Is NML or NMS more undervalued in 2026?

CirclFi compares 1525 (NML) and Nuveen Minnesota Quality Munici (NMS) using 13 institutional-grade valuation models. NML has a Quality of Company score of 1.8/10 while NMS scores 1.8/10.

Which is a better buy — NML or NMS?

Compare intrinsic values from 13 independent models against current market prices. CirclFi also calculates Value Trap scores. This is not financial advice — conduct your own analysis.

How do NML and NMS valuations compare?

NML at $9.96 and NMS at $12.23 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

NML vs NMS Valuation Comparison 2026 — Intrinsic Value Analysis

NML

Asset Management

1525

Quality

1.8

out of 10

Value Trap

—

Price

$9.96

Last close

Models

6/13

Active

NMS

Asset Management

Nuveen Minnesota Quality Munici

Quality

1.8

out of 10

Value Trap

—

Price

$12.23

Last close

Models

6/13

Active

Model-by-Model Comparison

Model	Type	NML Fair Value	NML Upside	NMS Fair Value	NMS Upside
Bayesian DCF	Intrinsic	$2.64	-73.5%	$3.24	-73.5%
First Chicago	Scenario	$•••.••	••.•%	$•••.••	••.•%
Markov DDM	Intrinsic	$6.29	-36.8%	$6.99	-42.8%
ML-RIV	Intrinsic	$•••.••	••.•%	$•••.••	••.•%
Sentiment SOTP	Hybrid	$•••.••	••.•%	$•••.••	••.•%
CUCE Ensemble	Ensemble	$•••.••	••.•%	$•••.••	••.•%

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NML vs NMS — Which Stock Is More Undervalued?

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

Comparing 1525 (NML) and Nuveen Minnesota Quality Munici (NMS) 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.

NML currently trades at $9.96 with a QOC of 1.8/10, while NMS trades at $12.23 with a QOC of 1.8/10.

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).