As the leader in predictive analytics and marketing in real estate,
SmartZip is often asked how and why agents and brokers would use
algorithmic predictions to help find listings to build their businesses.
The National Association of REALTORS states that 72 percent of
sellers will list with the first agent they speak with, not necessarily
with the most experienced agent. Therefore, Big data and predictive
analytics allow agents to focus their time and energy on those most
likely to list.
Before big data and predictive analytics were introduced to the real
estate market, agents may have attempted to produce similar results by
assigning scores to people in their territories, including their
As an example, they might assign individuals a score of 1-5,
depending on their knowledge of individual prospects and their intention
to sell within a predictable period. The reality is that human beings
are extraordinarily complex, and their buying and selling behavior is
usually the result of hundreds of converging factors. To truly uncover
predictions of human behavior, you need to analyze thousands of data
points to reveal the multitude of patterns that lead to transactions.
Attempting to complete this process and predict listing opportunity at a
human’s pace is daunting at best.
There is a solution. Big data and predictive analytics consider
thousands of data points providing in-depth information on the people
living in the home, the history of the home itself, and the history of
the market in which the home resides. Data in these areas are analyzed
to identify who may be signaling a near-future selling date.
We all know the 80/20 rule. Eighty percent of yield comes from 20
percent of a farm. An agent’s goal is to find the top 20 percent in
their market. The reason agents mass farm a territory is because they
are trying to find the top 20 percent. This is where big data and
predictive analytics come into play.
An agent’s goal is to find the top 20 percent in their market.
Let’s take the Joneses, for example. Perhaps over the past 20 years,
they move on average every five years and today has been four-and-a-half
since their last move. That’s interesting. But what if they also have
two children who are about a year or less away from college? That’s
interesting too. Now consider that the Joneses’ income history puts them
in a category of homeowners who typically sell in year seven and its
now year six. Here, we see that three different attributes align.
History of migration patterns, children and income. Those are just three
attributes that line up. However, when you stack several hundred of them on top of each other, you no longer have a coincidence. You have
This technology is all around us. A FICO score is simply a prediction
of our likelihood to make loan payments on time. The department store
Target made headlines when they showed they can predict accurately,
using big data and predictive analytics, which of their customers are
pregnant. Now, instead of telling everyone about their new baby products
(mass farming), they just focus on this subset of customers (smart
farming). This technology is not new; its usage is just new to those in
the real estate industry.
Companies use these predictions to determine factors such as whom to
contact, as well as when and why. Now real estate agents can too.
Traditionally, farming is arduous and costly. Big data and predictive
analytics will identify an agent’s top 20 percent, eliminating the 80
percent that often prove to be wasted time, effort and marketing (think
of all the mail that goes unopened or thrown away by the bottom 80
percent who have no intention of listing).
Of all the tools and technology now available to real estate agents
for prospecting, big data and predictive analytics are the most robust.
Nothing else currently matches these abilities at allowing agents to
hyper-focus their time and energy on the right prospects.
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