Leveraging the AHS to Better Estimate Worst Case Housing Needs Of Persons

with Disabilities:An Exploration

Danilo Pelletiere, National Low Income Housing CoalitionKathryn 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 non-elderly

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 non-elderly 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

• Consortium of Citizens with Disabilities (based on method in Nelson, 2008)

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 ACS-

identified severe problems

• About 40% of those assisted in AHS have ACS-

identified 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• 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

Y variables• 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

Min. Max. Mean Std. Dev. Min. Max. MeanStd. Dev.

Mean ACS/AHS

Ratio of household income to poverty 0.00 114.51 4.29 4.44 0.00 54.59 4.06 4.10 106%income to typical rent ratio 26.06 109.24 61.96 12.81 21.80 146.28 65.36 17.02 95%Food Stamp Receipt 0.00 1.00 0.10 0.30 0.00 1.00 0.06 0.24 175%Public Assistance receipt 0.00 1.00 0.03 0.16 0.00 1.00 0.02 0.13 145%Social Security Receipt 0.00 1.00 0.28 0.45 0.00 1.00 0.25 0.43 111%Retirement Receipt 0.00 1.00 0.17 0.38 0.00 1.00 0.14 0.35 123%Wages and Saleries 0.00 1.00 0.76 0.42 0.00 1.00 0.73 0.44 104%Married Couple 0.00 1.00 0.49 0.50 0.00 1.00 0.51 0.50 97%Black Householder 0.00 1.00 0.03 0.18 0.00 1.00 0.11 0.32 30%Hispanic Householder 0.00 1.00 0.06 0.23 0.00 1.00 0.13 0.33 46%Number of Kids 0.00 14.00 0.67 1.09 0.00 9.00 0.65 1.07 102%Number of People 1.00 20.00 2.51 1.47 1.00 14.00 2.53 1.45 99%Multifamily rental property 0.00 1.00 0.21 0.41 0.00 1.00 0.20 0.40 106%Building has 50 units or more 0.00 1.00 0.05 0.21 0.00 1.00 0.04 0.19 126%Built prior to 1939 0.00 1.00 0.14 0.34 0.00 1.00 0.15 0.36 90%Number of Rooms 1.00 28.00 5.90 2.33 1.00 21.00 5.74 1.80 103%Number of Bedrooms 0.00 14.00 2.76 1.14 0.00 10.00 2.79 1.04 99%Rent level 4.00 3900.00 797.83 498.72 1.00 4738.00 825.04 626.17 97%Severe cost burden 0.00 1.00 0.18 0.38 0.00 1.00 0.17 0.38 101%Plumbing 0.00 1.00 0.99 0.08 .00 1.00 1.00 .04 100%Crowded 0.00 1.00 0.03 0.18 0.00 1.00 0.02 0.15 142%MWMetro 0.00 1.00 0.17 0.37 0.00 1.00 0.19 0.39 91%Snonmetro 0.00 1.00 0.08 0.27 0.00 1.00 0.07 0.25 115%Wnonmetro 0.00 1.00 0.02 0.15 0.00 1.00 0.02 0.14 122%Smetro 0.00 1.00 0.29 0.45 0.00 1.00 0.30 0.46 96%Wmetro 0.00 1.00 0.20 0.40 0.00 1.00 0.20 0.40 98%MWnonmetro 0.00 1.00 0.06 0.24 0.00 1.00 0.04 0.20 144%NEnonmetro 0.00 1.00 0.02 0.14 0.00 1.00 0.02 0.14 99%

AHSACS

“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