MKL vs NODK

Markel Group Inc. vs NI Holdings, Inc. — Valuation Comparison 2026

MKL

Insurance - Property & Casualty
Markel Group Inc.
Quality
9.0
out of 10
Value Trap
12
SAFE
Price
$1846.37
Last close
Models
12/13
Active
VS

NODK

Insurance - Property & Casualty
NI Holdings, Inc.
Quality
7.0
out of 10
Value Trap
Price
$14.06
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType MKL Fair ValueMKL Upside NODK Fair ValueNODK Upside
Bayesian DCF Intrinsic $4653.63 +152.0% $1.87 -86.7%
Earnings Power Value Intrinsic $1305.31 -29.3% $4.37 -68.9%
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 MKL vs NODK — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

MKL vs NODK — Which Stock Is More Undervalued?

MKL scores higher with a 9.0/10 quality rating vs NODK's 7.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Markel Group Inc. (MKL) and NI Holdings, Inc. (NODK) 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.

MKL currently trades at $1846.37 with a QOC of 9.0/10, while NODK trades at $14.06 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).