This master's thesis is about the success of sharing economy in the accommodation segment in Prague. The thesis is based on theories conceptualizing sharing economy as a result of social and value change, not only as technological one. Using online review data, the user experience of shared accommodation via Airbnb and traditional via Booking are compared. Analysis is conducted with focus on users' satisfied needs and fulfilled values. For processing the data, text mining techniques (topic modelling and sentiment analysis) were employed. The major result is that in Prague the models of sharing economy accommodation meets the growing need in society to fulfil post-material values in the market much better than the models of traditional accommodation (hotels, hostels, boarding houses). In their experiences, Airbnb users reflect social and emotional values more often, even though most sharing economy accommodations in Prague do not involve any physical sharing with the host. The thesis thus brings a unique perspective on the Airbnb phenomenon in the Czech context and contributes to the discussion of why the market share of the sharing economy in the accommodation segment in Prague has been growing, while traditional models stagnated.

La presente investigación tuvo como objetivo analizar el impacto que generó el ingreso de Airbnb al mercado limeño en el sector hotelero a través de las 3 métricas más importantes para la evaluación económica de dicho sector como son los ratios de Tarifa Promedio Diaria (ADR), Tasa de Ocupación (OCC), Rentabilidad por Habitación Disponible (RevPAR). Esta investigación mixta se realizó con fines informativos y con miras a brindar un sustento válido a la industria hotelera para que pueda revisar el comportamiento de Airbnb y viceversa. Para la realización de la investigación, el primer paso fue la búsqueda, clasificación y análisis de las principales teorías de diferentes autores y temas relacionadas con nuestro tema. Seguidamente se realizó un análisis cuantitativo con la recopilación de data de la industria hotelera, de la oferta de Airbnb y de las variables de control para potenciar el modelo. Se determinó que la mejor manera de comprobar nuestra hipótesis de si el mercado hotelero fue afectado por el ingreso de Airbnb en Lima, es realizando una lectura conjunta del resultado de como la oferta de Airbnb afecta individualmente a cada uno de los ratios antes mencionados. De esta manera, complementamos la lectura del análisis cuantitativo con entrevistas realizadas a dos personas encargadas de tomar decisiones en el sector hotelero. La conclusión del análisis mixto concluyó que la hipótesis general se rechaza, es decir, que Airbnb no tiene un efecto negativo significante en el mercado hotelero. / The objective of this research was to analyze the impact generated by the Airbnb’s entry into the Lima market in the hotel sector through the 3 most important metrics for the economic evaluation in the sector, such as the Average Daily Rate (ADR), Rate Occupancy (OCC), Profitability per Available Room (RevPAR). This mixed research was made for informational purposes and for providing valid support to the hotel industry so that it can review Airbnb's behavior and reverse. To carry out the research, the first step was the search, classification and analysis of the main theories of different authors and topics related to our topic. Then, we made the quantitative analysis with the researched data from the hotel industry, Airbnb's offer and control variables to enhance the model. It was determined that the best way to check our hypothesis of whether the hotel market was affected by the entry of Airbnb in Lima, making a joint reading of the result of how the Airbnb offer individually affects each of the aforementioned ratios. In this way, it complements the reading of the quantitative analysis with interviews with two decision-makers in the hotel sector. The conclusion of the mixed analysis concluded that the general hypothesis is rejected, that is, that Airbnb does not have a significant negative effect on the hotel market. / Tesis

Three essays in this dissertation explore the behavior of individuals in response to the housing crisis and its consequences, and the impact of the pandemic on the short-term rental markets. The first essay examines the economic outcomes of young people who have returned to their parents' home, using data from 2003-2017 waves of the National Longitudinal Survey of Youth 1997 Cohort (NLSY 97). The economic outcomes of boomerang movers did not improve compared to the period of independent living, and the income gap with young people who remained independent widened. The residential movement of young people who make boomerang moves has an impact on their income, but this effect is short-lived. Going back to a parental house changes the region and urban form significantly, and movement of urban form from the central city to the suburban and from the suburban to out of the MSA has a negative impact on income. Findings from the study suggest implications. First, more affordable housing should be provided to reduce boomerang moves. Second, ways to increase job opportunities should be explored to reduce the short-term negative impact of boomerang move. Finally, education and vocational training opportunities must be increased to close the income gap among young people. The second essay seeks to answer the following questions through the experiences of individual households due to the foreclosure. First, did foreclosed households regain homeownership? Second, is there a relationship between socio-demographic characteristics of foreclosed household and regaining homeownership? Third, where do homeowners who have lost their homes migrate? Finally, what characteristics of the neighborhood help foreclosed households recover? While previous studies have focused on the resilience of housing markets and regions, this study explores the link between regional characteristics and individual household recovery. The recovery of financially disadvantaged households is an important issue for communities and states. Identifying the mechanism that is responsible for household recovery has implications for implementing programs to aid household recovery. This study primarily relies on the 2005 -2019 Panel Study of Income Dynamics (PSID). Since 2009, PSID has added survey questions about foreclosure; Whether a foreclosure process has begun, the year and month of the start, the result of the process, and whether a foreclosed home is a primary residence. The findings of this study suggest that the government's recovery assistance program should aim to support relocation to areas with lower poverty rates and higher job and educational opportunities. The final essay explores changes in short-term rentals resulting from the COVID-19 pandemic. To identify the impact of the COVID-19 pandemic, this study uses New York City's Airbnb listing data from Inside Airbnb (IA), as well as supplemental data such as American Community Survey (ACS) data. Change in the number of STRs is divided into (1) the number of units left the platform and (2) the number of new units. The former relates to the survival of existing STR units and, the latter to the location choice of new units. The results show that the impact of several variables on survival and generation mechanisms changed since the COVID-19 pandemic. Since the survival mechanism and the generation mechanism of short-term rentals are different, they should be considered separately in regulating the STR to stabilize local housing markets. / Doctor of Philosophy / Although research has been conducted on the housing crisis and recovery of the housing market, there are still unanswered questions from two aspects. First, have the individuals affected by the crisis recovered? Were the individual decisions in response to the crisis effective? Second, how has the new crisis caused by the COVID-19 pandemic impacted the housing market? Are different characteristics observed from previous housing crises? While the evidence is reported that the relationship between the new crisis and housing demand has changed, the impact of the pandemic on contemporary housing crises such as gentrification and reduced housing stock is unknown. This dissertation explores the trajectories of individual behavior in the housing market, using various data sources and methodologies. Of the three essays in this dissertation, the first two essays explore the behavior of individuals in response to the housing crisis and its consequences, and the final essay explores the impact of the pandemic on the short-term rental markets. The first essay investigates the economic outcomes of young people who return to their parental homes after periods of independent living using NLSY97 data. The second essay investigates the relationship between neighborhoods and the economic recovery of households using Panel Study of Income Dynamics. The third essay explores changes in the survival and generation mechanism of Airbnb units associated with the COVID-19 pandemic using New York City's Airbnb listing data. The results of each study commonly lead to the conclusion that housing affordability should be improved. It also suggests that more affordable housing should be provided in areas of greater opportunities. This dissertation ultimately contributes to identifying individuals at risk from external shocks and suggesting goals and strategies for a healthy housing market.

In an age where the sharing economy has proliferated as a preferred means of travel in the tourism industry, and the accommodation sharing platform Airbnb has risen to the forefront, there is much criticism and discussion about the need for such nascent platforms to operate in alignment with sustainable development. Currently, economic benefits for the host and guest lie at the core of Airbnb’s sustainability appeal, while few concrete steps have been taken to advance environmental and social values. Many have proposed a green rating system and sustainability-oriented search filters as a means to propagate these values and catalyze a necessary paradigm shift within the sharing economy. Through the lens of green architecture and construction, this study analyzes the extent and manner in which sustainability features and amenities are promoted by hosts on Airbnb in the high-desert mountain town of Taos, New Mexico. This case study approach selects and intriguing destination that is not only characterized by a long history of earthen building traditions by the Tiwa people, but was also the birthplace of the world-renowned, off-the-grid Earthship concept. An analysis of all active Airbnb listings was compared with a more targeted analysis of off-the-grid listings to reveal that hosts more often than not frame their sustainability features and amenities in terms of visitor comfort, convenience, and enjoyment. For instance, the valorization of earthen adobe building for its authenticity and cultural appeal in lieu of its energy efficient and natural qualities. This indicated a high level of unexploited potential, wherein hosts could enhance their listing’s sustainability appeal and educational value through reframing these features to potential guests, and off-the-grid listings could benefit from implementing and promoting sustainable practices and emphasizing the local culture. Most importantly, after quantitively analyzing the features that arose, this study assembled the content basis for a theoretical green rating system and sustainability search filters that could be applied to Taos as a localized system, or merely provide insight to other destinations and the Airbnb platform as a whole.

This thesis charts the increasing volume and changing nature of tourism in Europe. It was allowed by the liberalization of air travel market (the emergence of Low-Cost Carriers), and by the emergence of sharing economy (Airbnb). Followed by these changes a new segment of tourists, who were described as independent travelers, emerged. The thesis focuses on the interaction of those three factors and their effect on urban space - thus on its socio- geographic, socio-economic and socio-cultural fabric. Because of Low-Cost Carriers, tourists and travelers are concentrated in several European cities - those which were able to accommodate its environment for these airlines. In the cities, tourism spread from the concentric zones of the city center to the zones of the inner city, where a new tourist industry was adjusted for independent travelers. This touristification deepens the process of gentrification and spatial inequalities. Because of Airbnb, the limited hotel supply in the city center was surpassed, and the accommodation sector was integrated into residential fabric of the inner city. Those touristified spaces of the city become socio-culturally heterogenic. However, with increasing costs of living in the inner city, this space is more and more socio- economically homogenous. Consequently,...

This master thesis is a case study that deals with the concept of multilevel governance and its applicability in the regulation of the modern trend in shared economy - the digital platform Airbnb. The base resource for the study is the territory of the capital city of Prague, where this phenomenon has a massive impact on the problems in legislative issues. The political debate on this agenda fills the headlines of media news and tends to lead to major changes or mobilization of the electorate in the upcoming years. The enormous rise of developing this service has put politics at all levels of government ahead of serious legislative and political challenges. Application of the Airbnb service example on our theory forms the core of the empirical part of the research. The aim of this thesis is to examine the importance and role of each individual level based on the concept of multilevel governance in relation to the regulation of the Airbnb phenomenon. The main part of the thesis is devoted for processing semi-structured interviews with the main representatives of each individual level, media monitoring and analysis of available resources. Based on research is obvious that the basic postulates of the multi-level governance approach can be considered as valid because they are displayed at all selected...

Esta tesis doctoral tiene como fin realizar una revisión de los principios económicos del turismo desde una perspectiva de la economía de la innovación, analizar el potencial impacto de la aplicación de IA en la industria turística a todos los niveles, y el estudio del mercado turístico más disruptivo de las últimas décadas: la economía de plataforma, ejemplificada en el caso de estudio de Airbnb. En este Capítulo I se establece el hilo conductor de los apartados de los que consta esta tesis en formato compendio, inspirada en diversos trabajos, entre los que se incluyen los publicados por el doctorando en esta etapa predoctoral. Para ello se presenta el diseño de la investigación, explicando detalladamente todo el proceso realizado para lograr el planteamiento de la tesis y la consecución de los objetivos y se dedica un breve apartado para presentar las principales conclusiones de la tesis. El Capítulo II de esta investigación está dedicado a la revisión de la evolución del concepto de innovación y su importancia en la teoría económica. Para ello nos basaremos en referentes teóricos que han estudiado el papel de la tecnología y la innovación en el crecimiento económico, como Schumpeter, Solow, Romer o Lucas. Con ello se pretende comprender el impacto que están teniendo los cambios disruptivos que vivimos en la economía, para posteriormente aplicarlos a la transformación de la estructura de la industria turística. En el Capítulo III se realiza un análisis aplicado de la innovación y del impacto de las nuevas tecnologías en el sector turístico. En él se estudiará el estado de la innovación del sector, realizando importantes aclaraciones sobre la capacidad que tiene la industria para adaptar o desarrollar tecnologías disruptivas. Además, se explicarán los principios digitales que están transformando la industria turística y el nuevo ciclo de investigación derivado de la aparición del Big Data y que está protagonizado por técnicas basadas en algoritmos de Machine Learning, justificando así la elección del sector turístico como caso de estudio. En el Capítulo IV se realiza una revisión completa del proceso transformador que está viviendo la estructura de la industria turística debido al cambio de paradigma tecnológico. Así, se estudia cómo estos procesos innovadores están desarrollando una nueva demanda turística basada en los datos, cómo se está reinventando la cadena de valor turística, cómo se fijan los precios turísticos en un mercado con información casi perfecta, qué retos supone para el mercado laboral y formativo del sector, y qué papel juegan en el surgimiento de nuevos competidores de base tecnológica en el sector. En los Capítulos V y VI se escoge como caso de estudio aplicado el mercado alojativo, utilizando la información de Airbnb. Sin duda, esta empresa representa muchos de los desafíos a los que se enfrenta el sector en cuestiones tecnológicas, de regulación política, intervención de mercado, reinterpretación de la cadena de valor turística, aparición de shocks económicos o pandémicos a los que se deben enfrentar los investigadores. El Capítulo V tiene como objeto de análisis la ciudad de Madrid, cuarto destino por número de anuncios de Airbnb en Europa. Para este caso aplicado se estudia si la pandemia de la COVID-19 tuvo un impacto significativo en la estructura de la oferta y de la demanda de Airbnb. Para ello, el estudio parte de un modelo logit de datos de panel hedónicos, se aplican diferentes métodos alternativos de selección de variables y pruebas de verosimilitud para confirmar la existencia del cambio estructural que afecte a la toma de decisiones a la hora de alquilar un apartamento de la Plataforma. El Capítulo VI centra el estudio en la Comunidad Valenciana, uno de los principales destinos turísticos de sol y playa, para realizar un análisis sobre la fijación de precios del alojamiento turístico en la plataforma. Este caso de estudio tiene por objetivo analizar si la aplicación de algoritmos de ML permite a las empresas optimizar precios de una manera más eficiente que modelos tradicionales. Para ello, se enfrenta el rendimiento de un modelo de precios hedónicos tradicional frente a un modelo de estimación basado en redes neuronales, comprobándose el mejor ajuste en la capacidad predictiva de las técnicas basadas en machine learning a la hora de fijar precios. De este modo la tesis doctoral constituye una valiosa y novedosa aportación al nuevo ciclo de investigación del sector. Propone una exhaustiva revisión de todas las implicaciones y las aplicaciones que tienen las nuevas tecnologías en el turismo y de las ventajas del uso de técnicas de análisis basadas machine learning para los investigadores en su estudio.

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