POLICY ADVISORY GROUP
The Housing Affordability Gap for
Extremely Low-Income Renters in 2013
Josh Leopold, Liza Getsinger, Pamela Blumenthal, Katya Abazajian, and Reed Jordan
June 2015
Since 2000, rent s have risen while t he number of rent ers who need low-priced housing
has increased. These t wo pressures make finding affordable housing even t ougher for
very poor households in America. Nat ionwide, only 28 adequat e and affordable unit s
are available for every 100 rent er households wit h incomes at or below 30 percent of
t he area median income. Not a single count y in the Unit ed St at es has enough affordable
housing for all it s ext remely low-income (ELI) rent ers. The number of affordable rent al
homes for every 100 ELI rent ers ranges from 7 in Osceola Count y, Florida, t o 76 in
Worcest er Count y, Maryland.1
This brief provides information on national trends in housing affordability for ELI renter
households, as well as insights into which major counties are making the most and least
progress on closing the housing affordability gap. The findings are based on data from the
2000 Census as well as three-year averages from the 2005, 2006, and 2007 and the 2011,
2012, and 2013 1-year American Community Surveys. For the sake of simplicity we refer to
data averaged from 2011–13 estimates as 2013.
This brief is the first publication on housing affordability to combine detailed county-level
data on ELI renter households (those with incomes at or below 30 percent of the area median)
and the impact of US Department of Housing and Urban Development (HUD) rental
assistance. Its four key findings:

Supply is not keeping up wit h demand. B etween 2000 and 2013, the number of ELI renter
households increased 38 percent, from 8.2 million to 11.3 million. At the same time, the supply
of adequat e, affordable, and available rental homes for t hese households increased only 7
percent, from 3.0 million to 3.2 million.
The gap bet ween ELI rent er households and suit able unit s is widening over t ime. From 2000

to 2013, the number of adequate, affordable, and available rental units for every 100 ELI renter
households nationwide declined from 37 to 28.
Ext remely low-income rent ers increasingly depend on HUD programs for housing. M ore than

80 percent of adequate, affordable, and available homes for ELI renter households are H UDassisted, up from 57 percent in 2000.
The supply of adequat e, affordable, and available unit s varies widely across t he count ry.

Among the 100 largest US counties, Suffolk C ounty, which includes B oston, comes closest to
meeting its area’s need, wit h 51 units per 100 ELI renter households. Denton Count y, part of
the Dallas-Ft. Worth metropolitan area, has t he largest housing gap, wit h only 8 units per 100
ELI renters. Rust Belt areas (e.g., Detroit, MI; Chicago, IL, and Milwaukee, WI) have seen large
declines in adequate, affordable, and available units. Most count ies had fewer units available in
2013 than 2000. Notable exceptions to this trend include Suffolk, MA; Los Angeles, CA; and
M iami, FL, which have expanded their number of available units since 2000.
To expand on the well-documented challenges of housing affordability for low-income renters, our
brief provides county-level estimates of housing affordability, as well as national and state estimates.
2
O ur integration of household-level data on assisted households from H UD allows us to show the
impact, by county, of federal rental assistance programs on addressing housing needs for ELI renters. It
also allows for a more detailed trend analysis of changes in affordability driven by changes in the
economy, the rental market, and the availability of rental assistance.
These county estimates provide useful information to national and local policymakers, the media,
practitioners, and the public. Local decisionmakers can use this analysis to help guide policymaking and
programing toward the housing needs of ELI households.
The Affordability Crisis for Extremely Low-Income Renters
The nationwide lack of sufficient affordable housing for poor households is well documented (see, e.g.,
H UD 2013 and JC H S 2014). Affordability is a particular challenge for extremely low-income
households. H UD sets income limits for its programs, adjusting for household size. In 2013, the ELI limit
for a household of four ranged from $12,600 to $32,800, depending on location. In most counties the
income limit was $22,000 or less.
W ithout subsidies, it is nearly impossible to build and operate rental housing that is affordable to
ELI renters in most markets (JC H S 2014). Developers cannot make developments targeted to ELI
renters “pencil out,” meaning that the expected revenue stream from rents is too low to cover the costs
of maintaining the property and to pay back the debt incurred in development. T he largest subsidy
2
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
source for low-income housing development—t he Low-Income Housing Tax Credit —is designed to
make units affordable t o households wit h incomes at 50–60 percent of area median income (AMI)—up
to twice the ELI limit. The assistance available through federal block grant programs (such as the
Community Development Block Grant) and most st ate and local programs cannot keep housing
affordable to ELI rent ers over t he long t erm (Cunningham, Leopold, and Lee 2014).
Meanwhile, t he stock of nonsubsidized housing that is affordable to ELI renters has steadily
declined. Thirteen percent of unassist ed unit s with rents at or below $400 in 2001 had been demolished
by 2011. Nearly half (46 percent) of the remaining units were built before 1960, putt ing t hem at high
risk of demolition (JCHS 2013).
HUD’s rental assistance programs are increasingly the only source of affordable housing for ELI
renters in many areas. Unlike ot her safety net programs—like Social Security, food stamps, Medicaid, or
Medicare—housing assistance is not available to all eligible applicants; only 24 percent of the 19 million
eligible households receive assistance (JCHS 2013). As a result, millions of low-income individuals and
families face serious challenges ranging from severe cost burdens to overcrowding t o homelessness.
HUD’s biennial Worst Case Needs report documents housing needs for very low income rent ers
(people wit h incomes no greater than 50 percent of AMI) who do not receive rental assistance. HUD
considers two forms of worst-case housing needs: severe rent burden, which means spending 50
percent or more of household income on rent and ut ilit ies; and severely inadequat e housing, which
refers to housing with one or more serious heating, plumbing, and elect rical or maintenance problems.
HUD found 7.7 million very low income unassisted rent ers, or 42 percent of rent ers in this group, had
worst-case housing needs in 2013. Severe rent burdens account ed for more than 97 percent of these
cases (Steffen et al. 2015). Incidences of worst-case needs have decreased from their peak in 2011, as
renters’ incomes have risen; still, t he number of such needs is 49 percent great er in 2013 than in 2003
(Steffen et al. 2015).
Severe housing needs are so common partly because low-wage workers do not earn enough t o
afford adequate housing. A worker earning the federal minimum wage would need to work 104 hours a
week to afford a typical two-bedroom apartment. Renters on average earn $14.64 an hour, while fulltime wage earners on average need to earn $18.92 an hour t o afford a two-bedroom apartment (Arnold
et al. 2014). At t he stat e level, the average hourly wage a full-t ime worker needs t o earn to afford a twobedroom apartment range from $12.56 in Arkansas to $31.54 in Hawaii.
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
3
BOX 1
An Overview of Federal Rent al Assist ance
The Sect ion 8 Housing Choice Voucher Program (HCV) is the dominant federal program, wit h over $19
billion in spending in 2014. Through vouchers, it provides households the opportunity to find eligible
housing in t he privat e rental market. Approximat ely 2.1 million low-income families use t hese t enantbased vouchers, administered by a network of 2,230 public housing aut horities (Rice 2014). Vouchers
typically help pay t he difference between what a family can afford and the act ual rent of a unit that
meets HUD’s healt h and safety standards, up to a locally det ermined rent limit. Families are expected to
contribute t he larger amount of either 30 percent of family income or the minimum rent amount of up
to $50. The program part icularly targets extremely low-income families; by law, 75 percent of newly
admitted households must be ELI. Public housing authorities, or PHAs, can dedicate up to 20 percent of
their vouchers for linking vouchers t o a specific unit; these “project-based” units are sometimes
embedded in affordable multifamily buildings funded through the Low-Income Housing Tax Credit or
dedicat ed to supportive housing to provide an ongoing operating subsidy.
Sect ion 8 Project -Based Rent al Assist ance operates through an agreement between a private
property owner and HUD. The program serves 1.2 million families (CBPP 2013). Tenants must
contribute t he greater of 30 percent of their income or a minimum rent of $25, while the subsidy
compensat es t he landlord for the remaining costs of operating and maintaining the property. Like t he
HCV program, project-based rent al assistance targets ELI households: by law, at least 40 percent of the
assist ed units in a development must be designed for ELI households. However, approximat ely 73
percent of units wit h project-based assistance are occupied by ELI households. The vast majority of
developments were built between the 1960s and 1990s, and the program hasn’t added t o the supply of
new rental homes in many years (Treskon and Cunningham forthcoming).
Public housing unit s are owned and operat ed by local public housing agencies. The program
currently serves 1.2 million households, 72 percent of which have extremely low incomes. Some public
housing developments have been redeveloped as mixed-income propert ies, primarily through HOPE VI
and t he Choice Neighborhoods Initiat ive. Absent t hese efforts, new public housing is not being
developed, and many existing development s have large capital investment needs following years of use
and deferred maint enance. HUD’s Rent al Assistance Demonst rat ion provides a mechanism by which
public housing can be converted to property-based Sect ion 8 cont racts.
4
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
The National Trend Shows Economic Improvements for
Renters but Continued Loss of Affordable Rental Housing
From 2000 to 2013, the share of rental housing that was adequat e, affordable, and available to ELI
renters went from 37 units per 100 ELI rent ers to 28—a 24 percent decrease. The change in units is
primarily the result of losing unassist ed affordable units. While the number of HUD-assist ed units for
every 100 ELI rent ers has increased slightly during this period, from 21 to 23, the number of unassist ed
units has fallen from 16 to 5.
This analysis underscores t hat the private market alone does not provide enough affordable
housing. Federal rental assistance is an import ant mechanism t o preserve affordable and available units,
but it is far from keeping pace with need.
FIGURE 1
Available Housing for Ext remely Low-Income Rent ers Has Declined bet ween 2000 and 2013
Affordable units per 100 extremely low-income renter households
37
33
28
All
21
24
HUD-assisted
23
16
9
Unassisted
5
2000
2005–07
2011–13
Sources: 2000 Decennial Census, and three-year averages from the 2005,2006, and 2007 and 2011, 2012, and 2013 ACS 1-year
sample data from the Integrated Public Use Microdata Series merged with data from HUD on income limits and households
receiving rental assistance.
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
5
HUD Rental Assistance Programs Are the Predominant
Source of Affordable Housing for ELI Renters
In 2013, nearly 4.6 million households received rental assistance from HUD. Sevent y-five percent of
these households (3.4 million) had ext remely low incomes, ranging from 72 percent in public housing to
76 percent in the HCV program. The number of families HUD assists and t he prevalence of each
assistance type has changed between 2000 and 2013 (table 1). Nearly half of assist ed ELI rent ers (1.6
million) part icipat e in the Housing Choice Voucher program, which provides part icipants wit h a voucher
to rent housing in the privat e market. More than 750,000 ELI rent ers live in public housing, and nearly
900,000 live in project-based Section 8 housing.
TABLE 1
ELI Households in HUD-Assist ed Housing Have Increased since 2000
Housing Choice Voucher program
Multifamily Sect ion 8 program
Public housing
Other HUD programs
All
2000
2006
2013
839,420
701,519
497,019
811,378
1,364,437
857,415
692,354
986,448
1,609,798
893,257
769,864
1,048,131
2,147,817
3,043,239
3,427,793
Source: Data provided by the US Department of Housing and Urban Development from the Public and Indian Housing
Information Center and the Tenant Rental Assistance Certification System.
The growth in all programs reflects HUD’s strategic goal of increasing housing assist ance by
224,000 units, which it mainly achieved by pressing public housing authorit ies (PHAs) to use their full
budget authority and fix uninhabitable units. For example, the American Recovery and Reinvestment
Act provided $3 billion for capital improvements t o public housing. Some jurisdictions const ruct ed
mixed-income development s, shifting some of the public housing stock to vouchers. Progress was made,
as indicat ed in table 1, but sequestrat ion was a major disrupt ion.
Figure 2 shows t he t otal number of renter households and ELI renter households receiving HUD
assistance in 2000, 2006, and 2013. The numbers rise st eadily, even wit h a decline in assisted
households st emming from the 2013 budget sequest rat ion (Rice 2014). The proport ion of HUDassist ed rent ers that have extremely low incomes has st ayed more or less the same during this period.
6
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
FIGURE 2
Rent ers Receiving HUD Assist ance Have Risen St eadily since 2000
Total and extremely low-income (ELI) renters receiving HUD rental assistance, 2000–13
5,000,000
4,500,000
4,000,000
3,500,000
3,000,000
2,500,000
ELI Assisted
Renters
2,000,000
All Assisted
Renters
1,500,000
1,000,000
500,000
0
2000
2006
2013
Source: Data provided by the US Department of Housing and Urban Development from the Public and Indian Housing
Information Center and the Tenant Rental Assistance Certification System.
HUD rent al assistance does not guarant ee affordability. As shown in figure 3, 26 percent of HUDassist ed ELI rent ers pay more than 30 percent of their monthly income on housing. The HCV program
had t he highest percent age of rent-burdened households (42 percent). Rent burden was much lower in
public housing (14 percent) and t he multifamily Section 8 program (9 percent).
HUD programs provide assistance on a sliding scale, wit h assisted renters paying 30 percent of
their mont hly income, aft er cert ain adjustment s, on housing. However, assist ed households can still be
rent-burdened for several reasons:

M inimum rents: PH As can, and most do, establish a minimum monthly rent of up to $50.

Alternative rents: Some PH As have been given the flexibility to implement alternative rents
like flat rents, tiered rents, or rents that require households to pay higher percentages of their
incomes.

R enting above the payment standard: H ouseholds may rent units that cost more than the local
payment standard.
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
7
FIGURE 3
A Quart er of HUD-Assist ed ELI Rent ers Are Rent -Burdened
Share of ELI renters paying more than 30%of their income on rent
42%
26%
20%
14%
Housing Choice
Vouchers
Moderate
rehabilitation
9%
10%
Section 8
Other multifamily
Public housing
Total
Source: Data provided by the US Department of Housing and Urban Development from the Public and Indian Housing
Information Center and the Tenant Rental Assistance Certification System.
All PHAs set a payment st andard, by bedroom size, t hat dictat es t he maximum rent t hey will
subsidize for families in t he HCV program. If households choose t o rent over this limit—t o rent a unit in
a neighborhood with bett er schools, for example—t hey must pay t he difference bet ween the market
rent and the payment standard. In t heir first year in the program, households cannot have t heir rent
burden exceed 40 percent. The cap does not apply aft er the first year. Previous analysis has shown t hat
households rent ing over the payment standard are t he single biggest cause of rent burden, which
explains why rent-burden rates are so much higher in the HCV program than in other HUD programs
(McClure 2005).
Excluding rent-burdened households, HUD rental assist ance programs keep housing affordable for
nearly 2.6 million ELI rent ers. This is roughly four t imes the number of non-HUD-assisted ELI renters in
adequate and affordable housing (610,000). From 2000 to 2013, the number of ELI rent er households
with adequat e and affordable housing through HUD programs has increased from 1.7 million to 2.6
million. By contrast, the number of ELI rent ers wit h adequat e and affordable housing absent HUD
assistance has fallen from 1.3 million to 610,000. In 2000, 57 percent of ELI rent ers with adequat e and
affordable housing received HUD assistance; by 2013 that share had risen to 81 percent, reflecting the
loss of market-rat e affordable housing (figure 4).
8
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
FIGURE 4
HUD Assist ance Plays a Crit ical Role in Enabling ELI Rent ers t o Obt ain Adequat e and Affordable
Housing
Share of ELI renters in adequate and affordable housing with and without HUD assistance
57%
73%
79%
81%
HUD-assisted
Unassisted
43%
27%
2000
2006
21%
19%
2012
2013
Source: ACS and HUD data, 2000–13.
Availability of Adequate and Affordable Rental Housing
by County
Our int eractive map shows t he number of adequat e, affordable, and available housing units for ELI
renters in each county in the Unit ed Stat es. For this brief, we focus on the 100 counties with t he highest
3
populat ions as of 2013.
The Nort heast Has a Great er Supply of Affordable Housing for Ext remely LowIncome Rent ers t han t he Sout h or t he W est
Figure 5 shows t he gap bet ween the number of ELI renter households and t he number of affordable and
adequate rental units available to t hem in each county nationwide. The light est areas have t he least
available and affordable housing for ELI renters and t he darkest areas have t he most. The affordability
gap is lowest in the Northeast, Appalachia, t he Midwest, and t he Great Plains and is highest in t he Sout h
and t he West. Our relat ed feature article describes how different stat e and local housing policies can
contribute t o higher and lower gaps.
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
9
FIGURE 5
Number of Adequat e, Affordable, and Available Housing Unit s for Ext remely Low-Income Rent ers by
Count y, 2013
Sources: 2011, 2012, and 2013 ACS 1-year sample data from the Integrated Public Use Microdata Series merged with data from
HUD on income limits and households receiving rental assistance.
Table 2 shows which of the 100 largest US counties have the great est share of adequate, affordable,
and available rental unit s for ELI rent ers. Suffolk Count y, which includes Boston, is ranked highest; even
Suffolk, however, has only enough adequate, affordable, and available rental units for about half of it s
ELI renter households. Five of the 10 count ies with t he smallest affordability gap are in Massachusetts;
only one—San Francisco—is outside the Northeast. Counterint uit ively, some count ies wit h the most
expensive housing markets—including Boston, San Francisco, and Washington, DC—have the smallest
gap in units affordable to ELI rent ers. For the most part, these results reflect a higher proportion of
rental unit s target ed t o ELI rent ers, not fewer ELI renters. The higher share of affordable units may
reflect a local, stat e, or federal decision to focus on ELI households.
10
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
TABLE 2
Large Count ies wit h t he Smallest Gap in Affordable Unit s for ELI Rent ers, 2013
Count y
Suffolk, MA
Norfolk, MA
Essex, MA
District of Columbia
Worcester, MA
Middlesex, MA
Fairfield, CT
San Francisco, CA
Hartford, CT
Allegheny, PA
Populat ion
745,716
682,501
756,508
633,167
805,989
1,537,150
933,794
826,626
897,426
1,229,582
ELI rent er
households
74,262
23,018
40,208
52,633
37,265
60,809
38,710
64,697
43,454
51,549
Adequat e,
affordable, and
available unit s
37,703
10,222
17,733
22,300
15,612
25,376
14,511
23,112
15,442
18,260
Unit s per 100
rent ers
Rank
51
44
44
42
42
42
37
36
35
35
1
2
3
4
5
6
7
8
9
10
Source: Three-year averages from the 2011, 2012, and 2013 ACS 1-year sample data from the Integrated Public Use Microdata
Series merged with data from HUD on income limits and households receiving rental assistance.
Denton County, Texas, part of the Dallas-Ft. Worth met ropolitan area, has roughly 8 adequat e,
affordable, and available units for every 100 ELI rent ers, the great est gap of any large county (table 3).
Eight of t he 10 count ies wit h the biggest gap in affordability for ELI rent ers are in Georgia, Florida, or
Texas; Clark County, Nevada, which includes Las Vegas, and San Joaquin, California, which includes
Fresno, are the two except ions.
The count ies with t he largest affordability gap typically have both fewer ELI rent ers and far fewer
affordable rentals t han the counties with the smallest gap. For example, Suffolk County has a similar
total population as Denton County (745,716 vs. 707,550), and nearly five t imes as many ELI rent ers
(74,262 vs. 14,924). But Suffolk has more than 30 t imes more affordable units for ELI rent ers than
Denton (37,703 vs. 1,207). Clark County, Nevada, which includes Las Vegas, has a populat ion of more
than 2 million but one-t hird of the affordable units of Washington, DC, which has a populat ion of less
than 650,000. This disparity is partly the result of federal rental assistance not keeping pace wit h
populat ion growth in the South and Southwest. For example, Suffolk County has over 32,000 federally
assist ed units, and Denton has roughly 1,000, and partly a result of differences in st ate and local
invest ments in affordable housing development and preservation. For example, Massachusett s has a
4
number of stat e-run programs to supplement federal rental assist ance.
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TABLE 3
Large Count ies wit h t he Biggest Gap in Affordable Unit s for ELI Rent ers, 2013
Count y
Denton, TX
Gwinnett, GA
Cobb, GA
Orange, FL
Clark, NV
Lee, FL
DeKalb, GA
San Joaquin, CA
Travis, TX
Collin, TX
Populat ion
707,550
841,658
707,248
1,198,989
1,997,371
645,681
706,093
700,220
1,093,138
834,110
ELI rent er
households
14,924
17,155
19,510
37,165
66,336
13,059
30,682
22,831
48,056
13,433
Adequat e,
affordable, and
available unit s
1,207
1,494,
1,767
3,730
7,998
1,696
4,325
3,306
6,979
1,959
Unit s per
100 rent ers
8
9
9
10
12
13
14
14
15
15
Rank a
97
96
95
94
93
92
91
90
89
88
Source: Three-year averages from the 2011, 2012, and 2013 ACS 1-year sample data from the Integrated Public Use Microdata
Series merged with data from HUD on income limits and households receiving rental assistance.
a
Four of the 100 largest counties in the United States are in New York City. Because the five New York City counties are
combined in this analysis, the lowest ranking number is 97.
Bost on, Los Angeles, and Miami M ade t he M ost Progress in Closing t he Affordabilit y
Gap from 2000 t o 2013; Det roit Fell t he Furt hest Behind
Only 9 of the 100 largest counties increased the number of affordable units available per 100 ELI
renters from 2000 t o 2013 (table 4). Each county with a positive trend closed t he gap by increasing the
number of units affordable t o ELI rent ers rat her t han decreasing the number of ELI rent er households.
Suffolk County led the way, increasing the number of units available for every 100 ELI rent ers from 48
to 51. Unfort unat ely, while t hese counties saw improvements in the proportion of rentals affordable to
ELI renters, none were able to add enough units to match the increase in ELI rent ers. For example, Los
Angeles added roughly 38,200 units affordable to ELI renters between 2000 and 2013, but it had an
increase of 137,000 ELI renter households.
Wayne County, Michigan, which includes Det roit, and Will County, Illinois, provide contrasting
examples of how counties can lose ground in this area. In Wayne County, the negative trend is t he result
of a precipitous drop in the supply of affordable housing for ELI renters, from about 48,000 units to
about 24,500. By comparison, in Will County the number of units affordable t o ELI rent ers stayed more
or less the same, but the number of ELI rent er households nearly doubled, from 5,900 to 11,100. Many
counties t hat have lost t he most affordable housing per 100 ELI renters are large Midwestern count ies,
such as Wayne County, Cook County (Chicago), and Milwaukee County (Milwaukee).
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TABLE 4
Count ies wit h t he M ost Posit ive Affordabilit y Trends for ELI Rent ers, 2000–13
Count y
Suffolk, M A
Los Angeles, C A
K ern, C A
B ergen, N J
N ew York, N Y
San Francisco, C A
O range, C A
M iami-Dade, FL
Fresno, C A
San Diego, C A
ELI Rent er
Households
2000
2013
57,132
383,332
17,459
19,474
589,726
48,847
71,254
87,982
25,350
77,359
74,262
535,214
26,549
28,429
643,243
64,698
106,204
115,281
38,484
120,135
Adequat e,
Affordable, and
Available Unit s
2000
2013
27,281
58,780
2,377
4,905
192,995
16,882
11,532
22,203
4,549
13,566
37,703
94,672
4,239
7,775
220,121
23,112
18,108
29,789
6,987
20,376
Unit s per 100
Rent ers
2000
2013
47.8
15.3
13.6
25.2
32.7
34.6
16.2
25.2
17.9
17.5
50.8
17.7
16.0
27.3
34.2
35.7
17.1
25.8
18.2
17.0
Difference
3.1
2.4
2.4
2.2
1.5
1.2
0.9
0.6
0.2
-0.5
Rank
1
2
3
4
5
6
7
8
9
10
Source: 2000 Decennial Census and three-year averages from the 2011, 2012, and 2013 ACS 1-year sample data from the
Integrated Public Use Microdata Series merged with data from HUD on income limits and households receiving rental assistance.
TABLE 5
Count ies wit h t he W orst Affordabilit y Trends for ELI Rent ers, 2000–13
Count y
Wayne, MI
Shelby, TN
Will, IL
Lee, FL
Milwaukee, WI
Fulton, GA
Macomb, MI
Jefferson, AL
Duval, FL
Cook, IL
ELI Rent er
Households
2000
2013
88,945
33,966
5,921
7,568
47,944
43,626
13,249
25,237
23,391
249,920
99,699
40,861
10,080
13,059
66,421
49,586
22,435
29,591
33,141
255,759
Adequat e,
Affordable, and
Available Unit s
2000
2013
48,069
13,575
2,988
2,494
19,159
21,057
5,461
13,177
10,648
103,324
24,458
6,866
2,758
1,696
13,641
14,345
4,987
10,138
9,266
62,840
Unit s per 100
Rent ers
2000
2013
54.0
40.0
50.5
33.0
40.0
48.3
41.2
52.2
45.5
41.3
25.5
16.8
27.4
13.0
20.5
28.9
22.2
34.3
28.0
24.6
Difference
-28.5
-23.2
-23.1
-20.0
-19.5
-19.3
-19.0
-18.1
-17.5
-16.8
Rank a
97
96
95
94
93
92
91
90
89
88
Source: Three-year averages from the 2011, 2012, and 2013 ACS 1-year sample data from the Integrated Public Use Microdata
Series merged with data from HUD on income limits and households receiving rental assistance.
a
Four of the 100 largest counties in the United States are in New York City. Because the five New York City counties are
combined in this analysis, the lowest ranking number is 97.
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
13
Conclusion
Housing affordability is an ongoing challenge for households throughout the United States, but it
creates the greatest st ress for the poorest households. Since 2000 the number of extremely lowincome renters has increased substantially while t he stock of adequat e, affordable, and available rent al
units for these households has cont inued to erode. This erosion is driven by both the cont inued loss of
affordable market-rat e housing and the budget cuts to HUD rent al assistance programs. As t his brief
demonstrates, wit hout vital federal rental assistance, the magnitude of this problem would be much
great er. Simply put, virtually no affordable housing unit s would be available to ELI households absent
the continued invest ment in federally assisted rental housing.
The provision of adequate affordable housing for ELI households requires more t han federal
funding. It requires a funct ioning local housing market and ecosyst em t hat draws on resources from and
leverages coordinat ion bet ween federal, stat e, and local actors. The approach cities and counties take
to solving t he affordability crisis for ELI households is a function of several things, some wit hin the
control of a local jurisdiction and some not.
Local resource commitment: In the current const rained budget climat e, cities are able t o devot e
fewer resources to housing for ELI households. Yet some cities have creat ed local revenue sources,
either one t ime or ongoing, that can be used to build and maint ain affordable housing. Some of these
strat egies include using general obligat ion bonds, local housing trust funds, or property tax set-asides to
finance the const ruction of affordable rental housing and/or cover operating costs.
Resource targeting: Federal rental assistance can serve households earning up t o 80 percent of area
median income. In reality, it mostly serves households earning at or below 30 percent of AMI. However,
rent levels that are affordable t o ELI households often involve the creat ive layering of federal, st ate, and
local resources (such as tax credits and housing subsidies), or they require deep, ongoing subsidies for
property operations. Local communit ies can target this array of resources t o serve extremely lowincome households through local preservation st rat egies and other forms of rent al assistance. To makes
these approaches systemic versus episodic requires coordinated action and invest ment by local act ors
from the nonprofit, philanthropic, public, and for-profit sectors and a clear understanding of the target
populat ion and specific affordability challenges.
State support: The state-level fiscal, regulatory, and programmatic environment can eit her help or
hinder local act ion. Some st ates have adopted policies t hat outlaw local zoning practices t hat generat e
more affordable housing, such as inclusionary zoning. Other stat es have created housing assistance
programs or tax credit programs that supplement local action.
Legacy of federal investments: How and where federal housing resources are allocat ed is a function
of hist ory and past decisionmaking. These allocations were partly a function of city size and need at the
time. Older large cities such New York, Los Angeles, Balt imore, Boston, and Chicago benefited early
from federal housing invest ments. More recent large cit ies such Dallas, Houston, Phoenix, and San Jose
do not have the same distribution of federally assist ed housing, largely because their accelerat ed
14
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
growth happened aft er the major allocat ions of federal rental assistance. In addit ion to demonstrat ing a
large need, Northeast cit ies in particular had st rong local political will, which helped them benefit from
early federal invest ment in affordable rental housing. For the most part, these cit ies have been good
stewards of these early investments and have sought t o stem the loss of affordable rental housing and
even add to the stock. Some cities have more tools to work with than others, but cit ies and even states
cannot do it alone. As the need grows in cities and counties, these local governments are unlikely to
keep pace wit hout addit ional federal invest ment in rent al assistance for ELI households.
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
15
Appendix. Where Our Numbers Come From
The primary data source for this analysis is household-level records from t he 2000 Census and 3-year
averages of the one-year American Community Survey (ACS) for 2005, 2006, and 2007, and for 2011,
2012, and 2013. Household-level records from t his dataset were downloaded from t he University of
Minnesota’s Int egrat ed Public Use Microdata Series. This dataset provides informat ion on households’
income, demographics, housing unit s, and housing-relat ed expenses. We applied HUD data on income
5
limits to ident ify rent ers wit h ext remely low incomes.
To det ermine renters’ housing costs, we used the RENT variable from t he ACS, which asks “What is
the mont hly rent for this house, apart ment, or mobile home?” We applied HUD’s annual income limits t o
calculat e affordability: if t he report ed monthly rent and utilities from ACS were less than or equal t o 30
6
percent of the income limit for ELI households in t hat area, the unit was considered affordable. We
then added vacant units affordable to ELI renters. Finally, we subtract ed both vacant and occupied
subst andard units, defined as those wit h incomplet e plumbing or missing kitchen or cooking facilities.
This provided the t otal number of adequat e, affordable, and available units.
Units adequate, affordable, and available = Affordable occupied units + affordable vacant unit s –
to ELI renter households
units occupied by higher-income renters − substandard
occupied units − substandard vacant units
W e divided the number of adequate, affordable, and available units by the number of ELI renter
households, then multiplied by 100. T he result was the number of units per 100 ELI renter households,
both nationwide and by county.
Adequate, affordable , and available units = [(Total ELI renters − units affordable to ELI renters)/
per 100 ELI renters
Total ELI renters]*100
To examine the role of H UD ’s rental assistance programs, we used a dataset provided by H UD. The
dataset provided information by county on the number of assisted households, their income levels, and
7
rent burdens for each of H U D’s rental assistance programs from 2000 to 2013. W e took the total
number of units adequate, affordable, and available to ELI renters and subtracted units in which ELI
households were receiving H UD rental assistance to estimate how many rental units would be
affordable to ELI renters without H UD rental assistance programs.
Affordable units without H UD assistance = Total affordable units − (H UD-assisted, affordable, and
adequate units)
O ur methodology differs from our 2014 analysis of the affordability gap for ELI renter households
in two key areas. First, to increase our sample size and thus the reliability of local estimates, we used 3year averages rather than relying on the 1-year AC S estimates. Second, in last year’s report, we
assumed that all ELI renters receiving H UD assistance were in affordable housing. T hus, the number of
affordable units for ELI renters was calculated by subtracting all ELI renter households receiving H UD
assistance from the total number of affordable units. This year we received data from H UD on the rent
burden of ELI renters receiving H UD rental assistance. Using these new data, we removed the units of
16
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
HUD-assisted renters who were rent-burdened or in inadequate housing before calculating t he impact
of HUD programs on the affordability gap.
Our methodology has several import ant limitations, which we will work t o address in future
iterations of our analysis. The first limitation is small sample sizes for county-level est imat es. The ACS
typically samples roughly 1 percent of the total population (Census Bureau 2013). This process yields a
large sample for nat ional analysis, but the sample size for any particular county is much smaller; the
sample for a part icular subset within t hat county, such as ext remely low-income renters, is smaller still.
As a result, for smaller counties—those with fewer than 20,000 residents—we are unable t o reliably
provide a county est imat e and inst ead rely on stat ewide averages.
The second limitat ion is that the Census Bureau no longer includes a quest ion about households’
receipt of government housing assistance in eit her the ACS or t he decennial census. This creates
challenges when using ACS data t o measure housing affordability. Housing Choice Voucher recipients
should report the full rent amount (including what t he voucher covers) to t he ACS, but many report
their own monthly payment s inst ead. An int ernal Census Bureau analysis of subsidized rent ers in
California estimat ed t hat 40 percent of these households reported their own rent contribut ion to the
ACS, 32 percent report ed t he tot al monthly rent, and t he ot her 28 percent report ed an amount that did
not match either their rent contribution or the full mont hly rent. Conversely, some households
receiving t enant-based rent al assistance report the value of t heir voucher as part of their income t o the
Census Bureau—overstat ing the impact of rental assist ance on households’ rent burden.
For our analysis, we assume that subsidized rent ers report their monthly rent payments to t he ACS
rather than the full rent. However, based on t he Census Bureau’s analysis, this may be t rue for less than
half of assisted households. As a result, we may underestimat e the availability of affordable housing by
failing t o capture the value of the rent subsidy for households that report the full market rent of their
unit t o the ACS. For fut ure reports, we will explore whet her we can adjust our methodology to reflect
the uncertainty of how subsidized renters report their housing expenses to the ACS.
Also for fut ure reports, we hope t o incorporat e data from other federal rental assist ance programs,
such as t he US Department of Agricult ure’s Mult i-Family Housing Rental Assistance program and other
HUD rent al assistance programs, such as for Native Americans. Adding data from these programs will
provide a more complet e picture of rental assistance, particularly outside metropolitan areas.
Anot her limitation is that our data do not include homeless individuals, which constituted over
610,000 people at 2013’s point-in-time count (H enry, Cortes, and Morris 2013).
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
17
APPENDIX TABLE A.1
Availabilit y of Adequat e and Affordable Rent al Housing for Ext remely Low-Income (ELI) Rent ers in
t he 100 Largest US Count ies, 2013
From most to least affordable
Count y
St at e
Tot al
populat ion
Suffolk
Norfolk
Essex
District of Columbia
Worcester
Middlesex
Fairfield
San Francisco
Hartford
Allegheny
Philadelphia
Jefferson
New York
Essex
Hamilton
Jackson
Hennepin
Westchester
Jefferson
El Paso
Cuyahoga
New Haven
Lake
Davidson
Nassau
Hidalgo
Bexar
Fulton
Monmouth
Denver
Montgomery
King
Duval
Snohomish
Honolulu
Will
Bergen
Hudson
Erie
Alameda
MA
MA
MA
DC
MA
MA
CT
CA
CT
PA
PA
AL
NY
NJ
OH
MO
MN
NY
KY
TX
OH
CT
IL
TN
NY
TX
TX
GA
NJ
CO
MD
WA
FL
WA
HI
IL
NJ
NJ
NY
CA
745,716
682,501
756,508
633,167
805,989
1,537,150
933,794
826,626
897,426
1,229,582
1,546,770
658,601
8,341,122
787,615
802,659
677,502
1,184,060
962,233
751,312
824,916
1,266,434
863,217
701,763
647,670
1,348,563
805,497
1,785,855
970,400
629,754
634,685
1,004,242
2,007,779
879,131
733,797
974,683
681,537
919,049
652,921
919,332
1,554,725
18
ELI rent er
households
74,262
23,018
40,208
52,634
37,266
60,810
38,710
64,698
43,454
51,549
117,816
29,591
643,243
57,340
52,749
37,535
55,135
38,017
36,957
23,573
75,049
43,438
16,486
30,858
26,769
24,008
59,316
49,586
16,599
41,764
22,183
83,687
33,141
24,172
34,437
10,080
28,429
39,544
41,314
74,913
Adequat e,
affordable, and
available rent als
37,703
10,223
17,734
22,300
15,612
25,376
14,511
23,112
15,442
18,260
41,499
10,138
220,121
19,595
17,972
12,507
18,189
12,354
11,756
7,423
23,361
13,331
5,029
9,362
7,911
6,991
17,228
14,345
4,801
12,074
6,409
23,621
9,266
6,660
9,465
2,758
7,775
10,757
11,159
19,711
Affordable
unit s per 100
ELI rent er
households
Rank
50.8
44.4
44.1
42.4
41.9
41.7
37.5
35.7
35.5
35.4
35.2
34.3
34.2
34.2
34.1
33.3
33.0
32.5
31.8
31.5
31.1
30.7
30.5
30.3
29.6
29.1
29.0
28.9
28.9
28.9
28.9
28.2
28.0
27.6
27.5
27.4
27.3
27.2
27.0
26.3
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
Count y
St at e
Tot al
populat ion
Fairfax
Suffolk
Miami-Dade
Ventura
Wayne
Prince George’s
Cook
Franklin
Santa Clara
Contra Costa
Oakland
Middlesex
Macomb
Montgomery
Baltimore
Monroe
Bernalillo
Oklahoma
St. Louis
Milwaukee
DuPage
Wake
Hillsborough
Marion
Multnomah
San Mateo
Pima
Pinellas
Riverside
Fresno
Palm Beach
Salt Lake
Pierce
El Paso
Los Angeles
Sacramento
Orange
San Diego
Dallas
Shelby
Broward
Kern
Harris
Tarrant
Mecklenburg
VA
NY
FL
CA
MI
MD
IL
OH
CA
CA
MI
NJ
MI
PA
MD
NY
NM
OK
MO
WI
IL
NC
FL
IN
OR
CA
AZ
FL
CA
CA
FL
UT
WA
CO
CA
CA
CA
CA
TX
TN
FL
CA
TX
TX
NC
1,117,918
1,499,091
2,592,201
834,880
1,789,819
881,876
5,227,094
1,195,915
1,836,454
1,079,460
1,221,103
822,933
848,455
808,846
817,791
748,221
672,027
742,641
1,000,363
953,901
927,775
951,834
1,280,536
919,356
757,738
738,114
992,286
922,744
2,264,491
947,942
1,354,932
1,063,941
811,730
645,787
9,951,320
1,448,487
3,084,550
3,175,313
2,447,575
937,748
1,812,793
856,363
4,255,830
1,880,361
967,906
ELI rent er
households
22,323
31,588
115,281
23,113
99,699
29,694
255,759
59,062
65,983
36,578
30,690
29,979
22,435
18,697
25,404
35,118
29,411
30,468
29,835
66,421
16,001
28,487
41,766
51,544
40,498
22,430
40,447
26,608
56,844
38,484
40,267
27,523
25,763
18,978
535,214
66,416
106,204
120,135
101,007
40,861
57,465
26,549
152,692
61,493
35,788
Adequat e,
affordable, and
available rent als
Affordable
unit s per 100
ELI rent er
households
Rank
5,843
8,264
29,789
5,971
25,458
7,416
62,840
14,389
15,940
8,750
7,265
7,090
4,987
4,149
5,571
7,630
6,388
6,496
6,200
13,641
3,235
5,750
8,307
10,085
7,872
4,241
7,560
4,957
10,509
6,987
7,309
4,929
4,588
3,359
94,672
11,554
18,108
20,376
17,106
6,866
9,392
4,239
23,462
9,318
5,421
26.2
26.2
25.8
25.8
25.5
25.0
24.6
24.4
24.2
23.9
23.7
23.6
22.2
22.2
21.9
21.7
21.7
21.3
20.8
20.5
20.2
20.2
19.9
19.6
19.4
18.9
18.7
18.6
18.5
18.2
18.2
17.9
17.8
17.7
17.7
17.4
17.1
17.0
16.9
16.8
16.3
16.0
15.4
15.2
15.1
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
19
Count y
St at e
Tot al
populat ion
San Bernardino
Maricopa
Collin
Travis
San Joaquin
DeKalb
Lee
Clark
Orange
Cobb
Gwinnett
Denton
CA
AZ
TX
TX
CA
GA
FL
NV
FL
GA
GA
TX
2,076,322
3,939,668
834,110
1,093,138
700,220
706,093
645,681
1,997,371
1,198,989
707,248
841,658
707,550
ELI rent er
households
59,923
124,368
13,434
48,057
22,831
30,682
13,059
66,336
37,166
19,510
17,156
14,924
Adequat e,
affordable, and
available rent als
Affordable
unit s per 100
ELI rent er
households
Rank
8,857
18,346
1,959
6,980
3307
4,325
1,696
7,998
3,731
1,768
1,494
1,207
14.8
14.8
14.6
14.5
14.5
14.1
13.0
12.1
10.0
9.1
8.7
8.1
86
87
88
89
90
91
92
93
94
95
96
97
Sources: 2000 Decennial Census, and three-year averages from the 2005,2006, and 2007 and 2011, 2012, and 2013 ACS 1-year
sample data from the Integrated Public Use Microdata Series merged with data from HUD on income limits and households
receiving rental assistance.
Note: Four of the 100 largest counties in the United States are in New York City. Because the five New York City counties are
combined in this analysis, the lowest ranking number is 97.
20
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
APPENDIX TABLE A.2
Trends in Affordabilit y for Ext remely Low-Income (ELI) Rent ers in t he 100 Largest US Count ies,
2000–13
By most to least positive
Count y
St at e
Suffolk
Los Angeles
Kern
Bergen
New York
San Francisco
Orange
Miami-Dade
Fresno
San Diego
Sacramento
Pierce
Hennepin
Alameda
Monroe
Riverside
Montgomery
El Paso
Ventura
Prince George’s
Suffolk
San Mateo
El Paso
San Joaquin
Worcester
King
Travis
Essex
San Bernardino
Hudson
Broward
Baltimore
Fairfax
Middlesex
Philadelphia
DuPage
Bernalillo
Norfolk
Nassau
Salt Lake
MA
CA
CA
NJ
NY
CA
CA
FL
CA
CA
CA
WA
MN
CA
NY
CA
MD
TX
CA
MD
NY
CA
CO
CA
MA
WA
TX
MA
CA
NJ
FL
MD
VA
MA
PA
IL
NM
MA
NY
UT
ELI Rent er
Households
2000
2013
57,132
383,332
17,459
19,474
589,726
48,847
71,254
87,982
25,350
77,359
40,354
17,212
35,793
54,253
26,270
31,695
18,104
16,929
15,984
22,879
23,300
13,898
9,876
15,032
25,148
57,032
31,237
30,254
41,253
34,344
42,510
16,236
14,104
42,927
89,798
10,603
17,002
14,382
20,527
16,215
74,262
535,214
26,549
28,429
643,243
64,698
106,204
115,281
38,484
120,135
66,416
25,763
55,135
74,913
35,118
56,844
22,183
23,573
23,113
29,694
31,588
22,430
18,978
22,831
37,266
83,687
48,057
40,208
59,923
39,544
57,465
25,404
22,323
60,810
117,816
16,001
29,411
23,018
26,769
27,523
Adequat e,
Affordable, and
Available Unit s
2000
2013
27,281
58,780
2,377
4,905
192,995
16,882
11,532
22,203
4,549
13,566
7,272
3,181
12,161
14,822
6,004
6,248
5,498
5,607
4,394
6,095
6,504
2,880
1,953
2,519
11,200
17,737
5,474
14,292
7,426
10,491
8,502
4,207
4,253
19,625
35,264
2,577
4,388
6,979
6,982
3,664
37,703
94,672
4,239
7,775
220,121
23,112
18,108
29,789
6,987
20,376
11,554
4,588
18,189
19,711
7,630
10,509
6,409
7,423
5,971
7,416
8,264
4,241
3,359
3,307
15,612
23,621
6,980
17,734
8,857
10,757
9,392
5,571
5,843
25,376
41,499
3,235
6,388
10,223
7,911
4,929
Affordable Unit s
per 100 ELI Rent er
Households
2000
2013
47.8
15.3
13.6
25.2
32.7
34.6
16.2
25.2
17.9
17.5
18.0
18.5
34.0
27.3
22.9
19.7
30.4
33.1
27.5
26.6
27.9
20.7
19.8
16.8
44.5
31.1
17.5
47.2
18.0
30.5
20.0
25.9
30.2
45.7
39.3
24.3
25.8
48.5
34.0
22.6
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
50.8
17.7
16.0
27.3
34.2
35.7
17.1
25.8
18.2
17.0
17.4
17.8
33.0
26.3
21.7
18.5
28.9
31.5
25.8
25.0
26.2
18.9
17.7
14.5
41.9
28.2
14.5
44.1
14.8
27.2
16.3
21.9
26.2
41.7
35.2
20.2
21.7
44.4
29.6
17.9
Difference,
2000–13
3.0
2.4
2.4
2.2
1.5
1.2
0.9
0.6
0.2
-0.6
-0.6
-0.7
-1.0
-1.0
-1.1
-1.2
-1.5
-1.6
-1.7
-1.7
-1.8
-1.8
-2.1
-2.3
-2.6
-2.9
-3.0
-3.1
-3.2
-3.3
-3.7
-4.0
-4.0
-4.0
-4.0
-4.1
-4.1
-4.1
-4.5
-4.7
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
21
Count y
St at e
Santa Clara
Pima
Bexar
Snohomish
Westchester
Clark
Hillsborough
St. Louis
Contra Costa
Maricopa
Montgomery
Monmouth
Denton
Erie
Multnomah
Hartford
Middlesex
Allegheny
Essex
Dallas
Pinellas
Honolulu
Gwinnett
Orange
Harris
Palm Beach
Oklahoma
Denver
New Haven
DeKalb
Jefferson
Jackson
Lake
Franklin
Wake
Oakland
Collin
Hidalgo
Cobb
Tarrant
Hamilton
Marion
Cuyahoga
Fairfield
Davidson
CA
AZ
TX
WA
NY
NV
FL
MO
CA
AZ
PA
NJ
TX
NY
OR
CT
NJ
PA
NJ
TX
FL
HI
GA
FL
TX
FL
OK
CO
CT
GA
KY
MO
IL
OH
NC
MI
TX
TX
GA
TX
OH
IN
OH
CT
TN
22
ELI Rent er
Households
2000
2013
43,116
25,419
36,710
13,008
38,451
35,284
26,607
16,638
21,642
69,925
11,340
12,910
10,341
35,378
25,553
30,870
19,015
39,794
53,310
78,282
21,268
29,315
6,684
21,150
119,594
24,940
21,613
29,865
32,360
19,051
24,944
24,501
9,759
43,838
15,633
20,764
5,347
13,559
10,728
38,937
35,445
29,319
61,369
30,154
26,492
65,983
40,447
59,316
24,172
38,017
66,336
41,766
29,835
36,578
124,368
18,697
16,599
14,924
41,314
40,498
43,454
29,979
51,549
57,340
101,007
26,608
34,437
17,156
37,166
152,692
40,267
30,468
41,764
43,438
30,682
36,957
37,535
16,486
59,062
28,487
30,690
13,434
24,008
19,510
61,493
52,749
51,544
75,049
38,710
30,858
Adequat e,
Affordable, and
Available Unit s
2000
2013
12,489
6,043
12,674
4,303
14,747
6,587
7,081
4,607
6,681
15,236
3,316
4,728
1,667
12,414
7,112
13,566
6,126
17,520
22,806
20,070
5,817
10,639
1,189
4,061
29,672
6,950
6,726
11,582
13,157
4,747
10,642
10,824
4,042
15,513
4,908
7,275
1,390
5,514
2,211
10,650
16,699
9,644
27,296
15,412
11,908
15,940
7,560
17,228
6,660
12,354
7,998
8,307
6,200
8,750
18,346
4,149
4,801
1,207
11,159
7,872
15,442
7,090
18,260
19,595
17,106
4,957
9,465
1,494
3,731
23,462
7,309
6,496
12,074
13,331
4,325
11,756
12,507
5,029
14,389
5,750
7,265
1,959
6,991
1,768
9,318
17,972
10,085
23,361
14,511
9,362
Affordable Unit s
per 100 ELI Rent er
Households
2000
2013
29.0
23.8
34.5
33.1
38.4
18.7
26.6
27.7
30.9
21.8
29.2
36.6
16.1
35.1
27.8
43.9
32.2
44.0
42.8
25.6
27.4
36.3
17.8
19.2
24.8
27.9
31.1
38.8
40.7
24.9
42.7
44.2
41.4
35.4
31.4
35.0
26.0
40.7
20.6
27.4
47.1
32.9
44.5
51.1
44.9
24.2
18.7
29.0
27.6
32.5
12.1
19.9
20.8
23.9
14.8
22.2
28.9
8.1
27.0
19.4
35.5
23.6
35.4
34.2
16.9
18.6
27.5
8.7
10.0
15.4
18.2
21.3
28.9
30.7
14.1
31.8
33.3
30.5
24.4
20.2
23.7
14.6
29.1
9.1
15.2
34.1
19.6
31.1
37.5
30.3
Difference,
2000–13
-4.8
-5.1
-5.5
-5.5
-5.9
-6.6
-6.7
-6.9
-6.9
-7.0
-7.0
-7.7
-8.0
-8.1
-8.4
-8.4
-8.6
-8.6
-8.6
-8.7
-8.7
-8.8
-9.1
-9.2
-9.4
-9.7
-9.8
-9.9
-10.0
-10.8
-10.9
-10.9
-10.9
-11.0
-11.2
-11.4
-11.4
-11.5
-11.5
-12.2
-13.0
-13.3
-13.4
-13.6
-14.6
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
Rank
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
Count y
St at e
District of Columbia
Mecklenburg
Cook
Duval
Jefferson
Macomb
Fulton
Milwaukee
Lee
Will
Shelby
Wayne
DC
NC
IL
FL
AL
MI
GA
WI
FL
IL
TN
MI
ELI Rent er
Households
2000
2013
52,474
17,733
249,920
23,391
25,237
13,249
43,626
47,944
7,568
5,921
33,966
88,945
52,634
35,788
255,759
33,141
29,591
22,435
49,586
66,421
13,059
10,080
40,861
99,699
Adequat e,
Affordable, and
Available Unit s
2000
2013
30,365
5,580
103,324
10,648
13,177
5,461
21,057
19,159
2,494
2,988
13,575
48,069
22,300
5,421
62,840
9,266
10,138
4,987
14,345
13,641
1695.67
2,758
6,866
25,458
Affordable Unit s
per 100 ELI Rent er
Households
2000
2013
57.9
31.5
41.3
45.5
52.2
41.2
48.3
40.0
33.0
50.5
40.0
54.0
42.4
15.1
24.6
28.0
34.3
22.2
28.9
20.5
13.0
27.4
16.8
25.5
Difference,
2000–13
-15.5
-16.3
-16.8
-17.6
-18.0
-19.0
-19.3
-19.4
-20.0
-23.1
-23.2
-28.5
86
87
88
89
90
91
92
93
94
95
96
97
Sources: 2000 Decennial Census, and three-year averages from the 2005,2006, and 2007 and 2011, 2012, and 2013 ACS 1-year
sample data from the Integrated Public Use Microdata Series merged with data from HUD on income limits and households
receiving rental assistance.
Note: Four of the 100 largest counties in the United States are in New York City. Because the five New York City counties are
combined in this analysis, the lowest ranking number is 97.
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
Rank
23
Notes
1.
Three other counties also had 75 adequate, affordable, and available units for every 100 ELI renters: Allegan
County, Michigan, Lincoln County, Missouri, and Jefferson County, West Virginia. This analysis excludes
counties with fewer than 10 ELI renters surveyed as part of the 2013 American Community Survey.
2.
See the appendix for a detailed description of how we constructed county-level estimates from the Integrated
Public Use Microdata Series dataset.
3.
New York Cit y is t echnically five separat e counties, but for this analysis they are grouped as one.
4.
Matthew Johnson, “Stepping Up: How Cities Are Working to Keep America’s Poorest Families Housed,”
http://www.urban.org/features/stepping-how-cities-are-working-keep-americas-poorest-families-housed.
5.
HUD income limits are available at http:// www.huduser.org/portal/datasets/il.html.
6.
Because the ACS does not include a variable that indicat es whether utilit y costs are included in the rent, we
calculate the difference in gross rent and contract rent for each renter-occupied household, as explained in the
appendix.
7.
The HUD data were broken out into the following program cat egories: HCV program, public housing,
moderate rehabilitation program, multifamily Section 8 contracts, and other multifamily programs.
References
Arnold, Althea, Sheila Crowley, Elina Bravve, Sarah Brundage, and Christine Biddlecombe. 2014. Out of Reach 2014.
Washington, DC: National Low Income Housing Coalition.
http://nlihc.org/sites/default/files/oor/2014OOR.pdf.
CBPP (Center on Budget and Policy Priorities). 2013. “Section 8 Project-Based Rental Assistance.” Washington,
DC: CBPP. http://www.cbpp.org/sites/default/files/atoms/files/PolicyBasics-housing-1-25-13PBRA.pdf.
Cunningham, Mary, Joshua Leopold, and Pamela Lee. 2014. “A Proposed Demonstration of a Flat Rental Subsidy for
Very Low Income Households.” Washington, DC: Urban Institute. http://www.urban.org/research/publication/
proposed-demonstration-flat-rental-subsidy-very-low-income-households.
Henry, Meghan, Alvaro Cortes, and Sean Morris. 2013. The 2013 Annual Homeless Assessment Report (AHAR) to
Congress. Part 1: Point-in-Time Estimates of Homelessness. Washington, DC: US Department of Housing and
Urban Development, Office of Community Planning and Development.
HUD (US Department of Housing and Urban Development). 2013. “Picture of Subsidized Households.”
Washington, DC: HUD.
JCHS (Joint Center for Housing Studies). 2013. “America’s Rental Housing.” Cambridge, MA: JCHS.
http://www.jchs.harvard.edu/americas-rental-housing.
———. 2014. “State of the Nation’s Housing.” Cambridge, MA: JCHS.
http://www.jchs.harvard.edu/research/state_nations_housing.
McClure, Kirk. 2005. “Rent Burden in the Housing Choice Voucher Program.” Cityscape: A Journal of Policy
Development and Research 8 (2): 5–20.
Rice, Douglas. 2014. “Sequestration’s Rising Toll: 100,000 Fewer Low-Income Families Have Housing Vouchers.”
Washington, DC: CBPP. http://www.cbpp.org/research/housing/sequestrations-rising-toll-100000-fewerlow-income-families-have-housing-vouchers
Steffen, Barry L., George R. Carter, Marge Martin, Danilo Pelletiere, David A. Vandenbroucke, and Yun-Gann David
Yao. 2015. Worst Case Housing Needs: 2015 Report to Congress. Washington, DC: HUD.
Treskon, Mark, and Mary Cunningham. Forthcoming. “Moving to Work.” Washington, DC: Urban Institute.
US Census Bureau. 2013. “An Overview of the American Community Survey.” Washington, DC: US Census Bureau.
24
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
Acknowledgments
This brief was funded by Housing Authority Insurance, Inc. (HAI, Inc.), to provide fact-based analysis
about public and assisted housing. We are grat eful to t hem and to all our funders, who make it possible
for Urban to advance it s mission. Funders do not, however, det ermine our research findings or the
insights and recommendations of our experts. The views expressed are those of the authors and should
not be attributed to t he Urban Instit ute, its trust ees, or its funders.
The authors thank Rob Sant os, Tim Triplet t, and Doug Wissoker for their advice on data analysis. They
also t hank Erika Poet hig reviewing earlier drafts of this brief.
About the Authors
Josh Leopold is a research associat e in the Met ropolit an Housing and Communities
Policy Center at the Urban Institut e, where his work focuses on homelessness and
affordable housing policy.
Before joining Urban, Leopold was a management and program analyst at the US
Int eragency Council on Homelessness (USICH). At USICH, he helped implement the
Obama administration’s plan for ending chronic homelessness and homelessness
among vet erans by 2015; he also helped develop a nat ional research agenda relat ed to
homelessness. From 2006 t o 2011, he worked as an analyst for Abt Associates, where
he was involved in numerous studies, including the Annual Homeless Assessment
Report; the Costs of Homelessness study; the Study of Rents and Rent Flexibility in
Subsidized Housing; and an evaluation of t he AmeriCorps program.
Leopold has a bachelor’s degree from Grinnell College, Iowa, and a master’s degree in
informat ion science from t he University of Michigan.
Liza Getsinger is the policy and research program manager of Urban Institute’s Policy
Advisory Group. In t his capacity she coordinat es across Urban’s policy cent ers to
deepen engagement on cross-cutting topics—in particular cit ies and places. Her
expertise blends research, policy, and pract ice with a focus on developing act ionable,
evidence-based solut ions to inform policymakers, pract itioners, and philant hropic
invest ments.
Before joining Urban, Getsinger worked at the National Housing Conference, where
she helped craft policy, legislative, and programmatic initiatives on budget and tax
issues, HUD’s regulatory and programmat ic funct ioning, and neighborhood
restoration. In Austin, Texas, she held several posit ions focused on yout h services and
affordable housing. Getsinger started her career as a researcher in Urban’s
Met ropolit an Housing and Communit ies Policy Cent er.
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
25
A graduat e of DePaul University, Getsinger holds a mast er’s in public affairs with a
concentration in social and economic policy from the Lyndon B. Johnson School of
Public Affairs at t he University of Texas at Austin, where she was an endowed
fellowship recipient.
Pamela Blumenthal is a senior research associate with t he Policy Advisory Group and
the Metropolitan Housing and Communit ies Policy Center at the Urban Instit ute. Her
expertise is in affordable housing, land-use regulation, and economic resilience, wit h an
emphasis on qualitat ive research. She also works with other researchers to t ranslat e
their work int o actionable policy for urban policy makers.
Blumenthal spent over a decade as a regulat ory lawyer working on consumer financial
prot ection issues, bot h in the private sect or and at the Division of Consumer and
Community Affairs at the Federal Reserve Board. She blended her legal expert ise and
research skills as a member of the Consumer Financial Prot ection Bureau
implement ation team, leading work on development of new mort gage lending
applicat ion disclosures. She joined Urban Inst itut e from the Office of Policy
Development and Research at the US Department of Housing and Urban
Development.
Blumenthal has a BA in English from Cornell University, a JD from University of
Michigan School of Law, and a PhD in public policy and public administ ration from the
George Washington University.
Katya Abazajian is a research assistant in t he Metropolit an Housing and Communit ies
Policy Center at the Urban Institut e. She works in multiple subject areas including
consumer financial prot ection, neighborhood development, and t he preservation of
affordable housing in Washington, DC. Since starting at Urban, Abazajian has been
involved wit h the Nat ional Neighborhood Indicat ors Partnership, the
NeighborhoodInfo DC program, Promise Neighborhoods, and the DC Affordable
Housing Preservation Network.
Abazajian graduat ed from Claremont McKenna College in 2014 wit h a BA in both
economics and math. At Claremont McKenna, she was an undergraduate research
assistant at t he Rose Institute of Stat e and Local Government and the managing edit or
of the campus newspaper. Her undergraduate research focused on municipal t ax
systems, regional economic forecasting, and local politics.
Reed Jordan is a research associat e II in the Met ropolit an Housing and Communit ies
Policy Center at the Urban Institut e. Since joining Urban in 2012, he has been involved
in various nat ional. He is currently providing technical assistance to grant ees of the US
Depart ment of Education’s Promise Neighborhoods program and evaluating the early
implement ation of t he US Department of Housing and Urban Development's Choice
26
H O U SI N G A FFO RD A B I LI T Y GA P FO R EX T REM ELY LO W - I N CO M E REN T ERS
Neighborhoods init iative. His other int erests include performance management, lowincome housing, and educat ion policy.
Jordan graduat ed magna cum laude from Carlet on College in Northfield, Minnesota,
with honors in political science and depart mental distinction on his senior t hesis. While
at Carlet on, he was awarded a two-year grant from the Howard Hughes Medical
Inst itut e to perform environmental chemist ry research at Carlet on and signal analysis
research at Texas A&M University. He was also awarded a fellowship to fund a
community development and public health int ernship in Urubamba, Peru.
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