Key Takeaways
- 58% of reverse mortgage borrowers are aged 70 or older.
- Women comprise 58% of reverse mortgage borrowers.
- The average age of HECM borrowers is 72 years old.
- Reverse mortgages cover 40-60% of home equity depending on age.
- Borrowers retain 100% of home appreciation during the loan term.
- Annual MIP is 0.5% of outstanding balance.
- Reverse mortgage volume expected to grow 10% annually through 2028.
- Aging baby boomers will drive 50% market growth by 2030.
- Proprietary products projected to reach 20% market share by 2025.
- The average initial principal limit for HECMs is 52% of home value.
- Average HECM loan size was $310,000 in 2023.
- 68% of HECMs are taken as tenure payments.
- 10% of reverse mortgages enter default annually due to property charges.
- Foreclosure rate for HECMs is under 2%.
- 22% of loans had servicer advances exceeding $10,000 in 2022.
Most borrowers are women aged 70 plus, often debt free, using reverse mortgages mainly to fund living expenses.
Borrower Demographics
Borrower Demographics Interpretation
Financial Aspects
Financial Aspects Interpretation
Industry Trends
Industry Trends Interpretation
Loan Characteristics
Loan Characteristics Interpretation
Risks and Costs
Risks and Costs Interpretation
Usage and Volume
Usage and Volume 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.
Stefan Wendt. (2026, February 27). Reverse Mortgage Statistics. Gitnux. https://gitnux.org/reverse-mortgage-statistics
Stefan Wendt. "Reverse Mortgage Statistics." Gitnux, 27 Feb 2026, https://gitnux.org/reverse-mortgage-statistics.
Stefan Wendt. 2026. "Reverse Mortgage Statistics." Gitnux. https://gitnux.org/reverse-mortgage-statistics.
Sources & References
- Reference 1NRMLAONLINEnrmlaonline.org
nrmlaonline.org
- Reference 2HUDhud.gov
hud.gov
- Reference 3REVERSEMORTGAGEDAILYreversemortgagedaily.com
reversemortgagedaily.com
- Reference 4AARPaarp.org
aarp.org
- Reference 5CONSUMERFINANCEconsumerfinance.gov
consumerfinance.gov
- Reference 6HUDUSERhuduser.gov
huduser.gov
- Reference 7MBAmba.org
mba.org
- Reference 8FHFAfhfa.gov
fhfa.gov
- Reference 9REVERSEMORTGAGEreversemortgage.org
reversemortgage.org
- Reference 10FTCftc.gov
ftc.gov
- Reference 11GAOgao.gov
gao.gov
- Reference 12HOUSINGWIREhousingwire.com
housingwire.com
- Reference 13NMLSCONSUMERACCESSnmlsconsumeraccess.org
nmlsconsumeraccess.org







