{
  "id": 600503,
  "title": "Researchers create early warning system to monitor housing evictions",
  "url": "https://urgent.news/2026/08/11/researchers-create-early-warning-system-to-monitor-housing-evictions",
  "topic": "science",
  "section": "Science",
  "published": "2026-08-11T20:40:02.000Z",
  "source": {
    "name": "Phys.org",
    "slug": "phys-org",
    "url": "https://phys.org/news/2026-08-early-housing-evictions.html"
  },
  "original_language": "en",
  "account": "This report examines a new study that evaluates the efficacy of open-source tools and publicly available data in predicting housing evictions during the COVID-19 pandemic. Researchers from the University at Buffalo and three partner universities found that while open-source resources are accessible and cost-free, challenges remain in accurately forecasting eviction trends due to limited data detail and inconsistent updates.\n\nThe study, published in the Journal of Technology in Human Services, focused on New York’s Bronx County, one of the regions hardest hit by both the economic downturn and the initial surge in COVID-19 cases. Using open-source tools, the researchers analyzed publicly available datasets on eviction filings, demographics, and employment trends at the ZIP code level. They then developed statistical forecasting models to project eviction filings from January 2020 through July 2021, comparing these projections with the actual eviction numbers during the same period.\n\nThe results indicated that projected eviction filings were 2.6 to 3.3 times higher than the actual reported numbers. This discrepancy highlights the potential for open-source tools to provide early warning signals of rising eviction rates, which could have been far worse without government interventions like eviction moratoriums. The analysis also revealed that communities with a higher proportion of people of color experienced more significant employment declines early in the pandemic, underscoring the tool's potential to spotlight equity issues and guide resource allocation in disproportionately affected neighborhoods.\n\nMaria Rodriguez, an assistant professor in the University at Buffalo's Department of AI and Society, emphasized that while open-source tools can generate valuable insights, they require human service organizations to develop stronger data skills. With the right data management expertise, even small teams can leverage open data to make more informed decisions during future crises. The study's findings suggest that while open-source tools can be a powerful asset for predicting and responding to housing instability, they must be complemented by improved public data systems and organizational data literacy to maximize their impact on equitable outcomes.",
  "summary": "Human service organizations play an important role in connecting people to housing, health care, food access and other essential services. Yet forecasting community needs can be difficult in times of rapid change or crisis because of limited resources and restricted access to data. For instance, during the COVID-19 pandemic, concerns about a potential surge in evictions exposed gaps in the data…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}