If you have not read the story in Forbes (2/16/2012) entitled, "How Target Figured Out a Teen Girl Was Pregnant Before Her Father Did," you should. Google it. The story tells of a father who complained to the store manager because his daughter was receiving ads and coupons for products targeted at pregnant women. Turns out, she bought products over the preceding few months that Target's computer systems determined (with 87% certainty) were products expectant mothers purchase - unscented cocoa-butter lotion, a purse large enough to double as a diaper bag, zinc and magnesium supplements, and so on.
This type of predictive marketing is possible because of "Big Data." Retailers have collected this data for years. Point of sale terminals - instead of cash registers - had virtually no payoff when retailers first installed them over 30 years ago. Rather, they helped retailers determine who bought what and when. On a large scale, the collected data helped determine how much shelf space to allocate, what products to advertise at different times of the year, when to start discounting seasonal items, and what to advertise in generic circulars sent to everyone. Now retailers use this data to target their marketing, sending just the right advertising to just the right consumers.
Every day, these systems become more and more sophisticated and the data volume grows and grows. The more data, the more analysis. Big Data results in better decision-making, increased revenue and market share, lower operating costs, and accelerated profitability.
So, why is an appraiser writing about retailers and the data they have collected for 30 years? Because appraisers and the clients they serve (mostly banks) have also collected data for years about the value of real estate and the underlying information necessary to determine value. Some appraisers and some banks have continuously organized and stored that data; many have not. Most likely, the appraisers and banks collecting the data did not do so in a manner that allows the type of analysis that retailers can accomplish. The larger banks have much more data available for analysis. The larger the bank, the lower its cost to collect and organize the information. Additionally, the larger the lenders, the better prepared they are to weather the effects of a few non-performing loans. However, smaller banks have plenty of data to work with as well. They just need to organize and manage it better. Analyzing one property at a time cannot identify problem loans without the blessing of tremendous luck. And who can afford to analyze every property individually every year - or every month or every day? That was the objective of "mark-to-market" regulations that were not implemented because banks could not afford to comply.
Data properly stored and organized in a computerized database can be analyzed as often as we want. The analysis can be done quite cheaply once the data is collected. Properly organized data can be sliced and diced in as many ways as enlightened management wants to manage its portfolio.
Compliance becomes cheap and easy when the data is available, and that data can assist in developing predictive strategies for portfolio management. Bankers and appraisers are as smart as retailers; they just have to do a better job of collecting and organizing their data.
Like Target (and the father of the expectant teenager), bankers need to be smarter than their clients are. They need to be able to predict which real estate loans in their existing portfolio are likely to fail. They need to be able to identify when the worst of a recession is over and when it is time to be aggressive about lending again. Banks and appraisers are in the business of risk management. They cannot manage what they do not measure. And, these days, measurement usually involves Big Data.
Shaun Fitzgerald is owner of Fitzgerald Appraisals, Easton Mass., a past-president of the Mass. Board of Real Estate Appraisers and a director of Lender Sentinel, a risk management services provider.