Dataset

This dataset was created by Rod Rodrigues

Contents

Dataset

This dataset was created by Haowei Jing

Contents

This dataset contains the predicted prices of the asset Airbnb Inc Sailing over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Context and Methodology:
This dataset was created as part of a machine learning project for predicting Airbnb rental prices in the USA (year 2023). It is used to evaluate models (in particular a RandomForestRegressor) on training and test data that were previously cleaned and processed from a large raw dataset (AB_US_2023.csv). The training data were versioned in DBRepo and loaded into the Modeling_Regression.ipynb notebook via the API. A 10-fold cross-validation tuning on the training data optimizes the model’s three hyperparameters.

Technical Details:
The dataset is split into a training set and a test set. All output files (model pickle, metrics CSV, plots) are stored in the results/ folder at the repository root. Additional configuration and sample data can be found in data/sampled_data/. The notebook retrieves only the current training and test PIDs via the DBRepo API, so no local CSVs are versioned in the repo.

Further Notes:
A detailed setup guide (downloading large CSVs, adjusting paths) and all other preprocessing notebooks are documented in the README. License: CC-BY-4.0.

Dataset

This dataset was created by nagesh marshivane

Contents

Este conjunto de dados contém os preços previstos do ativo Airbnb Tokenized Stock (Ondo) nos próximos 16 anos. Estes dados são calculados inicialmente utilizando uma taxa de crescimento anual padrão de 5% e, após o carregamento da página, apresentam um componente de escala deslizante onde o utilizador pode ajustar a taxa de crescimento de acordo com as suas próprias projeções, sejam elas positivas ou negativas. A taxa máxima de crescimento ajustável positiva é de 100%, e a taxa mínima de crescimento ajustável é de -100%.

This dataset contains the predicted prices of the asset Airbnb Tokenized Stock (Ondo) over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Context

This dataset collected from airbnb. It is collected to see how airbnb is used in Turkey Istanbul.

Content

There are 16 columns which shows the latitude, longitude etc. It also shows the price. So, a regression problem such as finding the price of an house can be applied to this dataset. To see an example you can check my notebook from airbnb newyork dataset

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Airbnb Stock: Is It a Buy, Sell, or Hold for the Next 3 Months?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Context

Since 2008, guests and hosts have used Airbnb to travel in a more unique, personalized way. As part of the Airbnb Inside initiative, this dataset describes the listing activity of homestays in Boston, MA.

Content

This data file includes all needed information to about the listing details, the host, geographical availability, and necessary metrics to make predictions and draw conclusions. Basic data cleaning has been done, such as dropping redundant features (ex: city) and converting amenities into a dictionary. The data includes both numerical and categorical data, as well as natural language descriptions.

Acknowledgements

This dataset is part of Airbnb Inside, and the original source can be found here.

Inspiration

• Listing visualization
• What features drive the price of a listing up?
• What can we learn about different hosts and areas?
• What can we learn from predictions? (ex: locations, prices, reviews, etc)
• Which hosts are the busiest and why?
• Is there any noticeable difference of traffic among different areas and what could be the reason for it?

Context This dataset describes the listing activity and metrics in NYC, NY from 2008-2015.

Content This data file includes all the needed information to find out more about hosts, geographical availability, necessary metrics to make predictions and draw conclusions.

Acknowledgements This public dataset is part of Airbnb, and the original source can be found on this website.

Inspiration What can we learn about different hosts and areas? What can we learn from predictions? (ex: locations, prices, reviews, etc) Which hosts are the busiest and why? Is there any noticeable difference of traffic among different areas and what could be the reason for it?

Our travel datasets provide extensive, structured data covering various aspects of the global travel and hospitality industry. These datasets are ideal for businesses, analysts, and developers looking to gain insights into hotel pricing, short-term rentals, restaurant listings, and travel trends. Whether you're optimizing pricing strategies, analyzing market trends, or enhancing travel-related applications, our datasets offer the depth and accuracy you need.

Key Travel Datasets Available:

  Hotel & Rental Listings: Access detailed data on hotel properties, short-term rentals, and vacation stays from platforms like 
    Airbnb, Booking.com, and other OTAs. This includes property details, pricing, availability, guest reviews, and amenities.

  Real-Time & Historical Pricing Data: Track hotel room pricing, rental occupancy rates, and pricing trends 
    to optimize revenue management and competitive analysis.

  Restaurant Listings & Reviews: Explore restaurant data from Tripadvisor, OpenTable, Zomato, Deliveroo, and Talabat, 
    including restaurant details, customer ratings, menus, and delivery availability.

  Market & Trend Analysis: Use structured datasets to analyze travel demand, seasonal trends, and consumer preferences 
    across different regions.

  Geo-Targeted Data: Get location-specific insights with city, state, and country-level segmentation, 
    allowing for precise market research and localized business strategies.



Use Cases for Travel Datasets:

  Dynamic Pricing & Revenue Optimization: Adjust pricing strategies based on real-time market trends and competitor analysis.
  Market Research & Competitive Intelligence: Identify emerging travel trends, monitor competitor performance, and assess market demand.
  Travel & Hospitality App Development: Enhance travel platforms with accurate, up-to-date data on hotels, restaurants, and rental properties.
  Investment & Financial Analysis: Evaluate travel industry performance for investment decisions and economic forecasting.



  Our travel datasets are available in multiple formats (JSON, CSV, Excel) and can be delivered via 
  API, cloud storage (AWS, Google Cloud, Azure), or direct download. 
  Stay ahead in the travel industry with high-quality, structured data that powers smarter decisions.

Este conjunto de datos contiene los precios previstos del activo Airbnb Inc Sailing para los próximos 16 años. Estos datos se calculan inicialmente utilizando un porcentaje de crecimiento anual predeterminado del 5%, y, una vez cargada la página, se muestra un componente de escala móvil en el que el usuario puede ajustar aún más el porcentaje de crecimiento según sus propias previsiones, ya sean positivas o negativas. El porcentaje de crecimiento ajustable positivo máximo es del 100%, y el porcentaje de crecimiento ajustable mínimo es del -100%.

Since 2008, guests and hosts have used Airbnb to expand on traveling possibilities and present more unique, personalized way of experiencing the world. This dataset describes the listing activity and metrics in London UK in 2022.

This public dataset is part of Airbnb, and the original source can be found Here

Inspiration

• What can we learn about different hosts and areas?
• What can we learn from predictions? (ex: locations, prices, reviews, etc)
• Which hosts are the busiest and why?
• Is there any noticeable difference of traffic among different areas and what could be the reason for it?

Этот набор данных содержит прогнозируемые цены актива Airbnb Tokenized Stock (Ondo) на следующие 16 лет. Эти данные изначально рассчитываются с использованием стандартной годовой ставки роста в 5 процентов. После загрузки страницы появляется компонент с ползунком, который позволяет пользователю дополнительно корректировать ставку роста в соответствии с их собственными положительными или отрицательными прогнозами. Максимальная положительная регулируемая ставка роста составляет 100 процентов, а минимальная регулируемая ставка роста составляет -100 процентов.

Short-Term Rental Pricing Software Market Outlook

According to our latest research, the global Short-Term Rental Pricing Software market size reached USD 1.2 billion in 2024, demonstrating robust momentum fueled by the rising digitalization of the hospitality and property management sectors. The market is anticipated to register a strong CAGR of 13.6% from 2025 to 2033, projecting a value of approximately USD 3.8 billion by 2033. This remarkable growth is primarily driven by the increasing adoption of dynamic pricing tools among property managers and hosts seeking to maximize revenue in an increasingly competitive short-term rental environment.

One of the primary growth factors for the Short-Term Rental Pricing Software market is the exponential rise in short-term rental platforms and the evolving expectations of travelers. The proliferation of platforms such as Airbnb, Vrbo, and Booking.com has created a highly competitive landscape where pricing flexibility is crucial. Property owners and managers are increasingly leveraging advanced pricing algorithms to optimize occupancy rates and maximize returns. These software solutions utilize real-time data analytics, competitor benchmarking, and demand forecasting to recommend optimal pricing, enabling hosts to react swiftly to market changes. The growing sophistication of these tools, often powered by artificial intelligence and machine learning, is further enhancing their appeal and effectiveness across the industry.

Another significant driver is the expanding role of technology in streamlining property management operations. Short-term rental pricing software is no longer a standalone tool; it is often integrated into broader property management systems (PMS), channel managers, and customer relationship management (CRM) platforms. This integration allows for seamless automation of pricing updates, synchronization across multiple booking channels, and comprehensive analytics. The demand for such integrated solutions is particularly high among professional property managers and real estate agencies that oversee large portfolios, as it reduces manual workload, minimizes pricing errors, and ensures consistency. As the short-term rental market matures, the need for data-driven decision-making and operational efficiency is accelerating the adoption of advanced pricing software.

The market is also benefiting from the globalization of travel and the diversification of short-term rental use cases. The increasing popularity of remote work, digital nomadism, and extended stays has broadened the customer base for short-term rentals beyond traditional vacationers. This shift has prompted property owners to adopt more dynamic and granular pricing strategies to cater to varying lengths of stay, guest profiles, and seasonal demand patterns. Additionally, regulatory changes and economic uncertainties in key markets are pushing hosts to adopt sophisticated pricing tools to remain competitive and compliant. These trends are expected to sustain high demand for short-term rental pricing software over the coming decade.

Regionally, North America continues to dominate the Short-Term Rental Pricing Software market, accounting for over 42% of global revenue in 2024. This leadership is attributed to the region's high penetration of short-term rental platforms, tech-savvy property managers, and a strong ecosystem of software vendors. Europe follows closely, driven by a mature tourism industry and rising regulatory pressures that make efficient pricing essential. Meanwhile, the Asia Pacific region is emerging as a high-growth market, supported by rapid urbanization, a burgeoning middle class, and increased investment in digital infrastructure. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit from a smaller base, as market awareness and digital transformation initiatives gain traction.

Component Analysis

The Component segment of the Short-Term Rental Pricing Software market is bifurcated into software and services, both of which play pivotal roles in shaping the industry’s landscape. Software solutions constitute the core of this market, offering a range of functionalities from dynamic pricing algorithms to real-time market analytics and automated rate adjustments. These platforms are increasingly leveraging artificial intelligence and machine learning to deliver more accurate and responsive pricing recommendations. The

Context

Since 2008, guests and hosts have used Airbnb to expand on traveling possibilities and present more unique, personalized way of experiencing the world. This dataset describes the listing activity and metrics in NYC, NY for 2019.

Content

This data file includes all needed information to find out more about hosts, geographical availability, necessary metrics to make predictions and draw conclusions.

Acknowledgements

This public dataset is part of Airbnb, and the original source can be found on this website.

Inspiration

• What can we learn about different hosts and areas?
• What can we learn from predictions? (ex: locations, prices, reviews, etc)
• Which hosts are the busiest and why?
• Is there any noticeable difference of traffic among different areas and what could be the reason for it?

该数据集包含未来 16 年 Airbnb Tokenized Stock (Ondo) 资产的预测价格。这些数据最初使用默认的 5% 年增长率进行计算，页面加载后，用户可通过滑动比例组件根据自己的正面或负面预测进一步调整增长率。最大可调正增长率为 100%，最小可调增长率为 -100%。

此資料集包含資產 Airbnb Tokenized Stock (Ondo) 在未來 16 年的預測價格。此數據最初是使用預設的 5% 年增長獎勵率計算的，頁面加載後，它具有滑動比例組件，使用者可以根據自己的正面或負面預測進一步調整增長獎勵率。最高可調正獎勵率為 100%，最低可調獎勵率為 -100%。

Cet ensemble de données contient les prix prévisionnels de l'actif Airbnb Tokenized Stock (Ondo) pour les 16 prochaines années. Ces données sont initialement calculées à partir d'un taux de croissance annuel par défaut de 5 %. Après le chargement de la page, elles intègrent un composant à échelle mobile qui permet à l'utilisateur(trice) d'ajuster davantage le taux de croissance en fonction de ses propres projections positives ou négatives. Le taux de croissance positif maximal ajustable est de 100 %, tandis que le taux de croissance minimal ajustable est de -100 %.