Turning Standards into Rules — Part 5: Weighing the Factors in Capital Gains vs. Ordinary Income Decisions
(2019) 9 DTR 16 (Bloomberg BNA)
2 Pages Posted: 22 May 2019
Date Written: January 15, 2019
The question of whether gains or losses on the sale of real estate should be treated on account of ordinary income or capital gains is particularly relevant for individual taxpayers, since capital gains are taxed at a lower rate than ordinary income. ‘‘Capital assets’’ are defined by exclusion in tax code Section 1221. For the purposes of real estate dispositions, the key exclusion is paragraph 1221(a)(1), which states that a property is not considered a capital asset if it is ‘‘held by the taxpayer primarily for sale to customers in the ordinary course of his trade or business.’’ This exclusion figures largely in real estate tax cases because the outcome often turns on the extent of the taxpayer’s involvement in the real estate business. Courts have developed and used several different multi-factor tests to assist in the determination of whether a gain or loss on the sale of real estate is on account of income or capital. The tests most commonly referred to are the seven-factor test set out in United States v. Winthrop, and the more recent three-step test outlined in Suburban Realty Co. v. United States. Factor tests are useful in taking account of the facts of the situation, but mere enumeration of factors does not necessarily lead to an accurate prediction of the outcome. Unlike predictive methods that rely on tallying-up factors or employing more traditional forms of statistical analysis, machine learning allows for a nuanced approach that reflects the complexity of the legal question at hand. While lawyers can only draw on a handful of leading cases, machine learning can digest the facts of hundreds of cases in order to parse out the relative importance of each factor. In this article, we see that machine learning can generate accurate predictions of how the courts will characterize dispositions of real estate by individual taxpayers.
Keywords: machine-learning, capital gains, real estate, classification
JEL Classification: H2, H20, H29
Suggested Citation: Suggested Citation