In late March, as COVID-19 ground the U.S. economy to a halt, Dan Rosenbloom’s phone was buzzing with activity. Real estate owners, brokers and operators - many of whom Dan had known and worked with for decades - were inundating Cadre’s head of acquisitions with information about properties across the U.S.

As the calls piled up, Dan began to take particular interest in a few select markets that Cadre wasn’t investing in - places that hadn’t made the cut of the ‘Cadre 15’, the carefully curated list of what Cadre believes to be some of the top, most appealing real estate markets based on Cadre’s proprietary, data-driven market selection process. It occurred to Dan that the unprecedented pandemic had thrown the market into such immediate turmoil that it was worth reassessing all assumptions.

“I’ve been in this business a while and, based on my experience, contacts and my gut, I sensed that there’s something happening in some of these places - opportunities that might be very compelling,” he said.

So, Dan did what he usually does in circumstances when his intuition is piqued: he turned to his colleague, Ashwin Raghu, Cadre’s head of data science and engineering.

Ashwin shared Dan’s recognition that COVID-19 increased the timeliness of reassessing the market. Ashwin and his team of data scientists began collecting and aggregating supplemental data on markets across the U.S., including  historical transaction data, economic data, demographic trends - an array of information needed to be revisited due to the nature of the pandemic and run through the systems his team built to, in this case, determine if the environment was changing enough to shift the composition of the Cadre 15.

“We’re supplementing experience - in this case Dan’s decades of industry knowledge and his intuitive feel for markets - with science,” Ashwin said.

Call it the “qualitative quantification of real estate investing,” a Cadre process that leverages market subjectivity with rigorous data analyses, surfacing macro and micro trends. These insights manifest themselves in a number of ways: providing guidance into which markets and which assets to invest in, optimizing asset management, and helping inform the bidding process.

‘’Real estate has evolved from a technology perspective,” Ashwin says. “We’re able to inform experience and gut instincts with hard facts.”

Market selection 2.0

Compared with many other real estate investors, Cadre takes a somewhat different route in deploying technology.

Many real estate investors use a mix of past experience and best judgment to pick markets to invest in. The role of human intelligence and expertise in real estate investing shouldn’t be diminished, Ashwin says, but points out that firms that operate exclusively under this model do so at their risk. “Human decision making suffers from blind spots, limited attention spans, and confirmation biases,” he says.

One level up, using a more systematized, technology-enabled approach, some investors rank and screen markets by certain metrics - population or job growth, for instance. This, too, has its downsides as it usually involves arbitrary decisions about weighting. As a result, the process often produces rankings that tend to be more descriptive of where a market is, rather than where it is headed.

Cadre’s approach is different. One example that Ashwin shared is a hierarchical model that his team created as an advancement over conventional market selection techniques, for instance by incorporating a Recurrent Neural Network into a classical statistical framework known as Holt-Winters. The result is a predictive model that is designed to accurately capture the complex relationships that exist between the various variables that drive market performance such as population, income, rent, occupancy, and new construction and is designed to produce an unbiased and more accurate view of the market and its trajectory.

“We created a system to predict two-year asset-market performance and generate a relative ranking that highlights the investments we believe will outperform on a total return basis,” Ashwin says. “It’s not the only way, but we think it’s a better way,” Ashwin says, “and the role of quantitative methods in real estate investment is bound to grow as data in the asset class becomes more abundant and structured.”

Revealing insights

Dan points out that data has backed up and confirmed the decisions he makes based on his knowledge and experience on more than one occasion - providing him with additional confidence and opening up new markets for Cadre to invest in.

For example, recently the team has been scouting, among others, opportunities in the Washington D.C. area, which didn’t make the Cadre 15 cut originally because of lower projected appreciation in the market. But together, Dan and Ashwin were able to see that the post-COVID environment presented opportunities there. Their combined efforts went beyond the obvious recognition that the D.C. area has demonstrated resilience during economic downturns as well as an ability to stay liquid.

Other areas of interest include office properties both in the Los Angeles and San Francisco Bay area, which hadn’t initially warranted inclusion into the Cadre 15 because pricing was prohibitive, though projected appreciation had ranked very high. But back on the phone, Dan began hearing of some price dislocation due to the area’s outsized adoption of work-from-home immediately post-COVID. Persisting uncertainty nonetheless remains around the office sector, warranting deep due diligence at the asset level.

Proprietary insights can also come in handy when it comes time to consider exiting a market. One example of where the data and intel lined up to inform both the purchase and sale decision was a multifamily property that Cadre acquired in 2017 in Salt Lake City, where data was pointing to an influx of people and jobs. Two years later, the rest of the industry had largely caught on, creating an opportune time to exit as data began pointing to a potential peaking of leasing momentum. And this goes back in Cadre history  -  the first asset Cadre sold was a multifamily property close to home in New York, where data was suggesting a slowdown in rent growth. The team opted to sell and monetize on the appreciation  to-date (which in hindsight was indeed a good call as rent growth has since become more muted).

Informed bids and asset management

Dan says that the power of the data is not just that it informs but it confirms his market instincts.

“The data gives us an edge,” Dan says, “not only whether or not to be in a market, but also what we can bid.” He says that if Ashwin can show him that a market or asset has particularly bullish prospects otherwise impossible to detect without the data crunching, he can afford to outbid competitors and move quickly, with conviction.

“Data can show trends that can give me conviction in the upside of an investment - and conviction is a critical element for real estate investors,” Dan says.

There’s more: Once a property is purchased, Cadre’s asset management team can use real-time data - as opposed to monthly inputs - to track the business plan in numerous ways such as in identifying issues like raising or lowering rents and react in real time, adjusting marketing spends to attract tenants, and measuring premiums generated on newly renovated units which can help ensure that Cadre’s business plan is on track.

“We can identify assets better, have more inputs on the bidding process, manage properties with greater efficacy, and also have data to inform sale decisions,” Ashwin says. “This is just a modern way of investing in real estate, leveraging advanced analytic tools across the entire lifecycle of the asset, and supplementing the experience we have in the marketplace.”

It’s overstating the case to draw an analogy with the so-called Agricultural Revolution during the 18th and early 19th centuries, a period of technological innovation that boosted crop productivity by leaps and bounds. But perhaps, it’s an apt metaphor for Cadre’s relationship with data. “I’m a farmer planting seeds,” says Dan, “and if the data can point me toward more fertile fields, I’m going to increase my yield.”


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