CRESY vs DUO

Cresud S.A.C.I.F. y A. vs Fangdd Network Group Ltd. — Valuation Comparison 2026

CRESY

Real Estate
Cresud S.A.C.I.F. y A.
Quality
2.0
out of 10
Value Trap
Price
$11.91
Last close
Models
6/13
Active
VS

DUO

Real Estate
Fangdd Network Group Ltd.
Quality
4.9
out of 10
Value Trap
44
WARN
Price
$1.11
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType CRESY Fair ValueCRESY Upside DUO Fair ValueDUO Upside
Bayesian DCF Intrinsic $2.88 -75.8% $0.31 -71.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $69.03 +479.6%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $7.31 -38.6% $1.47 +32.7%
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 CRESY vs DUO — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CRESY vs DUO — Which Stock Is More Undervalued?

DUO scores higher with a 4.9/10 quality rating vs CRESY's 2.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Cresud S.A.C.I.F. y A. (CRESY) and Fangdd Network Group Ltd. (DUO) 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.

CRESY currently trades at $11.91 with a QOC of 2.0/10, while DUO trades at $1.11 with a QOC of 4.9/10.

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).