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Nevis Brands Inc (NEVIF) Fair Value & Analysis

Consumer Defensive · US · Market cap $278K

Price$0.0206
Fair Value$0.0184
Upside-10.6%
Quality95/100
Evidence: Low Range $0.0184 – $0.0184

Fair value as of: Jun 26, 2026

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Analysis

Nevis Brands Inc (NEVIF) currently trades at $0.0206, while our model-based Fair Value estimate is $0.0184 — implying the stock looks roughly 10.6% overvalued today. We read business quality at 95/100 (high quality), in the Consumer Defensive 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: low).

About the company

Nevis Brands Inc. engages in the development and sale of cannabis products in the United States. It offers hemp derived THC beverages under the Major brand name. The company was formerly known as Pascal Biosciences Inc. and changed its name to Nevis Brands Inc. in June 2023. Nevis Brands Inc. is headquartered in Seattle, Washington.

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Frequently asked questions

Is Nevis Brands Inc (NEVIF) undervalued?
As of Jun 26, 2026, our model estimates a fair value of $0.0184 versus a price of $0.0206 — about −11% (overvalued). Model-based estimate, not financial advice.
What is the fair value of NEVIF?
Our 21-model fair value for Nevis Brands Inc is $0.0184 (as of Jun 26, 2026), built from audited fundamentals. The current price is $0.0206.
What is the quality score of NEVIF?
Nevis Brands Inc has a Quality Score of 95/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.

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.