BRP Inc (DOO) Fair Value & Analysis
Consumer Cyclical · US · Market cap $4.4B
Analysis
BRP Inc (DOO) currently trades at $59.76, while our model-based Fair Value estimate is $116.92 — implying the stock looks roughly 95.6% undervalued today. We read business quality at 97/100 (high quality), in the Consumer Cyclical 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
BRP Inc., together with its subsidiaries, designs, develops, manufactures, and sells powersports vehicles and marine products in the United States, Canada, Europe, Asia Pacific, Latin America, and internationally. It operate4s through two segments, Powersports and Marine. The Powersports segment offers year-round products, such as all-terrain vehicles, side-by-side vehicles, and three-wheeled and two vehicles; seasonal products, including snowmobiles, personal watercraft, and pontoons; and OEM engines, which includes parts, accessories and apparel (PA&A), engines for karts, recreational aircraft and jet boats, and other services. The Marine segment includes boats, pontoons, related PA&A, and other services. It offers its products under SKI-DOO, LYNX, CAN-AM, SEA-DOO, QUINTREX, ROTAX brands. The company was formerly known as J.A. Bombardier (J.A.B.) Inc. and changed its name to BRP Inc. in April 2013. BRP Inc. was founded in 1937 and is headquartered in Valcourt, Canada.
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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.