Vection Technologies Limited (VR1) Fair Value & Analysis
Technology · AU · Market cap A$53.4M
Fair value as of: Jun 26, 2026
Analysis
Vection Technologies Limited (VR1) currently trades at A$0.0200, while our model-based Fair Value estimate is A$0.0260 — implying the stock looks roughly 30.0% undervalued today. We read business quality at 93/100 (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: low) — always confirm before acting.
About the company
Vection Technologies Limited, an enterprise-focused company, develops and commercializes integrated digital transformation technology solutions and services in Australia. It offers Mindesk, a virtual reality design review and real-time rendering in the unreal engine, for CAD and BIM; EnWorks, a solution to support training, manufacturing, and maintenance processes that enhances the work with augmented reality-powered visual assistance; 3DFrame, a no-code enterprise metaverse app for immersive product presentations; Algho, a no-code conversational artificial intelligence platform; Shelfzone, a software-as-a-service solution to build, test, and showcase work; and MYR, a software for the digitalization of the design process, prototyping and industrialization of the denim product. The company also offers 3D modeling and renderings, XR customizations and white labels, metaverse and web 3.0, ICT infrastructure, and kiosks for POS, biometrics, augmented reality, and virtual reality. It ser…
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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.