Foot traffic and commercial real estate analytics

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Real estate professionals rely on data and analytics to assess asset classes, develop location intelligence and make investment decisions. This is as true of real estate developers as it is in real estate investing, and it's now absolutely essential. Why?

Commercial real estate analytics is changing

The modern real estate industry is data driven, using machine learning, predictive analytics and data science to extract the most from real estate data.

As a result, analyzing real estate is at once getting easier to do and more complex to understand.

Where once real estate firms assess risks and opportunities on residential or commercial property based on real estate market data alone, now big data of all types has entered the picture.

ESG criteria and ratings, human mobility and foot traffic data, trade area or catchment area analysis, cross visitation, dynamic demographic profiles . . . they don't teach this stuff when you take your real estate license.

Only real estate professionals with particular focus in CRE, or perhaps retail site selection teams are likely to have the data science chops in-house to make much sense of it.

And that's a problem, because foot traffic data provides great predictive insights for real estate developers and investors.

Foot traffic is ideal for CRE market analysis

The commercial real estate industry is high stakes. CRE investors maximize their investment opportunities by leveraging data sources that go beyond property type and tenant data.

CRE data used to inform investment decisions is augmented with real time or near time location data inputs to measure average foot traffic in a given geographical area, and detect changes in behavioral trends such as frequency or length of visits.

Migration patterns in the United States may also be studied to understand changes in population in the wake of the Covid-19 pandemic, or based on evolving patterns of area economic development.

Migration patterns then affect population density, which drives foot traffic, which is the basis for understanding catchment areas, which helps CRE investors to decide where to put their investment.

Net-net foot traffic is not transactional data, it is behavioral data. It tells CRE investors and property developers how people are using an area, the way that's changing, and what it means.

The CRE industry is much more than a capital market. It's the vanguard of investment in the world future generations will live in. Real estate professionals need this data to do their job.

How to work with foot traffic data in commercial real estate analytics

Our datasets can help you understand foot traffic to any defined urban area, Point of Interest, or venue. You can make more informed decisions for site selection or deselection, and learn how to boost your competitive advantage. 

To do these things, we help you follow three key steps:

foot traffic analytics

Step 1: Gather raw data

Unacast uses GPS location data to measure foot traffic because it is the most reliable. It works by sending signals, or “pings,” from mobile devices to a constellation of satellites. GPS uses triangulation to determine where on the planet your device is, and describes that position using latitude and longitude. Each ping from a device also has a timestamp.‍

foot traffic analytics

Step 2: Contextualize the data

The raw pings are clustered by our algorithms into events that indicate activity, such as dwelling at a location, or traveling and assigning retail venues and brands. An array of latitudes and longitudes are of no use. That's why Unacast's data engine translates the raw data feed into something understandable by adding context.

We make sure that private information, such as people’s exact home location, are obfuscated and not discernible to an address. We can add areas together to measure neighborhoods, cities, counties, states, and even an entire country.

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Step 3: Create curated foot traffic datasets

We have datasets depending on your data maturity and needs: Foot Traffic Data, Dynamic Trade Areas Data, and Cross-Visitation Data.

It’s tempting to think that once you add some context to the GPS pings on maps you’re done but there is more to it than data visualization.

But there are literally an uncountable number of ways to make these location datasets say different things via catchment analysis, some of which are unhelpful.

This is where the team of data scientists and business strategists at Unacast helps you “ask” the data just the right questions.

Commercial real estate data and CRE analytics links

Want to know more? Schedule a meeting with a commercial real estate analytics expert.


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