Airbnb in the Age of a Housing Crisis: A Case Study of Housing Affordability and Vacation Rental Regulations in Niagara Falls, ON
Bibliographic record
Abstract
This research focuses on housing in the context of growing unaffordability and increasing popularity of Airbnb in Niagara Falls, Ontario. In a city like Niagara Falls, which sees 12 million tourists annually, vacation rentals have become a highly profitable business. However, Niagara Falls is also currently experiencing a housing crisis. Airbnb complicates this crisis by perpetuating discourses in which housing is viewed primarily as a commodity. Commodification of housing, through processes of neoliberalization, financialization and securitization, inflates housing prices. More importantly, many people have accepted the unaffordability of housing because of discourses related to homeownership, mortgage debt, and asset-based welfare. These discourses normalize the commodification of housing, making processes like privatization, gentrification and Airbnb conversions seem natural, if not desirable. Housing practices based on these discourses disproportionately affect the underhoused. My research questions include: How does the hegemony of homeownership affect the housing markets in Niagara Falls? What elements of the homeownership discourse are used to describe both long-term rentals and vacation rentals in Niagara Falls? What are the consequences of these discourses for housing affordability in Niagara Falls? To answer these questions, I conducted a content and discourse analysis of city documents and city council meeting transcripts. My intent is to explain how respondents conceptualize their experiences related to vacation rental regulations in the context of housing discourses. Furthermore, I shall be analysing prominent housing discourses to examine the relationship between Airbnb and housing affordability in Niagara Falls, Ontario. My findings show that multifaceted homeownership discourses guided the discussions. All of these tend to stigmatize rentals in general, and long-term renters in particular. I conclude that the current housing system privileges homeownership at the expense of the renter population. This system, in turn, has focused on homeowners’ expectations during the Airbnb debates with little concern for how it affects housing affordability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".