Vinci SA (VCISF) Fair Value & Analysis
Industrials · US · Market cap $81.8B
Analysis
Vinci SA (VCISF) currently trades at $147.86, while our model-based Fair Value estimate is $211.65 — implying the stock looks roughly 43.1% undervalued today. We read business quality at 91/100 (high quality), in the Industrials 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
Vinci SA, together with its subsidiaries, engages in concessions, energy, and construction businesses in France and internationally. It operates through Concessions, Energy Solutions, and Construction segments. The company's Concessions segment operates motorways, autoroutes, airports, highways, railways, and stadiums. Its Energy segment provides services to the manufacturing sector, infrastructure, building solutions and facilities management, and information and communication technology; and industrial and energy-related services, which includes development of renewable energy assets, as well as engineering, procurement, construction projects in the energy sector, and development of renewable energy production facilities of solar, and wind farms. The company's Construction segment engages in designing and undertaking projects, which includes general contractor; geotechnical, and structural engineering and related digital activities, as well as provision of services in nuclear engi…
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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.