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Seazen Group (SZENF) Fair Value & Analysis

Real Estate · US · Market cap $5.1B

Price$0.7000
Fair Value$0.1400
Upside-80.0%
Quality80/100
Evidence: Medium Range $0.1000 – $0.1700

Analysis

Seazen Group (SZENF) currently trades at $0.7000, while our model-based Fair Value estimate is $0.1400 — implying the stock looks roughly 80.0% overvalued today. We read business quality at 80/100 (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: medium).

About the company

Seazen Group Limited, together with its subsidiaries, engages in the investment, development, management, and sale of properties in the People's Republic of China. The company primarily develops and sells residential properties and mixed-use complexes, as well as provides commercial property management and other services. It also provides asset operation and management, information technology, and children entertainment services. In addition, the company offers business management consulting and pension services. Further, it develops energy technology; and engages in retail business. Seazen Group Limited was formerly known as Future Land Development Holdings Limited and changed its name to Seazen Group Limited in November 2019. The company was founded in 1993 and is headquartered in Shanghai, China. Seazen Group Limited operates as a subsidiary of Wealth Zone Hong Kong Investments Limited.

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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.