KW vs OZ

Kennedy-Wilson Holdings Inc. vs Belpointe PREP, LLC — Valuation Comparison 2026

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
VS

OZ

Real Estate
Belpointe PREP, LLC
Quality
4.3
out of 10
Value Trap
36
LOW
Price
$48.00
Last close
Models
4/13
Active

Model-by-Model Comparison

ModelType KW Fair ValueKW Upside OZ Fair ValueOZ Upside
Bayesian DCF Intrinsic $0.43 -96.1%
Earnings Power Value Intrinsic $19.84 +82.3%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $10.01 -9.1% $12.93 -73.2%
Dynamic NAV Asset-Based $16.41 -65.8%
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 KW vs OZ — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

KW vs OZ — Which Stock Is More Undervalued?

KW scores higher with a 5.9/10 quality rating vs OZ's 4.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Kennedy-Wilson Holdings Inc. (KW) and Belpointe PREP, LLC (OZ) 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.

KW currently trades at $11.01 with a QOC of 5.9/10, while OZ trades at $48.00 with a QOC of 4.3/10.

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).