How will big data analytics prevent future housing oversupply?

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Big Data Analytics to Inform Housing Development and Reduce Oversupply

Big data analytics will be integrated into housing development planning from next year, under the National Housing Policy 2026-2035 (DRN 2026-2035), to more accurately assess local needs, market demand, and income levels. This data-driven approach aims to ensure that the number and types of homes built align with specific district requirements, thereby mitigating the issue of unsold properties. The system will leverage artificial intelligence to identify suitable housing products, prices, and locations that best match local conditions.
Data-Driven Needs Assessment by District

The implementation of big data analytics will enable a granular assessment of housing requirements at the individual district and location level. This will include determining the precise number of housing units needed and the specific types of homes that are in demand within each area. For instance, if data indicates a need for 5,000 housing units in a particular district, developers can use this information to tailor their project planning.
Tailoring Affordable Housing Definitions

A key function of the big data analytics system will be to incorporate local income levels and property prices. This will allow the definition of "affordable housing" to be more scientifically aligned with the economic realities of different regions, moving away from a one-size-fits-all approach. Housing and Local Government Minister Nga Kor Ming highlighted that an affordable home price in Kuala Krai, Kelantan, should not be expected to be the same as in Bukit Bintang, Kuala Lumpur, underscoring the need for localized affordability metrics.
Guiding Developer Planning Decisions

Developers will have access to the insights generated by the big data analytics system to inform their project planning. The data will assist them in making informed decisions regarding the type of housing to construct, the appropriate pricing strategy, and the most suitable locations that will resonate with local demand. This proactive planning is intended to ensure that new developments are more responsive to actual market needs, thereby preventing a future oversupply of unsold homes.
Achieving the One Million Home Target

The overarching goal of providing one million homes by 2035, as set out in the DRN 2026-2035, is underpinned by this data-driven strategy. The policy aims to ensure that this target is met by providing Malaysians with homes that are not only available but also suited to their specific needs and financial capacities. Big data analytics is presented as a crucial tool to achieve this objective by ensuring that supply is closely matched with validated demand.

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How will big data analytics prevent future housing oversupply?

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