Unpacking Building Data Through the Story of Goldilocks and the Three Bears
Everyone knows the story of Goldilocks: A curious young girl, wandering past the boundaries of her normal day, stumbles upon the home of three bears, sampling the in-home facilities of each who lives there. Goldilocks endeavors to teach children, if nothing else, not to enter the homes of bears, let alone sample their meal, chair, or bed.
There is, however, another frontier facing a similar set of questions as Goldie did on her fateful visit to the home of the three bears, and that’s in our use of data about built environments and the business insights building data can bring forth.
The edifices we live, work, and play in aren’t just walls, doors, and windows. They’re sophisticated structures composed of varying materials meeting building norms of the region and era in which they were built. Similar to a fingerprint, no building is exactly like another. The components of each are pieces of information that share insight into how it should be managed, reinforced, or protected.
Building data can be used for any number of purposes. Insurance brokers use building data to secure commercial insurance coverage for clients. REITs use it to understand their exposure to extreme weather events. And most recently, ESG efforts are honing in on organization-level carbon outputs, a portion at least of which can come from facilities used to run the business.
But those who utilize building data to inform decision-making still face a critical question. How much data is too little or too much to make an informed decision?
As extreme weather events brought by climate change continue to make their presence known, commercial real estate and residential businesses alike will have big decisions to make. Is building multifamily housing for seniors on the coastlines of South Florida still a viable investment? Can a distribution warehouse in Glenn County, California stand up to the threat of wildfire? Is a hospital built in South Texas capable of sustaining a winter deep freeze? Is it wise to build a new high-rise near known fault lines?
Questions like these are already showing up in boardrooms, and the information needed to decide based on more than your gut has to be aggregated and accessible.
Solving the Goldilocks Quandary in Data of Built Environments
Data about anything can be a double-edged sword.
If you don’t have enough, you can’t use it to draw any meaningful conclusions. Too much, and you risk information overload. The framework for data also needs to be the right depth; enough about a single building to provide a full and informative picture, but not so much detail that the process can’t scale across a 1000-property portfolio.
Not too hot, not too cold – just right.
This information can then inform decisions made about whether to build, retrofit or even sell a property.
But how do you determine the right depth in architecting your data schema?
Understanding Market Applications of Building Data
Collecting data without understanding its practical application in the market puts the cart before the horse.
Insurance brokers, for instance, utilize building data for insurance policy renewals. Connecting with brokers to understand market challenges that may impact an upcoming renewal is just one way to gain insight into what the market needs and how you can aggregate the necessary documentation.
Knowing What to Look For
The best sources for improving data are your own building documents. This includes PCAs, seismic reports, roof reports, etc.
Rely on on-the-ground personnel that know the properties best to supply information.
High Priority Data
Focus efforts on gathering data and filling gaps in information for the most important properties in your portfolio.
This includes those with the highest total insurable value (TIV) and those in high-risk geographies where earthquakes, wind, and flooding are prevalent.
Move with the Changes
Be cognizant of changes in your portfolio, such as property acquisitions, disposals and upgrades. Make sure these changes are reflected in the data.
These are just a few of the top priorities for outlining your organizations building data strategy and how the data can inform critical business decisions.
Using the framework above will allow you to avoid the pitfalls of “Goldilocking” your way through, sampling each and every process until a bear of an event chases you out of your building. &