Mesa Laboratories, Inc (MLAB) Fair Value & Analysis
Technology · US · Market cap $525M
Analysis
Mesa Laboratories, Inc (MLAB) currently trades at $91.97, while our model-based Fair Value estimate is $26.73 — implying the stock looks roughly 70.9% overvalued today. We read business quality at 97/100 (high quality), in the Technology 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).
About the company
Mesa Laboratories, Inc. develops, designs, manufactures, sells, and services life sciences tools and quality control products and services in North America, Europe, the Asia Pacific, and internationally. The Sterilization and Disinfection Control segment offers biological, chemical, and cleaning indicators, used to assess the effectiveness of sterilization decontamination, disinfection, and cleaning processes in the pharmaceutical, medical device, and healthcare industries. This segment also provides testing and laboratory services to the dental and pharmaceutical industries. Its Clinical Genomics segment offers MassARRAY, a genetic analysis tool system, and related consumables, including chips, panels, and chemical reagent solutions used by clinical labs to analyze DNA samples for inherited genetic disease testing, pharmacogenetics, oncology testing, infectious disease testing, doping and toxicology testing, and other differentiated applications for use in research. The Biopharmace…
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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.