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Rome Accommodation Demand Analytics
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About
This dataset features detailed information on 4,599 hotels located in Rome, collected from TripAdvisor, a leading global travel platform. Its primary purpose is to explore how bubble ratings, specified down to half-bubbles, influence hotel popularity, which is quantified by the number of page views a hotel receives. The data facilitates an examination using a regression discontinuity design, revealing that the bubble presentation of ratings does not create significant jumps at cut-offs, a finding that contrasts with previous studies in other industries. A notable observation is that web users typically shortlist hotels with a bubble rating of at least 3. The dataset can be used to outline the potential for using page views as a proxy for demand in hospitality research.
Columns
- hotel_url: A unique URL for each hotel's page on TripAdvisor.
- name: The name of the hotel.
- views: The count of page views for a hotel, serving as a measure of popularity or demand, with values ranging from 0 to 88.
- views_binary: A binary indicator derived from the 'views' count, likely signifying whether a hotel achieved a certain popularity threshold.
- score_adjusted: An adjusted score or rating for the hotel, with values between 1 and 5.
- bubble_rating: The specific bubble rating given on TripAdvisor, detailed to half-bubbles, ranging from 1 to 5.
- category_hotel: A binary indicator (0 or 1) denoting if the accommodation is classified as a hotel.
- category_inn: A binary indicator (0 or 1) denoting if the accommodation is classified as an inn.
- category_specialty: A binary indicator (0 or 1) denoting if the accommodation is classified as a specialty lodging.
- class: The star rating or classification of the hotel, including "no stars" and other categories.
- class_4_5: A binary indicator (0 or 1) for hotels classified as 4-star or 5-star.
- class_3_4_5: A binary indicator (0 or 1) for hotels classified as 3-star, 4-star, or 5-star.
- n_reviews: The total number of reviews a hotel has received, with values ranging from 1 to 8286.
- location_grade: A grade or score indicating the hotel's location quality, with '100' being the most common value.
- discount: A binary indicator (0 or 1) showing if a discount is available for the hotel.
- discount_perc: The percentage of discount offered, ranging from 0 to 91.2.
- price_curr_min: The current minimum price for a stay, with many entries marked as 'NA'.
- price_min: The minimum price for a stay, with a number of entries marked as 'NA'.
- price_max: The maximum price for a stay, with several entries marked as 'NA'.
Distribution
This dataset is provided in a CSV file format and has a size of 3.23 MB. It contains 4,599 individual records, each representing a hotel in Rome.
Usage
This dataset is ideal for:
- Investigating the influence of online ratings, particularly bubble ratings, on hotel popularity and demand.
- Performing regression discontinuity analysis to understand thresholds in consumer behaviour.
- Developing models for hospitality demand estimation using page views as a proxy.
- Analysing consumer shortlisting patterns on travel platforms.
- Exploring pricing strategies and discount impacts within the hotel industry.
- Academic research into online review systems and their economic implications.
Coverage
The dataset focuses geographically on Rome, Italy. The information pertains to hotels within this specific city. A specific time range for data collection is not detailed, but it is indicated that the dataset is static and not expected to receive future updates. No particular demographic scope is mentioned beyond general "web users" interacting with the TripAdvisor platform.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
- Academics and Researchers: For studies in hospitality, economics, data science, and consumer behaviour, especially those focusing on online reviews and demand estimation.
- Hotel Management and Marketing Teams: To gain insights into how online ratings affect their hotel's visibility and to refine their marketing and pricing strategies.
- Data Scientists and Analysts: For developing predictive models on hotel popularity, demand, or for market analysis within the tourism sector.
- Travel Industry Stakeholders: To understand market trends, consumer preferences, and the dynamics of online travel platforms.
Dataset Name Suggestions
- Rome Hotel Popularity & TripAdvisor Ratings
- TripAdvisor Hotel Performance Data (Rome)
- Online Hotel Ratings Impact in Rome
- Rome Accommodation Demand Analytics
- Hotel Popularity in Rome: A TripAdvisor Dataset
Attributes
Original Data Source: Rome Accommodation Demand Analytics
QUALITY