Bowman Consulting Group (BWMN) Fair Value & Analysis
Industrials · US · Market cap $546M
Analysis
Bowman Consulting Group (BWMN) currently trades at $29.36, while our model-based Fair Value estimate is $14.67 — implying the stock looks roughly 50.0% 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
Bowman Consulting Group Ltd., provides engineering, technical consulting, and program management services in the United States. The company offers planning, engineering, program management, commissioning, environmental consulting, geospatial imaging, surveying, land procurement, and other infrastructure management services. It also provides site and civil design and planning, structural engineering, landscape architecture, mechanical, electrical and plumbing, fire prevention engineering, construction management, procurement and oversight, program and asset management, and environmental consulting; LNG pipeline systems, power generation facility design, utility undergrounding, grid resiliency planning, mission critical planning, data center feasibility and design, bridging power design and procurement, utility scale on-site energization, grid interconnect engineering, and transmission corridor design; and road, highway and bridge design, ports and harbors engineering, asset controls,…
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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.