Tutor Perini Corporation (TPC) Fair Value & Analysis
Industrials · US · Market cap $3.8B
Analysis
Tutor Perini Corporation (TPC) currently trades at $81.00, while our model-based Fair Value estimate is $63.00 — implying the stock looks roughly 22.2% overvalued today. We read business quality at 94/100 (high quality), in the Industrials 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: high).
About the company
Tutor Perini Corporation, a construction company, provides diversified general contracting, construction management, and design-build services to private customers and public agencies in the United States and internationally. It operates through three segments: Civil, Building, and Specialty Contractors. The Civil segment engages in public works construction and the replacement and reconstruction of infrastructure; and offers civil contracting services, including construction and rehabilitation of highways, bridges, tunnels, mass-transit systems, military and other government facilities, and water management and wastewater treatment facilities, as well as provides drilling, foundation, and excavation support for shoring, bridges, piers, roads, and highway projects. The Building segment offers various specialized building markets, such as hospitality and gaming, transportation, healthcare, commercial offices, government facilities, sports and entertainment, education, correctional an…
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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.