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October 14, 2020
Assessing the Size and Spread of Vulnerable Renter Households in the Southeast
"If you can get the right tool targeted and aimed at the right problem, you can solve that problem and make a lot of progress."
- Raphael Bostic, Atlanta Fed President, during Racism and the Economy webinar (around the 1 hour, 58 minute timestamp) on October 7, 2020
Many cities have begun their efforts to combat COVID-19-related housing effects on renters by estimating the number of affected households, claiming federal CARES Act relief funds, and distributing the funds to households in need through local organizations. More than likely, the number of vulnerable households will exceed the funds that have been made available to assist them. In this post, we continue our data exploration of households that are likely to be vulnerable to housing insecurity as a result of COVID-19.
Picking up where we left off in our July 13 blog post, we focus exclusively on renter households and explore regional variations in the estimated impacts to renters susceptible to COVID-19-related job loss across the Atlanta Fed's territory, the Sixth District, which covers much of the southeastern United States (Alabama, Florida, Georgia, and portions of Louisiana, Mississippi, and Tennessee). As we did for our national-level analysis, we adopt New York University's Furman Center methodology1 and we focus on the intersection of income, cost-burdened status, dwelling unit type, and race in an effort to better understand the distributional impact across renter households. The estimates we provide here can be used to highlight opportunities for targeted local responses. Localities can also use these estimates to assess whether, and how well, their disbursed funds are reaching those most in need. Further, this analysis can help policymakers and local leaders anticipate the gap between their pool of existing resources and the level of support they may ultimately need, given the estimated size and spread of renter household vulnerability. In short, we hope this work can help localities sharpen their tools and better identify their problems as they work to combat rental housing insecurity resulting from COVID-19.
Sixth District regional trends
We took a combined look at nine metropolitan statistical areas (MSAs) across the Sixth District: Atlanta, Georgia; Bay County, Florida; Birmingham, Alabama; Gulfport, Mississippi; Jacksonville, Florida; Miami, Florida; Nashville, Tennessee; New Orleans, Louisiana; and Orlando, Florida. We estimate there to be nearly 1.05 million renter households (or 16.9 percent of all renter households in the nine MSAs) that may be vulnerable to job loss tied to the COVID-19 pandemic. Using microdata from the Census Bureau's 2018 American Community Survey (ACS) provided by IPUMS USA, University of Minnesota,2 we estimate that:
- More than half of vulnerable renter households had incomes less than $50,000 (593,202, or 56.9 percent).
- Going into the pandemic, slightly more than half of vulnerable renter households (532,231, or 51 percent) were cost-burdened—that is, they were spending more than 30 percent of their income on housing costs.
- Looking deeper into the cross-section of income and housing-cost burden, a whopping 87.6 percent of cost-burdened renter households vulnerable to COVID-19-related job loss earned less than $50,000.
Targeting aid to cost-burdened households earning less than $50,000 could be important because these households face steeper challenges than others in maintaining emergency savings. In fact, according to data from the Federal Reserve's 2019 Survey of Household Economics and Decisionmaking, households earning under $50,000 are less likely to have a three-month rainy-day fund than households who earn more than $50,000, making it more difficult for this demographic to weather such a shock to their income as reduced work hours, temporary furlough, or job loss.
We also estimate that an overwhelming majority of renter households vulnerable to COVID-19-related job loss (902,656, or 86.7 percent) either lived in single-family rentals (392,978) or rentals in buildings with fewer than 50 units (509,678).
By ensuring that aid is directed to renter households residing in single-family houses, small multifamily buildings (two to four units), or medium multifamily buildings (five to 49 units), rental relief efforts are more likely to reach smaller-scale landlords as opposed to institutional landlords. Smaller landlords are more likely to operate on thinner margins, leaving them unable to offset an extended period of time when they can't collect rents. If these smaller-scale landlords fail, the stock of housing they provide—housing that tends to be more affordable3 and supports an overwhelming majority of renter households—could change hands and displace many lower-income tenants as a result.
And finally, we estimate that:
- Over two-thirds of vulnerable renter households (703,896, or 67.5 percent) self-identify as a race other than non-Hispanic white.4
- Nearly three-fourths of cost-burdened renter households vulnerable to job loss were categorized as a race other than non-Hispanic white (390,154, or 73 percent).
Acknowledging the disproportionate impact of COVID-19 related job loss on renter households of color and targeting aid towards these households could help to prevent any exacerbation of existing racial disparities.
While summary statistics at the regional level provide a helpful reference point, we also drill down further because policy responses such as emergency rent relief and eviction moratoria5 are much more likely to be implemented at the local level. The first animated GIF below presents estimates of vulnerable renter households by income, cost-burden status, and dwelling unit type.6
A few additional observations emerge beyond what we've cited at the regional level.
First, although the majority of renter households susceptible to job loss earn less than $50,000, metros with more than 450,000 people (specifically Atlanta, Miami, Nashville, and Orlando) tend to have vulnerable households across a broader income distribution. Put differently, in larger metro areas, renter households can have slightly higher incomes and still experience housing vulnerability.
Second, as we cited in the regional trends above, vulnerable renters are most likely to reside in single-family units or multifamily buildings with fewer than 50 units. It could be argued, though, that this is to be expected given that most renters in any metropolitan area live in single-family homes or small- to medium-sized multifamily buildings. We applied a chi-squared test to understand whether vulnerable renters were statistically more likely than other renters to reside in buildings with fewer than 50 units and found that, indeed, there was a statistical difference for all of the metropolitan areas we examined except for Jacksonville.
Third, Miami (59.5 percent), New Orleans (56.7 percent), and Orlando (51.8 percent) had shares of cost-burdened and vulnerable renters above 50 percent, which stood out as elevated relative to the other metropolitan areas we examined.
Finally, given the well-documented disproportionate impact that COVID-19 is having on people of color, we take our regional-level analysis one step further than our national-level analysis and substitute a racial lens (white versus nonwhite)7 for the cost-burdened lens in an effort to analyze the distributional impacts of COVID-19-related job loss by renter household race. The second animated GIF, below, presents estimates of vulnerable renter households by income, race, and dwelling unit type.
As the charts demonstrate, the distribution of race across income and dwelling unit type varies by metropolitan area. To better assess whether or not there might be disparate exposure to COVID-19 housing vulnerability across race, we compare the race of vulnerable renter versus the race of all renter households for each metropolitan area (see the table).
While each metropolitan area's share of renter households of color appears to be similar to the shares of estimated vulnerable renter households identifying as races other than non-Hispanic white, a chi-squared test revealed that the two shares are in fact statistically different for all of the metropolitan areas except Jacksonville.8 In other words, our analysis is consistent with the idea that the pandemic may be causing a disproportionate hardship on households of color. As a result, failure to respond with targeted policy interventions for vulnerable renter households is likely to have an outsized impact on households of color. Localities may be well-served to carefully consider race as they design and evaluate their programs that support vulnerable renters to ensure that they aren't inadvertently exacerbating existing racial disparities.
Taken together, the intersection of these metrics can help localities better understand the scale of the COVID-19-related rental vulnerabilities in their communities, particularly as those vulnerabilities may exacerbate existing inequities along racial lines. We hope that this analysis, which highlights the greatest housing vulnerabilities in communities across the Southeast, provides policymakers and leaders on every level with insights that will help them respond to the ongoing COVID-19 pandemic and emerge with an inclusive recovery plan. In that process, the estimates provided above can be used as a planning and advocacy tool, as well as a benchmark to help evaluate how needs have been met.
1 [back] The methodology we used was developed by New York University's Furman Center at the onset of the COVID-19 pandemic. It incorporated all occupations that may have been vulnerable to job loss at the time. We have not updated their methodology to consider the current work status of each occupation. Therefore, these estimates likely overcount affected households and therefore should be treated as a ceiling for the estimated number of vulnerable households.
2 [back] To look at the intersection of income, cost-burdened status, dwelling type, and race, we use microdata from the Census Bureau's 2018 American Community Survey, as provided by IPUMS USA, University of Minnesota. Census microdata are not (necessarily) available down to the city level, to protect the anonymity of respondents, so all analysis above was conducted at the metropolitan-level using aggregated PUMA, or Public Use Microdata Areas, data.
3 [back] B. Y. An, R. W. Bostic, A. Jakabovics, A. Orlando, and S. Rodnyansky, "Why Are Small and Medium Multifamily Properties So Inexpensive?" Journal of Real Estate Finance and Economics (2019).
4 [back] The ACS categorizes Hispanic/Latino/Spanish as an ethnicity and not a race. For the purpose of this analysis, we group Hispanic/Latino/Spanish identification with other nonwhite racial responses in an effort to track inequality between exclusively self-reporting white respondents and all other respondents. See H. V. Strmic-Pawl, B. A. Jackson, and S. Garner, "Race Counts: Racial and Ethnic Data on the U.S. Census and the Implications for Tracking Inequality," Sociology of Race and Ethnicity, 2018, Vol 4(I) 1–13.
5 [back] That latest status of eviction moratoria is being tracked by Princeton University's Eviction Lab (here) and the National Low-Income Housing Coalition (here). An overview of properties covered by the CARES ACT eviction moratorium can be found here.
6 [back] The data behind the animated GIFs, as well as an interactive tool presenting this same data, can be found here. In addition to selecting the metropolitan area of interest, the web tool gives the user the ability to filter the metropolitan-level household estimates by a more granular breakdown of race.
7 [back] As outlined in a previous footnote, we refer to all households coded by the 2018 American Community Survey as non-Hispanic white as "white." We refer to all households coded as anything other than non-Hispanic white as "nonwhite" or "households of color." Nonwhite and households of color include those households who identify as Hispanic.
8 [back] As we mention in footnote 4, we group households who identify as Hispanic/Latino/Spanish with nonwhite racial responses. As a robustness check, we recoded and reran our racial analysis a second time, shifting households who identified as Hispanic and white from the nonwhite category to the white category. Interestingly, this change in categorization does matter, as reflected by the different results. A chi-squared test revealed that the share of vulnerable nonwhite renter households is statistically different from the overall share of nonwhite renter households for all of the metropolitan areas we examined except Bay County and Nashville.
July 13, 2020
What's Being Done to Help Renters during the Pandemic?
As we pointed out in our most recent post, the principal policy response to the COVID-19 pandemic in the U.S. mortgage market has been forbearance. Support for renters, on the other hand, has been much less widespread. Now that states and cities have received CARES Act funds, allocation strategies are starting to surface (for example, here and here). A common element among these strategies is the formation of emergency assistance funds for renters.
While household income most certainly will be considered in qualifying renter households for aid, other factors—like household cost burden and property type—may serve not only to channel funds to those feeling the economic effect of COVID-19, but also to help municipalities preserve their limited stock of affordable housing units. In this post, we attempt to provide greater insight on the types of affected households with the goal of helping policymakers design a relief program that reaches households most in need.
New York University's Furman Center looked at New York City households and identified those that were likely to have members in occupations vulnerable to job layoffs. We applied the Furman Center methodology to national 2018 American Community Survey data and found that 37.8 percent of all U.S. households may be vulnerable to COVID-19-related income loss (we'll refer to these as vulnerable households). As chart 1 shows, the 34.3 million households vulnerable to COVID-related job loss span the income spectrum.
The majority of vulnerable households are owner occupied (58.4 percent, or 20 million). Moreover, these vulnerable owner-occupied households tend to have incomes greater than $50,000 per year, live in single-family homes, and are less likely to have been cost-burdened1 going into the COVID-19 downturn. As we noted above, the vast majority of these owner-occupied vulnerable households have access to relief via widespread mortgage forbearance.
Honing in on the estimated 14.3 million vulnerable renter-occupied households, we see a more concerning picture emerge (see chart 2). Renter-occupied households vulnerable to COVID-related job loss also span the income distribution. Strikingly, though not surprisingly, the share of renter-occupied households that cost-burdened and vulnerable is disproportionately concentrated at the bottom of the income distribution. Put differently, 86 percent of the cost-burdened and vulnerable renter-occupied households (that is, the dark blue shaded portion of chart 2) earn less than $50,000. In other words, going into the COVID-19 downturn, the lower-income renter-occupied households who were employed in occupations most likely to suffer wage disruptions were already stretched thin and spending more than 30 percent of their income on housing costs.
In contrast to the picture for all vulnerable households, cost-burdened and vulnerable renter households are more likely to reside in properties with fewer than 50 units.
Small and medium multifamily housing units and affordability
Rent shortfalls in single-family rental properties and rental properties with 2–49 units are particularly worrisome because these properties are more likely to be owned by "mom-and-pop" investors and not institutional investors, according to a May 26 Joint Center for Housing Studies post. Mom-and-pop investors may not have the financial cushion necessary to weather the shortfall and cover ongoing costs, in turn making these properties more likely to become distressed and be sold.
Importantly, a paper by An et al. has established that, controlling for important differences, properties with 2–49 units often sell at a discount compared to single-family properties and properties with 50 or more units, thus making them a source of unsubsidized affordable housing.2 For this reason, it seems clear that directing rental assistance to these vulnerable households below the $50,000 income threshold living in properties with 2–49 units would target those most in need of assistance. Moreover, such targeting could preserve the already-insufficient supply of affordable units and prevent a greater deficit. It seems clear that directing rental assistance to these vulnerable households below the $50,000 income threshold living in properties with 2–49 units would target those most in need of assistance. Moreover, such targeting could preserve the already-insufficient supply of affordable units and prevent a greater deficit.
States and municipalities across the U.S. are still in the process of trying to understand (1) how many households are suffering from lost wages due to COVID-19, (2) how much financial support is needed to help these households weather the storm, and (3) what is an equitable way to design the relief program so that it reaches households most in need? Much of the research we've cited in this post can provide insight into the number of households and the required degree of financial support.
While evidence suggests that the one-time stimulus checks and expanded unemployment insurance benefits under the CARES Act have helped households meet their obligations (see this June 17 report from the Urban Institute and this May 27 macroblog for more in-depth discussion), there are concerns about the balance sheets of households and impending housing market distress when these benefits expire. Of particular concern are renter households with members who are in occupations vulnerable to job layoffs—especially given that many areas are slowing or reversing the pace of reopening. The emergency rental assistance funds that cities are designing will likely serve as an important backstop for many rental households. Because the number of renter households in need of support will undoubtedly exceed the amount of funds that have been earmarked to support them, a strategic response may very well be one that directs the funds to households demonstrating the greatest need, and doing so would also work to preserve a limited stock of affordable units.
Though we provide a national-level snapshot in this post, this analysis can easily be tailored to finer levels of geography. In future posts, we will take a closer look at several of the largest cities in the Atlanta Fed's Sixth District to provide estimates for the number of vulnerable households while spotlighting similarities and differences in the distributions of the affected population.
1 [back] A household is considered cost-burdened if it spends more than 30 percent of its monthly income on housing costs (including utilities).
2 [back] B. Y. An, R. W. Bostic, A. Jakabovics, A. Orlando, and S. Rodnyansky, "Why Are Small and Medium Multifamily Properties So Inexpensive?" Journal of Real Estate Finance and Economics (2019).
March 2, 2018
Tax Reform's Effect on Low-Income Housing
The recently enacted Tax Cuts and Jobs Act of 2017 substantially reduced corporate taxes, from 35 percent to 21 percent. Some commentators and practitioners have voiced concerns about how the new tax law will affect demand for Low Income Housing Tax Credits (LIHTC), America's primary mechanism for producing new or refurbished affordable housing units. According to Dawn Luke, chief operating officer with Invest Atlanta, the lowering of the corporate tax rate continues to present challenges to the market in terms of LIHTC pricing, with credit prices being lowered by as much as 16 cents on the dollar for projects in the near-term pipeline. Luke says this means that several affordable housing projects could become bottlenecked as developers scramble to find subsidy to fill this gap. In addition, this firm expects that declining demand for LIHTCs will generate 20,000 fewer low-income housing units a year, a roughly 15 percent decline.
It's worth taking a few moments to review how the LIHTC actually works. The LIHTC program, created as part of the Tax Reform Act of 1986, allows developers to receive tax credits in exchange for committing to rent their units for 30 years to households earning less than 50 to 60 percent of the area's median income. Private developers apply to receive an LIHTC subsidy through their state housing authorities, and are allocated a subsidy equal to a percentage of construction and eligible soft costs. Developers awarded an allocation receive a 10-year annuity of nonrefundable tax credits that they can use to offset positive future federal income tax liability. For example, through the program, the developer of a $10 million apartment building could receive up to $1.17 million a year for 10 years. (This assumes that, to receive a basis boost, the developer would receive a 9 percent allocation and the project would be located in either a sufficiently low-income neighborhood or a high-rent metro area.)
Due to the rental restrictions, it is virtually impossible for LIHTC properties themselves to generate enough tax liability to claim the full value of allocated tax credits, so developers need to have either sufficient other federal income tax to offset or the income tax of a limited partner. These outside investors, usually organized through a partnership called syndication, would contribute a fixed dollar amount to the developer upon completion of the subsidized property in exchange for 99.9 percent of the equity, including allocated tax credits, of the project.
The allocated tax credits themselves offer a dollar-for-dollar reduction in future tax liability, so changing the corporate tax rate does not directly reduce their statutory value. So why might the after-market value of the credits fall with the new tax law?
First, the recent tax cuts reduce the pool of firms with sufficient tax liability. If a business has less tax liability than it has tax credits, that business would effectively leave money on the table. The business would have to at least wait until it had enough tax liability to claim the subsidy. Several past investors in LIHTC properties, including Fannie Mae, learned firsthand how illiquid their LIHTC investment actually was after the 2008 financial crisis. With the lower corporate rate and other favorable provisions that are coming out of the new tax law, some firms that previously may have found the investment profitable may well reconsider.
Even firms that expect to have large profits may now have greater uncertainty about their future taxes as they work through the 1,100-page bill. The increased risk could cause firms to value less any future reductions in their tax liability.
The owner of an LIHTC project, like owners of all residential buildings, gets to deduct the building’s depreciation over a 27.5-year schedule. These depreciation allowances, coupled with LIHTC rental restrictions and relatively high operation costs due to compliance with those restrictions, often result in large expected tax losses that go beyond the allocated tax credits. For example, the $10 million apartment building mentioned above would be expected to generate more than $290,000 in depreciation allowances a year that outside investors not limited by passive-loss restrictions (such as C corporations) could use to offset other taxable income. The reduction in the corporate rate from 35 percent to 21 percent would lead to about a $626,000 decrease in outside investors’ willingness to pay developers for those deductions under reasonable assumptions. (A potential headache is that depreciaton allowances are subject to recapture if the project is eventually sold for more than tax basis. This provision rarely needs to be enforced.) This represents a 5.9 percent reduction in the overall valuation of the investment, which could require additional debt on the property and perhaps make some projects no longer feasible.
At the same time, lower taxes should expand the supply of market-rate housing. Only a small fraction of low-income households occupies newly built, rent-capped homes produced under the LIHTC. Most of these households use their own earnings or HUD vouchers to pay the market rents for older, existing apartments. A recent study by Stuart Rosenthal in the American Economic Review showed that while newly constructed units are often unaffordable for most households, they eventually supply the majority of future low-income affordable housing. This "filtering down" occurs as a result of physical depreciation or shifts in style or location preferences. If lower taxes generate new market-rate construction—and thus increase the aggregate supply of housing—these lower taxes should lower rents throughout the market or increase landlord participation in HUD voucher programs.
Eriksen and Lang suggest two changes to the LIHTC program that would increase the supply of affordable housing produced under the program without increasing tax expenditures. The first, and most immediate, would be simply to make the allocated tax credits through the LIHTC program refundable, because uncertainty about future tax liabilities reduces both the pool of otherwise eligible investors and the market value of allocated tax credits. Making this change would also give some developers at least the option of claiming the credit themselves rather being forced to partner with outside investors. The second change would allow developers to claim an actuarially equivalent subsidy over a shorter time period than the currently required 10 years. Developers and LIHTC investors are thought to have a much higher cost of capital than the federal government. In the extreme, allowing developers to claim the full value of refundable tax credits when projects are completed would give them the greatest flexibility in financing their projects.
Increasing the supply of housing affordable to low-income families could be achieved using other policies that focus on reducing other barriers to increasing housing production, like state and local zoning laws that limit the location and density of multifamily housing. A bill working its way through the California legislature would appear to be in this spirit.
Chris Cunningham is a research economist and associate policy adviser at the Federal Reserve Bank of Atlanta; Mike Eriksen is associate professor of real estate in the Linder College of Business at the University of Cincinnati.
The views expressed here represent those of the authors and not the Federal Reserve Bank of Atlanta or the Federal Reserve System.
November 3, 2015
Keeping an Eye on the Housing Market
In a recent speech, Federal Reserve Bank of San Francisco President John Williams suggested that signs of imbalances were starting to emerge in the form of high asset prices, particularly in real estate. He pointed out that the house price-to-rent ratio had returned to its 2003 level and that, while it may not be at a tipping point yet, it would be important to keep an eye on the situation and act before the imbalance grows too large. President Williams is not the only one monitoring this situation. Many across the industry are keeping a watchful eye on the rapid price appreciation (see here, here, and here), including my colleagues and me at the Atlanta Fed.
While it is too soon to definitively know if a bubble is forming, the house price-to-rent ratio seems like a relevant measure to track. Why? Basically, because households have the option to rent or own their home, equilibrium in the housing market is characterized by a strong link between prices and rents. When prices deviate substantially from rents (or vice versa), the cost-benefit calculus in the rent-versus-own equation changes, inducing some households to make a transition. In effect, these transitions stabilize the ratio.
In an effort to better understand house price trends, we chart the house price-to-rent ratio at an annual frequency on top of a stacked bar chart depicting year-over-year house price growth (see chart below). Each stacked bar reflects the share of ZIP codes in each range of house price change. Shades of green indicate house price appreciation from the year-earlier level, and shades of red indicate house price decline. The benefit of considering house price trends through the lens of this stacked bar chart is, of course, that it provides a better sense for the distribution of house price change that is often masked by the headline statistic.
Looking at these two measures in concert paints an interesting picture, one that doesn't appear to be a repeat of the early 2000s. While the house price-to-rent ratio indicates that house prices on a national basis have been increasing relative to rents, the distribution of house price change looks a bit different. In 2003, roughly 20 percent of ZIP codes across the nation were experiencing house price appreciation of 15 percent or more on a year-over-year basis. In 2014 and 2015, less than 5 percent of ZIP codes experienced this degree of appreciation.
To better understand the regional variation, we repeated this exercise at a metro level using the Case-Shiller 20 MSAs (see charts below). (House price-to-rent ratios for Las Vegas and Charlotte were not calculated because the Bureau of Labor Statistics does not provide an owners' equivalent rent for primary residence series for these markets.) This more detailed approach reveals that elevated price-rent ratio readings were only present in a few, perhaps supply-constrained, metropolitan areas (see top right corner of each chart for the Saiz supply elasticity measure). Moreover, current home price appreciation across ZIP codes does not have the breadth that was present during the early 2000s.
Notes: (1) All price-to-rent ratios are indexed to 1998, except Dallas and Phoenix, which are indexed to 2002. (2) SE = Saiz's Supply Elasticities. Pertains to city boundaries, not metropolitan areas. For more information, see Albert Saiz, "The Geographic Determinants of Housing Supply," The Quarterly Journal of Economics (August 2010) 125
As John Krainer, an economist at the San Francisco Fed, pointed out in a 2004 Economic Letter, "it is tempting to identify a bubble as a long-lasting deviation in the price-rent ratio from its average value. But knowing how large and long-lasting a deviation must be to resemble a bubble is not obvious." We will continue digging and report back when we think we know something more.
Jessica Dill, economic policy analysis specialist in the Atlanta Fed's research department
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