Tianjin Development Holdings (TJSCF) Fair Value & Analysis
Industrials · US · Market cap $249M
Fair value as of: Jun 26, 2026
Analysis
Tianjin Development Holdings (TJSCF) currently trades at $0.2325, while our model-based Fair Value estimate is $0.4700 — implying the stock looks roughly 102.2% undervalued today. We read business quality at 80/100 (high quality), in the Industrials sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: medium) — always confirm before acting.
About the company
Tianjin Development Holdings Limited, through its subsidiaries, supplies water, heat, thermal power, and electricity in China and Hong Kong. It operates through six segments: Utilities, Pharmaceutical, Hotel, Electrical and Mechanical, Port Services, and Elevators and Escalators. The company engages in the supply of tap water, installation and maintenance of water pipes, technical consultancy, and retail and wholesale of water pipes and related parts, as well as water testing services; distribution of steam and heat for industrial, commercial, and residential customers; and supply of electricity, construction of electricity supply network, application of technology related to new energy and renewable energy, and electricity construction and related technical services, as well as energy storage technology services. It also manufactures and sells chemical drugs, Chinese medicines, and other healthcare products; researches and develops medicine technology and products; designs, manufac…
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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.