MTG vs NMIH

MGIC Investment Corporation vs NMI Holdings Inc — Valuation Comparison 2026

MTG

Insurance - Specialty
MGIC Investment Corporation
Quality
8.6
out of 10
Value Trap
12
SAFE
Price
$25.51
Last close
Models
12/13
Active
VS

NMIH

Insurance - Specialty
NMI Holdings Inc
Quality
8.0
out of 10
Value Trap
24
SAFE
Price
$36.28
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType MTG Fair ValueMTG Upside NMIH Fair ValueNMIH Upside
Bayesian DCF Intrinsic $43.18 +69.3% $82.34 +127.0%
Earnings Power Value Intrinsic $24.67 -3.3% $40.51 +11.6%
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 MTG vs NMIH — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

MTG vs NMIH — Which Stock Is More Undervalued?

MTG scores higher with a 8.6/10 quality rating vs NMIH's 8.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing MGIC Investment Corporation (MTG) and NMI Holdings Inc (NMIH) 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.

MTG currently trades at $25.51 with a QOC of 8.6/10, while NMIH trades at $36.28 with a QOC of 8.0/10.

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).