The business of commercial real estate feeds on data. Brokers depend on ownership information, developers require accurate demographic and demand information, lenders need risk intelligence, and so on. But while the need for data has been a front-burner item at countless talks and industry forums, the opposite end of the spectrum is worthy of discussion as well. Speaking on data use at a recent Propmodo Live panel, CohnReznick’s Paul Ricci said “the cleansing of the data…data governance or having a data czar or a group overseeing that data is becoming more and more important.”
Paul was speaking to an important issue, and his comment highlighted the dark side of information access: the growing amount of data—often inaccurate—that commercial pros of every color are having to face on a daily basis. From outdated records to conflicting degrees of detail, information overload can clog up workflows, throw professionals off the trail of real deals, and waste time at all steps of the transaction. This article will address the nature of data noise as well as several of its most impactful sources, and a couple of strategies to make sense of it all.
The idea that data overload is a problem is not a new one. All the way back in 2016, Bernard Marr wrote for Forbes that “if a company is already struggling to store and analyze its own data now, it will be drowning in data in the next few years.” A few years have passed, and we in real estate are feeling the weight of all this data like never before. Many of our own data giants are still towering figures (CoStar chief amongst them) but crop after crop of data provider startups have arrived and in many cases flourished. While these providers offer, by and large, very valuable services, getting these services to play well together can be a pitfall in and of itself.
As I discussed in a recent article for Propmodo, my brokerage workflow involves numerous data sources: CoStar, LandVision’s parcel data, my city assessor’s data, and LexisNexis-derived contact info. This represents not only a huge time sink to sift through but also numerous points of failure in the prospecting process. CoStar’s rents may be inaccurate, LandVision’s parcel data could be outdated, and LexisNexis’ contact information is frequently incorrect. Even my assessor’s data, straight from the City of Tucson as well (and something I once thought would be categorically the most accurate) is often out of date. In a hot market like we have now even weeks is enough to throw off an evaluation and possibly miss a good deal.
This is exacerbated by the fact that so many of the data providers offer overlapping and frequently contradictory information. Countless times I’ve found a particular subject property only to find that CoStar lists one owner, the assessor lists another, and LandVision lists a third. Infrequently-updated databases can be problematic for their users, but multiple independently-necessary yet often clashing databases are a genuine migraine.
Purchasers, leasing agents, and consumers, in general, have another headache to deal with: the omnipresence of high-resolution maps and Google Streetview can make snap judgments far harder to overcome. It’s always fun to see people waving at the camera in Google’s streetside camera snaps, but if that camera also caught a sidewalk torn up from construction or, maybe even more importantly, signs of damage or unsavory activities at a building itself. Businesses and owners can find themselves haunted by a bad neighborhood long after construction completes or damaged facades are repaired.
While some of these challenges will cast a shadow over all firms, others will affect some teams more than others. Our data-heavy world can democratize information, but for individuals or teams who struggle to keep up, their shortcomings will be amplified for all to see. Professionals in different parts of the industry already speak different languages in terms of modeling and analysis metrics. With growing data granularity and ever-increasing ease of access, real estate pros who fail to highlight the right data for the right audiences will find themselves outclassed by streamlined competitors who can.
There is no silver bullet solution to any of these issues, either. However, there are a couple of ways to combat this information overload. By striving to utilize best practices and remembering that in the world of data, there is no one-size-fits-all software solution, you’ll position yourself well to most efficiently wrangle your data into real insights.
In database work (or other work leveraging the power of databases), it is easy to start cutting corners: abbreviating words; adding different levels of detail from record to record based on convenience; batch updating values instead of at the time of use. Each of these shortcuts might save a few seconds here or a few seconds there, but they could also spell trouble down the road for your team. Abbreviations might mean one thing to you but another to your coworker who also uses the database. Different degrees of detail at the record level can quickly lead to a patchwork of granularities that can be maddening to make sense of and easily disrupt workflows. Batch updating your data can be great if you take good notes and don’t forget anything along the way. On the other hand, errors as mundane as confusing two similar records (perhaps neighboring properties) can lead to widespread data inaccuracies.
All of these challenges are magnified with each additional coworker. Take the time necessary to establish certain best practices across your entire team and reap the rewards later, and always remember the 80/20 rule: spend the most effort on the information that will make the biggest difference, and don’t stress about data that may or may not become relevant in the future.
Second, professionals should remember to find what works best for them at the individual or team level. The data gathering regimes utilized by providers in New York may not possess the same granularity, timeline, and accessibility as data in Los Angeles, and the same is also true from property type to property type. Just because Data Provider A works for Team X on the east coast, doesn’t necessarily mean that that provider will also be the best solution for Team Y in the west. Similarly, don’t fall into the trap of thinking that only certain tech platforms are worth your consideration. Even if a given niche appears to have an 800-lb gorilla option, take the time to thoroughly research the alternatives. Teams differ drastically in their levels of sophistication as well as their specific data priorities. Don’t invest time and money in a software option that will prioritize the display of data you don’t need.
This short piece has only just begun to scratch the surface of the data pitfalls now affecting real estate teams around the world. From uneven accuracy to differing expectations of measurables, the perils of our data-driven world could fill a whole series of articles. Hopefully, this article can inspire a discussion on how to prevent messy data from overwhelming you, and help build workflows and processes that keep your data working for your team and not the other way around.