NMT vs NMZ

Nuveen Massachusetts Quality Mu vs Nuveen Municipal High Income Op — Valuation Comparison 2026

NMT

Asset Management
Nuveen Massachusetts Quality Mu
Quality
1.8
out of 10
Value Trap
Price
$12.73
Last close
Models
10/13
Active
VS

NMZ

Asset Management
Nuveen Municipal High Income Op
Quality
1.7
out of 10
Value Trap
Price
$10.25
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType NMT Fair ValueNMT Upside NMZ Fair ValueNMZ Upside
Bayesian DCF Intrinsic $3.37 -73.5% $2.71 -73.5%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $6.73 -47.1% $9.72 -3.6%
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 NMT vs NMZ — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

NMT vs NMZ — Which Stock Is More Undervalued?

NMT scores higher with a 1.8/10 quality rating vs NMZ's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Nuveen Massachusetts Quality Mu (NMT) and Nuveen Municipal High Income Op (NMZ) 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.

NMT currently trades at $12.73 with a QOC of 1.8/10, while NMZ trades at $10.25 with a QOC of 1.7/10.

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).