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Seach Medical Group (SEMG) Fair Value & Analysis

Healthcare · Il · Market cap 78.2M ILA

Price2.51 ILA
Fair Value1.68 ILA
Upside-32.9%
Quality84/100
Evidence: Medium Range 1.47 ILA – 1.83 ILA

Fair value as of: Jun 25, 2026

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Analysis

Seach Medical Group (SEMG) currently trades at 2.51 ILA, while our model-based Fair Value estimate is 1.68 ILA — implying the stock looks roughly 32.9% overvalued today. We read business quality at 84/100 (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: medium).

About the company

Seach Medical Group Ltd manufactures and sells medicinal cannabis products worldwide. The company provides cannabis oil and medical cannabis inflorescence products. Seach Medical Group Ltd was incorporated in 1987 and is based in Ramat Gan, Israel.

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Frequently asked questions

Is Seach Medical Group (SEMG) undervalued?
As of Jun 25, 2026, our model estimates a fair value of 1.68 ILA versus a price of 2.51 ILA — about −33% (overvalued). Model-based estimate, not financial advice.
What is the fair value of SEMG?
Our 21-model fair value for Seach Medical Group is 1.68 ILA (as of Jun 25, 2026), built from audited fundamentals. The current price is 2.51 ILA.
What is the quality score of SEMG?
Seach Medical Group has a Quality Score of 84/100, 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.