ELF vs HNST

e.l.f. Beauty, Inc. vs The Honest Company, Inc. — Valuation Comparison 2026

ELF

Household & Personal Products
e.l.f. Beauty, Inc.
Quality
8.8
out of 10
Value Trap
24
SAFE
Price
$57.40
Last close
Models
12/13
Active
VS

HNST

Household & Personal Products
The Honest Company, Inc.
Quality
6.5
out of 10
Value Trap
30
LOW
Price
$3.71
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType ELF Fair ValueELF Upside HNST Fair ValueHNST Upside
Bayesian DCF Intrinsic $32.14 -44.0% $0.71 -80.9%
Earnings Power Value Intrinsic $9.84 -82.9% $1.35 -63.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
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 ELF vs HNST — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ELF vs HNST — Which Stock Is More Undervalued?

ELF scores higher with a 8.8/10 quality rating vs HNST's 6.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing e.l.f. Beauty, Inc. (ELF) and The Honest Company, Inc. (HNST) 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.

ELF currently trades at $57.40 with a QOC of 8.8/10, while HNST trades at $3.71 with a QOC of 6.5/10.

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).