Simpple Ltd (SPPL) Fair Value & Analysis
Industrials · US · Market cap $36.6M
Fair value as of: Jun 24, 2026
Analysis
Simpple Ltd (SPPL) currently trades at $3.83, while our model-based Fair Value estimate is $1.22 — implying the stock looks roughly 68.1% overvalued today. We read business quality at 95/100 (high quality), in the Industrials 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
Simpple Ltd. provides facilities management software in Singapore. The company offers SIMPPLE software, a platform comprising modules related to quality management, workflow management, and people management; SIMPPLE PLUS, a robotic solution in cleaning and security domains, and IoT devices and peripherals; SIMPPLE.AI, a facilities management autonomic intelligence engine that automates workflow processes in a built environment setting; and consultancy services to assist in delivering solutions in a built environment. It serves property developers or building owners, facilities management companies, and service contractors, such as environmental services companies, security agencies, horticulture, and maintenance companies. The company was founded in 2016 and is headquartered in Singapore. Simpple Ltd. operates as a subsidiary of Ifsc Founders Pte. Ltd.
Open the full interactive analysis →
Similar stocks
Frequently asked questions
Is Simpple Ltd (SPPL) undervalued?
What is the fair value of SPPL?
What is the quality score of SPPL?
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.