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Technology and Short-Term Rentals

Paper Session

Friday, Jan. 7, 2022 12:15 PM - 2:15 PM (EST)

Hosted By: American Real Estate and Urban Economics Association
  • Chair: Keren Horn, University of Massachusetts-Boston

Property Management Technology Adoption in the Short-Term Housing Rental Market

Jaime Luque
,
ESCP Business School
Sophia Göppinger
,
ESCP Business School

Abstract

Following the recent and growing literature that focuses on the eruption of Airbnb on the short-term rental housing market, this paper studies the impact of a new technology that provides information on market trends using Airbnb's scrapped data. We collected a sample of 5,392 housing units available on Airbnb in Madrid, Spain. We exploit a natural experiment whereby 14% of the properties in the sample were managed by property managers that adopted the technology at different points in time. We use Propensity Score Matching to estimate the effect of adopting this technology on occupancy rate, average daily price, and revenue. We find that enhanced market transparency through this technology adoption led to a 23% increase in occupancy, 525% increase in average daily price, and 289% increase in revenue. We conducted robustness checks using Augmented Inverse Propensity Weighting (AIPW), which showed effects of similar signs but smaller magnitudes. We discuss the implications of these results for the consolidation and competition in this market.

Does Airbnb Reduce Matching Frictions in the Housing Market?

Abdollah Farhoodi
,
University of Toronto
Nazanin Khazra
,
University of Toronto
Peter Christensen
,
University of Illinois

Abstract

There is an ongoing concern about the negative effect of home-sharing on housing availability and prices. To address this concern, we study the mechanisms through which Airbnb affects the housing market. We build a matching model and show how the growth of Airbnb improves matching quality in the housing market. We test our theoretical results using daily Airbnb data for the entire US and a novel shift-share approach. We provide evidences that the dominant effect of Airbnb on the housing market is through reductions in matching frictions. Our empirical results show that an increase in Airbnb increases house prices, reduces total sales, increases sellers' time on the market, and reduces the probability of selling a house. We interpret these results as evidence for improvement in matching quality. Home-owners can afford to hold on to their houses and wait to be well-matched. These findings support our micro-founded model and suggest reducing buyers' search cost as the primary mechanism for these effects. We also discuss the heterogeneity in response to the Airbnb increase using a causal machine learning model. Consistent with our theoretical model, we find that locations with lower elasticity of housing supply respond more to the growth of Airbnb.

Airbnb, COVID-19 Risk and Lockdowns: Local and Global Evidence

Adrian Lee
,
Deakin University
Maggie Hu
,
Chinese University of Hong Kong

Abstract

The COVID-19 pandemic has triggered an unprecedented crisis in the travel industry and short-term rental market. We study the impact of COVID-19 on global Airbnb booking activity from three aspects: the initial Wuhan lockdown, local COVID-19 cases, and local lockdowns. Using reviews and cancellations as proxies for Airbnb bookings, we find that local lockdowns result in a 57.8% fall in global booking activities, with an 8.8% fall after the Wuhan lockdown. Every doubling of newly infected cases is associated with a 4.16% fall in bookings. The sensitivity of bookings to COVID-19 decreases with geographic distance to Wuhan, and increases with government stringency of lockdown policies as well as human mobility within a market. Using London, United Kingdom as a case study, we find private rooms experience over 20% more cancellations than entire homes, consistent with host fears of infection. On the supply side, we find stable listings volume and lower booking price. Hosts charge an infection risk premium of 5.2% for letting private rooms relative to entire homes. Our study provides insights for hosts and policymakers to formulate recovery plans in a post COVID-19 economy.

Cleanliness is Next to Income: The Impact of COVID-19 on Short-Term Rentals

Sean Wilkoff
,
University of Nevada-Reno
Lily Shen
,
Clemson University

Abstract

The short-term rental market provides a close to real time signal of how events of regional and national importance can affect the demand for housing. We use Airbnb data from Austin, Texas to empirically investigate the impact of the onset of Corona Virus Disease 2019 (COVID-19) on the short-term rental market. Specifically, we employ a machine learning algorithm to create an extensive cleanliness dictionary to detect whether an Airbnb unit is clean. We use a difference-in-difference specification to value the change in income related to reviewer perceived cleanliness during the COVID-19 pandemic. We find the following results: first, available listings declined by 25% once the pandemic hit and those that remained lost 22% of their income and had occupancy decrease by 20%. Second, properties that were perceived to be clean increased their income by 17.5% and their occupancy by 16.5%, mitigating the negative shock due to COVID-19. Third, rental prices for clean Airbnb listings did not increase after COVID-19.

Discussant(s)
Amrita Kulka
,
New York University
Edward Kung
,
California State University-Northridge
Amanda Ross
,
University of Alabama
Andrew Bibler
,
University of Nevada-Las Vegas
JEL Classifications
  • R3 - Real Estate Markets, Spatial Production Analysis, and Firm Location