Categorical Eligibility and Fact-Specific Proxies Decrease Documentation Burden for ERA Program Recipients

As Treasury Department Emergency Rental Assistance (ERA) programs continue to ramp up, many face application backlogs. Guidance from the department in the form of frequently asked questions (FAQ) offers several solutions to decrease documentation burden, including allowing income eligibility determination based on fact-specific proxy and categorical eligibility. The FAQ document outlines how programs are incorporating these flexibilities to decrease the documentation burden for renter households. According to NLIHC’s tracking, only 2% of ERA programs have implemented fact-specific proxies, and only 17% have implemented categorical eligibility. Programs should act quickly to implement these flexibilities, along with self-attestation, to move funds quickly to renters most in need.

According to federal guidance, ERA programs may rely on three approaches to determine a household’s income eligibility: self-attestation alone, categorical eligibility, and fact-specific proxy. Programs should offer self-attestation alone as an option to applicants, but when programs use this method, they must reassess the household’s income every three months. Implementing categorical eligibility and fact-specific proxies are two methods that limit the need for income recertification and do not require additional documentation from the applicant. Fact-specific proxy allows jurisdictions to rely on an income proxy, such as income averages within a household’s neighborhood or census tract, to infer an applicant’s income. Categorical eligibility allows a household to be deemed eligible if it has been verified as low-income in another local, state, or federal program, such as SNAP, TANF, WIC, Medicaid, Housing Choice Vouchers, and others.

NLIHC is tracking ERA program implementation, including how many programs offer these flexibilities. As of early September, only ten programs offered income-eligibility determination based on fact-specific proxy. This includes five state programs: Kentucky, Virginia, Connecticut, Delaware, and South Carolina. While these programs have slightly different methods for calculating fact-specific proxy thresholds, they follow similar strategies. These programs use publicly available data to infer if households in a small geographic area – such as a ZIP code or census tract – are below 80% of the area median income for a larger geographic area – such as a county or state. Data sources include median income data from the American Community Survey, HUD area median income calculations, and HUD qualified census tracts. Several programs have integrated this data into their applications, so that when applicants enter their address, they are alerted to the fact that they do not need to provide additional income documentation.

NLIHC has identified 84 programs that allow income determination using data from other federal, state, or local assistance programs. In most cases, the programs require households to provide an income determination letter from another program. This may decrease barriers to providing income documentation as it allows households to provide one document rather than multiple pay stubs, invoices, and other proof of income. Some programs have eliminated the need for documentation altogether by accessing data from other assistance programs and integrating these into their application processing. Louisiana, for example, allows applicants to select an option to use data from the Louisiana Workforce Commission rather than providing income documentation. Philadelphia established a data-sharing agreement with the city’s Data Management Office and has begun using public benefits data (particularly Medicaid data) to verify income eligibility (see Memo 9/7).

To address application backlogs and decrease documentation burden, programs should implement these flexibilities to determine income eligibility. State and local programs offer examples of how this can be done, and more jurisdictions must follow suit to serve low-income renter households efficiently.