- Kenya has annual housing demand of 250,000 units with an estimated supply of 50,000 units
- This culminates to a housing deficit of 2 million units, an equivalent 80 per cent gap.
Image: DOUGLAS OKIDDY:
Kenyans living in informal settlements are not benefiting from formally constructed houses as the projects only target two percent of them, according to the World Bank.
The bank in its private sector development outlook notes that those residing in this areas are prone to high risk of communicable diseases such as malaria and cholera.
This has been a concern that has seen the government intervene through the affordable housing programme to boost the living standards of its citizens, at the same time narrowing the housing deficit in addressing inequality.
Data by Habitat for Humanity notes Kenya has annual housing demand of 250,000 units with an estimated supply of 50,000 units, culminating in a housing deficit of two million units, an 80 per cent gap.
Although the previous regime fell short of its target under the affordable housing initiative to deliver 500,000 units by 2022, the Kenya Kwanza government has reiterated its plans to fast track the project’s delivery by producing 200,000 units annually.
The Word Bank is calling for adoption of Artificial Intelligence (AI) into the programme in order to provide more targeted and efficient approach to housing development, management and finance.
It says through this, developing nations can address the issue of unlimited access to housing, especially for the informal sector.
“The use of AI in housing can help to make a real difference in the lives of people living in emerging markets. It can provide a targeted and efficient approach to housing development, management and finance," the lender says.
AI could also promote access to housing by analysing data on demand and supply, as well as data on the social and economic characteristics of households.
This by identifying areas where there is a high demand for housing in relation to population growth, urbanisation and migration to identify areas where housing demand is likely to increase in the future.
The information could then be used to guide the development of new housing projects and the allocation of resources, ensuring that they are targeted to areas where they are most needed.
“This depicts the designing of low-cost, energy-efficient and climate-resilient housing, which can be built using locally available materials and could help reduce the costs associated with housing construction and make it more accessible to informal households,” World Bank says.
It can further play a role in enabling informal households access finance for housing, the lender says.
Machine learning algorithms can be used to analyse financial data and assess the creditworthiness of households, which can help to identify those who are most in need of financial assistance.
Pointing towards digital transformation, the lender notes that private sectors and non-profit organisations should invest in research and development of AI and its application in housing to maximise on its benefits.
It would then seek to address the housing deficit concern which is a pressing issue that affects millions of people around the world.
According to the United Nations, the world needs to build an additional 18.6 million affordable and adequate housing units per year to meet the needs of the world's population.
This deficit is particularly acute in emerging markets, where rapid population growth, urbanisation and economic development are putting pressure on housing systems.