Runa Smart Equipment Co (301129) Fair Value & Analysis
Industrials · CN · Market cap 3.2B CNY
Analysis
Runa Smart Equipment Co (301129) currently trades at ¥22.90, while our model-based Fair Value estimate is ¥15.05 — implying the stock looks roughly 34.3% 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
Runa Smart Equipment Co., Ltd. provides smart heating solutions in China. It offers metering control, energy-consumption analysis, heating network monitoring, dispatching command and management, central control console, whole network balance, geographic information, customer service, operation and maintenance management, equipment management, room temperature prediction, and AI algorithm training systems products. The company also provides modular heat exchangers, ultrasonic heat meters smart IoT data terminals, intelligent control cabinets, intelligent balancing valves, and intelligent temperature control products. In addition, it offers energy-saving heating, room temperature monitoring, metering control plan, energy performance contract, pipeline network safety monitoring program, and second network intelligent balancing solutions, as well as intelligent control solutions for thermal power station. The company was founded in 2008 and is based in Hefei, 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.