EZRA vs LIFE

Reliance Global Group, Inc. vs Ethos Technologies Inc. — Valuation Comparison 2026

EZRA

Insurance Brokers
Reliance Global Group, Inc.
Quality
4.0
out of 10
Value Trap
49
WARN
Price
$3.69
Last close
Models
7/13
Active
VS

LIFE

Insurance Brokers
Ethos Technologies Inc.
Quality
7.3
out of 10
Value Trap
Price
$19.58
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType EZRA Fair ValueEZRA Upside LIFE Fair ValueLIFE Upside
Bayesian DCF Intrinsic $5.52 -71.8%
Earnings Power Value Intrinsic $8.33 -57.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $6.11 +65.5%
ML-RIV Intrinsic $0.03 -83.1% $13.44 -31.3%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for EZRA vs LIFE — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

EZRA vs LIFE — Which Stock Is More Undervalued?

LIFE scores higher with a 7.3/10 quality rating vs EZRA's 4.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Reliance Global Group, Inc. (EZRA) and Ethos Technologies Inc. (LIFE) across 13 institutional-grade valuation models reveals how each company's intrinsic value stacks up against its market price. CirclFi's engine processes SEC EDGAR 10-K and 10-Q filings, FRED macroeconomic data, and GDELT news sentiment to generate independent fair value estimates daily.

EZRA currently trades at $3.69 with a QOC of 4.0/10, while LIFE trades at $19.58 with a QOC of 7.3/10.

Both companies are analyzed with models spanning intrinsic (Bayesian DCF, EPV), scenario-based (First Chicago), regime-switching (Markov DDM, RCMH-DCF), machine learning (ML-RIV, FTNN Topology), and ensemble methods (CUCE).