Challenge
The real estate and private markets division of one of the world’s largest global private investment banks was seeking highly accurate foot traffic data to pursue commercial real estate investments with higher confidence. Our client needed to understand how migration shifts triggered demographic changes in order to meet KPIs associated with return on assets (ROA) and return on equity (ROE).
Challenged by a lack of timely, transparent datasets in the market, our client didn’t have access to the resources needed to ensure profitability through validated investment decisions. Static, census-based insights lacked representation of seasonal trends, which limited the team’s ability to accurately perform their own projections. What’s more, the bank was left unsatisfied with their adhoc suite of disparate data sources. Instead of trying to triangulate information from various sources, the bank needed a streamlined, convenient, one-stop-shop for all their location data needs, including migration and demographic details.
Solutions
The division saw that dynamic location data would contextualize existing datasets, and they employed Unacast’s Migration Patterns product to help both main and localized offices prioritize markets of interest. The product would provide greater insights into the movements of people throughout the United States, offering information on regional behavioral differences, permanent vs. temporary moves, and commercial hub locations. And by pairing Unacast migration data with inferences from additional third and first-party data sources, the bank would be able to create a results-driven investment strategy.
Results
Using the data to drive allocation to different portfolios, the real estate and private markets division was excited to better support market-specific transactions teams. Once identified as a market of interest, a regional office would receive documentation outlining where to make what types of investments. By partnering with Unacast, the bank was able to greatly increase the confidence in their models for real estate transactions with more accurate demand forecasting and stronger predictive analytics. This resulted in the real estate and private markets division’s ability to come closer to achieving their ROE and ROA goals.
