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Industrial Logistics Properties Trust (ILPT) Fair Value & Analysis

Real Estate · US · Market cap $540M

Price$8.70
Fair Value$9.64
Upside+10.8%
Quality83/100
Evidence: Medium Range $1.93 – $27.20

Analysis

Industrial Logistics Properties Trust (ILPT) currently trades at $8.70, while our model-based Fair Value estimate is $9.64 — implying the stock looks roughly 10.8% undervalued today. We read business quality at 83/100 (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

Industrial Logistics Properties Trust is a real estate investment trust focused on owning and leasing high quality industrial and logistics properties. As of March 31, 2026, ILPT's portfolio consisted of 409 properties containing approximately 59.6 million rentable square feet located in 39 states. Approximately 77% of ILPT's annualized rental revenues as of March 31, 2026, are derived from investment grade tenants, tenants that are subsidiaries of investment grade rated entities or Hawaii land leases. ILPT is managed by The RMR Group, a leading U.S. alternative asset management company with over 37 billion US dollars in assets under management as of March 31, 2026, and 40 years of institutional experience in buying, selling, financing and operating commercial real estate. ILPT is headquartered in Newton, MA. Industrial Logistics Properties Trust was incorporated in 2017 in Maryland, USA.

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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.