How Predictive Analytics Can Change Commercial Real Estate Brokerage
This guest post comes to us from Dan Spiegel, Executive Vice President of Operations for Colliers International in the United States.
It’s no secret commercial real estate is heavily data-driven, as the deals brokers and owners make in any given market depend on a number of key points: levels of availability, new space being built, employment trends — the list goes on and on. Every commercial real estate brokerage collects mountains of data around these characteristics.
But the industry still doesn’t view itself as a data business. Firms in retail, finance, and even residential real estate have started hiring data scientists to analyze massive swaths of information to help paint a picture of what the future will look like. Today, most brokers are just using small, localized pieces of available data to make individual deals, rather than looking at the whole picture to craft an effective long-term strategy.
That could soon change as the industry embraces predictive analytics. It’s only a matter of time before there’s an effective software solution that pulls in even more data and turns it into actionable analysis that enables brokers to be more proactive. Let’s dive into some of the ways predictive analytics can change the way brokers work.
Helping Owners Make Better Investment Decisions
Predictive analytics can give brokers insights on where the next investment opportunities are and even help them decide which ones they can pass on to owners in order to make deals happen. By aggregating disparate data points like cost of capital, demographic shifts, workforce trends, and countless others, brokers will be able to empirically demonstrate to owners how a building would drive value and fit into their firm’s investment model. Conversely, they would also be able to find assets in owners’ portfolios that have maximized their value and ought to be sold.
Predictive analytics could also empower brokers to think bigger than one city and bring investors a wider array of opportunities. Right now, most brokers develop a deep expertise around a local market and advise clients on investments within that market. With stronger data analysis tools, those brokers would be able to steer their clients toward opportunities in other markets that will meet their goals. So, for example, if an owner approached a Chicago broker about buying office space, that broker might be able to tell them that rental rates are actually more likely to increase in a nearby secondary market like Milwaukee.
Predictive analytics widen the scope of what brokers can do, help them land more deals, and make them more valuable to owners. Both sides win, and the entire commercial real estate marketplace becomes more efficient.
More Proactive Leasing Representation
While predictive analytics’ most obvious application comes on the investment side, it also has applications in leasing.
If agency brokers had the ability to predict tenant behavior, they could be more proactive in how they lease up vacant space. Instead of making 30 calls searching for potential tenants, they could narrow their list down to companies who are most likely looking to move based on their industry, headcount growth, and the behavior of similar firms under current market conditions. Same thing with tenant retention. Rather than wait for a company to say they’re planning to move, the broker could get in touch sooner and start a discussion on next steps.
On the tenant rep side, brokers could use predictive analytics to ensure their clients are renting the best possible space. This would be especially valuable for long-term leases. If a company is leasing an office for ten years, they want assurance the workforce they need will be there for the duration. Predictive analytics could give brokers the ability to say that with confidence.
What’s the Next Step?
The data commercial real estate needs to move into the predictive analytics era is out there. Commercial owners and brokerages have data around their assets, deal activity, and demand spread across multiple systems, and companies like Hightower are already helping them consolidate and analyze it more easily. Other information on population and workforce trends is readily available as well.
But even so, brokers aren’t experienced at pulling all that data together, analyzing it on a huge scale, and drawing out insights they can present to clients. They need software tools that can do the heavy lifting for them with sophisticated algorithms and machine learning.
Companies like Alteryx and Enodo Score are already working on this problem, but there’s no clear market leader yet. It’ll be a tough nut to crack, but when someone does, brokers will have a game-changing tool at their disposal.
