What is the Global Real Estate Transparency Index? The Global Real Estate Transparency Index, or GRETI, is JLL's biennial assessment of how reliable, accessible, and enforceable real estate data and regulation are in each market worldwide. On September 14, 2026, JLL (NYSE: JLL) released the 14th edition from Chicago, and it carried a finding that should change how commercial real estate investors read every AI-generated market report that crosses their desk: over 90% of occupiers and investors are now using AI tools to analyze market fundamentals. When nearly everyone is running the same models, the differentiator is no longer the model. It is the quality of the public data underneath it. For the broader framework, see our guide to AI real estate due diligence.
Key Takeaways
- JLL's 2026 Global Real Estate Transparency Index found that over 90% of occupiers and investors now use AI tools to analyze market fundamentals.
- Transaction volumes in the 13 Highly Transparent markets rose 64% over the past two years, outpacing the rest of the world by roughly 20 percentage points.
- Those 13 markets attracted $1.4 trillion of real estate capital and account for more than 80% of global direct investment.
- AI amplifies data asymmetry rather than erasing it: thin public data produces confident but unverifiable output, not a warning label.
- Treat a market's transparency tier as a confidence discount applied to any AI-generated market analysis before it reaches your investment committee.
What the 2026 Global Real Estate Transparency Index Found
The headline result is that transparency keeps paying. JLL reports that two-thirds of the global markets it tracks improved transparency over the past two years, and that transaction volumes in the top tier rose 64% across the same period, outpacing the rest of the world by about 20 percentage points. The 13 markets JLL classifies as Highly Transparent, a group that includes the United States, the United Kingdom, France, Australia, Canada, Germany, Japan, and Singapore, attracted $1.4 trillion of real estate capital. Those same 13 markets now represent 56% of total income-producing real estate worldwide and more than 80% of global direct investment.
JLL's 2026 edition evaluates 88 countries and territories and 146 city markets across 260 separate factors, spanning data availability, valuation standards, regulatory enforcement, and transaction process. The fastest improvers were India, Vietnam, and South Korea, with India and Vietnam together drawing roughly $12 billion in direct transaction volume over two years, both all-time highs. The mechanism JLL credits is specific: governments digitizing land registries, planning services, and public records so property data becomes real-time, disaggregated, and publicly accessible. You can read the full release on JLL's newsroom.
Why 90% AI Adoption Turns Transparency Into an Underwriting Risk
Here is the part most coverage will skip. A 90% AI adoption rate does not mean 90% of investors have better market analysis. It means 90% are now exposed to a failure mode: a large language model will produce a fluent, structured, confident market read whether or not the underlying data supports it. Scarcity of data does not make ChatGPT, Claude, Gemini, or Perplexity refuse the question. It makes them interpolate.
In a Highly Transparent market, that is mostly fine. If you ask an AI tool about Dallas industrial absorption or Phoenix multifamily concessions, it is drawing on dense public records, CoStar coverage, broker reports from CBRE and Cushman & Wakefield, and municipal filings. The model compresses research time without inventing much. Move down the transparency tiers and the same prompt, the same model, and the same apparent confidence now rest on a far thinner base. The output looks identical. The reliability does not.
This is why GRETI 2026 is more useful to a US investor than its global framing suggests. The index is, in effect, a map of where AI-assisted market analysis can be trusted and where it needs to be treated as a hypothesis. That matters domestically too, because transparency varies within the United States. Recording practices, assessor data quality, and rent disclosure differ sharply between a major metro and a tertiary market, the gap we cover in AI for out-of-state CRE investing.
The Transparency Tier as a Confidence Discount
The practical move is to stop treating AI market output as uniformly reliable and start discounting it by data environment. A workable approach for most acquisition teams:
- Tier 1, Highly Transparent: Accept AI-generated market summaries as a research accelerant. Spot-check headline figures such as vacancy, absorption, and average cap rate against one primary source before they enter a model.
- Tier 2, Transparent: Require a named source for every number that touches the pro forma. Treat unsourced NOI or rent growth assumptions as placeholders, not findings.
- Tier 3 and below: Use AI for structuring questions and identifying what you do not know, not for producing answers. Every figure needs independent confirmation from a broker, appraiser, or local operator.
An AI tool that cannot tell you where a number came from has not given you a number, it has given you a plausible sentence. That distinction drives the difference between real-time retrieval and model recall, which we break down in our comparison of real time data versus training data for CRE market analysis. For investors who want this built into a repeatable acquisition workflow rather than left to individual analysts, The AI Consulting Network specializes in exactly this kind of process design.
Where Transparency Is Improving Fastest: Alternatives, Debt, and Energy
JLL identifies alternative sectors, debt markets, and energy performance as the fastest-moving transparency frontiers in the 2026 index, with building energy-performance tracking among the most improved factors globally. Each of these maps directly onto a live allocation decision.
Data center disclosure improved because capital demanded it, and better data on power availability, lease structure, and tenant credit is what makes AI-assisted screening viable where the operative risks are technical rather than traditional. Debt transparency matters for the same reason: observable DSCR, loan structure, and spread data is data an AI tool can reason over. Energy disclosure is becoming an underwriting input as retrofit costs and compliance deadlines move into hold-period economics.
The pattern is consistent: AI becomes useful in a sector at roughly the moment its data becomes public and standardized. Ranking markets on fundamentals, the workflow we detail in AI for market selection in CRE, only produces defensible rankings where the fundamentals are actually published.
How CRE Investors Should Act on GRETI 2026
Three concrete steps. First, when you receive an AI-generated market report, ask what data environment produced it and require citations for anything load-bearing. Second, build the transparency tier into your market-entry screen as an explicit risk factor, the way you would treat regulatory or currency risk, because lower transparency raises diligence cost. Third, the arbitrage has shifted. When 90% of your competitors run the same models over the same public data, edge comes from proprietary data and the willingness to verify where others accept the output.
JLL's separate 2026 research reinforces the point, finding that AI and technology skills gaps are now the leading constraint on real estate transformation, cited by 36% of organizations worldwide. The constraint is not access to models. It is the capacity to use them well. CRE investors looking for hands-on AI implementation support can reach out to Avi Hacker, J.D. at The AI Consulting Network. Full index detail is available through JLL's Global Real Estate Transparency Index.
Frequently Asked Questions
Q: What is JLL's Global Real Estate Transparency Index?
A: GRETI is JLL's biennial index scoring how transparent real estate markets are across data availability, valuation standards, regulatory enforcement, and transaction process. The 2026 edition, released September 14, covers 88 countries and territories and 146 city markets across 260 factors.
Q: Does high AI adoption mean investors have better market data?
A: No. JLL found over 90% of occupiers and investors now use AI tools, but adoption measures usage, not accuracy. AI tools produce equally confident output regardless of whether the underlying public data is dense or thin, which makes source verification more important, not less.
Q: Why does market transparency affect AI underwriting?
A: AI models synthesize available data. In transparent markets with digitized registries and standardized reporting, that synthesis is grounded. In opaque markets, the model interpolates from sparse inputs and returns a confident answer anyway, so figures that reach your pro forma may have no verifiable source.
Q: Where does the United States rank in GRETI 2026?
A: The United States is one of 13 markets JLL classifies as Highly Transparent, alongside the United Kingdom, France, Australia, Canada, Germany, Japan, and Singapore. Those 13 markets attracted $1.4 trillion of capital and represent more than 80% of global direct investment.
Q: How should I adjust my AI workflow based on a market's transparency?
A: Apply a confidence discount by tier. In Highly Transparent markets, spot-check headline figures against a primary source. In less transparent markets, require a named source for every number that enters the pro forma and use AI to frame questions rather than produce answers.