Safety Insurance Group (SAFT) Fair Value & Analysis
Financial Services · US · Market cap $1.0B
Analysis
Safety Insurance Group (SAFT) currently trades at $73.57, while our model-based Fair Value estimate is $87.88 — implying the stock looks roughly 19.5% undervalued today. We read business quality at 95/100 (high quality), in the Financial Services 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: high) — always confirm before acting.
About the company
Safety Insurance Group, Inc. provides private passenger and commercial automobile, and homeowner insurance in Massachusetts, the United States. The company offers private passenger automobile policies that provide coverage for bodily injury and property damage to others, no-fault personal injury coverage for the insured/insured's car occupants, and physical damage coverage for an insured's own vehicle for collision or other perils. It also provides commercial automobile policies that offer insurance for commercial vehicles used for business purposes, including private passenger-type vehicles, trucks, tractors and trailers, and insure individual vehicles, as well as commercial fleets; and homeowners policies, which provide coverage for homes, condominiums, and apartments for losses to a dwelling and its contents from various perils, and coverage for liability to others arising from ownership or occupancy. In addition, the company offers business owners policies that cover apartments …
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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.