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10 | TH E M R EP O RT MDWELL Source: Housecanary.com/appraiser Regulatory Compliance Issues? No Problem. Big Data is Here. Appraisers save an hour on average per appraisal with intelligent forms and pre-populated data. A comprehensive tool that allows appraisers to analyze relevant data, select from the most accurate comps, and create reports is available at Housecanary.com/appraiser. I t's hard to deny the impact of big data in our everyday lives. Banks and insurance com- panies use it to find risk, reduce customer cost, and increase profit. Telecoms use it to fix problems before they escalate. And now, lenders can assess appraisal quality using an analytics approach—all thanks to Fannie Mae's new platform, Collateral Underwriter (CU). This tool low - ers lenders' risk with a warning when inappropriate comparable sales may be skewing values. Where regulatory compliance and big data intersect Appraisers have historically lacked the support of analytics to better define value boundar- ies and avoid setting off a CU warning—increasing the risk of revision requests (for appraisers) and buybacks (for lenders). And the reality is new regula- tions are not making things easier for the appraiser. Knowing that they may venture into the unknown terrain of data analyt- ics, the Appraisal Foundation (charged with writing the legally binding guidelines for professional appraisal practice) issued the third draft of Advisory Opinion 37. This opinion requires apprais - ers to verify and understand the analytics tools they use. Combining human experience and data analytics HouseCanary has examined how big data companies are responding to the changing landscape. It turns out that ana- lytics tools are coming to market. These tools help appraisers choose the right boundaries and find the most accurate comps—boosting industry confidence that apprais- ers stay compliant and decrease the potential for revision requests, while lenders worry less about audit rules and buybacks. A great example is an interac- tive heat map tool that provides a visualization of all the recent sales sold within that neighborhood. With pre-populated data, apprais- ers can leverage their industry knowledge and justify their valua- tions more accurately and quickly. Saving time and closing more deals So how quickly are apprais- ers completing their work using analytics? How much time are they expected to save? To find the answer, HouseCanary first exam- ined the average amount of time appraisers spend per transaction. Their data revealed that appraisers spend approxi- mately five hours per appraisal (completing an average of 264 per year). When apprais- ers use analytics, they can save at least one hour—averaging four hours per transaction, instead of the previous five. That means they can complete 42 more appraisals per year (completing 306 in total). As big data shapes the world around us, we're clearly seeing this industry shift to big data— making lending and appraisal simpler, faster, and more accurate. ACCURATE NEIGHBORHOOD BOUNDARIES USING INTELLIGENT DATA A look at how data analytics is changing lending and appraisal This interactive heat map provides a Similarity Score, giving appraisers insight into properties that have the smallest amount of gross adjustments to the subject property vs. ones that have the largest amount. The green color represents the most similar properties within that neighborhood, while red indicates the most dissimilar. SAVE TIME AND RAISE PRODUCTIVITY WITH PRE-POPULATED DATA Current Average (without data) New Average (with data) Average Number of Appraisals Per Year 264 306 264 306 1 2 3 4 5 6 7 8 9 10 11 12 350