Sharing Places: Local Socio-Economic Organization and Inequality in Contemporary Short-Term Rental Markets

Abstract

Research on markets distinguishes niche markets, characterized by local community engagement and specialization, from mass markets, characterized by arms-length exchange and large-scale production. Yet, this research often overlooks how inequality differentially underpins these forms of exchange. Building on this work, I explore how local socio-economic disparities may structure different segments of short-term rental markets in the platform (i.e., “sharing”) economy. Drawing on cross-sectional analyses of over 300,000 Airbnb listings clustered in 277 U.S. metropolitan areas, I find that microentrepreneurial short-term rental markets—involving small-scale exchanges that typically demand more personal investment and social interaction—are embedded in civically active communities struggling with economic and housing precarity. Large-scale short-term rental markets—typically involving more socially distant exchanges in which operators rent multiple properties—are prevalent in expensive housing markets, where there are real estate investment opportunities to capitalize on housing vacancies. This study thus builds on understandings of market formation and segmentation, incorporating the role of local inequality, while also illuminating the tensions within platform economy markets more broadly.

Keywords

Markets segmentation inequality platform economy socio-economic organization

Introduction

Sociological work on markets often differentiates exchanges that rely on arm’s-length transactions and mass production from those that rely on specialization, community connection, or notions of social intimacy and reciprocity (Carroll and Swaminathan 2000; Uzzi 1997; Willer, Flynn, and Zak 2012; Zelizer 2005). In line with these conceptual distinctions, recent work on market formation explores how various local communities shape markets posing as an alternative to traditional production processes and commodities. This work suggests that civic organizations, social movements, and locally embedded economic practices have generated new and unconventional markets such as micro-brewing, grass-fed beef, and wind power and have shielded communities from non-local and extractive forms of investment (Carroll and Torfason 2011; Goldstein 2018; Rao 2008; Sine and Lee 2009; Weber, Heinze, and DeSoucey 2008).

However, this work on market formation and segmentation often overlooks how market segments can be differentially structured by urban stratification and inequality. That is, this literature often emphasizes the assets of local communities, showing how their institutions, networks, and practices might orient market actors away from mass production and consumption, toward alternative exchanges that are understood as more meaningful (e.g., Carroll and Torfason 2011; Uzzi 1997; Weber et al. 2008). In doing so, this work downplays how communities also encompass stratified social and economic structures that can motivate different kinds of market participation. How, then, are local socio-economic organization and inequality reflected in market segmentation?

This question is particularly consequential for markets in the platform (i.e., “sharing”) economy, which draw in market participants with different motivations and resources. On the one hand, platforms adopting this label suggest that their technology, in crowdsourcing personal goods to rent, provide “microentrepreneurs” and consumers more economic opportunity and meaningful experiences than traditional markets (Curtis 2014; Frenken and Schor 2017; Zhang, Bufquin, and Lu 2019). On the other, they also attract enterprises of scale, who use the technology to draw in new customers and maximize profits. However, even though these forms of participation are markedly different, microentrepreneurial and large-scale exchanges get couched together as a broader market alternative through the platforms’ crowdsourcing model and the rhetoric of “sharing.”

In this article, I unpack this segmentation, exploring the distinct local socio-economic organization that characterizes short-term rental markets in the platform economy. Drawing on cross-sectional analyses of Airbnb listings in 277 U.S. metropolitan areas, I find that microentrepreneurial markets—involving small-scale exchanges that typically demand more personal investment and social interaction—are embedded in civically active communities struggling with economic and housing precarity. Large-scale markets—involving more socially distant exchanges in which operators rent multiple properties—are prevalent in expensive housing markets, where there are real estate investment opportunities to capitalize on housing vacancies. This study thus builds on understandings of market formation and segmentation, incorporating the role of local inequality, while also illuminating the tensions within platform economy markets more broadly.

Contemporary Short-Term Rental Markets in the Platform Economy

The last decade has seen an explosion of technology firms that span a range of markets including accommodation (e.g., Airbnb, Vrbo/HomeAway), transportation (Uber, Lyft, Turo), food (UberEats, Grubhub, Instacart, Doordash), clothing (Rent the Runway, Poshmark), and labor (TaskRabbit). Companies associated with the platform economy crowdsource their products and labor from a community of users, rent or sell those products out to customers, and mediate these exchanges through internet- or app-based platforms. Relying on exchanges between peers, this network-based model is often understood to be more collaborative, open, and altruistic than the more hierarchical exchanges offered by traditional firms (Powell 2016). Platform companies argue that, in addition to facilitating such exchanges, they help to provide extra income in an uncertain economy, repurpose unused goods, reduce waste, and encourage egalitarian communities characterized by diversity, hospitality, and trust (Frenken and Schor 2017; Schor et al. 2015).

However, platforms also often promote precarious working arrangements, relying on the “gig” labor of users without providing worker protections or benefits (Friedman 2014; Ravenelle 2019). Furthermore, even though they may facilitate the exchange of under-utilized assets among peers, companies often too liberally apply the rhetoric of “sharing” and “innovation” to exchanges that are actually more profit-oriented and conventional (Belk 2014; Cockayne 2016; Slee 2017). To be sure, the crowdsourcing model of the platform economy may invite both new participants, who engage in more collaborative and personal exchanges, and traditional participants, who largely follow the mass market practices of the hierarchical firm. More work, then, is needed exploring the nuances of these markets, including the ways in which they may be segmented by different users and forms of exchange (Lutz and Newlands 2018; Powell 2016; Schor et al. 2020).

Indeed, on the one hand, the platform technology allows private individuals, sometimes called microentrepreneurs (Curtis 2014; Frenken and Schor 2017; Zhang et al. 2019), to commodify their skills and assets with excess capacity; the income they earn often supplements or replaces the income they earn from conventional labor markets (Schor et al. 2020). Consumers renting from these microentrepreneurs often seek them out because they offer more alternative and social experiences than conventional markets can provide (Mao and Lyu 2017; Tussyadiah 2015). On the other hand, the technology also provides a previously untapped customer base to conventional market actors already circulating these goods and services at scale (i.e., large-scale operators). Companies in these markets do not mobilize personal assets or labor, but rather use the brokerage technology to identify new consumers for an already commercialized, but underutilized, fleet of goods. Both microentrepreneurial and large-scale markets in the platform economy, then, are a form of “prosumption” in which suppliers and producers, at different magnitudes, re-commodify goods and labor with excess capacity (Frenken and Schor 2017; Humphreys and Grayson 2008; Ritzer and Jurgenson 2010).

However, in contrast to traditional prosumption, many of these exchanges are often rentals and, as such, involve commodities that are returned to the initial owner after temporary use. In this context, prosumers place their faith in strangers, assuming that engaging in such an exchange will not incur damage or loss. While reputation systems help build mutual trust, securing a favorable review also demands significant emotional labor from providers, who must generate empathy and create memorable experiences for customers with thoughtful “small talk” and personal touches (Huurne et al. 2017; Lutz, Newlands, and Fieseler 2018; Raval and Dourish 2016). The stakes are especially high for microentrepreneurs, who, conducting one-to-one exchanges, offer more personalized items and engage more frequently with customers. The platform economy, thus, relies on exchange structures that, while providing several practical benefits and economic incentives to participants, can also require labor, resources, and socio-emotional investment.

These dynamics are apparent in one of the most popular platform economy markets, short-term rentals, where owners, tenants, or property managers (i.e., “hosts”) rent a housing unit to guests seeking short-term accommodation. For microentrepreneurs on short-term rental platforms, the income earned is typically supplemental; it does not often replace traditional forms of work since it is often less labor-intensive than other forms of gig labor (Ravenelle 2019; Schor et al. 2020). However, becoming a successful microentrepreneurial host does involve significant resources and skills, including having extra housing space in a desirable neighborhood, the know-how to effectively market that space, and an ability to cultivate personalized accommodation practices and amenities (Bucher et al. 2020; Ravenelle 2019). Platforms celebrate the “entrepreneurial ethos” of these hosts in their marketing and advertising, emphasizing the hard-working individuals who struggle to pay their bills and the way that short-term rentals support local economies through such alternative tourism services (Airbnb 2012; Ravenelle 2019).

At the same time, short-term rental platforms devote significant efforts to attracting business travel consumers and thus involve large-scale operators who list multiple properties with standardized amenities (Horn and Merante 2017). These hosts may be previous microentrepreneurial hosts who, finding success with short-term renting, are looking to scale up to further expand profits or real estate enterprises from the housing market that, in trying to identify new consumers, convert properties into quasi-hotels (Ravenelle 2019; Samaan 2015). Because they are listing at scale, large-scale operators are also more likely than microentrepreneurial hosts to outsource their labor to agents who manage units and interact with guests. Many large-scale forms of short-term rental exchange, then, offer less personal and less distinctive experiences than microentrepreneurial forms.

The similarities and differences between microentrepreneurial and large-scale short-term rentals are accordingly outlined in Table 1. Microentrepreneurial rentals, typically operated by individual owners or tenants with a spare room or house, are more niche and personal, promising travelers the experience of “living like a local” (Benner 2016). Large-scale rentals, on the other hand, resemble traditional hotel and short-term accommodation markets, offering more standardized features and more impersonal spaces and experiences.

Table 1.

Idealized Features of Microentrepreneurial and Large-Scale Exchanges in Short-Term Rental Markets.

Differentiating Features	Microentrepreneurial	Large-scale
Exchange Structure
Host/guest social distance and interaction	One-to-one/personal	One-to-many/managed en-masse
Accommodation experience	Local/idiosyncratic	Translocal/standardized
Resource Dependencies
Host labor	Individual homeowners/tenants	Real estate enterprise
Capital	Low to moderate	High
Housing	One room/unit	Multiple rooms/units

To be sure, these categories of exchange, and their described features, are idealizations and, as such, do not uniformly represent empirical reality. However, idealizations are analytically useful as “sensitizing devices” to analyze and compare empirical phenomena (Lopreato and Alston 1970; Strandbakken 2017; Weber 1949). Accordingly, I use the idealized scheme in Table 1, along with the literature described in the next section, to examine whether the similarities and differences outlined here align with patterns in empirical data on short-term rental exchanges.

The Local Socio-Economic Organization of “Sharing”

Markets develop from institutional structures (logics, rules, norms, etc.) and material resources that facilitate commodity exchange (Emigh 2008; Fligstein and Dauter 2007; Padgett and Powell 2012; Sewell 1992). Urban marketplaces reflect a “geography of production” (Storper 2013:7) specializing in market commodities based on how resources and institutions are spatially configured (Molotch, Freudenburg, and Paulsen 2000; Storper 2013). Thus, markets concentrate in urban areas where conducive institutional structures and resources can be easily adapted for production purposes (Fleming et al. 2012; Padgett and Powell 2012; Storper 2013).

I have highlighted some idealized features of short-term rental market exchange (see Table 1), including the varying (1) exchange structures associated with each type of short-term rental and (2) resource dependencies. In this section, I connect theories on markets to the literature on urban community, labor markets, and housing, showing how short-term rental markets might map onto local social stratification and inequality. I propose that microentrepreneurial and large-scale short-term rental markets may thrive in contexts with (1) an institutional ecology that favors alternative “sharing” practices and (2) labor, income, and housing inequalities that create the economic motivations and excess capacities for different types of short-term rental exchange.

Institutional Structures: Civic Capacity and Locally Embedded Economic Exchange

Sharing economy markets offer an alternative to conventional markets by circulating goods for temporary use. Typically, once a consumer purchases a good, it becomes privately owned, and thus excludable to others. The odd nature of this type of prosumption is that it embodies the conflicted orientations of buying and selling, combining an intrinsic act of “pleasing oneself” with an extrinsic act of “pleasing someone else” (Humphreys and Grayson 2008:12). Such a practice, then, has the potential to evoke a sense of “hostile worlds,” or that intimate practices should not be comingled with acts of economic exchange (Zelizer 2005, 2011). Sharing economy markets face an additional constraint in that they depend on temporary exchanges between strangers (Frenken and Schor 2017).

Local civic organizations can help to foster the engagement, community solidarity, and supportive social networks for microentrepreneurial exchanges in particular (Putnam 2001). An urban community with a strong institutional structure provides an opportunity for “blended social action”: creating settings for individuals first to congregate and form connections and then mobilize for the collective good (Sampson et al. 2005). A concentration of organizations in an area often matters more for such civic orientations than the size of these organizations’ membership (Sampson et al. 2005). As many in the United States retreat from associational life (Putnam 2001), however, these civic capacities often get channeled inward toward mass consumption (Cohen 2003). Markets thus have become a primary locus for civic activity, as both a facilitator and target (King and Pearce 2010; McDonnell, King, and Soule 2015; Walker 2014; Walker, Martin, and McCarthy 2008).

Active communities and social movements often orient their efforts into the creation of alternative markets challenging mass production. The emergence of markets for micro-brewing, grass-fed beef, wind power, and recycling, for example, relied on communities, social movements, and innovators to adapt originally social practices and meanings to economic exchanges (Carroll and Swaminathan 2000; Lounsbury, Ventresca, and Hirsch 2003; Rao 2008; Weber et al. 2008). Contexts with residential stability, a strong local identity, and civic infrastructure can also be protective, deterring nonoccupant investment, and commercial production and retail (Carroll and Torfason 2011; Goldstein 2018; Ingram and Rao 2004; Ingram, Yue, and Rao 2010). Such local social organization has implications for short-term rental markets, as it predisposes communities toward more alternative and socially engaged, rather than more commercial and extractive, market practices.

I anticipate, then, that microentrepreneurial exchanges, which require more personal investment and social interaction than large-scale exchanges, will be associated with socially active communities and locally embedded economic exchanges. In particular, I expect that marketplaces that have a high concentration of local organizations (i.e., the institutional infrastructure for blended social action) and local businesses will have higher rates of microentrepreneurial rentals. These communities will have more opportunities for interfacing and generating the personal connection and solidarity required for microentrepreneurial exchanges and will be more resilient to commercialized, large-scale forms of exchange.

Resources: Labor, Income, and Housing Precarity

The founding of many platforms closely followed the financial and subprime mortgage crisis of 2007, suggesting they correlate with the increased debt and economic insecurity facing many consumers and workers. Since the 1970s, the American economy has been defined by market deregulation, privatized risk, mass consumption practices, and an unprecedented expansion of the financial sector (Centeno and Cohen 2012; Ivanova 2011). These developments are accompanied by wage stagnation, growing inequality, and a retrenchment of worker benefits and union rights (Krippner 2005; Wilmers 2018; Wisman 2013). This is an untenable convergence: consumers are spending and acquiring more while facing mounting debt and more precarious labor arrangements.

This tension is expressed in urban housing markets, where financialization and an “ideology of homeownership” (Ronald 2008) together contribute to rising housing costs and consumers living beyond their means. First, deregulatory policies transformed traditional mortgages that once locally extended credit to homeowners into securities to be traded on a global stock market (Aalbers 2008). This development, along with local policies focused on returning professional classes to city centers (e.g., creative city initiatives), incentivized the investment of global capital in urban housing markets (Smith 2002; Wetzstein 2017). In attempts to cater to these professional classes and global investors, developers now produce a glut of luxury housing that few local residents can actually afford (Brash 2011; Linhart 2011; Stein 2018).

Yet, more people continue to aspire to own a home, living out their version of the American Dream (Drew 2013; Pattillo 2013; Ronald 2008; Schor 1998; Warren and Tyagi 2003). Since the deregulatory policies of the 1990s that made mortgages more accessible, homebuying is increasingly seen as an investment opportunity, resulting in booms in nonoccupant housing whereby buyers acquire properties not for their use value but to flip them into higher-cost housing or generate rental income (Goldstein 2018). The increased investment in urban housing from people of all incomes raises property values, making housing especially costly for homebuyers new to the housing market as well as for renters who, through rent, pay the expected returns of fictitious capital (Aalbers 2008; Smet 2016; Stephens 2007; Teresa 2016). These increases in housing costs correspond with wage stagnation, more debt, and a rise in new forms of precarious work.

The shift to a new economy based on technological innovation and services, along with diminishing social protections, has created a new class of workers, who, under flexible work arrangements, lack job stability, regular earnings and hours, and a safety net (Kalleberg 2009; Peck 2005; Scott 2006; Standing 2011). Additionally, with lackluster incomes and rising living costs, households are also increasingly turning to financial services to pay for their housing and living expenses (Fligstein and Goldstein 2015). Such developments correspond with an overextended, more economically insecure middle class (Leicht and Fitzgerald 2006; Schor 1998; Warren and Tyagi 2003).

The subprime mortgage and financial crises of 2007, then, likely prompted many consumers to turn to microentrepreneurship. The supplemental income offered by short-term rental markets potentially provided an incentive to those facing employment insecurity or stagnant wages but who could still afford extra housing space (Fligstein and Dauter 2007; Kalleberg 2009; Ravenelle 2019; Schor et al. 2020). I expect, then, that microentrepreneurial rentals will thrive in urban marketplaces where there are low unemployment rates but where working hours are insecure, and workers have extra labor capacity. I also expect microentrepreneurial rates to be higher in communities with moderate levels of income—where residents can afford the extra space—but also where the distribution of wealth is unequal and there are high housing costs relative to income (suggesting stagnating wages).

Urban housing trends would also suggest that short-term rental markets are an investment opportunity for real estate developers and global capital. If there is polarization in the short-term rental market between more and less capital intensive and propertied forms of exchange (see Table 1), I expect the more capital intensive, propertied forms, like large-scale rentals, to represent the investment decisions of capitalists who see housing—and the short-term rental of such housing—in expensive cities as a lucrative business opportunity. In such cases, this segment of the short-term rental market would not be underpinned by middle income earners trying to compensate for their high housing costs but rather real estate firms and landed elites who engage in hosting at scale to further accumulate capital on their luxury vacant units (Brash 2011; Wetzstein 2017). I expect, then, for large-scale operation to be more prevalent in contexts where there are large real estate markets and high levels of income. I also expect that large-scale operation will be popular in communities with high rents and high residential vacancies, reflecting the glut of luxury housing units that investors are trying to make profitable.

Microentrepreneurial and large-scale exchange rates may also positively correlate with housing costs, because of their potential effects on housing prices. That is, as opponents of short-term rentals argue, these rentals may deplete long-term housing stock in their conversion of rooms and units for transient use, thereby increasing rents and property values. Several studies (Barron, Kung, and Proserpio 2021; Garcia-López et al. 2020; Todd, Musah, and Cheshire 2022) have examined this question, and while some effect on local housing market supply and pricing has been observed, it is also still feasible that high housing prices also drive short-term rental participation. Under either condition, or both, I would expect both microentrepreneurial and large-scale exchange rates to be higher in contexts with expensive housing.

Research Design

In this study, I use negative binomial estimation with robust standard errors to regress short-term rental listings in a metropolitan area on local indicators of civic capacity, localism, labor market dynamics, income stratification, and housing costs and supply (see Tables 2 and 3). The two sets of estimates for the two dependent variables (microentrepreneurial and large-scale rentals) are presented in Table 4. Column 1 in each set excludes quadratic and interaction terms while Column 2 includes them. Column 3 adds a third interaction term that is theoretically relevant for large-scale rentals. For ease in interpreting the coefficients and interaction terms, the supplement includes plots of the marginal effects of each variable (Supplemental Figures S1 and S3) and the interaction terms (Supplemental Figures S2, S4, and S5). I also plot the differences in marginal effects across the two types of exchange (Supplemental Figures S6 and S7), to compare the effects across different models with the same variables (Mize, Doan, and Long 2019).

Table 2.

Anticipated Relationship between Types of Short-term Rental Markets and Measures.

Measure	Microentrepeneurial: Anticipated relationship	Large-scale: Anticipated relationship
Institutional Structures
Civic capacity
Nonprofit organizations per 10k capita	+
Locally embedded exchange
Walmart stores per 100k capita	−
% small business retail	+
Walmart stores per capita x % small business retail	+
Resource Dependencies
Labor market dynamics
% working <35 h per week	+
% unemployed	−
% working <35 h per week x % unemployed	+
% real estate firms		+
Income stratification
Median family income (10 thousands)	+	+
Median family income (10 thousands), squared	−
Income inequality gini index	+
Housing costs and supply
Median rent as a percentage of income	+
% of nonseasonal vacancies		+
Median contract rent (thousands)	+	+
% of nonseasonal vacancies x median contract rent		+

Table 3.

Means, Standard Deviations, Ranges, and Correlations, 2016 Airbnb.com, 2010-2015 ACS, 2012 Economic Census, 2006 Walmart data (Holmes 2011), and 2011 NCSS.

	Variable	Mean	SD	Min	Max	1	2	3	4	5	6	7	8	9	10	11	12
1.	Total listings (thousands)	1.15	3.52	0	42.78	1	0.98	0.92	0.08	−0.31	0.39	−0.07	0.08	0.28	0.27	0.25	0.14
2.	Microentrepreneurial listing rates (thousands)	0.70	2.46	0	32.98	0.98	1	0.82	0.10	−0.29	0.37	−0.08	0.06	0.23	0.28	0.25	0.10
3.	Large-scale listing rates (thousands)	0.45	1.21	0	9.81	0.92	0.82	1	0.03	−0.32	0.39	−0.04	0.11	0.35	0.22	0.23	0.21
4.	Nonprofit organizations per 10k capita	11.73	4.01	3.50	31.26	0.08	0.10	0.03	1	−0.21	0.06	−0.32	−0.33	−0.17	0.65	0.08	−0.03
5.	Walmart stores per 100k capita	1.10	0.51	0	2.85	−0.31	−0.29	−0.32	−0.21	1	−0.20	0.00	−0.24	−0.24	−0.37	−0.01	−0.25
6.	% small business retail	41.97	4.51	31.72	60.08	0.39	0.37	0.39	0.06	−0.20	1	0.25	0.29	0.29	0.09	0.25	0.36
7.	% working <35 h per week	43.70	4.49	30.00	57.10	−0.07	−0.08	−0.04	−0.32	0.00	0.25	1	0.49	0.14	−0.53	0.28	0.66
8.	% unemployed	5.08	1.22	2.40	9.70	0.08	0.06	0.11	−0.33	−0.24	0.29	0.49	1	0.13	−0.29	0.12	0.45
9.	% real estate firms	4.76	1.24	2.26	9.03	0.28	0.23	0.35	−0.17	−0.24	0.29	0.14	0.13	1	0.01	0.17	0.26
10.	Median family income (10 thousands)	6.47	1.16	3.69	10.81	0.27	0.28	0.22	0.65	−0.37	0.09	−0.53	−0.29	0.01	1	−0.06	−0.15
11.	Income inequality gini index	45.67	2.29	39.65	54.27	0.25	0.25	0.23	0.08	−0.01	0.25	0.28	0.12	0.17	−0.06	1	0.40
12.	Median rent as a percentage of income	31.06	2.44	24.80	38.60	0.14	0.10	0.21	−0.03	−0.25	0.36	0.66	0.45	0.26	−0.15	0.40	1
13.	% of nonseasonal vacancies	8.36	2.56	3.30	18.36	−0.14	−0.16	−0.10	−0.35	0.38	0.04	0.23	0.20	0.02	−0.50	0.23	0.06
14.	Median contract rent (thousands)	0.72	0.19	0.44	1.61	0.47	0.42	0.50	0.32	−0.57	0.37	−0.09	0.07	0.44	0.67	0.05	0.31
15.	Total population, ln	3.88	0.98	2.76	7.60	0.59	0.56	0.60	0.02	−0.38	0.29	−0.23	0.15	0.30	0.35	0.25	−0.01
16.	% of tourism firms	34.95	7.41	17.00	71.12	0.27	0.25	0.28	0.22	−0.34	0.15	−0.03	0.08	0.19	0.24	0.20	0.23
17.	% of seasonal vacancies, ln	1.18	0.74	0.18	3.65	0.05	0.00	0.15	0.02	0.01	0.39	0.30	0.01	0.33	−0.16	0.02	0.31
18.	% Non-White, ln	3.36	0.56	1.89	4.58	0.24	0.21	0.28	−0.39	−0.28	0.22	0.19	0.46	0.37	−0.13	0.33	0.32
19.	Index of dissimilarity	44.86	12.53	16.99	77.87	0.13	0.14	0.09	0.09	0.06	0.01	−0.16	−0.01	−0.32	0.05	0.22	−0.16
20.	Median age	37.38	4.35	24.50	57.30	0.02	0.01	0.04	0.26	0.07	0.36	0.07	0.01	−0.10	0.05	−0.09	−0.03
21.	Northeast	0.14	0.34	0	1	0.09	0.12	0.03	0.40	−0.13	0.15	−0.14	0.00	−0.33	0.33	−0.08	0.05
22.	Midwest	0.23	0.42	0	1	−0.12	−0.10	−0.16	0.16	0.10	−0.35	−0.22	−0.18	−0.36	0.09	−0.19	−0.27
23.	South	0.41	0.49	0	1	−0.06	−0.08	−0.01	−0.36	0.34	0.11	0.02	−0.07	0.16	−0.36	0.34	−0.02

	Variable	Mean	SD	Min	Max	13	14	15	16	17	18	19	20	21	22	23
13.	% of nonseasonal vacancies	8.36	2.56	3.30	18.36	1	−0.44	−0.13	−0.01	0.09	0.16	0.25	0.11	−0.17	−0.11	0.55
14.	Median contract rent (thousands)	0.72	0.19	0.44	1.61	−0.44	1	0.39	0.33	0.19	0.33	−0.24	0.01	0.14	−0.31	−0.24
15.	Total population, ln	3.88	0.98	2.76	7.60	−0.13	0.39	1	0.30	−0.20	0.32	0.36	−0.01	0.06	−0.07	−0.02
16.	% of tourism firms	34.95	7.41	17.00	71.12	−0.01	0.33	0.30	1	0.11	0.19	0.01	−0.04	0.15	−0.07	−0.01
17.	% of seasonal vacancies, ln	1.18	0.74	0.18	3.65	0.09	0.19	−0.20	0.11	1	−0.09	−0.23	0.45	0.10	−0.32	0.12
18.	% Non-White, ln	3.36	0.56	1.89	4.58	0.16	0.33	0.32	0.19	−0.09	1	−0.09	−0.41	−0.26	−0.37	0.26
19.	Index of dissimilarity	44.86	12.53	16.99	77.87	0.25	−0.24	0.36	0.01	−0.23	−0.09	1	0.18	0.13	0.37	0.04
20.	Median age	37.38	4.35	24.50	57.30	0.11	0.01	−0.01	−0.04	0.45	−0.41	0.18	1	0.28	−0.04	−0.03
21.	Northeast	0.14	0.34	0	1	−0.17	0.14	0.06	0.15	0.10	−0.26	0.13	0.28	1	−0.22	−0.33
22.	Midwest	0.23	0.42	0	1	−0.11	−0.31	−0.07	−0.07	−0.32	−0.37	0.37	−0.04	−0.22	1	−0.45
23.	South	0.41	0.49	0	1	0.55	−0.24	−0.02	−0.01	0.12	0.26	0.04	−0.03	−0.33	−0.45	1

Table 4.

Incident Rate Ratios for Negative Binomial Regression Listings of Short-Term Rental Listings on Metropolitan Characteristics (N = 277).

Independent Variables	Microentrepreneurial			Large-Scale
Independent Variables	1	2	3	1	2	3
Civic capacity and locally embedded economic exchange
Nonprofit organizations per 10k capita	1.03*(0.02)	1.03*(0.02)	1.03*(0.02)	1.00(0.02)	1.00(0.02)	1.00(0.02)
Walmart stores per 100k capita	0.91(0.10)	0.22*(0.15)	0.18*(0.12)	0.18(0.17)	0.18^†(0.17)	1.01(0.13)
% small business retail	1.02*(0.01)	0.98(0.02)	0.98(0.02)	1.04*(0.02)	0.99(0.03)	1.03*(0.02)
Walmart stores per capita x % small business retail	—	1.04*(0.02)	1.04*(0.02)	—	1.04^†(0.03)	—
Labor market, income, and housing dynamics
% working <35 h per week	1.02(0.02)	1.09**(0.03)	1.09**(0.03)	0.99(0.02)	1.01(0.03)	1.00(0.02)
% unemployed	0.85***(0.04)	1.54^†(0.35)	1.55^†(0.35)	0.81***(0.04)	1.00(0.32)	0.81***(0.04)
% working <35 h per week x % unemployed	—	0.99**(0.00)	0.99**(0.00)	—	1.00(0.01)	—
% real estate firms	1.14**(0.06)	1.12*(0.06)	1.10^†(0.06)	1.23**(0.08)	1.20**(0.08)	1.18**(0.08)
Median family income (10 thousands)	1.34***(0.08)	2.63**(0.76)	2.43**(0.76)	1.30***(0.09)	1.97^†(0.72)	1.12(0.11)
Median family income (10 thousands), squared	—	0.95*(0.02)	0.96*(0.02)	—	0.97(0.02)	—
Income inequality gini index	1.04(0.03)	1.05*(0.03)	1.06*(0.03)	1.00(0.03)	1.02(0.03)	1.03(0.04)
Median rent as a percentage of income	1.08**(0.03)	1.07**(0.03)	1.06*(0.03)	1.11***(0.03)	1.11***(0.03)	1.07*(0.03)
% of nonseasonal vacancies	1.03(0.02)	1.02(0.02)	0.97(0.07)	1.08**(0.03)	1.08**(0.03)	0.87^†(0.07)
Median contract rent (thousands)	—	—	1.20(0.96)	—	—	0.64(0.52)
% of nonseasonal vacancies x median contract rent	—	—	1.08(0.11)	—	—	1.37**(0.16)
Controls
Total population, ln	3.61***(0.23)	3.56***(0.22)	3.55***(0.22)	3.60***(0.32)	3.62***(0.32)	3.57***(0.32)
% of tourism firms	1.04***(0.01)	1.04***(0.01)	1.04***(0.01)	1.04***(0.01)	1.04***(0.01)	1.04***(0.01)
% of seasonal vacancies, ln	1.96***(0.17)	1.98***(0.16)	1.91***(0.17)	2.75***(0.28)	2.84***(0.29)	2.47***(0.28)
% Non-White, ln	0.73**(0.08)	0.79*(0.09)	0.74*(0.10)	0.87(0.12)	0.92(0.12)	0.74^†(0.12)
Index of dissimilarity	0.98***(0.00)	0.98***(0.00)	0.98***(0.00)	0.98**(0.01)	0.98**(0.01)	0.98**(0.01)
Median age	1.14^†(0.09)	1.12^†(0.08)	1.13^†(0.07)	1.43**(0.15)	1.42***(0.14)	1.44***(0.14)
Median age, squared	1.00^†(0.00)	1.00^†(0.00)	1.00*(0.00)	1.00***(0.00)	1.00***(0.00)	1.00***(0.00)
Northeast	0.62*(0.13)	0.59**(0.59)	0.62*(0.12)	0.52*(0.14)	0.50**(0.13)	0.58^†(0.16)
Midwest	0.80(0.16)	0.77(0.15)	0.82(0.16)	0.64^†(0.15)	0.63*(0.15)	0.75(0.18)
South	0.73^†(0.12)	0.70*(0.11)	0.73*(0.12)	0.60*(0.12)	0.57**(0.11)	0.68^†(0.14)
Constant	0.00***(0.00)	0.00***(0.00)	0.00***(0.00)	0.00***(0.00)	0.00***(0.00)	0.00***(0.00)
alpha	0.30(0.03)	0.29(0.03)	0.29(0.03)	0.46(0.05)	0.46(0.05)	0.45(0.05)
AIC	3238.77	3229.34	3231.49	3052.39	3053.09	3044.72
BIC	3318.50	3319.94	3329.33	3132.12	3143.69	3131.69

Notes: Robust standard errors are in parentheses.

^†< 0.10, *p < 0.05, **p < 0.01, ***p < 0.001 (two-tailed test).

Because this study is concerned with short-term rental markets, I use a common unit of analysis used in the study of other markets and meso-level urban phenomena: the metropolitan area. The Census Bureau defines these areas as adjacent communities that have an urban core of at least 10,000 people, at least one community that is more than 50,000 people, and a high degree of economic and social integration with the urban core (U.S. Census Bureau 2018). They most closely approximate marketplaces compared to other geographic delineations (census tracts, county, etc.), and are a well-established measure for local productive systems (Marquis and Battilana 2009).

Regional units of analysis, such as the metropolitan area, are important for understanding the spatial distribution of economic activity, as these geographies encompass dense interlinkages of resources, firms, industries, and labor pools (Fujita and Thisse 2013; Scott and Storper 2015; Storper 2013). The clustering of these institutional and economic structures can lead to regional specialization, which further facilitates or precludes the development of certain industries and market practices in a particular region (Molotch et al. 2000; Storper 2013). As such, metropolitan areas are frequently used in studies of labor markets, tourism trends, housing dynamics, and alternative market forms and segmentation (see Carroll and Torfason 2011; Goldstein 2018; Kadiyali and Kosová 2013; Kemeny and Storper 2012; Rugh and Massey 2010) to understand various geographic patterns of economic development. In line with this work, I use this unit of analysis to understand local patterns of short-term rental market development and segmentation.

Thus, I analyze a sample that includes 277 metropolitan areas with populations over 150,000. Because the Census Bureau’s American Community Survey provides the most comprehensive set of measures for the theoretical constructs outlined in this study, I geocoded all other data used in the study to the Federal Information Processing Specification (FIPS) codes for each metropolitan area. The sample, thus, represents mid-sized to large urban marketplaces in the United States.

Dependent Variables

I collected short-term rental listing data from May 2016 to October 2016 on the site Airbnb.com by writing a web-scraping program in Python that searched zip codes for host listings in the metropolitan areas in my sample. I chose the sample period because it reflects a season during which travel accommodation is highly popular, making the choice of whether to participate in hosting, or not, most stark. Furthermore, because this sample year is 8 years after the company’s founding, I minimize the effect that unfamiliarity with Airbnb might have on participation.

Using these data, I include models that use two different dependent variables: (1) the total number of listings operated by hosts with only one listing (i.e., microentrepreneurs) in a marketplace and (2) the total number of listings operated by hosts with more than one listing (i.e., large-scale operators) in a marketplace. These measures are intended to operationalize the conceptual difference between microentrepreneurial rentals (or one-to-one exchanges that are idealized as more personalized) and large-scale rentals (or exchanges at scale that are idealized as more standardized) outlined in Table 1. In categorizing these exchanges by the number of listings a host operates, I capture the conceptualized differences in magnitude that undergirds microentrepreneurial and large-scale forms of renting. Table 3 provides descriptive statistics on these two variables.

Independent Variables

I merged the dependent variables with demographic, economic, and nonprofit data from the 2011–2015 American Community Survey (ACS) estimates, the 2012 Economic Census, the 2011 National Center for Charitable Statistics (NCCS), and a dataset on Walmart store openings (Holmes 2011). These data serve as measures for civic capacity, local economic embeddedness, labor market dynamics, income stratification, and housing costs and supply. Table 2 summarizes the anticipated relationship between each measure and the dependent variables, based on the literature outlined above. Table 3 presents the descriptive statistics for each measure.

Measures of civic capacity and locally embedded economic exchange

I anticipate that microentrepreneurial rentals will be more prevalent in communities with high civic capacity and high local investment (see Table 2). In measuring organizations that cultivate “blended social action” and collective efficacy (Sampson et al. 2005), I use the count of nonprofits organizations in each metropolitan area (2011 NCCS) per 10,000 people in the residential population (2011–2015 ACS). In measuring localism, I use the prevalence of small business firms and an inverse measure, non-local business investment. Research on nonoccupant housing investments used Walmart data to demonstrate that communities with chain retail like Walmart are more vulnerable to such investment (Goldstein 2018). Using the same data, I calculated the count of Walmart stores in a metropolitan area (in 2006) per 10,000 people in the total residential population. However, I expect that this measure will be moderated by the degree of local business investment as well; that is, metropolitan areas may have a high concentration of Walmarts and still have a strong representation of small, local businesses. I thus also include the percentage of retail firms that have fewer than five employees (from the 2012 Economic Census) and interact the two variables in Models 2 and 3 (see Table 4).

Measures of labor, income, and housing precarity

I anticipate that the two short-term rental market segments will track differently with local labor market dynamics (see Table 2). That is, I anticipate that microentrepreneurial rentals will be prevalent where there is high employment insecurity but low unemployment, since often, the economic opportunities provided by microentrepreneurial rentals are enough to supplement work that is not full-time, but not enough to substitute other forms of employment entirely. To measure employment insecurity, I use the percentage of the population ages 16 to 64 who typically work less than 35 hours per week from the 2011–2015 ACS; to measure unemployment, I use the percentage of the labor force age 16 and older that is unemployed. I also interact these two variables (see Model 3 in Table 4), anticipating that microentrepreneurial markets will be most popular in metropolitan areas with a high percentages of workers who work less than 35 hours per week and low levels of unemployment. I expect that large-scale short-term rental markets will be more popular in large real estate markets, where there is a large investor community. To measure this, I use the percentage of total firms in a metropolitan area that are real estate establishments (from the 2012 Economic Census).

In measuring income inequality dynamics, I use median family income and an inequality index from 2011–2015 ACS. Median family income, in its linear form, is used to test the anticipated positive relationship between wealth and rates of large-scale rentals (see Table 2). I include the quadratic term for median family income in the second model, to test the anticipated relationship that moderate levels of income (not high or low) will have higher rates of microentrepreneurial rentals (see Table 2). I also use the Census’s inequality index, which captures the evenness of income across a community, to examine whether microentrepreneurial rates will be higher in places with stagnant wages and rising inequality (see Table 2).

Finally, I anticipate high housing costs to be associated with high rates of each form of short-term renting, consistent with other literature demonstrating short-term rental markets’ effects on housing prices (Barron et al. 2021; Garcia-López et al. 2020; Todd et al. 2022) or other possible alternative mechanisms theorized in this paper (see Table 2). One alternative possibility discussed is that microentrepreneurial rentals will be more prevalent in marketplaces with high rent burdens, as hosts may be compensating for wages that are too low in relation to the high cost of housing. To explore this anticipated relationship, I use median gross rent as a percentage of income from the 2011–2015 ACS, which measures how much income residents within a metropolitan area are dedicating to rent relative to other expenses. Another alternative possibility discussed is that large-scale rentals will be popular in marketplaces with high housing costs and high residential vacancies since real estate investors may be trying to convert their investments in long-term luxury units into profitable rentals on the short-term rental market. To explore this anticipated pattern, I use median contract rent and the percentage of nonseasonal vacant housing units from the 2011–2015 ACS and interact the two in the third model; places with high rents and high vacancies might suggest gluts in luxury housing.

Controls

The analyses also include controls from 2011–2015 ACS and 2012 Economic Census data. I include total population (2011–2015 ACS) with the expectation that metropolitan areas with higher populations will have a higher number of potential Airbnb listings and will be more popular tourist destinations; I use the natural logarithm of this variable to correct for skewness. Using the 2012 Economic Census’s industry categories, I combine the percentage of total firms in Accommodation and Food industries with the percentage of total firms in Arts and Recreation industries to create a broader tourism measure. I also include the logged percentage of seasonally vacant housing, as a measure for existing vacation rental markets.

The models also include non-White and median age, using demographic variables from the 2011–2015 ACS. Additionally, I constructed an index of dissimilarity, computing the percentage of Black residents that would have to exchange tracts with non-Black residents to achieve an even residential distribution. This has been noted as a reliable measure for segregation, representing the unevenness of a metropolitan area (Massey, White, and Phua 1996; Rugh and Massey 2010). These variables are more appropriate as controls, as without micro-level data, theorizing potential mechanisms might contribute to reifying racial categories (see the section on limitations that follows). Finally, I control for potential variation in tourism, labor markets, and other regional specialization using 2011–2015 ACS’s regional categories.

Multicollinearity diagnostics

Multicollinearity among independent variables can pose a problem for regression analysis as it can inflate standard errors and make coefficients less precise. One way to diagnose collinearity among variables is by examining their correlation; in examining the independent variables, no pairwise correlation exceeds 0.70 (see Table 3), or the more common cutoff of 0.80 (Berry and Feldman 1985). Additionally, I examined the variance inflation factors (VIF) on a linear regression model that excluded the interaction and quadratic terms; with this model, no VIF exceeded 5, with a mean VIF score calculated at 2.87; these scores suggest a low degree of multicollinearity (Belsley, Kuh, and Welsch 2005). While inclusion of the interaction and quadratic terms inflated these scores, this is inevitable given their mathematical construction, and should not affect interpretation of the results if they are significant (Belsley et al. 2005; O’Brien 2007). The results of these diagnostics, then, suggest I can proceed with the assumption that multicollinearity does not pose a problem for the analyses.

Limitations of the Data and Analyses

While this original dataset offers insight into understanding short-term rentals in the platform economy, it does have several limitations because of the collection methods of “scraping,” the joining of disparate sources of data, the examination of markets where privacy issues are a concern, and the level of analysis. First, given the timing of the project and inability to scrape retroactive listings on the Airbnb website, the dataset cannot capture the process of short-term rental market emergence, beginning with Airbnb’s launch in 2008, that may be possible with time-series data. I also had difficulty accessing retroactive data on markets that may have been predecessors to contemporary short-term rental platforms such as Couchsurfing and earlier forms of vacation rentals. The dataset, consisting of independent variables that span the years 2006 through 2015 and Airbnb listing counts from 2016, are thus treated cross-sectionally and cannot support any attempts at causal inference. Rather, I use the analyses to identify patterns between different types of short-term rental exchange and urban socio-economic organization, as well as to contemplate possible mechanisms for these patterns (see those described under “The Socio-Economic Organization of ‘Sharing’”).

Second, given the restrictive interface of Airbnb and apparent privacy concerns, the data do not capture actual short-term rental exchanges (i.e., “bookings”), the profitability of the firm, or the number of consumers who only use the site to book accommodation (i.e., guests). Airbnb has been very reluctant to share data, stating concerns about the privacy of users, and in special cases when it does, it does so in censored ways or when legally compelled (Martineau 2019). The dataset, then, offers only one measure market growth: short-term rental supply.

A final limitation of the study is the level of analysis (metropolitan areas), which raises the issue of “ecological fallacy,” or the argument that individual-level conclusions cannot be drawn from group-level data (van Poppel and Day 1996; Robinson 1950). For example, given the significant role racial categorization plays in social trust, economic stratification, residential segregation, and discriminatory housing practices (e.g., Abascal and Baldassarri 2015; Abrahao et al. 2017; Kennedy et al. 2021), it is also likely significant for short-term rental market formation. However, I do not have data on the racial identification of particular market participants, the more micro-level neighborhood environment in which they are directly embedded, and their subsequent decision-making and therefore cannot test hypotheses about relationships between race and short-term rental market participation with this study. If listing rates were significantly associated with marketplaces with particular racial compositions, it would be difficult to identify whether this is because of dynamics of social trust, discrimination, or several possible mechanisms that, without careful investigation, could potentially reify racial categories. Generally, I cannot assume that individual hosts within a metropolitan area who adopt the practice of short-term renting have the aggregated and generalized characteristics of the metropolitan area.

However, given that this study is largely concerned with the growth and segmentation of markets—organizational-level phenomena—rather than the reasons for individual host practices and interactions, I proceed cautiously, focusing on meso- and macro-level dynamics and forms of explanation. That is, in contemplating explanations for these market patterns, I describe mechanisms that explain collective processes, emphasizing meso-level socio-economic organization, labor dynamics, and housing markets (not individual persons or racial categories) with tendencies well established by theory. I also include percent non-White, a segregation measure, and median age as controls, with the caveat that the explanation for such relationships must be pursued further with survey, experimental, and qualitative research. Other research more carefully approaches the role of racial biases in host and guest decision-making (Abrahao et al. 2017; Edelman, Luca, and Svirsky 2017). This paper instead focuses on the urban institutional structures and inequalities that are associated with platform economy market growth, with the acknowledgment that other micro-level factors are also important to these processes.

Results

I anticipate that civic capacity and localism are associated with growth in platform economy markets, particularly microentrepreneurial markets. The results presented in Table 4 provide support for this anticipated relationship. Across all three models for microentrepreneurial listings, the measure for civic capacity (number of nonprofit organizations per 10,000 capita) has a significant (p < 0.05) positive association with the expected count of microentrepreneurial listings. For each one-unit increase in the variable, the predicted number of listings increases by three percent, controlling for all other indicators. This coefficient is more significant in the microentrepreneurial model than in the large-scale model. In marketplaces with extremely high nonprofit densities (around 20 to 30 organizations per 10,000 residents), the predicted number of listings is between 100 to 300 greater for microentrepreneurial listings than large-scale listings; this difference is within the 95% confidence intervals (see Supplemental Figure S6a).

The measures for localism—Walmarts per 100,000 people and percent small business retail—also have a significant association with microentrepreneurial rates, though less consistently. In the first model for microentrepreneurial rentals in Table 4, the Walmart measure does not have a significant association, but the percentage of small retail firms does (p < 0.05). In this model, each one-unit increase in the percentage of small business retail is associated with a two percent increase in the rate of microentrepreneurial rentals. However, when the interaction term between the two variables is included (Models 3 and 4), the small business coefficient is insignificant and the coefficient for Walmarts per 100,000 capita has a significant negative relationship (p < 0.05) with microentrepreneurial rates. The interaction of Walmarts per 100,000 capita with percent small business has a significant positive relationship (p < 0.05) with the rate of microentrepreneurial rentals. In these models, each additional Walmart per 100,000 people reduces the predicted microentrepreneurial rate by anywhere between 78 and 82%. However, the interaction results suggest that this association can be moderated by the presence of small business retail firms; that is, marketplaces where there are high densities of Walmarts can also have high rates of microentrepreneurial rentals, if there is also a high percentage of retail firms that are small business (see Supplemental Figure S2).

These findings are not as apparent in the large-scale models, where the interaction term between Walmart density and percent small business retail is not very significant (p < 0.10). The relationship between percentage of small retail firms and large-scale exchange rates, however, does appear to be significant; for each one percent increase in the percentage of small retail firms the predicted rate of large-scale rentals increases by three or four percent. These findings suggest that the large-scale segment does track, to some degree, with locally embedded economic exchange, perhaps as a more “local” alternative than national hotel chains, but still as a more standardized and mainstream segment than microentrepreneurial markets.

I anticipated that inequalities in labor markets, income, and housing are associated with growth in microentrepreneurial and large-scale short-term rental markets. The results presented in Table 4 generally provide support for these expectations. First, I expected that microentrepreneurial and large-scale rentals would be associated with different labor market dynamics, with microentrepreneurial rentals more prevalent in areas with low unemployment but high employment insecurity and large-scale rentals more prevalent in communities with large real estate markets. In Model 1 of the microentrepreneurial models, the unemployment rate has a highly significant negative relationship with the rate of microentrepreneurial rentals (p < 0.001) while the measure for employment insecurity, the percentage of those working less than 35 hours per week, has an insignificant association. However, this association changes in the second and third models, when the interaction between unemployment and percent less than full-time is included.¹

When the interaction term is included, the main coefficient for percentage of those working less than full-time is highly significant (p < 0.01); for each one percent increase, the rate of microentrepreneurial rentals increases by nine percent, controlling for all other factors. The main coefficient for unemployment, on the other hand, reverses to a positive association, though this is not very significant (p < 0.10). The interaction term suggests that unemployment moderates the relationship between employment insecurity and microentrepreneurial listings, with the predicted number of listings being highest in places with low unemployment but high levels of part-time workers. Based on Model 2, in places with the highest percentages of those working less than full-time and an unemployment rate one standard deviation below the mean (3.9%), the predicted number of microentrepreneurial listings is around 1,700; this predicted rate decreases by about 1,000 listings when the unemployment rate is one standard deviation above the mean (6.3%) (see Supplemental Figure S4).

For the large-scale models, the negative association between the unemployment rate and listing rates is more consistent. In the preferred models for large-scale rental markets,² controlling for other factors, each one percentage increase in the unemployment rate is associated with a 19% reduction in the rate of large-scale listings, a result that is highly significant (p < 0.001). The results for large-scale models also provide support for the expectation that large-scale markets will correlate with real estate industry patterns; in the large-scale models, each one percent increase in the percentage of total firms that are real estate firms is associated with an 18 to 23% increase in the predicted number of large-scale listings, a result that is highly significant (p < 0.01). This relationship is also significant (p < 0.05) for microentrepreneurial markets; however, the size of the effect is greater for large-scale markets. In marketplaces with a percentage of real estate firms below the mean (approximately five percent), large-scale markets have significantly fewer predicted listings than microentrepreneurial markets (see Supplemental Figure S7c).

The results in Table 4 also support the expectation that microentrepreneurial rentals would be more prevalent in communities with moderate levels of income and high income inequality. In Model 1 for microentrepreneurial listings, the linear term for median family income is highly significant (p < 0.001), suggesting that higher rates of microentrepreneurial listings occur in contexts where there are higher incomes, controlling for all other variables in the model. However, in Models 2 and 3, the significant quadratic term for median family income (p < 0.05) and comparative AIC/BIC scores³ suggest that this relationship may be more curvilinear. Microentrepreneurial rates appear to peak at around $100,000 in median family income, at which point they begin to drop (see Supplemental Figure S3g). Furthermore, the income inequality index also has a significant positive association with microentrepreneurial rates (p < 0.05); for each percentage increase in the inequality index, the anticipated rate of microentrepreneurial rentals increases by five to six percent.

The results on income for the large-scale model are more mixed. I anticipated that large-scale rentals would be prevalent in communities with high incomes, an expectation that seems to be supported by the first model, where the linear term for median family income is highly significant (p < 0.001). However, the third model, which includes median contract rent, diminishes this association and the term becomes insignificant. Median family income and median contract rent are correlated (0.67), so this finding suggests that rental housing dynamics may better capture the spatial patterns of the large-scale market.

I also suggested that both microentrepreneurial and large-scale markets would be prevalent in communities with high housing costs. For microentrepreneurial markets, this expectation is supported by the findings on median rent as a percentage of income in Table 4. Across all models, the positive association between rent burdens and listing rates remains significant (p < 0.05), controlling for all other variables; for each percentage increase in the rent burden, the anticipated rate of microentrepreneurial rentals increases by between six and eight percent. High rent burdens are also significantly (p < 0.05) and positively associated with large-scale rental listings. Large-scale rental listings are also highest in contexts where there are high contract rents and high nonseasonal vacancies (see Model 3 in Table 4 and Supplemental Figure S5), controlling for all other factors. The same interaction term between median contract rent and nonseasonal vacancies is insignificant in the microentrepreneurial model (see Model 3 in Table 4).

Discussion and Conclusion

I have suggested that the local popularity of short-term rental markets is a process of adapting existing institutional structures and resources (Emigh 2008; Fligstein and Dauter 2007; Padgett and Powell 2012; Sewell 1992; Storper 2013) to the needs of each type of exchange. As outlined in Table 1, microentrepreneurial and large-scale segments of the platform economy involve different exchange structures, labor, capital, and housing. These analyses indicate that short-term rental markets grow where there are institutional structures and resources for these different types of economic exchange.

As part of a broader market offering a “local” alternative to traditional travel accommodation experiences, both microentrepreneurial and large-scale markets are situated in contexts where economic exchanges are locally embedded. Based on the findings, both segments are associated with the prevalence of small business retail. However, microentrepreneurial rentals, as the “purer” alternative that relies on direct and intimate interaction, are also associated with civically oriented communities. In a metropolitan area, as the density of nonprofit organizations increases, the rates of microentrepreneurial listings are predicted to be significantly higher than the predicted number of large-scale listings. These findings are consistent with the literature on alternative markets (Carroll and Torfason 2011; Goldstein 2018; Rao 2008; Sine and Lee 2009; Weber et al. 2008), suggesting that institutions for community solidarity and “blended social action” (Sampson et al. 2005) can cultivate the social connection needed for alternative exchanges that rely on the more localized, intimate practice of short-term renting.

This study also builds on this literature, highlighting an often-overlooked sociological dimension of market formation: urban social stratification and inequality. As the analyses demonstrate, both microentrepreneurial and large-scale markets appear to thrive in places with different forms of economic stratification. Based on the findings, microentrepreneurial rentals appear to be prevalent in marketplaces where there is low unemployment, high levels of labor precarity, moderate levels of income, and high income inequality. Large-scale rentals, on the other hand, are more popular in marketplaces with high levels of income, or high rents and nonseasonal vacancies.

These findings reflect the increasing inequality between consumers of housing—who largely buy or rent it for its use value—and investors in housing, who seek increasing returns on their investments (Goldstein 2018). In the former case, housing consumers who are financially overextended and are facing less stable employment prospects (Fligstein and Goldstein 2015; Kalleberg 2009; Schor 1998), may have turned to microentrepreneurial forms of short-term renting to repurpose the excess capacities of their homes for supplemental income. In the latter case, wealthy or investor communities may be looking to expand profits on vacant housing through large-scale forms of short-term rentals (Teresa 2016; Wetzstein 2017). Of course, given that both markets are popular in marketplaces with high housing costs, the practice of short-term renting may also be a contributor to rising housing prices, as other research has indicated (Barron et al. 2021; Garcia-López et al. 2020; Todd et al. 2022).

Indeed, the findings on these markets’ relationship to housing markets also illuminate complicated housing politics. Given that both types of markets are popular in urban areas with high housing costs, policymakers in these contexts often view them as a cause of housing crises, and design regulatory policy accordingly. However, as I have suggested, this relationship could be more circular, with microentrepreneurial and large-scale rentals also emerging as symptoms of rising housing prices and high living costs in different ways. That is, the microentrepreneurial segment may reflect communities struggling economically whereas the large-scale segment may reflect increased capitalization of already-expensive housing, a difference that would warrant distinct policy approaches. While a contribution of this paper has been to illuminate these potential tensions, given the limitation of the cross-sectional analyses, more research should explore how and to what extent this segmentation does or does not affect housing prices and policy.

Relatedly, the findings from this paper suggest that local civic energies may be channeled into these new forms of market participation. To what extent may these market forms be absorbing civic engagement and reshaping the public sphere? Are platform economy markets becoming their own sites for “blended social action,” (Sampson et al. 2005) creating market participants and then advocates? To what degree are microentrepreneurial hosts and large-scale operators aligned in the field of policymaking? As platform economy markets become a more commonplace feature of urban experience, questions remain about the implications for the communities in which they thrive.

Supplemental Material

Supplemental Material - Sharing Places: Local Socio-Economic Organization and Inequality in Contemporary Short-Term Rental Markets

Supplemental Material for Sharing Places: Local Socio-Economic Organization and Inequality in Contemporary Short-Term Rental Markets by Yotala Oszkay in Social Currents

Footnotes

Acknowledgments

I would like to thank Edward T. Walker, Rebecca J. Emigh, Jacob G. Foster, Paavo Monkkonen, Emily Block, David Coles, Zachary Griffen, Andrew Herman, Kyle Nelson, Kevin Shih, Eleni Skaperdas, and my other UCLA colleagues in the Emigh Working Group for their very helpful feedback and brainstorming. Earlier versions of this paper were presented at the American Sociological Association Annual Meeting, the Society for the Advancement of Socio-Economics Annual Meeting, the Stanford/PAC Junior Scholars Forum, and the UCLA Markets, Organizations, and Movements Working Group; I thank their organizers and attendees for providing me additional spaces to discuss this work. Finally, I thank the editors and anonymous peer reviewers at Social Currents.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was partially funded by the National Science Foundation Doctoral Dissertation Improvement Grant (Award #: 1801745).

ORCID iD

Yotala Oszkay

Supplemental Material

Supplemental material for this article is available online.

Notes

References

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