BOC vs CRESY

Boston Omaha Corporation vs Cresud S.A.C.I.F. y A. — Valuation Comparison 2026

BOC

Conglomerates
Boston Omaha Corporation
Quality
5.9
out of 10
Value Trap
27
LOW
Price
$13.38
Last close
Models
12/13
Active
VS

CRESY

Conglomerates
Cresud S.A.C.I.F. y A.
Quality
2.0
out of 10
Value Trap
Price
$11.86
Last close
Models
6/13
Active

Model-by-Model Comparison

ModelType BOC Fair ValueBOC Upside CRESY Fair ValueCRESY Upside
Bayesian DCF Intrinsic $6.62 -50.5% $3.14 -73.5%
Earnings Power Value Intrinsic $1.47 -89.0%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $1.65 -87.6% $69.03 +482.0%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

BOC vs CRESY — Which Stock Is More Undervalued?

BOC scores higher with a 5.9/10 quality rating vs CRESY's 2.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Boston Omaha Corporation (BOC) and Cresud S.A.C.I.F. y A. (CRESY) 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.

BOC currently trades at $13.38 with a QOC of 5.9/10, while CRESY trades at $11.86 with a QOC of 2.0/10.

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).