Stella-Jones Inc (STLJF) Fair Value & Analysis
Basic Materials · US · Market cap $3.2B
Analysis
Stella-Jones Inc (STLJF) currently trades at $58.00, while our model-based Fair Value estimate is $67.39 — implying the stock looks roughly 16.2% undervalued today. We read business quality at 95/100 (high quality), in the Basic Materials 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
Stella-Jones Inc., together with its subsidiaries, manufactures and sells industrial pressure-treated wood products in Canada and the United States. It operates through two segments, Pressure-Treated Wood; and Logs and Lumber. The company offers wood utility poles, railway ties, residential lumber; and industrial products, such as timbers for railway bridges, crossings and construction, marine and foundation pilings, and coal tar-based products. It also sells logs, as well as excess lumber to local home-building markets. In addition, the company offers treated wood poles and crossarms, steel lattice towers, and steel transmission poles to electrical utilities companies; treated wood railway ties and timber for short line and commercial railroad operators; and manufactures and distributes treated residential lumber and accessories to retailers for outdoor applications. Stella-Jones Inc. was incorporated in 1992 and is headquartered in Saint-Laurent, 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.