GNT vs HASI

GAMCO Natural Resources, Gold & vs HA Sustainable Infrastructure C — Valuation Comparison 2026

GNT

Asset Management
GAMCO Natural Resources, Gold &
Quality
1.7
out of 10
Value Trap
Price
$8.12
Last close
Models
6/13
Active
VS

HASI

Asset Management
HA Sustainable Infrastructure C
Quality
6.7
out of 10
Value Trap
30
LOW
Price
$41.32
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType GNT Fair ValueGNT Upside HASI Fair ValueHASI Upside
Bayesian DCF Intrinsic $2.15 -73.5%
EROIC Spread Intrinsic $27.95 -32.4%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $6.34 -26.0% $5.48 -86.7%
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 GNT vs HASI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

GNT vs HASI — Which Stock Is More Undervalued?

HASI scores higher with a 6.7/10 quality rating vs GNT's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing GAMCO Natural Resources, Gold & (GNT) and HA Sustainable Infrastructure C (HASI) 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.

GNT currently trades at $8.12 with a QOC of 1.7/10, while HASI trades at $41.32 with a QOC of 6.7/10.

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).