ORGN vs TOMZ

Origin Materials, Inc. vs TOMI Environmental Solutions, I — Valuation Comparison 2026

ORGN

Industrial Organic Chemicals
Origin Materials, Inc.
Quality
5.1
out of 10
Value Trap
36
LOW
Price
$1.49
Last close
Models
6/13
Active
VS

TOMZ

Industrial Organic Chemicals
TOMI Environmental Solutions, I
Quality
4.4
out of 10
Value Trap
40
WARN
Price
$0.93
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType ORGN Fair ValueORGN Upside TOMZ Fair ValueTOMZ Upside
Bayesian DCF Intrinsic $0.89 -40.0% $0.04 -95.9%
Earnings Power Value Intrinsic $0.32 -54.3%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $7.49 +403.0% $0.83 -11.0%
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 ORGN vs TOMZ — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ORGN vs TOMZ — Which Stock Is More Undervalued?

ORGN scores higher with a 5.1/10 quality rating vs TOMZ's 4.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Origin Materials, Inc. (ORGN) and TOMI Environmental Solutions, I (TOMZ) 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.

ORGN currently trades at $1.49 with a QOC of 5.1/10, while TOMZ trades at $0.93 with a QOC of 4.4/10.

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).