CSL vs FBIN

Carlisle Companies Incorporated vs Fortune Brands Innovations, Inc — Valuation Comparison 2026

CSL

Building Products & Equipment
Carlisle Companies Incorporated
Quality
9.8
out of 10
Value Trap
6
SAFE
Price
$342.69
Last close
Models
12/13
Active
VS

FBIN

Building Products & Equipment
Fortune Brands Innovations, Inc
Quality
7.1
out of 10
Value Trap
19
SAFE
Price
$39.39
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType CSL Fair ValueCSL Upside FBIN Fair ValueFBIN Upside
Bayesian DCF Intrinsic $221.18 -35.5% $13.54 -65.6%
Earnings Power Value Intrinsic $20.82 -93.9% $3.52 -91.1%
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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CSL vs FBIN — Which Stock Is More Undervalued?

CSL scores higher with a 9.8/10 quality rating vs FBIN's 7.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Carlisle Companies Incorporated (CSL) and Fortune Brands Innovations, Inc (FBIN) 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.

CSL currently trades at $342.69 with a QOC of 9.8/10, while FBIN trades at $39.39 with a QOC of 7.1/10.

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).