BLNE vs UWMC

Beeline Holdings, Inc. vs UWM Holdings Corporation — Valuation Comparison 2026

BLNE

Mortgage Bankers & Loan Correspondents
Beeline Holdings, Inc.
Quality
3.7
out of 10
Value Trap
70
DANGER
Price
$1.31
Last close
Models
9/13
Active
VS

UWMC

Mortgage Bankers & Loan Correspondents
UWM Holdings Corporation
Quality
6.8
out of 10
Value Trap
51
WARN
Price
$3.06
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType BLNE Fair ValueBLNE Upside UWMC Fair ValueUWMC Upside
Bayesian DCF Intrinsic $0.09 -93.1% $10.31 +195.4%
Earnings Power Value Intrinsic $0.59 -80.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $0.29 -78.2%
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 BLNE vs UWMC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

BLNE vs UWMC — Which Stock Is More Undervalued?

UWMC scores higher with a 6.8/10 quality rating vs BLNE's 3.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Beeline Holdings, Inc. (BLNE) and UWM Holdings Corporation (UWMC) 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.

BLNE currently trades at $1.31 with a QOC of 3.7/10, while UWMC trades at $3.06 with a QOC of 6.8/10.

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).