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Big Data in Real Estate: 3 Important Non-Traditional Data Sets to Consider

November 15, 2019 2:35 PM By Jason Schedler

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Big data is a hot topic in many industries, and real estate is no different. For investors, it’s a great way to make predictions for future investments and increase ROI. In a 2014 study at MIT, 66% of executives reported a competitive edge from using data in their business. The most powerful competitive advantage of using big data comes from incorporating your traditional data with non-traditional external data. Doing so reveals hyperlocal trends that point to areas with potential for growth. 

The question is how do we use this data in an effective manner, and what kind of non-traditional data should we consider? People leave digital breadcrumbs—data that exists while conducting everyday activities—everywhere from a person’s physical location, often tagged on social media or on review apps like Yelp, to credit card use, and even health data from things like Fitbit. Understanding what’s important or where to start can seem like a daunting task. 

To get you started, we’ve put together a list of data sets you might want to consider, along with some real-world applications. 

Accessibility to Points of Interest 

Accessibility to points of interest refers to things like coffee shops, highly rated restaurants, movie theaters, and grocery stores, to name a few. Analyzing this type of data allows investors to take what they already know offline about the impact of community and apply it online with larger sets of data. The subtlety in points of interest is understanding that proximity itself is not the determining factor. For instance, less is sometimes more: Two specialty food stores within a specific area may mean an increase in property prices, while four within that same distance may lower prices.  

Why it’s important: What do Whole Foods, Trader Joe’s, and Starbucks have in common when it comes to real estate? They are a predictive measure in home values. In Seattle, over the past decade, apartment buildings within a mile of Whole Foods and Trader Joe’s appreciated quicker than others, and in Boston, from 1997 to 2014, home values within a quarter-mile of Starbucks increased by 45 percentage points over the rest of the city, according to a 2015 Zillow report. Looking at Yelp’s API, investors can analyze changes like an increase in highly rated restaurants. These types of changes can explain increases in rent. 

Environment

Environmental data includes, but is not limited to, CO2 emissions, air pollution exposure (including values exceeding World Health Organization guidelines), and greenhouse gas emissions. For real estate investors, analyzing environmental data like pollution may indicate changes in property values. 

Why it’s important: According to research, counties in the United States that were forced to reduce pollutants to meet federal clean air requirements saw home prices grow by 4.8% in the 1970s and 3.9% in the 1980s. Although not applicable to the United States, studies done in other countries have also shown a positive correlation between cleaner air and higher home values. Real estate investors know the saying “location, location, location,” and perhaps environmental data, in addition to other non-traditional data sets, may take that phrase even further. Big data alone will not improve air quality, but air quality does impact where people choose to live.

Infrastructure 

Infrastructure data includes things like electric power consumption, mobile cellular subscription data, fixed broadband subscriptions, and transportation data like air transfers and train lines. 

Why it’s important: Traffic congestion and the impact that deteriorating infrastructure has on commutes can delay normal business operations and job growth. It also impacts community development. A study by the Economic Policy Institute and the Urban Land Institute found that infrastructure is the most influential factor when it comes to real estate investment and development. For example, after the addition of a subway station in Somerville, a suburb of Boston, Federal Realty turned a neglected 45-acre industrial site into a neighborhood with housing, offices, and restaurants with pedestrian walkways, thus bolstering the value of the neighborhood. Additionally, office buildings near public transportation garner about 80% higher rents than ones farther away, according to JLL. Using this non-traditional data set alongside other data allows investors to get a richer idea of neighborhood growth and return on investment. 

When deciding what sets of data you want to examine, it’s important to understand that the process is not linear. Many of the data sets come together like a river, feeding and flowing together. In other words, your data sets, when paired with other data sets, like points of interest and infrastructure, come together for more predictive power. 

Bringing the data together is an important and often tedious first step in the process. The relationships in the data are what create a true 360-degree view. While looking at the data that one application can provide creates a great portrait, stitching together multiple systems is the data equivalent of creating a panorama. Leveraging Robotic Process Automation (RPA) can help immensely in transforming the data and creating automatic processes that keep your valuable data related. 

When data needs to be manipulated so that data keys match across applications or databases, RPA can be used to perform the transformation within the application (as a user would, but saving lots of time). In addition, RPA can be used to maintain data relationships going forward. As data is created in one application or external data set, a bot can be at the ready to insert or update the relevant components in one or more additional systems. When APIs and traditional integration tools are unavailable, RPA can be an incredibly powerful tool.

Data itself is only numbers, but the ability to extract data and forecasts from these numbers is the key to new strategies that will allow you a more competitive edge. These non-traditional data sets are not limited to the above, but they are a great place to start brainstorming. If you need help navigating big data or any part of your real estate management, we’re here to help. Fill out our contact sheet to understand how big data can take your specific business and business needs to the next level.