Info Edge (India) Limited (NAUKRI) Fair Value & Analysis
Communication Services · IN · Market cap ₹640B
Analysis
Info Edge (India) Limited (NAUKRI) currently trades at ₹1,012, while our model-based Fair Value estimate is ₹394.21 — implying the stock looks roughly 61.0% overvalued today. We read business quality at 89/100 (high quality), in the Communication Services 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: medium).
About the company
Info Edge (India) Limited operates as an online classifieds company in the areas of recruitment, matrimony, real estate, and education and related services in India and internationally. It operates through Recruitment Solutions, 99acres for Real Estate, and Other segments. The company offers recruitment services through naukri.com, an online job website for jobseekers and corporate customers, including hiring consultants; naukrigulf.com, a website catering to Gulf markets; quadranglesearch.com, a site that provides off-line placement services to middle and senior management; zwayam.com, an advanced application tracking system for the recruitment process; and codingninjas.com, a code learning platform, as well as operates other brands, that includes iimjobs.com, hirist.com, doselect.com, Naukri Fast Forward, a ambitionbox.com, and jobhai.com. It also provides 99acres.com, which offers listings of properties for sale, purchase, and rent; Jeevansathi.com, an online matrimonial platform…
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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.