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Sino Green Land Corporation (SGLA) Fair Value & Analysis

Industrials · US · Market cap $227M

Price$1.25
Fair Value$1.44
Upside+15.1%
Quality95/100
Evidence: Low Range $0.8591 – $1.87

Fair value as of: Jun 25, 2026

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Analysis

Sino Green Land Corporation (SGLA) currently trades at $1.25, while our model-based Fair Value estimate is $1.44 — implying the stock looks roughly 15.1% undervalued today. We read business quality at 95/100 (high quality), in the Industrials 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

Sino Green Land Corporation engages in the manufacturing and selling of recovered and recycled products in Malaysia. The company offers PET bottle flakes and strapping belts, and HDPE pellets. The company is based in Semenyih, Malaysia.

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Frequently asked questions

Is Sino Green Land Corporation (SGLA) undervalued?
As of Jun 25, 2026, our model estimates a fair value of $1.44 versus a price of $1.25 — about +15% (undervalued). Model-based estimate, not financial advice.
What is the fair value of SGLA?
Our 21-model fair value for Sino Green Land Corporation is $1.44 (as of Jun 25, 2026), built from audited fundamentals. The current price is $1.25.
What is the quality score of SGLA?
Sino Green Land Corporation 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.