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RealTrends Q32021 BrokerPulse sees brokers still optimistic about the market, wary of competition and wondering when inventory will rise.

2021 RealTrends Brokerage Compensation Report

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Knock.com’s Sean Black on the transaction revolution

Real estate is on its third revolution, from the digital revolution of the early 2000s to the information revolution kicked off by Trulia and Zillow to today's transaction revolution.

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Old School Predictive Analytics Generate Move Scores 

Predictive analytics is one of the new sciences that is capturing the imagination of our industry. One can use big data to analyze everything from social media and credit card activity to demographics and GPS movements; data scientists predict who is most likely to buy and sell real estate in the next year. Then, they sell this data as leads to real estate professionals and real estate companies. Guess what? We’ve been predicting buying and selling patterns for over 25 years using the old school, an analog method described below.  

As part of their annual business planning, have your associates go through the following process with their database. 

These People Probably Want to Buy/Sell Real Estate. 

Take out your list of people you know. Go through the names one at a time. Bring the person/family into your consciousness. Think about them and ask yourself these questions regarding their situation. If they fit the issue, write the number of that question next to their name. The names with the most numbers have the most change going on in their lives and have the highest move score.  Change drives their real estate. About 20 percent of your database should have a high move score.   

How many of the people you know:  

  1. Have had an increase in family size in the past year? 
  2. Have children age 10 and under? (Send them a “How to pay for college” with real estate brochure.)
  3. Have teenage children? The children are older, and the floor plan doesn’t work.
  4. Have children who have left home recently? Or an adult child has moved back in?
  5. Are living below or above their means?
  6. Have lived in their same house for 7 years or more? 
  7. Have had their employer/company expand in the past year? 
  8. Have had their employer/company downsize in the past year?
  9. Have had a significant health event or an elderly parent move in?
  10. Have received a substantial inheritance? 
  11. Own a building lot? 
  12. Are getting married or are recently married? 
  13. Are getting divorced or are recently divorced?
  14. Have a commute of _______ hours a day or more?
  15. Have a dream for “Wake-up Money” investment property?
  16. Have a dream to live somewhere specific?

 This list is composed of the people who have the most change going on in their lives and will probably want to buy and sell real estate in the next year. We call this a Warm List. These 16 questions came from 40 years of experience in documenting the changes that drive real estate activity. How accurate is this list with the highest move scores? Here’s an example.  

How Accurate? 

Several years ago, a friend of mine was the founder of one of the first companies to develop predictive analytics. He showed me how they could predict the 20 percent of the households in any zip code that would be most likely to move in the next year. Their analytics were amazing. Their business model was to sell zip codes to real estate companies. The cost to purchase our zip codes was in six figures.  

I suggested we experiment. I would give him the databases of three of our top producers, and he would run those households through his data analysis to provide us with the 20 percent with the highest move scores. I then compared his list with our top producers’ Warm List of highest move scores. It was almost a perfect match!   

Old School vs. New School 

Our old school, an analog system of predicting move scores was as accurate as the modern technology. And it was free! It’s also better because our associates have a personal relationship with their list of people with high move scores—compared to buying a cold list provided by a data analytics company. Want to install predictive analytics in your firm? Go old school.  It’s better, and it’s free.  

 

 

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