It\u2019s common knowledge that Big Data has arrived in the Mortgage Industry. One of the most important questions leaders in our industry need to ask themselves, of course, is \u201cWhere is it all going?\u201d. We\u2019re going to give you our take on this issue in just a moment. But first, let me give a short synopsis of what Big Data is and how it can greatly benefit mortgage companies.<\/p>\n
Historically, all the data computers used was set up in highly structured data bases. In other words, we had separate fields for each piece of data and we spent a lot of time and effort to make sure all the data was clean and accurate.\u00a0 Big Data does away with that. Big Data reads data that was never meant to be analyzed by a computer.\u00a0 This includes everything from Tweets and Facebook postings to newspaper clippings. All of these were written for human consumption, not for computer processing.<\/p>\n
Big Data cut through that.\u00a0 Big Data is able to read all of this unstructured, messy stuff that was never meant for computers and then makes sense of it.\u00a0 It other words, it can read Tweets and Facebook postings and data from hundreds of different sources that are written in incompatible styles and assign meaning to what it\u2019s reading.\u00a0 In the mortgage industry, this means that we can now tap into huge reservoirs of information that were always available to us before \u2013 data that is in the public domain, but we could never get a computer to work with it.<\/p>\n
Now let\u2019s take a look at where Big Data is going to take the mortgage industry.<\/em><\/p>\n We believe that Big Data is going to pre-populate mortgage applications. In other words, Big Data will mine data from bank records, publicly available data bases, social media sites, and other sites to collect all or nearly all the information required for a mortgage application. This will leave the applicant with the option of either clicking to ratify the pre-populated application as accurate or, on the other hand, edit a few fields here and there to fine tune the application.<\/p>\n Another approach here is for prospective home owners to complete their mortgage applications as they always have and then the mortgage company\u2019s computers will compare the pre-populated versions with the applicants\u2019 versions to identify discrepancies.<\/p>\n In either case, the objective of this exercise will be to enhance the accuracy of the data in the applications at the same time the system reduces the burden on the applicants and mortgage companies.<\/p>\n We can also see that computer algorithms will score mortgage applications using machine learning algorithms. These algorithms will approve or deny the applications immediately. Approved applications may be forwarded for processing right away. Rejected applications will qualify for a human review if the applicants don\u2019t feel they have been scored properly. The goal of this instant evaluation will be to eliminate the delays in the current manual evaluation process \u2013 delays that are often measured in weeks.<\/p>\n We can see that Big Data will be instrumental in projecting the number of applications for new mortgages or refinanced mortgages in specific geographies and specific time frames. Further, Big Data will project the total value of these mortgages. These projections will help mortgage companies reposition their people and processing power based on projected market demand.\u00a0 These projections would be based on the current mortgage portfolio the industry has in place in various geographic areas coupled with scenarios about shifts in mortgage interest rates.<\/p>\n We can expect to see Big Data analysis of non-monetary defaults on mortgages to become more common if not universal.\u00a0 Here, I\u2019m talking about flagging accounts where payments were made early and with an extra principal payment to being made on time with no extra payment. Or we will find homeowners whose home owner association is suing them. Or maybe the local government put a lien on the property on the grounds that the property is uninhabitable. Or the couple is getting divorced.\u00a0 These are all early warning signs that Big Data will track as a matter of course.\u00a0<\/strong><\/p>\n Residential property appraisals will become more objective and more accurate. Big Data will propose the most appropriate neighborhood comparable. It will develop appraisals using industry standards that will be driven by an algorithm. MReport<\/strong> claimed that, \u201cMore than 30 percent of loans fall short of the collateral valuation agreed to between customer and loan officer.\u201d Big Data will help fix that.<\/p>\n Big Data is bringing big changes<\/p>\n Spending on Big Data applications and technology will soar.\u00a0 In 2014, 2015, and 2017, we\u2019ve seen Big Data spending in the mortgage industry at $2.6 billion, $2.8 billion, and $3.2 billion respectively. We are going to see spending on Big Data continue to climb as the number of success stories grows.<\/p>\n Mortgage companies are going to suffer a severe shortage of Big Data analysts who know how to manipulate the huge and ever-growing quantities of data that will become available. We are going to need professionals who can manage the enquiries in ways that lead to highly defensible conclusions.\u00a0 The growth in the demand for Big Data analysts is going to outstrip the supply.<\/p>\n We are going to see the rapid growth of specialized firms that assist mortgage companies plan for and implement Big Data projects.\u00a0 This function is going to outsourced rather than treated as a core competence for several reasons.\u00a0 First, most mortgage companies will find it far too expensive to build their own in-house facilities.\u00a0 Second, the process of building their in-house facilities will take too long and are liable to face many dead-end alleys. Third, they will not be able to attract the talent they need at a price they can afford.\u00a0 Fourth, the management in existing mortgage companies will need to go through a steep learning curve that is best handled by a specialized firm.\u00a0 Over time, we can expect mortgage companies to build teams of in-house Big Data talent while leaving the technologies to cloud-based firms.<\/p>\n As a result, Small mortgage companies that cannot afford to buy the necessary technologies will be squeezed out of business. Larger companies will buy them.<\/p>\n Mortgage companies are going to increasingly focus on building higher quality portfolios with fewer staff.\u00a0 The only way to have a smaller staff complement and a larger mortgage portfolio is through automation.\u00a0 That should be obvious.\u00a0 Automation in general and Big Data in particular is the way of the future.<\/p>\n We are going to see many new, non-traditional players in the mortgage industry.\u00a0 They will spring from places like Silicon Valley.\u00a0 They will offer better service at lower costs than banks and traditional mortgage companies. For example, the Lending Club facilitated $3.6 billion in loans in the first six months of 2015.\u00a0 Likewise, Prosper is growing fast.<\/p>\n We can expect the Federal Housing Administration to develop a growing number of regulations that the mortgage industry must comply with.\u00a0 Many of these regulations will apply to a company\u2019s portfolio of mortgages rather than any given mortgage.\u00a0 Mortgage processors will continue to ensure that they comply with application specific compliance issues, but they cannot be expected to deal with portfolio-wide compliance issues.\u00a0 In fact, it is unlikely that it is humanly possible to do so.\u00a0 This means that mortgage companies will necessarily embrace Big Data to do that job for them.\u00a0 Failure to do so means that they will face stiff penalties in court.\u00a0 It is far better for these companies to catch non-compliance failures on their own and take action than to face their regulators in court.<\/p>\n Carl Pry, a managing director at Treliant Risk Advisors, said \u201cIt\u2019s in every bank\u2019s best interest to get one step ahead of the regulators and understand what that regulator is going to know and find. They need to resolve any discrepancies [and] do any file review analysis needed to be able to explain any disparities before the regulators find them.\u201d<\/p>\n Here are a few more examples of how Big Data helps keep Mortgage companies out of legal trouble:<\/p>\n Decades ago the local bank manager knew his customers well and was in a position to make an informed judgment call about the amount of credit to be extended.\u00a0 Bank managers rarely make those decisions in retail bank branches and mortgage companies today.\u00a0 Rather, those decisions are made by a committee \u2013 often in another city.\u00a0 We need to reinvest some humanity into the decision-making process. Incorporating social media will go a long way in that direction.<\/p>\n The mortgage approval process is going to become more transparent. At the moment, borrowers only know whether they are approved or rejected, but they rarely have an idea why they were slotted where they were.\u00a0 In the future, mortgage companies will be in a position to coach their applicants very specifically about what they need to do to be approved.<\/p>\n Additionally, Big Data is going to help reduce the risk in mortgage lending. Big Data will help brokers advise their clients about school performance and community crime rates. This will help the buyers make better-informed decisions and, ideally, lead to lower risk mortgages.<\/p>\n The privacy issue is going to become a big issue in Big Data.\u00a0 Although everything Big Data practitioners do is legal, the act of mining social media on a wholesale basis was never considered when social media sites were first introduced.\u00a0 We are going to see some interesting and instructive debates on ethical issues over the next decade before we see a consensus emerge.\u00a0 Any legislation passed before those ethical debates come to closure will prove to be ill-conceived and counterproductive.<\/p>\n Just to wrap up, I want to make it clear that Big Data is already having an impact on how the mortgage industry operates and we are still at the early stages. We are going to be in for a very interesting ride over the next few years.<\/p>\n If you want to learn more about this, feel free to get in touch with me directly.\u00a0 I\u2019m Eskinder Assefa, CEO of SOMAmetrics in Berkeley, California. We work with mortgage companies to help them realize their full business potential by improving their sales and marketing strategies and leveraging emerging technologies that have an impact on the bottom line.<\/p>\n","protected":false},"excerpt":{"rendered":"\u00a0<\/strong><\/h2>\n
Big Data Can Be Used to Streamline Processes at Mortgage Companies<\/strong><\/h2>\n
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Pre-populate mortgage applications<\/strong><\/h3>\n<\/li>\n<\/ol>\n
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Computer algorithms to score mortgage applications<\/strong><\/h3>\n<\/li>\n<\/ol>\n
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Big Data analysis of non-monetary defaults<\/strong><\/h3>\n<\/li>\n<\/ol>\n
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More Objective Residential Property Appraisals <\/strong><\/h3>\n<\/li>\n<\/ol>\n
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How is the Business of Big Data Affecting the Mortgage Industry?<\/strong>\u00a0<\/strong><\/h2>\n
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Increase in spending on Big Data<\/strong><\/h3>\n<\/li>\n<\/ol>\n
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Increased need for big data analysts in mortgage companies<\/strong><\/h3>\n<\/li>\n<\/ol>\n
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Increase in consultants<\/strong><\/h3>\n<\/li>\n<\/ol>\n
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Automation and Big Data will be important for mortgage companies<\/strong><\/h3>\n<\/li>\n<\/ol>\n
*Warning<\/strong>*: New Players<\/strong><\/h3>\n
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How Does Big Data Help Mortgage Companies Keep up with New Regulations and Laws?<\/strong><\/h2>\n
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Big Data, Mortgage Companies, and the Mortgage Buyer: How Relations Can Be Vastly Improved<\/strong><\/h2>\n
**Warning:**Potential future issues: Privacy<\/strong><\/h3>\n
Conclusion: Big Data and Mortgage Companies<\/strong><\/h3>\n