Miami decision profile

Miami, in structure.

Comparison market with strong published volume data but different price conventions.

Low data coverage · 3/7 official metricsCompare with others

Registered price

Data not published

No official €/m² series; US data is published per unit median.

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

Price direction

+4.8% median sale price, year on year

Price trend

Miami-Dade County · Q4 2025 · SWIP · Pahuma Group AS · partial confidence

Market activity

28,600 closed sales

Transaction volume

Miami-Dade County · 2025 · SWIP · Pahuma Group AS · partial confidence

Buyer mix

Data not published

Foreign buyer activity

Estimated in survey data only, not an official register series.

Finance

6.4% 30-year fixed

Mortgage context

United States · Q4 2025 · SWIP · Pahuma Group AS · partial confidence

Supply

Data not published

New-build activity

County permit series not loaded.

Regulation

Insurance and coastal resilience costs are a material ownership variable.
Condominium reserve legislation has changed carrying costs materially.

Lifestyle fit

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

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Data quality

Low data coverage

Not published: Official registered price, Housing price index, Foreign buyer activity, New-build activity

Buyer pressure model

Foreign participation not published
No official series
Lower transaction activity
-6% year on year
Rising registered prices
+4.8% year on year
Limited verified supply data
Permits not disclosed at this volume

SWIP analysis

Fewer registered transactions alongside rising registered prices. In a supply-constrained market this pattern is consistent with scarcity rather than weakening demand, but the published data does not by itself distinguish the two.

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

HIGH

2 recorded regulatory considerations
Insurance and coastal resilience costs are a material ownership variable.
Tourist-rental restrictions apply
Regulatory notes
Higher financing cost
6.4% average rate
Low official data coverage
3/7 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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