CARR vs JCI

Carrier Global Corporation vs Johnson Controls International — Valuation Comparison 2026

CARR

Air-Cond & Warm Air Heatg Equip & Comm & Indl Refrig Equip
Carrier Global Corporation
Quality
7.6
out of 10
Value Trap
Price
$63.87
Last close
Models
11/13
Active
VS

JCI

Air-Cond & Warm Air Heatg Equip & Comm & Indl Refrig Equip
Johnson Controls International
Quality
8.5
out of 10
Value Trap
12
SAFE
Price
$134.06
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType CARR Fair ValueCARR Upside JCI Fair ValueJCI Upside
Bayesian DCF Intrinsic $29.22 -54.2% $15.67 -88.3%
Earnings Power Value Intrinsic $27.68 -56.7% $15.34 -88.6%
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 $•••.•• ••.•% $•••.•• ••.•%
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CARR vs JCI — Which Stock Is More Undervalued?

JCI scores higher with a 8.5/10 quality rating vs CARR's 7.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Carrier Global Corporation (CARR) and Johnson Controls International (JCI) 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.

CARR currently trades at $63.87 with a QOC of 7.6/10, while JCI trades at $134.06 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).