With its population having more than tripled since the 1980s, Wake County in North Carolina, home of the state capital, has seen a significant growth in its real estate activity. The transition from a largely rural area into an increasingly populated district, brought many challenges, many of them related to the properties’ appraisal, something essential for tax purposes.  Up until last period, the assessments were performed by only 16 county assessors, who evaluated more than 20 variables for each of the 400,000 properties.  This highly manual job was presenting many challenges that needed to be tackled. On the one hand, the staff was not enough to attend the increasing activity and the new policies on appraisal frequency, being extremely challenging to hire and train new appraisers in a reasonable timeframe. On the other, the process was subject to biases or mistakes because it mostly relied on the judgement of the appraisers, who according to Marcus Kinrade, Wake County’s Revenue Director, may “tend to rely more on their experience rather than what the data may be saying”. 
Pursuing higher accuracy and fairness, Wake County introduced machine learning (ML) to improve the property value assessment process. The platform relies on ML models that take into account more than 100 variables, such as location, market activity, size, or property exterior, and learns/becomes more “intelligent” with every new transaction. This system addresses many of the abovementioned issues, including completing the assessments in half of the historical time (4 years versus 8), or getting more objective and uniform valuations.  Now the employees can enhance their judgement by comparing the properties’ assessment with the system report, which includes the property evaluation and comparable values in similar neighborhoods, enabling them to spend more time in value-added activities, such as understanding and analyzing changes. The overall output is a more objective, timely, accurate, thoughtful, and equitable property valuation, providing more confidence to both the County and the taxpayers.
The new appraisals will become effective in 2020.  Nevertheless, to fully untap the system potential, the County will require several years and additional employees. Kinrade expressed that the County’s goal is to continue increasing its “productivity by automating repetitive tasks and increase our accuracy through quality data analysis, sound decision making and the elimination of errors. We try to benefit our Wake County taxpayers by offering quality, accurate services for the lowest cost of operation possible”.  In this line, he explained that this project does not address the “tax rate side of the equation” , suggesting that longer term plans of the County may include ML to tackle issues such as tax rate determination, and improvement in the allocation of the budget towards the services that the Wake County’s residents value the most.
There are still recurring activities that this system does not address, which would be worth it to evaluate to continue tackling the challenges related to time/resources constraints and confidence of the assessments. In the long term, processing and evaluating the homeowners’ appeals on the assessed values  is a task that the system could eventually perform, if the County manages to further standardize the appealing process. Developing a module that can learn from the historic appeals and its resolutions, to then evaluate future complaints, can lead to appraisers spending less time on cases which the system has a high degree of confidence are not applicable, and more time in analyzing those that it predicts can lead to corrective actions from the County. In the short term, an intermediate task ML can improve is the processing of the appeals’ supporting documents and its classification by relevance, learning patterns from every new case. Many legal buffets are already using this kind of technologies when reviewing cases and performing due diligence.  Similarly, a professor involved in a study of the National Bureau of Economic Research on algorithms to support the justice system, claimed that ML can be very applicable even in the legal context “where there’s considerable human expertise being brought to bear”. 
Making municipalities more efficient and smarter by ML applications is not only good in terms of better resource allocation but also desirable in terms of equality among taxpayers. However, there are some open questions on which Wake County and other organizations going through similar processes, should still work on. The main one relates to how to make sure that the employees are sufficiently trained and understand the basis of the algorithms behind the predictions. The output of this new process will impact not only the local government budget, but the pockets of hundreds of thousands of residents. If the applications of ML are extended to work on resident’s preferences to better allocate the County’s budget, other open questions may include how to make sure that this sensitive information is used only for the intended purposes.
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 Rick Smith, “Wake County tax team enlists high-tech SAS help to tackle property assessments”, WRAL TechWire, August 20, 2018 – https://www.wraltechwire.com/2018/08/20/wake-county-tax-team-enlists-high-tech-sas-help-to-tackle-property-assessments/
 Colin Wood, “Machine learning is helping this North Carolina county keep up on property assessments”, Statescoop, September 4, 2018 – https://statescoop.com/machine-learning-is-helping-this-north-carolina-county-keep-up-on-property-assessments/
 Artificial intelligence improves assessment accuracy and productivity in Wake County, SAS Corporate Website, Customer Stories – https://www.sas.com/en_us/customers/wake-county.html
 Bernard Marr, “How AI And Machine Learning Are Transforming Law Firms And The Legal Sector”, Forbes, May 23, 2018 – https://www.forbes.com/sites/bernardmarr/2018/05/23/how-ai-and-machine-learning-are-transforming-law-firms-and-the-legal-sector/#57f18b5832c3
 Tom Simonite, “How to Upgrade Judges with Machine Learning”, MIT Technology Review, March 6, 2017 – https://www.technologyreview.com/s/603763/how-to-upgrade-judges-with-machine-learning/