Ground Rents Income Fund PLC (GRIO) Fair Value & Analysis
Real Estate · GB · Market cap 16.7M GBX
Fair value as of: Jun 26, 2026
Analysis
Ground Rents Income Fund PLC (GRIO) currently trades at p0.1675, while our model-based Fair Value estimate is p0.4190 — implying the stock looks roughly 150.1% undervalued today. We read business quality at 95/100 (high quality), in the Real Estate 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: medium) — always confirm before acting.
About the company
Ground Rents Income Fund PLC is a closed-ended real estate investment trust, listed on The International Stock Exchange and traded on the SETSqx platform of the London Stock Exchange. Schroder Real Estate Investment Management Limited (the Manager) was appointed as the Company Alternative Investment Fund Manager in May 2019 to support the Company Board with the headwinds related to building safety and leasehold reform. In 2023, the Board and Manager carried out an extensive shareholder consultation on proposals to change the Continuation Vote mechanism included in the Articles dating from 2012, as well as proposed changes to the Investment Policy. These proposals received strong support from shareholders and resulted in a new Continuation Resolution and Investment Policy. The new Investment Policy adopts a strategy of realising the Company assets in a controlled, orderly and timely manner for shareholders, whilst continuing to deliver best-in-class residential asset management inclu…
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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.