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«Mixed Messages on Mixed incoMes Volume 15, Number 2 • 2013 U.S. Department of Housing and Urban Development | Office of Policy Development and ...»

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Of U.S. couple households, from 2006 through 2010, an estimated 1 percent (652,791) were SSC households; the 95-percent confidence interval is 1.0 to 1.1 percent. The SSC percentage is much higher in metropolitan areas than in nonmetropolitan areas. Of nonmetropolitan couple households, only 0.7 percent were estimated to be SSC households. Of metropolitan couples, an estimated 1.2 percent were SSC households. An estimated 2.0 percent of couple households in central cities of metropolitan areas were SSC households compared with 0.9 percent of couple households in metropolitan areas outside of central cities.

The possible preferences of same-sex couples for cultural and other amenities available in more metropolitan areas might play a significant role in these differences. Another possible factor is that there might be more tolerance and less discrimination in metropolitan areas.

Exhibit 1 reports estimates of same-sex couples as a percentage of all couple households by state and for metropolitan and nonmetropolitan areas within states, and it includes 95-percent confidence intervals. Estimates are reported in descending order of state total estimates. The total SSC percentage varies from 0.5 percent in Mississippi to 5.7 percent in D.C., the median is 0.9 percent in North Carolina, and the mean is 1.1 percent. Four of the five highest percentage observations (D.C., Maine, Massachusetts, and Vermont) are in the eastern United States; the exception is Oregon. The five lowest percentage states are in the Midwest (North Dakota, Nebraska, and South Dakota) and South (Mississippi and West Virginia) census regions.

Nonmetropolitan estimates vary from 0.4 percent in Mississippi to 2.3 percent in Delaware, with a median of 0.6 percent in Michigan and a mean of 0.8 percent. Metropolitan estimates vary from

0.5 percent in North Dakota to 5.7 percent in D.C., with a median of 1.0 percent in Missouri and a mean of 1.2 percent. Note that some states’ estimates are missing from one column. For instance, New Jersey has no nonmetropolitan areas, whereas Wyoming has no metropolitan areas.

Although exhibit 1 is more informative than typical maps, D.C. remains an outlier. In exhibit 2, I replace the column of metropolitan estimates in exhibit 1 with estimates for metropolitan areas outside central cities and for central cities of metropolitan areas.2 Compared with exhibit 1, exhibit 2 better explains why D.C. is an outlier from the 50 states. D.C.

is the only observation for which the total population resides in a central city of a metropolitan area. When compared with central-city areas within states, D.C. is not such a significant outlier. In fact, Oregon central cities have a higher estimated SSC percentage—6.2 percent. Although Georgia ranks 17th overall, its central-city estimate of 5.2 percent ranks 3rd. The median for central-city areas within states is 1.6 percent in Texas, and the mean is 2.0 percent.

The ACS PUMS does not disclose central-city status for all households in metropolitan areas or for any metropolitan areas in Delaware, Montana, and North Dakota.

268 Graphic Detail Visualizing Same-Sex Couple Household Data With Linked Micromaps Exhibit 1 Same-Sex Couple Households As a Percentage of Couple Households (by metropolitan status)

–  –  –

Linked micromaps are powerful data-visualization tools, allowing for multiple columns of data to be reported next to maps. Geographic areas can be sorted and arranged in subgroups to facilitate visual comprehension.

Linked micromaps make clear that part of the state variation in the SSC percentage is because of differences in the proportion of the state population living in metropolitan areas, particularly in central cities. Compared with estimates for central cities within other states, the SSC percentage in Washington, D.C., is not so large.

Acknowledgments The author thanks Daniel Carr for helpful comments and R code for creating linked micromaps and thanks Ron Wilson for helpful comments.

Author Brent D. Mast is a social science analyst in the Office of Policy Development and Research at the U.S. Department of Housing and Urban Development.

References Carr, Daniel B., and Linda Williams Pickle. 2010. Visualizing Data Patterns With Micromaps. New York: Chapman and Hall/CRC.

Lofquist, Daphne. 2011. “Same-Sex Couple Households: American Community Survey Briefs.” U.S. Census Bureau Report ACBSR/10-03. Available at http://www.census.gov/prod/2011pubs/ acsbr10-03.pdf.

Ruggles, Steven, J. Trent Alexander, Katie Genadek, Ronald Goeken, Matthew B. Schroeder, and Matthew Sobek. 2010. “Integrated Public Use Microdata Series: Version 5.0.” Machine-readable database. Minneapolis: University of Minnesota. Available at http://usa.ipums.org/usa/.

Additional Reading O’Connell, Martin, and Daphne Lofquist. 2009. “Changes to the American Community Survey Between 2007 and 2008 and Their Potential Effect on the Estimates of Same-Sex Couple Households.” Washington, DC: U.S. Census Bureau. Available at http://www.census.gov/hhes/samesex/ files/changes-to-acs-2007-to-2008.pdf.

Cityscape 271Mast

272 Graphic Detail Impact A regulatory impact analysis must accompany every economically significant federal rule or regulation. The Office of Policy Development and Research performs this analysis for all U.S. Department of Housing and Urban Development rules. An impact analysis is a forecast of the annual benefits and costs accruing to all parties, including the taxpayers, from a given regulation. Modeling these benefits and costs involves use of past research findings, application of economic principles, empirical investigation, and professional judgment.

Refinancing Hospital Loans Alastair McFarlane U.S. Department of Housing and Urban Development The views expressed in this article are those of the author and do not represent the official positions or policies of the Office of Policy Development and Research or the U.S. Department of Housing and Urban Development.

Summary of Impact Analysis When the credit crisis developed, the Federal Housing Administration (FHA) allowed non-FHAinsured hospitals to refinance capital debt. FHA permitted the refinancing of non-FHA-insured loans under notices issued on July 1, 2009, and February 22, 2010. This final rule revised the regulations governing FHA’s Section 242 Hospital Mortgage Insurance Program to codify the refinancing of non-FHA-insured loans.

In offering this new insurance product, the U.S. Department of Housing and Urban Development (HUD) took an approach intended to attract hospital applicants with a higher degree of financial strength than many Section 242 applicants have had historically. The minimum operating margin and debt-service coverage ratio were set at the median values prevailing in the Section 242-insured portfolio (excluding hospitals on credit watch). The rule will not address the financing needs of all healthcare facilities: its goal is to assist those hospitals saddled with unexpectedly high interest rates and those in which refinancing is urgently needed for the hospital to continue operations and adequately serve its community.

HUD expects the rule to result in a $1.26 million transfer per year per healthcare facility. Among 10 facilities, the aggregate annual impact is $12.59 million. A multiyear scenario, in which the number of participants increases to 17, yields an aggregate annualized transfer to hospitals of $17.63 million by the third year of the program. HUD estimates that this program will raise net receipts of the federal government by $79 million (from $79 million to $158 million). Costs of Cityscape 273 Cityscape: A Journal of Policy Development and Research • Volume 15, Number 2 • 2013 U.S. Department of Housing and Urban Development • Office of Policy Development and Research McFarlane the rule include upfront application costs, which may be as high as $870,000 per applicant but are likely to be much lower, given that non-FHA-insured lenders impose transaction costs as well.

HUD does not have enough information to quantify or evaluate the opportunity costs or distortionary effects of the program. A benefit of reducing the probability of default includes reducing the expected social welfare loss from hospital foreclosures.

Motivation for the Rule The rule was promulgated to provide relief for those hospitals that are paying high penalty rates on auction-rate debt and variable-rate bonds and that are unable to obtain affordable refinancing from the private market, thereby placing in jeopardy the continued existence of the hospital and its ability to adequately serve the surrounding community. Auction-rate debt was a standard means for financing loans used by quasi-utilities, such as hospitals. The interest rates on auction securities are reset by auction periodically.1 The auction-rate securities are an alternative to more familiar types of bonds, such as fixed-rate bonds or variable-rate bonds, for which the rate is based on an index such as LIBOR (the London Interbank Offered Rate). Hospitals issued auction-rate securities because, before 2008, they provided low-cost financing. Investors purchased auction-rate securities because, before 2009, they were perceived as offering an advantageous balance between risk and expected return.

An auction for adjustable-rate securities fails when the offer of securities for sale exceeds the orders to purchase securities (demand exceeds supply).2 Before 2008, broker-dealers had managed to prevent most auction failures by putting in bids when demand for auction-rate securities threatened to be insufficient. In 2008, however, broker-dealers retreated and the auction-rate securities market was paralyzed.3 When an auction fails, the investor’s account is frozen until the next auction and the borrower is required to pay an interest penalty, which can be significant. The penalty is designed to compensate investors who bear the opportunity costs of illiquidity during volatile times.

Until the recent financial crisis, liquidity in the auction-rate securities market had been adequate for hospitals. In February 2008, the auction-rate securities market froze and many borrowers were subject to increases in interest payments during an economic period in which any increase in cost imposed a substantial burden on the borrower. In worst case scenarios, some borrowers experienced interest payment increases as great as 10 percentage points.4 Other debt-service costs exist, in addition to the interest-rate penalties. Variable-rate debt is typically collateralized by letters of credit issued by banks to the borrowers. During a financial crisis, a reduction in the liquidity and creditworthiness of banks adversely affects their ability to extend or reissue letters of credit. The consequences of nonrenewal of letters of credit can be the acceleration of outstanding debt balance.

Auctions occur at intervals of 7, 14, 28, or 35 days.

Lee (2008) provides an excellent description of auction-rate securities and setting interest rates via the Dutch auction.

The failure rate rose from 2.0 to 87.0 percent during February 2008.

The U.S. Securities and Exchange Commission issued a legal brief in 2011 supporting claims that investment banks failed to adequately warn investors of the risks of auction-rate securities (Preston and Gallu, 2011).

–  –  –

If a hospital finds itself in a disadvantageous position because of failed auctions, then refinancing is critical to its ability to repay its loan. Allowing for refinancing also leads to benefits by reducing the probability of default and reduces the expected social cost of a hospital foreclosure.

Transfers Resulting From the Rule The hospitals that are able to refinance into a lower cost loan because of FHA insurance are the primary beneficiaries of this rule. The objective of the healthcare facility to minimize financing costs among a variety of alternatives can be expressed as— mini[mARS, (mFHA + TFHA), (mM + TM)], (1) where m is the annual mortgage payment, T is the annualized closing cost, ARS indicates the auction-rate security financing cost or status quo, FHA indicates the mortgage payment and closing costs of an FHA-insured loan, and M indicates the lowest cost market alternative to the FHA loan.

If the FHA-insured loan is the least cost loan and the status quo (ARS) is the second best option,5 then the annual savings would be expressed as—

–  –  –

If the FHA-insured loan is the least cost loan and the alternative market refinancing (M) is the second best option,6 then the gain from refinancing is—

–  –  –

Before the liquidity crisis, the former scenario (2) was the norm for healthcare facilities. Since 2008, however, the latter scenario (3) has been more common. For healthcare facilities operated by local governments, the reduction of interest payments constitutes a transfer to the taxpayers.

The segment of the market served by FHA is composed of facilities with credit ratings of BBB (lower medium grade) or less or are not rated. By raising the hospital’s credit rating to AA (high grade), the FHA insurance enables considerable debt-service savings for the hospital. FHA is able to facilitate a transfer of (mM + TM) – (mFHA + TFHA) to hospitals through prudent risk pooling by FHA.

The estimate of the benefit of the FHA refinancing rule needs to account for the benefit to hospitals relative to other refinancing opportunities. In the former scenario, in which auction-rate security financing is the least expensive alternative to an FHA loan, the net annual gain of FHA refinancing for the hospitals is equal to the sum of the average market rate plus interest-rate penalties less the sum of the FHA annual fixed rate plus its insurance premium and other closing costs. For example, if the average annual rate that hospitals pay on auction-rate debt is 15.0 percentage points and the FHA-insured loan offers refinancing at 4.5 percentage points, then the benefit of the refinancing, accounting for the 0.5-percent premium, would be 15.0 percentage points annually for participants.

If private alternatives exist, however, such as the restructuring of debt that would reduce capital

–  –  –

costs to an interest rate of 7.0 percent (Franklin, 2009), then the net benefit of the rule relative to other opportunities would be 2.0 percentage points (7.0 percent–4.5 percent–0.5 percent),7 where

0.5 percent represents the annual FHA premium.

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