Impacts of Peer-To-Peer Rental Accommodation in Stockholm, Barcelona and Rio De Janeiro

DEGREE PROJECT IN COMPUTER SCIENCE AND ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2020

Impacts of peer-to-peer rental accommodation in Stockholm, Barcelona and Rio de Janeiro

An exploratory analysis of Airbnb’s data

IRENE SUÁREZ PACIOS

KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT

Impacts of peer-to-peer rental accommodation in Stockholm, Barcelona and Rio de Janeiro: an exploratory analysis of Airbnb’s data

Innovation Management and Product Development

IRENE SUÁREZ PACIOS

Master of Science Thesis 2020: TRITA-ITM-EX 2020:169 KTH Royal Institute of Technology School of Industrial Engineering and Management Machine Design SE-100 44 STOCKHOLM

Abstract

As a part of the growing movement called the “peer-to-peer” economy, Airbnb has changed the short-stay rental market and has become one of the world’s largest booking websites for finding an accommodation to stay. The platform has also affected the economy of tourism around the world, so, given the importance of the subject, in this thesis study, the impacts that the Airbnb rental accommodation model has on clients of Stockholm, Barcelona and Rio de Janeiro is studied. In this way, it has been analyzed how factors such as price, location and seasonality affect Airbnb customers in these cities. To do this, the three cities were first analyzed individually and then compared, using data from the Inside Airbnb website from 2010 to now. This research has been carried out through an exploratory analysis using the R programming language. The study has been divided into three parts:

First, the Spatial Data Analysis has shown that Airbnb´s presence in all three cities has increased significantly in the past decade, growing from the most touristy parts of the city to surrounding areas. In addition, it has been observed that the largest number of Airbnb properties are apartments located near the city center and touristic places, which also are the most valued areas by Airbnb customers and the most expensive to rent a property. Secondly, a Demand and Price Analysis has been carried out. In this part, the demand for Airbnb listings has been estimated over the years since 2010 and across months. A significant increase in demand has been appreciated inthe last decade, which also shows a seasonal pattern. In the three cases, the demand graph follows the city´s climate, showing the highest demand during the summer months, which corresponds to the most expensive period. Finally, through User Review Mining, customer opinion has been studied by applying text mining to reviews. In this part of the research, word clouds have been used to have a visual representation of the text data, showing the most frequent words and analyzing what makes customers feel comfortable and uncomfortable.

Key words: Airbnb, peer-to-peer economy, shared economy, exploratory analysis, text mining

Sammanfattning

I detta examensarbete har effekterna som Airbnbs hyresmodell har på kunder i Stockholm, Barcelona och Rio de Janeiro studerats. På detta sätt har det varit möjligt att analysera hur faktorer som pris, plats och säsongsvaror påverkar Airbnbs kunder i dessa städer. För att göra detta analyserades först de tre städerna individuellt och jämfördes sedan med data från webbplatsen Inside Airbnb från 2010 till nu. Denna forskning har genomförts genom en undersökande analys med programmeringsspråket R. Studien har delats in i tre delar:

För det första har den rumsliga dataanalysen visat att Airbnbs närvaro i alla tre städerna har ökat markant under det senaste decenniet och växte från att omfatta de delar av staden som är mest intressanta för turister till omgivande områden. Dessutom har det observerats att det största antalet objekt på Airbnb är lägenheter belägna nära centrum och platser intressanta för turister, som också är de mest värderade områdena av Airbnbs kunder och de som är dyrast att hyra i en fastighet. För det andra har en efterfrågan och prisanalys genomförts. I denna del har efterfrågan på Airbnbs registreringar uppskattats under åren sedan 2010 och över flera månader. En betydande ökning av efterfrågan under det senaste decenniet har uppskattats, vilket också visar ett säsongsmönster. I samtliga tre fall följer efterfrågan förändringarna i stadens klimat och visar den högsta efterfrågan under sommarmånaderna, vilket också motsvarar den dyraste perioden. Slutligen, i avsnittet Användarrecensioner, har återkoppling från kunderna studerats genom att använda textutvinning på recensioner. I denna del av forskningen har ordmoln använts för att få en visuell representation av textdata, som visar de vanligaste orden och analyserar vad som gör att kunderna känner sig bekväma och obekväma.

Nyckelord: Airbnb, peer-to-peer-ekonomi, delad ekonomi, undersökande analys, textutvinning

Acknowledgments

First, I want to thank Airbnb, since without their data the analysis carried out in this thesis would not have been possible.

Thank you to my two supervisors: Rafael Laurenti, for giving me the opportunity of doing this thesis at the Royal Institute of Technology and for your availability and guidance, and Joaquín Ordieres Meré, for agreeing to supervise my thesis remotely from the Polytechnic University of Madrid (UPM).

Doing this thesis would not have been the same without the friends that I have made in this Erasmus, especially Carmen and Sandra, who have become my Swedish family and who have encouraged and supported me at all stages of the project.

I also want to thank everyone who has been with me in the distance during this experience and that I have missed so much. To my friends from Majadahonda, my cousins fromRimor and my friends from university. Mainly Miguel, for all the fun times, for his support and for being the best friend.

Lately, I would like to thank my family for their love and support. To my parents, Aida and David for their advice and inspiration not only in this exchange but also during all my student years. Especially to my father, who has been an essential part of this project, for his patience and help.

Irene Suárez Pacios

Contents

1 Introduction 1 1.1 Background ...... 1 1.2 Purpose ...... 2 1.3 Delimitations ...... 3

2 Frame of Reference 5 2.1 Stockholm ...... 5 2.2 Barcelona ...... 6 2.3 Rio de Janeiro ...... 8

3 Method 10 3.1 Research Design ...... 10 3.2 Data Collection ...... 11 3.2.1 Airbnb Data ...... 11 3.2.2 Geographic, Social and Economic Data ...... 11 3.3 Data Analysis ...... 11 3.3.1 Description of Data ...... 11 3.3.2 Analysis of Data Quality ...... 14 3.3.3 Setting of the Exploratory Analysis ...... 16

4 Results and Discussion 19 4.1 Stockholm ...... 19 4.1.1 Spatial Data Analysis ...... 19 4.1.2 Demand and Price Analysis ...... 26 4.1.3 User Review Mining ...... 31 4.2 Barcelona ...... 34 4.2.1 Spatial Data Analysis ...... 34 4.2.2 Demand and Price Analysis ...... 40 4.2.3 User Review Mining ...... 45 4.3 Rio de Janeiro ...... 47 4.3.1 Spatial Data Analysis ...... 47 4.3.2 Demand and Price Analysis ...... 53 4.3.3 User Review Mining ...... 57 4.4 Comparison of the Cities ...... 60 4.4.1 Spatial Data Analysis ...... 61 4.4.2 Demand and Price Analysis ...... 65 4.4.3 User Review Mining ...... 71

5 Conclusions and Future Work 74

References 78

Appendix 83 A Hotels I

B Metro Maps II

C Climographs IV

D Neighbourhoods of Rio de Janeiro V

E Map of the Favelas of Rio de Janeiro VI

F Crime Map of Barcelona VII Chapter 1

Introduction

In this chapter, the background of the thesis is described along with the purpose and the delimitations.

1.1 Background

Airbnb was founded in San Francisco in 2007. A conference was being held in the city and two university graduates came up with the idea of offering three air mattresses on the floor intheir apartment to conference delegates. The students used a simple website to advertise their apartment as an “AirBed & Breakfast” for those conference delegates who were looking for a cheap way to spend the night to avoid the high hotel prices. After this, the two colleagues recruited another friend to exploit their business idea and developed the website to advertise for tourists. In 2009, the website was relaunched as Airbnb.com [1]. In February 2011, the platform reached one million booking nights and by August 2016 there were more than 2 million listings in 34,000 cities and 191 countries worldwide [2].

The Airbnb booking website allows the customer to search for accommodation based on destination, travel dates and number of guests. The website returns a list of available accommodations that can be filtered by different attributes such as prices, type of place, number of rooms… Apartment information is also available with descriptions, photos and reviews from previous guests. The platform allows tourists to rent different types of accommodation: from small rooms to an entire apartment or house, 57% of Airbnb listings are entire apartments and homes, 41% are private rooms and 2% are shared rooms [11]. In some cases, the host may be living in the space at the same time as the rental or may be absent. Payments are made through the website and the platform charges guests a fee under 13% of the booking subtotal and hosts a 3% fee [3].

Airbnb is part of the growing movement called the “shared” or “peer-to-peer” economy, which uses online and mobile technologies. This new model competes with traditional and physical businesses since this economy generally implies consumers maintaining access to goods and services without owning them [2]. Airbnb has changed the traditional model by providing an online marketplace that enables the rental of spaces from one ordinary person to another: it connects people who own idle accommodation assets (hosts) with those looking for a place to stay (guests) via digital marketplaces. The rise of Airbnb represents an innovation within the tourism accommodation industry, whose rapid growth has been enabled by two key factors: technology innovations and supply-side flexibility [4].

One of the main features that explain the success of Airbnb is its prices: its accommodations tend to be cheaper than traditional accommodation. Airbnb hosts can offer very competitive low prices because the hosts´ primary fixed costs, such as rent and electricity, are already covered. Moreover, there are no labor costs or they are minimal and hosts are usually not fully dependent on the revenue that they get from Airbnb. In addition to the economic aspects, Airbnb offers its hosts a compelling experience value proposition “Live like a local” [5], the company offers an

1 opportunity to experience local life, meet real neighbourhoods in non-touristic areas, interact with the host and have helpful local advice. Hosts may also appreciate staying in real homes over a hotel and having access to a kitchen, washing machine, dryer and other residential amenities [1]. Authenticity is also seen as one of the motivators for customers to choose Airbnb [6].

This new form of accommodation has changed the short-stay accommodation market, having an impact on the tourism economy. Airbnb, aware of this impact, has invested many resources in studies to analyze its real impact on the rental market. The platform insists that they expand the tourism market instead of competing directly with hotels. [2]. The company believes that they contribute positively to the tourism sector, since their hosts stay longer, spend more money and bring activity to local neighbourhoods (according to Airbnb, in many cities, over 70% of the spaces they offer are outside the central hotel districts [4]). Furthermore, since Airbnb listings are more scattered than hotels, hosts may be more likely to spend money in neighbourhoods that do not typically see much economic activity [1]. Airbnb also can absorb peak demand in destinations where the capacity of its hotel rooms is unable to sustain the tourist season or major events.

As for Airbnb, researchers have begun to examine its relationship to more traditional forms of hospitality. Several independent studies suggest that the platform has a negative impact on the income of local hotels and they state that Airbnb is expected to reduce hotel rates and revenues [2]. They consider that Airbnb is good for tourism but bad for hotels and that a large part of Airbnb listings compete directly with the traditional offer of hotels, since they share spaces [7]. However, other researches, such as Varma et al. investigation [8], suggest that hotels and Airbnb listings are quite complementary since their customers tend to be different, so they concluded that Airbnb hardly disrupts the industry.

The growth of Airbnb has been linked to protests against the high tourist presence in cities such as Barcelona, Amsterdam and Berlin. Residents’ annoyance with tourists has increased since in many cases, the high demand for Airbnb apartments has led to the displacement of residents and increased rental costs [5]. The discomfort with the platform is also related to the regulation debate, Airbnb has been accused of unfair competition in many sectors of the hospitality industry and there have been many discussions about how taxes should be imposed on the platform. When guests stay in traditional accommodations, they have taxes related to this. However, Airbnb does not charge them, so customers can generally avoid paying the taxes that are generally charged to this sector, which gives Airbnb rentals a competitive advantage over traditional accommodation. Taxes, along with local laws in the cities where Airbnb has listings, have led the platform to have legal battles against local governments and face many legal issues [1].

1.2 Purpose

The main objective of this project is to study the impacts of peer-to-peer rental accommodation in different scenarios using Airbnb data from 2010 until now. This thesis will focus on three different cities: Stockholm, Barcelona and Rio de Janeiro. The aim is to understand the effect that different patterns, such as location, price and season, have on customer’s demand for Airbnb properties. In addition, customer opinions will be studied by analyzing customer reviews and feedback drawn from the Airbnb dataset. In this way, it will be possible to know what customers mention most frequently and what makes them comfortable and uncomfortable.

To carry out this study, an exploratory analysis will be performed using the R programming language. Thus, it will be possible to deepen into what affects Airbnb customers in these cities and gain new knowledge about the Airbnb rental market. Some of the questions that are intended to be answered through the analysis are:

2 • How is the evolution of Airbnb´s spatial penetration?

• Which locations in each city are highly rated by guests?

• How do prices of listings vary by review score and location?

• What type of properties are there in each city? Do they vary by neighborhood?

• How does the demand for Airbnb rentals fluctuate throughout the year to study the seasonality of demand? And between years?

• Are the demand and prices of the rentals correlated?

• Are there any common themes that can be identified from the reviews? What aspects ofthe rental experience do people like and what aspects do they dislike?

After the analysis of each of the cities, a comparison will be made between the results of the three. With this comparison, the common and the different points will be obtained. In this way, it will possible to identify the common aspects that attract and deter customers.

This study is conducted for academic purposes so that it can contribute to understanding the impacts of a ”peer-to-peer” economy case study. This research involves an important analysis, since the platform has changed the market for short-term rentals that affects the tourism economy, soit may be interesting for many professionals and institutions such as the platform itself, economists, for research related to these fields, tourism booklets or local governments of the analyzed cities.

1.3 Delimitations

• This master thesis is carried out for twenty weeks, following the requirements of the Degree Project Course in Innovation Management and Product Development of KTH Royal Institute of Technology and the requirements of the Master Thesis in Industrial Engineering and Management of the Polytechnic university of Madrid (UPM).

• This study will solely incorporate analysis of three cities, which are Stockholm, Barcelona and Rio de Janeiro.

• The data collected for the analysis dates from 2010 until now (February 2020).

• Apart from the analysis of Airbnb variables, to complete the study, further research regarding the context of the city is needed. In this way, it is necessary to select several geographic, economic and social variables that affect the demand for Airbnb, since there are alarge number of factors that directly or indirectly influence customers. This selection will be made following the criteria of the student after the necessary documentation and the acquisition of the necessary knowledge on this topic.

• To study demand, the ”number of reviews” variable is used as an approximation of the number of bookings made over the past year, since the dataset does not have data on the bookings made over the years. It is assumed that about 50% of guests review the host and their stay on the listings.

• Occupancy rates will be studied using data from the ”calendar” table, which provides data for the next year. This will be used as an estimate of the occupancy, as no occupancy data is available from previous years.

• There are data that contain missing values and, because of the difficulty in working with them, the rows that contain them will be removed from the analysis.

3 • Because the reviews contain opinions in multiple languages, it is necessary to filter and delete non-English comments. This has to be done because the word clouds that will be created to display the most common words in reviews must be in English. Deleting non-English comments means removing less than 10 % of words, as the percentage of English words in reviews is higher than 90 % in the three cities.

• There is no scientific literature to compare with the results obtained from the analysis ofthe cities, since the study of the Airbnb rental model in Stockholm, Barcelona and Rio de Janeiro has not been carried out before.

4 Chapter 2

Frame of Reference

In this chapter, the frame of reference is presented. It summarizes the context of the three cities chosen for the study. The existing knowledge and the research of this section are later used in the analysis to interpret and discuss the results.

2.1 Stockholm

Stockholm is the capital and the most populous urban area of Sweden, with a population of 974,073 inhabitants in the municipality [9]. The city spreads across a Baltic Sea archipelago of fourteen islands. As can be seen in the map in figure 2.1, the Municipality of Stockholm is divided into 14 urban districts. The actual urban map of Stockholm is very influenced by the ”General plan for Stockholm 1952”. This plan was created to decentralize Stockholm and solve its housing shortage after the Second World War. It meant the creation of new suburbs built along the subway lines with commercial and public services, which allowed the population to live in the surroundings and get to the center to the city in a short period. This innovative urban model was called ”ABC City” (Work, Housing, and Centrum) [10].

Nowadays, due to high levels of immigration, a leading hub for high-tech start-up scene and having the continent’s highest birth rates, Stockholm is Europe’s fastest-growing capital, with a population that has grown by almost a quarter of a million in just seven years [11]. The capital is considered the “European Silicon Valley” in the production of successful high-tech startups [12], as it currently holds around 22,000 tech businesses (Stockholm has birthed successful global brands like are Spotify, Skype, and µTorrent) [13]. This scenario means the arrival of people to work in these companies, who cannot assure their new employees a place to stay and find it difficult to host their workers since there are strict housing regulations designed to stop firms renting apartments en masse in the same block [11].

This, together with strict building regulations and a lack of investment over de last decades, has led to a housing storage in the city [11]. Thus, the Swedish National Board of Housing, Building, and Planning has estimated that, to meet demand, around 600,000 apartments need to be built in Sweden over the next nine years [14]. All this explains the long waiting lines for rent-controlled housing. The average waiting time to get a rental apartment in 2016 was nine years and between fourteen and sixteen in the most popular neighborhoods [15]. Moreover, sharing a room becomes also very difficult since over half of households in Sweden (52 % of all households) are single-person properties [16].

As stated, the Swedish rental market is regulated. It is regulated the amount of rent the landlord can take, under what circumstances an apartment can be rented, for how long and the high taxes associated with it [17]. Under these market conditions, Airbnb plays a controversial role. There have been many discussions about Airbnb and its effect on Stockholm´s complex housing market. However, it is legal for tourists to rent an apartment through Airbnb [18]. It is profitable for

5 property owners to rent the apartment through online portals like Airbnb, as they can get a higher amount of renting per night instead of the traditional way of renting the apartment per month [19].

Notwithstanding, Airbnb seems the ultimate temporary solution to deal with the long waiting queues. The platform has helped the tenants to solve their accommodation challenges in an overcrowded market. Furthermore, it seems a suitable solution in this city, which is highly hi-tech driven.

• Älvsjö

• Bromma

• Enskede-Årsta-Vantör

• Farsta

• Hägersten-Liljeholmen

• Hässelby-Vällingby

• Kungsholmen

• Norrmalm

• Östermalm

• Rinkeby-Kista

• Skärholmen

• Skarpnäck

• Södermalm

• Spånga-Tensta

Figure 2.1: Stockholm neighbourhood map. Wikipedia. Stockholm Municipality Map. Retrieved March 4, 2020, from: https://es.wikipedia.org/w/index.php?title=Estocolmo&oldid=124459340

2.2 Barcelona

Barcelona is a city on the northeast coast of Spain. It is the second-most populous municipality in Spain, with a population of 1.6 million [9]. As can be appreciated in figure 2.2, the city is divided into 10 administrative districts. This city is recognized for its innovative urban planning. The transformation of Barcelona began in the second half of the 19th century with the ”extension” (Eixample) from the old town (Ciudad Vella) to the surroundings. It was designed by the architect Ildefons Cerdà, who created this new area characterized by long straight streets and a strict grid pattern crossed by wide avenues [20]. In the following years, the adjacent areas swallowed Barcelona’s growth and the city grew towards the surroundings [21]. The second great urban transformation was caused by the Olympic Games in 1992 when Barcelona spread new investments

6 across San-Martí and Saints-Montjuïc [22]. At present, Barcelona is undergoing a third wave of transformation, with the initiative “22@distric” to convert San-Martí into a technology and knowledge-driven economic powerhouse zone [20].

Nowadays, tourism involves early twelve percent of Barcelona´s economy [23], but it was not until the Summer Olympics that its international profile expanded to the current levels: Barcelona is currently considered the fifth most visited city in Europe and twentieth in the world[24] (the city receives almost 12 million visitors in total [25]). Many factors have contributed to the strong demand of the city: the support and encouragement of tourism from the Government; the successful advertising campaigns of Barcelona to the international market as a fun European destination, with good weather, beaches, lively nightlife and a wide variety of museums and architecture; the rise of budget airlines like Ryanair and the availability of a wide range of properties through short-rental portals such as Airbnb. Moreover, Barcelona leads the world ranking of congresses held [26]. Its location “at the entrance of Europe”, which makes the city accessible by sea, air, and road, and its Mediterranean climate with mild temperatures throughout the year (see climograph in Appendix C), make this city a good option to celebrate these type of events. Two examples of these are the Smart City Expo World Congress and the Barcelona Games World.

• Ciutat Vella

• Eixample

• Gràcia

• Horta-Guinardó

• Les Corts

• Nou Barris

• Sant Andreu

• Sant Martí

• Sants-Montjuïc

• Sarrià-Sant Gervasi

Figure 2.2: Barcelona neighbourhood map. Wikipedia contributors. 2019, December 28. Districts of Barcelona. In Wikipedia, The Free Encyclopedia. Retrieved March 4, 2020, from: https://es.wikipedia.org/w/index.php?title=Estocolmo&oldid=124459340

In this scenario, the growth of Airbnb plays an important role as a platform to host millions of tourists [23]. Airbnb´s entry in 2009 took place in the middle of the global financial collapse, which was received by many unemployed citizens as an easy way to receive cash. By 2010, the Government liberalized the rules related to short-term vacation rentals, which resulted in thousands of new licenses for apartment owners. This number quadrupled over the next four years [23]. But this climate changed radically in the following years: many residents began to show signs of discontent,

7 as they were tired of the “excessive tourism” and the so-called “binge-drinking tourism” [27]. All this, added to the one million illegal beds in the territory lead to complaints and protests from the citizens of Barcelona [28].

In response to the difficult situation, the Catalan Government, which has the competence of tourism in the region, launched an operation against illegal tourist apartments and tried to slow down Airbnb activity. They published a new law by which any rented apartment for visitors had to be registered in the Tourism Registry and have a permit [29]. They also took measures as a way to protect the locals and the hotel industry from what was considered “unfair competition”. To do this, in July 2015, Airbnb was punished with two fines of 30,000 euros (318,516 SEK each). However, since according to the municipal administration, Airbnb continued offering apartments without license numbers, they decided to apply the maximum fine of 600,000 euros (6,370,321 SEK)[30]. Given this situation, Airbnb appealed against the fines, and the platform, aware of the growing hostility began working more closely with local governments. Among other things, an action plan was introduced to identify hosts who were breaking rental laws and to close down unlicensed properties. At present, this situation is still unresolved. However, the supply of illegal apartments on offer has been reduced by 95%; 4,900 have been shut down and 6,500 fines issued23 [ ].

2.3 Rio de Janeiro

Rio de Janeiro is the capital of the state of Rio de Janeiro and the second-most populous municipality in Brazil. Home of more than 6 million people [9], and founded on the east of Guanabara Bay, this city is located in the southeastern region of Brazil, on the Atlantic coast. The municipality of Rio is administratively subdivided into nine subprefectures presented in the map of the figure 2.3, containing a total of 160 neighbourhoods (see the table in Appendix D).

The city was founded in the 16th century around what is now Centro Histórico e Zona Portuária, following the Spanish model of town planning (Laws of the Indies) and building organized and orthogonal streets. In the following years, the city grew and spread inland, surrounding the “morros” (small mountains). During the 19th century, due to the growing concern of the under- development of the city compared to other European cities, many changes were made imitating their urban planning [31]. In this way, Rio has witnessed many modifications from its origins to the present day: the development of the city has always influenced from first world countries that have mixed with its traditional Carioca culture, forming the so-called ”Carioca urbanism” (characterized by the union between the grid and the curve in the architecture and the city plan of the city) [32]. This mixture and the development attempt explain the irregular distribution of the urban areas of the city, which presents rich and touristic neighbourhoods and poor and dangerous zones, both very close to each other but very differentiated between them. The poor settlement slums inthe city are known as “favelas”, and, as can be seen in Appendix E, they are scattered across the city.

In recent decades, the city has experienced significant changes, but it still faces major problems and imbalances related to urban, social, and safety issues. By 2000, the city faced extreme levels of inequality, with an extremely functional division between different districts, showing an insufficient urban infrastructure, water, sanitation, and urban transport in the city´s poor neighbourhoods. Furthermore, the city was undeveloped as a tourism and business destination [33]. The Summer Olympics Games in 2016 supposed many changes driven by the goal of recovering the urban quality, infrastructure level, and social cohesion [33]. To host millions of visitors for the Games, almost 60,000 hotel rooms were available in the market [34], and Airbnb offered about 33,000 rooms and apartments in the Brazilian city [35]. Moreover, the city´s brand after the Games was positively affected, increasing interest in visiting the country [36].

The city has hosted other important events over the last decade, such as the Catholic World Youth Day in 2013 and the FIFA World Cup in 2014. Another event that takes place every year and brings

8 millions of visitors is the Carnival of Rio, which takes place in late February. A survey published by Riotur shows that that commerce, hospitality, and services raised 3.78 billion of Brazilian reals (8,5 billion SEKS) during the four days of festivities in 2019 (around 7 million people enjoy this event) [37]. The city also hosts ”Rio’s international film festival” in November, which is one of the biggest in Latin America and ”Festas Juninas” in June, which is one of the most important folkloric festivals in Brazil [38].

Currently, Airbnb offers around 34 thousand listings across the city, helps many families earn extra income, and spreads business to parts of the city that generally do not see much economic activity. Last year, host income and guest spending generated economic activity of 160 million dollars (approximately 1,5 billion SEK) [3].

Figure 2.3: Rio de Janeiro neighbourhood map.

9 Chapter 3

Method

In this chapter, the methodology chosen for the elaboration of the thesis is described and discussed.

3.1 Research Design

The study was carried out for 20 weeks in which the master’s student worked full time. The author of this project is an exchange student, so this thesis was carried out through collaboration between the Machine Learning Department of the Royal Institute of Technology KTH, the university where the student carries out the exchange, with the Industrial Management Department of the Polytechnic University of Madrid (UPM), the student’s home university. The author of the thesis was assisted by a supervisor from KTH and a supervisor from UPM.

Regarding the research field of this study, the peer-to-peer economy is currently widely studied. This explains why a thesis on a practical case of this economy is very interesting, since the Airbnb rental market has changed the short-term accommodation model. As explained in the introduction (see Purpose section), the objective of the research is to analyze the impacts of Airbnb on customers in three different cities. The cities considered in this study had to be cities where thisspecific analysis has not been done before. The expansion and penetration of Airbnb is widely studied and the platform model has been studied in cities such as London, New York and Paris. Taking this into account, the cities chosen for the study are Stockholm, Barcelona and Rio de Janeiro: • Stockholm is chosen for its peculiar situation regarding the Swedish controlled rental market, which makes the study unique and interesting. Moreover, it is the city where KTH University is settled and therefore, the city where this thesis has been carried out.

• The selection of Barcelona is made because it is a very touristic city that has witnessed a very controversial situation between the Catalonian Government and Airbnb. The high presence of Airbnb in the city has led to many protests, conflicts and a fine from the local government, which has changed the way Airbnb works in the city.

• Rio de Janeiro implies an interesting field of study since it represents the case of Airbnb ina South American country, very different from the other two cities chosen. Furthermore, the student has a special link with the three cities, which was necessary to deeply understand their context and urban map: the author knows Stockholm and Barcelona very well, and could ask for help to understand certain aspects of Rio de Janeiro, since the KTH supervisor is from there. This was essential for comparing and verifying descriptions obtained from literature and the internet and explains why these cities were chosen and not others on other continents such as Asia or Africa.

The study began with a literature study, in which aspects related to Airbnb and the cities of Stockholm, Barcelona and Rio de Janeiro were investigated. The theoretical knowledge was obtained from literature, web pages, scientific articles and journals that are related to thefield

10 of study. Once the necessary knowledge was obtained, the gathering of data and its analysis started.

Most of the Airbnb information used in the study was taken from the Inside Airbnb web page: http://insideairbnb.com/get-the-data.html. It hosts an available, independent and non-commercial set of tools that contains data with information about Airbnb in different cities around the world. Moreover, further investigation regarding the geographic, social and economic context of the cities was completed to develop a deep understanding of the different impacts of the peer to peer rental model. Once all the data was collected, it was examined using the R programming language and software environment.

3.2 Data Collection

As stated before, the data was gathered from the mentioned webpage and literature, web pages and scientific articles. In this section, the approach of how data was gathered and its classification is explained.

3.2.1 Airbnb Data Airbnb data was gathered from the Inside Airbnb webpage, which periodically publishes snapshots of Airbnb listings. In February 2020, data from the three cities was downloaded. The dataset is comprised of three main tables:

• Listings: detailed listing data showing attributes for each listing, such as prices, neighbourhoods, latitude and longitude.

• Reviews: detailed reviews given by the guests.

• Calendar: provides details about booking for the next year by listing.

3.2.2 Geographic, Social and Economic Data To obtain a deeper understanding of each city, several groups of variables have been analyzed, capturing their geographic, social and economic context. The sources of this data include OpenStreetMap (https://www.openstreetmap.org), Google Maps and a variety of official city websites.

3.3 Data Analysis

When all the data was gathered, the data analysis began. In order to conduct the complete study, first of all, a description of the data was performed, followed by an analysis of its qualityand an implementation of changes (to create the desired visualizations). Finally, the exploratory data analysis was performed.

3.3.1 Description of Data In this section, the data content is described in greater depth. The table 3.1 contains information about the dataset used in the study.

11 Category Metric Source Description

Airbnb insideairbnb.com Airbnb parameters that characterize Airbnb parameters each of the listings Distance to GoogleMaps Distance from each of the the center Geography neighbourhoods to the center of the city Points GoogleMaps Main tourist places or places of of interest interest in the city GoogleMaps and Public Public transport accessibility and OpenStreetMap transport frequency

Hotel GoogleMaps Distribution of hotels in the city distribution

Climate climate-data.org Climograph of the city

Popularity News magazines Popularity and trends of the Social neighbourhoods

Safety Police documents Crime, pickpockets and other risks factors related to the personal safety in a neighbourhood Median income Median estimate of Economic and median Various sources household income and household value value in a neighbourhood

Table 3.1: Dataset summary.

Airbnb Data The Airbnb dataset of each city is composed of three tables:

• Listings. The parameters extracted from the listings table, used for the analysis are:

– ID: discrete variable that represents the identification number of the listings. This variable is present in the three tables. – Name: categorical variable that contains the name that the property owner has given to the listing. – Host since: date when the listing was uploaded to Airbnb website for the first time. – Neighborhood: categorical variable with information of the neighborhoods where the listings are situated. – Latitude: discrete variable that indicates the latitude of the property. – Longitude: discrete variable that indicates the longitude of the property. – Property type: categorical variable that shows the property type of the listing, such as apartment, loft, house... – Price: continuous variable that shows the price of the listing. – Review scores location: continuous variable that represents the score that the user gives to the location where the Airbnb listing is situated.

12 • Reviews. The attributes of the reviews used in the analysis are:

– Listing ID: discrete variable with the listing identification. – Date: day, month and year of when the review was written. – Comments: textual variable that contains a written review of the host.

• Calendar. This data contains information on the bookings for the next year by listing.

– Listing ID: identifier of the listings. – Date: day, month and year of the booking. – Available: categorical variable that shows if the listing is available or not in the specified date.

A general overview of the data shows that there are:

• 6,328 unique Airbnb listings and 121,462 reviews in total in Stockholm. The first rental was up in March 2009 in Hägersten-Liljeholmen.

• 20,411 unique listings and 740,992 reviews in total in Barcelona. The first rental was up in September 2008 in Guinardó (Horta-Guinardó).

• 33,715 unique listings and 316,056 reviews in total in Rio de Janeiro. The first rental was up in March 2009 in Copacabana (Zona Sul).

Geographic, Social and Economic Data • Geographic data

– Distance to Center. This variable is found to be one of the most significant factors that explain the presence of Airbnb in an area [39]. For simplicity, the center of the city is considered the commercial, cultural and historical heart of the city. This way, the distance to the center is calculated as the approximate distance between the city center and the center of the neighbourhood under study. – Points of Interest. In this research a point of interest is considered a specific location that might be useful or interesting for tourists. Examples of points of interest include museums, town halls and churches. In a recent study conducted in London [39], a correlation between ”distance to center” and the ”tourism factor” has been detected, which has a great positive significance on the number of Airbnb offerings in that area. This phenomenon is also expected to be present in the cities analyzed, so that areas with a high concentration of points of interest, which have touristic attraction, will also have a high presence of Airbnb properties. To analyze the points of interest of a neighbourhood, GoogleMaps is used for each city, creating a map with this information to be able to visualize it. – Public transport. Transport links are a key component in the property prices of an area. Some tourists spend a lot of money on centric listings, however, many people choose to move away from the center to have cheaper apartments. For those who prefer cheaper listings, the location that they choose is highly influenced by the area´s connectivity to the city center. This is why the Airbnb website has added an option to check the public transits close to the selected listing. Each city has different public transport modalities, nonetheless, since the three cities studied have a metro network, this mode of transport has been chosen as an indicator of the strength of the public transport in the city.

13 – Hotel distribution. As previously mentioned, it is unclear if there is a relationship between Airbnb listings in an area and hotels, there are many studies and research on this topic, but there is no unanimous response. However, the author of the thesis wanted to contribute to the analysis of the relation between both. As there is no public dataset available for the number of hotels in all cities, the hotel data has been extracted from Google, searching for ”city name” and ”hotels”. In this way, taking into account the available data, the relationship between the location of hotels and Airbnb properties has been analyzed to determine if they are located in the same spaces and share locations in the cities studied. – Climate. The relationship between climate and demand is studied. The assumption is that customers tend to travel when weather conditions are good.

• Social data

– Popularity. The popularity of a neighbourhood can affect the demand for properties in a certain area, not only for Airbnb listings but also for people looking for a place to live. As stated earlier, one of the aspects that Airbnb offers its customers is the experience of living like a local. This could become a better experience if the neighbourhood has a rich local life and good services, which is generally related to those areas where people want to live and where tourists want to have their accommodation. – Safety. This parameter characterizes the personal safety of Airbnb users in case they want to stay in a listing situated in a particular neighbourhood. Issues such as criminality of the area or the presence of pickpockets are taken into account in this factor.

• Economic indexes

– Median income and median household value. These measures provide an indicator of the socio-economic structure of a city. The median income and the median household value of a neighbourhood affect the aesthetic of the area, the style and type of the buildings, the size of the properties, the average price of the houses... Additionally, a study on Airbnb in London showed that income is related to Airbnb since there are more people with low income that join Airbnb as hosts, possibly using the extra income generated from Airbnb to support themselves [40].

3.3.2 Analysis of Data Quality Previous to analyze the Airbnb data, it was necessary to perform several transformations on the dataset in order to be able to work with it and create the desired visualizations. Generally, there were no major inconsistencies or mismatches in the data, however, the following actions were taken:

• Initial verification To verify the validity of the data, 10 random listings were selected for each city and their presence on the original Airbnb platform and the accuracy of its information were double-checked. The following image shows an example of some of the information available on the Airbnb webpage and some data obtained from the Inside Airbnb website. It can be appreciated that the data from both sides match.

14 Number Number Number Location Name Host Neighbourhood Wifi of guests of beds of baths score Chic apartment nearly Sagrada Aline 6 4 1 4.8 Sant Martí Yes Familia VI

Figure 3.1: Screenshot of a property from the Airbnb website.

• Format changes The majority of changes made in the data consisted of changing the format on the columns to obtain them in a desirable way:

– Some changes were made regarding the language of the review comments. The dataset contains reviews in multiple languages, so it was filtered to remove the comments that were not in English. – The date format was changed to transform it from a mm-dd-yyyy format to obtain the day, month and year separately. – Some adjustments were required to get the price column in integer values, removing de ”$” symbol and the comma separator.

15 • Missing values The dataset had null values, so the rows that contained them were dropped. In order to visualize these missing values a vismiss plot was used. This R tool allows us to obtain the percentage of missing values and determine in which observations they are located. The image presented below shows an example of one of the representations made to analyze the missing values of several variables used in the exploratory analysis. Most of the data is available as the total percentage of missing values is 2.7% and most of these non-available data are found in the ”location score” variable.

Figure 3.2: Representation of missing data.

3.3.3 Setting of the Exploratory Analysis The exploratory analysis is divided into three sections:

• Spatial Data Analysis, in which questions regarding the impacts of Airbnb in different locations in the cities will be answered. This section is comprised of the following parts:

– Study of the evolution of the number of Airbnb listings over the years. – Spatial visualization of the distribution of Airbnb properties in each city using the Leaflet tool of R, which allows the student to visualize the location of the Airbnb listings in the map of the city. With this tool, it is possible to geographically visualize the different Airbnb properties in the city to get an idea of how the listings are distributed in the neighbourhoods. This way, by clicking on each listing, information related to the property can be obtained with details such as the listing name, hostname, price of the property, room type and neighbourhood. The following figure shows an example of some listings situated in the neighbourhoods of Östermalm and Norrmalm in Stockholm, it can be appreciated that one of the listings has been selected and its attributes are presented.

16 Figure 3.3: Image of the interactive graph of the Airbnb listings of Norrmalm and Östermalm.

– Creation of a map with pointers indicating the main places of interest of the city. This visualization will be used to understand the distribution of the principal touristic places in the city and if there is a correlation between the location of Airbnb properties and them. – Study of the correlation between the location of Airbnb properties and hotels in the city. – Analysis of the areas that Airbnb customers value the most. This study will be carried out using the Airbnb users’ rating of their stay, classifying the data to obtain the average location score for each neighbourhood. – Investigation of the prices in the different areas of the city, which will be conducted calculating the average price of the listings of each neighbourhood. – Counting of listing type by neighbourhoods. A bar chart will be made to study the relationship between property type and neighbourhood. Since there are plenty of types of listings it will be selected those that are predominant.

• Demand and Price Analysis. The aim of this part is to study the demand for Airbnb listings over the years since 2010 and across months of the year to understand seasonality. Moreover, a relation between price and demand will be established, in order to be able to answer whether prices of listings fluctuate with demand. To study the demand, the ”number of reviews” variable has been used as an approximation of the number of bookings made over the past year, since the dataset does not have data on the bookings made over the years. It has been assumed that about 50% of guests review the host and their stay on the listings. The following points will be studied in this section:

– Study of the average occupancy rate by neighbourhood in each city. The occupancy rate is calculated as follows: T otal booked properties in a neighbourhood ∗ Occupancy rate = T otal properties in a neighbourhood 100 Booked properties are properties that had been rented for the next year when the data was extracted from the Inside Airbnb dataset in February 2020. It is considered that there are generally spontaneous or last-minute bookings, so the occupancy will be higher than the calculated, especially in the long-term. This way, the minimum average occupancy rate for next year and for every neighbourhood will be studied. This can also be used as an estimate of the occupancy map for the coming year, as no occupancy data is available for previous years.

17 – Evolution of the number of listings over the years. A graph will be made to study the evolution of demand using the data extracted from the reviews table. – Seasonality in demand. For this part, three graphs will be presented, each with data from a different years (2017, 2018 and 2019). In these representations, the demand across months will be analyzed using data from the reviews table. – Variation of Airbnb prices throughout the year to investigate if the prices of the listings follow a pattern. For this, the daily average prices of the listings over the years, located in the calendar table, will be used. – Variation of Airbnb prices across the week. This will be studied using a box plot of average prices by day of the week. – Occupancy Rate by Month. Occupancy over the next year will be estimated using the number of bookings for next year obtained when the data was extracted from the dataset in February 2020. In order to perform this study, the percentage occupancy for each day will be calculated, analyzing what percentage of apartments have been already booked. This estimation allows the student to get an idea about the evolution of the occupancy and can be used as an estimation of the seasonal demand since there is no available data from the occupancy of the previous years. As it happened in the study of the occupancy rate by neighbourhood, the rates obtained are an aproximation and will not be the final occupation since it is considered that there are generally spontaneous or last-minute reservations, which means that the occupancy will be higher than the calculated, especially in the long-term.

• User Review Analysis. To analyze the customer´s opinion, text mining is applied to the customer reviews and feedback from the Airbnb dataset. This analysis is performed using the tm package of R, which provides the text mining framework needed.

– Creation of a word cloud to represent the most frequent words in the reviews. This will be done using the word cloud generator package wordcloud of R, in which the words have a size proportional to their relative frequency. – To analyze customer opinions more deeply, two more word clouds have been built with words that express what makes customers feel “comfortable” and “uncomfortable”. This can be carried out using word vectors. Word vectors place any given word in an n-dimensional space, where the proximity of any two words in this vector space is proportional to their “similarity”.

The analysis will be performed using R, which is an open-source programming language suitable for analysis. It is very useful for this project since it allows rapid prototyping and works with the datasets to design machine learning models.

18 Chapter 4

Results and Discussion

In this chapter, the answers to the questions of interest mentioned in the introduction and the exploratory analysis of the Airbnb data described in the method are detailed through a variety of different figures, maps, graphs and visualizations. First, each city is studied individually andthen a comparison is made between them. For each city, the study has been divided into three sections:

• Spatial Data Analysis

• Demand and Price Analysis

• User Review Mining

4.1 Stockholm

The first city analyzed is Stockholm. To begin with, a rough description of some data ofthecity is presented, which will be interesting for the comparison of this city with Barcelona and Rio de Janeiro:

• As mentioned before, Stockholm´s population is 974,073 [9] and there are 6,328 unique Airbnb listings in the city. To get an idea, this means that in Stockholm, there is one Airbnb property for every 154 citizens.

• The number of international visitors in 2018 was 2,604,600 [41] and it is estimated that this corresponds to half of the total number of visitors. Therefore, it is estimated that, in total, around 5 million tourists visit the city each year. This means that in Stockholm, there is one available Airbnb property for every 791 tourists.

4.1.1 Spatial Data Analysis In this section, an analysis of the impacts of some variables using spatial visualizations is presented.

Evolution of the Number of Airbnb Properties Intending to understand the large number of Airbnb listings in the city, the graph in figure 4.46a shows the evolution of the Airbnb properties in Stockholm over the years. In this graph, it can be appreciated that the number of Airbnb listings has increased significantly from 2010 to 2019: there were 31 properties in 2010; 2,901 in 2015 and there are currently 6,328. However, this rise is more significant from 2014 to the present.

19 Figure 4.1: Evolution of Airbnb listings over the years in Stockholm.

In order to visualize the rise of Airbnb properties over the years, an interactive graph has been created using the Leaflet tool in R:

(a) 2010 (b) 2015

Figure 4.2: Visualization of the growth of Airbnb listings in Stockholm.

20 Observing the figure 4.2c and the table below, which contains the number of Airbnb listings by neighbourhood at the moment, it can be seen that the maximum number of listings are clustered around Södermalm and Norrmalm, followed by Kungsholmen and Östermalm, which form the city center. Moreover, it can be appreciated that the mentioned central parts of the city have a higher number of properties and this number decreases as we move from the center towards the surroundings. This gradual reduction of properties follows the growth pattern that the city has followed (see frame of reference): Stockholm has grown from the old and central parts to the surroundings along the subway lines. This radial pattern is present in other aspects of the analysis and it will be explained further on.

Neighbourhood Number of listings Södermalm 2032 Norrmalm 970 Kungsholmen 799 Östermalm 753 Hägersten-Liljeholmen 738 Enskede-Årsta-Vantör 592 Bromma 438 Skarpnäck 437 Rinkeby-Kista 304 Farsta 204 Hässelby-Vällingby 172 Älvsjö 151 Spånga-Tensta 88 Skärholmen 85

Table 4.1: Number of Airbnb properties by neighbourhood in Stockholm.

Places of Interest The concentration of a high number of Airbnb properties around Södermalm, Norrmalm, Kungsholmen and Östermalm makes sense, especially considering that the principal touristic places are situated around these neighbourhoods, as can be appreciated in the following map, that represents the main places of interest of the city:

Figure 4.3: Principal places of interest in Stockholm.

21 Observing the maps 4.2c and 4.3, it can be said that the closer to the city center and the touristic places, the more Airbnb listings. The neighbourhood with the highest number of listings is Södermalm. This could be explained by its favorable geographical situation since it is very close to touristic areas. Furthermore, it is a very attractive area for Airbnb customers, since it is considered a trendy neighbourhood and the hub of creativity with many bohemian shops, cafes, vintage fashion stores and art galleries.

Presence of Hotels Regarding the distribution of hotels in Stockholm (see figure A.1a in Appendix A), it can be seen that most of them are located in Norrmalm (in the center of the city and close to the main places of interest). Therefore, the largest number of hotels are located in Norrmalm, while the neighbourhood with the largest number of Airbnb listings is Södermalm. This makes sense, since Södermalm is a popular neighbourhood full of narrow streets with small bars and shops, whereas Norrmalm is a busy commercial area and the cultural center, characterized by wide streets crowded with people. In addition, Norrmalm has the second-highest number of Airbnb listings, which could indicate that, in the case of Stockholm, it is true that a large part of Airbnb listings are found to be in the location where hotels are situated.

Location Score by Neighbourhood In figure 4.4, a representative map of the city with the average location score is shown. As can be seen in the representation, Norrmalm and Södermalm receive the highest location scores, followed by Östermalm and Kungsholmen. This is consistent with the analysis carried out in the previous section since these areas are located in the city center, which is very touristic and contains a high number of restaurants, shops and entertainment facilities.

It is worth mentioning that the distribution of the location score follows the expected radial pattern (related to the pattern of growth of the city, see Frame of references): the highest location scores are found at the center of the city and this score is reduced as we move away from the center to the surroundings following the metro lines. In many cases this also occurs with the number of stars of the hotels, most of the five-star hotels are situated at the center of the city (mainly in Norrmalm) and the hotels and hostels with a lower number of stars are generally located in the surroundings. This means that, in general, the neighbourhoods where Airbnb top-rated properties are situated coincide with the location where most five-star hotels can be found.

Hence, the neighbourhood score decreases as we move away from the city center. However, the low scores in some distant neighbourhoods may be due, in addition to the distance factor, to the loss of popularity caused by several cases of violence. This is the case of Rinkeby-Kista, Älvsjo, Skärholmen and Enskede-Årsta-Vantör. These neighbourhoods contain the so-called ”vulnerable areas” or “especially vulnerable areas”, which are terms applied by police in Sweden to areas with high crime rates and social exclusion [42]. The Älvsjo case is an exception since, although it is considered a ”vulnerable area”, it is in the middle of the Stockholm rating scale. This could be caused because the ”Stockholm International Fair”, which is the biggest rentable facility in northern Europe, is located in this neighbourhood. This facility arranges trade fairs, international congresses, seminars, general assemblies and music events, which brings many people from all over the world and many of them stay in Airbnb properties.

Another factor that may influence the location score is the availability and frequency of public transport. The map introduced in Appendix B (figure B.1) contains the metro map of the city. As can be seen, there are seven metro lines that follow an axial form: all lines go from one point of the city to another, passing through the central station (T-Centralen), which explains why the frequency of the metro is higher at stops near downtown and lower in the distant stops.

22 Figure 4.4: Stockholm location score.

Price by Neighbourhood As can be observed in figure 4.5, in many cases this map follows the previous map, since the highly rated location tend to be costly. Therefore, Söderman and Kungsholmen are the most expensive neighbourhoods (with an average price between 1223.84 and 1336.3 SEK), followed by Norrmalm and Östermalm (with an average price between 1111.37 and 1223.84 SEK).

Thirdly, Bromma has an average price between 998.9 and 1111.37 SEK. This is a high and medium-income residential neighbourhood, where the most expensive properties based on Stockholm´s average purchase price are located [43]. Fourthly, the properties situated in the south of the city, except for Skärholmen, receive an average price between 773.96 and 998.9 SEK, which coincides with the medium average score that these neighbourhoods receive.

The cheapest neighbourhoods (with an average price between 661.49 and 773.96 SEK) are those that are far from the center and also have the lowest location score: Rinkeby-Kista, Spånga-Tensta, Hässelby-Vällingby and Skärholmen.

23 Figure 4.5: Stockholm prices score.

Types of Listings in Stockholm In this section, the relationship between property type and neighbourhood is studied. Since there are many types of properties, the analysis focuses on the most frequent ones in the city. The plot in figure 4.49a shows the ratio of property type and the total number of properties in the borough.

Some key observations from the bar chart are:

• The top five property types selected for the analysis are apartments, houses, townhouses, lofts and condominiums.

• Apartments are the most common type of property in all of the neighbourhoods, with a presence rate between 51.5% (Älvsjö) and 94.7% (Östermalms).

• The second most common type of properties are houses, present in all the neighbourhoods at different levels. The neighbourhoods the highest presence of this type are Älvsjö with44.7% and Spånga-Tensta with 34.7%. It can be noted that, in general, houses are more present on the outskirts of the city and there is a lower percentage in the central neighbourhoods such as Kungsholmens, Norrmalm, Östermalms and Södermalms.

• The third most common properties are townhouses. The neighbourhood with the largest presence of this type is Hässelby-Vällingby, with a 14.2%.

24 • Lofts are present in the central neighbourhoods and the immediate surroundings of the city. This makes sense since construction areas in the city center are generally smaller and more expensive than in the surroundings. This matches the results obtained and explains why lofts and apartments are more common in these central parts, whereas in the surroundings there are more houses.

• Södermalm, Norrmalm, Kungsholmen and Östermalm do not have available townhouses to be rented on the webpage. This is curious, since Gamla Stan, the old town island and most touristic part of the city in Södermalm, is famous for its colorful townhouses. Norrmalm has also many characteristic townhouses, which have become a popular image of the city. The explanation of the lack of townhouses on the Airbnb webpage from these neighbourhoods could be that these types of properties are family buildings that are not in the market since they are kept from generation to generation as a symbol of family tradition. Assuming this hypothesis, this type of property is not usually rented and, if they are, they are rented in the traditional way, as regular apartments and not as Airbnb properties.

• It is worth mentioning that, in Stockholm, due to its geography of islands, boats can be rented as apartments. This type of property is a minority and is only present in those neighbourhoods with access to lakes or the sea, such as Hägersten-Liljeholmens, Östermalms, Kungsholmens and Södermalms.

Figure 4.6: Types of listings in Stockholm.

25 4.1.2 Demand and Price Analysis In this section, an investigation is conducted on some of the factors that affect the demand and prices of Airbnb properties in Stockholm. To carry out this study, the variable ”number of reviews” is used as an indicator of demand, as explained in the method.

Occupancy Rate by Neighbourhood in Stockholm To begin with, the average occupancy rate by neighbourhood is studied:

Neighbourhood Occupancy rate Enskede-Årsta-Vantör 85.33% Skarpnäck 85% Hägersten-Liljeholmen 84.9% Kungsholmen 84.7% Norrmalm 84.5% Östermalm 83% Södermalm 82.5% Farsta 81.5% Bromma 81.3% Älvsjö 76.8% Spånga-Tensta 76.25% Skärholmen 72.3% Hässelby-Vällingby 68.5% Rinkeby-Kista 40.9%

Table 4.2: Occupancy rate by neighbourhood in Stockholm.

Figure 4.7: Map with the occupancy rate by neighbourhood in Stockholm.

26 It can be seen that most neighbourhoods show an average occupancy rate between 80 and 85%, which is a high rate, since it means that, on average, more than 80-85% of Airbnb properties will be occupied. The neighbourhoods with these rates are those situated in the center of the city and in the immediate surroundings, which are also those with the highest number of Airbnb properties.

Taking into account that the number of Airbnb properties in each neighbourhood is considerably different (table 4.1) and knowing the average occupancy rate of each area (table 4.2), it is concluded that the areas situated in the city center will receive more visitors, followed by nearby neighbourhoods.

Demand Across Years in Stockholm The following graph represents the evolution of the demand of Airbnb listings in Stockholm:

Figure 4.8: Demand across years in Stockholm.

Some key observations from the bar chart are:

• The number of reviews has increased over the years, which, as discussed earlier, indicates an increase in demand.

• It can be appreciated that from 2012 to 2014 the demand hardly grew. However, from 2014 to 2018 it grew steadily with a significantly higher slope than in previous years. This slope has doubled in the last two years and shows a growing trend.

• It can be seen that the demand follows a seasonal pattern: each year there are peaks and drops, which indicates that certain months are busier compared to the others. This phenomenon is studied in the following section.

27 Seasonality in Demand in Stockholm To understand seasonality, the demand is studied through months for 2017, 2018 and 2019:

Figure 4.9: Seasonality in demand in 2017, 2018 and 2019 in Stockholm.

Analyzing the three graphs, it can be appreciated that the number of Airbnb reviews has raised from 2017 to 2019, which indicates that the demand for Airbnb has also increased. There seems

28 to be a pattern of how demand fluctuates across the year: the demand increases until August and then decreases, reaching its lowest point in December. Moreover, by looking at the attached climograph in Appendix C, which represents the monthly average temperatures and monthly average precipitations in the city, it can be seen that the demand for Airbnb properties follows a similar pattern. The extremely low temperatures and the frequent snow falls between December and April reduce tourism in these months, which increases considerably in summer due to the good weather.

Prices Across the Year in Stockholm With the aim of studying how prices vary throughout the year, the image below shows a representation of the variation of prices over the months:

Figure 4.10: Seasonality in price in Stockholm.

As can be seen in the graph, the average listing price hardly varies around 1,100 SEK. The price begins the year decreasing until March when it starts to increase until mid-September. Then it decreases again until the end of the year. This way, the highest average price occurs in August and the lowest in March. The shape of the graph is similar to the demand graph, except in January and February when prices fall while the number of reviews (indicative of demand) increases. This seems contradictory to common sense, as prices are expected to decrease with lower demand. It could be due to the assumption that the number of reviews is a reflection of demand, which might not always be the case.

Prices Across the Week in Stockholm As can be seen in the graph, Fridays and Saturdays are slightly more expensive compared to the other days of the week. However, it can be seen that the variation is small and the city hardly shows any difference between the days of the week.

29 Figure 4.11: Box plot of prices by day of the week in Stockholm.

Occupancy Rate by Month in Stockholm In this section a calendar heatmap of the occupancy rate of the Airbnb properties for 2019-2020 is presented:

Figure 4.12: Occupancy Rate by Month in Stockholm.

Some observations of the heatmap are:

• The occupancy rate appears to increase throughout the year, starting from the lowest occupancy in January to the busiest in November. This way, January and February seem to be the calmest months while November seems to be the busiest.

• It seems like there are three occupancy intervals: from January to February with low occupancy, from March to May with a medium occupancy and from June to November with a high occupancy rate.

• On this occupancy map, the highest occupancy rate can be found in the last three days of November of 2019. The high occupancy these days has its explanation on the date the data was gathered from the Airbnb website. The data was extracted in early February 2020, which means that the data from November 2019 to February 2020 correspond to actual bookings

30 that took place, while the data from February 2020 to November 2020 contain reservation data for the future. It makes sense that those days that have already passed have a higher occupancy rate since there are always last-minute bookings. • Stockholm occupancy map seems to match with the climograph of the city (Appendix C figure C.1a): the most demanded seasons of the year are those months when temperatures are not low, allowing tourists to enjoy the city, whereas December, January and February when temperatures are very low and there are a lot of precipitations, the occupancy rate is smaller. • This occupancy map coincides with the demand curve in all months except November, since according to the demand graph, the most demanded month is August and from this month the demand decreases. This difference between both representations could be caused bythe assumption that has been made for the demand graph, for which the number of reviews is a reflection of the demand, which might not always be the case.

4.1.3 User Review Mining This section presents the analysis of user reviews of Stockholm. The following image shows a word cloud of the city reviews:

Figure 4.13: Word cloud of Stockholm reviews. The analysis of the word cloud shows interesting trends: • Location seems to be key, as the words ”location”, ”area”, ”place”, and ”stay” are featured prominently in the word cloud. • Transportation features like ”train”, ”bus”, ”walk” and ”time” are also frequently named. Customers generally appreciate spending a short time going from one place to another, so they value the availability of transport means or the possibility of walking from the place where they stay in the tourist areas. Proximity to areas of interest such as ”bars” is also valued. • Airbnb properties are short-term rentals, however, people seem to lay stress on the comfort aspect of their stay, words like ”comfortable”, “warm”, ”quiet”, ”big” and ”clean” and are mentioned. • Availability of ”Amenities” such as ”bed”, ”WIFI” and ”kitchen” also find frequent mention, indicating that customers pay attention to the services provided by the tenant. • Words such as ”great”, ”lovely”, ”nice” and ”perfect” are present, indicating that most Airbnb customers express good feelings about Stockholm. • The word “host” finds a lot of mention; indicating the important role that hosts playin shaping the Airbnb experience.

31 Analysis of Customer Satisfaction The following word cloud presented is related to “uncomfortable” feelings. The reason this word has been chosen is that those words that appear along with it generally express discomfort. Therefore, the following word cloud facilitates the analysis of factors that annoy customers:

Figure 4.14: Word cloud of Stockholm reviews with words expressing annoyance. The analysis of this word cloud shows the following trends:

• Words like ”cramped”, ”crowded” and ”tight” indicate that lack of space is one of the most common complaints.

• ”Cold” shows that customers also complain because of temperature issues.

• Price is frequently mentioned, indicating that ”pricey” listings are one of the main customers’ protests.

• Words like ”inconvenient”, ”difficult”, ”strange”, ”bad”, ”annoying”, ”awkward” and ”messy” help tenants get an idea of those things that customers dislike.

• Customers like the ease of checking in, finding the apartment and contacting the host, so words like ”difficult”, ”tricky”, ”challenging” and ”hard” related to these issues are uncomfortable for them.

The second-word cloud is for the word “comfortable”. The analysis of this word cloud allows the identification of those things that have led the customers to positive experiences.

Figure 4.15: Word cloud of Stockholm reviews with words expressing comfort.

32 The analysis of the ”comfortable” word cloud shows the following trends:

• Customers value having enough space on the Airbnb property, therefore words such as ”spacious”, ”large” and ”big” are frequently mentioned.

• Frequent words like ”tidy”, ”warm”, ”quiet” and ”clean” demonstrate the importance of cleanliness, comfort and good condition of the properties. In addition, customers value the appearance of the property, which is why words like ”nice”, ”comfy”, ”cozy”, ”lovely” and ”stylish” are often mentioned as comfortable factors.

• The appearance of ”bed” as one of the most frequent words shows that customers value the quality of the bed to rest and be comfortable.

33 4.2 Barcelona

In this section, the city of Barcelona is analyzed. First of all, a rough description of some of the city´s data is presented:

• Barcelona´s population is 1,636,762 [9] and there are 20,411 unique Airbnb listings in total. This means that there is one Airbnb property for every 81 people living in the city.

• In 2018 the city received 11,977,277 visitors in total [25], of which 6,714,500 were international visitors [41]. This means that, in total, there is one Airbnb property for every 588 tourists that visit the city.

These figures show the large number of tourists that the city hosts each year and the highofferof Airbnb properties that the city currently has.

4.2.1 Spatial Data Analysis

Evolution of the Number of Airbnb Properties The next graph represents the evolution of the number of Airbnb properties in Barcelona over the years:

Figure 4.16: Evolution of Airbnb listings over the years in Barcelona.

It can be noticed that the presence of Airbnb has increased significantly in the last decade, so that the growth curve is almost straight with a slope around forty-five grades. There were 341 properties in 2010, which increased to 10,222 in 2015 and has almost duplicated in 2019 with 20,411 listings. This increase is linear and continuous since the beginning of Airbnb in the city, moreover, it seems that this growth has not stopped yet and the presence of Airbnb in the city will continue to grow in the following years.

This increase in Airbnb presence can be visualized in the following maps, which represent Airbnb properties in the different neighbourhoods of the city of Barcelona:

34 (a) 2010 (b) 2015

Figure 4.17: Visualization of the growth of Airbnb listings in Barcelona.

As can be seen on the maps, the first years of Airbnb in Barcelona are characterized by Airbnb properties located in the most central neighbourhoods (Eixample, Ciutat Vella and Gràcia). In the following years, the number of properties increased both in the center and in the surroundings. The current number of Airbnb properties in each neighbourhood is attached in the following table:

Neighbourhood Number of Airbnb listings Eixample 6719 Ciutat Vella 4943 Sants-Montjuïc 2363 Sant Martí 2174 Gràcia 1771 Sarrià-Sant Gervasi 757 Horta-Guinardó 674 Les Corts 421 Sant Andreu 354 Nou Barris 252

Table 4.3: Number of Airbnb properties by neighbourhood in Barcelona.

It should be noted that currently, Eixample and Ciutat Vella have considerably more properties than the rest of the neighbourhoods. Moreover, it can be observed that the downtown areas contain more properties and this number decreases as we move from the center to the surroundings.

35 Places of Interest Figure 4.18 shows the main places of interest in the city. Observing the map 4.17c and the table 4.3, it can be appreciated that the major quantity of properties is located in the neighbourhoods of Eixample and Ciutat Vella, which, as can be seen in the figure 4.18, are the areas that contain the largest number of places of interest of the city.

Figure 4.18: Principal places of interest in Barcelona.

Thus, it can be said that most Airbnb properties are situated in the most central and touristic parts of the city and the coastal neighbourhoods. The distribution of the places of interest and, therefore, the location of Airbnb properties, have a direct relation to the development of the urban planning of the city (see Frame of reference). In this way, the oldest part (Ciutat Vella) and the posterior extension (Eixample) are the most touristic parts of the city, followed by Sants-Montjuïc and Sant Martí, both with facilities and areas transformed for the Olympic Games.

Presence of Hotels Analyzing the map of the hotels in Barcelona attached in the Appendix A (figure A.1b), it can be noted that most of them are in those areas where the greatest number of places of interest are found, which also corresponds to those neighbourhoods with the highest number of Airbnb properties.

Location Score by Neighbourhood Observing the representation of the figure 4.19, it can be seen that Ciudad Vella, Eixample and Gràcia are the most valued neighbourhoods in terms of location, the three of them with an average score between 9.5 and 9.852. These neighbourhoods form the center and contain most of the principal places of interest of the city. They are followed by Sants-Montjuïc, with a score between 9.5 and 9.625. According to the website uniquespain.travel, these four neighbourhoods contain the “most trendy“ areas in the city at the moment, which coincide with the assumption that was initially made: popular areas tend to be more valued.

It can be seen that the location score follows the usual pattern of having better ratings in those neighbourhoods that are closer to the center of the city and to the touristic places (see figure 4.18). Therefore, as we move away from the central areas, the score decreases. This way, the neighbourhoods around the central core of the city have lower scores. This also happens with the number of stars of the hotels, since most of the five-star hotels are situated in the city center (Ciudad Vella, Eixample and Gràcia) and most of the four-star hotels are located both in the center

36 and in the neighbourhoods of Sant Martí and Sants-Montjuïc. Hotels with a lower number of stars expand from the center to the surroundings with low location scores. This means that the location score generally coincides with the number of stars of the hotels so that the hotels with the highest number of stars are located in the most valued neighborhoods and vice versa.

Figure 4.19: Barcelona location scores.

Les Corts, Sarrià-Sant Gervasi and Sant Martí have a location score between 9.375 and 9.5. In the case of Sant Martí, it is a modern area with recent buildings where investigation centers, universities, green areas and new apartments have been built in the last two decades [44]. Moreover, this area contains several beaches that attract tourists and is quite close to the center, making this neighbourhood a good option for visitors to stay. As for Les Corts, this neighbourhood contains the famous Barcelona football stadium (Camp Nou). Considering the huge popularity of Barcelona football club matches, it makes sense that this area is well connected to the center by public transport and therefore well-valued by customers. With regard to Sarrià-Sant Gervasi, it is the district with the highest per capita income, the largest proportion of university degrees and the lowest unemployment rate [45]. This can be appreciated in the good care of most of the buildings in the area, making it a very nice neighbourhood to stay and explains why customers value this area.

The neighbourhoods with the lowest score are those located in the north of the city: Sant Andreu, with an average location score between 9.125 and 9.25 and Horta-Guinardó and Nou Barris, with an average score in the range of 9 and 9.125. The lower score of Horta-Guinardó and Nou Barris could be explained, among other factors, due to its geographical situation, since they are known by the steepness of their streets, which is caused due to the hilly landscape in this area of Barcelona, surrounded by the hills of Collserola, Vall d’Hebron and Riera d’Horta [44]. This landscape makes the way to the centric parts more difficult and longer, decreasing the score of these neighbourhoods. Moreover, Nou Barris is the area of the city with the largest presence of immigrants, it has become the reception area of the strong foreign immigration flows since 2000, which has reduced

37 its popularity among locals and tourists [45]. On the other side, Horta-Guinardó is a residential area, full of green areas and considered a quiet place to live. This neighbourhood is chosen by those locals that run away from the bustle of the center, hence it is not very attractive for tourists that want to visit the center and get to know the local life of the city.

In figure B.2 presented in the Appendix B, the metro map of the city can be found. This is composed of twelve lines, with an irregular knot shape that connects the entire city. However, the central parts contain a larger number of stations, some very close to each other, allowing tourists to choose between multiple stops to get to the places of interest. This contributes to the higher score of the central areas, which are better communicated than the surroundings of the city.

Price by Neighbourhood The following map represents the average price of each neighbourhood in Barcelona:

Figure 4.20: Barcelona prices score.

In many cases, this map follows the location score map, since the highly rated locations tend to be costly. Analyzing the image 4.20, it can be seen that Eixample, Gràcia and Sarrià-Sant Gervasi are the most expensive boroughs, with an average price between 1719.5 and 1971.8 SEK. Eixample and Gràcia are very well valued by the customers so their average price matches their location score map. Regarding Sarrià-Sant Gervasi, this neighbourhood does not have the highest location score, but, as was mentioned before, it is the district with the highest per capita income and one of the most expensive areas of Barcelona [46].

Sant Martí has an average price between 1214.9 and 1467.2 SEK and it is followed by Sants-Montjuïc and Les Corts, with an average price between 962.6 and 1214.9 SEK. Horta-Guirnardó and Ciutat Vella have an average price between 710.3 and 962.6 SEK. The average price of Horta-Guirnardó

38 matches its location score. However, Ciutat Vella is highly valued in terms of the location, but it has a low average price. The reason for this is that this part of the city has become a ”low cost” neighbourhood for tourists, caused, among other factors, by the gentrification of this area [47]. All of this has made this neighbourhood, which is situated in a privileged central location, a very attractive cheap option for tourists. The tourist overcrowding of Ciutat Vella has caused the decrease of its popularity, which has also been affected by the high presence of pickpockets and other minor criminals that operate in this tourist area [48]. This can be observed in the image presented in the Appendix F, which shows the crime rate in Barcelona. The cheapest neighbourhoods are Sant Andreu and Nou Barris, with an average price between 458 and 710.3 SEK. These two areas may be the farthest from the center, and both of them receive a low location score.

Type of Listings in Barcelona In order to study the relationship between property types and neighbourhood, the following graph is analyzed:

Figure 4.21: Types of listings in Barcelona.

Some key observations from the bar chart are: • The types of property selected for the analysis, since they are the most predominant in Barcelona, are apartments, condominiums, houses, lofts and serviced apartments. • Apartments are the most common property type in all of the neighbourhoods, in all of them, apartments are present in more than 84.7%, which is the Horta-Guinardó case, with the lowest rate of apartments. • To a lesser extent, houses are the second more common type of listings. The neighbourhood with a larger percentage is Horta-Guinardó with 9.3%. • Condominiums and lofts are also present in small quantities in the neighbourhoods. Condominiums vary from 1.2% in Nou Barris to 3.82% in Sant Andreu. Lofts vary from 1.3% in Eixample to 3.8% in Gràcia.

39 4.2.2 Demand and Price Analysis Occupancy Rate by Neighbourhood in Barcelona In this section the average occupancy rate by neighbourhood is discussed:

Neighbourhood Occupancy rate Les Corts 52.9% Sant Andreu 52.1% Horta-Guinardó 51.8% Ciutat Vella 51.1% Sant Martí 50.6% Sants-Montjuïc 50.2% Gràcia 49.8% Nou Barris 48.5% Eixample 46.6% Sarrià-Sant Gervasi 45.8%

Table 4.4: Occupancy rate by neighbourhood in Barcelona.

Figure 4.22: Map with the occupancy rate by neighbourhood in Barcelona.

Looking at the table, it can be seen that the variation in the occupancy rate between neighbourhoods is very small. Occupancy rates range from 45.8 to 52.9%, indicating that, on average throughout the year, more than half of Airbnb properties will be occupied. However, it is important to mention that the number of properties in each neighbourhood is considerably different (table 4.3): the centric areas contain a considerably higher number of Airbnb properties (especially Eixample and Ciutat Vella) than the surroundings. Considering all this, it means that most Airbnb customers stay in areas located in the city center and the coastal areas: Eixample, Ciutat Vella, Gràcia, Sants-Montjuïc and Sant Martí.

40 Demand Across Years in Barcelona

In order to study the evolution of the demand for Airbnb listings in Barcelona, the following graph is analyzed:

Figure 4.23: Demand across years in Barcelona.

The key observations drawn from the graph are:

• The number of reviews has increased over the years, which means that, based on the initial assumption that the number of reviews represents demand, demand has also increased.

• The trend line shows almost exponential growth since the appearance of Airbnb in Barcelona.

• A seasonal pattern with peaks and drops in the demand can be observed each year. This phenomenon is studied in the next section.

Seasonality in Demand in Barcelona

To analyze the seasonal pattern of demand presented in the previous graph, the demand through months is studied for the last three years. Analyzing the graphics (figure 4.24), the following conclusions are made:

• Demand has increased from 2017 to 2019.

• All three charts have a similar shape: all of them start the year with a significant rise until May, then have a small increase during the summer period until September and finally decrease until the end of December.

• It can be seen that the shape of the graph is similar to the climograph of the city C.1b attached in the Appendix C. Therefore, those months with high temperatures and low precipitations, which are the summer months, correspond to the most demanded periods of the year. This makes sense, considering that Barcelona is a coastal city with beaches full of tourists.

41 • Observing the climograph of Barcelona, it can be noted that the city generally has a warm and temperate climate and the rain falls mostly in the winter, with relatively little rain in the summer. This climate is attractive to tourists and explains why, among other reasons, such as its central geographic location, Barcelona is a very demanded city where many international congress and events are held.

Figure 4.24: Seasonality in demand in 2017, 2018 and 2019 in Barcelona.

42 Prices Across the Year in Barcelona

After analyzing the seasonality of demand, it makes sense to study whether prices follow the same seasonality. The figure 4.52b shows the evolution of prices over the months.

Figure 4.25: Seasonality in price in Barcelona.

Observing the graph the following points can be observed:

• The demand and price curves follow a similar shape.

• Prices vary month by month in stages, with the exception of several peaks.

• There are three peaks of high price: one in early March, when the Mobile World Congress (MWC Barcelona) is held, in late November, when several events take place at the same time: Smart City Expo WorldCongress and NiceOne Barcelona (N1B) among others, and at the end of December, which correspond to the Christmas holidays.

• The cheapest period to rent an Airbnb property in Barcelona is in the period between December, after the festivity days due to the Constitution Day (December 6) and Immaculate Conception’s Day (December 8) and before of Christmas.

Prices Across the Week in Barcelona

As can be seen in the graph, Fridays and Saturdays are slightly more expensive compared to the other days of the weeks. However, the price variation during the week is minimal.

43 Figure 4.26: Box plot of prices by day of the week in Barcelona.

Occupancy Rate by Month in Barcelona The following figure presents a heat map with the occupancy rate by month in Barcelona:

Figure 4.27: Occupancy Rate by Month in Barcelona.

The following observations are obtained from the figure:

• It can be appreciated that the highest occupancy rate can be found on the first colored day of November 2019, with an occupancy rate of around 90%. As explained before, this happens because the period between November and January corresponds to the reservations that have already taken place. Therefore, this shows that December has a low occupancy rate and that it is a quiet month in comparison to the time interval between February and October.

• Many months have an occupancy rate around 50%, which indicates that in these months more than half of the Airbnb properties will be occupied.

• It can be appreciated that this occupancy map coincides with the demand graph, showing a rise from the beginning of the year to May, then a small increase during the summer period until October and finally a decrease until the end of December. Besides, the peaksin demand can also be observed in this occupancy map, one between the end of February and the beginning of March and another one during Christmas time.

44 • It can be deduced that the busiest months are those in the period between May and October, which correspond to a good climate in the city, with high temperatures and low rainfall (see climograph C.1b in the Annex C).

4.2.3 User Review Mining In this section, the user reviews of Barcelona are analyzed. To begin with, a word cloud of the city reviews is presented:

Figure 4.28: Word cloud of Barcelona reviews.

The analysis of the word cloud shows interesting trends:

• Location seems to be key, as the words ”location”, ”located”, ”area”, ”place”, ”central”, ”far”, ”time” and ”close” are featured prominently in the word cloud, indicating that the customer values the position of the Airbnb property and the time it takes to move from it to the places of interest. Therefore, transportation options such as ”metro”, ”station” and ”walk” are also frequently mentioned.

• Words like ”comfortable”, ”clean”, ”beautiful”, ”quiet”, ”cute”, ”cozy” and ”big” are frequent, meaning that hosts must take care of the appearance of their properties to make it as comfortable as possible.

• Customers value their interaction with the “host”, which means that hosts should try to be nice to their guests and be ”helpful”, ”easy” and ”friendly”.

• Words like “great”, ”perfect”, ”good”, ”nice” ,”wonderful”, ”excellent” ,”lovely” and ”fantastic” are present, indicating that most Airbnb customers in Barcelona express good feelings in the reviews.

• Safety is a key issue; customers look for listings where they feel ”safe”.

Analysis of Customer Satisfaction The first word cloud introduces what makes customers “uncomfortable”. The following word cloud makes it easy to analyze the factors that cause customer discomfort:

45 Figure 4.29: Word cloud of Barcelona reviews with words expressing annoyance.

The analysis of the word cloud shows the following trends: • Three of the most frequent words are ”mattress”, ”sofa” and ”couch”, which indicates that customers value the quality of their stay based on the comfort and size of the bed or the couch where they sleep. • ”Cold” shows the discomfort that the customers express due to temperature issues. • ”Cramped” states that lack of space is one of the most common complaints. • ”Inconvenient”, ”tricky”, ”challenge”, ”difficult”, ”hard” and ”complicated” show that customers do not want to have complications checking in or finding the apartment. Therefore, hosts should facilitate these processes and be as helpful as possible to guests. • Some other annoying aspects are ”noisy”, ”dark” and ”awkward” properties. The second-word cloud contains what makes customers “comfortable”. The analysis of this word cloud enables the identification of those things that have led them to positive experiences.

Figure 4.30: Word cloud of Barcelona reviews with words expressing comfort.

The key observations drawn from the word cloud are: • Customers value ”spacious” and ”large” Airbnb properties. • Words such as ”clean”, ”pleasant”, ”cozy” and ”nice” demonstrate the importance that customers place on the cleanliness, comfort, appearance and good condition of the properties. • ”Bed” is one of the most frequent words, which means that customers value the quality of the bed to rest and be comfortable. • Customers express appreciation of the properties with services with words like ”equipped”, ”kitchen” and ”modern”.

46 4.3 Rio de Janeiro

In this section, the city of Rio de Janeiro is analyzed. To begin with, a rough description of the city´s data is presented:

• Rio´s population is 6,718,900 [49] and there are 33,715 unique Airbnb listings in total, which means that there is around one Airbnb property for every 200 people living in the city.

• 2,278,300 international people visited the city in 2018 [41], and it is estimated that in total, around 5 million people visit the city each year, which means that for every 149 tourists there is one available listing.

4.3.1 Spatial Data Analysis

Evolution of the Number of Airbnb Properties To get started with the analysis, the evolution of Airbnb properties in Rio de Janeiro is presented:

Figure 4.31: Evolution of Airbnb listings over the years in Rio de Janeiro.

As shown in the graph, the number of Airbnb properties has increased over the years: from 197 listings in 2010, to 16,842 in 2015 and with 33,715 at the present time. Moreover, it seems that this growth will not stop yet, since the slope of the growth curve from 2018 to 2019 remains positive, despite being smaller than in previous years. This means that it is expected to continue increasing in the following years but to a lesser extent.

The growth of Airbnb experienced in Rio de Janeiro can be spatially visualized in the following maps:

47 (a) 2010 (b) 2015

Figure 4.32: Visualization of the growth of Airbnb listings in Rio de Janeiro.

The map shown in figure 4.32c reflects the current Airbnb properties distributed in Riode Janeiro. When comparing this image with the table 4.5, it can be seen that the largest number of properties are situated in the Zona Sul area, with a significant difference in properties compared to other areas. In addition, it can be noted that the south and southeast of the city contain more than half of the total Airbnb properties.

Neighbourhood Number of listings Zona Sul 19312 Barra da Tijuca e Baixada de Jacarepaguá 9021 Centro Historico e Zona Porturia 2415 Grande Tijuca 1568 Grande Méier 486 Zona Norte 291 Ilha do Governador e Zona da Leopoldina 275 Zona Oeste 274 Grande Bangu 73

Table 4.5: Number of Airbnb properties by neighbourhood in Rio de Janeiro.

48 Places of Interest The next figure contains a map with the principal places of interest of thecity:

Figure 4.33: Principal places of interest of Rio de Janeiro.

As indicated, most Airbnb properties are located in the southeast of the city, which, as can be seen in figure 4.33, corresponds to the neighbourhoods containing the main places of interest and the most famous beaches.

Presence of Hotels According to the figure A.1c presented in the Appendix A, which shows the distribution of hotels in Rio de Janeiro, the southeast of the city also corresponds to the areas where most of the hotels are located.

Comparing the distribution of Airbnb properties and hotels throughout the city, it can be observed that the hotel distribution is more concentrated in the main places of interest and beaches, whereas Airbnb properties are scattered throughout the city (with a major concentration in the southeast area).

Location Score by Neighbourhood Analyzing the map 4.34, it can be appreciated that Zona Sul, Grande Tijuca, Ilha do Governador e Zona da Leopoldina and Grande Oeste are the most valued areas of the city, with an average score between 9.67 and 9.84.

Zona Sul is the area with the largest number of Airbnb apartments and where most tourists generally stay. It is a residential area, where many hotels and Airbnb properties can be found [50]. This area embraces the famous neighbourhoods of Copacabana, Ipanema and Leblon and several beaches, all of them very popular among tourists. In respect of Grande Tijuca, this area contains the city´s most famous statue: Christ the Redeemer, which is a cultural icon of both Rio de Janeiro and Brazil [50]. There are many ways to get to the statue since there are many transport facilities in this part of the city, which also contributes to its high score. Also, this area comprises the highest financial income in the region and has one of the best Human Development

49 Indexes (HDI) in the municipality [50], which explains the good care of its streets and buildings and, therefore, the high value given by customers. Concerning Ilha do Governador e Zona da Leopoldina, this area contains one of the city´s airports, which explains the high availability of transport connections from this part of the city to others. Moreover, this part of the city is not far from the main touristic spots. It contains middle-class neighborhoods and has one of the best HDI of the city [50]. In short, in these three areas, good transport communication, a well-kept urban environment and the proximity to places of interest are significant factors that influence their high location score. Furthermore, it is worth mentioning that most of the neighbourhoods that the website culture trip considers the ”coolest neighbourhoods in Rio de Janeiro”, are located in the southeast of the city, which also contributes to their high location score. To continue, the high score of Zona Oeste is considered a particular case, since it is the largest region of the city and the second neighbourhood with the fewest Airbnb properties. Moreover, this area contains a great part of the forest, urban areas and favelas, which makes it very difficult to be classified51 [ ]. Deeper research would be necessary to explain the average score of this region.

Secondly, Barra da Tijuca e Baixada de Jacarepagu receive an average score between 9.5 and 9.67. In this case, the great part of the Airbnb properties is located in the south of the area, in the sub-neighbourhoods of Barra da Tijuca, Jacarepaguá and Recreio dos Bandeirantes. These two zones are close to the beach and also to some of the facilities used in the Olympic Games, which explains the existence of a high number of apartments in this part of the city and their high score.

Figure 4.34: Rio de Janeiro location score.

Thirdly, Zona Norte receives a score between 9.34 and 9.5, followed by Grande Bangu and Centro Historico e Zona Porturia, which receive a score between 9.17 and 9.34. Zona Norte and Grande Bangu are not as close as other parts of the city to touristic spots. Zona Norte has a large number of favelas [51] and Grande Bangu is mainly a rural area, these factors explain the reduced number of Airbnb properties in these areas and the low score. However, Centro Historico e Zona Porturia contains many places of interest and it does not receive a high score as the areas with this feature. This could be due to the presence of favelas and risk areas, since, as previously mentioned, one of the exploratory variables of the study is the safety factor, since Airbnb users generally tend to avoid those areas that involve lack of citizen security.

50 Grande Méier has the lowest location score. This neighbourhood contains residential areas where the middle and upper classes have their properties, but it also contains poor areas and some of the city´s most dangerous favelas [51]. Once again, the safety factor influences its low location score.

In addition, observing the metro map of the city (figure B.3 in the Appendix B), it can be appreciated that it has three lines, which connects the neighbourhoods of Centro Historico e Zona Porturia, Ilha do Governador e Zona da Leopoldina, Grande Tijuca, Grande Méier, Zona Sul and Zona Norte. The frequency of public transportation and proximity to metro stops are factors that customers generally value when rating Airbnb properties.

Price by Neighbourhood The following map represents the average price of each neighbourhood in Rio de Janeiro:

Figure 4.35: Rio de Janeiro prices score.

Barra da Tijuca e Baixada de Jacarepagu is the most expensive area, which, as previously stated, corresponds to a high-value zone that is close to beaches and Olympic facilities, both very attractive to tourists. Grande Tijuca, Grande Bangu and Zona Sul also correspond to the highest average price range, with prices between 1451.8 and 1644 SEK. It is worth mentioning that Grande Tijuca has properties close to the famous statue and is situated in a location well-valued by the customers, which explains its high price. Contrary to what might be instinctively assumed Grande Bangu has a high average price (despite its low location score), which could be explained by the reduced number of properties that can be found in this area, which can allow owners to increase the prices. On the other hand, Zona Sul, presents a high score and a high price.

In the third price segment, Zona Oeste has an average price between 1067.8 and 1259.8 SEK and is followed by Centro Historico e Zona Porturia. Both have an average price that matches their location score.

Zona norte and Grande Méier have an average price between 683.8 and 875.8 SEK. As was indicated in the location analysis, both have several characteristics that make its average price to be lower than in other areas. Ilha do Governador e Zona da Leopoldina has the lowest average price, yet it presents a high location score. The reason for this could be the proximity of

51 some of the properties to the airport since traditional accommodations near the airport are cheaper. It can also be caused because it is a residential area that might not be of interest to tourists [50].

Type of Listings in Rio de Janeiro In this section, the relationship between property type and neighbourhood in Rio de Janeiro is studied:

Figure 4.36: Types of listings in Rio de Janeiro.

The following issues are taken from the graph:

• Apartments are the most common type of property in all areas except Grande Bangu and Zona Oeste, where there are more houses. It makes sense since Grande Bangu and Zona Oeste are zones with a greater distance from the city center. Zona Oeste is a low middle-class area characterized by humble single-family houses. Grande Bangu is a middle-class area with residential neighbourhoods with small and modest houses but in better conditions than Zona Oeste. Zona Norte also has a high percentage of houses and is a modern area that combines apartments and single-family houses.

• Zona Sul and Grande Tijuca are the areas with the highest percentage of apartments. This makes sense since they both are highly demanded zones where most tourists stay due to their proximity to the most famous beaches.

• Condominiums are the third most common type in all the neighbourhoods. It has its maximum in Barra da Tijuca e Baixada de Jacarepaguá with 12.7%.

52 4.3.2 Demand and Price Analysis Occupancy Rate by Neighbourhood in Rio de Janeiro In this section, the average occupancy rate by area is presented and analyzed:

Neighbourhood Occupancy rate Zona Sul 52.54% Barra da Tijuca e Baixada de Jacarepaguá 50.55% Grande Tijuca 48.31% Centro Historico e Zona Porturia 46.9% Grande Méier 43.74% Ilha do Governador e Zona da Leopoldina 42.7% Zona Norte 39.35% Grande Bangu 39.05% Zona Oeste 35.13%

Table 4.6: Occupancy rate by area in Rio de Janeiro.

Figure 4.37: Map with the occupancy rate by neighbourhood in Rio de Janeiro.

As can be appreciated, the areas around the main places of interest and near the principal beaches have a higher occupancy rate. The case with the lowest occupancy rate is found in Zona Oeste with 35.13%, which means that all areas of the city will have more than a third of the listings occupied.

Demand Across Years in Rio de Janeiro The graph in figure 4.38 shows an estimate of the evolution of the demand for Airbnb properties in Rio during the last decade. As can be appreciated, the number of reviews has increased over the years, reaching its maximum in 2020. Looking at the trend curve, it can be noted that the number of reviews is practically non-existent until 2013, but from this year it begins to increase slowly and steadily in some intervals. In the interval between 2018 and 2020, the slope of the curve doubles compared to the previous trend, which means that in the last two years the city´s demand has

53 increased significantly. Moreover, it can be seen that the graph shows peaks and drops every year. To study these changes in more detail, this phenomenon is studied in the next section.

Figure 4.38: Demand across years in Rio de Janeiro.

Seasonality in Demand in Rio de Janeiro Demand during the months is studied for 2017, 2018 and 2019 (see figure 4.39). Observing the climograph C.1c located in the Appendix C, it can be seen that the climate in Rio has hot and humid summers and warm winters. Average temperatures do no exceed 30ºC or drop below 20ºC, but rain is significant most months of the year. Therefore, the climate coincides with thedemand trends, since the most demanded seasons are those months that belong to Summer (from December to February) when the temperatures are the highest. However, Rio also has high temperatures in the rest of the months, which makes the city attractive to tourists throughout the year. All of this explains the small variation that takes place in the demand curve during the year.

54 Figure 4.39: Seasonality in demand in 2017, 2018 and 2019 in Rio de Janeiro.

Prices Across the Year in Rio de Janeiro The seasonality in the price is shown in figure 4.39. The main points are discussed:

• The price chart has a similar shape to the demand graph: it decreases from January to mid-May and then increases until December.

• It should be noted that the summer months (from December to February), which correspond to the high season, are the most demanded and, therefore, the most expensive.

• There is a significant peak in prices in February, which is caused by the Carnival of Rio,which takes place in the last week of February. After this event, the prices drop substantially and rise steadily and slowly over the consecutive months.

• According to this graph, the cheapest time to visit the city is the second half of March or around the winter season (May to October).

55 Figure 4.40: Seasonality in price in Rio de Janeiro.

Prices Across the Week in Rio de Janeiro As can be seen in the following representation, Fridays and Saturdays are the most expensive days of the week to visit the city. Prices these days are slightly higher than the rest of the days of the week. In addition, it can be appreciated that there is barely a variation between the prices from Sunday to Thursday.

Figure 4.41: Box plot of prices by day of the week in Rio de Janeiro.

56 Occupancy Rate by Month in Rio de Janeiro To continue, a heat map with the occupancy rate by month in Rio de Janeiro is presented:

Figure 4.42: Occupancy Rate by Month in Rio de Janeiro.

• It can be seen that the highest occupancy rate can be found on the first colored day of November 2019. As was explained before, this happens because the period between November and January corresponds to the reservations that have already taken place. Therefore, this shows that December has a low occupancy rate and that it is a quiet month compared to the interval between May and November.

• It can be noted that, as happens in the demand graph, the variation throughout the year is small, the occupation usually varies from 45 to 50 %.

• There are two peaks in occupation: the first at Christmas, during the last week of December and the first week of January and the second at the last weekend of February, which corresponds to the Carnival of Rio de Janeiro.

4.3.3 User Review Mining In this section, an analysis of the user reviews of the city is performed. First of all, a word cloud with the most frequent words is present:

Figure 4.43: Word cloud of Rio reviews.

57 The analysis of the word cloud shows interesting trends:

• Location seems to be key, as the words ”place”, ”stay”, ”location”, ”local”, ”close”, ”area” and ”located” are featured prominently in the word cloud. The area where the Airbnb properties are situated is important to the customer, who values proximity to places of interest such as ”beach”, ”bars” and ”restaurants”. Safety is also important for customers, so they tend to look for apartments situated in ”safe” areas.

• Transportation options like ”metro”, ”station”, ”bus” and ”taxi” also find frequent mention. Customers generally look for apartments that are well connected by different means of transport or apartments that are not far from the center and where it is possible to go by ”walk” and spend short ”time” on the route.

• Airbnb customers value the quality of their stay, therefore words such as ”nice”, ”clean” and ”quiet” are frequent.

• Facilities on Airbnb properties such as ”WIFI” and ”tv” are valued.

• The word “host” finds a lot of mention; indicating the important role that hosts playin shaping the Airbnb experience.

• Words like ”super”, ”great”, ”perfect”, ”good”, ”amazing” and ”ideal” are present, showing that most Airbnb customers express good feelings about the city.

Analysis of Customer Satisfaction To continue, a word cloud with words related to “uncomfortable” is presented, expressing the things that annoy customers:

Figure 4.44: Word cloud of Rio de Janeiro reviews with words expressing annoyance.

The analysis of the word cloud of ”uncomfortable” shows the following trends:

• Customers generally value the quality of their sleep, thus they negatively evaluate uncomfortable beds with ”mattresses” in poor condition.

• The word cloud shows words like ”cramped” and ”small”, indicating that lack of space is one of the most common complaints.

• “Hot” and “cold” are some of the common temperature problems.

• Customers want to relax during their stay, so they dislike ”noisy” or ”loud” areas.

58 • It is important that the hosts analyze the opinions of the customers, so they can understand their expectations and improve the apartment in case they find it ”disappointing” or ”awkward”.

• The hosts should try to facilitate the stay of the guests: they should give a good description of how to get to the apartment, facilitate the check-in... Words like ”difficult”, ”challenging”, ”inconvenient”, ”hard”, ”nervous”, ”annoying” and ”tired” are mentioned very frequently to describe uncomfortable feelings.

The second word cloud shows “comfortable” feelings expressed by customers:

Figure 4.45: Word cloud of Rio de Janeiro reviews with words expressing comfort.

The analysis of the word cloud shows the following points:

• As mentioned above, lack of space causes inconvenience to customers, therefore ”space” and ”spacious” apartments are highly appreciated.

• Customers generally value the quality of their sleep, therefore comfortable ”beds” are highly valued.

• Prominently featured are words like ”quiet”, ”clean” and ”safe” demonstrating the importance of environment, location and cleanliness.

• Customers value the appearance of the properties, this explains why ”cozy”, ”modern”, ”nice”, ”lovely”, ”beautiful” and ”stylish” apartments are very well received.

• ”Equipped” apartments with amenities are also appreciated by customers.

59 4.4 Comparison of the Cities

In this section, a comparison between the cities of Stockholm, Barcelona and Rio de Janeiro is performed. Initially, a general comparison of the main characteristics of the cities has been made: • Stockholm and Barcelona are European cities and therefore are located in the northern hemisphere, whereas Rio de Janeiro is situated in South America, in the southern hemisphere. This affects the analysis, since the hemisphere where they are located determines inwhich months the seasons take place.

• The climograph of these cities, attached in the Appendix C, plays an important role that affects the arrival of visitors. As can be noted, the climate is very different ineachoneof them [52]: Stockholm has an oceanic climate when temperatures are above 0 °C and a humid continental climate when temperatures are below -3 °C. This city is characterized by cold winters, during which the average temperature is a few degrees below zero and mildly warm summers. Regarding Barcelona, this city has a Mediterranean climate, characterized by mild winters and hot summers, the rain falls mostly in the winter, with relatively little rain in the summer. In the case of Rio de Janeiro, due to its tropical climate, temperatures are more constant throughout the year, but rainfall is significant most of the months of the year.

• It can be appreciated that the three cases are coastal cities, however, sun and beach tourism are typical in Barcelona and Rio de Janeiro. In the cultural aspect, the three cities have many places of interest, combining modern attractions with historical charm. This explains the high attractive for tourists from all over the world and makes them very touristic destinations.

• In Stockholm, the expansion of the city was planned in the second half of the 20th century, in a way that new suburbs were constructed along the subway lines [10]. Barcelona’s transformation began in the second half of the 19th century with the ”extension” of Barcelona, creating a new area characterized by long straight streets and a strict grid pattern [20]. Rio de Janeiro has witnessed many changed from its origins to the present day: the development of the city has always been influenced by the mix of first world countries and their traditional Carioca culture [31]. This and the development attempt to explain the irregular distribution of the urban areas of the city, which presents rich and touristic neighbourhoods and favela zones, both very close to each other. Therefore, the city map of Stockholm and Barcelona is influenced by the urban planning that both have followed during the growth of the city, however, Rio has witnessed a development characterized by both the urban planning and the anarchic growth of favelas.

• In addition, it is worth mentioning that two cities in the analysis hosted the Olympic Games: Barcelona (1992) and Rio de Janeiro (2016). Both spread investments throughout the city and it meant many changes for both: an improvement in their infrastructures, the recovery of some areas of the cities and their image was positively affected. To continue, some reflections on data related to the three cities aremade. • Stockholm´s population is 974,073; Barcelona´s population is 1,636,762 and Rio de Janeiro´s population is 6,718,900 [9].

• It is estimated that around 5 million people visit Stockholm every year, around 12 million Barcelona and 5 around million Rio de Janeiro [41].

• The number of Airbnb properties in each city is 7,769 in Stockholm; 20,428 in Barcelona and 33,715 in Rio de Janeiro. Taking these numbers into account, it can be estimated that there is one Airbnb property for every 154 people living in Stockholm, one for every 81 in Barcelona and one for every 200 in Rio de Janeiro. These calculations show that Barcelona has a higher number of Airbnb properties in relation to its population compared to Stockholm (which is in second place) and Rio de Janeiro.

60 Furthermore, it can be noted that in Stockholm there is one Airbnb property for every 791 tourists; in Barcelona, there is one for every 588 and in Rio de Janeiro, one for every 149. This indicates that, in comparison to the other cities, there are fewer Airbnb properties for tourists in Stockholm.

4.4.1 Spatial Data Analysis Evolution of the Number of Airbnb Properties To begin with the spatial data analysis, the evolution of the available Airbnb properties in the three cities is presented:

(a) Stockholm (b) Barcelona

Figure 4.46: Evolution of the number of Airbnb properties in the three cities.

It can be observed that in all three cases, the number of properties has increased over the years. However, the growth has been different in the three cities: in Stockholm and Rio the rise ismore significant from 2014 to the present, whereas in Barcelona the increase is linear and continuous since the start of Airbnb in the city. Moreover, it can be seen that the current number of Airbnb properties is greater in Rio de Janeiro (33,715 properties) than in Barcelona (20,428) and Stockholm (7,769).

Comparing the expansion of Airbnb in the three cities, it can be seen that it is related to the growth pattern that each of the cities has experienced: • Stockholm and Barcelona have grown in an organized way, expanding from the old town (which is the tourist area) to the surroundings, which is why they have most of the Airbnb properties in the center and this number decreases as we move away from the center. • Rio de Janeiro does not follow the same pattern as Stockholm and Barcelona. This city has experienced anarchic and unequal growth, with lower building planning, which explains the

61 high differences between neighbourhoods that are next to each other and the unbalanced distribution of Airbnb properties.

Places of Interest All three cities have a higher number of Airbnb properties in neighbourhoods that are closer to the city center or tourist spots. The three cities are very touristy, however, the tourism they receive is very different: Stockholm tourists seek its cultural heritage, which is mainly situated in theoldtown and its immediate surroundings. This explains the high concentration of Airbnb properties around this area. Rio de Janeiro offers many cultural activities, however, its main attraction for tourists is its good weather and its beaches, which explains why most Airbnb apartments are located in the coastal areas and not in the old town. Barcelona tourism combines the city´s cultural appeal and beach tourism, which is why most Airbnb properties are located around the old town and the coastal areas of the city.

Presence of Hotels In most cases, most hotels are located in those areas with a major number of places of interest, which also correspond to the neighbourhoods with the largest number of Airbnb properties. This happens in the three cities of the analysis: regarding Stockholm, most of the hotels are located in Norrmalm, which is the second neighbourhood with more Airbnb properties. Barcelona has the majority of the Airbnb apartments located in the neighbourhoods of Eixample and Ciutat Vella, the central part of the city, which also contains the largest number of hotels. Concerning Rio de Janeiro, the southeast of the city corresponds to the areas where most of the hotels and Airbnb properties are situated since the most famous beaches and places of interest are found in this area. In addition, it can be appreciated that, in all three cases, the distribution of hotels is more concentrated in the main places of interest whereas Airbnb properties are more scattered throughout the city (but with a greater concentration in downtown areas).

Location Score by Neighbourhood The figure 4.47 shows the map of the three cities with the average location score. They all have the same color scale to facilitate the comparison. The key ideas extracted from the maps are the following: • Regarding Airbnb customers, there are many factors that affect them when they value an Airbnb property. This is a complex matter, so after researching this topic, a selection of the main geographic, social and economic aspects was made (see method: Geographic, social and economic data). Thus, the factors that have been taken into account for the analysis are location; distance to the city center and touristic places; public transport links; and safety, popularity and economic characteristics of the area where the property is situated. • It has been observed that the highest location scores generally correspond to the most touristic areas. In the cases of Stockholm and Barcelona, the highest scores are found in the central parts of the city, which are also the oldest and most touristic parts. However, in the case of Rio de Janeiro, the highest scores are achieved in the coastal neighbourhoods where the most famous beaches are situated, which are the most demanded by tourists. • In Stockholm and Barcelona, the location score decreases as we move from the downtown neighbourhoods to the surroundings, except in some particular cases. However, in Rio de Janeiro the distribution of scores is irregular and does not follow a pattern. In many cases this also occurs with the number of stars of the hotels, most of the five-star hotels are situated in the most touristic areas and the hotels and hostels with a lower number of stars are generally located in the surroundings. This means that, in general, the neighbourhoods where Airbnb top-rated properties are situated coincide with the location where most five-star hotels can be found.

62 • Stockholm receives a location score ranging between 9 and 10, while Barcelona´s location score ranges from 9 to 9.8 and Rio varies from 8.8 to 9.8. Moreover, the highest location score is reached in Stockholm, in the neighbourhoods of Norrmalm and Kungsholmen, with an average score between 9.8 and 10. In Barcelona and Rio de Janeiro, the maximum location score is between 9.6 and 9.8.

Figure 4.47: Location score by neighbourhood in the three cities.

Price by Neighbourhood In this section, a price comparison by neighbourhood is made using the same color scale to facilitate the comparison. The key ideas drawn from the maps presented in figure 4.48 are as follows:

• In many cases, it has been found a correlation between neighbourhood location score and price. Therefore, those factors that affect the location score (distance to the center, safety, popularity...) also affect the price.

• In the city of Stockholm, there is a clear pattern for which those neighbourhoods with the highest prices have also the highest location scores. This also happens in Barcelona, except the central neighbourhood of Ciutat Vella, which has become a low-cost area crowded with tourists. In Rio de Janeiro, the location and price score do not match in many cases.

• Barcelona has the most expensive neighbourhoods (Eixample, Gràcia and Sarrià-Sant Gervasi), and it is also the city with the biggest difference between the average prices of its areas. On the other hand, Stockholm is the city with the smallest difference between the prices of its neighbourhoods.

63 Figure 4.48: Price by neighbourhood in the three cities.

Types of Listings

The figure 4.49 shows the most common type of listings by neighbourhood in each city. Some key observations from the graphs are:

• The main type of properties in the three cities are: apartments, condominiums, houses, lofts, serviced apartments and townhouses.

• Apartments are the most common type of property in all three cities. In addition, it is the most common type in all the neighbourhoods, except in Grande Bangu and Zona Oeste in Rio de Janeiro.

• The second most common type of property is houses. This type of Airbnb property has a higher percentage of presence in Rio de Janeiro compared to the other cities. Regarding Stockholm, there is a clear difference between the city center, where there is a reduced number of houses and the surroundings, where this number is higher.

• It should be noted that townhouses are present in several neighbourhoods of Stockholm, nevertheless, this property type is not present in Barcelona and Rio de Janeiro. In the same way, serviced apartments are present in Barcelona and Rio but not in Stockholm.

64 (a) Stockholm (b) Barcelona

Figure 4.49: Types of listings in the three cities.

4.4.2 Demand and Price Analysis To continue, the results of the demand and price analysis for the three cities are compared.

Occupancy Rate by Neighbourhood When comparing the occupancy maps of the three cities, it can be observed that the occupancy rates are very different, so it makes no sense to compare them with the same color scaleina comparison map. The following key points are observed:

• The highest occupancy rate is in Stockholm, with percentages ranging from 65 % to 90 %. This is because this city has fewer listings for each visitor than Barcelona and Rio: there is a listing for every 791 tourists in Stockholm, while in Barcelona there is a listing for every 588 and in Rio for every 149 tourists.

• In all three cities, the highest occupancy rates are in neighbourhoods in the most touristic areas, which are also the places where most Airbnb properties are located. This indicates that those areas situated in the city center host more visitors.

65 Demand Across Years The following images represent the evolution of demand over the years in the three cities:

(a) Stockholm (b) Barcelona

Figure 4.50: Demand across years in the three cities.

These are the main ideas obtained from the comparison of the charts: • It can be appreciated that the number of reviews (indicative of demand) has increased in the last decade in the three cities. However, this growth is different in the three cases. In Stockholm, demand has grown steadily, increasing its slope at two years intervals, while Barcelona shows almost exponential growth. Rio de Janeiro presents a smaller growth in comparison to Barcelona, which is constant in some intervals. • Demand in Barcelona has grown more strongly than in Stockholm and Rio. Moreover, it can be noted that the current demand of Airbnb properties is significantly higher than in the other cases, which matches with the greater number of tourists visiting this city. • It all cases, demand follows a seasonal pattern: every year there are peaks and drops in the demand, indicating that certain months are busier compared to others.

Seasonality in Demand The graphs in the figures 4.51 represent the evolution of demand throughout 2019 in the three cities. The main points are discussed: • Assuming the hypothesis initially formulated, for which the evolution of the number of reviews represents the demand, it can be seen that demand in Barcelona is greater than in Stockholm and Rio. This makes sense, since, as was mentioned before, Barcelona has the highest number of visitors.

66 • The three curves are very different from each other, but all of them show a relationship with their climograph attached in the Appendix C. – In the case of Stockholm, its extremely low temperatures reached in winter (December to February) considerably reduce tourism at this time of the year. – Regarding Barcelona, this city usually has a warm and temperate climate, the rain falls mostly in the winter, with relatively little rain in the summer. This weather is attractive for the rest of the countries, which explains the large number of international events that take place in the city and the increase of demand in summer when the good temperatures attract tourists all over the world. – In the case of Rio de Janeiro, due to its tropical climate, temperatures are more constant throughout the year, but rainfall is significant most months of the year. The most demanded months to visit the city are the summer months, between December and February. Therefore, in the three cities, the most demanded period are the summer months, which take place at different times: Stockholm and Barcelona are in the northern hemisphere, sosummer months take place between June and September. However, Rio de Janeiro is in the southern hemisphere, so summer occurs between December and February.

(a) Stockholm (b) Barcelona

Figure 4.51: Seasonality in demand in the three cities.

• Following the previous point, it is understandable that Stockholm and Barcelona have a similar pattern in how demand fluctuates throughout the year: demand begins with its lowest point between December and January and increases until reaching its highest point in August when it begins to falls until the end of the year. However, Rio de Janeiro the highest demand takes place from December to February and decreases in the rest of the months.

67 • The variation in demand is very pronounced in Stockholm and Barcelona, nevertheless, it is very little pronounced in Rio de Janeiro. This could be caused because the winter months in Rio coincide with the summer season in countries situated in the northern hemisphere when many people are on vacation and visit this city as a holiday destination. In addition, in the three cases, there are peaks of demand that correspond to specific festivities or events.

Prices Across the Year The seasonality in the price in the three cities is shown below:

(a) Stockholm (b) Barcelona

Figure 4.52: Prices across the year in the three cities.

• It can be seen that in all three cases, the price graph shows a similar shape to the demand graph. However, in the case of Stockholm, this does not happen in January and February, when prices begin to fall while the number of reviews (indicative of demand) starts to increase. This seems contradictory to common sense, as the price is expected to decrease with a decrease in demand. This could be due to the assumption that the number of reviews is a reflection of demand, which might not always be the case.

• The prices vary differently in the three cases:

– While in Stockholm the average price varies slightly around 1,100 SEK, Barcelona and Rio present a great variation of the price throughout the year. This could be happening because, compared to the other two cities, Stockholm has a tighter market, with a number of visitors that are not excessively high and consistent with the number of listings offered. It should be noted that both Stockholm and Rio have a numberof visitors around 5 million, but still, Rio has more than four times the number of listings that Stockholm offers.

68 – Rio shows a significant fall of prices after the Carnival of Rio (end of February) andfrom this point the prices vary very little until the Christmas period. – Barcelona prices vary stepwise increasing considerably month by month and decreasing after the end of October.

• It can be noted that Summer months, which correspond to the high season, are more demanded and, therefore, more expensive. As mentioned above, this period in Stockholm and Barcelona takes place between July and September and in Rio de Janeiro between December and February.

• It is worth mentioning that peaks in demand also show a peak in price. It is noteworthy the case of the Carnival of Rio de Janeiro, which shows a significant peak of prices at the endof February.

• It can be seen a variation of prices throughout the months, which represents that the average prices on certain days were higher compared to other days. This phenomenon is studied in the following section.

Prices Across the Week To study the daily average price, a plot a box plot with the average prices by day of the week is presented:

(a) Stockholm (b) Barcelona

Figure 4.53: Prices across the week in the three cities.

• It can be seen that in all three cases, Fridays and Saturdays are more expensive compared to the other days of the week.

69 • As indicated in the previous section, Barcelona prices vary more throughout the year than Stockholm and Rio. This explains why the box plot of Stockholm is broader than the box plot in the other two cities.

Occupancy Rate by Month To continue, the occupancy rate between 2019 and 2020 is studied for the three cities:

(a) Stockholm

(b) Barcelona

Figure 4.54: Occupancy Rate by Month in the three cities.

• The maps shown in the figure 4.54 allow the student to have an idea about the evolution of the occupation in the three cities (there is no available data on the occupation from previous years) and can be used as an estimate of seasonal demand. In general, the occupancy map seems to coincide with the demand curve and the climograph of each city, except in some particular cases.

• Stockholm´s occupancy rate appears to be higher than the rest of the cities. It is caused by the lower number of listings that the city has for each visitor in comparison to the others.

70 • All three cities show peaks of occupancy when certain events or festivities take place. All of them show a peak during Christmas: last week of December and the first of January.

4.4.3 User Review Mining This section presents a comparison between the word clouds made for the three cities. These word clouds contain the most frequent words from the reviews of each city:

(a) Stockholm (b) Barcelona (c) Rio de Janeiro

Figure 4.55: Word cloud of the three cities.

The common points in the word clouds are as follows:

• In all three cases, location seems to be key, indicating that customers value the location of the Airbnb property and the time that it takes to move from it to tourist areas.

• In all three cities, the availability of different means of transport such as the subway or buses is highly valued.

• The analysis shows that customers feel comfortable in clean, warm, quiet and spacious apartments. This indicates that the hosts should take care of the appearance of their property to make it as comfortable as possible if they want guests to feel comfortable during their stay.

• The word “host” is widely mentioned; indicating the important role that hosts play in shaping the Airbnb experience. Customers value their interaction with the host, so they should try to be nice with their guests and be helpful and friendly.

• The availability of amenities find mention too, customers value having, among other things, WIFI and television. They also enjoy properties with clean and modern kitchens and they pay special attention to the quality of the beds.

• Safety is a key issue for customers in Barcelona and Rio de Janeiro: they look for apartments situated in ”safe” areas.

Analysis of Customer Satisfaction Two additional word clouds are presented in this section. The first contains words related to those things that are “uncomfortable” for Airbnb customers, representing factors that cause them annoyance. The second word cloud introduces what makes customers “comfortable”, to study what they enjoy and find convenient and easy.

71 Uncomfortable:

(a) Stockholm (b) Barcelona (c) Rio de Janeiro

Figure 4.56: Word cloud of the most uncomfortable things for Airbnb customers in the three cities.

The three cities have the following aspects in common related to customer discomfort:

• Lack of space is one of the most common complaints, they do not like about tight, cramped and small apartments.

• “Hot” and “cold” are common temperature issues.

• Hosts should try to facilitate the stay of the guests: they should give a good description of how to get to the apartment, facilitate check-in, be available in case there is any problem, etc. Words that express difficulty and annoyance are related to customer´s discomfort.

• In Barcelona and Rio de Janeiro, customers express their discomfort caused by poor conditions or the bad quality of the mattress or sofa where they sleep.

• Customers want to relax during their stay, so they express displeasure to noisy areas. Moreover, dark and awkward apartments are also a common reason for discomfort.

Comfortable:

(a) Stockholm (b) Barcelona (c) Rio de Janeiro

Figure 4.57: Word cloud of the most comfortable things for Airbnb customers in the three cities.

The three cities have the following aspects in common related to customer comfort:

• Customers value spacious and big apartments. As mentioned before, the lack of space causes them discomfort.

72 • The high-quality beds are very appreciated.

• The modern and equipped apartments with amenities are highly valued by customers.

• The appearance of the properties is highly valued among guests, who especially enjoy modern, cozy and stylish apartments.

• Prominently featured are words like ”quiet”, ”clean” and ”safe”, demonstrating that guests place great importance on the environment, location and cleanliness of the listings. They look for clean, safe and warm apartments.

73 Chapter 5

Conclusions and Future Work

In this chapter, the main conclusions and some recommendations for future work are presented.

In this thesis work, the peer-to-peer rental accommodation model of Airbnb has been studied in the cities of Stockholm, Barcelona, and Rio de Janeiro. The impacts that, factors such as location, price, and seasonality of Airbnb listings have on the customer have been individually analyzed for each city and then compared between them. This study has been carried out through an exploratory analysis using the R programming language gathering Airbnb´s data from the Inside Airbnb webpage from 2010 until now. The analysis has been divided into three parts: Spatial Data Analysis Since the arrival of Airbnb, its presence has increased significantly in the three cities, however, its growth has been different in each case: in Stockholm and Rio the rise was more significant from 2014 to the present, while in Barcelona the increase has been linear and continuous from the beginning.

Currently, most of Airbnb properties are situated near the most touristic areas and their distribution follows the urban growth pattern of the cities. In this way, most of the Airbnb properties in Stockholm are located close to the old town; in Rio de Janeiro they are mainly located near the main beaches and in Barcelona both in the old town and the beach areas. In addition, in Stockholm and Barcelona, the number of Airbnb properties gradually decreases as we move away from the center, but in Rio there are big differences between neighbourhoods that are next to each other and do not follow a pattern. This way, the three cities have the highest number of Airbnb properties in the neighbourhoods that are close to the tourist places, which also correspond to the areas where most hotels are located.

Regarding Airbnb customers, many factors affect them when they value an Airbnb property. This is a complex matter, so after researching this topic, a selection of the main geographic, social, and economic aspects was made. Thus, the factors that have been taken into account that summarize what customers value are: good location; short distance to the city center and touristic places; good public transport links, safety, and high popularity of the area where the property is situated. Following these points, it has been observed that the highest location scores given by Airbnb customers and prices generally correspond to the most touristic areas, which are located in the old town in Stockholm and Barcelona, and the zone of the main beaches in Rio.

In terms of the average price, a correlation has been found between the neighbourhood location score and its price. In Stockholm, a clear pattern has been detected for which neighbourhoods with the highest location scores also have the highest prices. This also happens in Barcelona, except for the Ciutat Vella neighbourhood, which has become a low-cost area crowded with tourists. In Rio de Janeiro, the location and the price score do not coincide in many cases. Finally, it is worth mentioning that the most common type of property in the three cities are apartments. However,

74 condominiums, houses, lofts, serviced apartments, and townhouses are also present, but to a lower extent.

Demand and Price Analysis In this part, the demand for Airbnb listings over the years since 2010 and across months has been studied. Moreover, the relationship between price and demand has been established:

The demand has increased in the last decade in all three cities. However, this rise has been different in each case: in Barcelona, the demand has grown more strongly, showing analmost exponential growth, which coincides with the greater number of tourists visiting this city. In Stockholm and Rio, demand has grown steadily, increasing its slope at intervals. Furthermore, in all three cases, demand follows a seasonal pattern. It has been observed that the demand graph shows a relation with the climograph of each city, thus, the most demanded periods are the summer months, which take place between June and September in Stockholm and Barcelona (both of them in the northern hemisphere) and between December and February in Rio de Janeiro (located in the southern hemisphere). In addition, there are peaks of demand that correspond to specific festivities or events that take place in the cities.

Demand trends have been compared to the price chart. It has been observed that in all three cities, the price graph shows a shape similar to the demand graph. In this way, summer months, are the most demanded months and, therefore, the most expensive. However, the prices are very different in the three cities and vary differently: while in Stockholm the average price varies slightly, Barcelona and Rio change to a greater extent throughout the year. Moreover, Fridays and Saturdays are more expensive than the other days of the weeks. However, on average, this variation is not very pronounced.

The occupancy rates of the city have been studied. In general, the occupancy rate of Stockholm seems to be higher than the rest of the cities, due to its lower number of listings for each visitor. Moreover, all three cities show peaks of occupancy when certain events or festivities take place. All of them show a peak at Christmas, during the last week of December and the first week of January.

User Review Mining Finally, Airbnb´s customer feedback has been studied by applying text mining to the reviews and creating word clouds. In all three cities, the most common words are those related to Airbnb property location, availability of transportation, the quality and appearance of the properties, and aspects related to the host.

To analyze customer opinions in more depth, it has been studied what customers find comfortable and uncomfortable. Most of the things that appeared in each word cloud were common in the three cities. So generally, Airbnb customers find annoying things related to lack of space, temperature issues, difficulty finding the apartment or contacting the host, uncomfortable mattresses, noisy areas, and dark and awkward apartments. On the other hand, guests find the following aspects comfortable: spacious apartments; pleasant beds; nice, equipped, modern, and stylish apartments; good location and cleanliness of the listings.

Future Work In this section, some recommendations for future research are presented:

• It would be interesting to research to determine if the presence of Airbnb in a city has an impact on rental prices so that a rise in Airbnb properties in a neighbourhood is associated with an increase in rents in the same area. According to Airbnb, their presence brings money

75 to cities and spread extra income to local neighbourhoods that are not always in traditional tourist areas. However, critics argue that home-sharing platforms raise the cost of living for local renters. This could be because by facilitating short-term rentals, some landlords prefer to switch their properties from long-term rentals (aimed at residents), to short-term rentals (aimed at visitors), increasing rental rates over time.

This has happened in cities like Barcelona, causing complaints and protests from locals, many of whom have moved from downtown tourist areas to live on the outskirts of the city, turning some central zones into ghost areas, mainly occupied by tourists. It would also be interesting to analyze this situation in the Swedish regulated market, in which, in long-term rentals, the amount of rent that the owner can charge is regulated, while the short-rental market does not apply this condition. Therefore, landlords can get a higher amount of renting per night instead of the traditional way of renting the apartment per month.

• The second proposal is to investigate whether regulating the presence of Airbnb would improve its situation in three main aspects:

– Airbnb customers want to enjoy Airbnb experience without the worry of doing something illegal or causing problems for local governments (like in Barcelona), so reaching an agreement with local authorities would facilitate their presence and improve the image of Airbnb. – Secondly, the Airbnb experience involves ”living like a local” which means enjoying the local boroughs and, if possible, getting to know the neighbors, who can give advice and recommendations to guests. For this to be possible, Airbnb must be well received by the neighbourhood community and this can only be achievable if there are rules regarding the behavior of Airbnb customers to facilitate coexistence in buildings or in the areas where the properties are located. Having regulations established by Airbnb in cooperation with the local government would facilitate coexistence and please the neighbors, who in many cases complain about the noise and misbehavior of tourists who stay in Airbnb listings. – Thirdly, renting an Airbnb property has become an activity that generates income, which can be seen as a business opportunity. Therefore, aspects such as taxes and regulation of the presence of apartments should be considered, so that this platform is a fair competition for the hospitality sector and to avoid the overcrowding of Airbnb apartments and the consequent increase in rents.

All of these reasons explain why, regulating the presence of Airbnb, taking into account the multiple stakeholders affected by the platform and the context of each city, would be beneficial for everyone. In this scenario, it would be interesting to investigate further the existing regulations on these matters and propose concrete regulations for Stockholm, Barcelona, and Rio de Janeiro.

• Taking into account the current situation of Coronavirus that the whole world is living, it would be interesting to study the impacts that these types of global catastrophes have on the Airbnb rental market.

Bibliography

[1] D. Guttentag. Airbnb: disruptive innovation and the rise of an informal tourism accommodation sector. Current Issues in Tourism, 18(12):1192–1217, 2015.

[2] N. Gurran and P. Phibbs. When tourists move in: How should urban planners respond to airbnb? Journal of the American Planning Association, 83(1):80–92, 2017.

[3] Airbnb. Airbnb webpage. https://news.airbnb.com/, 2020. [Online; downloaded 1-May-2020].

[4] G. Zervas, D. Proserpio, and J.W. Byers. The rise of the sharing economy: Estimating the impact of airbnb on the hotel industry. Journal of Marketing Research, 54(5):687–705, 2017.

[5] J. Oskam and A. Boswijk. Airbnb: the future of networked hospitality businesses. Journal of Tourism Futures, 2(1):22–42, 2016.

[6] D. Guttentag, S. Smith, L. Potwarka, and M. Havitz. Why tourists choose airbnb: A motivation-based segmentation study. Journal of Travel Research, 57(3):342–359, 2018.

[7] Caitlin Huston. As airbnb grows, hotel prices expected to drop. Market watch, August 2015.

[8] Arup Varma, Nenad Jukic, Almir Pestek, Clifford J Shultz, and Svetlozar Nestorov. Airbnb: Exciting innovation or passing fad? Tourism Management Perspectives, 20:228–237, 2016.

[9] City population. http://www.citypopulation.de/. [Online; accessed 20-Jan-2020].

[10] Thomas Hall. Stockholm: the making of a metropolis. Routledge, 2008.

[11] Maddy Savage. The city with 20 year waiting lists for rental homes. BBC Magazine, May 2016.

[12] Ben Schiller. How stockholm is creating a second silicon valley in scandinavia. Fast Company, 22th June 2016.

[13] Wharton; University of Pennsylvania. How stockholm became a unicorn factory. Wallenstam AB, November 2015.

[14] The housing market in sweden. Wallenstam AB, April 2018.

[15] Tom Turula. The official way to get an apartment in stockholm is to put your name onalist and wait 10 years. Business insider, 17th August 2017.

[16] Eurostat: European Commission. Over half of sweden’s households made up of one person. https://ec.europa.eu/, 5th September 2017. [Online; downloaded 16-January-2020].

[17] Be resident. Rent in sweden. https://www.beresidentsweden.com/. [Online; downloaded 15-January-2020].

[18] Routes north: Scandinavia travel guide. Is airbnb legal in sweden? https://www.routesnorth. com/. [Online; downloaded 2-February-2020].

[19] Tarek G. Honein. The Airbnb hosting experience: Lessons from Stockholm. Mid Sweden University, June 2017.

78 [20] Álex Maroño. Barcelona, the city of the future. https://elordenmundial.com/, July 2018. [Online; downloaded 27-April-2020].

[21] Mohorte. 2000 years of barcelona history, told on a fantastic interactive map. https://magnet. xataka.com/, November 2016. [Online; downloaded 27-April-2020].

[22] David Roberts. Barcelona’s remarkable history of rebirth and transformation. https://www. vox.com/, April 2019. [Online; downloaded 27-April-2020].

[23] Rebecca Mead. The airbnb invasion of barcelona. New Yorker magazine, 22th April 2019.

[24] Barcelona city council. Department of Economy Business and Employment. The tourism sector in barcelona. bcn.cat/barcelonagrowth, October 2014.

[25] Barcelona will promote montjuïc, collserola or poblenou as new poles of tourist attraction. 20 minutos magazine, 2020.

[26] Grupo Adriano. Spain and business tourism. https://grupoadriano.com/, 2019. [Online; downloaded 28-April-2020].

[27] Homa Khaleeli. From barcelona to malia: how brits on holiday have made themselves unwelcome. The guardian, 17th January 2017.

[28] Lluis Pellicer. Catalonia fines airbnb €30,000 for offering illegal tourist home rentals. El país, 7th Jul 2014.

[29] Mail online. Barcelona to fine airbnb and rival homeaway 600,000 euros each for ’unauthorised adverts’ for lodgings. https://www.dailymail.co.uk/, November 2016. [Online; downloaded 28-April-2020].

[30] El independiente. Colau’s ultimatum to airbnb and its rivals: it gives them a month and a half to announce only legal properties. El independiente, 2020 February.

[31] Professor Camilo Salazar. Urban theories: Rio de janeiro. architecture. Universidad Los Andes. Facultad de Arquitectura y Diseño, February 2012.

[32] Juan Cristobal Donoso. Rio de janeiro: The union between the grid and the vernacular curve. laciudadenlahistoria, November 2013.

[33] Ricard Frigola. The current urban reality of rio de janeiro: light and dark sides of the olympic legacy. ie University, February, 2018.

[34] Sophie Foggin. After effects of 2016 olympic games continue to impact upon rio de janeiro’s tourism industry. Brazil reports, August 2018.

[35] Mitra Sorrells. Latin america, part 4: Challenges and opportunities in travel distribution. PhocusWire, May 2019.

[36] Claudio M Rocha and Janet S Fink. Attitudes toward attending the 2016 olympic games and visiting brazil after the games. Tourism Management Perspectives, 22:17–26, 2017.

[37] Tomas Haupt. Carnival rio de janeiro 2019: more tourism revenue than expected. Tourism Review, ”Respected voice of tourism”, March 18, 2019.

[38] Lonely planet. Top events rio de janeiro. https://www.lonelyplanet.com/, 2020. [Online; accessed 2-April-2020].

[39] Greatorex A. Quercia D. et al. Quattrone, G. Analyzing and predicting the spatial penetration of airbnb in u.s. cities. EPJ Data Sci. 7, 31 (2018).

79 [40] Giovanni Quattrone, Davide Proserpio, Daniele Quercia, Licia Capra, and Mirco Musolesi. Who benefits from the “sharing” economy of airbnb? In Proceedings of the 25th International Conference on World Wide Web, page 1385–1394, Republic and Canton of Geneva, CHE, 2016. International World Wide Web Conferences Steering Committee.

[41] Wikipedia contributors. List of cities by international visitors. Wikipedia, the free encyclopedia. https://en.wikipedia.org/, 2020. [Online; accessed 30-March-2020].

[42] Sweden National Operations Department, Intelligence Unit. Stockholm. Criminal impact in the local community: A state of play for developments in vulnerable areas. July 2019.

[43] The local. These are the most expensive streets in sweden. The Local - Sweden’s News in English, 2020.

[44] Barcelona Turisme. Visit barcelona. https://www.barcelonaturisme.com/. [Online; accessed 25-Jan-2020].

[45] Barcelona activa. Barcelona city hall. Barcelona data sheet 2018. Main economic indicators for the Barcelona area. barcelona.cat/business, 2018.

[46] Certicalia. The most expensive neighbourhoods in barcelona. https://www.certicalia.com/, 2019. [Online; accessed 20-April-2020].

[47] Raval residents exonerate ’low cost tourists’ and denounce the gentrification of ciutat vella. La vanguardia, August 2014.

[48] Guy Hedgecoe. Barcelona crime wave tarnishes boom in tourism. BBC News, August 2019.

[49] Rio de Janeiro City population. Population in rio de janeiro. https://www.citypopulation.de/. [Online; accessed 3-Feb-2020].

[50] Wikipedia contributors. Rio de janeiro. Wikipedia, the free encyclopedia. https://en.wikipedia. org/, 2020. [Online; accessed 5-May-2020].

[51] City hallof rio de janeiro. Municipal Secretary of Tourism. Favelas rio de janeiro. Municipal institute of urbanism Pereira Passos IPP, 2019.

[52] CLIMATE-DATA.ORG. Climate data for cities worldwide. https://en.climate-data.org/. [Online; accessed 2-Apr-2020].

80 List of Figures

2.1 Stockholm neighbourhood map...... 6 2.2 Barcelona neighbourhood map...... 7 2.3 Rio de Janeiro neighbourhood map...... 9

3.1 Screenshot of a property from the Airbnb website...... 15 3.2 Representation of missing data...... 16 3.3 Image of the interactive graph of the Airbnb listings of Norrmalm and Östermalm. . 17

4.1 Evolution of Airbnb listings over the years in Stockholm...... 20 4.2 Visualization of the growth of Airbnb listings in Stockholm...... 20 4.3 Principal places of interest in Stockholm...... 21 4.4 Stockholm location score...... 23 4.5 Stockholm prices score...... 24 4.6 Types of listings in Stockholm...... 25 4.7 Map with the occupancy rate by neighbourhood in Stockholm...... 26 4.8 Demand across years in Stockholm...... 27 4.9 Seasonality in demand in 2017, 2018 and 2019 in Stockholm...... 28 4.10 Seasonality in price in Stockholm...... 29 4.11 Box plot of prices by day of the week in Stockholm...... 30 4.12 Occupancy Rate by Month in Stockholm...... 30 4.13 Word cloud of Stockholm reviews...... 31 4.14 Word cloud of Stockholm reviews with words expressing annoyance...... 32 4.15 Word cloud of Stockholm reviews with words expressing comfort...... 32 4.16 Evolution of Airbnb listings over the years in Barcelona...... 34 4.17 Visualization of the growth of Airbnb listings in Barcelona...... 35 4.18 Principal places of interest in Barcelona...... 36 4.19 Barcelona location scores...... 37 4.20 Barcelona prices score...... 38 4.21 Types of listings in Barcelona...... 39 4.22 Map with the occupancy rate by neighbourhood in Barcelona...... 40 4.23 Demand across years in Barcelona...... 41 4.24 Seasonality in demand in 2017, 2018 and 2019 in Barcelona...... 42 4.25 Seasonality in price in Barcelona...... 43 4.26 Box plot of prices by day of the week in Barcelona...... 44 4.27 Occupancy Rate by Month in Barcelona...... 44 4.28 Word cloud of Barcelona reviews...... 45 4.29 Word cloud of Barcelona reviews with words expressing annoyance...... 46 4.30 Word cloud of Barcelona reviews with words expressing comfort...... 46 4.31 Evolution of Airbnb listings over the years in Rio de Janeiro...... 47 4.32 Visualization of the growth of Airbnb listings in Rio de Janeiro...... 48 4.33 Principal places of interest of Rio de Janeiro...... 49 4.34 Rio de Janeiro location score...... 50 4.35 Rio de Janeiro prices score...... 51

81 4.36 Types of listings in Rio de Janeiro...... 52 4.37 Map with the occupancy rate by neighbourhood in Rio de Janeiro...... 53 4.38 Demand across years in Rio de Janeiro...... 54 4.39 Seasonality in demand in 2017, 2018 and 2019 in Rio de Janeiro...... 55 4.40 Seasonality in price in Rio de Janeiro...... 56 4.41 Box plot of prices by day of the week in Rio de Janeiro...... 56 4.42 Occupancy Rate by Month in Rio de Janeiro...... 57 4.43 Word cloud of Rio reviews...... 57 4.44 Word cloud of Rio de Janeiro reviews with words expressing annoyance...... 58 4.45 Word cloud of Rio de Janeiro reviews with words expressing comfort...... 59 4.46 Evolution of the number of Airbnb properties in the three cities...... 61 4.47 Location score by neighbourhood in the three cities...... 63 4.48 Price by neighbourhood in the three cities...... 64 4.49 Types of listings in the three cities...... 65 4.50 Demand across years in the three cities...... 66 4.51 Seasonality in demand in the three cities...... 67 4.52 Prices across the year in the three cities...... 68 4.53 Prices across the week in the three cities...... 69 4.54 Occupancy Rate by Month in the three cities...... 70 4.55 Word cloud of the three cities...... 71 4.56 Word cloud of the most uncomfortable things for Airbnb customers in the three cities. 72 4.57 Word cloud of the most comfortable things for Airbnb customers in the three cities. 72

B.1 Metro map of Stockholm...... II B.2 Metro map of Barcelona...... II B.3 Metro map of Rio de Janeiro...... III

C.1 Climograph of Barcelona ...... IV

E.1 Map of the favelas of Rio de Janeiro ...... VI

F.1 Crime rate Barcelona ...... VII

82 List of Tables

3.1 Dataset summary...... 12

4.1 Number of Airbnb properties by neighbourhood in Stockholm...... 21 4.2 Occupancy rate by neighbourhood in Stockholm...... 26 4.3 Number of Airbnb properties by neighbourhood in Barcelona...... 35 4.4 Occupancy rate by neighbourhood in Barcelona...... 40 4.5 Number of Airbnb properties by neighbourhood in Rio de Janeiro...... 48 4.6 Occupancy rate by area in Rio de Janeiro...... 53

83 Appendix A

Hotels

(a) Stockholm

(b) Barcelona

Google. (s.f.). [Map of the hotels in Stockholm, Barcelona and Rio de Janeiro in Google maps]. Retrieved March 30, 2020, from: https://www.google.com/maps

I Appendix B

Metro Maps

Figure B.1: Metro map of Stockholm. Stockholm T-Bana, Map by mapa-metro.com, Retrieved March 10, 2020, from: https://mapa-metro.com/en/Sweden/Stockholm/Stockholm-Tunnelbana-map.htm

Figure B.2: Metro map of Barcelona. Barcelona metro map, Map by Mapametrobarcelona.com, Retrieved March 10, 2020, from: https://www.mapametrobarcelona.com/en-index.php

II Figure B.3: Metro map of Rio de Janeiro. Map of Rio de Janeiro Subway Map, tube and underground MetroRio network Retrieved March 10, 2020, from: https://riomap360.com/rio-de-janeiro-metro-map

III Appendix C

Climographs

(a) Stockholm (b) Barcelona

Figure C.1: Climograph of Barcelona Climate-data.org. Stockholm, Barcelona and Rio de Janeiro climate. Retrieved March 30, 2020, from: https://en.climate-data.org/

IV Appendix D

Neighbourhoods of Rio de Janeiro

Area Neighbourhoods Bairro Imperial de São Cristóvão, Benfica Caju, Catumbi, Centro, Cidade Nova, Estácio, Gamboa, Lapa, Centro Histórico e Zona Portuária Mangueira, Paquetá, Rio Comprido, Santa Teresa, Santo Cristo, Saúde and Vasco da Gama Botafogo, Catete, Copacabana, Cosme Velho, Flamengo, Gávea, Glória, Humaitá, Ipanema, Jardim Botânico, Zona Sul Lagoa, Laranjeiras, Leblon, Leme, São Conrado, Urca, Vidigal Anil, Barra da Tijuca, Camorim, Cidade de Deus, Curicica, Freguesia de Jacarepaguá, Gardênia Azul, Barra da Tijuca e Baixada de Grumari, Itanhangá, Jacarepaguá, Joá, Praça Seca, Jacarepaguá Pechincha, Recreio dos Bandeirantes, Tanque, Taquara, Vargem Grande, Vargem Pequena, Vila Valqueire, Jardim Sulacap Bangu, Campo dos Afonsos, Deodoro, Gericinó, Grande Bangu Magalhães Bastos, Padre Miguel, Realengo, Santíssimo, Senador Camará, Vila Kennedy, Vila Militar Barra de Guaratiba, Campo Grande, Cosmos, Guaratiba, Zona Oeste Inhoaíba, Paciência, Pedra de Guaratiba, Santa Cruz, Senador Vasconcelos, Sepetiba Alto da Boa Vista, Andaraí, Grajaú, Maracanã, Praça da Grande Tijuca Bandeira, Tijuca, Vila Isabel Abolição, Água Santa, Cachambi, Del Castilho, Encantado, Engenho de Dentro, Engenho Novo, Higienópolis, Jacaré, Jacarezinho, Lins de Vasconcelos, Grande Méier Manguinhos, Maria da Graça, Méier, Piedade, Pilares, Riachuelo, Rocha, Sampaio, São Francisco Xavier, Todos os Santos Bonsucesso, Bancários, Cacuia, Cidade Universitária, Cocotá, Freguesia, Galeão, Jardim Carioca, Jardim Ilha do Governador e Zona da Guanabara, Maré, Moneró, Olaria, Pitangueiras, Leopoldina Portuguesa, Praia da Bandeira, Ramos, Ribeira, Tauá, Zumbi Acari, Anchieta, Barros Filho, Bento Ribeiro, Brás de Pina, Campinho, Cavalcanti, Cascadura, Coelho Neto, Colégio, Complexo do Alemão, Cordovil, Costa Barros, Engenheiro Leal, Engenho da Rainha, Guadalupe, Honório Gurgel, Inhaúma, Irajá, Jardim América, Zona Norte Madureira, Marechal Hermes, Oswaldo Cruz, Parada de Lucas, Parque Anchieta, Parque Colúmbia, Pavuna, Penha, Penha Circular, Quintino Bocaiuva, Ricardo de Albuquerque, Rocha Miranda, Tomás Coelho, Turiaçu, Vaz Lobo, Vicente de Carvalho, Vigário Geral, Vila da Penha, Vila Kosmos, Vista Alegre

V Appendix E

Map of the Favelas of Rio de Janeiro

Figure E.1: Map of the favelas of Rio de Janeiro Map of Rio de Janeiro favelas, Map by Adam Towle for LSE Cities Retrieved March 27, 2020, from https://pt.map-of-rio-de-janeiro.com/

VI Appendix F

Crime Map of Barcelona

Figure F.1: Crime rate Barcelona Barcelona Field Studies Centre, Crime in Barcelona’s Old Town Ciutat Vella and El Raval 2016 Retrieved March 27, 2020, from https://geographyfieldwork.com/ElRavalCrime2016.htm

VII

TRITA -ITM-EX 2020:169