Scientific Industries, Inc (SCND) Fair Value & Analysis
Technology · US · Market cap $6.7M
Fair value as of: Jun 25, 2026
Analysis
Scientific Industries, Inc (SCND) currently trades at $0.5640, while our model-based Fair Value estimate is $0.7100 — implying the stock looks roughly 25.9% 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: low) — always confirm before acting.
About the company
Scientific Industries, Inc. engages in the design, manufacture, and marketing of standard benchtop laboratory equipment, weight and measurement, and bioprocessing products in the United States, Germany, and internationally. The company offers laboratory vortex mixers, shakers, and accessories; rotators and rockers accessories; disruptors and shakers accessories; as well as incubators under the Genie brand name. It also provides pharmacy, laboratory, industrial digital scales, moisture analyzers, mechanical and automated pill counters, force gauges, and test stands products under the Torbal brand; and bioprocessing systems comprising cell growth quantifier for biomass monitoring in shake flasks; liquid injection systems for automated feeding in shake flasks; various coaster systems and flow-through cells for pH and DO monitoring and analytical software; and multi-parameter sensor and dissolved oxygen sensor pills. The company's products are used for research purposes by universities,…
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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.