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PICC Property and Casualty Company (PPCCY) Fair Value & Analysis

Financial Services · US · Market cap $44.4B

Price$46.22
Fair Value$78.32
Upside+69.5%
Quality95/100
Evidence: High Range $58.74 – $97.90

Analysis

PICC Property and Casualty Company (PPCCY) currently trades at $46.22, while our model-based Fair Value estimate is $78.32 — implying the stock looks roughly 69.5% undervalued today. We read business quality at 95/100 (high quality), in the Financial Services 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: high) — always confirm before acting.

About the company

PICC Property and Casualty Company Limited, together with its subsidiaries, engages in property and casualty insurance business in People's Republic of China. It operates through six segments: motor vehicle, accidental injury and health, agriculture, Liability, Commercial Property, and Other. The company offers motor vehicle, commercial property, cargo, liability, accidental injury, short-term health, agriculture, credit, surety, household property, marine hull, and other insurance products. It also provides reinsurance, investment and funds application, insurance agency services, training services, IT services, and property management services, etc. The company was founded in 2003 and is based in Beijing, China. PICC Property and Casualty Company Limited operates as a subsidiary of The People's Insurance Company (Group) of China Limited.

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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.