SCI Engineered Materials, Inc (SCIA) Fair Value & Analysis
Technology · US · Market cap $36.3M
Fair value as of: Jun 24, 2026
Analysis
SCI Engineered Materials, Inc (SCIA) currently trades at $7.75, while our model-based Fair Value estimate is $7.84 — implying the stock looks roughly 1.2% undervalued today. We read business quality at 95/100 (high quality), in the Technology 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: high) — always confirm before acting.
About the company
SCI Engineered Materials, Inc. manufactures and supplies materials for physical vapor deposition thin film applications in the United States. The company offers ceramic powders, metal sputtering targets, and backing plates for use in aerospace, automotive, defense, glass, optical coating, photonic, media storage, flat panel display, semiconductor, computer processor, thin film solar, and photovoltaic industries. Its materials are used to produce nano layers of metals and oxides for advanced material systems; and to apply decorative coatings for end uses, such as sink faucets and glasses, as well as to produce various electronic, photonic, and semiconductor products. The company serves domestic and multi-national corporations, universities, and research institutions. The company was formerly known as Superconductive Components, Inc. and changed its name to SCI Engineered Materials, Inc. in August 2007. SCI Engineered Materials, Inc. was incorporated in 1987 and is headquartered in Co…
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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.