DIRTT Environmental Solutions Ltd (DRTTF) Fair Value & Analysis
Industrials · US · Market cap $102M
Fair value as of: Jun 24, 2026
Analysis
DIRTT Environmental Solutions Ltd (DRTTF) currently trades at $0.5490, while our model-based Fair Value estimate is $0.6600 — implying the stock looks roughly 20.2% undervalued today. We read business quality at 95/100 (high quality), in the Industrials 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
DIRTT Environmental Solutions Ltd. engages in the industrialized construction business in the United States and Canada. The company designs and manufactures interior spaces; and offers ICE software, a platform that designs, visualizes, organizes, configures, prices, manufactures, assembles, and installs construction projects. It also provides solid, glass, combination, leaf folding, and headwalls, as well as doors, prefabricated module, casework, timer, electrical, networks, and access floors. The company serves healthcare, education, financial services, government and military, manufacturing, non-profit, energy, professional services, retail, technology, and hospitality industries. It sells its products and solutions through a network of construction partners, local sales representatives, internal industry specialists, business development professionals, and a construction partner support team. The company was incorporated in 2003 and is headquartered in Calgary, Canada.
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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.