

problems. Although, in previous analyses, we have found differing effects upon rent errors, we 

chose to model the independent project-caused errors to better understand the effect upon the overall 

system. Examining the pattern in which project and household factors account for project-caused 

errors may help housing management reduce such errors.

B. Data and Variables

We combined household and project data to conduct data processing, including examining data 

quality, missing data imputation, data editing and rescaling, and derived or composite variable 

construction. Before testing final multiple regression models, we conducted extensive initial data 

analysis in iteration with data processing to examine data quality of key measures, the bivariate 

relationship between predictor variables and outcome variables, and issues in preliminary multiple 

regression models. 

We matched the household records with the affiliated projects using the project identification code. 

The resulting data set contains 2,400 household cases affiliated with 531 projects.3

 However, only 

498 projects that were affiliated with these household responded to the PSQ survey. Replicate 

weights were attached to each record to calculate delete-a-group Jackknife variance estimates that 

account for the stratified, multistage cluster selection of the tenant sample. 

The Household Data. The household survey gathered detailed information about household 

characteristics as well as final measure of rent errors. An algorithm was developed to recode/rescale 

raw data items and construct composite variables.4 Using the algorithm, we produced measures 

of the following: 

● Project-caused errors 

● Household financial conditions (e.g., income and expenses) 

● Demographics (e.g., household size, number of bedrooms, elderly household with 

disabilities)

The Project Data. The PSQ file contained more than 200 raw data items, with many having a large 

number of categories that describe project characteristics and practices. 

3 Of the 538 sampled projects (or project-like entities, hereafter referred to as projects) for the PSQ, 

33 projects failed to respond; consequently, 148 households under these projects did not have PSQ data 

matched. A sensitivity analysis suggested that excluding these households may introduce bias to the 

analysis. Although they differed in some household variables, no difference was found in the rent error 

dependent variables. Due to the lack of difference in the dependent variables and due to the lack of 

knowledge about the type of these missing projects, we did not impute these records. 

4 Although, in previous years, we have used this algorithm to take the logarithm of the dollar value in 

order to tighten variables with skewed distribution, this year we chose to use a Tobit regression model, 

which effectively accounted for the skewed distribution of the gross error dollar amount and, therefore, 

did not use the logarithmic variable.
Appendix F: Multivariate Analysis

F-4 August 31, 2016

C. Methodology

Model Specification and Estimation. We conducted a combined multilevel, multivariate analysis 

to account for rent errors (gross rent error, subsidy overpayment, and subsidy underpayment) and 

project-caused errors. We used a multivariate regression model with logistic and linear (Tobit) 

regression outcomes to model both the probability and amount of the error. Modeling the probability 

(subsidy underpayment and subsidy overpayment) and amount together allowed us to differentiate 

the impact of the household and project characteristics and practices on both the direction and 

amount (intensity) of the rent error. In addition, we used a Tobit modeling approach to estimate 

the linear regression coefficients in order to account for the skewness of the data (i.e., a large 

majority of HUD applications do not have any rent error). We considered variables from all four 

factors: (1) project characteristics, (2) project practices, (3) project-caused errors, and (4) household 

characteristics. 

The full set of variables eligible for the model are presented in Exhibit F-1. We selected the final 

variables for the model in two steps. First, we calculated individual models for each variable and 

identified those that had a significant relationship with gross rent error, subsidy underpayment, or 

subsidy overpayment at p < 0.10. Second, we entered all the resultant variables into the model and 

used a backward selection algorithm to identify the final variables for the model.5 Only the variables 

from step 1 were included in the backward algorithm. Any variable that was significant at p < 0.05 

was included in the model. 

To examine factors underlying project-caused errors, we conducted multivariate analyses using 

project characteristics, project practices, and household characteristics to account for each measure 

of project-caused errors. There were five project-caused errors that were included in the model: 

overdue recertification error, allowance calculation error, income calculation error, other calculation 

error, and transcription error. These project-caused errors were coded so that an error was indicated 

by 1 and a 0 indicated no error and included in the model as intermediary variables (i.e., outcomes 

of household characteristics and predictors of rent errors). 

Unless otherwise noted, we conducted statistical analyses using MPLUS with underpayment subsidy 

and overpayment subsidy as categorical outcomes and gross rent error as a censored outcome with 

a floor effect. Additionally, we used a two-level complex analysis type with project characteristics 

and practices identified as between variables and household characteristics and project-caused errors 

in the within level of the model. Models were executed with the weight variable, cluster variable, 

and stratification variable identified. 

For descriptive statistics, we used SAS 9.4, specifically PROC SURVEYMEANS and PROC 

SURVEYFREQ. All statistics presented here were generated with sample weights and replicate 

weights, using the Jackknife procedure. 

5 The backward algorithm introduces all variables into the model and test for significance. Variables with 

the weakest non-significant relationship with the outcome measure (i.e., p value) are removed from the 

model. After each variable is removed from the model, the algorithm reevaluates the significance of other 

effects in the model. Those effects that are no longer significant are removed from the model one at a 

time, in order of the weakest relationship. 
HUDQC Final Report for FY 2015 F-5 

D. Findings

To address the first research question of identifying predictor variables that accounted for rent 

error and project-caused errors, we present bivariate tabulation, regression coefficients, and related 

significance test statistics to establish whether or not an effect exists beyond chance (i.e., whether 

the effect is statistically significant).

Gross Rent Error 

We tabulate descriptive statistics for the eligible predictor variables separately by two groups of 

households: those with and those without gross rent error. This approach offers a preliminary view 

of the predictor variables differentiated by gross rent error. Exhibit F-1 presents statistics of the 

variables6 by the indicator of subsidy underpayment (with or without an error of $5 or more). Exhibit 

F-2 presents statistics of the variables by the indicator of overpayment subsidy (with or without an 

error of $5 or more). Statistically significant differences at the p < 0.05 level are noted by an asterisk. 

The proportion of Public Housing Authority (PHA)-administered Section 8 projects is higher for 

households with subsidy underpayment errors, while the percentage of both PHA-administered 

Section 8 and Owner-administered projects is higher for households with subsidy overpayment 

errors. There are no project practices that are significant between households with rent error and 

those without error (underpayment or overpayment). There are a number of significant differences 

on both subsidy overpayment and underpayment for tenant characteristics and project-caused errors.

Exhibit F-1

Variables Evaluated For Rent Error Modeling: Households With and Without Underpayment Error 

(Original Scales, Weighted)

Households Without Error (Underpayment) Households With Error (Underpayment)

Variable Label n Mean

Standard 

Error

Lower 

95%

Upper 

95% n Mean

Standard 

Error

Lower 

95%

Upper 

95%

Project Characteristics

Public Housing 2,081 0.229 0.004 0.222 0.237 319 0.221 0.023 0.173 0.269

PHA-administered 

Section 8 2,081 0.468 0.004 0.459 0.476 * 319 0.520 0.027 0.464 0.576

Owner-administered 2,081 0.303 0.003 0.296 0.310 319 0.259 0.020 0.217 0.302

Response Across 

Project 2,081 0.948 0.013 0.922 0.975 319 0.932 0.020 0.890 0.974

Cases per Staff 

(in 100s) 1,957 1.038 0.043 0.948 1.127 * 295 1.158 0.091 0.968 1.349

Cases per Certification 

Staff (in 100s) 1,949 1.522 0.097 1.320 1.725 295 1.676 0.155 1.352 2.000

Cases per Experienced 

Certification Staff 

(in 100s)

1,948 2.361 0.405 1.516 3.207 292 2.579 0.403 1.738 3.421

6 For dummy variables (coded 1 for a “yes” and 0 for a “no” response), the means are equivalent to the 

percentage of households that had a value of 1.
Appendix F: Multivariate Analysis

F-6 August 31, 2016

Households Without Error (Underpayment) Households With Error (Underpayment)

Variable Label n Mean

Standard 

Error

Lower 

95%

Upper 

95% n Mean

Standard 

Error

Lower 

95%

Upper 

95%

Move-in Cases 

(in 100s) per Staff 

Member

1,957 0.140 0.008 0.123 0.157 295 0.146 0.013 0.119 0.174

Move-in Cases 

(in 100s) per 

Certification Staff 

Member

1,949 0.200 0.010 0.180 0.220 295 0.203 0.017 0.168 0.238

Move-in Cases 

(in 100s) per 

Experienced 

Certification Staff 

Member

1,948 0.303 0.046 0.208 0.399 292 0.308 0.052 0.198 0.417

Percentage of 

Experienced 

Certification Staff

1,949 85.124 1.731 81.513 88.735 295 84.313 2.733 78.612 90.015

Certification Staff 

Turnover Rate 1,957 1.684 0.447 0.751 2.616 295 1.964 0.835 0.223 3.706

Project Practices

Assigned Case by 

Transaction Type 1,957 0.199 0.028 0.140 0.258 295 0.169 0.030 0.106 0.232

Assigned Case by 

Activity 1,957 0.006 0.003 -0.001 0.013 295 0.008 0.006 -0.004 0.021

Assign Case Randomly 1,957 0.157 0.020 0.115 0.199 295 0.153 0.030 0.090 0.215

Assigned Case 

Nonsystematically 1,957 0.380 0.034 0.309 0.452 295 0.356 0.046 0.261 0.452

Number of Activities 

Using a Computer 1,957 7.799 0.084 7.624 7.974 295 7.814 0.106 7.593 8.035

Number of Activities 

Using Software 1,957 5.253 0.051 5.146 5.360 295 5.212 0.093 5.018 5.406

Project Characteristics

Contracted Out to 

Perform Certifications 1,957 0.116 0.019 0.077 0.154 295 0.123 0.032 0.056 0.190

Dedicated Training 

Department or 

Staff Trainer Within 

Organization

1,957 0.589 0.034 0.518 0.659 295 0.638 0.043 0.547 0.728

Number Reported 

Training Hours for New 

Staff

1,957 63.290 8.602 45.346 81.233 295 61.920 13.067 34.663 89.177

Total Number of 

Training Hours for New 

Staff on Specific Topics

1,957 195.381 25.604 141.973 248.790 295 195.127 29.602 133.377 256.876

Count of Methods for 

Training Used for New 

Staff

1,957 34.122 10.192 12.861 55.383 295 33.940 12.427 8.019 59.862

1+ Web-Based Training 

for New Staff 1,957 0.493 0.029 0.432 0.554 295 0.499 0.049 0.397 0.601
HUDQC Final Report for FY 2015 F-7 

Households Without Error (Underpayment) Households With Error (Underpayment)

Variable Label n Mean

Standard 

Error

Lower 

95%

Upper 

95% n Mean

Standard 

Error

Lower 

95%

Upper 

95%

Shadowing/ Mentoring 

Training for New Staff 1,957 0.725 0.028 0.666 0.784 295 0.729 0.041 0.644 0.814

Enterprise Income 

Verification (EIV) 

Training for New Staff

1,957 0.642 0.028 0.583 0.701 295 0.657 0.044 0.566 0.748

1+ Calculation Training 

for New Staff 1,957 0.627 0.031 0.562 0.691 295 0.636 0.045 0.541 0.730

Interviewing Tenants 

Training for New Staff 1,957 0.587 0.035 0.515 0.660 295 0.590 0.051 0.485 0.696

Total Number of 

Training Hours for 

Experienced Staff on 

Specific Topics

1,957 123.507 22.021 77.572 169.442 295 133.718 31.845 67.292 200.145

Count of Methods 

for Training Used for 

Experienced Staff

1,957 3.755 0.102 3.541 3.969 295 3.820 0.164 3.478 4.162

1+ Web-Based Training 

for Experienced Staff 1,957 0.562 0.023 0.514 0.611 295 0.573 0.046 0.477 0.669

Shadowing/ 

Mentoring Training for 

Experienced Staff

1,957 0.558 0.028 0.499 0.617 295 0.533 0.039 0.451 0.615

EIV Training for 

Experienced Staff 1,957 0.830 0.016 0.797 0.864 295 0.826 0.028 0.767 0.885

1+ Calculation Training 

for Experienced Staff 1,957 0.631 0.035 0.558 0.703 295 0.640 0.036 0.566 0.714

Interviewing 

Tenants Training for 

Experienced Staff

1,957 0.547 0.030 0.484 0.610 295 0.537 0.039 0.456 0.618

Staff With Goal-Setting 

Process for Performing 

Certifications

1,957 0.712 0.028 0.653 0.771 295 0.697 0.040 0.614 0.781

Dedicated QC Staff 1,957 0.736 0.027 0.679 0.792 295 0.772 0.030 0.708 0.835

Number of Errors 

Tracked 1,957 5.244 0.308 4.601 5.887 295 5.424 0.361 4.672 6.177

Certification Review 

Rate 1,951 39.248 2.456 34.125 44.371 293 40.033 3.322 33.103 46.963

Frequency Making 

Errors 1,950 17.119 0.362 16.364 17.873 292 16.913 0.393 16.094 17.732

100% Certifications 

Reviewed 1,957 0.162 0.026 0.107 0.218 295 0.150 0.031 0.086 0.214

Randomly Select for 

Review 1,957 0.632 0.020 0.590 0.674 295 0.669 0.038 0.589 0.748

New/Error-Prone/

Household Factor 

Select for Review

1,957 0.247 0.029 0.187 0.308 295 0.277 0.028 0.219 0.335

Review 5 Key Info 1,957 0.654 0.026 0.601 0.707 295 0.617 0.045 0.522 0.711
Appendix F: Multivariate Analysis

F-8 August 31, 2016

Households Without Error (Underpayment) Households With Error (Underpayment)

Variable Label n Mean

Standard 

Error

Lower 

95%

Upper 

95% n Mean

Standard 

Error

Lower 

95%

Upper 

95%

Review in 30 

Days Form HUD50058/50059 Submit1,957 0.886 0.015 0.854 0.917 295 0.878 0.029 0.818 0.939

Review 3-12 

Months Form HUD50058/50059 Submit1,957 0.882 0.018 0.845 0.919 295 0.867 0.031 0.802 0.933

Feedback Specifics 

and in Person 1,957 0.348 0.032 0.282 0.415 295 0.337 0.040 0.253 0.421

Feedback Monthly or 

Shorter 1,957 0.619 0.033 0.551 0.688 295 0.620 0.042 0.532 0.708

Feedback Quarterly or 

Longer 1,950 0.288 0.026 0.234 0.341 292 0.290 0.031 0.224 0.355

Review by Supervisor/

Leader 1,957 0.801 0.027 0.743 0.858 295 0.793 0.036 0.718 0.867

Review by Contract 

Administrator 1,957 0.150 0.032 0.083 0.217 * 295 0.192 0.037 0.115 0.269

Review by Office of 

Inspector General 

(OIG) Auditor

1,957 0.086 0.018 0.049 0.123 295 0.088 0.021 0.045 0.132

Review Prior Form 

HUD-50058/50059 1,957 0.732 0.028 0.674 0.790 295 0.692 0.045 0.599 0.785

Household Characteristics

Number of Household 

Members 2,081 2.031 0.040 1.947 2.116 * 319 2.535 0.105 2.316 2.754

Total Annual Income in 

thousands 2,081 12.932 0.385 12.129 13.736 * 319 19.014 0.713 17.526 20.502

Earned Income 593 16.905 0.877 15.074 18.735 * 166 19.681 1.089 17.409 21.953

Other Income 307 3.970 0.279 3.388 4.552 * 68 5.030 0.436 4.120 5.939

Public Assistance 

Income 158 4.046 0.419 3.173 4.920 22 3.445 0.593 2.207 4.683

Pension Income 1,302 11.763 0.177 11.393 12.133 * 174 13.378 0.619 12.087 14.669

Medical Expense 589 1.223 0.099 1.016 1.430 * 93 1.694 0.252 1.168 2.219

Total Number of 

Sources of Income/

Expenses

2,081 1.291 0.019 1.251 1.332 * 319 1.565 0.050 1.460 1.669

Total Number of 

Allowances 2,081 1.147 0.022 1.102 1.192 * 319 1.224 0.043 1.135 1.312

Age of Head of 

Household 2,081 53.152 0.619 51.861 54.443 * 319 50.170 1.115 47.843 52.496

Moving to Work 2,081 0.065 0.019 0.026 0.104 * 319 0.091 0.028 0.032 0.149

Elderly Household 2,081 0.599 0.021 0.557 0.642 * 319 0.444 0.030 0.381 0.507

Dependent in 

Household 2,081 0.380 0.014 0.350 0.409 * 319 0.542 0.030 0.479 0.604

Minor in Household 2,081 0.286 0.014 0.258 0.315 * 319 0.393 0.035 0.321 0.465

Disabled in Household 2,081 0.484 0.015 0.453 0.515 * 319 0.397 0.025 0.345 0.450

Total Number of 

Expenses 2,081 0.311 0.017 0.275 0.347 319 0.332 0.029 0.273 0.392
HUDQC Final Report for FY 2015 F-9 

Households Without Error (Underpayment) Households With Error (Underpayment)

Variable Label n Mean

Standard 

Error

Lower 

95%

Upper 

95% n Mean

Standard 

Error

Lower 

95%

Upper 

95%

Total Amount of 

Allowances 2,081 0.978 0.038 0.898 1.058 * 319 1.379 0.126 1.116 1.643

Actual Rent Paid by 

the Household 2,081 0.254 0.009 0.234 0.274 * 319 0.313 0.022 0.267 0.358

Project-Caused Errors

Percent of Items With 

Transcription Errors 2,081 0.352 0.013 0.324 0.380 * 319 0.849 0.030 0.786 0.912

Overdue Recertification 

Error 2,081 0.011 0.003 0.006 0.017 319 0.042 0.012 0.017 0.066

Allowance Calculation 

Error 2,081 0.010 0.002 0.005 0.015 * 319 0.020 0.006 0.007 0.033

Consistency Error 2,081 0.208 0.014 0.178 0.238 319 0.222 0.027 0.166 0.278

Income Calculation 

Error 2,081 0.022 0.006 0.011 0.034 319 0.029 0.009 0.010 0.048

Other Calculation Error 2,081 0.034 0.005 0.023 0.045 * 319 0.055 0.012 0.030 0.080

* The two groups differ significantly in the predictor variable (p < 0.05).

Source: HUDQC FY 2015 household-level data collection and PSQ
Appendix F: Multivariate Analysis

F-10 August 31, 2016

Exhibit F-2

Variables Evaluated For Rent Error Modeling: Households With and Without Overpayment Error 

(Original Scales, Weighted)

 Households Without Error (Overpayment) Households with Error (Overpayment)

Variable Label n Mean 

 Standard 

Error 

 Lower 

95% 

 Upper 

95% n Mean 

 Standard 

Error 

 Lower 

95% 

 Upper 

95% 

Project Characteristics

Public Housing 2,099 0.231 0.003 0.226 0.237 301 0.207 0.018 0.170 0.244

PHA-administered 

Section 8 2,099 0.465 0.003 0.458 0.472 * 301 0.541 0.021 0.498 0.585

Owner-administered 2,099 0.304 0.003 0.298 0.310 * 301 0.252 0.018 0.213 0.290

Response Across Project 2,099 0.945 0.014 0.915 0.975 301 0.952 0.011 0.930 0.974

Cases per Staff 

(in 100s) 1,969 1.022 0.040 0.939 1.105 * 283 1.267 0.105 1.047 1.486

Cases per Certification 

Staff (in 100s) 1,962 1.481 0.089 1.295 1.667 * 282 1.958 0.202 1.537 2.380

Cases per Experienced 

Certification Staff 

(in 100s)

1,958 2.275 0.353 1.539 3.011 * 282 3.168 0.731 1.644 4.693

Move-in Cases 

(in 100s) per Staff 

Member

1,969 0.139 0.008 0.123 0.155 283 0.152 0.016 0.118 0.186

Move-in Cases 

(in 100s) per Certification 

Staff Member

1,962 0.197 0.009 0.178 0.216 * 282 0.225 0.022 0.179 0.270

Move-in Cases 

(in 100s) per 

Experienced Certification 

Staff Member

1,958 0.295 0.040 0.212 0.378 * 282 0.365 0.089 0.179 0.550

Percent of Experienced 

Certification Staff 1,962 85.051 1.813 81.269 88.833 282 84.782 2.297 79.990 89.573

Certification Staff 

Turnover Rate 1,969 1.771 0.546 0.632 2.909 283 1.384 0.325 0.707 2.062

Project Practices

Assigned Case by 

Transaction Type 1,969 0.195 0.028 0.137 0.252 283 0.198 0.042 0.109 0.286

Assigned Case by 

Activity 1,969 0.007 0.004 -0.001 0.015 283 0.004 0.004 0.004 0.012

Assign Case Randomly 1,969 0.161 0.021 0.117 0.205 283 0.125 0.035 0.053 0.198

Assigned Case 

Nonsystematically 1,969 0.374 0.034 0.304 0.444 283 0.402 0.043 0.312 0.491

Number of Activities 

Using a Computer 1,969 7.799 0.079 7.635 7.963 283 7.817 0.132 7.541 8.092

Number of Activities 

Using Software 1,969 5.232 0.056 5.115 5.350 283 5.352 0.074 5.197 5.507
HUDQC Final Report for FY 2015 F-11 

Households Without Error (Overpayment) Households with Error (Overpayment)

Variable Label n Mean 

 Standard 

Error 

 Lower 

95% 

 Upper 

95% n Mean 

 Standard 

Error 

 Lower 

95% 

 Upper 

95% 

Project Characteristics

Contracted Out to 

Perform Certifications 1,969 0.118 0.020 0.077 0.159 283 0.104 0.027 0.048 0.160

Dedicated Training 

Department or 

Staff Trainer Within 

Organization

1,969 0.590 0.033 0.521 0.658 283 0.632 0.047 0.534 0.730

Number Reported 

Training Hours for New 

Staff

1,969 62.396 8.939 43.750 81.043 283 67.917 11.200 44.555 91.279

Total Number of 

Training Hours for New 

Staff on Specific Topics

1,969 200.840 26.935 144.654 257.025 * 283 158.226 20.162 116.169 200.284

Count of Methods for 

Training Used for New 

Staff

1,969 35.989 11.374 12.263 59.716 * 283 21.312 2.463 16.174 26.449

1+ Web-Based Training 

for New Staff 1,969 0.490 0.030 0.428 0.553 283 0.521 0.047 0.423 0.618

Shadowing/ Mentoring 

Training for New Staff 1,969 0.723 0.029 0.664 0.783 283 0.738 0.034 0.666 0.809

EIV Training for New 

Staff 1,969 0.638 0.028 0.579 0.697 283 0.683 0.041 0.598 0.769

1+ Calculation Training 

for New Staff 1,969 0.624 0.031 0.558 0.689 283 0.657 0.043 0.567 0.746

Interviewing Tenants 

Training for New Staff 1,969 0.586 0.036 0.512 0.661 283 0.599 0.041 0.513 0.684

Total Number of 

Training Hours for 

Experienced Staff on 

Specific Topics

1,969 125.737 23.722 76.254 175.220 283 118.962 28.583 59.339 178.586

Count of Methods 

for Training Used for 

Experienced Staff

1,969 3.762 0.104 3.545 3.979 283 3.775 0.161 3.440 4.110

1+ Web-Based Training 

for Experienced Staff 1,969 0.567 0.025 0.515 0.619 283 0.541 0.036 0.465 0.617

Shadowing/ 

Mentoring Training for 

Experienced Staff

1,969 0.560 0.029 0.500 0.621 283 0.517 0.037 0.441 0.593

EIV Training for 

Experienced Staff 1,969 0.826 0.017 0.792 0.861 283 0.853 0.019 0.813 0.893

1+ Calculation Training 

for Experienced Staff 1,969 0.629 0.033 0.560 0.697 283 0.656 0.043 0.566 0.745

Interviewing 

Tenants Training for 

Experienced Staff

1,969 0.547 0.029 0.486 0.609 283 0.533 0.043 0.444 0.623

Staff With Goal-Setting 

Process for Performing 

Certifications

1,969 0.709 0.027 0.653 0.765 283 0.720 0.045 0.626 0.814
Appendix F: Multivariate Analysis

F-12 August 31, 2016

Households Without Error (Overpayment) Households with Error (Overpayment)

Variable Label n Mean 

 Standard 

Error 

 Lower 

95% 

 Upper 

95% n Mean 

 Standard 

Error 

 Lower 

95% 

 Upper 

95% 

Dedicated QC Staff 1,969 0.736 0.025 0.684 0.788 283 0.771 0.041 0.687 0.856

Number of Errors 

Tracked 1,969 5.241 0.296 4.624 5.858 283 5.451 0.382 4.654 6.248

Certification Review 

Rate 1,961 39.652 2.498 34.442 44.862 283 37.336 3.154 30.758 43.915

Frequency Making 

Errors 1,960 16.906 0.338 16.201 17.611 * 282 18.351 0.600 17.099 19.603

100% Certifications 

Reviewed 1,969 0.161 0.027 0.106 0.217 283 0.156 0.029 0.096 0.216

Random Select for 

Review 1,969 0.634 0.021 0.591 0.676 283 0.662 0.030 0.600 0.723

New/Error-Prone/

Household Factor 

Select for Review

1,969 0.247 0.027 0.191 0.304 283 0.278 0.036 0.202 0.353

Review 5 Key Info 1,969 0.651 0.026 0.596 0.706 283 0.635 0.038 0.556 0.715

Review in 30 

Days Form HUD50058/50059 Submit1,969 0.880 0.017 0.845 0.915 * 283 0.917 0.018 0.880 0.955

Review 3-12 

Months Form HUD50058/50059 Submit1,969 0.875 0.020 0.833 0.917 * 283 0.918 0.019 0.879 0.957

Feedback Specifics and 

in Person 1,969 0.352 0.032 0.286 0.418 283 0.310 0.036 0.235 0.384

Feedback Monthly or 

Shorter 1,969 0.616 0.032 0.549 0.683 283 0.643 0.041 0.557 0.729

Feedback Quarterly or 

Longer 1,960 0.287 0.024 0.237 0.338 282 0.290 0.030 0.227 0.353

Review by Supervisor/

Leader 1,969 0.797 0.026 0.742 0.851 283 0.821 0.047 0.722 0.919

Review by Contract 

Administrator 1,969 0.158 0.032 0.091 0.224 283 0.142 0.034 0.072 0.213

Review by OIG Auditor 1,969 0.088 0.018 0.050 0.126 283 0.074 0.024 0.024 0.123

Review Prior Form 

HUD-50058/50059 1,969 0.730 0.028 0.670 0.789 283 0.709 0.041 0.623 0.795
HUDQC Final Report for FY 2015 F-13 

Households Without Error (Overpayment Households with Error (Overpayment)

Variable Label n Mean 

 Standard 

Error 

 Lower 

95% 

 Upper 

95% n Mean 

 Standard 

Error 

 Lower 

95% 

Variable 

Label

Household Characteristics

Number of Household 

Members 2,099 2.050 0.044 1.959 2.141 * 301 2.436 0.102 2.224 2.649

Total Annual Income in 

Thousands 2,099 13.601 0.399 12.770 14.432 301 14.787 0.486 13.774 15.800

Earned Income 637 18.256 0.807 16.573 19.939 * 122 13.794 0.953 11.806 15.781

Other Income 324 4.233 0.262 3.687 4.779 * 51 3.867 0.397 3.040 4.694

Public Assistance 

Income 154 3.889 0.415 3.022 4.756 26 4.461 0.906 2.571 6.351

Pension Income 1,295 11.808 0.231 11.325 12.291 * 181 12.886 0.483 11.878 13.894

Medical Expense 588 1.117 0.120 0.867 1.367 * 94 2.259 0.283 1.668 2.850

Total Number of 

Sources of Income/

Expenses

2,099 1.304 0.020 1.262 1.346 * 301 1.492 0.043 1.402 1.581

Total Number of 

Allowances 2,099 1.124 0.022 1.078 1.170 * 301 1.384 0.052 1.275 1.494

Age of Head of 

Household 2,099 52.980 0.666 51.591 54.369 * 301 51.180 1.091 48.904 53.456

Moving to Work 2,099 0.063 0.018 0.026 0.100 301 0.104 0.041 0.019 0.190

Elderly Household 2,099 0.580 0.022 0.535 0.625 301 0.568 0.034 0.496 0.640

Dependent in 

Household 2,099 0.383 0.015 0.352 0.413 * 301 0.530 0.034 0.459 0.600

Minor in Household 2,099 0.292 0.014 0.262 0.322 * 301 0.359 0.034 0.287 0.430

Disabled in Household 2,099 0.466 0.015 0.434 0.497 301 0.516 0.038 0.438 0.595

Total Number of 

Expenses 2,099 0.306 0.018 0.269 0.343 * 301 0.369 0.032 0.302 0.435

Total Amount of 

Allowances 2,099 0.963 0.036 0.888 1.038 * 301 1.501 0.111 1.269 1.733

Actual Rent Paid by 

the Household 2,099 0.255 0.010 0.234 0.276 * 301 0.308 0.016 0.275 0.341

Project-Caused Errors

Percentage of Items 

With Transcription 

Errors

2,099 0.365 0.011 0.342 0.388 * 301 0.786 0.038 0.706 0.865

Overdue 

Recertification Error 2,099 0.012 0.003 0.006 0.018 301 0.038 0.014 0.009 0.067

Allowance Calculation 

Error 2,099 0.010 0.002 0.005 0.015 * 301 0.020 0.009 0.002 0.038

Consistency Error 2,099 0.209 0.015 0.178 0.239 301 0.218 0.032 0.151 0.284

Income Calculation 

Error 2,099 0.022 0.005 0.011 0.032 301 0.034 0.013 0.007 0.060

Other Calculation Error 2,099 0.032 0.003 0.025 0.039 * 301 0.069 0.022 0.023 0.114

* The two groups differ significantly in the predictor variable (P < 0.05).

Source: HUDQC FY 2015 household-level data collection and PSQ
Appendix F: Multivariate Analysis

F-14 August 31, 2016

Multiple Logistic Regression Model Elements. In the multiple logistic regression elements, the 

regression coefficient estimates are typically evaluated in terms of odds ratios.7

 The odds ratio 

represents the odds that an event (e.g., gross rent error) will occur given a particular characteristic 

(e.g., administrative error), relative to the odds of the event occurring in the absence of that 

characteristic. The odds ratio is simply the probability of the event occurring divided by the 

probability of the event not occurring: OR = p/(1-p). For example, if the probability of a gross rent 

error without an administrative error is 25 percent, the odds of the event are 25 percent/75 percent, 

or 1 to 3. If an administrative error increases the probability of a gross rent error to 50 percent, the 

odds are 1 to 1. The odds ratio is the odds with the administrative error relative to the odds without 

an administrative error, or: 

The odds ratio is estimated as the given predictor’s relationship with the rent error, net of other 

predictor effects (hereafter, statements to interpret regression coefficient estimates are all qualified 

such that the estimated effect exists while holding other effects equal).

Multiple Linear Regression Models Elements. In the multiple linear regression elements of the 

rent error model, the regression coefficient estimate indicates the given predictor’s relationship with 

the rent error, net of other predictor effects (hereafter, statements to interpret regression coefficient 

estimates are all qualified such that the estimated effect exists while holding other effects equal). 

The estimated intercept represented a reference point for interpreting estimates of predictor effects 

on gross rent error from each model. For example, the intercept estimated for gross rent error in the 

final model was -0.01, which, although below the threshold of 0, still indicates expected average 

gross error of a “reference” group of households that had a zero value on each predictor variable in 

the model. For binary-coded predictors such as program type (e.g., Public Housing), the zero value 

represented the projects in programs other than the given one—in this case, PHA-administered 

Section 8 or Owner-administered programs. For project-caused errors, the zero value indicates there 

was not an instance of a particular error for that observation. 

A coefficient estimate for a predictor, if statistically significant, represents the difference from the 

“reference” value in gross rent error associated with this predictor. We focused on interpreting the 

regression coefficients that were statistically significant (p ≤ 0.05), as they represented effects that 

were unlikely to be due to chance.

Gross Error Element. Overall, the final model differed from previous years in that no project 

characteristics were found to significantly impact gross rent error. Alternatively, project practices, 

tenant characteristics, and project-caused errors continued to have significant findings. In the 

multilevel multivariate model, we found the following variables were significant predictors of the 

amount of gross rent error (n = 2,157):

● Project characteristics: No project characteristics were significant as related to the amount 

of gross rent error.

7 The estimated coefficients in the logistic model are the log odds ratios, log (OR). The odds ratios are 

obtained as eb, where e is a constant of approximately 2.718 and b is the estimated logistic regression 

coefficient.
HUDQC Final Report for FY 2015 F-15 

● Project practices: A single project practice was associated with the amount of rent 

error, when EIV training is conducted with new staff. However, the result suggests that 

conducting the training increases the amount of gross rent error ($6.54). One reason for 

this relationship is that staff may focus on the EIV as a resource and neglect to use other 

sources of income verification.

● Tenant characteristics: The amount of the rent error increases for households as household 

income increases ($3.74). Gross rent error also increases with the actual amount rent paid by 

the household ($0.15). Alternatively, increases in the amount of total allowances decreases 

the amount of gross rent error ($3.07).

● Project errors: Two project errors were significant predictors: overdue recertification error 

and transcription errors. Overdue recertification errors have the largest impact on the value 

of the rent error, with a $45.48 increase. Transcription errors also have a large impact on 

the amount of the error, with an increase in $16.62. 

The model continued to suggest that the amount of gross rent error is tied to the household financial 

measures, including annual income, rent, and total amount of allowances. While these metrics tend 

to conform to the models from previous years, more sociodemographic tenant measures seem to 

impact the amount of gross rent error indirectly through the project-caused transcription error. We 

will explore these indirect relationships later in this section. 

Figure F-1 presents the predictors found to be statistically significant in the gross rent error model. 
Appendix F: Multivariate Analysis

F-16 August 31, 2016

Figure F-1

Statistically Significant Predictors of Gross Rent Error: 

Multiple Regression Dollar Value Net Effects From the Final Model 

*p < 0.05, **p < 0.01, *** p < 0.001 (test with the null hypothesis that a coefficient = 0; a significant result indicates that the 

corresponding predictor is associated with the gross rent error)

Estimate represents the unique effect of a given predictor, net of other effects in the model. 

Significant effects based on design adjusted variance using delete-a-group Jackknife.

Source: HUDQC FY 2015 household-level data collection and PSQ
HUDQC Final Report for FY 2015 F-17 

Overpayment and Underpayment

As described earlier, we incorporated these two subsidy errors into the final model to help determine 

variables that impact the direction of gross rent error. As with the amount of gross rent error, we 

initially included all tenant characteristics, project characteristics, practices, and errors as predictors 

of the direction of rent error. Each of these rent errors were modeled using logistic regression 

analyses that were elements of the iterative full model. Figures F-4a and F-4b present statistically 

significant results. 

An analysis of overpayment and underpayment rent errors produced additional information that may 

improve program ability to deal with more specific rent errors. We estimated two equations with the 

same four sets of predictors, rescaled into a logarithm, as those used in modeling gross rent error to 

explain overpayment and underpayment. Figures F-4a and F-4b present the statistically significant 

results of underpayment and overpayment respectively. Many of the effects, especially in regard to 

subsidy underpayment, are similar to the amount of gross rent error, including transcription error, 

overdue recertification error, total annual household income, and household rent. Alternatively, the 

number of dependents in the household, number of allowances, and certified staff caseload also 

increase the possibility of subsidy overpayment, while having a transcription error also increases 

the likelihood of overpayment.

Figure F-2a and F-2b present the log odds of the statistically significant predictors of underpayment 

and overpayment. 
Appendix F: Multivariate Analysis

F-18 August 31, 2016

Figure F-2a

Statistically Significant Predictors of Underpayment Rent Error Indicators (1 = Error, 0 = No error): 

Logistic Regression Odds Ratios From the Final Model 

*p < 0.05, **p < 0.01, *** p < 0.001 (test with the null hypothesis that a coefficient = 0; a significant result indicates that the 

corresponding predictor is associated with the subsidy underpayment)

Estimate represents the unique effect of a given predictor, net of other effects in the model. 

Significant effects based on design adjusted variance using delete-a-group Jackknife.

Source: HUDQC FY 2015 household-level data collection and PSQ
HUDQC Final Report for FY 2015 F-19 

Figure F-2b

Statistically Significant Predictors of Overpayment Rent Error Indicators (1 = Error, 0 = No error): 

Logistic Regression Odds Ratios From the Final Model 

*p < 0.05, **p < 0.01, *** p < 0.001 (test with the null hypothesis that a coefficient = 0; a significant result indicates that the 

corresponding predictor is associated with the subsidy overpayment)

Estimate represents the unique effect of a given predictor, net of other effects in the model. 

Significant effects based on design adjusted variance using delete-a-group Jackknife.

Source: HUDQC FY 2015 household-level data collection and PSQ
Appendix F: Multivariate Analysis

F-20 August 31, 2016

Underpayment. A number of predictors of household characteristics and project-caused errors were 

associated with underpayment in patterns similar to those for gross error. We present the significant 

effects for modeling the probability of an underpayment (n = 2,147): 

● Project characteristics: There are no significant effects based on project characteristics. 

This is consistent with the FY 2013 and FY 2014 analyses.

● Project practices: There are no significant effects based on project practices differing 

from the findings in FY 2014. 

● Tenant characteristics: The odds of subsidy underpayment is 1.99 times higher when a 

tenant is in the Moving to Work program. In addition, the odds of underpayment are also 

increased by 1 percent for each $1,000 increase in the tenant’s total income. Alternatively, 

we see decreases in the probability of underpayment by 7 percent with incremental increases 

in actual rent paid by the household, and by 31 percent if there is an adult over 62 in the 

household.

● Project errors: Two project errors were significant predictors: transcription error and 

overdue recertification error. Transcription error was the largest effect in the model with 

an odds ratio of 9.74. An overdue recertification error increased the likelihood of a subsidy 

underpayment by 4.14 times.

Overpayment. In modeling overpayment error, there were fewer significant predictors than the 

underpayment and gross error model. We present the significant effects for modeling the probability 

of an underpayment and then the significant effects for modeling the value of the effect (n = 2,147): 

● Project characteristics: A higher number of cases per certified staff is associated with an 

increase in the possibility of overpayment. An increase of 100 cases per staff increases the 

possibility of overpayment by 10 percent. 

● Project practices: No project practices are significant predictors of the probability of a 

subsidy overpayment. 

● Tenant characteristics: There is a 69 percent increase in the odds of a subsidy overpayment 

for each increase in the number of tenant allowances. In addition, there is a 47 percent 

increase in the likelihood of a subsidy overpayment if there are dependents in the household. 

● Project errors: Transcription error is the only significant project error predicting the 

likelihood of subsidy overpayment. It had the largest effect on overpayment, increasing the 

odds of overpayment 5.70 times. 

While the probability of overpayment and underpayment subsidy only shared the transcription 

project-caused error, in both cases it appeared to have significant impact on the likelihood of 

subsidy error. While no household characteristics were identified as predictors of subsidy error, 

this may be an effect of modeling household characteristics as a predictor of project-caused errors 

as intermediate variables. 
HUDQC Final Report for FY 2015 F-21 

Project Error Models

In modeling gross rent errors, underpayments, and overpayments, we included five project-caused 

errors: overdue recertification error, transcription error, consistency error, income calculation 

error, allowance calculation error, and other calculation error. Of these, we found only overdue 

recertification error and transcription error to be significant predictors of the amount or direction 

of gross rent error. As this analysis is focused on the impact of variables directly and indirectly 

on gross rent error, we focus on the variables that were significantly related to these two projectcaused errors.

Overdue Recertification Error: While only a small number of cases reported overdue recertification 

error, the impact of overdue recertifications was significant on the amount of gross rent error and 

the likelihood of subsidy underpayment. Our model indicated that higher numbers of experienced 

certified project staff increased the percent likelihood (0.1%) of an overdue recertification error. 

While this finding may be counter-intuitive, it may suggest that although rare, larger projects may 

run the risk of not getting certifications or recertifications completed on time. 

Transcription Error: In contrast to overdue recertification error, transcription error was found 

to be a significant predictor of both the amount and direction (via subsidy underpayment and 

overpayment) of gross rent error. Additionally, our findings seem to suggest that the majority 

of household indicators and the Owner-administrator project type identified in previous years as 

significant predictors of gross rent error may actually effect rent error through transcription error. 

Specifically, household member characteristics reduced the likelihood of a transcription error by 

79 percent if there was at least one adult aged 62 or older, by 52 percent if there were dependents 

in the household, and by 40 percent if household included a minor. Alternatively, we did see that 

an increase in the age of the head of household increased the likelihood of a transcription error by 

1 percent. 

Alternatively, our findings showed that household financial variables were more complex in their 

impact on transcription errors. While the greater increase in expenses (e.g., medical, childcare, 

disability) a household had reduced the likelihood of a transcription error by approximately 

33 percent, total annual income and the number of sources of income increased the likelihood of a 

transcription error by 3 percent and 55 percent, respectively. In addition, we found that the number 

of different allowances a household reported was the largest predictor of likelihood, with each 

additional allowance increasing the likelihood of a transcription error by 4.24 times. 

Finally, the results also depicted that Owner-administered projects were 21 percent less likely to 

have a transcription error than other types of projects. 

Indirect Effects. The combined model allowed us to determine if the household characteristics, 

project practices and project characteristics that impact project-caused errors as proximal outcomes 

also significantly impacted gross rent error measures through project-caused errors. We found that 

all significant indicators of project-caused errors (previously described) also had significant indirect 

effects on gross rent errors and subsidy overpayment and underpayment. These results confirm our 

reasoning that household characteristics appear to act through project-caused errors and, therefore, 

were not found as significant direct indicators (i.e., differing from previous year models).

Figure F-3 presents the log odds of the statistically significant predictors of transcription error.
Appendix F: Multivariate Analysis

F-22 August 31, 2016

Figure F-3

Statistically Significant Predictors of Transcription Error Indicators (1 = Error, 0 = No error): 

Logistic Regression Odds Ratios From the Final Model 

*p < 0.05, **p < 0.01, *** p < 0.001 (test with the null hypothesis that a coefficient = 0; a significant result indicates that the 

corresponding predictor is associated with having a transcription error)Estimate represents the unique effect of a given predictor, net 

of other effects in the model. 

Significant effects based on design adjusted variance using delete-a-group Jackknife.

Source: HUDQC FY 2015 household-level data collection and PSQ
HUDQC Final Report for FY 2015 F-23 

E. Summary of Findings

The FY 2015 HUDQC multivariate modeling modified the approach used in previous years by 

creating a single combined model. The new approach continued to analyze variables representing 

project characteristics, project practices, tenant characteristics, and project-caused errors as they 

relate to gross rent error, subsidy underpayments, and subsidy overpayments. In addition, this year’s 

approach continued to examine the relationship between household and project characteristics 

and project-caused errors. However, instead of examining the relationships in separate models, 

we included all the relationships in a single iterative model that allowed us to perform logistic 

and linear modeling concurrently. We suggested that examining the full system of variables using 

an integrated modeling approach would allow us to better understand the impact of the predictor 

variables on project-caused errors and the amount and direction of gross rent error while accounting 

for the covariance between the outcome variables. Key findings are highlighted as follows.

Rent Errors

Project characteristics were not generally found to be predictive of the probability of a rent error. 

The only characteristic found significant was the cases per certified staff, which was positively 

associated with the likelihood of subsidy overpayment (more cases per certified staff caused an 

increasing likelihood of subsidy overpayment). 

Of numerous project practice indicators, only projects that conduct EIV training for new staff are 

associated with an increase in the probability of rent error. 

Project-caused errors represented the largest effects in measuring rent error. Two indicators 

were most salient: (1) transcription errors and (2) overdue recertification errors (just 2 percent 

of recertifications in FY 2015). Both predicted substantially higher rent error. With regards to 

the amount of gross rent error, overdue recertification errors had the largest effect. However, for 

underpayment and overpayment subsidy, transcription errors were more salient. The rent error model 

suggests that reducing project-caused errors will reduce rent errors. The model elements focused 

on predicting project-caused errors may demonstrate significant impacts on rent errors.

Household characteristics play a significant role in explaining project errors, and were, thereby, 

also found to impact gross rent error. Both household financial conditions and demographics present 

varying levels of challenge to transcription and overdue recertification processing. Over the years, 

the basic patterns have been quite consistent and fit common sense (i.e., a household’s complex 

financial situations raise the risk of errors in determining rent). Some key predictors are:

● Households with higher total income and fewer total allowances had higher amounts of 

gross rent error.

● Households with higher total income and without an elderly member were more likely to 

have subsidy underpayment; while those that had dependents were more likely to have 

subsidy overpayment.

● Household membership, including an adult over age 62, minors, and at least one dependent, 

decreased the likelihood of a transcription error.
Appendix F: Multivariate Analysis

F-24 August 31, 2016

● Complex household finances, including higher total income, more types of allowances, 

and more sources of income, indicated a higher likelihood of transcription error; however, 

fewer types of expenses decreased the possibility of a transcription error. 

Implications for Program Improvement

We underscored a number of implications for HUD’s subsidy improper payment management. 

First, the three housing programs were not substantially different in rent error after project and 

household variables were considered. This finding suggests that targeting a particular program 

type for strengthening financial integrity may not be as effective as targeting specific problems 

underlying rent errors across all program types (e.g., reducing project-caused mistakes in (re)

certification processing). 

Project-caused errors in certification processing represent the largest effects with rent error, 

particularly transcription errors. Such errors can lead to increased amounts of subsidy overpayment or 

underpayment. Reducing project-caused errors should be a priority for reducing the rate of improper 

payment. The significant project-caused variables are highly related to household characteristics 

(sources of income/expenses, number of allowances, household with elderly members or other 

dependents). Households with more complex financial situations are more susceptible to project 

errors and, thus, more susceptible to rent errors. 

Underpayment and overpayment relate to different issues, and may require different strategies to 

remedy. The only common effect among the two is transcription error. This is the strongest predictor 

of rent error probability for both the amount and direction (underpayment and overpayment) of rent 

error. Total annual income, actual household-paid rent, and overdue recertification processing are 

significant factors contributing to the amount of rent error and subsidy underpayment. In addition, 

the total amount of allowances also significantly impacts the amount of rent error, while having an 

adult over 62 in the household and being part of the Moving to Work program indicated potential 

for subsidy underpayment. Alternatively, having dependents in the household and large numbers 

of allowances increased the possibility of subsidy overpayment. 

Future Research

The new approach evaluated the effects of both direction and amount of rent error by examining 

both the logistic and linear relationships and the proximal (project-caused errors) and distal (gross 

rent errors) concurrently within the model. While a number of results are consistent with prior 

studies, the path through which these predictors impact the distal outcome of rent errors may have 

emerged. Increasing our understanding of these effects on rent errors as well as their relationship 

with other predictors (e.g., project-caused errors) will allow focused efforts to reduce errors. Our 

findings have shown that a number of housing characteristics impact rent error through the projectcaused transcription error. Using these characteristics (e.g., large sources of income, large numbers 

of allowances) to identify specific households that could be at risk for improper payment in advance 

of the (re)certification process could allow for increased quality control for these cases or the 

development of specific training around these at-risk households. 
HUDQC Final Report for FY 2015 F-25 

The project practices that emerged from the modeling seem to be inconsistent in their relationship 

with rent errors (e.g., increased training associated with increased rent error). Further investigating 

these counterintuitive effects could help identify whether the effect is truly associated with rent error 

or whether there are underlying factors that are confounded with rent error. This will improve clarity 

on specific challenges facing certification staff that lead to processing errors and payment errors. 

As stated in the FY 2014 report, historical analysis of the HUDQC data seems both feasible and 

desirable. Thus far, the analytic task has focused solely on a single year. With more than a decade of 

data accumulated by the annual surveys, a great deal of comparable measures of improper payment 

errors, project background/activities, and household characteristics are available. The large amount 

of data would allow in-depth analyses to describe the changing patterns of these important measures 

and their relationships. In a broad chronological framework, historical data analysis may shed light 

on how much progress HUD has made in dealing with housing subsidy improper payment, and how 

such progress can be attributed to changes in housing program practices in relation to changes in 

the sociodemographic characteristics of program participants. Information yielded from historical 

analysis could be useful for HUD’s long-term planning and program development to improve 

financial integrity.
Appendix F: Multivariate Analysis

F-26 August 31, 2016

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