A data point shared widely at the end of 2016 came to us as a bit of a shock: IBM’s “10 Key Marketing Trends for 2017” report declared that 90% of all of the world’s data existing today was created in the last two years alone. Those numbers make sense, since we now create 2.5 quintillion bytes of data every single day. Yep…quintillion.
Although it’s mind-bending to try to grasp the immensity of that figure, it’s actually important to realize that all of the data floating around isn’t just cat videos and political rants. In fact, those numbers make up real-world “big data,” which is now being harnessed by nearly every industry in the world — particularly commercial real estate — in combination with artificial intelligence (AI) and machine learning to help produce key insights. This curated information allows business decision makers to make smarter, more informed choices than ever before.
Commercial real estate is an industry that’s been relatively in the tech “dark ages” for quite some time. For more than ten years, commercial has been behind residential real estate, without access to key metrics (building selling prices, building square footage, etc.) that could help with making better business decisions. However, the commercial industry is finally reaching technological enlightenment by shifting to a big data-backed appraisal process, which has already been used by the residential real estate industry for nearly a decade.
Many years ago in residential real estate, the appraisal process shifted from being an offline, on-paper process to an online one. Appraisers and agents migrated information from home evaluation reports to the cloud — which afforded industry professionals access to big data that could be used to create visual reports on a home’s estimated value (based on comparable numbers in the system). As more and more appraisers and agents have used this system, the more accurate it’s become.
The commercial industry is now catching up to the residential space. Experts can now utilize Big Data for a more streamlined and accurate appraisal process. Big Data is also helping make smarter financial decisions, since stored information from lenders and insurance companies (like FICO scores, risk factors, trends) allows agents to create an algorithm that can quickly make decisions about loan offers.
Big Data doesn’t just help in decision making processes — it also helps with running buildings better. Commercial buildings can now be equipped with internet-connected devices (i.e., the Internet of Things, or IoT), that have sensors that can alert someone quickly if things go awry. This means a sensor can let us know, for example, if a water leak is starting, or whether an air conditioner goes out. Then, management companies can react faster — saving money on repairs and keeping happier tenants. Big Data can also create buildings that are more eco-friendly and don’t waste energy, which helps the environment while saving money on utilities.
One final way that Big Data has helped commercial real estate is by providing insights into whether an investment is worth it or not. For example, if you’re considering a mixed-use property, you can look at things like home price trends, retail availability, and demographics around the particular development, then determine easily whether it’s worth it to invest in the property. You can also do something similar for properties outside the region where you’re located. Big Data lets us take advantage of technology to analyze investment opportunities from afar — and you don’t even have to travel to determine whether the investment is a smart one to pursue.
Thanks to all those quintillion bytes of data that’s now being created, and the means to make sense of it through AI, the commercial real estate industry has emerged from the dark ages into a time where we have access to numbers that can help us do our jobs better, smarter, and more efficiently. As our world continues to change, the real estate industry will, too — and luckily, we’ll have plenty of numbers on our side that can help guide our decisions as we go.