MReport July 2022

TheMReport — News and strategies for the evolving mortgage marketplace.

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28 | M R EP O RT FEATURE W ell-trained associates at home improvement stores will often ask customers how they will be using a tool or product to narrow their recommenda- tions and match the tool to the customer's needs. They know the needs of a first-time homeowner are different than those of an avid do-it-yourselfer, and certainly different from those of a professional contractor. Bottom line: the right tool for one customer might not be best suited for another. Now, a similar approach is informing the development of the next generation of Automated Valuation Models (AVMs). Everybody Knows What an AVM Is…or Do They? M ortgage professionals understand that AVMs are generally used in purchase and refinance transactions to confirm that an appraised value is within a certain band of confidence for the underwriter. In some low loan-to-value (LTV) refinances, AVMs may even be used as the value indicator for the property. Additionally, AVMs are widely used by servicers and investors to make collateral-based decisions. The number of use cases for AVMs outside of lending con- tinues to grow as data sources, technology, computing power, and modeling techniques evolve and become more sophisticated. The real estate and mortgage industries, marketing firms, and consumers all have very differ- ent needs, and modelers now can provide an AVM that is best suited for each purpose. Why Are There So Many Types of AVMs? M ore than 25 AVMs have come to market in the past 25 years. Why so many? It's because the various modelers are using different data and modeling techniques to test if their assump- tions can produce more accurate valuations, a higher hit rate (in which the model can deliver a score on a higher percentage of properties), or increased confi- dence scores (the score within a certain tolerance band, e.g., 95% assurance that the score would be within 5% of the top and bottom model score). In many cases, AVM developers use back testing, the method in which machine- learning techniques are used to "teach" the model, to prove their efficacy against closed loan data, list price, sale price, and property characteristics. Generally, there are three grades of AVMs commonly available: marketing, consumer facing, and lender grade. Most effective AVMs use multiple sets of data and submodels, running on a base of MLS listing data and public record assessor data. What distinguishes one model from another is how the data, and how much of the data, is used. For example, if you're using an AVM for marketing purposes, you may want a model that has the highest hit rate, so that every property shows at least a rough value. To attain a higher hit rate, marketing AVMs may generate valuations using less data and fewer compa- rables (comps) and include fewer property characteristics. However, a lender-grade model may not produce a value for that same property due to a lack of available information needed to generate a value with the desired confidence score. Marketing-Grade AVMs T his category of AVMs provides values on 100% of properties at an extremely low cost per property, delivering valu- ations with a high hit rate and an acceptable level of accuracy. However, these models often sacrifice some degree of accuracy to attain the extremely high hit rate, as they use fewer comps and characteristics than higher- grade AVMs. Lenders often use a marketing model AVM to identify prospects for home equity lending AVMs: Why One Size Doesn't Fit All When it comes to AVMs, success depends on ensuring that the right tools are matched with the right needs. By Jon Wierks

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