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<\/figure><\/div>\n\n\n\nWritten by Steve Schroeder<\/strong> as part of the ALS Resolvion Insights Series<\/a><\/strong><\/p>\n\n\n\n Statement of the Problem<\/strong><\/p>\n\n\n\n Several factors converge to cause current and\nfuture constraints in the repossession industry that will impact lenders, forwarders<\/a> (also known as repossession management companies<\/a>) and agencies alike. These\nfactors will continue to increase in relevance over the next five to ten years\nplacing further pressure on the industry.<\/p>\n\n\n\n These factors include: <\/p>\n\n\n\n Let\u2019s review these factors and their resulting\nimpact on the industry. In a recent study (IBIS World)[i]<\/a> the following industry\ntrends were identified:<\/p>\n\n\n\n Regulations<\/strong>: The repossession industry\nhas undergone tremendous change over the last ten years with the introduction\nof stringent state and federal regulation around repossession\npractices<\/a>. These changes have resulted in a dramatic curtailment of the flexibility\nand methods available to field agents to perform repossessions. They have also\nintroduced an increase in time and effort on the part of the agency and the\nfield agent to comply with the regulations. <\/p>\n\n\n\n Rising Costs:<\/strong> Costs at the agency level\nare increasing at a faster rate than in prior years as a result of insurance\npremiums, increased fuel costs (in part due to traffic increases), and manpower\ncosts due to minimum wage laws and an increase in \u201cskills needed\u201d to comply\nwith regulatory requirements.<\/p>\n\n\n\n Traffic<\/strong>: Increasing traffic in the\nmajor metro areas of the United States dramatically impacts the time it takes\nto execute on a repossession. From investigation of an assignment to the time\nit takes to deliver the vehicle to a secure lot. Traffic in the major metro\nareas is forecast to get dramatically worse over the next ten years. In 2018\nthe DOT estimated that the average person of driving age lost 42 hours stuck in\ntraffic jams, spent $160B in fuel costs, $28B in cost of truck transport. These\nnumbers are projected to continue to increase (DOT). <\/p>\n\n\n\n Low Margins:<\/strong> Lower margins in business\nmeans less money is spent on innovation. The state of the software available to\nthe repossession industry is behind that of most industries. One major factor\nfor this is that with low margins there is less likelihood for larger\nconsolidation plays (a fractured industry). Furthermore, with low margins small\nbusinesses do not have the resources to invest in innovation. To compound this,\nfees the repossession industry used to include in their revenue model are being\nreduced and eliminated. <\/p>\n\n\n\n Innovations in Repossession<\/strong><\/p>\n\n\n\n Only\ntwo possible outcomes can evolve from the convergence of these factors. The two\noptions include either a rising cost of the fees paid to repossession\nagencies<\/a> (to drive investment, staff growth, new entrants) or for innovation to\nreduce cost and improve efficiency on a per assignment and per repossession\nbasis. <\/p>\n\n\n\n Innovation<\/strong>: In recognition of the\nconverging factors that will seriously constrain the repossession industry over\nthe next five years, the first ever model to improve the efficiency of the\nassignment and repossession process was developed. <\/p>\n\n\n\n LocateScore\nwas developed with the sole goal of segmenting repossession assignments into\ncategories of potential for success. By segmenting appropriately, several minor\nadjustments to the repossession process can offset a wide spread constraint in\navailable resources and result in higher recovery rates.<\/p>\n\n\n\n Let\u2019s explore the\nimplications before jumping into the numbers.<\/strong><\/p>\n\n\n\n If this\nhypothesis holds true than there is no efficiency to be gained in the\nrepossession industry and the only alternative is rising costs. The best method\nis to \u201cdrive\u201d assignments until either the asset is recovered, or enough time\nhas passed that the borrower is treated as a skip and subsequent research is\nperformed.<\/p>\n\n\n\n The impact of\nthis assumption at the agency level is that after one to three runs the\nassignment must be \u201cdiscarded\u201d by the agency to make room for new business\nwhich arrives on a flow basis. The equivalent of having a dam filled with\nwater. An equal amount of water must be let out as is coming in once the dam is\nfull. Keep in mind that repo agencies<\/a> consistently run at full\ncapacity.<\/p>\n\n\n\n Let\u2019s assume that not all assignments have the\nsame likelihood of recovery and that we can determine likelihood prior to making\ndecisions, allocating manpower, fuel, and assets. With this knowledge, additional\ndecisions become available to lenders, forwarders<\/a>, and agencies. <\/p>\n\n\n\n Lenders<\/em>:<\/p>\n\n\n\n With this knowledge in hand lenders can make process\nand financial decisions starting at the early stages of delinquency through to\ndefault and vehicle\nrepossession<\/a>. Prior knowledge of \u201ceasy\u201d vs \u201cdifficult\u201d repossession allows for\npathing of the early stage delinquency events. <\/p>\n\n\n\n Forwarders and Skip<\/a><\/em>:<\/p>\n\n\n\n Forwarders<\/a> can leverage this\ninformation in many ways as well. From client management, skip timing, agency\nmanagement<\/a>, internal process optimization. Exploring a few implications for a\nforwarder renders some interesting discussion items.<\/p>\n\n\n\n Repossession and Agencies<\/em>:<\/p>\n\n\n\n Agencies can leverage this knowledge to adjust\nthe allocation of manpower, assets, and run frequency. By choosing to focus and\nspend additional time on \u201chigh likelihood\u201d opportunities, repo\nagencies<\/a> can increase their recovery rates and profitability.<\/p>\n\n\n\n Usage at an agency level does raise many\npotential industry side effects. Lenders and forwarders<\/a> will need to have the same\nknowledge in order to address these side effects. <\/p>\n\n\n\n LocateScore Performance<\/strong><\/p>\n\n\n\n Extensive\ntesting has been performed on sources from lenders, skip\ncompanies<\/a>, and forwarders<\/a>. The predictive power of\nLocateScore ranges from 200 to 600 percent magnitude in recovery rates from low\nscore to high scores. Exploration of this performance is detailed further.<\/p>\n\n\n\n Low Scores:<\/strong> Low LocateScore results have\nsubstantially lower recovery rates than mid and high scores. Average recovery\nrates for this category are in the 5% to 8% range across several lines of\nbusiness. These scores do not mean that they are not recoverable. Through skip\nefforts and \u201ctalking\u201d the borrower into relinquishing the vehicle these\naccounts can be successfully closed.<\/p>\n\n\n\n This\ngroup of scores consists of approximately seven percent to ten percent of all\nassignments. Evaluating the impact of these low scores on agency performance\n(estimated 5,000 operating field agents x 7% & 10% = between 700,000 to 1,000,000-man\nhours per year) spent on these assignments based on a single assignment to one\nagency. Additionally, fuel expenditures are estimate at $3 per man hour for a\ntotal of $2.1m to $3m.<\/p>\n\n\n\n Repurposing\nmanpower and assets on mid and high LocateScore results would produce between\ntwo to three times as many recoveries for the same manpower allocation. In\nother words, result in a five percent to ten percent increase in recovered\nvehicles.<\/p>\n\n\n\n Middle Score: <\/strong>LocateScore result that lands\nin the middle of the range are expected to perform at rates equivalent to an\nunscored pool of data. These scores will operate as would a business that did\nnot have prior knowledge of the LocateScore value. <\/p>\n\n\n\n High Scores<\/strong>: High LocateScore results have between a 40%\nto 50% increase in recovery rates over unscored averages. A subsequent analysis\nof unsuccessful recoveries within this group is warranted to understand the\nvalue of multiple attempts on these locations. <\/p>\n\n\n\n Conclusion<\/strong><\/p>\n\n\n\n The\nLocateScore model helps predict recovery success prior to any manpower allocation,\nskip\ntracing<\/a> efforts, or data gathering process. This knowledge can best be used for\nchanging business, financial, and workflow methods around the Low LocateScore\ngroup. <\/p>\n\n\n\n While\nLocateScore has immediate utility for all three participants (lender, forwarder<\/a>, and agency), of equal\nimportance is understanding that other companies may have the information and\nmay be changing business practices. This has the potential of negatively or\npositively influencing an \u201cuninformed\u201d participant in ways that are hard to\npredict. <\/p>\n\n\n\n As of this writing, there are\nover 80 agencies equipped with this information and a percentage of them that\nare making decisions based upon this knowledge. Furthermore, there are multiple\nforwarders and lenders in the process of testing and evaluating how to best\nleverage this information. LocateScore is already available to lenders, skip\ntracing companies<\/a>,\nand repossession management forwarders<\/a> using the MasterQueue system.<\/p>\n\n\n\n By the end of 2019, it is\nestimated that several top lenders may alter pricing based upon this knowledge\nto ensure that their \u201cLow Score\u201d subset gets proper attention in forwarder<\/a>, skip\ntracing<\/a>, and agency handling.<\/p>\n\n\n\n Written by:
Steve Schroeder
LocateScore.com
(916) 304-4640<\/p>\n\n\n\n
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