Analyzing Airbnb and short-term rental (STR) data is all about helping investors make smarter decisions when buying properties for short-term rentals.
Analyzing Airbnb and short-term rental (STR) data is all about helping investors make smarter decisions when buying properties for short-term rentals. The main goal is to avoid spending too much on properties that might not bring in good profits, which can help prevent potential losses. The main goal of this analysis is identifying the most important factors that predict whether a property will do well as a short-term rental. These factors can be things like location, property size, pricing, amenities, and how the property has performed in the past. Exploratory data analysis (EDA) is a big part of this. It's like detective work with data. EDA helps investors dig deep into the data to find hidden patterns and insights that might not be obvious at first glance. This kind of analysis isn't one-size-fits-all. It's tailored to what each investor wants. It's about finding the right balance between making money and dealing with the costs and risks.
The dataset consist of data gathered from Airbnb bookings in european countries The dataset consist of Nine european cities each being a capity city
Amsterdam: Amsterdam is the capital city of the Netherlands. It is located in the western part of the country, in the North Holland province.
Athens: Athens is the capital city of Greece. It is situated in the southern part of mainland Greece, near the coast of the Aegean Sea.
Barcelona: Barcelona is a city in Spain. It is located in the northeastern region of Catalonia, along the Mediterranean coast.
Berlin: Berlin is the capital city of Germany. It is situated in the northeastern part of Germany, near the country's border with Poland.
Budapest: Budapest is the capital city of Hungary. It is located in the central part of Hungary, along the banks of the Danube River.
Lisbon: Lisbon is the capital city of Portugal. It is situated on the western coast of Portugal, along the Atlantic Ocean.
Paris: Paris is the capital city of France. It is located in the north-central part of France, along the Seine River.
Rome: Rome is the capital city of Italy. It is situated in the western part of Italy, in the Lazio region.
Vienna: Vienna is the capital city of Austria. It is located in the eastern part of Austria, near the country's borders with Hungary, Slovakia, and the Czech Republic.
My motivation for selecting this particular dataset stems from our profound interest in the realm of short-term rentals and Airbnb accommodations. While some businesses flourish in this industry, others face setbacks, often attributing their failures to the Airbnb platform itself, without delving into the critical factors of pricing and budget. This is precisely where our project steps in. We recognize the need to unravel the intricate web of success and failure within the short-term rental domain. We aim to investigate the multifaceted dynamics of this sector, shedding light on the vital role that pricing and budgeting play in the overall performance of businesses. By delving into this dataset, we intend to provide valuable insights that can guide prospective investors and existing stakeholders. Our research seeks to emphasize the significance of not solely placing blame on external factors but rather scrutinizing the internal mechanisms that underpin profitability and sustainability in the Airbnb and short-term rental landscape.
The primary objective of our analysis is to offer in-depth insights into the factors that influence property pricing within the dataset. To achieve this, we have devised an algorithm that relies on feature selection and employs the power of the random forest method. It is important to note that our algorithm is intentionally designed to be exceptionally simplified, prioritizing ease of comprehension for all users. Through this analysis, we endeavor to unravel the intricate web of variables that contribute to property pricing dynamics. We aim to identify and emphasize the key drivers behind pricing trends, offering a comprehensive understanding of the factors at play. Our algorithm, grounded in feature selection and the random forest technique, is tailored to distill complex information into a user-friendly format. By simplifying the process, we aim to make our findings accessible to a broad audience, ensuring that individuals with varying levels of expertise can grasp the insights we've gathered. In essence, our analysis and algorithm represent a concerted effort to demystify the pricing determinants for properties in the dataset, making this valuable information available to a wide and diverse audience.