Page 20 - THE GAP: The Affordable Housing Gap Analysis 2019

THE GAP New York City found that criminal defendants who were homeless were more likely to be re-arrested than those who were not homeless, indicating that permanent housing is associated with lower rates of re-arrest (Peterson, 2016). CONCLUSION e shortage of seven million rental homes a ordable and available to households with the lowest-incomes is a national problem a ecting nearly every community. As a result, families lack the foundation of a stable, secure home from which to achieve better health, A SHORTAGE OF AFFORDABLE HOMES, 2019 Data Center’s MABLE/Geocorr 2014 Geographic Correspondence Engine. If at least 50% of a PUMA was in a Core Based Statistical Area (CBSA), we assigned it to the CBSA. Otherwise, the PUMA was given nonmetropolitan status. Households were categorized by their incomes (as extremely low-income, very low-income, low- income, middle-income, or above median income) relative to their metropolitan area’s median family income or state’s nonmetropolitan median family income, adjusted for household size. Housing units were categorized according to the income educational advancement, and economic mobility. e private market cannot and will not, on its own, build and operate homes these families can a ord. We need a sustained public commitment to ensure the lowest-income and most vulnerable households in America have decent, stable, accessible, and a ordable homes. ABOUT THE DATA is report is based on data from the 2017 American Community Survey (ACS) Public Use Microdata Sample (PUMS). e ACS is an annual nationwide survey of approximately 3.5 million addresses. It provides timely data on the social, economic, demographic, and housing characteristics of the U.S. population. PUMS contains individual ACS questionnaire records for a subsample of housing units and their occupants. PUMS data are available for geographic areas called Public Use Microdata Sample Areas (PUMAs). Individual PUMS records were matched to their appropriate metropolitan area or given nonmetropolitan status using the Missouri Census 16 After households and units were categorized, we analyzed the extent to which households in each income category resided in housing units categorized as a ordable for that income level. For example, we estimated the number of units a ordable for extremely low- income households that were occupied by extremely low-income households and by other income groups. We categorized households into mutually exclusive household types in the following order: (1) householder or householder’s spouse were at least 62 years of age (seniors); (2) householder and householder’s spouse (if applicable) were younger than 62 and at least one of them had a disability (disabled); (3) non-senior non-disabled household. We also categorized households into more detailed mutually exclusive categories in the following order: (1) elderly; (2) disabled; (3) householder and Children of families who used housing vouchers to access affordable homes in lower-poverty neighborhoods were more likely to attend college, less likely to become single parents, and likely to earn more as adults needed to a ord the rent and utilities without spending more than 30% of income. e categorization of units was done without regard to the incomes of the current tenants. Housing units without complete kitchen or plumbing facilities were not included in the housing supply. NATIONAL LOW INCOME HOUSING COALITION