Schneider National, Inc (SNDR) Fair Value & Analysis
Industrials · US · Market cap $6.5B
Analysis
Schneider National, Inc (SNDR) currently trades at $35.36, while our model-based Fair Value estimate is $23.62 — implying the stock looks roughly 33.2% overvalued today. We read business quality at 95/100 (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
Schneider National, Inc., together with its subsidiaries, provides multimodal surface transportation and logistics solutions in the United States, Canada, and Mexico. It operates in three segments: Truckload, Intermodal, and Logistics. The Truckload segment offers over-the-road freight transportation services through dry van, bulk, temperature-controlled, lightweight, and flatbed trailers across dedicated or network configurations. Its Intermodal segment provides door-to-door container on flat car services through a combination of rail and dray transportation using company-owned containers, chassis, and trucks. The Logistics segment offers asset-light freight brokerage, supply chain, warehousing, and import/export services, as well as value-added services. The company also leases equipment, such as trucks to owner-operators and provides insurance to drivers and owner-operators. Schneider National, Inc. was founded in 1935 and is headquartered in Green Bay, Wisconsin.
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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.