PSA vs RC

Public Storage vs Ready Capital Corporation — Valuation Comparison 2026

PSA

Real Estate Investment Trusts
Public Storage
Quality
8.7
out of 10
Value Trap
18
SAFE
Price
$303.69
Last close
Models
13/13
Active
VS

RC

Real Estate Investment Trusts
Ready Capital Corporation
Quality
5.9
out of 10
Value Trap
28
LOW
Price
$1.78
Last close
Models
4/13
Active

Model-by-Model Comparison

ModelType PSA Fair ValuePSA Upside RC Fair ValueRC Upside
Bayesian DCF Intrinsic $258.29 -15.0%
Earnings Power Value Intrinsic $12.05 -96.0%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $1218.02 +301.1% $3.58 +101.0%
ML-RIV Intrinsic $180.61 -40.5% $1.77 -4.3%
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 PSA vs RC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

PSA vs RC — Which Stock Is More Undervalued?

PSA scores higher with a 8.7/10 quality rating vs RC's 5.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Public Storage (PSA) and Ready Capital Corporation (RC) 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.

PSA currently trades at $303.69 with a QOC of 8.7/10, while RC trades at $1.78 with a QOC of 5.9/10.

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).