Editas Medicine, Inc (EDIT) Fair Value & Analysis
Healthcare · US · Market cap $413M
Fair value as of: Jun 24, 2026
Analysis
Editas Medicine, Inc (EDIT) currently trades at $2.76, while our model-based Fair Value estimate is $0.8000 — implying the stock looks roughly 71.0% overvalued today. We read business quality at 95/100 (high quality), in the Healthcare 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: low).
About the company
Editas Medicine, Inc., a clinical stage genome editing company, focuses on developing transformative genomic medicines to treat a range of serious diseases. The company develops a proprietary gene editing platform based on CRISPR technology. Its lead program is EDIT-401, a one-time therapy designed to reduce LDL cholesterol through the upregulation of the LDL receptor to treat hyperlipidemia. The company also develops therapies to treat Sickle cell disease and transfusion-dependent beta thalassemia; and in vivo gene editing medicines indicated for other cells and tissues. It has a research collaboration with Juno Therapeutics, Inc. to develop alpha-beta T-cell experimental medicines for the treatment of solid and liquid tumors, and autoimmune disease. The company was formerly known as Gengine, Inc. and changed its name to Editas Medicine, Inc. in November 2013. Editas Medicine, Inc. was incorporated in 2013 and is based in Cambridge, Massachusetts.
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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.