CapitaLand Ascendas REIT (CLAR) (ACDSF) Fair Value & Analysis
Real Estate · US · Market cap $9.0B
Analysis
CapitaLand Ascendas REIT (CLAR) (ACDSF) currently trades at $1.80, while our model-based Fair Value estimate is $1.54 — implying the stock looks roughly 14.4% overvalued today. We read business quality at 95/100 (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: high).
About the company
CapitaLand Ascendas REIT (CLAR) is Singapore's first and largest listed business space and industrial real estate investment trust. It was listed on the Singapore Exchange Securities Trading Limited (SGX-ST) in November 2002. CLAR has since grown to be a global REIT anchored in Singapore, with a strong focus on technology and logistics properties in developed markets. As of 31 December 2025, its investment properties under management stood at 18.2 billion US dollars. It owns a total of 226 properties across three segments, namely Business Space & Life Sciences; Industrial & Data Centres; and Logistics. These properties are in the developed markets of Singapore, Australia, the US, and the UK/Europe. These properties house a tenant base of 1,731 international and local companies from a wide range of industries and activities, including data centres, information technology, engineering, logistics and supply chain management, biomedical sciences, financial services (backroom office supp…
Open the full interactive analysis →
Similar stocks
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.