Glass House Brands Inc (GLASF) Fair Value & Analysis
Healthcare · US · Market cap $1.2B
Fair value as of: Jun 24, 2026
Analysis
Glass House Brands Inc (GLASF) currently trades at $11.87, while our model-based Fair Value estimate is $4.21 — implying the stock looks roughly 64.5% 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: low).
About the company
Glass House Brands Inc., together with its subsidiaries, operates as an integrated cannabis company in the United States. It operates through three segments: Retail; Wholesale Biomass; and Cannabis-Related Consumer Packaged Goods. The Retail segment owns, and operates retail cannabis stores, and dispensary operation management services. The Wholesale Biomass segment includes propagation, nursery, flowering canopy, dries, processes, and distributes cannabis biomass. The Cannabis-Related Consumer Packaged Goods segment manufactures, extracts, infuse, conversion, packaging, and distributes cannabis products. The company cultivates, manufactures, and distributes cannabis bulk flowers and trims to wholesalers; and consumer packaged goods to third-party retail stores in California. It also offers raw cannabis, cannabis oil, and cannabis consumer goods; edibles; flower jars and bags; single, multi-pack, and infused pre-rolls; tinctures; biomass for distributors; and online payment processi…
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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.