Does Airbnb Reduce Matching Frictions in the Housing Market?

Abstract

There is growing concern about the impact of the home-sharing markets on housing affordability, yet the underlying mechanisms are not well-studied. We use a theoretical model to provide key results on the mechanisms through which home-sharing can improve the quality of matches between buyers and sellers in the housing market. We then test these predictions empirically using daily Airbnb data for the entire U.S. and a novel shift-share approach. We find that an increase in Airbnb increases house prices, reduces total sales, increases for-sales inventory, increases sellers' time on the market, and reduces the probability of selling a house. The empirical evidence supports the hypothesis that Airbnb has reduced matching frictions in the housing market. We then examine heterogeneous responses to Airbnb using Generalized Random Forest (GRF). Consistent with our theoretical model, results from the GRF model indicate that locations with a less elastic housing supply respond more to the Airbnb growth.