A successful AI strategy requires a competitive edge on data

Building a robust database is the critical first step, but what information is in that database is equally important to consider.

At his firm’s annual Groundbreakers conference in San Francisco last month, Prologis chief executive Hamid Moghadam highlighted one major obstacle to the widespread adoption of artificial intelligence in real estate.

“Before you can engage with AI and get value out of it you have to have data, and have it digitized,” he said. “Without data, AI is useless.”

Indeed, the subject of PERE’s October cover story, APG Asset Management, shows how establishing a robust database is the critical first step in developing a successful AI strategy. The Dutch pension investor began the digitalization of its real estate business seven years ago by incorporating significantly more data into its investment decision process.

“Disclosure very much improved over time, but also the amount of data that we could leverage into our investment process dramatically increased,” Rutger van der Lubbe, the Dutch pension investor’s head of global real estate investment strategy, said in his interview with PERE. To help its real estate team navigate these enhanced datasets, APG created the precursor to what eventually became its digital portfolio manager, Samuel. Fast forward to today, and APG’s real estate team relies on Samuel to quickly access the investor’s vast repository of data – as well as provide his own analysis of that data – to help make more informed investment decisions.

The availability of data, however, is the biggest challenge that real estate owners face in AI adoption. A lot of traditional real estate data, such as sales and leasing information, is typically held by data providers and brokerages and therefore not scalable for the purposes of modeling investment decisions.

For the world’s largest property owners, one way to plug the gap is to compile and analyze the data on their own portfolios to supplement the external data they are able to access. The increasing use of alternative data – which can encompass foot-traffic patterns, credit card spending, supply chain data, demographic patterns and hybrid working patterns – is another workaround. APG derives a significant amount of its data from both internal and alternative sources.

It is worth noting internal or non-traditional data sources can also give property owners a competitive edge, since such information is not available to the wider real estate industry.

For this reason, PGIM Real Estate’s launch of its RealAssetX innovation lab this week caught our attention. RealAssetX, which focuses on researching and developing new technologies for the real assets industry, will comprise three interconnected components: data intelligence, research and intelligence and investments.

According to a press release on the launch, data intelligence focuses on “building unique datasets and advanced analytics” that combine unstructured, or text-based, third-party data with data PGIM Real Estate has accumulated over the past 50 years through its investment activities. The firm, which already has differentiated data sets derived from a wealth of proprietary historical information, is looking to further sharpen its competitive edge by tapping into additional data sources through this initiative.

When it comes to AI adoption, many property owners have not even entered the race. But for those in more advanced stages of developing an AI strategy, building and augmenting data intelligence will only help to widen their lead.