​Five ways predictive analytics is changing the real estate industry

March 22, 2016 by Avi Gupta in Articles 

Marc Benioff - Predictive AnalyticsSoftware giant Salesforce is doubling down on predictive analytics, with CEO Marc Benioff saying that varying industries are calling for smarter systems that help them strategically draw in, score, manage and convert leads.

"This will be the huge shift going forward, which is that everybody wants systems that are smarter, everybody wants systems that are more predictive, everybody wants everything scored, everybody wants to understand what’s the next best offer, next best opportunity, how to make things a little bit more efficient," Benioff said at the Forbes CIO Summit.

At SmartZip, we’ve spent the last seven years helping agents and brokers identify, rank and convert the homeowners most likely to sell in their neighborhood areas. We know that real estate professionals can benefit from patterns and signals that go unnoticed to the naked eye but are easily identified by a smartly programmed algorithm.

But seller identification isn’t the only way that predictive analytics will move the real estate needle. The opportunities are truly endless and have the power to change nearly everything we know about business planning, intuitive follow-up, home search and broker recruiting.

Here are five ways predictive analytics will change the real estate industry (and how it already is):

1. Optimizing buyer search

It’s been theorized that Amazon’s predictive recommendations could have a success rate as high as 60% in some cases. What if real estate search engines and portals could capitalize on this same idea, and offer recommendations of local homes buyers may love? Teke Wiggin detailed the potential for property matching in this recent Inman article, showing how buyers may be introduced to new neighborhoods or home styles when they are shown properties that match their lifestyle rather than their search criteria.

In many ways, the idea of property matching is like traditional online dating vs. meeting someone organically. If you sign up for Match.com, you may refuse to date any men under 5’10”. But if you had a great conversation in line at Starbucks with a man who stands 5’8” in line at Starbucks, his height may not matter as much. Similarly, a buyer may say they need three bedrooms when they really need two bedrooms and an office area. By leveraging property matching or a “homes you may like” carousel -- rather than a listing alert email of homes matching specific criteria -- this buyer may find a 2-bedroom home with a nook that could double as an office or a garage that’s been converted to a workspace.

If our market continues to see low inventory, property matching could be the best way to get buyers to think outside the search box.

2. Planning and selling new construction

Home builders do a lot of research when determining their next development site, but some developments still sell like hotcakes while others grow stale. As a result, builders are left scratching their heads wondering why two seemingly similar developments ended up having such disparate sale numbers.

By using predictive analytics, builders could identify patterns of successful local developments and work to replicate those results. They could also analyze the features that buyers are selecting most often and create “spec homes” that incorporate these features (and leave room to be customized in short fashion). Again, in our sellers’ market, a shorter time to closing on newly built homes would be a game-changer.

3. Brokerage expansion

Expanding real estate brokerage

Whether you run Keller Williams’ brokerage expansion division or are trying to grow a boutique agency, you’re facing the same question: What part of the city (or state or country) is underserved and in need of more agents like ours?

Using predictive analytics, you could assess everything from local turnover to agent-to-buyer ratios in different areas to determine the next hot markets that fit your specialty or brand. When selecting a specific office location, you can even analyze local driving or walking patterns to find an office with the most passerby traffic.

4. Brokerage recruiting

As many agents retire, it’s critical to have a plan that targets recruits outside the industry. Not many college students join the industry after their graduation, but many will consider the career switch and you can be the one to entice that transition.

Predictive analytics can identify the personality types, education levels and even the current jobs of individuals who would make excellent agents. Once you have rankings of the types of people who may be interested in a career switch, and have the right makings of a successful agent, you can target them one-to-one or via strategic multi-channel recruiting methods.

5. Home improvement ROI

How many times have you had the home improvement ROI conversation with potential sellers? A dozen -- this week? What if, as an agent, you could guarantee your sellers that they’d earn more at closing by making just three main fixes to their home?

Theoretically, predictive analytics could analyze the upgrades and improvements of local homes to identify the types of properties that local buyers will pay more for, or the condition they expect at a certain price.

How long until predictive analytics is more than a buzzword?

predicitve analytics

Rome wasn’t built in a day, and neither are algorithms. Still, we’re seeing a massive shift in the use of big data and predictive analytics as companies build property-matching software, create seller identification and conversion platforms, and even leverage automated AVMs and appraisals to make instant offers.

Predictive analytics is already more than a buzzy term and it’s already changing the game for agents and brokers across the real estate landscape. The five trends noted above are only the beginning and in five years, the probate lists you relied on for seller leads will seem as antiquated as newspaper and park bench ads.

Avi Gupta is the president and CEO of SmartZip Analytics, the leader in predictive analytics for the real estate industry.

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