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Dynamic Pricing Modelling — Airbnb Amsterdam Case Study
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Airbnb has a flexible pricing model where hosts can choose whether they want to rent their property short term or long term, at a fixed price or at variable prices. To this end, there is usually a base price suggested by the platform that hosts tend to change based on what they think the price should be (or what they need on them to pay their costs).
Dynamic pricing (also known as demand pricing) is a pricing strategy where companies set flexible prices for products or services based on current market demands. Companies can adjust prices based on algorithms that take into account competitive pricing, supply and demand, and other external market factors. Hotels and other players in the hospitality industry use dynamic pricing to adjust the cost of rooms and packages based on supply and demand needs at a given time. The goal of dynamic pricing in this industry is to find the highest price that consumers in different market segments are willing to pay. It indicates an increase in price when demand is high and a decrease to stimulate demand when it is low. By having a variety of prices based on demand at different times, that means hosts can generate more revenue by attracting customers at the different prices they are willing to pay.
In this project I tried to create a dynamic pricing model that would help Airbnb hosts to choose the best property price for a selected property based on the listed accommodation (demand, competition, availability, etc.) and compare their results with a fixed pricing plan current .
For this project I obtained the data from the “Inside Airbnb” platform which in turn retrieves it from publicly available information from the Airbnb website. As a case study I used the data available for the city of Amsterdam in the Netherlands. In particular, I used the data from the files “list” which contains details about real estate listings in Amsterdam (property type, hosts, properties of each property, etc.) and “Calendar” which contains detailed data about a calendar their property buildings and reserves. . all time In my listing sample there were listings for 19278 properties with 106 properties, but the corresponding calendar data for the same listings for one year had 7037006 listings.
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The idea was to sort the ads into groups and then find an example case study and find our dynamic pricing equation. To this end, I first analyzed the data with information about real estate listings in Amsterdam, selected the characteristics I wanted to use for my data collection, and divided the listings into groups that I would later use for my sample.
For clustering I used a k-means algorithm which is an unsupervised algorithm with the aim of separating n observations into k clusters where each observation belongs to the group with the closest mean (cluster centers or cluster center), serving as a prototype of the cluster By grouping my data I can focus on one of the classes and get more meaningful results by running my valuation model using parameters from the same class instead of considering all the data from potentially irrelevant lists.
In a second step we will investigate how the weather affects prices and in particular if there is seasonality (higher demand in some months) and booking options related to which day of the week it is. Such a dependency would help me refine my model to reflect daily and monthly price movements for each entry.
The final step is to create my dynamic pricing equation and run it against a sample case and see what results it would provide each year compared to existing pricing data. The characteristics used for such a model/equation should be ones that change regularly (daily) rather than fixed ones which would make sense in a forecasting model but not in a dynamic forward pricing model.
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I chose several features that I thought would make sense for my collection that represent the main features of the building and the hosts. I deliberately removed some of the generic data (i.e