Mensch und Maschine Software SE (MSHHF) Fair Value & Analysis
Technology · US · Market cap $786M
Analysis
Mensch und Maschine Software SE (MSHHF) currently trades at $48.34, while our model-based Fair Value estimate is $22.75 — implying the stock looks roughly 52.9% overvalued today. We read business quality at 91/100 (high quality), in the Technology 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
Mensch und Maschine Software SE provides technical software and digitization solutions in computer aided design, manufacturing and engineering, product data and lifecycle management, and building information modeling and management areas. It operates in two segments, M+M Software and M+M Digitalization. The M+M Software segment develops standard software for the areas CAD and CAM, BIM and engineering, garden and landscaping, and CAE. The M+M Digitalization segment develops tailor-made software solutions. This segment is also involved in the customer-specific adaptations, training, and the Autodesk business. In addition, the company offers hyperMILL CAM software, a virtual machine; SOFiSTiK for structural analysis and reinforcement software for civil engineering; DATAflor software solutions for gardening, landscaping, and earthworks; eXs, a computer aided engineering software; and MapEdit, which connects geo and land register data with other data sources. Further, it provides PDM boo…
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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.