Summary of Cleanliness is Next to Income: The Impact of COVID-19 on Short-Term Rentals, with Sean Wilkoff. In Austin, Texas, Airbnb listings fell 25 percent at the onset of COVID-19 and surviving listings lost 22 percent of income, but units reviewers perceived as clean, measured with a machine-learning cleanliness dictionary, gained 17.5 percent in income and 16.5 percent in occupancy.
Summary of Information Value of Property Description: A Machine Learning Approach, with Stephen L. Ross. A machine-learning measure of the soft information in listing text raises sale prices by 15 percent per standard deviation in a hedonic model and 10 percent in a repeat-sales model, and price indices that ignore it overstate prices by 10 to 23 percent and mistime the post-recession stabilization.
Summary of Information in Financial Contracts: Evidence from Securitization Agreements, with Brent W. Ambrose, Yiqiang Han, and Sanket Korgaonkar. A machine-learning comparison of the pooling and servicing agreements behind commercial mortgage-backed securities shows substantial variation across deals, underwriters, and time, partly explained by collateral, and correlated with later loan and bond performance.
Summary of The Odd One Out? The Impact of Property Uniqueness on Selling Time and Selling Price, with Thomas M. Springer. Machine-learning measures of uniqueness from listing text show that a one-standard-deviation increase in market uniqueness raises sale prices by 13 percent ($48,490) while delaying sale by 1.7 days; universal uniqueness raises prices 11 percent ($41,030) at a 3-day delay.