Sino Land Company (SNLAY) Fair Value & Analysis
Real Estate · US · Market cap $12.5B
Analysis
Sino Land Company (SNLAY) currently trades at $6.99, while our model-based Fair Value estimate is $3.75 — implying the stock looks roughly 46.4% overvalued today. We read business quality at 95/100 (high quality), in the Real Estate 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
Sino Land Company Limited, an investment holding company, invests in, develops, manages, and trades in properties. It operates through Property Sales, Property Rental, Hotel Operations, Investments in Securities and Financing, and Property Management and Other Services segments. The company's property portfolio includes shopping malls, offices, industrial buildings, residentials and car parks. In addition, it provides cleaning, building construction and management, financing, administration, security, mortgage loan financing, secretarial, management, project management, securities investment, consultancy, and deposit placing services, as well as operates hotels. Further, the company engages in real estate agency and trustee related services. Sino Land Company Limited operates approximately 18.9 million square feet of attributable floor area in Mainland China, Hong Kong, Singapore, and Sydney. The company was incorporated in 1971 and is based in Tsim Sha Tsui, Hong Kong. Sino Land Co…
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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.