Service: Taxation and the automatization of property evaluation

For years, and with varying degrees of success, the Danish government has tried to centralize and automate tax administration. Attempts to centralize and automate tax collection in 2005 with EFI (Et Fælles Inddrivelsessystem or One Shared Tax collection system) failed, and EFI was shelved in 2015 (Alfter, 2019, 51). A new system, to collect billions of Danish crowns owed by taxpayers, was expected to be finalized by mid-2019, however, in September 2019, the Auditors of the Danish Parliament, Rigsrevision, published a critical report indicating that tax collection would only be fully up and running by the end of 2020, or later (Rigsrevisionen, 2019). Missing out on collecting billions of Danish crowns in tax revenue, however, is just one of several automatization concerns on the table of every new minister of finance. One heated discussion is about automatizing the estimation of property values – particularly in a context where housing costs are growing significantly in the capital and near larger cities while they are dropping elsewhere, thus affecting property owners in a country where large parts of the population own their own home. Passed in 2017, the Ejendomsvurderingslov (law on property assessment, 2017) states that automatic real estate valuation assessment will become standard practice. In preparation for the law, this fully automated system was described as “machine assessments” (Motzfeldt & Abkenar, 2019, p. 36-37). The system is set to include “sales prices related to the neighborhood, location and size. Sales price levels are adjusted to take into account the distance to forests and recreation areas, schools, streets, railways”. The system will also take into account whether or not there is the possibility to build on the surrounding terrain and whether or not ground pollution is an issue (Motzfeldt & Abkenar, 2019, p. 37). A total of 19 parameters will be used to automatically calculate property value (Version2 Editorial, 2019). A preparatory committee prepared an analysis to guide the government ahead of the legislation. The committee followed the overall line to move from “subjective to objective” assessments, from human to digital assessments, and from manual to automatic administration, to improve assessments and lower administration costs (Engbergudvalget, 2014, p. 68-69). While it does not explicitly mention it, this model uses a level of machine learning to develop a “statistical predictive model” (Motzfeldt & Abkenar, 2019, p. 37). Furthermore, the overall assumption of the preparatory committee was that machine assessments were more objective than human assessments, where human assessments are considered subjective. As with many other systems, the automated property assessment system has been significantly delayed. Along the way, fears surfaced that it did not comply with Danish administrative law (Mølsted, 2018). It also proved to be more expensive than the estimate in the initial report regarding the cost of a property valuation calculated by a human (Motzfeldt, 2019-2). This project, and other problematic attempts by the Danish tax authorities to incorporate IT projects, have been described as “the ministers’ (…) IT-phantoms” (Version2 Editorial, 2019). The law to assess property values allows a variation of 40% in the value of a given property – 20% either side of the price calculated by the system – “and one does not need to hold a PhD in human behavior to predict that this will lead to protests if or when the system comes into use. Worst case we will see headlines that two newly built and identical houses will be taxed very differently. Could we blame citizens if they ask critical questions why they had to spend billions for a system with such a low level of precision” (Motzfeldt, 2019-2).