The digital revolution has unleashed a wave of CRE data tools, but not all data vendors were created equal. Here’s how to choose the most effective one.
There’s been considerable hype in the commercial real estate (CRE) industry about the latest business buzzword du jour: data. What efficiencies can it enable? What does it mean for traditional brokerage? How will it impact market research?
But while the CRE industry’s interest in data emerged in the wake of the digital revolution, digital technologies have not, for the most part, expanded the scope of information with which brokers work. The industry’s foundational data — cost of capital, demographic shifts, workforce trends, ownership histories, zoning regulations, etc. — remains the same, and no amount of technological innovation is going to change it.
What has changed is the ease with which brokers and other CRE professionals are able to access the high-value information they need to jump on opportunities as soon as they materialize. Information that was once accessible only to those patient enough to rummage through microfiches in some forgotten corner of a municipal office is now available to everyone on multiple devices.
That said, in a business like CRE, it’s the quality of the data that matters — more than both quantity or accessibility. According to one study, “only 50 percent of organizations with low data quality see accurate data analysis, while that number jumps to 83 percent for organizations with high data quality.”
This means CRE professionals interested in leveraging newly accessible market data should be looking beyond the sleekest new interfaces, and consider the underlying standards and practices to which different vendors hold the information they provide. To that end, here are four things every broker should look for when in the market for a new CRE data platform.
1. Is Your Data of High Quality?
As highlighted above, the quality of a vendor’s data is of unparalleled importance. While there isn’t a universal definition of “quality,” most experts agree that completeness, consistency, validity, and timeliness are all hallmarks of high-quality data.
Not only does incomplete data make it difficult for a platform’s users to perform rudimentary operations, it can also have knock-on effects. For instance, if a platform is missing the square footage for a particular building, not only will the building not show up in a simple square footage query, but its secondary measurements — cost per square foot, square feet per seat, etc. — will be thrown off, as well.
If a platform’s datasets are inconsistent or invalid, on the other hand, users are likely to receive either inaccurate or, more frequently, duplicate information that only serves to muddle what should be a straightforward search. This is a persistent problem for CRE data aggregators that simply draw as much data as they can from numerous sources without carefully cross-referencing the information. It’s important to ensure that the platform you’re using not only verifies the validity of all the data points its providing, but also leverages advanced algorithms designed to avoid redundancies.
In reality, no data platform is immune to errors or inconsistencies, but some take greater care to minimize them than others. In an industry as fast-paced as CRE, the more frequently data is validated, the more relevant it will be to the brokers who depend on it. In turn, brokers with access to up-to-date information will be better equipped to jump on potential opportunities.
2. Where Is Your Data Coming From?
As we worked to scale our offerings at Reonomy, we quickly realized that providing high-quality, comprehensive CRE data — especially on a national scale — would be all but impossible without strategic partnerships. The criteria according to which we evaluated potential providers were strict: they needed to produce high-quality datasets drawn from a variety of sources using sophisticated approaches that weren’t reliant on error-prone manual processes.
Measures like these are designed to ensure we’re presenting complete asset profiles for our users, as several records may exist in reference to the same property or “entity.” By applying machine learning algorithms to disparate datasets, we’re able to aggregate information about entities regardless of differences in record shape, storage location, or curator style or preference.
Conducting this process manually almost inevitably results in either duplicate pieces of information or incomplete asset profiles, making it difficult for brokers to do their jobs effectively and at scale.
3. Are You Equipped to Operate in Every CRE Market Across Asset Classes?
CRE data platforms that work well in one set of circumstances doesn’t necessarily work well in all circumstances — something that brokers should keep in mind if they work in multiple markets or asset classes.
The nearly 50 million commercial properties in the U.S. are sprinkled throughout more than 3,000 counties, each of which presents unique challenges to efficient data aggregation.
In New York City, for instance, most locally-managed CRE data is structured in a way that is machine-readable, making aggregation fairly straightforward. By contrast, sprawling cities like Los Angeles — L.A. County alone is comprised of 89 incorporated municipalities, each with its own approach to collecting and organizing property data — present many more obstacles to building complete (let alone consistent) datasets.
In order to source mortgage or sales documents in L.A., for example, one would need to call the Land Records Information office, submit a request, and wait days to receive the information. In New York City, on the other hand, that same information is more structured and accessible. Not What’s more, there is also a tremendous amount of metadata surrounding an event recorded in New York City. This metadata serves as a reference chain that helps data consumers understand how different events relate to one another, and allows consumers to infer important information — for example, if a mortgage has been released, assumed, or assigned.
As such, it’s important to assess a data vendor’s capabilities across all of your areas of operation, not just one or two of your niches.
4. Do You Offer Analytics Capabilities?
Advanced analytics capabilities can be a game-changing bonus for brokers looking for a competitive edge. After all, CRE success pivots not only on high-quality data, but on how brokers and investors make sense of it — and the strategic decision-making it empowers.
By analyzing seemingly unrelated data points within a broader dataset, effective CRE analytics tools can help brokers better understand the underlying factors driving business in their specific market or niche. Truly sophisticated platforms can follow digital breadcrumbs to move beyond surface level information, and uncover essential information on property ownership, sales comparables, debt history, and likelihood to sell.
This additional step from information to insight can often be the decisive factor in whether a broker is able to capitalize on the most lucrative opportunities, making it an invaluable add-on to any CRE data platform.
A Roadmap to CRE Dominance
After decades spent manually gathering critical information, CRE professionals finally have instant access to the coveted data they need to operate efficiently and strategically. But it’s up to them to discern between data that has the potential to drive meaningful business and data that may only complicate existing processes.
By keeping the above considerations in mind, brokers will be well on their way to making the data-driven decisions that will differentiate them from the competition.
About the Author : Richard Sarkis is CEO and co-founder of CRE data engine Reonomy