Interest is growing in creating zoning data for the purpose of conducting applied urban research on zoning reform, housing affordability, and other topics. Recently, two new sources of zoning information have emerged that complement previous data sources that were often limited in detail or spatial extent. First, the National Zoning Atlas (NZA) has implemented a standardized methodology for the manual construction of variables from zoning codes, and this methodology has been applied in a growing number of states. Second, researchers are exploring the use of natural language processing (NLP) to automate the creation of variables from zoning codes. Mleczko and Desmond (2023) presented one noteworthy example of NLP methods, the National Zoning and Land Use Database (NZLUD), in a paper accompanied by & lstrok;a nationwide dataset of variables for 2,639 jurisdictions. In this article, we assess the quality and usefulness of that new data source by comparing it with data previously created using the NZA method, which provides us with a high-quality ground truth. To do this, we translated the NZA variables for 14 jurisdictions in West Michigan into relevant NZLUD variables by aggregating numerical data reported at the level of zoning districts into categorical data reported at the level of jurisdictions. We then compared out results against NZLUD data for the same jurisdictions by calculating correlation coefficients or matching scores. The results show variable-level correlations ranging from 0.04 (maximum permitted density) to 40 (minimum required parking). These results suggest that although datasets such as the NZLUD may provide initial insights for planners and policymakers seeking to address exclusionary zoning, accurate and detailed analysis will continue to require manual methods like the NZA. This article provides a detailed discussion of the structure of two leading national zoning datasets, a specific quality analysis, and alternative approaches to creating this important type of data
Disabled veterans face unique housing challenges shaped by the intersection of service-related and disability-related barriers. This article uses 5-year public use microdata from the Integrated Public Use Microdata Series (IPUMS) database from 2018 and 2023, coupled with a unique state-level database of veterans' resources, to compare the likelihood of poor housing conditions between the general population and groups defined by veteran status, disability status, and the combination of both to assess whether those factors protect against poor housing outcomes. The results indicated that disability status and veteran status alone offer specific protections for people to prevent negative housing outcomes but, when combined, may instead generate negative housing outcomes for individuals at the intersection of both statuses: disabled veterans. Further, existing policies and resources are insufficient to meet the specific challenges of disabled veterans.
Rent burden, the ratio of rent to income, is a housing-need statistic readily developed from the American Community Survey (ACS). ACS rent burden statistics are heavily used in policy advocacy. In this article, we compare aggregate administrative data for households in housing subsidy programs with the ACS microdata sample for Massachusetts. We find that the ACS overstates the prevalence of high-rent burden among Extremely Low-Income households. By this finding, we do not intend to diminish the perceived urgency for housing development to meet substantial needs at all income levels. In fact, our finding may be interpreted to increase the measured need for "Very" (as opposed to "Extremely") Low-Income households. Our primary conclusion is that ACS rent burden data should always be presented along with administrative data and should never be presented alone, at least for Extremely Low-Income households. Policymakers depend on accurate assessments of housing need.
Communication and coordination among partners are critical to achieving a shared goal, especially when addressing homelessness. The Supportive Services for Veteran Families (SSVF) program is a partnership between the U.S. Department of Veterans Affairs (VA) and community nonprofit organizations (grantees) that aims to prevent and end homelessness among veterans. The study team conducted a qualitative study with SSVF program stakeholders, including SSVF grantee employees, VA homeless services providers, and veterans using SSVF services. The study team focused on themes related to communication and coordination. This article highlights these findings.The study team conducted semi-structured telephone interviews with 20 grantee employees, 21 VA homeless services providers, and 83 veterans receiving SSVF program services. The VA homeless services providers and SSVF grantee employees described their relationships as positive, with effective communication and coordination. However, these key informants reported communication challenges regarding changes in policy and eligibility requirements. Some veterans reported difficulties receiving timely and clear communication from grantees. All three groups recognized the high staff turnover and burnout among SSVF grantee employees, which negatively impacts the program. Participants offered strategies for improving communication, including additional meetings between VA homeless services providers and SSVF grantees, establishing consistent points of contact, and ensuring greater transparency and access to critical information needed to serve veterans. Additional recommendations included addressing staffing challenges both at the VA and grantees, enhancing education on program roles and services, expanding training opportunities for grantee employees, and maintaining an updated list of available resources and policy changes.
Housing cost burden (HCB), the share of a household's income dedicated to housing and related expenses, is a useful metric for understanding housing insecurity trends. The share of veteran households experiencing HCB-a spending ratio greater than 30 or 50 percent on housing needs-is considerably less than that of nonveterans, potentially because of the additional income veterans receive as compensation for their service, such as disability compensation, pensions, and so on. Research on HCB typically considers an aggregated measure of household income from all sources. This article uses the Survey of Income and Program Participation to understand how the share of households experiencing HCB differs when considering different income streams-for example, HCB relative to private income only, HCB relative to private income plus benefits from the U.S. Department of Veterans Affairs (VA), and so on. Including income from VA benefits, especially VA disability compensation, in the HCB calculation contributes drastically to housing security for veteran households. Adding income for non-VA public sources, such as Social Security benefits, provides additional housing security. These results highlight the importance of VA benefits and other nonwage income in combating housing insecurity for veteran households that depend on these benefits
