Average achieved hotel room rate in Hong Kong in the past five years

The Hotel Room Booking & Customer Orders Dataset This is a rich, synthetic dataset meticulously designed for data analysts, data scientists, and machine learning practitioners to practice their skills on realistic e-commerce data. It models a hotel booking platform, providing a comprehensive and interconnected environment to analyze booking trends, customer behavior, and operational patterns. It is an ideal resource for building a professional portfolio project from initial exploratory data analysis to advanced predictive modeling.

The dataset is structured as a relational database, consisting of three core tables that can be easily joined:

rooms.csv: This table serves as the hotel's inventory, containing a catalog of unique rooms with essential attributes such as room_id, type, capacity, and price_per_night.

customers.csv: This file provides a list of unique customers, offering demographic insights with columns like customer_id, name, country, and age. This data can be used to segment customers and personalize marketing strategies.

orders.csv: As the central transactional table, it links rooms and customers, capturing the details of each booking. Key columns include order_id, customer_id, room_id, booking_date, and the order_total, which can be derived from the room price and the duration of the stay.

This dataset is valuable because its structure enables a wide range of analytical projects. The relationships between tables are clearly defined, allowing you to practice complex SQL joins and data manipulation with Pandas. The presence of both categorical data (room_type, country) and numerical data (age, price) makes it versatile for different analytical approaches.

Use Cases for Data Exploration & Modeling This dataset is a versatile tool for a wide range of analytical projects:

Data Visualization: Create dashboards to analyze booking trends over time, identify the most popular room types, or visualize the geographical distribution of your customer base.

Machine Learning: Build a regression model to predict the order_total based on room type and customer characteristics. Alternatively, you could develop a model to recommend room types to customers based on their past orders.

SQL & Database Skills: Practice complex queries to find the average order value per country, or identify the most profitable room types by month.

Dataset

This dataset was created by Ploy Wa

Contents

Estonia Accommodation Establishment: Avg Cost per Night data was reported at 40.000 EUR in Sep 2018. This stayed constant from the previous number of 40.000 EUR for Aug 2018. Estonia Accommodation Establishment: Avg Cost per Night data is updated monthly, averaging 31.000 EUR from Jan 2002 (Median) to Sep 2018, with 201 observations. The data reached an all-time high of 41.000 EUR in May 2018 and a record low of 24.000 EUR in Jan 2004. Estonia Accommodation Establishment: Avg Cost per Night data remains active status in CEIC and is reported by Statistics Estonia. The data is categorized under Global Database’s Estonia – Table EE.Q004: Accommodation Establishments. The average cost of one guest night in an accommodation establishment, which includes the value-added tax and the cost of breakfast if it is sold with accommodation services.

This data set contains booking information for a city hotel and a resort hotel and includes information such as when the booking was made, length of stay, the number of adults, children, and/or babies, and the number of available parking spaces, among other things.

All personally identifying information has been removed from the data.

NOTE: Names, Emails, Phone Numbers, and Credit Card numbers in the data are synthetic and not real information from people. The hotel data is real from the publication listed above.

Data Column Reference

ITA12 - Mean nightly accommodation costs of overnight foreign resident visitors. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Mean nightly accommodation costs of overnight foreign resident visitors...

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

Russia Avg Consumer Price: HU: Housing: Hotel 2 * Overnight Stay data was reported at 1,792.230 RUB/Person in Feb 2025. This records an increase from the previous number of 1,759.450 RUB/Person for Jan 2025. Russia Avg Consumer Price: HU: Housing: Hotel 2 * Overnight Stay data is updated monthly, averaging 1,397.350 RUB/Person from Jan 2021 (Median) to Feb 2025, with 50 observations. The data reached an all-time high of 1,792.230 RUB/Person in Feb 2025 and a record low of 1,186.320 RUB/Person in Jan 2021. Russia Avg Consumer Price: HU: Housing: Hotel 2 * Overnight Stay data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PA021: Average Consumer Price: Housing and Utilities Services.

This dataset provides detailed information about tourist behaviors and preferences. It includes data on various aspects of travel, such as spending, accommodation, and travel purposes.

Dataset Columns:

Tourist_ID: Unique identifier for each tourist. Country: Country of origin of the tourist. Age: Age of the tourist. Gender: Gender of the tourist. Travel_Purpose: Primary reason for travel. Preferred_Destination: Type of destination preferred by the tourist. Stay_Duration_Days: Number of days the tourist stays at the destination. Spending_USD: Total spending during the trip in USD. Accommodation_Type: Type of accommodation used by the tourist. Travel_Frequency_per_Year: Number of trips taken per year. Average_Spending_Accommodation_USD: Average spending on accommodation per day in USD. Average_Spending_Transport_USD: Average spending on transport per day in USD. Average_Spending_Food_USD: Average spending on food per day in USD. Average_Cost_Per_Day_AED: Average cost per day in AED. With_Family: Indicates if the tourist is traveling with family or alone.

ITM07 - Mean Nightly Accommodation Costs and Daily Day-to-Day Expenditure of Foreign Resident Overnight Visitors. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Mean Nightly Accommodation Costs and Daily Day-to-Day Expenditure of Foreign Resident Overnight Visitors...

Flights without a full package, Annual Average, Use electronic programs, Government Procedures and Bureaucracy, Workers, Total tourism establishments, Constraints facing setting up or practicing economic activities, Electricity Price, Access to Telecommunication (Phone & Internet), Road Passenger Transport, Number of international passengers, Water Price, Percentage distribution of tourism establishments which use electronic programs, Flights within a full package, Low demand, Percentage %, Labour Laws & Regulations, Electricity Supply (without interruption), Laptop, Percentage distribution of tourism establishments which use social media, Employees, Hotel rooms, Salaries and wages, Availability of Skilled Labour, Inbound international flights, Tourism Direct Gross Value Added, Water Passenger Transport, Number of local passengers, Average duration of residence in accommodation units, Other Activities, Professionals, Other Activities, Non-cloud Data, Railways Passenger Transport, Percentage distribution of devices used in tourism establishments, Operating surplus, Fuel Price, Managers, nights, Operating expenditure, Main Activity, Non-Saudi, Operating rate of international flights, Major performance indicators for passengers transport services, Licenses & Permits, Do not use social media, Cultural Activities, Outbound international flights, Land Passenger Transport, Workers problems, Furniture Apartments, Major challenges facing business environment development, There are constraints, Female, Transport Equipment Rental, Percentage distribution of tourism establishments that have cloud data, Number of available seats for international flights, Average daily price for accommodation units in Saudi Riyal, Number of available seats for local flights, Operating revenues distribution, Food and Beverage Serving Activities, Local Competition, Travel Agencies and Reservation Services, Cloud Data, Operating rate of local flights, Do not have Accounting Books, Security & Stability, Benefits and allowances, Water Supply (without interruption), Railway Passenger Transport, Percentage distribution of accounting books or budget usage, Percentage of sold flights for passengers by flight type, Operating revenues, Other Specific Tourism Characteristic Services, Government Inspection Procedures, Number, Fuel Supply (without interruption), Access to Finance, Thousands Riyals, Accommodation for Visitors, Total compensations, Gross value added of the tourism industries = operating revenues - operating expenses, Technicians, Local flights, Saudi Riyal per day, Total, Do not use electronic programs, Land / Rent of Space, Have Accounting Books, Retail trade of Country-Specific Tourism Characteristic Goods, No constraints, Air Passenger Transport, Employment percentage, Saudi, Occupancy rate for accommodation units, Handheld or tablet, Average daily income for accommodation units in Saudi Riyal, Desktop (PC), Specialists, -, Sports and Recreational Activities, Use social media, Number of employees, wages and Salaries, compensation, Flight, Tourism Establishments Survey, Economic Activity, Occupations

Saudi Arabia

Explore the Tourism Establishments Survey dataset in Saudi Arabia to uncover key insights on economic activities, workers, government procedures, and more. Access data on airline passengers, accommodation rates, transportation services, and challenges facing the tourism industry. Discover valuable information to enhance your understanding of the tourism sector in Saudi Arabia.Follow data.kapsarc.org for timely data to advance energy economics research..Preliminary estimated data based on supply and use tables.Gross value added of the tourism industries = operating revenues - operating expenses.Notes:Full package deals: packages that include the flight ticket as well as other services, such as the hotel booking, car rental... etc. Source: Administrative data from the Ministry of Human Resources and Social Development, Ministry of Tourism and the Saudi Railway Company

The current average price per night globally on Airbnb is $137 per night.

Russia Avg Consumer Price: Hotel 3 * Overnight Stay data was reported at 1,746.320 RUB/Person in 10 Jan 2022. This records a decrease from the previous number of 1,915.430 RUB/Person for 27 Dec 2021. Russia Avg Consumer Price: Hotel 3 * Overnight Stay data is updated weekly, averaging 1,960.560 RUB/Person from Jan 2021 to 10 Jan 2022, with 52 observations. The data reached an all-time high of 2,299.440 RUB/Person in 07 Jun 2021 and a record low of 1,675.500 RUB/Person in 25 Jan 2021. Russia Avg Consumer Price: Hotel 3 * Overnight Stay data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PA001: Average Consumer Price: Weekly.

Introduction The Price Indexes of New Single-Family Houses Sold Including Value of Lot are a set of price indexes designed to illustrate inflation in new houses built for sale. These indexes do not include contractor-built houses, owner-built houses, or houses built for rent.

Data Collection The data used to compute these indexes are obtained from the U.S. Census Bureau's Survey of Construction. This survey gathers information on the physical characteristics and prices of new single-family houses through monthly interviews with the builders or owners of a national sample of new houses.

Price Index Design – Laspeyres Type Indexes The Constant Quality Price Indexes of New Single-Family Houses Sold Including Value of Lot are Laspeyres type indexes. The basic form of a Laspeyres type price index is:

∑ 𝑖 ( 𝑞 0 𝑖 ⋅ 𝑝 𝑡 𝑖 ) ∑ 𝑖 ( 𝑞 0 𝑖 ⋅ 𝑝 0 𝑖 ) ∑ i ​ (q 0i ​ ⋅p 0i ​ ) ∑ i ​ (q 0i ​ ⋅p ti ​ ) ​

where 𝑝 0 𝑖 p 0i ​ and 𝑝 𝑡 𝑖 p ti ​ are the prices in the base and current periods, respectively, and 𝑞 0 𝑖 q 0i ​ are the quantities in the base period. This ratio represents the current cost of the quantity of goods purchased in the base year compared to the cost in base year prices of the same quantity of goods. The denominator is the price of the average base period house. To compute this index, the prices must be derived from a regression model since only the total house and land price are collected.

Regression Model Experience has shown that regression estimation of the price in the following multiplicative model is superior to estimation for the above additive model:

Avg Consumer Price: HU: Housing: Hotels: Overnight Rate data was reported at 1,796.060 RUB/Person in Jan 2019. This records an increase from the previous number of 1,770.350 RUB/Person for Dec 2018. Avg Consumer Price: HU: Housing: Hotels: Overnight Rate data is updated monthly, averaging 818.060 RUB/Person from Jan 1995 (Median) to Jan 2019, with 289 observations. The data reached an all-time high of 1,948.970 RUB/Person in Jun 2018 and a record low of 23.540 RUB/Person in Jan 1995. Avg Consumer Price: HU: Housing: Hotels: Overnight Rate data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PA020: Average Consumer Price: Housing and Utilities Services.

Russia Avg Consumer Price: HU: Housing: Hotels: Student Dormitory Rent data was reported at 798.410 RUB/Person in Jan 2019. This records an increase from the previous number of 657.020 RUB/Person for Dec 2018. Russia Avg Consumer Price: HU: Housing: Hotels: Student Dormitory Rent data is updated monthly, averaging 139.990 RUB/Person from Jan 1995 (Median) to Jan 2019, with 289 observations. The data reached an all-time high of 798.410 RUB/Person in Jan 2019 and a record low of 8.590 RUB/Person in Jan 1995. Russia Avg Consumer Price: HU: Housing: Hotels: Student Dormitory Rent data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PA020: Average Consumer Price: Housing and Utilities Services.