Is NUW or NXG more undervalued in 2026?

CirclFi compares Nuveen AMT-Free Municipal Value (NUW) and 733085 (NXG) using 13 institutional-grade valuation models. NUW has a Quality of Company score of 1.7/10 while NXG scores 1.7/10.

Which is a better buy — NUW or NXG?

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 NUW and NXG valuations compare?

NUW at $14.12 and NXG at $58.77 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

NUW vs NXG Valuation Comparison 2026 — Intrinsic Value Analysis

NUW

Asset Management

Nuveen AMT-Free Municipal Value

Quality

1.7

out of 10

Value Trap

—

Price

$14.12

Last close

Models

5/13

Active

NXG

Asset Management

733085

Quality

1.7

out of 10

Value Trap

—

Price

$58.77

Last close

Models

6/13

Active

Model-by-Model Comparison

Model	Type	NUW Fair Value	NUW Upside	NXG Fair Value	NXG Upside
Bayesian DCF	Intrinsic	$3.74	-73.5%	$15.56	-73.5%
First Chicago	Scenario	$17.15	+22.9%	$70.33	+23.1%
Markov DDM	Intrinsic	—	—	$57.08	+2.8%
ML-RIV	Intrinsic	$•••.••	••.•%	$•••.••	••.•%
Sentiment SOTP	Hybrid	$•••.••	••.•%	$•••.••	••.•%
CUCE Ensemble	Ensemble	$•••.••	••.•%	$•••.••	••.•%

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NUW vs NXG — Which Stock Is More Undervalued?

Both NUW and NXG score 1.7/10 on quality. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Nuveen AMT-Free Municipal Value (NUW) and 733085 (NXG) 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.

NUW currently trades at $14.12 with a QOC of 1.7/10, while NXG trades at $58.77 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).