Astral Limited (ASTRAL) Fair Value & Analysis
Industrials · IN · Market cap ₹408B
Analysis
Astral Limited (ASTRAL) currently trades at ₹1,523, while our model-based Fair Value estimate is ₹419.45 — implying the stock looks roughly 72.5% overvalued today. We read business quality at 97/100 (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
Astral Limited engages in the manufacture and marketing of pipes and fittings, water tanks, and adhesives and sealants in India and internationally. It operates in two segments, Plumbing, and Paints and Adhesives. It offers plumbing pipes and fittings, sewerage and drainage pipes and fittings, agriculture pipes and fittings, cable protection system, industrial piping system, fire protection system, urban infrastructure, ancillary products, and column pipe; and roto moulding, blow moulded, loft, and anti-viral copper shield water tanks. It also provides bath-ware products comprising faucets, sanitarywares, cisterns, and showers; paints, such as interior and exterior emulsions, distempers, enamels, and primer and wall putty undercoats; adhesives and sealants, including epoxy, PVA, rubber, anaerobic tapes, silicone, acrylic, hybrid, instant, solvent, specialised, industrial adhesives, and epoxy putty; specialized valves, such as compact true, single union, and industrial ball valves; c…
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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.