FPI vs FVR

Farmland Partners Inc. vs FrontView REIT, Inc. — Valuation Comparison 2026

FPI

Real Estate Investment Trusts
Farmland Partners Inc.
Quality
8.7
out of 10
Value Trap
18
SAFE
Price
$10.27
Last close
Models
12/13
Active
VS

FVR

Real Estate Investment Trusts
FrontView REIT, Inc.
Quality
5.9
out of 10
Value Trap
6
SAFE
Price
$17.74
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType FPI Fair ValueFPI Upside FVR Fair ValueFVR Upside
Bayesian DCF Intrinsic $1.01 -90.3% $0.94 -94.7%
Earnings Power Value Intrinsic $0.80 -92.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $7.52 -57.6%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 FPI vs FVR — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

FPI vs FVR — Which Stock Is More Undervalued?

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

Comparing Farmland Partners Inc. (FPI) and FrontView REIT, Inc. (FVR) 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.

FPI currently trades at $10.27 with a QOC of 8.7/10, while FVR trades at $17.74 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).