||| FROM SCIENCE DIRECT || Re-posted at request of Orcasonian reader


Abstract

Online short-term rental (STR) platforms such as Airbnb have grown spectacularly. We study the effects of regulation of these platforms on the housing market using a quasi-experimental research design. 18 out of 88 cities in Los Angeles County have severely restricted short-term rentals by adopting Home Sharing Ordinances. We apply a panel regression-discontinuity design around the cities’ borders. Ordinances reduced listings by 50% and housing prices by 2%. Additional difference-in-differences estimates show that ordinances reduced rents also by 2%. These estimates imply large effects of Airbnb on property values in areas attractive to tourists (e.g. an increase in house prices of 15% within 2.5km of Hollywood’s Walk of Fame).

1. Introduction

Short-term housing rentals (STRs) have become very important due to the rise of online STR-platforms, such as Airbnb, which provide opportunities for households to informally offer accommodation to visitors. The surge in popularity of STR-platforms has led to substantial opposition because of a decrease in housing affordability (Samaan, 2015Sheppard, Udell, 2016), unfair competition, and illegal hotelization (CBRE, 2017). Negative externalities (e.g. noise, reduction in perceived safety) due to the presence of tourists in residential buildings are also frequently mentioned (see e.g. Lieber, 2015Williams, 2016Filippas, Horton, 2018).

Local governments around the globe have responded quite differently towards regulating STRs. Most cities have not significantly regulated these platforms, but a limited number of cities have recently put severe restrictions in place. Berlin, for instance, requires STR-hosts to occupy the property for at least 50% of the time (O’Sullivan, 2016). San Francisco imposes a 14% hotel tax (i.e. a Transient Occupancy Tax) and a cap of maximum 90 rental days per year (Fishman, 2015). Amsterdam even imposes a maximum cap of 30 rental days per year as of 2019.

In this paper, we aim to measure the impact of Airbnb, by far the largest STR-platform, on housing markets. We focus on the effects of policies that restrict the market for STRs. There are arguably three main mechanisms of how regulation of short-term renting impacts property markets:

1.Efficient use effect. Short-term rentals generate income from idle space, increasing value due to additional income opportunities. Moreover, residential properties can now be used by their most profitable use (i.e. by short-term renters). This should be an efficiency gain that spurs housing demand, which increases house prices (see e.g. Turner et al., 2014).

2.Rental housing supply effect. Short-term rentals may in turn lead to a reallocation of existing housing stock away from the long-term rental market towards privately-owned housing, which increases rents (see e.g. Quigley et al., 2005).

3.Externality effect. Short-term rentals may create negative nuisance externalities, lowering nearby property values. If neighbors fear turnover or unfamiliar people in their neighborhood, this may reduce demand for housing (see e.g. Filippas and Horton, 2018).

To identify the effects of short-term housing rentals regulation on the housing market, we exploit exogenous variation provided by the implementation of so-called Home-Sharing Ordinances (HSOs) in Los Angeles County. 18 out of 88 cities implement regulations that essentially ban informal vacation rentals; hosts renting out entire properties are now subject to the same formal regulations as regular hotels and bed and breakfasts. Short-term home-sharing is not always prohibited, albeit restricted in those cities.

There are several reasons why we focus on Los Angeles County. First, it is an area that is attractive to tourists and has thousands of listings on Airbnb. It is in the global top 10 of the cities with the most Airbnb listings and is the second most popular Airbnb city in the US after New York. Second, there is substantial spatio-temporal variation in the implementation of HSOs within this county. For example, HSOs have been implemented in cities that receive many tourists (e.g. Santa Monica), as well as in cities that are more at the edge of the Los Angeles Conurbation (e.g. Pasadena). We think this might add to the external validity of the results shown in the paper. Third, by focusing on 18 cities, rather than on the introduction of an HSO in one single city, we substantially reduce the likelihood that our results are contaminated by an unobserved event (e.g., a change in a city-specific policy) that occurs around the same time as the introduction of the HSO. Fourth, in Los Angeles County, in contrast to for example New York, renters are (usually) not allowed to list a property on Airbnb (Lipton, 2014).3 This facilitates the interpretation of the distributional consequences of our results: renters generally lose from Airbnb-induced higher rents (and hardly benefit from the opportunity of subletting to short-term renters).

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