Ultimate Products Plc, (ULTP) Fair Value & Analysis
Consumer Cyclical · GB · Market cap 37.4M GBX
Fair value as of: Jun 25, 2026
Analysis
Ultimate Products Plc, (ULTP) currently trades at p0.4600, while our model-based Fair Value estimate is p1.29 — implying the stock looks roughly 180.4% undervalued today. We read business quality at 95/100 (high quality), in the Consumer Cyclical 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
Ultimate Products Plc, together with its subsidiaries, supplies branded household products in the United Kingdom, Rest of Europe, and internationally. The company offers a range of household products, including vacuum cleaners, food prep, storage solutions, and airers, as well as fans, heaters, and electric fires under the Beldray brand; cookware, kitchen electrical, and scale products under the SALTER brand; audio products under the Intempo brand; non-electrical kitchen and laundry products under the Russell Hobbs brand; and travel luggage and accessories under constellation and ZFrame brands. It also provides floorcare, healthy living, and party appliances under the PROlectrix brand; bone China kitchenware products under Portobello brand; pillows, duvets, mattress, protectors, and toppers under Dreamtime brand; and bakeware, cookware, and textile products under George Wilkinson brand. In addition, the company offers party and kitchen products under GILES & POSNER brand; kitchen pr…
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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.