Data Science for the Real Estate Industry

World's major industries, such as Financial Services, Telecom, Advertising, Healthcare, Education, etc, have attracted the attention of the KDD community for decades. Hundreds of KDD papers have been published on topics related to these industries and dozens of workshops organized---some of which have become an integral part of the conference agenda (e.g. the Health Day). Somewhat unexpectedly, the KDD conference has barely addressed the real estate industry, despite its enormous size and prominence. The reason for that apparent mismatch is two-fold: (a) until recently, the real estate industry did not appreciate the value data science methods could add (with some exceptions, such as econometrics methods for creating real-estate price indices); (b) the Data Science community has not been aware of challenging real estate problems that are perfectly suited to its methods. This tutorial provides a step towards resolving this issue. We provide an introduction to real estate for data scientists, and outline a spectrum of data science problems, many of which are being tackled by new "prop-tech" companies, while some are yet to be approached. We present concrete examples from three of these companies (where the authors work): Airbnb -- the most popular short-term rental marketplace, Cherre -- a real estate data integration platform, and Compass -- the largest independent real estate brokerage in the U.S.