How Predictive Analytics is Disrupting CRE

How Predictive Analytics is Disrupting CRE

Guest Author: Ron Rossi, Vice President of Customer Success, IMS

In the webinar, How Predictive Analytics Is Disrupting CRE, the disruption taking place in commercial real estate is discussed. This disruption is driven in part by technology and predictive analytics. Ron Rossi, VP of Customer Success at RealPage IMS moderated the panel and was joined by Michael Walsh, CEO & Publisher of The Real Reporter, Mark Armstrong, East Region Sales Manager of Xcalar; and Jeramiah Carr. More than 400 CRE professionals registered for the event, where panelists provided an overview of how predictive analytics is disrupting CRE and engaged in a great, thought-provoking discussion.

The Power of Data

Many industries have already experienced the positive impact from the power of data, from retail and healthcare to automotive and legal. Data can be quite valuable, and when harnessed it can offer actionable insights, provide a competitive advantage, and fuel growth. Data has changed the way businesses operate by creating efficiencies that have led to cost savings and innovation that has driven sales and the value of their companies. Aside from that, Millennials continue to enter the workplace, and they are drawn to companies that utilize technology.

Data’s impact on the CRE industry will be based on how well firms leverage predictive analytics. First, you should recognize the need. The pace of adoption is still relatively slow, but the industry is waking up. It’s happening all around us; the way people work and access technology is changing the way real estate is developed. Second, you need to get your data ready. Data in its raw form is difficult to wrangle, normalize, and keep up to date.  Now that you have taken care of the first two steps, you need to have the tools and people in place to make it all happen. As you consider whether to build, buy, or partner, you must weigh the investment in time, people, and software.

 

The Power of Predictive Analytics

In the early stages of leveraging predictive analytics there will be a number of questions you should expect to answer:

  • Descriptive – What happened and where did it happen?
  • Diagnostic – Why did it happen?
  • Predictive – What will happen?
  • Prescriptive – What should I do?
  • Full Automation – Data is doing the work for me!

When you have reached the full automation stage you will then be in a position to apply Machine Learning (ML).  Machine Learning is the precursor to Artificial Intelligence (AI) and is broken into two types.

  • Supervised learning, meaning you know a lot of the answers and are trying to find more.
  • Unsupervised learning, meaning you are unfamiliar with the answers but want to see if anything important exists. Unsupervised learning is often used for finding anomalies.

By collecting data around demographics, job growth, and construction starts, you will start to predict and forecast an asset’s performance with trending and variance analysis against budgets and pro-forma underwriting. There are millions of data points that our brains interpret to help us make a decision, and machine learning enables you to reconstruct that decision-making process with technology.

 

The Future of Predictive Analytics

What does the future hold for predictive analytics? Gartner predicts by 2020, predictive and prescriptive analytics will attract 40% of enterprises’ net new investment in business intelligence and analytics. Having real time access to information, being able to visualize data – especially in real time and on mobile devices – will be the norm in CRE. Further, Millennials will drive the future of data and analytics. They require data and transparency, and they want to work with and for companies that leverage technology in innovative ways.

Watch the on-demand webinar to learn more about the need for predictive analytics in corporate real estates, the types of insights predictive can provide, and how CRE firms can leverage predictive analytics to drive efficiency, value, and informed decision-making.

 

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