Kumagai Gumi Co (KMGGF) Fair Value & Analysis
Industrials · US · Market cap $1.8B
Analysis
Kumagai Gumi Co (KMGGF) currently trades at $10.74, while our model-based Fair Value estimate is $6.78 — implying the stock looks roughly 36.9% 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
Kumagai Gumi Co.,Ltd. operates as a construction company in Japan and internationally. The company constructs tunnels, roads and bridges, railways, dams, and airports/marine ports, as well as water and sewerage, environmental, energy, renewal, ICT civil engineering, housing/mansion, office/government, hotel, commercial, culture, education/research, sports/entertainment, medical/social welfare, plant, distribution, timber and other projects. It also engages in the production of asphalt mixture and the paving of roads, airports, ports, and bridges; building renovation business, including building survey/diagnosis, antiseismic diagnosis/antiseismic reinforcement, and the formulation of long-term renovation plans, as well as manufacture of steel products for shield tunnels and construction machinery; and operates a leasing business for a steel construction. In addition, the company offers construction and building plans; software services utilizing CAD and information systems to create …
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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.