Team Internet Group (TIGXF) Fair Value & Analysis
Communication Services · US · Market cap $140M
Fair value as of: Jun 25, 2026
Analysis
Team Internet Group (TIGXF) currently trades at $0.5600, while our model-based Fair Value estimate is $2.14 — implying the stock looks roughly 282.1% undervalued today. We read business quality at 88/100 (high quality), in the Communication Services 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: medium) — always confirm before acting.
About the company
Team Internet Group plc provides domain name services. It operates through three segments: Domains, Identity & Software (DIS), Comparison, and Search. The DIS segment conducts business as a distributor of domain names through a network of channel partners, as well as sells domain names and ancillary services to end users, monitoring services to protect brands online, technical and consultancy services to corporate clients and licensing the group's in-house developed registry management platform. The Comparison segment provides product comparison platforms that enable digital consumers to make swift yet well-informed purchasing decisions. This segment also offers high-quality traffic to its partners by connecting the right consumers to their products, creating a better-connected online shopping ecosystem. The Search segment creates privacy-safe AI based customer journeys that help online consumers make informed choices. It operates in the Americas, Ireland, Luxembourg, Germany, rest …
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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.