It’s easy to draw correlations between big data and certain areas of the business world. For example, it makes sense that a tech company would leverage big data, or that a software company would use it to develop a cutting edge SaaS offering. But in real estate, does big data really have a place at the table? Those inside the industry would say yes – and resoundingly so!
Big Data’s Role in Real Estate
Over the past 10 years, big data has played an increasingly important role in the real estate industry. Some of these influences can be clearly seen, while others fly beneath the radar. Let’s take a closer look at some of these interactions and what they mean for those inside the industry:
1. Smarter Decision Making
“As it is the case with any venture, there are some risks that are inherent to the housing market. Being able to mitigate these risks and navigate various market trends is essential for generating a profit,” Mashvisor notes. “This is where real estate analytics comes in. In fact, the use of real estate data has entirely replaced gut decisions with metric-driven practices. Investors can now make decisions that are grounded in data rather than vague and untested strategies.”
The combination of big data, AI, and predictive analytics makes it far easier to search for properties and zero in on the ones that have the greatest chance of being profitable. If nothing else, it allows investors to eliminate properties that are considered high-risk.
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2. Improves Tenant Selection
For rental property investors, selecting the right tenants means the difference between a profitable, long-term investment and a property that drains cash and causes headaches.
In addition to hiring a property management service to screen tenants on your behalf, you can also use big data to help you focus on the best possible candidates. One option is predictive real estate data analytics, which rely on big data and real time insights to help you determine a renter’s likelihood of being a good tenant.
From a macro perspective, certain analytics tools can help investors determine who tenants will be in the coming years. In other words. What will the renter pool look like in two years or five years down the road? And based on this information, which type of renter should you target?
3. Stitch Together Data
One of the big problems with big data is that there are so many sources of data. If you aren’t careful, it’s easy to become overwhelmed with everything. And ironically enough, too much data is just as useless as too little data. But thanks to advanced analytics and machine learning algorithms, it’s simple to aggregate the right data sources and interpret even the most disparate sources of information. You can do this simply by building a real estate portfolio on an online software.
For example, if you’re a developer who is looking to identify underused parcels zoned for development, you might pull data from previous transactions on the MLS. But in isolation, these databases don’t tell you much about potential. They simply reveal what’s already happened.
“Advanced analytics can quickly identify areas of focus, then assess the potential of a given parcel with a predictive lens,” McKinsey & Company reports. “A developer can thus quickly access hyperlocal community data, paired with land use data and market forecasts, and select the most relevant neighborhoods and type of buildings for development.”
Based on all of these findings, that developer can optimize timing, types of property, and even price segmentation to further max out value.
4. Automated Valuation Computations
Have you ever wondered how websites like Zillow can estimate a property’s valuation with such accuracy? It comes down to something called Automated Valuation Models, or AVMs. These platforms – which every major real estate company in the world uses – pull data from millions of listings to create ballpark figures and estimates of home valuations. Based on this information, investors can make smarter offers that are more likely to produce a positive return on investment.
AVMs are especially helpful in situations where there might not be enough recent transactions for a real estate agent to make an educated guess on pricing. If someone looks at a dozen different properties in a specific market – likely a rural one – and there’s no recent sold history for comps, AVM provides a great starting point to understand value. It might not lead an investor to a specific offer price, but it at least sets the parameters. (And if the seller doesn’t have a similar tool in their corner, they could find themselves at a significant disadvantage – potentially leaving money on the table.)
Big Data as a Competitive Advantage
Big data doesn’t change the fundamentals of real estate investing. It does, however, increase things like access, profitability, and opportunity. Those that leverage big data as the resource that it is will enjoy more upside (while suppressing the downside) in the years to come.
