Seneca Foods Corporation (SENEB) Fair Value & Analysis
Consumer Defensive · US · Market cap $987M
Analysis
Seneca Foods Corporation (SENEB) currently trades at $158.50, while our model-based Fair Value estimate is $138.99 — implying the stock looks roughly 12.3% overvalued today. We read business quality at 95/100 (high quality), in the Consumer Defensive 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
Seneca Foods Corporation provides packaged fruits and vegetables in the United States and internationally. The company offers canned, frozen, and jarred fruits and vegetables; and packaged snack chips and other food products under the private label, as well as under various national and regional brands that the company owns or licenses, including Aunt Nellie's, CherryMan, Green Giant, Green Valley, Libby's, READ, and Seneca. In addition, it packs canned and frozen vegetables. Further, the company engages in the sale of cans, ends, and seeds, as well as aircraft operations. It provides its products to grocery outlets, including supermarkets, mass merchandisers, limited assortment stores, club stores, and dollar stores; specialty retailers; and food service distributors, restaurant chains, industrial markets, other food packagers, and export customers in approximately 55 countries, as well as federal, state, and local governments for school and other feeding programs. Seneca Foods Cor…
Open the full interactive analysis →
Similar stocks
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.