PGR vs PRHIZ

Progressive Corporation (The) vs Presurance Holdings, Inc. - 9.7 — Valuation Comparison 2026

PGR

Fire, Marine & Casualty Insurance
Progressive Corporation (The)
Quality
9.6
out of 10
Value Trap
12
SAFE
Price
$190.40
Last close
Models
11/13
Active
VS

PRHIZ

Fire, Marine & Casualty Insurance
Presurance Holdings, Inc. - 9.7
Quality
4.4
out of 10
Value Trap
35
LOW
Price
$18.29
Last close
Models
7/13
Active

Model-by-Model Comparison

ModelType PGR Fair ValuePGR Upside PRHIZ Fair ValuePRHIZ Upside
Bayesian DCF Intrinsic $192.41 +1.1%
Earnings Power Value Intrinsic $171.53 -9.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $308.83 +62.2% $0.90 -94.9%
PWERM Option-Based $334.64 +75.8% $8.12 -55.6%
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 PGR vs PRHIZ — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

PGR vs PRHIZ — Which Stock Is More Undervalued?

PGR scores higher with a 9.6/10 quality rating vs PRHIZ's 4.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Progressive Corporation (The) (PGR) and Presurance Holdings, Inc. - 9.7 (PRHIZ) 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.

PGR currently trades at $190.40 with a QOC of 9.6/10, while PRHIZ trades at $18.29 with a QOC of 4.4/10.

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).