Smart Powerr Corp (CREG) Fair Value & Analysis
Utilities · US · Market cap $7.6M
Fair value as of: Jun 25, 2026
Analysis
Smart Powerr Corp (CREG) currently trades at $2.30, while our model-based Fair Value estimate is $2.72 — implying the stock looks roughly 18.3% undervalued today. We read business quality at 95/100 (high quality), in the Utilities 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: low) — always confirm before acting.
About the company
Smart Powerr Corp., through its subsidiaries, provides energy saving solutions and services in China. The company provides energy saving and recovery facilities for various energy intensive industries; waste energy recycling systems for use in nonferrous metal plants; and clean-technology and energy-efficient solutions to reduce air pollution and energy shortage problems. It also designs, finances, constructs, operate, transfer, and installs the waste energy recycling project to mid to large-size enterprises involved in high energy-consuming businesses; offers project investment, investment management, economic information consulting, technical, financial leasing, and financial leasing transactions and consulting services; purchases, repairs, and disposes financial leasing assets; and sells and leases energy saving systems and equipment. In addition, the company offers various waste-to-energy technologies and solutions comprising waste gas power generation system that utilizes flamm…
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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.