Real estate investment company CONTI Capital says that better transaction and psychographic data combined with machine learning tools has led it to greater efficiency with a data-driven screening approach that it calls the CONTI Index. In a white paper, the company calls it a "site selection model designed to identify outperforming metropolitan areas, submarkets and zip codes for multifamily investment opportunities."

Prompting the company was a view that the Covid-19 pandemic had created "idiosyncrasies" and not "structural shifts" in housing markets. "We required a tool to see through the current market dislocations, to identify which market trends were permanent and which were transitory," they wrote.

Four factors led to the ability to create the data-driven approach they now use. Listing platforms "improved the quality, granularity, and timeliness of market and submarket rental rate data." Third parties were making real-time demographic data tied to "individuals that live within specific geographies" rather than waiting for more abstracted information from the Census Bureau. Psychographic data and methods, "typically used in marketing campaigns to categorize individuals into segments based on shared characteristics," allowed better understanding of consumer behavior. Finally, a combination of better-quality data with a shift from econometric to machine learning models allowed analysis without pre-existing explicit assumptions about the data.