New tech can speed up Europe’s single-family rental sector.

As we end the last quarter of 2021, a second volatile year due to the pandemic, institutional investment in Europe’s residential sector is on the rise as landlords look to satisfy the demand for stable returns.

Single-family rental (SFR) is an untapped asset class, where units of rental property can be used to build highly diversified, low-risk portfolios. But in order to deliver this at speed and scale, harnessing data and technology isn’t just an idea, it’s a necessity.

According to the Organisation for Economic Co-operation and Development (OECD), Germany has the second highest rental rate of OECD nations, with 54.5% of its population renting their homes. The US, in comparison, has a rental rate of only 34.9% (OECD: Society at a Glance 2019), however, SFR as an asset class is already relatively established, with major landlords such as Blackstone, each owning tens of thousands of SFR units.

In August, The Daily Telegraph reported that housing affordability in the UK’s cities has reached a record low, with salaries failing to maintain the rapid double-digit property price growth over the past year. This was confirmed by a recent Halifax report, commenting that cities in the UK have experienced a meteoric 10.3% rise in house prices. By comparison, in the past 12-months, wages in UK cities only increased by 2.1%, highlighting the inaccessibility of homeownership in the residential sector.

Worsening house affordability is a similar story emerging across Europe and a cultural shift towards renting is inevitable. Consumers now choose to rent rather than buy, in order to retain flexibility of where they live and spend their money.

The residential market in Europe is now estimated at €40tn, offering a wealth of opportunity for those who can unlock it. IMMO Capital is the first SFR asset manager in Europe to develop an advanced AI-enabled platform to identify and assemble SFR rental portfolios at incredible speed and scale.

A key part of its process is identifying ‘gateway cities’ and within those locations it finds the best properties that match investors’ exacting requirements. Machine learning is used to process over one million data points within 5 x 5 mile zones, dynamically identifying pockets for investment and growth. Both traditional and non-traditional data points are analysed to determine the most accurate potential of an asset and location.

Using an example in Germany, Cologne’s GDP and employment growth has steadily outperformed the German average. Between 2005 and 2020, Cologne’s GDP grew at a compound annual growth rate (CAGR) of 1.52% compared to the German average, which stood at 1.04%. 

During the same period, Cologne’s employment grew at a rate of 1.47%, versus the German average of 0.88%. These are indicators that support a capital and rent growth opportunity, not only in the Cologne market, but also in its surrounding areas. Similarly, in many areas of Germany, high rental price growth continues to outstrip supply and this growth isn’t limited to the cities, but is also in its commuter regions. 

Taking Cologne again – where rentals make up 71.9% of housing – and one of its neighbouring commuter districts, Rhein-Erft-Kris, as an example: the growing demand for rental properties within these two districts has resulted in rental price growth of 12% over the past five years and a significant drop in the average time it takes for an apartment to rent, falling from around 10 weeks to around five weeks in Rhein-Erft-Kreis, and from around six weeks to around three weeks in Cologne. With population and disposable incomes forecast to grow annually by 1.2% and 1.0% respectively over the next 10 years, demand and rental prices will continue to drive upwards in both regions.

To then identify the best areas within these regions, traditional data sources can be examined. These include distances to local amenities and transport links, commute times to major employment hubs, the number and ratings of local schools or nurseries, proximity to green spaces, the evolution of local retail offerings and their review ratings, pollution levels, noise levels and more. The result
is that every individual asset has its own score to assess not only the rentability of the unit today, but also its potential into the future. 

Less than 2% of the transaction market is made up of residential assets typically accessible to institutions – built-to-rent (BTR), multi-family or secondary portfolio acquisitions. The remaining 98% has been impossible for institutions to access, as the traditional processes associated with aggregating and managing individual units at scale are too inefficient. Today, this is no longer the case. 

Technology is now capable of creating data-led investment strategies to identify and rapidly underwrite a large volume of individual assets, unlocking the possibility to efficiently source directly from consumer sellers. 

Within a single city, IMMO Capital’s technology assessed over 20,000 bespoke investment leads within 12-months with a total value of over €3.5 billion. The resulting portfolio assembled for investors delivered stable cash flows, with a strong gross entry yield and +10% uplift in value-to-purchase-price (per CBRE valuation) over an average hold of only 18-months. Building portfolios of dispersed SFR assets at this scale and speed is only made possible by technological efficiencies across the entire value chain – from sourcing, acquisition and portfolio management through to lettings and property management.

By working with leading, tech-driven asset managers to establish SFR real estate portfolios from existing residential stock, investors no longer need to target only 2% of the market that is multi-family blocks. Coupling an end-to-end SFR platform with expert market intelligence provides investors with attractively priced, well-located residential portfolios that deliver stable financial returns within weeks as opposed to years.