HMN vs LMND

Horace Mann Educators Corporati vs Lemonade, Inc. — Valuation Comparison 2026

HMN

Insurance - Property & Casualty
Horace Mann Educators Corporati
Quality
7.7
out of 10
Value Trap
18
SAFE
Price
$46.35
Last close
Models
11/13
Active
VS

LMND

Insurance - Property & Casualty
Lemonade, Inc.
Quality
7.0
out of 10
Value Trap
12
SAFE
Price
$58.04
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType HMN Fair ValueHMN Upside LMND Fair ValueLMND Upside
Bayesian DCF Intrinsic $150.65 +225.0% $20.03 -65.5%
Earnings Power Value Intrinsic $19.91 -57.1% $19.37 -65.8%
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 HMN vs LMND — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

HMN vs LMND — Which Stock Is More Undervalued?

HMN scores higher with a 7.7/10 quality rating vs LMND's 7.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Horace Mann Educators Corporati (HMN) and Lemonade, Inc. (LMND) 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.

HMN currently trades at $46.35 with a QOC of 7.7/10, while LMND trades at $58.04 with a QOC of 7.0/10.

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).