HNI Corporation (HNI) Fair Value & Analysis
Consumer Cyclical · US · Market cap $2.2B
Analysis
HNI Corporation (HNI) currently trades at $34.25, while our model-based Fair Value estimate is $23.69 — implying the stock looks roughly 30.8% overvalued today. We read business quality at 95/100 (high quality), in the Consumer Cyclical 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
HNI Corporation, together with its subsidiaries, engages in the manufacture, sale, and marketing of workplace furnishings and residential building products primarily in the United States and Canada. It operates through two segments, Workplace Furnishings and Residential Building Products. The Workplace Furnishings segment offers panel-based and freestanding office furniture systems, seating, storage, benching, tables, architectural products, storage, and ancillary and hospitality products, as well as social collaborative items. This segment sells its products through independent dealers, wholesalers, office product distributors, e-commerce focused resellers, and wholesalers, as well as directly to end-user customers and governments. The Residential Building Products segment provides various gas, wood, electric, and pellet-fueled prefabricated fireplaces; and inserts, hearth stoves, facings, outdoor fire pits and fire tables, and accessories. This segment sells its products primarily…
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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.