Malaysia property transactions

Name: Malaysia property transactions — NAPIC (JPPH) open sales data
Creator: National Property Information Centre (NAPIC), Valuation and Property Services Department (JPPH)

Official transaction records from NAPIC (JPPH) open sales data — actual sold prices, not asking prices. Filter by state, district, sector and property type.

559,927 official NAPIC transaction records · latest published month Mar 2026 · last synced 58 days ago.

Filter transactions

469,869 records

Sector

State

District

Property type

Month	Type	Scheme / road	District	Level	Sqft	Tenure	Price	PSF
Aug 2024	1 - 1 1/2 Storey Terraced	PINGGIRAN BERTAM PERDANA (BP4) · LORONG PINGGIRAN BERTAM 19	Seberang Perai Utara · Pulau Pinang	—	949	Freehold	RM360,000	RM379
Aug 2024	1 - 1 1/2 Storey Terraced	TAMAN BERTAM INDAH · LORONG DBERTAM INDAH 43	Seberang Perai Utara · Pulau Pinang	—	794	Freehold	RM380,000	RM479
Aug 2024	1 - 1 1/2 Storey Terraced	TMN PENAGA · LORONG PENAGA 1	Seberang Perai Utara

Where this data comes from

NAPIC-sourced, official JPPH open data. All records come from the National Property Information Centre’s Open Sales Data publication by the Valuation and Property Services Department (JPPH), Ministry of Finance Malaysia — 559,927 records, last synced 58 days ago.

How we handle the data

A daily job checks NAPIC’s publication for a new release and re-ingests the full dataset when one lands (NAPIC refreshes roughly quarterly, in place).
Fields are stored verbatim — property types, tenure, districts and prices are never renamed, adjusted, modelled, or AI-generated.
The only computed figures are unit conversions (sq.m → sqft) and the PSF (price ÷ floor area); rows without a usable floor area show no PSF rather than an estimated one.
Medians on this page are plain aggregates over the displayed records.

What to keep in mind

NAPIC publishes transaction dates at month precision, with a reporting lag.
The publication is a rolling ~5-year window — older transactions drop out as new quarters are added.
Records don’t identify individual units or buyers; some rows legitimately look like duplicates (several identical units transacting in the same month).
Past prices don’t guarantee future ones.