Transcript Leveraging the AHS to Better Estimate Housing Needs Of

Leveraging the AHS to Better Estimate
Worst Case Housing Needs Of Persons
with Disabilities:
An Exploration
Danilo Pelletiere, National Low Income Housing Coalition
Kathryn P. Nelson, Consultant
Paper prepared for presentation at the American Housing
Survey User Conference,
Washington, DC, March 8, 2011.
Comparing the ACS and AHS
• Specifically, are Worst Case Needs* of disabled nonelderly adults undercounted by AHS?
– Should they be adjusted to better control totals?
– Could multivariate estimates of WCN from ACS help?
• More generally, because ACS is broad while AHS has
depth on housing and is more flexible,
• can strengths of both be linked to produce better
estimates, particularly for small areas?
– How easy is doing this for outside researchers trying to
answer specific questions?
• *WCN=rent burden>50% of income or severely
inadequate housing for unassisted very low-income
renters (income <= 50% of Area Median Income)
Paper structure
• History of HUD’s estimates of WCN among the
disabled: improving an imperfect proxy
• But the ACS, NHIS, and SIPP counted many more
than the AHS as disabled in past
• Do the 2009 AHS and 2009 ACS similarly cover
disabled adults and housing problems?
• Exploring multivariate estimates of WCN among
the disabled
• Conclusion: both AHS and ACS estimates of WCN
should be adjusted to better data
HUD’s estimates of WCN for nonelderly adults: history and critique
• Proxies based on income from SSI, SS, and welfare
first developed in 1994, compared to better
control totals
• 1995 #s compared to SSI Stewardship Review
Sample; proxy & controls both improved since
• CCD: 2005 and 2007 WCN comparisons against
sources with better data on persons with
disabilities imply that WCN of non-elderly disabled
were more than double even the improved AHS
estimates from income proxies
2007 comparisons
2007 Estimates of WCN Using AHS and Various Controls
Childless
With children
1400
1220
1009
907
880
653
602
404
AHS/HUD
ACS
NHIS
SIPP
Controls
• Consortium of Citizens with Disabilities (based on method in Nelson, 2008)
Source: Authors tabulations
Our research
• In 2009 AHS and ACS both asked a six-question
sequence recommended by a Census advisory panel
to identify persons with disabilities
• We ask three technical questions
– Do the AHS and ACS now similarly identify disabled?
– Do the AHS and ACS similarly identify severe housing
problems?
– Can we develop multivariate approaches that improve on
simple ratios to estimate worst case needs of non-elderly
adults with disabilities from AHS and ACS?
Answers: No, Yes, and Maybe
• Even with same questions, 2009 ACS’ estimates of the
number disabled are some 50% above AHS
• And, evidence suggests that ‘really’ both are low
• Incidence of severe and moderate problems is quite
similar in ACS & AHS
• This supports past ACS evidence of higher WCN among
disabled and confirms desirability of adjusting AHS
estimates to better control totals
• Initial multivariate estimates of subsidized households
and WCN in the ACS look reasonable
• Multivariate methods may pay off for some questions
1. Unexpectedly, many more report
disabling conditions in ACS
• For all renters, 2009 ACS estimate of disabled is 52%
higher, 8.8 million rather than 5.8 million
• ACS counts for the 6 specific conditions asked about
are 38-99% above AHS
• Among non-elderly very low-income renters, 43%
more report disabling conditions on the ACS, 3.3 rather
than 2.7 million
AHS-ACS 6-question sequence
undercounts number of disabled
• AHS with disability or SSI income would raise
estimates of disabled VLI renters by 21-55%
• Disabled veterans would add 1.7M (7%) to ACS
• Before 2008, ACS asked about work-activity limitations
– Study of 2008 CPS: omitting work question understates
size of working age population with disabilities by 30%,
especially among poorest
– e.g., in 2007 ACS, the limiting-work question would have
added 0.9M VLI renters (or 9%) as disabled
2. AHS and ACS count common
housing problems similarly
• AHS and ACS cover rent burden and crowding
• Incomplete kitchen or plumbing are ACS’ only
indicators of housing quality
• AHS and ACS quite similarly count moderate and
severe cost burden
• Rates of severe problems are slightly lower for all
households in ACS, but similar for VLI renters
Estimating WCN from ACS?
• Essentially all with worst case needs in AHS have ACSidentified severe problems
• About 40% of those assisted in AHS have ACSidentified severe problems
• Identifying which renters are assisted is key to
estimating worst case needs from ACS
3. A multivariate approach?
1. Estimate a binary logit model to predict
which VLIR households have housing
subsidies in the AHS
2. Use this model to predict which VLIR
households are subsidized in the ACS and
thereby derive those with WCN
=
The AHS model
• S is the Subsidized Housing Variable
• Xij are the household’s characteristics
• Xik are their unit’s and building’s
characteristics
• Xil are their location characteristics
• All independent variables must be “shared” by
both surveys
ACS and AHS shared variables
X variables
Y variables
• Ratio of household income to
poverty
• Food Stamp Receipt
• Public Assistance receipt
• Social Security Receipt
• Retirement Receipt
• Wages and Salaries
• Married Couple
• Black Householder
• Hispanic Householder
• Number of Kids
• Number of People
• Multifamily rental property
• Building has 50 units or more
• Built prior to 1939
• Number of Rooms
• Number of Bedrooms
• Rent level
• Severe cost burden
• Plumbing
• Crowded
Z variables
• Median inc. to typical rent ratio
• Region/Metro
How the Variables Compare
ACS
Min.
Ratio of household income to poverty
income to typical rent ratio
Food Stamp Receipt
Public Assistance receipt
Social Security Receipt
Retirement Receipt
Wages and Saleries
Married Couple
Black Householder
Hispanic Householder
Number of Kids
Number of People
Multifamily rental property
Building has 50 units or more
Built prior to 1939
Number of Rooms
Number of Bedrooms
Rent level
Severe cost burden
Plumbing
Crowded
MWMetro
Snonmetro
Wnonmetro
Smetro
Wmetro
MWnonmetro
NEnonmetro
Max.
AHS
Mean
Std. Dev.
Min.
Max.
Mean
Std.
Dev.
Mean
ACS/AHS
0.00
114.51
4.29
4.44
0.00
54.59
4.06
4.10
26.06
109.24
61.96
12.81
21.80
146.28
65.36
17.02
95%
0.00
1.00
0.10
0.30
0.00
1.00
0.06
0.24
175%
0.00
1.00
0.03
0.16
0.00
1.00
0.02
0.13
145%
0.00
1.00
0.28
0.45
0.00
1.00
0.25
0.43
111%
0.00
1.00
0.17
0.38
0.00
1.00
0.14
0.35
123%
0.00
1.00
0.76
0.42
0.00
1.00
0.73
0.44
104%
0.00
1.00
0.49
0.50
0.00
1.00
0.51
0.50
97%
0.00
1.00
0.03
0.18
0.00
1.00
0.11
0.32
30%
0.00
1.00
0.06
0.23
0.00
1.00
0.13
0.33
46%
0.00
14.00
0.67
1.09
0.00
9.00
0.65
1.07
102%
1.00
20.00
2.51
1.47
1.00
14.00
2.53
1.45
99%
0.00
1.00
0.21
0.41
0.00
1.00
0.20
0.40
106%
0.00
1.00
0.05
0.21
0.00
1.00
0.04
0.19
126%
0.00
1.00
0.14
0.34
0.00
1.00
0.15
0.36
90%
1.00
28.00
5.90
2.33
1.00
21.00
5.74
1.80
103%
0.00
14.00
2.76
1.14
0.00
10.00
2.79
1.04
99%
4.00 3900.00 797.83
498.72
1.00 4738.00 825.04 626.17
106%
97%
0.00
1.00
0.18
0.38
0.00
1.00
0.17
0.38
101%
0.00
1.00
0.99
0.08
.00
1.00
1.00
.04
100%
0.00
1.00
0.03
0.18
0.00
1.00
0.02
0.15
142%
0.00
1.00
0.17
0.37
0.00
1.00
0.19
0.39
91%
0.00
1.00
0.08
0.27
0.00
1.00
0.07
0.25
115%
0.00
1.00
0.02
0.15
0.00
1.00
0.02
0.14
122%
0.00
1.00
0.29
0.45
0.00
1.00
0.30
0.46
96%
0.00
1.00
0.20
0.40
0.00
1.00
0.20
0.40
98%
0.00
1.00
0.06
0.24
0.00
1.00
0.04
0.20
144%
0.00
1.00
0.02
0.14
0.00
1.00
0.02
0.14
99%
“Final” Model Results
Overall, coefficients
differ from zero, the
model fits the data, and
predictions are correct
81% of the time.
Predicting Subsidies in the ACS
• Use coefficients and “corresponding” ACS variables in
the following equation for each household (j)
• Those with probability over 50% (S > =.5) are predicted
to be subsidized
Summary of National Results
• National estimates reasonably distributed but not
close enough.
- 2.5 million subsidized VLIR households (vs. 4.2 million in
AHS)
- 9.2 million WCN (7.1 million AHS)
- 9.6 million w/o est. subsidies in the WCN proxy
- 2.6 million VLIR disabled adult households with WCN (2.0
AHS WCN controlled to SIPP disabled total)
- 1.8 million of these households are nonelderly or with
children
State Results
Source: Authors’ estimates using American Community Survey data
Technical Conclusions
• Compared to ACS, AHS estimates of persons with disabling
conditions are low, and 6 questions apparently undercount
the disabled in AHS and ACS
• AHS estimates of WCN should be adjusted to control totals
from better data on disabling conditions, including ACS and
SIPP, and reflect ongoing research on best ways to count
the disabled
• Multivariate approach appears worth pursuing
– Next steps: better match variables and test other model
specifications to improve fit and prediction rate
– Unclear if it will ultimately improve on univariate method
– ACS variables are limited
– housing subsidies are diverse and idiosyncratic non-entitlements,
method may work better for other variables and questions
Policy Implications
• Evidence that as many as 18 to 29% of 2009 worst case
renters are non-elderly disabled implies 811 and other
HUD programs should direct more assistance to needy
disabled renters
• Families with children and disabled adults particularly
need housing assistance
• ACS and AHS appear to complement each other – may
funding for both long continue