Guest view: Redefining residential investment – a data-driven future

In the evolving landscape of the residential market, institutional investors stand on the cusp of change. As the UK government implements ambitious reforms in the private-rented sector, the need for energy-efficient, secure, high-quality and affordable homes is paramount.

Institutional investors are well placed to step further into the residential market to fund the delivery of safe, stable, quality homes.

Government initiatives have so far focused on constructing new homes to meet housing demand, but this has fallen short. With 233,000 new builds constructed in 2021/22, failing to meet the 300,000 target, a more comprehensive solution is still required to meet residential needs for quality housing.

For investors looking to bridge this gap while maximising returns, the refurbishment and retrofitting of existing housing stock presents an immediate opportunity.

The truth is, whether investors looking at the private rented sector are scaling up portfolios of new build or existing homes, the trouble often lies with the granularity of the market. Traditional, manual processes are not only labour-intensive but highly inefficient, offering a secular and inaccurate view of the market.

A smarter approach to investment is required, where data analytics and technology are not just tools but essential components in identifying opportunities, driving smart investment decisions and efficiently managing residential portfolios.

Underused data and the role of AI

The traditional metrics used by institutional investors, such as historical price trends and rental yields, are now being outpaced by the need for more nuanced, granular data. These conventional metrics fail to capture the local market dynamics which are crucial for spotting hidden investment opportunities and micro-trends that drive investment returns.

The greatest untapped potential lies in leveraging sophisticated data analytics to provide a more comprehensive view. By integrating non-traditional data sets such as localised retail data, consumer behaviour insights and so on, investors can gain a competitive edge.

Naturally, artificial intelligence (AI) is at the forefront of this revolution. By employing machine learning algorithms, investors can predict trends and make informed and sustainable decisions faster than ever before. AI’s predictive capabilities enable the early identification of areas poised for growth or decline, allowing investors to act before these changes are reflected in market prices.

In practice, this can also mean a better understanding of supply and demand dynamics within micro-locations by analysing the relationships between commute times (by public transport or driving), rental growth rates, rental yields, incomes, time-to-let data etc. across the commuter regions of various cities.

This can help to identify the best micro-locations that will deliver yield premiums at scale, whilst being the most attractive to residents from a location and affordability perspective, resulting in lower voids and more stable cash flows for an investor.

Advanced analytics can also incorporate factors such as neighbourhood development plans, green spaces, local amenities, local employment rates, or even the quality of nearby schools - elements that significantly affect residential desirability and, consequently, investment performance.

The wider implications of a technological revolution

The potential of technology in real estate is vast and still largely untapped. It’s possible to envision a sector where technology plays a central role in every aspect of residential investment - from identifying potential investments to managing properties and engaging with communities.

The integration of technology in the residential market demonstrates how its utility extends beyond mere financial gain and offers the chance to positively impact the environment and local communities. For example, investors should be aware that to avoid the risk of accumulating stranded assets, they must increasingly consider how their investments measure up against rising pressures and regulations around sustainability.

In practice, investors can utilise algorithms to identify and prioritise sustainability-focused retrofitting projects which can yield higher returns than new construction investments. In addition, AI automates the consumption of data which enables investors to quickly gain insights into their portfolio and spot areas of risk or opportunity relating to sustainability measures.

We’ve reached a pivotal point in the market’s timeline; before us lies a future where technology and investment practices merge to create a real estate market that is more profitable and also a means to foster sustainable living.

As the opportunity of unlocking this new asset class becomes realised and investors work out how to deploy into this sector, technology becomes a crucial tool for developing the value chain.

Institutional investors at the forefront of the technological revolution not only stand to reap the reward of unlocking a new asset class, but also the peace of mind that their investments will stand the test of time in a sustainable future.