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The spatial distribution of Airbnb providers in Brussels. Different drivers for different types of hosts?
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Brussels’ urban and suburban landscape has changed considerably since the 1980s. The consolidation of socioeconomic fractures inside the city, a reinforcement of long-lasting disparities between the city and its prosperous hinterland, as well as the increasing diversification of migration flows—both high- and low-skilled—contributed to these disparities. Recent evolutions of these patterns, however, have not been investigated yet and therefore remain unknown. Besides, the extent to which segregation is primarily related to economic inequalities and to migration flows—or a combination/interaction between the two—so far has not been studied. This chapter offers a detailed overview of the socio-spatial disparities in the Brussels Functional Urban Area. Our analyses relied on fine-grained spatial data, at the level of statistical sections and of individualised neighbourhoods built around 100 m x 100 m grids. We analysed socioeconomic segregation measures and patterns, as well as their evolution between 2001 and 2011. Socioeconomic groups were defined based on individuals’ position with respect to national income deciles. In line with previous research, our results show very marked patterns of socioeconomic segregation in and around Brussels operating both at a larger regional scale and at the local level.
The book “Urban Socio-Economic Segregation and Income Inequality: a Global Perspective” investigates the link between income inequality and residential segregation between socio-economic groups in 24 large cities and their urban regions in Africa, Asia, Australia, Europe, North America, and South America. Author teams with in-depth local knowledge provide an extensive analysis of each case study city. Based on their findings, the main results of the book can be summarised as follows. Rising inequalities lead to rising levels of socio-economic segregation almost everywhere in the world. Levels of inequality and segregation are higher in cities in lower income countries, but the growth in inequality and segregation is faster in cities in high-income countries, which leads to a convergence of global trends. In many cities the workforce is professionalising, with an increasing share of the top socio-economic groups. In most cities the high-income workers are moving to the centre or to attractive coastal areas, and low-income workers are moving to the edges of the urban region. In some cities, mainly in lower income countries, high-income workers are also concentrating in out-of-centre enclaves or gated communities. The urban geography of inequality changes faster and is more pronounced than city-wide single-number segregation indices reveal. Taken together, these findings have resulted in the formulation of a Global Segregation Thesis.
‘Claudia’ is neither a real name nor an owner who puts a room at the service of the collaborative economy. It is a pseudonym used by a transnational company which manages short-rentals apartments: 211 Airbnb listings in Madrid, 138 of which are in the city centre. This paper's main arguments are based on the fact that Madrid city centre is experiencing a process of airbnbisation which is driven by professional actors specialized in the short-term rental business. The analysis of this model includes an in-depth examination of the professionalization, concentration and monopolization of Airbnb in Madrid, from a temporal and territorial perspective. The paper concludes that Airbnb in Madrid is dominated by professional actors specialized in the business of renting apartments as short-term rentals, who mainly operate within the city's Central District, and whose activity does not comply with the principles of the sharing economy. This model has more to do with traditional forms of accommodation than with new hospitality models based on the sharing economy principles, and generates negative impacts on the economic sustainability of the city and its inhabitants.
The paper examines the size, structure, distribution, dynamics, and use of Airbnb accommodation offer in 167 countries. Web-scrapping Airbnb website in fall 2018 and 2019 resulted in a datasets on 5.7 million listings, including 3.6 million active listings which have been rented out (reviewed) during the last year. Listings are divided into four groups based on types of properties and numbers of offers hosted by one platform user. The results show that the platform is most commonly used to rent out entire apartments by multi-hosts. The numbers of Airbnb listings in countries depend on the level of economic development and size of inbound tourism. One third of Airbnb supply is located in big cities, another one third near seacoasts. Airbnb offer grows most quickly in its relatively new markets, while in primary urban destinations of some European countries it is stable or decreases. The offer of professional hosts is growing more quickly than of peer-to-peer hosts. Differences in the frequency of use and prices of listings exaggerate the geographical unevenness in benefits and impacts of Airbnb activity. Airbnb supply is not a uniform segment of tourist accommodation and its effects on destinations should be considered in relation to territorial context.
Through detailed empirical analysis of a central area of Lisbon, the paper explores whether short-term rental platforms such as Airbnb channel investment in residential real estate and the way in which the local community is affected by the proliferation of apartments rented to visitors. Between 2015 and 2017 we conducted fine-grained fieldwork in the historical neighborhood of Alfama and identified both the producers and socio-spatial consequences of short-term rentals. Our research did not find evidence of a sharing economy. Rather, it found a process of buy-to-let investment in which different players make profits from rents and displace residents with tourists. The paper develops two main arguments: first, we suggest that Airbnb acts as an instrument that contributes to the financialization of housing. Compared to the traditional rental market, short-term rentals offer a number of benefits that enhance market efficiency for property owners, making them increasingly attractive for both local and global investors. We found that the suppliers of short-term rentals are primarily investors that use housing as an asset to store capital. The main advantage of the short-term rental market for investors is that while they can make profits by renting properties to visitors they can also sell them tenant-free at any moment. Second, Airbnb gives way to a hyper-flexible rental market that for tenants implies increasing insecurity and displacement concerns. We portray Airbnb as an example of buy-to-let gentrification that is experienced by residents as a process of social injustice.
Hedonic modelling techniques have frequently been used to examine real estate valuation, and they have recently started to be applied to short-term rental valuation. Relying on a web-scraped data set of all Airbnb transactions in New York City (NYC) between August 2014 and September 2016, this paper presents the first hedonic regression model of Airbnb to take into account neighbourhood effects and to predict both average price per night and revenue generated by each listing. The model demonstrates that locational factors – above all, transit accessibility to jobs – and neighbourhood variation have a large impact on both price per night and monthly revenue, and further reveals how professionalization of the short-term rental market is driving more revenue to a narrower segment of hosts. Further, the findings suggest that Airbnb hosts earn a significant premium by converting long-term housing in accessible residential neighbourhoods into de facto Airbnb hotels. This premium incentivizes landlords and hosts with properties in accessible neighbourhoods to replace long-term tenants with short-term guests, forcing those in search of housing to less accessible neighbourhoods.
This article explores Airbnb accommodation spatial distribution and it estimates the main determinants of its location choice. It employs spatial bivariate correlations and spatial econometrics to understand the heterogeneous spatial relationship between established hotels and Airbnb for three kinds of local tourism destinations: sun and beach, nature-based, and city. The case study concerns the Canary Islands where a good mixture of these attractions can be found. The main conclusion drawn is that Airbnb regulation needs to distinguish the kind of tourism. More precisely, Airbnb supply overlaps established hotels in city tourism, but it does not so clearly in sun and beach nor nature-based destinations. Airbnb supply matches tourist visits spatial distribution better than established hotels in city and nature-based destinations, but not in sun and beach destinations, where the incumbent hotels are closer to the tourism resources. Finally, the results from the spatial econometrics model shows that population size and the number of tourist visits matters as determinants of Airbnb location. However, the main determinant is price, which has got a much larger elasticity.
The objective of this article is to provide an analysis of the spatialities of Airbnb in Sofia, Bulgaria. Relying on an analysis of both quantitative and qualitative data, this article firstly explores the diffusion and concentration of Airbnb listings in the city’s districts. It questions whether the platform’s self-proclaimed contribution to a more diversified offering of tourism accommodation indeed applies to the context of Sofia. It then identifies which listings are most popular among Airbnb guests, and examines who reaps the benefits and profits from this “sharing” economy and who does not. In doing so, this article aims to provide a more nuanced understanding of the power relations in the production and consumption of Airbnb experiences. Whilst discussing the socio-spatial impacts of Airbnb in Sofia, this article takes into account some of the broader urban transformations that have taken place in the city since the end of the socialist regime in 1989. The findings suggest that the large majority of Airbnb listings tend to concentrate in those districts that are marked by commercialization and gentrification and are home to a privileged higher-income population. These areas generally also already benefit from a high concentration of official tourism accommodation and tourist attractions. As such, the article concludes that, like in other European cities, Airbnb benefits a selective number of hosts and potentially further exacerbates an already problematic private rental market.
The Airbnb phenomenon as part of the broader growth of the so-called collaborative economy has grabbed the attention of a growing number of tourism researchers. Among the topics explored have been investigations as to the spatial tendencies of Airbnb in cities and discussions concerning its effects, inter alia, on gentrification, over-touristification and eventual resident displacement. Recognizing that the majority of extant studies have been conducted either in major cities, which in their own right attract large numbers of visitors or in tourism-intensive smaller communities we chose to investigate what Airbnb growth means for a mid-sized city with a highly diversified economy, which is not yet over-touristified. Our focus was on the Dutch city of Utrecht. Through a geospatial and statistical analysis of AirDNA data, we explored the growth of Airbnbs in the city overall, focusing specifically on the phenomenon's effects on the Lombok neighbourhood, a nascent ‘neo-bohemia’ neighbouring the city-centre tourist bubble. Our analysis reveals that although Airbnb activity in this neighbourhood is relatively recent there are signs suggesting that further touristification of parts of Lombok has ignited increased Airbnb activity. Moreover, there is a distance decay of Airbnb activity as one moves away from the city centre and from established tourism services including restaurants. These findings suggest that in an emerging neo-bohemian space such as Lombok, Airbnb takes on a role as instigator of urban tourism bubble expansion. The study ends with a call for further investigations to better understand the implications expanded Airbnb activity has, among others, on social justice within cities. For example, future investigations could examine the manner in which Airbnbs influence the everyday life of the residents of urban spaces and investigate the conflicts that might arise in Airbnb ghettoes between visitors and locals.
Airbnb and other short-term rental services are a topic of increasing interest and concern for urban researchers, policymakers and activists, because of the fear that short-term rentals are facilitating gentrification. This article presents a framework for analyzing the relationship between short-term rentals and gentrification, an exploratory case study of New York City, and an agenda for future research. We argue that Airbnb has introduced a new potential revenue flow into housing markets which is systematic but geographically uneven, creating a new form of rent gap in culturally desirable and internationally recognizable neighbourhoods. This rent gap can emerge quickly—in advance of any declining property income— and requires minimal new capital to be exploited by a range of different housing actors, from developers to landlords, tenants and homeowners. Performing spatial analysis on three years of Airbnb activity in New York City, we measure new capital flows into the short- term rental market, identify neighbourhoods whose housing markets have already been significantly impacted by short-term, identify neighbourhoods which are increasingly under threat of Airbnb-induced gentrification, and measure the amount of rental housing lost to Airbnb. Finally, we conclude by offering a research agenda on gentrification and the sharing economy.
This paper examines the impact of a variety of variables on the rates published for Airbnb listings in five large metropolitan areas in Canada. The researchers applied a hedonic pricing model to 15,716 Airbnb listings. As expected, the results show that physical characteristics, location, and host characteristics significantly impact price. Interestingly, more reviews are associated with a drop in price. This information is useful to hosts who are forming a pricing strategy for their listings as well as for Airbnb, who needs to support them. The paper raises important questions about pricing in the sharing economy and suggests avenues for future research in this area.
In recent years, what has become known as collaborative consumption has undergone rapid expansion through peer-to-peer (P2P) platforms. In the field of tourism, a particularly notable example is that of Airbnb. This article analyses the spatial patterns of Airbnb in Barcelona and compares them with hotels and sightseeing spots. New sources of data, such as Airbnb listings and geolocated photographs are used. Analysis of bivariate spatial autocorrelation reveals a close spatial relationship between Airbnb and hotels, with a marked centre-periphery pattern, although Airbnb predominates around the main hotel axis and hotels predominate in some peripheral areas of the city. Another interesting finding is that Airbnb capitalises more on the advantages of proximity to the main tourist attractions of the city than does the hotel sector. Finally, it was possible to detect those parts of the city that have seen the greatest increase in pressure from tourism related to Airbnb's recent expansion.
This contribution considers the spatial distribution of foreigners in Brussels. Fifteen nationalities are considered, among which a group of affluent foreigners linked to the international functions of the city (EU Capital and NATO headquarters) and a poor group whose beginnings can be traced to the ‘guestworkers’ immigration in the late sixties and early seventies. Firstly, the population structure of Brussels and the position of its foreigners are outlined in a historical perspective. Then, the housing market structure and its spatial distribution are explained. Both elements are crucial to the understanding of the contrasting residential distribution of the affluent foreigners and the guestworkers. Finally, the changes in the composition of the foreign communities between 1981 and 1991 are examined and related to processes of urban restructuring. They express the passage from a Fordist to a post-Fordist city whereby spatial patterns merely change, but deepening contrasts in the social structure appear.
We study the causal impact of the negative shock on short-term rentals caused by covid-19 in the tourist-intensive city centre of Lisbon. Our difference-in-differences strategy uses a parish-level treatment relying on the pre-pandemic intensity of short-term rentals, using data between Q3 2018 and Q3 2020. The results suggest that landlords relocated properties into the long-term rental market, in which prices de-crease 4.1%, while listed quantities increase 20% in the treated civil parishes vis-`a-vis comparison ones. We also find evidence of an incremental negative impact on sale prices of 4.8% in treated areas. Our results are robust to the inclusion of Porto.
This paper examines the potential economic spillover effects of a home sharing platform—Airbnb—on the growth of a complimentary local service—restaurants. By circumventing traditional land-use regulations and providing access to underutilized inventory, Airbnb attracts visitors to outlets that are not traditional tourist destinations. Although visitors generally bring significant spending power, it is unclear whether visitors use Airbnb only primarily for lodging and thus do not contribute to the adjacent economy. To evaluate this, we focus on the impact of Airbnb on restaurant employment growth across locales in New York City (NYC). Specifically, we focus on areas in NYC that did not attract a significant tourist volume prior to the emergence of a home-sharing service. Our results indicate a salient and economically significant positive spillover effect on restaurant job growth in an average NYC locality. A one-percentage-point increase in the intensity of Airbnb activity (Airbnb reviews per household) leads to approximately 1.7% restaurant employment growth. Since home-sharing visitors are lodging in areas that are not accustomed to tourists, we also investigate the demographic and market-structure-related heterogeneity of our results. Notably, restaurants in areas with a relatively high number of White residents disproportionately benefit from the economic spillover of Airbnb activity, whereas the impact in majority-Black areas is not statistically significant. Thus, policy makers must consider the heterogeneity in the potential economic benefits as they look to regulate home-sharing activities.
Purpose
This study aims to analyze the COVID-19 pandemic’s impact on the peer-to-peer (p2p) market for tourist accommodation.
Design/methodology/approach
Using monthly panel data from Airbnb listings in 22 cities worldwide, the authors run a differences-in-differences analysis comparing the period of February–October 2020 to the previous year.
Findings
Besides a decline in accommodation supply, the pandemic made prices and demand fall in all cities significantly, after controlling for room characteristics, host traits, booking policies and individual fixed effects. There is also evidence of an alteration of the influence on prices of certain variables such as superhost and instant booking.
Research limitations/implications
The main limitations are related to the reference spatial and temporal environment. Besides, the samples are limited to listings that stayed before and after the pandemic; therefore, it is possible that the real effect on review growth and/or prices is actually more negative.
Practical implications
The analysis performed shows a scenario that represents an opportunity for public managers to test more imaginative regulations that overcome the limitations of those implemented so far. Likewise, hosts who aspire to make their accommodations profitable must adapt to the conditions imposed by the economic environment of the cities in which they operate.
Originality/value
This is the first study to econometrically estimate the impact of COVID-19 on prices in the p2p market for tourist accommodation in a set of cities worldwide.
We study professional players and their roles in peer-to-peer (P2P) markets. Most notably, P2P home-sharing platforms (e.g., Airbnb) consist of both professional hosts and nonprofessional individual hosts. What are the roles of the professionals? Should home-sharing platforms regulate their participation? Professional hosts may primarily offer properties that nonprofessional hosts would not supply and attract more guests—the differentiation effect. Or they may mostly supply similar properties and compete with the nonprofessionals—the competition effect. Using a unique dataset of Airbnb listings, we first find that professional hosts’ properties are more expensive and have superior characteristics than nonprofessionals’. Second, we capitalize on a quasi-experiment in which Airbnb capped the number of properties a host can manage in several cities in the United States to determine the roles of professional hosts. With different predictions (about the policy impacts) under the differentiation versus competition effects, we find evidence suggesting the dominance of the latter. In particular, the policy increased the supply from nonprofessional hosts, and the price level of nonprofessional properties as a group went up after the policy. However, our findings of heterogeneity in policy impacts suggest that the dominance of competition is less prominent in certain markets. Lastly, we find that the platform was not worse off in attracting reservations or securing revenue after the policy. Our findings contribute to both theory and practice as they reveal the roles of professional players and how P2P platforms can manage their participation.
We assess the impact of Airbnb on residential house prices and rents: using a data set of Airbnb listings from the entire United States and an instrumental variables estimation strategy, we show that Airbnb has a positive impact on house prices and rents.
This research crucially investigates COVID-19 variables’ impacts on the changing distributions of travel and leisure industry returns across 65 countries via a quantile regression model that uses daily data from December 2019 to May 2020 to provide early evidences from a panel of countries. We find that the change rate in COVID-19 deaths exerts more substantial negative effects on industry returns at majority quantiles than does the impact from the number of confirmed cases. The latter number only saliently and negatively influences the lowest return quantiles, revealing a nonlinear effect of confirmed cases. The study identifies a V-shape correlation between the number of cases recovered and travel and leisure industry returns (i.e. a negative impact at the lower quantiles, but a positive impact at higher quantiles) across return quantiles. This likely denotes that confirmed cases grow exponentially and that their effect may overwhelm the impact of the number of recovered cases. Lastly, this study presents a positive correlation between government response stringency index and returns.
Although Airbnb's impact on hotels has been quantified for major hotel markets in the United States, these effects have not been quantified in international hotel markets. Accordingly, the purpose of this study is to examine the effects of Airbnb listings on key hotel performance metrics in an international context. In particular, we examine the effects of Airbnb listings on hotel revenue per available room (RevPAR), average daily rate (ADR), and occupancy rate (OCC) in major international hotel markets, namely London, Paris, Sydney and Tokyo. The results show that Airbnb listings in these major cities have been increasing more than 100% year over year and that the effect of Airbnb on hotel RevPAR and OCC is negative and statistically significant. In particular, a 1% increase in Airbnb listings decreases hotel RevPAR by between 0.016% and 0.031% in these hotel markets. The implications of these findings for destinations and hoteliers are discussed.
Red flags are increasingly being raised over the contribution of short term tourism rentals (mediated by platforms such as Airbnb) to socio-spatial inequalities and residential displacement. In many cases, the most vocal reactions have come from social movements claiming the Right to the City through particular geographies of resistance that underpin protest counter-narratives in the digital and non-digital spheres. In order to evaluate this relationship, a digital content analysis based on a sample of around 16,000 tweets illustrates the depth and diversity of tourism counter-narratives within the Twitter activity of social movements in opposition to tourism saturation in the city of Barcelona. This approach is triangulated with a spatial analysis of Airbnb-mediated rentals in Barcelona, based on GIS mapping, as well as correlations with a variety of geo-referenced data sources and the application of different socio-economic variables. The relationship between these two dimensions is key for policymakers in influencing regulatory approaches to collaborative platform activity and mitigating socio-spatial inequalities generated by short term rentals and the platforms that mediate them.
Research on Airbnb has provided significant evidence that it has an adverse impact on hotel performance. However, the impact of a more recent Airbnb-related phenomenon that remains under-explored is the increasing professionalization of Airbnb and the prevalence of multi-unit
hosts who offer more than one listing on the platform and are typically more dynamic in terms of issues like managing inventory and providing more standardized experiences. This professionalization begs the question of whether Airbnb should be considered a sharing economy
platform or a lodging corporation (Airbnb 2.0). To answer this question, the present study identifies which types of Airbnb properties (entire homes, private rooms, or shared rooms) and host structures (single- or multi-unit hosts) are the biggest threats to traditional lodging
companies in the U.S., and which states are most affected by the presence of Airbnb. The findings have significant implications for researchers and many practitioners associated with the phenomenon.
The main aim of this article is to examine the characteristics of Airbnb networks in the context of the potential impact on local residents and the traditional hotel industry. The analysis is based on a unique dataset that was constructed by the web-scraping of Airbnb listing data and hotel offers available at Booking.com in Paris, Barcelona, Berlin, and Warsaw. The empirical analysis reveals that only a minority of Airbnb listings can be classified as sharing economy services, while commercial offers constitute a significant share of listings on the platform. Although Airbnb facilitates the spread of tourism to areas not covered by the traditional hotel industry, it is also highly concentrated in neighbourhoods that have long attracted travellers, and thus contributes to increasing pressure from tourism. This comparative study also shows that Airbnb and traditional hotels compete for travellers across a wide range of market segments, but the substitutability of their offers is constrained by their complementary relationships in specific city areas. The study addresses crucial research gaps, presents a robust methodology, and provides a detailed market analysis.
This article evaluates whether Airbnb rentals affect the rents in the private rental sector in eight cities in France. We estimate a hedonic equation for each city on individual data for apartments, allowing for heteroscedasticity and spatial error autocorrelation of unknown forms and using a large variety of structural and contextual characteristics of the apartments. We show that the density of Airbnb rentals puts upward pressure on rents in Lyon, Montpellier, and Paris, whereas it has no significant effect in other cities. If we restrict the analysis to the professional business of Airbnb rentals, which we define as the lodgings owned by an investor who rents either several “entire home” dwellings (regardless of the number of days) or an “entire home” dwelling for more than 120 days a year, we find a greater effect, which concerns only the two largest cities of France, that is, Marseille and Paris. When we focus on new tenancy agreements, the impact is even higher and concerns Paris, Marseille, and Montpellier. The impact of the Airbnb activity on rents is shown to increase with the proportion of owner-occupiers and decrease with hotel density, both in Montpellier and Paris. However, the share of second homes leads to contrasting effects.
The rapid uptake of the sharing economy is disrupting many socially established models of services, especially in the housing and transportation sectors. This paper builds upon the growing critical scholarship examining the urban equity implications of the giant online sharing accommodation platform, Airbnb. It focuses on Airbnb listings in two major Australian metropolitan regions, Sydney and Melbourne. The paper raises important questions about the socio-economic patterns of Airbnb offering, and basically asks who is taking part in the sharing. It examines the Airbnb listings against the Census-based Socio-Economic Indexes for Area (SEIFA), and housing tenure data. Findings clearly show the biased distribution of Airbnb listings, presenting Airbnb hosting as a relatively affluent phenomena with education as the strongest socio-economic factor determining host participation on the platform. Nevertheless, areas in strategic tourist locations have been identified as places where Airbnb is having a pressing impact, and where imminent regulatory intervention is required.
The growth of the sharing economy has received increasing attention from economists. Some researchers have examined how these new business models shape market mechanisms and, in the case of home sharing, economists have examined how the sharing economy affects the hotel industry. There is currently limited evidence on whether home sharing affects the housing market, despite the obvious overlap between these two markets. As a result, policy makers grappling with the effects of the rapid growth of home sharing have inadequate information on which to make reasoned policy decisions. In this paper, we add to the small but growing body of knowledge on how the sharing economy is shaping the housing market by focusing on the short-term effects of the growth of Airbnb in Boston neighborhoods on the rental market, relying on individual rental listings. We examine whether the increasing presence of Airbnb raises asking rents and whether the change in rents may be driven by a decline in the supply of housing offered for rent. We show that a one standard deviation increase in Airbnb listings is associated with an increase in asking rents of 0.4%.
One of the major differences between markets that follow a “sharing economy” paradigm and traditional two-sided markets is that the supply side in the sharing economy often includes individual nonprofessional decision makers, in addition to firms and professional agents. Using a data set of prices and availability of listings on Airbnb, we find that there exist substantial differences in the operational and financial performance of professional and nonprofessional hosts. In particular, properties managed by professional hosts earn 16.9% more in daily revenue, have 15.5% higher occupancy rates, and are 13.6% less likely to exit the market compared with properties owned by nonprofessional hosts, while controlling for property and market characteristics. We demonstrate that these performance differences between professionals and nonprofessionals can be partly explained by pricing inefficiencies. Specifically, we provide empirical evidence that nonprofessional hosts are less likely to offer different rates across stay dates based on the underlying demand patterns, such as those created by major holidays and conventions. We develop a parsimonious model to analyze the implications of having two such different host groups for a profit-maximizing platform operator and for a social planner. While a profit-maximizing platform operator should charge lower prices to nonprofessional hosts, a social planner would charge the same prices to professionals and nonprofessionals.
Sharing economy platforms have become extremely popular in the last few years, and they have changed the way in which we commute, travel, and borrow among many other activities. Despite their popularity among consumers, such companies are poorly regulated. For example, Airbnb, one of the most successful examples of sharing economy platform, is often criticized by regulators and policy makers. While, in theory, municipalities should regulate the emergence of Airbnb through evidence-based policy making, in practice, they engage in a false dichotomy: some municipalities allow the business without imposing any regulation, while others ban it altogether. That is because there is no evidence upon which to draft policies. Here we propose to gather evidence from the Web. After crawling Airbnb data for the entire city of London, we find out where and when Airbnb listings are offered and, by matching such listing information with census and hotel data, we determine the socio-economic conditions of the areas that actually benefit from the hospitality platform. The reality is more nuanced than one would expect, and it has changed over the years. Airbnb demand and offering have changed over time, and traditional regulations have not been able to respond to those changes. That is why, finally, we rely on our data analysis to envision regulations that are responsive to real-time demands, contributing to the emerging idea of ``algorithmic regulation''.
Peer-to-peer markets, collectively known as the sharing economy, have emerged as alternative suppliers of goods and services traditionally provided by long-established industries. The authors explore the economic impact of the sharing economy on incumbent firms by studying the case of Airbnb, a prominent platform for short-term accommodations. They analyze Airbnb's entry into the state of Texas and quantify its impact on the Texas hotel industry over the subsequent decade. In Austin, where Airbnb supply is highest, the causal impact on hotel revenue is in the 8%-10% range; moreover, the impact is nonuniform, with lower-priced hotels and hotels that do not cater to business travelers being the most affected. The impact manifests itself primarily through less aggressive hotel room pricing, benefiting all consumers, not just participants in the sharing economy. The price response is especially pronounced during periods of peak demand, such as during the South by Southwest festival, and is due to a differentiating feature of peer-to-peer platforms-enabling instantaneous supply to scale to meet demand.
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