PRA vs ROOT

ProAssurance Corporation vs Root, 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

ROOT

Insurance - Property & Casualty
Root, Inc.
Quality
7.4
out of 10
Value Trap
18
SAFE
Price
$52.50
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType PRA Fair ValuePRA Upside ROOT Fair ValueROOT Upside
Bayesian DCF Intrinsic $1.75 -92.9% $207.95 +296.1%
Earnings Power Value Intrinsic $0.98 -95.9% $87.12 +65.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
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 ROOT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

PRA vs ROOT — Which Stock Is More Undervalued?

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

Comparing ProAssurance Corporation (PRA) and Root, Inc. (ROOT) 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 ROOT trades at $52.50 with a QOC of 7.4/10.

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).