hk_property_neighbourhoods

PREMIUM 175 rows

Per-neighbourhood (HMA) volumes, buildings and geo

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Hong Kong neighbourhood (HMA / 中原樓市片區) browse dimension below the district: one row per Centaline neighbourhood with bilingual names, its parent district code, region, registered-sale volume, building count and a representative coordinate.

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The evidence

Data coverage
Aug 2026 – present
Volume
175 rows
Last refreshed
18 October, 2026 (1 hour ago)
Data through
18 October, 2026 (1 hour ago)
Schema & column quality (11 columns)

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Column Type Description Populated Distinct
hma_id String Centaline neighbourhood id (HMA###) — primary identifier — —
hma_zh Nullable(String) Neighbourhood name in Chinese (e.g. 上環) — —
hma_en Nullable(String) Neighbourhood name in English where Centaline supplies one (most do not have one; use hma_zh) — —
district_code Nullable(String) Parent official district code (A..T; Centaline sub-areas U/V folded to Q/S) — links to hk_property_districts — —
region_zh Nullable(String) Region/territory in Chinese — —
transaction_count Int64 Registered sales (1995+) with a known neighbourhood scope attributed to this HMA — —
building_count Int64 Number of buildings located in this neighbourhood — —
latitude Nullable(Float64) Latitude (WGS84): autocomplete point, else the mean of the neighbourhood's buildings — —
longitude Nullable(Float64) Longitude (WGS84): autocomplete point, else the mean of the neighbourhood's buildings — —
updated_at DateTime When a registered sale in this neighbourhood was last observed in Centaline's transaction feed (UTC) — the neighbourhood's freshness, not an event date — —
id String Stable unique identifier for this row — the last column of every export. Use it to deduplicate, to key an incremental load, or to reference one specific row. It is additive: the entity and join keys you already use (hma_id) are unchanged. — —

Sample data

Every neighbourhood, full schema, no row cap. The table is small enough to ship in full.

SELECT * FROM renavon_exports.hk_property_neighbourhoods LIMIT 50 0.02s
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