Dubai decision profile

Dubai, in structure.

Highly transparent transaction register, very different ownership and tax framework.

Partial data coverage · 4/7 official metricsCompare with others

Registered price

AED 1,640 / sq ft

Dubai · Q4 2025 · Dubai Land Department · high confidence · Reported in local units; not directly comparable per m² without conversion.

This is a structured decision model, not a prediction and not a valuation.

Price direction

+12.4% year on year

Price trend

Dubai · Q4 2025 · Dubai Land Department · high confidence

Market activity

168,400 registered sales

Transaction volume

Dubai · 2025 · Dubai Land Department · high confidence

Buyer mix

Data not published

Foreign buyer activity

Nationality split is not published as a standard series.

Finance

Data not published

Mortgage context

No comparable official average-rate series.

Supply

High off-plan completion pipeline

New-build activity

Dubai · 2025 · Dubai Land Department · partial confidence

Regulation

Freehold ownership is limited to designated areas.
No annual property tax; transfer and service charges apply instead.

Lifestyle fit

Recorded strengths: Business opportunity, Sun, Marinas, International community. Build your SWIP profile to personalise this.

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Data quality

Partial data coverage

Not published: Housing price index, Foreign buyer activity, Mortgage context

Buyer pressure model

Foreign participation not published
No official series
Rising transaction activity
+18% year on year
Rising registered prices
+12.4% year on year
Verified new-build activity recorded
High off-plan completion pipeline

SWIP analysis

Prices and activity are both recorded higher. That is a straightforward reading, though the registered average is sensitive to which segments happened to trade.

How this was built

Model rules: No score is produced. Each statement restates one published series; the analysis names the pattern and the limits of reading it.
Input data: Only metrics carried in the SWIP source library. Each figure keeps its publisher, geography, period and confidence.
Periods: Latest published period per series. Periods differ between series and are shown per figure.
Geographies: Geography is stated per figure. Where a municipal figure does not exist, a regional or national series may be shown as context and is labelled as such.
Limitations: Averages are sensitive to the mix of properties that happened to transact. Low-volume municipalities produce volatile averages. Nothing here is a valuation or a forecast.
Interpretation method: Models combine published values using stated rules. No weighting is hidden and no score is generated that the rules above do not fully explain.

Ownership friction model

MEDIUM

2 recorded regulatory considerations
Freehold ownership is limited to designated areas.
Tourist-rental restrictions apply
Regulatory notes
No official financing series
Cost of debt must be verified locally
Partial official data coverage
4/7 metrics published
Cross-border ownership structure required
Jurisdiction differs from SWIP's home market

How this was built

Model rules: One point per recorded regulatory consideration. Two points where tourist-rental restrictions are recorded. Two points where average financing cost is 4% or above; one point where no official series exists. One or two points for partial or low official data coverage. One point where ownership sits outside SWIP's home jurisdiction. 0–4 LOW · 5–7 MEDIUM · 8+ HIGH.
Input data: Only metrics carried in the SWIP source library. Each figure keeps its publisher, geography, period and confidence.
Periods: Latest published period per series. Periods differ between series and are shown per figure.
Geographies: Geography is stated per figure. Where a municipal figure does not exist, a regional or national series may be shown as context and is labelled as such.
Limitations: Averages are sensitive to the mix of properties that happened to transact. Low-volume municipalities produce volatile averages. Nothing here is a valuation or a forecast.
Interpretation method: Models combine published values using stated rules. No weighting is hidden and no score is generated that the rules above do not fully explain.

Cross-sectional decision profile

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