IPI vs LEU

Intrepid Potash, Inc vs Centrus Energy Corp. — Valuation Comparison 2026

IPI

Mining & Quarrying of Nonmetallic Minerals (No Fuels)
Intrepid Potash, Inc
Quality
7.1
out of 10
Value Trap
6
SAFE
Price
$39.07
Last close
Models
12/13
Active
VS

LEU

Mining & Quarrying of Nonmetallic Minerals (No Fuels)
Centrus Energy Corp.
Quality
8.5
out of 10
Value Trap
20
SAFE
Price
$182.47
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType IPI Fair ValueIPI Upside LEU Fair ValueLEU Upside
Bayesian DCF Intrinsic $34.32 -12.2% $52.90 -71.0%
Earnings Power Value Intrinsic $10.40 -73.4% $44.08 -75.8%
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 LEU — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

IPI vs LEU — Which Stock Is More Undervalued?

LEU scores higher with a 8.5/10 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 Centrus Energy Corp. (LEU) 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 $39.07 with a QOC of 7.1/10, while LEU trades at $182.47 with a QOC of 8.5/10.

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).