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Imperial Pacific Limited (IPC) Fair Value & Analysis

Financial Services · AU · Market cap A$10.9M

PriceA$2.00
Fair ValueA$0.5800
Upside-71.0%
Quality95/100
Evidence: High Range A$0.4300 – A$0.7200

Fair value as of: Jun 26, 2026

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Analysis

Imperial Pacific Limited (IPC) currently trades at A$2.00, while our model-based Fair Value estimate is A$0.5800 — implying the stock looks roughly 71.0% overvalued today. We read business quality at 95/100 (high quality), in the Financial Services 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

Imperial Pacific Limited provides investment and financial services in Australia. The company was formerly known as Belmont Holdings Limited and changed its name to Imperial Pacific Limited in 2011. Imperial Pacific Limited was founded in 1936 and is based in Sydney, Australia.

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Frequently asked questions

Is Imperial Pacific Limited (IPC) undervalued?
As of Jun 26, 2026, our model estimates a fair value of A$0.5800 versus a price of A$2.00 — about −71% (overvalued). Model-based estimate, not financial advice.
What is the fair value of IPC?
Our 21-model fair value for Imperial Pacific Limited is A$0.5800 (as of Jun 26, 2026), built from audited fundamentals. The current price is A$2.00.
What is the quality score of IPC?
Imperial Pacific Limited has a Quality Score of 95/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.

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.