Oisix ra daichi Inc (OISXF) Fair Value & Analysis
Consumer Cyclical · US · Market cap $259M
Fair value as of: Jun 25, 2026
Analysis
Oisix ra daichi Inc (OISXF) currently trades at $7.47, while our model-based Fair Value estimate is $12.99 — implying the stock looks roughly 73.9% undervalued today. We read business quality at 95/100 (high quality), in the Consumer Cyclical 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: high) — always confirm before acting.
About the company
Oisix ra daichi Inc. engages in the online and catalogue sale of organic vegetables, agricultural products, additive free processed foods, and other food products and ingredients to general consumers in Japan. The company provides food products through Oisix stores; Oisix order services that enables consumers to order hard to find seasonal products; Oitoku, a regular delivery services that provides customers with pre-registered products at discounted prices; Daichi wo mamorukai, a household delivery services for organic agricultural products; and Tavelty for seasonal vegetable novelty packs. In addition, it offers advertising services; solutions support to companies for repeat sales; website user interface/user experience enhancement; temperature-controlled food distribution; new online customer acquisitions; and know-how on omni-channel retailing. The company was formerly known as Oisix.daichi Inc., and changed its name to Oisix ra daichi Inc. in July 2018. The company was incorpor…
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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.