Submitted:
03 October 2023
Posted:
04 October 2023
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Abstract
Keywords:
1. Introduction
1.1. Purpose of the Study
1.2. Scope of the Paper
- The paper provides a unified theoretical understanding of the interactions between Airbnb and urban housing ecosystems, serving as a foundational resource for academics and researchers exploring similar themes.
- The exploration of current regulatory landscapes and their efficacies in mitigating the impacts of short-term rental platforms stands as a crucial contribution, offering critical insights and recommendations for policymakers and urban planners seeking sustainable solutions.
- The paper furnishes a detailed analysis of how Airbnb affects housing prices, availability, and neighborhood dynamics, thereby contributing to the broader dialogue about sustainable urban development and the future of housing in the sharing economy.
2. Literature Review
2.1. Airbnb and Urban Housing Markets
2.2. Theoretical Insights
3. Methodology
3.1. Approach
3.2. Sources of Information
3.2.1. The Effect of Home-sharing on Residential Home Prices and Rentals
- Economic Policy Institute [39] reveals concerns about Airbnb’s influence on local markets, highlighting apprehensions about its potential to affect local home prices, residential area quality of life, hospitality sector job quality, and local government’s capacity to implement governmental codes and collect taxes.
- Marketing Science [40] provides empirical correlations between Airbnb listings and property prices and rental rates, underscoring a positive relationship between the increase in Airbnb listings and a subsequent rise in rental rates and home prices, especially in cities witnessing population growth.
3.2.2. Airbnb’s Influence on the Local Rental Housing Market
- IEB Working Paper [41] and Economic Policy Institute [39] elucidate the detrimental aspects of Airbnb’s proliferation, emphasizing its role in reducing housing availability and escalating housing costs for local inhabitants. They present arguments about how the conversion of long-term rental homes to Airbnb units can induce significant price rises due to the inelastic nature of housing demand.
3.2.3. Potential Advantages of the Introduction and Expansion of Airbnb in US Cities
- National Bureau of Economic Research [42] and Journal of Marketing Research [4] provide insights into the potential economic advantages stemming from Airbnb’s operations. They illustrate how Airbnb facilitates diverse revenue streams for property owners and fosters economic activity in cities by attracting tourists who subsequently spend on local services.
4. The Airbnb Business Model
4.1. Operational Overview
4.2. Market Demographics
4.3. Regulatory Landscape
5. Qualitative Insights: Impact on Urban Housing Markets
5.1. Housing Prices and Rents
5.1.1. Detailing the Phenomenon
5.1.2. Regional Variations
5.2. Housing Availability
5.3. Neighborhood Dynamics
6. Policy Implications and Regulatory Challenges
6.1. Regulations and their Impact
6.2. Policy Recommendations
- Balanced Regulation: Cities should strive to create balanced regulations that address the concerns related to housing and neighborhoods without stifling innovation and tourism. Striking the right balance can ensure the sustainable growth of the sharing economy while protecting the interests of residents, especially in areas with high housing demand.
- Enhanced Enforcement: To ensure compliance with established regulations, cities need to bolster enforcement mechanisms. This may include regular inspections, substantial fines for violations, and collaboration with platform providers to monitor and report non-compliant listings.
- Community Engagement: Engaging local communities in decision-making processes is essential for developing effective and inclusive regulations. Gathering input from residents can provide valuable insights into the specific concerns and needs of different neighborhoods and foster a sense of ownership and commitment to adhering to the regulations.
- Data Transparency: Encouraging Airbnb and other short-term rental platforms to share data can facilitate better-informed policy-making. Access to reliable and com-prehensive data can aid cities in assessing the impact of short-term rentals on housing markets and tailor regulations to address the identified issues effectively.
- Adaptive Policy Frameworks: Given the dynamic nature of the sharing economy, cities should adopt adaptive policy frameworks that can evolve in response to changing circumstances, emerging trends, and new insights. Regular reviews and updates of regulations can ensure their continued relevance and effectiveness in addressing the impacts of Airbnb on urban housing markets.
6.3. Enforcement Challenges
- Identification of Listings: The transient nature of short-term rentals makes the identification and monitoring of listings a daunting task. The frequent changes in availability and the sheer volume of listings complicate enforcement efforts.
- Limited Resources: Enforcement agencies often operate with limited resources, hampering their ability to conduct thorough inspections and audits, thereby affecting the overall efficacy of regulatory compliance.
- Non-Compliance: The varied levels of compliance among hosts, driven by a lack of awareness or deliberate evasion, pose significant challenges to enforcement agencies in ensuring adherence to regulations.
- Collaboration Hurdles: Achieving seamless collaboration between platform providers, local governments, and enforcement agencies is often marred by conflicting interests and data privacy concerns, impeding effective enforcement of regulations.
- Evolving Landscape: The constant evolution of the sharing economy necessitates continuous adjustments in enforcement strategies to cope with emerging trends, new business models, and shifting user behaviors.
7. Discussions
7.1. Balancing Benefits and Drawbacks
7.2. Future of Airbnb in U.S. Cities
7.3. Wider Implications for the Sharing Economy
8. Limitations and Future Research
8.1. Limitations of the Study
8.2. Suggestions for Future Research
9. Conclusion
Conflicts of Interest
References
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| Study | Key Findings | Focus Area |
|---|---|---|
| [24] | Impact on housing prices | Economic |
| [25] | Changes in neighborhood dynamics | Sociological |
| [26] | Shifts in housing availability | Economic |
| [27] | Altered community interactions | Anthropological |
| [28] | Regulatory challenges | Legal |
| [29] | Influence on local economies | Economic |
| [30] | Impact on local communities | Sociological |
| [31] | Changes in housing demand | Economic |
| [32] | Transformation in hospitality sector | Business |
| [33] | Evolution of sharing economy | Economic |
| Age Group | % of Hosts | % of Guests | Average Income | Preferred Property Type |
|---|---|---|---|---|
| 18-24 | 10% | 20% | $30,000 | Shared Rooms |
| 25-34 | 30% | 35% | $50,000 | Entire Homes/Apartments |
| 35-44 | 25% | 25% | $70,000 | Private Rooms |
| 45-54 | 20% | 10% | $90,000 | Entire Homes/Apartments |
| 55-64 | 10% | 5% | $110,000 | Private Rooms |
| 65+ | 5% | 5% | $130,000 | Entire Homes/Apartments |
| City | AHP (USD) | IHP (%) | ARR (USD) | RRR (%) | NAL |
|---|---|---|---|---|---|
| New York | 800,000 | 20 | 3,500 | 25 | 50,000 |
| Los Angeles | 750,000 | 18 | 3,200 | 23 | 45,000 |
| Chicago | 350,000 | 15 | 2,200 | 20 | 25,000 |
| City | Decrease in LTR | Increase in STL | Average LoS (days) |
|---|---|---|---|
| New York | Sharp Decline | High Increase | 3 |
| Los Angeles | Moderate Decline | Moderate Increase | 5 |
| Chicago | Significant Decline | Sharp Increase | 4 |
| City | Neighborhood Cohesion | Security Concerns | Neighborhood Character |
|---|---|---|---|
| New York | Varied Influence | Present | Transformative |
| Los Angeles | Diverse Impact | Evident | Modifying |
| Chicago | Mixed Effects | Observable | Altering |
| Limitation | Implication | Potential Mitigation | Future Research | |||
|---|---|---|---|---|---|---|
| Qualitative Approach | Limited Generalizabilityand Quantifiability | Incorporation of Quanti-tative Methods | Development of Compre-hensive Models | |||
| Focus onCities | Major U.S. | Non-universal Applica-bility to Smaller Cities orDifferent Contexts | Comparative Studieswith Smaller Cities | Expansion to Diverse Ge-ographical Contexts | ||
| Lack ofAnalyses | Quantitative | Inability to EstablishCausal Relationships orMagnitude of Impacts | Integration of EmpiricalAnalyses | Exploration of Causal In-ference Methods | ||
| Limited Temporal Scope | Potential Non-applicability to FutureContexts | Longitudinal Studies | Exploration of Emerg-ing Trends and Develop-ments | |||
| Selection Bias | Potential Overemphasison Specific Instances | Diverse Sampling Meth-ods | Investigation of Under-studied Areas | |||
| Subjectivity in Interpreta-tion | Possibility of Bias and In-accuracies | TriangulationSources | of | Data | Enhancement of Objec-tive Analytical Methods | |
| Research Area | Research Methodologies | Potential Contribution | Expected Outcomes |
|---|---|---|---|
| Quantitative Analyses | Statistical Models, Surveys | Establishment of Causal Re-lationships | Measurement of Impacts,Validation of Hypotheses |
| Diverse Geographic andSocio-Economic Contexts | Comparative Studies, CaseStudies | Enhanced Generalizability | Insights into Varied Con-texts |
| Evolving Dynamics andTrends in the Sharing Econ-omy | Trend Analysis, Market Re-search | Understanding of EmergingInnovations | Identification of Future De-velopments and SustainableBusiness Models |
| Integration with Other Plat-forms | Cross-Platform Studies,User Experience Research | Exploration of Inter-Platform Dynamics | Insights into User Behaviorand Platform Interactions |
| Community-IntegratedModels | Community Studies, Quali-tative Interviews | Development of SustainableBusiness Models | Enhancement of Commu-nity Relations and Integra-tion |
| New Service Categorieswithin Airbnb | Service Analysis, User Feed-back Analysis | Insight into Service Diversi-fication | Identification of User Needsand Preferences |
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