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Property and labor formalization in the age of the sharing economy: Airbnb, housing affordability, and entrepreneurship in Havana

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Property and labor formalization in the age of the sharing economy: Airbnb, housing affordability, and entrepreneurship in Havana

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Property and Labor Formalization in the Age of the Sharing
Economy
Airbnb, Housing Affordability, and Entrepreneurship in Havana

Raúl Santiago-Bartolomei

A dissertation presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA

In partial fulfillment of the requirements for the degree of
Doctor of Philosophy
Urban Planning & Development
December 2019

Dissertation Committee:
Marlon G. Boarnet, Ph.D.
Annette M. Kim, Ph.D.
Nicole E. Esparza, Ph.D.
Jorge De la Roca, Ph.D.

© Raúl Santiago-Bartolomei. All Rights Reserved.
ii

Acknowledgements

This dissertation is the result of the help, influence, mentorship, solidarity, and love of many
people that have shaped my formation and perspectives. Completing this manuscript required
overcoming multiple obstacles, many of which consisted of creative hurdles, but also economic
hardships, strained relationships, and even hurricanes. Each one proved to be a necessary step to
reach new levels in my professional and personal development, but each one was excelled with
the help of many invaluable people, many of which should be acknowledged in their own
language.
Primero que todo, quisiera expresar mi eterno agradecimiento a quién es mi mejor amiga
y compañera de vida, mi esposa Claudette M. Esparra Ramos. Claudia, siempre me has
acompañado en todo este proceso a través de tempestades (literalmente), un sinnúmero de
situaciones difíciles y, ahora, en esta etapa de paternidad. Te amo por todo lo que eres y por lo
que motivas ser. Las veredas las creamos andando juntos.
También agradezco a las luces de mi vida, mis hijos Alejandro y Mateo, quienes nacieron
en el transcurso de mi doctorado. Son mi principal motivación para todo lo que hago y todo lo
que quiero ser. Pero más que eso, me han dado el gran privilegio de ser parte del crecimiento y
formación de lo que serán, sin duda, dos seres excepcionales.
Mis queridos padres, Raúl Santiago Meléndez y Zoraida Bartolomei Guzmán, quienes
son una fuente incondicional e inagotable de amor, sabiduría, comprensión y apoyo. A mis
hermanos Livia Santiago Rosado, Laura Beatriz Santiago Bartolomei y Luis Manuel Santiago
Bartolomei por su entrañable amor, apoyo y humor en los momentos precisos. También recibí
apoyo y amor incondicional por parte de mi abuela Zoraida Guzmán, mis tías Marilú, Celeste,
iii
Jessica y Tati y mi tío Pedro, al igual que mis primos Natalia Robles, Celeste Robles y David
Bartolomei.
A mis suegros, Olga Ramos Cosme y Ricardo Esparra Caratini, les agradezco su ayuda,
apoyo, paciencia, y amor en todos estos años. De igual manera, no se me pueden quedar mis
cuñados Ricardo (Bebo) Esparra Ramos y Gabriel Esparra Aponte. Y por ultimo, no puedo dejar
sin mencionar la ayuda y consejos del gran Epifanio Esparra (que en paz descanse), la gran Olga
Cosme e Irma (Yiyi) Ramos.
Por otro lado, este trabajo no hubiese sido posible sin la ayuda y amor de quienes se han
convertido en mi familia extendida en La Habana: Marisel Cateura y su compañero Pablo,
Fernando Cateura, Yuliet Carbonell Cateura y José Carbonell Cateura. Igualmente quedo en
deuda con Alejandro Moya y Ricardo Hernández por toda su ayuda durante mis visitas.
This dissertation would not have come into fruition without the dedicated mentorship,
support, and patience from my advisor, Marlon G. Boarnet. In the face of creative stagnation,
academic crisis, and even post-disaster hardships after Hurricane María, you have always
provided a guiding hand in helping me maintain my focus, sense of purpose, and clarity of mind
in approaching my work, as well as helping me find the scholarships and grants that helped fund
this work. Your dedication and pedagogical approach have become the standard of what I aspire
to as a scholar and teacher.
I also want to express my deepest gratitude to my dissertation committee for their help
and support, even in the worst of times: Annette M. Kim (whose classes, teaching style and work
have been highly influential and formative for me), Nicole E. Esparza (thank you for taking the
time in helping me define myself as a scholar in those reading sessions), and Jorge de la Roca
iv
(gracias por tu disposición y apoyo incondicional). I also would like to thank those professors
who have also played an essential role in my development as a scholar (and as a person): Carmen
M. Concepción (Millie), Luis E. Santiago Acevedo, Raphael Bostic, Tridib Banerjee, Jeff
Nugent, Lisa Schweitzer, David Sloane, Manuel Castells, and Peter Monge. Special thanks to
Jorge Duany at the Cuban Research Institute at Florida International University, whose advise
was essential for the research design and fieldwork in Havana.
Thank you very much to Chris Wilson and Julie Kim at the Price School for all their help
during these years. I would not had made it to finish line without you two.
These years spent in the doctorate and in Los Angeles would have not been fully
enjoyable and timeless without the friendship and support from my fellow Ph.D. cohorts: Seva
Rodnyansky, Nathan Hutson, Soledad De Gregorio, Breanna Hawkins, Soyoon Cho, Hue-Tam
Jamme, Maria Francesca Piazzoni, Julia Harten, Madison Swayne, Anthony Orlando, Brian An,
Jung Ho Park, Robert Jackman, Carmen Mooradian, Zeewan Lee, Linna Zhu, Danielle Williams,
Gene Burinsky, Johanna Thunel, Lee White, and Heejin Cho.
Many thanks to my newfound family in the Center for a New Economy, especially
Deepak Lamba-Nieves and Mike Soto, by giving me the opportunity for being engaged in
research and work that lies deep within my interests and for all the support top finish this
dissertation.
Finally, this research was made possible through the funding provided by the Lincoln
Institue of Land Policy and the Lusk Center for Real Estate in USC.

v
Abstract

This dissertation examines how the increasing diffusion of the sharing economy can disrupt
expected policy outcomes from urban formalization in the Global South. Specifically, it shows
how home-sharing can affect expected policy outcomes from property and labor formalization
through three mechanisms: (1) its potential for generating gentrification and displacement
through the rent gap; (2) its effect over labor formalization in the form of self-employed
landlords; (3) its relation with housing affordability after recent property formalization reforms. I
use Havana, Cuba as a case study. Cuba has undergone recent economic reforms that include
property and labor formalization that has coincided with the arrival of home-sharing in the
island. Its example can help inform the nuances and idiosyncrasies of how home-sharing,
because of its inherent technological diffusion and transnationalism, adds additional layers of
complexity to urban landscapes in post-colonial and/or post-socialist contexts.

vi
Table of Contents
I. INTRODUCTION................................................................................................................. 1
Property Regularization Policies ................................................................................................................................ 2
Informality and entrepreneurship .............................................................................................................................. 5
The sharing economy and informal labor .................................................................................................................. 9
The Sharing Economy and Home-Sharing .............................................................................................................. 11
Home-Sharing and Housing Affordability............................................................................................................... 14
Property Regularization and Home-Sharing........................................................................................................... 15
Equity Concerns of Home-Sharing........................................................................................................................... 17
II. ECONOMIC REFORMS IN CUBA AND HOME-SHARING IN HAVANA.............. 20
Reforms in Cuba ........................................................................................................................................................ 21
Setting the Terrain for Home-Sharing in Cuba ...................................................................................................... 25
Havana ........................................................................................................................................................................ 29
Airbnb trends in Havana ........................................................................................................................................... 32
III. HOME-SHARING AND THE RENT GAP IN FORMALIZED PROPERTY
MARKETS IN HAVANA .......................................................................................................... 37
The Rent Gap and Gentrification ............................................................................................................................. 38
Home-Sharing and the Rent Gap ............................................................................................................................. 41
Rent Gap as Displacement of Housing Units ........................................................................................................... 42
Rent Gap as Potential Increase in Capitalized Ground Rent ................................................................................ 43
Rent Gap Through Property Hoarding ................................................................................................................... 48
Conclusion ................................................................................................................................................................... 50
IV. HOME-SHARING AND HOUSING AFFORDABILITY IN HAVANA .................. 53
Home-Sharing and Housing Affordability in Emerging Markets ......................................................................... 54
Hedonic Pricing .......................................................................................................................................................... 55
Results ......................................................................................................................................................................... 67
Rental rates .............................................................................................................................................................. 67
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Home prices ............................................................................................................................................................. 72
Conclusion ................................................................................................................................................................... 77
V. HOME-SHARING, LABOR FORMALIZATION, AND ENTREPRENEURSHIP IN
HAVANA ..................................................................................................................................... 79
Methods ....................................................................................................................................................................... 80
Findings ....................................................................................................................................................................... 83
Who Are the Landlords? ......................................................................................................................................... 83
Regulations, bureaucracy, and startup capital for market entry .............................................................................. 85
Networks and linkages ............................................................................................................................................... 92
Conclusion ................................................................................................................................................................... 98
VI. CONCLUDING REMARKS ....................................................................................... 100
VII. REFERENCES: ............................................................................................................ 105
VIII. APPENDIX 1: LANDLORD/HOME-OWNER INTERVIEW QUESTIONS ........ 117
IX. APPENDIX 2: STAKEHOLDER INTERVIEW QUESTIONS................................... 119

viii
List of Figures
Figure II-1: Non-state employment in Cuba. Source: Anuario Estadístico 2016, Tabla 7.2 - Ocupados en la economía
según situación del empleo, Oficina Nacional de Estadísticas ........................................................................... 23
Figure II-2: Change in population and housing units in Cuba and Havana between 2002 and 2012. Source: Anuario
Estadístico, Indicadores seleccionados del Censo de Población y Viviendas, Oficina Nacional de Estadísticas
............................................................................................................................................................................ 26
Figure II-3: Construction of housing units in Cuba. Source: Anuario Estadístico 2016, Tabla 12.1 - Viviendas
terminadas por provincias, Oficina Nacional de Estadísticas............................................................................. 27
Figure II-4: Location of Havana, Cuba ........................................................................................................................ 30
Figure II-5: Municipalities of Havana.......................................................................................................................... 31
Figure II-6: Monthly number of Airbnb listings in Havana. Seasonally adjusted. Source: AirDNA .......................... 33
Figure II-7: Monthly number of Airbnb reservations in Havana. Seasonally adjusted. Source: AirDNA .................. 34
Figure II-8: Monthly total revenue from Airbnb rentals in Havana. Seasonally adjusted. Source: AirDNA .............. 34
Figure II-9: Heatmap of total revenue per Airbnb listing. Source: AirDNA, OpenStreetMap .................................... 35
Figure III-1: Development of the rent gap through time. Redrawn by author from Smith (1979). ............................. 39
Figure IV-1: Centroids and estimated boundaries of sub-municipal neighborhoods in Havana ................................. 63
Figure V-1: Samples of CUC rental signs in Havana (highlighted in red). Pictures taken by author on June 2017. .. 87
Figure V-2: Samples of CUP rental signs in Havana (highlighted in red). Pictures taken by author on June 2017 ... 88
Figure V-3: Rental registry notebook. Pictures taken by author on June 2017 ........................................................... 89
Figure V-4: Example of housing upgrade by short-term rental landlord in Havana. Left: Homeowner himself, using
tools from his job at a state-owned enterprise, is starting works to install a rubble slab and build a bar for
visitors in his kitchen (picture taken by author on June 2017). Right: Bar at the kitchen after works are finished
(picture taken by author on February 2018) ....................................................................................................... 90
Figure V-5: Screenshots of online classified ads webpages commonly used in Cuba ................................................ 95

ix
List of Tables
Table II-1: Average number of people per housing unit in Cuba and Havana between 2002 and 2012. Source:
Anuario Estadístico, Indicadores seleccionados del Censo de Población y Viviendas, Oficina Nacional de
Estadísticas ......................................................................................................................................................... 26
Table II-2: Population of municipalities in Havana in 2012 ........................................................................................ 32
Table II-3: Breakdown of Airbnb listings in Havana by municipality. Source: AirDNA. .......................................... 36
Table III-1: Potential housing unit displacement by Airbnb, by dwelling type. .......................................................... 43
Table III-2: Breakdown of online rental ads data. ....................................................................................................... 45
Table III-3: Comparison of Airbnb revenue and potential long-term rental revenue by municipality. ....................... 46
Table III-4: Comparison of Airbnb revenue and potential long-term rental revenue for one-bedroom units by
municipality. ....................................................................................................................................................... 47
Table III-5: Comparison of Airbnb revenue and potential long-term rental revenue for two-bedroom units by
municipality. ....................................................................................................................................................... 48
Table III-6: Largest twenty Airbnb hosts in Havana by total number of listings. ....................................................... 49
Table IV-1: Long-term rental listings summary .......................................................................................................... 57
Table IV-2: Home sales listings summary ................................................................................................................... 58
Table IV-3: Summary of Airbnb listings and amenities and services ......................................................................... 60
Table IV-4: Summary of amenities and services in each municipality ....................................................................... 61
Table IV-5: Variable list .............................................................................................................................................. 64
Table IV-6: Variable summary statistics per municipality .......................................................................................... 66
Table IV-7: Results for rental rates, using number of Airbnb listings as independent variable. Standard errors
clustered at the municipal level .......................................................................................................................... 69
Table IV-8: Results for rental rates, using density of Airbnb listings as independent variable. Standard errors
clustered at the municipal level .......................................................................................................................... 71
Table IV-9: Results for housing prices using number of Airbnb listings at the municipal level as independent
variable. Standard errors clustered at the municipal level .................................................................................. 74
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Table IV-10: Results for housing prices using number of Airbnb listings at the sub-municipal level as independent
variable. Standard errors clustered at the municipal level .................................................................................. 75
Table IV-11: Results for housing prices using number of density of Airbnb listings at the municipal level as
independent variable. Standard errors clustered at the municipal level ............................................................. 76

1

I. Introduction
On November 2, 2011, the Government of Cuba enacted a law allowing its citizens to buy, sell,
and rent properties in the island. This law is part of a broader set of reforms implemented in the
island to allow greater economic liberalization among its citizens as a way to cope with
decreasing state capabilities and foster greater entrepreneurship (Domínguez, Villanueva,
Barberia, & Cluster, 2017; Spadoni, 2014). As part of these reforms, the Cuban Government
established an emerging real estate market and the following distinct rental markets: (1) short-
and long-term rental markets for Cuban citizens; and (2) short-term rental markets for tourists
and foreigners. On one hand, this has resulted in an increase in the number of self-employed
Cuban entrepreneurs (cuentapropistas) that rent units to Cuban nationals or foreign tourists
(Carmelo Mesa-Lago, González, Mederos, Rojas, & Liñan, 2016; Ritter & Henken, 2014).
With the advent of greater internet access and digital technologies, Cuba has also been a
site ripe for the arrival of the sharing economy in the form of home-sharing, particularly from
Airbnb. This puts Cuba among the countries in the Global South that has seen an increase in
home-sharing, prompting questions mostly studied in the North on how digital platforms for
short-term rentals can affect both entrepreneurship and housing affordability.
To gain further insight into how these goals could be working in tandem, it is important to
discern how these changes in property rights affect both renters and the new landlords in the form
of cuentapropistas. I examine whether these changes in property rights in Cuba could be achieving
one of these goals to the detriment of the other. More specifically, I will use Havana as a case
study to answer the following questions:
2
1. How does the recent emergence of entrepreneurial activity in Cuba hinder or foster access
to affordable housing for its citizens?
2. How does the underlying institutional context foster or hinder individual landlords
(microentrepreneurs) from entering the housing rental market?
The purpose of this research project is to provide some insight into how overlapping policies
related to property regularization and the formalization of entrepreneurial activity affect each other
in the Havana province in Cuba. This goal will be achieved by:
1. Analyzing the association between the housing market and the emerging entrepreneurial
activities in the form of short and long-term rentals; and
2. Identifying and analyzing the interactions between actors involved in the rental markets
and the underlying institutions and social networks that are relevant for these dynamics.
Property Regularization Policies
Property rights are understood to be essential in reducing transaction costs in economic
exchanges, since they reduce the possibilities of externalities and other market failures that can
distort economies; they also provide the means by which property can be commodified and
exchanged (Barzel, 1997; Coase, 1960; Demsetz, 1968). Once property rights are acknowledged
and codified, they must be enforced by trustful and transparent institutions. Such institutions
allow sustaining fair competition and owning capital that can be converted into inputs for
production (Barzel, 1997; De Soto, 2000).
Property regularization has been advocated as an essential step towards securing social
mobility (Feder & Nishio, 1998). By addressing tenure insecurity or liberalizing land markets,
uncertainty and transaction costs in previously informal property markets are reduced, allowing
3
households to turn their homes into secure and more valuable assets. This would push
households into increasing spending to improve housing quality, provide greater legibility and
security in property markets, and greater economic inclusion.
Indeed, property regularization has long been posited as an almost universal policy
measure for social mobility (De Soto, 2000). It is also an attractive policy measure for
governments because it is perceived to reduce costs in the form of property enforcement,
political costs stemming from unpopular interventions like evictions and slum clearance, and
from targeted interventions to reduce poverty (since this newly created markets would spur
greater social mobility), all while allegedly increasing state revenue from taxation (Brueckner &
Selod, 2009).
In Peru, tenure regularization fostered greater spending on housing upgrading, while, by
reallocating time allotted to home surveillance to prevent displacement, there was also increased
labor participation among households (Field, 2005, 2007; Field & Torero, 2006). In Buenos
Aires, Galiani & Schargrodsky (2010) found that land titles awarded to households in informal
settlements was also related to increased spending on physical upgrading, as well as increased
fertility rates. Likewise, after tracking titling programs in the Colonias in southern Texas, Ward,
de Souza, and Giusti (2004) also confirmed that regularization resulted in physical upgrading
among households that received land titles. What the latter research also found was that property
market transactions and valuation were largely left unchanged. Those results notwithstanding,
each of these studies shared an important finding in common, and that is that land titling did not
spur greater financial access, thus showcasing that the possibilities of economic inclusion
stemming from regularization is likely overstated.
4
Another result from property regularization is the reduction in transaction costs for
entrepreneurship, particularly in the form of market entry, given that the now formalized assets
can be capitalized by their owners (Li & Wu, 2014). One manifestation of this is the newly
created rental markets, where homeowners can become landlords, and, thus, providing
additional, presumably taxable, profits from recent property reforms, while increasing housing
options for potential tenants. Such is the case of rental markets that emerged from regularization
in South Africa, but at the behest of housing quality for tenants (Gilbert, Mabin, & Watson,
1997; Watson & McCarthy, 1998).
Such reforms in property rights, however, can further entrench inequality without
complementary reforms in other areas, such as reforms addressing surface rights, use rights, and
tenant protections (Deininger & Binswanger, 1999; Durand-Lasserve & Selod, 2009; Gilbert,
2012; Payne, Durand-Lasserve, & Rakodi, 2009). Much overlooked when designing property
regularization policies are the spatial distribution of costs and benefits stemming from these
reforms. New homeowners located near geographies of opportunity can reap greater benefits
from homeownership and/or renting their property, thus fostering rental hikes for possible low-
income tenants.
Regularized properties in prime location are also subject to external pressures such as
gentrification and displacement. Informal settlements close to employment centers can
experience such pressures through de-facto regularization, where properties are deemed eligible
for transactions in submarkets that are typically thought of to encompass housing in the formal
realm because of interventions such as infrastructure improvements and increased policing to
improve safety and reduce crime. Many households in favelas in Rio de Janeiro that underwent
infrastructure construction projects and greater police surveillance during preparations for the
5
2010 World Cup and the 2012 Olympics experienced gentrification, evictions and displacement
as landlords wanted to take advantage of the improved conditions and their location to areas of
greater economic opportunity (Pearlman, 2016).
In hindsight, regularization seems to fall short of achieving its promised outcomes. It has
mostly not been able to translate in improved access to credit, decreased poverty, protections
against evictions, expropriations, and other kinds of displacement, while improvements in
infrastructure, service provisions, and housing quality are not as substantial as expected (Gilbert,
2002; Payne et al., 2009). Nonetheless, increased tenure security has been a positive result from
regularization policies and, previously shortcomings notwithstanding, it is still a popular and
widely advocated measure, especially for fiscally-constrained and institutionally-weak
governments where market-based approaches that reduce public interventions are deemed
preferable.
Informality and entrepreneurship
In market economies (i.e. capitalist societies), the informal economy, as a manifestation
of informal institutions, is often the result of the need for cheaper goods and labor that cannot be
provided within state sanctioned economic activities. In contrast, the informal sector in statist
economies responds to an excess in rigidity to the central planning approach, where actors
engage in informal transactions that often parallel economic activity by the State. The result is
that, while informal economic activity is outside state purview in both market and statist
economies, it is complementary of formal economic activity in the former and it undermines the
central planning approach of the latter (Portes & Böröcz, 1988). Thus, informal economic
activity in statist economies will be seen as a threat to the State.
6
Indeed, in transitioning economies, the move towards a market economy necessitates a
shift of power from state mediators that work as redistributors, to the direct producers of goods
and services (Nee, 1992). In both the post-socialist Eastern Europe and the transitioning China,
the economy was geared to be more market based by decentralizing the state and rearranging
organizational structures in economic production to allow flexible arrangements that spur
exchanges of capital and knowledge between actors (Nee, 1992; Stark, 1996). The transitioning
process, however, results in potential conflict between a State that has a historical role in
centralizing decision making and ensuring egalitarianism, and private economic actors that yearn
for greater autonomy to improve economic outcomes (M Castells, 2011). This conflict is evident
in Cuba’s case as well.
For the potential cuentapropistas, formalizing and enforcing property rights might not be
enough to ensure social mobility. On this point, Erica Field’s research on the urban titling
program in Lima, Peru, shows that greater household tenure security in informal communities
can improve social welfare through greater labor participation and increased investment in home
improvements (Field, 2007). In this case, households without titles resorted to informal rules to
secure tenure, but are still kept away from the formal labor market for fear of losing their homes.
What this research shows is that the benefits stemming from the formalization of tenure do not
reach everybody equally and access to jobs, social capital, or financial services remain important
linkages, showing the importance of implementing parallel policies that provide a network of
supportive institutions for households.
In studying such networks, it would be useful to distinguish between “weak” and
“strong” ties between actors (Granovetter, 1977). In economic development, “strong” ties in
networks consist of formal exchanges between actors that tend to be reciprocal and difficult to
7
dissolve, which include formal economic transactions, contracts, and agreements. “Weak” ties
are informal exchanges or links between actors, examples include interlocking board members,
kinship, common membership in organizations of actors in different networks, or face to face
interaction.
While “strong” ties can provide the means to ensure economic sustenance, “weak” ties
allow the possibility to incorporate learning and measures of adaptation when facing external
shocks (Uzzi, 1997). These “weak” ties cause structural holes in networks, that provide the
opportunity for individual actors to exchange information and ideas with actors in different
communities that can result in innovation and learning that can benefit the network in the long
run (Burt, 2004).
Much of these aspects have been incorporated in the international development literature
in the form of social capital. Social capital relates to those structural characteristics in social
networks that allow actors to access resources through their ties in ways that can result in
improved economic outcomes (Coleman, 1988). Closed networks of kinship can allow actors to
access resources and establish ties of mutual support that can improve the possibilities of
developing individual capabilities, like educational attainment or economic security, with the
aggregate effect of improving human capital (Coleman, 1988). Such advantages notwithstanding,
social capital can have detrimental aggregate effects if such tight kinships result in exclusionary
practices or prohibit the exit of individuals that could achieve even greater economic gains
outside the network (Portes, 1998; Portes & Sensenbrenner, 1993).
Webb, Tihanyi, Ireland, and Simon (2009) identify different types of informal
entrepreneurs: (1) those seeking to replace current income; (2) those aspiring to achieve a certain
lifestyle; and (3) those committed to growing their ventures. The authors further state that at the
8
individual level, institutional arrangements allow entrepreneurs to identify and exploit
opportunities in the informal economy. At the institutional level, formal institutions deem
different activities ineligible for legal and legitimate exploitation, economic opportunities
emerge for those entrepreneurs that are willing to operate outside formal boundaries. And
looking at group effects, the authors show how entrepreneurs rely on cooperative groups, which
coalesce through shared identification, enter the informal economy and exploit existing
opportunities.
Webb et al. (2013) lay out a series of factors that determine the degree to which informal
economic activities become attractive: (1) the stringency of policies and rules; (2) the degree in
which a transition to formalization requires incremental changes vis a vis radical changes; (3)
how bureaucratic arrangements facilitate or hinder formalization; (4) the ease by which
avoidance and manipulation can bypass enforcement; (5) ambiguous jurisdiction and conflicting
interests across institutional actors; (6) the existence of informal trade/market associations that
protect entrepreneurs; (7) costs of operating formally; (8) an increasing distrust towards formal
institutions; (9) lack of access to legitimate means; (10) precarious socioeconomic conditions;
(11) the possibilities for leveraging family resources; (12) the possibility of applying resource
allocation strategies to address uncertainty and risk; and (13) increasing availability of
illegitimate means to achieve higher social status.
Research on the social capital of entrepreneurs in newly created markets in the global
south and transitioning economies shows that entries to markets are hindered by “institutional
voids”, or absence of formal actors that link individuals to needed resources, and results in
inequities across gender and income level (Mair & Marti, 2009; Mair, Martí, & Ventresca, 2012;
Puffer, McCarthy, & Boisot, 2010). In these situations, actors depend on informal transactions
9
with other actors that can provide the necessary “bridging” to access information or resources
(Batjargal, 2003).
The sharing economy and informal labor
Cities across the world have experienced a rapid increase in the diffusion of digital platforms that
provide the necessary arrangements for the exchange of goods and services among peers. These
platforms and activities are deemed as the “sharing economy”, “collaborative economy”, or
“collaborative consumption”, and they have altered significantly how people work and
experience their lives in cities.
Platforms that partake in the sharing economy can provide peer-to-peer (P2P), business-
to-business (B2B), or business-to-consumer (B2C) exchange of services and assets for a defined
period of time (Codagnone & Martens, 2016; Schor, 2016). Popular examples include the ride-
hailing company Uber, home-sharing giant Airbnb, and digital labor provider TaskRabbit,
among many others. These digital platforms allow suppliers to exchange or sell their assets or
skills to consumers that are seeking such commodities. The companies that provide these
platforms can also own the assets themselves, but allow P2P exchanges among consumers. As a
result, much of the sharing economy functions as two-sided markets (2SM) or multi-sided
markets (MSM), which are characterized for their network effects in diffusing access, price non-
neutrality, direct interaction among peers, and platform affiliation (Codagnone & Martens, 2016;
Einav, Farronato, & Levin, 2016; Hagiu, 2007).
The popularity of the sharing economy has been exacerbated by the following common
underlying conditions throughout the world (Davies, Donald, Gray, & Knox-Hayes, 2017;
Graham, Hjorth, & Lehdonvirta, 2017; Kovács, Morris, Polese, & Imami, 2017): (1) increased
10
access to the internet and to information and communication technologies; (2) precarious labor
resulting from labor flexibilization, reduced wages and benefits, reduced unionization,
decreasing labor participation, increased unemployment, and/or a sizeable informal workforce;
and (3) a significant stock of undervalued or underutilized assets.
Thus, sharing economy enthusiasts see the proliferation of these platforms as an
opportunity to foster the entrepreneurial spirit of the underemployed (or unemployed) by
providing greater labor autonomy and by providing opportunities for them to capitalize and
monetize their assets. Critics, on the other hand, point out that the sharing economy does not
provide sufficient labor security and stability for workers to overcome precariousness, it
encourages discrimination, and it fails to provide workers with a gateway to escape labor
exploitation due to lack of robust regulations, all while entrenching inequality among informal
workers, since opportunities are not evenly distributed geographically (Ravenelle, 2017; J. B.
Schor & Attwood-Charles, 2017). It is important to note that many of the companies that are
emblematic to the sharing economy are multinational firms that have access to capital around the
world, whose platforms allow international transactions among actors, and are notoriously
known for circumventing local regulations when establishing in cities across the world,
effectively “plugging in” workers in a global market of exchanges of assets and services that
transcends local boundaries (Parente, Geleilate, & Rong, 2018).
Research on the sharing economy and informal labor in countries in Global South has
shown that digital platforms have allowed workers to find more stable and economically
rewarding labor opportunities, as is the case of Uber and Ola drivers in India (Surie &
Koduganti, 2016). On the other hand, such stability is not equitable among digital workers, and
11
many face situations of precariousness and exploitation due to inadequate regulations, as is the
case of digital gig workers in South Africa (Graham et al., 2017).
Such shortcomings notwithstanding, many cities and policy circles have embraced the
sharing economy, seeing a potential source of wealth creation and labor improvements. The
World Bank (Bakker & Twining-Ward, 2018) has recently argued that P2P platforms for home-
sharing can be an instrumental in fostering tourism through micro-entrepreneurship. Airbnb
(Meier, 2017) has identified opportunities for greater and sustained expansion in emerging
economies. Both the World Bank and Airbnb argue that home-sharing provides opportunities for
poor households to capitalize and monetize underutilized assets, increase their income, and
achieve a more autonomous labor status through micro-entrepreneurship. Both organizations
posit that expanding the possibilities for home-sharing require regularization, improved digital
infrastructure, increased access to the internet. Recently, both organizations signed an agreement
to collaborate in fostering tourism in rural areas in countries of the Global South1.
The Sharing Economy and Home-Sharing
Cities around the world are experiencing a greater number of goods and services being
exchanged through digital platforms, signaling increasing diffusion of the “sharing economy”..
Among the most common activities that fall within the “sharing economy” is home-sharing,
which consists of short-term rentals of what otherwise is long-term rental or residential housing
units.

1 See Elliot, M. (2017). Airbnb partners World Bank to boost rural tourism. Travel Daily. Available at:
https://www.traveldailymedia.com/airbnb-partners-world-bank-boost-rural-tourism/
12
Home-sharing companies provide online, digital platforms where hosts can list available
dwellings and provide digital search engines that allow users (potential renters) to determine the
best match for their interests depending on location, dwelling type, price, and amenities. These
platforms also provide a host review process, where users can evaluate their stay with a
particular host, resulting in a reputation score system that lets other users determine if a host is
likely to provide a positive experience. Most of these companies allow for a P2P review process,
where hosts can review their experience with a particular user, allowing other hosts to determine
if they should allow said user to make use of their dwellings in the future.
There are many companies that provide digital platforms where homeowners, as hosts,
can list their property for rent, including HomeAway, VRBO, and Booking.com, but the largest
one by far, in terms of total number of listings, is Airbnb. By 2016, Airbnb had over 4 million
listings in over 191 countries. Types of dwellings vary widely, and can include single rooms,
apartments (with any number of rooms) and single-family units (with any number of rooms).
Airbnb has also managed to include buildings that are dedicated lodging businesses, albeit
ilegally in many cases, such as bed and breakfasts and hostels, where hosts offer individual
rooms. Some hosts also offer lodging options in trailers, boats, and accessory dwelling units.
The diversity of dwelling types offered by home-sharing sites implies different types of
hosts. In Airbnb, many hosts offer spare rooms from their own home, making them more likely
to be present throughout the stay of their guests. Other hosts offer secondary properties or
vacation homes and tend to be absent or distant when their units are occupied. Lastly, there is an
increasing trend of landlords, many of which are corporate landlords, that offer multiple units for
home-sharing, such as the aforementioned lodging businesses that likely do so illegally in many
cities.
13
Due to its disruptive nature, the prevalence of home-sharing, particularly in the case of
Airbnb, has been met with controversy in many cities. Because home-sharing offers short-term
rentals, its offering is mainly targeted towards non-residents that are looking for cheaper lodging
alternatives, which puts hosts in direct competition with traditional hotels and lodging services.
Contrary to chain or small hotels, motels, and hostels, short-term rentals offered through home-
sharing sites were originally intended for residential uses and tend to fall outside typical lodging
regulations and legal requirements, prompting traditional lodging businesses to claim that they
are unfairly burdened with the legal responsibilities (Gurran, 2018; Gurran & Phibbs, 2017;
Guttentag, 2015). Research in cities in the United States show that Airbnb listings do, indeed,
tend to push hotel prices down, although, in terms of welfare effects, the net result is positive for
non-residents (Farronato & Fradkin, 2018).
Airbnb and other home-sharing companies have been taken to task after residents
throughout cities in the US and Europe have complained that the prevalence of short-term rentals
promotes further gentrification and displacement in already distressed and unaffordable
communities (Wachsmuth & Weisler, 2018; Wegmann & Jiao, 2017; Yrigoy, 2018). The central
claim in this regard is that landlords can expect greater returns from home-sharing than long-
term rentals, spurring them to switch markets to attract visits from non-residents that want to
reap the benefits of local amenities. Thus, short-term rentals tend to remove otherwise available
housing units from the long-term market, constraining supply and spurring further rental
increases and possible evictions, making low-income groups all the more susceptible to these
market disruptions. At the same time, some hosts see in home-sharing an opportunity to
capitalize on undervalued assets as a way to generate supplemental income, especially in
environment where labor is increasingly precarious in terms of wages and benefits.
14
Other complaints stemming from home-sharing have to do with discriminatory practices.
Users have often made the case that many hosts across cities have refused them accommodation
due to racism, xenophobia, homophobia or sexism (Cheng & Foley, 2018; B. G. Edelman &
Luca, 2014; B. Edelman, Luca, & Svirsky, 2017). Also, because short-term rentals tend to be
much more concentrated in places with more touristic appeal, economic activity, and access to
amenities, there are spatial inequalities that tend to favor households that live near such areas,
providing them with a far greater share of opportunities to benefit from home-sharing.
Home-Sharing and Housing Affordability
Nascent and prevalent concerns about home-sharing and its effect on housing affordability
notwithstanding, there are few empirical studies that address this perceived problem. Regardless,
claims on short-term rentals adversely affecting housing affordability are increasingly common
across cities. As mentioned, the main causal mechanism that is posited by opponents of home-
sharing is that landlords foresee greater profitability from entering the short-term rental market,
therefore removing housing units that would otherwise be available for the long-term rental
market. Although home-sharing does imply a reallocation of housing units to the short-term
rental market, Barron, Kung, & Proserpio (2018) detail additional factors that need to be
considered before establishing a causal link between home-sharing and reduced housing
affordability.
P2P markets in home-sharing can reduce frictions and improve market efficiency to allow
homeowners to capitalize on underutilized assets in a way that they might not be able to do
otherwise. When this happens, housing units are indeed reallocated from the long-term rental
market to the short-term rental market, but also from local residents to non-local renters. Of
course, there has to be an existing or latent demand from non-local renters for short-term rentals
15
that is driven, among other reasons, by inefficiencies in the traditional lodging markets because
of excessive lodging rates, insufficient occupancy capacity, or both.
This demand from non-local renters is very geographically driven, as more “touristy”
areas or areas of greater economic activity will be the most attractive locations for these renters
and, thus, will have the greater share of short-term rentals. This reallocation of housing units
from local to non-local renters can, therefore, produce local externalities that disrupt housing
markets.
Another important consideration stems from the landlords themselves. The likelihood for
changes in rental rates due to home-sharing increases when the share of non-owner-occupier
landlords is greater, as owner-occupier hosts tend to rent available rooms at lower rates. Non-
owner-occupier hosts, on the other hand, tend to rent entire housing units at far greater rates, thus
increasing the likelihood for reallocating units to the short-term rental market from the long-term
rental market.
Taken together, the interplay of these factors can not only affect rental rates, but housing
prices can increase as well. Specifically, if home-sharing spurs an increase in housing prices that
is greater than the increase in rental rates, there is an increase in the price-rent ratio. What this
entails is that home-sharing can blur economic borders in housing submarkets, as it links short-
term rental markets with long-term rental markets and real estate markets, further entrenching
market segmentation throughout geographies.
Property Regularization and Home-Sharing
Academic literature on home-sharing and land regularization or liberalization is scant. Even the
literature on the presence of home-sharing in informal development is lacking. Nonetheless,
16
policy circles and home-sharing companies have been aggressive in promoting a digital
accommodation industry in emerging economies and countries in the Global South (Bakker &
Twining-Ward, 2018; Meier, 2017). At the same time, home-sharing is increasing in informal
settlements in large cities throughout the world.
Home-sharing companies have also increased their presence in informal settlements.
“Slum tourism” has been furthered enabled in informal settlements in India (D’Cunha, 2016) and
Indonesia (Kamenetz, 2013), while favelas in post-Olympics Rio de Janeiro have seen an
increase in online bookings for short-term rentals (Frenzel, 2016). Rio de Janeiro’s case is all the
more interesting because home-sharing was made possible after improved infrastructure and
increased policing and most listings are concentrated in neighborhoods with better location
regarding amenities and local attractions (Pearlman, 2016), highlighting the geographical
unevenness of home-sharing.
As regularization results in a liberalization of housing markets, the same causal
mechanisms between home-sharing and housing affordability described by Barron, Kung, &
Proserpio (2018) can take place in emerging land markets. There are, however, some instances in
which home-sharing might not affect housing affordability. After regularization, the market for
short-term rentals may be very small compared to the market for long-term rentals. This could
take place if the new landlords prefer the stability associated with long-term tenants, or if the
startup capital needed to improve homes to make them desirable for tourists is exceedingly large.
The other instance in which home-sharing might not affect housing affordability is if the
market for short-term rentals is dominated by units that would otherwise be vacant or
underutilized. In some emerging land markets, supply is very constrained as regularization does
not go hand-in-hand with increased development and improved credit access. Thus, households
17
would tend to optimize the existing stock of housing by renting out available rooms or accessory
dwelling units.
Equity Concerns of Home-Sharing
Cities around the world are experiencing an exponential increase in home-sharing amidst
controversies regarding the possibility of gentrification, displacement, reduced housing
affordability, and discrimination, leading to substantial conflicts between residents and home-
sharing companies, with city officials struggling to develop sensible and efficient regulations.
Home-sharing mostly stems from multinational companies that allow homeowners
(hosts) to list available dwellings for short-term renters (guests) around the world through a
digital platform and search engines that help determine the best match for the guests’ interests
depending on location, dwelling type, price, and amenities. These platforms also provide a P2P
review process, where users can evaluate their stay with a particular host and vice versa,
resulting in a reputation score system for both hosts and guests.
There are many home-sharing companies, among them are HomeAway, VRBO, and
Booking.com. But the largest one by far, in terms of total number of listings, is Airbnb. By 2016,
Airbnb had over 4 million listings in over 191 countries. Types of dwellings vary widely, and
include spare rooms, secondary properties, trailers, boats, and accessory dwelling units.
apartments (with any number of rooms) and single-family units (with any number of rooms).
Many hosts list buildings that are dedicated lodging businesses, many illegally, such as bed-and-
breakfasts and hostels, where hosts offer individual rooms and hotel-like accommodations.
One controversy regarding home-sharing, particularly in the case of Airbnb, is their direct
competition with traditional hotels and lodging businesses. Hotels around the world commonly
18
claim that they are unfairly burdened with the legal responsibilities because, unlike traditional
lodging businesses, short-term rentals tend to fall outside typical lodging regulations and legal
requirements (Gurran, 2018; Gurran & Phibbs, 2017; Guttentag, 2015). Research in cities in the
United States show that Airbnb listings tend to push hotel prices down (Farronato & Fradkin,
2018).
Another area of controversy in home-sharing is the large number of incidents of
discrimination. Research on Airbnb has shown that many hosts across cities have refused them
accommodation to guests due to racism, xenophobia, homophobia or sexism (Cheng & Foley,
2018; B. G. Edelman & Luca, 2014; B. Edelman et al., 2017).
The most heated debate on home-sharing, however, has to do with concerns that the
prevalence of short-term rentals will further spur gentrification and displacement in already
distressed and unaffordable cities (Wachsmuth & Weisler, 2018; Wegmann & Jiao, 2017;
Yrigoy, 2018). The main causal mechanism in this claim is that homeowners and landlords can
expect greater returns from short-term rentals, spurring them to switch from long-term rental
markets to attract visits from non-residents that want to reap the benefits of local amenities. The
result is a removal of housing units from the long-term market, further constraining supply,
increasing rental rates and displacement through possible evictions, making low-income groups
more susceptible to gentrification and displacement. Another important consideration stems from
the fact that there are inherent spatial inequities in home-sharing, as short-term rentals tend to be
much more concentrated in places with more touristic appeal, economic activity, and access to
amenities.
It could be argued that differential pricing between short-term rentals and long-term
rentals could result in market segmentation across geographical units (Bourassa, Hoesli, & Peng,
19
2003; Goodman & Thibodeau, 1998; Wu & Sharma, 2012), which, alongside supply constraints,
could cause short-term rentals to generate a crowding-out effect over housing for residents.
There is already anecdotal evidence on how home-sharing is affecting cities in the Global South,
like the aforementioned cases in India, Indonesia and Brasil.
Such concerns notwithstanding, policy circles and home-sharing companies have been
aggressively promoting short-term rentals in emerging economies in the Global South. The
World Bank (Bakker & Twining-Ward, 2018) has recently made case for P2P home-sharing
platforms as instrumental in fostering tourism and micro-entrepreneurship to increase household
wealth. At the same time, Airbnb (Meier, 2017) has focused its energies on emerging economies,
where opportunities for greater and sustained expansion await. Recently, both organizations
signed an agreement to collaborate in fostering tourism in rural areas in emerging economies2, as
they characterize home-sharing as a reliable source of opportunities for poor households to
capitalize and monetize underutilized assets, increase their income, and achieve a more
autonomous labor status through micro-entrepreneurship. Such a venture will require
regularization, improved digital infrastructure, increased access to the internet, further
complicating an analysis on how all this borne out.

2 See Elliot, M. (2017). Airbnb partners World Bank to boost rural tourism. Travel Daily. Available at:
https://www.traveldailymedia.com/airbnb-partners-world-bank-boost-rural-tourism/
20
II. Economic Reforms in Cuba and Home-Sharing in Havana
On November 2, 2011, the Government of Cuba enacted a law allowing its citizens to buy, sell,
and rent properties in the island. This law is part of a broader set of reforms implemented in the
island to allow greater economic liberalization among its citizens as a way to cope with
decreasing state capabilities and foster greater entrepreneurship (Domínguez et al., 2017;
Spadoni, 2014). As part of these reforms, the Cuban Government established an emerging real
estate market and the following distinct rental markets: (1) short- and long-term rental markets
for Cuban citizens; and (2) short-term rental markets for tourists and foreigners. On one hand,
this has resulted in an increase in the number of self-employed Cuban entrepreneurs
(cuentapropistas) that rent units to Cuban nationals or foreign tourists (Carmelo Mesa-Lago et
al., 2016; Ritter & Henken, 2014).
With the advent of greater internet access and digital technologies, Cuba has also been a
site ripe for the arrival of the sharing economy in the form of home-sharing, particularly from
Airbnb. This puts Cuba among the countries in the Global South that has seen an increase in
home-sharing, prompting questions mostly studied in the North on how digital platforms for
short-term rentals can affect housing affordability.
The real estate market liberalization policies are viewed by the Cuban government as a
means to optimize the housing stock for its citizens (C Mesa-Lago & Pérez-López, 2013). I
examine whether these changes in property rights in Cuba could be achieving this goal to the
detriment of housing affordability.
21
Reforms in Cuba
The collapse of the Soviet Union in 1989, pushed Cuba to adopt multiple policies across
different stages that would eventually lead to the approval of the economic reforms in 2011.
After the dissolution of the Soviet Union in 1989, Cuba lost its main trading partner. Beginning
in 1989 and ending in 1994, Cuba underwent a period of economic depression known as the
“Special Period” that saw its GDP fall by over 35% (Ritter & Henken, 2014; Spadoni, 2014).
Before the “Special Period”, the single biggest export in Cuba was sugar, which was sold
above market prices through agreements with the Soviet Union. At the same time, goods such as
medicine, food, and fuel were imported at below market prices through subsidies from the Soviet
Union. Thus, once the Soviet Union collapsed, Cuba accumulated large levels of debt and
experienced food, fuel, and medicine shortages that led to periodic blackouts, food rationing,
undernourishment, and the reversal of developmental achievements made in prior decades (C
Mesa-Lago & Pérez-López, 2013; Ritter & Henken, 2014).
During the “Special Period”, the Cuban Government saw limits to the centralization of
decision making and the statist approach to the economy that were characteristic of the Cuban
Revolution. The aggravating socioeconomic consequences of the economic depression
overstretched the capabilities of the Cuban State in conjuring appropriate policies. In response,
the Cuban Government underwent different forms of decentralization that resulted in somewhat
greater territorial self-government and development initiatives at the neighborhood level, the
creation of the consejos populares (people’s councils) in 1990 being an example of the former
and the microbrigades for neighborhood revitalization in 1989 as an example of the latter
(Anguelovski, 2013; Hearn, 2008).
22
With the collapse of its sugar industry and struggling extractive and agricultural sectors,
Cuba saw new economic opportunities in tourism. As a result, many informally self-employed
entrepreneurs in the Cuban economy started their private ventures illegally, such as paladares
(small in-home restaurants), casas particulares (private lodgings similar to bed and breakfasts),
and taxi services (Henken, 2002). The Cuban Government recognized their potential to improve
household livelihoods and started to loosen restrictions on their operations by 1993 (Ritter &
Henken, 2014). By 1997, self-employment and private business licenses were issued by the
Government for over 150 economic activities, including transportation, construction, private
lodging, and restaurants (Henken, 2005).
During this time, a double currency system was implemented with the purpose to
“convert” transactions in United States (US) dollars and earnings gathered through exchanges of
goods and services with non-residents into higher returns for the Cuban population. The Cuban
Convertible Peso (CUC) is pegged to the US dollar and is primarily used for currency exchange,
while the Cuban Peso (CUP) is used for transactions between Cuban citizens. The use of the
CUC as the exchange currency, and its elevated value in comparison to the CUP, put Cuban
entrepreneurs that engage non-residents, or are related to the tourism industry, in a distinct
socioeconomic advantage over other Cuban citizens.
By 1994, the turn towards tourism and the loosening of restrictions for self-employment
had paid off enough to lift Cuba out of the “Special Period”, but the island’s economy was still in
a precarious state. Nonetheless, by 1997, the Cuban Government, at the behest of Fidel Castro,
opted to implement more stringent regulations and requirements for self-employment and private
businesses (Ritter & Henken, 2014). The increasing inequality and the difficulty in monitoring
earnings and practices in these emerging businesses drove the Government to deem self-
23
employment and private businesses as contrary to the egalitarian objectives of the Cuban
Revolution (C Mesa-Lago & Pérez-López, 2013).
In 2006, Fidel Castro temporarily ceded power to his brother, Raúl Castro, due to health
reasons. Raúl Castro was elected by the National Assembly as the President of Cuba’s Council of
State and Council of Ministers in 2008 and was re-elected in 2013. Recognizing the need to spur
greater levels of growth and with decreasing state capabilities to vertically implement policies
that address the needs of Cuban citizens, the Cuban Communist Party, under the leadership of
Raúl Castro, approved sweeping changes to the economy. By 2010, self-employment restrictions
were loosened considerably, while public-sector enterprises and agencies laid-off over 600,000
workers (Spadoni, 2014). The result was an increasing trend in self-employment and private
sector employment that has not stopped since (Figure II-1).

Figure II-1: Non-state employment in Cuba. Source: Anuario Estadístico 2016, Tabla 7.2 - Ocupados en la
economía según situación del empleo, Oficina Nacional de Estadísticas

0
200
400
600
800
1000
1200
1400
1999 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Employment (in thousands)
Cooperatives Non-agricultural cooperative Private employment Self-employed
24
The new property reforms (Vega, 2014) approved in 2011, Cuban nationals were allowed
to own, sell, and buy real estate property at privately established prices. They were allowed to
own, at most, one residence and one vacation home. They could also apply for uncollaterized
loans, although mortgages are still non-existent. The Cuban government expected that, by
allowing citizens to access credit, homeowners would invest in home upgrades to improve the
housing quality in order sell or rent their property. A notorious result has been the booming
short-term rental markets through home-sharing, particularly with the advent of Airbnb3. It
should be noted, that only the housing structure or building structure itself is subject to private
ownership and transactions, as the land in Cuba is still state-owned.
Cuba’s recent reforms and decentralization efforts from the late 1980’s and early 1990’s
have yielded a significant set of civil society and private actors that can provide potential
homeowners with many opportunities. Increased access to the internet (although still very
limited), an increasing number of local non-government organizations, historical transnational
ties with the Cuban diaspora, greater local government autonomy, the creation of non-
agricultural cooperatives, and emerging private businesses can be beneficial for entrepreneurs to
access necessary information, financial resources, and services to start and operate new
businesses, including home rentals (Hansing & Orozco, 2014; Hearn, 2008; C Mesa-Lago &
Pérez-López, 2013; Ritter & Henken, 2014).

3 In 2017 Airbnb had over 22,000 listings spread across 70 different cities and townsin Cuba, as stated in their report
Airbnb and Cuba: Two years of connecting people and generating economic opportunity for individuals and
families, available here: https://2sqy5r1jf93u30kwzc1smfqt-wpengine.netdna-ssl.com/wp-
content/uploads/2017/06/Airbnb_CubaReport_2017.pdf

25
Setting the Terrain for Home-Sharing in Cuba
Since 1960, when the Cuban Government approved the Urban Law, Cuba achieved tenure security
for its citizens by nationalizing all land and structures and allocating them through adverse
possession or rent agreements (Vega, 2014). This resulted in a homeownership rate of 85%. While
the law was active, housing occupants could move only through swaps, since selling, buying, or
renting property was prohibited (C Mesa-Lago & Pérez-López, 2013).
Since then, the housing stock in Cuba experienced serious shortages and deterioration
(Grein, 2015.; Núñez, 2008), prompting the Cuban government to address the situation. Even
though there has been a decrease in population and an increase in housing units between 2002 and
2012 (Figure II-2), the average number of people per housing unit has remained somewhat stable
(Table II-1). On November 2011, the Cuban Government approved new property reforms to
liberalize housing markets by allowing Cuban nationals to sell and buy real estate property and
apply for uncollaterized loans to invest in home improvements to improve the quality of housing
in order sell or rent their property. (Vega, 2014). There are, however, important limitations set
forth in these reforms. First, Cuban citizens are limited to owning one residence and one vacation
home. And second, only the housing structure or building structure itself is subject to private
ownership and transactions, since land in Cuba is still state-owned.
26

Figure II-2: Change in population and housing units in Cuba and Havana between 2002 and 2012. Source: Anuario
Estadístico, Indicadores seleccionados del Censo de Población y Viviendas, Oficina Nacional de Estadísticas

Table II-1: Average number of people per housing unit in Cuba and Havana between 2002 and 2012. Source:
Anuario Estadístico, Indicadores seleccionados del Censo de Población y Viviendas, Oficina Nacional de
Estadísticas

2002 2012
Cuba 3.16 2.88
Havana 3.24 2.97

Although banks in Cuba (which are all state-owned) have yet to provide mortgages, they
do provide personal loans, while the 2010 reforms to ease restrictions on self-employment and
private businesses allowed the creation of non-agricultural cooperatives that can provide loans for
house improvements. Private construction businesses and self-employed skilled workers could be
hired by homeowners for construction projects. Many of these construction projects are not only
for in-situ home improvements, but also for housing subdivisions or the addition of independent
units. These changes notwithstanding, the supply of new housing units is primarily state-driven
11,177,743 11,173,151
3,534,327
3,885,900
2,201,610
2,105,291
679,917 709,895
2002 2012
Population in Cuba Housing units in Cuba
Population in Havana Housing units in Havana
27
(Grein, 2015), and while it has been growing consistently, it has done so at a very slow rate (Figure
II-34).

Figure II-3: Construction of housing units in Cuba. Source: Anuario Estadístico 2016, Tabla 12.1 - Viviendas
terminadas por provincias, Oficina Nacional de Estadísticas

With the reforms set in place in conjunction with increasing internet access (although still
severely limited), Cuba was fertile ground for short-term rentals, especially through online home-
sharing company Airbnb5. The Cuban government also established a series of regulations to
address home-sharing. First, landlords must acquire a self-employment license in order to list their
property for rent. Second, they must keep a ledger of all tenants. And third, they must pay monthly

4 The sudden peak in housing units built in 2006 coincides with reconstruction efforts after hurricane Dennis
damaged over 120,000 housing units in Cuba (source: http://www.minrex.gob.cu/es/dennis-provoco-perdidas-por-1-
400-millones-de-dolares)
5 See Airbnb and Cuba: Two years of connecting people and generating economic opportunity for individuals and
families, available here: https://2sqy5r1jf93u30kwzc1smfqt-wpengine.netdna-ssl.com/wp-
content/uploads/2017/06/Airbnb_CubaReport_2017.pdf

41,170
20,030
57,318
15,352
111,373
23,003
0
20,000
40,000
60,000
80,000
100,000
120,000
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Housing units built
28
taxes from their generated income. In conjunction with the two-property limit, this set of
regulations establishes a stringent guardrail for monitoring and controlling short-term rentals.
In Cuba’s case, however, the purposeful creation of two rental housing submarkets
(rentals for citizens vis a vis rentals for non-residents) can lead to the two markets affecting each
other. Units that otherwise would be rented to Cuban citizens could potentially be converted into
short-term tourist rentals, or capital that would have created more long-term rentals may be
diverted to the short-term market. If so, this would imply that the recent housing policies in Cuba
could inhibit the State from achieving more equitable outcomes for its citizens, specifically, its
renters.
Given the recent reforms and the housing situation in Cuba, it is possible that the
presence of home-sharing might disrupt both rental and housing markets, especially if housing
supply is very constrained. By the same token, the supply constrain might foster more “small-
time” hosts that rent spare rooms, rather than entire housing units, that wouldn’t otherwise be
available in the long-term rental market. This makes Cuba an interesting case study to assess
how home-sharing could affect emerging rental and housing markets.
One important caveat is that recent housing reforms in Cuba regularized properties by
liberalizing housing markets. Unlike regularization in informal settlements, the reforms did not
address tenure security since this has been mostly addressed in the 1960 Urban Law.
Nonetheless, such changes follow a similar vein as property liberalization policies in
transitioning countries, particularly China and Vietnam, albeit without the accompanying
financial reforms that took place in the latter cases.

29
Havana
This research uses Havana, Cuba as a case study in how home-sharing affects housing prices and
long-term rental rates in liberalized housing markets. As the capital and largest city in Cuba,
Havana is a prime location for both the island’s tourism activity and emerging housing markets.
The city, which is officially one of Cuba’s provinces, is located at the northern coast in Cuba
(Figure II-4), adjacent to the Atlantic Ocean and just south of the Florida Keys in the United
States.
Havana has a population of over 2 million and is composed of 15 municipalities (Figure
II-5) that differ significantly in their densities, population, and economic activity, providing
potential contrast to assess the rental market (Table II-2). The municipalities with the greatest
economic activity are Playa, Plaza de la Revolución, and Habana Vieja, while Centro Habana is
near all three and is the most densely populated municipality.
30

Figure II-4: Location of Havana, Cuba

31

Figure II-5: Municipalities of Havana

32
Table II-2: Population of municipalities in Havana in 2012
Municipality Population
Playa 176,614
Plaza de la Revolución 152,318
Centro Habana 151,174
Habana Vieja 90,682
Regla 42,707
Habana del Este 172,783
Guanabacoa 113,728
San Miguel del Padrón 158,268
Diez de Octubre 212,171
Cerro 129,196
Marianao 132,976
La Lisa 133,350
Boyeros 184,647
Arroyo Naranjo 210,214
Cotorro 74,670
Total 2,135,498
Source: Oficina Nacional de Estadísticas

Airbnb trends in Havana
Havana has been central in the booming of Airbnb listings in Cuba. Data from AirDNA, a firm
based in Barcelona that provides market data and analysis on Airbnb listings, shows that,
between August 2015 and August 2017, Airbnb listings in Havana went from a little over 1,800
to more than 22,000, 1,139% increase (Figure II-6).
33

Figure II-6: Monthly number of Airbnb listings in Havana. Seasonally adjusted. Source: AirDNA

The monthly number of reservations has also soared in this period. Between August of
2015 and March 2017, the total number of Airbnb reservations increased by over 12,000%
(Figure II-7). The same trend is seen in the total monthly revenue generated by Airbnb, where
total earnings in that same period increased by over 16,000%, going from a little over $63,000 in
August 2015, to over $10 million in in March 2017 (Figure II-8).
0
5000
10000
15000
20000
25000
8/1/2015
9/1/2015
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4/1/2017
5/1/2017
6/1/2017
7/1/2017
8/1/2017
34

Figure II-7: Monthly number of Airbnb reservations in Havana. Seasonally adjusted. Source: AirDNA

Figure II-8: Monthly total revenue from Airbnb rentals in Havana. Seasonally adjusted. Source: AirDNA

0
5000
10000
15000
20000
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$0.00
$2,000,000.00
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$10,000,000.00
$12,000,000.00
8/1/2015
9/1/2015
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8/1/2017
35
Revenue, however, is unevenly distributed spatially. The most affluent properties are
overwhelmingly concentrated in the Habana Vieja, Centro Habana, Plaza de la Revolución, and
Playa, with particular focus in the Cayo Hueso, Miramar and Vedado neighborhoods (Figure
II-9). These municipalities also have the largest share of listings (Table II-3). When looking at
average annual revenue per municipality, however, the story is somewhat different, as the largest
figures are recorded in Playa, Cotorro (which is much more rural), and Diez de Octubre.

Figure II-9: Heatmap of total revenue per Airbnb listing. Source: AirDNA, OpenStreetMap

36
Table II-3: Breakdown of Airbnb listings in Havana by municipality. Source: AirDNA.
Municipality
Total
Airbnb
listings
Number of entire
apartment/house Airbnb
listings
Number of
private
room
Airbnb
listings
Number of
shared room
Airbnb
listings
Average
annual
revenue per
listing
Boyeros 188 68 118 2 $1,963.40
Centro Habana 5555 2173 3303 79 $2,481.19
Cerro 357 143 199 15 $2,312.24
Cotorro 4 4 0 0 $5,531.50
Diez de Octubre 361 148 213 0 $4,950.97
Guanabacoa 67 33 34 0 $1,710.52
La Habana del Este 923 555 363 5 $3,149.01
La Habana Vieja 4221 1903 2299 19 $3,704.21
La Lisa 61 24 37 0 $2,236.03
Marianao 118 54 64 0 $1,053.80
Playa 3453 2231 1217 5 $6,130.02
Plaza de la Revolucion 8383 4274 4082 27 $3,446.43
Regla 65 41 24 0 $1,975.65
San Miguel del Padron 80 30 50 0 $881.40
Total 24113 11806 12155 152 $3,581.07

37
III. Home-Sharing and the Rent Gap in Formalized Property
Markets in Havana
There are increasing concerns in cities around the world on the potential role that home-sharing
(i.e. the use of digital platforms for listing and booking private housing units for short-term rentals)
can have in exacerbating gentrification and displacement. The large bulk of the research that
examines how home-sharing, particularly looking at home-sharing giant Airbnb, relates to forms
of gentrification, dispossession, and housing affordability has taken place in cities in industrialized
countries in the Global North. The issue, however, is no less controversial in cities in the South,
as there is accumulating anecdotal evidence on greater presence of home-sharing in urban areas,
including informal settlements, but it is decidedly understudied. How this dynamic bears out in
settings where property has been regularized is still a lingering question.
Empirical studies of Airbnb and gentrification have thus far focused in analyzing how
Airbnb can exacerbate the rent gap across neighborhoods. Applying a similar approach to contexts
where property has been recently regularized might shed light on how the presence of home-
sharing could foster gentrification and displacement, hindering the policy outcomes expected from
regularization.
Using Havana, Cuba as a case study, this paper highlights some ways in which using rent
gap analysis in the South differ on the experience from the North: (1) alternative forms of tenure
and occupation can have significant effects on the possibility of achieving the “highest and best
use” as a driver of redevelopment and gentrification, a crucial element in rent gap theory; (2)
comparing capitalized ground rents between short- and long-term rentals can be inadequate in
contexts where recently regularized properties fall outside the purview of financial capital,
38
resulting in supply constraints that limit the possibilities of redevelopment; and property hoarding
for short-term rentals can exacerbate rent gaps, which require considering local and transnational
spillovers.
The Rent Gap and Gentrification
First proposed by Neil Smith (Smith, 1979), rent gap theory seeks to explain gentrification
through different stages of urban growth and decline as a function of land valuation and
capitalization. In summary, rent gap theory posits that properties experience capital divestment
through time, rendering them increasingly incompatible with the “highest and best use”, leading
towards depreciation and blight (spurred even quicker through mechanisms like redlining,
blockbusting, and blow out) before economic pressures causes reaching a turning point where
reinvestment and redevelopment become present. In the process of capital depreciation, the
turning point towards redevelopment is determined by the dynamic between two central
concepts, the capitalized ground rent (CGR) and the potential ground rent (PGR).
CGR refers to the rent that is extracted by the landowner; it is reflected in housing prices
and rental rates. PGR, on the other hand, refers to the amount that can be capitalized if the
property was developed in to the “highest and best use”. Immediately upon initial development,
CGR and PGR start at the same value, but they tend to intersect as capital depreciation increases
through time (Figure III-1). The turning point for redevelopment, however, occurs once PGR
exceeds CGR. The difference between PGR and CGR is thus known as the rent gap (RG) (Smith,
1979). Therefore, redevelopment and gentrification are more likely to take place if RG = PGR –
CGR results in a positive value.
39

Figure III-1: Development of the rent gap through time. Redrawn by author from Smith (1979).

Rent gap theory is not without critics and retractors. As CGR and PGR are abstract
concepts that partly reflect land prices and inherent value, the operationability of rent gap theory
and its incompatibility with neoclassical theories of land prices and markets in urban economics
have been identified as problematic (Bourassa, 1993; Ley, 1987). Nonetheless, such critiques
have been characterized as either reflecting a fundamental misunderstanding of the concepts of
CGR and PGR in rent gap theory (Smith, 1987, 1996), or as reductionist because of how they
understate the role of time and political economy in fledging out the rent gap cycle (Clark, 1995).
40
On the other hand, supporters of rent gap theory have looked for ways to expand and improve it.
One important consideration is that the process of capital depreciation and the turning point
towards redevelopment and gentrification is not a passive process, but rather reflects a broader
political process of contestation, societal relations, and power struggles (Clark, 1995). Another
consideration refers to the scale at which the gentrification cycle takes place, as CGR is
determined largely at the neighborhood level (as a function of socioeconomic characteristics and
surrounding land use) and PGR is determined at the metropolitan scale (in relation to
metropolitan economic activity, size, proximity to geographies of opportunity, etc.) (Hammel,
1999).
While early development of rent gap theory focused on cities in the Global North, many
of its concepts have been used to explain patterns of urbanization in the Global South to argue
that its underlying principles have universal reach. Gentrification, it is argued, is a “global urban
strategy”, where the emergence of globalization and the free circulation of capital throughout the
world is the main driver of new forms of depreciation, eviction, and displacement at massive
scales (Smith, 2002). The increasing financialization of economies around the world has made it
easier for speculative activity to drive more exclusive forms of urbanization, leading to new,
planetary expressions of gentrification (Lees, Shin, & López-Morales, 2016; Slater, 2017). In
Santiago, Chile, deliberate state-driven efforts to devalue land that was targeted for renewal,
producing a form of social dispossession where developers would reap a greater amount of CGR
than existing owner-occupiers (Lopez-Morales, 2011), as well as exclusionary displacement by
broadening the rent gap (López-Morales, 2016).
D. Asher Ghertner (Ghertner, 2014, 2015), however, makes the case for how
gentrification and rent gap theory falls short of explaining a large swath of displacement
41
mechanisms in urban India, including slum clearance, land privatization, vertical slums, and
periurban enclosures. Specifically, Ghertner identifies four key areas where gentrification theory
fails in India: (1) Gentrification theory posits that reinvestment of capital occurs in already
capitalized spaces, which is not the case in many cities that recently started to experience
capitalist forms of production of space; (2) Gentrification theory was developed in advanced
capitalist countries strict formal property rights, while displacement in India is primarily the
result of private property in the form of urban enclosures; (3) Gentrification theory assumes that
reinvestment is geared towards the “highest and best use”, which has failed to materialize in
urban India; and (4) Gentrification theory tends to fail in accounting for extraeconomic forces,
particularly political contestation, which is likely the main driver of urban change in India and
much of the Global South. In summary, because gentrification theory is embedded in a “Euro-
American property system”, it fails to extend its underlying assumptions to postsocialist and
postcolonial contexts that “have strong traditions of public landownership, socialist land policies,
or diverse, non-ownership-based tenure regimes” (Ghertner, 2014).
Nonetheless, gentrification theory, it is argued in response to Ghertner, can be broadened
and is flexible enough to consider how diverse property and tenure arrangements interact with
extraeconomic forces, namely political, social, and cultural factors, in determining how
displacement and dispossession takes place in cities in the Global South (Krijnen, 2018).
Home-Sharing and the Rent Gap
Research on the potential gentrification and displacement resulting from home-sharing
has focused on analyzing how Airbnb listings can affect the rent gap. Using rent gap analysis in
Barcelona (Yrigoy, 2018) and New York City (Wachsmuth & Weisler, 2018), results show that
Airbnb can increase the rent gap in two ways: (1) by capturing a greater share of long-term rental
42
units; and (2) by extracting revenues that far exceed those generated through long-term rentals,
thus generating differential CGR. That increase in CGR can further exacerbate the rent gap,
rendering those neighborhoods with the greater differential in CGR as the ones more susceptible
to increases in Airbnb listings and, therefore, the ones most at risk of being gentrified. How this
dynamic might play out in a post-colonial and post-socialist settings is still an open question, one
which the recent experience in Havana can shed some light on.
Rent Gap as Displacement of Housing Units
Both Yrigoy (2018) and Wachsmuth & Weisler (2018) include similar measures of potential
displacement by estimating the share of short-term rentals from total rental units in Barcelona
and New York City, respectively. Given that the housing reforms took place relatively recently,
Cuba’s Oficina Nacional de Estadísticas (ONE) does not include rental housing in its estimates
of the housing stock for the Cuban census at the moment, which makes it impossible to
reproduce the analysis performed by Yrigoy (2018) and Wachsmuth & Weisler (2018).
Given the historic housing shortages in Cuba, ONE breaks down the housing stock in the
following manner: (1) number of housing units refers to individual structures, be it single-family
or multi-family development, that has a full set of features and accommodations (e.g. rooms,
bathrooms, and kitchen) and provides permanent residence to Cuban nationals; (2) temporary
dwelling units refers to housing units or single rooms or dormitories that provide shelter to
Cuban nationals or non-residents for a specified period of time; and (3) total dwelling units
which consists of the sum of housing units and temporary dwellings. Estimating potential
displacement of housing units by Airbnb in Havana can thus have more than one measure and
implication.
43
Using data from ONE’s 2012 census (the latest decennial census), Airbnb listings cover
3.4% of total dwelling units, a small but considerable figure (Table III-1). When comparing to
housing units, the number of Airbnb listings is limited to those that offer entire homes, which
represents less than 2% total housing units in Havana. Estimating potential displacement for
temporary dwelling units results in more alarming figures, since these types of dwellings are
classified as temporary because of its use, not housing characteristics, and includes entire homes
as well as single rooms. In Havana, Airbnb listings could potentially displace 76.4% of
temporary dwelling units.
Table III-1: Potential housing unit displacement by Airbnb, by dwelling type.
Type of dwelling Total units (2012 Census) Airbnb listings Percent of total
Dwelling units 709,895 24,113 3.4%
Housing units 678,302 11,806 1.7%
Temporary dwelling units 31,593 24,113 76.3%
Source: ONE 2012 Cuban Decennial Census and AirDNA.

Rent Gap as Potential Increase in Capitalized Ground Rent
The gist of the argument put forth by Yrigoy (2018) and Wachsmuth & Weisler (2018) is that
home-sharing, and Airbnb in particular, generates increases in CGR in ways that can further
increase switching from long-term rentals to short-term rentals, leading to housing affordability
and displacement issues. To better assess this dynamic, both studies develop measures that
compare revenue generated from Airbnb with rents generated from long-term rentals (Yrigoy
(2018) used a renters’ survey from Barcelona, while Wachsmuth & Weisler used data from the
American Community Survey). Their results show that CGR generated by Airbnb rentals are
substantially higher than the CGR generated through long-term rentals, several times more. The
idea here is to reproduce the same analysis for Havana.
44
To reproduce this analysis for Havana, I used online rental ads scraped from websites
such as revolico.com, cubisima.com, porlalivre.com, and detrasdelafachada.com. The data were
scraped and extracted between January 2015 and December 2017 using the Web Scraper plugin
for the Google Chrome browser, as well as the web-scraping packages rvest, xml, and xml2 in R.
Although there was an initial draw of 12000 rental ads, of which 1700 were located in Havana,
441 ads provided enough detail for statistical analysis (Table III-2). Long-term rentals are
expressed in monthly rates.
Using data from online rental ads could be reliable enough for studying housing (Boeing
& Waddell, 2017; Kim, 2016), but they must be accompanied with field visits to corroborate its
robustness for analysis (Harten & Kim, 2018). Between July 2017 and February 2018, I carried
out site visits in Havana, where I corroborated some online ads with landlords; these provided
that anecdotal evidence showing that final rental rates negotiated with tenants do not deviate
significantly from the online classified sites. Finally, annual long-term rental revenue was
estimated by projecting the monthly rental rates, resulting in a proxy value for actual capitalized
rents, but one that should be conservative, nonetheless.

45
Table III-2: Breakdown of online rental ads data.
Municipality
Total long-
term rental
listings
Apartment listings
Single-family housing
listings Single room listings
Number
Percent
total Number
Percent
total Number
Percent
total
Arroyo Naranjo 3 2 67% 1 33% 0 0%
Boyeros 6 3 50% 2 33% 1 17%
Centro Habana 45 32 71% 5 11% 8 18%
Cerro 11 6 55% 1 9% 4 36%
Cotorro 0 0 N/A 0 N/A 0 N/A
Diez de Octubre 30 20 67% 6 20% 4 13%
Guanabacoa 11 0 0% 8 73% 3 27%
Habana del Este 15 6 40% 9 60% 0 0%
Habana Vieja 5 3 60% 2 40% 0 0%
La Lisa 3 1 33% 2 67% 0 0%
Marianao 7 4 57% 3 43% 0 0%
Playa 136 88 65% 38 28% 10 7%
Plaza de la Revolucion 166 128 77% 27 16% 11 7%
Regla 1 1 100% 0 0% 0 0%
San Miguel del Padron 2 1 50% 1 50% 0 0%
Total 441 295 67% 105 24% 41 9%
Source: revolico.com, cubisima.com, porlalivre.com, and detrasdelafachada.com
There are a few caveats in applying this analysis to Havana. First, revenue from long-
term rentals cannot be estimated, since the ONE decennial census in Cuba does not account for
rental housing in the island, at least not explicitly, and the online ads do not have this
information displayed. Second, performing such a comparison with Cuba can underestimate
CGR in either direction since rates do not account for land, given that all lands in the island still
belong to the state. Third, the analysis cannot be done at the neighborhood level, since the online
rental ads identify municipalities at the most. Fourth, the proxy estimate for annual long-term
rental revenue does not reflect the actual revenue generated by long-term rentals, which likely
has considerable seasonal variations. Fifth, the number of long-term rental ads used for the
46
analysis is likely too low, especially across municipalities, to yield reliable comparisons with the
Airbnb data.
An initial assessment shows that both the number of Airbnb listings and average Airbnb
annual revenue seem to be correlated with the monthly rental rates across municipalities (Table
III-3). When projecting rental revenue and comparing it with Airbnb revenue, however, the data
show that rarely do Airbnb rentals are more profitable than long-term rentals. Exceptions to this
are the municipalities of Guanabacoa, La Lisa, and Regla, where the percent difference between
Airbnb revenue and long-term rents are positive, although there is a low number of observations
in the latter two that are likely skewing this finding.
Table III-3: Comparison of Airbnb revenue and potential long-term rental revenue by municipality.
Municipality
Average monthly
rental rates
Projected annual
rental revenue
Average
Airbnb
annual
revenue
Percent
difference
between
Airbnb
revenue
and long-
term rents
Number
of rental
listings
Number
of Airbnb
listings
Arroyo Naranjo $ 113.33 $ 1,360.00 $1,094.10 -19.6% 3 277
Boyeros $ 193.33 $ 2,320.00 $1,963.40 -15.4% 6 188
Centro Habana $ 302.89 $ 3,634.67 $2,481.19 -31.7% 45 5555
Cerro $ 212.00 $ 2,544.00 $2,312.24 -9.1% 11 357
Cotorro N/A N/A $5,531.50 N/A 0 4
Diez de Octubre $ 168.63 $ 2,023.60 $4,950.97 144.7% 30 361
Guanabacoa $ 90.91 $ 1,090.91 $1,710.52 56.8% 11 67
Habana del Este $ 365.00 $ 4,380.00 $3,149.01 -28.1% 15 923
Habana Vieja $ 418.00 $ 5,016.00 $3,704.21 -26.2% 5 4221
La Lisa $ 111.67 $ 1,340.00 $2,236.03 66.9% 3 61
Marianao $ 164.29 $ 1,971.43 $1,053.80 -46.5% 7 118
Playa $ 693.63 $ 8,323.59 $6,130.02 -26.4% 136 3453
Plaza de la Revolucion $ 450.55 $ 5,406.58 $3,446.43 -36.3% 166 8383
Regla $ 90.00 $ 1,080.00 $1,975.65 82.9% 1 65
San Miguel del Padron $ 145.00 $ 1,740.00 $881.40 -49.3% 2 80
Total $ 458.22 $ 5,498.67 $3,581.07 -34.9% 441 24113
Source: AirDNA. revolico.com, cubisima.com, porlalivre.com, and detrasdelafachada.com
47
In an effort to have a more direct comparison, I performed the same analysis for one-
bedroom listings for both Airbnb rentals and long-term rentals (Table III-4). The results show a
similar trend, although Cerro and Diez de Octubre now show a positive percent difference,
showing that one-bedroom Airbnb rentals are likely more profitable in those municipalities.
Repeating the same analysis for two-bedroom units shows similar trends (Table III-5), although
Cerro is no longer a profitable area for Airbnb.
Table III-4: Comparison of Airbnb revenue and potential long-term rental revenue for one-bedroom units by
municipality.
Municipalities
Airbnb listings for one-
bedroom units
Long-term rental listings for one-
bedroom units Percent difference
between Airbnb
revenue and long-term
rents
Average
annual
revenue
Number of
listings
Average
rental
price
Projected
annual rental
revenue
Number
of listings
Arroyo Naranjo $1,306.60 42 $110.00 $1,320.00 2 -1.0%
Boyeros $1,222.73 11 $153.33 $1,840.00 3 -33.5%
Centro Habana $2,140.64 927 $246.60 $2,959.20 25 -27.7%
Cerro $1,456.60 79 $77.75 $933.00 4 56.1%
Cotorro N/A 0 N/A N/A 0 N/A
Diez de Octubre $3,408.00 51 $142.78 $1,713.33 18 98.9%
Guanabacoa $935.43 7 $81.67 $980.00 9 -4.5%
La Habana del Este $1,338.12 117 $122.50 $1,470.00 4 -9.0%
La Habana Vieja $3,740.76 848 $30.00 $360.00 1 939.1%
La Lisa $1,207.83 6 $100.00 $1,200.00 1 0.7%
Marianao $736.27 15 $200.00 $2,400.00 3 -69.3%
Playa $1,492.26 516 $304.80 $3,657.63 71 -59.2%
Plaza de la
Revolucion $2,224.06 1569 $267.27 $3,207.20 75 -30.7%
Regla $159.64 14 $90.00 $1,080.00 1 -85.2%
San Miguel del
Padron $772.42 12 $145.00 $1,740.00 2 -55.6%
Total $2,365.18 4214 $245.42 $2,945.04 219 -19.7%
Source: AirDNA. revolico.com, cubisima.com, porlalivre.com, and detrasdelafachada.com

48
Table III-5: Comparison of Airbnb revenue and potential long-term rental revenue for two-bedroom units by
municipality.
Municipalities
Airbnb listings for two-
bedroom units
Long-term rental listings for two-
bedroom units Percent difference
between Airbnb
revenue and long-term
rents
Average
annual
revenue
Number of
listings
Average
rental
price
Projected
annual rental
revenue
Number
of listings
Arroyo Naranjo $818.56 50 $120.00 $1,440.00 1 -43.2%
Boyeros $958.92 25 $265.00 $3,180.00 2 -69.8%
Centro Habana $3,622.37 862 $358.68 $4,304.21 19 -15.8%
Cerro $1,741.07 44 $633.33 $7,600.00 3 -77.1%
Cotorro 7223.666667 3 N/A N/A 0 N/A
Diez de Octubre $3,773.14 56 $261.88 $3,142.50 8 20.1%
Guanabacoa $308.20 10 N/A N/A 0 N/A
La Habana del Este $2,486.45 206 $273.75 $3,285.00 4 -24.3%
La Habana Vieja $5,250.52 727 $515.00 $6,180.00 4 -15.0%
La Lisa $10,265.00 3 $117.50 $1,410.00 2 628.0%
Marianao $1,434.13 23 $150.00 $1,800.00 3 -20.3%
Playa $2,462.34 686 $842.76 $10,113.14 42 -75.7%
Plaza de la
Revolucion $3,777.70 1677 $535.85 $6,430.17 59 -41.3%
Regla $4,340.31 13 N/A N/A 0 N/A
San Miguel del
Padron $4,007.25 12 N/A N/A 0 N/A
Total $3,643.82 4397 $559.94 $6,719.27 147 -45.8%
Source: AirDNA. revolico.com, cubisima.com, porlalivre.com, and detrasdelafachada.com

Rent Gap Through Property Hoarding
As mentioned before, the Cuban housing law explicitly prohibits Cuban nationals from owning
more than two properties, landlords must obtain a self-employment license in order to rent their
property, must pay monthly taxes on their properties, and keep a ledger on tenants. This applies
for both short- and long-term landlords. Long-term landlords must reach contractual agreements
with tenants and register the contract with a local notary. This results in tightly regulated rental
market.
Data from Airbnb in Havana shows that such measures have been inadequate to avoid
large landlords that hoard properties for short-term rentals. Looking into the top twenty Airbnb
49
hosts, identified using Airbnb’s Host ID and ranked by the number of listings each have in the
platform (using Airbnb’s Property ID), the Airbnb data in Havana show that there are hosts with
over 400 listings that have made well over $1million between August 2015 and August 2017
(Table III-6). The top host by number of listings has 629 listings attributed to them, while the
highest earning host made more than $6 million. In sum, these twenty hosts, from a total of more
than 8,000 hosts in Havana, possess about a fourth of the total number of listings in the city, and
have made over 17% of the total revenue.
Table III-6: Largest twenty Airbnb hosts in Havana by total number of listings.
Host
Ranking
Total number of
listings
Total revenue
generated 2015-2017
1 629 $832,076.00
2 542 $232,153.00
3 490 $1,269,357.00
4 407 $1,020,897.00
5 374 $200,834.00
6 367 $18,940.00
7 308 $215,805.00
8 295 $1,416,319.00
9 285 $26,007.00
10 212 $11,828.00
11 177 $1,135,264.00
12 173 $242,280.00
13 135 $6,678,017.00
14 129 $106,235.00
15 123 $152,794.00
16 117 $208,762.00
17 116 $106,608.00
18 116 $64,031.00
19 115 $30,509.00
20 103 $2,260,231.00
Total 5213 $16,228,947.00
Source: AirDNA

50
Conclusion
The preceding analysis has various implications for the use of rent gap analysis of home-sharing.
First, with Cuba recognizing alternative forms of tenure and occupation, performing a direct
comparison of Airbnb listings with the total rental housing stock becomes problematic.
Therefore, such a situation warrants understanding how home-sharing presents a source of
potential displacement of housing units under each form of tenure and occupancy. As platform
companies, such as Airbnb, have more stringent and homogenizing market and occupancy
arrangements for housing in the short-term rental market, another consideration is understanding
how home-sharing presents areas of contestation as it forces owner-occupants to withdraw from
existing alternative tenure-occupancy frameworks to enter an international market of short-term
rentals. In Havana’s case, Airbnb can be a potential source of displacement for Cuban nationals
that need temporary dwelling while they work or address other affairs in the city, as well as for
some groups of non-residents (such as international college students).
Second, performing a comparative analysis of change in CGR between short- and long-
term rentals can be inadequate in some contexts. Much like Barcelona and New York, Airbnb
listings tend to concentrate more in places with higher rental rates. In my analysis, the
municipalities with the largest CGR differences appear to be those that are more likely to
experience an increased presence of Airbnb listings in the future, much like the studies of
Barcelona (Yrigoy, 2018) and New York (Wachsmuth & Weisler, 2018). However, unlike
Barcelona and New York, the overall trend was that Airbnb-generated CGR is less than CGR
from long-term rentals. This points to several considerations when applying rent gap analysis in
contexts similar to Havana, that is, contexts where there has been a recent formalization of
property and housing supply is severely constrained. In these circumstances, there is no figure
51
analogous to a developer that actively seeks to redevelop to achieve the “highest and best use”,
other than the state and the owner-occupiers. As Cuba has been facing severe economic
constraints and housing shortages, as well as declining housing quality, households would opt to
capitalize on their property to improve their structural conditions and have an additional source
of income. The easiest way to achieve this would be by renting out spare rooms. Indeed, the
Airbnb data shows that over half of the listings in Havana are private rooms. At the same time,
less than 10% of the long-term rentals are single rooms, indicating that there is a low probability
that spare rooms that are listed in Airbnb could potentially be switched to the long-term rental
market.
Third, when looking at individual Airbnb hosts, it becomes apparent that there are
extractive arrangements taking place in Havana, where a few hosts hoard a disproportional share
of the listings and the total revenue generated. Such a manifestation of inequality is taking place
in spite of comprehensive and stringent regulations put forth by the Cuban government. To better
understand the implications of this phenomenon, it is important to not lose sight of the fact that
home-sharing is enabled by multinational companies that provide digital platforms that allows
potential hosts and guests to book listings and transact payments from anywhere in the world. It
is also important to bear in mind that Cuba’s regulations on homeownership, renting and self-
employment are local in scope. What this entails is that, since nothing prevents non-residents to
become Airbnb hosts and either acquire property in Havana themselves (if they are Cuban
nationals living abroad) or come into agreements with residents to acquire properties through
them to profit from short-term rentals, is that the mechanisms that could produce a rent gap
become increasingly diffuse because of digital technology and are contingent upon transnational
52
dynamics. As Wachsmuth & Weisler (2018) state, Airbnb can become a gateway for
transnational gentrification, resulting in transnational spillover effects.

53
IV. Home-Sharing and Housing Affordability in Havana
The advent of the sharing economy, particularly in the form of home-sharing, has been a source
of concern to cities throughout the world. Among its most pressing issues, is how these digital
platforms for short-term rentals can hinder housing affordability. Most of the research conducted
on this issue has been on cities in Global North, however. There are lingering questions on how
such dynamics play out in cities of the Global South, especially after property regularization has
taken place. Seen as a policy that provides greater security and opportunities for capitalizing
assets, affordability outcomes from property regularization can potentially be seriously disrupted
by the diffusion of home-sharing.
The real estate market liberalization policies in Cuba are viewed by the Cuban
government as a means to optimize the housing stock for its citizens (C Mesa-Lago & Pérez-
López, 2013). I examine whether these changes in property rights in Cuba could be achieving
this goal to the detriment of housing affordability. More specifically, I use Havana as a case
study to answer the following question: How does the recent emergence of home-sharing in
Cuba hinder or foster access to affordable housing for its citizens?
I perform a series of hedonic analyses to determine how home-sharing is related to
housing affordability in Havana, by evaluating the dynamics between Airbnb listings and both
the long-term rental and home sales markets. Using data from online classified ads, results show
that increased presence of home-sharing has a statistically significant relationship to increased
rental rates and housing prices.
54
Home-Sharing and Housing Affordability in Emerging Markets
As regularization results in a liberalization of housing markets, the same causal mechanisms
between home-sharing and housing affordability as described in Chapter I can take place in
emerging land markets. There are, however, some instances in which home-sharing might not
affect housing affordability. After regularization, the market for short-term rentals may be very
small compared to the market for long-term rentals. This could take place if the new landlords
prefer the stability associated with long-term tenants, or if the startup capital needed to improve
homes to make them desirable for tourists is exceedingly large.
The other instance in which home-sharing might not affect housing affordability is if the
market for short-term rentals is dominated by units that would otherwise be vacant or
underutilized. In some emerging land markets, supply is very constrained as regularization does
not go hand-in-hand with increased development and improved credit access. Thus, households
would tend to optimize the existing stock of housing by renting out available rooms or accessory
dwelling units.
Given the recent reforms and the housing situation in Cuba, it is possible that the
presence of home-sharing might disrupt both rental and housing markets, especially if housing
supply is very constrained. By the same token, the supply constrain might foster more “small-
time” hosts that rent spare rooms, rather than entire housing units, that wouldn’t otherwise be
available in the long-term rental market. This makes Cuba an interesting case study to assess
how home-sharing could affect emerging rental and housing markets.
55
Hedonic Pricing
Using data from the real estate and rental ad sources used in Chapter III, I developed hedonic
pricing models that will allow us to determine if the new short-term rentals, long-term rentals
and real estate markets in Havana are linked. Specifically, I examined the following relationship:
𝑃 𝑟 𝑖 𝑐𝑒 𝑖 = 𝛼 + 𝛽𝐴 𝑖 𝑟 𝑏𝑛 𝑏 𝑚 + ∑ 𝛾 𝑡𝑖
𝑋 𝑡𝑖
𝑛 𝑡 = 1
+ 𝛿 𝐴 𝑚 𝑒 𝑛 𝑖 𝑡𝑖 𝑒 𝑠 𝑖𝑚
+ 𝘀 𝑡
Where Pricei refers to either long-term rental rates or housing prices for each observation i,
Airbnbm is a measure of Airbnb supply at the municipal or neighborhood level, Xni refers to a
vector of n number of housing characteristics for each observation i, Amenitiesim is the total
number of amenities and services per category located in each municipality or neighborhood, and
i is the error term.
Scraped short-term and long-term rental ads have information that includes: listed price
(in Cuban convertible pesos, or CUC), size (in square meters), number of rooms and bathrooms,
location, year of construction, and additional amenities (e.g. air conditioning, swimming pools,
hot water, etc.). The same 441 long-term rental ads extracted for statistical analysis in Chapter III
were used for the hedonic analysis in this chapter (Table IV-1). Home sales listings were
extracted from the same webpages as the long-term rental ads. The initial draw had more than
30,000 observations, which was reduced to a final dataset of 7,292 observations once duplicates
and incomplete ads were removed (Table IV-2). Geocoding could not be completed for this
analysis due to incomplete and irregular information among the stated addresses in the listings,
but the listings are matched to municipalities.
56
The vast majority of the long-term rental observations (78%) are concentrated in three
municipalities: Plaza de la Revolución, Playa, and Centro Habana (Table IV-1). These is not
surprising given that these municipalities either locate the main economic centers of Havana,
Miramar in Playa and Vedado in Plaza de la Revolución, or are the most densely populated
municipalities (as is the case of Centro Habana). For the same reason, these municipalities have
the highest average rental rates for both one- and two-bedroom units. There were no observations
in Cotorro, and there were a scant number of observations for about half of the municipalities,
including Regla, San Miguel del Padrón, La Lisa, and Arroyo Naranjo. It is worth noting that
average rent prices for long-term rental is skewed in some municipalities, but that is mostly due
to the small sample sizes in such instances. About two thirds of all rental listings consists of
apartments, while 24% are single-family units, and the remainder 9% are single rooms.
Observations for housing prices are more evenly distributed among municipalities,
although the largest share of observations are located in Playa, Plaza de la Revolución, Diez de
Octubre, Habana del Este, and Centro Habana (Table IV-2). In terms of prices, the largest
average prices for one-, two, and -three bedroom units are located in Playa, Plaza de la
Revolución, and Habana Vieja, while the lowest prices are found in Cotorro, Guanabacoa, and
La Lisa. Total home sales listings have an almost equal share of apartments and single-family
units, although these proportions vary significantly among individual municipalities.
57
Table IV-1: Long-term rental listings summary
Municipality
Total
long-
term
rental
listings
Apartment listings
Single-family housing
listings Single room listings One bedroom units Two bedroom units
Number Percent total Number
Percent
total Number Percent total
Average
rental price Number
Average
rental price Number
Arroyo Naranjo 3 2 67% 1 33% 0 0% CUC 110.00 2 CUC 120.00 1
Boyeros 6 3 50% 2 33% 1 17% CUC 153.33 3 CUC 265.00 2
Centro Habana 45 32 71% 5 11% 8 18% CUC 246.60 25 CUC 358.68 19
Cerro 11 6 55% 1 9% 4 36% CUC 77.75 4 CUC 633.33 3
Cotorro 0 0 N/A 0 N/A 0 N/A N/A 0 N/A 0
Diez de Octubre 30 20 67% 6 20% 4 13% CUC 142.78 18 CUC 261.88 8
Guanabacoa 11 0 0% 8 73% 3 27% CUC 81.67 9 N/A 0
Habana del Este 15 6 40% 9 60% 0 0% CUC 122.50 4 CUC 273.75 4
Habana Vieja 5 3 60% 2 40% 0 0% CUC 30.00 1 CUC 515.00 4
La Lisa 3 1 33% 2 67% 0 0% CUC 100.00 1 CUC 117.50 2
Marianao 7 4 57% 3 43% 0 0% CUC 200.00 3 CUC 150.00 3
Playa 136 88 65% 38 28% 10 7% CUC 304.80 71 CUC 842.76 42
Plaza de la Revolución 166 128 77% 27 16% 11 7% CUC 267.27 75 CUC 535.85 59
Regla 1 1 100% 0 0% 0 0% CUC 90.00 1 N/A 0
San Miguel del Padron 2 1 50% 1 50% 0 0% CUC 145.00 2 N/A 0
Total 441 295 67% 105 24% 41 9% CUC 245.42 219 CUC 559.94 147

58

Table IV-2: Home sales listings summary
Municipality
Total
home
sale
listings
Apartment
listings
Single-family
housing listings One bedroom units Two bedroom units Three bedroom units
Number
Percent
total Number
Percent
total
Average sales
price Number
Average sales
price Number
Average sales
price Number
Arroyo Naranjo 466 165 35% 301 65% CUC 7,250.43 117 CUC 15,328.00 233 CUC 23,189.01 91
Boyeros 419 172 41% 247 59% CUC 7,379.38 80 CUC 11,337.55 205 CUC 24,147.99 100
Centro Habana 651 374 57% 277 43% CUC 10,752.06 235 CUC 17,557.25 255 CUC 34,010.98 83
Cerro 486 268 55% 218 45% CUC 8,581.13 212 CUC 17,264.58 167 CUC 26,030.38 79
Cotorro 236 104 44% 132 56% CUC 6,764.58 48 CUC 7,824.28 124 CUC 10,931.67 60
Diez de Octubre 722 327 45% 395 55% CUC 9,227.00 237 CUC 19,627.39 245 CUC 42,479.59 147
Guanabacoa 354 153 43% 201 57% CUC 6,446.07 89 CUC 10,879.72 212 CUC 14,017.72 47
Habana del Este 688 352 51% 336 49% CUC 7,035.59 59 CUC 9,785.05 422 CUC 17,112.06 176
Habana Vieja 288 188 65% 100 35% CUC 11,545.81 118 CUC 19,009.83 113 CUC 50,560.31 35
La Lisa 388 179 46% 209 54% CUC 7,501.84 81 CUC 10,057.78 197 CUC 17,316.64 88
Marianao 478 218 46% 260 54% CUC 8,266.22 260 CUC 12,827.19 160 CUC 34,769.77 43
Playa 831 393 47% 438 53% CUC 13,177.27 219 CUC 34,057.30 280 CUC 76,514.51 173
Plaza de la Revolución 826 549 66% 277 34% CUC 19,494.79 163 CUC 38,548.36 304 CUC 76,149.10 210
Regla 45 17 38% 28 62% CUC 6,954.55 11 CUC 15,666.67 24 CUC 37,214.29 7
San Miguel del Padron 414 141 34% 273 66% CUC 6,834.51 149 CUC 8,768.29 192 CUC 20,925.00 64
Total 7292 3600 49% 3692 51% CUC 9,895.32 2078 CUC 17,544.44 3133 CUC 39,623.57 1403

59
Since Airbnb provides the market platform for the vast majority of short-term rentals in
Havana, data from the site was purchased from Airdna, an Airbnb market analysis firm. The data
includes short-term rental listings between August 1, 2015 and September 1, 2017. The total
number of listings is 24,113 (Table IV-3). At the municipal level, the number of listings ranges
from 4 in Cotorro, to 8,383 in Plaza de la Revolución. The final dataset does not include listings
for boats, camping tents, and other sites not associated with traditional housing. There is a
slightly larger share of listings that correspond to single rooms, which is consistent with the
housing supply constrains common in Cuba. When the supply of Airbnb listings is expressed as
density (number of listings per Km2 in each municipality), Centro Habana is by far the densest
municipality in terms of listings, while Habana Vieja and Plaza de la Revolución are a distant
second and third, respectively.

60
Table IV-3: Summary of Airbnb listings and amenities and services
Municipality
Total
Airbnb
listings
Number of
entire
apartment/house
Airbnb listings
Number
of single
room
Airbnb
listings
Number
of
shared
room
Airbnb
listings
Airbnb
density
(number of
listings/Km2)
Boyeros 188 68 118 2 1.301
Centro Habana 5555 2173 3303 79 1457.710
Cerro 357 143 199 15 31.962
Cotorro 4 4 0 0 0.052
Diez de Octubre 361 148 213 0 27.317
Guanabacoa 67 33 34 0 0.481
La Habana del Este 923 555 363 5 5.771
La Habana Vieja 4221 1903 2299 19 858.857
La Lisa 61 24 37 0 1.501
Marianao 118 54 64 0 4.905
Playa 3453 2231 1217 5 93.171
Plaza de la Revolucion 8383 4274 4082 27 619.648
Regla 65 41 24 0 5.784
San Miguel del Padron 80 30 50 0 2.936
Total 24113 11806 12155 152 30.225

Data on municipal amenities and services were obtained from OpenStreetMap (Table
IV-4) and its categories includes historical sites, lodging establishments, historical sites and
museums, shops, bars, nightclubs, and Wi-Fi parks, but also government service offices (e.g.
post offices, drug stores, internet card stores, and water and electricity utilities office), and
hospitals and health clinics. Amenities and services were estimated as the total number in each
municipality and as density (number of sites per Km2 in each municipality), with Plaza de la
Revolución, Habana Vieja, and Playa having both the largest number of total sites and density.
61
Table IV-4: Summary of amenities and services in each municipality
Municipality
Number of
lodging sites
Number of
historic sites
Number of service
offices
Number of
recreation sites
Shops and
markets
Number of
hospitals and
clinics
Restaurants,
bars, and
nightclubs
Number of wi-
fi access sites
Arroyo Naranjo 33 4 7 4 74 8 17 3
Boyeros 83 20 72 10 279 13 53 7
Centro Habana 489 17 46 7 283 8 182 17
Cerro 35 4 36 11 183 10 16 3
Cotorro 7 2 15 1 45 8 18 0
Diez de Octubre 69 3 41 9 227 7 41 7
Guanabacoa 15 3 12 3 79 6 10 5
La Habana del Este 281 21 29 8 142 5 87 11
La Habana Vieja 429 106 97 14 289 4 237 25
La Lisa 17 0 25 5 110 4 22 5
Marianao 22 7 40 11 194 12 16 6
Playa 192 11 158 19 430 24 196 18
Plaza de la Revolucion 500 232 168 21 701 48 319 44
Regla 11 4 18 3 70 9 11 2
San Miguel del Padron 11 4 8 0 72 3 11 1
Total 2194 438 772 126 3178 169 1236 154

62
The extracted information was codified and tabulated (See variable list in Table IV-5).
The data were used to carefully craft hedonic pricing models that incorporate specific
characteristics of each housing unit as well as spatial effects resulting from adjacent short-term
rental activity. I developed regressions that model, first, long-term rental rates and, second,
housing prices as a function of the number of short-term units offered for rent online in the same
geographic market, controlling for unit characteristics, local amenities and services, and
municipal level fixed effects. Sub-municipal neighborhood fixed effects, at the Reparto or
Consejo Popular level, were not included on the regressions for rentals because not all
observations yielded this information. However, a subset dataset of 2,871 home sales listings
included sub-municipal neighborhoods.
Maps or shapefiles that detail the boundaries of the sub-municipal neighborhoods in
Havana are not publicly available. The only publicly available source that identifies the
geographic neighborhoods in Havana was obtained in the form of shapefiles from
OpenStreetMap, and are geocoded as centroids rather than as polygons. In order to generate
neighborhood-level variables for the home sales regressions, it is important to establish their
boundaries. To do this, I developed an approximation of the neighborhood boundaries using
Voronoi polygons (Figure IV-1).
63

Figure IV-1: Centroids and estimated boundaries of sub-municipal neighborhoods in Havana
The main independent variable, supply of Airbnb listings, is the total number of listings
per municipality. Additional regressions were tested using density of Airbnb listings per
municipality (calculated as the total number of listings divided between surface area) as an
alternative independent variable for more robust testing for possible endogeneity associated with
using the total number of listings. Density of Airbnb listings was not estimated for sub-municipal
neighborhoods.

Neighborhood centroids
Municipalities
Neighborhood polygons
Legend
64
Table IV-5: Variable list
Variable Description
Variable
type
Sales price Listed sale price Continuous
Number of rooms (sales) Number of rooms for homes on sale Continuous
Number of baths (sales) Number of rooms for homes on sale Continuous
Sales housing type:
apartment Home on sale is a multi-family unit Binary
Sales housing type: house Home on sale is a single-family unit Binary
Rental rate Listed rental price Continuous
Number of rooms (rentals) Number of rooms for rentals Continuous
Number of baths (rentals) Number of baths for rentals Continuous
Rentals housing type:
apartment Rental is a multi-family unit Binary
Rentals housing type: house Rental is a single-family unit Binary
Rentals housing type: room Rental is a single room Binary
Number of Airbnb listings Number of Airbnb listings in each municipality or neighborhood Continuous
Density of Airbnb listings Density of Airbnb listings in each municipality (number/Km2) Continuous
Number of lodging sites Number of lodging establishments in each municipality or neighborhood Continuous
Number of historic sites Number of museums and historic sites in each municipality or neighborhood Continuous
Number of service offices
Number of banks and government service offices in each municipality or
neighborhood Continuous
Number of recreation sites Number of parks and sports venues in each municipality or neighborhood Continuous
Shops and markets Number of shops and markets in each municipality or neighborhood Continuous
Number of hospitals and
clinics Number of hospitals and clinics in each municipality or neighborhood Continuous
Restaurants, bars, and
nightclubs
Number of restaurants, bars, and nightclubs in each municipality or
neighborhood Continuous
Number of wi-fi access sites Number of wi-fi access sites in each municipality or neighborhood Continuous

Summary statistics (Table IV-6) show that the minimum rental rates was 10 CUC per
month and the largest value was 10,000 CUC per month, with a median of 280 CUC and an average
of 464.90 CUC. Home prices ranged from 1,000 CUC to over 1 million CUC and a median value
of 10,000 CUC. Sales listings showed properties that ranged from 1 room and no bathrooms, to 10
rooms and 10 bathrooms. Rental listings, on the other hand, showed properties that ranged from 1
room and no bathrooms, to 6 rooms and 6 bathrooms. I did not estimate Airbnb density as the
65
fraction of short-term rentals of the entire rental market because official census data for Cuba has
yet to identify and measure rental housing units.
66
Table IV-6: Variable summary statistics per municipality
Variable Minimum First Quartile Median Mean Third Quartile Maximum
Sales price CUC 1,000.00 CUC 7,500.00 CUC 10,000.00 CUC 31,037.00 CUC 27,000.00 CUC 1,112,223.00
Number of rooms (sales) 0 1 2 2.131 3 10
Number of baths (sales) 0 1 1 1.23 1 10
Rental rate CUC 10.00 CUC 100.00 CUC 280.00 CUC 464.90 CUC 600.00 CUC 10,000.00
Number of rooms (rentals) 1 1 2 1.723 2 6
Number of baths (rentals) 0 1 1 1.388 2 6
Number of Airbnb listings 4 118 362 2,204 3,462 8,383
Density of Airbnb listings 0.052 3.064 27.317 251.147 93.171 1457.710
Number of lodging sites 7 22 83 185.700 281 500
Number of historic sites 0 4 11 38.110 20 232
Number of service offices 7 25 41 64.340 97 168
Number of recreation sites 0 5 9 10.110 14 21
Shops and markets 45 110 227 262.300 289 701
Number of hospitals and clinics 3 6 8 13.910 13 48
Restaurants, bars, and nightclubs 10 17 53 105.500 196 319
Number of wi-fi access sites 0 5 7 13 18 44

67
The results showcase one of the first hedonic analyses of Cuba’s emerging private rental
market. Results from the hedonic pricing models whether the short-term rental market for
foreigners and the long-term rental and real estate markets for Cuban citizens in Havana are
linked, and if so, how. This hedonic pricing analysis can help determine if the short-term rental
market for foreigners could potentially have a crowding-out effect on affordable housing in
Havana. This would indicate if any changes in prices in the rental and real estate markets
resulting from adjacent short-term rentals can adversely or positively affect the possibilities of
accessing affordable housing in the city.
Results
Rental rates
Results from the regressions are consistent in showing that rentals are priced according to their
housing characteristics (Table IV-7). Using number of Airbnb listings as the main independent
variable, models 1, 2, and 3 shows that, controlling for housing characteristics, an increase in the
number of short-term rentals is related to a statistically significant increase in the rental price of
long-term rentals. Specifically, after controlling for property characteristics being equal, an
increase of 100 listings of short-term rentals in a given municipality can result in a 0.83% to
0.87% increase in average monthly rent for long-term rentals.
After controlling for local effects (i.e.) municipal amenities, however, the number of
Airbnb listings does not have a statistically significant relationship with rental rates, indicating
that local effects outweigh the presence of short-term rentals. Nonetheless, it is important to not
lose sight of the fact that many municipalities have a small number of observations and the
dataset is not particularly large (n = 441). Not having additional observations and a constrained
68
vector of housing characteristics also limit the explanatory capabilities of all the models, where
the largest R2 was 0.198 in model 4.
Using only the online listings and Airbnb data to determine the number of long-term and
short-term rentals, respectively, rather than the entire market for both rentals, could have
potentially resulted in selection bias, rendering the parameter estimates questionable in their
consistency. These limits notwithstanding, these results suggest that the online rental data and
Airbnb listings data are reliable enough for statistical analysis.

69
Table IV-7: Results for rental rates, using number of Airbnb listings as independent variable. Standard errors
clustered at the municipal level
Dependent variable: log(Rental rate)
(1) (2) (3) (4)
Number of Airbnb listings 8.25e-05** 8.72e-05** 8.22e-05** 2.04e-04

(3.53e-05) (3.86e-05) (3.67e-05) (1.98e-04)

Number of rooms (rentals)

0.474** 0.426** 0.462**

(0.158) (0.146) (0.148)

Number of baths (rentals)

-0.476** -0.424* -0.489**

(0.237) (0.227) (0.204)

Rentals housing type: house

0.099 0.104

(0.133) (0.111)

Rentals housing type: room

-1.08*** -0.962***

(0.236) (0.194)

Number of lodging sites

0.004

(0.003)

Number of historic sites

0.01***

(0.002)

Number of service offices

0.016

(0.019)

Number of recreation sites

-0.092**

(0.004)

Shops and markets

0.002

(0.002)

Number of hospitals and
clinics

0.008

(0.074)

Restaurants, bars, and
nightclubs

(0.006)

(0.019)

Number of wi-fi access sites

0.085

(0.074)

Constant 5.010*** 4.84*** 4.95*** 3.992***

(0.306) (0.173) (0.178) (0.188)
Observations 441 441 441 441
R2 0.034 0.076 0.132 0.198
Adjusted R2 0.032 0.0701 0.122 0.173
Residual Std. Error 1.351 (df = 439) 1.325 (df = 437) 1.287 (df = 435) 1.249 (df = 434)
F Statistic
15.54*** (df = 1;
439)
12.06** (df = 3;
437)
13.20*** (df = 5;
435)
8.114*** (df = 13;
427)
Note: *p<0.1; **p<0.05; ***p<0.01; standard errors in parenthesis

Using density of Airbnb listings as the main independent variable yielded very different
results (Table IV-8), showing an even more spurious relationship between Airbnb supply and
long-term rental rates in Havana. None of the models resulted in a statistically significant
70
relationship between Airbnb density and rental rates. Like the previous results, the regressions
have a very low R2 value, which means that variations are poorly explained by these results (R2 =
0.198 in Model 4).

71
Table IV-8: Results for rental rates, using density of Airbnb listings as independent variable. Standard errors
clustered at the municipal level
Dependent variable: log(Rental rate)
(1) (2) (3) (4)
Density of Airbnb listings 1.03e-04 1.22e-04 1.51e-04 -2.03e-05

(3.07e-04) (3.15e-04) (2.9e-04) (2.95e-04)

Number of rooms (rentals)

0.461** 0.425** 0.464***

(0.169) (0.153) (0.147)

Number of baths (rentals)

-0.431* -0.373 -0.489**

(0.239) (0.231) (0.204)

Rentals housing type: house

(0.018) 0.101

(0.129) (0.111)

Rentals housing type: room

-1.19*** -0.971***

(0.269) (0.195)

Number of lodging sites

0.005**

(0.002)

Number of historic sites

-0.01**

(0.003)

Number of service offices

0.031*

(0.019)

Number of recreation sites

-0.097**

(0.043)

Shops and markets

-1.70e-04

(0.002)

Number of hospitals and clinics

-0.013

(0.015)

Restaurants, bars, and
nightclubs

-0.021

(0.014)

Number of wi-fi access sites

0.122

(0.073)

Constant 5.376*** 5.17*** 4.256*** 4.07***

(0.306) (0.153) (0.157) (0.266)
Observations 441 441 441 441
R2 0.001 0.04 0.102 0.198
Adjusted R2 0.001 0.034 0.092 0.173
Residual Std. Error 1.374 (df = 439) 1.35 (df = 437) 1.309 (df = 435) 1.249 (df = 434)
F Statistic
0.476 (df = 1;
439)
6.1** (df = 3;
437)
9.926*** (df = 5;
435)
8.093*** (df = 13;
427)
Note: *p<0.1; **p<0.05; ***p<0.01; standard errors in parenthesis

72
Home prices
Results for home prices benefit from a much larger dataset (n = 7,292) (Table IV-9). After
controlling for housing characteristics, model 3 shows that an increase of 100 Airbnb listings in a
given municipality is related to a 0.5% increase in housing prices. However, controlling for both
housing characteristics and municipal amenities, results in model 4 indicate that an increase of
100 Airbnb listings in a given municipality is related to a 1% decrease in housing prices. This
reversal in direction is due to collinearity between the Airbnb measure and the number of
different amenities, as supply of Airbnb likely reflects multiple local characteristics and effects.
A similar trend is observed at the sub-municipal neighborhood level (Table IV-10),
where an increase of 100 Airbnb listings in a given municipality is related to a 0.8% increase in
housing prices but it becomes statistically insignificant after controlling for local amenities.
Therefore, the effect of home-sharing over housing prices is increasingly less relevant when
controlling for local effects, even more so than in the long-term rental market. This finding is
contrary with the findings from Barron, Kung, & Proserpio (2018) after looking at the effect of
Airbnb listings over housing prices throughout the United States, where Airbnb rentals had a
larger effect over housing prices than on rental rates, thus increasing the price-rental ratio. The
data I used for the analysis, however, is more limited than that used by Barron, Kung, &
Proserpio.
The models from this set of regressions have much better explanatory capabilities than
the rental rates regression, as R2 values reach up to 0.637. The larger datasets also yield much
higher numbers of observations across municipalities and neighborhoods, although the models
73
could benefit greatly from having additional data on housing characteristics (e.g. surface area,
year of construction, architectural typology, and access to utilities).

74
Table IV-9: Results for housing prices using number of Airbnb listings at the municipal level as independent
variable. Standard errors clustered at the municipal level
Dependent variable: log(Sales price)
(1) (2) (3) (4)
Number of Airbnb listings 1.35e-04*** 8.40e-05*** 4.94e-05*** -9.72e-05**

(2.68e-05) (1.72e-05) (1.23e-05) (3.31e-05)

Number of rooms
(rentals)

0.219*** 0.201*** 0.208***

(0.044) (0.004) (0.004)

Number of baths (rentals)

0.898*** 0.854*** 0.797***

(0.078) (0.008) (0.007)

Rentals housing type: apartment

0.987*** 0.91***

(0.009) (0.105)

Rentals housing type:
house

0.422*** 0.399***

(0.007) (0.006)

Number of lodging sites

-2.72e-03***

(3.58e-04)

Number of historic sites

-3.90e-03***

(5.02e-04)

Number of service offices

-1.42e-02***

(2.70e-03)

Number of recreation
sites

2.96e-02***

(3.49e-03)

Shops and markets

2.67e-03***

(5.60e-04)

Number of hospitals and clinics

3.85e-03

(2.62e-03)

Restaurants, bars, and nightclubs

1.05e-02***

(2.23e-03)

Number of wi-fi access
sites

5.67e-03

(5.38e-03)

Constant 9.274*** 7.81*** 7.567*** 1.723

(0.099) (0.129) (0.152) (1.102)
Observations 7,292 7,292 7,292 7,292
R2 0.127 0.51 0.602 0.62
Adjusted R2 0.127 0.51 0.602 0.62
Residual Std. Error 1.006 (df = 7290) 0.754 (df = 7288) 0.681 (df = 7286) 0.664 (df = 7278)
F Statistic
1,062*** (df = 1;
7290)
2,532*** (df = 3;
7288)
2,186*** (df = 5;
7286)
915*** (df = 13;
7278)
Note: *p<0.1; **p<0.05; ***p<0.01; standard errors in parenthesis

75
Table IV-10: Results for housing prices using number of Airbnb listings at the sub-municipal level as independent
variable. Standard errors clustered at the municipal level
Dependent variable: log(Sales price)
(1) (2) (3) (4)
Number of Airbnb listings 8.70e-05*** 7.17e-05*** 7.90e-05*** 2.495e-04

(2.47e-05) (1.87e-05) (1.99e-05) (1.83e-04)

Number of rooms
(rentals)

0.311*** 0.286*** 0.284***

(0.138) (0.122) (0.011)

Number of baths (rentals)

0.565*** 0.550*** 0.505***

(0.056) (0.056) (0.031)

Rentals housing type:
house

0.155** 0.201***

(0.056) (0.052)

Number of lodging sites

-1.385e-02***

(0.004)

Number of historic sites

-6.917e-03

(0.010)

Number of service offices

-3.942e-02***

(0.011)

Number of recreation
sites

-1.410e-02

(0.022)

Shops and markets

2.166e-02***

(0.004)

Number of hospitals and clinics

-8.533e-02***

(0.022)

Restaurants, bars, and nightclubs

-7.968e-03

(0.006)

Number of wi-fi access
sites

8.229e-02**

(0.030)

Constant 1.047*** 8.81*** 8.802*** 8.806***

(0.158) (0.093) (0.094) (0.031)
Observations 2,871 2,871 2,871 2,871
R2 0.053 0.598 0.603 0.637
Adjusted R2 0.053 0.597 0.602 0.635
Residual Std. Error 0.92 (df = 2869) 0.599 (df = 2867) 0.596 (df = 2866) 0.571 (df = 2858)
F Statistic
161.7*** (df = 1;
2869)
1,420*** (df = 3;
2867)
1,087*** (df = 4;
2866)
915*** (df = 12;
2858)
Note: *p<0.1; **p<0.05; ***p<0.01; standard errors in parenthesis

Using density of Airbnb listings show an even less relevant relationship between home-
sharing and housing prices (Table IV-11). This measure of supply of Airbnb listings did not
yield any statistically significant effect over housing prices before controlling for local
76
(municipal) amenities. Controlling for local amenities, however, results in a decreasing
relationship between Airbnb density and housing prices.
Table IV-11: Results for housing prices using number of density of Airbnb listings at the municipal level as
independent variable. Standard errors clustered at the municipal level
Dependent variable: log(Sales price)
(1) (2) (3) (4)
Density of Airbnb listings 3.203e-04 2.151e-04 7.801e-05 -1.525e-04**

(3.16e-04) (1.91e-04) (1.05e-04) (6.4e-05)

Number of rooms (rentals)

0.221*** 0.203*** 0.208***

(0.044) (0.041) (0.041)

Number of baths (rentals)

0.977*** 0.899*** 0.797***

(0.084) (0.083) (0.072)

Rentals housing type:
apartment

1.083*** 0.909***

(0.119) (0.105)

Rentals housing type: house

0.418*** 0.399***

(0.071) (0.062)

Number of lodging sites

-2.295e-03***

(3.15e-04)

Number of historic sites

-3.371e-03***

(4.53e-04)

Number of service offices

-0.011***

(0.002)

Number of recreation sites

0.028***

(0.044)

Shops and markets

0.002***

(5.95e-04)

Number of hospitals and
clinics

0.004*

(0.002)

Restaurants, bars, and
nightclubs

0.007***

(0.002)

Number of wi-fi access sites

0.005

(0.007)

Constant 9.491*** 7.845*** 7.579*** 7.368***

(0.154) (0.132) (0.147) (0.015)
Observations 7,292 7,292 7,292 7,292
R2 0.018 0.472 0.587 0.62
Adjusted R2 0.018 0.471 0.587 0.62
Residual Std. Error 1.067 (df = 7290) 0.7827 (df = 7288) 0.692 (df = 7286) 0.664 (df = 7278)
F Statistic
131.6*** (df = 1;
7290)
2,169*** (df = 3;
7288)
2,070*** (df = 5;
7286)
914.3*** (df = 13;
7278)
Note: *p<0.1; **p<0.05; ***p<0.01; standard errors in parenthesis

77
Conclusion
Results in this chapter shows that the prevalence of home-sharing could hinder housing
affordability in an emerging property market by having an adverse relation with both long-term
rental rates and housing prices, albeit marginally, if not significantly at all, once local effects and
housing characteristics are taken into account. More to the point, home-sharing can have a
greater effect over rental rates than housing prices, decreasing the price to rent ratio. This last
finding is in opposition to the findings from research conducted in the US on the sharing
economy and housing affordability.
Cuba’s case does highlights how home-sharing could be related with hindered housing
affordability even when supply is severely constrained, and a large share of short-term rentals
offered in digital platforms are single rooms. The effect, however, is much lower than the
research in the US shows, likely reflecting how different tenure arrangements and dwelling types
can reduce the possible causal mechanisms by which home-sharing affects housing affordability.
In other words, as most of the Airbnb listings are for single rooms, it is less likely that it will
affect housing prices and long-term rental rates for entire units.
There are, however, important considerations that limit the generalizability of the
findings from Havana. First, the process of regularization in Cuba is one of market liberalization
to allow residents to formally monetize and capitalize on their assets, rather than a mechanism of
addressing tenure security, as is the case in many other countries in the Global South. Second,
both formal rental and real estate markets are still relatively young and could still be in the
process of addressing issues related to principal agent problems and pricing equilibriums. Third,
the data used for this analysis has multiple shortcomings, including a low number of
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observations for long-term rentals, lack of key housing characteristics, specific addresses to
better account for local effects, and a more complete set of housing data from Cuba’s census.
Even accounting for such pitfalls, however, the analysis does provide enough empirical
basis to support further research on how the dynamics between the sharing economy and
property regularization can have favorable or unfavorable outcomes.

79
V. Home-Sharing, Labor Formalization, and Entrepreneurship in
Havana
Economic informality has long been identified a group of economic activities intrinsic to both
market and statist economies (Portes & Böröcz, 1988). As such, it is the result of reduced access
the formal economy, social exclusion, an interdependent relationship with formal endeavors to
access cheap labor and materials, labor precarization, and State laws and regulations that creates
informal economic opportunities by delimiting formal boundaries (Manuel Castells & Portes,
1989; Portes & Sassen-Koob, 1987). Thus, many countries have been pushing for economic
formalization, with the expectations that, through harmonization of property rights, labor
autonomy will foster entrepreneurial activity among informal workers, in a way that would allow
them to capitalize on their assets and reduce dependency on seemingly inefficient State
interventions (De Soto, 2000).
Results of economic formalization have not been as rosy as initial expectations would lay
out, but it has not withered in popularity among governments. With the advent of the sharing
economy, innovations in information and communications technologies provide new
opportunities in the form of digital labor through platforms that foster exchanges of good and
services. Thus, the sharing economy has disrupted multiple markets, including taxi services in
the form of ride-hailing, hotels and lodging businesses through home-sharing, and labor staffing
companies through digital labor services. It is then reasonable to expect that these digital
technologies can have disruptive effects over formalized endeavors.
It is, therefore, important, to understand how the arrival of digital platforms can disrupt
labor formalization in traditional markets in Havana, particularly how home-sharing results in
80
new dynamics among emerging landlords in short- and long-term rental markets. Given that the
Government of Cuba has recently allowed its citizens to buy, sell, and rent property as part of a
broader set of reforms that foster land market liberalization and micro-entrepreneurship by way
of self-employment (cuentapropistas), this chapter seeks to explore how the dynamics between
the underlying institutional context, social networks, and digital platforms foster or hinder
individual landlords from entering the housing rental market. Through interviews and field
observations, I identified and analyzed the interactions between actors involved in the rental
markets and the underlying institutions and social networks that are relevant for these dynamics.
This research showcases how formalization of entrepreneurial activity in the face of the sharing
economy can result in spillover effects, new dynamics, and social structures.
Methods
I used a snowball approach and semi-structured interviews to lay out the institutional
channels and barriers, as well as other actors through which they establish the ties that allow
these individuals the means by which they can access capital and resources to improve, acquire,
sell or rent their property. This included identifying actors that fall within formal roles (e.g.
registered real estate brokers, non-agricultural cooperatives, and local financial institutions) and
informal roles (e.g. family members that send remittances and unregistered brokers or
contractors) in the established framework of property rights in Cuba.
The findings from the interviews were complemented with field observations taken
during the course of fieldwork in Havana. This included spending hours and days with Cuban
landlords to observe daily tasks, transactions, and housing improvements, as well as observing
Cuban renters to understand commuting patterns and labor practices, and participants of informal
retail markets.
81
The resulting information from the interviews was used in the following two ways: 1)
generate a descriptive framework that contrasts the formal and informal dynamics that shape the
real estate and rental markets in Havana; 2) determine which actors play central and bridging
roles and what are the actor attributes that drive the ties in the different networks. This analysis
sheds light on the procedural and social hurdles that might prevent market entry by some
homeowners, which would lead to additional equity issues. Specifically, it will determine
whether the economic and political reforms (i.e. legalization of non-agricultural cooperatives and
the increasing presence of multiple civil society actors) have provided the necessary social ties
for entrepreneurs to become landlords in Havana or if access to resources and knowledge depend
largely on kinship and other informal networks. If so, it would be important to assess if this
varies between landlords that have entered the sharing economy to rent to non-residents and
short- and long-term rental landlords that rent to Cuban nationals.
The findings from the interviews and social network analysis lay out the process by
which informal ties between actors improve or hinder access to capital and resources for
households to become cuentapropistas that rent their property. If households are too reliant on
transnational ties and other informal actors to access the necessary resources, then existing
inequities in market entry among potential landlords in Havana would become more prevalent as
the gap between those with access to capital and those without becomes wider. Data collected
during the interviews and surveys are kept confidential and are managed following the standards
set by the University of Southern California’s (USC) Institutional Review Board (IRB).
Interviews with landlords in Havana provided insight to understand how the underlying
institutional context, social networks, existing economic linkages, and the landlords’ socio-
economic profile structure the possibilities of market entry. Although the reforms that have
82
provided the grounds for the current booming rental market in Havana were initially approved in
2011, various landlords rented their property before these reforms were implemented. At the
same time, some of the interviewees became self-employed renters less than a month before the
interview. This provides some contrast of experience, which can help assess how the 2011
reforms marked a change from previous years.
The interviews took place during two fieldtrips to Havana in June 2017 and February
2018. Thirteen self-employed landlords were interviewed, of which 9 offered short-term rentals
to non-residents, and 4 offered both short and long-term rentals to Cuban nationals. Only two
landlords offered to rent an entire home, the rest offered private rooms. Finally, all the properties
that were offered by the interviewed landlords were located either in the Vedado ward at the
Plaza de la Revolución municipality, in the Miramar ward at the Playa municipality, the Centro
Habana municipality, or Habana Vieja municipality. These contiguous wards and municipalities
(Figure…) are both the main employment, public services, and tourism centers in the Havana
province. Such locations, therefore, provide the most appealing housing options for both non-
residents looking for lodging through home-sharing and for Cuban nationals that are either
searching for short- or long-term rentals as they seek employment opportunities or services.
In addition to the interviewed landlords, a representative of Cuba’s Oficina Nacional de
Administración Tributaria (ONAT; Cuba’s tax collection agency) and the Physical Planning
Institute (IPF for its Spanish acronym; Cuba’s central planning agency) were also interviewed
regarding tax practices and urbanism policies applicable to the private rental market in Havana.
Two local long-term tenants were also interviewed, as well as two gestores de turismo, or
tourism intermediaries.
83
Findings
Who Are the Landlords?
Most of the landlords that were encountered were women, which is contrary to the trends
observed for self-employment in the island in general (Mesa Lago et al., 2016). Although there
were interviewees that were as young as 19, most were middle-aged or older. Most interviewees
identified themselves as being white or mulatto; only one of the interviewees identified as being
black.
All the interviewees can be described as self-employed entrepreneurs who see renting as
an opportunity towards economic success, or in the very least, as a way to make ends meet.
While many landlords offer their property for rent as a source of supplemental income, others
depend on their rentals as their main source of income. There is greater disposition to become
full-time entrepreneurs among short-term landlords that participate in home-sharing, meaning
that short- and long-term landlords that rent to Cuban nationals tend to incur in renting as a
complementary effort to their main source of income. As one long-term rental landlord
expressed:
What motivated me? Well, the country allowed work through self-employment
and we [their family] had the conditions for it. The place we live in lends itself
for it [renting to Cubans], there is a market that allows it and we created the
conditions to be a part of it. And we decided that it would be an additional
source of income for the family, to help the family, one more job. And with all
our papers in order we complied with the requirements and started renting the
room.

Some of the interviewees transitioned into self-employment after retiring from their
previous employment, others moved towards self-employed entrepreneurs after leaving their
84
jobs, and some became self-employed landlords while retaining other jobs or other forms of self-
employment. Many landlords in both short-term rentals to non-residents and rentals to Cuban
citizens transitioned from jobs in state-owned enterprises, as the wages from these jobs was
deemed too low. Others were already working odd jobs along with their job in state-owned
enterprises before transitioning into renting, others already experienced unsuccessful, stressful or
extenuating ventures in self-employment, be it as an at-home hairdresser or as a taxi driver, as is
the case for the following interviewees:
My motive was that I was working as a hairdresser and my physical well-being
was deteriorating. Renting is less stressful, it’s easier. Even if I had to invest, I
had greater earnings than when I was working as a hairdresser. As a
hairdresser I had to invest in products that are usually unavailable here in
Cuba, see? To rent the room I had to invest to fix the bathrooms, do other
repairs that I have yet to do. I’ve had to invest in the room, the bed, the
mattress, the air conditioner, some plumbing works, and other details for
improvements, but I’ve yet to fully bear the fruit of the rental. But trust me, it is
still more profitable to rent than suffer the physical exhaustion of hairdressing.
I prefer to rent than work as a hairdresser. – Short-term rental landlord
interviewee in Centro Habana
I used to work on transport in Havana. Man, there was no end to that! I used
to start early in the morning, and sometimes it would hit midnight and I was
still working. And it was one hell of a responsibility. And then, well, I also had
this [the rental], and I couldn’t do both things at the same time and this [the
rental] requires attention. – Short-term rental landlord interviewee in Centro
Habana

One interviewee took to renting rooms of their home after retirement because the
experience provided them with a sense of fulfillment and purpose:
I started to rent my home because I needed to feel that I could still be useful,
that at my age I could still become an entrepreneur.

85
Long-term landlords stated similar goals and opportunities in their interest in the rental
market. However, while long-term landlords see their renters as tenants with responsibilities to
fulfill within a mutually reached agreement, short-term landlords see their renters as guests or
customers. Tenants for long-term renters tend to be internal migrants (young former state
employees) looking for jobs in the new private service sectors for higher wages (mostly tourism
based) and Cuban and non-resident college students. One of them stated that they left a technical
government job in Pinar del Río (a province located in the Northwest of Cuba, more than 100
miles west of Havana) that payed close to 30 CUC per month and moved to Havana to work as a
bartender in a private bar, where they are payed near 600 CUC per month.
Regulations, bureaucracy, and startup capital for market entry
The process and experience of registering as a self-employed landlord in Havana was considered
to be smooth and efficient across all the interviewees. To become a self-employed landlord in
Havana, the household must first acquire the property title and present it to the local housing
division of the Physical Planning Institute. Given that from the early onset of the Cuban
Revolution housing and property titles were made widely available to the population through
rental agreements with the state, none of the interviewees had any issues in this regard.
Once the property title is acquired, the next step is to pay a 100.00 CUC fee in their local
bank for the self-employment license. Although the interviewees did not express any obstacles in
gathering the 100 CUC for the fee, this is certainly a substantial amount for any Cuban national,
considering that average monthly income is approximately 25 CUC. Once the self-employment
fee is paid, the household can then apply for the self-employment license for landlord through
the local Direction of Labor and must register as a landlord in ONAT for tax purposes. The
86
whole process would usually take between 1 and 2 weeks, to 1 month, as expressed by all the
interviewees.
Landlords can then opt for two types of licenses: (1) one is for landlords that use CUC for
all their transactions (arrendador divisa), as they will presumably tend to provide short-term
rentals for non-residents; and (2) one for landlords that use CUP for their transactions
(arrendador moneda nacional), which tend to rent to Cuban nationals. Landlords that have a
CUC license are required to display a sign in front of their property with a symbol similar to a
blue anchor (Figure V-1), while landlords that have a CUP license are required to display a sign
with the same symbol but colored in red (Figure V-2). Walking through the streets of Havana,
especially in neighborhoods in Vedado, Miramar, Habana Vieja, and Centro Habana, the amount
of CUC rental signs are overwhelming. Signs for CUP rentals are also numerous, but much less
common.
87

Figure V-1: Samples of CUC rental signs in Havana (highlighted in red). Pictures taken by author on June 2017.

88

Figure V-2: Samples of CUP rental signs in Havana (highlighted in red). Pictures taken by author on June 2017

Once registered as certified self-employees, landlords must pay a 10% tax rate on
earnings in a monthly fashion, with business expenses subject to tax deductions. Landlords that
are licensed to rent to Cuban nationals pay a base monthly tax of 100 CUP (Cuban pesos or
moneda nacional) or 4 CUC, while landlords that rent to non-residents pay a base monthly tax of
100 CUC. Landlords are required to have a renters registry notebook for bookkeeping purposes
(Figure V-3). The registry notebook can be audited by ONAT at any moment. Landlords must
also keep records of their tenants (including non-residents), their identification information
(passport number in the case of non-residents), rental rates, and period of occupation. Landlords
89
can deduce rental-related expenses from their monthly tax payments, as long as such deductions
are evidenced by receipts. Such expenses include utilities, repairs, construction materials,
toiletries, and other retail purchases such as bedsheets. This monthly process, however, is made
all the more tedious to short-term rental landlords due the lack of essential goods in state-owned
stores, which pushes landlords to purchase goods from informal markets and forfeiting any
possibilities of claiming tax deductions.

Figure V-3: Rental registry notebook. Pictures taken by author on June 2017

One aspect that seemed to first present itself as a major obstacle for households to
achieve market entry is the need for startup capital, which is usually for home repairs,
remodeling, and improvements. In this regard, the amount interviewees had to invest initially in
90
their homes varied widely; one landlord stated that they had to spend $300.00 US dollars in
home repairs, while another interviewee spent over $10,000.00 US dollars. The access to funding
for this initial investment seemed to be the biggest determinant to whether a particular landlord
would decide to rent to Cubans or non-residents: the inability to afford significant repairs to
accommodate was one of the mains stated reason that the landlords that rent to Cubans decided
to do so. It is common to stroll around Havana and find homes that are undergoing repairs and
other construction works in order to improve housing conditions for the rental business (Figure
V-4).

Figure V-4: Example of housing upgrade by short-term rental landlord in Havana. Left: Homeowner himself, using
tools from his job at a state-owned enterprise, is starting works to install a rubble slab and build a bar for visitors in
his kitchen (picture taken by author on June 2017). Right: Bar at the kitchen after works are finished (picture taken
by author on February 2018)
91
Since short-term rentals typically require physical improvements that could significantly
alter the layout of these housing units, the IPF performs random inspections routinely in
coordination with municipal authorities to ensure that such works are up to code and in
compliance with local ordinances. Given that most of the short-term rental units are located in
historic and touristic zones in Havana, ordinances are stricter regarding housing improvements.
Such ordinances, however, do not focus in fostering mixed-uses (although there are some
restrictions as to where locate new private businesses), commercial clustering or affordable
housing. Rather, these are geared towards maintaining the physical characteristics of the
structures.
One notable observation is that short-term renters tend to overcrowd their homes to
accommodate tenants/guests. Also, given the considerable entry capital, most short-term renters
believe it is highly unlikely that they would turn into long-term renters if their business wasn't
successful in the future. In fact, among the chief reasons as to why landlords in short-term rental
markets are reluctant, or outright dismissive, to rent to Cuban nationals are the expectations of
greater revenue by lodging non-residents (since Cuban nationals cannot afford rents that are on
par to short-term rental rates for tourists), as well as discrimination on the grounds of mistrust
towards Cuban nationals, either because these are perceived to be careless towards third-party
property or because they are perceived to be more susceptible to commit theft and other
misdemeanors.
Well, because Cubans tend to be more careless. And, well, foreigners pay
much better and that’s why I rent to foreigners. Cubans are too untrustworthy
to have as renters. It’s not the same - Short-term rental landlord interviewee in
Plaza de la Revolución on why they only rent to non-residents

92
An interesting development among short-term landlords is a greater interest in venture
expansion. Many of these landlords are acquiring, or contemplating to acquire, additional
properties in the emerging real-estate markets to rent them to non-residents in the short-term
markets. Although Cuban nationals are prohibited to own more than 2 housing units, some
landlords I met while doing fieldwork circumvent these regulations by buying additional units,
either through loans by family abroad or by using their own savings, and registering them under
their children, siblings, or other family members. Some of these landlords mentioned that they
are aware of non-residents that are attempting to stock properties for short-term rentals by either
marrying Cuban nationals, or by channeling funds to locals after achieving a verbal agreement, a
risky proposition given that non-residents are prohibited from owning properties and such
agreements have no legal validity. This assertion, however, could not be corroborated during
fieldwork.
Networks and linkages
There were two aspects that determined how each landlord formed their own social networks
related to their rental businesses: 1) access to resources and 2) access to information. Access to
resources refers to those social linkages that each landlord forms to acquire funds or materials to
enable them to operate their rental business. Access to information refers to the channels of
communication that allows landlords to learn and incorporate information relevant for their
rentals, this information can be related to regulations or the idiosyncrasies of the rental business.
Most landlords that could access the necessary start-up capital to afford home repairs and
improvements would do so through their relatives, especially those that live abroad. In this case,
many landlords would receive loans from their relatives through remittances. Although there
were some interviewees that used their personal savings to fund their home repairs and
93
improvements to rent to non-residents, those landlords that were unable to access funding for
repairs were those that rented to Cubans. It should be noted that even though local banks can
provide personal loans, the interviewees opted to not apply for these because either they felt that
the minimum amount set by the banks often exceeded the amount needed by the landlords6, they
would rather access funding through their relatives as a work-around of the banks’ bureaucratic
process, or they simply never considered this option.
Loans from relatives are much more lax on requirements than unsecured loans from
banks in Cuba. The interviewed landlords paid relatives according to monthly earnings, with no
interest rates and no contractual obligations. Agreements were always verbal.
I was going to ask the banks for a loan but dealing with the bank is tedious. I
am required to have a bank account, because businesses must have a bank
account [to apply for a loan]. So, I asked my siblings, who live in Florida, to
loan me the money. I am paying them back with the money I am earning now -
Short-term rental landlord interviewee in Centro Habana on how they access
start-up capital

Although accessing startup capital, or other monetary resources, through kinship,
particularly from relatives abroad, is almost the norm among short-term landlords, landlords that
rent to Cuban nationals tend to turn either turn to relatives or, even more common, use their own
savings to cover their startup costs. As mentioned, startup funding tends to be significantly less
for landlords in long-term rentals than landlords in short-term rentals, as the latter must face

6 It seems plausible that many landlords are unaware of the possibility of applying for loans in any of the State-
owned banks in Cuba. Unsecured loans for self-employed Cubans have a minimum amount requirement of 120
CUC (or 3,000.00 CUP), according to the Central Bank of Cuba, and interest rates can range between 2.25% and
7.75% for repayment periods of 3 and 120 months, respectively.
94
much stiffer competition in attracting increasingly demanding non-residents from other
landlords, local hotels, and hosts from other cities in Cuba and abroad.
Field observations and the interviews clearly reveals that short-term rental landlords that
lived in areas like Miramar and Vedado, which were elite neighborhoods prior to the Cuban
Revolution, benefit greatly from living or owning the larger housing units in these
neighborhoods. Many of these households were living in these larger units since before the
economic and property reforms in 2011 (although none had possession or occupied those units
prior to the Revolution), while others recently bought housing units in these areas using money
sent by relatives living abroad. These short-term rental landlords tended to subdivide these larger
housing units into more independent and shared rooms to rent them separately, thus increasing
their profits. Households located in places like Cayo Hueso in Centro Habana tend to live in
much smaller housing units that were developed after the Revolution, pushing them to
overcrowd their homes in order to accommodate their short-term rental listing.
Another resource-related matter is the way landlords, particularly those that rent to non-
residents, receive payment. Most of the interviewees advertise their rentals through Airbnb, but
do not receive payment directly from the home-sharing company. Because of the US embargo,
Airbnb cannot issue payments directly to Cuban nationals, so it relies on an authorized
intermediary, VaCuba, a Miami-based company that provides multiple services for Cuban
expatriates, to send payments. Interviewees that rely on this system to receive payment have
complained that payments are delayed by 2 to 4 months since the time of rental. Because of this,
the interviewees that do not advertise through Airbnb, or other home-sharing sites, have
established a reliable local word-of-mouth reference system that ensures them a constant flow of
renters that pay cash-in-hand. This more complex arrangement was exclusively observed for the
95
more experienced landlords that have been in the rental business for several years. In
comparison, landlords for Cuban nationals either advertise in local online websites for classified
ads (Figure V-5) or use the same word-of-mouth system some landlords in the short-term rentals
use. Worth observing is the fact that landlords that have CUP licenses tend to advertise online
using CUC for rental rates.

Figure V-5: Screenshots of online classified ads webpages commonly used in Cuba

One less common practice, but which was still present among the interviewees, is the use
of paid intermediaries to acquire supplies and groceries and assist in bureaucratic transactions,
particularly those pertaining to taxes, since they have to be paid on a monthly basis. This,
however, was never mentioned as common practice among landlords that provide short and long-
term rentals for Cubans.
96
All interviewees in the short-term rental markets expressed concern with the availability
and pricing of supplies and groceries that are essential in operating their rentals. This includes
hardware like tools, tiles, and other materials for home improvements, as well as everyday items
like toilet paper. These items, with the exception of groceries that can be acquired in agricultural
cooperatives, are available through state-owned stores throughout the city. The problem is that
these items are in short supply, causing landlords to visit 3 to 4 locations to purchase a single
item; this is made even more complicated when purchasing items in batches.
Well, products are too expensive here. I could offer breakfast to my guest, but
milk is very expensive. That’s why we charge foreigners the way we do,
because it’s very hard to find products at the store. I don’t know if you have
walked around much, ¿have you walked around here much? It’s hard to even
buy water. If you find a single product in the store, whatever it is, you should
take it because it may not be there the day after. Toilet paper is an example. -
Short-term rental landlord interviewee in Playa on scarcity of goods.

Another consequence of supply shortages is the hoarding of goods by the aforementioned
informal resellers when these do become available, giving them free reign to engage in price
gouging. This, in turn, causes landlords to buy overpriced items that are only available in the
informal market. As previously stated, these items become ineligible to be declared as expenses
for tax deduction purposes.
This situation has prompted the interviewees in the short-term rental markets, almost in
unanimity, to say that the state should develop stores that sell items in wholesale at a discounted
price for licensed cuentapropistas. This also suggests that the rental market is dependent upon
more effective supply chain management, which could be improved by further fostering the
development of more non-agricultural cooperatives that can sell many of these items.
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One interesting on-going development in the short-term rental market is the emergence of
gestores de turismo, or professional intermediaries that provide business-improving services to
landlords. Such services include creating and maintaining the rental listing online, with
professional photos and carefully curated advertisements of the property to make them more
appealing, record monetary transactions, and answer inquiries from potential renters. These
intermediaries, however, require reliable access to high-speed internet to perform such services,
which is typically out of reach for the Cuban population. Thus, many of these intermediaries
work in state-owned enterprises that have high-speed internet, and perform tasks pertaining to
the short-term rental market along with the full-time tasks required by their state employer. This
is a risky endeavor since it is a fireable offense in these state-owned companies.
Long-term landlords are not affected by the shortcomings or benefits of the economic
linkages that are inherent in the short-term rental markets, since they designate their tenants with
the responsibility of maintaining their rental units when low-cost repairs are required. Daily
supplies are also the sole responsibility of the tenants, although the situation with supply
shortages does affects landlords that provide short-term rentals to Cuban nationals.
The interviewees overwhelmingly relied on other landlords and acquaintances to access
information regarding the requirements to register property and acquire the self-employment
license, regulations regarding the rental market, and changes in legal framework. Although many
expressed that state-owned media was an important source of information when they were first
learning on the possibilities of self-employment and rental housing, landlords would turn to their
peers to learn the details on how to start their venture.
Here among Cubans, if you’ve noticed, there exists a communication where we
tell each other where there are opportunities for business improvements,
where are the hotspots, how can you do it and gain from it. In other words, we
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all talk to each other to know, within our economic possibilities, what are the
renting opportunities available to create your own business - Short-term rental
landlord interviewee in Habana Vieja on who they seek advice from regarding
short-term rentals

Likewise, landlords would rely on friends, kinship, and other landlords to better ascertain
the idiosyncrasies related to the rental business. These include common amenities to be made
available to tenants, cultural sensitivities regarding non-resident tenants, contacts for home
repairs and improvements, and, more importantly, factors to consider when pricing their
property. Long-term landlords, however, preferred to learn on-the-go, rarely relying on their
peers and kinship to sort out the daily workings of the long-term rental business.
What is also prevalent among short-term landlords is the use of the digital platforms,
particularly Airbnb, to access information on how to manage their property. Through Airbnb,
landlords receive suggested rental rates that account for local market trends; they keep track on
how pricing changes among properties within the same neighborhood; and they learn new ways
to better promote their listing by providing better descriptions, including new amenities, and
displaying better pictures of their property. Of course, Airbnb’s peer review process also pushes
these landlords to provide improved accommodations alongside competitive prices, in order to
meet the needs and expectations of increasingly demanding tourists who, according to many
short-term landlords, are unaware of the material difficulties taking place in Cuba.
Conclusion
The findings would suggest that the short-term rentals to non-residents and long-term rentals to
Cuban nationals are different non-competing markets. The price of market entry is different
among these markets, as are the regulatory requirements and the economic linkages that sustain
99
each sector. It is clear that short-term landlords tend to rely more heavily on kinship abroad to
ensure market entry. In this sense, short-term renting as a venture in Cuba benefits, and depends,
more from the transnational exchange and diffusion of ideas, knowledge, and behaviors, as well
as funding for start-up capital. This places short-term rentals in Cuba among the ventures where
social and monetary remittances become vehicles of transnational development (Levitt, 1998;
Levitt & Lamba-Nieves, 2011).
However, given that both short-term and long-term markets deal with the same durable
and immutable goods, i.e. housing units or rooms, it is reasonable to conclude that these markets
are linked. On the one hand, the prospects of greater revenue generated from home-sharing are
too much a temptation to overcome, effectively reducing housing units that could serve Cuban
nationals. On the other hand, given the overwhelming reluctance from short-term landlords to
rent to Cuban nationals, it is highly likely that the investments for improving housing units in
home-sharing would not have taken place.
The possibility of optimizing existing housing stocks in constrained markets through
increased rental supply is hindered by disruptive technologies that provide access to wealthier
possibilities in tourism, albeit in a scenario where short-term rental landlords not only compete
among themselves, but with offerings in other cities that are comparably attractive for tourists.
This suggests that Havana, far from a completely sui-generis case, provides comparable lessons
for cities in the Global South that see economic opportunity in the intersection between property
regularization of informal communities and greater access to multisided platforms like Airbnb.

100
VI. Concluding Remarks

States throughout the world continue to wrestle with economic informality. In spite of
shortcomings, economic formalization is still a sought-after set of policies that are expected to
foster entrepreneurial activity among informal workers, in a way that would allow them to
capitalize on their assets and reduce dependency on seemingly inefficient State interventions. With
innovations in information and communication technologies, the increasing presence of the
sharing economy has moved enthusiastic governments and policy circles to seek new opportunities
in the form of digital labor through platforms that reduce costs for the peer-to-peer exchange of
good and services.
The findings from this research that the recent economic reforms regarding rental housing
in Cuba need further improvement if they want to address the dual goals of increasing the number
of available housing units through a more efficient use of existing units and increase household
wealth through self-employment and entrepreneurship. The emerging short-term rentals for
foreigners have an adverse effect over the affordability of long-term rentals for Cuban nationals,
although this should be studied in greater detail. This suggests that, despite it mostly being a statist
economy, the reforms in Cuba have not left it immune to the controversy resulting from short-term
rentals in the sharing economy observed in market-based countries. The analysis could greatly
benefit from more observations as these markets mature.
Examining home-sharing through rent gap analysis in the Global South, especially in a
context of recent property formalization and housing market liberalization, requires additional
nuances than similar research conducted in European and American contexts. The underlying
economic and political conditions can determine how possible drivers of gentrification or
101
displacement will manifest themselves. Changes in property rights regimes might legally allow
households to sell and rent their homes but still remain outside the purview of financial capital,
reducing the likelihood of redevelopment geared towards the “highest and best use”. Likewise,
alternative tenure and occupancy arrangements would require other approaches to estimate
potential displacement of housing units resulting from home-sharing, as well as the vulnerable
populations in each form of tenure. Finally, focusing on average effects over capitalized ground
rent can overlook how home-sharing can foster extractive structures through local and
transnational networks.
Havana’s case also highlights an additional consideration that relates to the regulatory
tools that local and national governments have to avoid local and transnational spillover effects
that could result in gentrification and displacement. Cuba has established a local registry of
landlords, has capped the accumulation of properties for the local population, taxes income
generated by rentals on a monthly basis, and requires hosts to keep a registry of tenants.
Nonetheless, this regulatory framework has clearly failed in preventing extractive practices that
deepen inequality. The findings suggest that rent gap analysis in such contexts must take into
account alternative forms of tenure and occupation, that comparing capitalized ground rents
between short- and long-term rentals can be inadequate, while rent gaps can be spurred by local
and transnational spillovers.
A first step to address this would be to monitor rentals and transactions digitally and
require home-sharing platforms to share information on the hosts and properties. This, however,
would require a substantial improvement in information and communication technology
infrastructure in the island to achieve the former, and a willingness on the part of the home-
102
sharing companies to share the necessary information to achieve the latter. Both are unlikely to
take place, likely being a similar hindrance in other cities in the Global South.
By having an adverse relation with both long-term rental rates and housing prices, home-
sharing can hinder housing affordability in formalized property markets, but its effect is
significantly reduced once local effects and housing characteristics are taken into account. More
to the point, home-sharing can have a greater effect over rental rates than housing prices,
decreasing the price to rent ratio in opposition to the findings from research conducted in the US
on the sharing economy and housing affordability. Cuba’s case does highlights how home-
sharing can be related with hindered housing affordability even when supply is severely
constrained, and a large share of short-term rentals offered in digital platforms are single rooms.
The interviews showed that the short-term rentals to foreigners and long-term rentals to
Cuban nationals are different non-competing markets. The price of market entry is different among
these markets, as are the regulatory requirements and the economic linkages that sustain each
sector. It is clear that short-term renters tend to rely more heavily on social and monetary
remittances to ensure market entry. The wedge between short-term and long-term rental landlords
is defined by the social ties, especially through transnational networks of kinship, that determine
the possibilities of market entry, resulting in greater economic and social inequities, rendering
platform technologies as sources of disruption that could hinder the goals of economic
formalization. The trade-off is that, as the chapter on housing affordability shows, is that increasing
participation in home-sharing can lead to adverse effects on house prices and long-term rental
rates.
The situation could be amended if policies are enacted that can simultaneously marginally
disincentivize landlords from opting for short-term rentals to foreigners and incentivize using
103
existing units for long-term rentals for Cuban nationals. One possibility is to ease the transition
between self-employment licenses for rental to foreigners, to self-employment licenses for rentals
to Cuban nationals in those cases where short-term rentals for foreigners does not result in
successful endeavors for households. Further fieldwork and analysis for this research project
would be geared towards identifying a policy that could achieve such goals.
Nonetheless, it is reasonable to expect that such a tradeoff between short- and long-term
rental markets in Havana will not remain static or permanent. It is possible that if the short-term
rental market draws in more income, that increase in income could in the long-run lead to
construction of larger quantities of both short- and long-term rentals. Indeed, many of the
interviewees suggested that the main obstacle to opting to rent long-term to Cuban nationals was
the low revenue expectations from this market. Although the reforms are too recent to determine
if such a situation is taking place, longer-term research can shed light on this possibility.
The experiences shared in the interviews to landlords in Havana point to institutional
voids stemming from the reforms that have resulted in significantly unequal opportunities for
market entry in the rental business. Most of these voids result from the need to further improve
access to funding and credit for landlords. They also result from insufficient consideration for the
economic linkages that are important for landlords to sustain their rental businesses. Of particular
importance is the informal retail markets that have surged from supply chain inefficiencies,
pointing to needed reforms that bring stability, reduce uncertainty, and possibly formalize,
informal retailing (Dewar & Watson, 2018; Grant, 2013; Ligthelm, 2005; Simon, 1998).
Thus, from these observations, the interviews seem to suggest that economic reforms
should be geared towards: (1) making micro-loans available either through the local state-owned
banks or through financial cooperatives, (2) allow the creation of state or collectively-owned
104
wholesale stores that sell supplies to self-employed landlords at a discounted price, and (3) allow
the creation of small supply stores through self-employment as way to reduce price-gouging
through the informal market. These policies require further analysis to determine how well they
would address the issues stemming from economic linkages. Additional fieldwork and data
analysis can better inform if such policies identified by the interviewees are viable for Cuba’s
context.
What this study points towards is that addressing housing supply constraints and wealth
creation by formalizing land markets and fostering renting through micro-entrepreneurship, in
the context of a globalized economy and increasingly digitalized platform markets, can result in
conflicting goals. As home-sharing is liable to disrupt nascent housing markets, it is important to
account how the increasing diffusion of P2P or B2C digital platforms for short-term rentals can
hinder housing affordability. Such outcome would have to be stacked on top of other outcomes
(e.g. lack of access to formal credit, insufficient protections from displacement and evictions,
lack of improved efficiency in housing markets) that have made property regularization fall short
of its promises.

105
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117
VIII. Appendix 1: Landlord/home-owner interview questions
Background questions
1) Do you own this property? If so, how long ago did you become an owner of this property? If
not, how long ago have you been occupying this
2) How long ago did you start renting this property? What was the motive or motives for renting
this property?
3) Is this property your main residence or a vacation property?
4) Do you rent to foreigners or Cubans? Why?
5) Did you have to transition to self-employment? Would you describe how that transition took
place? Did you have to face any obstacles in that process?
Information access and registration questions
1) How did you learn that the government now allows property rentals? How did you become
acquainted with the process involving self-employment licenses, property registry, and
renting?
2) Did you consult with anyone about the process required to register and rent this property?
With who? About what?
3) What was the process that you followed to acquire your self-employment license and to
register this property? Did you have to face obstacles of any kind? If so, please elaborate
4) Did you consult with anyone about how to administer and manage the property rental as a
business? With who? About what?
5) How did you determine the rental price for this property?
Access to resources
1) Did you have to incur in costs prior to renting this property? On what? How much? How
were you able to gather the money?
2) If you had to invest in improvements in the property, who performed the improvement
works? What kind of improvements did you require? How long did those take?
Administration and management
1) Who performs tasks related to administration and management (e.g. payments and registry)?
2) Who oversees repairs when necessary?
3) How much do you spend in recurring costs to run this business (taxes, fees, services, repairs,
rent if state-owned)? Could you break down those costs?
4) What are the most pressing problems that you are facing as a landlord/business owner?
5) How can your experience as a landlord/business owner, and the rental market more
generally, be improved? What obstacles do you think are in the way of those improvements?
118
Socioeconomic profile
1) Sex
a) Male
b) Female
2) Age:
a) Less tan 18 years
b) 18-24 years
c) 25-34 years
d) 35-44 years
e) 45-54 years
f) 55-64 years
g) 64-74 years
h) 75 years or older
3) Head of household
a) Yes
b) No
4) Employment
a) Government employee
b) Private employer employee
c) Self-employed
d) Unemployed
5) Monthly household income: _______
6) Educational attainment
a) Elementary school degree
b) Middleschool degree
c) Highschool degree
d) Associate or technical degree
e) Bachelors degree
f) Graduate degree
7) Marital status
a) Single
b) Married
c) Divorced
d) Widowed
8) Race
a) White
b) Black
c) Mulatto
d) Other: _____________
9) Province of residence: __________________
10) Municipality of residence: __________________
119
IX. Appendix 2: Stakeholder interview questions
Stakeholder interview questions
Professional background
1. What is your position within the organization?
2. How long have you been working in the organization?
3. What tasks are you responsible for?
Organization/agency background
1. What is the main role of your organization?
2. What tasks is the organization responsible for?
3. Since when has the organization been responsible for this role and tasks?
4. How many employees does your organization have?
5. How much is the budget for your organization?
6. Does your organization have to be self-sustaining or does it depend on transfers and
concessions to operate?
Tasks and responsibilities before and after the legalization of the real estate market
1. How would you describe your organization’s role in Havana’s real estate market?
2. What was your organization’s role before the real estate and rental markets were
formalized in Cuba in 2011?
3. What changes did your organization had to undertake after the real estate and rental
markets were formalized in 2011?
4. How would you describe the process of implementing these changes in your
organization? What obstacles did you have to overcome?
5. What tasks related to the rental market does your organization undertake?
6. What obstacles has your organization faced during the implementation of these tasks?
How were these addressed?
7. Does your organization work or coordinate with other organizations or agencies? Which
ones? To what end do they coordinate? What tasks is each organization responsible for?
8. How would you qualify the relationship between your organization and the other
organizations or agencies?
9. What aspects would you consider that should be addressed in order to improve your
organization’s role in the rental market? Do you consider that it would be possible to
implement the required changes? Why?

Abstract (if available)

Abstract This dissertation examines how the increasing diffusion of the sharing economy can disrupt expected policy outcomes from urban formalization in the Global South. Specifically, it shows how home-sharing can affect expected policy outcomes from property and labor formalization through three mechanisms: (1) its potential for generating gentrification and displacement through the rent gap

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Asset Metadata

Creator Santiago-Bartolomei, Raúl (author)

Core Title Property and labor formalization in the age of the sharing economy: Airbnb, housing affordability, and entrepreneurship in Havana

School School of Policy, Planning and Development

Degree Doctor of Philosophy

Degree Program Urban Planning and Development

Publication Date 10/03/2019

Defense Date 05/03/2019

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Airbnb,Cuba,entrepreneurship,Housing,housing affordability,labor formalization,OAI-PMH Harvest,property formalization,sharing economy

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Boarnet, Marlon (committee chair), De la Roca, Jorge (committee member), Esparza, Nicole (committee member), Kim, Annette (committee member)

Creator Email raul@grupocne.org,rsantiagobartolomei@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-222574

Unique identifier UC11674985

Identifier etd-SantiagoBa-7844.pdf (filename),usctheses-c89-222574 (legacy record id)

Legacy Identifier etd-SantiagoBa-7844.pdf

Dmrecord 222574

Document Type Dissertation

Rights Santiago-Bartolomei, Raúl; Santiago-Bartolomei, Raul

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

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