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Mortgage Leads with Data Appends

Good mortgage leads are harder than ever to find. Data appends help to qualify mortgage leads before they're sold, increasing close rates for mortgage brokers struggling to stay in the business.

The mortgage liquidity crisis has hit independent mortgage brokers hard. Fewer consumers are qualifying for loans, and those who do are requiring more time to do so. In addition, home sales are declining in most areas, so the number of new home loans is down. All of this means fewer clients and less business for the average mortgage broker. Fortunately, new developments in the internet lead market are helping some brokers thrive, even in the credit crunch.

Traditional internet mortgage leads are gathered from consumers who go online and request a quote for any kind home loan: new, refinance, second, home improvement, and debt consolidation. The completed form—with the consumer’s name, address, phone number, and other data—is sold to a broker as a sales lead. The company generating the lead “scrubs” the data to prevent bogus information from reaching the broker. The lead generator accomplishes the validation by “pinging” the data against various databases. The process is automated, so it takes only seconds to verify the accuracy of the lead. Because the validation process is instantaneous and the leads are immediately emailed to the broker, internet-generated mortgage leads are often marketed as “real time” leads.

For the past decade, the industry has made only incremental improvements in lead validation and delivery. A few years ago lead generation companies introduced “live transfer” leads in which they call the consumer, verify interest in the loan, then transfer the call to the broker. Recently, however, some lead generation companies have changed internet mortgage leads in a way that can only be described as revolutionary.

Taking advantage of newly formed strategic alliances, internet lead companies are not only accessing databases to verify the accuracy of consumer information, but they also are appending queried data to their leads to make them more valuable to mortgage brokers. For example, iLeads.com of Newport Beach, California, has aligned with First American Financial CoreLogic to append detailed property and loan data to its mortgage leads at no additional charge. Instead of receiving a short-form lead containing only the basics about the consumer, brokers buying leads from iLeads.com obtain a long-form lead that includes detailed information such as property size, APN code, the original lender, appraised value, and first and second mortgage amounts. With this information in hand, the broker can evaluate the prospect and select the appropriate product before making the call.

The data appends solve another problem that has vexed the internet lead industry: consumer inaccuracy. A study by Bankrate.com reveals that 34% of consumers do not even know what type of loan they have. Others know the type, but not details about interest rates or loan balances. As a result, the information they provide often turns out to be inaccurate. Long-form leads with appended data take the guesswork out of the process. They replace erroneous consumer input with accurate data. This eliminates time-consuming question-and-answer sessions on the phone, allowing the broker to make more calls and close more deals. It also helps the broker proceed with confidence, knowing there will be few if any surprises as they proceed with the loan.

Long-form mortgage leads won’t solve the credit crisisFree Web Content, but they can help brokers work more efficiently and profitably.

Source: Free Articles from ArticlesFactory.com

ABOUT THE AUTHOR


An award-winning author of 28 nonfiction books for children and young adults, Bradley Steffens is a frequent contributor to online and print publications, including Discovery Channel Magazine and the Los Angeles Times. His 2007 book, Ibn al-Haytham: First Scientist, is the first biography to be published in English about the medieval Islamic scholar known in the West as Alhazen.



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