Marshalls plc (MSLH) Fair Value & Analysis
Basic Materials · GB · Market cap 343M GBX
Fair value as of: Jun 24, 2026
Analysis
Marshalls plc (MSLH) currently trades at p1.40, while our model-based Fair Value estimate is p1.00 — implying the stock looks roughly 28.6% overvalued today. We read business quality at 95/100 (high quality), in the Basic Materials 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: high).
About the company
Marshalls plc, together with its subsidiaries, manufactures and sells landscape, building, and roofing products in the United Kingdom and internationally. It offers paving products, such as flag paving, natural stone paving, block paving, permeable paving, tactile paving, concrete, granite, sandstone, yorkstone, 600x600 slabs, 450x450 slabs, charcoal block, buff, grey, 50mm slabs, linear, council slabs, 80mm block, and permeable block, as well as accessories, steps, rail products, marker blocks, setts and cobbles, access and estate roads, and pedestals; kerbs; and combined kerb and drainage systems, pipes and manholes, linear drainage systems, retaining walls, below ground drainage, specialist precast and offsite solutions. The company also provides facing bricks, walling, engineering bricks, blocks, and utility bricks; landscape protection products, including hostile vehicle mitigation, post and rail, seating, planters, litter bins, cycle parking and storage, tree protection, table…
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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.