FRPH vs KW

FRP Holdings, Inc. vs Kennedy-Wilson Holdings Inc. — Valuation Comparison 2026

FRPH

Real Estate
FRP Holdings, Inc.
Quality
6.8
out of 10
Value Trap
16
SAFE
Price
$23.11
Last close
Models
10/13
Active
VS

KW

Real Estate
Kennedy-Wilson Holdings Inc.
Quality
5.9
out of 10
Value Trap
52
WARN
Price
$11.01
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType FRPH Fair ValueFRPH Upside KW Fair ValueKW Upside
Bayesian DCF Intrinsic $18.12 -21.6% $0.43 -96.1%
Earnings Power Value Intrinsic $19.84 +82.3%
EROIC Spread Intrinsic $18.63 -19.4% $2.25 -79.6%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 FRPH vs KW — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

FRPH vs KW — Which Stock Is More Undervalued?

FRPH scores higher with a 6.8/10 quality rating vs KW's 5.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing FRP Holdings, Inc. (FRPH) and Kennedy-Wilson Holdings Inc. (KW) 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.

FRPH currently trades at $23.11 with a QOC of 6.8/10, while KW trades at $11.01 with a QOC of 5.9/10.

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).