TREE vs VOYA

LendingTree, Inc. vs Voya Financial, Inc. — Valuation Comparison 2026

TREE

Financial Conglomerates
LendingTree, Inc.
Quality
8.5
out of 10
Value Trap
17
SAFE
Price
$38.45
Last close
Models
11/13
Active
VS

VOYA

Financial Conglomerates
Voya Financial, Inc.
Quality
7.4
out of 10
Value Trap
12
SAFE
Price
$80.08
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType TREE Fair ValueTREE Upside VOYA Fair ValueVOYA Upside
Bayesian DCF Intrinsic $13.54 -64.8% $130.04 +62.4%
Earnings Power Value Intrinsic $51.87 +34.9% $57.81 -27.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 TREE vs VOYA — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

TREE vs VOYA — Which Stock Is More Undervalued?

TREE scores higher with a 8.5/10 quality rating vs VOYA's 7.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing LendingTree, Inc. (TREE) and Voya Financial, Inc. (VOYA) 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.

TREE currently trades at $38.45 with a QOC of 8.5/10, while VOYA trades at $80.08 with a QOC of 7.4/10.

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).