This guide outlines how Almanac can be used to run a data review of a prospective real estate location.
↳ Who’s this guide for?
This guide is for users working within the real estate function who are involved in site selection.
If you’re looking to screen a prospective site before you send the team there, or if you’ve found a site that has promise but you need to do further due diligence - this guide is for you.
1. Find your retail center
Above: The search modal for Centers
Start by clicking the Centers button from the navigation bar. This will open the search modal seen above.
There are two ways to search for a Center:
Search by name
Search by location
Above: Using the name search to find a Center
↳ Search by name
Enter the name of the retail center in the search box in the side panel. If you can’t find the center you’re looking for, try searching by location.
Above: Using the location search to find a Center
↳ Search by location
Enter the retail center’s location into the search box that sits on top of the map. Here you can search for and select the street address, the city, or even nearby points of interest to load all retail centers in the area.
Once you’ve found your retail center, select it from the side panel on the left or by clicking through on the map.
💡Tip: If you can’t find your Center, try creating a Site Report. This will enable you to review retail centers that aren’t in our database, as well as standalone sites.
2. Analyze Center performance
Above: An analysis page for the Mall of America Center
You’re now on an analysis page for your Center. Here you can find a range of analytics that are powered by pass_by’s foot traffic data.
To understand how the Center is performing as a whole, there are two key analytics:
Above: The Benchmark Metrics analytic, displaying year-on-year growth compared to the wider market
↳ Benchmark Metrics
These are three cards which enable you to compare the Center’s performance against other Centers in that state:
The Center Index card displays the year-on-year growth in foot traffic.
The State Center Index card displays the average growth for all Centers in that state.
The Difference card subtracts the State Center Index from the Center Index to indicate whether this Center is over- or under-performing the market.
Above: The Foot Traffic Trend analytic, displaying visits to the Center over time
↳ Foot Traffic Trend
This analytic displays the number of visits as a time series, enabling you to understand its performance over time.
You can adjust the date range using the date pickers in the top right hand corner of the analytic. You can also change the aggregation between Monthly and Quarterly.
Between these two analytics you can understand the Center’s growth, how it’s performed over time, and how it’s performing compared to other Centers in the state.
This is a good start, but now it’s time to drill down into what’s driving performance.
3. Analyze tenant performance
Above: The Tenant Ranking analytic, where store performance can be analyzed
The next analytic on this page is called Tenant ranking. This lets you understand how individual stores within the retail center are performing.
↳ Tenant ranking
This analytic contextualizes the performance of the stores located in the retail center. For each one, their performance is ranked against other stores in the same retail chain.
As well as the ranking, a percentile rank is provided. This is a way of comparing performance against retail chains with different amounts of stores.
🎓 Explainer: The percentile rank indicates the percentage of stores that have that score or lower. If a store was in the 90th percentile, it means that the number of visits was equal to or higher than 90% of other stores in that chain.
The percentile ranks are color coded, so you can easily gauge the performance of individual stores. If you see a sea of green - the stores are performing well! If you see a lot of yellows and reds - these stores are underperforming and may be at risk of closure.
You can filter the tenant ranking by category if you’re interested in how other stores in your market are performing.
With this analysis you can get a clearer picture of which businesses are finding success in the Center, and which businesses might cut their losses soon.
At this point you understand the overall performance of the Center and the individual performance of tenants. Now it’s time for the final piece of the puzzle; who’s visiting this Center?
4. Analyze visitors
Above: The Household Income analytic, one of five analytics that look at the traits of visitors
At the bottom of the analysis page, there are five analytics that look specifically at the visitors to this Center.
These five charts look at the demographics traits of the people who visit this Center, including:
Household income
Age range
Occupation
Educational attainment
Homeownership
Each analytic is displayed three different ways.
Above: A visitor trait displayed as a percentage difference to the wider population
↳ Difference compared to population
Here you can see the demographics of the visitors displayed as a percentage increase or decrease. If a value is positive, it means that type of consumer is overrepresented compared to the national population. If it’s negative, they’re underrepresented.
Above: A visitor trait displayed as a simple distribution of values
↳ Simple distribution
The next option is a simple distribution of values. Here you can see which type of consumer has the highest propensity to visit the Center.
Above: A visitor trait compared directly against the population values
↳ Comparison with wider population
Finally, you can see both the visitor values and the national average values displayed on the same graph. This lets you see which type of consumer is most likely to visit and which type of consumer is over- or underrepresented.
With these analytics, you can identify whether the visitors of this Center match up with your target audience. If they do, then this is another good sign that this Center is a promising site.
💡Tip: If you want to get an up to date analysis of who your actual audience are, head to the Chains section to run the same analysis on the visitors to your stores.
↳ Summary
So now you know how to use Almanac to run a data review of a prospective real estate site. You’ve seen how to evaluate the performance of the Center as a whole; how to review the performance of individual tenants; and finally how to compare the Center’s visitors with your own target audience.
Now you can make a data-driven decision on whether to progress with this site. Great job!