Vacation rental websites such as Airbnb have become increasingly popular where rentals are typically short-term and travels or vacations related. Reasonable rental prices play a crucial role in improving user experiences and engagements in these websites. However, the unique properties of their rentals challenge traditional house rentals that are often long-term and study or work related. Therefore, in this paper we investigate the novel problem of price recommendation in vacation rental websites. We identify some important factors that affect the rental prices and propose a framework that consists of Multi-Scale Affinity Propagation (MSAP) to cluster houses, Nash Equilibrium filter to remove unreasonable price and Linear Regression model with Normal Noise (LRNN) to predict the reasonable prices. Experimental results demonstrate the effectiveness of the proposed framework. We conduct further experiments to understand the important factors in rental price recommendation.

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