PRHIZ vs TIPT

Presurance Holdings, Inc. - 9.7 vs Tiptree Inc. — Valuation Comparison 2026

PRHIZ

Fire, Marine & Casualty Insurance
Presurance Holdings, Inc. - 9.7
Quality
4.4
out of 10
Value Trap
35
LOW
Price
$17.44
Last close
Models
7/13
Active
VS

TIPT

Fire, Marine & Casualty Insurance
Tiptree Inc.
Quality
8.8
out of 10
Value Trap
28
LOW
Price
$17.47
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType PRHIZ Fair ValuePRHIZ Upside TIPT Fair ValueTIPT Upside
Earnings Power Value Intrinsic $0.62 -96.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $1.62 -90.7% $9.09 -47.9%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $0.90 -94.9% $23.91 +36.8%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $0.38 -97.8% $13.74 -21.3%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

PRHIZ vs TIPT — Which Stock Is More Undervalued?

TIPT scores higher with a 8.8/10 quality rating vs PRHIZ's 4.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Presurance Holdings, Inc. - 9.7 (PRHIZ) and Tiptree Inc. (TIPT) 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.

PRHIZ currently trades at $17.44 with a QOC of 4.4/10, while TIPT trades at $17.47 with a QOC of 8.8/10.

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).