RVSB vs SFBC

Riverview Bancorp Inc vs Sound Financial Bancorp, Inc. — Valuation Comparison 2026

RVSB

Banks - Regional
Riverview Bancorp Inc
Quality
7.7
out of 10
Value Trap
8
SAFE
Price
$5.64
Last close
Models
11/13
Active
VS

SFBC

Banks - Regional
Sound Financial Bancorp, Inc.
Quality
7.9
out of 10
Value Trap
27
LOW
Price
$43.29
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType RVSB Fair ValueRVSB Upside SFBC Fair ValueSFBC Upside
Bayesian DCF Intrinsic $3.21 -43.1% $68.98 +59.3%
Earnings Power Value Intrinsic $4.83 -14.4% $109.78 +153.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 RVSB vs SFBC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

RVSB vs SFBC — Which Stock Is More Undervalued?

SFBC scores higher with a 7.9/10 quality rating vs RVSB's 7.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Riverview Bancorp Inc (RVSB) and Sound Financial Bancorp, Inc. (SFBC) 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.

RVSB currently trades at $5.64 with a QOC of 7.7/10, while SFBC trades at $43.29 with a QOC of 7.9/10.

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).