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A wide variety of businesses and government agencies support the U.S. real estate market. Examples would include sales agents, national lenders, local credit unions, private mortgage and title insurers, and government sponsored entities (Freddie Mac and Fannie Mae), to name a few. The financial performance and overall success of these organizations depends in large part on the health of the overall real estate market. According to the National Association of Home Builders (NAHB), the construction of one single-family home of average size creates the equivalent of nearly 3 new jobs for a year (Greiner, 2015). The economic impact is significant, with residential construction and related activities contributing approximately 5 percent to overall gross domestic product. With these data points in mind, the ability to accurately predict housing trends has become an increasingly important function for organizations engaged in the real estate market. The government bailouts of Freddie Mac and Fannie Mae in July 2008, following the severe housing market collapse which began earlier that year, serve as an example of the risks associated with the housing market. The housing market collapse had left the two firms, which at the time owned or guaranteed about $5 trillion of home loans, in a dangerous and uncertain financial state (Olick, 2018). Countrywide Home Loans, Indy Mac, and Washington Mutual Bank are a few examples of mortgage banks that did not survive the housing market collapse and subsequent recession. In the wake of the financial crisis, businesses within the real estate market have recognized that predicting the direction of real estate is an essential business requirement. A business acquisition by Radian Group, the Philadelphia-based mortgage insurance company, illustrates the importance of predictive modeling for the mortgage industry. In January 2019, Radian Group acquired Five Bridges Advisors, a Maryland-based firm which develops data analytics and econometric predictive models leveraging artificial intelligence and machine learning techniques (Blumenthal, 2019).
Matthew Thom
