To better target your marketing dollars, real estate brokerages are leveraging predictive analytics to reach those who are most likely to buy or sell.
Predictive analytics is a way to forecast future trends and results by analyzing historical data with statistical algorithms. New technology has made it more accessible in recent years to businesses of all sizes.
Imagine being able to target a consumer who is contemplating selling before they even reach out to any real estate professionals. By reaching a client on the front end, you could motivate them to act sooner while making yourself a more appealing candidate in the process.
Predictive analytics “help agents get better at their jobs because consumers’ expectations and questions are getting more precise,” says Martin Morzynski, chief marketing officer with HouseCanary, a real estate data analytics firm. “It brings a level of accuracy and granularity into the process that changes the game.”
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 spheres.
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.
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 statistical relevance.
Jay Macklin, broker-owner with RE/MAX Platinum Living in Scottsdale, Ariz., uses SmartZip analytics to target homeowners who might be the most likely to sell their homes soon. The service uses U.S. Census data to track sellers’ life cycle, based on public records like divorces, deaths, and marriages. The data can then predict the percentage of homeowners who are most likely to sell.
With the onset of predictive analytics and cognitive analytics comes machine learning. Suddenly, artificial intelligence is no longer a plot line in sci-fi movies, but a viable decision making tool for business. Combine the ensuing age of AI with a justifiable obsession for big data and analytics, and business leaders must now confront the reality that when the process of analyzing analytics becomes fully automated, machines may dictate our next move.
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; it’s usage is just new to those in the real estate industry.
Very recently we RE/MAX Gold built an exclusive relationship with Offer.com A predictive analytic company that finds sellers. Their claim of 70% accuracy is the best in the industry. If you’re looking for sellers we should talk. You can only access Offrs.com by being apart of the #GoldNation.
Source: “Predictive Analytics: The Next Big Thing in Real Estate?” RISMedia (June 29, 2017) and “How to Use Predictive Analytics in Your Real Estate Business,” RISMedia (June 10, 2017)
What Predictive Analytics, Big Data And The Rise Of Artificial Intelligence Mean For Real Estate (Forbes Magazine Jan 22, 2018)