UPXI vs WD

Upexi, Inc. vs Walker & Dunlop, Inc — Valuation Comparison 2026

UPXI

Finance Services
Upexi, Inc.
Quality
4.6
out of 10
Value Trap
39
LOW
Price
$1.19
Last close
Models
9/13
Active
VS

WD

Finance Services
Walker & Dunlop, Inc
Quality
5.7
out of 10
Value Trap
37
LOW
Price
$50.19
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType UPXI Fair ValueUPXI Upside WD Fair ValueWD Upside
Bayesian DCF Intrinsic $0.40 -70.5% $283.05 +464.0%
Earnings Power Value Intrinsic $59.26 +18.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $0.96 -19.1% $25.98 -49.8%
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 UPXI vs WD — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

UPXI vs WD — Which Stock Is More Undervalued?

WD scores higher with a 5.7/10 quality rating vs UPXI's 4.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Upexi, Inc. (UPXI) and Walker & Dunlop, Inc (WD) 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.

UPXI currently trades at $1.19 with a QOC of 4.6/10, while WD trades at $50.19 with a QOC of 5.7/10.

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).