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Host Professionalization in Short-Term Rentals: An Empirical Analysis of the Parisian Airbnb Market

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dc.contributor.advisorRigobon, Daniel
dc.contributor.authorAnderson, Justin
dc.date.accessioned2026-07-22T16:39:56Z
dc.date.available2026-07-22T16:39:56Z
dc.date.issued2026-04-09
dc.description.abstractThis thesis examines the extent to which Airbnb activity in Paris represents a professionalized market structure, a shift from its original peer-to-peer sharing platform. Using listing-level data for June 2025, this paper analyzes the distribution of listings across hosts, the spatial concentration of activity, and the characteristics associated with professional operators. The results reveal a highly skewed distribution of supply, with a small fraction of multi-listing hosts accounting for a disproportionate share of listings; in particular, the top 1% of hosts control nearly a quarter of the market. Spatial analysis using optimized hot spot methods shows that professional listings are concentrated in central, high-demand areas of the city, while non-professional activity is more diffuse. Regression results indicate that professional hosts exhibit distinct behavioral patterns, including higher availability and pricing strategies consistent with revenue optimization. These findings suggest that Airbnb in Paris operates as a hybrid market, combining a large base of casual hosts with a significant presence of professional operators who resemble commercial firms in both scale and behavior. This has important implications for housing markets and regulatory policies, particularly in distinguishing between casual and professional participants on the platform.
dc.identifier.urihttps://theses-dissertations.princeton.edu/handle/88435/dsp01pc289n59r
dc.language.isoen
dc.titleHost Professionalization in Short-Term Rentals: An Empirical Analysis of the Parisian Airbnb Market
dc.typePrinceton University Senior Theses
dspace.entity.typePublication
dspace.workflow.startDateTime2026-04-09T16:37:41.436Z
pu.contributor.authorid920320070
pu.date.classyear2026
pu.departmentOps Research & Financial Engr
pu.minorStatistics and Machine Learning

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