Project Planning Service Public Company (PPS) Fair Value & Analysis
Industrials · TH · Market cap 146M THB
Fair value as of: Jun 26, 2026
Analysis
Project Planning Service Public Company (PPS) currently trades at 0.1900 THB, while our model-based Fair Value estimate is 0.0700 THB — implying the stock looks roughly 63.2% 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
Project Planning Service Public Company Limited engages in the engineering consultation service, construction project consultation and management, and utility system construction project businesses in Thailand and internationally. The company offers construction design and planning services; computer services, and media services and concert; repair, maintenance, and installation services; consultant services for energy efficient and green buildings; advisory services for investment in information technology system and other investments; services for trading, renting, leasing, exchanging, mortgage, and consignment related. It also provides educational operations services, consulting, survey of building outlines and area development; design services for utilities and public infrastructure; consultation for airport runway and taxiway construct project; property development exchanging services; and construction services, an estimate or bidding for construction and design works. In addit…
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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.