CarGurus, Inc (CARG) Fair Value & Analysis
Consumer Cyclical · US · Market cap $2.5B
Analysis
CarGurus, Inc (CARG) currently trades at $30.44, while our model-based Fair Value estimate is $35.18 — implying the stock looks roughly 15.6% undervalued today. We read business quality at 95/100 (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
CarGurus, Inc. operates an online automotive platform for buying and selling vehicles in the United States and internationally. The company offers dealer subscription fees, advertising from auto manufacturers and other brand advertisers, and partnerships with financing services companies. It provides an online automotive marketplace that connects large audience of car shoppers with extensive network of dealers, anchoring integrated suite of products. It also offers Digital Deal which allows consumers the option to start their vehicle purchase process online for eligible listings; Dealership Mode which provides consumers with on-the-lot support during visits to participating dealers through the CarGurus app; Sell My Car " Top Dealer Offers which allows dealers to make tailored trade-in offers; and IMV Scan enables dealers to scan a VIN using their smartphone to access IMV data; and PriceVantage, an advanced pricing software powered by machine learning that leverages real-time consume…
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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.