Industrias Peñoles, S.A. (IPOAF) Fair Value & Analysis
Basic Materials · US · Market cap $19.9B
Fair value as of: Jun 24, 2026
Analysis
Industrias Peñoles, S.A. (IPOAF) currently trades at $45.00, while our model-based Fair Value estimate is $62.53 — implying the stock looks roughly 39.0% undervalued today. We read business quality at 80/100 (high quality), in the Basic Materials sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: medium) — always confirm before acting.
About the company
Industrias Peñoles, S.A.B. de C.V., together with its subsidiaries, engages in the exploration, extraction, and sale of mineral concentrates and minerals in Mexico, Europe, Canada, Asia, the United States, South America, and internationally. It operates through Mines of Precious Metal, Base Metals Mines, Metallurgical, and Other segments. The company primarily explores for zinc, lead, copper, gold, silver, cadmium, and magnesium deposits. It is also involved in the smelting and refining of non-ferrous metals. In addition, the company produces and sells chemical products, such as sodium sulfate, magnesium oxide, magnesium hydroxide, and magnesium sulfate, as well as copper sulfate, zinc sulfate, ammonium sulphate and bisulfite, deprezinc, sodium chloride, sulfuric acid, and antimony trioxide; production of inorganic chemical products. The company was founded in 1887 and is based in Mexico City, Mexico.
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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.