As the retail industry paves new paths to growth, data-fueled business intelligence can help tenants and shopping center owners alike make smarter decisions. To unlock the insights you need, it’s helpful to understand the value of big data for real estate brokers, tenants, landlords, and investors.
What is big data, and how can you use it to gain a competitive advantage? Read on to discover answers to top questions about the uses of big data in retail real estate.
Real estate brokers, landlords, tenants, buyers, and sellers have always relied on data to make decisions. However, analyzing a handful of data points—such as square footage, vacancy rates, sales per square foot, and traffic scores—is not the same as leveraging big data.
More than a buzzword, big data is a term that describes a large volume of data from a variety of sources. The interpretation of a wide range of data variables is a valuable tool for real estate professionals and can lead to not only more deals but also better deals done faster.
The information age has made it possible for you to access infinite data about almost any topic. And now, innovators are finding new ways to collect and analyze that infinite data to create business intelligence. Retail real estate is no exception.
In addition to traditional data used in real estate transactions, such as demographics and comps, a variety of other variables can help you predict the future and make stronger decisions. Non-traditional data might include customer reviews, social media updates, and even shopper movements within buildings.
Whether you’re a property owner or a broker, big data can create impactful insights. The trick is learning how to harness it. That’s where analytics tools come into play.
Conventional methods of analyzing data—like complex Excel spreadsheets—don’t work with big data. Analysts simply can’t bring together and sift through tens of millions of data points quickly enough to deliver real-time, actionable insights.
That’s where technology solutions come in. Machine learning algorithms make it significantly easier to aggregate and interpret disparate sources of data.
However, collecting the data and building accurate algorithms takes time and skilled talent. That’s why many CRE brokers and landlords subscribe to database solutions to aggregate the data they need.
If you are considering such a solution, it’s important to ensure the company has a robust process for ensuring data accuracy and timeliness. Quality databases pull information from multiple sources, provide transparency on the origination of each data point and go the extra mile to ensure accuracy and timeliness.
When seeking new tenants, retail property landlords and investors want more than a retailer that can pay the rent check next month. They want to attract brands with strong long-term growth prospects. New tenants should complement their existing tenant mix and boost the overall image of the shopping center or mall.
How do you identify the retailers that meet that definition of good tenants? Enter, big data and predictive analytics.
By analyzing comprehensive data sets, you can narrow down your prospect list to a few retailers that would be a perfect match for your space. You’ll want access to a database of retailers’ site selection criteria so you know what they look for in their space. This involves frontage and size requirements, parking ratios, traffic counts, demographics, and co-tenancy with other businesses. Then, you can compare that with your space to determine if there’s a match.
Meanwhile, mobility and point of sale data can tell powerful stories about the foot traffic and sales a retailer sees at their other locations. That information, combined with social media conversations and customer reviews, will give you a better understanding of the brand’s strength and the impact it can have on the overall sales per square foot of the property
Big data can also help you predict when you might have tenants vacating soon. For example, Retailsphere’s closures prediction algorithm scores tenants on their likelihood to close, based on a combination of variables. Equipped with this knowledge, you’ll be better prepared to fill their space if they do need to close the location.
Just as you can use big data to find tenants, retailers are using big data to identify the places where they need to have a presence. In fact, retailers allocate millions of dollars to location intelligence to better understand who visits their stores, for how long, and where else they shop – before and after the store visit.
Mobile devices allow retailers to access a treasure trove of information on consumers’ habits and traffic patterns by “geo-fencing” a property or even an individual store. That data—combined with other information such as demographics, drive times, and proximity to competitors—helps inform site selection decisions with a high degree of precision due to the granular level of trade area information.
Additionally, location analytics gives retailers insights into how their competitors are doing and helps them establish benchmarks for individual stores. Location-based insights can also be used to improve merchandising. That might include offering a better selection of products based on local demographics or changing a store layout.
Big data has many other uses beyond tenant prospecting and site selection. The use cases continue to expand as more information is collected every day through sensors and other devices connected with Internet of Things (IoT) technology.
For example, complex data sets and predictive analytics can play a powerful role in forecasting property valuations and lease rates.
Research by McKinsey found that analyzing non-traditional data, such as proximity to a 4-store hotel or the number of cafes within a mile, can more accurately predict areas with strong potential for price appreciation than traditional data alone.
Big data can also help property managers improve efficiencies and make capital expenditure decisions. Smart building systems generate a constant stream of data about equipment performance, energy consumption, and more.
Harnessing this data can help you reduce your carbon footprint and avoid capital expenditures by fixing equipment before it breaks down.
Real estate agents, brokers, and owners have traditionally relied on a combination of intuition and historical data to make decisions. Thanks to big data, you now have the ability to layer on predictive analytics to fuel even greater business intelligence.
Schedule a demo to discover how Retailsphere can help you tap into the power of big data.
