Data analytics for mixed-use developments

Mixed-use projects are increasingly popular among developers around the world. But success depends on precisely tailoring the offerings to customer demands. Sophisticated data analytics hold the key. By Paul Allen

At Kerten Hospitality (KH), every piece of data that flows into its mixed-use projects is analysed to maximise the returns and efficiency of the assets in its schemes. The global hospitality group manages and operates hotels, branded residences, serviced apartments, co-working hubs and dining outlets, creating destinations to stay, work, shop, eat and socialise across 12 countries. Assets are run with a focus on the guest experience, delivering efficient operating models and maximising space to extend seasonality and improve the ROI.

Using technology tools such as hotel revenue management solutions, building management systems, quality  control tools like ReviewPro and point of sale systems, KH captures and analyses a wealth of data to plan its resource needs, and drive efficiency and productivity, explains Chief Operating Officer Wafik Youssef. Data it collects includes customer profiles and target age groups, booking and spending behaviour, social media interactions and followers, venue footfall, waste management, energy usage versus occupancy and levels of cross-selling between project units “Such analysis provides us with the details needed to optimise our cost and revenue strategy, helping to drive maximum profit per square metre,” Youssef says.

Data-driven repurposing

Mixed-use developments provide a risk hedge for developers and investors by diversifying income streams and eliminating dependency on one asset class. Many, like the KH model, are hospitality anchored. The retail sector, where long-term disruption has left swathes of empty high street spaces and shopping centres, is particularly ripe for mixed-use repurposing. Aging offices in non-prime locations may face similar demand challenges in a post Covid, hybrid-working, sustainability- focused world. Data analytics can help determine what the most financially viable use mix should be.

Such analysis provides us with the details needed to optimise our cost and revenue strategy, helping to drive maximum profit per square metre.
Wafik Youssef, Chief Operating Officer, Kerten Hospitality

Verbraucherverhalten während des gesamten Lebenszyklus erfassen

For agent JLL, the first step is to use data to identify which buildings or large scale schemes will or won’t work as “investible places” and where to concentrate the firm’s efforts, says Tim Vallance, JLL’s Head of Investor Services and Retail Chairman. “Through data, we can now work out the alternative uses of thousands of properties at the touch of a button.”

JLL then tracks data during a building’s lifecycle to assess how the asset is performing and, most importantly, what consumers want. “The consumer is telling us they love mixed-use spaces,” says Vallance. “They like everything to be convenient and authentic. They like to work in a place, be able to walk outside and find a nice restaurant around the corner, and not have to travel too far to where they live.”

Versteckte Erfolgsfaktoren erkennen, optimale Nutzung ableiten

The most important factor in a property’s success is that the economic fundamentals must be right. The second is location. “Connectivity is hugely important,” Vallance notes. Where data analytics plays a vital role, is in uncovering the hidden factors that influence a development’s performance, especially in property adaptations and repurposing, to create the optimal combination of asset types. Clearly a shopping centre in a big gateway city such as London, Paris or Berlin will likely work for residential, Vallance adds. If it is on a motorway junction, it will work for logistics. The data gems that really help identify where to invest, though, are based around demographics.

“Our data team have the demographic data for every postcode in Europe. They can overlay how people are voting, what industries they work in, how people in those postcodes tend to behave, whatthey might respond to. Based on the data, the team can tell you how many shops are required, how many offices and beds are needed, whether the beds should be private renters, socially rented or privately bought.”

Through data, we can now work out the alternative uses of thousands of properties at the touch of a button.
Tim Vallance, Head of Investor Services and Retail Chairman, JLL

And as the data collection tools expand, datasets get richer and analytics capabilities improve, mixed-use developments will become ever more targeted. “Right now, we see data analysis on the go, which helps us make faster decisions.

This will continue to evolve until we have constant visibility on the business aspects in real time and can anticipate future prospects in a seamless way,” KH’s Youssef concludes.

By Paul Allen

Title image: JLL

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