Do short-term rental platforms affect housing markets? Evidence from Airbnb in Barcelona

Overview of the Study

This research, published in the Journal of Urban Economics (Volume 119, September 2020), investigates how short‑term rental platforms, specifically Airbnb, influence housing markets in Barcelona. The authors—Miquel‑Àngel Garcia‑López, Jordi Jofre‑Monseny, Rodrigo Martínez‑Mazza, and Mariona Segú—are scholars with expertise in urban economics and housing policy. Their analysis combines detailed micro‑data on Airbnb listings, transaction prices, and posted rents, offering a comprehensive view of market dynamics in a major European tourist city.

Data Sources and Scope

The study uses multiple high‑quality datasets: (1) Airbnb listings scraped from InsideAirbnb covering 2009–2017, (2) transaction price records from the Catalan Tax Authority (2009–2017), and (3) posted rent and price ads from Idealista (December of each year 2007–2017). The geographic unit of analysis is the Basic Statistical Area (BSA), of which 221 BSAs are retained after data restrictions, each averaging about 7 000 inhabitants. Airbnb activity is measured by the number of listings receiving at least one review per quarter, yielding an average of 54 listings per BSA by 2016 and 178 listings in the top decile.

Main Empirical Findings

Baseline regressions show that an increase of 100 Airbnb listings raises rents by 1.9 %, transaction prices by 4.6 %, and posted prices by 3.7 % on average. In high‑Airbnb neighborhoods (top decile), rents rise by roughly 7 %, transaction prices by 17 %, and posted prices by 14 %. The impact on prices consistently exceeds that on rents, reflecting higher returns from short‑term rentals. Event‑study analyses confirm that these effects emerge after 2013, when Airbnb activity expands, while pre‑2013 trends are parallel across neighborhoods.

Mechanisms Behind Price Increases

The authors’ theoretical model predicts that Airbnb reduces the supply of long‑term rentals, pushing up rents. Empirical tests on household counts support this mechanism: a 100‑listing increase lowers the number of households by about 2.4 % and modestly reduces household size and population, indicating displacement of long‑term residents. Instrumental‑variable estimations—using a shift‑share instrument that combines proximity to tourist amenities with Google Trends for “Airbnb Barcelona”—produce similar positive effects, reinforcing causal interpretation.

Implications for Sustainable Housing

The findings suggest that short‑term rental platforms can exacerbate housing affordability challenges in dense urban areas, especially where tourism demand is high. Barcelona’s case illustrates how a 5 % share of housing units listed on Airbnb (2.06 % of total units, 6.84 % of rented units) can translate into measurable rent and price pressures. Policymakers aiming for sustainable housing must consider regulatory tools that balance tourism benefits with the need to preserve long‑term rental stock, such as licensing requirements, caps on rental periods, or taxation of short‑term rentals.

Key Statistics at a Glance

• Average Airbnb listings per BSA (2016): 56 (overall), 179 (top decile)
• Share of housing units listed on Airbnb: 2.06 % of total, 6.84 % of rented units
• Average rent increase per 100 listings: 1.9 % (overall), 7 % (top decile)
• Average transaction price increase per 100 listings: 4.6 % (overall), 17 % (top decile)
• Average posted price increase per 100 listings: 3.7 % (overall), 14 % (top decile)

Policy Context and Future Research

Barcelona has implemented measures to curb unlicensed short‑term rentals, reflecting broader European concerns about “touristification.” The study’s robust methodologies—including fixed‑effects, BSA‑specific trends, detrending, IV, and event‑study designs—provide a template for evaluating similar dynamics in other cities. Further research could explore long‑term welfare effects on residents, the role of platform regulation, and comparative analyses across European housing markets.