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Foncière Vindi Société Anonyme (MLVIN) Fair Value & Analysis

Real Estate · FR · Market cap €87.0M

Price€4.56
Fair Value€1.10
Upside-75.9%
Quality86/100
Evidence: Low Range €0.8200 – €1.65

Fair value as of: Jun 26, 2026

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Analysis

Foncière Vindi Société Anonyme (MLVIN) currently trades at €4.56, while our model-based Fair Value estimate is €1.10 — implying the stock looks roughly 75.9% overvalued today. We read business quality at 86/100 (high quality), in the Real Estate 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: low).

About the company

Foncière Vindi Société Anonyme operates as a real estate company in France. The company engages in the development, rental, and sale of real estate properties. It also invests in real estate companies. Foncière Vindi Société Anonyme is headquartered in Paris, France.

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Frequently asked questions

Is Foncière Vindi Société Anonyme (MLVIN) undervalued?
As of Jun 26, 2026, our model estimates a fair value of €1.10 versus a price of €4.56 — about −76% (overvalued). Model-based estimate, not financial advice.
What is the fair value of MLVIN?
Our 21-model fair value for Foncière Vindi Société Anonyme is €1.10 (as of Jun 26, 2026), built from audited fundamentals. The current price is €4.56.
What is the quality score of MLVIN?
Foncière Vindi Société Anonyme has a Quality Score of 86/100, 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.