Jiangsu Jiangnan Water Co (601199) Fair Value & Analysis
Utilities · CN · Market cap 5.3B CNY
Analysis
Jiangsu Jiangnan Water Co (601199) currently trades at ¥5.92, while our model-based Fair Value estimate is ¥5.65 — implying the stock looks roughly 4.6% overvalued today. We read business quality at 95/100 (high quality), in the Utilities 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
Jiangsu Jiangnan Water Co., Ltd. supplies water in China. The company engages in the drainage and water treatment-related business; water supply engineering design and technical consulting; water quality testing; water meter measurement and testing; and public infrastructure investing business. The company owns 3 surface water plants with a daily water supply capacity of 1.1 million cubic meters. The company also manages and constructs drainage pipe network and drainage pumping stations; operation management, inspection, maintenance and dredging of drainage network facilities and drainage pumping stations; operates and manages sewage treatment related facilities; provides sewage treatment and industrial water purification services; designs and constructs water supply and drainage pipelines; installs water supply and drainage equipment; and offers pipeline repairs and other services. Jiangsu Jiangnan Water Co., Ltd. was founded in 1966 and is based in Jiangyin, China.
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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.