U.S. Department of Housing and Urban Development 

Office of Policy Development and Research 

Quality Control for Rental

Assistance Subsidies Determinations

Final Report 

June 20, 2001 
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Final Report 

Quality Control for Rental 

Assistance Subsidies Determinations 

Prepared for: 

Office of Policy Development and Research 

Department of Housing and Urban Development 

Washington, DC 20410 

Contract #: C-OPC-21179 

Prepared by: 

ORC/Macro 

11785 Beltsville Drive 

Calverton, MD 20705-3119 

June 20, 2001 
Officer in Charge 

JoAnn Kuchak 

Project Manager 

Mary K. Dent Sistik 

Analysts 

Patrick Boxall - KRA Corporation

Tigran Markaryan

Pedro Saavedra, Ph.D.

Douglas Thompson, Ph.D.

Wendy Wyatt

Expert Consultants 

Linda Campbell - HUD Policy

Amy Jones - HUD Policy

Suzanne B. Loux - Survey Research

Z. Frank Wann - Statistical Programmer

Charles Wilkins, Jr. - HUD Policy

Field Supervisors 

Jagruti Patel 

Maria Pereira 

Karen Smith 

Laura Webb 

The contents of this report are the views of the contractor and do not necessarily reflect 

the views or policies of the U.S. Department of Housing and Urban Development or the 

U.S. Government. 
Foreword

Early in 2001 the Department of Housing and Urban Development (HUD) took an 

important step to improve the effectiveness of its rental housing assistance programs. 

Through the Rental Housing Integrity Improvements Program (RHIP), HUD worked to 

identify the best methods of reducing costly errors in the local administration of both 

the public housing and Section 8 programs. The Quality Control for Rental Assistance 

Subsidies Determinations study provided the impetus for much of this effort. The 

study found that substantial errors were being made in the income and rent 

determinations that set the amount HUD pays on behalf of families receiving public 

housing and Section 8 program assistance. Through miscalculation of income and 

other errors, overpayments of more than $600 million were made, whereas rent 

underpayments totaled $1.7 billion―nearly three times the amount of the 

overpayments. 

The data collection for this study was done in mid-2000. On-site tenant interviews, file 

review, and independent third-party income verifications were conducted by an 

independent contractor for a nationally representative sample of families who receive 

public housing and Section 8 assistance. Using these data and adhering to all HUD 

requirements, the Department made income, rent, and subsidy determinations based 

on adherence to all HUD guidelines. These determinations were then compared to 

those made by local public housing and Section 8 project staff. This allowed the 

Department to identify the most serious errors, their costs, and their apparent causes, 

and formed the basis for many of the corrective actions that are now being developed. 

Successive Quality Control studies will provide periodic performance indicators and 

verify the effectiveness of HUD's corrective actions. 

The Department acknowledges and thanks the many public and assisted housing 

project staff who participated in this study. Their contributions and those of HUD staff 

in identifying and correcting the major causes of subsidy determination errors will help 

the Department to achieve its goal of assuring that the correct amount of benefits go 

to eligible tenants, allowing it to serve as many low-income households as possible. 

Alphonso Jackson 

Deputy Secretary 
Table of Contents 

Page Number 

Executive Summary ....................................................................................................... ES-I 

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

A. Purpose of the Quality Control for Rental Assistance Subsidies

Determinations Study........................................................................................ 1

B. Background of the Study................................................................................... 1

C. Organization of This Report.............................................................................. 1

II. Methodology................................................................................................................. 3

A. HUD Requirements and Study Standards ........................................................ 3

B. The Sample ...................................................................................................... 3

C. The Data Collection Process ............................................................................ 4

D. Data Sources .................................................................................................... 5

E. Time Periods..................................................................................................... 6

F. Constructing the Analysis Files......................................................................... 6

G. Rules for Matching Verification and Income and Expense Items ..................... 6

H. HUD Rent Formulae ......................................................................................... 7

I. Calculation of Rent Error .................................................................................. 8

J. Quality Control Rent ........................................................................................ 8

K. HUD Requirements Affecting the Analysis ...................................................... 9

III. Study Objectives and Analytic Methods..................................................................... 11

IV. Findings ................................................................................................................... 16

A. Rent Error ....................................................................................................... 17

B. Sources of Error.............................................................................................. 27

C. Errors Detected Using Information Obtained from Project Files .................... 32

D. Project Analysis.....................................................................................38

E. Error Prone Profiling ...................................................................................... 39

F Occupancy Standards..................................................................................... 40

G. Comparison With TRACS/MTCS Data........................................................... 42

V. Rent Reasonableness Determinations ...................................................................... 46

A. Methodology.................................................................................................... 47

B. Housing Authority Procedures ........................................................................ 48

C. Housing Authority Practice.............................................................................. 57

D. Rent Comparability ......................................................................................... 62

E. Recommendations.......................................................................................... 69

VI. Recomendations ....................................................................................................... 72

Definitions ................................................................................................................................ 79

35005

Table of Contents-i 
Appendices 

Page Number 

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

Appendix B: Weighting Procedures ...................................................................................B-1

Appendix C: Sample Sizes for Analysis ............................................................................ C-1

Appendix D: Analysis Tables............................................................................................. D-1

Appendix E: Sample List of Cases ....................................................................................E-1

Appendix F: Consistency Errors.........................................................................................F-1

Appendix G: The Impact of Administrative Error and Component Error

on Dollar Rent Error...................................................................................... G-1

Appendix H: The Impact of Tenant Characteristics on Rent Error ................................... H-1

Appendix I: The Impact of Project Characteristics and Practices on Error .......................I-1

35005

Table of Contents-ii 
Quality Control For Rental Assistance Subsidies Determinations 

Final Report 

Executive Summary 

The Department of Housing and Urban Development (HUD) Quality Control for Rental Assistance 

Subsidies Determinations studies provide national estimates of the extent, severity, costs, and sources of 

rent errors for the Public Housing and Section 8 programs. The current study also examined whether rents 

charged for Section 8 tenant-based program units are reasonable in relation to rents in the private, 

unassisted market. Data for the current study were collected during May through August 2000. 

For purposes of this study, “error” is defined as any rent calculation or eligibility determination that 

differs from what would have occurred if the PHA/owner had followed all HUD income certification and 

rent calculation requirements during the most recent income certification/ recertification. When 

appropriate, study findings are compared to findings from the previous study. 

It is important to note that this study was primarily designed to measure the extent of administrative error 

by housing providers, not to measure how much additional tenant contributions could be realistically 

collected in a cost-efficient manner. The extent of the identified error is sensitive to a number of 

assumptions made in the study. Changes in the error threshold, for example, would affect the overall 

dollar error estimates. Perhaps more importantly, it is likely that some tenants with large rent increases 

resulting from corrected calculations would leave the program, reducing potential subsidy reductions; 

while those with decreases in their rents would be more likely to remain, increasing subsidy requirements. 

These corrections are desirable outcomes, but it is unclear what their net impact would be on subsidy 

costs. The most appropriate use of this study is as a tool for strengthening HUD’s procedures for ensuring 

administrative compliance with regulations. The improvements recommended will require more rather 

than fewer resources in the short-term. Significant reductions in error can only be expected after progress 

is made in providing the type of program simplifications and the additional instructions, forms, training, 

and monitoring discussed in the report. Some budgetary savings may be achieved at some future date if 

the recommendations of the study are adopted. However, the necessary changes will take two to four 

years to start to achieve measurable results, and the major benefit that these changes ensure is an 

improvement in the accuracy and equity of subsidy determinations. 

METHODOLOGY 

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

handbooks and notices, all HUD requirements relevant to the determination of rent were consolidated into 

a set of HUD requirements. Nationally recognized experts were involved in establishing and reviewing 

the standards used in this study. 

The Sample. A nationally representative sample of 600 projects in the United States and Puerto Rico was 

selected for this study. These projects were selected from the universe of the three program types covered 

by the study: 

• Public Housing 

• PHA-administered Section 8 (Certificates, Vouchers, and Moderate Rehabilitation) 

Executive Summary June 20, 2001 ES-i 
•	 Owner-administered Section 8 (New Construction, Substantial Rehabilitation, Property 

Disposition, and Loan Management). 

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

unusually large projects. The final study data set includes responses from 2,403 households. 

The Data Collection Process. The data collection effort included creating and automating over 30 data 

collection instruments, contacting and interviewing PHA/owner staff, hiring and training over 60 data 

collectors, and selecting the tenant sample. Data collectors obtained data from tenant files, interviewed 

tenants using Computer Assisted Personal Interviewing (CAPI) software developed for this study, 

electronically transferred data to Macro headquarters on a daily basis for review, and requested third-party 

verifications related to income and expenses. The data collection process was facilitated by the use of 

built-in consistency and edit checks that prompted interviewers to probe inconsistent and anomalous 

responses. 

A related but different data collection effort also occurred for a sub-sample of Section 8 Certificate and 

Voucher units with the objective of learning how rent reasonableness requirements were being 

implemented. Data on PHA rent reasonableness policies and practices were collected through project 

telephone interviews and on-site data collection. Rent comparability studies were then conducted for each 

unit by licensed appraisers to learn how program-approved rents compared with private market rents paid 

for comparable units. 

Calculation of Rent Error. A quality control rent (QC Rent) was calculated for each household using 

the information reported by the household and verified. Rent error was determined by comparing the QC 

Rent to the actual tenant rent (the rent from the HUD Form 50058 or 50059). A discrepancy of $5 or less 

between the actual and QC Rent was not counted as an error. This was done to eliminate minor 

calculation discrepancies that have little impact on program-wide subsidy errors. 

MAJOR ERROR FINDINGS 

Percent of Households With Rent Errors. The analysis of tenant files, tenant interview, and income 

verification data indicates that: 

• 34 percent of all households paid at least $5 less than they should (with an average error of $95) 

•	 44 percent of all households paid the correct amount of rent within $5 (32 percent paid exactly 

the right amount) 

• 22 percent of all households paid at least $5 more than they should (with an average error of $56) 

The percent of error varied by program type. The highest rate of underpayment of rent (42 percent) was 

found in the PHA-administered Section 8 programs. The lowest rate of overpayment (20 percent) was 

found in both the PHA-administered Section 8 and Public Housing programs. Underpayment of rent was 

found in 33 percent of Public Housing households and 27 percent of owner-administered Section 8 

households. Overpayment of rent was found in 20 percent of Public Housing households and 25 percent 

of owner-administered Section 8 households. The chart that follows summarizes this information. 

Executive Summary June 20, 2001 ES-ii 
Rent Error by Program Type 

Program 

Rent Underpayment 

(Subsidy Overpayment) 

33% 

42% 

27% 

34% 

Rent Overpayment 

(Subsidy Underpayment) 

20% 

20% 

25% 

22% 

Public Housing 

PHA-Administered Section 8 

Owner-Administered Section 8 

Total 

Dollar Error Impact of Rent Errors. The error rate is calculated by dividing the sum of the dollar 

amount of gross rent error (i.e., differences in excess of $5 between actual and QC rents) by the sum of 

the dollar amount of the QC rent. Major findings were: 

•	 Rent Underpayments of Approximately $1.7Billion Annually. For tenants who paid less monthly 

rent than they should pay (34 percent), the average monthly underpayment was $95. For purposes 

of generalization, total underpayment errors were spread across all households (including those 

with no error and overpayment error) to produce a program-wide average monthly underpayment 

error of $32. Multiplying the $32 by the approximately 4.3 million units represented by the study 

sample results in an overall annual underpayment dollar error of approximately $1.7 billion per 

year. 

•	 Rent Overpayments of Approximately $.6 Billion Annually. For tenants who paid more monthly 

rent than they should pay (22 percent), the average monthly overpayment was $56. When this 

error was spread across all households, it produced an average monthly overpayment of $12. 

Multiplying the $12 by the approximately 4.3 million assisted housing units represented by the 

study sample results in an overall annual overpayment dollar error of approximately $634 million 

per year. 

•	 Net Overall Gross Rent Error of $1.04 Billion Annually. When combined, the average gross rent 

error per case is $44 ($32 + $12). Overpayment and underpayment errors partly offset each other. 

The net average rent error is $20 ($32-$12). HUD subsidies for Public Housing and Section 8 

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

means that rent errors have a dollar-for-dollar correspondence with subsidy payment errors. The 

study found that the net subsidy cost of the under- and over-payments was approximately $1.04 

billion per year ($1.669 billion - $.634 billion). 

Subsidy overpayment and underpayment dollars are presented in the chart below. 

Type Dollar Error 

Average Monthly Per Tenant Error 

for Households With Errors 

Average Monthly Per Tenant Error 

Across All Households 

Total Annual Program Errors 

Total Annual Errors – 

Subsidy Dollar Error 

Subsidy Overpayment 

$95 (34%) 

$32 

$1.669 billion 

$1.42 - $1.92 billion 

Subsidy Underpayment 

$56 (22%) 

$12 

$634 million 

$483 million - $785 million 

95% Confidence Interval 

Executive Summary June 20, 2001 ES-iii 
Eligibility of Newly Certified Households. A separate analysis of newly certified households (9 percent 

of the sample) was conducted to determine if these households were eligible for HUD housing assistance. 

There was only one newly certified household in the sample who was not income-eligible based on the 

QC income determination. However, 16 percent of the newly certified households failed to document 

social security numbers (or certify non-assignment of a number) for one or more family members (at least 

six years of age), and 22 percent lacked the signed consent forms needed to authorize verification of 

income and assets (for each member of the household at least 18 years of age). In addition, 21 percent 

lacked the signed declaration forms accepted as proof of citizenship. 

Overdue Recertifications. At the time of this study, HUD required that every household be recertified 

annually. Recertifications for 6 percent of the households were overdue. However, the majority of these 

households were overdue by less than 4 months. 

Occupancy Standards. Eleven percent of all households occupied a unit that had more bedrooms than 

permitted under normal occupancy standards. Two percent had fewer than needed bedrooms. As found 

in the past study, most of the errors involved one-person households in two-bedroom units. This could 

not be explained by program rules. Excluding certificate and voucher units, which can legitimately have 

more bedrooms than needed if the landlord discounts the rent, did not change this relationship. 

Rent Reasonableness. This component of the study examined the extent to which housing authorities 

have effectively implemented the HUD requirement that Section 8 voucher and certificate units may not 

have rents set in excess of those paid for comparable private market units. Major findings were: 

•	 Almost all (98%) housing authorities have adopted some type of formal rent reasonableness 

policy, although the methods and documentation requirements vary significantly. The prospect 

of being penalized under the SEMAP system for not having such policies appears responsible for 

much of the recent activity associated with establishing or revising such policies. 

•	 Most housing authorities implement the policies they have established—at least one rent 

reasonableness determination had been conducted for 89 percent of all units sampled. 

•	 Analysis of how program rents compared with rents for comparable private market units showed 

that: 

Relationship of Section 8 Rents to Market Rents 

Relationship 

to Market Rents 

Percent Dollar Difference 

of Units Per Month 

Units with rents 5% or more 62% -$179 

below comparable market rents 

Units with rents within 5% of 16% -$3 

comparable market rents 

Units with rents 5% or more 22% $76 

above comparable market rents 

Program average 100% -$95 

Executive Summary June 20, 2001 ES-iv 
Units with rents 5 percent or more above market comparable rents appear likely to be the result of flawed 

rent reasonableness procedures. The large percentage of units with rents considerably below comparable 

private market rents appears due to a complex mix of factors: tenure discounts for long-standing tenants, 

the reluctance of many landlords to raise rents for extremely low-income tenants (especially elderly 

tenants), the sympathy of some landlords (especially small-scale landlords) for the affordability problems 

of program participants, and Section 8 program practices and rent adjustment constraints that have 

suppressed rent increases (often over a period of years). 

SOURCES OF ERROR 

Rent errors are often due to a mix of different errors. For purposes of this study, administrative errors 

(e.g., calculation errors, transcription errors, failure to recertify on time, and failure to verify information) 

are analyzed separately from specific component errors (income and expense items used to calculate 

rent). Component errors often result when project staff do not conduct a thorough tenant interview or do 

not verify the information obtained during the interview. However, component error may also occur when 

the tenant supplies incorrect information either intentionally or unintentionally. 

Administrative Errors. The two most common administrative errors are calculation errors and failure 

to verify and make use of verified income and expense information. The HUD TRACS and MTCS data 

systems check the rent calculations on forms 50058 and 50059. For tenants for whom data are submitted 

(and corrected if required), these systems virtually eliminate rent determination calculation errors for the 

items included on the forms. However, not all cases are reported and some cases returned to program 

sponsors for correction are ignored. Calculation errors decreased significantly between 1992 and 2000, 

which is also the period when use of TRACS and MTCS increased significantly. It is worth noting that 

subsidy overpayment errors were higher for households for which TRACS/MTCS data had not been 

submitted. Improvement should continue as data for more and more households are submitted to these 

data systems. 

Verification of income and expenses remains a problem. HUD requires that information provided by 

tenants be verified. Verification rates have generally improved since the last study. With the exception 

of other income1

 (which was only verified 63 percent of the time), income items were verified at least 82 

percent of the time. Earned income, which is the rent component most often in error, was verified 82 

percent of the time. However, a third (33 percent) of the verified amounts did not match the amount of 

earned income used on the 50058/50059 form to calculate rents. Earnings were not verified in 75 percent 

of the households with QC earned-income related rent errors. Failure to use verified income and expense 

amounts was also highly correlated with other sources of rent determination error. 

Note that obtaining income verification is often difficult. Employers sometimes don’t respond to requests 

for verification, even when repeated requests are made. However, some program sponsors do a much 

better job than others, and the QC study shows that it is reasonable to expect all program sponsors to have 

as high a success rate as the current high performers. The study also shows that there is significant room 

for improvement in using the verifications that are obtained, which are often collected consistent with 

procedures but then filed and never used. 

1

 Other income includes sources of income other than earned income, social security or pensions, public assistance, or 

income from assets. Examples of other income are: unemployment, workers’ compensation, child support, alimony, gifts 

and contributions, and income from rental property. 

Executive Summary June 20, 2001 ES-v 
Component Errors. Incorrect income and deduction amounts were by far the most significant sources 

of error in determining rents. All but 8 percent of households with rent errors had an income or expense 

component error. Earned income (27 percent), pension income (14 percent), and medical expenses (15 

percent) had the greatest error frequency. The following table shows the frequency of the most serious 

component errors and the average error for that component for households with the same type error. 

Errors are ordered by their impacts on program subsidy levels, which means that both the error cost per 

case as well as the frequency of that type error was considered. 

Households in Error: Rent Components Responsible for the Largest Dollar Error 

Rent Component 

Earned Income


Other Income


Pension, Etc. Income


Asset Income


Public Assistance


Child Care Allowance


Medical Allowance


Dependent Allowance


Disability Allowance


Elderly/Disabled Allowance


No Component Error


Total


Number of Percent of Average Dollar 

Households Households Amount 

647,000 26.9% $6,641 

289,000 12.0% $3,853 

326,000 13.6% $3,701 

103,000 4.3% $3,450 

227,000 9.4% $2,816 

75,000 3.1% $2,333 

360,000 15.0% $1,157 

112,000 4.7% $1,060 

2,000 0.1% $600 

85,000 3.5% $400 

180,000 7.5% 0 

2,406,000 100% $3,472 

No attempt was made to determine whether these errors were caused by project staff or the tenant. 

However, to respond to HUD’s interest in understanding the cause of errors, the QC rent and rent error 

was recalculated without income and expense items identified during the household interview that were 

not present in the tenant file. The income and expense items identified during the household interview 

account for over half ($916 million) of the annual underpayment dollar errors. In addition, not using 

income and expense items identified during the household interview increases the annual overpayment 

error dollars by $91 million. The table below presents the percent of households in error and the total 

annual program dollar errors with and without income and expense items identified during the household 

interview. 

Error Based on All Income and Expense 

Items Identified During the Study 

Error Without Income and Expense 

Items Identified during the Household 

Interview 

Percent of Households in Error Total Annual Dollar Errors 

Subsidy 

Overpayment 

Subsidy 

Underpayment 

Subsidy 

Overpayment 

Subsidy 

Underpayment 

34% 22% $1.669 billion $634 million 

24% 25% $.916 billion $725 million 

Executive Summary June 20, 2001 ES-vi 
This information indicates that a complete detailed interview will identify additional sources of income 

and expenses, and result in a more accurate rent calculation. However, even if a tenant interview is 

thoroughly conducted, tenants may not disclose all sources of income. This may be due to forgetfulness, 

language problems, misunderstanding the questions, or other difficulties. Research conducted by HUD’s 

Office of Inspector General and its Real Estate Assessment Center (REAC), however, suggest that much 

of this non-disclosure is intentional. One effective way of detecting most unreported sources of income 

and assets is through income matching with State or Federal data systems. 

HUD has established a system available to all program sponsors that provides information on Social 

Security benefits. It also matches TRACS/MTCS data with IRS and Social Security Administration data 

to determine if there are any significant discrepancies with the income and asset amounts reported on the 

HUD 50058/50059 data. Full-scale implementation of IRS matching has only recently been initiated and 

is complicated by legal, procedural, staffing, and data problems. However, it provides information which 

would otherwise be unavailable. 

The last statistical income matching study completed by REAC was with tax year 1998 data. Depending 

on the type of subsidy received, it used a $4,000 to $8,000 income matching threshold to screen out 

differences which might be due to timing or definitional differences between how HUD and the IRS count 

income. It estimated that rent underpayments due to income misreporting exceeded $.6 billion. 

There is overlap in the QC and REAC error estimates, since the QC study found that thorough 

interviewing produced information on more earnings and assets than found by program sponsors. Also, 

program sponsor failure to use verified income amounts shows up as a discrepancy for both the QC and 

REAC studies. On the other hand, REAC had access to additional information and past OIG and REAC 

studies seem to suggest that most of the larger discrepancies they find are due to intentional misreporting. 

The extent of overlap between the REAC agreed-upon-procedure and the QC study estimates can only 

be determined by a future study that combines the two study approaches. 

Error-Prone Modeling: Two types of error-prone modeling were conducted for this study. A path 

analysis was conducted to examine the relationship between project characteristics and practices and 

administrative errors such as incorrect calculation and erroneous transcription. This analysis identified 

the number of units in a project (smaller projects do better), staff training (because it leads to better 

verification), third-party verification, and the number of income/expenses as the characteristics and 

practices that have the most impact on rent error. An analytical approach known as Classification and 

Regression Tree (CART) was employed to identify which tenant characteristics had a substantial impact 

on QC Rent error. Among the key indicators identified during the analysis are: at least two sources of 

earned income, at least one source of public assistance income (for households with earned income), and 

at least one source of other income (given other conditions). 

An analysis of the relationship between 50058/50059-detectible errors and QC errors was also conducted. 

There were no sufficiently strong predictive relationships found between QC errors and errors from the 

50058/50059. 

Executive Summary June 20, 2001 ES-vii 
--

--

--

RECOMMENDATIONS 

Policy Implications: This study was not designed to provide recommendations regarding basic program 

objectives and policies. However, the findings from this study suggest that some major procedural 

changes should be considered when establishing and revising policy. 

•	 Federal laws, regulations, and HUD requirements should be simplified to the extent possible. The 

current statutory environment poses substantial obstacles to efficient, accurate income and rent 

calculations. It contains dozens of requirements which may all be well-intentioned and have 

potentially desirable impacts but which, taken as a whole, make the income and rent 

determination process incomprehensively complex. The current income and rent determination 

requirements are detailed, complex, sometimes ambiguous, and subject to relatively frequent 

legislative changes. While determining which income to count, which expenses to allow, and 

annualizing that information will always be somewhat complicated, overly complex policies 

which only apply to a portion of the population could be eliminated or simplified. Two examples 

of such policies follow: 

- Disallowance of Earned Income from Public Housing Rent Determinations. Legislation 

passed in 1998 related to employment incentives provides an example of the complexities 

associated with rent determinations. The legislation provides special rent treatment for 

families: 

whose income increases as a result of employment of a member of the family who was 

previously unemployed for one or more years; 

whose earned income increases during the participation of a family member in a family 

self-sufficiency or other job training program; or, 

who is or was, within six months assisted under any State program for temporary 

assistance for needy families funded under part A of title IV of the Social Security Act 

and whose earned income increases. 

Families that qualify under these provisions are not subject to rent increases related to 

increased income from the specified training or employment for a 12 month period. After that 

period, the rent may be increased but the increase may not be greater than 50 percent of the 

amount of the total rent increase that would be otherwise applicable. 

In practice, low income tenants often have jobs with little security and move in and out of 

employment and training programs. Regulations needed to define the range of circumstances 

that occur and adequately document eligibility for this provision are necessarily long and 

somewhat complex. Keeping track of rent increase constraints imposes a significant added 

burden on PHAs and adds to rent determination errors. As with many provisions associated 

with rent and income determinations, there apparently was little thought given to striking a 

balance between a policy objective and administrative feasibility. A flat dollar or percentage 

income deduction for any training or earned income, for instance, would have provided a 

more direct and understandable incentive, and would have been easier for program sponsors 

to implement and for HUD to monitor. 

Executive Summary June 20, 2001 ES-viii 
- Medical Expenses. Elderly and disabled families are eligible for a medical expense deduction 

which is intended to cover prospective medical costs. Determining the amount that a family 

anticipates spending on medical needs is a difficult thing to do. Elderly tenants often keep 

poor records, and there is limited reason to believe that the medical expenses claimed have 

a close relationship with actual expenses, which HHS data suggest are, on average, higher. 

Verifying medical expenses is a burdensome process for program sponsors. Calculating the 

medical expense allowance would be far less complicated if HUD would substitute a flat 

medical allowance for the inexact science of estimating future expenses. If some provision 

for exceptionally high expenses was considered essential, then the requirement could be that 

actual expenses could be claimed if in excess of some relatively high percentage of a family’s 

income (e.g., 20 percent). This approach would be welcomed by the many elderly who resent 

the intrusion of housing staff into their very personal medical affairs (many verifications by 

their very nature reveal the type of clinics being visited, the practice of doctors being seen, 

and the names and dosages of prescriptions drugs being taken). 

Expecting what are often relatively low-paid, minimally trained, high turn-over project staff to 

correctly implement unnecessarily complex rules is unrealistic. Some program sponsors do a 

remarkably good job, but expecting a generally high level of accuracy in rent and subsidy 

determinations may be unrealistic within the context of the current system. The legislative 

changes affecting tenant rent determinations made every one or two years usually affect a 

relatively small percentage of tenants, but are sufficient to substantially reduce incentives to 

design and implement comprehensive forms, procedures, and data systems that cover all aspects 

of income and rent determinations. 

•	 HUD should consider expanding support of the occupancy function and conducting an outreach 

campaign to PHAs and owners informing them of the Department’s occupancy related-resources. 

Specifically, HUD should develop or expand a nationwide, consistent, credible approach to 

providing guidance and support to PHAs and owners. As one example, the Department could 

offer a monthly-televised program highlighting a specific occupancy topic leaving at least half 

of the program time for call-in questions on any occupancy topic. HUD could then make the 

taped program available for Internet access to reach a larger audience (as the Department does 

now with many video programs.). Commonly, PHA managers and staff are unaware of the 

resources that HUD has to offer—especially those originating from headquarters. Even when 

HUD’s customers are aware of some of the Department’s direct assistance options, owners and 

PHA staff are still reluctant to use them. The PHA may hesitant to call HUD staff for fear that 

their questions will bring a closer scrutiny of their operations. Some PHAs may have had past 

experiences with getting different answers to the same questions from different HUD staff, or 

may be aware that their HUD contact person has a different perspective than that expressed by 

another HUD staff to a neighboring PHA. For these and other reasons, it is important that the 

PHA/owner community know that there are HUD-approved resources that they can trust to 

provide consistent guidance and quick, reliable answers to questions. 

Also, it is critical that there be a close link between the team that responds to field concerns and 

the staff responsible for writing HUD notices and guidance documents. The team responding to 

field questions and concerns knows what the problems are that face the field. These problems 

should be the subject of the guidance that comes from HUD. 

Executive Summary June 20, 2001 ES-ix 
•	 HUD should provide the PHA/owners with the forms, training, and other tools needed to 

determine rent correctly. Rent calculation error could be reduced if HUD would provide 

structured forms for interviewing tenants, obtaining verification, and calculating rent. Ideally, 

these tools would be provided in the form of computer-assisted interview software that minimizes 

the number of questions that need to be asked. Such systems would ensure that tenants are asked 

about all income sources and expenses that affect their rent. Manuals and training materials 

explaining how to implement requirements correctly and calculate rent accurately should be 

provided. 

Federal and local housing staff should be given an opportunity to work together to develop these 

tools and systems needed to reduce rent error. Many local PHA/owners have already developed 

forms, training materials, manuals, automated systems, and monitoring processes that have 

enabled them to provide accurate efficient service to the tenants they serve. HUD should learn 

from these PHA/owners and develop materials which will help those PHA/owners who for one 

reason or another have not been as successful. 

•	 PHA/owners should be held accountable for implementing HUD regulations and calculating rent 

accurately. An on-site monitoring system should be developed that includes reviews at both the 

local and Federal level. PHA/owners with excessive errors should be required to develop 

corrective action plans and show improvement within specified time periods. Over the last several 

years, in its efforts to down-size staff and to use risk management concepts, HUD effectively 

stopped monitoring the occupancy function. Determining the correct amount of rent is critical to 

HUD management. 

Monitoring can be conducted at a variety of different levels. We recommend that HUD require 

PHA/owners to perform quality control reviews on a percentage of income determinations and 

rent calculations. Agencies which have aggressively sought to improve performance of their 

programs, often including establishment of quality control review procedures, have had some 

significant successes. In addition, HUD Field Offices and/or other national level well-trained staff 

should conduct a re-review of a percentage of the cases reviewed at the local level to ensure that 

the quality control reviews are being conducted correctly. This type of oversight not only 

identifies errors, but prevents them. In addition, it demonstrates HUD’s concern and focuses 

PHA/owner attention on tenant income and rent. 

There is evidence that PHA/owners will respond to HUD directives if they are monitored and 

held accountable. The Section 8 SEMAP system, for instance, appears responsible for much of 

the recent burst of improvement in activity by public housing agencies to implement or improve 

the rent reasonableness determination requirement. MTCS reporting, which is part of the new 

PHA rating systems, has improved partly as a result of the potential penalty from low rating 

scores, and the higher reporting and associated calculation checks appear responsible for much 

of the reduction in rent and subsidy calculation errors for units passing the MTCS data system 

edits. 

Recommendations for Modifying the Quality Control Process: The current quality control study 

methodology is based on the successes and failures of previous studies, and generally performed well. 

Some minor changes in the next study appear desirable. These include more letters and contact with HUD 

Field Office and project staff to keep them informed of the study’s progress, hiring more back-up data 

collectors to be assigned to sites as needed, and automating additional components of the data collection 

process. It also may be desirable to include two possible sources of rent determination error that were not 

Executive Summary June 20, 2001 ES-x 
addressed in the current study—determining if the correct utility allowance and the correct payment 

standard are used. Also, HUD may wish to consider if further research on Section 8 rent reasonableness 

is desirable. 

There is one major change in the QC process that is also worth considering—integrating the QC study 

process with the HUD Real Estate Assessment Center’s (REAC) annual 1,000 case income matching 

effort. Combining efforts would involve use of a common sampling methodology and changing the timing 

of the QC data collection to maximize overlap with tax year reporting. At the end of the normal QC 

process all data would be made available to REAC for use in confidential income matching. This 

approach would permit normal QC reports to be produced, but also give REAC most of the information 

needed to develop consolidated estimates of subsidy errors for HUD’s major housing subsidy programs. 

If the QC study process obtained data for multiple points of time within a tax year, REAC would be able 

to measure the extent to which income matching discrepancies appeared due to tenant non-disclosure of 

income as opposed to some type of program sponsor error. 

Recommendations Related to Rent Reasonableness: Making recommendations related to rent 

reasonableness is difficult because of the limited amount of information available. The rent 

reasonableness task was essentially an exploratory study. Very little was known about the rent 

reasonableness procedures followed by PHAs, or whether the rents approved by PHAs would fall within 

the acceptable guidelines set by HUD. In fact, the study found that the average monthly contract rent for 

Section 8 certificate and voucher units was $95 less than the estimated rents for comparable unassisted 

units. While this is relatively good news for HUD, it was not the expected outcome, and leaves many 

questions unanswered. Follow-up rent reasonableness studies should include questions that explore why 

some tenant rents are less than comparable market rents, and if possible include a methodology for the 

field data collector to pursue follow-up questions related to the specific outcome of each case reviewed 

while still on site. Information obtained through this study can be used to design a more thorough data 

collection process that will include situations that were not anticipated for this study. 

The finding from this study did, however, provide enough information to make some observations. First, 

the study reveals a “profile”of cases that are more likely to have “unreasonable” rents. These include units 

with longer tenure under the program, originally assisted under the certificate program, with a household 

headed by an elderly disabled member, located in an elevator apartment building, and a part of a relatively 

low-cost submarket. 

Second, the study also shows that it is not enough to just complete a rent reasonableness determination. 

How the determination is done is important, and the determination must be timely. The study suggests 

that combined approaches to determining rent reasonableness work best. In other words, no single 

approach can ensure reasonable rents for all units. Although, in general, market based approaches work 

better than unit-to-unit approaches. 

Study findings support some of the PHAs recommendations regarding standardization of the rent 

reasonableness requirement. HUD should take some reasonable steps to provide program resources and 

technical assistance for those agencies that need additional help developing systems and procedures for 

implementing an effective rent reasonableness process. Care needs to be taken not to require a particular 

structure. PHAs should be allowed to select the method or combination of methods for determining 

reasonable rents that works best for them. 

Executive Summary June 20, 2001 ES-xi 
Quality Control for Rental Assistance 

Subsidies Determinations 

Final Report 

June 20, 2001 
I. Introduction 

A. Purpose of the Quality Control for Rental Assistance Subsidies Determinations Study 

The purpose of this study is to provide national estimates of the extent, severity, costs, and sources of 

errors occurring in the certification and recertification2

 procedures used by Public Housing Agencies 

(PHAs) and owner-administered (owner) assisted housing programs. This study included a task to 

determine whether the rents charged for assisted units in the Section 8 tenant-based programs are 

reasonable in relation to rents in the private, unassisted market. To fulfill the purpose of this evaluation, 

HUD identified thirteen study objectives related to types of errors and cost issues; this report addresses 

each of these objectives. For purposes of this study, “error” is defined as any rent calculation or eligibility 

determination that differs from what would have occurred if the PHA/owner had followed all Department 

of Housing and Urban Development (HUD) income certification and rent calculation requirements during 

the most recent (re)certification. The analysis also identifies errors in assigning appropriate size units to 

households and certain procedural errors (i.e., situations in which PHAs/owners did not follow HUD 

procedures but no dollar error resulted). 

B. Background of the Study 

This project is the second in a series of studies designed to identify current HUD eligibility, income, and 

rent determination regulations; translate these regulations into survey instruments; develop an error 

detection system; and provide nationally representative estimates of error. The final report for the first 

study, conducted by ORC/Macro International Inc. (Macro) and KRA Corporation (KRA), was published 

in April 1996. Work on the current project began in October 1998. Tasks completed prior to data 

collection included designing the research and survey methodology, compiling HUD’s regulations for the 

programs included in the study (public housing, Section 8 tenant-based, and Section 8 project-based), 

obtaining approval from the Office of Management and Budget (OMB), automating the data collection 

process, and conducting a pretest of the study. Data were collected from a nationally representative 

sample of HUD-assisted housing projects and project residents during April through August 2000. Data 

were uploaded via the Internet, and analysis progressed as soon as the data were collected. 

C. Organization of This Report 

This report is organized as follows: 

• Section I: Introduction 

• Section II: Methodology 

• Section III: Study Objectives 

• Section IV: Findings 

• Section V: Rent Reasonableness Determinations 

2

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

“certification”) and thereafter an annual recertification of each household’s rent (a “recertification”). In addition, interim 

recertifications are completed as needed. In this report, the term (re)certification refers to certifications, interim recertifications, 

and annual recertifications. 

Final Report June 20, 2001 1 
I. Introduction


• Section VI: Recommendations 

•	 Appendices


A. Rent Calculations


B. Weighting Procedures


C. Sample Size for Analysis


D. Analysis Tables


E. List of Cases in Error


F. Consistency Errors


G. The Impact of Administrative Error and Component Error on Dollar Rent Error


H. The Impact of Tenant Characteristics on Rent Error


I. The Impact of Project Characteristics and Practices on Error


Definitions of key terms used throughout this report are found at the end of Section VI.


Final Report June 20, 2001 2 
II. Methodology 

A. HUD Requirements and Study Standards 

Using the Code of Federal Regulations and official HUD handbooks and notices, all HUD requirements 

relevant to the determination of rent were consolidated into a set of HUD requirements. The requirements 

identified changes in Federal legislation that were expected to impact this study. These legislated changes 

were monitored and the requirements were revised as the new legislation became effective. The complete 

set of requirements was reviewed by an independent team of HUD policy experts to ensure that all 

requirements were included and correct. 

These requirements were used to create a set of standards that identify errors in eligibility determination, 

rent calculation, and unit assignment for the housing programs included in the study. The standards 

converted the requirements into a uniform set of rules that could be followed when determining rent error. 

In general, the standards followed the requirements. However, there were some requirements for which 

standards had to be created to make it possible to collect data in a uniform manner. For a complete list 

of standards used in this study, see the Data Collection Standards.3 

B. The Sample 

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

randomly selected from each project. Projects were selected with probabilities proportional to size 

without replacement, but projects whose size exceeded the sampling interval were selected for eight, 

twelve, or more households in the project, and were counted as more than one project for purposes of 

determining the sample size. Therefore, because these large projects were selected multiple times, the 

study sample included 524 distinct projects in 54 geographic areas across the United States and Puerto 

Rico. 

The sampling design required approximately equal allocations for three program types: Public Housing, 

PHA-administered Section 8 (Certificates, Vouchers, and Moderate Rehabilitation) and owneradministered Section 8 (New Construction, Substantial Rehabilitation, Property Disposition, and Loan 

Management). For additional information on the sampling procedures, see the Sampling Report, Quality 

Control for Rental Assistance Subsidies Study.4 

A random sample of four households (plus ten potential “replacement” households, for use in the study 

if a sampled household did not meet the study requirements or was not available to be interviewed) was 

selected from most projects. However, as noted above, additional households were selected for some large 

projects. For example, the New York City Housing Authority Section 8 Certificate and Voucher program 

had a household sample size of 36. This procedure resulted in a tenant sample of 2,400 households. One 

household was excluded from the sample after data collection was completed because the household was 

not an assisted household. In four of the 524 projects one additional household was selected for the study 

because of an error in classification of project type. Rather than excluding households originally selected, 

these cases were added to the final data set. Therefore, the final data set includes responses from 2,403 

households in the 524 projects. 

3

Macro unpublished report to HUD dated March 31, 1999. 

4

Macro unpublished report to HUD dated May 30, 1999. 

Final Report June 20, 2001 3 
II. Methodology 

C. The Data Collection Process 

This study used a multiple stage data collection effort to obtain all required information. Interviews were 

conducted with project staff prior to the on-site data collection. Field data collection included abstracting 

data from the household’s file, interviewing the tenant, and requesting verification5

 from third parties. 

Preparing for and conducting this data collection involved several major tasks. Each of these tasks is 

discussed briefly below. 

Creating the Data Collection Instruments. Over 30 data collection forms were designed for this study. 

These forms were created to collect all the data needed to determine if 1) there were errors in the 

eligibility determination, 2) the household’s rent was calculated correctly, and 3) units were correctly 

assigned according to the study standards. Each form was created by a survey research specialist and 

reviewed by a HUD policy expert. All data collection forms were approved by OMB. 

Automating the Data Collection Process. Most of the forms created for this study were automated and 

incorporated into a data collection system designed for laptop computers. Data abstracted from the tenant 

files were entered directly into the system, and the tenant was interviewed using a CAPI (computer 

assisted personal interviewing) process. As sections of the instruments were completed, the system 

compared the data to expected responses or data previously entered, allowing the data collector to correct 

data entry errors while in the field. The system required that the data be collected in the correct order, and 

that all the appropriate skip patterns be followed. This automated process greatly reduced the time needed 

to edit, code, and clean the data after data collection was completed. 

Contacting and Interviewing PHA/Owner Staff. PHA/owner contact names were obtained from HUD 

Field Office staff. Letters were sent to PHA/owner staff advising them of the study and requesting their 

participation. 

A CAPI interview was conducted with the project staff person most knowledgeable about certification 

and recertification procedures. This interview served as a means for verifying the project type and size; 

obtaining information about local policies and procedures; and requesting a master list of assisted 

households (for sampling purposes) and project specific materials. 

Hiring and Training Data Collectors. Over 60 data collectors were hired to complete the field data 

collection. Data collectors typically lived in the same general area as the projects selected for the study. 

Two 9-day training sessions were held (half of the data collectors were trained at each session). This 

detailed training covered: project background, HUD programs and requirements, survey procedures, 

automated data collection, and administrative procedures. 

Field Data Collection. Each data collector was assigned a group of projects. Data collection activities 

at each of these projects included: contacting the project, selecting the tenant sample,6

 identifying the 

month for which data were collected, abstracting data from the tenant file, contacting the tenant, 

5

Verification is a process of obtaining information about income or expenses from a third party who can attest to the accuracy 

of the information provided by the household. HUD requires that most information provided by the household be verified by 

a third party or substantiated from documents (such as bank statements). 

6 Many of the tenant samples were selected by Macro staff prior to on-site data collection; however, if the PHA/owner 

did not provide the list of households ahead of time, the data collector selected the sample on-site. 

Final Report June 20, 2001 4 
II. Methodology 

interviewing the tenant, and completing the release forms requesting verification from third parties. Data 

were transferred to Macro electronically on a daily basis. Field data collection began in April, 2000 and 

ended in August, 2000. 

D. Data Sources 

Data for each sampled household came from the sources listed below. Abbreviated terms used in this 

report are shown for each of the data sources in parentheses following the form’s official title. 

•	 HUD Form 50058/50059 (50058/50059)—PHAs must complete a HUD Form 50058 for each 

household in public housing, moderate rehabilitation, certificate, and voucher programs at 

certification and recertification. A HUD Form 50059 is required for all other programs in the study; 

it must also be completed if an interim recertification is conducted. Data from these forms were 

entered directly into the HUD QC Automated Data Collection System (ADCS) on each data 

collector’s laptop computer. As the data were entered, the system identified potential data entry errors 

(such as incorrect codes or numbers based on internal calculations and consistency checks) allowing 

the data collector to make the appropriate corrections immediately. 

•	 Documentation and Other Verification From PHA/Owner Files (D Forms)—Macro collected 

information from the tenant files that supported and explained the information used by PHA/owners 

to determine eligibility and calculate tenant rent. The D Form module also collected information 

indicating whether the income, asset, or expense used by the PHA/owner was verified. 

•	 Household Interview Data (Household Questionnaire)—An adult member of each household 

included in the sample (preferably the head of the household) was interviewed in person via CAPI. 

Questions primarily addressed family composition, and sources and amounts of income, assets, and 

applicable expenses. Data were collected for the same point in time as the (re)certification was 

conducted. See Section II, E. 

� Third-Party Verification Data (Release Forms)—If there was no evidence that the PHA/owner 

verified the information used in calculating rent, or the verification obtained by the PHA/owner did 

not meet the requirements agreed upon by HUD and Macro for this study, the appropriate third-party 

source was sent a form requesting verification. Verification was also requested from third parties 

when the household interview identified a new source or different amount of income then that shown 

in the tenant’s file. Release forms designed to collect verification of information for specific time 

periods were signed by the tenant during the household interview and then sent to the third party for 

completion and return to Macro. 

•	 Match with Social Security Administration Data. The list of household members in the sample was 

matched with Social Security Administration files by HUD. It was expected that this match would 

provide benefit data for all household members receiving Social Security and SSI benefits. However, 

because of Social Security matching issues, benefit data was only received for approximately 55 

percent of the household members who (according to QC data) were receiving SSA/SSI benefits. This 

benefit data was used in the final QC rent determination. 

Only HUD-specified procedures were used in collecting tenant income, expense, and allowance data, and 

verification information from third-party sources. These procedures were followed so that the study 

would only identify errors that occurred because the PHA/owner did not follow HUD requirements. 

Final Report June 20, 2001 5 
II. Methodology 

E. Time Periods 

Data were collected for a particular point in time, referred to as the Quality Control Month (QCM). This 

month represents the date the most recent rent calculation was completed. That action may be a 

certification, annual recertification, or interim recertification. The QCM is the month in which the project 

manager (or other authorized housing project staff member) signed the 50058/50059 form, certifying that 

the information contained on the form was correct. If no signature was available on the 50058/50059 

form, the data collector used other documentation in the tenant file to determine when the action was 

taken. 

If the recertification was overdue (more than 12 months had passed since the last (re)certification), the 

respondent was asked about circumstances for the month in which the recertification would have occurred 

had housing project staff processed it on time. If the recertification was overdue by more than 12 months, 

the QCM was moved forward in 12-month intervals to a point in time within 12 months of the date on 

which the data were collected. 

F. Constructing the Analysis Files 

The study database initially contained information at both the household-member level and the household 

level, and income and expense information in hourly, weekly, monthly, or annual amounts. To calculate 

rent, Macro constructed an analysis file that aggregated all income and expense data to an annual amount 

at the household level. For some items, this calculation was relatively easy (e.g., when there were stable 

income items, such as Social Security); for others, the calculation was more complicated. Special attention 

was paid to households with multiple sources of earned income that started or stopped during the year 

covered by the (re)certification period (e.g. seasonal agricultural or holiday earnings) to assure that the 

income was annualized correctly. 

The database initially consisted of five separate files that contained the information collected from the 

50058 forms, the 50059 forms, the tenant file using the D form module, the household interview via the 

CAPI process, and the release forms. For the calculation of rent error, the final analysis file contained 

income and expense/allowance data aggregated at the household level in annual amounts; rent data were 

in monthly amounts. Separate files were created for the analysis of issues such as verification, internal 

50058/50059 errors, and occupancy standards. 

G. Rules for Matching Verification with Income and Expense Items 

For purposes of this study, verification was considered acceptable if it was in writing from a third party. 

In addition, the verification had to be dated within 60 days before or 30 after the date the certification, 

annual recertification, or interim recertification was conducted. Note that if the most recent action was 

an interim recertification, verification of the items that changed must have been dated within 60 days 

before or 30 after the date the interim recertification was completed. Items that did not change must have 

been dated within 60 days before or 30 after the date the most recent annual recertification was 

completed. This rule reflects HUD’s requirement that only items that change be verified during an interim 

recertification. 

As each income or expense item was identified during the data collection process, it was assigned a 

unique identification code. This code (which links the member number, income or expense type, and a 

consecutive number) was used to match the specific item to the verification from the third party when it 

was received. 

Final Report June 20, 2001 6 
II. Methodology 

H. HUD Rent Formulae 

HUD specifies the formula for determining tenant rents for each of its programs. The formula for 

determining the Total Tenant Payment (TTP) is the same for all programs. The Total Tenant Payment is 

the greater of: 

•	 30 percent of a household’s adjusted monthly income, which is one-twelfth of the total of all 

household members’ earned and unearned income (other than those amounts specifically excluded 

by HUD or PHA policy), less allowances for elderly/disabled households and for household 

dependents, and deductions for disability, medical, and child care expenses. 

• 10 percent of a household’s gross monthly income with no allowances or expense deductions. 

•	 The welfare rent in “as-paid” states. (Does not apply to Section 8 Vouchers with a (re)certification 

effective date prior to October 1, 1999.) 

•	 The minimum rent ($25 for owner-administered projects, or an amount established by the PHA, not 

to exceed $50). 

Although the TTP is defined the same for all programs, there are 11 different rent calculations used to 

calculate the actual amount of the household’s rent (depending on the program type and the household’s 

specific situation) for the programs included in this study. These 11 rent calculations include: 

• Public Housing 

• Section 8 Regular Certificates and Moderate Rehabilitation 

• Section 8 Over-the-Fair-Market-Rent-Tenancy Option (OFTO) 

• Section 8 Vouchers 

• Section 8 Preservation Certificates 

• Section 8 Preservation Vouchers 

• Section 8 Project-Based (except Moderate Rehabilitation) 

• Manufactured Home Space Rental for Section 8 Regular Certificates 

• Manufactured Home Space Rental for Section 8 Vouchers - Pre-Merger 

• Manufactured Home Space Rental for Section 8 Vouchers - Post-Merger 

• Manufactured Home Space Rental for Section 8 Over-FMR-Tenancy Option (OFTO) 

The household’s rent was calculated after the data from all sources was collected. When calculating rent, 

a cap was placed on the maximum amount of rent the tenant was required to pay. For all Section 8 

programs, this is the Gross Rent. In the Public Housing program, this is the Flat Rent or the Ceiling Rent. 

Final Report June 20, 2001 7 
II. Methodology 

If the Flat Rent or the Ceiling Rent was not available, the Fair Market Rent for the appropriate county was 

used to cap the rent. 

Additional rent calculations are also necessary for households that include ineligible non-citizens. 

Determining the correct rent for these households is a multi-part process including determining if the 

household is entitled to continuation of assistance, or temporary deferral of termination of assistance; and 

prorating the rent if appropriate. Two proration formulas were used—one for Public Housing and one for 

all Section 8 programs. 

The formulas used for the specific rent calculations can be found in Appendix A. These are the formulae 

that PHAs/owners should use in determining tenant rent, and the formulae Macro used in determining 

if tenant rents were calculated correctly. 

I. Calculation of Rent Error 

The monthly rents Macro used in determining the national estimates of error are as follows: 

•	 Actual Rent: The monthly rent indicated on the 50058/50059 forms. If this item was missing on the 

50058/50059 form, the Actual Rent was calculated based on the other information on the 

50058/50059 form.

7 

•	 Quality Control (QC) Rent: The monthly rent calculated by Macro using the information 

reported by the household and verified.

8 

Rent error was determined by comparing the QC Rent to the Actual Rent (i.e., the Actual Rent minus the 

QC Rent). A discrepancy of $5 or less between the Actual and QC Rent was not considered to be an error. 

The $5 window was used to allow for minor calculation and rounding errors, and to focus the analysis 

of the data on the major sources of error. For an exploratory analysis, a rent calculated solely on the 

information contained on the 50058/50059 forms was used to determine if errors could be identified using 

only information contained on the 50058/50059 forms. 

J. Quality Control Rent 

Macro researchers calculated a QC Rent using the best source of information available. When determining 

which data to use in the QC rent calculation, every effort was made to use data that would have been 

available to the PHA/owner. Macro researchers used the verification that came from the project files 

whenever possible. If acceptable verification was not available from the tenant file (see Section II-G for 

a discussion of acceptable verification), verification was requested from an appropriate third party. If this 

verification was not returned by the third party and the tenant file did not include verification, income and 

expense information obtained from the household interview was used to calculate the QC Rent. The 

following special procedures were followed when appropriate: 

• Income that started after the QCM was not counted when calculating the QC Rent. 

7

 Rent Roll data was not used as a substitute for Actual Rent because the previous study found that the rent roll 

sometimes included amounts to make up for previous unpaid rent, fines, or damages, etc. 

8

 Attempts were made to verify items that were not verified by PHA/owner staff; however, verification was not always 

obtained. See Sections II-K, and IV-A for further discussion regarding verification. 

Final Report June 20, 2001 8 
II. Methodology 

•	 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 with a frequency of once per year were not counted. 

•	 Temporary Assistance to Needy Families (TANF) and Other Welfare were treated as the same 

source of income so that income listed as TANF on one form (e.g. the household questionnaire), 

and Other Welfare on another form (e.g, the Documentation forms) would not be counted twice. 

•	 Welfare (TANF and Other Welfare) income, Child Support income, and Child Care expenses 

were treated at the household level instead of the member level so that the same source of income 

associated with one member (e.g. the head of household) on one form, but another member (e.g. a 

child) on another form would not be counted twice. 

•	 Passbook rates (for determining the imputed income from assets) were taken from information 

provided by PHA/owner staff. If the rate was missing, the average rate for the geographic area 

was used. 

•	 For new certifications, the low and very low income limits were taken from information provided 

by PHA/owner staff. If the limits were missing, the average for the program type was used. 

K. HUD Requirements Affecting the Analysis 

Several HUD requirements affected the data collection methodology and subsequent analysis. As noted 

in Section II-A above, relevant HUD requirements were incorporated in the study standards used to 

determine error. All data collection procedures and analysis were based on these study standards. 

Although most standards were easily implemented, several were more problematic. Those standards that 

complicated the data collection or analysis are discussed below. 

Anticipated Income. The amount of rent a household will pay is based on anticipated household income 

and deductions for the 12 months following (re)certification. For households with a stable income, such 

as Social Security or steady employment, determining anticipated annual income for the next 12 months 

is fairly reliable. However, many assisted households have members who are seasonally employed or who 

move in and out of the household, changing the total household income. Additionally, certain expenses 

such as medical expenses (for elderly/disabled households) and child care costs may be very difficult to 

anticipate. Determining whether such income and expense amounts were figured correctly at the time of 

recertification is very difficult when data are collected after the changes occurred. 

Every effort was made to treat questionable income or expenses the same as they were treated by 

PHA/owner staff. Several of the special procedures presented in Section II-J were created for this 

purpose. 

Verification. HUD regulations require that the information supplied by residents at (re)certification be 

verified by third parties (e.g., employers, the Social Security Administration, banks, medical personnel). 

Data collectors obtained release forms from the households when evidence of verification was not present 

in the tenant’s file and then requested verification from the appropriate third parties. However, some third 

parties did not respond, others returned information for incorrect time periods, and other problems were 

encountered in obtaining the correct verification. Follow-up requests for missing verification were not 

Final Report June 20, 2001 9 
II. Methodology 

made in all cases due to study time constraints. In calculating the rents, codes were assigned indicating 

which rents were based on verified information and those for which the income/expense information was 

only partially or not verified. 

Macro, in consultation with HUD, established a set of verification rules to determine whether an item was 

verified. The rules used to determine if verification was acceptable and to match each item used in the 

rent calculation to the verification are found in Section II-G. Information regarding the percent of 

verification obtained for different rent components is found in Table 1 (in the appendix) and Exhibit IV-1 

in Section IV-A. 

Recent Changes in Legislation. Several major changes in Federal HUD regulations went into effect in 

October 1999. While these changes were included in the standards prepared for this study, it was agreed 

that the study would not find a household in error simply because a project had not implemented a new 

regulation on the date required. Therefore, during the interview conducted with PHA/owners, staff were 

asked whether they had implemented these new rules. If the PHA/owner stated that they had implemented 

the new rule, households in that project were evaluated on the basis of the new requirement. Of course, 

if the most recent case action was prior to October 1999, the new rules were not applicable. 

Training Programs and Income Exclusions. HUD regulations (and the study standards) allow for all 

or part of the income of certain household members to be excluded if those household members were 

enrolled in or completed a training program (as defined by HUD). To ensure that these rules were 

implemented correctly, questions concerning participation in training programs were added to the data 

collection systems. A HUD policy expert reviewed these data as well as the household’s income and 

expenses to determine if any portion of the household’s income should be excluded. 

Less than 5 percent of the respondents reported they were currently enrolled in, or had completed, an 

employment training course. Most of these households were not eligible for a training income exclusion 

when calculating their rent. However, there were a few households (less than 1 percent) who benefitted 

from these regulations. 

Ineligible Non-citizens. HUD regulations require that rent be prorated for households that include 

ineligible non-citizens (as well as citizens or eligible non-citizens) unless the household meets certain 

criteria that allow continuation of full assistance. Macro conducted a special review of all households that 

included ineligible non-citizens to ensure that the rent was calculated correctly. Less than 1 percent of the 

households in the study included an ineligible non-citizen. 

Final Report June 20, 2001 10 
III. Study Objectives and Analytic Methods 

This section presents the thirteen study objectives and a brief description of the methodology used to meet 

those objectives.

9 

Objective 1: Identify the various types of errors and error rates and related variance estimates. 

The types of errors and error rates detected in the 1996 report published by HUD are replicated in this 

analysis. These errors include percent of households paying correct and incorrect rent, dollar error 

amount, and dollar error rate. Variance estimates (standard errors) are provided for the 2000 data. 

Errors were determined by recalculating the tenant rent based on verified QC information and subtracting 

the tenant rent indicated on the 50058/50059 forms (Actual Rent). A household is found to be in error 

if the difference between the QC Rent and the Actual Rent is greater than $5. Simple percentages of the 

number of households paying the correct rent are reported, as well as the percent of households in error 

per program, the average gross dollars in error, and the percent of rent dollars in error. 

Errors are categorized, as in the 1996 report, by the following types: 

• Misreporting of income sources or amounts 

• Calculation errors 

• Transcription errors (the tenant file documentation does not match the 50058/50059 data) 

• Incorrect use of allowances 

• Failure to conduct a recertification in a timely manner 

Misreporting is defined as a discrepancy between tenant-reported information and that supplied by thirdparty verification. This is primarily a tenant-caused error, but can be affected by how well the PHA/owner 

conducts the tenant interview. The other four types of error (calculation, transcription, incorrect use of 

allowances, and failure to conduct recertifications in a timely manner) are errors attributable to the 

PHA/owner. 

Transcription errors and failure to conduct a recertification in a timely manner are procedural errors that 

may or may not result in a payment error. Misreporting of income sources or amounts, calculation errors, 

and incorrect use of allowances usually result in payment error. 

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

Three different types of dollar error estimates were calculated, the first of which describes error in the 

amount of rent. The remaining two dollar error estimates describe error in the income and expense items 

used in calculating rent. 

9

See Analysis Plan, an unpublished Macro report to HUD, dated March 15, 1999, for a more detailed description of the 

methodology. 

Final Report June 20, 2001 11 
III. Study Objectives and Analytic Methods 

•	 Dollar Rent Error—The difference between the monthly Actual Rent and the monthly QC Rent 

(i.e., Actual Rent minus QC Rent). Rent was considered in error if the monthly QC Rent and 

Actual Rent differed by more than five dollars. For households who were ineligible when initially 

certified, the QC Rent is the amount of rent in the absence of any subsidy; the dollar error is this 

amount minus the Actual Rent.

10 

•	 Total Component Dollars in Error—The absolute sum (i.e., the sum of the positive and 

negative amounts, ignoring the plus or minus signs) of all individual income and expense 

component errors. These errors are combined to provide an overall Total Dollars in Error and are 

presented as annual amounts. A dollar amount of rent overpayment and underpayment was 

calculated for each component with identified error; however, some of these errors were 

overlapping or offsetting. (For example, earned income may have been underreported while— 

perhaps because of a calculation error—Supplemental Security Income may have been 

overstated). The net difference could be zero, or a positive or negative number. This calculation 

disregards these offsets. 

•	 Largest Component Dollar Error—The annual dollar amount of error for the income or 

expense components with the largest error. Income and expense components include the five 

sources of income (earned, pensions, public assistance, other income, and assets) and the five 

types of deductions (medical, child care, and disability assistance expenses, dependent allowance, 

and elderly/disabled allowance). If the component with the largest error is earned income, the 

largest dollar error would reflect the difference between the earned income used by the 

PHA/owner, and the earned income used in the QC Rent calculation. 

The first measure of error, Dollar Rent Error, is used to estimate the National Rent Error Rate. Tenant 

overpayments and underpayments of rent are measured separately. They are also combined arithmetically 

to produce a Net Rent Error (Actual Rent minus QC Rent) and combined absolutely to produce a Gross 

Rent Error (the sum of under- and overpayments ignoring the plus and minus signs). The Dollar Rent 

Error rate is determined by dividing the sum of the dollar amount of Gross Rent Error by the sum of the 

QC Rents. Note that rent error is reported on a monthly basis. 

The second, Total Component Dollars in Error, is useful in analyzing income and expense components 

in error. Note that the total dollars in error are annual figures. 

The third measure, Largest Component Dollar Error, identifies the rent component contributing the most 

to the error in the household’s rent. It is a useful diagnostic tool in identifying the major sources of error 

so that program improvements can be targeted to the areas contributing the most to error. 

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

This analysis includes determining the National Rent Error Rate, the numbers and proportions of 

households found to be in error, and the dollar amount of rent error and the proportion of total dollars 

found to be in error. Sample data were weighted to provide national estimates. 

10As an operational matter, for public housing households, the underpayment due to ineligibility is defined as the Flat Rent 

(if it is available), the HUD-approved ceiling rent (if available), or the Section 8 Existing Fair Market Rent, minus the actual 

total tenant payment. 

Final Report June 20, 2001 12 
III. Study Objectives and Analytic Methods 

Objective 4: Determine the relationship between errors detectable using the HUD 50058 and HUD 

50059 forms and total errors. 

This analysis determines whether the errors that can be identified using only information contained on 

the 50058/50059 forms were representative of the total errors. This analysis was conducted by first 

identifying errors based on data contained on the 50058/50059 form and then comparing these calculation 

errors and consistency errors with the rent errors identified by the QC process. 

Objective 5: Determine whether error rates and error costs have statistically significant differences 

from program to program. 

In addition to national estimates of the number of households and dollars of rent error, household and 

dollar error rates for projects operated by PHAs were compared to those operated by owners. Within each 

of these program administration types, Macro analysts looked at specific types of projects. The projects 

were categorized as follows: 

•	 PHA-Administered Projects 

- Public Housing 

- Section 8 Certificate 

- Section 8 Voucher 

- Section 8 Moderate Rehabilitation 

• Owner-Administered Projects 

- Section 8 New Construction and Substantial Rehabilitation 

- Section 8 Loan Management and Property Disposition 

In addition to replicating the results from the 1996 report, analysis was conducted with error rate, rather 

than error costs, as the dependent variable. This determined whether error rates and error costs have 

statistically significant differences from program to program. 

Objective 6: Determine the extent to which households are overhoused relative to HUD’s 

occupancy standards. 

This objective addresses whether households reside in units with the correct number of bedrooms. 

Generally acceptable HUD guidelines

11 specifying the appropriate size unit for assisted households are 

shown in Exhibit III-1 below. 

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

Final Report June 20, 2001 13 
III. Study Objectives and Analytic Methods 

Exhibit III-1


PHA Section 8 Unit Size Standards


Number of Persons in Household 

Number of Bedrooms 

Minimum Maximum 

0 1 1 

1 1 2 

2 2 4 

3 3 6 

4 5 8 

5 7 10 

For most programs, the rules are not based solely on household size and allow discretion on the part of 

the project staff. All programs allow exceptions to these rules. In this analysis, Macro used the guidelines 

shown in the above table. 

Objective 7: Determine the extent to which errors are concentrated in projects and programs. 

Further descriptive analysis was conducted to determine the degree to which errors are concentrated in 

programs or whether they are randomly distributed. In addition, an analysis of variance was completed 

to determine if errors are concentrated in projects. Further analysis aimed at determining if errors are 

concentrated in projects was not conducted because of changes in the sampling methodology which 

increased the number of projects in the sample and decreased the number of units per project. 

Objective 8: Identify the percentage of newly certified tenants who were incorrectly determined 

eligible for program admission. 

Newly certified households were reviewed to determine whether they met the eligibility requirements. 

Five criteria are reviewed at initial certification that are not a part of the recertification process: definition 

of family, citizenship, verification of social security numbers, signing consent forms, and low and very 

low income limits. This study did not investigate definition of family because it is determined by the PHA 

or owner. Therefore, findings are provided on four of the five initial certification criteria. In addition, this 

study did not include suitability factors that PHA/owners may use in selecting tenants—factors such as 

tenancy histories, histories of drug use or criminal activity. 

Objective 9: Estimate the total positive and negative errors in terms of HUD subsidies. 

Proper payments are those in which the Actual Rent equals the QC Rent. Errors can be either 

overpayments (Actual Rent greater than QC Rent) or tenant underpayments (Actual Rent less than QC 

Rent). Overpayment error rates were calculated by dividing the total amount of overpayment by the total 

QC Rent; underpayment error rates were calculated similarly by dividing the total amount of 

underpayments by the total QC Rent. 

Final Report June 20, 2001 14 
III. Study Objectives and Analytic Methods 

Objective 10: 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 discussed under Objective 1, errors are categorized into five types: misreporting of income sources 

or amounts, calculation errors, transcription errors, incorrect use of allowances, and failure to conduct 

a recertification in a timely manner. Calculation errors, transcription errors, incorrect use of allowances, 

and failure to conduct recertifications in a timely manner are errors attributable to the PHA/owner. 

However, discrepancies between the information used by the project to calculate rent and what is obtained 

through the QC process cannot always be attributed to the tenant or the project. For this reason, we view 

discrepancies between information used by the PHA/owner to calculate rent and information used in the 

QC Rent calculation as sources of error, rather than ascribing cause to tenants or project staff. 

This report defines source of error as the type of income, asset, expense, or allowance that caused (or 

contributed to) errors. Macro identified source errors using the 10 income and expense components found 

on the 50058/50059 forms for calculating rent. The five income components are employment income, 

Social Security and pensions, public assistance, other income, and asset income. The five 

expense/allowance components are elderly/disabled allowance, dependent allowance, medical expenses, 

child care expenses, and disability expenses. This report discusses both household and dollar error 

according to these categories. 

Objective 11: Determine whether other tenant or project characteristics on which data are 

available are correlated with high or low error rates. 

To respond to this objective we used error-prone modeling techniques to identify households with a high 

probability of being in error. Two separate equations were developed, one using household characteristics 

and the other using project characteristics as independent variables to predict level of household error. 

Objective 12: Determine whether households for which 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 household sample was matched to the TRACS/MTCS data. Analysis was conducted to compare 

the average dollars in error for households included in TRACS/MTCS and those who were not. For those 

households found in TRACS/MTCS where the effective date of action also matched, analysis was 

completed on key data fields. 

Objective 13: Determine the extent to which Section 8 Certificate rents are consistent with market 

rate rents for comparable units in comparable locations. 

Meeting this objective involved three major tasks: examining the policies and procedures housing 

authorities adopted when implementing the rent reasonableness requirement, determining the extent to 

which housing authorities actually implement rent reasonableness policies and procedures, and assessing 

the comparability of rents charged under the Section 8 tenant-based assistance program to the private, 

unassisted market. 

Final Report June 20, 2001 15 
IV. Findings 

Analyses were conducted using weighted data for the sample of 2,403 households. (Appendix B presents 

the procedure used in weighting the data.) There was insufficient information on five of these households 

to determine the amount of error or the source of error.12 Appendix C shows the distribution of 

households available for each type of analysis, for both the sample and the weighted data. 

When appropriate, data are presented by the three program types that served as the basis for the sampling 

design—Public Housing, PHA-administered Section 8 (Certificates, Vouchers, and Moderate 

Rehabilitation), and owner-administered Section 8 (New Construction, Substantial Rehabilitation, 

Property Disposition, and Loan Management). 

Each of the major study findings, the reasons for the errors, and other background information concerning 

these errors are discussed below. In many of the exhibits throughout the report the data collected during 

the current study (referred to as the 2000 data) are compared to the data collected in a previous study. The 

data for this earlier study was collected in 1992; the analysis was completed in 1995. However, the final 

report for the earlier study, published by HUD in April 1996, is usually referred to by HUD as the 1996 

report. Therefore, the exhibit headings and most references in the text refer to the data collected in 1992 

as “1996”. Dollar figures for the 1996 report are given as both actual dollars (1992 dollars) and current 

dollars (2000 dollars). The Consumer Price Index (CPI) increased by 23 percent from summer 1992 to 

summer 2000. HUD estimates the change in median family income during that period to be 30 percent. 

The CPI is believed to be a more appropriate tenant income adjustment factor based on the information 

available. Therefore, an adjustment factor of 23 percent was used for the 1992 data. 

This discussion is divided into seven parts: the errors in the rent amount based on the QC data (rent error), 

the errors in sources of income and expenses (component errors), the errors found using only project file 

data (administrative error), project analysis, error prone profiling, occupancy standards, and comparisons 

with MTCS/TRACS data. The first three parts present different types of error. 

Rent error is error that results in an actual dollar error. A dollar error means the household paid too much 

rent (an overpayment) or the household paid less rent than it should have paid (an underpayment). 

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

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

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

medical expenses, child care expenses, and disability expenses. 

Administrative errors are errors that result from mistakes in procedure. They consist of: 

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

form 

• Calculation errors—arithmetic errors within subsections of the 50058 or 50059 form 

•	 Transcription errors—errors in transferring information from documentation in the tenant file to 

the 50058 or 50059 form 

12 One of the households had no Actual Rent or Total Tenant Payment on the 50058/59 form. There were 4 households where 

the QC Rent could not be calculated because critical information was missing. PHA/owners were unable to provide this 

information when it was requested during follow-up telephone calls. Therefore, most tables are based on data for 2,398 

households. 

Final Report June 20, 2001 16 
IV. Findings 

• Failure to conduct a recertification in a timely manner 

• Failure to verify information. 

Component errors and administrative errors may or may not result in rent errors. Administrative errors 

tell us at what point in the process the error occurred, while the component errors tell us which income 

or expense caused the error. 

Data supporting the discussion are presented in tables located in Appendix D. The chart at the beginning 

of Appendix D presents each of the objectives and the tables that include data responding to those 

objectives. 

A. Rent Error 

Overview. Rent errors13 were identified by subtracting the QC Rent from the Actual Rent. The QC Rent 

was calculated using third-party verification whenever possible. If third-party verification was not 

available, information from the Documentation forms or Household Questionnaire was used. The Actual 

Rent is the Tenant Rent from the 50058/50059 form. As noted above, a household was considered to be 

correct (proper payment) if the QC Rent and the Actual Rent matched within $5. All exhibits included 

in this report (except IV-2) and all tables in Appendix D define households whose Actual and QC Rents 

matched within $5 as proper payments, except for the supplemental tables (designated by the letter “S”), 

which are based on exact matches between these two rents. 

A list of cases where the Actual and QC rent vary by more than $100 is provided in Appendix E. This 

list provides the reader with examples of the different types of errors identified in this study. 

Definitions of Rent Errors. Dollar error can be determined by comparing the rent the household should 

have paid to what it was paying, or by identifying the percent of the Federal subsidy that was paid in 

error. In this study, error was determined by the first method. The rent errors presented throughout this 

report were calculated in the following manner: 

� Dollar Rent Error was calculated at the household level by subtracting the household’s QC Rent 

from the Actual Rent. Note that these are monthly rents. A negative number indicates an 

underpayment, meaning the household paid less than it should have paid, and that HUD’s 

contribution was higher than it should have been. A positive number indicates a household 

overpayment, meaning HUD’s contribution was less than it should have been. 

� Gross Rent Error is the absolute value (i.e., the sum of the absolute value of positive and negative 

Rent Error) of the Dollar Rent Error for the sample as a whole or a specified group of households. 

The Gross Rent Error functions simply as a measure of the magnitude of the errors. The dollar 

amounts presented in the tables are Gross Rent Error values, unless otherwise indicated. 

� Net Rent Error is the arithmetic value (i.e., the sum of the negative and positive values of overand underpayments) of the rent error. 

� Error Rate is calculated by dividing the sum of the Gross Rent Error by the sum of the QC Rent, 

for the entire sample or a specified group of households. 

13 Rent error is based on Tenant Rent; not Total Tenant Payment. Tenant Rent is calculated using the formulas listed in 

Section II G. and provided in detail in Appendix A. 

Final Report June 20, 2001 17 
IV. Findings 

Verification Used in Determining the QC Rent. As indicated above, a set of verification rules were 

established for this study (see Section II-G). If an income or expense component involved in the 

calculation of rent was not verified by the PHA/owner, an effort was made by Macro staff to verify it. 

However, even though the study spent considerable resources on this task, verification could not be 

obtained for all items. 

Exhibit IV-1 presents the percentage of each rent component that was verified by either the PHA/owner 

or Macro. The first column presents the 1996 findings. The remaining two columns present the 2000 

findings, first using the same verification requirements as used in the 1996 report (third party in writing, 

third-party verbal, or documentation); and second using the more stringent verification requirements for 

this study (third-party in writing). 

Exhibit IV-1


Percent of Households Fully Verified by Either the PHA/Owner or Macro


Third-Party Verbal 

or In Writing, or 

Documentation 

Third-Party Verbal or 

In Writing, or 

Documentation 

Third-Party 

In Writing 

Rent Component 

1996 2000 

Earned Income 

Pensions, etc. 

Public Assistance 

Other Income 

Asset Income 

Child Care Expense 

Disability Expense 

Medical Expense 

62% 72% 62% 

72% 88% 78% 

73% 75% 66% 

46% 52% 48% 

57% 57% 49% 

51% 51% 47% 

1% 20% 20% 

41% 52% 40% 

Source: Table 1, Appendix D 

Tables 1a and 1b in Appendix D provide additional verification information by rent component. They 

present the number of households for which the income or expense component was not verified, partially 

verified, or fully verified. Table 1a includes items that were verified by third parties in writing or verbally, 

or with documentation. Table 1b provides data for items verified in writing by third parties (as required 

by the study). 

Proper Payments. Exhibit IV-2 presents the percent of households with proper payments by program 

both for households where the Actual and QC Rents matched within $5 and for households where the 

Actual and QC Rents matched exactly. At (re)certification, the rent was calculated correctly (within $5) 

in more than a third of the households (44 percent), down 3 percent from 1996’s total of 47 percent. 

About a third matched exactly for 2000 (32 percent), down 1 percent from 1996’s 33 percent. 

Final Report June 20, 2001 18 
IV. Findings 

Exhibit IV-2


Percent of Households with Proper Payments


Percent of Households 

Within $5 

Percent of Households 

Matched Exactly 

Administration Type 

1996 2000 1996 2000 

Public Housing 46% 47% 32% 34% 

PHA-Administered Section 8 47% 38% 36% 30% 

Total PHA-Administered 47% 42% 34% 32% 

Section 236 59% n/a 53% n/a 

Owner-Administered w/o Section 236 47% 48% 31% 32% 

Total without Section 236 47% 44% 33% 32% 

Source: Table 3, Appendix D 

Households with QC Rent Error. Exhibit IV-3 presents the percent of households in error, the average 

dollar amount in error, and error rate by program. The exhibit indicates that 56 percent of the households 

include a rent error greater than $5. As can be seen, this is up from 53 percent in 1996. The average gross 

dollars in error, calculated by dividing the sum of the dollar amount of gross error (i.e., the sum of the 

absolute values of under- and overpayments) by the total number of households is $45 in 2000 and $36 

in current 1992 dollars. The gross dollar error rate, calculated by dividing the sum of the dollar amount 

of Gross Rent Error by the sum of the dollar amount of the QC Rent, is 24 percent. 

Exhibit IV-3


Percent of Households with Error, Average Dollars in Error, and Dollar Error Rate


for Households with Error Greater Than or Less Than $5


Percent of Households 

with Error 

Average Gross Dollars 

in Error 

Gross Dollar 

Error Rate 

1996 

Administration Type 

1996 2000 

Actual $ Current $ 

2000 1996 2000 

Public Housing 54% 53% $30 $37 $41 18% 20% 

PHA-Administered Section 8 53% 62% $29 $35 $59 17% 31% 

Total PHA-Administered 53% 58% $30 $37 $51 17% 26% 

Section 236 41% n/a $29 $35 n/a 1% n/a 

Owner-Administered w/o 236 53% 52% $28 $34 $32 17% 18% 

Total without Section 236 53% 56% $29 $36 $45 17% 24% 

Source: Table 2 and 3, Appendix D 

It is important to note that this study was primarily designed to measure the extent of administrative error 

by housing providers, not to measure how much additional tenant contributions could be realistically 

collected in a cost-efficient manner. The extent of the identified error is sensitive to a number of 

assumptions made in the study. Changes in the error threshold, for example, would affect the overall 

dollar error estimates. Perhaps more importantly, it is likely that some tenants with large rent increases 

resulting from corrected calculations would leave the program, reducing potential subsidy reductions; 

while those with decreases in their rents would be more likely to remain, increasing subsidy requirements. 

These corrections are desirable outcomes, but it is unclear what their net impact would be on subsidy 

costs. The most appropriate use of this study is as a tool for strengthening HUD’� ���������� ���

ensuring administrative compliance with regulations. The improvements recommended will require more 

rather than fewer resources in the short-term. Significant reductions in error can only be expected after 

Final Report June 20, 2001 19 
IV. Findings

Final Report June 20, 2001 20

progress is made in providing the type of rule simplifications, and additional instructions, forms, training,

and monitoring discussed in the report. While it is likely that budgetary savings can be achieved at some

future date if the recommendations of the study are adopted, the necessary changes will take two to four

years to start to achieve measurable results.

Underpayment and Overpayment Households. Exhibit IV-4a and 4b present the percent of households

and average dollar amount of error for all households, when errors of $5 or less are not counted. Exhibit

IV-4a presents the error for underpayment households. Exhibit IV-4b presents the error for overpayment

households. Thirty-four percent of all households paid more than $5 less than they should have in 2000,

compared with 27 percent in 1996. For these households, the average monthly payment was $95 and $73

(in 2000 dollars), respectively, less than it should be. While 26 percent of all households in 1996 paid

more than $5 more than they should have, that figure declined to 22 percent for 2000. The average

monthly overpayment was $56 in 2000, down from $61 in 1996. The underpayment and overpayment

average dollar figures for 2000 are $95 and $56, respectively.

Exhibit IV-4a

Underpayment Households

Percent of Households and Average Monthly Dollar Amount of Error

Percent of Average Dollar Amount of Error

Households in

Error For Underpayment Households

(with errors < $5)

For All Households

1996 1996

Administration Type

1996 2000

Actual $ Current $

2000

Actual $ Current $

2000

Public Housing 28% 33% $66 $81 $84 $18 $23 $28

PHA-Administered Section 8 28% 42% $55 $67 $109 $15 $19 $46

Total PHA-Administered 28% 38% $61 $75 $100 $17 $21 $38

Section 236 22% n/a $97 $119 n/a $21 $26 n/a

Owner-Administered w/o 236 26% 27% $55 $68 $81 $14 $18 $22

Total without Section 236 27% 34% $59 $73 $95 $16 $20 $32

Source: Table 3 and 4, Appendix D

Exhibit IV-4b

Overpayment Households

Percent of Households and Average Monthly Dollar Amount of Error

Percent of Average Dollar Amount of Error

Households in

Error For Overpayment Households

(with errors > $5)

For All Households

1996 1996

Administration Type

1996 2000

Actual $ Current $

2000

Actual $ Current $

2000

Public Housing 26% 20% $46 $56 $63 $12 $15 $13

PHA-Administered Section 8 14% 20% $54 $66 $67 $13 $16 $13

Total PHA-Administered 25% 20% $49 $60 $65 $12 $15 $13

Section 236 19% n/a $40 $49 n/a $7 $9 n/a

Owner-Administered w/o 236 27% 25% $50 $61 $41 $14 $17 $11

Total without Section 236 26% 22% $49 $61 $56 $13 $16 $12

Source: Table 3 and 4. Appendix D
IV. Findings 

Figure IV-1 presents the percent of underpayments, proper payments, and overpayments by program type. 

Programs were grouped into three categories—Public Housing, PHA-administered Section 8, and owneradministered Section 8. Note that PHA-administered Section 8 programs have significantly more 

underpayment error than the other programs. 

Figure IV-1 [Figure not available electrically] 

As indicated above, a household was considered to be correct (proper payment) if the Actual Rent and 

the QC Rent matched within $5. In response to concerns that the threshold (the difference between the 

Actual Rent and the QC rent) for matching data should have been larger, dollar rent error was determined 

using varying income thresholds. The graph provided in Figure IV-2 presents these findings for all 

households. Similar graphs for each program type—public housing, PHA-administered Section 8, and 

owner-administered Section 8 can be found at the end of Appendix D. Thresholds are presented in 

income equivalents so they are more comparable to the REAC annual income match findings. For 

example, a rent error of $5 is equal to $200 in income (assuming the tenant rent equals 30 percent of 

adjusted annual income). Figure IV-3 provides the percent of households in error by these same income 

thresholds. 

Figure IV-2 

Effect of Varying Income Thresholds on Total Dollar Error 

$1,750 

$1,500 

$1,250 

$1,000 

$750 

$500 

$250 

$0 

Income Thresholds 

Underpayments (millions) Overpayments (millions) 

Final Report June 20, 2001 21 
IV. Findings 

Figure IV-3


Effect of Varying Income Thresholds on Percent of Households in Error


40 

30 

20 

10 

0 

Income Thresholds 

Underpayments Overpayments 

Gross and Net Dollars in Error. Exhibit IV-5 presents the gross and net average dollars in error and 

their associated standard error.

14 To obtain the Gross and Net Rent Error, the dollar amount of 

overpayments is added to the dollar amount of underpayments, first using the absolute values for gross 

error, and then the arithmetic values for the net error. The net error measures the dollar cost of the errors 

and is -$20 (indicating a tenant underpayment) for 2000; the average gross dollar error is $45 for 2000 

and represents the dollars associated with the errors (the magnitude of the errors). 

Exhibit IV-5


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


Gross Rent Error Net Rent Error 

Average Dollars in Error Standard 

Error 

Average Dollars in Error Standard 

Error 

Administration Type 

1996 1996 

Actual $ Current $ 

2000 2000 

Actual $ Current $ 

2000 2000 

Public Housing $30 $37 $41 $4.39 -$ 6 -$ 8 -$15 $3.68 

PHA-Administered Section 8 $29 $35 $59 $3.67 -$ 2 -$ 3 -$32 $5.06 

Total PHA-Administered $30 $37 $51 $3.13 -$5 -$6 -$25 $3.52 

Section 236 $29 $35 n/a n/a -14 -$17 n/a n/a 

Owner-Administered w/o 236 $28 $34 $32 $3.80 -$1 -$1 -$11 $3.67 

Total without Section 236 $29 $36 $45 $2.72 -$3 -$4 -$20 $2.75 

Source: Table 5, Appendix D 

14 Standard Errors for the 1996 report are not provided because they are not comparable with the 2000 data. 

Final Report June 20, 2001 22 
IV. Findings 

Error Rates by Program. An analysis was conducted to determine the differences in error rates by 

programs. A summary of these error rates is shown in Exhibit IV-6. These include Gross Error Rate, 

which is the sum dollar amount of gross error divided by the sum dollar amount of QC Rent, and the Net 

Error Rate, which is the sum dollar amount of net error divided again by the sum dollar amount of QC 

Rent. The Error Rate for both Gross Error and Net Error is much greater for PHA- administered Section 

8 programs than for either Public Housing or owner-administered programs. 

Exhibit IV-6


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


Error Rates 

Administration Type Gross Error 

Rate 

Standard 

Error 

Net Error 

Rate 

Standard 

Error 

Public Housing 20% 2.3% -7 % 1.9% 

PHA-Administered Section 8 31% 2.2% -17% 2.2% 

Total PHA-Administered 26% 1.7% -12% 1.7% 

Section 236 n/a n/a n/a n/a 

Owner-Administered w/o Section 236 18% 1.9% -6 % 1.9% 

Total 24% 1.4% -11% 1.3% 

Source: Table 5, Appendix D 

An analysis of variance (ANOVA) was completed for program administration type. Although there is a 

great deal of variation in gross errors within each group, there was a statistically significant difference 

between the means of the PHA-administered and owner-administered programs, producing a definite 

program effect. When we separate the PHA-administered Section 8 program from Public Housing we see 

that PHA-administered Section 8 households have a much higher average gross dollar error rate. 

Certifications/Recertifications. The sample households included both certifications (i.e., newly admitted 

households) and recertifications. Certifications were analyzed to determine if these households were 

eligible for HUD housing assistance. A separate analysis was also conducted for overdue recertifications. 

Figure IV-4 presents the breakdown of cases by case type—certifications, recertifications, and overdue 

recertifications. 

[Figure IV-4 not available electronically] 

Exhibit IV-7 shows the breakdown of the percent of certifications, recertifications not overdue, and 

recertifications overdue, by program type. The exhibit indicates in 2000 that 9 percent of the households 

were certifications and 6 percent of the households were overdue recertifications. These findings indicate 

a decrease in the percentage of certifications from 1996 (from 14 percent to 9 percent) and an increase 

in the percentage of overdue certifications (from 2 percent to 6 percent). 

Final Report June 20, 2001 23 
IV. Findings 

Exhibit IV-7


Certifications and Recertifications by Administration Type


Certifications Non Overdue 

Recertifications 

Overdue 

Recertifications 

Administration Type 

1996 2000 1996 2000 1996 2000 

Total 

Public Housing 13% 8% 83% 85% 4% 7% 100% 

PHA-Administered Section 8 14% 10% 85% 85% 2% 5% 100% 

Total PHA-Administered 14% 9% 83% 85% 3% 6% 100% 

Section 236 23% n/a 71% n/a 6% n/a 100% 

Owner-Administered w/o Section 236 14% 9% 86% 86% <1% 5% 100% 

Total without Section 236 14% 9% 84% 85% 2% 6% 100% 

Source: Table 6, Appendix D 

Certifications. Exhibit IV-8 presents a summary of the findings related to eligibility criteria. The analysis 

of newly certified households found a significant percentage of households that did not meet all the 

certification criteria and thus may have been certified in error. The criteria reviewed included citizenship, 

social security number, signing the appropriate consent form, and qualifying as low income or very low 

income households. However, only those households that did not meet the appropriate low or very low 

income limit were definitely not eligible for assistance. The total gross income of 99 percent of the 

households (according to the QC Rent calculation) fell within the appropriate low or very low income 

limit. 

Exhibit IV-8


Percent of Newly Certified Households Meeting Certification Criteria


Percent of Households Certification Criteria 

Met 

Criterion 

Did Not Meet 

Criterion 

Unable to Determine 

Whether Tenant Met 

Criterion* 

Citizenship 79% 21% 0 

Social Security Number 84% 16% 0 

Consent Form 71% 23% 6% 

Low and Very Low Income 99.5% <1% 0 

Meets All Eligibility Criteria 53% 47% 0 

Exhibit 8a provides the percent of newly certified households meeting the certification criteria by program 

type. 

Final Report June 20, 2001 24 
IV. Findings 

Exhibit IV-8a


Percent of Newly Certified Households


Meeting Certification Criteria by Program Type


Percent of Households Meeting the Criteria 

Certification Criteria 

Public Housing 

PHA-Administered 

Section 8 

Owner-Administered 

Section 8 

Citizenship


Social Security Number


Consent Form


Low and Very Low Income


Meets All Eligibility Criteria


75% 81% 81% 

84% 85% 84% 

65% 76% 73% 

100% 99% 100% 

46% 59% 54% 

A household met the citizenship or social security number criteria if there was evidence in the tenant file 

that the citizenship or social security number was verified. The data indicate that a citizenship code 

(indicating whether each household member was a citizen, eligible noncitizen or ineligible noncitizen) 

and a social security number was available (from either the tenant file or the household interview) for 

each household member. However, 21 percent of the households had at least one household member for 

whom there was no verification of citizenship. To meet the citizenship verification requirement, the file 

must have contained (for each household member) a signed declaration of U.S. citizenship or eligible 

immigration status; proof of age documentation; an INS card; or INS system verification of citizenship 

status, or documentation that the member was in process for verification or an INS hearing. 

Sixteen percent of the households had at least one member age six or over for whom there was no 

verification of their social security number. To meet the social security number verification requirements 

the file must have contained (for each household member six years of age or older) a copy of the social 

security card, or statement from the Social Security Administration verifying the social security number 

or a certification indicating the member does not have a social security number. 

In 71 percent of the households there was a signed consent form, dated within 15 months of the QCM 

(the date for which data was collected), for all members age 18 or over. Twenty-two percent of the 

households included at least one household member (age 18 or over) for whom a consent form (dated 

within 15 months of the QCM) was not in the file. For the remaining 7 percent of the households, we 

were unable to determine whether the household met the criteria because of missing information. 

Note that not meeting the social security number, citizenship, and consent form criteria may not mean the 

household was not eligible for assistance; rather, the project did not follow the HUD requirements in 

documenting the information. 

Underpayments and Overpayments for Certifications, Recertifications, and Overdue 

Recertifications. Exhibit IV-9 presents a summary of the households with overpayments and 

underpayments by the type of case—certification, non-overdue recertification, and overdue certification. 

The Average Dollar Amounts are based on the sum of the dollar amounts for payment errors (either 

underpayment or overpayment) for the type of household (certification, overdue recertification, or nonoverdue recertification) divided by the number of households with that payment type (for whom a QC 

Rent could be calculated). For example, the sum of the dollar amounts for new certifications with monthly 

underpayments ($10,927) was divided by the total number of certifications in the sample for whom QC 

Rent could be calculated (382,000). The result is an underpayment average dollar amount of $29. 

Final Report June 20, 2001 25 
IV. Findings 

The data indicate that the amount of dollar error in new certifications in 2000 is less than the amount for 

recertifications. However, there is essentially no difference in the dollar error for overdue and nonoverdue recertifications. This is different than the findings in the 1996 report which show a significant 

difference between the error in the overdue and non-overdue recertifications. We believe the reason why 

the 2000 data does not show a difference in the average dollar amount between the overdue and nonoverdue recertifications is that 70 percent of the overdue recertifications were overdue by three months 

or less. 

Exhibit IV-9


Average Monthly Underpayment and Overpayment Dollar Amount


Averaged Across All Households


Underpayment 

Average Dollar Amount 

Overpayment 

Average Dollar Amount 

1996 1996 

Household Type 

Actual $ Current $ 

2000 

Actual $ Current $ 

2000 

Certifications $15 $18 $29 $11 $14 $ 9 

Non-overdue Recertifications $16 $20 $33 $13 $16 $13 

Overdue Recertifications $30 $37 $35 $25 $31 $12 

Total $16 $20 $32 $13 $16 $12 

Source: Table 7, Appendix D 

Subsidies. The actual cost of errors to HUD is expressed in terms of subsidy payments. HUD subsidies 

for assisted housing programs equal the allowed expense level or payment standard minus the total tenant 

payment or tenant share. The subsidy is correct if the Actual Rent equals the QC Rent (within $5). A 

negative subsidy error occurs when the tenant pays too much rent (QC Rent < Actual Rent). A positive 

subsidy error occurs when the tenant pays too little rent (QC Rent > Actual Rent). These subsidy errors 

by program type are summarized in Exhibit IV-10a and 10b, below. The subsidy errors by certification 

status are summarized in Exhibit IV-11. 

Exhibit IV-10a


Negative Subsidy Households (Under-subsidies)


Percent of Households and Average Monthly Dollar Amount of Error


Percent of Average Dollar Amount

Households in 

Error For Negative Subsidy 

Households (with errors > $5) 

For All Households 

1996 1996 

Administration Type 

1996 2000 

Actual $ Current $ 

2000 

Actual $ Current $ 

2000 

Public Housing 26% 20% $46 $56 $63 $12 $15 $13 

PHA-Administered Section 8 14% 20% $54 $66 $67 $13 $16 $13 

Total PHA-Administered 25% 20% $49 $60 $65 $12 $15 $13 

Section 236 19% n/a $40 $49 n/a $7 $9 n/a 

Owner-Administered w/o 236 27% 25% $50 $61 $41 $14 $17 $11 

Total without Section 236 26% 22% $49 $61 $56 $13 $16 $12 

Source: Tables 3 and 4, Appendix D 

Final Report June 20, 2001 26 
IV. Findings 

Exhibit IV-10b


Positive Subsidy Households (Over-subsidies)


Percent of Households and Average Monthly Dollar Amount of Error


Percent of Average Dollar Amount

Households in 

Error For Positive Subsidy 

Households (with errors < $5) 

For All Households 

1996 1996 

Administration Type 

1996 2000 

Actual $ Current $ 

2000 

Actual $ Current $ 

2000 

Public Housing 28% 33% $66 $81 $84 $18 $23 $28 

PHA-Administered Section 8 28% 42% $55 $67 $109 $15 $19 $46 

Total PHA-Administered 28% 38% $61 $75 $100 $17 $21 $38 

Section 236 22% n/a $97 $119 n/a $21 $26 n/a 

Owner-Administered w/o 236 26% 27% $55 $68 $ 81 $14 $18 $22 

Total without Section 236 27% 34% $59 $75 $95 $16 $20 $32 

Source: Tables 3 and 4, Appendix D 

Exhibit IV-11


Average Monthly Dollar Amounts of Error for Negative (Under-) and Positive (Over-) Subsidies


Averaged Across All Households


Negative Subsidy Average Dollar 

Amount of Error 

Positive Subsidy Average Dollar 

Amount of Error 

1996 1996 

Household Type 

Actual $ Current $ 

2000 

Actual $ Current $ 

2000 

Certifications $11 $14 $ 9 $15 $18 $29 

Non-overdue Recertifications $13 $16 $13 $16 $20 $33 

Overdue Recertifications $25 $31 $12 $30 $37 $35 

Total $13 $16 $12 $16 $20 $32 

Source: Table 7, Appendix D 

B. Sources of Error 

In addition to identifying the number of households in error and the associated dollars in error, analysis 

was conducted to determine which income and expense components contributed the most to 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. In addition, the sum of the component errors is greater 

than net rent errors because of off-setting errors. For example, the household presented below has earned 

income and child care costs with errors in both components. The total component error is $600 ($400 + 

$200); however, the adjusted net income error (the amount used to determine the household’s rent) is only 

$200. 

Final Report June 20, 2001 27 
IV. Findings 

Component File Data QC Data Dollar Error 

Earned Income $2,200 $2,600 $400 

Child Care $ 400 $ 600 $200 

Adjusted Net Income $1,800 $2,000 $200 

Exhibit IV-12 presents each income and expense component included in the rent calculation and the 

percent of households where this component contributed the most to the gross error. The exhibit indicates 

that earned income caused the largest dollar error in the highest percentage of households (27 percent). 

Pension income was in error 14 percent of the time and medical expenses was in error 15 percent of the 

time. The average dollar amount associated with earned income is $6,641, substantially higher than the 

average dollar amount associated with pension income and medical expenses where the average dollar 

amount was $3,701 and $1,157 respectively. 

Exhibit IV-12


Rent Components Responsible for the Largest Dollar Error


For Households with Rent Error (listed by amount of dollar error )


Percent of Households in Error Average Dollar Amount 

1996 

Rent Component 

1996 2000 

Actual $ Current $ 

2000 

20% 27% $4,896 $6,022 $6,641 

14% 12% $2,865 $3,624 $3,853 

15% 14% $3,653 $4,493 $3,701 

5% 4 % $1,864 $2,293 $3,450 

12% 9 % $2,831 $3,482 $2,816 

3% 3 % $2,058 $2,531 $2,333 

22% 15 % $1,957 $2,407 $1,157 

4% 5 % $ 618 $ 760 $1,060 

<1% <1% $ 483 $ 594 $ 600 

2% 4 % $ 400 $ 492 $ 400 

4% 8 % 0 0 0 

Earned Income 

Other Income 

Pensions 

Asset Income 

Public Assistance 

Child Care Expenses 

Medical Expenses 

Dependent Allowance 

Disability Expenses 

Elderly/Disabled Allowance 

No Rent Component Error 

Source: Table 8, Appendix D 

Note that for some households the rent error is not caused by one of the ten components listed; rather it 

is caused by other arithmetic errors or by using the wrong rent calculation formula. The number of 

households in this category increased from 4 percent in 1996 to 8 percent in 2000, possibly because some 

of the rent calculations (for certificates and vouchers) have become more complicated. The percent of 

households in error stayed the same or increased for most rent components, with the highest increase for 

earned income (a 7 percent increase). However, there was a 7 percent decrease in the percent of 

households where the medical expense component was in error. 

Final Report June 20, 2001 28 
IV. Findings 

Total and Largest Component Dollar Error. Exhibit IV-13 presents the dollar amounts associated with 

the total dollars in error ( the sum of the absolute value of the errors in all rent components) and the 

largest dollars in error (the largest error for each household attributable to a specific source), by program 

type. Both the average amount of the total dollars in error and the average amount of the largest dollar 

error are greater for PHA-administered projects than for owner-administered projects, a finding 

unchanged from 1996 to 2000. 

Exhibit IV-13


Total and Largest Component Dollars in Error


For Households with Rent Error


Average Total 

Dollars in Error 

Average Largest 

Dollars in Error 

1996 1996 

Administration Type 

Actual $ Current $ 

2000 

Actual $ Current $ 

2000 

Public Housing $4,605 $5,664 $4,837 $3,222 $3,963 $3,723 

PHA-Administered Section 8 $4,080 $5,019 $5,070 $2,780 $3,419 $3,860 

Total PHA-Administered $4,364 $5,368 $4,975 $3,018 $3,712 $3,803 

Section 236 $5,239 $6,444 n/a $2,315 $2,848 n/a 

Owner-Administered w/o Sec.236 $2,703 $3,325 $3,351 $2,556 $3,144 $2,709 

Total without Section 236 $3,789 $4,660 $4,484 $2,775 $3,414 $3,472 

Source: Table 9, Appendix D 

QC Rent Components by Payment Type and Administration Type. Exhibit IV-14 provides the 

percentage of the total number of households with (and without) component error by component type and 

payment type. For example, 14 percent of all households with underpayment rent error had earned income 

errors; 3 percent of all households with proper rents had earned income errors; and 6 percent of all 

households with overpayment rent error had earned income errors. It also provides this information for 

PHA- and owner-administered households. This exhibit reflects component errors in proper payment 

households when the component dollar error is $5 or less. The exhibit indicates that earned income is the 

rent component that has the highest percent of error (20 percent), followed by pension income (17 

percent) and medical expense (16 percent). 

Final Report June 20, 2001 29 
IV. Findings

Final Report June 20, 2001 30

Exhibit IV-14

Component Error by Payment Type for All Households

Component Underpayment Proper Payment Overpayment

PHA Owner Total PHA Owner Total PHA Owner Total

Earned Income 17% 8% 14% 4% 1% 3% 7% 4% 6%

Pension Income 11% 10% 11% 8% 12% 9% 5% 10% 6%

Public Assistance Income 8% 4% 7% 2% 2% 2% 3% 2% 3%

Other Income 11% 5% 9% 4% 2% 3% 3% 2% 3%

Asset Income 4% 8% 5% 3% 8% 4% 2% 8% 4%

Dependent Allowance 6% 2% 5% 2% <1% 1% 4% 1% 3%

Elderly Household Allowance 4% 3% 3% 2% <1% 2% 2% 2% 2%

Child Care Allowance 3% 2% 3% 1% 0% <1% 3% 1% 2%

Disability Assistance Expense 0% <1% 0% 0% 0% 0% <1% <1% <1%

Medical Expense 6% 13% 8% 5% 12% 7% 5% 14% 8%

No Rent Component Error 3% 1% 2% 24% 26% 25% 2% 2% 2%

Source: Table 10, Appendix D

Exhibit IV-15 presents the standard errors for the total number of households with (and without)

component error by component type and payment type.

Exhibit IV-15

Percent of Households and Standard Error by Rent Component and Payment Type

Underpayment Proper Payment Overpayment

Component

Percent of

Total

Households

Standard

Error

Percent of

Total

Households

Standard

Error

 Percent of

Total

Households

Standard

Error

 Earned Income 14.0% 1.1% 2.9% .4% 6.2% .7%

 Pension Income 10.7% .8% 8.8% .9% 6.2% .6%

 Public Assistance 6.9% 1.0% 2.1% .3% 2.6% .3%

 Other Income 8.7% .9% 3.1% .5% 3.0% .4%

 Asset Income 5.1% .6% 4.3% .6% 4.3% .7%

 Dependent Allowance 4.5% .7% 1.2% .3% 2.8% .5%

 Elderly/Disabled Allowance 3.4% .6% 1.5% .4% 2.3% .3%

 Child Care Expenses 2.6% .4% .4% .1% 2.1% .3%

 Disability Expenses .0% .0% 0 0 .1% .1%

 Medical Expenses 8.0% .7% 7.4% .9% 8.1% .9%

 No Rent Component Error 2.2% .3% 24.5% 1.6% 2.0% .4%

Source: Table 16, Appendix D
IV. Findings 

Allowances. Analysis was conducted of both elderly/disabled and dependent allowances15 to determine 

whether these allowances were being applied correctly. These findings are summarized in Exhibit IV-16. 

Exhibit IV-16


Elderly/Disabled Allowances and Dependent Allowances


Elderly Allowance 

Non-Elderly/ 

Disabled 

Households 

Elderly/ 

Disabled 

Households 

All 

Households 

Households 

Without 

Dependents 

Households 

With 

Dependents 

All 

Households 

No Allowance 97% n/a 44% 99% n/a 52% 

Incorrect Allowance 3% 10% 7% 1% 17% 9% 

Correct Allowance n/a 90% 49% n/a 83% 40% 

Source: Table 11, Appendix D 

The exhibit presents the percent of elderly/disabled and nonelderly/disabled households for which an 

elderly/disabled allowance was correctly or incorrectly applied. Elderly/disabled allowances were 

incorrectly used in 7 percent of the households in 2000. Ten percent of the elderly/disabled households 

received an incorrect allowance, while three percent of non-elderly/disabled households received an 

allowance. 

The exhibit also presents the percent of households with and without dependents for which a dependent 

allowance was correctly or incorrectly applied. The dependent allowances were incorrect in 9 percent of 

the households. In 1 percent of the households, a dependent allowance was given to a household that did 

not have dependents. For the remainder of the households in error (17 percent), either a dependent 

allowance was not given when it should have been or the wrong allowance amount was given. 

A review of the data for the households with dependent allowance errors indicates that errors in 

dependent allowances are made by PHA/owners for the following reasons: 

•	 The tenant revealed more family members to our data collectors than were shown in the 

tenant file (some newborns, but mostly older children or adults). 

•	 The PHA/owner did not recognize adult full-time students, who were not the head, spouse, or 

co-head of the household, as being eligible for a dependent allowance. 

•	 The PHA/owner was confused about whether a full-time student, youth, or a disabled adult 

should be treated as a co-head or as another adult. 

• The PHA/owner continued to count a child who turned 18 as a dependent. 

•	 The PHA/owner only applied a single $480 dependent allowance regardless of the number of 

dependents. 

Dependent Allowance 

15 Households with an elderly or disabled head or spouse are entitled to one $400 allowance (i.e., deduction from gross 

annual income) in calculating rent. Households are entitled to a $480 allowance for each dependent (defined as children 

under 18, full-time students, and disabled members other than the head or spouse). 

Final Report June 20, 2001 31 
IV. Findings 

• The PHA/owner made mathematical errors or typographical errors. 

Individual cases with elderly/disabled allowance errors were also reviewed to better understand the nature 

of the errors. We found that PHA/owners do a very good job of giving the elderly/disabled allowance 

when the head of the household (or spouse) is elderly. Elderly status is clear cut based on the date of 

birth. However, more errors are made for households with a head or spouse who is disabled for whom 

the rules and documentation requirements are more complex. The elderly/disabled allowances in the QC 

rent calculation for households with a disabled head or spouse were based on the tenant’s statement. This 

may have resulted in tenants who said they were disabled who were not, as well as tenants who said they 

were not disabled who were. If we assume the tenant answered correctly (which we did), then the 

PHA/owner either did not ask the household whether the head or spouse was disabled, or the PHA/owner 

did not obtain verification of the disability. 

C. Errors Detected Using Information Obtained From Project Files 

The QC rent and rent error was recalculated using only income and expense items identified in the tenant 

file. That is, without income and expense items identified during the household interview. The income 

and expense items identified during the household interview account for over half ($916 million) of the 

annual underpayment dollar errors. In addition, not using income and expense items identified during the 

household interview increases the annual overpayment error dollars by $91 million. The table below 

presents the percent of households in error and the total annual program dollar errors with and without 

income and expense items identified during the household interview. 

Exhibit IV-17


Households in Error and Dollar Error Using Only Project File Information


Percent of Households in Error Total Annual Dollar Errors 

Subsidy 

Overpayment 

Subsidy 

Underpayment 

Subsidy 

Overpayment 

Subsidy 

Underpayment 

Error Based on All Income and 

Expense Items Identified During the 

Study 

Error Without Income and Expense 

Items Identified during the Household 

Interview 

34% 22% 

24% 25% 

$1.669 billion $634 million 

$ .916 billion $725 million 

Analysis of the errors on the 50058/50059 form was conducted to determine whether the errors that can 

be identified using only the information on the 50058/50059 are representative of the total errors in the 

program. These analyses included the identification of calculation error and consistency error. 

Calculation error was determined by using the information recorded on the 50058/50059 form (i.e., 

income amounts, expenses, and allowances) to calculate the rent amount. This calculation did not take 

into account whether dollar amounts were verified or whether the recertification was conducted on time. 

It simply determined whether, using the information on the 50058/50059 form, a correct rent was 

calculated. This analysis identified errors because of mistakes in arithmetic or in the incorrect use of a 

formula. Items that were not completed but should have been were considered incorrect. This analysis 

did not include identifying households where items were recorded in the wrong place on the 50058/50059 

forms, although improper use of a field on the 50058/50059 forms can result in a calculation error. Table 

Final Report June 20, 2001 32 
IV. Findings 

12 in Appendix D presents the number of households with 50058/50059 forms that contained calculation 

errors by the rent component contributing to the error. 

Consistency errors were identified by determining whether there was logical conformity between 

elements within the 50058 or 50059 form. For instance, transaction type and assistance status must 

correspond. Elderly status information should be consistent with information provided about the age of 

the head of the household or spouse. The number of dependents should not exceed the number of 

household members. Table 13 in Appendix D presents the number of households that contain consistency 

errors on the 50058/50059 forms. Rather than list each individual item that might have a consistency 

error, these data are summarized according to the subsections of the forms. Appendix F provides the data 

items by subsection that were included in this analysis. 

Exhibit IV-18 presents the percent of households with calculation and consistency errors in different 

sections of the 50058 and 50059 forms. It is important to emphasize that the 50058 is formatted 

differently and in some sections provides more line items of information than the 50059. Therefore, the 

number and types of calculation and consistency errors on the forms are different, and the findings from 

the two forms should not be compared. However, the large number of calculation errors (particularly on 

the 50058 forms) is potentially a contributing factor to QC error, even though a calculation or consistency 

error does not necessarily lead to a rent error. The PHA/owner may make an error when completing one 

section of the form, and still calculate the rent correctly. 

Exhibit IV-18 

Percentage of Households with Calculation and Consistency Errors 

Percent of Households 

Calculation Errors Consistency Errors 

50058/50059 Item 

50058 50059 Total 50058 50059 Total 

General Information n/a n/a n/a 20% 6% 16% 

Household Composition 32% 20% 28% 19% 13% 17% 

Net Family Assets and Income 25% 15% 50% 14% 4% 11% 

Allowances and Adjusted Income 82% 28% 65% 8% 3% 6% 

Family Rent and Subsidy Information 47% 17% 37% 11% 2% 8% 

Source: Table 12 and 13, Appendix D 

Comparison of 50058/50059 Errors to QC Error. A comparison was made between the errors in the 

calculation of rent on the 50058/50059 forms and errors identified through the QC Rent calculation. The 

purpose of this comparison was to determine if the errors identified using only the 50058/50059 data 

could predict the rent error that would be found in a quality control review. When using only the 

50058/50059 data to calculate the Actual Rent, errors were found in 14 percent of the households in 2000, 

a significant improvement from 1996’s figure of 21 percent. The QC error calculation found error in 56 

percent of the households in 2000, up from 1996’s 53 percent. The households were compared to 

determine if the same households were similarly identified as correct or incorrect. Forty-three percent of 

the households were identified as correct and 12 percent were identified as incorrect by both calculations; 

the remaining 45 percent were identified differently by the two calculations. This emphasizes that data 

from the 50058/50059 forms alone cannot accurately identify rent error. Exhibit IV-19 summarizes these 

findings for 1996 and 2000. 

Final Report June 20, 2001 33 
IV. Findings 

Exhibit IV-19


50058/59 Rent Calculation Error Compared to QC Rent Error


Percent of 

Households 

Correct 

Percent of 

Rent Calculation ouseholds Incorrect

1996 2000 1996 2000 

Using Information on the 50058/50059 Form 79% 86% 21% 14% 

According to the QC Rent Calculation 47% 44% 53% 56% 

Both 50058/50059 Calculation and QC Rent Calculation 39% 43% 14% 12% 

Verification errors were identified by determining whether an item was verified by the project and, if 

it was, whether the correct information was transferred to the 50058/50059 form. An error occurs when 

the verified amount obtained by the project is not recorded properly on the 50058/50059 forms (and, 

presumably, not used in the rent calculation). When determining whether the amount of the income or 

expense provided in the verification matched the amount used on the 50058/50059 form, a variance of 

$100 was allowed to accommodate potential rounding errors when annualizing data. 

Table 14 in Appendix D presents the number of households where verification was not obtained, where 

it was obtained but the verified amount did not match the amount used on the 50058/50059, and where 

verification was obtained and the verified amount did match the 50058/50059. Table 14a includes items 

that were verified by third parties in writing or verbally, or with documentation. Table 14b provides data 

for items verified in writing by third parties (as required by the study). The data from the first table is used 

to compare the 2000 data to the 1996 report because the 1996 report does not reflect the more stringent 

verification requirement. 

Exhibit IV-20 summarizes the findings in Table 14. In general, the PHA/owner is obtaining more 

verification and making better use of the verification. The percentage of items that were verified increased 

for all rent components except for medical expenses (which only shows a slight reduction). The 

percentage of time the verification matched the 50058/50059 data also increased for all rent components. 

However, the number of households where verification was obtained and used by the PHA/owner varies 

greatly depending on the rent component. For example, earned income, one of the main sources of error, 

was verified 82 percent of the time in 2000, compared to 63 percent in 1996. However, the correct 

amount of earned income was only used 55 percent of the time. Other income was fully verified 63 

percent of the time, but only matched the 50058/50059 data 42 percent of the time. Medical expenses 

continued to be verified three-quarters of the time, but in 2000 the amount of households where the 

verification matched the 50058/50059 data increased from 37 percent in 1996 to 52 percent. 

Final Report June 20, 2001 34 
-- --

IV. Findings 

Exhibit IV-20 

Verification of 50058/50059 Rent Components 

No Project 

Verification 

Rent Component 

1996 

Earned Income 

Pensions 

Public Assistance 

Other Income 

Asset Income 

Child Care Expense 

Disability Expense 

Medical Expense 

Item Verified 

by Project 

Verification Matched 

50058/59 within $100 

2000 1996 2000 1996 2000

37% 18% 63% 82% 34% 55% 

28% 13% 72% 87% 45% 71% 

22% 16% 78% 84% 52% 67% 

46% 37% 54% 63% 34% 42% 

16% 12% 84% 88% 70% 75% 

32% 27% 68% 73% 47% 53% 

100% 63% 0 37% 0 26% 

21% 28% 79% 72% 37% 52% 

Source: Table 14a, Appendix D 

Exhibit IV-20a provides case file verification information by program type. It provides both the percent 

of households where the rent component was verified, as well as the percent of the verification for each 

rent component found in the tenant file that matched the data on the 50058/50059 form within $100. 

Exhibit IV-20a


Verification of 50058/50059 Rent Components by Program Type


Public Housing 

PHA-Administered 

Section 8 

Owner-Administered 

Section 8 

Rent Component Verified Matched* Verified Matched* Verified Matched* 

Earned Income 

Pensions 

Public Assistance 

Other Income 

Asset Income 

Child Care Expense 

Disability Expense 

Medical Expense 

* Matched within $100 

83% 53% 80% 53% 86% 64% 

81% 65% 86% 73% 91% 74% 

83% 64% 86% 69% 82% 66% 

60% 35% 68% 50% 56% 32% 

85% 66% 96% 82% 87% 75% 

64% 42% 74% 53% 79% 65% 

17% 17% 58% 58% 

79% 56% 60% 42% 73% 54% 

Source: Table 14a, Appendix D 

Tenant File Verification Compared to QC Error. Households with error identified through the QC 

process were examined to determine if failure to verify sources of income and expenses was a contributor 

to error. 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 indicates that missing 

verification does have a major impact on error. Verification was missing for about 60 percent or more 

of all households with QC error for each rent component. 

Final Report June 20, 2001 35 
IV. Findings 

Exhibit IV-21


QC Error Households With Missing Verification


50058 50059 

Households with 

QC Error 

Households with 

QC Errors and 

Missing 

Verification 

Households with 

QC Error 

Households with 

QC Errors and 

Missing 

Verification 

Rent Component 

1996 2000 1996 2000 1996 2000 1996 2000 

Earned Income 

Pensions 

Public Assistance 

Other Income 

Asset Income 

Child Care Expense 

Handicapped Expense 

Medical Expense 

No Component Error 

15% 24% 80% 74% 14% 12% 77% 77% 

23% 16% 91% 76% 17% 19% 89% 59% 

22% 11% 71% 71% 10% 5% 81% 75% 

15% 14% 55% 82% 11% 7% 51% 88% 

6% 6% 72% 83% 7% 16% 80% 81% 

4% 6% 46% 81% 3% 3% 45% 75% 

1% 1% 100% 100% <1% 1% NA 77% 

19% 12% 70% 95% 28% 25% 75% 86% 

30% 49% n/a n/a 35% 53% n/a n/a 

Summary of 50058/50059 Errors. Exhibit IV-22 provides a summary of the errors identified from the 

50058/50059 forms. These include consistency errors (see page 36), calculation errors, and overdue 

recertifications. The exhibit presents the percent of households in error, the average dollar error, and the 

standard errors for both households with recalculated 50058/50059 error (error determined using only 

the 50058/50059 form), and households with QC Rent error. This information is provided for households 

with error, and households without error for each error type. In addition, an unduplicated count of 

50058/50059 error is provided. The exhibit indicates that individual types of 50058/50059 errors can not 

predict QC Rent Error. However, 50058/50059s with any type of error (consistency, calculation or 

overdue recertifications) can predict QC Rent Error in 58 percent of the households. 

Final Report June 20, 2001 36 
Exhibit IV-22


50058/50059 Procedural Error: Percent of Households, Average Dollars in Error


Error Type Based on 50058/59 Recalculation Households with Recalculated 50058/9 Error Households wit

Percent of 

Households 

in Error 

Standard 

Error of 

Percent 

Average 

Dollar 

Error 

Standard 

Error of 

Mean 

Percent of 

Households 

in Error 

Standard 

Error of 

Percent 

Households with Consistency Error 53% 5.31% $ 98 $21.67 37% 3.27% Households without Consistency Error 47% 5.31% $ 58 $ 5.57 63% 3.27% Households with Allowance Calculation Error 10% 3.45% $146 $49.40 15% 2.22% Households without Allowance Calculation Error 90% 3.45% $ 71 $ 9.41 85% 2.22% Households with Income Calculation Error 4% 1.46% $ 62 $30.66 6% 1.32% Households without Income Calculation Error 96% 1.46% $ 79 $14.14 94% 1.32% Households with Other Calculation Error 27% 3.61% $113 $25.72 30% 1.82% Households without Other Calculation Error 73% 3.61% $ 66 $ 9.55 70% 1.82% Overdue Recertifications 5% 1.68% $ 66 $13.91 6% .75% On-time Recertifications 85% 3.12% $ 84 $15.81 86% 1.00% Certifications 10% 2.47% $ 39 $ 9.95 8% .77% Unduplicated Count, Any Type of 50058/50059 Error 66% 4.60% $ 93 $19.30 58% 2.86% Unduplicated Count, No 50058/50059 Error 34% 4.60% $ 50 $ 4.82 42% 2.86% Total Households 100% $ 79 100% 

Final Report June 20, 2001 
IV. Findings 

Summary of Administrative Errors. As outlined in the study objectives earlier, administrative errors 

are primarily errors in procedures and include calculation errors, transcription errors, failure to recertify 

on time, as well as failure to apply allowances appropriately. Exhibit IV-23 provides the Gross Rent Error 

and Net Rent Error for households with each type of administrative procedural error. 

Exhibit IV-23


Administrative Error: Percent of Households, Average Dollars in Error


For All Households with 50058/50059 Recalculated Rent


Gross Rent Error Net Rent Error 

Error Type 

Percent of 

Households 

in Error 

Percent of 

Households 

in Error 

Average Standard 

Error of 

Mean 

Average Standard 

Error of 

Mean 

Dollars 

in Error 

Dollars 

in Error 

Transcription Errors 50% $12 $ 2.87 50% -$ 6 $ 3.09 

Calculation Errors-Allowances 8% $26 $13.37 8% -$17 $13.99 

Calculation Errors-Income 3% $10 $ 4.69 3% $ 2 $ 5.02 

Calculation Errors- Other 23% $18 $ 6.04 23% -$ 8 $ 6.34 

Overdue Recertifications 6% $9 $ 3.53 6% $ 4 $ 2.84 

Any Administrative Errors 64% $12 $ 2.70 64% -$ 5 $ 2.88 

In addition, regression analyses using administrative errors combined with income components as 

independent variables was completed to examine to what degree these errors affect the QC Dollar Rent 

Error. The model identified several variables with strong correlations to Dollar Rent Error. These include 

all forms of income, especially earned income. The overall insight of the underpayment model is not the 

type of administrative error that affects the level of dollar error, but that income components cause that 

error. A more detailed discussion of this analysis is found in Appendix G. 

D. Project Analysis 

An analysis of variance (ANOVA) was conducted to determine whether Gross Rent Errors were 

concentrated in projects. If gross error occurred at the project level we would expect some projects to 

have errors for most of its tenants, while other projects would be error-free. On the other hand, if errors 

were not concentrated at the project level, there would be a random distribution of errors across all 

households. 

The ANOVA was conducted to determine if there was a systematic project effect. The complex study 

design made the use of standard ANOVA methods problematic. To make sure that projects not appear 

significant when the effect was artifactual, we conducted an analysis of variance followed by 20 

simulations where the error measures were randomly assigned to tenants. None of the twenty simulations 

accounted for a larger percent of the variance (.22) than the actual ANOVA (.26). This indicated that 

there was some concentration of error among projects, but that it was quite small. It was significant 

enough to justify a project path analysis (discussed below), while still indicating the need to conduct 

tenant-level analyses as well. 

Project Characteristics and Practices. Two analytical approaches were used to determine how project 

characteristics and practices were related to errors. First, a path analysis was conducted to examine direct 

and indirect pathways leading from project characteristics and practices to administrative errors such as 

incorrect calculation and erroneous transcription. Second, logistic regression was used to examine how 

project characteristics and practices were related to significant rent errors, i.e., those with absolute values 

Final Report June 20, 2001 38 
IV. Findings 

exceeding five dollars. Two logistic regression models were created. The first identifies project 

characteristics and practices that predict rent error. The second adds tenant characteristics to project 

characteristics and practices to find factors that predict rent errors. 

The path model indicates that project characteristics and practices both have an impact on administrative 

errors. The project characteristics and practices that have the most impact on error are: the number of 

units in the project, training (because it leads to better verification), third-party verification, and the 

number of sources of income/expenses. The logistic regression analysis also shows that the number of 

sources of income/expenses contributes to error. This analysis also indicates that elderly/disabled projects 

have less error and obtaining third-party verification reduces error. 

When tenant characteristics are added to the logistic regression model, more specific predictors of error 

are identified. These include: sources of earned income, other income, and medical expenses. A more 

detailed discussion of this analysis is found in Appendix I. 

F. Error Prone Profiling 

The incidence of rent error may be related to tenant characteristics, project characteristics, or both. An 

analytical approach known as Classification and Regression Tree (CART) was employed to break down 

characteristics and relate them to the significant rent errors, i.e., those with absolute values greater than 

five dollars. This analysis seeks to discover which tenant characteristics had a substantial impact on rent 

error. In particular, this analysis was designed to develop profiles of tenant characteristics that lead to 

error. 

The CART analysis identified four key tenant characteristics which had a substantial impact on QC Rent 

error. The four key indicators identified during the analysis are: at least two sources of earned income, 

at least one source of public assistance income (for households who also have earned income), at least 

one source of other income (given other conditions), and at least three sources of pension income (given 

other conditions). 

One clear observation about the final model is that having more than one source of earned income in the 

household is the single strongest predictor of rent error. A more detailed discussion of this analysis is 

found in Appendix H. 

Models focusing on project contributions to rent error were also employed. Two analytic approaches were 

used to determine how project characteristics and practices were related to errors. First, a path analysis 

was conducted to examine direct and indirect pathways leading from project characteristics and practices 

to administrative errors such as incorrect calculation and erroneous transcription. Second, logistic 

regression was used to examine how project characteristics and practices were related to significant rent 

errors, i.e., those with absolute values exceeding five dollars. 

The path analysis, which focuses on the project as the unit of analysis, indicates that project 

characteristics and practices both have an impact on administrative errors. Some of the more interesting 

conclusions are: projects with more units show more 50058/50059 calculation errors, workshop training 

is helpful because it leads to better verification, third-party verification leads to a reduction in errors, more 

sources of income/expenses leads to more documentation-related errors. 

Final Report June 20, 2001 39 
IV. Findings 

Two logistic regression models, which focus on the household as the unit of analysis, were completed. 

The first predicted rent errors from project characteristics and practices. This analysis supported the 

findings of the path analysis—obtaining verification from third parties, being an elderly/disabled project, 

and the number of sources of income/expenses were all directly related to administrative error. The 

second logistic regression model predicted rent errors from tenant characteristics and project 

characteristics/practices. This analysis found that sources of earned income, other income, and medical 

expenses all have a significant impact on rent error. Overall, the results of the four models (i.e., path 

model, CART, and the first and second logistic regression models) are quite consistent. A more detailed 

discussion of this analysis is found in Appendix I. 

F. Occupancy Standards 

Exhibit IV-24 presents a summary of the analysis conducted to determine whether households are 

assigned units with the correct number of bedrooms. It shows the percent of households, by number of 

bedrooms, that were residing in units with the correct number of bedrooms according to the guidelines 

used in the study. It also shows the percent of households in units with too many and too few bedrooms 

by administration type. Note that the guidelines used in this study are generally acceptable HUD 

guidelines. However, the Section 8 Certificate and Voucher programs sometimes allow households to rent 

units with fewer or more bedrooms then specified in the guidelines. In addition, all programs allow 

exceptions to the rules established by HUD. 

Exhibit IV-24


Percent of Households in Units with the Correct Number of Bedrooms


(According to Study Guidelines)


PHA Administered 

Public Housing Section 8 

Owner 

Administered Total 

Number of 

Bedrooms 

1996* 2000 1996 2000 1996 2000 1996 2000 

0 n/a 100% 97% 100% 97% 97% 92% 99% 

1 n/a 99% 99% 99% 100% 100% 99% 99% 

2 n/a 72% 76% 82% 86% 76% 85% 78% 

3 n/a 83% 85% 85% 87% 83% 89% 84% 

4 n/a 67% 67% 63% 83% 69% 75% 66% 

5 n/a 40% 25% n/a n/a n/a 19% 39% 

All Units n/a 84% 87% 86% 93% 92% 91% 87% 

Source: Table 15 * 1996 data for PHA-administered public housing households is not available. 

The data indicate that 13 percent of all households in 2000 occupied a unit with too many or too few 

bedrooms, according to the guidelines used for this study. This number is up slightly from 1996, where 

9 percent of all households occupied a unit with an incorrect number of bedrooms. Approximately 15 

percent of all PHA-administered households were over-or underhoused. For owner-administered 

households, 8 percent were incorrectly housed in 2000, slightly up from 7 percent in 1996. 

Final Report June 20, 2001 40 
IV. Findings

Final Report June 20, 2001 41

Exhibits IV-24a and IV-24b display the percent of households that met these guidelines for each bedroom

size. The shaded cells indicate the percent of households that fall outside study guidelines. These exhibits

present 1996 and 2000 data respectively.

Exhibit IV-24a

Percent of All Households

by Number of Bedrooms and Number of Household Members

1996

Number of Household Members

Number of

Bedrooms

1 2 3 4 5 6 7 8 9

0 92% 6% 2%

1 90% 9% 1% <1%

2 11% 46% 29% 10% 2% <1% <1%

3 2% 7% 27% 35% 18% 10% 1% <1% <1%

4 2% 4% 2% 17% 22% 21% 24% 5% 2%

5 13% 6% 12% 13% 25% 12% 19%

Exhibit IV-24b

Percent of All Households by

Number of Bedrooms and Number of Household Members

2000

Number of Household Members

Number of

Bedrooms

1 2 3 4 5 6 7 8 9 10 17

0 99% 1%

1 89% 10% <1% <1%

2 20% 41% 26% 11% 2% <1% <1% <1%

3 4% 10% 30% 32% 16% 6% 2% 1%

4 7% 8% 18% 25% 25% 12% 4% 2%

5 10% 13% 15% 13% 9% 30% 10%

6 75% 25%

8 100%

Source: Table 15, Appendix D

A supplementary analysis of two-person households in two-bedroom units indicates that 64 percent of

two-bedroom units with two persons include a child under 18, while 18 percent include another adult in

addition to the head of household. In elderly/disabled households, 30 percent of the two bedroom units

with two persons included the head and spouse (or co-head); 31 percent included the head and a youth

under 18; and 30 percent included the head and another adult.
IV. Findings 

Exhibit IV-24c presents the percent of households in project-based programs (Public Housing and all 

owner-administered projects) that met these guidelines for each bedroom size. The shaded cells indicate 

the percent of households that fall outside study guidelines. This is 2000 data; similar data for 1996 is not 

available. 

Exhibit IV-24c


Percent of Project-Based Households by


Number of Bedrooms and Number of Household Members


2000 

Number of Household Members 

Number of 

Bedrooms 

1 2 3 4 5 6 7 8 9 10 17 

0 99% 2% 

1 92% 8% <1% <1% 

2 24% 43% 22% 8% 2% <1% <1% 

3 4% 11% 28% 35% 13% 7% 1% <1% <1% 

4 11% 4% 16% 22% 24% 15% 6% 3% 

5 10% 14% 15% 10% 9% 31% 10% 

6 75% 25% 

G. Comparison With TRACS/MTCS Data 

The households included in this study were matched against the TRACS/MTCS data files, using the social 

security number of the head of the household. Active MTCS records were found for 90 percent of the 

households in PHA-administered projects. Active TRACS records were found for 68 percent of the 

households in owner-administered projects. (This percentage increases to 75 if designated inactive 

records which appear to have been miscategorized are included.) 

Analysis was conducted to compare the average dollars in error for households that matched 

TRACS/MTCS with those that did not. Exhibits IV-25 provides the percent of households in each of the 

three program types present and absent in TRACS/MTCS, and the average dollars in error for each 

program type. The average dollars in error for households not in TRACS/MTCS was higher for all three 

groups. Exhibit IV-26 presents the percent of households and average dollars in error for households 

found/not found in TRACS/MTCS by payment type. While the percent of households with over and 

underpayments is about the same for households present and absent from TRACS/MTCS, the average 

underpayment dollars in error was higher for those households that were not found in the TRACS/MTCS 

data file. 

Final Report June 20, 2001 42 
IV. Findings 

Exhibit IV-25 

Average Dollars in Error by Program and TRACS/MTCS Data 

TRACS/MTCS PRESENT TRACS/MTCS ABSENT 

Administration 

Type Percent of All 

Households in Error 

Average Dollars in 

Error 

Percent of All 

Households in Error 

Average Dollars 

in Error 

PHA-administered 

Public Housing 

Section 8 

Owner-administered 

Section 8 

TOTAL 

88% $38 12% $58 

92% $59 8% $67 

68% $28 32% $40 

83% $44 17% $49 

Source: Table 17, Appendix D 

Exhibit IV-26


Average Dollars in Error by Payment Type and TRACS/MTCS Data


Payment Type TRACS/MTCS PRESENT TRACS/MTCS ABSENT 

Percent of Average Dollars Percent of Average Dollars in 

Households in in Error Households Not in Error 

TRACS/MTCS TRACS/MTCS 

Overpayment 21% $56 25% $ 57 

Underpayment 35% $92 32% $110 

Proper Payment 44% n/a 43% n/a 

Total 100% $44 100% $49 

Source: Table 18, Appendix D 

Additional analysis was conducted to identify the number of households where the effective date of action 

on the 50058/50059 used in the study matched the effective date of action in the TRACS/MTCS file. 

Sixty-two percent of the TRACS households and 55 percent of the MTCS households that matched on 

Social Security number also matched on effective date. This was fewer cases than expected. However, 

we believe this occurred because of the time that passed between data collection and analysis. During that 

period, subsequent actions would have required that project staff update the information in the 

TRACS/MTCS data files. Therefore, the most recent action in the TRACS/MTCS files would no longer 

be the same as the most recent action at the time of the study data collection. 

For those households that matched on effective date of action, further analysis was conducted to 

determine if certain key variables matched. For owner-administered households (TRACS cases) these 

variables included gross income, net income, tenant rent, and total tenant payment(TTP). For PHAadministered households (MTCS cases) the key variables were gross income, net income, and total tenant 

payment (TTP). 

Final Report June 20, 2001 43 
IV. Findings 

These key variables matched for the majority of all households. Ninty-four percent of the PHAadministered households, and 91 percent of the owner-administered households matched on all the key 

variables. The discussion below provides additional information on the specific variables reviewed. 

However, there were several households where more than one variable did not match. 

Only 2 percent of the TRACS and MTCS households failed to match on gross income. A sightly higher 

percentage of TRACS (5 percent) and MTCS (3 percent) of households failed to match on net income. 

Four percent of TRACS households and 5 percent of MTCS households failed to match on Total Tenant 

Payment and 8 percent of TRACS households failed to match on Tenant Rent (tenant rent data was not 

available for MTCS households). This information is summarized in Exhibit IV-27. 

Exhibit IV-27


Percentage of Matched and Non-Matched Dollar Amounts for Key Variables


Matching Variables from the 50058/50059 Form and MTCS/TRACS Data Files


Match Gross Income Net Income Total Tenant Payment Tenant Rent

Status 

TRACS MTCS TRACS MTCS TRACS MTCS TRACS 

No Match 2.4% (8) 2% (16) 4.5%(15) 3.2% (25) 4.3% (14) 5.4% (42) 8% (27) 

Match 97.6% (329) 94% (734) 95.5% (321) 92.8% (724) 95.8% (323) 87.2% (681) 92% (310) 

Subtotal 100% (337) 96% (750) 99.7% (336) 749 100% (337) 92.6% (723) 100% (337) 

Missing - 4% (31) .3% (1) 4% (32) - 7.4% (58) -

Total 100% (337) 100% (781) 100% (337) 100% (781) 100% (337) 100% (781) 100% (337) 

The discrepancies in gross income, net income, and total tenant payment were not concentrated in any 

particular owner-administered projects. However, there were four projects with more than two cases 

(there were usually only four cases selected per project) where the tenant rent on the 50059 did not match 

with TRACS data. For PHA-administered projects discrepancies in gross income and net income were 

not concentrated in any particular projects. However, the majority of households that did not match on 

TTP were from the same projects. These particular projects are projects that do not use the standard 

50058 form. 

There are several households in Exhibit IV-27 reported as missing for the MTCS match with the 50058 

form information. In virtually all cases, the missing refers to a blank on the 50058. Of the 58 households 

where TTP is missing, 20 are also missing gross income and net income. When the specific projects 

involved were examined, it was discovered that 50058 forms with multiple missing items are usually 

projects that do not use the standard 50058 form. 

The households which included variables where the 50058/50059 data did not match the TRACS/MTCS 

data were reviewed to determine if these household’s rent was calculated in error. Exhibit IV-28 displays 

the cases with discrepancies in gross income, net income, total tenant payment, and tenant rent, and the 

percentages that also have rent errors. As can be seen the percentages of no matches with gross errors all 

are very similar between TRACS and MTCS and across gross income and net income. For total tenant 

payment, TRACS is higher at 86 percent than MTCS at 74 percent, but since the numbers are so small 

the difference may be simply by chance. 

Final Report June 20, 2001 44 
IV. Findings 

Exhibit IV-28 

Percentage of Gross Dollar Rent Errors for Cases Where Key Variables Did Not Match 

Item That Did Not Match 

Gross Income Net Income Total Tenant Payment Tenant 

Rent 

TRACS MTCS TRACS MTCS TRACS MTCS TRACS 

Rent Error 80% (8) 88% (14) 88% (15) 88% (22) 86%(12) 74% (31) 81% (22) 

No Rent Error 20% (2) 12% (2) 12% (2) 12% (3) 14% (2) 26% (11) 19% (5) 

Total 100% (10) 100% (16) 100% (17) 100% (25) 100% (14) 100% (42) 100% (27) 

Analysis was also conducted to determine if the households which included variables where the 

50058/50059 data did not match the TRACS/MTCS data also included consistency, transcription or 

calculation errors within the 50058/50059. Exhibit IV-29 presents these households by type of error. 

Transcription error in TRACS households was the single largest type of error, ranging from 75 percent 

for gross income and 87 percent for net income to 79 percent for TTP and 81 percent for tenant rent. 

MTCS households did not display a single type of error in the key variables that failed to match on dollar 

amounts, but the number of households with transcription and consistency errors was high. 

Exhibit IV-29 

Percentage of Administrative Errors for Cases Where Key Variables Did Not Match 

Item That Did Not Match 

Gross Income Net Income Total Tenant Payment Tenant 

Rent 

TRACS MTCS TRACS MTCS TRACS MTCS TRACS 

Consistency Error 25% (2) 44% (7) 27% (4) 56% (14) 21% (3) 64% (27) 19% (5) 

Allowance 13% (1) 25% (4) 33% (5) 40% (10) 36% (5) 21%(9) 22% (6) 

Calculation Error 

Income Calculation 

Error 

- 12% (2) 7% (1) 12% (3) - 19%(8) -

Other 

Calculation Error 

13% (1) 44% (7) 33% (5) 44%(11) 29% (4) 36%(15) 19% (5) 

Transcription 

Error 

75% (6) 63% (10) 87% (13) 76%(19) 79% (11) 60%(25) 81% (22) 

Final Report June 20, 2001 45 
V. Rent Reasonableness Determinations 

The rent reasonableness task examined the rent determinations that housing authorities make for 

dwellings leased in the Section 8 tenant-based program. HUD regulations require that housing 

authorities ensure that rents charged for assisted units are reasonable in comparison to the rent paid 

for comparable unassisted units in the private market. 

Adequate rent reasonableness determinations are essential for the prudent economic operation of the 

Section 8 program. Permitting rents in the Section 8 program to be higher than those paid by other 

families for comparable, unassisted units drives up program costs and increases the amount that assisted 

families must pay. Additionally, unmonitored Section 8 rents could drive up rents for all renters in some 

neighborhoods. 

Faulty or inadequate rent reasonableness determinations can be very costly in terms of the level of 

subsidies required to operate the Section 8 program. In the short term, unreasonably high rents increase 

individual subsidies, thereby limiting the number of households that the program can serve. In the longer 

term, increased per unit costs can drive up overall program spending. Recently, HUD decided to increase 

the Fair Market Rent (FMR) to the 50th percentile for some locations that are experiencing very tight 

rental markets. The housing authorities in these targeted areas must be especially vigilant in applying rent 

reasonableness determinations to keep program costs down. 

A failure to accurately determine rent reasonableness also may increase the amount that assisted families 

pay for rent. When Section 8 rents exceed those for comparable unassisted units, tenants’ payments are 

unreasonably high. In addition, unrestricted Section 8 rents may have a detrimental effect on a 

neighborhood’s entire rental housing market. Higher Section 8 rents may put upward pressure on all rents 

in the neighborhood. 

Despite its importance to the overall functioning of the program, rent reasonableness requirements have 

been called “the hardest job that Section 8 administrators have to carry out and one of the least well 

defined.”

16 HUD regulations provide basic standards that housing authorities must apply in determining 

rent reasonableness. The regulations also specify when housing authorities must determine rent 

reasonableness and how to conduct unit comparability. Furthermore, because of its importance, rent 

reasonableness is a major factor in the Section 8 Management Assessment Program (SEMAP). However, 

little information is available about how housing authorities comply with HUD’s regulations or the extent 

to which Section 8 rents are, in fact, consistent with market rents for comparable units. This study 

addresses these questions and provides information that HUD can use to give more guidance to local 

agencies on implementing the requirement. 

This task has three specific objectives: 

•	 To examine the policies and procedures housing authorities adopt in their implementation of the 

rent reasonableness requirement. 

•	 To determine the extent to which housing authorities actually implement rent reasonableness 

policies and procedures. 

16 See Turner, M., S. Popkin, and M. Cunningham, Section 8 Mobility and Neighborhood Health: Emerging Issues and 

Policy Challenges, Urban Institute, April 2000. 

Final Report June 20, 2001 46 
V. Rent Reasonableness Determinations 

•	 And, to assess the reasonableness of rents charged under the Section 8 tenant-based assistance 

program in comparison to rents in the private, unassisted market. 

A. Methodology 

The rent reasonableness task involved a multifaceted data collection strategy, which was designed and 

carried out by a team consisting of ORC/Macro and its subcontractors KRA Corporation and 

CountryWide Inspections. The task involved three main steps. 

First, KRA Corporation conducted a telephone interview with representatives from a nationwide sample 

of housing authorities. This interview solicited information about the housing authorities’ policies and 

procedures for implementing the rent reasonableness requirement. 

Second, ORC/Macro field staff collected information on a sample of Section 8 certificate and voucher 

households at each PHA. This effort, which was integrated with the data collection for the main quality 

control study, provided details about the length and terms of each household’s tenure in their current unit. 

It also examined the most recent rent reasonableness determination for each unit, if any had been 

conducted, and provided information on each unit related to the criteria that HUD requires housing 

authorities to consider in determining rent reasonableness. This information was compiled through a 

review of PHA files and via in-person interviews with assisted households. 

Finally, staff with CountryWide Inspections surveyed three comparable, unassisted units for a Section 

8 certificate and voucher sub-sample. 

The universe for the rent reasonableness task consists of a subset of the universe for the main quality 

control study—that is, 1,555,544 households assisted under the Section 8 tenant-based assistance program 

in the continental United States, Hawaii, and Puerto Rico. Exhibit V-1 indicates the characteristics, size, 

and purpose of each of the nested samples used in the study to represent the universe. 

Exhibit V-1 

Study Samples 

Sample Size Description 

Primary Sampling Units 54 Geographic clusters from which subsequent samples were drawn. 

Project Sample 130 A sample of Section 8 certificate and voucher projects from the primary 

sampling units. 

Housing Authority Sample 107 The unduplicated number of housing authorities represented by the project 

sample. 

Household Sample 752 A sample of households using a Section 8 certificate or voucher under 

sampled projects. 

Comparability Sample 396	 A sub-sample of Section 8 certificate and voucher households that excludes 

Section 8 units located in properties with project-based subsidies; units that 

consist of an owner-occupied manufactured home on a rented pad; and units 

located in an independent group residence, congregate housing, or shared 

housing. 

Sample of Comparables 1188	 A sample of unassisted units selected for each of the units in the study’�

comparability sample. 

Final Report June 20, 2001 47 
V. Rent Reasonableness Determinations 

B. Housing Authority Procedures 

The task’s first objective is to examine the policies and procedures that housing authorities use in their 

implementation of the rent reasonableness requirement. HUD regulations provide overall parameters that 

housing authorities are expected to meet; however, little is known about how housing authorities translate 

these general requirements into practice. The study aims to fill this gap. 

This part of the task relies exclusively on the findings from a telephone survey conducted with staff at 

105 housing authorities located throughout the United States and Puerto Rico. It should be noted that 

these findings are not statistically representative of all housing authorities because the sample for the rent 

reasonableness task was selected to represent Section 8 certificate and voucher tenants (or units), not 

housing authorities. Having said that, the housing authorities surveyed for this research include a diverse 

mix of agencies in terms of their location, type of housing market, and the number of certificates and 

vouchers under contract. 

The task examined several aspects of the policies and procedures housing authorities use in implementing 

the rent reasonableness requirement. In general we found that: 

•	 Almost all housing authorities have some type of formal rent reasonableness procedures in place. 

Only 2 out of 105 housing authorities reported that they do not have specific procedures for 

assessing rent reasonableness. 

•	 A majority (86 out of 105) of housing authorities determine rent reasonableness by comparing the 

rent for a Section 8 unit to the rent for one or more comparable units. Most other agencies 

determine reasonableness by using data on average market rents. Some agencies use a combination 

of both methods. 

•	 A majority (87 out of 105) of housing authorities rely on agency staff to compile information on 

private, unassisted housing for rent reasonableness determinations, even though respondents 

identified this responsibility as the most time consuming and most difficult facet of the entire 

requirement. 

•	 Most housing authorities incorporate the criteria that HUD specifies for use in determining the 

reasonableness of Section 8 rents. Many now do so by using a point system, which assigns a score 

to each unit according to each of HUD’s criteria. 

•	 Almost all housing authorities document the outcome of the rent reasonableness determination in 

their case files. Only 5 out of 105 housing authorities indicated that they do not require tenant file 

documentation. 

•	 Almost all (99 out of 105) housing authorities say they conduct rent reasonableness determinations 

at lease-up, and a majority (57 out of 105) also say they do so prior to a rent increase. The task did 

not specifically examine PHA policies for determining rent reasonableness when the FMR is 

decreased by 5 percent or more—the third trigger mandated by HUD. However, a sizable minority 

(44 out of 105) of agencies reports that they conduct rent reasonableness determinations at annual 

recertification, even though this goes beyond HUD’s basic requirements. 

•	 Almost all housing authorities give the personnel responsible for determining rent reasonableness 

some degree of discretion in carrying out the determination and ultimately in deciding what is a 

“reasonable” rent. This means that units are not unnecessarily excluded from the program if, for 

example, there is valid reason for a Section 8 unit to have a rent higher than its comparables. It also 

Final Report June 20, 2001 48 
V. Rent Reasonableness Determinations 

means that a PHA’s success in ensuring that Section 8 rents are reasonable will depend on how 

formal rent reasonableness policies and procedures are implemented. 

•	 Finally, a majority (83 out of 105) of all housing authorities report they had either recently updated 

their rent reasonableness procedures or plan to do so soon. Changes to the rent reasonableness 

procedures appear to have been spurred by the implementation of SEMAP, greater scrutiny from 

HUD auditors, and the housing authorities’ own interest in improving the efficiency and accuracy 

of their rent reasonableness determinations. 

These and related findings are discussed in greater detail below. 

Comparing Section 8 Rents to Private, Unassisted Housing 

To comply with the rent reasonableness requirement, housing authorities must determine that Section 8 

rents are reasonable in comparison to rents for similar housing in the private, unassisted market. A PHA’s 

overall approach to the requirement can be defined by the method it uses to determine the rent for private, 

unassisted housing. This decision drives many other aspects of a PHA’s rent reasonableness procedures, 

including everything from the kind of data that must be collected to the ultimate definition of what 

constitutes a “reasonable” rent. What do housing authorities use as a point of comparison of Section 8 

rents? 

The study reveals that, with some exceptions, housing authorities use two main approaches when 

conducting rent reasonableness determinations—a unit-to-unit comparison or a unit-to-market 

comparison. 

A unit-to-unit comparison is akin to a standard real estate appraisal technique in which the rent for a 

Section 8 unit is compared to the rent for one or more private, unassisted units selected as comparables. 

As shown in Exhibit V-2, the survey indicated that 86 out of 105 housing authorities use the unit-to-unit 

comparison method at least part of the time. By contrast, a unit-to-market comparison requires housing 

authorities to use data on private, unassisted rents to estimate the “market” rent for different unit 

categories. A Section 8 rent is then compared to the average market rent or rent range for similar units. 

According to the survey, approximately 1 in 5 housing authorities (23 out of 105) use this type of rent 

reasonableness method at least part of the time. Although it is possible to broadly group housing 

authorities’ rent reasonableness procedures into these two categories, this characterization fails to capture 

the full diversity in approaches that housing authorities pursue. 

Exhibit V-2


Process Used To Determine If Rent Is Reasonable


Rent Reasonableness Method Number of PHAs Percent 

Unit-to-Unit Comparison 80 76% 

Unit-to-Market Comparison 17 16% 

Combination of Unit-to-Unit and 6 6% 

Professional Judgement 2 2% 

Total 105 100% 

Source: Derived from the Project Staff Survey 

Two of the housing authorities surveyed for this research indicated that they do not use either of these 

methods. Instead, these housing authorities base their assessment of rent reasonableness solely on the 

market knowledge and professional judgment of their own personnel, without conducting a formal 

comparison of Section 8 and private, unassisted housing. 

Final Report June 20, 2001 49 
V. Rent Reasonableness Determinations 

The survey also revealed considerable diversity among the housing authorities that do use the unit-to-unit 

or unit-to-market approaches to rent reasonableness. To begin with, several of the surveyed housing 

authorities do not use one approach—they use both. Six of the surveyed housing authorities use a 

combination of the two approaches. 

Two of the authorities that use both unit-to-unit and unit-to-market approaches also use a method that 

does not fit either category. At these authorities, which operate in the same community with heavily 

regulated rents, rent reasonableness is sometimes determined according to local rent regulations. Here, 

a proposed Section 8 rent is compared to the previous rent for the same unit with allowances made for 

annual and vacancy increases as well as increases resulting from allowable improvements to the unit. This 

approach, which assumes that rents determined according to local regulations are by definition 

reasonable, is used as the primary method for determining rent reasonableness at one agency and as an 

alternate approach at the other. 

Even among those housing authorities that only use a unit-to-unit or only use a unit-to-market approach 

for determining rent reasonableness, there are several agencies that have more than one way of applying 

the single approach. This type of variability sometimes occurs because the particular method used to 

determine comparability is driven by the availability of data on unassisted housing from particular data 

sources. It also occurs because many authorities’ procedures have been in flux over recent years, and they 

have continued to support multiple approaches to the rent reasonableness requirement as procedural 

changes have been implemented. 

Finally, it should be noted that although only two housing authorities rely solely on the professional 

judgment of their own staff to determine rent reasonableness, most if not all housing authorities do this 

to a lesser or greater extent. That is, even when a PHA pursues a formal unit-to-unit or unit-to-market 

approach to the requirement, there usually are a number of points at which staff can assert their own 

discretion over the determination of “reasonable” rent. This and other aspects of the complexity involved 

in determining rent reasonableness are explored further below. 

Compiling Information on Unassisted Housing 

A PHA’s overall approach to rent reasonableness—whether unit-to-unit, unit-to-market, or a 

hybrid’determines the kind of information that it needs to compile on unassisted housing. But there are 

many other choices housing authorities have to make in deciding how to compile information for rent 

reasonableness determinations, including decisions about who should collect the data, what data sources 

should be employed, and how the data should be maintained for day-to-day use, if at all. 

These choices are important for two main reasons. First, compiling information on private, unassisted 

housing is, according to the housing authorities themselves, the most time-consuming and costly aspect 

of the entire requirement, and frequently the most difficult. Second, the quality of data on private, 

unassisted housing is critical to the overall effectiveness of a PHA’s procedures. No matter what a PHA’s 

rent reasonableness procedures otherwise consist of, they cannot accurately assess the reasonableness of 

Section 8 rents if the PHA’s information on private, unassisted housing is not accurate. 

The study reveals that despite the time and effort required to compile information on private, unassisted 

housing, most agencies choose to assign this responsibility to one or more of their regular staff as opposed 

to relying on an outside contractor. Among the housing authorities surveyed for this research, 87 out of 

105 use agency staff to gather information on unassisted units. Approximately 1 out of every 10 

authorities hire a contractor to collect these data (or 11 out of 105); 5 agencies report using data collected 

by another group such as a local government agency. A majority of housing authorities decide, therefore, 

that it is most cost effective and/or administratively efficient to conduct this function in-house. 

Final Report June 20, 2001 50 
V. Rent Reasonableness Determinations 

Housing authorities that opt to use in-house staff to compile information on private, unassisted housing 

do so using a wide variety of data sources. Broadly speaking, however, the data sources fall into two main 

categories. 

The most frequently used method to compile information on unassisted units is through direct contact 

with landlords or property managers. Several housing authorities undertake formal, random surveys of 

area rental housing, although this type of data collection is more likely to be undertaken with the 

assistance of an outside contractor. More common is for agencies to employ their own staff who are in 

the community inspecting Section 8 units to simultaneously collect data on unassisted housing. Inspectors 

might compile such information, for example, on unassisted units in the same properties where Section 

8 units are located, or simply by following up on “For Rent” signs they come across in the course of their 

daily business. Several housing authorities also require landlords that participate in the Section 8 program 

to provide information on other unassisted units that they own for rent reasonableness determinations. 

Finally, some housing authorities use data on units that leave the Section 8 program as comparables, 

following-up as necessary with landlords to keep information on units up to date. 

Housing authorities also find data on unassisted rental units from secondary sources such as local 

newspapers, apartment guides, real estate agents, multiple listing services, and the Internet. Secondary 

data sources can be advantageous because they avert the need for direct contact with landlords, which 

many housing authorities identify as a particularly time consuming and, if landlords are not cooperative, 

difficult task. However, the information that can be gleaned from such sources is seldom as detailed as 

the data that can be compiled through direct contact with landlords or rental agents. Newspaper listings, 

for example, might provide information on unit location, size, amenities, and utilities, but they are 

unlikely to provide information on other criteria that HUD requires housing authorities to consider in 

determining rent reasonableness, such as unit age and quality. Moreover, with the exception of multiple 

listing services, secondary data sources usually only report how much a landlord would like to rent a unit 

for, which may not be the same as the rent that the market ultimately supports. For these reasons, it is not 

uncommon for housing authorities to use secondary data sources as an alternative when satisfactory data 

are not available directly from landlords, or as a preliminary data source supplemented, as necessary, by 

direct follow-up with landlords to compile more detailed information. As Exhibit V-3 indicates, 57 out 

of 87 housing authorities that compile their own data on unassisted units use a combination of these two 

methods. 

Exhibit V-3


Sources Housing Authorities Use to Locate Data On Comparable Units


Type of Data Source Number of PHAs Percent* 

Only Direct From Landlord 18 21% 

Only Secondary Source 12 14% 

Combination of Both Sources 57 66% 

Total 87 100% 

Source: Derived from the Project Staff Survey 

* Percentages do not sum to 100 due to rounding. 

Once data are collected, housing authorities typically prepare for their rent reasonableness determinations 

by producing a reference file on unassisted rental units that can be used to compare against Section 8 

units. This file takes the form of either a “database” of unassisted rental units (for unit-to-unit 

comparisons) or a schedule of rents (for unit-to-market comparisons). However, some housing authorities 

that make unit-to-unit comparisons also collect information on unassisted units on an as-needed basis. 

In other words, they will collect information on comparables if warranted for a particular rent 

reasonableness determination. For a small minority of housing authorities (3 out of 86 that use a unit-toFinal Report June 20, 2001 51 
V. Rent Reasonableness Determinations 

unit approach), this is the only way that they compile information on comparables; hence they do not 

maintain a database on unassisted housing. 

The survey revealed that almost half of all housing authorities maintain a database of comparables or 

schedule of rents in an electronic format (47 out of 105). Several housing authorities do this using 

commercial software that they also use to administer other aspects of the Section 8 program. In other 

instances, housing authorities (or their contractors) design and build their own searchable database on 

comparable or market rents for the sole purpose of the rent reasonableness requirement. 

It should be noted that it is equally common for housing authorities to maintain information for the rent 

reasonableness requirement in a paper format (50 out of 105). Some housing authorities continue to rely 

on a paper format because the quantity and complexity of their data on market rents or comparable units 

does not warrant an electronic system. However, in other instances, the fact that rent reasonableness data 

are in a paper format reflects that the systems, and even the data themselves, have not been updated in 

many years. Furthermore, in some instances the format of the database on unassisted housing raises 

questions about the extent to which a “database” of comparables can be effectively searched according 

to HUD-specified criteria. At one PHA, for example, the information on unassisted housing is kept in a 

“comparables box,” literally a box filled with cuttings of newspaper and other secondary source listings 

on comparable units. PHA procedures for incorporating HUD’s comparability criteria are described next, 

below. 

Incorporating HUD’s Comparability Criteria 

HUD requires housing authorities to consider nine factors in determining that Section 8 rents are 

reasonable—location, unit type, size, age, quality, amenities, owner-paid utilities, maintenance, and 

housing services. However, HUD provides little guidance on how housing authorities should incorporate 

these factors into their decision-making process. 

The study revealed that nearly all housing authorities incorporate most of HUD’s comparability factors 

into rent reasonableness assessments, although they do so in a number of different ways. One way for 

housing authorities to incorporate HUD’s factors is to use these criteria when selecting comparable units 

(for unit-to-unit comparisons) or in defining unit categories (for unit-to-market comparisons). As shown 

by the top panel of Exhibit V-4, unit location, size, amenities, and type are the only criteria used by a 

majority of housing authorities to select comparable units or define unit categories. However, the survey 

did indicate that 21 housing authorities include all nine of HUD’s criteria in their rent reasonableness 

procedures, often by using a point system that accounts quantitatively for each factor. 

Final Report June 20, 2001 52 
V. Rent Reasonableness Determinations 

Criteria 

Exhibit V-4 

Incorporation of HUD’s Comparability Criteria 

Number of PHAs Percent (n=105) 

Criteria Used to Select Comparables and Define Unit Categories 

Location


Size


Amenities


Unit Type


Owner-Paid Utilities


Age


Quality


Housing Services


Maintenance


85 

81 

65 

65 

50 

45 

35 

33 

31 

Criteria Used to Adjust Rents for Comparison Purposes 

Amenities


Owner-Paid Utilities


Location


Size


Quality


Age


Unit Type


Housing Services


Maintenance


25 

16 

13 

11 

11 

6 

4 

4 

4 

81% 

77% 

62% 

62% 

47% 

43% 

33% 

31% 

29% 

24% 

15% 

12% 

10% 

10% 

6% 

4% 

4% 

4% 

Source: Derived from the Project Staff Survey 

Housing authorities that do not consider HUD’s criteria in selecting comparables (or defining market 

categories) may incorporate HUD’s criteria in calculating adjusted rents that are used for comparison 

purposes. For example, a PHA that uses a unit-to-unit approach might adjust rents for differences in 

owner-paid utilities before making a rent reasonableness determination to ensure that comparability is 

based on a comparison of gross rents. As indicated by the bottom panel of Exhibit V-4, housing 

authorities most often make this type of adjustment to account for differences between the Section 8 unit 

and comparable units (or unit category) in terms of amenities. 

Finally, even if housing authorities do not incorporate HUD’s criteria at either the selection stage or in 

determining an adjusted rent for comparison purposes, they may do so in making the final determination 

of rent comparability. Housing authorities indicated that, without any formal mechanism in place, the staff 

that are responsible for ultimately determining rent reasonableness will, at their own discretion, account 

for any unusual unit characteristics. The issue of how housing authorities define “reasonable” rent is 

discussed further below. 

The study also examined how housing authorities operationalize HUD’s factors. For example, a majority 

of housing authorities consider unit location in identifying comparable units or unit types, but what 

criteria are actually used to establish that a Section 8 unit and a private, unassisted unit are in a similar 

location? 

Final Report June 20, 2001 53 
V. Rent Reasonableness Determinations 

The study shows that, of all the HUD criteria, housing authorities vary most in how they define unit 

location. Some housing authorities, for instance, control for location by requiring that comparable units 

be within a certain radius of the subject unit. Another method is to match a Section 8 unit and comparable 

units by zip code or census tract. Qualitative approaches such as accessibility to public services (such as 

public transportation) or neighborhood amenities (such as shopping) are also used to gauge location. 

Housing authorities vary much less in how they operationalize the other HUD criteria for the selection 

of comparable units. For example: 

•	 Unit type—units may be categorized as single-family, townhouse, garden-style apartment, and so 

on 

• Size—units may be classified by number of bedrooms or square feet 

• Age—units may be classified by age (in years) or year built 

•	 Quality—units may be categorized by the rating produced through the Housing Quality Standards 

inspection 

•	 Amenities—units may be classified according to their in-unit amenities such as kitchen appliances, 

and site amenities such as swimming pools or playgrounds 

•	 Maintenance and housing services—units may be classified according to the provision of on-site 

management or maintenance services. 

Operationalizing HUD’s criteria is, according to the surveyed housing authorities, one of the more 

problematic aspects of the rent reasonableness requirement. Several housing authorities bemoaned the 

fact that HUD gives little guidance on what kinds of definitions are acceptable for each of the criteria. 

There does, however, appear to be a widespread acknowledgment of the importance of developing 

procedures that incorporate all of HUD’s criteria. The use of a point system, in which different aspects 

of a unit are rated and given a score, has been a solution for many housing authorities. 

Determining Rent Reasonableness 

Although a PHA’s overall approach to rent reasonableness, its methods for collecting data on unassisted 

housing, and its incorporation of HUD’s criteria all have a bearing on the quality of its comparability 

assessments, the outcome of its rent reasonableness procedures may ultimately depend more on when 

determinations are made and how a “reasonable” rent is defined. 

HUD requires that housing authorities conduct rent reasonableness determinations at lease-up, whenever 

there is an increase in rent, if there is a 5 percent decline in the Fair Market Rent, and if required to do 

so by HUD. Accordingly, agencies should assess rent comparability at any point at which there is a 

possibility for the Section 8 rent to deviate from the rent for similar, unassisted housing. The survey 

indicated that housing authorities for the most part have procedures that comply with HUD’s 

requirements. Almost all housing authorities conduct rent reasonableness determinations at lease-up (99 

out of 105), and a majority also do so when there is a rent increase (57 out of 105). The study did not 

specifically examine PHA policies for determining rent reasonableness when the FMR is decreased by 

5 percent or more. However, a sizable minority of agencies (44 out of 105) reports that they conduct rent 

reasonableness determinations at annual recertification, even though this goes beyond HUD’s basic 

requirements. 

Final Report June 20, 2001 54 
V. Rent Reasonableness Determinations 

The operational definition of what constitutes a reasonable rent is another fundamental characteristic of 

a PHA’s approach to the requirement. No matter how elaborate are a PHA’s procedures to control for 

differences between Section 8 units and comparable, unassisted housing, the rent reasonableness 

determination ultimately comes down to a simple question. How do PHAs determine that the contract rent 

is reasonable? 

The housing authorities surveyed for this research indicate that there are three main approaches to 

determining what is a reasonable rent. These different approaches can be described in terms of the extent 

to which the individual PHA staff that assess rent reasonableness can exert their own discretion in making 

a determination. 

At one end of the spectrum there are housing authorities that give full discretion to the staff making the 

comparability assessment. This category includes the two housing authorities surveyed for this research 

that have no formal procedures in place for determining rent reasonableness, relying instead entirely on 

professional judgement. However, this group also includes housing authorities that do have formal 

procedures in place. The study revealed that many agencies that use formal rent reasonableness 

procedures do so in name only. At these housing authorities rent reasonableness procedures ensure 

compliance with the HUD requirement, but the comparability of a Section 8 rent effectively is based on 

a staff person’s experience with the program and familiarity with the community. For example, at one 

small rural PHA that administers less than 100 Section 8 units, rent reasonableness determinations are 

conducted (for regulatory purposes) using an electronic database of comparable units. However, the 

database of comparables includes less than 30 units. And, in practice, if a landlord requests a rent that the 

PHA staff believes is “unreasonable,” this is communicated directly to the landlord before any rent 

reasonableness paperwork is filled out. 

At the other end of the spectrum are housing authorities with procedures that formally define what can 

be considered a reasonable rent, with no (or very minimal) discretion given to staff responsible for 

making the determination. At one large urban PHA, for example, the rent for the Section 8 unit must be 

less than the average rent for the selected comparables, with no exceptions. 

In the middle is a category that incorporates the vast majority of housing authorities at which the ultimate 

definition of reasonable rent is determined at least in part at the discretion of the staff person making the 

determination. The extent to which PHA personnel can exert their discretion varies considerably across 

these agencies. At some authorities, for example, there are formal guidelines in place that require a 

specific justification for any variance from an allowable rent. At others there are looser “rules of thumb” 

that generally apply in determining what is a reasonable rent. What unifies this group of authorities is that 

the ultimate decision on how to define reasonableness rests with the person making the determination. 

It is worth noting that when the entire rent reasonableness process is considered, almost every PHA 

permits some degree of staff discretion. In fact, if a unit-to-unit approach is used, it may be easier for 

PHA personnel to exert discretion in the identification of comparable units than in the final determination 

of reasonableness. For example, while it is true that the PHA cited above only certifies a rent as 

reasonable if it is less than the average rent for comparable units, this formal requirement fails to capture 

other points of discretion in the agency’s procedures. If there is a significant rent range among the 

comparable units selected from the agency’s standing rent reasonableness database, then the responsible 

staff member can request a secondary survey of comparable units in the Section 8 unit’s neighborhood. 

Furthermore, like several other authorities surveyed for this research, the agency will allow the landlord 

to provide comparables that justify the proposed Section 8 rent. 

There are, therefore, several points at which housing authorities can and do exert discretion in 

determining rent reasonableness. This means that units are not unnecessarily excluded from the program 

if, for example, there is valid reason for a Section 8 unit to have a rent higher than its comparables. But 

it also means that PHA personnel may back into a determination through their interpretation of what 

Final Report June 20, 2001 55 
V. Rent Reasonableness Determinations 

constitutes a “reasonable” rent or by their selection of comparables that will support a proposed rent. A 

PHA’s success in ensuring that Section 8 rents are reasonable depends not only on the formal rent 

reasonableness policies and procedures the agency has in place, but on the rigor with which such policies 

and procedures are implemented. 

Documenting Rent Reasonableness 

Although it will not have a direct impact on the comparability of Section 8 rents, documentation is the 

most important component of a PHA’s procedures in terms of being able to demonstrate compliance with 

the requirement. 

The study examined how housing authorities demonstrate that they test for rent reasonableness and found 

that virtually all housing authorities (100 out of 105) use some type of standard form to document their 

rent reasonableness determinations. These forms typically document the Section 8 unit’s address and 

information (rent, gross rent, number of bedrooms, unit type, etc.) as well as a specified number of 

comparable units and their information. The forms frequently also document whether or not the Section 

8 unit rent is certified as reasonable, with the signature of the PHA staff person attesting to this 

determination. 

With the use of electronic databases to maintain information of unassisted housing, it is quite common 

for rent reasonableness documents to be computer-generated. However, all of the housing authorities that 

use such systems indicated that they also keep a hard copy of the rent reasonableness documents in every 

case file. 

Changes in Rent Reasonableness Procedures 

The final noteworthy aspect regarding housing authorities’ procedures for determining rent 

reasonableness is the clear shift that is underway in how housing authorities go about meeting the 

requirement. 

The survey indicated that almost 8 out of every 10 housing authorities (83 out of 105) have either updated 

their procedures over the last 2 years or are currently planning changes to their procedures. Only 22 of 

the surveyed housing authorities did not report any changes over the same period, and even among this 

group several housing authorities reported that they had made changes shortly before the period in 

question. 

What is driving so many housing authorities to change their procedures for determining rent 

reasonableness? On the surface, most housing authorities cite concerns about the accuracy of their 

determinations and the efficiency with which they can make determinations as the reasons for updating 

their procedures. However, it is clear from the respondents’ own comments that the changes are in large 

measure being driven by SEMAP as well as specific HUD audit findings related to rent reasonableness. 

With additional scrutiny from HUD, it is becoming more important for authorities to have a system in 

place that is at once defensible from a programmatic standpoint and, due to the extra demands of 

complying fully with the requirement, more efficient. 

The types of solutions that are being implemented reflect these driving factors. For example, many 

housing authorities have implemented new procedures that will for the first time explicitly account for 

all of the comparability criteria required by HUD. Such procedures include some variants of a points 

system. Many agencies also have pursued a technical solution by purchasing commercial software that 

will support rent reasonableness determinations. Such software typically includes a database shell into 

which housing authorities can enter information on unassisted units, a module for comparing the proposed 

Final Report June 20, 2001 56 
V. Rent Reasonableness Determinations 

Section 8 rent with rents for comparable unassisted units or unit categories, and a module for generating 

documents to certify that the rent is reasonable. These systems usually incorporate all of HUD’s 

comparability criteria, with the added benefit of promising greater efficiencies. 

In the context of these changes, it is significant that many housing authorities expressed concern about 

the lack of detailed guidance from HUD as to what kinds of solutions will satisfy the rent reasonableness 

factor under SEMAP. Section E revisits this issue and others related to the housing authorities’ 

recommendations for changes to the requirement. 

C. Housing Authority Practice 

The previous section shows that housing authorities almost universally have formal guidelines in place 

for determining rent reasonableness. This section examines the extent to which the housing authorities 

actually apply these policies and procedures. 

PHA staff may face considerable hurdles and disincentives in implementing rent reasonableness. This is 

especially likely, perhaps, if personnel lack adequate training on the requirement, if they are not held 

accountable for the accuracy of rent determinations, or if they are under pressure to accept units for the 

Section 8 program due to owner reticence or tight rental market conditions. In practice, therefore, housing 

authorities may view the rent reasonableness requirement more as a regulatory hurdle than as a tool for 

ensuring the equity and efficiency of the Section 8 program. 

The study investigated two broad questions related to PHA implementation of the rent reasonableness 

requirement. First, are housing authorities actually conducting rent reasonableness determinations? And, 

second, are they doing so according to their own procedures and as required by HUD? These questions 

are answered below using information gathered through the project staff survey and abstracted from case 

files for a nationally representative sample of Section 8 certificate and voucher units. 

Incidence of Rent Reasonableness Determinations 

The study uses a simple benchmark to measure the incidence of rent reasonableness determinations— 

whether or not a PHA has conducted at least one rent reasonableness determination on the unit currently 

occupied by a Section 8 household, as demonstrated by case file documentation. 

This is, in fact, a minimal standard. Housing authorities are required to conduct a rent reasonableness 

determination at lease-up, every time the owner of Section 8 unit requests an increase in rent, if there is 

a 5 percent decline in the published Fair Market Rent, and if directed to do so by HUD. Housing 

authorities also have the discretion to assess rent reasonableness at other times and, as discussed in 

Section B above, many opt to do so at annual recertification regardless of other circumstances. It is not 

uncommon, therefore, for housing authorities to conduct more than one rent reasonableness determination 

for a single unit. The study did not attempt to determine how many rent reasonableness determinations 

had been made for a particular unit, only whether or not any determination had been made. 

As Exhibit V-5 indicates, an estimated 89 percent of Section 8 certificate and voucher units meet the 

standard applied here, or approximately 1.39 million out of the total 1.56 million units nationwide. This 

rate is relatively high; however, it does not meet the 98 percent target established by the SEMAP Final 

Rule. 

Final Report June 20, 2001 57 
V. Rent Reasonableness Determinations 

Exhibit V-5


Incidence of Rent Reasonableness Determinations


Status Number of Units Percent 

At least one determination documented 1,391,636 89% 

No determination documented 136,011 9% 

Status not determinable 29,898 2% 

Total* 1,557,544 100% 

Source: Derived from Project Staff Survey and Review of Housing Authority Files 

* Total does not equal sum due to rounding. 

The study found that an estimated 9 percent of Section 8 certificate and voucher units do not have any 

documentation of a rent reasonableness determination. It should be noted, however, that the absence of 

documentation for a rent reasonableness determination does not necessarily indicate that no determination 

was made. Instead, the absence of documentation may indicate that PHA staff failed to document a rent 

reasonableness assessment appropriately. 

The status of the remaining 2 percent of cases was undeterminable because the PHA�� �������������

not require documentation (or because we were unable to ascertain whether the PHA�� ����������

require documentation). 

Implementation of Determinations 

While the study reveals a relatively high incidence of rent reasonableness determinations, it also shows 

there are numerous irregularities in the implementation and documentation of the determinations. Taken 

alone, none of these anomalies involve a majority of all tenant-based Section 8 units. Nor do they 

necessarily indicate a failure to meet HUD’s requirements. However, taken together, they underscore the 

flexibility that housing authorities exert in carrying out reasonableness determinations and the possibility 

that current procedures are not effectively guarding against excessive rents. 

The timeliness of a rent reasonableness determination is clearly important in ensuring the reasonableness 

of Section 8 rents in comparison to rents for unassisted, comparable housing, especially in volatile 

housing markets. The more time that has elapsed since the most recent rent reasonableness determination, 

the less likely it is that the “reasonable” Section 8 rent will be truly comparable with similar unassisted 

units. In particular, out-of-date assessments may pose a problem if a Section 8 unit is located in a 

neighborhood that sees a decline in rents or if the rent for a Section 8 unit subsequently increases without 

a review for reasonableness. 

The study reveals that in a majority of the units where a rent reasonableness determination was made, the 

most recent determination was made after lease-up (57 percent). Furthermore, as shown by the top panel 

on Exhibit V-6, in two-thirds (66 percent) of the units the most recent rent reasonableness determination 

was made within the last year. For some units, however, the time elapsed since the most recent rent 

reasonableness determination is much longer, with 13 percent not having a determination made for more 

than 2 years. The maximum time that had elapsed since the most recent rent reasonableness determination 

was 14 years. 

Final Report June 20, 2001 58 
V. Rent Reasonableness Determinations 

Exhibit V-6


Time Elapsed Since Most Recent Rent Reasonableness Determination


Time Period Number of Units Percent

All Housing Authorities 

Less than 1 year 913,152 66% 

1 to 2 years 161,779 12% 

More than 2 years 184,965 13% 

Insufficient information 131,740 10% 

Total* 1,391,636 100% 

Authorities that Conduct Determinations at Annual Recertification 

Less than 1 year 490,184 77% 

1 to 2 years 62,400 10% 

More than 2 years 31,476 5% 

Insufficient information 50,310 8% 

Total* 634,370 100% 

Source: Derived from Project Staff Survey and Review of Housing Authority Files. 

*Excludes cases with no documented rent reasonableness determination. 

Forty-three percent of the housing authorities surveyed for this research(see Section B) reported that they 

conduct rent reasonableness determinations as part of the regular, annual recertification process. This 

local policy, which goes beyond HUD’s minimum requirements, may guard against the possibility of 

Section 8 rents not keeping pace (either up or down) with comparable, unassisted housing. But, do 

housing authorities actually implement this policy? Exhibit VIII-6 also presents information on the time 

elapsed since the most recent rent reasonableness determination at housing authorities that reported they 

conducted determinations every year. As indicated, the share of units that have had a rent reasonableness 

determination in the last year (77 percent) is greater for this group of housing authorities than among the 

full sample. However, there is still a significant minority of units (15 percent) for which the local, annual 

standard was not met. Furthermore, the figures in the bottom panel on Exhibit VIII-6 do not include an 

estimated 36,000 units at such housing authorities for which there was no documented rent 

reasonableness determination at all. 

Rent reasonableness determinations may not occur exactly according to the schedule called for under 

locally developed PHA procedures, but do they occur when they are supposed to under HUD’s 

regulations? In particular, do they occur when there is a rent increase? Exhibit V-7 explores the timing 

of the most recent rent reasonableness determination relative to the most recent rent increase. 

Final Report June 20, 2001 59 
V. Rent Reasonableness Determinations 

Exhibit V-7 

Timing of Most Recent Rent Reasonableness Determination 

Relative to Most Recent Rent Increase 

Rent Reasonableness Determination: Number of Units Percent 

More than 4 months before increase date 165,055 20% 

Up to 4 months before increase date 491,026 58% 

After increase date: 

Determination at Annual Recertification 45,10 5% 

No Determination at Annual Recertification 87,005 10% 

Insufficient information 58,108 7% 

Total* 846,299 100% 

Source: Derived from Review of Housing Authority Files


*Excludes cases with no rent increase since lease-up or no documented rent reasonableness determination.


For 58 percent of these units, the most recent rent reasonableness determination occurred in the same 

month as the rent increase or up to four months before the rent increase took effect. Some housing 

authorities begin the annual review process 120 days prior to the anniversary date. Therefore, any test that 

occurs up to four months prior to the effective date of a rent increase probably meets HUD’s requirement 

that a comparability assessment be conducted prior to an increase in rent. 

For approximately one third of all units, though, the timing of the most recent rent reasonableness 

determination does not appear to meet HUD’s requirement. As Exhibit VIII-7 indicates, one in every five 

units (20 percent) last had a rent reasonableness determination more than 4 months before the rent 

increase. For these units, the rent reasonableness determination probably dates from a prior rent increase 

or from lease-up. For another 10 percent of units, the most recent rent reasonableness determination 

occurred after the most recent rent increase took effect, and therefore after the point at which a 

determination could be expected to effectively guard against unreasonable rent. 

For an additional 5 percent of units, the most recent rent reasonableness determination also occurred after 

the most recent rent increase. However, these units are managed by agencies that conduct a rent 

reasonableness determination at every annual recertification, so the most recent determination likely 

occurred in the absence of a rent increase. 

An examination of other aspects of the rent reasonableness determinations for the sampled units reveals 

additional procedural anomalies that, like the irregularities in timing, occur for a minority of units. For 

example, the study found that rent reasonableness documentation for many units is not filled out 

completely, not filled out according to PHA policy, or otherwise in error. In some instances, the 

documentation for rent reasonableness is not filled out at all. Because of the lack of conformity in the 

forms used to document rent reasonableness, the type of documentation irregularities that occur varies 

considerably. Exhibit V-8 highlights the incidence of two specific types of anomalies that occur across 

different housing authorities—namely, disparities between the number of comparables required by the 

PHA and the number of comparables documented in the case file, and the absence of signatures certifying 

to the comparability of the subject Section 8 unit’s rent. 

Final Report June 20, 2001 60 
V. Rent Reasonableness Determinations 

Exhibit V-8 

Documentation Anomalies 

Number of Units 

44,199 

480,936 

273,890 

799,025 

Percent 

Number of Comparables: 

More than expected 

Same as expected 

Less than expected 

Total* 

6% 

60% 

34% 

100% 

94% 

6% 

100% 

7% 

79% 

14% 

100% 

Documentation Signatures: 

Signed 

Not Signed 

Total** 

930,728 

64,132 

994,860 

Variance Between Documented Reasonable Rent and Contract Rent 

Reasonable rent > contract rent 47,006 

Reasonable rent = contract rent 534,726 

Reasonable rent < contract rent 95,535 

Total*** 677,266 

Source: Derived from Project Staff Survey and Review of Housing Authority Files. 

*Excludes cases at housing authorities that do not use a unit-to-unit rent reasonable determination approach, 

cases with no documented rent reasonableness determination, and cases for which there was insufficient 

information to make a comparison. 

** Only includes cases where photocopied documentation indicated that PHA signature was required. 

***Only includes data with photocopied rent reasonableness form indicating reasonable rent. 

The compilation of information on comparables is one of the critical components in conducting a unit-tounit determination of rent reasonableness. The unit-to-unit method, which is used by a majority of the 

housing authorities surveyed for this research, is premised on an assumption that a carefully selected 

sample of similar unassisted units can provide a valid benchmark for determining rent comparability. 

Housing authorities that use this method were asked to report the minimum number of comparables that 

they require in conducting a unit-to-unit assessment of rent reasonableness. As Exhibit VIII-8 indicates, 

the documentation for a majority of the rent reasonableness determinations conducted by these housing 

authorities had the same number of comparables as stated in their policy (60 percent), or more than 

expected (6 percent). However, housing authorities failed to use the number of comparable units required 

by their own procedures in about a third (34 percent) of the cases for which there was enough information 

to make an assessment. Because the samples used to determine comparability in a unit-to-unit approach 

are usually small, the failure to document the number of comparables stipulated in PHA policy raises a 

question about the validity of the housing authorities’ assessment results. 

Another documentation anomaly that occurs across different housing authorities relates to the final 

certification of a “reasonable” rent. At many housing authorities, the documentation for rent 

reasonableness consists of worksheets used to record information on the Section 8 unit and unassisted 

housing plus a separate sheet or statement that the responsible staff person must sign to indicate that the 

proposed rent is in fact reasonable. The study shows that the staff person�� ���������������������������

approximately 6 percent of the cases where such a certification is required. The extent of this type of 

oversight simply may reflect the short-cuts that PHA staff take in a time-pressured work environment 

more than any substantive shortfall in the comparability of the subject Section 8 rents. On the other hand, 

it does indicate that housing authorities overlook an important control point in the rent reasonableness 

process, as prescribed by their own procedures. 

Final Report June 20, 2001 61 
V. Rent Reasonableness Determinations 

The final facet of the rent reasonableness process examined here pertains to the variance between the 

reasonable rent as documented by the public housing authorities and the actual contract rent for Section 

8 certificate and voucher units. A comparison of the documented reasonable rent and the contract rent 

for the sampled units shows that the contract rent is less than or equal to the reasonable rent in 86 percent 

of all cases for which sufficient information was available—see the bottom panel on Exhibit VIII-8. In 

other words, the certified “reasonable” rent is, as required by HUD, the same as or greater than the current 

contract rent. 

The contract rent for Section 8 units is greater than the documented “reasonable” rent in 14 percent of 

the cases. This may occur when the reasonable rent documented in the case file is derived from 

comparable units that differ significantly from the Section 8 unit—for example, if 4-bedroom units are 

used to determine the reasonable rent for a 5-bedroom Section 8 unit. However, the Section 8 rent also 

will exceed the documented reasonable rent if, as described above, housing authorities fail to assess rent 

reasonableness every time there is a rent increase. In other words, the actual Section 8 contract rent may 

start out the same as the documented reasonable rent, but the two rents will start to deviate if there are 

subsequent rent increases that are not subject to a rent reasonableness determination. If rents for 

comparable unassisted housing do not keep pace with the rent for the Section 8 unit, then Section 8 

contract rent will be “unreasonable.” 

In approximately 7 percent of the cases the contract rent for the Section 8 unit is less than the amount of 

rent approved as a reasonable rent through the rent reasonableness determination. Clearly it is 

advantageous for both the PHA and the assisted household to pay less in contract rent. After all, a rent 

is only “unreasonable” if it exceeds the rent for similar, unassisted housing. But this difference between 

the approved and executed contract rent begs a question about the housing authorities’ rent 

reasonableness procedures. Why would a landlord request a contract rent that is less than the amount the 

PHA itself has determined to be a “reasonable” rent? Section D discusses some of the reasons why 

contract rent may be less than the documented “reasonable” rent in reviewing the findings of the study’s 

comparability assessment. 

None of these anomalies is significant enough to conclude that the contract rents for Section 8 units are 

unreasonable—there could be a valid reason for every type of anomaly. At a minimum, though, the 

pervasiveness of such anomalies underscores that housing authorities allow considerable flexibility in 

implementing the rent reasonableness requirement. They also show that even when housing authorities 

conduct rent reasonableness determinations, they may do so without strictly conforming to the 

requirements laid out by HUD or their own procedures. 

D. Rent Comparability 

The task’s final objective is to assess the outcome of local PHA procedures and practice. Regardless of 

how housing authorities comply with the rent reasonableness requirement, are the rents charged for 

Section 8 certificate and voucher units, in fact, reasonable in comparison to rents in the private, unassisted 

market? 

The study examined the comparability of rents for a subset of the full sample of Section 8 certificate and 

voucher households. For policy and methodological reasons, the study attempted to exclude three types 

of units from the reasonableness assessment: units located in properties with project-based subsidies (for 

example, under the HOME Investment Partnerships Program) that could result in below-market contract 

rents; units that consist of an owner-occupied manufactured home on a rented space or pad; and units 

located in an independent group residence, congregate housing, or shared housing. Excluding these unit 

categories produced a universe for the reasonableness assessment of an estimated 1,451,208 units, or 93 

percent of all Section 8 certificate and voucher units included in the main quality control study. 

Final Report June 20, 2001 62 
V. Rent Reasonableness Determinations 

Data collection and analysis for the comparability assessment involved several steps. ORC/Macro field 

data collectors compiled information on the key characteristics of the sampled units from PHA files and 

through in-person household interviews. Real estate inspectors then conducted a survey of the 

neighborhoods in which the Section 8 units are located, and selected and collected data on three 

comparable units for each sampled Section 8 unit. The data on the comparable units was used to estimate 

the market rent for each Section 8 unit, using standard real estate appraisal techniques.17 This estimate 

was then used to determine the extent to which the Section 8 rents deviate from the market rent for 

comparable units. 

Summary of the Findings. The results of this analysis indicate that a majority of Section 8 units have 

a contract rent that is less than the estimated market rent for similar, unassisted units. In fact, the study 

found that more than 7 out of every 10 (71 percent) Section 8 units have a contract rent that does not 

exceed estimated market rent. 

While these 71 percent of Section 8 units can be assumed, by definition, to have a “reasonable” contract 

rent, it does not necessarily follow that the balance of the units have “unreasonable” rents. The study did 

not attempt to define how much a Section 8 rent can exceed estimated market rent before it is 

unreasonable, due to the pitfalls inherent in setting a nominal standard for rent reasonableness. Instead, 

the study examined the degree to which Section 8 rents exceeded reasonableness thresholds. 

Figure V-1 presents the percent of units by the difference in Section 8 and estimated market rents in 5 

percent intervals. 

Figure V-1 

Estimated Market Rent as a Percentage of Section 8 Leased Rent 

25 

20 

15 

10 

5 

0 

17 A standard real estate appraisal determines the rent (or sales price) that the market will bear for a particular housing 

unit by comparing that unit to a small sample of similar units in the same or similar location. Differences between the 

subject unit and comparables are accounted for by adjusting the rent (or sales price) for the comparable units. In 

administering the comparability assessment for this study, every attempt was made to match sample Section 8 units and 

comparable, unassisted units on several key characteristics’ unit type, number of bedrooms, square footage, location, 

construction type, and age. To derive an estimated market rent, a dollar adjustment was then added to each comparable’s 

contract rent based on the extent to which the comparable unit differed from the Section 8 unit on these and other 

characteristics, including: interior and exterior condition, owner-provided appliances and amenities, parking, ownerprovided services, and access to neighborhood services. Estimated market rent is the average (mean) of the adjusted rents 

for each Section 8 unit’s three comparables. The adjustments employed for this analysis varied by region based on a 

market assessment conducted by CountryWide Inspections, the inspection subcontractor. In addition to the characteristics 

itemized above, rents were adjusted to account for differences between the Section 8 and comparable units in owner-paid 

utilities. Utility adjustments were made using each PHA’s utility allowance schedule. 

Final Report June 20, 2001 63 
V. Rent Reasonableness Determinations 

Section 8 Rents in Excess of Market Rents. Exhibit V-9 shows the comparability of Section 8 rents 

using two types of reasonableness thresholds. The exhibit’s top panel shows the extent to hich Section 

8 rent exceed market rent using thresholds defined by the percent difference between the Section 8 and 

estimated market rents. As the exhibit indicates, less than one-third (29 percent) of all Section 8 units in 

the comparability study universe have rents that exceed estimated market rent—or 415,934 units 

nationwide. For about a third of these units (or 9 percent of all units), the monthly rent variance is 5 

percent or less of the estimated market rent. Approximately one out of every 25 units (or 4 percent of all 

units) has a rent that exceeds estimated market rent for similar, unassisted housing by more than 25 

percent. 

Exhibit V-9


Share of Section 8 Units Exceeding Alternative Reasonableness Thresholds


Section 8 Rent Exceeds Estimated Market Rent By: Number of Units Percent 

Percent Difference 

0-5% 125,944 9% 

6 -10% 103,452 7% 

11-15% 60,412 4% 

16-25% 70,605 5% 

More than 25% 55,521 4% 

Total 415,934 29% 

Dollar Difference 

$0-$25 114,853 8% 

$26-$50 104,529 7% 

$51-$75 67,193 5% 

$76-$100 76,304 5% 

More than $100 53,055 4% 

Total 415,934 29% 

Source: Derived from Survey of Comparables 

The bottom panel on Exhibit V-9 presents the findings of the reasonableness survey using a second set 

of thresholds based on the dollar variance between the Section 8 and estimated market rent. These 

findings echo the pattern shown for the percent difference in rents. Of those units that have Section 8 

rents that exceed estimated market rent, a majority has rents that exceed market rent by $50 or less per 

month. An estimated 4 percent of all Section 8 units in the comparability study universe have a rent that 

exceeds estimated market rent by more than $100. 

Section 8 Rents Below Market Rents. One unexpected finding from the reasonableness assessment 

merits further discussion—that is, the extent to which the study shows that Section 8 rents fall short of 

the estimated market rent for similar unassisted housing. 

The study found that the average monthly contract rent for a Section 8 certificate or voucher unit is $95 

less than the estimated market rent for a comparable, unassisted unit, or an average of 10 percent less. 

Furthermore, as illustrated by Figure V-1 above, approximately one in every five Section 8 units (20 

percent) were found to have a rent more than 25 percent below estimated market rent. 

This disparity begs a question regarding landlord participation in the program, namely: Why would 

landlords accept rents under the Section 8 program that are so much less than the rents supported by 

similar units in the unassisted market? To help address this question, follow-up calls were made to the 

Final Report June 20, 2001 64 
V. Rent Reasonableness Determinations 

estimated market rents.18 This effort highlighted several program-related issues that help explain the 

difference in rents.19 

Factors That Impact the Difference in Section 8 and Estimated Market Rents. Several factors that 

impact the difference in the Section 8 and estimated market rents are discussed below. Exhibit VIII-10 

summarizes these factors and provides the average amount of rent for households within the factor 

subcategories. 

Tenure. The study reveals that the longer a unit is in the program, the more likely it is for rent to deviate 

either below or above market rent. As indicated by Exhibit V-10, the longer a household has used Section 

8 assistance for a particular unit, the greater the average disparity between the Section 8 and estimated 

market rents. Rent for units where the household has used Section 8 for less than a year is, on average, 

$71 less than estimated market rent compared to a difference of $135 for units that have been rented 

under the program for more than five years. This effect is amplified by focusing just on Section 8 units 

where rents are significantly less than market rent. Among these units, units that have been rented by the 

same Section 8 tenant for more than 5 years have rents that are on average $245 less than the market rent 

for similar unassisted units. 

However, the study also reveals that units that have rented under Section 8 for more than 5 years are more 

likely to have rents that significantly exceed market rent than units that have been in the program a shorter 

period of time. Approximately one-fourth (27 percent) of the units that have rented under the program 

for more than 5 years were found to have rents that exceeded market rent by at least 5 percent, compared 

to just 16 percent of units that have been in the program less than a year. In other words, although a 

longer period of tenure under the program on average results in greater rent discounts, it also increases 

the likelihood that Section 8 rents will be “unreasonable.” 

Head of Household Type. Another reason why a landlord might be willing to provide a rent discount 

under the Section 8 program pertains to the characteristics of the assisted households. For example, 

landlords may be willing to forgo a portion of the rent they could demand in the unassisted market if they 

can use the program to rent their unit to an elderly household. In comparison to other households, 

especially families with children, elderly households are perceived to cause less physical wear and tear 

on a unit, and are thought to be less likely to cause problems with other tenants or neighbors. As indicated 

by Exhibit V-10, the study shows that the greatest disparity between Section 8 and estimated market rent 

exists for units with an elderly (non-disabled) head of household. The difference between the Section 8 

rent and market rent is, on average, almost twice as great for elderly households (-$149) as it is for nonelderly, non-disabled households (-$87). 

Exhibit V-10 also shows, however, that the rent discount landlords are willing to make for units occupied 

by elderly Section 8 recipients does not apply evenly to all elderly households. In fact, if a Section 8 

household is headed by an elderly person who is also disabled, then their unit on average rents for $80 

less than market rent—the smallest disparity in rents among any of the demographic groups listed. This 

suggests that combination elderly and disabled households may require unit characteristics and housing 

services that command higher rents than for non-elderly and non-disabled households. The rent premium 

that is required for elderly and disabled households is illustrated further when examining data just for 

Section 8 units where the rent exceeds market rent by 5 percent or more. Among this group of units, the 

18 The follow-up calls focused on the units where the Section 8 rent was at least 25 percent less than the estimated 

market rent. Information was compiled on 64 of the 85 sampled Section 8 units that fell in this category. 19The follow-up calls also revealed methodological issues that help explain the disparity between Section 8 and 

estimated market rent for at least a subset of the comparability sample. For example, housing authorities indicated that 

some of the units in the sample did have project-based subsidies, and therefore a below-market rent even though the study 

design called for the exclusion of such units. This applied to approximately 1 in every 5 of the units examined through 

follow-up calls. 

Final Report June 20, 2001 65 
--

V. Rent Reasonableness Determinations 

greatest disparity in Section 8 and market rent occurs for combination elderly and disabled household— 

$104 per month. 

Exhibit V-10


Disparity in Monthly Section 8 and Estimated Market Rents


Average Dollar Difference* 

Units with Rents 5% 

or More Below 

Comparable Value 

Units with Rents 5% 

or More Above 

Comparable Value 

All Units 

(100 % of Sample) 

Dollars Percent Dollars Percent 

All Units -$95 $179 62 % $76 22 % 

Tenure (Length of time in current unit with Section 8 assistance.) 

Less Than 1 Year 

1 to 5 Years 

More Than 5 Years 

Head of Household Type 

Elderly


Disabled


Elderly & Disabled


Not Elderly or Disabled


Type of Assistance 

Certificate 

Voucher 

-$71 -$132 61% $65 16% 

-$107 -$175 67% $61 20% 

-$135 -$245 63% $69 27% 

-$149 -$221 69% $53 8% 

-$101 -$179 67% $71 26% 

-$80 -$204 56% $104 32% 

-$87 -$169 60% $74 21% 

-$120 -$216 63% $83 20% 

-$66 -$134 61% $69 24% 

Number of Rent Reasonableness Determinations 

At least One Determination -$93 -$177 62% $80 22% 

No Determination -$126 -$200 68% $51 21% 

Status Undeterminable -$77 -$154 55% $28 37% 

Method for Determining Rent Reasonableness 

Unit-to-Unit 

Combination 

Unit-to-Market 

Professional Judgement 

Unit Type 

Single Family Detached 

Duplex-Row 

Manufactured Home 

Apartment Walk-Up 

Apartment Elevator 

Neighborhood Housing Cost 

Relatively Low Cost Submarket 

Relatively High Cost Submarket 

Source: Table 17, Appendix D 

-$85 -$173 59% $78 24% 

-$119 -$119 100% 0% 

-$143 -$217 71% $78 14% 

-$25 -$110 39% $28 61% 

-$76 -$159 62% $92 24% 

-$122 -$208 64% $59 22% 

-$53 -$72 78% $47 8% 

-$82 -$162 60% $70 23% 

-$197 -$362 59% $130 12% 

-$14 -$81 47% $77 32% 

-$244 -$274 90% $55 3% 

*Section 8 rent minus estimated market rent--negative values indicate that the estimated market rent exceeds the Section 8 rent. 

Final Report June 20, 2001 66 
V. Rent Reasonableness Determinations 

Type of Assistance. While landlords may have their own reasons for offering a rent discount (or 

demanding a rent premium) under particular circumstances, there are also a number of provisions in the 

Section 8 program that may have depressed rents, historically. In particular, housing authorities cite the 

constraint of the FMR and the Annual Adjustment Factor (AAF) for units subsidized under the premerger certificate program as a reason why Section 8 rents are out-stripped by market rents. Under the 

certificate program, the rent-to-owner (or contract rent) for units occupied by assisted households was 

constrained (except under certain circumstances) by the FMR. In certain markets, this may have had the 

effect of keeping Section 8 rents under the rent that could be charged for similar, unassisted units in the 

same location. Similarly, the AAF that governed rent increases under the pre-merger certificate program 

may have fallen short of increases in rent for unassisted unit rents. 

Housing authorities cite the constraints posed by the FMR and AAF as a major reason for the disparity 

between Section 8 and market rents for about a quarter (23 percent) of the units examined through followup calls. These constraints are also illustrated by the reasonableness assessment. As indicated by Exhibit 

V-10, the rent for certificate units is, on average, $120 less than the estimated market rent for assisted 

units compared to $66 for voucher units. 

It should be noted, however, that there may be some markets where the application of the FMR and AAF 

in the certificate program have had the opposite effect. Some markets may have unassisted rents (and rent 

increases) that, historically, have been outstripped by the FMR and AAF. Under such circumstances, the 

rent for Section 8 certificate units may be expected to exceed estimated market rent. This pattern is 

illustrated by units that rent for an amount 5 percent or more greater than the market rent, there is a 

greater disparity for certificate holders ($83) than for voucher holders ($69). 

The 40 percent cap on the share of a household’s income that can go to pay initial rent under the merged 

Section 8 program may also constrain contract rent. While this requirement was implemented less than 

a year before the data collection for this study and was not cited by the housing authorities contacted 

through follow-up calls, it is likely to prompt landlords to tradeoff contract rent with the desirability of 

particular tenants, like the other provisions highlighted. In other words, it is likely to become one in a 

complex mix of factors, including the rent reasonableness requirement, that must be weighed by the 

tenant, landlord, and PHA in determining the contract rents that will be paid under the tenant-based 

Section 8 program. 

Number of Rent Reasonableness Determinations. The study found that a majority of Section 8 units 

(89 percent) have had at least one rent reasonableness determination (see Exhibit V-5, above). But, what 

affect does the application of the rent reasonableness requirement have on rent comparability? Exhibit 

V-10 shows that among those units where the Section 8 rent exceeds the estimated market rents by 5 

percent or more, the disparity in rents is greatest for units where there has been at least one rent 

reasonableness determination. At face value this finding seems to be counter-intuitive, but it may point 

to a couple of issues. First, that rent reasonableness status is a minimal standard, as applied in this study— 

there may have been “at least one determination,” but that determination may be out-of-date or otherwise 

in error. Second, it may point to self-regulation by PHAs in implementing the requirement, according to 

the “need” to verify rent reasonableness based, say, on their knowledge of the local market. 

Method for Determining Rent Reasonableness. The study’s examination of rent reasonableness 

procedures started with the basic method that PHAs use to determine reasonableness. The report divides 

PHAs into three main groups—those that directly compare the Section 8 rent to the rent for a discrete 

number of unassisted units; those that compare Section 8 rents to average market rents for similar units, 

and those that use a combination of methods. There also were two PHAs that did not have any formal 

method for determining rent reasonableness—in other words, they depend solely on professional 

judgment. (See Exhibit V-2, above.) Among units where Section 8 rents exceed market rents by 5 percent 

or more, there is no distinction between the PHAs that only use a unit-to-unit or unit-to-market method. 

Under either method, Section 8 rents exceed market rent by an average of $78. However, it is more likely 

for units administered by PHAs that only use a unit-to-unit method to have rents that significantly exceed 

Final Report June 20, 2001 67 
V. Rent Reasonableness Determinations 

market rent. Approximately one-fourth (24 percent) of the units overseen by this type of agency had rents 

that exceeded market rent by at least 5 percent, compared to just 14 percent of units at agencies that used 

just a unit-to-market method. It is also noteworthy that none of the units at housing authorities that use 

a combination of methods have rents that exceed market rents by 5 percent or more. This suggests that 

to effectively guard against unreasonable rents, housing authorities (and HUD) need to recognize that no 

single method can be expected to work for all of an agency’s units. 

Unit Type. Unit type is important to rent reasonableness because it is one of the key characteristics that 

will determine what constitutes similar unassisted housing. Landlords may be more willing to discount 

the rent for certain types of units in return for a guaranteed stream of income (possibly because of the 

types of tenants that these units attract). Also, it may be more difficult to effectively monitor rent 

reasonableness for certain unit types. 

Exhibit V-10 reveals an interesting pattern for elevator apartments. On the one hand, elevator apartments 

have the lowest rents in comparison to market rent among those units that have relatively low rents. On 

average, the Section 8 rent for this type of unit is $362 less than comparable rent. On the other hand, 

elevator apartment rents exceed comparable rent by a greater amount than other types of units with rents 

that exceed comparable rent by 5 percent or more. One factor that may be driving this pattern is the 

presence of project-based subsidies. We know from follow-up calls that a portion of the units included 

in the comparability analysis have project-based subsidies. The extent to which such units are in high-rise 

properties may explain why elevator apartment rents should—under certain circumstances--fall so far 

short of comparable rent. Another likely factor is the extent to which Section 8 elderly households—who 

landlords are more likely to provide a rent discount—reside in highrise apartments. 

Neighborhood Housing Cost. Local market conditions for each unit in the comparability study were 

measured using the ratio of comparable rent to the 2000 FMR for a unit of the same size. While the 

comparable rent is a composite market rent for the Section 8 unit, or a snapshot of the rent for a very 

specific submarket, the FMR is an area-wide benchmark pegged by HUD (in 2000) to the 40th percentile 

rent. As defined here, units in relatively low cost submarkets have comparable rents that equal or are less 

than the FMR, whereas units in relatively high cost submarkets have comparable rents that exceed the 

FMR. 

It is not surprising that Section 8 rents fall short of market rent in relatively high cost areas, due to the 

historical limitations on program rents especially in the certificate program. However, given that 

approximately one-third of the units in the comparability study are in relatively high cost areas, the 

disparity between the two types of submarket is striking. 

Units in relatively high cost submarkets almost universally have rents less than comparable rent. Overall, 

such units rent for $244 less than comparable rents. Only 3 percent of such units have rents that exceed 

market rent by 5 percent or more. 

By contrast, rents in relatively low cost submarkets are, on average, very close to comparable rents—as 

indicated, just $14 less than comparable rent. That is not to say, however, that there are not disparities 

between Section 8 and comparable rents in this type of submarket. To the extent that relatively low cost 

submarkets coincide with submarkets with relatively weak housing demand (which they should do over 

time) then landlords may have a greater incentive to participate in the Section 8 program, and possibly 

to offer rent discounts to “good” Section 8 tenants, to ensure a guaranteed stream of income. On the 

otherhand, if the payment standard (or FMR) exceeds the rents in a particular submarket, then there is a 

possibility to charge an unreasonable rent under Section 8 if the PHA is not vigilant. Approximately one 

third (32 percent) of the units in relatively low-cost submarkets, as defined here, have rents that exceed 

comparable market rent by 5 percent or more. 

Final Report June 20, 2001 68 
V. Rent Reasonableness Determinations 

E. Recommendations 

This section discusses policy recommendations offered by participating housing authorities and derived 

from the study findings, and suggests changes to methodology for the follow-up rent reasonableness 

study. 

PHA Project Staff Recommended Changes 

As part of the Project Staff Survey, representatives from the sample of housing authorities were asked 

what recommendations they would make to change the rent reasonableness requirement. Broadly 

speaking, the housing authorities’ recommendations fall in two categories���������������������������������

of eliminating the requirement and, second, regarding possible improvements in how the requirement is 

implemented. 

Elimination of the Requirement. Many of the surveyed housing authorities voiced support for the 

requirement and acknowledged its importance to the operation of their tenant-based Section 8 program, 

but other authorities, some quite bluntly, urged HUD to consider its elimination. However, the arguments 

posed by those agencies interested in eliminating the procedure varied: 

•	 Several agencies said that the requirement is no longer required with the recent reforms to the tenantbased Section 8 program. Specifically, agencies indicated that, together with the FMR, the new 40 

percent of income cap on tenant payments provides a sufficient guard against excessive rents. Others 

voiced a more philosophical belief that with the shift to an entirely voucher-based system, the rent 

reasonableness procedure interferes with the legitimate choice assisted households should have in 

deciding how much they pay in rent. If the principle of the voucher system is to subsidize qualified 

households up to a certain payment standard, and then require the household to pay the difference in 

rent, then why not allow the household to pay as much as they wish? 

•	 Other agencies voiced opposition for more pragmatic reasons. In particular, agencies argued that the 

benefits of implementing the requirement are outweighed by the requirement’s costs in terms of time, 

staff resources, and scarce administrative funds. This opinion was voiced by agencies in a mix of 

different markets, and of different sizes, but was expressed in particular by those agencies overseeing 

small programs in rural areas where the staff relied in large part on their own discretion in making 

determinations. Why go through the exhaustive effort of documenting rent reasonableness when the 

staff members know their own market and what kinds of rents are reasonable? 

Improvements to the Requirement. Other respondents fell short of recommending elimination of the 

requirement, choosing instead to focus their recommendations on how the requirement could be 

improved. These recommendations fell into three categories: 

•	 Some agencies argued for the simplification of the requirement, often for the same pragmatic reasons 

outlined for elimination of the requirement above. In other words, these agencies view the 

requirement, especially as it is now monitored through SEMAP, as being overly burdensome. 

Individual respondents suggested, for example, that HUD do away with the requirement that specific 

criteria be considered in determining rent reasonableness (in particular, age, square footage, and 

management services). Others suggested that the requirement only be enforced for certain types of 

units and in certain types of markets where the risk of excessive program rents is particularly high. 

Another agency argued that a determination should only be required at lease-up. And several agencies 

recommended that the requirement be implemented with more flexibility. 

•	 On the other hand, several respondents suggested that the best way for HUD to improve the rent 

reasonableness requirement would be to enforce more standardization. Specifically, agencies argued 

that additional guidance is required from HUD, that HUD should develop standardized forms to be 

used in assessing comparability (and integrate these data requirements with existing forms similar to 

Final Report June 20, 2001 69 
V. Rent Reasonableness Determinations 

the request for lease approval), and that the requirement should focus in more detail on unit amenities. 

One agency called for greater consistency in HUD enforcement of the requirement across different 

agencies because the inconsistent application of the requirement causes problems in working with 

landlords who participate in more than one PHA’s program. 

•	 Other housing agencies focused their recommendations on the problem of finding, then collecting 

data on comparable units. Several agencies called for HUD to get more involved in this process by 

conducting surveys for housing authorities or providing satisfactory information from other HUD 

sources, by changing the requirement to put more of the burden on landlords, or by allowing agencies 

to offer landlords an incentive for providing information on comparables. 

Policy Implications 

In light of the study findings on the reasonableness of Section 8 rents, the recommendation for complete 

elimination of the requirement does not have merit. Although the study found that most Section 8 units 

have a contract rent that is considerably less than the estimated market rent, almost 30 percent of all 

Section 8 units have rents that exceed estimated market rent. 

The recommendation for greater discretion in where and how the requirement is implemented may 

warrant further consideration for two reasons: first, because so many Section 8 units do have rents that 

fall short of market rent for similar unassisted units; and, second, because of the degree to which housing 

authorities permit some measure of discretion in making rent reasonableness determinations. The project 

staff survey suggested that many housing authorities go through the process of conducting rent 

reasonableness determinations to meet the regulatory requirement, but in practice rely largely on the 

professional judgment of agency personnel to ensure that rents are not excessive. To the extent that the 

agencies that exercise discretion in this fashion overlap with those in which Section 8 rents fall short of 

market rents, then HUD may want to consider some variability in how the requirement is applied. 

The study findings also support some of the housing authorities’ recommendations regarding 

standardization of the requirement. It is difficult to see how the procedures could be standardized so that 

one size fits all agencies, but HUD could take some reasonable steps to provide program resources and 

technical assistance for those agencies that need additional help. In effect, the study shows that 

standardization is occurring in part because of the demands of SEMAP, and in part because of the 

proliferation of technical assistance, database systems, and other commercial material that support rent 

reasonableness. But several agencies still operate with relatively antiquated systems. Standardization and 

technical guidance should help improve the procedures at such agencies, and standardization should also, 

over the long term, simplify the process of compiling information on comparable units. 

Recommended Changes to the Rent Reasonableness Study 

Recommendations for changes to a follow-up rent reasonableness study include potential improvements 

to the data collection that was carried out under the first study, as well as possible new directions for the 

research. 

All aspects of the data collection for the rent reasonableness study could be improved: 

•	 Telephone Survey—The telephone survey needs to be simplified and focused more tightly on the 

issues of greatest concern to HUD. This study successfully captured the variability of housing 

authorities’ rent reasonableness procedures, but it did not focus in detail on any particular issue or 

type of approach. Having conducted the first survey, the design team will have a much better idea 

about what kinds of issues can be investigated in more depth. In particular, its would be worthwhile 

to focus on the new types of procedures and systems that housing authorities are implementing in 

response to SEMAP. Some field research on PHA procedures may be warranted to supplement 

telephone data collection on types of procedures that are of greatest interest to HUD. 

Final Report June 20, 2001 70 
V. Rent Reasonableness Determinations 

•	 File Abstraction—The abstraction of data should be redesigned to permit a more thorough and 

comprehensive audit of issues related to implementation of the requirement. A careful redesign of the 

documentation form should permit the collection of more useful information with the addition of a 

limited number of data items. Doing so should eliminate the need to photocopy information from the 

case file. In particular it would be useful to trace the rent history for units in the program so that 

housing authorities’ compliance with HUD requirements regarding the timing of rent reasonableness 

determinations can be examined more definitively. It would also be useful to collect more systematic 

information on the comparable units (or unit categories) employed to make rent reasonableness 

determinations, since this is one of the major control points in which PHA personnel can exert their 

personal discretion. These enhancements also would make it easier to interpret findings from the 

comparability assessment. 

•	 Comparability Assessment—Several aspects of the comparability assessment are candidates for 

redesign. One option for improving the quality of information on assisted and unassisted units would 

be to have the inspection subcontractor collect data on both sets of units, instead of the split that was 

used for the current study (however, this option has some logistical and budget implications). The 

next rent reasonableness study also needs to look more closely at the kinds of adjustments that are 

made to arrive at estimated market rent. While adequate for determining the overall reasonableness 

of Section 8 rents, the current study’s use of regional adjustment criteria and PHA utility data could 

be improved by using adjustments tailored better to local market conditions. Finally, the current 

survey reveals that it is necessary to follow-up on the comparability survey to determine the valid 

reasons, if any, for the disparity between the Section 8 rent and estimated market rent. Such a followup effort might involve contact with housing authorities and landlords, whose perspective is largely 

missing from the current study. 

Several new topics are suggested by the current study, which could be addressed to a lesser or greater 

extent by the existing data strategy. For example: 

•	 Do housing authorities that use newer rent reasonableness procedures such as point-based systems 

do a better job than others, all things being equal, in determining rent reasonableness? 

•	 How are housing authorities responding to the ongoing implementation of SEMAP, with its specific 

requirements related to rent reasonableness? 

•	 What is the impact of exception payment standards and FMRs being set at the 50th percentile for rent 

reasonableness in some market areas? 

•	 To what extent do PHA personnel exercise their own professional discretion over rent reasonableness 

determinations? 

• How comparable are housing authorities’ comparables? 

•	 What factors affect the reasonableness of Section 8 rents? Are those agencies that use more discretion 

in determining rents more or less likely to have reasonable rents? What is the impact of other agency 

characteristics, policies, and procedures? How do market characteristics influence rent 

reasonableness? 

•	 What causes landlords to rent units under the program below comparable market rent? What are 

landlords’ opinions about the application of the rent reasonableness requirement? 

The design for the next survey should consider these and other HUD research priorities in the context of 

ongoing changes to the tenant-based Section 8 program. 

Final Report June 20, 2001 71 
VI. Recommendations 

This section discusses recommended changes to the study that will improve the data collection process 

or the quality of the data used in the analysis. Sections A through C provide a general discussion of the 

quality control review process with a summary of specific recommendations. Section D discusses error 

components that could be included in a future study. 

A. The Quality Control Review Process 

Conducting a study to produce national estimates of the errors occurring in the household certification 

and recertification process is a major undertaking that requires many tasks. The following major tasks are 

part of this process: 

� Establishing a uniform set of standards to follow when determining error. 

� Constructing the sampling frame. 

� Selecting the sample from which data should be collected. 

� Obtaining project identification information from HUD field offices. 

� Identifying the sources of information from whom data should be collected. 

� Establishing the time period for data collection. 

� Designing the data collection instruments. 

� Automating the data collection process. 

� Hiring and training data collectors. 

� Collecting the data. 

� Editing the data. 

� Constructing the analysis file. 

� Calculating the QC (i.e., the correct) rent. 

� Determining the amount and sources of error. 

The Quality Control for Rental Assistance Subsidies study conducted by Macro was funded by HUD to 

obtain national estimates of error. While most financial assistance programs, such as TANF, Food 

Stamps, and Medicaid, have been conducting quality control reviews of their programs for years, HUD 

is still in the preliminary stages of developing a quality control system. Conducting a quality control 

review for HUD programs is more difficult than conducting reviews for other programs for two major 

reasons. First, most public welfare programs are administered at the State level. Quality control reviews 

are conducted by the State to identify the error rate, and Federal re-reviews are conducted to verify this 

error rate. Because HUD programs are not administered at the State level and there are thousands of 

PHAs and owners responsible for implementing the (re)certification process, no central or local 

organizations/agencies exist that could easily conduct quality control reviews. Therefore, the entire quality 

control process must be conducted at the Federal level. 

Final Report June 20, 2001 72 
VI. Recommendations 

Secondly, HUD housing subsidies are funded through many different sections of the Federal regulations, 

each of which has different rules regulating the (re)certification process and rent determination. 

Conducting quality control reviews for HUD programs therefore requires reviewers who are familiar with 

several sets of eligibility requirements, policy exceptions, and options. On the other hand, financial 

assistance programs outside of HUD usually follow one set of basic rules. While options are allowed 

under these programs, they almost always have an impact on the entire State. State quality control 

reviewers need to be familiar with only one set of requirements. 

B. Basic Components of a HUD Quality Control Study: Actual and QC Rent 

As noted above, there are many components of a quality control study. However, there are two basic 

requirements of any quality control study that must be included in the study design. First, the data 

collected must include the amount of rent the PHA/owner has determined the household should pay. This 

is the amount calculated using the standard HUD formula, without any adjustments for prior over- or 

underpayments, fines, maintenance fees, and the like. This information is only available from project 

files.20 Therefore, a file review is required. 

Second, the data collected must include all elements necessary to calculate the amount of rent the 

household should be paying. For the HUD formula, this includes income, expense items for certain 

households, age of household members, and several other household characteristics. There are three 

sources of these data, as follows: 

� Project files—These data are available from project files if the (re)certification has been conducted 

correctly and if all relevant information is maintained in the file. Relevant information includes all 

sources of income and expenses and timely verification of these amounts, as well as verified 

information on household member characteristics such as social security number and citizenship. 

� Household interview—Because many project files are not complete or the project did not collect all 

the required information during (re)certification, a household interview is needed to increase the 

likelihood of obtaining all relevant information. 

� Third-party verification—The accuracy of the data, whether obtained from the project files or the 

tenant, must be verified by the source (e.g., employers, hospitals, the Social Security Administration). 

This verification may be found in the project files or obtained from the third-party. Obtaining 

verification directly from the third-party source is preferred (and is the procedure used by other 

Federal assistance programs). Since a release form signed by the tenant is needed to obtain the 

verification an in-person contact with the tenant is required. 

Exhibit VI-1summarizes the sources of the data needed to meet the requirements of a quality control 

study. The issues associated with collecting the required data are discussed below. 

20 TRACS/MTCS data contains information on the amount of rent the household should pay. However, TRACS/MTCS 

data may not match the 50058/50059 file information, and the TRACS/MTCS files do not include data on all assisted 

households. 

Final Report June 20, 2001 73 
VI. Recommendations 

Exhibit VI-1 

Data Collection Sources 

Type of Information Project Household 3rd Party 

Rent and Rent Components 

Actual Rent paid by household X


Income/expense items used by project to calculate rent X


Actual sources of income/expenses X X


Verification 

Items verified by project X


Items not verified by project X X


Rent Amount. The amount of rent the household is required to pay (ignoring adjustments for previous 

late payments, fees, etc.) must be collected from the project because this is the only source of this 

information for all assisted households. These data can be collected from the 50058/50059. 

Income/Expense Items Used by Projects to Calculate Rent. To conduct analysis of what income or 

expense items caused errors in the rent amount, all income and expense data used in the 50058/5009 

calculation must also be collected. The project files are the only source of this information. 

Sources of Income/Expenses. The sources of income/expenses reported by the household can usually 

be obtained from the project file. The files (for the most part) contain information on sources of income 

and expenses for each household member. However, if project files are relied on to identify all sources 

of income/expenses, some sources may be missed. If project staff did not understand the regulations or 

did not ask the right questions, the project file may not include all the sources of income or expenses. 

Therefore, an in-depth interview with the household is needed to obtain more complete information about 

the income and expenses of all household members. Although this is a labor-intensive activity, an 

interview with the household is essential to gathering complete information about all the sources of 

household income and expenses as well as household composition. 

Verification. The project file should also contain verification of the information used in the rent 

calculation. If this verification is current and complete, it can be used to validate the amount of the income 

or expense. However, if verification is missing, out of date, or incomplete, third-party verification must 

be obtained At a minimum, income and expenses not verified by the project staff must be verified by a 

third party. Verification can be collected from third parties through the mail or it can be collected in 

person. In either case, signed release forms are needed from the tenant. 

Verification could also be requested from sources other than those from which the household claims they 

have income to determine other possible sources of income. For example, if a family with children has 

little or no income, the social services office would be contacted to determine if the household was 

receiving TANF. This is known as verifying negative allegations, and is part of the quality control process 

for financial assistance programs such as TANF and Medicaid.21 Because HUD does not require 

verification of negative allegations, this study only verified reported sources of income and expenses. 

The data collection process as designed for the current study meets all of these requirements. It is 

recommended that the general data collection design remain the same. While verifying negative 

allegations could be added to the design of the data collection process, there is no indication that HUD 

21Typically, sources of income that are checked for negative allegations are local banks and agencies administering programs 

that appear relevant to a low-income household’s situation, such as TANF for families with children, SSI for elderly 

households, etc. 

Final Report June 20, 2001 74 
VI. Recommendations 

wishes to formalize its quality control process to the same degree as those processes used by other 

financial assistance programs. In addition, the cost of adding this component is significant. 

C. Other Study Components 

Standards. A comprehensive set of all HUD requirements that have an impact on the rent calculation will 

continue to be needed to assure that the correct data are collected and the QC Rent is calculated correctly. 

The standards used to identify errors22 will need to be updated to reflect changes in HUD requirements. 

Use of MTCS/TRACS Databases for Sampling and 50058/50059 Data. A sampling frame of all PHAand owner-administered projects was created for the current study. After updating the list of projects, a 

similar frame can be used for future studies. However, there is an interest in determining if the project 

sample can be selected using TRACS and MTCS data. 

A match of the households included in this study with MTCS/TRACS data is being conducted now. If 

all households are included in the MTCS/TRACS databases, consideration should be given to using these 

data as a base for selecting the household sample. Using the MTCS/TRACS data for selecting the 

household sample may not be appropriate, either because the data are not current or because of the delay 

between when the sample is drawn and when the actual data collection occurs. In the current study, the 

procedure of drawing the sample prior to the site visits resulted in a large number of ineligible households 

being included in the original sample. 

If using the MTCS/TRACS data is not possible or practical, it is recommended that the household sample 

be selected by the data collector while at the project. Selecting the household sample prior to field data 

collection did not prove as advantageous as expected due to the high number of ineligible households 

included in the sample (due to turnover between the time the sample was drawn and actual data 

collection). In addition, not all projects sent their list ahead of time; therefore, all data collectors had to 

be trained in the sample selection procedures. 

When researching the MTCS/TRACS sampling option, a determination should also be made as to 

whether the MTCS/TRACS data can be used in place of abstracting 50058/50059 data from the tenant 

file. Even if MTCS/TRACS cannot be used for sampling, it may be feasible to use the 50058/50059 data, 

thereby eliminating the need to abstract such data (for at least some of the households) during on-site data 

collection. 

Letters to HUD Field Offices, Projects, and Tenants. Throughout the study, letters explaining the study 

and asking for cooperation are sent to Field offices, projects, and tenants. Each of these letters serves a 

very specific purpose. The letters designed for the current study can easily be used for future studies; 

however, we recommend that additional letters be used as well. A follow-up letter should be sent to the 

Field offices and to the projects just prior to the actual data collection. Under current study procedures, 

the time delay between the initial letter to the Field offices and projects and the actual data collection is 

substantial. A follow-up letter will keep the Field offices informed and allow the projects to be more 

responsive to our data collectors. These follow-up letters should include the name of the data collector 

and the date the data collector will be contacting the project. 

The letter used to inform the tenant of the study and the requirement for them to participate should be 

revised so there are multiple versions of the letter to use depending on circumstances. Some tenants 

require a very firm letter, while others, particularly the elderly, need a less formal approach. The data 

collectors should be provided with several letters to use as appropriate. 

22See Data Collection Standards, an unpublished Macro report to HUD dated March 31, 1999. 

Final Report June 20, 2001 75 
VI. Recommendations 

Data Collectors. While the majority of the data collectors for the current study were excellent, there were 

several data collectors who had to be replaced for a variety of reasons. For future studies, we recommend 

that several back-up data collectors, who are willing to travel to sites as needed, be hired. These back-up 

data collectors will attend the regular data collector training session and be assigned to sites as needed. 

Automation Issues. Most of the data for the current study were collected using an automated data 

collection system. This system simplified the data collection process, reduced the number of data 

collection errors, and eliminated the need to code the data after data collection. While the existing system 

worked well, there are many improvements that can be made to the system. Recommended changes 

include expanding the data system used for on-site data collection to include the tracking and monitoring 

of verification forms sent to third parties, and automated tracking of replaced households. For example, 

the third-party verification system should be tied in to the field data collection software. This will reduce 

the level of effort involved in initiating and tracking third-party verification, and expedite the matching 

of verification received from third parties to project data. 

The following is a summary of the recommended changes discussed above. 

• Update study standards to reflect changes in HUD requirements. 

•	 Consider the use of MTCS/TRACS data for selecting the sample and obtaining 50058/59 file 

data. 

•	 Select the tenant sample on site during field data collection (unless it is determined that 

MTCS/TRACS data can be used for the tenant sample). 

•	 Send follow-up letters to the Field offices and projects immediately before data collection 

will begin. 

•	 Develop multiple letters to tenants taking into consideration different household 

circumstances. 

• Hire and train several back-up data collectors to be assigned to sites as needed. 

•	 Automate additional components of the data collection process and enhance the entire 

automated process within contract budget and time frames for future studies. 

D. Error Components That Could Be Added to the Study 

While the Quality Control for Rental Assistance Subsidies study, as currently designed, looks at most 

factors that contribute to rent error, there are other factors that the study does not address. Two of these 

factors are: 

•	 Utility Allowances. The amount equal to the PHA or HUD estimate of the monthly cost of a 

reasonable consumption of utilities and other services for the unit by an energy-conservative 

household of modest circumstances consistent with the requirements of a safe, sanitary, and 

healthful living environment, if the cost of utilities (except telephone) and other housing 

services is not included in the tenant rent, but is the responsibility of the family occupying the 

unit. 

Final Report June 20, 2001 76 
VI. Recommendations 

•	 Payment Standard. In a voucher tenancy the maximum subsidy payment for a family 

(before deducting the total tenant payment). For a voucher tenancy, the PHA generally sets 

the payment standard in the range from 90 percent to 110 percent of the current FMR (the 

basic range). With HUD approval the PHA may set the payment standard higher or lower 

than the basic range. 

In the current study, we assumed the utility allowance and Payment Standard used by the PHA/owners 

were correct. If these figures were not correct, the amount of rent the tenant pays and, therefore, the HUD 

subsidy, may be incorrect. The process of determining whether one or more of these figures is correct 

could be added to this study. However, the tasks involved in making this determination need to be 

examined closely before a decision regarding their inclusion is made. Any changes in the study design 

will likely impact the study budget or time frame. Some issues regarding the impact of the utility 

allowance and the payment standard are discussed below. 

Utility allowances. PHA/owners are required to calculate utility allowances under certain circumstances 

and they are required to review their utility allowance schedules at least annually to ensure currency. 

The calculations used in creating utility allowances, particularly for the tenant-based Section 8 programs, 

can be very complex given there are separate allowances for each bedroom size, for each unit type, and 

for each possible combination of utilities (e.g., gas cooking, oil heating, electric refrigeration). Many 

utilities have different winter and summer rates; many also have different rates for differing levels of 

consumption. Some PHAs create separate allowances for each (of perhaps dozens) of water or garbage 

service providers in their jurisdiction. Once a complex network of allowances is created, the PHA/owner 

must be exact in determining which water/sewer company, which garbage company, electric company, 

gas company, etc., provides the service to the particular assisted unit. An error can be made if records 

reflect the wrong type of utility (i.e., gas heating instead of electric). 

A household receives too large a utility allowance if the PHA/owner incorrectly believes that the tenant 

is responsible for a particular utility payment when the owner actually assumes that responsibility. 

Conversely the allowance would be too small if the household were not given credit for all of the utilities 

for which they were responsible. 

To include utilities in the study, HUD must first determine the level at which the review should be 

completed. Possibilities include: checking the basis for the utility allowance schedule itself (i.e., rates, 

consumption assumptions, arithmetic calculations), determining if the utility standard is current, or 

checking the accuracy of applying the schedule to the sample households. Once this decision is made, the 

data to be collected by the on-site data collectors need to be identified. 

At a minimum to determine if the PHA/owner is using the correct utility allowance, the data collector 

would have to collect additional data from several sources including: 

•	 The utility allowance schedules covering the period of time for which the study collected data 

(generally two calendar years) and the effective dates of these utility allowance schedules 

•	 The utility types ascribed to the unit when determining the utility allowance (from the tenant 

file) 

•	 The breakdown of utility responsibilities, either tenant or PHA/owner, used by the 

PHA/owner (typically listed on the lease) 

Final Report June 20, 2001 77 
VI. Recommendations 

•	 The utilities actually paid by the tenant according to the tenant (during the household 

interview) 

This information would be used to determine the correct utility allowance, and whether this allowance 

is the same as the allowance used by the PHA/owner. 

Payment Standard. Payment Standards must be kept current and set between 90 and 110 percent of the 

fair market rent (FMR). If a PHA does not ensure that their payment standards are within this range, or 

they misunderstand how new FMRs affect exception payment standards, errors in tenant rent 

determinations will result. 

PHAs may apply payment standards incorrectly resulting in errors in tenant rents. A PHA may have 

several Payment Standard areas with complex borders, sometimes making it difficult to select the correct 

Payment Standard for any given address within the jurisdiction. PHAs may also err by applying the 

family-size Payment Standard (the size authorized for the family as shown on the voucher) in lieu of the 

lesser of the family-size Payment Standard or the Payment Standard for the unit size (number of 

bedrooms in the unit). Other potential areas for error include whether a PHA has been authorized to use 

FMRs based on the 50th percentile of 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 eligible for these higher subsidy standards. Another complication allows PHAs to change the Payment 

Standard only at the time of the annual recertification or prior to moving to a new address. Thus, even 

if a change in family composition requires an interim recertification with several family members moving 

in or out, the Payment Standard used in determining the rent should not be changed at the interim 

recertification. 

Based on a recent revision to the Housing Act of 1937, HUD has a particular interest in monitoring the 

correct use of the payment standard. Section 545 of the Quality Housing and Work Responsibility Act 

of 1998 calls on HUD to monitor rent burdens and review any payment standard that results in a 

significant percentage of the families occupying units of any size paying more than 30 percent of adjusted 

income for rent. 

To determine if the PHA is using the correct Payment Standard, the following information is required: 

•	 The Fair Market Rents for each unit size for each geographic area within the PHA’s 

jurisdiction 

•	 The Payment Standard for each unit size for each geographic area within the PHA’s 

jurisdiction 

• Any exception Payment Standard area approved by HUD, and the boundaries of those areas 

• The number of bedrooms in the unit the family selects 

• The payment standards for both the family size and the unit size 

• The Payment Standard area in which any given assisted unit is located 

This information would be used to determine the correct Payment Standard, and whether this amount is 

the same as the amount used by the PHA. 

* * * * 

Final Report June 20, 2001 78 
Definitions 

Actual Rent—the tenant rent from the 50058/50059 form 

Administration Type—PHA or owner 

Case Type—certification, recertification, and overdue recertification 

Abstract Month—the month in which the data collection process for any given household was initiated 

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

of the QC Rent. 

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

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

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

error 

Overpayment—results when the household paid more than it should have paid; HUD’s contribution was 

less than it should have been 

Payment Type—underpayment, proper payment, and overpayment 

Program Type—Public Housing, Section 8 Certificates, Section 8 Vouchers, Section 8 Moderate 

Rehabilitation, Section 8 Substantial Rehabilitation and New Construction, Section 8 Loan Management, 

Section 8 Property Disposition, and Section 8 Preservation 

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

Quality Control (QC) Rent—calculated by Macro using the Household Questionnaire and verification 

data 

Rent Component—the five sources of income (earned, pensions, public assistance, other income, and 

assets) and the five types of deductions (medical, child care, and disability assistance expenses, dependent 

allowance, and elderly/disabled allowance) 

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

Total Component Dollars in Error—the absolute sum (i.e., the sum of the positive and negative 

amounts, ignoring the plus or minus signs) of all individual income and expense component errors. These 

errors are combined to provide an overall Total Dollars in Error and are presented as annual amount. 

Underpayment—results when the household paid less than it should have paid; HUD’s contribution was 

higher than it should have been. 

Final Report June 20, 2001 79 
Appendix A


Rent Calculations

Rent Calculations 

A. Public Housing 

a. Obtain the Total Tenant Payment (TTP). 

b.	 Determine if the family includes any ineligible noncitizens. IF YES, continue. 

If NO, go to d. 

c.	 Determine if the family includes any citizens or eligible noncitizens. IF YES, go to 

Continuation of Assistance. IF NO, go to Temporary Deferral of Termination of Assistance. 

MARKER (marks the return point after determining continuation of assistance status). 

d. Obtain the Utility Allowance. 

e. Determine if the PHA has a Flat Rent. IF YES, continue. IF NO, go to g. 

f. Determine if the tenant selected the Flat Rent. IF NO, go to g. IF YES, the QC RENT equals 

the flat rent. 

g. Determine if the PHA has a Ceiling Rent. 

IF YES, obtain the amount of the ceiling rent. The amount of the tenant�s rent (QC RENT) 

is the lower of the Ceiling Rent or a. (TTP) minus b. (Utility Allowance). 

IF NO, the amount of the tenant�s rent (QC RENT) is a. (TTP) minus d. (Utility Allowance). 

B. Section 8 Regular Certificates and Moderate Rehabilitation 

a. Obtain the TTP. 

b. Determine if the family includes any ineligible noncitizens. IF YES, continue. If NO, go to 

d. 

c.	 Determine if the family includes any citizens or eligible noncitizens. IF YES, go to 

Continuation of Assistance. IF NO, go to Temporary Deferral of Termination of Assistance. 

MARKER (marks the return point after determining continuation of assistance status) 

d. Obtain the Utility Allowance. 

e. Subtract d. (Utility Allowance) from a. (TTP). 

6.	 If e. is less than or equal to the gross rent, it is the QC RENT. If e. is greater than the gross 

rent, the gross rent is the QC rent. 

Rent Calculations A-1 
C. Section 8 Over-the-Fair-Market-Rent-Tenancy Option (OFTO)


a. Obtain the TTP.


b. Obtain the Gross Rent.


d. Obtain the Fair Market Rent.


d.	 Subtract d. (Fair Market Rent) from c. (Gross Rent) to determine the amount by which the


Gross Rent exceeds the Fair Market Rent.


e. Add a. to d. to determine the Family Share.


f. Determine if the family includes any ineligible noncitizens. IF YES, continue. If NO, go to


h.


g. Determine if the family includes any citizens or eligible noncitizens. IF YES, go to


Continuation of Assistance. IF NO, go to Temporary Deferral of Termination of Assistance.


MARKER (marks the return point after determining continuation of assistance status). 

h. Obtain the Utility Allowance.


i. Subtract h. (Utility Allowance) from e. (Family Share) to determine QC RENT.


D. Section 8 Vouchers


a. Obtain TTP.


b. Obtain Gross Rent.


c. Obtain 30 percent of monthly ADJUSTED INCOME.


d. Obtain 10 percent of Monthly TOTAL INCOME.


e. Obtain Minimum Rent.


f. Obtain Payment Standard.


g. Determine if the Gross Rent (b.) equals the Payment Standard (f).


IF YES, the Family Share equals a. (TTP). Go to v.


IF NO, continue.


h.	 Determine if the Gross Rent exceeds the Payment Standard.


IF YES, subtract f. (Payment Standard) from b. (Gross Rent).


IF NO, go to o.


Rent Calculations A-2 
i. Add a. to h.


j.	 Determine if the (re)certification was effective on or after 10/1/99. IF YES, continue.


IF NO, the Family Share = i. Go to v.


k.	 Determine if this is the FAMILY�s initial assisted lease for this unit ( D-9, Q.1b = No) .


IF YES, continue. IF NO, the Family Share equal i. Go to v.


l. Obtain Monthly Adjusted Income.


m. Divide i. by l.


n.	 Determine if m. is equal to or less than .40. IF YES, the Family Share equal i. Go to v.


IF NO, procedural error. Family Share equals i. Go to v.


o. Determine if the family was admitted to the program between 12/20/98 and 10/1/99. IF NO,


go to q. IF YES, continue.


p. Determine if the assisted unit is in the same unit or in the same complex where the FAMILY


lived immediately prior to receiving assistance (D-9, Q.1c = Yes). IF YES, the Family Share


equals the greater of c. or d. Go to v. IF NO, continue.


q.	 Determine if the (re)certification was effective prior to 10/1/99. IF YES, continue. IF NO,


go to u.


r. Subtract a. (TTP) from f. (Payment Standard) to determine HAP.


s. Subtract r (HAP) from b. (Gross Rent).


t. The Family Share equals the higher of d., e., or s. Go to v.


u. The Family Share equals a.


v. Determine if the family includes any ineligible noncitizens. IF YES, continue. If NO, go to


x.


w.	 Determine if the family includes any citizens or eligible noncitizens. IF YES, go to


Continuation of Assistance. IF NO, go to Temporary Deferral of Termination of Assistance.


MARKER (marks the return point after determining continuation of assistance status) 

x. Obtain the Utility Allowance.


y. Subtract x. (Utility Allowance) from the Family Share (g., j., k., n., p., t., or u.). This is the


QC RENT.


Rent Calculations A-3 
E. Section 8 Preservation Certificates


a. Obtain the TTP.


b. Obtain Monthly Adjusted Income.


c. Obtain the Monthly Adjusted Income of the FAMILY at the time of mortgage prepayment


or voluntary termination.


d.	 Obtain the gross rent the FAMILY was paying on the date of prepayment or voluntary


termination.


e. Record the greatest of a. or d.


f.	 Determine if the PHA implemented the regulation related to the FAMILY�s income declining


by 15 percent or more. IF YES, continue. IF NO, record e. as the Family Share, and go to


l.


g. Subtract b.(current monthly adjusted income) from c. (previous monthly adjusted income),


and divide the result by c. (previous monthly adjusted income). (This determines the percent


by which the family�s income decreased.)


h.	 Is g. greater than or equal to 15 percent? IF YES, continue. IF NO, record e. as the Family


Share, and go to l.


i. Divide d. by c. to determine the percentage of income being paid by the FAMILY at the time


of mortgage prepayment or voluntary termination.


j. Determine the greater of 30 percent or the percentage determined in i.


k. Multiply b. (Monthly Adjusted Income) by j. to determine Family Share.


l.	 Determine if the family includes any ineligible noncitizens. IF YES, continue.


If NO, go to n.


m.	 Determine if the family includes any citizens or eligible noncitizens. IF YES, go to


Continuation of Assistance. IF NO, go to Temporary Deferral of Termination of Assistance.


MARKER (marks the return point after determining continuation of assistance status). 

n. Obtain the utility allowance.


o. Subtract n. (utility allowance) from f., h., or k. (Family Share). This is the QC RENT.


Rent Calculations A-4 
F. Section 8 Preservation Vouchers


a. Obtain the new Gross Rent.


b. Obtain the Payment Standard.


c. Obtain 30 percent of Monthly Adjusted Income.


d. Obtain 10 percent of Monthly Total Income.


e. Obtain the Monthly Adjusted Income.


f.	 Obtain the Monthly Adjusted Income of the FAMILY at the time of mortgage prepayment


or voluntary termination.


g.	 Obtain the applicable Rent the FAMILY was paying on the date of prepayment or voluntary


termination.


h. Obtain the Minimum Rent.


i. Determine if the FAMILY stayed in their unit or moved within the project to an appropriate


size unit (D-9, Q. 1b = Yes, or D-9, Q. 6g. = Yes). IF YES, continue IF NO, go to l.


j. Determine if a. (Gross Rent) exceeds b. (Payment Standard). IF YES, continue.


IF NO, go to l.


k. Record the greatest of c., d., g., and h. Go to q.


l. Subtract c. (30% of monthly ADJUSTED INCOME) from b. (Payment Standard).


m. Record the greatest of d., g., or h.


n. Subtract m. from a. (Gross Rent).


o. Record the lesser of l. or n. to determine HAP.


p. Subtract o. (HAP) from a. (Gross Rent).


q. Determine if the PHA implemented the regulation related to the FAMILY�s income declining


by 15 percent or more. IF YES, continue. IF NO, record k. or p. as the Family Share.


Go to w.


r. Subtract e.(monthly adjusted income) from f. (previous monthly adjusted income), and divide


the result by f. (previous monthly adjusted income). (This determines the percent by which


the family�s income decreased.)


Rent Calculations A-5 
s. Determine if r. is greater than or equal to 15 percent? IF YES, continue. IF NO, record k. or


p. as the Family Share. Go to w.


t. Divide g. by f. to determine the percentage of income being paid by the FAMILY at the time


of mortgage prepayment or voluntary termination.


u. Determine the greater of 30 percent or the percentage determined in t.


v. Multiply e. (Monthly Adjusted Income) by the percentage in u. to determine Family Share.


w.	 Determine if the family includes any ineligible noncitizens. IF YES, continue.


If NO, go to y.


x. Determine if the family includes any citizens or eligible noncitizens. IF YES, go to


Continuation of Assistance. IF NO, go to Temporary Deferral of Termination of Assistance.


MARKER (marks the return point after determining continuation of assistance status). 

y. Obtain the Utility Allowance.


z. Subtract y. (Utility Allowance) from k., p., or v. (Family Share) to obtain the QC RENT.


G. Section 8 Project-Based (except Moderate Rehabilitation)


a. Obtain the Gross Rent.


b. Obtain the TTP.


c. Determine if b. (TTP) is less than a. (Gross Rent). IF YES, continue. IF NO, mark as


Procedural Error. This case should not be an assisted household, and should not have been


in the sample. However, to determine the amount of the subsidy error, the tenant rent capped


by the gross rent must be calculated. Continue.


d. Determine if the family includes any ineligible noncitizens. IF YES, continue. If NO, go to


f.


e.	 Determine if the family includes any citizens or eligible noncitizens. IF YES, go to


Continuation of Assistance. IF NO, go to Temporary Deferral of Termination of Assistance.


MARKER (marks the return point after determining continuation of assistance status). 

f. Obtain the Utility Allowance.


g. Subtract f. (Utility Allowance) from b. (TTP). This is the QC RENT.


Rent Calculations A-6 
H. Manufactured Home Space Rental for Section 8 Regular Certificates


a. Obtain FMR for manufactured home space rental.


b. Obtain the amortization cost.


c. Obtain the Utility Allowance.


d. Obtain the Rent to Owner.


e.	 Determine if d. (Rent to Owner) is greater than a. (FMR).


IF YES, procedural error, continue. IF NO, continue.


f. Add b., c., and d. to determine the manufactured home space cost.


g. Obtain the TTP.


h. Subtract g. (TTP) from f. (manufactured home space cost).


i. Select the lesser of d. (rent to owner) or h. to determine HAP.


j.	 Determine if the family includes any ineligible noncitizens. IF YES, continue.


If NO, go to l.


k.	 Determine if the family includes any citizens or eligible noncitizens. IF YES, go to


Continuation of Assistance. IF NO, go to Temporary Deferral of Termination of Assistance.


MARKER 

l. Subtract i. (HAP) from d. (rent to owner) to determine the QC RENT.


I. Manufactured Home Space Rental for Section 8 Vouchers - Pre Merger


a. Obtain the Manufactured Home Space Rental FMR.


b. Obtain the amortization cost.


c. Obtain the Utility Allowance.


d. Obtain the Rent to Owner.


e. Add b., c., and d.


f. Obtain 30 percent of Monthly Adjusted Income.


g. Subtract f. from e.


h. Obtain 10 percent of Gross Monthly Income.


Rent Calculations A-7 
i. Obtain Minimum Rent.


j. Determine the higher of h. or i.


k. Subtract j. from d. (rent to owner).


l. Determine the lesser of g. or k. to determine HAP.


m.	 Determine if the family includes any ineligible noncitizens. IF YES, continue.


If NO, go to o.


n.	 Determine if the family includes any citizens or eligible noncitizens. IF YES, go to


Continuation of Assistance. IF NO, go to Temporary Deferral of Termination of Assistance.


MARKER 

o. Subtract l. (HAP) from d. (Rent to Owner) to determine QC Rent.


J. Manufactured Home Space Rental for Section 8 Vouchers - Post Merger


a. Obtain the Payment Standard.


b. Obtain the Total Tenant Payment.


c. Obtain the maintenance and management charges.


d. Obtain the Utility Allowance.


e. Obtain the rent to owner.


f. Subtract b. (Total Tenant Payment) from a. (Payment Standard).


g. Add c. (Maintenance and management charges), d. (Utility Allowance), and e. (Rent to


Owner).


h. Subtract b. (Total Tenant Payment) from g.


i. Determine lesser of f. or h to determine HAP.


j. Determine if the family includes any ineligible noncitizens. IF YES, continue. If NO, go to


l.


k.	 Determine if the family includes any citizens or eligible noncitizens. IF YES, go to


Continuation of Assistance. IF NO, go to Temporary Deferral of Termination of Assistance.


MARKER 

l. Subtract i. (HAP) from e. (Rent to Owner) to obtain QC Rent.


Rent Calculations A-8 
K. Manufactured Home Space Rental for Section 8 Over-FMR Tenancy Option (OFTO)


a. Obtain the Manufactured Home Space Rental FMR.


b. Obtain the amortization cost.


c. Obtain the utility allowance.


d. Obtain the Fair Market Rent.


e. Add b., c., and d.


f. Obtain the TTP.


g. Subtract f. from e.


h. Obtain the rent to owner.


i. Obtain the minimum rent.


j. Subtract i. from h.


k. Select the lesser of g. or j. to determine HAP.


l.	 Determine if the family includes any ineligible noncitizens. IF YES, continue.


If NO, go to n.


m.	 Determine if the family includes any citizens or eligible noncitizens. IF YES, go to


Continuation of Assistance. IF NO, go to Temporary Deferral of Termination of Assistance.


MARKER 

n. Subtract k. from h. to determine QC RENT.


Rent Calculations A-9 
Special Calculations for Households with Ineligible Non-Citizens 

A. Continuation of Assistance 

a. Determine if the family was receiving assistance on June 19, 1995. IF YES, continue. IF NO, 

the FAMILY maybe eligible for pro-rated assistance; go to Proration Formula. 

b.	 Determine if the FAMILY head or spouse is a citizen or eligible noncitizen. IF YES, 

continue. IF NO, the FAMILY may be eligible for pro-rated assistance; go to Proration 

Formula. 

c.	 Determine if the FAMILY includes any ineligible members other than the head, spouse, child 

or parent of the head or spouse. IF NO, continue. IF YES, the FAMILY maybe eligible for 

pro-rated assistance; go to Proration Formula. 

d. Determine if the FAMILY was granted continuation of assistance before 11/29/96. IF YES, 

the FAMILY is eligible for full continuation of assistance, Return to MARKER. IF NO, the 

FAMILY maybe eligible for pro-rated assistance; go to Proration Formula. 

B. Temporary Deferral of Termination of Assistance 

a. 	 Determine if the FAMILY includes a refugee under Section 207 of the Immigration and 

Naturalization Act or an individual seeking asylum under Section 208 of that Act. IF YES, 

the FAMILY is entitled to ongoing deferral of termination. Go to MARKER. IF NO, 

continue. 

b. 	 Determine if temporary deferral of termination of assistance was granted prior to 11/29/96. 

IF YES, continue. IF NO, go to e. 

c.	 Determine if more than 36 months have passed since temporary deferral of termination of 

assistance was granted. IF YES, go to g. IF NO, the FAMILY is entitled to temporary 

deferral of termination of assistance. go to MARKER. 

d. 	 Determine if more than 18 months have passed since temporary deferral of termination of 

assistance was granted. IF YES, continue. IF No, the FAMILY is entitled to temporary 

deferral of termination of assistance. go to MARKER. 

e. Determine if there are eligible members. 

IF YES, go to f. 

IF NO, go to g. 

f.	 Determine if the household elected pro-rated assistance. 

IF YES, go to Proration Formula. 

g. Determine if the FAMILY is exercising its hearing rights. If YES, go to MARKER. 

IF NO, continue. 

h. Determine if the PHA is making reasonable efforts to evict. IF YES, go to MARKER. IF NO, 

Procedural Error, HOUSEHOLD IS INELIGIBLE. 

Rent Calculations A-10 
C. Proration Formula for Public Housing


a. Determine if this is a Public Housing case? IF YES, continue. IF NO, go to 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.�

f. Subtract d. (TTP) from e. (maximum rent) to determine maximum subsidy.


g. Divide f. (maximum subsidy) by b. (number of FAMILY members) and multiply by c.


(number of eligible members) to determine the eligible subsidy for the FAMILY.


h. Subtract g. (eligible subsidy) from e. (maximum rent) to obtain the pro-rated TTP.


i. Obtain the Utility Allowance.


j. Determine if the PHA has a Flat Rent. IF YES, continue. IF NO, go to l.


k. Determine if the tenant selected the Flat Rent. IF NO, go to l. IF YES, the QC RENT equals


the flat rent. Go to q.


l.	 Determine if the PHA has a Ceiling Rent.


IF YES, go to m.


IF NO, go to o.


m. Obtain the amount of the ceiling rent.


n. Determine if the Ceiling Rent is lower tHAn h. (prorated TTP) minus i. (Utility Allowance).


IF YES, the QC RENT is the Ceiling Rent. STOP


IF NO, the QC Rent is h. (prorated TTP) minus i. (Utility Allowance). Go to p.


o.	 The amount of the tenant�s rent (QC RENT) is h. (prorated TTP) minus i. (Utility


Allowance).


p.	 Did the Family accept the prorated rent? Y/N


IF NO: Go to Temporary Deferral of Termination of Assistance.


Rent Calculations A-11 
D. Proration Formula for All Section 8 Programs


a. Calculate the HAP.


b. Record the number of FAMILY members.


c. Record the number of eligible FAMILY members.


d. Obtain the Gross Rent.


e. Divide a. (HAP) by b. (total number of FAMILY members), and then multiply the result by


c. (number of eligible FAMILY members) to obtain the prorated HAP.


f. If Manufactured Home Space Rental, return to MARKER.


g. Subtract e. (prorated HAP) from d. (Gross Rent) to obtain the prorated Family Share.


h. Obtain the utility allowance.


i. Subtract h. (utility allowance) from g. (prorated Family Share) to determine the prorated QC


RENT.


Rent Calculations A-12 
Appendix B 

Weighting Procedures 
Weighting Procedures 

This appendix describes the procedures followed in weighting the sample data. 

Study Population. The universe under study included all projects and tenants located in the 

continental United States, Alaska, Hawaii, and Puerto Rico. 

The following programs were given an opportunity to appear in the sample: 

• Public Housing 

•	 PHA-administered Section 8 projects: 

- Moderate Rehabilitation 

- Certificates 

- Vouchers 

•	 Owner-administered projects: 

- Section 8 New Construction/Substantial Rehabilitation 

- Section 8 Loan Management 

- Section 8 Property Disposition 

The following programs/project types are excluded from the sample: 

� Mutual Help projects 

• Indian Housing projects 

• Section 23 projects 

� A small number of Section 8 projects that are in development but were not expected to be 

operational during the study period 

� Section 236, including projects without tenant-based subsidy, with Rental Assistance 

Payments (RAP) subsidy, and with the Section 8 subsidy 

Weighting Strategy. There are two weighting strategies that may be followed in a design, such as 

this one, where Primary Sampling Units (PSUs) and projects may be selected more than once 

depending on whether they exceed the sampling interval. One approach is to determine how many 

times a PSU has been selected and use the conditional probabilities given that it was selected that 

number of times. We then determine the number of times a project was selected and use the 

conditional probabilities given that the tenant was selected that number of times. Therefore, if a 

sample was drawn twice, the same household could have different weights depending on how many 

times the associated PSU or project was selected. 

Weighting Procedures B-1 
Instead of the above approach we used inverse probabilities of selection. This means that we 

calculated every possible way a household could have been selected to determine its overall probality 

of selection. We then trimmed the weights and post-stratified in order to obtain the final weights. For 

this reason, instead of speaking of PSU weights and project-level weights we discuss probabilities 

of selection at each stage. 

Primary Sampling Unit Probabilities. Each PSU was sampled with probabilities proportional to 

size. The size measure used was the number of tenants adjusted to obtain equal expectation for the 

three major types of programs in the study. The number of tenants of each kind in a PSU was 

multiplied by an inflation factor to make all three numbers equal. The size measures were then added 

and the PSU probability of selection was its size measure divided by the sum of the size measures 

nationwide, multiplied by the number of PSUs to be selected (which was 60). PSUs with 

probabilities greater than 1 could be selected more than once. Its probability for weighting purposes 

was still set to 1.0. 

Project Probabilities. Replacements were treated just like initially selected projects. The projects 

were selected with probabilities proportional to adjusted size (using the same adjustment as that used 

for PSUs). Ten projects were selected from each PSU and, if a PSU was selected several times, ten 

projects were selected for each time the PSU was selected. In turn a project might be selected more 

than once. 

If a PSU had a probability of selection less than one, the probability of selection of the projects, 

given that the PSU was selected, is p=10s/�sj where s is the adjusted size of the project, 10 is the 

number of projects to be sampled, and the summation is taken over the entire PSU. This is a 

conditional probability and it must be multiplied by the probability that the PSU was selected at all 

in order to obtain the unconditional probability of selection of the project. 

If the PSU had a probability of selection equal to 1, then the PSU could have been selected m or m+1 

times. The probability that the PSU was selected m times and the probability that the project was 

selected if the project was selected m times was then calculated. As a result the overall probability 

of the project being selected was calculated as follows. Let x be the event that the PSU was selected 

m times, and x� the probability that the PSU was selected m+1 times. Then p(x)+p(x�)=1. Let y be 

the event that the project was selected. Then p(y)=p(x)p(y|x)+p(x�)p(y|x�). Since the probability of 

the PSU is already 1.0, this was also a probability given that the PSU was selected. In other words, 

we calculated the probability of selecting the projects by following what would have happened had 

the PSU been seleceted m times and what would have happened had the PSU been selected m+1 

times. 

Tenant Probabilities. In order to calculate tenant weights we followed a procedure analogous to that 

for calculating project weights. If a project had no chance of being selected more than once, 

probability of selection of the tenant was 4/Nij , where Nij was the number of tenants in project j in 

PSU i and four was used since this was the number of tenants selected from the project. Note than 

since projects were selected proportional to Nij, had accurate data been available for all projects the 

probabilitiess would have been the same for tenants in the three major groups. It is only because the 

number of tenants in the projects was seldom exactly the number in the frame that tenant weights 

and probabilities differ. 

Weighting Procedures B-2 
If a project could be selected more than once, the total probability of selection was calculated 

following every possible path. This may have entailed calculating four different conditional 

probabilities, considering the possibilities of the PSU being selected m or m+1 times, and the project 

being selected k or k+1 times. Once the overall probability was calculated, this incorporated the PSU 

and project probabilities of selection. 

At this point a tenant weight was calculated as the multiplicative inverse of the tenant probability 

of selection. 

Adjustments to Weights. Up to this point the assumption was that we would obtain the exact 

number of tenants in the project and that we would sample exactly four tenants for every time the 

project was selected. In practice we sampled tenants who were not in scope, and in a very few cases 

we sampled a number other than the one called for in the design. This happened because an out-ofscope tenant was discovered too late or because a tenant thought to be out-of-scope and replaced 

turned out to be in scope. 

If some tenant was found to be out-of-scope, the tenant weights were multiplied by n/(n+m) where 

n is the number of in-scope tenants sampled and m is the number of out-of-scope tenants sampled. 

This multiplication has the effect of reducing the Nij to account for the fact that some of the tenants 

making up that number are out of scope. 

If the number sampled in a project was n� and the number that was supposed to be sampled was n, 

then weights in the project were multiplied by n/n�in order to spread the weights accordingly while 

retaining the sum of the weights provided by the project. 

Post-Stratification. The sample was designed to obtain similar numbers of tenants in each of three 

categories of projects: 

� Public Housing 

� PHA-administered Section 8 

� Owner-administered Section 8 

HUD provided approximate totals for each of the three categories. The frame totals did not 

correspond to these numbers, and in any case required extensive adjustments. Thus it was decided 

to adjust the weights so they added up to those provided by the external source. 

Trimming the Weights. Extreme weights tend to add to the variance of the estimates. There is an 

approach that allows the reduction of the extreme weights. This approach uses the NAEP procedure 

(so called because it was used in the National Assessment of Education Progress). The procedure 

essentially works as follows: 

� For each of the three post-strata, the NAEP criterion is defined as sqrt(10*(� w

2 /n)). 

� If any weight exceeds the criterion it is set to the criterion. 

Weighting Procedures B-3 
� The weights in the post-strata are adjusted to add up to the same value as before. 

� The procedure is repeated as many times as is necessary. 

This procedure was applied both before and after post-stratification. It essentially had the effect of 

reducing extreme weights while retaining the total number in each post-stratum. 

Weighting Procedures B-4 
Appendix C 

Sample Sizes for Analysis 
Analysis Purpose 

Number of 

Weighted Cases 

Sample Sizes for Analysis 

Sample Cases Exclude

Number of Because 50058/50059 M

Sample Cases 

Percent Included in the Cannot Calculate 

of Universe Analysis Actual Rent 

Case Characteristics (recertification,


certification, unit size, verification) 4,303,000 100% 2,403 n/a 

Rent Error 4,290,000 99.8% 2,398 1* 

* 50058/50059 missing Actual Tenant Rent or Total Tenant Payment ** Cases missing critical information needed to calculate rent 

Sample Sizes for Analysis 
Appendix D 

Analysis Tables 
Tables Responding to Objective(s) 

OBJECTIVE TABLE 

Objective 1: Identify the various types of errors and error rates and 1. Verification of QC Rent Components 

related estimated variances. 2. Dollar Rent Error by Program Type 

3. Percent of Households by Payment Type and Prog4. Dollar Error Amount by Payment Type and Progra5. Gross and Net Rent Error by Program Type 6. Case Type by Program Type 7. Dollar Error Amount by Payment Type and Case T8. Largest Component Error For Households with Re10. QC Rent Components by Payment Type and Adm11. Allowances 

Objective 2: Identify the dollar costs of the various types of errors. 2. Dollar Rent Error by Program Type 

4. Dollar Error Amount by Payment Type and Progra5. Gross and Net Rent Error by Program Type 7. Dollar Error Amount by Payment Type and Case T8. Largest Component Error For Households with Re9. Total and Largest Dollar Error by Program Type fo

Rent Error 

Objective 3: Estimate the national-level costs for total error and 2. Dollar Rent Error by Program Type 

major error types. 4. Dollar Error Amount by Payment Type and Progra

5. Gross and Net Rent Error by Program Type 7. Dollar Error Amount by Payment Type and Case T8. Largest Component Error For Households with Re9. Total and Largest Dollar Error by Program Type fo

Rent Error 

Objective 4 Determine the relationship between errors detectable 12. Calculation Errors on Form 50058/59 

using the HUD 50058 and HUD 50059 forms and 13. Consistency Errors on Form 50058/59 total errors. 14. Verification of Form 50058/59 Rent Components 

Objective 5: Determine whether error rates and error costs have 2. Dollar Rent Error by Program Type 

statistically significant differences from program to 3. Percent of Households by Payment Type and Progprogram. 4. Dollar Error Amount by Payment Type and Progra

5. Gross and Net Rent Error by Program Type 6. Case Type by Program Type 9. Total and Largest Dollar Error by Program Type fo

Rent Error 

Objective 6: Determine the extent to which households are 15. Occupancy Standards 

overhoused relative to HUD�s standards. 

Objective 7: Determine the extent to which errors are concentrated 2. Dollar Rent Error by Program Type 

in projects and programs. 16. Percent of Cases and Standard Error by Rent Com

Type 

Analysis Tables 
OBJECTIVE TABLE 

Objective 8: Estimate the percentage of newly certified tenants who 

were incorrectly determined eligible for program 

admission. 

Objective 9: Estimate the total positive and negative errors in terms 

of HUD subsidies. 

Objective 10: 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 the 

program sponsor staff. 

Objective 11: Determine whether other tenant or project 

characteristics on which data are available are 

correlated with high or low error rates. 

Objective 12: Determine whether cases for which 50058/59 data had 

been submitted to HUD were more or less likely to 

have errors than those for which data had not been 

submitted. 

Objective 13: Determine the extent to which Section 8 certificate and 

voucher rents are consistent with market rate rents 

for comparable units in comparable locations. 

6. Case Type by Program Type 7. Dollar Error Amount by Case Type 3. Percent of Households by Payment Type and Prog4. Dollar Error Amount by Payment Type and Progra7. Dollar Error Amount by Case Type 16.	 Percent of Cases and Standard Error by Rent Com

Type 

Multivariate regression analysis with error sources and er

independent variables, and QC error as the dependent va

Multivariate error prone analysis using tenant and project 

independent variables and QC error as the dependent va

17. Program Type by TRACS/MTCS Data 18. Payment Type by TRACS/MTCS Data 

19. 	 Disparity in Monthly Section 

Estimated Market Rents 

Terms:	 Rent Component: The five sources of income (earned, pensions, public assistance, other, and asset), three types of exp

child care, and disability expenses), and two allowances (dependent and elderly allowances) 

Rent Error: The difference between the Actual Rent and the QC Rent; net rent error is the algebraic sum of over- and unde

is the sum of the absolute values of under- and overpayments. 

Program Type: Public Housing, Section 8 Certificates, Section 8 Vouchers, Section 8 Moderate Rehabilitation, Section 8 

and New Construction, and Section 8 Loan Management and Property Disposition. 

Administration Type: PHA or Owner Payment Type: Underpayment, proper payment, and overpayment Case Type: Certification, recertification, and overdue recertification 

Analysis Tables 
[Remaining files not available electronically] 

Analysis Tables D-3

Appendix E 

Sample List of Cases 
Sample List of Cases 

This appendix includes all cases where the Actual and QC rents vary by more than $100. The list 

includes the data from both the 50058/50059 (the first line) and the QC calculations (the second line) 

for each observation. The program type for each case is listed in the second column with the rent 

formula used for the QC tenant rent in the third column. Program Types include: 

PH = Public Housing


C/V = Certificate or Voucher


OA = Owner Administered


Rent formulas include: 

A Public Housing


B Section 8 Regular Certificates and Moderate Rehabilitation


C Section 8 Over-the-Fair-Market-Rent-Tenancy Option (OFTO)


D Section 8 Vouchers


G Section 8 Project-Based (except Moderate Rehabilitation)


Rent formulas for households with ineligible non-citizens also include proration. An asterisk (*) next 

to the rent formula indicates the household included an ineligible non-citizen and required proration. 

Each of the rent formulas are provided in detail in Appendix A 

For each source of information (50058/59 and QC) the following items are listed: 

� HH Status - Elderly or Non-elderly


� Number of Dependents


� Earned Income


� Pension Income


� Public Assistance Income


� Other Income


� Asset Income


� Total Income


� Dependent Allowance


� Child Care Allowance


� Disability Allowance


� Medical Allowance


� Elderly Allowance


� Total Allowances


� Adjusted Income


� Total Tenant Payment


� Utility Allowance


� Ceiling Rent (for Public Housing) or Gross Rent (for Section 8)


� Payment Standard


� Tenant Rent


Sample List of Cases E-1 
Tenant Rent 

The tenant rent is the amount of rent the tenant pays. It is found on the 50058 or 50059 form in the 

following places. The information in parenthesis is the instruction from the 50058 form. There is no 

instruction on the 50059 form in the TRACS instructions for this data item. 

50059 All Owner-Administered Programs	 Item # 51 on the paper form 

Item # 66 on the TRACS Basic Record 

50058 Public Housing Item 10e or 10s if prorated 

(total rent amount the family pays, or the total credit amount the family received to pay utilities) 

Section 8 Certificates Item 11s or 11ak if prorated 

(total rent amount the family pays to the owner, or the total credit amount the family receives to pay utilities) 

Section 8 Vouchers Item 12x or 12ai if prorated 

(Total rent amount the family pays to the owner) 

Moderate Rehabilitation Item 13k or 13x if prorated 

(total rent amount the family pays to the owner, or the total credit amount the family receives to pay utilities) 

Manufactured Home Owner Renting the Space Item 14w or 14ag if prorated 

(total rent amount the family pays to the owner) 

Tenant Rent is not the same as Total Tenant Payment. In fact the two figures are only the same in 

about one-third of the households. Reasons for the difference include adjustments for utility 

allowances, ceiling or gross rents, households where the payment standard is less than the gross rent, 

households where the TTP exceeds the gross rent, and households with ineligible noncitizens which 

require proration. 

Specific Case Examples 

To help the reader interpret the information provided in the attached list of cases, a description of 

a sample of these cases is provided. 

Obs. #1:	 This is a voucher household. The QC data identified earned income and a child care 

allowance not used by the project staff. The QC data also indicates that the �other�

income is less than reported on the 50058. The gross rent and the payment standard are 

the same. The QC TTP for this household is $283. However, the tenant rent is $226. 

The difference between the TTP and the tenant rent is the utility allowance. 

Obs. #7:	 This is a public housing household with one member who is an ineligible non-citizen. 

This household was incorrectly identified on the 50058 as a non-elderly household and 

not credited with the elderly allowance. Additionally, the pension amount recorded on 

the 50058 was higher than the QC data, and a source of income was left out. The QC 

TTP is $903, however the QC tenant rent is $663. The difference between the TTP and 

the tenant rent is the prorated rent based on the ineligible non-citizen, minus the utility 

allowance ($159), capped by the ceiling rent ($579). 

Sample List of Cases E-2 
Obs. # 8: 	 This is an owner-administered Section 8 household with one member who is an 

ineligible non-citizen. The QC data found a small amount of income not recorded on 

the 50058. In this case the 50059 TTP is $50, and the QC TTP is $54; however the QC 

tenant rent differs from the 50059 tenant rent by $125. The difference is accounted for 

by the prorated assistance for the ineligible non-citizen. 

Obs. # 20:	 This is a voucher household for which the QC TTP and the 50058 TTP are the same 

($356). The payment standard is greater than the gross rent. Therefore, the QC tenant 

rent ($295) is the TTP ($356) minus the utility allowance ($61). 

Obs. # 30:	 This is an owner-administered Section 8 household where the QC TTP is greater than 

the gross rent. The QC data indicates employment income not found on the 50058, and 

a smaller amount of other income than recorded on the 50059. As a result, the adjusted 

annual income is much larger than that recorded on the 50059 form. The gross rent is 

$1224 and the QC TTP is $1229. This household technically should not have been 

assisted and should not have been in the sample. However, to determine the amount of 

the subsidy error, the QC tenant rent was calculated and capped by the gross rent. The 

tenant rent ($1166) is the gross rent minus the utility allowance ($58). 

Obs. #56:	 This is an owner-administered Section 8 household. The QC data identified two sources 

of income not recorded on the 50059. The total QC income is more than double the 

income recorded on the 50059. The QC tenant rent is the same as the QC TTP of $312. 

Obs. #72:	 This is a voucher household where the payment standard is less than the gross rent. The 

QC data found the 50058 form under-recorded the amount of earned income for the 

household. In this case the QC TTP is $566 and the QC tenant rent is $579. The tenant 

rent is the TTP plus the difference between the gross rent ($618) and the payment 

standard ($502) minus the utility allowance ($103). 

Obs. #96:	 This is a voucher household. The QC data found that earned income was over-recorded 

on the 50058 form. In this case the QC TTP is $97 and the QC tenant rent is $146. 

When the payment standard is less than the gross rent, the tenant rent is the TTP plus 

the difference between the gross rent ($983) and the payment standard ($851) minus 

the utility allowance ($83). 

Obs. #313:	 This is a certificate household. The QC data found two new sources of income and one 

source of income that was under-recorded on the 50058 form. The QC total income is 

4 times as much as that recorded on the 50058. This household was also incorrectly 

identified as non-elderly by the project staff and therefore not credited with the elderly 

allowance. The QC TTP for this case is $1,267 however, the rent is capped by the gross 

rent. Therefore, the QC tenant rent is $929. 

[Table not available electronically] 

Sample List of Cases E-3 
Appendix F 

Consistency Errors 
50058 - Consistency Errors 

ITEM ERROR 

General Information: 

1d. Program


2a. Type of Action


2b. Effective Date of Action


Household Composition: 

3g. Sex


3h. Relationship


3i. Citizenship


3k. Race


3m. Ethnicity


3s. Family Subsidy Status


3t. Effective Date


Net Family Assets and Income 

6a. Family Member No. 

7a. Family Member No. 

7b. Income Code 

8a. Total Annual Income 

8e. Earnings Made Possible by Disability 

Assistance Expense 

Allowances and Adjusted Income 

8d. Maximum Disability Allowance 

8f. Allowable Disability Assistance 

Expense 

8g. Total Medical Expenses 

8i. Medical/Disability Assistance 

Allowance 

8j. Elderly/Disability Allowance 

8m. Dependent Allowance 

8p. Yearly Child Care Cost That Is Not 

Reimbursed (Child Care Allowance) 

Must equal P, CE, VO, MR, MC, or B


Must equal 1 through 8


Cannot be earlier than Date of Admission to the Program (2c)


Must equal M or F


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


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


Must equal 1 through 4


Must equal 1 or 2


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


Should not be blank if 3s equals C or T


Must equal a number used in 3a.


Must equal a number used in 3a.


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


Must equal Total Annual Income (7m)


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

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

Should only be completed if any member is disabled 

Should be <= 8d (Maximum Disability Allowance) 

Should be 0 if 8c (Medical/Disability Threshold) is > 8d 

Should be 0 or blank if 8d is 0 or blank 

Should only be completed if the head or spouse is 62 or over, 

or disabled; otherwise it should be blank 

Should equal 8h minus 8c if 8d is blank or 8d is less than 8c 

Should equal 8c if 8d is >= 8c 

Should be $400 if head or spouse is 62 or over, or disabled; 

otherwise it should be 0 or blank 

Must be completed if the household contains a member 

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

head, spouse, foster child or adult, or live-in attendant) 

Should only be completed if any member is less than 13 

years old 

Consistency Errors F-1 
ITEM ERROR 

Family Rent and Subsidy Information 

10a., 11q, 12k, 13j, 14s TTP 

10a. through 14ag. Rent Calculations 

Must equal TTP (9j) or blank 

- If Program (1d) = P, items 10a., 10c., and 10e. must be 

completed; items 11a. through 14ag. must be blank. 

- If Program (1d) = CE or MC, items 10a. through 10u. and 

12a. through 13z. must be blank. 

- If Program (1d) = VO items 10a through 11an, and 13a. 

through 13z. must be blank. 

- If Program (1d) = MR, items 13f., 13g., 13h., 13j., 13k., and 

13m must be completed; items 10a. through 12ak., and 14a 

through 14ag. must be blank. 

50059 - Consistency Errors 

ITEM 

General Information: 

1. Effective Date


6a. Action Processed


6b. Action Processed


7. Type of Subsidy


9a. Race of Head of Household


9b. Ethnicity of Head of Household


Household Composition 

16. Sex 

19. Special Status Code 

21. Eligibility Code (Citizenship) 

Net Family Assets and Income 

26b. C or I 

28. Family Member No. 

28a. Care Code 

Allowances and Adjusted Income 

36. Dependent Allowance 

37. Child Care Allowance 

ERROR 

Cannot be earlier than Date Tenant Moved into Project (2.)


Must equal 1 through 5


Must equal 1 through 4, or blank


Must equal 1 through 6


Must equal 1 through 4


Must equal 1 or 2


Must equal M or F


Must equal E, S, H, F, J, or blank; should be E if Age > 61


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


Must equal C or I


Should not be greater than the total number of members listed 

in item 13 (Family Member Number) 

If the family member is greater than 18 years of age, then this 

code should be C, H, CH, HC or blank 

Must be completed if the household contains a member under 

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

spouse, foster child or adult, or live-in attendant) 

Should only be completed if any member is less than 13 years 

old 

Consistency Errors F-2 
ITEM 

39a. Total Handicapped Expenses 

39b. Handicapped Allowance 

40a. Total Medical Expenses 

41. Elderly Household Allowance 

Family Rent and Subsidy Information: 

51. Tenant Rent 

52. Utility Reimbursement 

ERROR 

Should be 0 or blank if Item 28a (Care Code) is not equal to H,


or CH


Should be <= 39a (Handicap Expenses)


Should be 0 if 38 (3% of Annual Income) is > 39a


Should be 0 or blank if 39a is 0 or blank


Should only be completed if the head or spouse = H or E, or


age 62 years old or older


Should be $400 if the Special Status Code for the head or


spouse = H or E; otherwise it should be 0 or blank


Should equal the maximum of Item 50 (TTP) minus Item 45


(Utility Allowance) or 0


Should be blank if Item 45 < Item 50


Consistency Errors F-3 
Appendix G 

The Impact of Administrative Error and Component 

Error on Dollar Rent Error 
The Impact of Administrative Error and Component Error on Dollar Rent Error 

Objective of the Analysis 

The primary purpose of this analysis was to investigate the relationship between administrative errors and 

component error and provide insight into their effect on the level of QC Dollar Rent Error. This analysis 

was intended to amplify and further explain observations displayed in Exhibit IV-12, Rent Components 

Responsible for the Largest Dollar Error, which shows the relative contribution of each of the rent 

components to QC Dollar Rent Errors. 

Administrative errors are errors that result from mistakes in procedure. They consist of: 

• Consistency errors 

• Calculation errors 

• Transcription errors 

• Failure to conduct a recertification in a timely manner 

• Failure to verify information. 

Component errors are the ten income and expense components used to calculate rent. The five income 

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

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

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

Administrative errors tell us at what point in the process the error occurred, while the component errors 

tell us which income or expense caused the error. It is the impact of the two combined on the level of QC 

Rent Error that we investigated during this analysis. We used multivariate regression to create the models 

that predict error. 

Data Considerations 

The unit of analysis was the household. The analysis included 2,398 households. All components of error 

were aggregated to the household level. 

Understanding that the model produced as a result of this analysis would exclude some other more 

complex factors (e.g., number of sources of income), other analytic approaches were used to help explain 

the causes of error. Such analyses are discussed in Sections VI and VII of this report. 

Dependent Variable 

The dependent variable was the level of QC Rent Error. Net Error was used to define QC Rent Error 

because it is a continuous variable which allowed us to finely measure changes in the dollar amount of 

QC Rent Error. Although we considered using Gross Error (the absolute value of net error) to define QC 

Rent Error, it was determined that there was no simple linear relationship between gross error and the 

variables at the component level. 

The Impact of Administrative Error and Component Error on Dollar Rent Error G-1 
More of the errors are errors in underpayments rather than in overpayments, indicating a possible bias 

towards errors in underpayment. If this bias were due to different behaviors driving overpayments versus 

underpayments, with the behaviors associated with underpayments being simply more common, then 

separate models would be required. However, assuming that the bias comes from a natural tendency to 

settle questions in favor of the recipients of housing assistance, one model was constructed for all net 

dollar error, with the assumption that the same types of administrative errors cause both underpayment 

and overpayment errors. Whether this assumption was warranted was determined by the quality of the 

fit for one model. The good fit of the model validated the assumption that similar administrative errors 

drive both underpayment and overpayment errors. 

Independent Variables 

As discussed earlier in this report, data was collected from multiple sources. These include: 

• The 50058 or 50059 form 

• Other information found in the tenant file including verification (recorded on Documentation forms) 

• Verification from third parties (when the verification was not found in the tenant file) 

• The household interview 

Each type of administrative error is identified by taking data from one or more of these different sources 

of data. 

Consistency errors are based on information from the 50058/50059 form. These errors were identified 

by determining whether there was logical conformity between elements within the 50058 or 50059 form. 

(Internal 50058/50059 consistency checks.) 

Calculation errors are also based on information from the 50058/50059 forms. The information recorded 

on the 50058/50059 form was used to calculate the rent. An error occurred when the rent calculated using 

the rent components on the 50058/50059 form did not match the actual rent amount recorded on the 

50058/50059 form. (Recalculated 50058/50059 components minus actual 50058/50059 rent components.) 

Transcription errors were identified by comparing the information on the 50058/50059 form to the other 

information found in the tenant file. The rent was calculated using the information from the tenant file. 

An error occurs if this information does not match the information on the 50058/50059 file. (File 

information minus 50058/50059 data.) 

Errors based on failure to conduct a recertification in a timely manner were identified by looking at the 

effective date of the most recent action in the tenant file. If the effective date was more than 12 months 

prior to the date the data were collected, the household’������������������������������

Errors resulting from failure to verify information were identified by comparing the tenant file 

information with the QC information. If the PHA/owner verified the information correctly, there is no 

error. If they did not, the error is based on failure to verify or use the verified information. (File 

information minus QC data.) 

Three of the administrative errors—calculation, transcription, and failure to verify—can be associated 

with one or more of the rent components. Therefore, each of these administrative errors when combined 

with the rent components represents ten different variables for a total of 30 independent variables. 

Consistency errors and errors resulting from failure to conduct a recertification in a timely manner are 

The Impact of Administrative Error and Component Error on Dollar Rent Error G-2 
not associated with rent components. Therefore, each of these errors represents one independent variable, 

for a total of 32 independent variables. 

During the analysis, another set of rent component variables was added to the list of independent 

variables. These variables were based on the difference between the recalculated rent components (the 

rent calculated using on the information on the 50058/50059 forms) and the QC rent components. They 

represent a combination of the errors identified when comparing the recalculated 50058/50059 rent 

component errors to the file information (transcription errors), and the errors identified when comparing 

the rent components documented from the file information to the QC rent components (verification 

errors). They were added because the number of variables in earlier versions of the model that predicted 

error were quite large, and better predictors of error were needed if available. 

Exhibit G-1 below presents each of the independent variables based on combining rent component with 

administrative error. 

Exhibit G-1


Administrative/Component Independent Variables


Component Calculation 

(Errors identified 

using 58/59 data) 

Earned Income	 Calculation-Earned 

Income 

Pension Income	 Calculation-Pension 

Income 

Public Assistance Calculation-Public 

Income Assistance 

Other Income	 Calculation-Other 

Income 

Asset Income	 Calculation-Asset 

Income 

Medical Expenses CalculationChild Care 

Expenses 

Disability 

Expenses 

Elderly/Disabled 

Allowance 

Dependent 

Allowance 

Medical Expenses 

Calculation-Child 

Care Expenses 

CalculationDisability Exp. 

CalculationElderly/Disabled 

Allowance 

CalculationDependent 

Allowance 

Administrative Error 

Transcription 

(Errors identified 

using file data) 

TranscriptionEarned Income 

TranscriptionPension Income 

TranscriptionPublic Assistance 

TranscriptionOther Income 

TranscriptionAsset Income 

TranscriptionMedical Expenses 

TranscriptionChild Care Exp. 

TranscriptionDisability Exp. 

TranscriptionElderly/Disabled 

Allowance 

TranscriptionDependent 

Allowance 

Verification 

(Errors identified through 

the QC process) 

Verification-Earned 

Income 

Verification-Pension 

Income 

Verification-Public 

Assistance 

Verification-Other 

Income 

Verification-Asset 

Income 

Verification-Medical 

Expenses 

Verification-Child Care 

Expenses 

Verification-Disability 

Expenses 

VerificationElderly/Disabled 

Allowance 

Verification-Dependent 

Allowance 

Combined Transcription 

and Verification Errors 

Trans./Verif.-Earned 

Income 

Trans./Verif.-Pension 

Income 

Trans./Verif.-Public 

Assistance 

Trans./Verif.-Other 

Income 

Trans./Verif.-Asset 

Income 

Trans./Verif.-Medical 

Expenses 

Trans./Verif.- Child Care 

Expenses 

Trans./Verif.-Disability 

Expenses 

Trans./Verif.-

Elderly/Disabled 

Allowance 

Trans./Verif.-Dependent 

Allowance 

Model Construction 

In order to select specific variables for the regression equation from the 32 variables, we examined 

correlations of the variables with Net Rent Error. In addition, we looked at the correlation between 

predictor variables to ensure as much independence among predictors as possible. Although consistency 

errors and overdue recertifications failed to have a strong correlation with net error for overpayments as 

well as underpayments, they were retained in the hope that they might still prove useful as predictors. 

The Impact of Administrative Error and Component Error on Dollar Rent Error G-3 
When developing a model for one payment type, the data with the opposite sign was set to zero so as not 

to reduce the sample size. Models were checked afterwards (without the extra data) to make certain that 

including the extra households did not influence the fit of the model unduly. In addition, only those errors 

greater than plus or minus five dollars constituted an overpayment or underpayment. For variable 

selection from the pool, we first ran correlations on the variables and Net Rent Error. 

Choosing the most promising variables (those with highest correlations) for the pool of variables, we then 

used stepwise regression for variable selection for each model. Several tests were used to determine the 

adequacy of the model. These included R-square, F-test, collinearity tests, and t-tests for the significance 

of individual coefficients. The final models were those where the R-square Change Score (the change in 

the percentage of fit) decreased significantly, i.e., .009 or less, and the F Change Score changed direction 

(if possible). All models were screened for outliers and standardized residuals examined against the fit. 

The Model 

Variables were screened for inclusion through correlations with Net Rent Error. Several variables 

exhibited strong correlations, including all forms of income error, especially earned income. 

The results of the regression are shown below in Exhibits G-2 and G-3. As can be seen in Exhibit G-2 

from the adjusted R-square, net error exhibited a good fit: the adjusted R-square was .68. This means that 

the final model was able to account for 68 percent of the net error. The model was checked for fit without 

including the proper payment data (or zero error) to determine if it changed the fit significantly. The 

adjusted R-Square was 0.682, indicating that the inclusion of the zero error data did not significantly alter 

the fit of the model. 

Exhibit G-2


Fit of the Model


R R-Square Adjusted R-Square Standard Error of Estimate 

0.825 0.680 0.679 68.6681 

In Exhibit G-3, the coefficients show a large effect on net error. The unstandardized coefficient is the 

percent effect that a variable has in describing net error. For the last coefficient listed, Trans./Verif.-

Medical Expense error, the unstandardized coefficient is a negative .16 percent. The interpretation here 

is that the negative sign of the coefficient tells us that an under-reporting in medical expenses results in 

overpayment in rent (or that an overreporting in medical expenses results in an underpayment in rent). 

The size of the coefficient tells us the impact this has on net error. The amount that the model estimates 

that this variable contributes is .16 percent, a very small impact. That the error was from the Trans./Verif. 

process means that the QC process in addition to information from the tenant file (but not from 

recalculating the 50058/59 information), was able to estimate what should have been claimed as medical 

expenses. 

The Impact of Administrative Error and Component Error on Dollar Rent Error G-4 
Exhibit G-3


Table of Coefficients - Total Model


Unstandardized Coefficients Standardized Coefficients 

B 

Standard 

Error Beta t 

Sig. 

- 5.198 2.033 -2.557 .011 

0.001779 0.000 0.731 46.214 .000 

0.001846 0.000 0.332 20.807 .000 

0.001945 0.000 0.323 20.192 .000 

0.002005 0.000 0.226 14.460 .000 

0.001513 0.000 0.164 10.544 .000 

-0.001586 0.000 -0.125 - 8.020 .000 

Constant 

Trans./Verif.-Earned 

Income Error 

Trans./Verif.- Pensions 

Income Error 

Trans./Verif. - Other 

Income Error 

Trans./Verif. - Public 

Assistance Income Error 

Trans./Verif. - Asset 

Income Error 

Trans./Verif.-Medical 

Expense Error 

While the unstandardized coefficient describes the impact on the error, the standardized coefficients 

describe the relative contribution of the variable to the fit. In the case of medical expenses this was 

negative 12.5 percent. The largest contribution was the Trans./Verif.-Earned Income error with a 

standardized coefficient of 73 percent. This means that 73 percent of the fit of the model (which accounts 

for 68 percent of the error) is from mistakes in reporting earned income. The model also includes the 

Trans./Verif.-Pension Income, Trans./Verif.- Other Income, Trans./Verif.-Public Assistance Income, 

Trans./Verif.-Asset Income and Trans./Verif.- Medical Expense Expenses. All the variables included in 

the model were significant as displayed by the t-test and p-value (Sig.) in the table above. From it we see 

that most of the variables are related to sources of income. Only the last variable is from medical 

expenses. As expected, Trans./Verif.- Earned Income error was important. 

When breaking down income by types we can see that the three types of income error with the most 

impact are Trans./Verif.-Earned Income, Trans./Verif.-Pension Income, and Trans./Verif.-Other Income 

(in descending order). The standardized coefficients show that the first three variables all contribute 

strongly to the model relative to the other variables. The other errors are much smaller in comparison and 

include Trans./Verif.-Public Assistance Income, Trans./Verif.-Asset Income, and Trans./Verif.-Medical 

Expenses error. Importantly, none of the other administrative errors contributed to the model. 

This model exhibited a good fit but an examination of the residuals revealed some interesting phenomena. 

Examination of residuals is crucial before accepting a “good” model, as they reveal what has not been 

accounted for by the model. Systematic patterns in residuals mean that either the form of the model 

requires respecification (restating) or that more remains to be explained. The residuals from the total 

model are shown in Exhibit G-4. As can be seen, the residuals (or the remainder unaccounted for by the 

model) revealed a unique two- line pattern. Both pieces were linear patterns not explained by the model. 

Although we conducted an extensive prior examination of correlations, more variables (unrelated to the 

model at hand) were examined for possible correlations without success. The fact that the two pieces 

intersect near the origin suggested that there may be some other factors at work. These factors were 

systematic and seem to multiply the error by one of several constants. Close examination of several 

possible factors such as number of people in the household, number of elderly, and number of bedrooms 

all failed to account for either of these factors. That we were unable to identify these factors suggests that 

the phenomena underlying the residuals are complex and would require much more investigation. 

The Impact of Administrative Error and Component Error on Dollar Rent Error G-5 
The Impact of Administrative Error and Component Error on Dollar Rent Error G-6

Exhibit G-4

Fit of Residuals Against the Net Error

Summary of Findings

One model was presented for both overpayment and underpayment. The overall insight of the

administrative error model is that the type of administrative error does not affect the level of dollar error,

but rather that the income components cause that error. This is not surprising, as one would expect that

dollar errors in income would have the greatest impact on dollar level rent errors due to its proportionate

size compared to allowances and expenses. That is, income dollar error is usually many times larger than

allowance or expense dollar error. Finally, although the total model is a good one, the residuals for the

error model suggested that some other additional factor not described by this model are at play.

Administrative Error Model

-$800

-$600

-$400

-$200

$0

$200

$400

$600

-$1,000 -$800 -$600 -$400 -$200 $0 $200 $400 $600 $800

Residuals
Appendix H 

The Impact of Tenant Characteristics 

on Rent Error 
The Impact of Tenant Characteristics on Rent Error 

The incidence of rent error may be related to tenant characteristics, project characteristics, or both. An 

analytical approach known as Classification and Regression Tree (CART) was employed to break down 

characteristics and relate them to the significant rent errors, i.e., those with absolute values greater than 

five dollars. The analysis presented below seeks to discover which tenant characteristics had a substantial 

impact on rent error. A model focusing on project contributions to rent error is presented in the next 

appendix of this report. 

Objective of the Analysis 

The objective of this analysis was to determine how various tenant characteristics were related to the 

magnitude of rent error. In particular, this analysis was designed to develop profiles of tenant 

characteristics that lead to error. Different tenant characteristics may impact the incidence of rent errors. 

For example, tenants who are elderly may have multiple sources of medical expenses. The amount of 

effort required to verify these expenses would be greater for these households than for households 

without medical expenses. It would be reasonable to expect that each additional source of medical 

expenses presents more opportunities to make mistakes. In other words, we would expect households 

with multiple sources of medical expenses to have more rent errors than those without medical expenses. 

It is this type of relationship to rent error that this analysis investigated. 

Sample 

The household was the unit of analysis. This analysis included 2,398 households. Slightly different 

numbers of households participated at the different stages of the model-building process because of 

missing values among some of the explanatory variables such as age, race, ethnicity, and gender of the 

head of household. The final model included 2,314 households because 84 households were missing 

information on the age of the head of household. 

Measures 

Measures of Rent Error. The dollar amount of rent error was used in this analysis. A household’s rent 

was considered to be in error if the gross error (defined as the absolute value of the difference between 

the household’s Actual Rent and the household’s QC Rent) exceeded five dollars. 

Measures of Tenant Characteristics. The tenant characteristics used in the analysis were divided into 

two groups. The first group included demographic information such as the age, sex, race, and ethnicity 

of the head of household; the number of members, dependents, and elderly in the household; and whether 

the household constituted a single adult. It also included the urbanization level of tenant location 

(measured as urban vs. non urban), and number of years the household participated in the program. 

The second group of measures contained the number of various income and expense sources of the 

household. Specifically, it included the number of sources of Earned Income, Asset Income, Pension 

Income, and Other Income, as well as the number of sources of Medical and Child Care Expenses. The 

number of sources of income and expenses was used instead of the simple presence or absence of the type 

of income or expense so it would capture the complexity of working with multiple sources of income and 

expenses, each requiring their own documentation and verification. This type of variable structure yields 

more specific results and, if the item is not important, is still able to distinguish between the presence or 

The Impact of Tenant Characteristics on Rent Error H-1 
absence of the variable of interest. Citizenship status and disability expenses were not included in the 

analysis because there were not enough households in these subgroups. 

Two types of measures of the tenant characteristics were used, categorical and continuous. All the 

variables in the second group were simple counts. The categorical variables were: sex; race/ethnicity (for 

analysis purposes race and ethnicity were combined resulting in four categories: white, black, Hispanic, 

and other); presence or absence of elderly head, spouse, or co-head; single adult household; and 

urbanization level. 

Approach 

CART is capable of handling a mix of categorical and continuous (or continuous) predictors. Tree-based 

modeling is an exploratory technique commonly employed to uncover structure in data, whether the data 

are linear or non-linear. Some of the uses of tree-based modeling are: devising prediction rules that can 

be rapidly evaluated, screening variables, assessing the adequacy of linear models, and summarizing 

multivariate data. CART employs a procedure know as binary recursive partitioning to successively split 

the data into smaller and smaller groups; the resulting structure is known as a regression or classification 

“tree”. This algorithm recursively splits the data in each group or node until the node is either 

homogeneous or contains too few observations. 

Compared to linear and additive models, tree-based models have the following advantages. They are: 

•	 Easier to interpret when the predictor variables are a mix of categorical and continuous 

(ordinal) measures. 

• Invariant to monotonic transformation of predictor variables. 

• More adept at capturing non-additive behavior. 

•	 Better able to address general (i.e., other than a particular multiplicative form) interactions 

between predictor variables. 

All of these features make CART attractive for this analysis, especially the ability to profile groups or 

population characteristics that may have differential propensities for error. 

Model Construction 

The model-building process started with all the tenant characteristic measures described above. In order 

to avoid obtaining small nodes, the following two restrictions were imposed on the tree building process: 

nodes were not split if they had less than 50 households, and they were not split if this would result in a 

node with less than 20 households. 

CART produced a large number of partitions that made the result seem over-complicated. A “pruning” 

procedure to remove nodes that failed to contribute much to the overall goodness-of-fit was employed. 

Pruning successively trimmed the least important splits through specification of a cost-complexity 

measure. This measure indicated the importance of a “branch” of the tree in terms of a deviance measure. 

The plot of the deviance measure by the complexity measure displayed a large drop at the point 

corresponding to a 12-node solution. This model at the intersection of the most complexity with the least 

deviance was adopted as the final model. Several other cutoff points were examined and misclassification 

rates (the number of misclassified households divided by the total number of households) were compared. 

The Impact of Tenant Characteristics on Rent Error H-2 
The final model exhibited a good fit with the data: the misclassification rate, or the percentage of 

households that the model failed to classify correctly, was less than 31 percent. 

Presentation of Results 

The final model tree is shown in Figure H-1 (at the end of this appendix). The boxes denote the nodes 

in the tree; red boxes depict the terminal nodes, i.e., the nodes that do not branch out. All the terminal 

nodes constitute a mutually exclusive partition of the whole sample: all members of the sample are 

accounted for by all the terminal nodes and no household can be in more than one terminal node. The 

following information is presented on the tree: the splitting condition (the value of the predictor variables 

used for the split); the number of households (N) in the node; the proportion (P) of households with rent 

error (actually, an estimate of the probability of being in error); and the mean dollar error (μ). The legend 

in the exhibit provides a description of the variable names used on the tree. 

At the top or root node is the full sample. The percentage of those in error in the total sample population 

was 55 percent. To determine the probability of a specific type of household being in error, follow the 

tree from the root node at the top down to the node of interest, according to the splits at the interior nodes. 

Significant Findings 

The following were among the key indicators related to large differences in error: 

At least two sources of Earned income. Among households with two or more sources of Earned 

Income, 88 percent exhibited some dollar error (the mean dollar error was $134.11). In contrast, only 64 

percent of the households with one source of Earned Income exhibited some dollar error (mean dollar 

error = $63.43). Combining the households with at least one source of Earned Income, 68 percent 

exhibited some dollar error (mean dollar error = $76.45) whereas only 48 percent of the households with 

no Earned Income exhibited a dollar error (mean error = $24.94). In other words, households with more 

sources of earned income exhibited greater rates of dollar error. Households with two or more sources 

of earned income exhibited the greatest error rates. 

At least one source of Public Assistance. Among households with one source of earned income and no 

public assistance income, 58 percent exhibited some dollar error (mean dollar error = $52.94). A much 

greater proportion (i.e., 85 percent) of households with one source of earned income and one or more 

sources of public assistance income exhibited some dollar error (mean dollar error = $105.30). In other 

words, among households with one source of earned income, those with public assistance income 

exhibited higher error rates than did those without public assistance income. 

At least one source of Other Income. Among households with no earned income and no medical 

expenses but at least one dependent, 92 percent of those with Other Income exhibited some dollar error 

(mean dollar error = $101.54). Fifty-three percent of the households with no earned income and no 

medical expenses but at least one dependent and no Other Income exhibited some dollar error (mean 

dollar error = $33.13). That is, within the subset of respondents with no earned income, no medical 

expenses, and one or more dependent(s), those with Other Income had higher rates of error than did those 

with no Other Income. 

Mean overpayments and underpayments were calculated along with the mean dollar error (absolute value) 

to determine if either underpayments or overpayments were a major contributor to error. As expected, 

underpayments were a significant contributor to error in most of the groups. The overpayments were over 

$20.00 in only three groups—at least two sources of Earned Income (and no Other Income), at least one 

The Impact of Tenant Characteristics on Rent Error H-3 
source of Child Care expenditures (give other conditions), and only one source of Earned Income (given 

other conditions). 

Summary 

A Classification and Regression Tree model was developed to profile tenant characteristics associated 

with rent error. One clear observation that can be made about the final model is that having more than one 

source of Earned Income in the household is the single strongest predictor of rent error. This is further 

demonstrated by the fact that households with multiple sources of Earned Income display no other 

distinguishing characteristics. 

The number of sources of Other Income is a strong identifier of rent error when considered in conjunction 

with other qualifying characteristics. Different characteristics had different dollar errors associated with 

them, but the dollar error associated with the number of sources of Other Income is two to three times 

greater when there are sources of Other Income present. 

While the number of sources of Other Income and Earned Income appears to be a powerful identifier of 

rent error, it only accounts for error in slightly more than half of the sample. Nonetheless, when looking 

for a simple indicator of rent error, the presence of multiple sources of Earned or Other Income works 

quite well. 

The profiles for the rest of the sample, however, are quite complex, splitting several times into groups 

with distinguishing characteristics. It appears that among households having only one source of earned 

income, the number of sources of income from Public Assistance becomes a strong secondary identifier, 

with those having at least one source of Public Assistance having a mean error twice as high ($105.30 

vs. $52.94) as those without Public Assistance. Finally, those households with no sources of Earned 

Income and more than one source of Medical Expenditures amounts to almost one-fifth of the sample but 

the associated mean dollar error is both small ($24.15) and not much different than those with no Medical 

Expenditures and no sources of Earned Income ($24.85). Consequently, although multiple sources of 

Medical Expenditure may account for a large number of errors (when error is defined categorically as 

presence or absence of error), they do not result in a significant dollar error when error is considered as 

a continuous variable. 

The Impact of Tenant Characteristics on Rent Error H-4 

Appendix I 

The Impact of Project Characteristics and Practices on 

Error 
The Impact of Project Characteristics and Practices on Error 

Three study objectives addressed whether rent errors are related to project characteristics (e.g., project 

size) and to project practices (e.g., the training of project staff). Two analytical approaches were used to 

determine how project characteristics and practices were related to errors. First, a path analysis was 

conducted to examine direct and indirect pathways leading from project characteristics and practices to 

administrative errors such as incorrect calculation and erroneous transcription. (This analysis relates to 

Objective 10 of the study.) Second, logistic regression was used to examine how project characteristics 

and practices were related to significant rent errors, i.e., those with absolute values exceeding five dollars. 

(This analysis relates to Objective 11 of the study.) 

A. Pathways From Project-Level Factors to Administrative Errors (Path Model) 

Objective of the Path Analysis 

The objective of this analysis was to determine how administrative errors are related to project 

characteristics and practices. Administrative errors were defined as improper steps in 

certification/recertification resulting in rent errors, such as improper calculation or improper transcription 

of documented sources of income. Project characteristics were defined as project features that are not 

under the control of project staff, such as number of housing units. Project practices were defined as 

project features that are at least partly under the control of project staff, such as training procedures. 

Project characteristics and practices may impact administrative errors directly, or they may impact errors 

indirectly by affecting other factors that in turn directly influence errors. For example, the size of a project 

may impact procedures for training staff, and staff training may in turn affect errors. In this example, 

project size impacts errors indirectly by affecting staff training, while staff training affects errors directly. 

To determine how administrative error relates to project characteristics and procedures, the analysis 

investigated indirect and direct pathways leading from project characteristics and practices to 

administrative errors. Path analysis is a useful technique for addressing this kind of research question. 

Path analysis provides estimates (coefficients) for a series of interconnected regression-like equations. 

The estimates indicate the strength and direction of direct and indirect connections among a set of 

variables. A path model with a good “fit” will portray a series of connections among variables that closely 

mirrors the observed relations (covariance matrix) among the variables in the sample data. 

Sample 

This analysis focused on connections among project-level factors and administrative errors. Therefore, 

project was the appropriate unit of analysis. The analysis included only projects that were studied in-depth 

via the project staff questionnaire. Staff from 512 projects completed the Project Staff Questionniare. 

Project data from the project staff questionnaire were linked with the data for the sampled households 

within each project (typically four households per project). Sufficient household-level data were not 

available for two of the projects for which we had project staff questionnaire data. Therefore the analysis 

included only the 510 projects for which sufficient household-level data was available. 

The Impact of Project Characteristics and Practices on Error I-1 
Measures 

Measures of Administrative Errors. The incidence of administrative errors was the main outcome of 

interest in the model. We examined three types of administrative errors: 

•	 Errors based on the 50058/50059 forms. These were errors in calculation based on the data 

recorded on the 50058/50059 forms. 

•	 Errors based on file data. These were errors in transcription: the information on the 

50058/50059 forms did not match the documents in tenants’ files. 

•	 Errors based on the QC process. Typically, these errors were due to insufficient 

documentation, e.g., verification needed for correct rent calculation was lacking. 

Initially, the three types of errors were determined at the household level. A household was considered 

to exhibit a given type of administrative error if the dollar amount of the household rent was impacted 

by that type of error. To compute project-level measures of administrative errors, the percentage of 

households in error per project was determined; this was done separately for each of the three error types. 

The dollar amount of errors (as opposed to the presence versus absence of errors, regardless of dollar 

amount) was also considered as a measure. However, this exhibited weaker, less interpretable relations 

with project characteristics and practices; therefore it was not used in the final model. 

Measures of Project Characteristics and Practices. Measures of project characteristics and practices 

were available from two sources: 1) the project staff questionnaire; and 2) household data from the files 

and the interviews. 

Principal components analysis was used to reduce the Project Staff Questionniare data to a smaller set 

of meaningful variables. The principal components represent common patterns of responses to the project 

staff questionnaire. For example, some response patterns were related to training practices (described 

below). Based on this analysis, component scores were computed and used as measures of project 

characteristics and practices. There was one exception: to measure requests for third-party verification, 

we computed the percentage of verified items for which staff reported that they requested third-party 

verification; this was done to enhance comparability with household-level file data on how much thirdparty verification was actually obtained by the projects. 

Measures of project characteristics and practices were also taken from the household data. Most of these 

measures differed among households within a project, e.g., the number of sources of income or expenses 

that were verified through third parties. For such measures, we computed an average across households 

within a project, similar to the computation of the administrative error measures described above. 

Initially we hypothesized that project characteristics would indirectly affect errors by affecting project 

practices, which would in turn directly affect errors, that is, characteristics → practices → errors. 

Accordingly, in order to select characteristics and practices for the path analysis, we first examined 

relationships among practices and errors. The practices most strongly related to errors were selected. 

Next, relationships between characteristics and practices were examined, and the characteristics most 

strongly related to practices were selected. If two variables showed basically the same connections with 

other variables in the model (i.e., they were redundant), but one of the variables had connections with 

larger coefficients, the variable with weaker connections (smaller coefficients) was dropped from the final 

The Impact of Project Characteristics and Practices on Error I-2 
model. Variables that were neither directly nor indirectly connected with administrative errors were also 

dropped from the final model. 

The measures of project characteristics and practices included in the final model were defined as follows. 

Project characteristics 

• Number of units: the number of housing units in a project. 

• Elderly/disabled project: designation as an elderly or disabled project. 

•	 Number of sources of elderly- or disability-related income/expenses: the number of 

sources of income or expenses of types that are particularly likely to occur among the elderly 

or disabled, i.e., medical expenses, social security and pensions, asset income, and disability 

expenses. An average was computed over the sampled households in each project. 

•	 Number of sources of income/expenses (all types): the number of sources of income or 

expenses of all types that were measured, i.e., medical expenses, social security and pensions, 

asset income, disability expenses, public assistance, other income, childcare expenses, and 

earned income. An average was computed over the sampled households in each project. 

Project practices 

•	 Workshop-based training: a training pattern characterized by extensive, recent participation 

in training workshops (usually by 4 or more project staff members), especially those offered 

by Nan McKay. 

•	 Request verification from third party: the percentage of items verified (income, medical 

expenses, etc.) for which staff reported asking for written documentation from third parties 

for verification purposes. 

•	 Obtain verification from third party: in contrast to the previous variable, which is a selfreport measure of attempts to verify via third-party documentation, this variable is the 

percentage of verified items for which third-party documentation was actually received. 

Although strictly speaking verification obtained from a third party is not a project practice, it was 

included in the model to help explain the pathway from requests for third-party verification to errors. 

In addition, the measures of sources of income/expenses may not seem appropriate as measures of project 

characteristics, because they are based on household characteristics. While it is true that these measures 

are based on household characteristics, because these measures are averaged across all households within 

a project, they may be indicative of project practices. 

Model Construction 

Data screening was conducted to ensure that the data were appropriate for path analysis. The number of 

levels for each variable, the number of households, and the parameter-to-case ratio all exceeded the 

minimum required for path analysis. All variables were standardized to ensure comparable standard 

The Impact of Project Characteristics and Practices on Error I-3 
deviations and some variables were transformed to reduce kurtosis, which can be problematic for path 

analysis models. 

Preliminary models were examined and modified to optimize model fit with the data. Wald and Lagrange 

tests, standardized residuals, and interpretability considerations were used to decide which paths among 

variables to add or remove. The final model exhibited a good fit with the data: the model was 

overidentified, as required for path analysis; the model chi-square was over two times its degrees of 

��������� 2

 = 82.73, df = 31); the residuals were fairly small; and the comparative fit index (CFI) and the 

non-normed index both exceeded .90 (CFI = .96, non-normed index = .94). 

Presentation of Results 

The final model is shown in Figure I-1 (found at the end of this section). The boxes show the variables 

in the model. The arrows show connections among the variables. The causal flow moves from left to right 

in the figure, that is, variables to the left may influence variables to the right but not vice versa. A variable 

with an arrow pointing to it may be thought of as an “effect,” which is “caused” by the variable with the 

arrow pointing from it. (The quotations are used because, strictly speaking, the data do not permit tests 

of causality; however, for purposes of discussion, we will assume that the underlying processes are 

causal.) Solid lines indicate positive relationships, that is, an increase in one variable is related to an 

increase in the other. Dashed lines indicate negative relationships, i.e., an increase in one variable is 

related to a decrease in the other. Single-headed arrows represent causal paths and the double-headed 

arrow represents a covariance. The number beside each arrow is an estimated coefficient, which indicates 

the strength of the relationship; the larger the coefficient, the stronger the relationship. The coefficients 

are interpreted as regression coefficients. For example, the coefficient next to the path leading from 

number of units to workshop-based training is .26; therefore, given a 1-unit increase in the number-ofunits measure, one would expect a .26-unit increase in the measure of workshop-based training. All 

coefficients in the model were significant at the .05 level or better. 

Significant Findings 

The model indicates that project characteristics and practices both have an impact on administrative 

errors. Some of the more interesting conclusions consistent with this model are highlighted below. 

Project size is important. Projects with more units show more administrative errors based on 

50058/50059 forms, which are basically calculation errors. Elderly/disabled projects, which are relatively 

small (note the negative covariance between elderly/disabled project and number of units), show lower 

rates of such errors. In large projects, the need to process a large number of certifications/recertifications 

may cause more staff to make a higher proportion of calculation errors. Also, it is possible that larger 

projects serve more varied tenant populations, making completion of the forms a less standardized and 

routine procedure, which might lead to more calculation errors. Project size also matters for training: 

Projects with more units utilize more workshop-based training, possibly because it is efficient to train 

large numbers of staff within a standardized workshop format. 

Workshop training is helpful because it leads to better verification. Workshop-based training led to a 

reduction in administrative errors, apparently by encouraging staff to use sound verification practices, i.e., 

to request verification via third parties. 

Third-party verification is important. As one would expect, obtaining more third-party verification leads 

to a reduction in errors. Emphasizing the importance of third-party verification may be one of the most 

direct ways to reduce errors. 

The Impact of Project Characteristics and Practices on Error I-4 
More sources of income/expenses leads to more documentation-related errors. Not surprisingly, it 

appears that having to document more sources of income and expenses leads to more documentation 

errors. That is, projects with households that on average have more sources of income/expenses tend to 

make more documentation-related errors, specifically errors based on file data (transcription errors) and 

errors based on the QC process (improper verification). Because households in elderly/disabled projects 

tend to have more sources of elderly- or disability-related income/expenses (e.g., medical expenses, 

pensions), such projects must deal with more sources of income/expenses overall, which in turn leads to 

an increase in documentation-related errors. 

While the model makes sense and it exhibits a good fit with the sample data, two caveats are in order. 

First, while the variables in the model account for a respectable chunk of the variance in errors based on 

the QC process (R2 = .31), they account for less of the variance in errors based on 50058/50059 forms 

(R2 = .10) and in errors based on file data (R2

 = .08). The remaining variance may be due to unmeasured 

project-level variables, or to tenant- or household-level factors, or to some other unknown factors. A 

second caveat is that the self-report measure of third-party verification appears to be less than ideal. While 

staff reports of requests for third-party verification were significantly and positively related to actually 

obtaining such verification (as one would expect), the relationship was surprisingly weak. This may be 

because the Project Staff Questionniare did not measure the extent to which staff emphasize third-party 

verification. Staff were asked whether third-party verification was one among several forms of 

verification that they request. There was no indication of whether third-party verification was the 

preferred form, and there was no indication of how persistent staff are in their attempts to obtain thirdparty verification. Measures that tap these important aspects of third-party verification might be included 

in future versions of the Project Staff Questionniare. 

B. Relationships Between Project Characteristics/Practices and Rates of Rent Errors (Logistic 

Regression Models) 

Objective of the Logistic Regression Models 

The path model described above, focusing on the project as the unit of analysis, shows how administrative 

errors are related to project characteristics and practices. Another approach is to examine the usefulness 

of project characteristics/practices as predictors of rent error at the household level, i.e., using household 

as the unit of analysis. This approach was taken in the analyses described below. 

One objective was to examine whether project characteristics and practices are useful predictors of 

household rent errors. A second objective was to determine whether tenant characteristics provide 

additional predictive power, above and beyond that provided by project characteristics and practices. The 

CART analysis (described in Appendix H) pointed to tenant characteristics that are predictive of 

household rent errors; income-related items appeared to be most predictive of rent errors among the 

variables considered in the model. Combining these tenant characteristics with project characteristics and 

practices might lead to an even more powerful model of household rent errors. 

A household’s rent was considered to be in error if the difference between the household’s Actual Rent 

and the household’s QC Rent exceeded five dollars. Because the dependent variable was categorical (i.e., 

rent error vs. no rent error), logistic regression was an appropriate analytic technique. In the first stage 

of the analysis (predicting rent errors from project characteristics and practices), stepwise logistic 

regression was utilized to identify a set of project characteristics/ practices that was useful for predicting 

rent errors. In the second stage of the analysis, tenant characteristics were added to the logistic regression 

model to see if tenant characteristics provided additional predictive utility. 

The Impact of Project Characteristics and Practices on Error I-5 
Sample 

The sample data were derived from the household dataset and from the project staff questionnaire. The 

household was the unit of analysis. 

Measures 

In all models, rent error (as defined above) served as the dependent variable. The predictor variables 

submitted for analysis were: 1) all project characteristics and practices that were included in the path 

model; 2) additional project characteristics and practices that might help to predict rent errors, such as 

PHA- vs. owner-administered, staff-to-tenant ratio, Public Housing vs. Section 8, and so forth; and 3) all 

tenant characteristics that contributed to the prediction of rent errors in the CART analysis (see Appendix 

H). All measures were defined exactly as they were defined for purposes of the path and CART models, 

to facilitate cross-model comparisons. Measures from the path model as well as other measures derived 

from the project staff questionnaire were converted to the household level, that is, the same values were 

assigned to all sampled households within a given project. 

Before the analyses were conducted, all predictor variables were standardized. Conversion of all variables 

to a common scale facilitates evaluation of the relative impact of different variables. 

Logistic Regression Model 1: Predicting Rent Errors from Project Characteristics and Practices 

Model Construction. The first model utilized stepwise logistic regression to identify a set of project 

characteristics/practices that were useful for predicting rent errors. The variables in the final model— 

coefficients, standard errors, Wald tests, and odds ratios—are shown in Exhibit I-1. The model correctly 

predicted error for 64.6 percent of the households, while the remaining 35.4 percent were not correctly 

predicted. Thus, while the model fits reasonably well it is far from perfect. Diagnostics were examined 

to see if dropping some unusual influential households would improve the prediction. However, the 

removal of such households did not result in a noticeably improved model. 

Exhibit I-1


The Stepwise Logistic Regression Model Predicting Rent Errors


from Project Characteristics and Practices.


Variable Coefficient SE ����� 2 p-Value Odds Ratio 

(Intercept) .09 .05 3.22 <.07 N/A 

Number of sources of income/expenses (all .32 .05 36.31 <.0001 1.38 

types) 

Obtain verification from third party -.42 .05 79.46 <.0001 .66 

Elderly/disabled project -.41 .06 52.03 <.0001 .44 

Significant Findings. Rent errors were positively related to the number of sources of income/ expenses– 

that is, more sources = more errors. In contrast, errors were negatively related to obtaining third-party 

verification (i.e., less verification = more error) and being in an elderly/disabled project (i.e., elderly/ 

disabled projects have less error). The three variables have coefficients of comparable size; therefore, 

within this model, it appears that the strength of their relationship with error is approximately equal. 

The relationships depicted in this model are very similar to those in the path model, despite the fact that 

the path model was done with project as the unit of analysis, and error was defined differently in the path 

model (the path model focused on administrative errors as opposed to rent errors). This suggests that the 

findings are quite robust – they hold across different units of analysis and across different definitions of 

The Impact of Project Characteristics and Practices on Error I-6 
error. In the path model, obtaining verification from third parties, being an elderly/disabled project, and 

the number of sources of income/expenses were all directly related to administrative errors, and in the 

logistic regression model these same measures were predictive of rent errors. One difference between the 

models is that in the path model, number of units was directly connected with administrative errors, but 

this measure did not enter the stepwise logistic regression model. This does not mean that number of units 

is unrelated to household rent errors, it simply means that number of units contributes nothing additional 

to the prediction, once the other variables have been accounted for. 

Logistic Regression Model 2: Predicting Rent Errors from Tenant Characteristics and Project 

Characteristics/Practices 

As stated above, in the model including project characteristics and practices, error was incorrectly 

predicted for 35.4 percent of the households. This suggests that project characteristics and practices do 

not tell the whole story. Tenant characteristics may also be important. Adding tenant characteristics might 

enhance the model’s predictive power. To examine this possibility, tenant characteristics were added to 

the logistic regression model. 

Model Construction. The model including tenant characteristics as well as project characteristics/ 

practices is shown in Exhibit VII-2. 

Exhibit VII-2


The Logistic Regression Model Predicting Rent Errors


from Tenant Characteristics and Project Characteristics and Practices


Variable 

Odds 

Coefficient SE ����� 2 p-Value Ratio 

Intercept .24 .06 19.32 <.001 N/A 

Project Characteristics/ Practices 

Number of sources of -.03 .07 .17 .68 .97 

income/expenses (all types) 

Obtain verification from third party -.39 .05 60.70 <.0001 .68 

Elderly/disabled project -.17 .07 6.41 .01 .71 

Tenant Characteristics 

Number of sources of other income .51 .06 68.17 <.0001 1.67 

Number of sources of earned income .73 .08 90.45 <.0001 2.08 

Number of sources of medical .52 .07 50.25 <.0001 1.67 

expenses 

Receives elderly allowance -.08 .09 .89 .35 .85 

Number of sources of pension income, .28 .07 16.91 <.0001 1.33 

social security, etc. 

Number of sources of public assistance .38 .06 43.06 <.0001 1.46 

Age of head of household .05 .08 .31 .58 1.04 

Number of Dependents -.01 .07 .01 .94 1.00 

The Impact of Project Characteristics and Practices on Error I-7 
Adding tenant characteristics to the model resulted in improved predictions. The model with tenant 

characteristics exhibited a significantly improved fit with the data, compared to the model including only 

��������������������������������������� 2

 = 2[(-1303.44, i.e., log likelihood for full model)—(-1429.73, i.e., 

log likelihood for model including only project factors)] = 252.58, df = 9, p<.0001). The full model—that 

is, the model including tenant characteristics as well as project characteristics/ practices—correctly 

predicted rent error for 73.1 percent of the households, which is an improvement over the 64.6 percent 

correctly predicted by the model including only project characteristics/practices, although the prediction 

is still far from perfect. 

Significant Findings. Several tenant characteristics related to sources of income and expenses appeared 

to be particularly good predictors of error. Namely, sources of earned income, other income, and medical 

expenses all had coefficients greater than .50. By comparison, the largest coefficient among the project 

characteristics/practices was -.39 for obtaining third-party verification. Therefore, within this model it 

appears that tenant characteristics tend to be more strongly related to rent errors, although project 

characteristics/practices do make a significant contribution to the model’s predictive power. 

Overall, the results of the four models (i.e., path model, CART, and the first and second logistic 

regression models) are quite consistent. As noted above, most of the measures that were directly related 

to administrative errors within the path model were also predictive of rent errors in the first logistic 

regression model. Further, two of the three project measures that were predictive of rent errors in the 

initial logistic regression model were also predictive of errors in the final logistic regression model with 

tenant characteristics added. Also, in the final logistic regression model, all of the tenant characteristics 

except the age of the head of household made significant contributions to the prediction of errors, 

providing support for the CART analysis findings. Although there were some discrepancies among the 

models, the discrepancies are ones that (with hindsight) one might well expect. Number of sources of 

income/expenses (all types) predicts errors in the initial logistic regression model but not in the final 

model. This is not surprising when one considers that rent errors are strongly related to various sources 

of income/expenses at the household level (i.e., considered as tenant characteristics). After the householdlevel income/expense measures have been accounted for, the average number of income/expenses across 

all households within a project would not necessarily be expected to make an additional contribution to 

the prediction of error. Along similar lines, the impact of the household-level measure of age (i.e., age 

of head of household) may be drowned out by more powerful and direct age-related measures such as 

elderly/disabled project and elderly allowance. 

C. Possibilities for Future Work 

The path analysis and logistic regression models provide insight into how project characteristics and 

practices are related to errors. However, more work could enrich and strengthen these findings. 

First, as noted above, in future versions of the project staff questionnaire, additional measures of thirdparty verification might be useful. It would be worthwhile to ask project staff if third-party verification 

is the preferred form, and how persistent they tend to be in their attempts to obtain third-party verification. 

Second, hierarchical linear modeling (HLM) could provide further insights into how errors are related 

to tenant characteristics, project characteristics/practices, and interactions among the two levels. An HLM 

model would provide a relatively simple, unified picture of relationships at the project and household 

levels. 

The Impact of Project Characteristics and Practices on Error I-8