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66 no pre-installed software. In the following years, by adding additional data sources and submodels, AVMs evolved into a more sophisticated, regression and rules- based technology which could emulate market prices and property characteristics and then apply retrogression analysis to track the history of home prices at the neighborhood level. By the early 2000s, several major companies were offering AVMs and the GSEs had even developed their own versions for use with their own automated underwriting systems. Even with those advancements, issues persisted. Some models would come up with more hits (a "hit" is the ability of an AVM to actually render a value result on a particular property) than others, and some AVMs performed better in different markets. To address this, the concept of an AVM cascade was created. An AVM cascade uses multiple AVM models that "rotate" from one model to another until one returns a valuation at a certain confidence score level. Users could tailor the AVM cascade models used in specific markets or situations for optimum accuracy and hit rates. ese cascade models became increasingly popular, and AVMs became even more mainstream. By the mid-2000s, the key to the growing acceptance of AVMs was their increasing accuracy and performance. is was being achieved through expanding access to multiple data sources, database management, and high- speed machine computing and learning. e ability to update the property information on a weekly, and then daily, basis moved AVMs even closer to real-time. AVM providers were internally testing literally billions of valuations produced by these AVMs. Today, AVM testing has expanded ten-fold to build better confidence scores. Accurate confidence scores have become vital to understanding the valuation model's reliability. As a result of the mortgage crisis from 2008-2010, the use of hybrid valuation methods that combined technology, modeling, and human review with photos of the property was introduced. e human element in hybrid AVMs addressed risk concerns that a fully automated solution might present. Hybrids, in general, were cheaper than a full appraisal but more expensive than a traditional AVM. ese hybrid models were also driven by the shrinking population of certified residential appraisers and competition among lenders to secure deals and to give borrowers a cheaper and faster alternative to determine values. Hybrids enabled lenders and the GSEs to select what combination of appraisal and automated tool that best fit the risk profile of the transaction. Combinations included just an AVM, or an AVM with an inspection, a drive- by review, a desk review, a broker price opinion (BPO), or even a full appraisal. roughout this time, these solutions were in place primarily for high-equity refinance transactions, servicing solutions, and distressed asset disposition. Today, the use of hybrid valuation models is increasing, and they are even being used to support certain types of lower-risk purchase transactions. Appraisal waivers and the resulting ability to use hybrid solutions have seen a sharp increase over the last few years, even before COVID-19, but accelerating sharply due to the pandemic. It would appear that hybrid valuation solutions are here to stay. WHERE ARE WE TODAY AND WHERE ARE WE GOING? AVM modelers are now looking at more ways to access even more data points that can add nuance to existing models beyond recorded sale prices. is data includes local price volatility, list-to-sale price ratios, time on market, geospatial information, the impact of natural disasters (e.g., fire, flood, storm surge), and probably the most important— property condition. New technology is also being considered and implemented in AVM modeling, such as text mining on MLS listings, image recognition, artificial intelligence, and more. Cloud computing platforms are being used to model, run, and test AVMs and provide lenders, servicers, and investors with instant access to the valuation outcomes. Leading AVM companies are also looking into the ability to input even more technology-driven sources of data, including drone inspections of a property's outer condition in the front and backyard, and even borrower-supplied data from mobile apps. New geospatial data technology is also being leveraged to determine true neighborhood boundaries. As data sources, technology, and testing methodologies evolve, the AVM industry is moving towards significantly advanced AVM testing based on artificial intelligence and machine learning, allowing users to know exactly what performance to expect in real world applications. e industry is also evolving and creating different versions of AVMs to address the different use cases needed. For example, one version of AVMs for general consumers who are looking at values on a real estate website, another for white-label values for tools like mortgage calculators, a premium level for lenders, and yet another for portfolio management and investors. From their humble beginnings to help appraisers save time determining comps, AVMs are now universally used by the entire housing industry. AVMs have saved consumers, lenders, servicers, investors, and appraisal management companies (AMCs) hundreds of millions of dollars in cost and time and have helped mitigate potential risk of over- or under-valuing a property. While the valuable contributions and services that are provided by certified residential appraisers will never cease, AVMs will continue to grow in supporting the needs of the valuation industry. For this AVM geek, on a personal level, it's been a great source of pride to have been part of the AVM's past and bright future. Jon Wierks is VP, Analytics, for First American Data & Analytics, a division of First American Financial Corporation (NYSE: FAF). In this position, he leads the development and support of First American's valuation solutions. Wierks has over 25 years' experience in the real estate and valuation space as a creator/co-creator of several of the industry's highly regarded AVMs and has earned six valuation-related pending or issued patents. Feature By: Jon Wierks

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