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Whitefield Income Limited (WHI) Fair Value & Analysis

Financial Services · AU · Market cap A$208M

PriceA$1.31
Fair ValueA$0.8200
Upside-37.2%
Quality76/100
Evidence: Medium Range A$0.6100 – A$1.02

Fair value as of: Jun 26, 2026

Analysis

Whitefield Income Limited (WHI) currently trades at A$1.31, while our model-based Fair Value estimate is A$0.8200 — implying the stock looks roughly 37.2% overvalued today. We read business quality at 76/100 (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: medium).

About the company

Whitefield Income Limited operates as an investment company. It focuses on investing in Australian Securities Exchange listed equity securities. Whitefield Income Limited was founded in 1923 and is based in Sydney, Australia.

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Frequently asked questions

Is Whitefield Income Limited (WHI) undervalued?
As of Jun 26, 2026, our model estimates a fair value of A$0.8200 versus a price of A$1.31 — about −37% (overvalued). Model-based estimate, not financial advice.
What is the fair value of WHI?
Our 21-model fair value for Whitefield Income Limited is A$0.8200 (as of Jun 26, 2026), built from audited fundamentals. The current price is A$1.31.
What is the quality score of WHI?
Whitefield Income Limited has a Quality Score of 76/100, 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.