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The Psychology of Real Estate: Using Psychographic Data to Find a Great Self-Storage Location

By Patrick C. O'Connor Comments
Continued from page 1

The household with an average propensity to rent self-storage is Type C, with a ratio of 100 percent. Conversely, Type E households are only 20 percent as likely to rent self-storage as the typical household. Being aware of the concentration of psychographic profiles that more likely to rent self-storage provides insight to site selection.

Analyzing Current Tenants

Let's get back to Bill and Joe. To determine why some of their properties outperform others with similar demographic profiles, they hire a consulting firm to evaluate and compare tenants from all 10 of their properties against other households in their areas of business. The firm takes their list of tenants and compiles an index with the tendency for each psychographic profile to rent self-storage vs. the overall population.

Bill and Joe have two properties that are exceptional performers, six that are good performers and two that are laggards. The demographics for all 10 facilities—site quality, visibility, level of competition and traffic counts—are similar. The consultant reports that the two exceptional performers are in areas where there’s an exceptional number of Type A and Type B psychographic profiles. The two laggards are in areas with an above-average number of Type D and Type E profiles. The practical implications of the data make it easy for Bill and Joe to see that when investigating new areas in which to build, it makes sense to look for those with a high concentration of Type A and B households.

Scouting a Great Location

Like many storage operators, Bill and Joe have always started the development process by looking for a prospective site and then getting a feasibility study. Psychographics enable a different approach. The partners ask their consultant to identify geographic areas with high levels of Type A and B households. They can then evaluate the level of competition in those areas, identifying which have an average, above average or below average number of storage facilities. They can begin searching for prospective sites after they’ve identified the locations with above-average levels of households likely to rent self-storage coupled with a low or reasonable level of competition.

Using psychographic data doesn’t negate the value of a feasibility study. After all, one bad deal can offset the benefits of five or even 10 smart real estate investments. A feasibility study is cheap insurance against building a poorly performing property, even if the psychographic profile of the area is excellent.

The use of psychographics is just the latest in a long series of innovations since the inception of self-storage. Though it’s not critical to use psychographics to determine if a site is suitable for a successful project, the insight gleaned can increase your number of exceptional performing facilities and minimize the potential for laggard sites.

Patrick O’Connor is co-author of “Big Data in Real Estate—Be a Millionaire,” and president of, a U.S. real estate database that provides data and consulting services. He also owns O’Connor & Associates, a U.S. property-tax appeal firm that offers cost segregation, federal tax reduction and appraisal services. He’s been a business entrepreneur since graduating from Harvard Business School in 1983. For more information, e-mail

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