Data quality

Which CoC rates can we trust?

An undercount-risk audit across all 384 Continuums of Care

A "low" homelessness rate isn't always a real low. Communities that count well can appear worse than communities that count poorly. This audit scores every CoC on signals of likely undercount and flags the ones whose headline numbers should not be trusted at face value.

Method: 5-signal additive scoring (sheltered share, rural classification, YoY volatility, funding per homeless, right-to-shelter exemptions) Sample: 384 CoCs · 29 flagged as likely undercounting Sources: HUD PIT counts · HUD CoC Program Awards · UCSF BHHI population data Updated: May 2026

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Cite this dataset

Stephens, G. (2026). Data quality audit: undercount risk per Continuum of Care. Gaither Research. https://gaitherresearch.org/research/data-quality

Stephens, and Gaither. 2026. "Data quality audit: undercount risk per Continuum of Care." Gaither Research. https://gaitherresearch.org/research/data-quality.

Stephens, Gaither. "Data quality audit: undercount risk per Continuum of Care." Gaither Research, 2026, https://gaitherresearch.org/research/data-quality.

@misc{stephens2026data,
  author = {Stephens, Gaither},
  title  = {Data quality audit: undercount risk per Continuum of Care},
  year   = {2026},
  publisher = {Gaither Research},
  url    = {https://gaitherresearch.org/research/data-quality},
  orcid  = {0009-0002-7543-7365},
  urldate = {2026-05-01}
}

Author ORCID iD: 0009-0002-7543-7365