CVNA vs KMX

Carvana Co. vs CarMax Inc — Valuation Comparison 2026

CVNA

Auto & Truck Dealerships
Carvana Co.
Quality
8.7
out of 10
Value Trap
12
SAFE
Price
$73.49
Last close
Models
13/13
Active
VS

KMX

Auto & Truck Dealerships
CarMax Inc
Quality
5.9
out of 10
Value Trap
20
SAFE
Price
$43.90
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType CVNA Fair ValueCVNA Upside KMX Fair ValueKMX Upside
Bayesian DCF Intrinsic $7.42 -89.9%
Earnings Power Value Intrinsic $12.65 -82.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $60.62 -17.5% $134.35 +217.9%
Markov DDM Intrinsic $82.61 -79.8% $8.09 -78.9%
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 CVNA vs KMX — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CVNA vs KMX — Which Stock Is More Undervalued?

CVNA scores higher with a 8.7/10 quality rating vs KMX's 5.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Carvana Co. (CVNA) and CarMax Inc (KMX) 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.

CVNA currently trades at $73.49 with a QOC of 8.7/10, while KMX trades at $43.90 with a QOC of 5.9/10.

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).