IPI vs SMG

Intrepid Potash, Inc vs Scotts Miracle-Gro Company (The — Valuation Comparison 2026

IPI

Agricultural Inputs
Intrepid Potash, Inc
Quality
7.1
out of 10
Value Trap
6
SAFE
Price
$39.43
Last close
Models
12/13
Active
VS

SMG

Agricultural Inputs
Scotts Miracle-Gro Company (The
Quality
7.2
out of 10
Value Trap
12
SAFE
Price
$60.48
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType IPI Fair ValueIPI Upside SMG Fair ValueSMG Upside
Bayesian DCF Intrinsic $34.34 -12.9% $11.54 -80.9%
Earnings Power Value Intrinsic $10.40 -73.6% $6.41 -89.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 IPI vs SMG — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

IPI vs SMG — Which Stock Is More Undervalued?

SMG scores higher with a 7.2/10 quality rating vs IPI's 7.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Intrepid Potash, Inc (IPI) and Scotts Miracle-Gro Company (The (SMG) 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.

IPI currently trades at $39.43 with a QOC of 7.1/10, while SMG trades at $60.48 with a QOC of 7.2/10.

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).