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China Hospitality Market Data for Shenzhen
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About
This dataset provides a detailed collection of features pertinent to Oyo rental properties in Shenzhen, China, designed for market analysis and rental price prediction. OYO, an Indian multinational hospitality chain founded by Ritesh Agarwal in 2012, established itself as China's largest hotel chain by operating locally, with a significant presence across 337 cities and 500,000 rooms. In FY19, OYO China contributed $307 million, representing 32.3% of OYO's global revenue. This dataset offers a unique insight into the operational specifics and customer experience aspects that influence rental prices within the dynamic Chinese hospitality market, specifically focusing on the city of Shenzhen. It contains various property and host-related attributes, along with review scores, to facilitate the development of predictive models and market intelligence.
Columns
- accommodates: The number of people the property can accommodate. Valid entries range from 1 to 16, with a mean of 4.39.
- amenities: A list of facilities available at the property, with the majority falling into an "Other" category (97%).
- availability_30: The number of days the property is available in the upcoming 30-day period. Values range from 0 to 30, with a mean of 16.5.
- bathrooms: The number of bathrooms in the property. Most properties have between 0.8 and 1.6 bathrooms, with a mean of 1.48. There is a 1% missing data rate for this column.
- bed_type: The type of bed available. "Real Bed" is the most common type, accounting for 97% of entries.
- bedrooms: The number of bedrooms in the property. Most properties have between 1 and 2 bedrooms, with a mean of 1.74. There is a 0% missing data rate for this column.
- beds: The total number of beds in the property. Values range from 1 to 16, with a mean of 2.21. There is a 0% missing data rate for this column.
- calculated_host_listings_count: The number of listings the host has in the trending list. Values range from 1 to 17, with a mean of 1.9.
- cancellation_policy: The cancellation policy for bookings. "strict" is the most common policy, at 40%.
- guests_included: The number of guests included in the base price. Values range from 0 to 16, with a mean of 1.88.
- has_availability: A boolean indicating if the property has availability, consistently true for all records.
- host_is_superhost: A boolean indicating if the host is a superhost. 86% of hosts are not superhosts, and 14% are. There is a 0% missing data rate for this column.
- host_listings_count: The total number of listings by the host. Values range from 1 to 339, with a mean of 11.8. There is a 0% missing data rate for this column.
- instant_bookable: A boolean indicating if the property is instantly bookable. 91% of properties are not instantly bookable.
- latitude(North): The northern latitude coordinate of the property. Values range from 22.5 to 22.8, with a mean of 22.6.
- longitude(East): The eastern longitude coordinate of the property. Values typically hover around 114, with a mean of 114.
- maximum_nights: The maximum number of nights a property can be booked. Values range widely from 1 to 26,800, with a mean of 747.
- number_of_reviews: The total number of reviews received. Values range from 0 to 314, with a mean of 10.8.
- property_type: The type of property. "House" is the most common, at 61%, followed by "Apartment" at 32%.
- review_scores_checkin: Review scores for the check-in process. Values range from 2 to 10, with a mean of 9.83. This column has a 35% missing data rate.
- review_scores_communication: Review scores for host communication. Values range from 2 to 10, with a mean of 9.84. This column has a 35% missing data rate.
- review_scores_location: Review scores for the property's location. Values range from 4 to 10, with a mean of 9.47. This column has a 35% missing data rate.
- review_scores_rating: Overall review scores. Values range from 20 to 100, with a mean of 95.4. This column has a 35% missing data rate.
- review_scores_value: Review scores for value for money. Values range from 2 to 10, with a mean of 9.42. This column has a 35% missing data rate.
- room_type: The type of room available. "Entire home/apt" is most common at 70%, followed by "Private room" at 28%.
Distribution
The dataset is provided in CSV format and is approximately 1.94 MB in size. It contains 25 distinct columns and typically features 5834 records across most fields, though some columns, particularly review scores, have a notable percentage of missing values (35%). The dataset is structured for tabular analysis. Updates are expected to occur annually.
Usage
This dataset is ideal for:
- Developing predictive models for rental prices of OYO properties in Shenzhen.
- Conducting market analysis to understand key drivers of property value and guest satisfaction.
- Identifying popular property features and amenities within the Chinese hospitality sector.
- Assessing the impact of host performance on review scores and booking behaviour.
- Supporting strategic planning for hospitality businesses and real estate investors.
Coverage
The dataset's geographic scope is concentrated on Shenzhen, China. While a specific time range for the data collection is not detailed, the dataset is expected to be updated annually. It covers various aspects of accommodation, from property types and amenities to host attributes and guest reviews, offering insights into the hospitality market in a major Chinese city.
License
CC0: Public Domain
Who Can Use It
This dataset is suitable for:
- Data Scientists and Machine Learning Engineers for building and evaluating regression models to predict rental prices.
- Market Analysts interested in understanding the dynamics and trends of the Chinese hospitality and rental market.
- Researchers studying urban economics, tourism, and consumer behaviour in accommodation services.
- Real Estate Developers and Investors seeking insights into property valuation and investment opportunities in Shenzhen.
- Hospitality Management Professionals aiming to optimise property offerings and pricing strategies.
Dataset Name Suggestions
- Shenzhen OYO Rental Price Predictor
- China Hospitality Market Data for Shenzhen
- OYO Property Features and Pricing (Shenzhen)
- Shenzhen Accommodation Review and Pricing
- Chinese Rental Property Insights
Attributes
Original Data Source: China Hospitality Market Data for Shenzhen
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