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US Hotel Bookings and Economic Trends
Data Science and Analytics
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About
Hotel booking information for a City Hotel and a Resort Hotel is combined with key economic indicators for the United States. This data includes 119,390 observations of hotel bookings that occurred between July 2015 and August 2017. Its primary purpose is to enable the creation of predictive models, such as an LTSM model, to forecast hotel booking demand by correlating booking patterns with economic factors. While the hotels' locations are not specified, they are assumed to be in the United States to align with the provided economic data, which includes indicators like GDP, CPI, inflation, fuel prices, and consumer sentiment.
Columns
- hotel: The name of the hotel ("City Hotel" or "Resort Hotel").
- is_canceled: A binary indicator showing if the booking was cancelled (1 for True, 0 for False).
- lead_time: The number of days between the booking date and the arrival date.
- arrival_date_year: The year of arrival.
- arrival_date_month: The month of arrival.
- arrival_date_week_number: The week number of the year for the arrival date.
- arrival_date_day_of_month: The day of the month for the arrival date.
- stays_in_weekend_nights: The number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay.
- stays_in_week_nights: The number of weeknights (Monday to Friday) the guest stayed or booked to stay.
- adults: The number of adults.
- children: The number of children.
- babies: The number of babies.
- meal: The type of meal package booked (e.g., BB for Bed & Breakfast).
- country: The country of origin for the guest.
- market_segment: The market segment designation (e.g., "Online TA" for Travel Agents).
- distribution_channel: The booking distribution channel used (e.g., "TA/TO" for Travel Agent/Tour Operator).
- is_repeated_guest: A binary indicator showing if the booking was made by a repeated guest (1 for True, 0 for False).
- previous_cancellations: The number of previous bookings cancelled by the customer.
- previous_bookings_not_canceled: The number of previous bookings not cancelled by the customer.
- reserved_room_type: The code for the room type that was reserved.
- assigned_room_type: The code for the room type assigned to the guest at check-in.
- booking_changes: The number of modifications made to the booking.
- deposit_type: Indicates if a deposit was made to secure the booking.
- agent: The ID of the travel agency that made the booking.
- company: The ID of the company that made the booking.
- days_in_waiting_list: The number of days the booking was on a waiting list before being confirmed.
- customer_type: The type of booking customer (e.g., "Transient").
- adr: The Average Daily Rate.
- required_car_parking_spaces: The number of car parking spaces requested by the customer.
- total_of_special_requests: The total number of special requests made by the customer.
- reservation_status: The final status of the booking (e.g., "Check-Out", "Canceled").
- reservation_status_date: The date when the last reservation status was updated.
- MO_YR: The month and year of the stay.
- CPI_AVG: The average Consumer Price Index for the United States.
- INFLATION: The inflation rate in the United States.
- INFLATION_CHG: The change in the inflation rate.
- CSMR_SENT: A measure of consumer sentiment.
- UNRATE: The unemployment rate.
- INTRSRT: The interest rate.
- GDP: The Gross Domestic Product of the United States.
- FUEL_PRCS: The price of fuel.
- CPI_HOTELS: The Consumer Price Index specifically for hotels.
- US_GINI: The GINI ratio for the United States, measuring income inequality.
- DIS_INC: The disposable income per capita.
Distribution
- Format: The data is provided as a single CSV file named
hotel_bookings_raw.csv. - Size: The file is 24.97 MB.
- Structure: The dataset is tabular, containing 119,390 rows and 43 columns.
Usage
- Forecasting Hotel Booking Demand: Ideal for building time-series models (like LTSM) to predict future booking volumes.
- Economic Impact Analysis: Analyse the correlation between macroeconomic indicators and hotel booking patterns.
- Customer Behaviour Analysis: Understand booking cancellations, lead times, and the impact of pricing on customer decisions.
- Hospitality Market Research: Explore trends in the travel and accommodations sector.
Coverage
- Geographic: The economic data pertains to the United States. While the hotel locations are not specified, they are assumed to be in the US for analytical purposes. The dataset includes guest nationality, with many guests from Portugal (PRT) and Great Britain (GBR).
- Time Range: The dataset covers hotel bookings from 1 July 2015 to 31 August 2017. The associated economic data corresponds to this period.
- Demographic: Guest nationality is included.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For building predictive models to forecast demand and cancellations.
- Economists: To study the relationship between economic indicators and consumer spending in the hospitality industry.
- Hotel Managers and Revenue Analysts: To inform pricing strategies and operational planning.
- Students and Researchers: For academic projects related to economics, business analytics, and tourism.
Dataset Name Suggestions
- Hotel Booking Demand with US Economic Indicators
- US Hotel Bookings and Economic Trends (2015-2017)
- Economic Drivers of Hotel Booking Demand
- Hospitality Industry Bookings and Macroeconomic Data
- Predictive Analytics for Hotel Reservations
Attributes
Original Data Source: US Hotel Bookings and Economic Trends
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