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Nanosonics Limited (NNCSF) Fair Value & Analysis

Healthcare · US · Market cap $705M

Price$2.55
Fair Value$0.9700
Upside-62.0%
Quality95/100
Evidence: High Range $0.7900 – $1.09

Analysis

Nanosonics Limited (NNCSF) currently trades at $2.55, while our model-based Fair Value estimate is $0.9700 — implying the stock looks roughly 62.0% overvalued today. We read business quality at 95/100 (high quality), in the Healthcare 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

Nanosonics Limited operates as an infection prevention company in Australia, North America, Europe, the United Kingdom, the Middle East, and Asia Pacific. The company manufactures and distributes trophon ultrasound probe disinfector, and its related consumables and accessories; and research, develops, and commercialize of infection control and decontamination products and related technologies. It also provides trophon2 a device with an enhanced design and new functionality, including AcuTrace TM for audit-ready digital record-keeping and capabilities to connect trophon 2 with hospital IT systems; AuditPro nanosonics, an infection control workflow compliance management; Trophon EPR, a patented sonicated mist technology that provides high level disinfection of both endocavitary and surface ultrasound probes; and CORIS, an instrument reprocessing product platform. Nanosonics Limited was incorporated in 2000 and is headquartered in Macquarie Park, Australia.

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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.