Key Takeaways
- In the US, 37.5% of renter households (about 22.7 million) spent more than 30% of income on rent in 2022
- In the US, 47.2% of renter households spent more than 30% of income on rent in 2019
- In the US, 20.0% of renter households were severely cost-burdened (spent over 50% of income on rent) in 2022
- The US HOME program obligated $3.3 billion in FY 2023
- The US HOME program had 4,058 participating jurisdictions in FY 2023
- In FY 2023, HUD awarded $800 million for the National Housing Trust Fund (NHTF)
- In the EU, about 10.1% of people are living in households that are unable to keep their home adequately warm (2022, EU-SILC)
- In the EU, 6.3% of people cannot afford basic household services (2022)
- In the EU, 7.4% of people live in overcrowded housing (2022)
- In the US, HUD’s Housing Choice Voucher program has an average payment standard factor of 1.00 for local area, which ties to fair market rent (policy)
- HUD’s Fair Market Rent (FMR) is published annually for every county
- In 2024, HUD’s FMR documentation specifies methodology uses 40th percentile gross rent
- In the US, 44% of households in need are extremely low-income renters
- In the US, 38% of housing need is for very low-income renters
- In 2022, extremely low-income renter households numbered 11.1 million (US)
In 2022, 43% of US renters were cost-burdened, and millions faced severe rent pressures.
Related reading
US Housing Costs and Need
US Housing Costs and Need Interpretation
More related reading
US Policy, Programs, and Funding
US Policy, Programs, and Funding Interpretation
More related reading
Global Affordable Housing Context
Global Affordable Housing Context Interpretation
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Housing Supply, Construction, and Outcomes
Housing Supply, Construction, and Outcomes Interpretation
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Program Impacts, Populations Served, and Demographics
Program Impacts, Populations Served, and Demographics Interpretation
How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Aisha Okonkwo. (2026, February 13). Affordable Housing Statistics. Gitnux. https://gitnux.org/affordable-housing-statistics
Aisha Okonkwo. "Affordable Housing Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/affordable-housing-statistics.
Aisha Okonkwo. 2026. "Affordable Housing Statistics." Gitnux. https://gitnux.org/affordable-housing-statistics.
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