AG1 (AG1) Fair Value & Analysis
Consumer Cyclical · DE · Market cap €5.0B
Fair value as of: Jun 24, 2026
From 15 valuation models · updated 6 days ago
Fair value updated Jun 24, 2026 — revised from €1.24 to €6.68 (+438.7%) since Jun 23, 2026. Share price +33.4% over the past month.
Price vs Fair Value (12 months)
12‑month range €14.63 – €30.72 · fair‑value band €5.01 – €9.30 · the €25.36 price screens above the €6.68 fair value. As of Jun 24, 2026.
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AG1 (AG1) currently trades at €25.36, while our model-based Fair Value estimate is €6.68 — implying the stock looks roughly 73.7% overvalued today. We read business quality at 95/100 (high quality), in the Consumer Cyclical sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: high).
Net debt stands at €1.4B. Fundamentals as of Jun 24, 2026
Revenue & earnings trend
FY2021 – FY2025 · reported fiscal years
AG1 reported revenue of €8.2B in FY2025 versus €4.8B in FY2021, a compound +14.4%/yr. Reported net income was €77.9M in FY2025.
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6 more Auto & Truck Dealerships stocks, each showing price versus our Fair Value estimate (as of Jun 24, 2026).
| Stock | Price | Fair Value | vs Fair Value |
|---|---|---|---|
| Carvana Co CVNA | $66.67 | $38.48 | -42% |
| Penske Automotive Group PAG | $171.02 | $150.41 | -12% |
| D'Ieteren Group SIEVF | $193.00 | $125.79 | -35% |
| Hotai Motor Co 2207 | 495.00 TWD | 626.30 TWD | +27% |
| CarMax, Inc KMX | $47.15 | $30.27 | -36% |
| Lithia Motors, Inc LAD | $295.15 | $500.63 | +70% |
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How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.