{"id":32867,"date":"2018-11-13T15:51:46","date_gmt":"2018-11-13T20:51:46","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/wake-county-using-machine-learning-for-property-appraisal\/"},"modified":"2018-11-13T15:51:46","modified_gmt":"2018-11-13T20:51:46","slug":"wake-county-using-machine-learning-for-property-appraisal","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/wake-county-using-machine-learning-for-property-appraisal\/","title":{"rendered":"WAKE COUNTY USING MACHINE LEARNING FOR PROPERTY APPRAISAL"},"content":{"rendered":"

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\u2019 appraisal, something essential for tax purposes. [1] 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. [2] 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\u2019s Revenue Director, may \u201ctend to rely more on their experience rather than what the data may be saying<\/em>\u201d. [1]<\/p>\n

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 \u201cintelligent\u201d 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. [3] Now the employees can enhance their judgement by comparing the properties\u2019 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.<\/p>\n

The new appraisals will become effective in 2020. [3] Nevertheless, to fully untap the system potential, the County will require several years and additional employees. Kinrade expressed that the County\u2019s goal is to continue increasing its \u201cproductivity 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<\/em>\u201d. [1] In this line, he explained that this project does not address the \u201ctax rate side of the equation<\/em>\u201d [1], 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\u2019s residents value the most.<\/p>\n

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\u2019 appeals on the assessed values [2] 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\u2019 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. [4] \u00a0Similarly, 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 \u201cwhere there\u2019s considerable human expertise being brought to bear<\/em>\u201d. [5]<\/p>\n

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\u2019s preferences to better allocate the County\u2019s budget, other open questions may include how to make sure that this sensitive information is used only for the intended purposes.<\/p>\n

(Word count: 796)<\/p>\n

[1] Rick Smith, \u201cWake County tax team enlists high-tech SAS help to tackle property assessments\u201d, 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\/<\/a><\/p>\n

[2] Colin Wood, \u201cMachine learning is helping this North Carolina county keep up on property assessments\u201d, Statescoop, September 4, 2018 – https:\/\/statescoop.com\/machine-learning-is-helping-this-north-carolina-county-keep-up-on-property-assessments\/<\/a><\/p>\n

[3] 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<\/a><\/p>\n

[4] Bernard Marr, \u201cHow AI And Machine Learning Are Transforming Law Firms And The Legal Sector\u201d, 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<\/a><\/p>\n

[5] Tom Simonite, \u201cHow to Upgrade Judges with Machine Learning\u201d, MIT Technology Review, March 6, 2017 – https:\/\/www.technologyreview.com\/s\/603763\/how-to-upgrade-judges-with-machine-learning\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Wake County in North Carolina has implemented machine learning to improve the property value assessment process, something extremely important for tax purposes. The new assessments will be effective in 2020 and will be the result of a new process that intends to get more objective, timely, accurate, thoughtful, and equitable property valuations that provide more confidence to both the County and the taxpayers.<\/p>\n","protected":false},"author":11819,"featured_media":32868,"comment_status":"open","ping_status":"closed","template":"","categories":[4794,346,4793,1793,775],"class_list":["post-32867","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-government-innovation","category-machine-learning","category-property-appraisal","category-public-sector","category-real-estate","hck-taxonomy-organization-wake-county","hck-taxonomy-industry-public-administration","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nWAKE COUNTY USING MACHINE LEARNING FOR PROPERTY APPRAISAL - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/wake-county-using-machine-learning-for-property-appraisal\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"WAKE COUNTY USING MACHINE LEARNING FOR PROPERTY APPRAISAL - Technology and Operations Management\" \/>\n<meta property=\"og:description\" content=\"Wake County in North Carolina has implemented machine learning to improve the property value assessment process, something extremely important for tax purposes. 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