Machine learning applied to Dubai’s public property data, to support decisions that used to rest on instinct. It informs the advice; it doesn’t replace site visits, legal checks or judgment.
The short answer
AI property intelligence applies statistical models to Dubai’s public property data (DLD transactions, RERA project registrations, rental and service-charge indexes) to estimate developer delivery risk, yield ranges by district and fair price ranges. It supports, rather than replaces, site visits, legal checks and the final decision, and is used within advisory engagements.
There is plenty to learn from: Dubai recorded more than 270,000 real estate transactions worth AED 917 billion in 2025, according to the Dubai Media Office. Patterns across that many deals are what models are good at.
“…a more mature and sustainable market underpinned by transparency, governance, and data-driven policies.”
Omar Hamad Bu Shehab, Director General, Dubai Land Department, on the 2025 results
Supply is the hardest variable to see by eye: about 452,000 units were under construction at the end of 2025, two-thirds at 0–20% completion, on DLD data compiled by DXB Interact.
Every output comes with its assumptions and ranges, and the final call is made with you, not by the model.
AI helps with patterns across thousands of transactions, ranges and outliers.
People are essential for site visits, developer conversations, fitting a purchase to your goals, spotting when the market has changed character, and negotiation.
Mainly the Dubai Land Department and RERA (transactions, project registrations, rental and service-charge indexes), plus portal listings and developer disclosures.
No. It uses structured numerical data and statistical models built for regression, classification and forecasting. Language models are used, if at all, only to summarise.
Yes, within an engagement, so the ranges and assumptions can be explained properly.
Your data stays in systems you control where possible, is processed under a written agreement, and is never used to train public models.
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