Telefônica Brasil S.A (VIV) Fair Value & Analysis
Communication Services · US · Market cap $21.1B
Fair value as of: Jun 25, 2026
Analysis
Telefônica Brasil S.A (VIV) currently trades at $13.15, while our model-based Fair Value estimate is $16.01 — implying the stock looks roughly 21.7% undervalued today. We read business quality at 95/100 (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: high) — always confirm before acting.
About the company
Telefônica Brasil S.A., together with its subsidiaries, operates as a mobile telecommunications company in Brazil. Its fixed line voice services portfolio includes local, domestic long-distance, and international long-distance calls; and mobile portfolio comprises voice and broadband internet access through 3G, 4G, 4.5G, and 5G, as well as value-added, prepaid plans with data sharing features, family plans, voice mail, caller identification, voice minutes in unlimited bundles to mobile and fixed-line phones, and digital services, and wireless roaming services. The company also offers data services, including broadband and mobile data services; fixed-line local services consist of activation, monthly subscription, public telephones and measured services. In addition, it provides pay TV services through IPTV technologies; network services, such as rental of facilities and other services; wholesale services, including interconnection services to users of other network providers; and di…
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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.