GOOD vs GOODN

Gladstone Commercial Corporatio vs Gladstone Commercial Corporatio — Valuation Comparison 2026

GOOD

Lessors of Real Property, NEC
Gladstone Commercial Corporatio
Quality
8.6
out of 10
Value Trap
12
SAFE
Price
$12.61
Last close
Models
10/13
Active
VS

GOODN

Lessors of Real Property, NEC
Gladstone Commercial Corporatio
Quality
8.2
out of 10
Value Trap
12
SAFE
Price
$22.63
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType GOOD Fair ValueGOOD Upside GOODN Fair ValueGOODN Upside
Bayesian DCF Intrinsic $13.74 +8.9% $30.37 +34.2%
First Chicago Scenario $6.47 -48.7% $3.60 -84.1%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 GOOD vs GOODN — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

GOOD vs GOODN — Which Stock Is More Undervalued?

GOOD scores higher with a 8.6/10 quality rating vs GOODN's 8.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Gladstone Commercial Corporatio (GOOD) and Gladstone Commercial Corporatio (GOODN) 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.

GOOD currently trades at $12.61 with a QOC of 8.6/10, while GOODN trades at $22.63 with a QOC of 8.2/10.

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).