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The housing quality questions asked in the survey were originally developed to test damages to the housing stock after Hurricanes Katrina and Rita and are not specific enough to judge the severity of applicants’ housing problems. Nonetheless, some of the problems reported by applicants are potentially very serious. Almost one-fourth of applicants lived in housing with mold or water damage, which is positively associated with asthma and other respiratory problems (Bush et al., 2006).
More than 10 percent of applicants reported problems with their electric, plumbing, or heating systems. Some of these problems may be only inconveniences, whereas others are potentially unsanitary and unsafe. In addition, 18 percent of applicants lived in overcrowded housing, which has been associated with higher risk of meningitis, tuberculosis, and other respiratory problems (Office of the Deputy Prime Minister, 2004) and food insecurity (Cutts et al., 2011). The housing quality problems reported by applicants are especially troubling because most of these households include young children.
The high incidence of housing quality problems suggests that the lack of adequate, affordable housing is a public health issue as well as an economic and social problem. Further research is needed to determine the severity of these housing quality problems and whether they are representative of problems among very low-income households in general. These results suggest, however, that the quality of the housing stock for very low-income households remains a problem even if incidences of severely substandard housing have become rare.
Finally, the survey of applicants raises some questions about whether rental assistance is being targeted to households with the greatest housing needs. Of new admits, 16 percent were already receiving some form of housing assistance. Although subsidized applicants were not immune from having severe rent burdens or other housing hardships, they were less likely to experience these
problems than were applicants not receiving a subsidy. Assisted applicants, however, were as likely to be selected from the waiting list as were unassisted applicants. PHAs may want to consider prioritizing waitlisted households that are not already receiving housing assistance.
The treatment of doubled-up applicants is a larger issue for rental assistance policy. Among all rental assistance applicants, 40 percent were living with family or friends. Because these applicants were typically extremely low income, they appear to be more likely to be selected from the waiting list than applicants living independently without a subsidy. Doubled-up applicants, however, were less likely than applicants living independently to be severely rent burdened or to live in housing with quality problems and, counterintuitively, were also less likely to live in overcrowded housing.
These findings raise the question of whether doubling up is a solution to a housing affordability problem or is a serious housing need itself. The answer depends in part on the stability of the doubled-up arrangement. The survey results suggest that living with friends is often not a viable long-term living arrangement. Applicants living with friends had the shortest average tenure at their current address and were the most likely to report being literally homeless at some point during the past 12 months. By contrast, applicants living with family had been in their current address for an average of 5 years, suggesting that, for many applicants, living with other family members was a stable, long-term living arrangement.
Of course, anyone who has ever moved back in with their parents recognizes that this is not an ideal long-term living arrangement and many doubled-up applicants have a strong desire to form their own households. A number of studies have established that rental assistance is a means by which families can create their own household (Shroder, 2002). Household formation has psychological benefits; assisted households have reported decreases in stress and depression as a result of having their own home rather than having to “mooch” off of family or friends (Wood, Turnham, and Mills, 2009). Household formation may also have positive effects for the development of human capital and for the overall economy (Painter, 2010; Shroder, 2002). Some policymakers may not see household formation as one of the primary goals of rental assistance, however, and may prefer to see scarce resources allocated to applicants who are homeless, severely rent burdened, or living in substandard housing.
294 Refereed Papers The Housing Needs of Rental Assistance Applicants Appendix A. Formulas for Comparing Rental Assistance Applicants With Very Low-Income Households in the Same Metropolitan Area The comparison of incidences of severe rent burden and overcrowding between the two groups was done using a weighted average approach. The mean was calculated for each site, and the weighted mean was the average across all sites weighted by the number of respondents in each site such that where so that where Ms = the mean outcome for the survey sample;
Ns = the total number of survey respondents;
Msj = the mean outcome for the survey sample in the site;
Nj = the number of survey respondents in the jth site; and Xij = the outcome for the ith respondent in the jth site.
The calculation used to determine the variance of each population was where P = the weighted mean outcome; and N = the number of respondents.
Acknowledgments A Development and Dissemination grant from Abt Associates supported this article. The author thanks Jill Khadduri, Jacob Klerman, Stephen Kennedy, Bulbul Kaul, and Larry Buron for their support and insights.
Author Josh Leopold is a management and program analyst at the United States Interagency Council on Homelessness.
References Belsky, Eric S., Jack Goodman, and Rachel Drew. 2005. Measuring the Nation’s Rental Housing Affordability Problems. Cambridge, MA: Harvard University, Joint Center for Housing Studies.
Bostic, Raphael. 2011. “Foreword.” In Worst Case Housing Needs 2009: Report to Congress.
Washington, DC: U.S. Department of Housing and Urban Development, Office of Policy Development and Research.
Buron, Larry, Jill Khadduri, Josh Leopold, and Sarah Gibson. 2010. Study of Rents and Rent Flexibility: Final Report. Report by the Urban Institute and Applied Real Estate Analysis.
Washington, DC: U.S. Department of Housing and Urban Development, Office of Public Policy and Legislative Initiatives.
Bush, Robert K., Jay Portnoy, Andrew Saxon, Abba Terr, and Robert Wood. 2006. “The Medical Effects of Mold Exposure,” Journal of Allergy and Clinical Immunology 117: 326–333.
Cutts, Diana B., et al. 2011. “Housing Insecurity Associated With Food Insecurity and Poor Health in Children,” Journal of Clinical Outcomes Management 18 (9): 395–398.
Grigsby, William G., and Steven C. Bourassa. 2004. “Section 8: The Time for Fundamental Program Change,” Housing Policy Debate 15: 805–834.
Jacob, Brian A., and Jens Ludwig. 2008. The Effects of Housing Assistance on Labor Supply:
Evidence From a Voucher Lottery. Working Paper 14570. Cambridge, MA: National Bureau of Economic Research.
Khadduri, Jill. 2008. Housing Vouchers Are Critical for Ending Family Homelessness. Washington, DC:
National Alliance to End Homelessness.
Koebel, C. Theodore, and Patricia Renneckar. 2003. A Review of the Worst Case Needs Measure.
Washington, DC: U.S. Department of Housing and Urban Development, Office of Policy Development and Research.
McClure, Kirk. 2011. Reduction of Worst Case Housing Needs by Assisted Housing. Washington, DC:
U.S. Department of Housing and Urban Development.
National Low Income Housing Coalition (NLIHC). 2004. A Look at Waiting Lists: What Can We
Learn From the HUD Approved Annual Plans? NLIHC Research Note #04-03.Washington, DC:
National Low Income Housing Coalition.
Office of the Deputy Prime Minister. 2004. The Impact of Overcrowding on Health and Education:
A Review of the Evidence and Literature. London, United Kingdom: Office of the Deputy Prime Minister. Also available at http://www.communities.gov.uk/documents/housing/pdf/140627.pdf.
Painter, Gary. 2010. What Happens to Household Formation in a Recession? Washington, DC:
Research Institute for Housing America.
Sharfstein, Joshua, Megan Sandel, Robert Kahn, and Howard Bauchner. 2001. “Is Child Health
at Risk While Families Wait for Housing Vouchers?” American Journal of Public Health 91 (8):
Shroder, Mark. 2002. “Does Housing Assistance Perversely Affect Self-Sufficiency? A Review Essay,” Journal of Housing Economics 11: 381–417.
Steffen, Barry L., Keith Fudge, Marge Martin, Maria Teresa Souza, David A. Vandenbroucke, and
Yung Gann David Yao. 2011. Worst Case Housing Needs 2009: Report to Congress. Washington, DC:
U.S. Department of Housing and Urban Development, Office of Policy Development and Research.
U.S. Department of Housing and Urban Development (HUD). 2011. The 2010 Annual Homeless Assessment Report to Congress. Washington, DC: U.S. Department of Housing and Urban Development, Office of Community Planning and Development.
Wood, Michelle, Jennifer Turnham, and Gregory Mills. 2009. “Housing Affordability and Family
Well-Being: Results From the Housing Voucher Evaluation,” Housing Policy Debate 19 (2):
298 Refereed Papers Graphic Detail Geographic Information Systems (GIS) organize and clarify the patterns of human activities on the earth’s surface and their interaction with each other. GIS data, in the form of maps, can quickly and powerfully convey relationships to policymakers and the public.
This department of Cityscape includes maps that convey important housing or community development policy issues or solutions. If you have made such a map and are willing to share it in a future issue of Cityscape, please contact firstname.lastname@example.org.
Geographic Patterns of Regional Unemployment Versus Unemployment Compensation in the United States—2009 Ron Wilson U.S. Department of Housing and Urban Development The opinions expressed in this article are those of the author and do not necessarily reflect those of the U.S.
Department of Housing and Urban Development.
In 2009, the unemployment rate was the highest it has been in the United States since 1982 (BLS, 2012a). Cresting at 10 percent, the unemployment rate coincided with one of the most serious economic downturns in U.S. history. State governments respond to unemployment by providing compensation through insurance. Unemployment insurance comes from state-managed funding that provides monetary compensation to workers who have suffered job loss.1 Unemployment compensation acts as a stabilizer for both family incomes and local economies. Individual state policies affect unemployment compensation amounts and eligibility. Unemployment compensation, then, may have geographic patterns that differ from unemployment rates and reveal the extent to which states are attempting to buffer the fallout from unemployment.
Location Quotients (LQs) used in this analysis highlight relative differences in the geographic patterns of unemployment rates (BLS, 2012b) and compensation levels (BEA, 2012) across the For a general description of unemployment benefits, see http://en.wikipedia.org/wiki/Unemployment_benefits.
nation. The LQ is simply the ratio of the county unemployment rate, or the share of unemployment benefits in the county’s personal income, to its national counterpart. If a county’s LQ is 1, it has the same unemployment rate (dependence on unemployment benefits) as the nation. A divergent color scheme for both unemployment rates and compensation levels shows whether counties have a similar (white), lesser (light gray), or greater (dark gray) LQ than the nation.
Exhibit 1 shows regional unemployment patterns by county in 2009, with clear regional distinctions. Approximately 51 percent of counties had rates of unemployment similar to the national rate (LQs between 0.76 and 1.24). An extensive and cohesive pattern of lower unemployment rates dominates the Great Plains states of Montana, North Dakota, South Dakota, Nebraska, Iowa, and Oklahoma. Nebraska, North Dakota, and South Dakota are made up almost entirely of counties with unemployment rates that were less than one-half the national rate.
The Northeastern states from Maine to Virginia show a regional pattern with similar to lower unemployment rates compared with the national rate. Michigan, California, and Oregon had a much higher than normal unemployment level, with most counties in these states having an unemployment rate of 1.25 to nearly 3 times greater than the national rate. Several localized clusters in the Southern states have unemployment rates higher than the national rate.
Exhibit 1 County Shares of the Unemployment Rate in 2009 for the Contiguous 48 States–– (manual classification of location quotient breaks)
Exhibit 2 also shows clear regional patterns of unemployment insurance benefits by county in 2009.2 Unemployment insurance patterns in exhibit 2 are far more geographically divergent than the unemployment rates shown in exhibit 1. Only 34 percent of counties had similar levels (LQ values between 0.76 and 1.24) of unemployment compensation compared with the national level. Rust Belt and West Coast states had an extensive, cohesive pattern of counties with 1.5 to 3 times greater levels of unemployment compensation than the national level. Local clusters of unemployment insurance compensation are also present in the Southern states but are somewhat more geographically extensive than the unemployment rate pattern in exhibit 1. Clusters of extreme values in Indiana, Illinois, Wisconsin, Minnesota, and central Pennsylvania are visible in exhibit 2 that have no counterparts in exhibit 1.