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ContextLogic Holdings (LOGC) Fair Value & Analysis

Consumer Cyclical · US · Market cap $426M

Price$9.15
Fair Value$11.63
Upside+27.1%
Quality80/100
Evidence: Low Range $8.73 – $14.54

Fair value as of: Jun 24, 2026

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Analysis

ContextLogic Holdings (LOGC) currently trades at $9.15, while our model-based Fair Value estimate is $11.63 — implying the stock looks roughly 27.1% undervalued today. We read business quality at 80/100 (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: low) — always confirm before acting.

About the company

ContextLogic Holdings Inc. focuses on seeking to develop and grow a de novo business and finance potential future bolt-on acquisitions of assets or businesses. The company was incorporated in 2010 and is headquartered in Oakland, California.

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Frequently asked questions

Is ContextLogic Holdings (LOGC) undervalued?
As of Jun 24, 2026, our model estimates a fair value of $11.63 versus a price of $9.15 — about +27% (undervalued). Model-based estimate, not financial advice.
What is the fair value of LOGC?
Our 21-model fair value for ContextLogic Holdings is $11.63 (as of Jun 24, 2026), built from audited fundamentals. The current price is $9.15.
What is the quality score of LOGC?
ContextLogic Holdings has a Quality Score of 80/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.

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.