PW vs REFI

Power REIT (MD) vs Chicago Atlantic Real Estate Fi — Valuation Comparison 2026

PW

Real Estate Investment Trusts
Power REIT (MD)
Quality
6.1
out of 10
Value Trap
38
LOW
Price
$0.78
Last close
Models
3/13
Active
VS

REFI

Real Estate Investment Trusts
Chicago Atlantic Real Estate Fi
Quality
8.7
out of 10
Value Trap
20
SAFE
Price
$11.37
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType PW Fair ValuePW Upside REFI Fair ValueREFI Upside
Bayesian DCF Intrinsic $20.10 +76.7%
Earnings Power Value Intrinsic $12.74 +12.0%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $1.05 +34.1% $36.15 +218.0%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $1.29 +65.1% $12.17 +7.1%
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 PW vs REFI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

PW vs REFI — Which Stock Is More Undervalued?

REFI scores higher with a 8.7/10 quality rating vs PW's 6.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Power REIT (MD) (PW) and Chicago Atlantic Real Estate Fi (REFI) 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.

PW currently trades at $0.78 with a QOC of 6.1/10, while REFI trades at $11.37 with a QOC of 8.7/10.

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).