ABOUT LAND LEASE INTELLIGENCE
We built the publication we wanted to read.
AI-powered. Human-edited.
Land Lease Intelligence began with our own itch. As investors and operators in land lease communities, we wanted a better way to keep up with the market information that matters to our work.
A company presentation here. A planning decision there. Housing data, site rents, construction costs and resident experiences scattered across different sources. We started LLI to bring those pieces together and make sense of what was changing.
How we put it together
AI is central to how we build LLI. It helps us find information across a wide range of sources, analyse and synthesise it, and connect the dots that might otherwise be considered only in isolation. The findings are brought together for human interpretation and editing.
Human judgement shapes what makes the edition, how the evidence is contextualised and what matters to readers. We check definitions, compare changes over time and link published reporting to public sources, with analysis clearly identified. Every edition is reviewed and edited by a human before publication.
A wider view of the sector
A well-rounded perspective requires a variety of sources, each helping us make better sense of the others. The useful insight is often in how those pieces fit together.
Our questions span home prices, site rents and sales velocity; development economics, community operations and resident experience; valuations and how costs are accounted for; and strategy, capital and partnerships. A price increase means more when considered alongside sales velocity. Rent growth deserves to be understood through both operating performance and the resident's experience.
That is why LLI ranges across finance and markets, operators and deals, housing and affordability, construction, and planning and policy. These sections help build a connected view of the Australian land lease sector for investors and operators.
Always improving
We want each edition to be more useful than the last. That means refining our sources, asking better questions and sharpening the analysis, so readers can spend less time searching and more time understanding what matters.
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