2016-15682. Housing Choice Voucher Program-New Administrative Fee Formula

AGENCY:

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

ACTION:

Proposed rule.

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.

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 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.

The new language for § 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 § 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:

○ The FY 1993 FMR for a 2 bedroom existing dwelling unit in the market area, or

○ 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.

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 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.

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 http://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]

(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]

(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 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: http://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 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 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:

Table 1—Base Fee Formula Calculation

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

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.

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.

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 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.

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.

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.

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.

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-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.

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 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 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.

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 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 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 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 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 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]

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.

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 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 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 × .36 = .252) and wages are 45 percent of the total cost (.70 × .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.

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.

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.

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 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 § 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.

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 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 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 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—§ 982.152: Administrative fees under the HCV program are governed by § 982.152. The ongoing administrative fee provision in § 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 (§ 982.152(b)(2)) and to pay a lower fee for PHA-owned units (§ 982.152(b)(3)).

The proposed rule would revise § 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 (§ 982.152(b)(1)(vii)(B)).

Portability: Administration by initial and receiving PHA—§ 982.355(e)(1). Under § 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 § 982.355(e)(1). In addition, § 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 administrative fees, the proposed rule would remove § 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 http://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

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

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

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

§ 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 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 § 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.

* * * * *

3. In § 982.355:

a. Revise paragraph (e)(1);

b. Remove paragraph (e)(3);

c. Redesignate paragraphs (e)(4), (5), (6), and (7), as (e)(3), (4), (5) and (6).

The revision reads as follows:

§ 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.

* * * * *

Dated: June 8, 2016.

Lourdes Castro Ramírez,

Principal Deputy Assistant Secretary, Office of Public and Indian Housing.

Footnotes

1.  The draft final report for this study was published in April 2015 and the final report was published in August 2015.

2.  A cost driver is a factor that triggers a change in the cost of an activity.

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).

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 (http://www.huduser.org/​portal/​hcvfeestudy.html) and the Solicitation of Comment reflected the updated coefficients.

5.  The study can be found at: http://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.

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.

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.

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.

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.

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

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.

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.

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 http://www.census.gov/​population/​metro/​.

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.

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.

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.

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.

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

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

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

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.