How to Use Python for Airbnb Market Analysis to Develop a Pricing Strategy?

Market Analysis: Obtaining Accurate and Reliable Data

Accurately positioning your product within the market is crucial for the success of your business. Before establishing a pricing strategy, conducting a comprehensive market analysis is essential. This analysis allows rental companies to understand the pricing landscape in their territory and develop a data-driven strategy.

While some property managers opt to purchase data from third-party vendors, this approach has potential drawbacks. Factors such as data currency, errors, or skewed outliers can undermine the accuracy and relevance of the information. Upfront costs can be a barrier to entry, and the reliability of the data provided may be uncertain.

To mitigate these issues, gathering data directly from the source is beneficial whenever possible. By leveraging the power of Python and appropriate data acquisition techniques, rental companies can obtain raw and reliable data directly from platforms like Airbnb, allowing for a more informed and effective pricing strategy. This approach ensures up-to-date and reliable information that can be used for more accurate market analysis.

Data Acquisition: Building a Web Scraper for Airbnb Listings

To access curated and raw data from Airbnb, we developed a web scraper using Python to enable seamless data acquisition. While we was in France, specifically in the Deauville area, we wanted to gather information about the total number of one-bedroom rentals and their pricing quartiles for a specific period (12/23/21 - 01/02/21). Initially, a simple search on Airbnb yielded 300 listings, but we suspected there were more options available.

To ensure comprehensive data collection, we divided the search results based on price ranges. This approach allowed me to determine the breaking point of 300 listings by adjusting the price ranges (e.g., ≤60, 60–75, 180–290, 290≤) to capture slightly fewer than 300 results in each query. This ensured that no data points were missed.

Using a Scrapy spider in Python, we created a web scraper to automate the data acquisition process. We ran the scraper for each price range, resulting in eight JSON files that contained ≤300 listings per file. This approach enabled me to obtain a complete and extensive dataset for further analysis and pricing strategy development.

Data Cleaning: Formatting the Data for Manipulation

Now that we have obtained the data, the next step is to clean and format it for easy manipulation. To accomplish this, we can follow the steps outlined in the notebook (link provided).

First, the eight JSON files are read into separate data frames. Each data frame is then added to a list called "dfs." Using the pd.concat, drop_duplicates(), and reset_index(drop=True) functions, we can merge the data frames and remove any duplicate entries. This process results in a new data frame called "listings" that contains 2209 unique observations.

By performing these data cleaning operations, we ensure that the dataset is structured and can be easily manipulated and analyzed for pricing strategy development.

To facilitate aggregations, we need to transform the data into a suitable format for easy manipulation. First, I use regular expressions to remove the "€" symbol from the price variable. Then, I convert the remaining value into an integer using the "astype(int)" method.

To process the bedrooms variable, we perform several transformations. First, we replace the string "Studio" with "0" to represent a studio apartment. Then, we use regular expressions to remove the text "bedroom(s)" from the variable. Finally, we convert the remaining value into an integer.

Having cleaned up the price and bedrooms variables, we can now visualize the price distribution of one-bedroom listings in Deauville using a boxplot. By filtering the dataset for one-bedroom listings and plotting the price variable using a boxplot, we can visualize the spread and distribution of prices for these listings. The boxplot provides insights into the median, quartiles, and any potential outliers in the price distribution. This visualization helps us understand the range of prices for one-bedroom listings in Deauville and identify any significant price variations.

To gain more insights from our data, let's investigate the listings with prices greater than 300 €. This will help us understand what is happening with these high-priced listings.

After further inspection, we have identified some listings that are not relevant to our study. Specifically, the units at index locations [2197, 2201, 2206] are renting out multiple rooms in a hotel, which means they are not one-bedroom listings. To ensure the accuracy of our analysis, we will exclude these listings from our data.

By dropping the rows corresponding to the irrelevant outliers from the dataset, we obtain a new dataset called "relevant_listings." We then create a boxplot using this filtered dataset to visualize the price distribution of the remaining relevant one-bedroom listings in Deauville.

The updated boxplot provides a more accurate representation of the price spread for one-bedroom listings after removing the irrelevant outliers. This allows us to analyze the price distribution with greater confidence and make more informed decisions based on the normalized data.

By using the one_br.shape attribute, we can determine the number of unique one-bedroom observations in our "one_br" dataframe.

Running this code will display the count of unique one-bedroom observations in the "one_br" dataframe. In this case, the output shows that there are 939 unique one-bedroom observations in our dataset.

To determine the market rate percentiles of rental units in the one-bedroom category, we can use the "np.percentile" function from the NumPy library. This function allows us to calculate any desired percentile of our price data.

Here is the code to calculate the 25th, 50th, and 75th percentiles and assign them to the variables "one_br_25", "one_br_50", and "one_br_75" respectively:

Running this code will display the respective percentiles of the price data for one-bedroom units. By knowing these market rate percentiles, you can gain insights into how your pricing compares to similar products in the market and make informed decisions when setting your rental prices.

To visualize the distribution of the price data and highlight the percentiles, we can create a Kernel Density Estimate (KDE) plot using the Seaborn library. The KDE plot will provide a smooth representation of the density of observations in the dataset.

After obtaining the market percentiles, we can conduct sentiment analysis to gain insights into each price range. This analysis can provide information about the location, number of bathrooms, amenities, and guest reviews, helping us create a profile for each price range.

Here is a general breakdown of the characteristics associated with each percentile range:

Lower Quartile (<25%): This range represents the low-cost providers with the lowest prices. These properties may be located in inconvenient areas, possibly new to the market. They may offer shared bedrooms/bathrooms and have limited amenities or services.

25%-50%: This range includes focused low-cost providers or integrated low-cost differentiators. The prices are still competitive, but there may be added value in terms of convenient locations and modest amenities or services.

50%-75%: In this range, properties differentiate themselves with above-median prices. They offer great locations and higher quality amenities, perks, or services compared to the lower percentiles.

Upper Quartile (>75%): This range represents focused differentiators with the highest prices. These properties offer luxury units in the best locations with excellent amenities, perks, and services.

It's important to note that the quartile model may not be suitable for every market. Some markets may align better with quintiles, octiles, or deciles. As a subject matter expert of your market, you can use your judgment to determine the appropriate pricing model based on the unique characteristics of your market.

By conducting sentiment analysis and understanding the profile associated with each price range, you can make informed pricing decisions that align with the market and cater to the preferences and expectations of your target audience.

Conclusion

In conclusion, when developing a pricing strategy for vacation rental properties, it is crucial to concentrate on substitute offerings in your property's exclusive categories rather than solely comparing similar-size units. Each unit profile attracts its own segment of customers with varying levels of price sensitivity.

To gain a comprehensive understanding of the market, you can perform this study using different date sets to account for seasonality, such as holidays, off-season, regular-season, or events. Additionally, keep in mind that many companies utilize dynamic pricing tools that adjust property prices based on factors like days until coming and possession levels.

Determining market percentiles is just one aspect of developing a successful pricing strategy. It's likely that you will need to make real-time adjustments based on the feedback loop and market conditions.

It's important to note that Airbnb does not provide an official API to the public, and excessive scraping of their website may violate their terms of service, potentially resulting in being blocked. If you choose to use the provided code, please be aware of the associated risks and refer to Scrapy Documentation about Download Delay before implementing your own project.

By conducting through market analysis, utilizing data scraping techniques, and continuously monitoring market dynamics, you can develop a pricing strategy that positions your vacation rental properties competitively and maximizes profitability.

For more information, contact Actowiz Solutions now! You can also call us for all your web scraping service or mobile app scraping requirements.