PRA vs PRHI

ProAssurance Corporation vs Presurance Holdings, Inc. — Valuation Comparison 2026

PRA

Insurance - Property & Casualty
ProAssurance Corporation
Quality
7.4
out of 10
Value Trap
12
SAFE
Price
$23.90
Last close
Models
12/13
Active
VS

PRHI

Insurance - Property & Casualty
Presurance Holdings, Inc.
Quality
4.5
out of 10
Value Trap
35
LOW
Price
$0.65
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType PRA Fair ValuePRA Upside PRHI Fair ValuePRHI Upside
Bayesian DCF Intrinsic $1.75 -92.9% $0.36 -44.3%
Earnings Power Value Intrinsic $0.98 -95.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $25.09 +5.0% $0.33 -52.9%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 PRA vs PRHI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

PRA vs PRHI — Which Stock Is More Undervalued?

PRA scores higher with a 7.4/10 quality rating vs PRHI's 4.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing ProAssurance Corporation (PRA) and Presurance Holdings, Inc. (PRHI) 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.

PRA currently trades at $23.90 with a QOC of 7.4/10, while PRHI trades at $0.65 with a QOC of 4.5/10.

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).