Local Landscapes of Assisted Housing: Reconciling Layered and Imprecise Administrative Data for Research Purposes
The New Jersey State of Affordable Housing (NJSOARH) project seeks to understand the state of rental housing affordable to low-income and very low-income households in New Jersey and the processes that shape housing insecurity. Though the availability of affordable, stable, high-quality housing is widely recognized as a critical challenge, we have an incomplete view of the existing landscape of rental housing affordable to lower-income households, the lived experience of low-income renters, and the complex and often interwoven processes that shape housing insecurity in NJ.
The NJSOARH team of Shiloh Deitz, Will B. Payne, Eric Seymour, Kathe Newman and Lauren Nolan recently published a new article in Cityscape: A Journal of Policy Development and Research, the journal of the Office of Policy Development and Research (PD&R) of HUD User.
Read the article at https://bit.ly/4cZgD8O
Abstract
Understanding the stock of rental housing affordable to lower-income households is a crucial task for local governments aiming to meet rising demand and inform policy priorities. However, enumerating the number of units with public housing, Project Based Section 8, and Low-Income Housing Tax Credit (LIHTC) assistance and identifying precisely where those units are located is deceptively challenging. Although federal datasets with that information are easily accessible, development and building location information may be unavailable or imprecise. Critically, identifying units that receive more than one form of assistance is hard, especially units with LIHTC. To address these challenges in New Jersey, the authors developed a largely automated and replicable process for precisely placing subsidized housing units into tax parcels. Doing so enables linking units across federal programs and with state and local data and to more accurately aggregate counts to integrate with decennial census and American Community Survey (ACS) data from the U.S. Census Bureau. Within New Jersey, the research team re-geocoded records in three datasets using two commercial geocoding services, assigned them confidence scores, designated records for manual handling, and then assigned them to parcels. Following those steps, they identified more than 15,000 units statewide with overlapping federal subsidies, which would lead to a 12-percent overcount of subsidized units in the state if the three datasets were used as given (and up to a 40-percent overcount in individual municipalities). By reusing and reconciling those datasets at the parcel level, researchers can more accurately enumerate rental units associated with different levels of subsidy depth and duration, a crucial task for identifying housing needs within and beyond the assisted rental stock.
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