Learning Market Dynamics for Optimal Pricing

My Take

When it comes to building multi-level hierarchical models in a business context, there is a persistent tension between local relevance and global optimization. Aphoristic as it may be, every market truly is different, and the people who live there tend to have a very intuitive sense of how it operates. So, purely probabilistic approaches to modeling individual markets are likely to met with push-back and suspicion. On the other hand, structural models are constrained by the information available to the modeler – they don’t know what they don’t know. Perhaps AirBnB has figured out a way to find a happier medium between the two.

Their Take

Market dynamics plays a key role in matching guests with hosts in two-sided marketplaces such as Airbnb. Supply and demand vary drastically across different locations, different check in dates and different lead times until check-in. It is important for us to understand and forecast these spatial and temporal trends in order to find better matches for our community of hosts and guests.


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