Using Location Analytics to Vet Retail Real Estate Investments
From The Buxton Co
All real estate investments come with a measure of risk, but managing risk in the retail real estate industry can be especially challenging for REITs and other property investors.
A crucial aspect of managing risk is properly vetting potential real estate investments. When evaluating a property, there are many factors to consider. The cost of the investment, average rents in the market, historical rents at the property, average percent occupancy, the expected payback period, and more are all helpful metrics to consider.
Traditional methods of evaluating retail real estate investments are good, but emerging technology and data sources can provide an additional layer of insight to help inform your decisions. In this blog, we’ll explore how REITs and other investors can use location analytics to better understand dynamics at potential retail investment properties.
Defining Terms: What Is Location Analytics?
Location analytics, mobile data analytics, mobile GPS data, and massive mobile data are all buzzwords used to describe a similar type of data analysis that has become increasingly popular in many industries. In short, location analytics involves using de-identified GPS signals from mobile devices to study traffic volume trends, understand where visitors are coming from, and define the typical consumer profile of visitors to a selected place.
Location analytics comes in a variety of forms, from custom studies to on-demand, DIY analysis tools. Some may be tempted to purchase the raw datasets for in-house analysis but be warned: raw GPS data requires significant processing before it is useful for analysis.
Applying Location Analytics to Assess Potential Retail Real Estate Investments
Location analytics can play a helpful role in the due diligence process for a potential retail real estate investment. Using these analytics, investors can go beyond financial statements to better understand how consumers are interacting with the property – and whether those patterns are changing.
Define the Typical Trade Area Size for the Property
How far are people traveling, on average, to visit the shopping center? When you look at historical visits data, has the trade area size remained consistent or has a new competitor in the market shifted the trade area size?
Track Foot Traffic Volume Trends
Has traffic at the property been rising or declining? Is there any seasonality to the trend? How does traffic compare to traffic at a nearby competing shopping center? This information can help you to better understand the strength of the property’s consumer draw. A shopping center with stellar financials for the last 10 years but declining consumer foot traffic for the last two years may require closer investigation.
Define the Consumer Profile of Who is Visiting
When de-identified consumer visit data is merged with demographic and psychographic (lifestyle) information about the device owner, it provides a peek into the types of consumers most frequently visiting the shopping center of interest.
This knowledge can be used as part of a qualitative assessment of the property’s strength and vulnerability to risk.
Are these the types of consumers who are more or less sensitive to changes in the economic cycle? Are they likely to tighten their belts during a recession or do they have enough discretionary income to keep spending regardless? Combining this knowledge with knowledge of the center’s tenant mix (ratio of consumer discretionary to consumer staples retailers) can be helpful.
Has the consumer profile of visitors to the center been changing over time? This might help to inform any observed shifts in center performance.
How does the profile compare to the average profile of consumers in that market? You might be considering a market known for a certain type of customer, but is the center attracting the types of people you expect?
Assess the Potential Tenant Mix
Another emerging application of location analytics is using consumer profile data to assess whether a specific brand is a good fit for a property by comparing the property’s visitors to visitors at the brand’s other locations in similar markets. Before making the investment in a property, you can start identifying which brands would be good tenants. If you are considering an investment in a new upscale lifestyle center but the data shows that most luxury brands wouldn’t be a good fit for the area, then that may be a red flag.
The Bottom Line
Location analytics can provide unique insights to add additional color to your due diligence analysis. By combining consumer and traffic insights about the property with traditional financial analysis, real estate investors can better assess risk and make wise investment decisions.
Buxton is the leading customer analytics firm that helps organizations identify who their customers are, where those customers are located, and the value those customers have to the organization.
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