Reviews and reputation · Empirical analysis joining Airbnb reviews with official crime data in five cities; counterfactual simulations of review visibility.
Safety Reviews on Airbnb: An Information Tale
Aron Culotta, Ginger Zhe Jin, Yidan Sun, Liad Wagman (2025). Safety Reviews on Airbnb: An Information Tale. Marketing Science. https://doi.org/10.1287/mksc.2023.0552.
Plain-English take
Safety reviews and personal safety experiences reduced bookings; removing vicinity-safety reviews could increase Airbnb reservation revenue under unchanged beliefs, but highlighting/removing effects reverse under rational updating; review–crime correlation is stronger in low-income/minority neighborhoods. For your marketing: Use it as a behavioral mechanism or experiment-design reference, not a direct forecast for a SaaS funnel.
How much weight to give this
2.5/5 Evidence needs cautious interpretation
The 1–5 evidence-check score is a practical reading aid, not a formal peer review or a probability that a conclusion is true. It combines design strength (2 points), sample reporting (1), comparison/control clarity (1), and how much of the paper could be checked (1). Access affects how confidently this directory can assess a study, not the study's underlying quality. Blinding is reported separately because it is not relevant to every design.
Empirical analysis joining Airbnb reviews with official crime data in five cities; counterfactual simulations of review visibility.
Airbnb listings/reviews and official crime data in five major U.S. cities.
Judged from the publisher-described design and comparison; consult the paper for complete analysis.
Publisher page and abstract were retrieved directly; full text was not assumed open.
Double-blind? Blinding was not established from the retrieved publisher record.
Research question
What the authors investigated
How does visibility of vicinity-safety reviews affect bookings, revenue and neighborhood sorting?
Design
Empirical analysis joining Airbnb reviews with official crime data in five cities; counterfactual simulations of review visibility.
Sample and setting
Airbnb listings/reviews and official crime data in five major U.S. cities.
Reported result
What the study found
Safety reviews and personal safety experiences reduced bookings; removing vicinity-safety reviews could increase Airbnb reservation revenue under unchanged beliefs, but highlighting/removing effects reverse under rational updating; review–crime correlation is stronger in low-income/minority neighborhoods.
This is a paraphrase of the source. Check the original paper for definitions, estimates, statistical uncertainty and analysis details.
Scope and transfer
Limitations to keep in view
Evidence is limited to five major U.S. cities and model-based counterfactuals; the publisher reports conditional outcomes depending on belief updating and learning.
Possible founder application
Use it as a behavioral mechanism or experiment-design reference, not a direct forecast for a SaaS funnel.
Evidence access
Publisher record and abstract retrieved; consult the linked paper for full methods and tables.
Publisher abstract states the five-city Airbnb/crime-data merge and conditional counterfactual results. Direct publisher-page retrieval was recorded on 2026-10-09; this profile uses publisher metadata/abstract and does not claim independent review of unavailable full text.
If only an abstract or metadata was available, the profile is limited to claims supported there. A paper's paywall does not change its evidentiary status, but it limits what we can substantiate from accessible text.