How will data-driven planning address the unsold property issue?

Question

Accepted Answer

Data-Driven Planning to Address Property Oversupply

Starting next year, housing development will be guided by local needs and market demand, leveraging big data analytics to reduce the oversupply of unsold properties. This initiative, part of the new National Housing Policy 2026-2035, aims to ensure that new housing projects align with actual requirements in terms of quantity, type, and price points across different areas. The system will incorporate local income levels and property prices to define affordability more accurately, moving away from a one-size-fits-all approach.
Assessing Housing Needs with Big Data

The core of this new approach involves using big data analytics to meticulously assess housing needs at the district and location level. This will include determining the precise number of homes required and the specific types of residences that are in demand. By analyzing granular data, the system will provide a more precise understanding of market gaps, moving beyond generalized assumptions.
Incorporating Local Economic Conditions

A crucial aspect of the data-driven planning is the integration of local economic factors, such as income levels and existing property prices. This ensures that the definition of "affordable housing" is tailored to the specific economic realities of each area, preventing mismatches where housing priced for one locale is unsuitable for another. For instance, the policy aims to avoid a situation where housing in a high-cost urban centre like Bukit Bintang, Kuala Lumpur, is priced similarly to that in a less expensive region like Kuala Krai, Kelantan.
Developer Subscription and AI Support

Developers will have the option to subscribe to this data system, although its use will not be mandatory. The insights derived from the data are intended to assist developers in making informed decisions about the type of housing, pricing strategies, and optimal locations that best match local demand. Furthermore, artificial intelligence will enhance the system by identifying suitable housing products, price ranges, and locations based on the analyzed local conditions, providing an advanced decision-support tool.
Ensuring Demand for New Homes

The overarching goal of providing one million homes by 2035 is directly supported by this data-driven methodology. The focus is on ensuring that there is a demonstrable need for these homes before development commences. This strategy is designed to prevent the future accumulation of unsold properties by ensuring that supply is a direct response to identified and validated demand, thereby promoting a more balanced and sustainable housing market.

Reading history

Hide →

This history is cleared when the page is closed.

How will data-driven planning address the unsold property issue?

Loading

Was this article helpful?

Helpful

Not helpful

Report an issue

1

0

0

Share with friends