Hill & Smith PLC (HILS) Fair Value & Analysis
Industrials · GB · Market cap 2.1B GBX
Analysis
Hill & Smith PLC (HILS) currently trades at p28.05, while our model-based Fair Value estimate is p17.90 — implying the stock looks roughly 36.2% overvalued today. We read business quality at 95/100 (high quality), in the Industrials 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
Hill & Smith PLC manufactures and supplies infrastructure products in the United Kingdom, the rest of Europe, North America, the Middle East, the rest of Asia, and internationally. It operates through US Engineered Solutions, UK & India Engineered Solutions, and Galvanizing Services segments. The US Engineered Solutions segment provides composite and steel solutions for infrastructure construction, such as energy transmission and distribution, data centres, waterfront protection, transportation, and other industrial facilities, engineered support for water, power, and liquid natural gas markets, seismic protection solutions for road work zone safety products, and off-grid solar lighting and power solutions. The UK & India Engineered Solutions segment offers hostile vehicle mitigation and off-grid solar lighting solutions for energy markets. The Galvanizing Services segment offers hot-dip galvanizing and powder coating services, increasing the sustainability and maintenance-free life…
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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.