Shenzhen Ridge Engineering Consulting Co (300977) Fair Value & Analysis
Industrials · CN · Market cap 2.7B CNY
Fair value as of: Jun 24, 2026
Analysis
Shenzhen Ridge Engineering Consulting Co (300977) currently trades at ¥17.21, while our model-based Fair Value estimate is ¥3.33 — implying the stock looks roughly 80.7% overvalued today. We read business quality at 95/100 (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
Shenzhen Ridge Engineering Consulting Co., Ltd. provides third-party evaluation, management, and consulting services. The company provides construction project quality and safety; project management; engineering management and consulting; enterprise management consulting; building material evaluation services; urban construction and supporting quality and safety evaluation services; electronic information technology and high-tech services; research, develops, and sells computer software research and development, sales, and technology transfer. It also offers research and development and sales of data platform software for construction projects; construction project quality testing; environmental testing; environmental monitoring; product quality testing; fire protection technical consulting services; and work safety. In addition, the company provides non-residential real estate leasing; residential rental; and system integration services for artificial intelligence industry applicat…
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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.