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«A Journal of Policy Development and Research HoPe VI Volume 12, Number 1 • 2010 U.S. Department of Housing and Urban Development Office of Policy ...»

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We showed previously that HUD gave 50 percent of the total weight to shortage of ELI rental units, and a 25-percent weight to housing problems of ELI renters, corresponding with the law’s requirement for targeting 75 percent of rental housing funds to ELI households. HUD then gave equal weights of 12.5 percent for shortage of VLI units and for severe cost burdens of VLI renters.

To examine the importance of this weighting for allocation outcomes, HUD also ran the allocation formula with alternative weight structures. The first alternative was to retain the 50-percent priority weight for Factor 1 but remove the overweighting of Factor 3 so that it equals Factors 2 and 4, resulting in a 50-16.7-16.7-16.7 structure. HUD also tested two additional levels of preference for Factor 1, one applying a weight 10 percentage points below and the other 10 points above the proposed 50-percent value. Both alternatives provide equal weights for the other factors.

The Government Accountability Office (GAO) recently examined the adequacy of the two major data sources that potentially could address Insular Areas—the Current Population Survey (CPS) and the American Community Survey (ACS)—in a review of data adequacy for the Medicaid program. GAO concluded that CPS and ACS data are not available for the Insular Areas, except for the Commonwealth of Puerto Rico. Like HERA, the Medicaid statute defines states to include insular Areas, of which Puerto Rico is one. HUD’s decision to treat Puerto Rico like the 50 states and District of Columbia in allocating HTF therefore hews more closely to HERA than other federal programs have done when faced with similar statutory definitions and data limitations. (GAO, 2009).

162 Impact The Impact of Formula Allocation Discretion in the Housing Trust Fund Alternative 1: 50-16.7-16.7-16.7 weights.9 Relative to the proposed formula’s 50-12.5-25-12.5 weighting, removing the additional preference for Factor 3 has distributional effects. Under a $1 billion total appropriation and using 2005-to-2007 ACS data, the alternative 50-16.7-16.7-16.7 formula structure would provide additional benefits exceeding $500,000 to the states of California, Florida, Nevada, New Jersey, and New York. Reductions of $500,000 or more would occur for Illinois, Indiana, Kentucky, Michigan, Minnesota, Missouri, Ohio, Pennsylvania, Texas, and Wisconsin. Relative to the proposed allocation formula, 10 states would receive more under this option and 35 would receive less, although, for 20 states, the change would be less than 1 percent from the proposed allocation.

Alternative 2: 40-20-20-20 weights. Without the overweighting of Factor 3, the two weighting alternatives for Factor 1—10 points higher or 10 points lower—do not produce the roughly symmetrical gains or losses that might be anticipated for any given state. Notably, California and Florida would benefit relative to the proposed rule whether the Factor 1 prioritization were stronger or weaker. Overall, reducing the weight of Factor 1 through the 40-20-20-20 structure has a result similar to that of eliminating the Factor 3 overweight, but with slightly more concentrated effects. The number of gainers (11) and losers (34) relative to the proposed allocation is similar, but the average gain and average loss both are greater, primarily because of large gains by California and Florida and larger losses by the same states affected by alternative 1.

Alternative 3: 60-13.3-13.3-13.3 weights. Compared with the first and second alternatives, increasing the weight of Factor 1 to 60 percent in the 60-13.3-13.3-13.3 structure produces smaller changes in allocations relative to the proposed formula. The 14 states that gain would receive an average of $312,000 more in their allocations, while the 31 states that lose would average $141,000 less. A significant gain by California makes it the single major outlier under this alternative.

Selection of Alternative for Proposed Rule In eliminating the alternatives discussed previously, HUD’s decision is complicated by the fact that increasing the weight on the VLI factors (2 and 4) might have the effect of shifting funding from states with relatively softer rental housing markets, such as Alabama, Ohio, Pennsylvania, and Michigan, to housing markets that in the 2005-to-2007 period had very high rental costs relative to income, such as California, Florida, Nevada, and New York. It is worth noting that the appropriate use of HTF funds might vary by the type of housing shortage. State and local housing markets that have the highest shortages of housing for both ELI households and VLI households might have the greatest need for new units. Those markets with a shortage primarily in ELI units have a greater need for funds to reduce operating costs and renovate affordable housing so that decent affordable housing will be available to ELI renters.

HUD’s analysis of the sensitivity of state allocations to various prioritizations of the needs of ELI renters under Factor 1 and Factor 3 revealed that approximately one-half of the states are not Although the weights are rounded to facilitate presentation, those in the calculation process use repeating decimals so as to sum to 100 percent.

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affected greatly by any of the weighting alternatives, because 20 to 29 states experienced changes of less than 1 percent. For larger states, effects tend to be more pronounced, yet only rarely exceed 3 percent relative to HUD’s proposed formula. HUD concluded that providing priority weighting for both ELI factors in the proposed 50-12.5-25-12.5 structure accommodates states for which ELI needs take different forms, while responding as closely as feasible to the statutory requirement that 75 percent of rental assistance funds provided by the HTF should serve ELI households.





Summary of Impacts As noted previously, the statute is very specific about the factors to be used in the formula and different weighting schemes have only a modest effect on allocation grants. The largest effect on allocation grants is the amount made available for the program. Under current statute, the HTF would be funded through profits from the government-sponsored enterprises Fannie Mae and Freddie Mac. Because those agencies currently do not have profits, for the FY 2010 HUD budget request to Congress, President Obama requested that $1 billion be appropriated for the program as a transfer from the federal government to state governments. The direct federal cost of the program will be the amount eventually provided by Congress.

HTF grants will be used to support the development of primarily rental housing affordable to ELI households. Under the formula described here, this program provides funding to add affordable housing supply to markets in which strong evidence indicates an inadequate supply. This program represents a strong complement to HUD’s demand-side program, the Housing Choice Voucher Program (HCVP), which provides a tenant-based subsidy for primarily ELI households to afford existing privately owned rental housing. A limitation of the HCVP is that tenants are less likely to use their vouchers successfully in tight markets (Finkel and Buron, 2001), a problem that the careful targeting of HTF dollars in this rule to markets with inadequate supply is intended to address.

The primary benefits of the HTF are expected to be similar to the HCVP. The large-scale random assignment evaluation of the voucher program by Mills et al. (2006) reports that a primary benefit of housing assistance programs is reducing homelessness and the doubling up tendency among ELI families.10 Thus, the primary benefit of the program against no funding or funding without targeting will be to reduce the number of homeless families and individuals in relatively tight housing markets.

The economic effect of the HTF formula rule was classified in HUD’s submission to the Office of Management and Budget as a transfer from the federal government to states in the amount of the appropriation. More explicitly, and perhaps more accurately, the transfer is from taxpayers to direct beneficiaries of housing assistance, thus increasing the beneficiaries’ consumer surplus.

Despite the simple transactional implication of a transfer, the economic costs and benefits are in reality far more complex. Even ignoring the state-level distributional effects of the discretionary design of a formula, the evaluations cited previously hint at the indirect benefits and effects of the Mills et al. (2006) also report various other effects of relatively modest size, both positive (for example, deconcentration of poverty) and negative (for example, lower earnings).

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housing subsidy. Increasing the supply of affordable housing would mitigate the severe shortage of affordable housing units and the current crowding out of households with greater needs.11 Greater affordable housing supply would produce external benefits arising from reduction of homelessness and improved housing consumption by low-income households.

HUD is not in a position to assess a number of economic effects. An incomplete list of such factors might include the deadweight losses that result from higher federal taxes and borrowing, the discount value that HTF beneficiaries place on housing subsidies compared with cash grants—offset by possible increases in their labor supply compared with cash grants, and increases in resources used by developers or program applicants in competing for HTF grants. The current lack of data and analytic capacity has prevented HUD from addressing these issues, although such analysis would be of great interest.

Acknowledgments The authors gratefully acknowledge the perspective and comments provided by Alistair MacFarlane and Mark Shroder of HUD’s Office of Policy Development and Research.

References

Finkel, Meryl, and Larry Buron. 2001 (November). Study on Section 8 Voucher Success Rates Volume I:

Quantitative Study of Success Rates in Metropolitan Areas. Washington, DC: U.S. Department of Housing and Urban Development.

Khadduri, Jill, Kimberly Burnett, and David Rodda. 2003. Targeting Housing Production Subsidies:

Literature Review. Washington, DC: U.S. Department of Housing and Urban Development.

Mills, Gregory, Daniel Gubits, Larry Orr, David Long, Judie Feins, Bulbul Kaul, Michelle Wood, Amy Jones & Associates, Cloudburst Consulting, and The QED Group. 2006. Effects of Housing Vouchers on Welfare Families. Washington, DC: U.S. Department of Housing and Urban Development.

U.S. Department of Housing and Urban Development (HUD). 2007. Affordable Housing Needs 2005:

Report to Congress. Washington, DC: U.S. Department of Housing and Urban Development. http:// www.huduser.org/publications/affhsg/affhsgneeds.html (accessed January 27, 2010).

U.S. Government Accountability Office (GAO). 2009. Federal Medicaid and CHIP Funding in the U.S.

Insular Areas. GAO-09-558R. Washington, DC: U.S. Government Accountability Office.

In 2005, only 39.9 affordable rental units were available for every 100 ELI households and 76.8 units for every 100 VLI households (HUD, 2007). Further, about 2.76 million households with incomes above the ELI threshold were occupying ELI-affordable units in 2005 (HUD, 2007).

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Contents Symposium

HOPE VI

Guest Editor: Jennifer A. Stoloff Guest Editor’s Introduction

Better Neighborhoods, Better Outcomes?

Explaining Relocation Outcomes in HOPE VI

by Edward G. Goetz

Neighborhood Institutions, Facilities, and Public Space:

A Missing Link for HOPE VI Residents’ Development of Social Capital?

by Alexandra M. Curley HOPE VI Neighborhood Spillover Effects in Baltimore

by Nina Castells

HOPE VI and Neighborhood Economic Development:

The Importance of Local Market Dynamics

by Sean Zielenbach and Richard Voith Refereed Papers

Moving Toward a Shrinking Cities Metric:

Analyzing Land Use Changes Associated With Depopulation in Flint, Michigan......... 133 by Justin B. Hollander Departments Graphic Detail Combining Data on Residential Vacancy Rates and Mortgage Foreclosures Provides a Picture of Neighborhood Change

by Robert N. Renner and Jamie W. Wolf Impact The Impact of Formula Allocation Discretion in the Housing Trust Fund

by Todd Richardson and Barry Steffen

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