D2L Inc (DTLIF) Fair Value & Analysis
Technology · US · Market cap $392M
Fair value as of: Jun 24, 2026
Analysis
D2L Inc (DTLIF) currently trades at $7.21, while our model-based Fair Value estimate is $9.38 — implying the stock looks roughly 30.1% undervalued today. We read business quality at 97/100 (high quality), in the Technology 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
D2L Inc. provides cloud-based learning software for higher education institutions, kindergarten to grade 12 schools and districts, and private sector enterprises in Canada, the United States, and internationally. The company provides personalized, flexible and modern learning experiences for people of all ages. It offers core cloud-based learning innovation platform, Brightspace, serves three distinct markets, such as kindergarten to grade 12 schools (K-12), higher education, and corporate markets. In addition, the company provides performance+, advanced analytics package, achievement+, and creator+ easy-to-use authoring tools; performance+ advanced analytics package, achievement+, which streamlines achievement reporting on learning outcomes reporting; and Course Merchant, a digital storefront that enables institutions to sell courses directly to learners. Further, it offers Lumi, a human-centered artificial intelligence; and D2L Link that enables seamless connections and streamline…
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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.