The economic impacts of shortterm rentals and regulations: A literature review

ALBERTO HIDALGO

FRANCISCO J. VELÁZQUEZ

Documento de Trabajo 2026/02

Febrero 2026

Alberto Hidalgoa∗

Francisco J. Velazquezb

aUniversitat de les Illes Balears, Spain bUniversidad Complutense de Madrid, Spain

Short-term rental (STR) platforms such as Airbnb have reshaped urban and tourism economies worldwide. By lowering transaction costs and enabling peer-to-peer accommodation at scale, they have expanded visitor capacity beyond traditional hotel districts and channeled tourism demand directly into residential neighborhoods (Zervas et al., 2017; Farronato and Fradkin, 2022). This transformation affects multiple urban markets simultaneously (housing, local economic activity, tourist accommodation, and neighborhood dynamics), making STR one of the most consequential developments in contemporary urban policy.

The economic effects of STR are both substantial and two-sided. On the positive side, STR increase consumer surplus from expanded variety and flexibility relative to hotel-only alternatives, especially during peak periods (Farronato and Fradkin, 2022; Schaefer and Tran, 2023). They redistribute tourist spending toward residential neighborhoods, stimulating employment and business activity in consumption-oriented services (Basuroy et al., 2020; Alyakoob and Rahman, 2022; Hidalgo et al., 2024). They also raise the option value on dwellings, the capitalized value of flexibly switching between residential and short-term rental use, incentivizing renovation and creating income opportunities for homeowners (Xu and Xu, 2021; Bekkerman et al., 2023).

On the negative side, STR impose significant costs that grow with scale and spatial concentration. The most prominent concern is housing affordability: by shifting dwellings from long-term residential use to short-term tourist accommodation and capitalizing expected revenues into property values, STR expansion raises housing prices (Garcia-López et al., 2020; Franco and Santos, 2021; Barron et al., 2021) and rents (Horn and Merante, 2017; Garcia-López et al., 2020; Barron et al., 2021). Renters disproportionately bear these costs, while property owners and professional hosts capture the benefits (Calder-Wang, 2021; Bibler et al., 2021). Beyond housing, STR disrupt neighborhoods through increased transiency and changes in criminal opportunity (van Holm and Monaghan, 2021; Lanfear and Kirk, 2024; Maldonado-Guzmán et al., 2024). In the accommodation sector, they erode pricing power for lower-end hotels while redistributing surplus toward consumers (Zervas et al., 2017; Li and Srinivasan, 2019; Chang and Sokol, 2022). STR thus generate both efficiency gains and distributional conflicts across markets, actors, and neighborhoods. Whether winners could in principle compensate losers remains an open empirical question, as no study has attempted comprehensive welfare accounting across all affected markets.

∗alberto.hidalgo@uib.cat Department of Applied Economics, University of Balearic Islands. javel@ccee.ucm.es Department of Applied & Structural Economics and History, Universidad Complutense de Madrid.

Because these impacts include neighborhood externalities that market mechanisms alone cannot internalize, STR have become a focal point of urban policy. Governments have responded with diverse instruments (bans, caps, licensing, taxation, data-sharing obligations) while platforms have developed self-regulatory mechanisms (Guttentag, 2019; Mont et al., 2020; Mosaad et al., 2023). Yet despite this proliferation of policy responses, existing reviews typically either focus on impacts while treating regulation as background, or catalogue regulatory instruments without linking them to the externalities they target and the outcomes they produce (Nawi et al., 2022; White and Cordon, 2025).

This paper bridges this gap by providing a systematic review that integrates the analysis of STR impacts with a detailed examination of regulatory instruments, their design, and their observed effectiveness. By connecting what we know about STR impacts with how regulation addresses them, the paper offers a coherent framework for understanding STR governance and a foundation for more effective policy design. To ensure broad coverage, we apply systematic review techniques following PRISMA guidelines. The selection process, detailed in Appendix A, identified 429 papers spanning a wide range of methodologies and empirical contexts. While this broad literature helps map the main analytical trends, the in-depth analyses focus primarily on high-quality quantitative studies, described in Appendix B and Appendix C. The paper is organized as follows. Section 2 examines the documented impacts of STR across four domains: housing markets, local economies, tourist accommodation, and neighborhood dynamics. Section 3 systematizes the regulatory responses (government regulation, self-regulation, co-regulation, and collective governance) and assesses their prominence and effectiveness. Section 4 draws conclusions and distills policy implications for the design of STR governance.

STR platforms simultaneously affect multiple urban markets: they shift housing from residential to tourist use, redirect visitor spending toward residential neighborhoods, introduce flexible competition into the accommodation sector, and alter the social dynamics of the communities in which they operate. Unlike traditional tourism infrastructure, which concentrates in designated commercial zones, STR embed commercial activity within the residential areas of cities, creating a distinctive pattern of spatially dispersed, cross-cutting impacts (Mosaad et al., 2023).

This section documents these effects, organizing the evidence around four interrelated domains, the housing market, the local economy, the traditional tourist accommodation sector, and neighborhood dynamics, which the literature identifies as the primary channels through which STR reshape urban environments. Figure 1 synthesizes the impacts identified in the literature, while Table 1 reports the frequency across the 365 papers in which these impacts have been documented using a wide range of quantitative and qualitative methodologies. The frequency distribution is strikingly lopsided: housing-market effects dominate (over

97% of papers), followed by neighborhood externalities such as commercial gentrification (20.0%) and quality of life (18.9%). The local economy and tourist accommodation sectors receive comparatively less attention, suggesting that the research agenda has been shaped more by political salience than by the breadth of affected markets.

The subsections that follow examine each domain in depth, drawing primarily on highquality quantitative studies that employ quasi-experimental or structural methods. The analysis begins with the most extensively studied domain, housing markets (Section 2.1), then turns to local economic effects (Section 2.2) and competitive dynamics in the accommodation sector (Section 2.3), before concluding with the most recently developed strand, neighborhood externalities (Section 2.4). A brief synthesis (Section 2.5) highlights the cross-domain interactions that complicate policy design.

Table 1. Main impacts and externalities of short-term rentals.

Notes: Percentages calculated over the 365 papers that include some form of impact assessment. Categories are not mutually exclusive. The high percentages for “Purchase & rental prices” and “General market conditions” reflect that most housing-market papers address prices at least implicitly; “Property values and rents” captures studies with explicit causal price estimates using quasi-experimental designs.

STR platforms have reshaped housing markets in many cities over the past decade. By enabling property owners to supply accommodation to tourists on a short-term basis, these platforms alter the allocation of housing between residential and visitor uses, affecting both

Figure 1. Main impacts and externalities of short-term rentals by affected stakeholder.

rental and owner-occupied markets. The net impact on housing outcomes is theoretically ambiguous: while STR activity reduces the supply of long-term rental housing and capitalizes expected tourism revenues into property prices, it can also generate income for residents and stimulate local economic activity. This ambiguity has placed STR platforms at the center of policy debates on housing affordability and urban regulation.

In response to these concerns, a growing body of empirical work has examined whether and to what extent the diffusion of STR has affected housing rents and prices.1 Researchers exploit rich spatial variation in STR activity across neighborhoods and cities, often combined with regulatory changes or quasi-experimental designs, to identify causal effects. Studies span a wide range of geographic contexts, including large U.S. metropolitan areas, major European cities, and smaller tourism-intensive regions, allowing researchers to assess both the average effects of STR expansion and their dependence on local housing market conditions and regulatory environments.

Five main channels drive STR’s housing market effects: supply reallocation, demandside capitalization, income effects from home-sharing, amenity adjustments, and negative externalities. Supply reallocation and price capitalization operate simultaneously and often in opposing directions, implying that the net effect of STR on rents and house prices is theoretically ambiguous and context-dependent.

The most direct channel runs through supply reallocation. By allowing property owners to earn higher returns from short-term renting, STR create incentives to shift dwellings from the long-term rental market toward short-term use. In practice, this occurs when a landlord converts a EUR 900/month long-term rental into a EUR 120/night Airbnb listing, removing one unit from residential supply while tripling gross income per square meter. In Berlin, each commercially-operated listing displaces 0.23–0.37 long-term rental units, whereas regulation targeting non-commercial (occasional) hosts had no measurable effect on rental supply (Duso et al., 2024). This reallocation reduces the effective supply of housing available to residents and, holding demand constant, exerts upward pressure on rents and property prices. The strength of this channel depends on both the prevalence of conversion and the elasticity of housing supply, how responsive new construction is to price increases, a parameter that varies dramatically across cities, from below 1 in supply-constrained coastal metros to above 3 in less constrained inland areas (Saiz, 2010). In inelastic markets, even modest reallocations can generate sizable price responses; in elastic markets, new construction absorbs much of the shock. Empirical studies consistently find stronger price effects in areas with a higher share of non-owner-occupied or investment properties, where landlords are more likely to switch to STR activity, while predominantly owner-occupied neighborhoods tend to exhibit weaker responses (Barron et al., 2021).

STR also shift housing demand by expanding the asset’s income-generating potential: price capitalization. The introduction of STR expands the set of uses for housing, increasing its option value as an income-generating asset. Anticipated STR revenues become capitalized into housing prices, attracting new buyers, such as investors or second-home owners, who compete with residents in the housing market. This demand-side channel raises equilibrium prices even in the absence of substantial physical conversion of units. Conceptually, housing becomes a more valuable asset because it can flexibly serve both residential and touristic purposes. Multiple studies confirm that this capitalization mechanism reinforces the supply effect by embedding expected STR profits into asset values (Sheppard and Udell, 2016; Garcia-López et al., 2020; Duncan and Ross, 2025).

1For a full list of recent contributions on the effects of short-term rentals on the housing market, see Table A1 in the Appendix.

For homeowners who rent out spare rooms, STR generate income effects that partially offset rising costs. By renting out spare rooms or occasionally vacant units, hosts gain an additional income stream. This income allows some households to remain in neighborhoods they might otherwise be priced out of, even as overall housing prices rise. Theoretical models show that while the introduction of STR markets tends to increase equilibrium-housing prices, it also redistributes surplus toward households able to participate as hosts. As a result, affordability deteriorates on average while improving for a subset of homeowners who leverage home-sharing income (Bibler et al., 2021). Empirical evidence following regulatory crackdowns is consistent with this interpretation, suggesting that the removal of STR income can increase financial distress among affected owners.

STR also reshape housing markets indirectly through amenity adjustments. Increased tourist presence induces changes in the local business environment, shifting consumption amenities toward restaurants, nightlife, and services catering to visitors (Hidalgo et al., 2024). Different demographic groups value these changes differently, which alters residential sorting patterns. Structural models with endogenous amenities (i.e., models in which the local mix of shops, restaurants, and services changes in response to STR activity) show that some residents, particularly younger households whose preferences align more closely with tourist-oriented amenities, experience welfare gains that partially compensate for higher rents, while others face net losses (Almagro and Domínguez-Iino, 2025). This channel implies that housing market effects cannot be fully understood without considering how neighborhood characteristics evolve in response to STR activity.

Working against these channels are the negative externalities associated with STR. High concentrations of STR can generate noise, congestion, and reduced social cohesion, lowering the desirability of affected neighborhoods for long-term residents. Because individual hosts do not internalize these nuisance costs, decentralized STR decisions lead to socially excessive home-sharing. Theoretical work shows that collective decision-making at the building or neighborhood level can yield more efficient outcomes by internalizing externalities (Filippas and Horton, 2018). Empirical findings indicate that in areas where nuisance effects dominate, STR activity can depress housing values, and that restricting STR may increase prices by restoring neighborhood quality (Garcia et al., 2025).

These five channels interact in complex ways. Supply reallocation and capitalization of STR income opportunities tend to push rents and prices upward, while amenity changes and nuisance externalities can mitigate or even reverse these effects in specific contexts. The observed impact of STR on housing markets therefore reflects the net outcome of these competing mechanisms, motivating the need for careful empirical identification, which we discuss in Appendix B. Turning from theory to evidence, the empirical record points consistently in the same direction across a wide range of contexts: STR expansion increases housing prices and, in most cases, rents. This effect is strongest in locations with high tourist demand and relatively inelastic housing supply, where the reallocation of dwellings toward short-term use meaningfully reduces the availability of long-term housing. In such markets, STR activity is capitalized into asset values and, with some lag, into rents, raising housing costs for residents.

Evidence from European cities illustrates the upper bound of these effects. In Barcelona, estimated impacts imply rent increases of around 2% and transaction price increases of roughly 4–5% at the neighborhood level; in the top decile of Airbnb activity, these figures rise to 7% and 17%, respectively (Garcia-López et al., 2020). Similar magnitudes are reported for Lisbon and Porto, where rapid post-2014 STR growth translated into sizable increases in housing prices, particularly in historic city centers (Franco and Santos, 2021). These southern European cases demonstrate that in tourism-intensive cities, STR can account for a non-trivial share of observed house price appreciation over relatively short periods. At the national scale, Allam et al. (2026) provide the first country-wide causal estimates for a major European economy: across more than 25,000 French municipalities, a 1% increase in STR listing density raises transaction prices by approximately 11%, an order of magnitude larger than comparable U.S. estimates, with effects ranging from 9.2% in central cities to 8.6% in non-touristic rural areas where platforms have created entirely new accommodation markets. Evidence from Italian cities confirms that effects concentrate in historic centers and attenuate with distance from tourist cores, with substantial within-city heterogeneity (Congiu et al., 2025).

In the United States, where STR listings typically represent a smaller share of the housing stock, estimated effects are more modest in elasticity terms but remain economically meaningful. Using nationwide variation, Barron et al. (2021) show that increases in STR listings lead to statistically significant rises in both rents and house prices across metropolitan areas. While the per-unit effects are small, they accumulate over time: Airbnb growth accounts for approximately one-fifth of actual rent growth in affected zip codes during the platform’s expansion phase. City-specific studies broadly reinforce these conclusions. Early evidence from Boston links higher STR penetration to faster rent growth (Horn and Merante, 2017), while quasi-experimental evidence from New Orleans shows that removing the option to operate STR leads to sharp declines in property values in affected areas, indicating that STR profitability had previously been capitalized into prices (Valentin, 2021). Together, these results support the interpretation that STR raises housing costs primarily by increasing the option value on housing and reducing effective long-term supply.

An important nuance emerges from several studies: STR’s estimated impact on house prices often exceeds its estimated effect on rents. This divergence supports forward-looking capitalization, buyers price in expected future STR income even when rents adjust slowly due to lease frictions or regulation. Multiple studies document increases in price-to-rent ratios in areas with growing STR activity, and sharp price responses when regulations curtail STR profitability, even as rents adjust more gradually (Barron et al., 2021; Valentin, 2021). Overall, the dominant finding holds across contexts: STR expansion tends to make housing more expensive, with particularly pronounced effects in high-demand urban and tourist markets.

These average effects, however, mask considerable variation. Although the average effect of STR on housing prices is positive, its magnitude, and in some cases even its sign, varies substantially across locations, housing segments, and regulatory environments. A central source of heterogeneity is spatial: STR’s impact concentrates in neighborhoods that are attractive to tourists, such as historic centers or amenity-rich districts, while purely residential or peripheral areas experience much smaller effects. Within-city analyses show that price effects often attenuate sharply with distance from tourist cores, although spillovers into adjacent neighborhoods are common as STR activity expands outward (Garcia-López et al., 2020; Franco and Santos, 2021; Congiu et al., 2025).

Heterogeneity also arises across housing market segments. In some contexts, STR disproportionately affects higher-end properties, particularly in vacation-oriented markets where large or high-quality homes generate especially high STR returns (Safari et al., 2025). In others, more affordable units are the primary margin of adjustment, as smaller apartments yield high percentage returns when converted to STR. Structural estimates indicate that lower-end rental units often account for a large share of both renter losses and host gains, implying that STR can tighten affordability precisely where housing is most scarce (Li et al., 2022). This finding is particularly concerning for policymakers because it means that STR worsen housing conditions for the most vulnerable households.

Regulation further shapes heterogeneous responses. Policies that cap listings, restrict entire-home rentals, or intensify enforcement reduce STR activity on average, but their effects on housing outcomes are uneven and sometimes counterintuitive. When Lisbon’s STR market collapsed during COVID-19, landlords shifted properties back to the long-term market, producing a 4.1% rent decrease and a 20% increase in rental listings (Batalha et al., 2022). By contrast, Santa Monica’s Home-Sharing Ordinance eliminated 60% of entire-home listings yet had no significant effect on rents, because the regulated units did not return to the long-term market (Chaves Fonseca, 2025); and in Sydney, STR regulation actually increased long-term rents by 2.7–3.6%, possibly by signaling tighter future supply constraints (Roudnitski and Sarkar, 2025). In Paris, stricter enforcement disproportionately affects casual or inexperienced hosts, while professional operators adapt more easily, shifting activity across platforms or locations (Tripathi et al., 2025). These varied responses confirm that regulation can alter who captures STR-related rents without necessarily improving affordability, and often displaces activity toward less regulated areas.

A small but informative subset of studies documents cases where negative externalities dominate. In areas with extremely high concentrations of STR, nuisance effects, noise, congestion, and loss of neighborhood cohesion, can reduce residential desirability. In such settings, STR density has depressed property values, and subsequent restrictions have led to price increases as neighborhood quality improves (Garcia et al., 2025). While these cases are atypical, they suggest the existence of threshold effects beyond which the sign of STR’s impact may reverse. STR’s effects may also vary over time: in Sydney, the Airbnb rent premium declined from 106% to 78% between 2015 and 2018 as landlords increasingly shifted properties to the short-term market, simultaneously raising long-term rents and compressing Airbnb returns (Hill et al., 2023). This temporal heterogeneity implies that cities at different stages of STR penetration may experience different price dynamics even under similar regulatory regimes.

Beyond average effects, STR generate substantial distributional consequences. The most consistent divide is between renters and property owners: STR-driven increases in rents and house prices benefit owners through capital gains and hosting income, while renters face higher housing costs and greater displacement risk. Both structural and reduced-form evidence indicates that these transfers can be large, resulting in net welfare losses for renters and gains for owners (Calder-Wang, 2021). Within the group of owners, the distribution of benefits is highly skewed. Hosts operating multiple listings or entire-home rentals capture a disproportionate share of STR revenues, while casual home sharers receive more modest gains. Over time, professionalization, the shift from occasional home-sharing to year-round, multi-property commercial hosting, and regulation may further concentrate earnings among larger or more sophisticated operators (Hill et al., 2023; Tripathi et al., 2025).

Among renters, impacts vary by location and demographic characteristics: rent increases are often largest in central, high-demand neighborhoods, which disproportionately affects higher-income renters, even as broader market tightening ultimately burdens lower-income households as well. In New York, aggregate renter welfare losses reach $2.4 billion, with over 97% of renters worse o! by roughly $125 per year at the median (Calder-Wang, 2021). In Madrid, STR-driven price increases reduce net residential inflows, with the displacement concentrated among residents without a college degree, while more educated households are attracted to STR-dense neighborhoods, consistent with a gentrification dynamic (Chaves Fonseca, 2024). Distributional effects also extend across space: when regulations restrict STR in one jurisdiction, activity and associated housing pressure often shift to neighboring areas, redistributing rather than eliminating impacts (Ellis et al., 2025). These housing-market pressures, moreover, do not remain confined to the residential sector: by reshaping the economic geography of neighborhoods, they also alter the conditions under which local businesses operate.

When New York City tightened STR enforcement in 2023, restaurant spending fell by approximately 10% (Zhao et al., 2026), revealing how deeply STR-driven tourism demand had become embedded in local economies. This finding illustrates a broader pattern: by redistributing tourist demand across space and altering incentives for private investment and business activity, STR platforms reshape the economic geography of neighborhoods.2 The rent increases documented in the previous subsection translate directly into higher costs for neighborhood businesses, but they also reflect rising property values driven in part by the commercial stimulus that STR generate. Understanding how these local economic effects arise requires examining four complementary channels: spatial reallocation of tourism demand, recomposition of local amenities, labor-market effects, and investment incentives (Basuroy et al., 2020; Xu and Xu, 2021; Alyakoob and Rahman, 2022; Hidalgo et al., 2024; Almagro and Domínguez-Iino, 2025; Milone et al., 2025).

The primary channel is spatial reallocation of tourism demand. STR platforms allow visitors to stay in areas with limited hotel supply, expanding the geographic footprint of tourism within cities. By lowering accommodation constraints and bypassing land-use regulations that restrict traditional hotels, STR redirect tourist presence toward residential neighborhoods. Multiple studies show that this redistribution increases foot traffic and local demand for nearby restaurants and consumption services, particularly in areas that were previously weakly exposed to visitor flows (Alyakoob and Rahman, 2022; Hidalgo et al., 2024; Almagro and Domínguez-Iino, 2025). From this perspective, STR operate as a market-expansion shock at the neighborhood level rather than merely substituting for existing accommodation.

2For a detailed list of recent contributions on the effects of short-term rentals on the local economy, see Table A4 in the Appendix.

A complementary channel operates through recomposition of local amenities. Tourist demand is systematically skewed toward food, beverage, nightlife, and short-stay convenience goods. As a result, STR-induced demand growth often leads to entry and expansion in tourist-oriented activities, while resident-oriented retail and services stagnate or decline. Importantly, this reallocation is visible at the area level, where tourism-oriented business categories expand while resident-oriented categories stagnate or decline, reshaping the composition of local amenities (Garcia-López and Rosso, 2023; Hidalgo et al., 2023; Almagro and Domínguez-Iino, 2025). Consequently, aggregate measures of local economic activity can increase even as the availability of resident-serving amenities declines. This distinction matters for policy: headline employment or revenue figures paint an overly optimistic picture if they obscure the loss of services that residents depend on.

STR expansion also generates labor-market and complementary-service effects. Increased visitor presence raises labor demand in food services and hospitality-adjacent sectors, as well as in cleaning, maintenance, transportation, and construction.Empirical work documents positive employment responses operating through both extensive margins—new establishments—and intensive margins—higher staffing levels among incumbents (Hidalgo et al., 2024)—with effects that vary systematically with baseline tourism intensity and competition from traditional hotels (Gálvez-Iniesta et al., 2023; Hidalgo et al., 2024).

A fourth channel runs through investment incentives. By raising expected returns to residential property, STR platforms increase the expected profitability of renovation and redevelopment. The investment incentives extend beyond housing to commercial space, as rising tourist demand increases the profitability of retail and service investment. Empirical work demonstrates that STR expansion causally increases residential renovation activity, the value of retail and service renovations, and net business entry in hospitality-related sectors, with effects concentrated among professionalized (multi-listing) operators and in neighborhoods where baseline redevelopment incentives were weak (Xu and Xu, 2021). Complementary findings confirm that STR regulations reduce residential permits alongside listings, reinforcing the interpretation that STR stimulate capital investment rather than merely reallocating short-run consumption (Bekkerman et al., 2023).

The four mechanisms (demand reallocation, amenity recomposition, labor-market stimulus, and investment incentives) work in concert to expand measured economic activity in STR-active neighborhoods, while simultaneously reshaping the composition of that activity in ways that create winners and losers. The empirical evidence confirms the importance of these channels. On the demand side, STR exposure increases activity in food-and-beverage services, measured through revenues, establishment counts, and employment. Employment effects capture both the creation of new businesses and increased staffing among incumbents, suggesting that STR affect local labor markets through multiple margins (Basuroy et al., 2020; Alyakoob and Rahman, 2022; Hidalgo et al., 2024). In Madrid, each additional 14 Airbnb rooms in a census tract is associated with one additional food-and-beverage establishment, and the same increase at the neighborhood level generates 11 new food-andbeverage workers (Hidalgo et al., 2024). A recurring finding is that these effects are stronger in areas with lower baseline commercial intensity or limited hotel presence, where STR are more likely to expand the effective tourism market rather than substitute for existing accommodation (Alyakoob and Rahman, 2022; Gálvez-Iniesta et al., 2023).

At the same time, several studies document systematic compositional change. Growth in tourist-oriented activities often coincides with stagnation or decline in resident-oriented retail and services. Firm-level evidence from Portugal shows that STR exposure increases exit rates among low-performing firms while surviving incumbents expand and new touristoriented firms enter, leading to a re-sorting of local economic activity rather than uniform growth (Cruz et al., 2024). Complementary analyses confirm the pattern at both the area level, where tourism-oriented license categories expand while resident-oriented categories contract (Garcia-López and Rosso, 2023), and the establishment level, where STR exposure increases entry of tourist-oriented businesses and raises closure rates among resident-oriented ones (Hidalgo et al., 2023). The resulting compositional shifts imply that aggregate gains can mask distributional effects and the adjustment costs that specific firms and consumers bear.

Investment responses play a central role in these outcomes. STR expansion increases residential renovation activity and commercial reinvestment, with effects concentrated among professionalized operators and in neighborhoods where redevelopment was previously less attractive (Xu and Xu, 2021). Evidence that STR regulations reduce residential permits by 11% alongside listings, with accessory dwelling units declining by an even larger 16.5%, further supports the view that STR sustain capital flows into housing types well-suited for short-term accommodation rather than merely shifting short-run spending (Bekkerman et al., 2023). Importantly, investment responses are not uniform across asset types, indicating that STR reorient development toward uses that are more flexible or better suited to short-term accommodation.

In sum, STR stimulate local demand, employment, and investment, but at the cost of reshaping neighborhoods toward tourism-oriented activities (Basuroy et al., 2020; Xu and Xu, 2021; Hidalgo et al., 2023). The trade-o! is genuine: restricting STR preserves residentserving amenities but dampens the localized economic stimulus. The same tension between market expansion and distributional costs plays out in the traditional accommodation sector, where STR have altered competitive dynamics in ways that directly affect both hotel incumbents and consumers.

STR constitute a flexible-capacity entrant into accommodation markets characterized by high fixed costs, capacity constraints, and pronounced seasonality.3 By lowering entry barriers and allowing accommodation supply to scale rapidly with demand, STR alter competitive conditions, pricing power, and the allocation of surplus in accommodation markets. The central questions are whether STR primarily substitute for hotel stays or expand overall market demand, how hotels adjust prices and quality in response, and how regulation shapes these competitive interactions (Zervas et al., 2017; Li and Srinivasan, 2019; Schaefer and Tran, 2023; Farronato and Fradkin, 2022; Jiménez et al., 2022).

Do STR substitute for hotel stays, or do they expand overall accommodation demand? This question lies at the heart of the competitive effects literature, and the answer depends on three closely related mechanisms: substitution and price competition, capacity flexibility, and strategic incumbent responses. At the core is substitution and price competition. Travelers perceive STR as imperfect substitutes for hotels, with substitution elasticities (i.e., how readily consumers switch between STR and hotels in response to price changes) varying systematically by hotel quality, traveler type, and location. By expanding available accommodation capacity, particularly during periods of high demand, STR constrain hotels’ ability to raise prices, leading to lower average daily rates and reduced revenue per room. Structural demand models indicate that substitution is systematically stronger for budget segments than for high-end hotels, and that a majority of Airbnb guests represent genuine market expansion rather than hotel diversion, structural estimates suggest that between half and two-thirds of bookings would not have resulted in a hotel stay absent the platform, with the share rising sharply during peak-demand periods (Farronato and Fradkin, 2022); a complementary structural analysis finds that Airbnb both mildly cannibalizes hotel sales and expands overall accommodation demand, with the balance depending on market conditions such as seasonality, consumer composition, and Airbnb supply elasticity (Li and Srinivasan, 2019). This competitive pressure is strongest for lower-end hotels and properties oriented toward leisure travelers, where differentiation is limited and substitution is more direct (Zervas et al., 2017).

Capacity flexibility amplifies this competitive pressure. Unlike hotels, which face rigid capacity constraints, STR supply can expand and contract rapidly in response to fluctuations in demand. Structural models show that this flexibility dampens peak-season price spikes and erodes the quasi-rents hotels would otherwise extract when binding capacity constraints allow them to charge above marginal cost. As a result, STR both displace hotel demand and expand total market capacity in high-demand states, generating welfare gains that concentrate precisely when hotels are most constrained (Li and Srinivasan, 2019; Farronato and Fradkin, 2022; Schaefer and Tran, 2023). For consumers, this means lower prices and greater availability during the periods when accommodation is traditionally most expensive.

Hotels’ responses to this competitive pressure are heterogeneous and strategic. Lowerquality hotels tend to compete primarily on price, while higher-quality hotels adjust along nonprice margins, increasing staffing and service quality and repositioning toward higher-end market segments. This vertical differentiation by incumbents in response to new competition mitigates competitive pressure for some segments while amplifying it for others, making the hotel industry more heterogeneous rather than producing uniform decline (Chang and Sokol, 2022; Yang et al., 2022). The competitive effects are highly localized, reflecting the geographically concentrated nature of competition between STR and hotels and the limited extent of multihoming across accommodation types (Schaefer and Tran, 2023).

3For a full list of recent contributions on the effects of short-term rentals on the hotel sector, see Table A5 in the Appendix.

The empirical evidence largely resolves this substitution-versus-expansion debate in favor of a nuanced middle ground: both forces operate, but their relative strength varies by market segment, time period, and geography. Across settings and methodologies, several consistent findings emerge. First, STR entry reduces hotel prices and revenues on average: intensified price competition drives these effects rather than large declines in occupancy. Revenue losses concentrate among lower-end hotels, independent properties, and hotels oriented toward leisure demand, while higher-end are comparatively insulated due to weaker substitution and stronger differentiation (Zervas et al., 2017; Li and Srinivasan, 2019).

Second, STR exert disproportionate effects during peak-demand periods: by flexibly expanding supply, they limit hotels’ pricing power under binding capacity, and structural estimates indicate that a substantial share of consumer-surplus gains arises in these highdemand states (Farronato and Fradkin, 2022; Schaefer and Tran, 2023). Third, hotel responses are heterogeneous and strategic rather than uniform. Low-quality hotels primarily respond by lowering prices, whereas high-quality hotels raise prices and increase investment in service quality, effectively repositioning toward higher-end market segments. This process reshapes the competitive environment by increasing dispersion in hotel performance rather than producing an industry-wide contraction (Chang and Sokol, 2022). Finally, recent synthesis work confirms that this heterogeneity is systematic. Meta-analytic evidence shows that STR competition affects high-end hotels less severely, has weakened over time, and is less pronounced in European markets than in Asian markets, consistent with learning, adaptation, and institutional differences (Yang et al., 2022). Regulatory restrictions on STR, by contrast, substantially benefit hotels through higher prices rather than increased quantities, indicating that regulation primarily reallocates surplus from consumers to incumbent firms. When New York City effectively banned most STR in 2023, hotel average daily rates rose by $14–19 per night, generating a revenue windfall of $2.1–2.9 billion over the first eighteen months, driven largely by higher prices rather than increased occupancy (Anastasi et al., 2025).

On balance, consumer gains from STR (greater variety, lower prices, reduced peakseason scarcity) substantially exceed hotel profit losses (Farronato and Fradkin, 2022). Restricting STR primarily transfers surplus back to hotels through higher prices rather than improving efficiency (Anastasi et al., 2025). Unlike the housing market, where STR generate distributional costs borne primarily by renters, the accommodation sector presents a more nuanced welfare calculus in which the debate is fundamentally about redistribution rather than efficiency. Yet even where the market-level welfare calculus is favorable, STR generate a distinct set of externalities at the neighborhood level that market prices do not capture.

A growing body of research studies STR as a neighborhood-level shock: by reallocating housing toward transient visitors, STR can change routine activity patterns, alter the strength of informal social control, and generate nuisance externalities that are highly localized and salient to residents (van Holm and Monaghan, 2021; Han et al., 2022; Lanfear and Kirk, 2024).4 Unlike the market-level impacts discussed in previous subsections, these externalities often materialize through public-safety outcomes, quality-of-life disruptions, and shifts in local social capital and political behavior (Ozer et al., 2024; Fontana, 2025; Rodon et al., 2025). Empirically, however, identification is difficult: listings locate where tourism demand is rising and where neighborhood trajectories (gentrification, nightlife, policing) are already changing (Roth, 2021; Lanfear and Kirk, 2024; Maldonado-Guzmán et al., 2024).

Among the cleanest quasi-experimental estimates come from Chicago, where buildinglevel STR restrictions near prohibited buildings reduced burglaries by 11%, even though the broader citywide ordinance had no discernible effect on aggregate crime (Jin et al., 2024). This quasi-experimental result points to a broader set of mechanisms through which STR penetration reshapes neighborhood outcomes. Four linked channels connect STR to these effects: criminal opportunity, weakened social cohesion, nuisance externalities, and political feedback. At the most basic level, STR penetration can be understood as a localized increase in transiency and a reallocation of activity across residential and public space. When housing units shift from long-term occupancy to short-term use, neighborhood routines change and the set of actors with incentives to monitor and enforce local norms is altered. This shift generates linked predictions for criminal opportunity, nuisance, and downstream political responses, helping reconcile findings across outcomes that at first glance appear heterogeneous (van Holm and Monaghan, 2021; Lanfear and Kirk, 2024; Rodon et al., 2025).

One central channel operates through routine activity theory (a criminological framework positing that crime occurs when a motivated offender, a suitable target, and the absence of a capable guardian converge in time and space), guardianship, and criminal opportunity. STR increase the presence of short-term visitors who differ systematically from long-term residents in their daily routines and their incentives to provide informal monitoring. Visitors are more likely to be unfamiliar with the area and to carry valuables, increasing their suitability as targets for property crime, while frequent guest turnover reduces the density of capable guardians in residential space (Lanfear and Kirk, 2024). Empirical support for this channel is uneven: while London data confirm the predicted pattern (Lanfear and Kirk, 2024), a study of Austin, Texas, finds no significant association between entire-unit STR listings and crime, though private room listings were positively associated with alcohol offenses (Roth, 2021). This mechanism predicts stronger effects on property crime and disorder than on serious violent crime, particularly in neighborhoods where STR activity concentrates in entire-home listings that substitute away from stable residential occupancy (van Holm and Monaghan, 2021).

4For a detailed list of recent contributions on the effects of short-term rentals on neighborhood externalities, see Table A6 in the Appendix.

A closely related mechanism operates through neighborhood instability and weakened social cohesion. High STR penetration can increase residential turnover and erode repeated interactions among neighbors, undermining collective efficacy (i.e., residents’ shared willingness and capacity to intervene in maintaining local order) and reducing informal social control. From an economic perspective, collective efficacy functions as a local public good: individual monitoring effort generates positive externalities for neighbors, and high population turnover from STR undermines the repeated interactions that sustain cooperative equilibria. Cross-neighborhood comparisons in Spanish cities reveal substantial within-city heterogeneity in the relationship between STR activity and crime, consistent with the idea that STR effects depend on baseline neighborhood characteristics and the extent to which short-term renting disrupts population stability (Maldonado-Guzmán, 2022; Maldonado-Guzmán et al., 2024).

Beyond crime, this mechanism also links STR exposure to civic outcomes: persistent turnover can weaken neighborhood-based social capital and reduce informal civic engagement, even as it increases political salience and mobilization around regulation (Fontana, 2025; Rodon et al., 2025). In London, sustained STR exposure is linked to increased loneliness and reduced civic engagement among remaining residents (Fontana, 2025). More broadly, the displacement driven by housing-market dynamics (Section 2.1) weakens the social ties that sustain neighborhood order, meaning that STR may simultaneously erode the informal ties that sustain day-to-day neighborhood life while galvanizing organized political responses.

Nuisance externalities represent another important channel, but an essential distinction separates nuisance from nuisance complaints. STR can intensify late-night activity, crowding, and disruptive behavior, yet administrative data typically capture complaints rather than underlying disturbance. This creates a measurement wedge: reporting behavior may change even if true nuisance does not increase proportionally. Consistent with this interpretation, Ozer et al. (2024) show that STR entry in New York City reduces residential noise complaints, particularly in areas with lower occupancy and stronger tourist amenities, suggesting that guests spend less time inside the unit. At the same time, street-level noise increases in STR-dense areas, implying spatial displacement of nuisance rather than its elimination (Ozer et al., 2024). These findings caution against interpreting complaint-based measures as direct proxies for nuisance and reinforce the role of reporting, host mediation, and enforcement in shaping observed outcomes (van Holm and Monaghan, 2021; Ozer et al., 2024).

These mechanisms imply a regulatory and political feedback loop. As localized externalities accumulate, whether through increased disorder, reallocated nuisance, or perceived deterioration in neighborhood quality, residents may become more politically engaged and shift electoral support toward platforms advocating stricter STR regulation (Rodon et al., 2025). At the same time, regulation and platform self-regulation can alter the composition or intensity of STR activity, attenuating some neighborhood externalities. Evidence from both regulatory interventions and platform-driven delisting supports such feedback effects: platform self-regulation that removed listings in U.S. cities reduced assault, robbery, and burglary, though theft increased as the remaining shared-home arrangements created new opportunities for property crime between hosts and guests (Han et al., 2022); formal regulation in Chicago and New York City similarly reduced burglary and aggregate crime, respectively (Jin et al., 2024; Chen et al., 2025).

Assessed from strongest to weakest identification, four consistent regularities emerge from this evidence. First, STR activity is most consistently associated with outcomes linked to criminal opportunity and disorder rather than large, uniform increases in serious violent crime. Evidence from U.S. cities (van Holm and Monaghan, 2021) and London (Lanfear and Kirk, 2024) shows stronger associations for property crime than for violent offenses, consistent with routine-activity mechanisms, while spatial evidence from Spanish cities similarly emphasizes heterogeneity across neighborhoods and the role of local structural factors in mediating STR–crime relationships (Maldonado-Guzmán, 2022; Maldonado-Guzmán et al., 2024). Second, nuisance effects are central but highly sensitive to measurement: STR exposure is linked to quality-of-life concerns, yet complaint-based outcomes may move differently from underlying disturbance because reporting and enforcement respond endogenously to STR presence, as the decline in residential noise complaints documented by Ozer et al. (2024) illustrates. Third, interventions that reduce STR exposure tend to improve neighborhood outcomes, with evidence from both platform self-regulation and formal regulation indicating declines in multiple categories of crime and disorder following listing removals (Han et al., 2022; Chen et al., 2025). Finally, neighborhood disruption has downstream civic and political consequences: STR exposure shifts electoral behavior toward regulatory backlash (Rodon et al., 2025), while sustained short-term renting weakens informal civic engagement through both population turnover and attitudinal changes among remaining residents, eroding social capital (Fontana, 2025). Together, these results suggest that STR reshape not only neighborhood conditions but also the local political economy of regulation.

In sum, neighborhood externalities operate through linked channels (criminal opportunity, weakened social control, nuisance, and political backlash) that are highly localized and causally linked to STR density (Lanfear and Kirk, 2024; Chen et al., 2025; Rodon et al., 2025). Reducing STR exposure improves neighborhood outcomes, but the effects are uneven, and regulation struggles to reverse deeper processes of touristification once established. This political feedback loop, in which localized harms generate demands for regulatory intervention, provides a natural bridge to the analysis of regulatory responses that follows.

Across all four domains, the evidence points in the same direction: STR generate private gains (hosting income, consumer surplus, localized economic stimulus) but impose costs that are spatially concentrated and socially uneven: distributional consequences transmitted through housing and labor markets, and neighborhood externalities that the price system does not capture. Property owners, platform operators, and tourists capture the benefits disproportionately, while renters, long-term residents, and lower-end incumbent firms bear the costs. Crucially, the four domains interact rather than operating in isolation: housing price increases amplify displacement, which weakens neighborhood social capital, which in turn increases crime opportunity, while the local economic stimulus from tourism creates coalitions that resist regulation. Barcelona illustrates the full chain: rising housing costs driven by STR expansion contributed to the displacement of lower-income residents, while simultaneously fueling a tourism-oriented business recomposition that eroded residentserving amenities; the resulting neighborhood transformation generated electoral backlash that ultimately produced some of the most restrictive STR regulations in Europe. Similar cascading dynamics have been documented in Berlin, where housing regulations reshaped the composition of STR supply without fully restoring residential availability, and in New York, where the interplay between hotel lobbying and neighborhood discontent culminated in an effective ban with far-reaching consequences for both accommodation markets and housing investment.

These interdependencies carry a clear implication for the regulatory analysis that follows: because the social costs of STR span multiple markets simultaneously, instruments targeting a single domain risk producing unintended consequences in others. The effectiveness of regulation therefore depends not only on the design of individual instruments but on whether the overall policy architecture can manage trade-offs across housing, local economic activity, accommodation competition, and neighborhood quality. The next section examines how jurisdictions have responded to this challenge.

By 2020, major tourism cities worldwide had adopted some form of STR regulation, yet the relationship between regulatory design and market outcomes remains poorly understood (von Briel and Dolnicar, 2021). The cross-domain impacts documented in Section 2 complicate the task: instruments targeting one market generate unintended consequences in others. This section systematizes the main regulatory approaches (government regulation, platform self-regulation, co-regulatory arrangements, and collective governance), documents their relative prominence across published studies, and synthesizes the empirical evidence on their effectiveness.

The regulatory debate is shaped by two opposing perspectives. Proponents emphasize efficiency gains and consumer welfare from platform-mediated exchange (Cohen and Sundararajan, 2015; Sundararajan, 2016), while critics point to negative externalities, regulatory arbitrage, and erosion of established standards (Frenken and Schor, 2017; Guttentag, 2019). A growing body of review literature maps the evolving governance landscape (Belk, 2014; Mont et al., 2020; Nawi et al., 2022). These tensions reflect a basic regulatory dilemma: the social costs of STR are real but highly heterogeneous across listings, neighborhoods, and time periods, making uniform rules either too permissive (allowing harmful commercial operations) or too restrictive (eliminating beneficial home-sharing). Most jurisdictions therefore adopt context-specific instruments that balance these competing concerns. The literature broadly distinguishes between government regulation and self-regulation by platforms (Mosaad et al., 2023). Government regulation refers to rules designed and enforced by public authorities at different levels of government, while self-regulation encompasses rules and enforcement mechanisms voluntarily adopted by platforms to govern behavior within their ecosystems. In practice, these approaches frequently coexist and interact, giving rise to hybrid or co-regulatory arrangements. However, these regulatory frameworks are not always effective, at least from the perspective of the communities and citizens affected by STR expansion. As a result, citizens, residents in the most affected areas, and even property owners themselves mobilize either against platforms and large operators or to protect themselves from the platforms’ market power. This has led to the emergence of a new form of governance, primarily grounded in social and political influence, which we refer to as community governance.

Four approaches structure the analysis that follows: government regulation (encompass-ing avoiding, limiting, and guiding mechanisms), platform self-regulation, co-regulatory arrangements, and collective governance (see Figure 2).

Government regulation: avoiding, limiting, and guiding mechanisms Mosaad et al. (2023) identify three core mechanisms through which governments regulate sharing economy activities: avoiding, limiting, and guiding. These mechanisms differ in their degree of restrictiveness and in the way they address STR-related impacts.

Avoiding mechanisms represent the most restrictive form of government intervention. They involve outright bans or exclusions that prevent certain sharing economy activities from taking place. In the context of STR, avoiding mechanisms include total prohibitions within a municipality, bans in specific zones, restrictions based on land-use categories, or incompatibility with urban planning regulations. Policymakers typically invoke these mechanisms when perceived social costs outweigh potential benefits, or when regulatory capacity proves insufficient to manage activity through more nuanced instruments. In housing-constrained cities, for example, concerns about housing availability and residential displacement motivate bans on STR in central neighborhoods (Guttentag, 2019).

However, the literature generally views avoidance as a blunt instrument. By eliminating both harmful and benign forms of activity, prohibitions may suppress innovation, reduce tourism revenues, and encourage informal or illegal markets. Most scholars therefore treat avoiding mechanisms as transitional or exceptional responses rather than sustainable long-term solutions (Mosaad et al., 2023).

Limiting mechanisms seek to contain, rather than eliminate, sharing economy activity. They do so by imposing quantitative constraints on scale, intensity, or growth. In STR markets, common instruments include caps on the number of rental days per dwelling, limits on the number of properties per owner or manager, density thresholds at building or neighborhood level, moratoria on new licenses, and zero-net-growth rules.

From an economic standpoint, limiting mechanisms aim to mitigate congestion and housing market pressures by restricting aggregate supply. They are particularly relevant in urban contexts where STR activity competes directly with long-term rental housing, contributing to rent increases and reduced availability of long-term accommodation (Barron et al., 2021). Limiting instruments also serve a political function, signaling responsiveness to resident concerns while preserving some degree of market access. The effectiveness of limiting mechanisms depends critically on how well authorities can monitor compliance and access transaction data. Platforms hold detailed information on listings and transactions, while regulators face persistent information asymmetries. Poorly designed limits therefore invite strategic behavior: hosts list across multiple platforms, professionalize supply through corporate structures, or exploit definitional loopholes (Guttentag, 2019).

Guiding mechanisms represent the most nuanced form of government regulation. Rather than focusing primarily on quantities, they shape the conditions under which sharing economy activities operate. These mechanisms are inherently multidimensional and target different actors, including platforms, service providers, and consumers.

For platforms, guiding regulation includes obligations related to registration systems, data sharing with authorities, transparency requirements, and cooperation in enforcement. Data sharing has emerged as a central element of STR regulation, enabling authorities to identify illegal listings, enforce taxation, and monitor compliance without direct intervention in individual transactions (Mosaad et al., 2023). For providers, guiding mechanisms encompass licensing requirements, registration obligations, primary-residence conditions, safety standards, and fiscal obligations. These instruments seek to align STR activity with existing accommodation and housing regulations while allowing differentiation between occasional and professional hosts. The design of registration procedures matters considerably: in Asturias, replacing administrative authorization with a responsibility statement more than doubled registered vacation rental supply, partly by formalizing previously unregistered operators and thereby bringing them within the reach of monitoring and taxation (Boto-Garcia et al., 2023).

Finally, governments may guide both platforms and providers jointly through tax collection arrangements, differentiated fees or penalties, insurance requirements, and educational initiatives. Such measures recognize the interdependence between platform design and host behavior and aim to distribute responsibility accordingly.

Self-regulation by platforms: entry, operation, and monitoring Self-regulation operates through three interrelated mechanisms. Market entry requirements (identity verification, documentation checks, property standards, and in some jurisdictions proof of registration) function as private certification systems that reduce adverse selection and build trust, though they also raise concerns about exclusion and the private exercise of regulatory power (Mont et al., 2020). Operational mechanisms include pricing tools, non-discrimination policies, insurance arrangements, dispute resolution systems, and host segmentation through tiered status labels. Dynamic pricing algorithms enhance allocative efficiency but may exacerbate price volatility in high-demand areas, while tiered status systems can improve service standards yet contribute to the professionalization and concentration of supply (Guttentag, 2019; Bosma, 2022).

Monitoring mechanisms, primarily reputation and review systems, supplemented by algorithmic detection and internal sanctions, enable platforms to oversee behavior at scale. Despite their scalability, these tools suffer from bias, manipulation, and algorithmic opacity. Most scholars now view platform self-regulation as a complement to, rather than a substitute for, public enforcement (Calo and Rosenblat, 2017; Mosaad et al., 2023), although some advocate for a more central regulatory role for platforms through delegated self-regulation (Cohen and Sundararajan, 2015).

Co-regulation and hybrid governance arrangements The limitations of both government regulation and platform self-regulation have spurred the development of a third approach. In practice, regulation of the sharing economy rarely relies exclusively on either public authority or private governance. Instead, hybrid arrangements combining elements of both approaches now appear in most major jurisdictions. Examples include platforms collecting and remitting tourist taxes, sharing data with authorities, or enforcing public rules through private contractual mechanisms. These co-regulatory models exploit the comparative advantages of each actor: governments provide legitimacy and coercive authority, while platforms contribute technological capacity and access to granular data. At the same time, such arrangements raise concerns about accountability, regulatory capture, and the redistribution of enforcement costs (Davidson and Infranca, 2016; Ferreri and Sanyal, 2018; Nawi et al., 2022).

Collective governance in STR regulation Where formal regulation and platform governance fall short, communities themselves step in. Collective governance emerges as a complementary mechanism in the STR literature, where homeowners’ associations (HOAs) enforce norms through internal covenants, conditions, and restrictions. This private governance approach allows residents to address STR-related disruptions, including transient occupancy, increased costs for common-area maintenance, and changes in community character (Kochan, 2018).

HOAs have amended governing documents to restrict or prohibit short-term rentals, imposing minimum lease durations, requiring board approval of rental arrangements, and levying fines for violations. Courts have consistently upheld these measures, recognizing that restrictions on transient use fall within the permissible scope of private land-use regulation. However, enforcement depends on the specificity of governing documents and on homeowner participation, and disputes frequently arise between owners seeking to capitalize on STR income and those who prefer residential stability (Kochan, 2018).

In summary, the STR regulatory framework comprises four complementary approaches that operate at different scales and through different mechanisms. Government regulation remains the primary tool, deploying avoiding, limiting, and guiding instruments to address STR impacts directly. Yet its effectiveness hinges on platform cooperation through coregulatory arrangements, on self-regulatory mechanisms that govern day-to-day market conduct, and on community engagement that addresses externalities formal rules cannot easily reach.

How frequently different regulatory instruments appear across empirical and conceptual studies offers an informative proxy for their perceived relevance in policy debates and research agendas. While frequency of mention does not directly measure effectiveness, instruments discussed repeatedly across heterogeneous contexts likely reflect widespread adoption, strong regulatory salience, or a recognized capacity to shape market outcomes. Instruments that rarely appear may signal marginal policy use or limited enforceability (see Table 2).

Figure 2. Interventions and specific mechanisms of regulation in the short-term rental market.

Table 2. Percentage of mentions in the analyzed papers of each type of regulation or instrument.

Government regulation: the dominance of guiding instruments Guiding instruments dominate the government regulation category, far outpacing both avoiding and limiting mechanisms. This dominance likely reflects their political palatability, guiding instruments allow governments to regulate STR without appearing to ban a popular service. Type-specific regulation (54.1%) and primary residence requirements (50.6%) lead, reflecting a broad consensus that differentiating accommodation types and anchoring STR to primary residences are central strategies for mitigating housing market pressures. Prior registration (45.3%), prior licensing (38.8%), and fines for non-compliance (40.8%) form a closely linked enforcement cluster, confirming that administrative tools function as sanctioning mechanisms rather than mere formalities. By contrast, transferable licenses (3.1%), permanent licenses (4.2%), and tax incentives (5.5%) remain marginal, indicating that scholars frame STR regulation primarily as restriction rather than positive inducement.

Within avoiding mechanisms, jurisdiction-wide bans appear in only 6.2% of papers, reflecting their exceptional nature. Urban planning incompatibilities (40.1%) and zonal prohibitions (30.4%) attract far more attention, illustrating the central role of spatially differentiated land-use regulation. Among limiting instruments, caps on days rented (31.8%) stand out, reflecting the widespread adoption of 60-, 90-, or 120-day rules in major cities and their analytical tractability. Caps on properties per host (9.2%) and guest numbers (10.1%) receive much less attention, while income-based limitations (4.3%) remain peripheral.

Self-regulation: selective emphasis on monitoring and segmentation Scholarly attention to self-regulation concentrates on two dynamics that determine market structure: who participates and how quality is signaled. Self-regulatory instruments receive uneven scholarly attention. Provider segmentation (41.5%) and reputation systems (40.0%) dominate, reflecting scholarly focus on two core platform dynamics: the distinction between casual and professional hosts, and the role of user reviews in disciplining behavior. Operational tools—dynamic pricing (21.2%), customer management (18.3%), and conflict resolution (14.3%)— attract less attention, typically discussed in relation to market efficiency rather than as regulatory instruments per se.

The most striking gap concerns algorithmic transparency, mentioned in only 2.7% of papers. This may reflect the difficulty of studying proprietary algorithms, but it also suggests that platform governance remains a blind spot in the regulatory literature, even as algorithms play a decisive role in structuring market visibility, pricing, and host segmentation.

Co-regulation: platforms as institutional intermediaries Co-regulation attracts the highest individual instrument frequencies in the entire typology, reflecting a consensus that sustainable regulation requires leveraging platform infrastructure. Platforms as institutional intermediaries (51.2%) ranks among the most prominent instruments overall, reflecting widespread recognition that platforms now function as de facto regulators, enforcing public rules through private infrastructure. Gatekeeping instruments (exclusive inclusion of authorized STR (33.3%) and aligned entry requirements (25.8%)) feature prominently, as does compliance with community rules (45.5%).

Data-related instruments reveal a telling asymmetry: data sharing on properties (25.2%)

and revenues (22.8%) receive more scholarly attention than platform-based tax collection (18.6%) or tax guidance (8.2%), suggesting that information provision matters more to the academic debate than direct fiscal intermediation, even though the latter may carry greater practical consequences. Deactivation of non-compliant listings (26.5%) shows that effective regulation now depends on platforms’ capacity to operationalize sanctions at scale.

Collective governance: the salience of social sanctions Social sanctions such as shaming appear in 60.1% of analyzed papers, exceeding many formal regulatory instruments. The high visibility of social sanctions likely reflects a widespread recognition that formal regulation alone cannot manage the everyday frictions (noise, overcrowding, loss of community) that STR generate in residential settings. Homeowners’ associations (39.6%) and collective bargaining groups (25.5%) further illustrate the role of organized community action. Together, these findings indicate that scholars now conceptualize STR governance as a multi-actor process in which bottom-up pressures complement, and sometimes substitute for, formal regulation.

The distribution of mentions reveals a clear hierarchy: classification, registration, residence-based differentiation, and platform intermediation dominate, while financial incentives, tradable rights, and algorithmic transparency remain marginal. The prominence of guiding and co-regulatory instruments over blunt restrictions signals a shift toward information-intensive, institutionally hybrid approaches that require coordination among public authorities, platforms, and local communities.

The frequency data reveal which instruments scholars prioritize; a complementary question is which impacts these instruments are designed to address. Table 3 reports how often the 89 empirical studies examining regulatory effectiveness focus on specific impact dimensions.

Table 3. Effects of regulation on short-term rental impact dimensions in the literature.

Notes: Percentages calculated over the 89 papers that include some form of regulatory effectiveness assessment. Categories are not mutually exclusive.

Housing dominates the regulatory effectiveness literature by a wide margin. Nearly half of the analyzed papers focus on the supply of housing for purchase or long-term rental (49.4%), while substantial shares examine property values, rents, and prices (36.0%) and general housing market conditions (34.8%). No other impact dimension comes close to this concentration of scholarly attention, which clearly positions housing as the central axis around which STR regulation has been conceptualized and evaluated.

This dominance is closely aligned with the most frequently discussed regulatory instruments. Measures such as primary residence requirements, limits on the number of days rented, type-specific regulation of STR, and mandatory registration and licensing are all explicitly designed to prevent the conversion of residential housing into full-time tourist accommodation. Their prominence reflects a widespread consensus that the principal social cost associated with STR expansion lies in its impact on housing availability, affordability, and residential stability. By constraining the intensity, duration, or professionalization of STR activity, these instruments aim to preserve housing stock for permanent residents.

The salience of zoning-based instruments and urban planning incompatibilities further reinforces this housing-centered framing, confirming that housing market effects are understood not only as aggregate outcomes but also as geographically concentrated phenomena requiring fine-grained spatial targeting.

Interestingly, despite the centrality of housing concerns, Table 3 shows that professionalization of STR within the housing market dimension is directly addressed in only 1.1% of papers. This apparent paradox can be explained by the way professionalization is regulated indirectly. Rather than focusing on explicit caps on portfolio size or direct prohibitions of commercial activity, most regulatory frameworks rely on housing-based instruments, such as primary residence rules or zoning restrictions, to limit large-scale operations. As a result, most studies analyze professionalization through its housing impacts rather than as a distinct market dynamic. This indirect approach matters because professionalization is precisely the mechanism that most often undermines regulatory effectiveness, as the next subsection documents.

If housing is the primary lens through which scholars view STR regulation, competition with hotels constitutes a secondary but non-trivial concern. Some 24.7% of papers examine effects on accommodation prices, yet only 4.5% address general conditions in the traditional accommodation sector. This gap reveals that scholars treat hotel competition as a side effect of housing-oriented regulation rather than as a standalone policy objective. Indeed, tools that would directly target accommodation-market competition (differentiated taxation, explicit capacity controls) appear far less frequently than housing-oriented instruments, and when studies do analyze accommodation effects, these typically emerge as by-products of density caps or licensing schemes designed primarily to protect residential supply.

While 16.9% of papers analyze general tourism-related economic activity, only 1.1% examine employment effects and a mere 2.2% focus on fiscal capacity. This fragmented engagement with economic impacts stands in marked contrast to the dense coverage of housing impacts.

Fiscal instruments occupy a particularly marginal position. Tax incentives, platformprovided tax guidance, and systematic platform-based tax collection all receive far less attention than housing-oriented tools, suggesting that scholars and policymakers treat fiscal outcomes as ancillary benefits of broader regulatory frameworks rather than as primary objectives. Employment effects fare even worse: although STR generate jobs in cleaning, maintenance, and local services, methodological challenges in isolating these impacts, combined with their diffuse and politically less salient nature, relegate them to the analytical periphery.

This pattern carries a significant policy implication. By prioritizing spatially concentrated and socially contentious impacts over broader economic channels, current research may inadvertently undervalue the opportunity costs of strict regulation, costs that fall disproportionately on neighborhoods where STR-driven spending represents a genuine economic lifeline.

Why do social sanctions appear in over half of all analyzed papers when quality-oflife impacts appear in only 10.1%? This apparent paradox, high regulatory visibility combined with low empirical coverage, defines the neighborhood externality dimension. Displacement (5.6%), commercial gentrification (4.5%), and urban renewal (1.1%) round out a set of impacts that, while less frequently studied than housing effects, exert outsized influence on regulatory debates. The prominence of informal governance instruments (social sanctions, homeowners’ associations, collective bargaining) reflects a recognition that formal regulation struggles to manage everyday frictions such as noise and eroded social cohesion, yet instruments explicitly designed to mediate such conflicts are rarely analyzed, suggesting that social externalities serve as triggers for regulation rather than as targets of systematic policy design.

Displacement and commercial gentrification point to longer-term neighborhood transformations that intersect with broader urban dynamics and remain difficult to attribute directly to STR regulation. Nevertheless, a growing subset of studies is beginning to engage with these deeper socio-spatial consequences.

The distribution of the impacts of regulations in Table 3 reveals a research agenda oriented toward spatially visible and politically salient impacts, above all housing, while fiscal capacity, employment, and algorithmic governance remain underrepresented. STR regulation has been shaped less by a full assessment of all affected dimensions than by those that are most immediately observable and socially contentious. Recognizing these priorities and blind spots is essential for interpreting the effectiveness evidence that follows.

How effective are STR regulations, and effective at what? Any regulation that restricts STR activity below the socially optimal level generates deadweight loss by suppressing transactions whose private surplus exceeds the social cost, a risk that is particularly relevant for blunt instruments such as jurisdiction-wide bans (Farronato and Fradkin, 2022). The answer depends on which outcomes we examine and in which institutional contexts. The evidence is organized below around four dimensions: the housing market, the traditional accommodation sector, the local economy, and neighborhood-level externalities.

The housing market constitutes the primary arena in which STR regulation has been debated and empirically evaluated. As documented in Section 2, STR expansion shifts dwellings from long-term residential use to short-term tourist accommodation, reducing effective supply and pushing up rents and prices. The central policy question is whether regulation can reverse or contain this reallocation (Ekeroma, 2023). Empirical evidence suggests that strict and enforceable regulations can partially mitigate these pressures, particularly in dense and supply-constrained urban environments. Studies employing quasi-experimental methods, such as difference-in-differences or regression discontinuity designs, consistently find that outright bans, zoning restrictions, or tightly enforced licensing regimes are associated with statistically significant reductions in rental prices. The magnitude of these effects is generally moderate, often in the range of 2–3 percent, but these studies stress that such changes are economically meaningful in markets characterized by low supply elasticity (Koster et al., 2021; Seiler et al., 2024). Day-cap regulations, which limit annual rental days per property, reduce STR supply while partially redirecting units to the long-term market, though the reallocation to long-term rental use is incomplete and significant non-compliance persists (Gauß et al., 2024). An important methodological contribution concerns the measurement of rents: Seiler et al. (2024) show that regulatory impacts are more clearly identified when using contracted rents rather than advertised or “asking” rents, which tend to be downwardly rigid and may not fully adjust to policy-induced changes. This distinction helps explain why some earlier studies failed to detect strong effects and reinforces the conclusion that regulation can be effective, provided that appropriate outcome measures are used.

These observed effects capture only part of the picture. A growing body of literature evaluates regulatory effectiveness through counterfactual analysis, asking how housing markets would have evolved in the absence of intervention. This perspective is particularly relevant in cities where affordability pressures predate STR regulation and where full price reversals are unlikely even under stringent rules. Barcelona illustrates the value of this approach: using counterfactual modeling, Gyódi et al. (2025) estimate that without the regulatory framework implemented since 2015, the number of STR listings would have nearly doubled, implying that regulation prevented a substantial additional share of the city’s rental stock from being converted into tourist accommodation. From this standpoint, effectiveness lies not in reversing past losses but in preventing further erosion of residential supply. Conversely, Santa Monica’s Home-Sharing Ordinance eliminated 60% of entire-home listings yet had no effect on residential rents, because the regulated units did not return to the long-term rental market (Chaves Fonseca, 2025). Several studies identify further important limitations. Restrictions on listing numbers are often accompanied by increased intensity of use among remaining listings. In Barcelona, demand, measured through review counts, continued to grow even in zones subject to growth caps, suggesting that remaining operators compensated through higher occupancy and more intensive exploitation of licensed properties (Benítez-Aurioles, 2021). This pattern reveals a structural weakness of many regulatory frameworks: they are more effective at constraining the extensive margin of STR activity than the intensive margin.

A deeper structural limitation constrains all these instruments. Professionalization significantly weakens regulatory effectiveness. Berlin’s experience provides the clearest illustration: its 2016 reform targeting commercial listings increased long-term rental supply, whereas the 2018 reform affecting non-commercial hosts had no measurable effect on the rental market (Duso et al., 2024). More broadly, regulations disproportionately affect casual or non-professional hosts, typically individuals operating a single listing, while multi-property operators display far greater resilience and adaptability. Professional hosts circumvent regulatory constraints through multiple channels, including corporate structures that obscure ownership, multi-platform listing that fragments enforcement across competing platforms, and systematic non-compliance with day-limit regulations through o!-platform bookings and seasonal arbitrage between short- and medium-term rental categories (Gauß et al., 2024). Paris’s algorithmic quantity regulation illustrates a novel enforcement model: platformimposed caps on listing days reduced housing prices, but spillovers to competing platforms such as Vrbo were immediate, and inexperienced hosts bore the cost disproportionately while professional operators adapted (Tripathi et al., 2025).

Evidence from crisis periods further reveals the structural advantage of professional operators. During the COVID-19 pandemic, professionalized hosts temporarily reallocated properties across STR platforms and medium-term rental markets (Heinermann, 2022). This market-segment arbitrage, facilitated by the same corporate infrastructure that enables regulatory evasion, meant that regulatory gains in housing availability proved fragile and reversible. Cities that initially celebrated the pandemic-driven return of units to longterm rental markets found those gains evaporating within months of tourism’s recovery. This dynamic is particularly problematic because professional listings are precisely those most likely to represent full-unit, year-round removals from residential supply. In cities like Barcelona, London, and Paris, where professional operators manage a disproportionate share of entire-home listings, regulation has reshaped the composition of STR supply without fully restoring housing availability (Shabrina et al., 2022; Gyódi et al., 2025). The implication is stark: regulations designed around the model of an occasional host sharing a spare room systematically fail when the market is dominated by commercial operators running portfolio businesses.

Dynamic supply-side responses further complicate the assessment. Regulatory effectiveness must also be evaluated in terms of its impact on housing investment and construction. While regulation frees up existing dwellings for residential use, it can simultaneously reduce incentives for new development. Bekkerman et al. (2023) find that stricter STR regulations are associated not only with reductions in listings but also with declines in residential building permits, particularly for new construction and structural additions.

Moreover, even regulations that successfully curtail investor listings may have muted effects on housing prices, because a significant portion of STR’s price impact operates through homeowners’ increased willingness to accept higher sale prices once their properties can generate rental income, a channel that persists as long as occasional home-sharing remains legal (Duncan and Ross, 2025). This introduces an important policy trade-o!: in the short run, regulation eases pressure on existing housing stock, but in the medium to long run it can dampen supply growth, partially offsetting affordability gains. Evaluating effectiveness solely on immediate price responses therefore risks overlooking broader investment dynamics and supply-side adjustments (Franco and Santos, 2021; Cunha and Lobão, 2022). These design choices matter enormously. Sydney’s 180-day annual cap did not limit the number of dwellings operating as STR but merely constrained usage intensity, inadvertently encouraging more properties to enter the short-term market and raising long-term rents by 2.7–3.6% (Roudnitski and Sarkar, 2025). The lesson is that day-caps without accompanying limits on the total stock of STR properties can produce perverse effects by spreading activity more thinly across a larger number of dwellings.

The local economic effects of STR regulation are more ambiguous than those observed in housing or accommodation markets. Several studies document positive associations between STR activity and employment growth in local service sectors, particularly restaurants and food services, with effects strongest in residential neighborhoods outside traditional tourist districts, where STR guests bring spending to areas previously lacking visitor traffic (Hidalgo et al., 2024). From this perspective, strict regulation entails opportunity costs by dampening localized economic revitalization, and evaluating effectiveness solely in terms of housing outcomes risks overlooking broader economic trade-offs. Indeed, Zhao et al. (2026) find that New York City’s 2023 STR regulation led to a 10% reduction in restaurant spending, with an estimated annual revenue loss of approximately $3 billion concentrated among higher-priced and tourist-oriented establishments. However, these gains are unevenly distributed and potentially transient: STR-driven demand can contribute to rising commercial rents and the replacement of resident-oriented services with tourist-oriented businesses, undermining long-term neighborhood sustainability (Esposito, 2023). A distinct but complementary mechanism operates through fiscal channels. Fiscal regulation constitutes a central link between STR activity and local economic governance. Agreements whereby platforms collect and remit taxes directly on behalf of hosts significantly slow the growth of housing costs by raising the effective cost of STR operation (Ellis et al., 2025). By reducing the net profitability of STR activity, such mechanisms also indirectly reinforce housing and accommodation policy goals.

The contrast between data-sharing and self-reporting regimes is stark. Denmark’s 2018 agreement requiring Airbnb to transmit income data to the tax agency reduced listing propensity by 14% and raised prices by 11%, but exits were concentrated among single-property hosts while multi-property operators expanded, accelerating the very professionalization that regulation sought to contain (Garz and Schneider, 2023a). Norway’s contemporaneous rental-income tax, which relied on self-reporting, had no measurable effect: hosts neither exited Airbnb nor increased their prices, despite an average annual tax liability of approximately $735 (Garz and Schneider, 2023b).

A third key dimension of regulatory effectiveness concerns competition between STR and traditional accommodation providers. Strong evidence indicates that STR regulations tend to benefit the hotel sector, particularly in cities where STR and hotels compete for similar segments of demand. Falk and Yang (2021) estimate that in regulated European cultural destinations, hotel overnight stays increased by approximately 9 percent following the introduction of stringent STR rules, and difference-in-differences evidence from the United States confirms positive effects on lower-scale hotel performance following STR restrictions (Yeon et al., 2020). These gains are concentrated among low-cost and mid-range hotels, which compete most directly with entire-home STR listings.

U.S. city-level studies reinforce this conclusion. In Chicago, regulatory reforms backed by platform data feeds led to substantial declines in STR revenues, particularly in areas with high hotel density, suggesting a partial recovery of market share by traditional accommodation providers (Jin et al., 2024). The most dramatic case is New York City, where the 2023 ban raised hotel average daily rates by $14–19 per night and generated a revenue windfall of $2.1–2.9 billion over eighteen months, driven largely by higher prices rather than increased occupancy (Anastasi et al., 2025). The hotel industry’s political contributions prior to the ban, an order of magnitude larger than Airbnb’s, raise concerns about regulatory capture.

These aggregate gains mask important heterogeneity across different types of STR activity. Restrictions primarily affect entire-home listings, while private-room rentals and genuine home-sharing arrangements are much less affected (Falk and Scaglione, 2024). This distinction has important implications for regulatory design: blanket restrictions risk eliminating socially beneficial forms of occasional home-sharing without substantially improving competitive conditions, whereas targeted measures aimed at commercial STR activity are more likely to restore balance without unnecessary collateral effects (Oszkay Febres-Cordero, 2022).

A growing body of evidence links high STR density to increased crime, including assault, robbery, and burglary, through the turnover of transient guests in residential buildings (Han et al., 2022). The quasi-experimental evidence from Chicago discussed in Section 2.4 confirms that building-level restrictions can reduce burglaries by double digits (Jin et al., 2024), suggesting that highly targeted regulations outperform broad citywide ordinances in improving neighborhood safety. Beyond measurable crime outcomes, recent studies place growing emphasis on subjective well-being and social cohesion. Residents frequently report feelings of alienation and loss as neighborhoods become oriented toward transient tourist use, an experience that Dinan et al. (2025) describe as solastalgia, capturing emotional harm linked to place transformation rather than physical displacement. Yet while regulation often slows the growth of STR supply, it often fails to reverse deeper processes of commercial and cultural change. As a result, regulatory effectiveness in this dimension remains limited, particularly when policies focus narrowly on listing numbers rather than broader urban dynamics.

A further persistent challenge is spatial spillovers. Cross-city comparisons confirm that zoning-based restrictions frequently displace STR activity into adjacent neighborhoods, exporting rather than eliminating negative externalities. In New Orleans, zoning restrictions pushed STR activity into adjacent neighborhoods (Valentin, 2021). Comparative analysis of Amsterdam, Berlin London and Bordeaux shows that cities follow highly individualized regulatory approaches, with no one-size-fits-all solution (Hübscher and Kallert, 2023; Robertson et al., 2024). Effectiveness depends critically on enforcement capacity: comparative evidence from Barcelona and Paris shows that even narrowly targeted zonal restrictions can avoid spillovers when supported by aggressive inspection campaigns, whereas broader temporal restrictions prove harder to monitor (Bei, 2025).

The evidence across all four dimensions converges on a single structural insight: regulation selects against casual hosts while leaving professionalized operators largely intact (Smith, 2021). This asymmetry explains why aggregate STR indicators often decline after regulation while the housing and neighborhood effects that motivated intervention persist. The concluding section distills the policy implications of this finding.

This paper has reviewed the rapidly expanding empirical literature on STR impacts and the regulatory responses designed to address them. The central finding is that STR constitute a simultaneous multimarket entry: their expansion reallocates housing between residential and tourist use, redirects demand toward local services, and reshapes competitive conditions in accommodation, all at once, generating private efficiency gains alongside distributional consequences transmitted through affected markets and neighborhood externalities that the price system does not capture. In housing markets, the evidence is unambiguous: STR expansion raises prices and, in most contexts, rents, with effects strongest in tourism-intensive, supply-constrained areas where five channels (supply reallocation, price capitalization, income effects, amenity adjustments, and negative externalities) interact to determine the net impact. In the accommodation sector, STR act as a flexible-capacity entrant that intensifies price competition and generates substantial consumer surplus, particularly during peak demand, while imposing uneven costs on lower-end hotels; regulatory restrictions primarily restore hotel pricing power rather than improving efficiency, as New York City’s 2023 ban vividly demonstrates (Anastasi et al., 2025). Local economies benefit from redirected tourism spending and investment stimulus, but at the cost of compositional change that replaces resident-serving businesses with tourist-oriented activities. At the neighborhood level, STR penetration increases criminal opportunity, weakens social cohesion, and generates nuisance externalities through mechanisms rooted in increased transiency and reduced informal monitoring. Crucially, these four domains interact rather than operating in isolation: housing price increases amplify displacement, displacement weakens social capital, weakened social capital increases crime opportunity, and tourism-driven business recomposition creates coalitions that resist regulation. This interdependence means that policies targeting a single domain risk producing unintended consequences in others, and that regulatory effectiveness cannot be meaningfully assessed along any single dimension.

Regulation can moderate these pressures, but its effectiveness is conditional rather than guaranteed. The evidence converges on a structural insight that cuts across all four domains: regulation selects against casual hosts while leaving professionalized operators largely intact. Professional operators circumvent constraints through multi-platform listing, corporate structures, primary-residence manipulation, and market-segment arbitrage between shortand medium-term rentals. This asymmetry explains why aggregate STR indicators often decline after regulation while the housing and neighborhood effects that motivated intervention persist. Moreover, regulation is more successful at preventing further deterioration than at reversing past losses: Barcelona’s licensing moratorium prevented a near-doubling of listings, yet remaining operators intensified use of existing licenses; Santa Monica eliminated 60% of entire-home listings with no measurable effect on rents; and Sydney’s day-cap perversely raised long-term rents by 2.7–3.6% (Gyódi et al., 2025; Chaves Fonseca, 2025; Roudnitski and Sarkar, 2025). Enforcement capacity and data access, not nominal rule strictness, determine whether regulation achieves its stated objectives. The contrast between Denmark’s data-sharing agreement (which reduced listing propensity by 14%) and Norway’s self-reporting tax (which had no measurable effect) crystallizes this lesson: without platformlevel data transparency, fiscal and administrative regulation risks remaining symbolic (Garz and Schneider, 2023a,b).

The review also reveals important gaps in the analytical framework that should guide future research. Most empirical work adopts a partial-equilibrium perspective, measuring STR impacts within a single market, yet the cross-market feedback loops documented throughout this review call for general-equilibrium analysis that can trace cascading effects and quantify net welfare; no study has yet attempted comprehensive welfare accounting across all affected markets. The host-platform relationship warrants closer scrutiny: platforms profit from maximizing transaction volume regardless of externality costs borne by neighbors, creating a moral-hazard problem that self-regulation alone is unlikely to resolve. Coregulatory arrangements, while informationally efficient, carry inherent risks of regulatory capture when platforms serve simultaneously as regulated parties and rule-enforcers, a concern underscored by the hotel industry’s political contributions preceding New York City’s STR ban (Anastasi et al., 2025). Fiscal instruments, algorithmic governance, and communitybased governance mechanisms remain weakly theorized and empirically underexplored, while the measurement of subjective welfare losses, solastalgia, erosion of social capital, loss of neighborhood identity, has barely begun. More comparative, longitudinal, and institutionally informed research is needed to understand how regulatory designs perform across different settings and how platform strategies co-evolve with the rules designed to constrain them.

The evidence yields four guiding principles for policy design. Because STR simultaneously affect housing, local economic activity, accommodation competition, and neighborhood quality, regulatory frameworks must explicitly manage trade-offs across these dimensions rather than optimizing outcomes in any single domain. Housing affordability should remain the central policy objective in supply-constrained jurisdictions, as the literature consistently identifies housing-market impacts as the most salient and politically contentious consequence of STR expansion. Policymakers should calibrate expectations, however: regulation prevents further deterioration more reliably than it restores past affordability.

Principle 1: Differentiate by operator type. Large-scale, professionalized STR operations drive negative externalities disproportionately, not occasional home-sharing, yet most regulatory frameworks fail to distinguish adequately between these forms of activity. Instruments anchored in primary residence rules and type-specific classifications, caps on rental days for primary residences versus outright restrictions on dedicated investment properties, are well suited to this distinction. Regulation should target the extensive margin (the number of full-time commercial listings) rather than the intensive margin (occupancy rates among remaining hosts), since restrictions on listing numbers prove far more effective than usage caps that professional operators circumvent through intensified exploitation. A sobering caveat applies, however: even successful restrictions on investor listings have limited effects on housing prices, because a significant portion of STR’s price impact operates through homeowners’ increased willingness to accept higher sale prices once their properties can generate rental income, a channel that persists as long as occasional home-sharing remains legal (Duncan and Ross, 2025).

Principle 2: Invest in enforcement infrastructure, not just rules. Enforcement capacity and data sovereignty are decisive determinants of regulatory effectiveness. The Denmark–Norway contrast is instructive: Denmark’s 2018 data-sharing agreement with Airbnb reduced listing propensity by 14%, while Norway’s contemporaneous rental-income tax, which relied on self-reporting, had no measurable effect on host behavior (Garz and Schneider, 2023a,b). Access to granular information on listings, transactions, and revenues is a core element of policy design, not a secondary technical detail: without such access, regulatory frameworks risk becoming symbolic, generating compliance costs without meaningful impact. Co-regulatory arrangements, in which platforms enforce public rules through private infrastructures, exploit platforms’ technological capacity and informational advantages, but policymakers should approach them as necessary yet imperfect solutions requiring clear legal mandates, independent monitoring mechanisms, and explicit safeguards against regulatory capture.

Principle 3: Target spatially and manage spillovers. Spatially differentiated instruments, zonal restrictions, density thresholds, improve precision but can generate spillovers into adjacent areas, as documented in New Orleans (Valentin, 2021), though Barcelona’s experience shows that aggressive enforcement can prevent such displacement (Gyódi et al., 2025; Bei, 2025). Cross-city comparisons confirm that no one-size-fits-all approach exists (Hübscher and Kallert, 2023).

Principle 4: Build adaptive, multi-stakeholder governance. STR markets evolve rapidly as platforms adjust algorithms, hosts professionalize, and demand patterns shift. Fixed regulatory thresholds become obsolete or counterproductive over time, as illustrated by the COVID-19 period when professional operators temporarily shifted to long-term rentals before reverting once tourism recovered (Dolnicar and Zare, 2020; Cocola-Gant et al., 2021; Heinermann, 2022). Continuous monitoring, periodic evaluation, and the ability to adjust instruments in response to observed outcomes are essential for maintaining effectiveness. STR regulation inevitably redistributes surplus across renters, homeowners, hosts, hotels, tourists, and platforms, and regulatory conflict often arises from perceived inequities rather than efficiency losses per se; explicitly acknowledging these distributional effects can improve the legitimacy and durability of policy interventions. Community governance mechanisms, homeowners’ associations, neighborhood organizations, and informal social sanctions, complement formal regulation by addressing externalities that top-down rules cannot easily reach, and policymakers should consider how formal rules interact with these bottom-up dynamics.

In sum, the evidence supports a conception of STR regulation as an ongoing governance process rather than a one-o! intervention. Effective policy requires differentiating between occasional home-sharing and commercial operations, investing in enforcement infrastructure anchored in platform data, targeting instruments spatially while managing spillovers, and building adaptive governance that can respond to rapidly evolving market conditions. The research frontier lies in general-equilibrium analyses that capture cross-market feedback loops, comparative studies that track regulatory performance across institutional settings, and a deeper understanding of how platform strategies co-evolve with the rules designed to constrain them. As cities worldwide continue to grapple with the consequences of platform-mediated accommodation, the stakes of getting this regulatory balance right will only grow.

References

1. Allam, Y., Breuillé, M., and Le Gallo, J. (2026). From sharing to capitalizing: Evaluating the rise of Airbnb in housing prices. Journal of Regional Science.
2. Almagro, M. and Domínguez-Iino, T. (2025). Location sorting and endogenous amenities: Evidence from Amsterdam. Econometrica, 93(3):1031–1071.
3. Alyakoob, M. and Rahman, M. S. (2022). Shared prosperity (or lack thereof) in the sharing economy. Information Systems Research, 33(2):638–658.
4. Anastasi, S. C., Marsella, A., Melo, V., Stephenson, E. F., and Wagner, G. A. (2025). Short-term rental bans and the hotel industry: Evidence from New York City. European Journal of Political Economy, page 89:102725.
5. Ayouba, K., Breuillé, M.-L., Grivault, C., and Le Gallo, J. (2020). Does Airbnb disrupt the private rental market? An empirical analysis for French cities. International Regional Science Review, 43(1-2):76–104.
6. Barron, K., Kung, E., and Proserpio, D. (2021). The effect of home-sharing on house prices and rents: Evidence from Airbnb. Marketing Science, 40(1):23–47.
7. Basuroy, S., Kim, Y., and Proserpio, D. (2020). Estimating the impact of Airbnb on the local economy: Evidence from the restaurant industry. Available at SSRN 3516983.
8. Batalha, M., Gonçalves, D., Peralta, S., and Dos Santos, J. P. (2022). The virus that devastated tourism: The impact of covid-19 on the housing market. Regional Science and Urban Economics, 95:103774.
9. Bei, G. (2025). The spatial effect of short-term rental regulations: The comparison between Barcelona and Paris. Cities, 158:105603.
10. Bekkerman, R., Cohen, M. C., Kung, E., Maiden, J., and Proserpio, D. (2023). The effect of short-term rentals on residential investment. Marketing Science, 42(4):819–834.
11. Belk, R. (2014). You are what you can access: Sharing and collaborative consumption online. Journal of Business Research, 67(8):1595–1600.
12. Benítez-Aurioles, B. (2021). A proposal to regulate the peer-to-peer market for tourist accommodation. International Journal of Tourism Research, 23(1):70–78.
13. Benitez-Aurioles, B. and Tussyadiah, I. (2020). What Airbnb does to the housing market. Annals of Tourism Research, page 90:103108.
14. Bibler, A. J., Teltser, K. F., and Tremblay, M. J. (2021). Is sharing really caring? the effect of Airbnb on housing prices and foreclosures.
15. Bosma, J. R. (2022). Platformed professionalization: Labor, assets, and earning a livelihood through Airbnb. Environment and Planning A: Economy and Space, 54(4):595–610.

References

1. Boto-Garcia, D., Baños-Pino, J. F., Del Valle, E., and Sustacha, I. (2023). Vacation rental market regulation and accommodation supply growth. Tourism Economics, 29(6):1551– 1576.
2. Brunstein, D., Casamatta, G., and Giannoni, S. (2025). Using machine learning to estimate the heterogeneous impact of Airbnb on house prices: Evidence from Corsica. Journal of Housing Economics, 67:102044.
3. Calder-Wang, S. (2021). The distributional impact of the sharing economy on the housing market. Available at SSRN 3908062.
4. Calo, R. and Rosenblat, A. (2017). The taking economy: Uber, information, and power. Colum. L. Rev., 117:1623.
5. Chang, H.-H. (2020). Does the room sharing business model disrupt housing markets? empirical evidence of Airbnb in Taiwan. Journal of Housing Economics, 49:101706.
6. Chang, H.-H. and Sokol, D. D. (2022). How incumbents respond to competition from innovative disruptors in the sharing economy—The impact of Airbnb on hotel performance. Strategic Management Journal, 43(3):425–446.
7. Chaves Fonseca, C. (2024). The effect of short-term rentals on house prices and residential mobility: Evidence from Madrid.
8. Chaves Fonseca, C. (2025). Short-term rentals and residential rents: evidence from a regulation in Santa Monica. International Journal of Housing Markets and Analysis, 18(5):1169–1203.
9. Chen, R., Jiang, H., and Zhang, S. (2025). Safer and cleaner: The impacts of Airbnb regulation on neighborhood quality. Available at SSRN 5885462.
10. Chen, W., Wei, Z., and Xie, K. (2022). The battle for homes: How does home sharing disrupt local residential markets? Management Science, 68(12):8589–8612.
11. Cocola-Gant, A., Jover, J., Carvalho, L., and Chamusca, P. (2021). Corporate hosts: The rise of professional management in the short-term rental industry. Tourism Management Perspectives, 40:100879.
12. Cohen, M. and Sundararajan, A. (2015). Self-regulation and innovation in the peer-to-peer sharing economy. University of Chicago Law Review Dialogue, 82:116–133.
13. Congiu, R., Pino, F., and Rondi, L. (2025). The uneven effect of Airbnb on the housing market: Evidence across and within Italian cities. Journal of Regional Science, 65(2):339– 377.
14. Cruz, R., Nobre, F., and Pereira dos Santos, J. (2024). The economic footprint of short-term rentals on local businesses: Evidence from Portugal. Available at SSRN 4982275.

References

1. Cunha, A. M. and Lobão, J. (2022). The effects of tourism on housing prices: applying a difference-in-differences methodology to the Portuguese market. International Journal of Housing Markets and Analysis, 15(4):762–779.
2. Davidson, N. M. and Infranca, J. J. (2016). The sharing economy as an urban phenomenon. Yale Law & Policy Review, 34:215–279.
3. Dinan, M. M., Lutz, C., and Poli, N. (2025). Residents’ perspectives on short-term rental platforms through a sustainability lens. Current Issues in Tourism.
4. Dogru, T., Hanks, L., Mody, M., Suess, C., and Sirakaya-Turk, E. (2020). The effects of Airbnb on hotel performance: Evidence from cities beyond the United States. Tourism Management, 79:104090.
5. Dolnicar, S. and Zare, S. (2020). COVID19 and Airbnb–disrupting the disruptor. Annals of tourism research, 83:102961.
6. Duncan, D. and Ross, J. (2025). The effect of Airbnb on housing prices: Evidence from the 2017 solar eclipse. Technical report, ZEW Discussion Papers.
7. Duso, T., Michelsen, C., Schäfer, M., and Tran, K. D. (2024). Airbnb and rental markets: Evidence from Berlin. Regional Science and Urban Economics, 106:104007.
8. Ekeroma, J. E. (2023). The Airbnb phenomenon: A qualitative analysis of its consequences on urban housing markets. Preprints.
9. Ellis, J. A., Kenchington, D. G., Smith, J. D., and White, R. M. (2025). Better tax enforcement moderates Airbnb’s pressure on housing costs. Journal of Financial and Quantitative Analysis, pages 60(7):3591–3621.
10. Esposito, A. (2023). Tourism-driven displacement in Naples, Italy. Land Use Policy, 134:106919.
11. Falk, M. and Scaglione, M. (2024). Effects of regulations on the Airbnb market in Geneva. Tourism Economics, 30(3):615–632.
12. Falk, M. and Yang, Y. (2021). Hotels benefit from stricter regulations on short-term rentals in European cities. Tourism Economics, 27(7):1526–1539.
13. Farronato, C. and Fradkin, A. (2022). The welfare effects of peer entry: the case of Airbnb and the accommodation industry. American Economic Review, 112(6):1782–1817.
14. Ferreri, M. and Sanyal, R. (2018). Platform economies and urban planning: Airbnb and regulated deregulation in London. Urban Studies, 55(15):3353–3368.
15. Filippas, A. and Horton, J. J. (2018). The tragedy of your upstairs neighbors: Externalities of home-sharing. In New York University Working Paper.
16. Fontana, N. (2025). Who is my neighbour? short-term renting and civic engagement in London. Technical report, Trinity College Dublin, Department of Economics.

References

1. Franco, S. F. and Santos, C. D. (2021). The impact of Airbnb on residential property values and rents: Evidence from Portugal. Regional Science and Urban Economics, 88:103667.
2. Frenken, K. and Schor, J. (2017). Putting the sharing economy into perspective. Environmental Innovation and Societal Transitions, 23:3–10.
3. Gálvez-Iniesta, I., Groizard, J. L., and Portella-Carbó, F. (2023). Sharing my place: the local labor market impact of the P2P technology shock. Technical report, Universitat de les Illes Balears, Departament d’Economía Aplicada.
4. Garcia, B., Miller, K., and Morehouse, J. (2025). In search of peace and quiet: The heterogeneous impacts of short-term rentals on housing prices. The Center for Growth and Opportunity.
5. Garcia-López, M.-À., Jofre-Monseny, J., Martínez-Mazza, R., and Segú, M. (2020). Do short-term rental platforms affect housing markets? evidence from Airbnb in Barcelona. Journal of Urban Economics, 119:103278.
6. Garcia-López, M.-À. and Rosso, G. (2023). Let’s visit the consumer city: The role of tourism in reshaping urban amenities. Available at SSRN 4959434.
7. Garz, M. and Schneider, A. (2023a). Data sharing and tax enforcement: Evidence from short-term rentals in Denmark. Regional Science and Urban Economics, 101:103912.
8. Garz, M. and Schneider, A. (2023b). Taxation of short-term rentals: Evidence from the introduction of the “Airbnb tax” in Norway. Economics Letters, 226:111120.
9. Gauß, P., Gensler, S., Kortenhaus, M., Riedel, N., and Schneider, A. (2024). Regulating the sharing economy: The effects of day caps on short- and long-term rental markets and stakeholder outcomes. Journal of Public Economics, pages 52:1627–1650.
10. Guttentag, D. (2019). Progress on Airbnb: A literature review. Journal of Hospitality and Tourism Technology, 10(4):814–844.
11. Gyódi, K., Mazur, J., and Cocola-Gant, A. (2025). Barcelona as a case study for the effectiveness of short-term rental market regulations. Cities, 162:105915.
12. Han, W., Wang, X., Ahsen, M. E., and Wattal, S. (2022). The societal impact of sharing economy platform self-regulations—An empirical investigation. Information Systems Research, 33(4):1303–1323.
13. Heinermann, L. (2022). Lisbon’s Airbnb market and gentrification: Current developments through the pandemic and the implications for public policy. Master’s thesis, Nova School of Business and Economics.
14. Hidalgo, A., Riccaboni, M., and Velazquez, F. J. (2023). When local business faded away: the uneven impact of Airbnb on the geography of economic activities. Cambridge Journal of Regions, Economy and Society, 16(2):335–348.

References

1. Hidalgo, A., Riccaboni, M., and Velázquez, F. J. (2024). The effect of short-term rentals on local consumption amenities: Evidence from Madrid. Journal of Regional Science, 64(3):621–648.
2. Hill, R. J., Pfeifer, N., and Steurer, M. (2023). The Airbnb rent premium and the crowdingout of long-term rentals. Journal of Housing Economics, 61:101935.
3. Horn, K. and Merante, M. (2017). Is home sharing driving up rents? Evidence from Airbnb in Boston. Journal of Housing Economics, 38:14–24.
4. Hübscher, M. and Kallert, T. (2023). Taming Airbnb locally: Analysing regulations in amsterdam, berlin and london. Tijdschrift voor Economische en Sociale Geografie, 114(1):6–27.
5. Jiménez, J. L., Ortuño, A., and Pérez-Rodríguez, J. V. (2022). How does AirBnb affect local Spanish tourism markets? Empirical Economics, 62(5):2515–2545.
6. Jin, G. Z., Wagman, L., and Zhong, M. (2024). The effects of short-term rental regulation: Insights from Chicago. International Journal of Industrial Organization, 96:103087.
7. Kim, Y. R. and Yeon, J. (2025). Do short-term rental platforms affect housing markets? Evidence from Airbnb in London. Tourism Management, 111:105204.
8. Kochan, D. J. (2018). The sharing stick in the property rights bundle: The case of short-term rentals and HOAs. University of Cincinnati Law Review, 86(3):893–922.
9. Koster, H. R., Van Ommeren, J., and Volkhausen, N. (2021). Short-term rentals and the housing market: Quasi-experimental evidence from Airbnb in Los Angeles. Journal of Urban Economics, 124:103356.
10. Lanfear, C. C. and Kirk, D. S. (2024). The promise and perils of the sharing economy: The impact of Airbnb lettings on crime. Criminology, 62(4):769–798.
11. Li, H., Kim, Y., and Srinivasan, K. (2022). Market shifts in the sharing economy: The impact of Airbnb on housing rentals. Management Science, 68(11):8015–8044.
12. Li, H. and Srinivasan, K. (2019). Competitive dynamics in the sharing economy: An analysis in the context of Airbnb and hotels. Marketing Science, 38(3):365–391.
13. Maldonado-Guzmán, D. J. (2022). Airbnb and crime in Barcelona (Spain): testing the relationship using a geographically weighted regression. Annals of GIS, 28(2):147–160.
14. Maldonado-Guzmán, D. J., Chamizo-Nieto, F. J., and Reyes-Corredera, S. (2024). Home sharing or crime sharing? Evidences of the relationship between Airbnb, crime and structural factors in Malaga, Spain. GIScience & Remote Sensing, 61(1):2384330.
15. Milone, F. L., Paolucci, E., and Raguseo, E. (2025). Do digital platforms create entrepreneurial opportunities? Evidence from marginal areas. Strategic Entrepreneurship Journal.

References

1. Mont, O., Voytenko Palgan, Y., Bradley, K., and Zvolska, L. (2020). A decade of the sharing economy: Concepts, users, business and governance perspectives. Journal of Cleaner Production, 269:122215.
2. Mosaad, M., Benoit, S., and Jayawardhena, C. (2023). The dark side of the sharing economy: A systematic literature review of externalities and their regulation. Journal of Business Research, 168:114186.
3. Nawi, N. F., Anuar, A., Manshor, N. M., and Abdul Latif, R. (2022). Policy, legal and regulation research in the sharing economy: A bibliometric analysis and systematic literature review. IIUM Law Journal, 30(S1).
4. Oszkay Febres-Cordero, Y. (2022). Contentions with Sharing: The Local Structures and Politics of Short-Term Rental Markets. PhD thesis, UCLA.
5. Ozer, G. T., Greenwood, B. N., and Gopal, A. (2024). Noisebnb: An empirical analysis of home-sharing platforms and residential noise complaints. Information Systems Research, 35(4):1824–1847.
6. Robertson, C., Dejean, S., and Suire, R. (2024). “Airbnb in the city”: Assessing short-term rental regulation in bordeaux. The Annals of Regional Science, 72(2):647–682.
7. Rodon, T., Maria Raya, J., and Llaneza, C. (2025). Unfairbnb! the effect of short-term letting on electoral behaviour. Political Studies.
8. Roth, J. J. (2021). Home sharing and crime across neighborhoods: An analysis of Austin, texas. Criminal Justice Review, 46(1):40–52.
9. Roudnitski, A. and Sarkar, S. (2025). The effect of policy regulations in the short-term rental platform market on long-term rental prices: A case study of Airbnb in Sydney. Environment and Planning B: Urban Analytics and City Science, page 23998083251350410.
10. Safari, N., Zhang, L., and Komarek, T. M. (2025). The impact of short-term rental activity on house prices: Evidence from coastal Virginia. The Annals of Regional Science, 74(1):6.
11. Saiz, A. (2010). The geographic determinants of housing supply. The Quarterly Journal of Economics, 125(3):1253–1296.
12. Schaefer, M. and Tran, K. D. (2023). Airbnb, hotels, and localized competition. NET Institute Working Paper No. 23-04.
13. Seiler, M. J., Siebert, R. B., and Yang, L. (2024). Airbnb or not Airbnb? that is the question: How Airbnb bans disrupt rental markets. Real Estate Economics, 52(1):239–270.
14. Shabrina, Z., Arcaute, E., and Batty, M. (2022). Airbnb and its potential impact on the London housing market. Urban Studies, 59(1):197–221.
15. Sheppard, S. and Udell, A. (2016). Do Airbnb properties affect house prices. Williams College Department of Economics Working Papers.

References

1. Smith, J. P. (2021). Essays on the Airbnb Market: Superhosts and Local Policy Determinants and Effects. PhD thesis, Middle Tennessee State University.
2. Sundararajan, A. (2016). The Sharing Economy: The End of Employment and the Rise of Crowd-Based Capitalism. MIT Press.
3. Tripathi, S., Kyriakou, H., and Petropoulos, G. (2025). Assessing the impact of algorithmic quantity regulations on sharing platforms: Evidence from Airbnb in Paris. Available at SSRN 5226013.
4. Valentin, M. (2021). Regulating short-term rental housing: Evidence from New Orleans. Real Estate Economics, 49(1):152–186.
5. van Holm, E. J. and Monaghan, J. (2021). The relationship of Airbnb to neighborhood calls for service in three cities. Cities, 116:103241.
6. von Briel, D. and Dolnicar, S. (2021). The evolution of Airbnb regulation—An international longitudinal investigation 2008–2020. Annals of Tourism Research, 87:102983.
7. White, K. K. and Cordon, J. (2025). Short-term rental regulations and residential housing affordability: Bridging the gap between policy and enforcement. Cornell Journal of Law and Public Policy, 34:203–260.
8. Xu, M. and Xu, Y. (2021). What happens when Airbnb comes to the neighborhood: The impact of home-sharing on neighborhood investment. Regional Science and Urban Economics, 88:103670.
9. Yang, Y., Nieto García, M., Viglia, G., and Nicolau, J. L. (2022). Competitors or complements: A meta-analysis of the effect of Airbnb on hotel performance. Journal of Travel Research, 61(7):1508–1527.
10. Yeon, J., Song, H. J., and Lee, S. (2020). Impact of short-term rental regulation on hotel industry: A difference-in-differences approach. Annals of Tourism Research, 83:102939.
11. Zervas, G., Proserpio, D., and Byers, J. W. (2017). The rise of the sharing economy: Estimating the impact of Airbnb on the hotel industry. Journal of Marketing Research, 54(5):687–705.
12. Zhao, K., Shoshani, T., and Proserpio, D. (2026). How short-term rental regulations reshape urban spending: Evidence from New York City’s restaurant sector. Available at SSRN.

This study follows a systematic literature review process structured according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, ensuring transparency, replicability, and methodological rigor in the identification and selection of relevant studies on the regulation of short-term rentals (STR), their impacts, and the effectiveness of regulatory frameworks.

The identification phase began with a broad search of academic databases, primarily Web of Science (WoS) using in the search the topics “short-term rentals”, “vacation homes”, “Airbnb”, “Short-term accommodation”, “Short-term lodging”, “Temporary accommodation”, “Vacation rentals”, “holiday rentals”, “Peer-to-peer accommodation”, “Platformmediated rentals”, “Home-sharing”, which yielded a total of 4,873 records. To ensure the quality and consistency of the dataset, duplicate records were identified and removed, resulting in the exclusion of 223 duplicates. After this initial cleaning process, 4,650 unique records remained and were subjected to the screening phase.

During screening, titles and keywords were first reviewed to assess the relevance of each record to the scope of the review. This step led to the exclusion of 2,972 records that were clearly unrelated to short-term rentals, regulatory issues, or policy impacts. Subsequently, abstracts of the remaining studies were examined in detail, resulting in the exclusion of an additional 1,100 records that did not directly address STR regulations, their impacts, or their effectiveness. As a result of this two-stage screening process, 578 studies were identified as potentially relevant and were sought for full-text retrieval.

Of the studies sought for retrieval through database searches, 14 could not be accessed, leaving 564 studies available for full-text assessment. These studies were evaluated for eligibility based on predefined inclusion criteria, focusing explicitly on empirical or theoretical contributions that analyzed STR regulations, assessed their impacts, or evaluated their effectiveness. Following this full-text review, 163 studies were excluded.

In parallel, an additional identification process was conducted using other methods, specifically citation tracking. This complementary strategy identified 43 additional studies through references cited in the articles retrieved from databases. All 43 studies were sought for retrieval, of which 5 could not be accessed. The remaining 38 studies were assessed for eligibility using the same criteria applied to the database-derived literature. After full-text evaluation, 10 of these studies were excluded for not meeting the core focus on STR regulations or their impacts and effectiveness.

The final inclusion stage integrated the eligible studies from both identification pathways. In total, 429 studies met all inclusion criteria and were incorporated into the final metasample used for the literature review. This large corpus provides a solid foundation for analyzing the evolution of STR regulations, their socioeconomic, spatial, and housing-market impacts, and the effectiveness of different regulatory approaches across jurisdictions.

By combining database searches with citation-based identification and applying a rigorous multi-stage screening and eligibility process, this review ensures broad coverage of the relevant literature while maintaining a clear and focused analytical scope aligned with the research objectives.

Estimating the causal impact of STR on housing markets is challenging because the platform’s expansion is highly endogenous. STR listings tend to grow disproportionately in neighborhoods that are attractive to tourists, experiencing rising demand, or undergoing broader processes such as gentrification. As a result, simple correlations between STR activity and housing outcomes may reflect unobserved demand shocks rather than a causal effect. To overcome this challenge, the literature relies on three main approaches: (i) quasi-natural experiments generated by policy interventions affecting short-term rentals, (ii) instrumental-variable strategies that exploit variation in STR exposure unrelated to local housing-market conditions, and (iii) complementary approaches including structural models and machine-learning methods.

Policy-based quasi-natural experiments A large share of the literature exploits regulatory interventions that directly restrict or alter the profitability of short-term rentals. These policies, including bans, caps, registration requirements, enforcement actions, tax agreements, or algorithmic quantity controls, generate sharp and plausibly exogenous changes in STR activity across space or time.

Early and influential examples exploit geographically targeted regulations. In New Orleans, the introduction of zoning-based restrictions on short-term rentals created sharp spatial variation in STR exposure, which is used to identify capitalization effects in housing prices (Valentin, 2021). Similarly, Santa Monica’s Home-Sharing Ordinance, which effectively eliminated absentee hosting, provides a natural experiment exploited to study housing-market responses to a sudden contraction in STR activity (Garcia et al., 2025; Chaves Fonseca, 2025). Related designs are applied in Los Angeles (Koster et al., 2021), Berlin (Duso et al., 2024), London (Kim and Yeon, 2025), Sydney (Roudnitski and Sarkar, 2025), and Paris (Tripathi et al., 2025).

A more recent strand of work exploits staggered or partial policy implementations. Tax enforcement agreements between STR and local governments in Florida counties generate variation in hosting profitability that is plausibly orthogonal to local housing trends (Ellis et al., 2025). Platform-level policy changes, such as algorithmic quantity regulation in Paris or enforcement-driven listing removals, have also been used to construct quasi-experimental settings (Chen et al., 2022; Tripathi et al., 2025). In all these cases, identification hinges on the assumption that, absent the policy intervention, treated and control areas would have followed parallel housing-market trajectories.

Policy-based designs are attractive because they generate large, discrete changes in STR supply. Their main limitation is external validity: regulatory shocks may not fully capture the effects of organic platform growth, and policy adoption itself may respond to local housing pressures.

Instrumental-variable strategies A second major strand of the literature identifies STR’s housing-market effects using instrumental variables (IV). These approaches construct exogenous variation in STR activity that is uncorrelated with unobserved local shocks to housing demand or supply.

The most common IV strategy is shift-share (Bartik-style) instrumental variable strategy. The core idea is to interact a measure of a location’s predetermined tourist appeal, such as historical hotel presence, proximity to attractions, or baseline tourism intensity, with aggregate growth in STR demand. This interaction predicts differential local STR expansion driven by global platform trends rather than by local housing-market dynamics. Prominent applications of this approach include studies of U.S. metropolitan areas (Barron et al., 2021), Barcelona (Garcia-López et al., 2020), Madrid (Chaves Fonseca, 2024), Italy (Congiu et al., 2025), Corsica (Brunstein et al., 2025), and U.S. counties more broadly (Duncan and Ross, 2025).

Under the exclusion restriction, tourist appeal affects housing outcomes only through STR-related activity (and associated tourism demand), not through contemporaneous housing supply shocks. While this assumption is inherently untestable, IV estimates across these studies tend to be consistent with, or larger than, fixed-effects estimates, suggesting that naive regressions may understate STR’s true impact due to measurement error or offsetting local trends (Barron et al., 2021; Garcia-López et al., 2020).

IV strategies offer broader external validity than policy experiments, as they capture variation closer to organic platform growth. However, their credibility rests on careful instrument construction and robustness checks, and many studies therefore combine IV estimation with rich fixed effects and alternative specifications.

Complementary approaches In addition to policy experiments and IV designs, the literature employs several complementary methods. High-dimensional panel regressions with neighborhood fixed effects and flexible time controls are widely used to absorb time-invariant heterogeneity and common shocks (Horn and Merante, 2017; Ayouba et al., 2020). While these approaches help mitigate confounding, they do not by themselves resolve endogeneity concerns and are typically used as baseline or supporting evidence.

Structural models provide an alternative framework by explicitly modeling equilibrium interactions between long-term and short-term rental markets. Such models allow for counterfactual simulations and welfare analysis but rely on parametric assumptions and rich data (Li et al., 2022; Calder-Wang, 2021; Almagro and Domínguez-Iino, 2025). More recently, machine-learning methods have been used to uncover heterogeneous treatment effects, offering flexibility in functional form while still relying on panel variation and identifying assumptions similar to reduced-form approaches (Brunstein et al., 2025).

The literature converges on a coherent identification logic: credible causal inference relies either on exogenous policy-induced variation in STR activity or on quasi-experimental instruments that mimic random exposure to platform growth. The fact that qualitatively similar results emerge across these distinct strategies strengthens confidence in the causal interpretation of STR’s effects on housing markets.

Identifying the causal impact of STR on local economic activity is challenging because platform growth is endogenous to neighborhood trajectories: STR expand where amenities improve, demand rises, or redevelopment is already underway. The literature therefore relies on quasi-experimental variation generated by regulatory interventions or by differential exposure to aggregate platform growth.

One approach exploits policy-based quasi-natural experiments. Regulatory changes, such as bans, caps, zoning restrictions, or enforcement regimes, shift the feasibility or profitability of STR in ways that are plausibly exogenous to short-run neighborhood trends. Studies using these designs compare outcomes before and after regulatory changes across affected and unaffected areas, documenting effects on business activity, employment, and investment. Evidence that STR restrictions reduce residential permitting alongside listings supports the interpretation that STR sustain investment incentives rather than merely reallocating consumption (Xu and Xu, 2021; Bekkerman et al., 2023). Moreover, the timing and the uneven distribution of short-term rentals entry has been exploited as a source of exogenous variation to isolate the impact of short-term rental from other local shocks (Basuroy et al., 2020; Alyakoob and Rahman, 2022).

A second strand employs instrumental-variable strategies, often of the shift-share (Bartik) type, to isolate plausibly exogenous variation in STR exposure. A prominent contribution instruments local STR growth using predetermined housing-market characteristics, such as the pre-existing share of rental housing, interacted with aggregate measures of platform demand, thereby isolating variation driven by broader STR expansion rather than local economic shocks (Hidalgo et al., 2024). Related IV designs exploit regulatory timing or spatial exposure to predict STR activity and estimate impacts on employment and business dynamics at broader geographic scales (Gálvez-Iniesta et al., 2023) or exposure to tourism using the spatial distribution of historic monuments (Almagro and Domínguez-Iino, 2025).

Identifying the causal impact of STR on hotel outcomes is challenging because STR entry is endogenous to local demand conditions: adoption is faster in locations experiencing tourism growth or positive demand shocks that would independently affect hotel performance. The literature therefore relies on a combination of reduced-form, structural, and policy-based approaches to isolate plausibly exogenous variation in STR exposure.

Early studies employ difference-in-differences designs exploiting variation in the timing and intensity of STR entry across locations. By comparing hotel outcomes before and after STR expansion in treated versus untreated markets, these studies estimate reduced form effects on hotel prices, revenues, and occupancy while controlling for seasonality and location-specific trends (Zervas et al., 2017). While transparent and intuitive, these designs primarily capture average treatment effects and do not separately identify substitution patterns or welfare channels.

A second strand adopts structural demand and supply models that explicitly model consumer substitution between hotels and STR under capacity constraints. These approaches estimate preferences over accommodation types and locations and simulate counterfactual scenarios without STR entry. This framework allows researchers to quantify substitution elasticities and to decompose welfare effects into price competition, expanded variety, and capacity-related mechanisms (Farronato and Fradkin, 2022; Schaefer and Tran, 2023).

Finally, policy-based natural experiments exploit regulatory interventions that sharply reduce STR supply. Sudden bans or enforcement shocks generate plausibly exogenous variation to estimate how hotel prices and revenues respond when STR competition is removed. These designs are particularly informative about incidence and redistribution, identifying the extent to which regulation restores pricing power to incumbent hotels rather than expanding quantities (Anastasi et al., 2025).

Causal identification is challenging because STR listings locate non-randomly in areas with tourism growth, changing nightlife patterns, and evolving enforcement. One approach exploits panel fixed-effects designs that relate within-neighborhood changes in STR activity to changes in crime or calls for service over time, controlling for time-invariant neighborhood characteristics and common shocks (Roth, 2021; van Holm and Monaghan, 2021). While transparent, these designs may remain sensitive to time-varying confounders correlated with both STR entry and neighborhood outcomes (Lanfear and Kirk, 2024).

A stronger set of designs exploits quasi-experimental reductions in STR supply induced by regulation or platform self-regulation. Registration requirements, enforcement campaigns, and platform-imposed listing removals generate discrete changes in STR exposure that can be used to estimate neighborhood responses. Evidence from such interventions shows that reductions in STR activity lead to declines in several categories of crime and disorder, although effects differ by offense type and can include substitution toward less severe crimes (Han et al., 2022; Jin et al., 2024; Chen et al., 2025). These designs are particularly informative because they isolate the removal margin rather than gradual market expansion.

Tab le A 1 . Main cont ribut ions of t he lit erat ure on t he impact of short-t erm rent als on t he housing market ( Part 1 of 3 ) .

Notes : “ IV ” denotes instrumental-variables or closely related quasi-exogenous designs ; “ Policy ” denotes natural experiments based on rule changes , bans , caps , discontinuities , or enforcement /regulatory interventions .

Tab le A 2 . Main cont ribut ions of t he lit erat ure on t he impact of short-t erm rent als on t he housing market ( Part 2 of 3 ) .

Notes : “ IV ” denotes instrumental-variables or closely related quasi-exogenous designs ; “ Policy ” denotes natural experiments based on rule changes , bans , caps , discontinuities , or enforcement /regulatory interventions .

Tab le A 3 . Main cont ribut ions of t he lit erat ure on t he impact of short-t erm rent als on t he housing market ( Part 3 of 3 ) .

Notes : “ IV ” denotes instrumental-variables or closely related quasi-exogenous designs ; “ Policy ” denotes natural experiments based on rule changes , bans , caps , discontinuities , or enforcement /regulatory interventions .

Table A4 . Main contribut ions of the literature on the impact of short-term rentals on the lo cal economy.

Notes : “ IV ” denotes instrumental-variables or closely related quasi-exogenous designs ; “ Policy ” denotes natural experiments based on rule changes , bans , caps , discontinuities , or enforcement /regulatory interventions .

Tab le A 5 . Main cont ribut ions of t he lit erat ure on t he impact of short-t erm rent als on t he hot el sect or .

Notes : “ IV ” denotes instrumental-variables or closely related quasi-exogenous designs ; “ Policy ” denotes natural experiments based on rule changes , bans , caps , discontinuities , or enforcement /regulatory interventions . “ DiD ” denotes difference-in-difference .

Tab le A 6 . Main cont ribut ions of t he lit erat ure on t he impact of short-t erm rent als on neighb orho o d ext ernalit ies ( Part 1 of 2 ) .

No tes : denotes instrumental-variables or closely related quasi-exogenous designs ; “ Policy ” denotes natural experiments based on rule changes , bans , caps , discontinuities , or enforcement /regulatory interventions .

Tab le A 7 . Main cont ribut ions of t he lit erat ure on t he impact of short-t erm rent als on neighb orho o d ext ernalit ies ( Part 2 of 2 ) .

Notes : “ IV ” denotes instrumental-variables or closely related quasi-exogenous designs ; “ Policy ” denotes natural experiments based on rule changes , bans , caps , discontinuities , or enforcement /regulatory interventions .