The Research and News Roundup is a monthly blog series that features a curated list of recent news and research related to housing instability, homelessness, and affordable housing. Together, these topics provide insights about the full housing continuum and provide community stakeholders with information about emergent research, promising practices, and innovative solutions related to housing and homelessness.

This month’s Research and News Roundup features recent research on the impact of cost-driven moves on children’s access to safety net services; an analysis of homeless rates in major cities; and the deployment of artificial intelligence (AI) to improve housing affordability.

HOUSING INSTABILITY

Disrupted Safety Net Access for Children

New research published in Pediatrics this month examined the relationship between residential moves due to unaffordable housing costs and disruptions in access to Medicaid, the Supplemental Nutrition Assistance Program (SNAP), and the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). The study found that across the 9344 children included in the study, those who were displaced from their homes due to their housing costs were more likely to experience disruptions in SNAP, WIC, and Medicaid. Social safety net programs such as these provide critical assistance for food and healthcare costs. Cost-driven moves which are often sudden can cause challenges with the administrative tasks required to retain access to services. Disruptions in mail-based communication, relocation to a new jurisdiction that requires reassessment of eligibility, and challenges in providing documentation or attending phone or in-person appointments during cost-based moves all can contribute to disruption of safety net services. Disruption in these services in turn can further exacerbate housing instability by negatively impacting a family’s already precarious financial situation. Streamlining administrative processes, enabling dual eligibility, or transitioning to remote delivery methods may help decrease service access disruption among households facing housing insecurity.

HOMELESSNESS

Homelessness Trends Among Major Cities

The Brookings Institute released an analysis of homeless trends in major cities. In most of the 44 major cities that completed full Point-in-Time (PIT) counts in 2023, the overall homeless rates either remained stable or declined. Among these major cities, Charlotte-Mecklenburg ranked 31^st out of 44 in the number of people who were experiencing homeless per 100,000 people (167 per 100,000) and 39^th out of 44 in the number of people who were unsheltered homelessness per 100,000 people (25 per 100,000). The authors offer several short and long-term policies that target the structural conditions that influence homelessness. These recommendations include but are not limited to aligning housing, land use, and homelessness plans to increase the supply of all types of housing; scaling pandemic-era prevention programs; adopting non-police alternative crisis response models; bolstering re-entry supports; and adopting a cross-jurisdictional approach to addressing homelessness.

AFFORDABLE HOUSING

Using AI to Promote Equitable and Affordable Housing

In December 2023, the National Council of State Housing Agencies convened a symposium to explore the challenges, opportunities, and policy implications associated with integrating artificial intelligence (AI) into the housing ecosystem. The use of AI for this purpose comes with both challenges and opportunities. Some potential opportunities for using AI within the housing space include to detect patterns of racism or bias within housing systems, to enhance equity and efficiency in matching people to the most appropriate resource, and to improve housing affordability by using AI to streamline permitting and identify optimum locations for affordable housing. A major challenge is that the datasets used to train AI may be inherently biased and reflect the historic and ongoing consequences of inequitable policies and there is concern that AI would incorporate those biases from the training data sets into its decision-making protocols. There are also equity concerns about which systems, organizations, and communities lack the resources to implement AI and thus not be able to access the benefits of this resource. Well executed and intentional policies will be essential to mitigate risks, optimize benefits, ensure AI systems center equity and empower organizations and communities.