I have had the good fortune to get a close up view of many chain store operators in action over the past 20 years. It’s amazing to see the wide variety of approaches used to find, open, relocate, and sometimes close stores. There are many different org charts and reporting structures that sometimes place the real estate function directly below the CEO and in other cases reporting to the CFO or VP Marketing. There are Real Estate Research Directors who have large staffs and tight controls over deal approval as well as companies who give the dealmakers responsibility for research. Continue reading
Years ago I was trying to sell site evaluation software to the commercial lending group at Freddie Mac. They had fifteen underwriters around the country with huge piles of loan requests on their desks and very little time to analyze each deal. The person I was working with described his problem like this:
“There are only about ten criteria we need to evaluate in order to approve or reject the loan. Unfortunately the ten criteria are usually different for each deal!” Continue reading
The retail marketplace is a complex system that is constantly changing and is full of surprises. Sometimes these surprises are positive; and other times they are not! It never ceases to amaze me how many different combinations of factors lead to success and failure in chain store performance. Complexity drives statistical models crazy (as well as the people who use them). There seem to be as many exceptions as there are rules, and the best way to understand these exceptions is by listening to experts tell their stories about what happened.
“Site Selection Surprises – Stories from the Field”
Please click on the new page above for the rest of the story…
Not too many years ago, chain store real estate was almost entirely a “people” business. The ICSC was the formal organization that provided regular gatherings among landlords, tenants, brokers, and all the other suppliers to the shopping center/chain store industry. “Going to Vegas” has become an annual pilgrimage for dealmakers since 1986 (the first convention was held in New York in 1958). The telephone and the automobile were the primary research tools. The research department was often located at one of the many bars, restaurants, coffee shops, and golf courses across the country. Continue reading
1. What’s the best way to make better real estate decisions?
Make better real estate decision-makers.
2. How do you make better real estate decision makers?
3. What’s the hot trend among chain operators today to make better real estate decisions?
Replacing decision-makers with mathematical models and training the remaining ones to be better bureaucrats. Continue reading
After you think about art and science in decision-making for a while, it can be addictive. I can’t even shoot arrows in my back yard without thinking about it. Continue reading
It’s hard to miss the similarity between baseball and site selection, right?
“That store is a home run.”
“We’re going to have to step up to the plate and do the deal.”
“She threw me a curve ball with that percentage rent clause.”
“We’re going to have to knock it out of the park to catch up to our competiton.” Continue reading
What’s worse? Too little science or too much science?
Ahh, trick question. The answer is that science is not a quantity that can be measured and compared to a standard. Most people would agree with this, but we often hear people say “we need a little more science in our site selection process.” Continue reading
All of us in the chain store industry would love to believe that we are making better real estate decisions today than before we had digitial maps, demographic data, and predictive models. There is no question that some companies have reduced capital losses from store closings and increased profitability of their stores with these tools. Continue reading
Most chain store modeling experts will tell you that a “good” sales forecasting model will estimate sales +/- 20% in 80-90% of the cases.
Most chain store real estate dealmakers believe that they need a model with no more than +/- 15% error 85% of the time.
Most people don’t agree on how this error is measured or what the role of human judgment should be in determining the “official” sales estimate used in calculating the projected return on investment. Continue reading