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Zigup Plc (ZIG) Fair Value & Analysis

Industrials · GB · Market cap 1.1B GBX

Pricep4.63
Fair Valuep3.82
Upside-17.5%
Quality95/100
Evidence: High Range p3.65 – p7.29

Analysis

Zigup Plc (ZIG) currently trades at p4.63, while our model-based Fair Value estimate is p3.82 — implying the stock looks roughly 17.5% 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

Zigup Plc engages in the provision of mobility solutions and automotive services to business and personal customers in the United Kingdom, Spain, and Ireland. The company offers vehicle provision services that includes vehicle rental, service, and maintenance solutions through vans, e-LCVs, and specialist vehicles with refrigerated, traffic management, and support; and fleet support and services comprising service scheduling, telematics, driver liaison, training, and downtime management, as well as electric vehicle (EV) fleet consulting and charging, and solar installation for businesses and consumers. It also provides claims support and accident management, such as accident claim handling, incident reporting, repair and insurer management, and legal support services for vehicles, drivers, and passengers; and replacement vehicle services through direct and credit hire arrangements. In addition, the company offers vehicle damage repairs for cars and LCVs, including plastic welding, a…

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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.