The commercial real estate industry has long ranked cities and regions into tiers for the purpose of market analysis, based on their investment potential or growth characteristics. However, the lack of a uniform approach and differences in variables – including the rise of certain industries in specific markets, changing rates of growth, and strengths in particular sectors such as office or industrial – can lead to confusion in the marketplace as real estate practitioners sift through competing reports on which markets are considered ideal for investment.
Currently, various models use a range of labels to describe rankings, such as “Primary Markets,” “Global Cities,” “First-Tier Markets” and “24-hour Cities,” according to the report. While most reports place a familiar set of large, well-known markets in the primary group, they seldom agree on the same group composition, ranking groups, and rankings within groups.
How then, can commercial real estate practitioners stratify and rank particular markets? A new research paper published by the NAIOP Research Foundation, “A New Look at Market Tier and Ranking Systems,” examines the issue and proposes a more complex method that compares multiple factors such as the type of sector and industry. In compiling the report, the authors interviewed many of the brokers, consultants and academics who develop these ranking systems, reviewed current ranking methodologies and compared these to their own analysis of market and census data.
The authors of the study conclude that differences between models and methodologies stem from a core unresolvable problem: the largest, most reliable markets are rarely the most active and dynamic. Tier and ranking systems compress each market’s complex characteristics into a single dimension to rank markets higher or lower than each other.
Moving to a two-dimensional comparison of markets’ size and activity could potentially resolve this conundrum,” say the authors of the study, Maria Sicola, Charles Warren, Ph.D., and Megan Weiner, three founding partners of CityStream Solutions. For example, a continuum of risk vs. reward could be charted against values such as general population and job growth, new construction and expanding inventory, or rising average
“The census can identify the largest markets by population,” say the authors. “However, there is a notable difference once the employment estimates are further filtered into the Technology, Advertising, Media and Information (TAMI) categories. Houston, Atlanta and suburban New Jersey drop out of the top 10, replaced by San Francisco, San Jose and Seattle.”
Similarly, there is a notable difference if one refers to employment estimates associated with industrial assets: wholesale and warehousing employment. Instead of the Bay Area and Seattle for technology, the Inland Empire (Riverside and San Bernardino) in California, along with Midwest hubs Columbus, Ohio, and Indianapolis, rise to the top as the largest industrial markets.
New models are evolving with advances in technology. For example, using data from Apartments.com, CoStar has developed a model that predicts what iPhone users in New York
City are willing to pay for an apartment rental based on their monthly data consumption.
“There may never be one “magic bullet” solution among the specialized ways in which markets are
grouped and ranked,” according to the authors.
The study concludes that disentangling markets’ size from their risk-return characteristics and plotting these on a chart would allow commercial real estate professionals to more easily compare different markets to each other and screen markets for the characteristics in which they’re most interested.