Housing Choice Voucher Program-New Administrative Fee Formula, 44099-44125 [2016-15682]

Agencies

• DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT

[Federal Register Volume 81, Number 129 (Wednesday, July 6, 2016)]
[Proposed Rules]
[Pages 44099-44125]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2016-15682]



[[Page 44099]]

Vol. 81

Wednesday,

No. 129

July 6, 2016

Part II





Department of Housing and Urban Development





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24 CFR Part 982





Housing Choice Voucher Program--New Administrative Fee Formula; 
Proposed Rule

Federal Register / Vol. 81 , No. 129 / Wednesday, July 6, 2016 / 
Proposed Rules

[[Page 44100]]


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DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT

24 CFR Part 982

[Docket No. FR-5874-P-03]
RIN 2577-AC99


Housing Choice Voucher Program--New Administrative Fee Formula

AGENCY: Office of the Assistant Secretary for Public and Indian 
Housing, HUD.

ACTION: Proposed rule.

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SUMMARY: This rule proposes a new methodology for determining the 
amount of funding a public housing agency (PHA) will receive for 
administering the Housing Choice Voucher (HCV) program--one that uses 
factors that a recently completed study demonstrates are more 
reflective of how much it costs to administer the HCV program. Ongoing 
administrative fees under the HCV program are currently calculated 
based on the number of vouchers under lease and a percentage of the 
1993 or 1994 local fair market rent, with an annual inflation 
adjustment. The new administrative fee formula proposed by this rule is 
based on a study conducted by Abt Associates for HUD that measured the 
actual costs of operating high-performing and efficient HCV programs 
and recommended a new administrative fee formula. In this rule, HUD 
proposes to adopt the recommended formula with modifications based 
largely on comments HUD received in response to a June 26, 2015 notice 
that solicited comment on the study.
    This rule proposes an ongoing administrative fee for a PHA that 
would be calculated based on six variables: Program size, wage rates, 
benefit load, percent of households with earned income, new admissions 
rate, and percent of assisted households that live a significant 
distance from the PHA's headquarters. The PHA's fee would be calculated 
each year based on these cost factors and a revised inflation factor 
would be applied to the calculated fee. This proposed rule also 
provides HUD with the flexibility to provide additional fees to PHAs to 
address program priorities such as special voucher programs (e.g., the 
HUD-Veterans Affairs Supportive Housing program), serving homeless 
households, and expanding housing opportunities.

DATES: Comment Due Date: October 4, 2016.

ADDRESSES: Interested persons are invited to submit comments regarding 
this proposed rule to the Regulations Division, Office of General 
Counsel, Department of Housing and Urban Development, 451 7th Street 
SW., Room 10276, Washington, DC 20410-0500. Communications must refer 
to the above docket number and title. There are two methods for 
submitting public comments. All submissions must refer to the above 
docket number and title.
    1. Submission of Comments by Mail. Comments may be submitted by 
mail to the Regulations Division, Office of General Counsel, Department 
of Housing and Urban Development, 451 7th Street SW., Room 10276, 
Washington, DC 20410-0500.
    2. Electronic Submission of Comments. Interested persons may submit 
comments electronically through the Federal eRulemaking Portal at 
www.regulations.gov. HUD strongly encourages commenters to submit 
comments electronically. Electronic submission of comments allows the 
commenter maximum time to prepare and submit a comment, ensures timely 
receipt by HUD, and enables HUD to make them immediately available to 
the public. Comments submitted electronically through the 
www.regulations.gov Web site can be viewed by other commenters and 
interested members of the public. Commenters should follow the 
instructions provided on that site to submit comments electronically.

    Note: To receive consideration as public comments, comments must 
be submitted through one of the two methods specified above. Again, 
all submissions must refer to the docket number and title of the 
rule.

    No Facsimile Comments. Facsimile (fax) comments are not acceptable.
    Public Inspection of Public Comments. All properly submitted 
comments and communications submitted to HUD will be available for 
public inspection and copying between 8 a.m. and 5 p.m. weekdays at the 
above address. Due to security measures at the HUD Headquarters 
building, an advance appointment to review the public comments must be 
scheduled by calling the Regulations Division at 202-402-3055 (this is 
not a toll-free number). Individuals with speech or hearing impairments 
may access this number via TTY by calling the Federal Relay Service, 
toll-free, at 800-877-8339. Copies of all comments submitted are 
available for inspection and downloading at www.regulations.gov.

FOR FURTHER INFORMATION CONTACT: Amy Ginger, Director, Office of 
Housing Voucher Programs, Office of Public and Indian Housing, 
Department of Housing and Urban Development, 451 7th Street SW., Room 
4228, Washington, DC 20410; telephone number 202-402-5152 (this is not 
a toll-free number). Persons with hearing or speech impairments may 
access this number by calling the Federal Relay Service at 800-877-8339 
(this is a toll-free number).

SUPPLEMENTARY INFORMATION:

I. Executive Summary

A. Purpose of This Proposed Rule

    The purpose of this rule is to establish a formula for determining 
fees to be paid to PHAs for administration of an HCV program that 
better captures the costs of the program and that therefore better 
compensates PHAs for their administration of an HCV program. The 
existing fee formula was established in 2008 and calculates two fee 
rates (1) a fee rate that applies to the first 7,200 voucher unit 
months under lease; and (2) a fee rate that applies to all subsequent 
unit months under lease. Both fee rates are based on a percentage of 
the 1993 or 1994 fair market rent, limited by floor and ceiling 
amounts, and multiplied by an inflation factor that captures the 
increase in local wage rates over time. Since 2008, administrative fees 
for the HCV program have been prorated to remain within the amounts 
authorized under HUD's annual appropriations acts.
    As noted in the Summary, the formula proposed in this rule is based 
on a study conducted by Abt Associates \1\ and their recommendation 
that the formula be based on specific cost factors that are discussed 
in detail in this preamble. The proposed formula would not be tied to 
FMRs, as is currently the case. The study advised that FMRs do not have 
a strong link to administrative costs. For the reasons presented in 
this preamble and the accompanying Regulatory Impact Analysis, HUD 
believes that the formula proposed in this rule better captures the 
costs of administration of an HCV program.
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    \1\ The draft final report for this study was published in April 
2015 and the final report was published in August 2015.
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B. Summary of Major Provisions of This Proposed Rule

    The major provisions of the proposed rule relate to HUD's 
regulations in 24 CFR 982.152, which are the regulations for the 
administrative fee. This proposed rule would revise the regulations in 
paragraph (b) of this section, which sets out the formula for 
determining the ``ongoing'' administrative fee. The ongoing 
administrative fee is paid to a PHA for each unit under a housing 
assistance payment (HAP) contract. The proposed rule replaces the 
existing language in

[[Page 44101]]

this paragraph with a new formula that is based on the study, HUD's 
further analysis of the study results, and comments received on the 
June 26, 2015 Solicitation of Comment, and highlighted in the Summary 
and Section I.A. of this preamble. Section 982.152(b), as proposed to 
be revised by this rule, lists the formula cost factors used to 
determine the administrative fee. These factors are based on an 
analysis of the actual relationship between specific cost drivers \2\ 
and a PHA's administrative costs, using the most recent available data 
for the following factors: PHA program size, the wage index, the 
benefit load, the percent of households with earned income, the new 
admissions rate, the percent of voucher holders living more than 60 
miles from the PHA's headquarters and any additional factors that may 
be established by HUD, as determined relevant to calculation of a fee 
that will reflect the actual costs of administration of the HCV 
program.
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    \2\ A cost driver is a factor that triggers a change in the cost 
of an activity.
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    The new language for Sec.  982.152 provides that HUD will adjust 
the administrative fee determined under the new calculation if 
necessary to stay within maximum and minimum administrative fee amounts 
determined by HUD. The proposed rule provides (as discussed further 
below) that for PHAs outside the U.S. Territories, the maximum ongoing 
administrative fee is based on $109, adjusted for inflation, and the 
minimum ongoing administrative fee is based on $42, adjusted for 
inflation. For PHAs in the U.S. Territories, the proposed rule provides 
(as discussed further below) that the maximum ongoing administrative 
fee is based on $109, adjusted for inflation, and the minimum ongoing 
administrative fee is based on $54, adjusted for inflation. The 
proposed rule provides that the ongoing administrative fee ceiling and 
floor amounts will be adjusted annually for inflation in accordance 
with Sec.  982.152(b)(1)(iii).
    The proposed rule includes an inflation factor that will be used to 
account for inflation that has taken place between 2013, when the 
ongoing administrative fee formula's cost drivers were measured, and 
the point in time at which the amount of the ongoing administrative fee 
is determined annually by HUD. As further discussed below, the 
inflation factor is a blended rate, where 70 percent of the inflation 
rate captures changes in the cost of employee wages and benefits and 30 
percent captures changes in the general cost of goods and services.

C. Costs and Benefits of This Proposed Rule

    The proposed rule advances a new methodology for determining the 
amount of funding a PHA will receive for administering the HCV program. 
The methodology is expected to provide a more accurate estimate of PHA-
specific costs than the current method, which is based on FMRs. The 
most substantive economic impact of the rule will be a transfer from 
lower-cost to higher-cost PHAs. Approximately, $122 million will be 
transferred between PHAs, primarily from large to small PHAs. The 
aggregate transfer depends upon the assumed level of appropriation 
($1,642 million) for HCV administration. For the base case scenario, 
the transfer represents 7.4 percent of administrative funds. Despite 
the large transfer, these funds remain within the HCV Program and 
continue to assist similar households.
    The benefits and costs of the rule are qualitative. A benefit of 
the rule will be the improvement in the allocation of funds. Allocating 
funds in accordance with the estimated cost of operation will lead to a 
better-run program. However, transition to the new formula may incur 
some negligible administrative costs.

II. Background

The Current Housing Choice Voucher Administrative Fee Formula
    HUD provides funding to over 2,200 PHAs to administer more than 2.2 
million HCVs nationwide, using a formula that was established by 
statute in 1998 and applies from 1999 forward. This administrative fee 
formula is based primarily on fair market rents (FMRs) from Fiscal 
Years (FY) 1993 or 1994, and is found in section 8(q)(1) of the United 
States Housing Act of 1937 (1937 Act), which was established in its 
current form by Title V, section 547 of the Quality Housing and Work 
Responsibility Act (Pub. L. 105-276, approved October 21, 1998).
    The FY 1999 calculation is found in section 8(q)(1)(B) of the 1937 
Act (42 U.S.C. 1437f(q)(1)(B)), and provides that the monthly fee for 
which a dwelling unit is covered by an assistance contract shall be as 
follows:
     For a PHA with 600 or fewer units (i.e., 7,200 unit months 
leased (UML) or less), 7.65 percent of the base amount.
     For a PHA with more than 600 units, the fee is 7.65 
percent of the base amount for the first 600 units and 7.0 percent of 
the base amount for additional units above 600.
    The base amount is calculated as the higher of:
    [cir] The FY 1993 FMR for a 2 bedroom existing dwelling unit in the 
market area, or
    [cir] The amount that is the lesser of the FY 1994 FMR for the same 
type of unit or 103.5 percent of the 1993 FMR for the same type of 
unit.
    HUD currently adjusts these amounts annually based on an inflation 
factor that is calculated using the Bureau of Labor Statistics 
Quarterly Census for Employment and Wages (QCEW). The inflation factor 
reflects the percentage change in local government wages since 1993, 
based on the most recent annual data available at the time the fee is 
being calculated.
    For years after 1999, section 8(q)(1)(C) of the 1937 Act (42 U.S.C. 
1437f(q)(1)(C)) provides that HUD shall publish a Federal Register 
notice setting the administrative fee for each geographic area. The fee 
is to be based on changes in wage data or other objectively verifiable 
data that reflect the costs of administering the program, as determined 
by HUD. Despite this broad statutory authority, HUD has not--until 
now--proposed updating the administrative fee formula based on changes 
in wage data or other objectively measurable data that reflect the 
costs of operating the voucher program.
Funding for Administrative Fees
    Before 2003, PHAs generally received Housing Assistance Payment 
(HAP) funding for all the units under their authority and the full 
amount of administrative fees authorized by the fee formula in place 
for all leased units. After 2003, administrative fees began to be 
reduced in different ways. In 2003, PHAs still received fees based on 
the number of units leased. However, the fees received were reduced by 
the amount of the PHA's administrative fee reserves in excess of 105 
percent of their calendar year (CY) 2002 fees.\3\ Fees for CY 2004 
through CY 2007 were not based on the number of units leased but rather 
on the previous year's fee eligibility, adjusted for any new units 
allocated after 2003. Therefore, in these years, fees were essentially 
frozen at the CY 2003 level with the only increase to the fee base 
coming from new units.
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    \3\ The 2003 reduction is in Public Law 108-7, Consolidated 
Appropriations Resolution, 2003, Div. K, Tit. II, numbered paragraph 
(5) under the Public and Indian Housing--Housing Certificate Fund 
account section, as well as the annual administrative fee notice in 
the Register, 68 FR 24078 (May 6, 2003).
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    Beginning in CY 2008, administrative fees were once again earned on 
the basis of vouchers leased in accordance with section 8(q) of the 
1937 Act. During this

[[Page 44102]]

time, administrative fees were prorated in order to stay within the 
amounts appropriated under HUD's appropriations acts. From CY 2008 
through CY 2010, the administrative fee proration was 90 percent or 
higher, meaning that PHAs received 90 percent (or more) of the 
administrative fees they would have received if full funding were 
available. Since 2011, however, the annual proration to the 
administrative fee has decreased, reaching a low in 2013 of 69 percent 
as a result of Federal budget sequestration but rising to 79.8 percent 
in 2014.
    Although the HCV program as a whole has grown in the past 5 years, 
PHAs have generally received less funding for the administration of the 
program. Indeed, because of funding challenges, some PHAs have opted to 
give up their HCV programs--requesting HUD to transfer their programs 
to other entities. Since 2010, more than 160 PHAs have transferred 
their HCV programs to other entities.
    In an environment with constrained funding, it is critical for HUD 
to have accurate, reliable information on how much it costs to 
administer a well-run HCV program. HUD therefore initiated, and 
Congress funded, an HCV Administrative Fee Study to ascertain how much 
it costs a PHA to run a high-performing and efficient HCV program, 
identify the main factors that account for the variation in 
administrative costs among PHAs, and develop a new administrative fee 
formula for reimbursing PHAs based on the study's findings.
HCV Administrative Fee Study
    The HCV Program Administrative Fee Study Draft Final Report was 
published on April 8, 2015 and the HCV Program Administrative Fee Study 
Final Report \4\ was published on August 21, 2015.\5\ The study: (1) 
Identified a diverse sample of 60 PHAs administering high performing 
and efficient HCV programs to participate in the study; (2) tested 
different direct time measurement methods; (3) collected detailed 
direct time measurement data using Random Moment Sampling (RMS) via 
smartphones; and (4) captured all costs incurred by the HCV program 
(labor, non-labor, direct, indirect, overhead costs) over an 18 month 
period at the 60 sample PHAs. A large and active expert and industry 
technical review group (EITRG)--consisting of representatives from the 
major affordable housing industry groups, executive directors and HCV 
program directors from high-performing PHAs, affordable housing 
industry technical assistance providers, housing researchers, and 
industrial engineers--reviewed the study design and results at separate 
stages in the study and provided invaluable feedback.
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    \4\ The main differences between the draft and the final report 
involve slight changes to the coefficients because of a more 
accurate way of calculating the new admissions rate. This affects 
chapters 6 and 7 and is explained in footnote 90 in the final report 
(chapter 6, pg. 118). Other changes in the final report involved 
clarifications to table notes, copy edits, corrections of 
typographical errors, and adding the executive summary to the final 
report. The formula tools and spreadsheets that were posted on the 
study Web site (https://www.huduser.org/portal/hcvfeestudy.html) and 
the Solicitation of Comment reflected the updated coefficients.
    \5\ The study can be found at: https://www.huduser.org/portal/hcvfeestudy.html. In addition to the study, HUD comprehensively 
described the study's methodology and findings in the Solicitation 
of Comment discussed below.
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    In accordance with the guidelines for ``peer review'' of 
``influential and highly influential scientific information'' in the 
Information Quality Bulletin of the Office of Management and Budget 
(OMB), dated December 16, 2004, and published in the Federal Register 
on January 14, 2005, 70 FR 2664-2677, HUD's Office of Policy 
Development and Research asked two industrial engineers who are experts 
in time-and-motion research (Dr. Nicola Shaw and Dr. Kai Zheng) and one 
economist who is an expert in assisted housing (Dr. Edgar Olsen) to 
review the HCV Program Administrative Fee Study Draft Final Report. The 
results of the peer review are posted on the study's Web site at https://www.huduser.gov/portal/hcvfeestudy.html.
    The study represents the most rigorous and thorough examination of 
the cost of administering a high-performing and efficient HCV program 
conducted to date, and provides the basis for calculating a fee formula 
based on actual PHA costs across a diverse sample of PHAs. Both the 
study's recommended formula and the formula proposed by this regulation 
are based on variables with better theoretical and statistical 
connection to the administrative costs of the HCV program than the 1993 
or 1994 FMRs.
    The study analyzed over 50 potential cost variables. The study's 
recommended administrative fee formula was based on a regression model 
using the following seven variables:
    (1) Program size: The number of vouchers under lease, including 
port-ins and excluding port-outs. PHAs receive an additional fee per 
voucher if they have fewer than 750 vouchers under lease, with the most 
additional fee received by PHAs with 250 or fewer vouchers under 
lease.\6\
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    \6\ The study found that PHAs with 500 or fewer vouchers under 
lease had significantly higher per unit costs. In a fee formula, a 
binary variable that separates PHAs into two groups--one with 500 
vouchers or fewer and one with more than 500 vouchers--would result 
in a cliff effect; that is, a substantial drop-off in fees after a 
PHA exceeds 500 vouchers under lease. To avoid the cliff effect, the 
formula provides additional fees to PHAs with fewer than 750 
vouchers under lease on a sliding scale. The study found that the 
250-to-750 range minimized the cliff effect without weakening the 
formula's accuracy in predicting costs.
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    (2) Wage index: The ratio of the statewide average metropolitan or 
nonmetropolitan wage rate for local government workers in the PHA's 
state, to the national average wage rate for local government 
workers.\7\
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    \7\ If the PHA's headquarters is located in a metropolitan 
county, the PHA is assigned the average local government wage for 
the metropolitan counties in the PHA's state. If the PHA's 
headquarters is in a nonmetropolitan county, the PHA is assigned the 
average local government wage for the nonmetropolitan counties in 
the PHA's state.
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    (3) Health insurance cost index: The ratio of the cost (to 
employers) of health insurance in the PHA's state, to the national 
average cost (to employers) of health insurance.
    (4) Percent of households with earned income: The percentage of HCV 
households served by the PHA that has income from wages.
    (5) New admissions rate: The number of households admitted to the 
PHA's HCV program (as a result of turnover or new allocations of 
vouchers) as a percentage of the total households served.
    (6) Small area rent ratio: A measure of how the average rents in 
the areas where a PHA's voucher participants live compare with the 
average rents for the overall area.
    (7) 60 miles: The percentage of HCV households served by the PHA 
that live more than 60 miles away from the PHA's headquarters.
    Since the recommended formula predicts the per-unit costs for 
administering the program from July 1, 2013, through June 30, 2014, the 
formula must be adjusted to reflect changes in the cost of goods and 
services over time. That is, the formula needs a factor to account for 
inflation. The study recommends a blended inflation rate that 
distinguishes between (i) change in wage rates over time; (ii) change 
in health insurance costs over time; and (iii) change in non-labor 
costs over time.
    The study's recommended formula would also change the method by 
which PHAs are reimbursed for the administrative costs associated with 
tenant portability. This proposed rule

[[Page 44103]]

incorporates the study's recommendation on administrative fees for 
portability, which is described in detail later in this preamble.
    The study's recommended formula accurately predicts 63 percent of 
the variance in agency costs among the 60 PHAs studied. Given the 
complexity of the HCV program and the heterogeneity of the United 
States, this is an extremely high predictive value. The current formula 
only accounts for 33 percent of the variance in agency costs, so the 
study's formula represents a nearly 100 percent increase over the 
current formula in terms of its predictive value. While 63 percent is a 
very high predictive value, the study notes that there are costs that 
may not be accounted for in the proposed formula. An example of this is 
the up-front time to establish a HUD-Veterans Affairs Supportive 
Housing (VASH) program. Moreover, the study notes that program rules 
may change which could impact costs. For example, PHAs may adopt 
streamlining activities that result in fewer inspections and may result 
in lower administrative costs. Finally, the study identifies four areas 
for further analysis and consideration in developing the administrative 
fee formula: (i) Administering the HUD-VASH program; (ii) serving 
homeless households; (iii) providing PHAs performance incentives; and 
(iv) expanding housing opportunities.
Solicitation of Comment on HCV Administrative Fee Study
    On June 26, 2015, at 80 FR 36382, HUD published a Federal Register 
notice seeking public comment on the variables identified by the HCV 
Administrative Fee Study as impacting administrative fee costs and on 
how HUD might use the study findings to develop a new administrative 
fee formula (Solicitation of Comment Notice). In particular, HUD 
requested comment on the 7 formula factors that comprised the study's 
recommended formula (wages, program size, health insurance cost index, 
percent of households with earned income, new admissions rate, small 
area rent ratio, and percent of households more than 60 miles from the 
PHA's headquarters); the inflation factor used to adjust the 
administrative fee formula; proposed administrative fee floors; maximum 
administrative fee funding; adjusting administrative fees for future 
program changes; and reducing funding disruptions for the relatively 
small number of PHAs that would likely have a decrease in funding under 
the study's proposed formula. In addition, HUD sought comment on 
modifications to the formula or supplemental fees to support PHAs in 
addressing program priorities, strategic goals, and policy objectives 
at the local and national level (as discussed in section 7.7 of the HCV 
Administrative Fee Study).

III. HUD's Proposed New Administrative Fee

    Significant modifications to the study's recommended formula 
variables in HUD's proposed formula. In response to comments received 
on the June 26, 2015, notice, HUD made three significant modifications 
to the study's recommended fee formula in developing HUD's proposed 
administrative fee formula. These three modifications affect the 
proposed formula by changing variables as follows:
     First, for PHAs in metropolitan areas, the wage index 
formula variable is based on the average local government wage rate for 
the PHA's metropolitan Core Based Statistical Area (CBSA), rather than 
the average local government wage rate for all of the metropolitan 
counties in the PHA's state.
     Second, the health insurance cost index formula variable 
has been replaced with a new ``benefit load'' formula variable, which 
is designed to more accurately reflect the variation in costs for all 
benefits that are paid on behalf of HCV employees, as opposed to using 
health insurance costs as a proxy to account for the variation in all 
benefit costs.
     Third, the small area rent ratio (SARR) variable has been 
removed from the proposed formula. HUD is sensitive to the concerns 
that the SARR may be more of an artifact of where PHA jurisdictions are 
located than an indicator of the level of additional effort to expand 
housing opportunities or recruit landlords in what may be more 
expensive rental markets. HUD was also concerned about the instability 
of the variable when tested with other combinations of variables in 
different regression models.
    HUD received 95 comments in response to the June 26, 2015, notice. 
The public comments can be found at: https://www.regulations.gov/#!docketDetail;D=HUD-2015-0058. HUD addresses significant issues raised 
by the commenters, explains the bases for the changes that HUD made to 
its proposed administrative fee formula that differ from the study's 
recommended administrative fee formula, and seeks specific comment on 
several issues in Section IV of this preamble.

IV. Factors Considered by HUD in Development of Its Proposed 
Administrative Fee Formula

    The administrative fee formula proposed by this rule is largely 
based on the recommended formula developed as part of the HCV 
Administrative Fee Study. The formula is created by a regression model 
which explains the relationship between the actual administrative costs 
and 6 cost drivers for the 60 study PHAs. Each of the 6 cost drivers 
(also known as formula variables) has both a theoretical and empirical 
basis for affecting administrative costs across all PHAs. The formula 
variables are discussed below, as is the rationale for eliminating the 
small area rent ratio (SARR) variable that was included in the study's 
recommended formula but dropped from the proposed formula set forth by 
this rule.
    The following provides an overview of how HUD's new proposed 
administrative fee formula was developed.
    Objective of the formula: One of the main objectives of the HCV 
Administrative Fee Study was to develop a fee formula that would more 
accurately account for the variation in the cost of administration 
among PHAs. As noted earlier, the current formula is based on an 
assumption that the differences in FMRs correlate with the differences 
in wage rates and other variables that account for the variation in PHA 
administrative costs. Unlike the current formula, the study's 
recommended formula is based on an analysis of the actual relationship 
between specific cost drivers and the PHAs' administrative costs. That 
analysis was used to appropriately incorporate the impact of the most 
significant cost drivers into the calculation of the administrative fee 
for individual PHAs.
    Measuring actual administrative costs per unit months leased (UML): 
The first step in developing the administrative fee formula proposed in 
this rule was to measure the actual administrative costs per UML at 
each of the 60 PHAs in the study. The study used RMS time measurement 
and cost data collection to capture all of the costs associated with 
operating a high performing and efficient HCV program at each of the 60 
PHAs. The study measured a total annual HCV administrative cost for 
each PHA, which included labor, non-labor, and overhead costs. Because 
the PHAs in the sample ranged in size from just over 100 vouchers to 
more than 45,000 vouchers, the study divided each PHA's total yearly 
administrative costs by its

[[Page 44104]]

number of UMLs over the year to arrive at an administrative cost per 
UML for each PHA in the study. The costs were collected for the year 
2013, and the administrative cost per UML ranged from $42.06 to $108.87 
across the 60 PHAs.
    Assessing the wide variation in UML administrative costs: After 
measuring the actual administrative costs for each PHA, the next step 
was to identify the PHA, program, and market characteristics that help 
explain the wide variation in UML administrative costs observed across 
the 60 PHAs. The PHA, program, and market characteristics are the 
factors that affect or drive each PHA's administrative costs, referred 
to in the study as cost drivers. The study team, in consultation with 
HUD and the expert and industry technical review group (EITRG), 
identified and tested more than 50 potential cost drivers that could 
theoretically be expected to affect HCV administrative costs.
    Use of ordinary least squares (OLS) to determine potential cost 
drivers that have most impact on HCV administrative costs: The study 
team used a statistical method known as OLS multivariate regression to 
determine which of the 50 potential cost drivers had the most impact on 
HCV administrative costs and which factors, in combination with one 
another, could best explain or predict the administrative costs per UML 
measured for the 60 PHAs in the study. OLS multivariate regression 
finds the best linear fit to the data when the analyst knows that two 
or more variables affect the outcome of interest, which is clearly the 
case when the outcome is UML administrative cost. OLS regressions have 
a dependent variable that the model is trying to explain (in this case, 
UML administrative cost) and the independent variables (also referred 
to as ``explanatory'' variables), such as PHA employee wages, program 
size, and other cost drivers. In addition to determining the best 
linear relationship between the dependent variable and the independent 
variables of the sample PHAs, the regression model then allows the 
statistician to better predict the value of the dependent variable for 
PHAs outside of the sample, based on the values of the independent 
variables for those PHAs.
    The significance of a coefficient: In a regression model, the 
independent variables, or cost drivers, are coefficients in the model. 
A coefficient can either have a positive or a negative value and can 
have different levels of statistical significance. In the study's 
model, a positive coefficient means that PHAs with higher values for 
the tested variable also have higher UML administrative costs. A 
negative coefficient means that PHAs with higher values for the tested 
variable have lower UML administrative costs.
    In addition to assigning each coefficient a positive or negative 
value, the regression model calculates the statistical significance of 
the coefficient or variable. The study's regression model identified 
variables as statistically significant at the 1 percent, 5 percent, and 
10 percent level, or not statistically significant. The percent level 
indicates the degree of confidence that the analyst and the public can 
have in the variable's relationship to the UML administrative cost. In 
empirical studies, all statistical relationships are measured with 
random error introduced by sampling only a random portion of the 
population instead of the whole population.
    Statisticians have developed yardsticks for the risk of error 
associated with the measurement of any particular relationship. If the 
variable is statistically significant at the 1 percent level, that 
means there is a less than 1 percent probability that the true 
relationship between that variable and UML cost is zero. For example, 
if the coefficient is positive, that means that the analyst and the 
public can be at least 99 percent sure that the variable is 
consistently associated with a higher UML cost. If a variable is 
statistically significant at the 10 percent level, there is a less than 
10 percent probability that the variable and the administrative cost 
per unit month relationship have a true correlation of zero, so the 
analyst would have at least 90 percent confidence that the variable was 
consistently associated with higher cost. Both variables are 
statistically significant, but the analyst and the public will have 
more confidence in the measurement if it is statistically significant 
at the 1 percent level. Variables that are not statistically 
significant may still affect UML administrative cost, but the analyst 
and the public will not be able to make confident and objective 
assertions about their impact.
    As noted above, the dependent variable the administrative fee 
formula is predicting through the OLS regression is the UML 
administrative cost. The actual administrative cost per UML was 
determined for the 60 study PHAs through the measurement of staff time 
spent on HCV administration using random moment sampling (RMS) and cost 
data collection. The OLS regression tested the relationship between the 
actual UML administrative costs and various combinations of independent 
variables to determine how much each cost driver affected the 
administrative costs for the sample PHAs, holding the other factors 
constant, and the consistency of the relationship between the proposed 
cost driver and the UML cost when the other factors are controlled for.
    The process for testing cost drivers: The study team started with a 
simple regression model with two cost drivers: Program size and local 
wage rates. Each of these cost drivers was found to be highly 
significant. The team then added each of the remaining potential cost 
drivers one at a time to test their significance once program size and 
local wage rates were taken into account. For example, one potential 
cost driver was the rate of new admissions to the HCV program, which 
the study team and EITRG reasoned could impact a PHA's administrative 
costs. Numerous combinations of variables were tested to find the set 
of factors that best explained the observed variation in UML 
administrative cost for the 60 study PHAs. Readers are encouraged to 
read chapters 6 and 7 of the HCV Program Administrative Fee Study Final 
Report for a complete list and description of all the potential cost 
drivers that were tested, the results of those tests, and the rationale 
through which the study team decided on the cost factors that were 
ultimately included in the study's recommended formula.
    The cost drivers that were identified as the best explanatory 
variables for the fee formula under this proposed rule are program 
size, wage index, benefit load, percent of households with earned 
income, new admissions rate, and percent of households residing more 
than 60 miles from the PHA's headquarters. The OLS regression uses the 
actual values of these explanatory variables for each PHA to predict 
the PHA's administrative cost per UML, which becomes the ongoing 
administrative fee for the PHA under the fee formula.
    Measuring regression by R-squared value: A key explanatory measure 
of a regression is the R-squared value. The R-squared of a regression 
is the percentage of the variance in the dependent variable (in this 
case UML administrative cost) that is accounted for by the model. The 
R-squared for the regression model used to develop the proposed formula 
under this rule is 0.62, which means that the combination of the six 
independent variables explains 62 percent of the observed variation in 
UML administrative cost across the 60 PHAs. Although the predictive 
value of the study's recommended formula was slightly

[[Page 44105]]

higher (63 percent), HUD believes that the benefits of the changes made 
as a result of the comments received in response to the Solicitation of 
Comment Notice outweigh the small decrease in the R-squared. The 
predictive value of the administrative fee formula in this proposed 
rule is still a much higher R-squared than the study expected, given 
the wide variety of factors that could potentially affect HCV 
administrative costs. (As discussed earlier, the current FMR-based 
formula only accounts for 33 percent of the variation of costs.)
    Formula calculation for HUD's proposed rule: The proposed ongoing 
administrative fee formula calculation based on the OLS regression 
model is as follows:
---------------------------------------------------------------------------

    \8\ The coefficients in this table reflect the proposed rule 
model, which, as described above, is a modified version of the model 
recommended by the HCV Program Administrative Fee Study. The 
variables and coefficients in the proposed fee model are similar to 
but not the same as those in the study model.
    \9\ The intercept for the model is -33.47. The intercept in a 
linear regression is simply the point at which the regression line 
crosses the y axis (the point at which the value of x--the 
independent variable--is 0). The intercept, along with the slope of 
the line, determines the value of dependent variable (in our case 
administrative fee per UML) based on the values of the independent 
variables. In a regression model, the slope of the line and the 
relationship between the x and y variables may result in a y-
intercept that is not meaningful in a practical sense. For instance, 
it is not possible for all of the formula variables to be zero for a 
PHA, so the intercept is meaningless in terms of an actual 
administrative fee value, and in reality there would never be such a 
thing as a negative administrative fee. Rather, it is simply an 
adjustment to the fee calculation that is necessary for the fee 
amounts to reflect the predicted administrative cost per UML as 
determined by the formula variables through the regression.

                  Table 1--Base Fee Formula Calculation
------------------------------------------------------------------------
       Formula variable             Applies to        Calculation \8\
------------------------------------------------------------------------
Program size 1................  PHAs with 250 or   + $13.94 ($13.94 x
                                 fewer units.       1).
Program size 2................  PHAs with 251 to   + $13.94 x [1-(units-
                                 749 units.         250)/500].
Program size 3................  PHAs with 750 or   + $0 ($13.94 x 0).
                                 more units.
Wage index....................  All PHAs.........  + $31.53 x PHA's wage
                                                    index.
Benefit load..................  All PHAs.........  + $0.78 x PHA's
                                                    benefit load.
Percent of households with      All PHAs.........  + $1.02 x % of PHA's
 earned income.                                     households with
                                                    earned income.
New admissions rate...........  All PHAs.........  + $0.15 x % of PHA's
                                                    households that are
                                                    new admissions.
Percent of households more      All PHAs.........  + $0.83 x % of PHA's
 than 60 miles from PHA HQ.                         households living
                                                    more than 60 miles
                                                    from PHA HQ.
Intercept \9\.................  All PHAs.........  -$33.47.
Fee...........................  Per Unit Month     = $.
                                 Leased (UML).
------------------------------------------------------------------------

    Each variable in the administrative fee formula has a monetary 
value that is equal to the positive coefficient estimate determined by 
the regression model. The formula coefficient is then multiplied by the 
individual PHA's variable value.\10\ For example, assume that the PHA 
had a wage index of 1.21. The dollar value of the wage index for this 
PHA is calculated by multiplying the wage index coefficient of $31.53 
by the PHA's variable value of 1.21, which equals $38.15. Another 
example is the percentage of households that have earned income. For 
each 1 percent of the PHA's assisted families that have earned income, 
the PHA receives an additional $1.02 in its base administrative fee 
amount (which is paid for all vouchers under lease, not just those 
where the family has earned income). The dollar amounts for all six 
formula variables for the PHA are then added together (and adjusted by 
the intercept) to determine the PHA's base fee per UML.
---------------------------------------------------------------------------

    \10\ Both the formula coefficients and the PHA variable values 
are rounded to two decimal places before the formula calculations 
take place. The inflation factor is rounded to four decimal places.
---------------------------------------------------------------------------

    Application of an inflation factor: An inflation factor is applied 
to the PHA's fee per UML to adjust for the increase in costs since 
2013, the year for which the study determined the administrative costs 
upon which the formula model is based.
    The PHA receives the administrative fee from HUD for each unit 
month leased for all of the vouchers it is administering, including any 
vouchers under lease that the PHA is administering as a receiving PHA 
under the portability billing procedures. However, the PHA does not 
receive the administrative fee for any of its vouchers administered by 
other PHAs under the portability procedures billing option. Instead the 
PHA will receive a separate portability administrative fee for those 
ported-out vouchers directly from HUD that is equal to 20 percent of 
the PHA's ongoing administrative fee. (Under this proposed rule, PHAs 
no longer bill for administrative fees under the portability 
procedures.)
    On an annual basis, the administrative fee is re-calculated by HUD 
based on the updated variable values for the individual PHA and 
adjusted for inflation.

V. Public Comment Received in Response to Solicitation of Comment 
Notice

    This section highlights the significant issues raised by the 
commenters and HUD's response to these issues. This section also 
solicits comment on certain specific issues.
Comments on Program Size
    Program Size. The study's cost regression models consistently found 
that programs with more than 500 vouchers under lease had significantly 
lower per unit costs than programs with 500 vouchers or fewer. In order 
to avoid a cliff effect--where a PHA administering 499 vouchers would 
receive a significantly higher fee than a PHA administering 501 
vouchers--the proposed formula gradually reduces the amount of the fee 
for different voucher program sizes rather than sharply reducing the 
fee when the voucher program size reaches 501 units under lease.
    Variable Calculation: The program size variable provides an amount 
equal to $13.94 to the UML administrative fee if the PHA has 250 or 
fewer vouchers. PHAs with 251 vouchers to 749 vouchers under lease 
receive a percentage of that $13.94 depending on the number of vouchers 
(the fewer vouchers under lease, the greater the amount the PHA would 
receive under this cost variable). The UML administrative fee amount 
for PHAs with 750 or more vouchers under lease would not be adjusted to 
account for added costs related to program size.

[[Page 44106]]

    Vouchers under lease include all port-in vouchers that are 
administered by the PHA but exclude the PHA's port-out vouchers 
administered by other PHAs.
    The UML administrative fee for the PHA is recalculated every year. 
The program size variable value for the PHA would be updated based on 
the most recent twelve months of data available from HUD's Voucher 
Management System (VMS) for unit months under lease (plus port-ins 
minus port-outs) at the time the new administrative fee is calculated.
    Dollar value of the program size adjusted for very small PHAs: In 
response to the Solicitation of Comment, commenters raised questions 
about the dollar value of the program size adjustment for very small 
PHAs. Commenters stated that the dollar value of the program size 
variable was proportionately very large in terms of the average 
administrative fee per UML of $70 under the proposed formula, and that, 
from a budgetary and public policy standpoint, it would be more 
sensible to expect local communities that wish to maintain very small, 
autonomous programs to continue to contribute their own resources to 
cover the additional administrative cost, instead of shifting all of 
that cost to the program and the Federal taxpayer. Concerns were raised 
that such a large dollar adjustment for small programs would discourage 
small PHAs from pursuing opportunities to increase administrative 
efficiencies through voluntary consortia or consolidation efforts. 
Another comment suggested that the formula only make the program size 
adjustment for small PHAs that are geographically isolated and 
represent the only existing option for program administration in the 
region or geographic area where they have jurisdiction.
    Gradual reduction and phase-out of fee adjustments as program size 
increases: Other comments focused on the formula's approach to 
gradually reducing and then phasing out the fee adjustment as the 
program size increases from 250 to 750 leased vouchers. For example, it 
was noted that this approach did not recognize that an increase in 
program size within the 250 to 750 leased unit range could actually 
increase, not decrease, administrative costs. An increase in size might 
result in a PHA having to hire more staff to handle the additional case 
load or to create a HCV program manager position, both of which would 
increase the PHA's administrative costs. Another comment questioned why 
the reduction in the fee adjustment would start at 250 units if the 
study determined that the correlation to lower costs was based on 
programs with more than 500 units.
    Provide size adjustments for greater number of program size 
thresholds: Some comments encouraged HUD to provide size adjustments 
for a greater number of program size thresholds (e.g., 1-500 vouchers, 
501-1,000 vouchers, 1,001-2,500 vouchers, etc.) as opposed to the 
straight proportional decrease proposed by the study. For example, a 
PHA with 750 vouchers would not be able to recognize the same economies 
of scale as a PHA with 10,000 vouchers but the study's recommended 
formula does not make any type of adjustment for program size beyond 
750 vouchers.
HUD Response
    HUD has not changed the program size variable from the approach 
recommended by the study for the administrative fee formula that would 
be implemented in accordance with this proposed rule. The study 
identified HCV program size as one of the most significant drivers of 
administrative costs and HUD believes that on that basis alone it 
merits inclusion in the formula at the proposed rule stage. For 
example, when just the program size of 500 vouchers or fewer under 
lease variable and the wage index variable were combined, that base 
model had an R-squared value of 0.347, meaning that it explained 34.7 
percent of the observed variation in cost among the 60 PHAs, which is 
greater than the current formula's predictive value. Also, the reality 
is that most PHAs that administer the voucher program are relatively 
small. For example, in CY 2014, 1,521 PHAs (68 percent of HCV 
administering PHAs) had 500 or fewer vouchers under lease (including 
port-ins and excluding port-outs).\11\ The number of PHAs that had 250 
or fewer vouchers under lease was 1,131 (50 percent of HCV 
administering PHAs). That said, HUD understands the concerns that the 
program size variable may direct limited administrative fee resources 
to small PHAs at the expense of more efficiently sized programs.
---------------------------------------------------------------------------

    \11\ The PHA counts and percentages in this sentence and the 
following sentence pertain to non-MTW agencies.
---------------------------------------------------------------------------

    Specific solicitation of comment #1:
    1a. HUD specifically seeks comment on whether HUD should consider 
constraining the coefficient estimate for program size.
    The program size variable is one of the most powerful variables in 
the formula and consequently the resulting fees favor small PHAs. 
Constraining the coefficient estimate in the regression model would 
reduce the dollar value of the program size adjustment in the formula 
calculation and provide greater weight to the other cost variables 
while still providing small programs with an adjustment in the base fee 
amount. For example, a fee formula could reduce the program size 
coefficient of $13.94 by 10, 20, or 30 percent.
    1b. Alternatively, HUD seeks comment on whether the proposed rule 
should reduce the impact of the formula's program size adjustment for 
only certain categories of small PHAs, such as small PHAs that have 
overlapping jurisdictions with other PHAs that administer the HCV 
program, as opposed to constraining the size coefficient estimate in 
the regression model. For example, the formula could impose limits or 
restrictions on the percentage or amount by which the covered PHA's fee 
could increase in response to the comment that communities that wish to 
maintain very small, more administratively expensive independent 
programs should continue to bear some of the responsibility for the 
financial cost of that decision under the new formula. HUD further 
seeks comment on the criteria that should be used to establish such a 
category of PHAs, as well as the methodology that would be used to 
adjust the fee.
    Specific solicitation of comment #2:
    2a. With regard to the unit size threshold based on 500 leased 
units and the approach of gradually reducing the dollar amount of the 
cost variable as program size increases between 250 and 750 units, HUD 
believes that gradual approach is preferable to a binary model where a 
PHA would see a significant change in the per unit fee as the result of 
leasing or not leasing a handful of vouchers. The study determined that 
500 units appeared to be the strongest threshold to use in terms of 
program size.
    However, HUD specifically seeks comment on whether to increase the 
unit size threshold and the corresponding adjustment range from 500 
leased units (250 to 750 unit range) to 750 leased units (500 to 1,000 
unit range) or 1,000 leased units (750 to 1,250 unit range). In keeping 
with the same methodology as the formula, if the unit size threshold 
was 750 units instead of 500 units, the dollar amount for the size 
variable could start to decrease at 500 units and would phase out at 
1,000 units (which would address the concern raised that there should 
be no increase in the program size adjustment for any program size 
below 500 units). Alternatively, if the unit size threshold was 1,000 
units, the dollar amount for the program size variable could start to 
decrease at 750 units, and

[[Page 44107]]

would phase out at 1,250 units. Another possible approach on which HUD 
seeks comment would be to narrow the range over which the adjustment is 
made, for example from 400 to 600 units or from 500 to 750 units. This 
would help address the concern that there should be no increase in the 
program size adjustment for any program size below 500 units while 
still providing protection against a cliff effect.
    The study tested different size categories of vouchers under lease 
\12\ as well as a continuous variable for the number of vouchers under 
lease. The coefficients on the other size variables were not 
statistically significant, and the continuous variable measure of size 
was not significant, so the study results were unable to identify where 
an increase in vouchers might result in an increase in UML 
administrative costs.
---------------------------------------------------------------------------

    \12\ Program with 500 or fewer vouchers; program with 501 to 
5,249 vouchers, program with 5,250 to 9,999 vouchers; program with 
10,000 plus vouchers.
---------------------------------------------------------------------------

    2b. HUD specifically seeks comment on whether the program size 
variable value for the PHA should be updated based on the average 
vouchers under lease for the most recent 12 months of data available at 
the time the new administrative fee is calculated, as is being 
proposed, or for a longer period of time, such as the most recent 24 or 
36 months. Using a 2- or 3-year average for the program size variable 
would lessen the short-term impact of a reduction in per unit fee 
associated with a major increase in program size, as might happen if a 
PHA received a large allocation of new vouchers or absorbed another 
PHA's program.
    Specific solicitation of comment #3: In response to concerns that 
the size variable would discourage creating greater efficiencies 
through consortia \13\ or consolidation, HUD specifically seeks comment 
on this issue. For example, the formula could apply a different program 
size value for a certain period (e.g., first three years following the 
consolidation or formation of the consortium) than the standard 
calculation under the proposed administrative fee formula. This interim 
program size value could be calculated based on the number of vouchers 
under lease (prior to the consolidation or formation the consortium) 
for the PHA that had the greatest number of vouchers under lease at 
that time of the consolidation or formation of the consortium. Under 
this approach, the formula would generate a higher per unit fee for the 
time period in question or could be gradually phased out. This 
adjustment would also help to defray start-up costs and other 
transitional expenses of consolidating programs or forming the 
consortia.
---------------------------------------------------------------------------

    \13\ On July 11, 2014, HUD published a proposed rule on 
``Streamlining Requirements Applicable to Formation of Consortia by 
Public Housing'' (79 FR 40019) proposing to allow PHAs to form 
single-ACC consortia. Under the proposed rule, PHAs that form a 
single-ACC consortium would receive administrative fees based on the 
total vouchers under lease for the consortium.
---------------------------------------------------------------------------

    HUD is seeking comment not only on this option, but is also 
interested in any other ideas on how the size variable could be 
adjusted with respect to consortia or consolidated programs.
    Specific solicitation of comment #4: HUD also specifically seeks 
comment on adopting such a policy for a small PHA when another PHA has 
overlapping jurisdiction.
Comments on Wage Index
    Wage Index. The study's analysis of cost drivers showed that wage 
index--a geographic index of local government wages constructed from 
data collected through the Bureau of Labor Statistics Quarterly Census 
of Employment and Wages (QCEW)--is a very strong driver of per unit 
administrative costs. PHAs with higher local wages relative to the 
national average have higher per unit administrative costs and PHAs 
with lower local wages relative to the national average have lower per 
unit administrative costs. This is consistent with the theory that PHA 
employees are paid at different wage rates based in part on the 
prevailing wage in the part of the country in which the PHA is located. 
As a result, PHAs operating in areas with higher than average 
prevailing wage rates will have higher administrative costs.
    Variable Calculation: The fee calculation for the wage index 
variable is $31.53 multiplied by the PHA's wage index ratio. The 
possible values for the wage index variable are limited to the highest 
and lowest values for the 60 PHAs in the study sample, which are 1.46 
and 0.64 respectively. (The reasons for limiting the value of the 
variable to the maximum and minimum values observed in the study sample 
are discussed further below.)
    For PHAs located in metropolitan areas, the wage index is the local 
government wage for the metropolitan Core Based Statistical Area (CBSA) 
in which the PHA headquarters is located divided by the national 
average local government wage.\14\ If the local government wage for a 
metropolitan CBSA is missing or unavailable, the wage index is the 
average local government wage for the counties with available data in 
the metropolitan CBSA in which the PHA headquarters is located divided 
by the national local government wage. If neither the CBSA data nor the 
county data is available, the wage index is the State average local 
government wage for metropolitan areas divided by the national average 
local government wage.
---------------------------------------------------------------------------

    \14\ Core Based Statistical Area (CBSA) is a collective term for 
metropolitan and micropolitan statistical areas (metro and micro 
areas). A metro area contains a core urban area of 50,000 or more 
population, and a micro area contains an urban core of at least 
10,000 (but less than 50,000) population. Each metro or micro area 
consists of one or more counties and includes the counties 
containing the core urban area, as well as any adjacent counties 
that have a high degree of social and economic integration (as 
measured by commuting to work) with the urban core. For more 
information, see https://www.census.gov/population/metro/.
---------------------------------------------------------------------------

    For PHAs located in micropolitan areas, if the local government 
wage for a micropolitan CBSA is missing or unavailable, the wage index 
is the average local government wage for the counties with available 
data in the micropolitan CBSA in which the PHA headquarters is located 
divided by the national local government wage. If the county data are 
not available, the wage index is the State average local government 
wage for non-metropolitan areas (including micropolitan areas) divided 
by the national average local government wage.
    For all other PHAs, the wage index is the state's average local 
government wage for non-metropolitan areas (including micropolitan 
areas) divided by the national average local government wage.\15\ As 
part of the annual adjustment of the administrative fee, the wage index 
for the PHA is recalculated each year using the most recent annual data 
available from the QCEW.
---------------------------------------------------------------------------

    \15\ The QCEW does not publish data on local government wages 
for the U.S. Virgin Islands, Guam, and the Northern Mariana Islands. 
PHAs in these places are assigned the national average local 
government wage, resulting in a wage index value of 1.
---------------------------------------------------------------------------

    The study's recommended formula used a wage index that was based on 
the average local government wage for metropolitan areas of the State 
and the average local government wage for non-metropolitan areas of the 
state. If the PHA headquarters was in a metropolitan county, the PHA 
was designated as a metropolitan PHA, and if the PHA headquarters was 
in a non-metropolitan county, the PHA was designated a non-metropolitan 
PHA. For each state, the study team calculated the average government 
wage for metropolitan counties and the average government wage for non-
metropolitan counties. For a metropolitan PHA, the wage index was the 
state's average government wage for metropolitan counties divided by 
the national average wage rate. For a non-

[[Page 44108]]

metropolitan PHA, the wage index was the state's average government 
wage for non-metropolitan counties divided by the national average wage 
rate.
    Several commenters expressed concern that the use of a State 
average is unfair to PHAs in high-cost, high-wage metropolitan areas. 
The commenters believed that relying on the State average to account 
for wage variations among individual PHAs significantly understates the 
costs of salaries in higher cost metropolitan areas while overstating 
the cost of wages in lower cost metro areas of the same state.
HUD Response
    The failure of the statewide average wage index to account for a 
potentially wide range of local government wages within a State is a 
significant concern. As an alternative approach for the formula under 
this proposed rule, HUD considered two alternatives to the study's QCEW 
wage index model. One model used county level data and substituted the 
State metro average or non-metro average, as applicable, for any county 
that was missing data. The other model used CBSA-level data for 
metropolitan areas and micropolitan areas, where available, and the 
State non-metropolitan average for other areas. The CBSA-level model is 
preferable to the county level model in that it explains a higher share 
of the observed variation in PHA costs and better approximates the 
labor markets in which PHAs are operating. HUD has adjusted the wage 
index formula variable accordingly for the fee formula that would be 
implemented under this proposed rule by using the CBSA-level data, 
where available, for PHAs in metropolitan and micropolitan areas, as 
described above.
Comments on Benefit Load (Health Insurance Cost Index in the Study's 
Recommended Formula)
    Benefit Load. The benefits provided to HCV staff are an important 
component of labor costs and may vary differently from the local wage 
rates captured by the wage index variable. The benefit load variable 
replaces the Health Insurance Cost index in the study formula. The 
reason for the change is discussed in detail in the comment section 
below.
    Variable Calculation: Using the information that PHAs report in the 
Financial Data System (FDS), HUD created a benefit load for each state. 
This State benefit load is calculated in the following manner. For each 
state, the total benefits paid by PHAs in the State for HCV employees 
for the most recent three years is divided by the total salaries paid 
by PHAs in the State for HCV employees for the same three years.\16\ 
The State benefit load is the average benefit load for all the PHAs in 
the state. The fee calculation for the benefit load variable is $0.78 
multiplied by the PHA's State benefit load. The possible values for the 
benefit load variable are limited to the highest and lowest values for 
the 60 PHAs in the study sample, which are 60.48 and 22.56 
respectively.
---------------------------------------------------------------------------

    \16\ In calculating the benefit load percentage, only data from 
approved submissions were used. When available, the approved audited 
data were used. Approved unaudited data were used for cases where 
the audited submission was not approved or submitted yet or the PHA 
was not audited.
---------------------------------------------------------------------------

    As part of the annual adjustment of the administrative fee, the 
State benefit load for the PHA would be recalculated each year using 
the most recent three years of data available for all PHAs from the 
FDS.
    As noted earlier, the study's recommended formula did not include 
this variable. The study's recommended formula addressed the variation 
in benefits costs through the Health Insurance Cost Index variable.
    Before discussing the comments on this indicator, some background 
on how the study arrived at the Health Insurance Cost Index would be 
helpful. The study team originally tested two different approaches to 
addressing the variation in benefits costs. In both cases the study 
team created an index of benefits costs. The first index was based on 
the Bureau of Labor Statistics Employer Cost for Employee Compensation 
(ECEC) survey. This survey measures employer costs for wages, salaries, 
and employee benefits for nonfarm private and State and local 
government workers. Unfortunately, estimates of benefits costs were not 
available other than at the national level for State and local 
government workers. As a result, the total benefits cost index the 
study team created for each PHA (the total benefits cost for the PHA's 
census division divided by the average total benefits cost for nation 
as a whole) under this approach was based on private industry workers, 
not State and local government employees. Furthermore, the estimates of 
benefit costs for private industry workers were only available at a 
census region and division level, which resulted in a benefits index 
based on a relatively broad geographic area.
    The second approach created a health insurance cost index based on 
the Department of Health and Human Services' Medical Expenditure Panel 
Survey (MEPS), which provides state-level data on the health insurance 
costs, but unfortunately also of private employers. The health 
insurance cost index was created by first subtracting the average total 
employee contribution from the average employee-plus-one premium for 
each State in order to develop a measure of employer health insurance 
cost. The study team then averaged the employer health insurance cost 
across the states to produce a national average. The health insurance 
cost index for each State is calculated by dividing each state's 
employer health insurance cost by that national average. The PHA was 
assigned the health insurance cost index that corresponded to the State 
in which it is located.
    Both of the study's approaches had positive coefficients in the 
combined cost driver model (meaning that higher local benefits costs 
are associated with higher per unit administrative costs) but neither 
was statistically significant. The study ultimately chose to include 
the MEPS-based model for benefits costs for the health insurance cost 
index in the proposed formula as the better proxy. The study 
recommended inclusion of the health insurance cost variable in the 
formula, despite its lack of statistical significance, in recognition 
of the importance of addressing the variation in benefits costs among 
PHAs.
    The Solicitation of Comment Notice asked for comments on whether 
health insurance costs are a good proxy for the benefits costs facing 
PHAs and if the variable, given its weak statistical significance, 
should be included as part of the formula under this proposed rule.
    Comments were generally supportive of including a formula variable 
that addressed the variation in benefits costs. However, concerns were 
expressed that an index based on the statewide average of health 
insurance costs does not adequately represent the full range (and 
consequently the full variation) of benefits costs that PHAs incur. 
Commenters mentioned the cost of pensions as a prime example of a major 
expense that could vary by PHA and that is not accounted for in the 
study's recommended formula. Commenters encouraged HUD to find a data 
point that would more accurately capture variation in the costs of all 
benefits, as opposed to solely relying on a health insurance cost 
index.
HUD Response
    As noted earlier in this preamble, HUD has replaced the health 
insurance cost index with a new variable designed to more directly 
address the variation in total benefits costs for PHAs. Using the 
information that PHAs report in the

[[Page 44109]]

FDS, HUD created a new ``benefit load'' index for each state.
    The benefit load is calculated in the following manner. For the 
most recent three years of data available in FDS, the sum of the total 
benefits paid to HCV employees is divided by the sum of the total 
salaries paid to HCV employees from the PHA's FDS submission. The total 
benefits cost comes from line items on the FDS that capture PHA 
contributions to employee benefit plans such as pension, retirement, 
and health and welfare plans. In addition, the included line items 
record administrative expenses paid to the State or other public agency 
in connection with a retirement and other post-employment benefit plans 
(if such payment is required by State law), and with trustee's fees 
paid in connection with a private plan (if such payment is required 
under the plan contract).
    The average benefit load for the PHAs in the State is calculated by 
dividing the total benefits paid to HCV employees (across all PHAs in 
the state) by the total salaries paid to HCV employees (across all PHAs 
in the state). PHAs with missing or negative benefit load were not 
included in this calculation. Each PHA is assigned the average benefit 
load for its state.
    When added to the regression model, the benefit load variable has a 
positive coefficient (PHAs in the sample with a higher benefit load had 
higher per unit administrative costs) and is statistically significant. 
The other advantage of this approach is that it directly accounts for 
all benefits that would contribute to cost variations between PHAs, not 
just health insurance costs. In addition, it relies on data that apply 
exclusively to PHAs, as opposed to the ECEC or MEPS data approaches 
that used private sector data as a proxy.
    The use of a state-wide average and a three year average in 
calculating the benefit load is intended to mitigate the distorting 
effects of year-to-year fluctuations in benefit costs. By using the 
State average and three years of cost data, HUD hopes that the formula 
will reflect the cost variation in benefits such as health care, 
pensions, and other retirement plans from State to state, without 
unduly influencing the amount of total benefits provided by individual 
PHAs.
    Specific solicitation of comment #5: HUD specifically seeks comment 
on the new benefit load variable. Is it a better proxy for variations 
in benefits than the original health care cost variable or should the 
final rule revert to the study's original health insurance cost index? 
Or is there a preferable alternative to addressing the variation in 
benefit costs, such as reconsidering using the ECEC-model the study 
tested or some other approach?
Comments on Small Area Rent Ratio
    Small Area Rent Ratio. The study's recommended formula included the 
small area rent ratio variable, also referred to in the study as the 
SARR. The SARR variable described the extent to which HCV participants 
are located in neighborhoods that are harder, or easier, to serve at 
payment standards set within the basic range of the HUD published Fair 
Market Rent (FMR). The SARR was intended to capture the local housing 
market conditions that PHAs are working under and could also reflect 
outcomes associated with expanding housing opportunities.
    For PHAs in metropolitan areas, the SARR was calculated as the 
median gross rent for the zip codes where voucher holders live, 
weighted by the share of voucher holders in each zip code, divided by 
the median gross rent for the metropolitan area. The theory behind the 
SARR is that having more voucher families leased in more expensive zip 
codes will increase administrative costs because it is more difficult 
for the PHA to recruit landlords and because voucher families might 
need more guidance and assistance in finding housing in unfamiliar 
neighborhoods.
    For PHA in non-metropolitan areas, data on gross rents by zip code 
are not available. For these agencies, the SARR was calculated as the 
unadjusted two-bedroom FMR for the non-metropolitan counties where the 
PHA operates divided by the published FMR. The SARR would usually equal 
one for non-metro PHAs as HUD does not measure any variation in rents 
with these non-metropolitan counties. However, for some counties the 
FMR is set at the State minimum rather than the 40th percentile rent in 
the county. PHAs operating in these counties should have relatively 
lower costs in placing tenants because the HUD FMR is more generous, 
and the SARR was designed to adjust for that condition for those non-
metro counties.
    Many commenters questioned the study's assumption that the SARR 
would be reflective of the actual cost and effort to expand housing 
opportunities, or that the SARR is a legitimate proxy for the variation 
in administrative costs related to the challenges of leasing units in 
more expensive markets. For example, some comments questioned if the 
SARR largely benefited the wrong PHAs if the objective was to recognize 
and account for efforts to expand housing opportunities. Because the 
SARR is based on metro-area rents, PHAs operating in higher cost 
suburban areas would typically receive higher fees while those 
operating in disadvantaged urban cores would receive lower fees 
regardless of the agencies' respective efforts to expand housing 
opportunities. Commenters suggested that the SARR simply reflects the 
degree to which a PHA's jurisdiction and hence their participating 
families are housed in more expensive areas of the metropolitan area. 
While in some cases the zip code areas in which the families reside may 
be an indication of staff time and effort to expand housing 
opportunities, commenters noted that in other cases the SARR only 
reflects where the jurisdiction's rental units are concentrated or 
where the PHA jurisdiction happens to be located within the metro area. 
Furthermore, the SARR is impacted by a range of factors beyond the 
administrative elements and PHA effort, including the accuracy of the 
FMR, the PHA's available HAP, and the availability of rental housing 
units in high cost parts of the community. In addition, the fact that 
the SARR was not consistently statistically significant when tested 
with a variety of different variables may be cause for concern that the 
relationship between the SARR and administrative cost per unit is not 
particularly robust.
    Other comments were concerned that the methodology of the SARR too 
closely paralleled HUD's small area FMR methodology. Commenters noted 
that it is premature to make any assumptions on administrative costs 
based by replicating the small area FMR demonstration approach into a 
cost variable since the demonstration is still ongoing. The comments 
noted HUD has yet to release its evaluation on whether the small area 
FMR demonstration achieved its objectives and to what extent small area 
FMRs resulted in additional administrative cost and complexity for the 
demonstration PHAs.
    A number of commenters suggested that the SARR either be 
supplemented or replaced with add-on fees outside of the fee formula 
that would better incentivize or directly recognize efforts to expand 
housing opportunities.
HUD Response
    After careful consideration of the comments, HUD decided to remove 
the SARR from the formula that would be implemented in accordance with 
this proposed rule. HUD is sensitive to the concerns that the SARR may 
be more of an artifact of where PHA jurisdictions are located than an 
indicator of the level of additional effort to expand housing

[[Page 44110]]

opportunities or recruit landlords in what may be more expensive rental 
markets. HUD was also concerned about the instability of the variable 
when tested with other combinations of variables in different 
regression models.
    Specific solicitation of comment #6. HUD is specifically requesting 
comment on whether the SARR or some other indicator that would address 
the variation in administrative cost as it relates to locational 
outcomes and expanding housing opportunities should be reconsidered for 
inclusion in the core formula. For example, one possibility is to 
include a variable that measures the degree to which voucher families 
are not overly represented in racially or ethnically concentrated areas 
of poverty (R/ECAPs) compared to the distribution of rental units 
within the PHA jurisdiction.\17\ Another possibility is to include a 
variable that examines the degree to which the percentage of a PHA's 
families that reside in areas of concentrated poverty is declining.
---------------------------------------------------------------------------

    \17\ Racially or ethnically concentrated area of poverty means a 
geographic area with significant concentrations of poverty and 
minority populations (24 CFR 5.152). To assist communities in 
identifying R/ECAPs for the Assessment of Fair Housing, HUD has 
developed a census tract-based definition of R/ECAPs that involves a 
racial/ethnic concentration threshold and a poverty test. The 
racial/ethnic concentration threshold is that for metropolitan 
areas, R/ECAPs have a non-white population of 50 percent or more. 
For non-metropolitan areas, R/ECAPs have a non-white population of 
50 percent or more. The poverty threshold is that R/ECAPs must have 
a poverty rate that exceeds 40 percent or is three or more times the 
average tract poverty rate for the metropolitan/micropolitan area, 
whichever threshold is lower. See ``Data Documentation'' posted at 
https://www.huduser.gov/portal/affht_pt.html#affhassess-tab.
---------------------------------------------------------------------------

    An additional option is to base the indicator on the number of 
families that initially lease in low-poverty areas or that move out of 
areas with high concentrations of poverty or R/ECAPs to less 
concentrated areas. Alternatively, HUD could base the indicator on the 
extent to which the overall percentage of the PHA's families residing 
in low-poverty areas increases, and/or the extent to which the overall 
percentage of the PHA's families residing in areas with high 
concentration of poverty or residing in R/ECAPs decreases from year to 
year. Both measures would take into consideration the locational 
outcomes of families that moved out of the of the PHA's jurisdiction 
under the portability procedures.
    Given the challenges that determining the actual cost and effort in 
terms of locational outcomes posed for the study, HUD recognizes it may 
be very difficult to design an indicator that is statistically 
significant and truly reflects the cost variation for locational 
outcomes among the sample PHAs in the regression model. HUD seeks 
public comment on whether the locational outcomes indicator should 
nevertheless be included in the core formula if it is not found to be 
statistically significant, similar to the new admissions indicator, 
which is not significantly significant but has a strong theoretical 
basis. An alternative approach is to address locational outcomes 
through the use of supplemental fees, which would be provided in 
addition to the administrative fee that is based on the regression 
model. Additional cost factors and supplemental fees are discussed 
later in this preamble. HUD is specifically seeking comment on fees for 
locational outcomes and expanding housing opportunities (see Specific 
solicitation of comment #21).
Comments on Households With Earned Income
    Households with Earned Income. This variable is the percentage of 
the PHA's voucher households with any income from wages. The PHA's 
voucher households are defined as the PHA's vouchers under lease in its 
jurisdiction plus any port-in vouchers under lease that the PHA is 
administering on behalf of other PHAs, minus its port-out vouchers that 
are administered by other PHAs.
    Variable calculation: The fee calculation for the households of 
earned income variable is $1.02 multiplied by the most recent three 
year average of the percentage of the PHA's households that had earned 
income reported in the PIH Information Center (PIC) as of their last 
recertification during the measurement year. The possible values for 
the households with earned income variable are limited to the highest 
and lowest values for the 60 PHAs in the study sample, which are 56.11 
and 15.58 respectively.
    As part of the annual adjustment of the administrative fee, the 
percentage of households with earned income would be recalculated each 
year using the most recent three years of PHA data from PIC (or its 
successor program).
    The study tested many different measures of the characteristics of 
the HCV population to see if these different family characteristics 
impacted administrative costs. Of all the family characteristic 
variables that were tested, seven were statistically significant when 
added to the base model of wage index and program size. Among the five 
variables associated with higher cost--percent of households that are 
family households; percent of households with three or more minors 
(hard-to-house families); percent of households with 6 or more members 
(large families); percent of households with majority of income from 
earnings; and percent of households with any income from earnings--the 
study determined that the percent of household with any income from 
earnings was the strongest cost driver when controlling for local wage 
rates and program size.
    The majority of family households have earned income so there is 
substantial overlap between family households and households with 
earned income. Because of this overlap and correlation, percent of 
households that are family households was no longer significant when 
the study team attempted to put both the family and earned income 
variables in the same model. Therefore, the study team retained the 
earned income variable in the recommended formula but dropped percent 
of households that are family households.
    In addition to the extra work required to verify wage income, the 
study suggested that another reason why the percent of wage earning 
households is a significant cost driver is because family households 
(highly-correlated with wage earning households) are substantially more 
likely to receive interim reexaminations than non-family households and 
are more likely to change units. Interim reexaminations and move 
processing represent extra work for the PHA, adding to administrative 
costs.
    Many comments raised concerns about this particular formula 
variable. Some comments stated that the study's findings did not match 
the commenters' experiences at their PHAs. These comments expressed the 
view that assisting elderly and disabled families was just as 
administratively costly as assisting families with earnings. For 
example, it was stated that calculating deductions for unreimbursed 
medical expenses can be very time-consuming and cumbersome. In 
addition, elderly and disabled families may be more likely to have 
special needs or reasonable accommodations. For instance, PHA staff may 
need to conduct annual examinations at the family's unit as opposed to 
requiring the family to come to the PHA's office.
    Other comments focused less of the accuracy of the study's findings 
and more on the potential unintended consequences of a formula that 
provides PHAs with a higher fee for assisting more working families. 
The weight and wide range of the variable can have a significant impact 
on the PHA's administrative fee (for example, the potential range of 
the dollar value for percentage of families with earned

[[Page 44111]]

income variable under this proposed rule is between $15.89 and $57.23). 
Commenters expressed concern that the value of this cost variable in 
the fee formula would force PHAs to establish admission preferences for 
working families and/or eliminate preferences for disabled or homeless 
families in order to increase the number of families with earned income 
and generate higher administrative fees. Commenters suggested that the 
recommended formula, combined with the need to maximize administrative 
fee revenue, would ultimately have a detrimental impact on household 
types less likely to have income from wages if the variable is included 
in the formula.
HUD Response
    HUD did not eliminate or modify the households with earned income 
variable for the fee formula under this proposed rule. While 
recognizing that the study's cost data and time reporting is limited to 
the 60 PHAs in the study sample, the study's data collection simply 
does not substantiate the comments that contend that assisting elderly 
and disabled families is as administratively costly as assisting 
families with earned income. On the contrary, the study's correlation 
analysis specifically examined the relationship between the percentage 
of households with non-elderly disabled heads and elderly headed 
households and HCV administrative costs. In both cases the coefficient 
value for the variable was negative, not positive. This means that the 
higher the percentage of non-elderly disabled headed households and the 
higher the percentage of elderly households assisted by the PHA, the 
lower the UML administrative cost for the agency. The actual RMS 
collection data also conclusively showed that elderly and disabled 
families took less time on the most time consuming aspect of the 
program (annual recertifications) and were therefore less costly than 
assisting non-elderly and non-disabled families for the sample PHAs. 
Both the data collection and the regression analysis on elderly and 
disabled families support the study's ultimate determination that the 
percentage of families with earned income variable is a significant 
cost driver in the administration of the HCV program.
    This formula variable is not in any way intended to force or 
pressure PHAs into serving more families with earned income at the 
expense of the people with disabilities or elderly people. On the 
contrary, it is included so that PHAs are not discouraged from serving 
families with earned income as a result of the higher administrative 
costs associated with those families by compensating PHAs for those 
higher costs.
    That said, HUD remains concerned that this variable could 
potentially have unintended consequences in terms of the types of 
families that the program serves.
    Specific solicitation of comment #7:
    7a. HUD specifically seeks comment on whether this variable should 
be removed from the formula despite the strong correlation between it 
and administrative costs.
    7b. HUD also specifically seeks comment as to whether the formula 
should constrain the coefficient estimate for the percent of households 
with earned income variable. This would reduce the dollar value of the 
households with earned income adjustment in the formula calculation and 
provide greater weight to the other cost variables while still 
providing an adjustment in the base fee amount for households with 
earned income. For example, the formula could reduce the earned income 
coefficient of $1.02 by 50 percent or some other percentage. HUD is 
particularly interested to know if there is a specific amount of 
percentage decrease or other constraint that the commenter would 
propose and the rationale for the commenter's recommendation.
    7c. HUD also seeks comment on other ideas to broaden or modify this 
particular formula variable.
    7d. HUD also seeks comment on how to address concerns related to 
this indicator on efforts to assist the homeless. Unlike elderly and 
disabled families, the simple regression analysis did indicate that 
PHAs that had a strong admissions preference for homeless had a 
positive coefficient (meaning that the PHAs had higher administrative 
costs) although it was not statistically significant.
    Elsewhere in this preamble, HUD is proposing to provide an 
additional fee for new admissions from the waiting list that are 
homeless families. In this regard, HUD seeks comment on those 
particular issues later in the rule.
    Specific solicitation of comment #8:
    8a. Would the homeless new admission add-on fee adequately address 
the concerns that the fee formula may inadvertently create a 
disincentive for PHAs to serve the homeless?
    8b. Alternatively, should a formula variable for homeless new 
admissions or current participants who were formerly homeless be 
included in the base fee calculation? For example, one possibility is 
to revise the percent of households with earned income variable to 
include formerly homeless families (e.g., homeless families that were 
admitted within the most recent three years) in addition to families 
with earned income when calculating the percentage that is the PHA 
variable value. One concern about this approach is the quality of the 
data reported to HUD on homeless admissions. It is evident that many 
PHAs do report this data, but in other cases it appears that the data 
is not reported.
    8c. HUD is interested on hearing from PHAs and other stakeholders 
on their experiences with homeless data and reporting homeless data, 
whether the data reporting would be reliable enough to include in the 
model, and whether there are changes in guidance or other approaches 
HUD could take to improve the accuracy, completeness, and reliability 
of homeless admissions data in the HCV program.
Comments on New Admission Rate
    New Admissions Rate. Based on the amount of time that PHAs spend on 
intake, voucher issuance, and lease-up for households newly admitted to 
the program, a relatively higher percentage of new admissions in a 
PHA's program should increase per unit administrative costs. This 
formula variable is defined as the number of new households admitted to 
the voucher program as a result of voucher turnover or new allocations 
of vouchers in the year, divided by the number of vouchers under lease 
(including port-in but excluding port-out vouchers). Although the 
study's cost driver analysis did not find that the new admissions rate 
was significantly associated with costs, the rate of new admissions had 
such a strong theoretical reason for impacting costs the study team 
decided it should still be included as a component of the fee formula. 
HUD has retained the new admission rate variable in the fee formula 
under this proposed rule.
    Variable Calculation: The fee calculation for the new admissions 
rate variable is $0.15 multiplied by the most recent three year average 
of the percentage of the PHA's households that were reported in PIC as 
new admissions at any time during the measurement year. The possible 
values for the new admissions rate variable are limited to the highest 
and lowest values for the 60 PHAs in the study sample, which are 52.19 
and 2.93 respectively.
    As part of the annual adjustment of the administrative fee, the new 
admissions rate for the PHA would be recalculated each year using the 
most recent three years of PHA data from PIC (or its successor 
program).
    The comments were generally supportive of including the new

[[Page 44112]]

admissions rate as a formula variable despite the fact it was not 
statistically significant in the regression model. There were a number 
of concerns that the impact of the variable may be understated because 
during the study period many PHAs had stopped or severely reduced 
leasing due to sequestration funding cuts.
    The study attempted to address the concerns regarding the reduction 
in HAP funding and the impact on leasing in 2013 by testing two 
measures of new admissions in the cost driver analysis: The rate of new 
admissions in 2013 and the rate of new admissions in 2012. The HAP 
funding proration in 2012 was 99.6 percent as compared to the 94 
percent HAP funding proration in 2013.
    For purposes of developing the proposed formula model, the study 
used the new admissions from 2012. The study team determined that the 
2012 new admissions rate was more representative of the cost data 
collected than the 2013 new admissions rate because many PHAs reduced 
their leasing substantially in 2013 in response to the reduced HAP 
funding. The HAP funding proration in 2012 was equal to or exceeded the 
HAP funding pro-rations in 2011, 2010, and 2009 (99.5 percent, 99.5 
percent, and 99.1 percent respectively). Furthermore, the study cost 
estimates included upward cost adjustments to account for any staff 
reductions that took place before the study's data collection period in 
order to approximate the level of staffing that was needed by the PHAs 
in 2012.
    Another comment concerned the impact of incoming families under the 
portability procedures. It was noted that many of the tasks the 
receiving PHA does to assist an incoming portability family lease in 
its jurisdiction are the same as what the PHA would do for any other 
new admissions.
HUD Response
    The new admissions rate currently does not include incoming 
portability families unless the PHA has absorbed the family into its 
own program.
    Specific solicitation of comment #9: HUD specifically requests 
comment on whether the numerator for the new admissions rate should 
include families that initially leased in the PHA's jurisdiction under 
the portability procedures to capture the increased cost for the 
receiving PHA, regardless of whether the PHA chooses the billing option 
instead of absorbing the family into its own program.
Comments on 60 Miles Variable
    60 miles. The 60 miles variable is a measure of the size of the 
PHA's jurisdiction. The variable is defined as the percentage of 
voucher households that live more than 60 miles from the PHA's 
headquarters. The study determined that PHAs that serve large 
geographic areas have higher costs. The reasons for these higher costs 
may include inspectors having to travel greater distances to units or 
that the PHA may need to establish and operate satellite offices.
    Formula Variable: The fee calculation for the 60 mile variable is 
$0.83 multiplied by the percentage of families that reside more than 60 
miles from the PHA's headquarters, based on the addresses reported in 
PIC. The possible values for the 60 mile variable are limited to the 
highest and lowest values for the 60 PHAs in the study sample, which 
are 47.39 and 0 respectively.
    As part of the annual adjustment of the administrative fee, the 60 
mile variable would be recalculated each year using the most recent 
year of PHA data from PIC (or its successor program).
    The study's recommended formula calculated the percentage by 
geocoding the addresses of individual voucher families and the address 
of the PHA's headquarters and calculating the shortest distance between 
the two points. (Port-out vouchers were not included in the 
calculation.) The cost driver analysis found that the percent of 
households living more than 60 miles from the PHA's headquarters is 
significantly and positively associated with administrative costs.
    The study found that 87 percent of PHAs had no voucher families 
living more than 60 miles from the PHA's headquarters, so this variable 
mainly affects a minority of PHAs with very large jurisdictions and 
statewide PHAs. However, the variable range was very broad (from 0 to 
47.39) and adds $0.83 (under the formula in this proposed rule) for 
each percentage increase in the percent of families living more than 60 
miles from the PHA headquarters. So although the variable does not 
apply to most PHAs, it has a dramatic effect on the per unit 
administrative fee for the relatively few agencies with higher 
percentages of families living more than 60 miles from the PHA 
headquarters.
    Some commenters expressed concern about how the distance from PHA 
headquarters was measured. It was noted that the 60 mile standard was 
calculated as the shortest point to point distance between the PHA 
headquarters and the family's unit. Comments noted that this would be 
problematic for agencies where a significant percentage of families 
might live within a 60 mile radius of the PHA headquarters, but the 
travel distance by road was in excess of 60 miles.
    Other commenters questioned the basic premise of the 60 mile 
variable, noting that some State agencies or PHAs subcontract their 
operations to other agencies or entities, and that those entities 
operate in their respective service areas, using their own employees 
and office buildings. In those cases, the PHA is not required to have 
its own inspectors cover large distances or operate satellite offices. 
Other commenters specifically questioned the validity of the 60 mile 
variable for State agencies. These comments pointed out that State 
agencies, by their very nature, are established and designed to 
administer programs across the entire state, and as such already have 
regional facilities and staff available to accomplish their state-wide 
mission. It was noted that as a result of the distance variable, many 
State agencies would see large increases in their administrative fees. 
A commenter stated that if it so much more expensive to administer the 
program over a large geographic area, it would make more sense to 
require the State agency to port families beyond the 60 mile radius to 
local agencies that may also have jurisdiction over the area.
HUD Response
    In cases where an agency has a large jurisdiction, HUD recognizes 
the agency may subcontract its administrative responsibilities or 
utilize an existing administrative structure (including resources and 
offices) that does not require inspectors to travel large distances or 
for the agency to open stand-alone satellite offices to effectively 
administer the HCV program. However, HUD believes that it is not 
feasible to create different distance variables based on a wide variety 
of different administrative models employed by PHAs, nor is it fair to 
completely exclude PHAs from a particular variable solely on the basis 
that they are a State agency and therefore should be expected to absorb 
any additional cost of administration related to distance. In addition, 
a PHA that chooses to subcontract administrative responsibilities to 
other entities to cover specific service areas may not have to maintain 
satellite offices or require inspectors to cover significant distances 
but will incur additional administrative costs to monitor those 
contracts, conduct quality control on the subcontractors' work, and 
otherwise ensure that the subcontractor is carrying out the 
administrative responsibilities that the PHA is ultimately accountable 
for under its

[[Page 44113]]

Consolidated Annual Contributions with HUD.
    With respect to concerns about the 60 mile distance being 
calculated as a point to point calculation as opposed to being based on 
actual road distance, HUD will consider changing the measure for 
purposes of the administrative fee formula in the final rule. For now, 
the 60 mile threshold remains determined by calculating the shortest 
distance from the unit to the PHA headquarters. Determining the 
distance by road is more cumbersome than the straight line method, and 
would not necessarily reflect road closures, traffic congestion, tolls, 
etc., that would impact travel time and administrative cost as well as 
distance.
    Specific solicitation of comment #10:
    10a. HUD specifically requests comment on another alternative, 
which is to reduce the distance from 60 miles to a shorter distance of 
50 miles to account for the potential deficiencies in the 60 mile 
``point to point'' calculation method instead of attempting to map the 
distance by road each year. The study tested 50 miles as an alternative 
distance formula variable. The 50 mile variable also had a positive 
coefficient sign when tested, meaning that PHAs is the study sample 
with a higher percentage of families residing 50 miles from the PHA 
headquarters had higher per voucher administrative costs. The variable 
was statistically significant but did not explain as much of the 
variation in cost.
    10b. HUD also specifically seeks comment on whether the formula 
should constrain the coefficient estimate for the 60 miles variable. 
This would reduce the dollar value of the 60 miles adjustment in the 
formula calculation and provide greater weight to the other cost 
variables while still providing an adjustment in the base fee amount 
for PHAs that serve households residing more than 60 miles from the PHA 
headquarters. For example, the formula could reduce the 60 miles 
coefficient of $0.83 by 50 percent or some other percentage.
Additional Comments on Distance Measurement
    Other comments questioned whether distance was the appropriate 
measure of the variation in cost to administer the program in a given 
area. For example, agencies in urban areas, while traveling shorter 
distances, may have greater time and cost burdens than a larger rural 
area, due to traffic congestion, the cost of parking, the need to rely 
on a variety of transportation options, etc.
    The study examined the subject of PHA jurisdictional size and type 
in detail. One of the tested cost drivers was the urban PHA variable, 
which was defined as the percent of the overall population within the 
PHA's jurisdiction that lives in urban areas based on the 2010 census 
definition. The problem with the urban PHA cost driver was that there 
was not a strong theoretical basis for its effects on HCV program 
costs. For example, many of the reasons why costs would be higher 
(e.g., such as traffic congestion adding to inspection times) might be 
offset by time-saving characteristics, such as HCV units tending to be 
less dispersed. Another weakness was that when a related variable was 
tested that measured the percentage of HCV households in the PHA 
program that reside in urban areas, the coefficient for that variable 
was negative (meaning that PHAs in the sample with higher percentages 
of HCV families living in urban areas tended to have lower costs) and 
not statistically significant. The study team did not include the urban 
PHA variable in the recommended formula because it was not clear how 
operating in a jurisdiction with a more urban population would increase 
program costs while serving more HCV households in urban areas 
decreases costs.
    By contrast, the distance variable was positive and statistically 
significant, both at 50 and 60 miles, leading the study to conclude 
that it was a significant cost driver that should be included in the 
formula.
    Other commenters suggested that HUD consider the overall area of 
the PHA's jurisdiction in terms of square miles, rather than the 
percentage of families that live a certain distance from PHA 
headquarters. However, it is unclear as to why the overall size of the 
PHA jurisdiction would have a significant impact on costs unless the 
HCV participants were dispersed throughout the entire jurisdiction. In 
addition, the study tested the area (in square miles) of the PHA 
jurisdiction and found that in the study sample the variable was not 
statistically significant and had a negative coefficient sign.
HUD Response
    In the Solicitation of Comment Notice HUD noted that one of the 
potential weaknesses of using the average distance of voucher families 
from PHA headquarters is that if an agency primarily serves households 
in a relatively small area but the area is more than 60 miles from the 
PHA headquarters, the variables' impact on PHA costs could be 
significantly over-stated.
    Specific solicitation of comment #11: HUD seeks comment on how to 
address this concern and specifically requests comments on how HUD 
should establish an additional threshold that would adjust the formula 
variable for cases where a significant portion of the PHAs families are 
clustered beyond the distance threshold from the PHA headquarters. For 
example, if the majority or the greatest concentration of voucher 
families are located within 60 miles of an alternative location as 
opposed to the PHA headquarters, the distance variable could be 
calculated from that reference point, as opposed to the PHA 
headquarters, which might be located in a distant State capital but 
does not reflect where the PHA's main operations center is (or should 
be expected to be) located. Alternatively, the formula could use a 
measure of dispersion--how far HCV participants live from one another--
to capture the extra administrative costs involved in serving 
households over a large area.
Comments on Other Suggested Cost Drivers
    A number of comments suggested that the study's recommended formula 
should have included other cost drivers that could significantly impact 
the variation in administrative costs between PHAs.
    Comments on success rates. Some commenters noted that PHAs do a 
substantial amount of work for voucher holders who do not ultimately 
lease units and therefore PHAs with lower success rates (the percentage 
of families who are issued a voucher that ultimately succeed in leasing 
a unit under the program) would have higher administrative costs than 
PHAs with relatively higher success rates. These commenters urged HUD 
to include a success rate variable in the fee formula.
    HUD Response: The study acknowledged that voucher success rates 
have a strong theoretical basis for impacting administrative costs. For 
example, a PHA with a lower success rate would have to conduct more 
eligibility determinations and issue more vouchers than a PHA with a 
higher success rate in order to maintain leasing. Unfortunately, the 
study team was unable to test the relationship of voucher success rates 
to UML administrative costs because reliable data on success rates was 
not available. While both voucher issuances and new admissions are 
recorded in HUD's PIC system, the data on voucher issuances was not 
reliable enough for the study team to calculate the success rates with 
any confidence. Even if HUD were to request that the study PHAs provide

[[Page 44114]]

information on their success rates directly for purposes of testing its 
relationship to administrative cost and statistical significance (as 
suggested by a commenter), HUD would still need to use the voucher 
issuance data to calculate the dollar adjustment to the PHA 
administrative fee for the broader universe of PHAs.
    Another area of concern in terms of a success rate variable is 
whether a high success rate is necessarily always indicative of a less 
challenging rental market. For instance, a PHA may have achieved a high 
success rate through a very aggressive approach to landlord outreach 
and housing search assistance, figuring that those extra administrative 
costs would be mitigated or off-set by the savings the PHA realizes by 
not having to process as many families to lease a unit.
    A fee formula that provided higher fees to PHAs with lower success 
rates would be disadvantageous to a PHA that had achieved a high 
success rate through an aggressive approach to landlord outreach and 
housing search assistance. Furthermore, a poor success rate may be the 
result other factors besides the rental market, such as inadequate 
owner outreach or payment standards that are set at the low end of the 
basic range. Just as commenters expressed concerns over the potential 
unintended consequences of the percentage of families with earned 
income formula variable, similar concerns might arise that the formula 
was ``rewarding'' PHAs for achieving low success rates, rather than 
encouraging and supporting PHAs that have expended administrative 
effort and incurred costs to improve the likelihood that their families 
successfully lease with their vouchers. By providing higher fees for 
low success rates, the formula might perversely discourage PHAs from 
increasing their administrative efforts to improve success rates and 
reduce the number of families that ultimately fail to find housing. An 
alternative approach, discussed below, to addressing the relative 
challenges and cost impacts of different market areas might be to 
reconsider vacancy rates or other market indicators of the availability 
of affordable housing rather than focusing on success rates as a proxy 
for market challenges.
    Comments on availability of affordable housing: Several commenters 
expressed concern that the fee formula did not include any variable 
that measured the relative availability of affordable housing units in 
the PHA's jurisdiction. In theory, a PHA's administrative costs should 
be higher in tight rental markets, since the PHA may have issued a 
greater number of vouchers and/or have intensive landlord outreach and 
housing search assistance in order for families to successfully lease 
units with voucher assistance.
    HUD Response: The study team tested several variables to proxy the 
availability of affordable housing, including (1) the vacancy rate from 
the 5-year ACS (2008-2012) for rental units in census tracts in the PHA 
jurisdiction; (2) the third quarter 2013 vacancy rate from the US 
Postal Service (USPS) for residences in census tracts in the PHA 
jurisdiction; and (3) the third quarter 2013 vacancy rate from the USPS 
for multifamily dwelling units in census tracts in the PHA's 
jurisdiction.
    The ACS vacancy rate had the advantage of covering only rental 
units, as opposed to all residential units, but it was based on data 
collected from 2008 and 2012 and therefore did not represent the most 
up-to-date market conditions for the time period the administrative 
study was covering.
    The USPS tracks residential vacancies on a quarterly basis but does 
not provide data separately for rental units and consequently may not 
be a good proxy for the market conditions that impact the HCV program. 
The study team worked with HUD to isolate the vacancy rate for 
multifamily units in the USPS vacancy data--which could be a closer 
approximation to the rental vacancy rate than the overall residential 
rate.
    Ultimately, however, none of these three variations was 
statistically significant when tested in the simple correlation 
analysis. Furthermore, when added to the combined cost driver model, 
the coefficients on all three vacancy rate variables remained 
insignificant and--contrary to expectations--the USPS multifamily 
variable's coefficient was positive (meaning the higher the vacancy 
rate, the higher the administrative unit cost for the PHA), which was 
the opposite of what was expected. Consequently, the study team 
concluded that residential vacancy rates, at least as captured by the 
available data, could not be included as a cost driver for 
consideration for the proposed fee formula.
    Specific solicitation of comment #12: HUD specifically requests 
comment on whether there are other approaches to measuring rental 
markets in order to determine what, if any, impact this factor may have 
on variations in administrative costs and to incorporate it into the 
formula, if appropriate.
    Comments on end of participation and frequency of moves. A number 
of comments suggested that the formula should include variables for end 
of participation (EOP) and frequency of moves. For example, it was 
suggested that EOP data might be a better measure of the variation in 
costs brought about by the relative turnover in the voucher program 
than the new admissions rate variable. Other comments noted that the 
frequency of voucher participant moves would have an impact on 
administrative costs among PHAs in terms of the number of unit 
inspections, rent reasonableness determinations, rent calculations, HAP 
contract executions, etc., the PHA would have to conduct. This 
variation in administrative costs would not be captured in the new 
admissions variable.
    HUD Response: With respect to EOP, the study team tested two 
measures of EOP: EOP as a percentage of total vouchers under lease in 
2013 and EOP as a percentage of total vouchers under lease in 2012. 
Neither of these measures was statistically significant when tested 
against the base model of program size and wages. The study team 
retested the 2012 variable and included it in near-final versions of 
the formula model, once in addition to the new admissions variable and 
once as a substitute for the new admissions variable. In both cases the 
EOP variable was not significant and the coefficient was negative (PHAs 
with higher percentages of EOPs had lower unit administrative costs), 
which was not in the expected direction. As a result, the EOP variable 
was not included in the study's recommended formula. The EOP variable 
was tested again in the model developed for this proposed rule and was 
not statistically significant.
    Concerning the frequency of moves, HUD agrees that higher rates of 
moves among voucher families should result in higher administrative 
costs, given all the work associated with processing a move request, 
issuing the voucher, and inspecting and ultimately placing a new unit 
under HAP contract. The study team tested a move variable for each PHA 
in the study sample, which was the number of moves in 2013 divided by 
the number of vouchers under lease. In the simple regression model with 
program size and wage index, the coefficient on the frequency of moves 
variable was negative (meaning that the higher the move rate, the lower 
the administrative cost per unit), which was not the expected 
direction, and the variable was not statistically significant. When 
combined with other cost drivers, the frequency of moves variable 
remained statistically insignificant and the coefficient remained 
negative. As a result the variable was not included in the study's fee 
formula. The variable

[[Page 44115]]

was tested again in the model developed for this proposed rule and 
although the coefficient became positive it was not statistically 
significant.
    Comments on limitation on the range of the formula variables: As 
discussed in detail in the HCV Program Administrative Fee Study Final 
Report (section 7.3.1), each variable in the proposed formula has a 
range of values. The regression model for the formula was based on both 
the per-unit costs estimated for the 60 PHAs in the study and the 
values for the input variables observed across those PHAs. In most 
cases, the 60 PHAs in the study are very close to all HCV PHAs in the 
mean and median values observed for the formula values. However, some 
PHAs have variable values outside of the range of values observed for 
the 60 sample sites. Since the formula is based on a sample of PHAs 
with input values within a certain range, the cost estimates do not 
necessarily apply in cases where an individual PHA may have a value 
outside the range tested. To eliminate those extreme values where the 
costs and inputs are not likely to have the same relationship as found 
in the model, the study recommended restricting the range of allowable 
values to those observed in the PHA sample.
    For example, the highest percentage of new admissions among the 60 
study sites was 52.19 percent. If a PHA's share of new admissions 
exceeded 52.19 (e.g., 60.00), the PHA's value for this variable would 
be capped at 52.19. Likewise, the lowest percentage of new admissions 
for the 60 study sites was 2.93. Even if a PHA's share of new 
admissions was below 2.93 (e.g., 0), the PHA's value for this variable 
would still be 2.93.
    HUD Response: The limitation on the range of the formula values 
would apply at both the implementation of the new fee formula and to 
the subsequent annual recalculations of the PHA administrative fee that 
is based the PHA's variable values.
    Specific solicitation of comment #13: HUD has retained this 
limitation on the PHA values in the proposed administrative fee 
formula, but is specifically seeking comment on whether this 
restriction should be modified or removed at the final rule for some or 
all of the formula variables. For example, HUD is seeking comment on 
whether the limitation on the range of PHA values should be established 
at the 25th and 75th percentile of all PHAs, rather than the minimum 
and maximum values that were observed for the 60 sample PHAs, for the 
percent of households with earned income and the new admissions 
variable. Establishing limits based on the values for all PHAs (e.g., 
at the 25th and 75th percentile or some other percentile cutoff) would 
ensure that the formula is not imposing archaic limits or the range of 
PHA variables and makes adjustments as circumstances dictate. Another 
approach would be to revisit the limits on the formula value ranges 
periodically (e.g., every 5 years or in the event of a major program 
change that would significantly impact a formula variable) and make 
adjustments when necessary.
    Comments on PHA variable value calculations: The PHA's ongoing 
administrative fee would be updated each year based on the most recent 
available data. The study noted that an important issue to consider in 
terms of these adjustments is the year-to-year volatility in the data. 
If a PHA's values for the formula variables are highly volatile from 
year to year, the result could be significant swings in the fee rate 
amount that would be difficult to predict and would further complicate 
program administration.
    The study team analyzed the volatility of the formula variables. As 
a result of this analysis, the study recommended that while the PHA's 
values for the program size, wage index, and 60 miles variables should 
be based on the most recent year of data, the fee formula should use 
three year averages for the remaining variables--health insurance cost 
index (now replaced by benefit load), percent of households with earned 
income, and new admissions rate. The three year average is the average 
of the latest year where data is fully available and the two preceding 
years. The PHA's values for the variable would continue to be subject 
to the maximum and minimum limits (the range) for that particular 
variable.
    Some commenters suggested using a 5-year average to further reduce 
the risk of volatility of the formula variables and the potential 
impact on the administrative fee.
    HUD Response: HUD is retaining the 3-year average approach for 
benefit load, households with earned income, and new admissions rate, 
but is specifically seeking comment on whether to consider a 3-year 
averages or alternative averages for the other variables in the formula 
to further reduce the risk of volatility.
    Specific solicitation of comment #14: HUD also seeks comment on 
whether HUD should use a longer time period, such as a 5 year average, 
for some or all of the variables.
    Comments on fee floors and ceilings: The study found that across 
the 60 study PHAs, the average administrative cost per voucher for CY 
2013 ranged from $42.06 per UML to $108.87 per UML. A straight 
application of the study formula for the more than 2,200 PHAs would 
result in predicted fees that fall below the lowest observed cost of 
$42 per UML for two percent of PHAs overall. All of the other PHAs in 
the study had costs that exceeded $42 and the formula is designed to 
capture those actual costs.
    Because $42 per UML is the lowest cost the study observed under 
which a PHA with very low cost drivers could operate a high-performing 
and efficient program, the study recommended that the formula establish 
a floor of $42 per UML. However, the 80 PHAs in the U.S. Territories 
may have costs that the fee formula is not capturing as reflected in 
their current funding levels. Due to those concerns and to minimize the 
funding disruption, a floor of $54 per UML was proposed for the U.S. 
Territories. The study did not measure costs for any PHAs located in 
the U.S. Territories. The study recommended $54 per UML as the floor 
for the U.S. Territories, which is an approximation of the lowest cost 
per UML in the U.S. Territories at the time of the study. The $54 floor 
fee was equal (at the time of the study) to the lowest prorated fee 
received by PHAs in the U.S. Territories increased by four percent. 
Four percent is the difference between the cost per UML and the 
prorated fee per UML for the lowest cost PHA in the study sample.
    Some commenters believed that the fee floor of $42 per UML was 
inadequate. Suggested alternatives included the average cost per unit 
observed by study ($70) or the fee the PHA was receiving immediately 
prior to the transition to the new fee formula. Other comments 
questioned the rationale and fairness of imposing a separate floor for 
the U.S. Territories and not for other areas that have a 
disproportionate share of decliners compared to the nation as a 
whole.\18\
---------------------------------------------------------------------------

    \18\ ``Decliners'' refers to PHAs that would receive less 
funding under the proposed rule fee formula than they would have 
received under the current formula.
---------------------------------------------------------------------------

    HUD Response: HUD has retained the $42 per UML floor for the 
administrative fee and the separate $54 per UML floor for the 
administrative fee for PHAs in the U.S. Territories for the fee formula 
that would be implemented in accordance with this proposed rule. The 
PHA's administrative fee, pre-inflation, would never be less than this 
fee floor, even if the fee calculation based on the six variables and 
the PHA values for those variables would otherwise have resulted in a 
lower amount.

[[Page 44116]]

    HUD does not agree that establishing a floor based on the average 
cost per unit of $70 observed by the study would accurately reflect the 
minimum fee necessary to administer the program, as a significant 
number of the effective, high-performing PHAs in the study sample were 
in fact administering the program for less than that amount. HUD also 
does not believe establishing a fee floor at whatever fee the PHA 
happened to receive under the current formula is defensible, given that 
the study found that the current formula does not account for the 
actual cost drivers of program administration. However, HUD agrees that 
any decrease in the fee as a result of the new formula must be 
implemented in a manner that reduces the risk of disruption to PHA 
operations and gives the agency sufficient time to prepare and adjust 
to a decrease in the administrative fee.
    HUD is proposing to limit the amount by which a PHA's fee may 
decrease from the actual administrative fee amount the PHA was 
previously receiving prior to the effective date of the adjustment, 
both at the initial implementation of the new fee formula and for any 
subsequent year adjustment. (This limitation is discussed in detail 
later in this preamble.)
    With respect to imposing separate fee floors for other areas of the 
country beyond the U.S. Territories, HUD is declining to do so in the 
proposed rule. HUD believes that the study sample was diverse enough in 
terms of geography, PHA size, market factors, etc., that it is not 
evident why establishing separate floors would be justified for areas 
other than the U.S. Territories. Under the fee formula that would be 
implemented in accordance with this proposed rule, only six PHAs 
outside the U.S. Territories would receive the fee floor of $42 per 
UML.
    In addition to retaining the $42 per UML floor for the 
administrative fee and the separate $54 per UML floor for the 
administrative fee for PHAs in the U.S. Territories recommended by the 
study, HUD proposes to establish a maximum fee of $109 per UML (prior 
to inflation) for all PHAs. HUD's rationale is that $109 per UML is the 
highest cost measured by the study for a high-performing and efficient 
HCV program. Under the fee formula that would be implemented in 
accordance with this proposed rule, two percent of PHAs overall would 
have predicted fees in excess of $109 per UML (prior to inflation). 
These PHAs would receive the maximum fee of $109 per UML, prior to the 
inflation adjustment. In 2014, none of the PHAs that would have 
received the ceiling fee of $109 per UML under the proposed formula 
($111.36 after the inflation adjustment) would have experienced a loss 
in funding relative to what they received under the current formula.
    In sum, under the fee formula that would be implemented in 
accordance with this proposed rule, PHAs would be subject to a fee 
floor of $42 per UML prior to inflation adjustment and a fee ceiling of 
$109 per UML prior to inflation adjustment.
    Specific solicitation of comment #15: HUD seeks comment on this 
proposed approach to setting fee floors and ceilings.
    Comments on limitations on overall decreases and increases in the 
PHA administrative fee at initial implementation and subsequent fee 
adjustments:
    The study recommended that HUD consider a transition or phase-in 
plan to allow PHAs time to adjust to the new fees. The study recognized 
that a transition or phase-in plan would be particularly important for 
PHAs that would experience a decrease in their administrative fee under 
the new formula. The purpose of a transition period to full 
implementation is to minimize the disruption to program operations for 
those PHAs that would experience a decrease in fee funding.
    The study suggested HUD consider a simple phase-in approach that 
would distribute the loss in fees gradually over a number of years so 
that the PHA does not experience a decrease in fees above a certain 
percentage in any given year. For example, a 5-year phase-in plan would 
result in a decliner PHA seeing its fees reduced each year for the 
first five years of implementation. In the fifth year, the PHA would 
receive the fee amount calculated under the new fee formula with no 
adjustments. The study noted that HUD could adjust the time period for 
the phase-in (e.g., use 3 years instead of 5 years) and could limit the 
phase-in to a subset of PHAs (such as only to PHAs experiencing a 
decrease over a certain percentage threshold.) Another alternative 
suggested by the study was for HUD to limit the extent of individual 
gains or losses from the funding received the year before the formula 
implementation.
    Many comments expressed concern that implementation of the new 
formula could result in disruptions to PHA operations. Commenters were 
not only concerned about the negative impact on agencies that would see 
a decline in their fee as a result of the formula change but also 
expressed fears that implementation, if coupled with insufficient 
appropriations to fund the new formula, could be harmful to numerous 
PHAs.
HUD Response
    One of HUD's main objectives in undertaking the study and 
developing a new fee formula was to bring a level of consistency and 
stability to the administrative fee funding that PHAs rely upon to 
carry-out their administrative responsibilities under the program. HUD 
recognizes the difficulties that uncertainty and unexpected 
fluctuations in administrative fees create for PHAs in terms of their 
ability to budget and manage their HCV programs beyond the immediate 
calendar year. Through this proposed rule HUD seeks to alleviate the 
concerns of the commenters that implementation of the formula would 
have immediate and potentially devastating impacts on PHA operations 
due to severe funding reductions.
    The proposed fee formula already seeks to reduce the potential 
volatility in administrative fees introduced by the new formula by 
restricting the ranges of the variable values and by using three year 
averages rather than one year of data for the cost drivers that are 
most at risk of dramatic changes from year to year. In addition, HUD is 
proposing to implement an overall cap on the percentage by which the 
PHA's administrative fee, pre-inflated, may decrease from the previous 
administrative fee amount it received, both at the initial 
implementation of the new fee formula and the subsequent annual 
recalculations of the administrative fee thereafter.
    HUD considered the 5 year and 3 year phase-ins but was concerned 
that those approaches could be relatively cumbersome. Since the PHA's 
fee would be changing each year during the 3 year or 5 year phase-in 
period, the fee calculation could for some PHAs become somewhat 
complicated, especially if the PHA's fee under the new formula was 
increasing and/or decreasing throughout the transition period to full 
implementation. Placing a limitation on how much the recalculated 
administration fee could decrease from the previous fee amount received 
by the agency would be far easier to calculate and explain.
    Under the fee formula that would be implemented in accordance with 
this proposed rule, the PHA administrative fee per UML could be no less 
than 95 percent of the ongoing administrative fee per UML the PHA 
received from HUD for the year prior to the effective date of the new 
per UML fee amount, adjusted for inflation. In other words, the PHA 
administrative fee per UML

[[Page 44117]]

could not decrease by more than 5 percent per year as a result of the 
new formula implementation or the subsequent annual recalculation based 
on the changes in the PHA's variable values.
    In addition to limiting the percent by which a PHA's administrative 
fee may decrease at implementation and in subsequent years, HUD is 
proposing to limit the percentage increase in the administrative fee at 
implementation and in subsequent annual recalculation of the 
administrative fee based on changes in the PHA's variable values. Under 
the fee formula that would be implemented in accordance with this 
proposed rule, the PHA administrative fee per UML in any given year 
could be no more than 140 percent of the administrative fee per UML 
that the PHA received for the year prior to the effective date of the 
new per UML fee amount, adjusted for inflation. HUD believes that 40 
percent still represents a very significant increase in an 
administrative fee for the impacted PHAs. By capping the percentage 
increase in a PHA's fee to no more than 40 percent, the formula covers 
the cost of limiting the decrease for the decliner PHAs without 
increasing the amount of funding that would be necessary to fully fund 
the fee formula if there was no transition under the new formula. In 
other words, the protection for the decliner PHAs does not increase the 
overall cost of the new formula if HUD also limits the annual increase 
for gainers to no more than 40 percent of the previous year's 
administrative fee.
    Applying the proposed caps on both the percent by which the PHA 
administrative fee per UML could decrease in any given year and the 
percent by which the PHA administrative fee per UML could increase in 
any given year, the fee formula that would be implemented in accordance 
with this proposed rule would work as follows. In the first year that 
the new fee formula is implemented, the PHA's fee per UML would be the 
maximum of the new formula fee per UML or 95 percent of the fee per UML 
received in the previous year under the existing formula, not to exceed 
140 percent of the fee per UML received in the previous year under the 
existing formula. After the first year of formula implementation, the 
point of reference would be the fee received in the previous year under 
the new formula. In other words, in the second year of implementation, 
the PHA's fee per UML would be the maximum of the current year's fee 
per UML based on the new formula or 95 percent of the fee per UML 
received in the previous year under the new formula, not to exceed 140 
percent of the fee per UML received in the previous year under the new 
formula. In this way, each PHA will eventually receive the fee per UML 
calculated by the new formula based on the PHA's variable values, but 
the increase or decrease in fees will take place gradually in order to 
minimize the risk of disruption to PHA operations.
Comments on Limiting Increases to the Fee
    In general, most comments were opposed to establishing a limit on 
increases to the fee. On one hand HUD is reluctant to impose limits on 
increases in administrative fees brought about by the new formula. The 
formula is designed to reflect the actual costs of administering the 
HCV program, and phasing in or limiting the increases in a PHA's 
administrative fee would delay the time when the PHA's fee would 
reflect those costs. On the other hand, one of the more common concerns 
expressed in the comments was the potential adverse impact of 
insufficient administrative fee appropriations and resulting pro-
rations on the new formula at implementation, especially for agencies 
that would experience a decline in funding as the result of the new 
formula.
HUD Response
    Limiting the annual increase of the administrative fee to a 
reasonable standard as part of the formula reduces the overall cost and 
increases the likelihood that the appropriations funding would not 
result in significant pro-rations. The study and a new fee formula 
based on the study's findings provide evidence-based justification for 
HUD's Budget Requests for administrative fee funding. HUD believes that 
implementation of the new formula will help to reduce the risk of deep 
pro-rations in administrative fee funding for the HCV program. However, 
the availability of appropriated funding is not within HUD's control.
    In the event that the appropriated funding is not sufficient to 
limit the fee reduction for decliner PHAs to no more than 5 percent 
from the previous year's fee per UML, under this proposed rule HUD 
would have the authority to reduce the maximum percentage increase from 
the previous year's fee per UML from 40 percent to a lower percentage 
(e.g., 20 percent). HUD would reduce the maximum annual percentage 
increase only to the extent necessary to limit the fee reduction for 
decliner PHAs to no more than 5 percent from the previous year's fee 
per UML.
    Specific solicitation of comment #16:
    16a. HUD seeks comment on this proposed approach to limiting 
decreases and increases. Specifically HUD seeks comment on the proposed 
limitation on increases and decreases as the result of the formula 
(fees may not decrease by more than 5 percent from year to year or 
increase by more than 40 percent from year to year as the result of the 
formula) as well as the following alternatives.
    (a) There is no limit on increases as a result of the formula.
    (b) There is no limit on decreases as the result of the formula.
    (c) The limit on increases is changed to 20 percent.
    (d) The limit on increases is changed to 30 percent.
    (e) The limit on decreases is changed to 10 percent.
    16b. HUD is also specifically requesting comment on the proposal 
that would allow HUD to further constrain the maximum percentage 
increase for gainer PHAs when necessary to ensure that the decliner 
PHAs' fees do not decrease by more than 5 percent annually. Are such 
additional constraints on gainer PHAs appropriate in the event of 
insufficient appropriations or should fees be prorated equally in such 
a circumstance, regardless of whether a PHA is a gainer or a decliner? 
Should parameters be established to ensure that the gainer PHAs receive 
at least a minimum percentage increase? For example, the formula could 
provide that in cases where the maximum percentage gain must be further 
constrained beyond the normally applicable 40 percent cap, the maximum 
cap would not be set below a 10 percent increase.
    If funds were still insufficient to fund administrative fees after 
the gainer PHAs were capped, what further adjustments should be made to 
the administrative fees to cover the funding shortfall? For example, in 
such an instance should the maximum percentage decline be adjusted from 
5 percent to a different amount (e.g., 10 percent) to cover or reduce 
the remaining shortfall? Or should all PHAs' administrative fees (both 
gainers and decliners) simply be equally prorated downward at that 
point? More broadly, are there other, preferable approaches to 
addressing the gains and declines in administrative fees if 
administrative fee funding is insufficient to cover the need?
    16c. In light of the comments expressing concerns about 
insufficient funding and the potential adverse

[[Page 44118]]

impact on the new formula's implementation, HUD is specifically seeking 
comment on whether the rule should provide that implementation of the 
new formula shall or may be delayed or suspended in the event that 
administrative fee funding is insufficient to the degree that 
implementation may seriously disrupt or impair PHA operations.
    As discussed above, in the event that the appropriated funding is 
not sufficient to limit the fee reduction for decliner PHAs to no more 
than 5 percent from the previous year's fee per UML, under this 
proposed rule HUD would have the authority to reduce the maximum 
percentage increase from the previous year's fee per UML from 40 
percent to a lower percentage (e.g., 20 percent). However, there could 
be circumstances where HUD, despite further restricting the fee 
increases, may not have enough funding to implement the new formula 
without imposing significant fee prorations to the new fees.
    In such a circumstance, the rule could allow for implementation to 
be delayed and instead provide, for example, that HUD shall simply 
apply an inflator factor to the PHA's administrative fee for the 
previous year and prorate all fees accordingly. However, delaying 
implementation (or further restricting the percentage by which a PHA's 
fee may increase under the new formula for that matter) could be 
disadvantageous to those PHAs that are gainers under the new formula. 
How severe would a funding shortfall need to be to delay 
implementation? What specific thresholds should be used to delay or 
suspend the implementation of the new formula under such a policy? For 
instance, the threshold could be based on: The level of funding 
appropriations as a percentage of the level of estimated need; the 
share of PHAs that would be decliners under the new formula; the 
maximum increase that could be provided to gainers under the new 
formula; or some other factor.
Comments on Inflation Adjustment
    After the new fee rate is calculated for the PHA, but prior to the 
implementation of limitations on increases and decreases described 
above, an inflation factor would be applied to account for cost 
increases since 2013 (the year for which the study estimated costs and 
upon which the administrative fee formula coefficients are based). The 
study recommended a blended inflation rate that takes into account the 
three types of costs: Wages, benefits, and non-labor costs. The blended 
rate is the weighted average of an inflation rate for each of these 
costs, based on the share of HCV administrative costs that each 
represented in the study sample of PHAs.
    The study team calculated that on average, direct labor costs 
(wages plus benefits) accounted for 70 percent of total direct costs 
and direct non-labor costs represented 30 percent of costs. The study 
then used BLS ECEC \19\ data to determine the benefits costs as a 
percent of total employer costs for local and State government 
employers. In 2014, benefits were 36 percent of total employer costs 
for local and State government employers. Since labor costs are 70 
percent of the total costs and benefits costs are 36 percent of the 
labor costs, this means that benefits costs are 25 percent of the total 
costs (.70 x .36 = .252) and wages are 45 percent of the total cost 
(.70 x .64 = .448). So the weights for the three inflation rates are 
0.45 for labor costs (wages), 0.25 for labor costs (benefits), and 0.30 
for non-labor costs.
---------------------------------------------------------------------------

    \19\ Bureau of Labor Statistics Employer Costs for Employee 
Compensation.
---------------------------------------------------------------------------

    To measure wage inflation, the study recommended the national 
average wage for local government workers from the BLS QCEW,\20\ which 
is the same source of data as is used to calculate the wage index 
variable. The inflation rate is calculated as the percent change in the 
national average wage for local government workers for the most recent 
year for which the data are available and the national average wage for 
local government workers in the formula's base year of 2013.
---------------------------------------------------------------------------

    \20\ Bureau of Labor Statistics Quarterly Census of Employment 
and Wages.
---------------------------------------------------------------------------

    To measure inflation in benefits costs, the study recommended that 
HUD use the national average cost of health insurance for private 
sector employees from the HHS MEPS.\21\ The HHS MEPS is the data source 
that the study used for the health insurance cost variable in the 
proposed formula. The inflation rate would be calculated as the 
percentage change in the national average health insurance cost for the 
most recent year for which the data are available and the national 
average health insurance cost in the study's base year of 2013.
---------------------------------------------------------------------------

    \21\ Department of Health and Human Services Medical Expenditure 
Panel Survey.
---------------------------------------------------------------------------

HUD Response
    As discussed earlier, HUD dropped the health insurance cost index 
from the proposed formula and replaced it with the benefit load. The 
same concerns related to the health insurance cost index would apply to 
the use of the HHS MEPS as a proxy for inflation for all benefits. 
Because health insurance is just one component of benefits costs, it 
may not be a particularly effective proxy to use to estimate the 
inflationary impact on PHA benefits costs.
    HUD believes a simpler approach to measuring inflation in both 
wages and benefits is to use the BLS ECEC. As the reader may recall 
from the benefit load variable discussion, the study considered using 
the ECEC as a measure of variation in the cost of benefits, since it 
measures employer costs for wages, salaries, and all employee benefits 
for State and local government workers, as opposed to only health 
insurance costs. The ECEC ultimately was not used as a measure for the 
benefits variable in the regression model because it did not make 
estimates of benefits costs for State and local government workers 
available below the national level. However, the ECEC does provide 
quarterly data on the total cost of compensation (wages plus all types 
of benefits) for State and local government workers for the nation as a 
whole, which allows HUD to calculate a wage and benefits inflation 
factor to be included in the blended inflator factor. Using the ECEC 
data also allows HUD to use one source for measuring inflation in wages 
and benefits, rather than using two different sources with different 
methodologies. Consequently, the proposed formula uses ECEC data on 
total cost of compensation for State and local government employees to 
calculate the inflation rate that would apply to the labor component of 
HCV administrative costs, which the study found represents 70 percent 
of total costs, as discussed above.
    The inflation rate for labor costs (wages and benefits) is 
calculated as the percent change in the ECEC national average for total 
cost of compensation (cost per hour worked) for State and local 
government workers based on the most recent data available, compared to 
the ECEC national average for total cost of compensation for State and 
local government workers for the formula's base year of 2013.
    To measure non-labor costs, which represents 30 percent of total 
costs, the study recommended that the formula use the BLS Consumer 
Price Index (CPI). The CPI measures change over time in the prices paid 
by urban consumers for a market basket of consumer goods and services. 
The most comprehensive CPI is the All Items Consumer Price Index for 
All Urban Consumers (CPI-U). The CPI-U's market basket of goods and 
services includes most items purchased for routine operations by PHAs. 
The inflation rate is calculated as the change

[[Page 44119]]

in the national CPI-U between the most recent CPI-U data available and 
the CPI-U from the study's base year of 2013. The study team also 
considered the Producer Price Index (PPI). The PPI measures change over 
time in the selling prices received by domestic producers of goods and 
services. The study team concluded that the CPI is the better option to 
use as an inflation factor for non-labor costs in the formula, because 
it is the most widely used measure of price change and it measures 
inflation as experienced by consumers in their day-to-day living 
expenses.
    The blended inflation rate is calculated as follows:

Blended inflation rate = the wage and benefits inflator (0.70 
multiplied by the percent change in BLS ECEC total cost of 
compensation for State and local government workers from base year 
of 2013) + the non-labor cost inflator (0.3 multiplied by the change 
in BLS national CPI-U from the base year of 2013.)
Comments on Use Regional or Local Inflation Factor Instead of a 
National Inflation Factor
    A few commenters suggested that HUD consider using regional or 
local inflator factors instead of a national inflator factor.
HUD Response
    HUD did not make this change for the proposed rule. The underlying 
wage index and benefit load variables that are used to recalculate the 
PHA's pre-inflated fee each year already account for the cost 
variations that may be attributable to metropolitan and State 
differences. Data are available at a regional level for non-labor costs 
from the CPI-U. However, data from the ECEC on wage and benefits costs 
are not available at the regional level for State and local government 
workers.
    Specific solicitation of comment #17: HUD specifically seeks 
comment on the blended inflation rate, particularly the methodology 
proposed to account for inflation in wage and benefits costs and 
whether HUD should consider using regional data for the inflation 
factor where available.
Comments on Administrative Fees for Vouchers Administered Under the 
Portability Procedures
    The study found that PHAs with higher percentages of units that are 
port-ins (family originally moved into the PHA's jurisdiction with a 
voucher issued by another PHA under the portability procedures) had 
higher average costs, supporting the theory that there is additional 
time associated with processing port-ins and then continuing to work 
with the initial PHA under the billing option.
HUD Response
    Since the study was issued, HUD updated its portability regulations 
with the publication in the Federal Register of the Housing Choice 
Voucher Program: Streamlining the Portability Process Final Rule, on 
August 20, 2015. Under Sec.  982.355(e)(3), the initial PHA must 
``promptly reimburse the receiving PHA for the lesser of 80 percent of 
the initial PHA's ongoing fee or 100 percent of the receiving PHA's 
ongoing administrative fee for each program unit under HAP contract on 
the first day of the month for which the receiving PHA is billing the 
initial PHA.'' \22\ The proposed formula would eliminate billing 
between the PHAs for administrative fees. Notwithstanding the recent 
portability rule change, eliminating billing for administrative fees 
will produce a more efficient process and a more equitable result. In 
place of having the receiving PHA bill the initial PHA for a portion of 
their administrative fee, the study recommends that the receiving PHA 
receive 100 percent of their own fee directly from HUD for any port-in 
vouchers under HAP contract. The initial PHA would not receive a 
regular administrative fee from HUD for vouchers that had ported out of 
its jurisdiction since HUD is compensating the receiving PHA directly. 
However, the initial PHA would receive a separate fee from HUD equal to 
20 percent of their own fee for any voucher for which the initial PHA 
is being billed for HAP under the portability option.
---------------------------------------------------------------------------

    \22\ Prior to the rule change, when portability billing 
occurred, the initial PHA was required to pay the receiving PHA 80 
percent of its administrative fee for each month that a family 
received assistance through the receiving PHA, unless the PHAs 
mutually agreed to a different billing amount. The rule change was 
designed to eliminate the incentive for a receiving PHA with a lower 
administrative fee from billing the initial PHA with a higher 
administrative fee. The overall intent of the change was to reduce 
PHA billing.
---------------------------------------------------------------------------

Comments on Eliminating Billing for HAP
    Comments generally did not oppose the proposal to eliminate 
administrative fee billings between PHA by allowing the receiving PHA 
to receive 100 percent of its own administrative fee directly from HUD 
for administering the portable voucher, while the initial PHA would 
receive a separate portability fee from HUD for its continued 
administrative responsibilities under the portability procedures. Some 
comments suggested that HUD should eliminate the billing for HAP as 
well as administrative fees to reduce administrative burden and 
streamline the process. Other comments suggested that 20 percent of the 
initial PHA's administrative fee may not be a sufficient amount for the 
portability fee.
HUD Response
    While HUD understands that there are many good reasons to eliminate 
HAP billings between PHAs for HAP as well as for administrative fees, 
the change is beyond the scope of this proposed rule. HUD will continue 
to explore options to reduce or eliminate portability billings and 
other streamlining efforts to reduce administrative burden, including 
technology and business re-engineering solutions. In the interim, the 
proposed change in how administrative fees are handled under 
portability should better compensate PHAs for portability costs and 
reduce some administrative complexity and burden.
    HUD believes that 20 percent of the initial PHA's administrative 
fee is the appropriate amount for the separate portability fee to be 
paid to the initial PHA for port-out vouchers under billing 
arrangements. Using the time data collected, the study team developed a 
regression model to estimate the time PHAs spent on the continuing work 
required as an initial PHA in a billing arrangement compared to the 
time spent initially processing each port-out transaction. The study 
team estimated that on average each voucher under a billing arrangement 
took about 24 minutes of time during the 8 week RMS period, or about 
156 minutes over a full year. On average, PHAs in the study sample 
spent a little over two and a half hours per year for each voucher that 
ported-out and was under a billing arrangement. The average time spent 
on all frontline voucher activities was 13.8 hours per voucher under 
lease per year. This means that the average time spent by the PHAs on 
billing activities as an initial PHA was about 19 percent of the time 
spent administering their non-port vouchers. HUD is comfortable that 
the portability fee for initial PHAs is reasonable based on the study's 
findings and has retained it in this proposed rule.
Comments on Additional Cost Factors and Supplemental Fees
    The study noted that in addition to modifying the formula, HUD 
should consider developing specific fees that would be provided 
separately to PHAs outside of the ongoing fee formula. The study's 
recommended administrative fee structure already includes one fee that 
is outside of the ongoing administrative fee formula--the portability 
fee that is

[[Page 44120]]

paid directly to initial PHAs by HUD for port-out vouchers under 
billing arrangements. The study recognized that there are many 
strategic goals, program priorities, and policy objectives where PHA 
efforts may need to be addressed through the provision of additional 
fees. Furthermore, a number of cost drivers that were not statistically 
significant in either the simple regression or the combined regression 
model may still merit consideration for a separate fee, as there is a 
strong theoretical basis by which to conclude that they have 
considerable impact on a PHA's administrative costs. HUD's Solicitation 
of Comment Notice specifically requested comment on whether additional 
compensation should be provided for four specific cost drivers 
identified by the study, and any other areas that the commenters might 
wish to identify.
    The four cost drivers identified in the study for consideration, 
and the comments that pertain to each are as follows:
    (1) Homeless households. The results of the study's time 
measurement were not conclusive about the time spent serving households 
that are homeless at admission compared to serving other household 
types, and the study's simple regression analysis did not find the 
share of homeless households to be a significant cost driver. However, 
several PHAs reported that serving formerly homeless households is more 
time consuming than assisting other voucher families, and the study 
acknowledged it was possible that in reporting their time through RMS, 
front-line PHA staff may not always have been aware of when they were 
working with a homeless client. (Time spent on homeless households only 
accounted for 3 percent of the total data points collected by household 
type, and only 12 of the 60 PHAs recorded any time spent working with 
homeless households.)
    Comments. As noted earlier, many of the comments expressed concern 
that including a cost variable for the percentage of families with 
earned income in the fee formula would have a detrimental impact on 
efforts to expand the use of vouchers to serve the homeless. Commenters 
pointed out that HUD's Family Options Study demonstrated the 
effectiveness of offering a voucher to a homeless family, and that HUD 
should be doing more, not less, to encourage and support PHA efforts to 
increase the percentage of formerly homeless families who are assisted 
under the HCV program. A number of PHA commenters stated that in their 
experience, serving the homeless--both at initial lease-up and in terms 
on ongoing activities--was more time consuming and administratively 
costly than any other household type. Reasons included the fact that 
many homeless families have poor credit histories and lack landlord 
references, making the housing search more problematic, and are more 
likely to have mental health and addiction challenges than a typical 
voucher household, complicating retention efforts.
    (2) Special voucher programs. In addition to measuring time spent 
on the regular voucher program, the study measured time spent on eight 
types of special vouchers: (i) Project-based, (ii) tenant protection, 
(iii) Veterans Affairs Supportive Housing (HUD-VASH), (iv) non-elderly 
disabled (NED), (v) family unification program (FUP), (vi) 5-year 
mainstream, (vii) disaster, and (viii) homeownership vouchers. 
Collecting time data related to special vouchers was challenging 
because of the very small size of the special programs. Nine of the 60 
study PHAs had no special vouchers at all, and all the special vouchers 
combined represented only 15 percent of the voucher portfolio for the 
remaining PHAs. As a result the study was only able to examine the time 
spent per voucher per year for three special voucher types: HUD-VASH, 
project-based vouchers, and homeownership vouchers.
    HUD-VASH. Two of the 21 PHAs in the study sample that administered 
HUD-VASH vouchers recorded very large amounts of time on HUD-VASH 
during the RMS data collection period. Both of these PHAs were in the 
process of developing new HUD-VASH programs and logged a large amount 
of time developing partnerships and procedures with their Veterans 
Affairs Medical Center (VAMC) counterparts. While a larger sample size 
would be necessary for the study to draw a definitive conclusion, the 
experience of those two agencies suggests that HUD-VASH is very time 
consuming in its early stages.
    The study results were inconclusive in terms of the amount of time 
spent on the HUD-VASH program after it is established. PHAs in the 
study reported that HUD-VASH is a very time-consuming program even 
after the start-up phase. However, the study's time estimates did not 
demonstrate that HUD-VASH vouchers took more time to administer on an 
ongoing basis than regular vouchers. The study team noted that the time 
spent on the voucher program may have been underestimated because the 
program is so small or PHA staff may have had difficulty in 
differentiating among different voucher types for some activities and 
recorded their time under regular vouchers if they were in doubt.
    Project-based Vouchers. The study team was able to develop time 
estimates for project-based vouchers for 27 PHAs in the study sample. 
For the one PHA in the process of developing a request for proposals 
(RFP) during the RMS data collection period, the time study revealed 
that the PHA expended a great deal of time on PBV compared to regular 
vouchers. The other 26 PHAs spent on average about the same amount of 
time per voucher for project-based vouchers as for regular vouchers. 
However, the 26 PHAs had wide variations in the time each PHA spent per 
voucher on project-based vouchers. Therefore, the study did not draw 
any definitive conclusions in terms of the workload associated with 
project-based vouchers compared to the regular vouchers.
    Homeownership Vouchers. The study was able to develop time 
estimates on homeownership vouchers for 27 PHAs. The study found that 
PHAs spend substantially more time per voucher on homeownership 
vouchers than on regular vouchers. Excluding time spent on inspections, 
the PHAs spent on average 22.3 hours per homeownership voucher per year 
as opposed 13.6 hours per regular voucher per year. However, the study 
cautioned that substantial variation existed with regard to the time 
spent on homeownership vouchers across the 27 PHAs. It is also 
important to note that the study did not find that administering the 
voucher homeownership program to be a significant cost driver. The 
study team hypothesized that this may be because the overall number of 
homeownership vouchers was too small relative to the number of regular 
vouchers to make a measurable difference in the PHAs' overall costs.
    Comments: A number of commenters supported additional fees for HUD-
VASH vouchers. Some comments focused on the amount of work involved to 
get a new allocation of vouchers off the ground and suggested that HUD 
employ a preliminary fee model to compensate agencies (e.g., providing 
additional administrative fee funding up-front along with the new 
allocation of vouchers to the administering PHA). Other commenters 
noted that HUD-VASH administration continues to be more 
administratively burdensome and costly even after initial lease-up, 
pointing out that HUD-VASH participants are more likely to suffer from 
substance abuse, mental illness, and other challenges that require 
greater vigilance and casework on behalf of

[[Page 44121]]

PHA staff to ensure the family remains successfully housed.
    Comments generally were supportive of supplemental fees for 
homeownership. For example, one commenter suggested that the $200 that 
HUD currently pays as a special fee for a successful homeownership 
closing be retained.
    With respect to project-based vouchers, some commenters advocated 
for a supplemental fee to address the additional up-front costs to 
PHAs. Another suggestion was for HUD to limit supplemental fees for 
project-based vouchers to cases where the project was expanding housing 
opportunities in low-poverty areas or providing housing for homeless or 
other persons with disabilities, depending on the cost variables 
included in the fee formula or other supplemental fees for expanding 
housing opportunities or serving the homeless or other persons with 
disabilities.
    Expanding Housing Opportunities and PHA Performance Incentives. The 
study suggested that HUD consider providing additional fees or fee 
adjustments for PHAs that score highly on program performance measures 
such as SEMAP or that achieve positive outcomes related to expanding 
housing opportunities.
    The study concluded that time spent on expanding housing 
opportunities was not a reliable cost driver for including in the 
administrative fee formula. Very little time was recorded on expanding 
housing opportunities during the RMS time data collection, and PHAs 
reported that they did not have the resources to invest substantial 
staff time in expanding housing opportunities even though they valued 
those activities. Another difficulty is that there is no existing data 
point by which to determine the level of effort a PHA is expending on 
expanding housing opportunities (beyond the data collection which is 
only available for the 60 study PHAs). Also, because the study did not 
collect data on the outcomes of the expanding housing opportunity, it 
was unclear if those PHAs that recorded time on expanding housing 
opportunities actually had any better outcomes than those PHAs that did 
not. The study concluded that the SARR, which captures the extent to 
which HCV families live in relatively more expensive areas, would be a 
preferable approach to addressing locational outcomes and the 
associated administrative costs until these issues could be addressed.
    Comments: As noted in the discussion above on the SARR variable, 
some comments recommended that HUD eliminate the SARR from the ongoing 
fee formula and address expanding housing opportunity as a supplemental 
or add-on fee. In addition, one commenter--who was supportive of the 
SARR--still encouraged HUD to also provide supplemental fees for 
expanding housing and de-concentration efforts, and suggested that HUD 
should not only compensate PHAs that are successful in location 
outcomes but also provide supplemental fees to PHAs that make progress 
on improving locational outcomes for families.
    Other commenters noted that the study found that many of the study 
PHAs lacked the resources to devote such time or staff to expanding 
housing opportunities. The comments included a suggestion that HUD 
study the costs of successful MTW mobility programs in order to 
estimate what an appropriate fee would be to address housing 
opportunity efforts.
    A number of commenters supported the concept of providing 
supplemental or additional administrative fees to high performing PHAs. 
It was noted, for instance, that HUD currently provides financial 
incentives based on performance in the Performance-Based Contract 
Administration (PBCA) program. It was also suggested, however, that 
performance incentives should not be part of the fee formula itself, 
which should simply address the administrative costs of running the 
program and not be designed to incentivize or drive PHA policy.
HUD Response
    HUD is appreciative of the many comments submitted on the subject 
of cost drivers and/or incentives for which HUD may wish to consider 
providing a supplemental or add-on fee in addition to the ongoing 
administrative fee covered by the formula. The proposed rule includes a 
section that provides HUD may provide supplemental fees in addition to 
the ongoing administrative fees. HUD would describe each of these 
additional fees and how those fees are calculated in a Federal Register 
Notice.
    In terms of the supplemental fees proposed for consideration by the 
study and in light of the cost variables in the fee formula that would 
be implemented in accordance with this proposed rule, HUD anticipates 
that it would establish a new additional fee for new homeless 
admissions from the PHA waiting list. The homeless admissions fee would 
be a one-time fee equal to 30 percent of the PHA's administrative fee 
annualized (i.e., the administrative fee multiplied by 12, which the 
PHA would receive for each homeless new admission reported in PIC. (For 
example, if a PHA's administrative fee is $70 per UML under the new 
proposed formula, the PHA would receive a one-time fee of $252 for each 
homeless new admission reported in PIC.) The average cost of intake, 
eligibility, and lease-up represents a little over 15 percent of the 
total cost per voucher leased as determined by the study. The homeless 
new admission fee roughly doubles that percentage to 30 percent, which 
would be provided as a separate fee to the PHA in addition to the 
regular ongoing fee the PHA would earn for the voucher being under 
lease. This fee would be made in recognition of the additional 
administrative effort to assist the homeless family both during the 
admissions and leasing process and during the family's initial 
transition to permanent housing. The proposed homeless new admissions 
fee is also intended to mitigate some of the concerns that the 
households with earned income variable in the proposed formula might 
inadvertently discourage PHAs from prioritizing the homeless through 
local admissions preferences.
    Specific solicitation of comment #18: HUD is specifically seeking 
comment on the homeless new admissions fee and how it relates to the 
ongoing administrative fee set forth in this proposed rule. HUD is 
particularly interested in whether commenters believe the fee amount is 
appropriate and whether this additional fee would alleviate concerns 
about the how the households with earned income variable might 
inadvertently impact homeless admissions.
    With regard to additional fees for HUD-VASH, HUD also anticipates 
that it would establish a policy to provide a one-time fee for new 
allocations of HUD-VASH vouchers. HUD recognizes that because only two 
PHAs were in the midst of implementing a new HUD-VASH program at the 
time of the RMS time data collection, the sample is too small to draw 
definitive conclusions. However, the time data collection for those two 
PHAs clearly supports the belief that a new allocation of HUD-VASH 
vouchers involves a significant amount of additional work for the 
administering PHA. Furthermore, it is reasonable to conclude that any 
new allocation of vouchers that requires the PHA to partner with 
another entity for family referrals (e.g., the family unification 
program) would similarly require additional administrative effort 
beyond what the PHA would normally experience in leasing a new 
allocation of vouchers. These additional administrative fees would be 
provided at the time that the new allocation of vouchers is obligated 
to the PHA to provide the PHA with resources to

[[Page 44122]]

establish or strengthen the partnership with the entity upon which the 
PHA must rely for the family referrals and any other applicable 
services. (Note that the fee for a new allocation of HUD-VASH or other 
vouchers targeted for the homeless would be paid in lieu of, not in 
addition to, the special fee being contemplated above for assisting 
homeless families.)
    For both the homeless new admissions fee and additional fees for 
HUD-VASH, HUD is seeking comment on whether providing these 
supplemental fees would be appropriate in the event that Congressional 
appropriations for HCV administrative fees are not sufficient to fund 
the supplemental fees without reducing per unit fees for PHAs overall. 
Also, HUD is requesting comment on any potential unintended 
consequences of providing these supplemental fees.
    Specific solicitation of comment #19: HUD is specifically seeking 
comment on what amount would be appropriate for this new allocation 
fee, but is initially thinking that the fee would be equal to 30 
percent of the PHA's annualized ongoing administrative fee multiplied 
by the number of vouchers in the new allocation. (Using the example 
above, where the PHA's administrative fee is $70 per UML under the new 
proposed formula, a PHA with a new allocation of 50 HUD-VASH vouchers 
would receive a one-time fee of $12,600.)
    HUD is less certain if additional fees beyond the regular 
administrative fee should be provided for the ongoing HUD-VASH 
activities. Although the PHAs in the study reported HUD-VASH vouchers 
were generally more administratively burdensome than regular vouchers 
(which is consistent with what many HUD-VASH PHAs have reported to HUD 
informally over the years), the study's RMS time measurement data was 
not helpful on this point. In August 2015, HUD sent a letter to all 
PHAs administering the HUD-VASH program, inviting those agencies to 
apply for extraordinary administrative fees to cover necessary or 
extraordinary related expenses that are incurred to increase lease-up 
success rates or decrease the time it takes for a veteran to locate and 
move-in to a unit. In order to apply for these funds, the PHA was 
required to justify and document actions specifically for administering 
the HUD-VASH program. HUD will review the applications and 
justifications for these extraordinary administrative funds to identify 
common activities and costs that would incurred by HUD-VASH PHAs to 
improve or maintain HUD-VASH leasing rates, and the extent to which 
this information might help inform the discussion on possible 
additional fees for ongoing HUD-VASH administration.
    Specific solicitation of comment #20: HUD is specifically seeking 
comment on the proposed new allocation fee for HUD-VASH and other 
voucher allocations that require partnership with another entity for 
applicant referrals and other services, as well as whether an 
additional fee for ongoing HUD-VASH administration is warranted and, if 
so, what would be the appropriate amount and rationale in support of 
such a fee.
    On the basis of the comments regarding homeownership vouchers, HUD 
would retain the current policy of providing a homeownership fee when a 
family purchases a home under the HCV homeownership program.
    As previously noted (specific solicitation of comment #6), HUD is 
also considering incentive fees to encourage and support PHAs in their 
efforts to improve locational outcomes for families, including but not 
limited to cases where the PHA is project-basing vouchers in areas of 
opportunity.
    Specific solicitation of comment #21: As previously discussed in 
specific solicitation of comment #6, HUD has dropped the SARR indicator 
but is seeking comment on whether the SARR or some other indicator that 
would address the variation in administrative cost as it relates to 
locational outcomes should be reconsidered for inclusion in the core 
formula. As an alternative approach, HUD is also seeking comment on how 
to effectively structure an incentive fee for improving locational 
outcomes of HCV households. For example, HUD could provide a separate 
fee to a PHA based on the number of families that initially leased in 
low-poverty areas or that move out of areas with high concentrations of 
poverty. As discussed earlier, an alternative measure might be the 
number of families that move from R/ECAPs to less concentrated areas. 
Other options could include the extent to which the overall percentage 
of the PHA's families residing in areas with high concentrations of 
poverty or R/ECAPs decreases from year to year. Both measures would 
take into consideration the locational outcomes of families that moved 
out of the PHA's jurisdiction under the portability procedures.
    HUD is not inclined to establish an additional fee for PHAs based 
on their SEMAP score and rating designation at this time. Since HUD is 
currently in the midst of an effort to revise SEMAP, it is premature 
for HUD to determine whether or not to provide a performance incentive 
fee based on the PHA's SEMAP score and how to calculate and structure 
such a fee if warranted. HUD will revisit this possibility as the SEMAP 
reform effort progresses.

VI. This Proposed Rule--Regulatory Structure of New Administrative Fee 
Formula

    This proposed rule would amend HUD's regulations in 24 CFR part 982 
that govern Section 8 Tenant-Based Assistance: Housing Choice Vouchers 
to revise the method for determining the amount of funding a PHA will 
receive for administering the HCV program.
    Administrative Fee--Sec.  982.152: Administrative fees under the 
HCV program are governed by Sec.  982.152. The ongoing administrative 
fee provision in Sec.  982.152(b)(1) provides that the amount of the 
ongoing fee is determined by HUD in accordance with section 8(q)(1) of 
the 1937 Act (42 U.S.C. 1437f(q)(1). The rule also allows HUD to pay a 
higher fee for a small program or a program operating over a large 
geographic area (Sec.  982.152(b)(2)) and to pay a lower fee for PHA-
owned units (Sec.  982.152(b)(3)).
    The proposed rule would revise Sec.  982.152(b)(2) to establish a 
new, significantly more detailed method for determining the ongoing 
administrative fee. In addition, the proposed rule would provide that 
the actual fee formula calculation would be presented in a notice 
published in the Federal Register. If HUD subsequently decides to 
update the formula coefficient values as the result of changes in 
program requirements or the availability of data, HUD will publish a 
notice in the Federal Register that describes the proposed change and 
provides an opportunity for public comment for a period of no less than 
60 calendar days. After consideration of public comments, HUD would be 
required to publish the revised formula coefficient values in a final 
notice in the Federal Register before implementing any changes (Sec.  
982.152(b)(1)(vii)(B)).
    Portability: Administration by initial and receiving PHA--Sec.  
982.355(e)(1). Under Sec.  982.355(e)(1), the receiving PHA may bill 
the initial PHA for housing assistance payments and administrative 
fees. The revised administrative fee formula would eliminate 
portability billing for administrative fees. Therefore, the proposed 
rule would eliminate the reference to billing for administrative fees 
in Sec.  982.355(e)(1). In addition, Sec.  983.355(e)(3) establishes 
the requirements governing the initial PHA's reimbursement of 
administrative fees to the receiving PHA. Given the elimination of 
portability billing for

[[Page 44123]]

administrative fees, the proposed rule would remove Sec.  
983.355(c)(3).

VII. Findings and Certifications

Regulatory Planning and Review
    OMB reviewed this proposed rule under Executive Order 12866 
(entitled ``Regulatory Planning and Review''). This rule was determined 
to be an economically significant regulatory action, as provided in 
section 3(f)(1) of the Order.
    This rule proposes a new methodology for determining the amount of 
funding a PHA will receive for administering the Housing Choice Voucher 
(HCV) Program based on six variables that better reflect the costs of 
administering the program than the current formula. The rule would 
result in transfers of funding among stakeholders of more than $100 
million a year. Approximately $122 million will be transferred between 
PHAs. The transfer is dependent upon an assumed level of appropriation 
($1,642 million) and will vary correspondingly.
    The formula will lead to a transfer to PHAs that are: Smaller; 
whose residents are dispersed more widely; have a higher rate of new 
admissions and household with labor income; and are located in areas 
with higher labor costs. The transfer to the PHA will depend on the sum 
of all of the effects. It is possible that cost-drivers could counter-
balance one another. For example, a small PHA in a low-wage area may 
experience no change in its administrative fees.
    The accompanying Regulatory Impact Analysis (RIA) for this rule 
addresses the costs and benefits that would result if this rule were to 
be implemented in greater detail than this summary can provide, and can 
be found in the docket for this rule at https://www.regulations.gov. The 
docket file is available for public inspection between the hours of 8 
a.m. and 5 p.m. weekdays in the Regulations Division, Office of General 
Counsel, Department of Housing and Urban Development, 451 7th Street 
SW., Room 10276, Washington, DC 20410-0500. Due to security measures at 
the HUD Headquarters building, an advance appointment to review the 
docket file must be scheduled by calling the Regulations Division at 
202-708-3055 (this is not a toll-free number). Hearing- or speech-
impaired individuals may access this number through TTY by calling the 
toll-free Federal Relay Service at 800-877-8339.
Unfunded Mandates Reform Act
    Title II of the Unfunded Mandates Reform Act of 1995 (2 U.S.C. 
1531-1538) (UMRA) establishes requirements for Federal agencies to 
assess the effects of their regulatory actions on state, local, and 
tribal governments and the private sector. This rule does not impose 
any Federal mandate on any state, local, or tribal government or the 
private sector within the meaning of UMRA.
Environmental Impact
    This proposed rule sets forth the establishment of a rate or cost 
determination and external administrative procedures related to rate or 
cost determinations which do not constitute a development decision 
affecting the physical condition of specific project areas or building 
sites. Accordingly, under 24 CFR 50.19(c)(6), this proposed rule is 
categorically excluded from environmental review under the National 
Environmental Policy Act of 1969 (42 U.S.C. 4321).
Regulatory Flexibility Act
    The Regulatory Flexibility Act (RFA) (5 U.S.C. 601 et seq.), 
generally requires an agency to conduct a regulatory flexibility 
analysis of any rule subject to notice and comment rulemaking 
requirements, unless the agency certifies that the rule will not have a 
significant economic impact on a substantial number of small entities.
    The proposed administrative fee formula would apply to all PHAs 
across the board, including small entities, defined for the purpose of 
the Regulatory Impact Analysis (RIA) as PHAs that administer fewer than 
500 units. The proposed formula provides for an upward fee adjustments 
for PHAs that administer fewer than 750 units, with the largest 
adjustment provided to PHAs that administer 250 vouchers or fewer. 
Using 2014 data, the RIA finds that 1,143 of the 1,521 PHAs with less 
than 500 units would have a net increase in funding relative to the 
existing formula, while 378 will have a decrease in funding ($7.9 
million) for a net gain of $23.45 million. The $7.9 million decline is 
relative to an assumed level of funding of $1.642 million, which is 
based on the proposed formula's calculations using 2014 data (the level 
of funding required for future years would be different).
    Thus, most small PHAs are expected to increase their level of 
administrative fee funding under the proposed rule relative to the 
current administrative fee formula. Furthermore, as described in the 
preamble, the proposed formula sets a lower bound on per unit fees at 
95 percent of the previous year's per unit fee, so no PHA would 
experience a fee decrease of more than 5 percent in a given year. This 
would affect the 378 small PHAs that would experience a decrease in 
funding under the new formula--the decrease would be spread over as 
many years as necessary so that no PHA would experience a decrease of 
more than 5 percent in any given year.
    Finally, the new formula does not impose any additional 
administrative burden on PHAs, as all the formula inputs come from 
administrative data already being collected by HUD. For these reasons, 
HUD has determined that this rule will not have a significant economic 
impact on a substantial number of small entities.
Executive Order 13132, Federalism
    Executive Order 13132 (entitled ``Federalism'') prohibits, to the 
extent practicable and permitted by law, an agency from promulgating a 
regulation that has federalism implications and either imposes 
substantial direct compliance costs on State and local governments and 
is not required by statute or preempts State law, unless the relevant 
requirements of section 6 of the Executive Order are met. This rule 
does not have federalism implications and does not impose substantial 
direct compliance costs on State and local governments or preempt State 
law within the meaning of the Executive Order.

Catalog of Federal Domestic Assistance Number

The Catalog of Federal Domestic Assistance number for 24 CFR part 
982 is 14.871.

List of Subjects in 24 CFR Part 982

    Grant programs--housing and community development, Grant programs--
Indians, Indians, Public housing, Rent subsidies, Reporting and 
recordkeeping requirements.

    Accordingly, for the reasons stated in the preamble, HUD proposes 
to amend 24 CFR part 982 as follows:

PART 982--SECTION 8 TENANT-BASED ASSISTANCE: HOUSING CHOICE VOUCHER 
PROGRAM

0
1. The authority citation for part 982 continues to read as follows:

    Authority: 42 U.S.C. 1437f and 3535(d).

0
2. In Sec.  982.152, paragraph (a)(2) and paragraph (b)(1) are revised 
to read as follows:


Sec.  982.152  Administrative fee.

    (a) * * *
    (2) Administrative fees may only be paid from amounts appropriated 
by the Congress.
* * * * *
    (b) Ongoing administrative fee. (1) The PHA ongoing administrative 
fee is

[[Page 44124]]

paid for each unit under HAP Contract on the first day of the month. 
The amount of the ongoing administrative fee is determined annually by 
HUD based on the most recent available data for the cost factors listed 
in this paragraph (b) at the time of fee calculation and will be 
published in the Federal Register consistent with the requirements of 
section 8(q)(1)(C) of the 1937 Act (42 U.S.C. 1437f(q)(1)(C)).
    (i) Formula cost factors used to calculate fee. The formula for 
determining the ongoing administrative fee for each PHA is based on the 
following cost factors:
    (A) PHA program size. The PHA size is determined by the number of 
vouchers under lease. The number of vouchers under lease includes 
vouchers under lease that the PHA is administering on behalf of other 
PHAs as the receiving PHA under the portability procedures. The number 
of vouchers under lease does not include any vouchers under lease for 
which the PHA is the initial PHA under the portability procedures and 
is billing the receiving PHA (those vouchers are counted as part of the 
receiving PHA's vouchers under lease).
    (B) Wage index. The wage index is the average annual wage for local 
government workers in the area where the PHA's headquarters is located, 
divided by the national average annual wage for local government 
workers.
    (C) Benefit load. The benefit load is the average employee benefits 
as a percentage of salary paid to PHA employees working on the HCV 
program in the State in which the PHA is located.
    (D) Percent of households with earned income. The percent of 
households with earned income is the percent of the PHA's active HCV 
households that had any income from employment as of their most recent 
recertification.
    (E) New admissions rate. The new admissions rate is the percent of 
the PHA's active HCV households that were new admissions to the 
program.
    (F) Percent of voucher holders living more than 60 miles from the 
PHA's headquarters. The percent of the PHA's active households living 
more than 60 miles away from the PHA's headquarters, where distance is 
calculated as the shortest distance between two points.
    (G) Additional factors. Any additional factors established by HUD 
in accordance with paragraph (b)(1)(viii) of this section.
    (ii) Fee ceiling and floor adjustments. The administrative fee will 
be adjusted if necessary to stay within maximum and minimum 
administrative fee amounts determined by HUD. For PHAs outside the U.S. 
Territories, the maximum ongoing administrative fee is based on $109, 
adjusted for inflation, and the minimum ongoing administrative fee is 
based on $42, adjusted for inflation. For PHAs in the U.S. Territories, 
the maximum ongoing administrative fee is based on $109, adjusted for 
inflation, and the minimum ongoing administrative fee is based on $54, 
adjusted for inflation. The ongoing administrative fee ceiling and 
floor amounts will be adjusted annually for inflation in accordance 
with paragraph (b)(1)(iii) of this section.
    (iii) Inflation factor. An inflation factor will be used to account 
for inflation that has taken place between 2013, when the ongoing 
administrative fee formula's cost drivers were measured, and the point 
in time at which amount of the ongoing administrative fee is determined 
annually by HUD. The inflation factor is a blended rate, where 70 
percent of the inflation rate captures changes in the cost of local 
government employee salaries and wages and 30 percent captures changes 
in the general cost of goods and services.
    (iv) Fee amount. The ongoing administrative fee amount is 
determined for each PHA using the most recent available data for the 
formula cost factors and the ceiling and floor adjustments, in 
accordance with paragraphs (b)(1)(i) and (ii) of this section and 
multiplied by the annual inflation factor in accordance with paragraph 
(b)(1)(iii) of this section.
    (v) Restrictions on year-to-year changes in fee amount. The amount 
by which a PHA's ongoing administrative fee may increase or decrease 
from the previous year under the formula is restricted as follows:
    (A) The ongoing administrative fee for a PHA may not exceed 140 
percent of the PHA's ongoing administrative fee for the previous year, 
adjusted for inflation.
    (B) The ongoing administrative fee for a PHA may not be lower than 
95 percent of the PHA's ongoing administrative fee for the previous 
year, adjusted for inflation.
    (C) In the event that administrative fee funding is insufficient, 
HUD may further reduce the maximum fee increase from the previous 
year's fee per UML if necessary to limit the reduction in the ongoing 
administrative fee for PHAs in accordance with paragraph (b)(1)(v)(B) 
of this section.
    (vi) Portability. For vouchers under HAP contract that are 
administered under the portability billing procedures at Sec.  
982.355(e), administrative fee payment is as follows:
    (A) The receiving PHA is paid 100 percent of its ongoing 
administrative fee for each unit under HAP contract on the first day of 
the month; and
    (B) The initial PHA is paid an ongoing administrative fee that is 
equal to 20 percent of the initial PHA's regular ongoing administrative 
fee for each unit under HAP contract.
    (vii) Fee formula calculation and formula variable coefficient 
changes. (A) HUD shall publish the formula calculation used to 
determine the ongoing administrative fee in a notice in the Federal 
Register. The notice shall include the specific formula variables, the 
formula variable coefficients, the data collection periods, the fee 
floor and ceiling values, and the inflator factor used in the 
calculation of the ongoing administrative fee.
    (B) Any subsequent changes to the formula variable coefficients as 
the result of changes in program requirements or the availability of 
data will first be proposed in a notice published in the Federal 
Register and will provide an opportunity for public comment of no less 
than 60 days. After consideration of public comments, HUD will publish 
the final formula calculation with the revised variable coefficients in 
a notice in the Federal Register.
    (viii) Modifications and supplemental fees. HUD may modify 
allocations or provide supplemental administrative fees to address 
program priorities such as special voucher programs (e.g., the HUD-
Veterans Affairs Supportive Housing program), serving homeless 
households, PHA performance incentives, and expanding housing 
opportunities. Any modifications or supplemental fees will be published 
in the Federal Register.
* * * * *
0
3. In Sec.  982.355:
0
a. Revise paragraph (e)(1);
0
b. Remove paragraph (e)(3);
0
c. Redesignate paragraphs (e)(4), (5), (6), and (7), as (e)(3), (4), 
(5) and (6).
    The revision reads as follows:


Sec.  982.355  Portability: Administration by initial and receiving 
PHA.

* * * * *
    (e) Portability billing. (1) To cover assistance for a portable 
family that was not absorbed in accordance with paragraph (d) of this 
section, the receiving PHA may bill the initial PHA for the housing 
assistance payments.
* * * * *


[[Page 44125]]


    Dated: June 8, 2016.
Lourdes Castro Ram[iacute]rez,
Principal Deputy Assistant Secretary, Office of Public and Indian 
Housing.
[FR Doc. 2016-15682 Filed 7-5-16; 8:45 am]
 BILLING CODE 4210-67-P