Kinrade noted the SAS investment is not about generating property tax revenue. This helps us to quickly identify neighborhoods that may require additional review to determine what is causing the divergence and make any necessary adjustments.” Properties are currently selling quickly in the county, and these sales, in turn, influence market values for all properties.Īccording to Kinrade, “SAS Viya performs an independent, data-driven, objective analysis for each property that can be compared with our appraisers’ analysis and assumptions to validate accuracy or identify divergence. Human judgment enhanced by artificial intelligenceĬounty appraisers first perform their own analysis and determine values for each property, then turn to SAS Viya for an objective second opinion. These interactive reports give Wake County appraisers access to information such as updated property evaluations, a list of the five most similar neighborhoods for any given address, and comparisons of estimates calculated with SAS versus actual sale prices. To support the model, SAS generates and hosts a series of reports using SAS Visual Analytics and SAS Visual Statistics, both available on the SAS Viya suite of products. It is powered by the artificial intelligence capabilities of SAS ® Visual Data Mining and Machine Learning, a tool that supports end-to-end data mining and machine learning with a comprehensive, visual interface that handles all tasks in the analytical life cycle – from data to discovery to deployment. ![]() The Wake County tax assessment model was built with repeatability in mind so that it can be adopted by other governments with minimal customization. With every home sale, the model is refined automatically to become more precise. The model employs decision trees to estimate sale prices based on a series of decisions: How much heated area does the house contain? Does the property have vinyl siding or a brick exterior? And so on. There are dozens of inputs that affect property value, such as location, size and finishes. 1, 2020, market value for each property based on recent sales data. SAS built cloud-based, machine learning models for Wake County that consider hundreds of factors and daily property sales to offer timely, objective, highly accurate market forecasts. Wake County turned to SAS and the SAS Viya platform to identify changing market trends every day for every property. Machine learning on the SAS ® Viya ® platform “We needed unbiased support to analyze our volumes of data, and SAS was the obvious choice,” Kinrade says. More than 3,000 parcels are sold each month, and even the simplest condominium unit can have more than 25 variables analyzed. With such a dynamic market, Wake County officials need real-time information on how market values are changing. “We couldn’t possibly hire and train enough appraisers and support staff fast enough to get our work done accurately on a shorter reappraisal cycle,” Kinrade says. ![]() This work will now be completed every four years. Previously, the Wake County Revenue Department had eight years to complete the general reappraisal on nearly 400,000 properties. Compounding this growth is a shorter reappraisal cycle. Kinrade is charged with overseeing property assessments in a flourishing county, which has added 20,000 parcels since the last general reappraisal in 2016. Meet Marcus Kinrade, Wake County’s Revenue Director. Explosive growth has sparked a robust building environment and increasing property values – creating a moving target for those challenged with assessing property values in a booming real estate market. By 2016, the population exceeded 1 million. In 1980, Wake County, North Carolina, was home to just over 300,000 people. Artificial intelligence improves assessment accuracy and productivity in Wake County
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