A simple univariate outlier identification procedure on ratio data collected by the Department of Revenue for the state of Kansas

Abstract

In order to impose fair taxes on properties, it is required that appraisers annually estimate prices of all the properties in each of the counties in Kansas. The Department of Revenue of Kansas oversees the quality of work of appraisers in each county. The Department of Revenue uses ratio data which is appraisal price divided by sale price for those parcels which are sold during the year as a basis for evaluating the work of the appraisers. They know that there are outliers in these ratio data sets and these outliers can impact their evaluations of the county appraisers. The Department of Revenue has been using a simple box plot procedure to identify outliers for the previous 10 years. Staff members have questioned whether there might be a need for improvement in the procedure. They considered the possibility of tuning the procedure to depend on distributions and sample sizes. The methodology as a possible solution was suggested by Iglewicz et al. (2007). In this report, we examine the new methodology and attempt to apply it to ratio data sets provided by the Department of Revenue.