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Real Estate Case Study

How Real Estate Companies Use Location Data in Investment Assessments

Situation

‘Good Living LLC’ is a fictitious real estate company that’s looking for mobility data to improve their investment assessments. Knowing that swaths of tech talent recently left California for Texas, Good Living wants to capitalize on potentially lucrative migration trends by investing in areas where high net-worth tenants actually want to live. They were looking for a location intelligence partner to help them invest with confidence – and they chose Spectus.

Challenge

Specifically, Good Living wanted to test their hypothesis that employees that work at the Tesla Gigafactory in Austin live in the surrounding communities like Round Rock and commute into the city for work. They wanted to know the routes they take to the factory, the places they visit after work, and their aggregated home locations to understand a detailed picture of high-net worth tenants’ activity in the area. To continue investing in the areas surrounding Austin, Good Living needed high-quality mobility data to understand the local amenities that this target group enjoys, and to show that the commute into the city is reasonable.

Solution

Spectus’ data clean room produces custom location-based solutions in a privacy-focused environment, and provided Good Living with the mobility insights they needed to inform their investment assessments.

Spectus produced 4 analyses:

  • Trajectories to work
  • Places visited after work
  • Aggregated home counties
  • Factory activity

Trajectories

To show the routes that employees take to work, Spectus analyzed the trajectories of devices that started or ended at the Tesla Gigafactory between August 1st – August 10th, 2022. Hover over a trajectory line to see the time a device began its journey to the factory and the length and duration it traveled.

Popular Locations

To show the places employees visit after work, Spectus analyzed the POIs that the devices stopped at. Click the dots to see the place name and visit count — the larger the dot, the greater the visit count. Some popular locations include gas stations, malls, and coffee shops.

Aggregated Home Counties

To visualize the breakdown of employees’ inferred home counties, Spectus analyzed where devices spent the night, and aggregated, upleveled, and de-identified the data to preserve user privacy. Hover over the counties to see the percentage of employees that live there – the darker the county, the greater the percentage.

Factory Activity

To visualize activity within the factory, Spectus analyzed how long devices stopped at various places over time. Click a dot to see how long a device stayed there – the larger the dot, the longer the dwell time.

How does the data clean room work?

Spectus’ data clean room can be used to produce nearly any level of mobility analysis you can imagine by converting raw device-level data into upleveled privacy-safe insights. Spectus converts billions of pings from millions of devices into actionable metrics including Stops, Trajectories, and Demographics. 

The data clean room addresses data providers’ concerns about user privacy because privacy protection is baked into the Spectus platform by design. In addition, Spectus filters out Sensitive Points of Interest (SPOIs) such as locations related to health services, religious facilities, and locations with vulnerable populations to ensure that data is handled responsibly and not exploited for unethical purposes. 

Interested in learning more about how mobility insights can strengthen your investment strategy?

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