SACH vs SBAC

Sachem Capital Corp. vs SBA Communications Corporation — Valuation Comparison 2026

SACH

Real Estate Investment Trusts
Sachem Capital Corp.
Quality
6.1
out of 10
Value Trap
18
SAFE
Price
$1.20
Last close
Models
11/13
Active
VS

SBAC

Real Estate Investment Trusts
SBA Communications Corporation
Quality
8.6
out of 10
Value Trap
12
SAFE
Price
$203.16
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType SACH Fair ValueSACH Upside SBAC Fair ValueSBAC Upside
Bayesian DCF Intrinsic $0.48 -60.1% $47.00 -76.9%
Earnings Power Value Intrinsic $0.76 -45.2%
EROIC Spread Intrinsic $2.39 +99.2% $8.62 -96.1%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
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 SACH vs SBAC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

SACH vs SBAC — Which Stock Is More Undervalued?

SBAC scores higher with a 8.6/10 quality rating vs SACH's 6.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Sachem Capital Corp. (SACH) and SBA Communications Corporation (SBAC) 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.

SACH currently trades at $1.20 with a QOC of 6.1/10, while SBAC trades at $203.16 with a QOC of 8.6/10.

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).