Trends in Data

Why Nontraditional Data Points are More Effective

by Galen Faurot-Pigeon

Commercial real estate investors and asset managers have been leveraging the same data sources for decades. With continued cap rate compression across markets and asset quality grades, there needs to be a way to win deals that will outperform in a highly competitive industry.

Savvy investors have been getting ahead of the curve by leveraging granular, nontraditional data for a much more effective method of analyzing markets and rent growth potential. Utilizing these nontraditional metrics in commercial real estate allows investors to explore new opportunities in submarkets that have been overlooked and make confident, data-driven decisions. 

Robust insights in a centralized location provide crucial, real-time information on market trends while allowing acquisitions and research teams to spend more time on tactical strategies and decision making. This is highly valuable for multifamily investors, owners and operators not only for the competitive advantage, but also because it streamlines the underwriting process and research efforts in different markets. 

Here are some of the ways nontraditional data points are more effective:

Hyper-localized insight 

Real estate is a hyper-localized industry and any insight on a granular level is not only more accurate, but also more predictive. Real-time metrics on employment, income and population growth when viewed on the zip code — or even block-level group — are much more telling than traditional data, which may only exist at the MSA level.

Census data is a good jumping off point for analyzing real estate investment opportunities, but it lacks the granularity and recency critical for research and planning.  The ability to view communities within a block-level group can provide a better snapshot of market activity around a specific asset or custom submarket. 

Higher correlation to rent growth drivers

Nontraditional metrics on a granular level also carry a much higher correlation to rent growth than traditional sources. An analysis of Markerr’s granular income and employment data shows an 80% higher correlation to rent growth relative to BLS job growth numbers.

Commercial real estate investors can drill down key growth drivers — notably income and employment growth — to specific areas and better analyze the types of rents they can implement at new or potential communities. 

For example, Markerr’s income & employment dataset highlights markets with the highest concentration of affluent renters in the country drilled down to the zip code level. The report indicates the percentages of six-figure earners in a market — a good proxy for affluent renters — along with the total number of employees, and identifies multifamily acquisition and expansion opportunities in specific neighborhoods. 

Increased efficiency 

Having nontraditional, granular datasets in a centralized dashboard is also an efficiency play for multifamily investors and asset managers. The most important, impactful data points are easily accessible and digestible, allowing investors to spend less time digging through the data and more time on meaningful decision making. 

One of the biggest pain points in data research and analysis is tracking down different data sources and points and sifting through all the information. The idea behind using data is not to replace people, but to empower people to free up time spent digging for data and create more time for strategic projects. Looking at employment, income, housing and demographic trends in real time, in one place, is highly valuable in speeding up the underwriting process and research in different markets. 

Nontraditional, granular data sets are making waves in commercial real estate investment as the next competitive advantage. Investors, developers and asset managers can get a better glimpse into key rent growth drivers while better analyzing demographic trends in real time. Not only do these data points carry a higher correlation to rent growth, but they increase efficiencies across the board and provide ample space for better data-driven decision making.

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