Landstar System, Inc (LSTR) Fair Value & Analysis
Industrials · US · Market cap $7.4B
Analysis
Landstar System, Inc (LSTR) currently trades at $207.50, while our model-based Fair Value estimate is $72.92 — implying the stock looks roughly 64.9% 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
Landstar System, Inc. provides transportation management solutions in the United States, Canada, Mexico, and internationally. It operates in two segments, Transportation Logistics and Insurance. The Transportation Logistics segment offers a range of transportation services, including truckload, less-than-truckload and other truck transportation, rail intermodal, air cargo, ocean cargo, expedited ground and air delivery of time-critical freight, heavy-haul/specialized, hazardous materials, cold chain/temperature-controlled, U.S.-Canada and U.S.-Mexico cross-border, intra-Mexico, intra-Canada, project cargo, and customs brokerage services, as well as transportation services to other transportation companies, such as third party logistics and less-than-truckload service providers. This segment serves the automotive parts and assemblies, consumer durables, building products, metals, chemicals, foodstuffs, heavy machinery, retail, electronics, military equipment and general commodities i…
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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.