Abstract
Peer-to-peer (P2P) marketplaces have seen exponential growth in recent years, featuring unique offerings from individual providers. However, scalable quantification of visual uniqueness and their impacts on platforms like Airbnb remains largely unexplored. We address this gap by developing, validating, and applying an unsupervised machine learning model to automatically extract uniqueness from images and quantify its impact on demand. We first construct a machine learning model, informed by cognitive psychology, to assess visual uniqueness in 481,747 property images, achieving high accuracy and interpretability. Next, we validate our model through three studies involving various participant populations and methods, confirming that the model’s predictions of visual uniqueness align with human judgment. Finally, we apply this model to demand data of Airbnb properties in New York City spanning 13 months. We find an inverted U-shaped relationship between visual uniqueness and demand, with two significant moderation effects: properties with higher response rates or overall ratings benefit more from visual uniqueness. This research provides valuable insights for P2P platforms like Airbnb, highlighting the strategic use of visual uniqueness to enhance visual appeal and market performance. It also offers a new methodological roadmap for integrating psychological insights into the development and validation of unsupervised machine learning models.
Accepted manuscripts
