Microlise Group (SAAS) Fair Value & Analysis
Technology · GB · Market cap 48.0M GBX
Fair value as of: Jun 25, 2026
Analysis
Microlise Group (SAAS) currently trades at p0.4200, while our model-based Fair Value estimate is p0.6500 — implying the stock looks roughly 54.8% undervalued today. We read business quality at 95/100 (high quality), in the Technology 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: medium) — always confirm before acting.
About the company
Microlise Group plc provides transport management technology solutions in the United Kingdom, Rest of Europe, and internationally. The company offers fleet telematics products, including fleet tracking, fleet utilization, driver performance, driver communications, trailer telematics, temperature monitoring, and focus telematics; fleet safety management, multi-camera vehicle systems, AI distraction cameras, tachograph management, bridge strike warning, and vehicle health products; journey management products comprising schedule and route management, planning and optimization, customer communications, and workforce and resource management; and delivery management products, such as electronic proof of delivery and sub-contractor management. It also provides distribution and remote asset management, and predictive maintenance services. It serves transport, logistics, light commercial, fleet, defense, security, plant, agriculture, original equipment manufacturer, and channel industries, …
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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.