LendingTree, Inc (TREE) Fair Value & Analysis
Financial Services · US · Market cap $533M
Analysis
LendingTree, Inc (TREE) currently trades at $38.21, while our model-based Fair Value estimate is $28.78 — implying the stock looks roughly 24.7% overvalued today. We read business quality at 95/100 (high quality), in the Financial Services 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
LendingTree, Inc., through its subsidiary, operates online consumer platform in the United States. The company operates through three segments: Home, Consumer, and Insurance. The Home segments offer purchase mortgage, refinance mortgage, and home equity loans and lines of credit. The Consumer segment provides credit cards; personal, small business, and auto loans; deposit accounts; and other credit products, such as debt settlement services. The Insurance segment includes information, tools, and access to insurance quote products, including automobile, home, life, and health and Medicare through which consumers are matched with insurance lead aggregators to obtain insurance offers and policies. This segment also offers QuoteWizard, a marketplace for insurance comparison; and ValuePenguin, a personal finance website that offers consumers objective analysis on various financial topics. The company was formerly known as Tree.com, Inc. and changed its name to LendingTree, Inc. in Januar…
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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.