To turn the sector around, urgent intervention is required. Retrofitting projects need to become more attractive to investors, and take precedence to improve housing quality and energy efficiency, reducing renter and landlord bills in turn. Equally, our sector needs to move beyond a reliance on legacy management systems that have long-created huge inefficiencies, preventing a holistic view of the housing sector, and ways in which to better cater to renter needs.

Adoption and application of emerging technologies, namely artificial intelligence (AI) and machine learning (ML), presents housing’s best chance to evolve toward a more efficient, sustainable, and renter-centric future.

The challenges of the housing sector

Across the housing sector, it’s common knowledge that management, underwriting, and renting processes have suffered long standing inefficiencies, with service levels, viable investments, and housing quality declining in turn.

Shelter’s mass survey in 2021 found that one in three adults in Britain (34%) were impacted by the housing emergency, meaning they did not have access to a safe and inhabitable home.

Also, with a 2022 McKinsey report detailing that 80% of housing available by 2050 has already been built, it’s clear that the quality of UK property will unlikely meet the required living standards, meaning even more adults will find themselves at the mercy of housing with failing safety and quality standards in the years to come.

A Eurofound report revealed that 46% of renters will have to leave their accommodation in the next three months as they will no longer be able to afford it.

The rental market in particular has a fine balance to strike: improve housing quality while reducing renting costs. Then there’s the challenge of lowering energy expenses for renters and landlords. Properties need to be retrofitted with sustainability in mind and reflected in energy consumption costs per household. Equally, investors need to be able to source opportunities that will increase the frequency of retrofitting projects.

However, the cost of renting rose in every UK region, bar Northern Ireland which stayed level, between April 2022 and April 2023. The IFS reports an increase in low-income households private renting in 2023 due to a decline in social housing, despite 25% of their homes failing to meet the Decent Homes Standard required of social housing, compared with 18% of owner-occupied homes.

This data clearly shows us that the retrofitting of UK properties is not being delivered at an acceptable pace which is increasing rental costs. When it comes to landlords, data revealed nearly two-thirds say they’ll have no choice but to put up rents by at least 10% in the next 12 months, in light of market pressures and energy bill inflation.

Solving these issues is a monumental task, one that requires institutional capital which in turn needs technology to help deploy effectively, and at scale.

Leveraging emerging technology

Sector challenges are cross-disciplinary, but there are particular problem sets where technology offers immediate advantages.

Artificial intelligence

Investing in real estate requires sifting through copious amounts of documents to collect information needed to evaluate an asset accurately. This is a lengthy process that, when done manually, increases the chance of error or oversight and drags out investment deals. Ultimately, adhering to manual processes slows down any progress the housing sector is trying to make.

Using AI language models, investors can automatically scan large volumes of data across hundreds of documents, allowing investment experts to understand assets much faster and spend more time on value-add activities. When this data is combined with comparable housing information based on a variety of factors, such as room number, location, energy rating, and mobility access, investors can look beyond newly built properties and find ones that, once retrofitted, could garner greater returns.

By helping investors find the right deals, AI will prove invaluable to solving the key challenge of housing quality for years to come.

Machine learning

The cost of data oversights within housing can fall directly on renter budgets. One key process in underwriting properties is the identification of suitable ‘comparable transactions’ to accurately value the asset. This process often takes trained investment experts many hours to complete. Without them, guesswork comes into play, and estimations become inaccurate.

Leveraging machine learning allows tech-enabled property companies to continually develop algorithms to consume much larger volumes of comparable properties than is possible via manual processes. Landlords can then use these platforms to accurately compare and contrast their property portfolios in far less time, providing more accurate and current prices to tenants thereafter. In essence, the time taken for landlords and investors to analyse comparable properties is cut from several days to almost instantly.  

Machine learning therefore greatly assists in the accurate pricing of properties. Equally, landlords can identify differences in their portfolios, and those similar, helping them better understand the housing quality and price them accurately. Investors can also use these algorithms to source and prioritise sustainability-focused retrofitting, which can be more lucrative than new build investment. Simultaneously, this works to reduce energy costs for landlords and overarching renter bills, two long standing pain points in our industry.

By leveraging these technologies, we can remove inefficiencies and speed up processes, which eliminates unnecessary costs allowing landlords to thereby provide the best deals for renters.

One thing is certain: there is hope for a revived renter-centric housing market that also caters to the needs of landlords and investors, with technology assisting the drive towards change.