IDE vs IIM

Voya Infrastructure, Industrial vs Invesco Value Municipal Income — Valuation Comparison 2026

IDE

Asset Management
Voya Infrastructure, Industrial
Quality
1.8
out of 10
Value Trap
Price
$13.96
Last close
Models
6/13
Active
VS

IIM

Asset Management
Invesco Value Municipal Income
Quality
1.8
out of 10
Value Trap
Price
$12.38
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType IDE Fair ValueIDE Upside IIM Fair ValueIIM Upside
Bayesian DCF Intrinsic $3.70 -73.5% $3.28 -73.5%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $10.49 -24.9% $8.13 -34.3%
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 IDE vs IIM — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

IDE vs IIM — Which Stock Is More Undervalued?

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

Comparing Voya Infrastructure, Industrial (IDE) and Invesco Value Municipal Income (IIM) 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.

IDE currently trades at $13.96 with a QOC of 1.8/10, while IIM trades at $12.38 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).