Analyzing patterns in Airbnb listing prices and their classification in London through geospatial distribution analysis

Advancements in technology and societal changes have profoundly altered lifestyles, leading to an increased desire for global travel. Alongside this trend, the concept of the 'sharing house' has emerged as a popular alternative to traditional hotel accommodations. Sharing houses offer benefits such as flexible rental periods, a variety of housing options, and competitive pricing, making them increasingly attractive to travelers. Airbnb stands out as a leading platform facilitating this model. Analyzing Airbnb data provides valuable insights for government policy-making, urban planning, travel planning for renters, host profitability, and strategic decisions for Airbnb itself. This study utilizes data from Airbnb's London listings, focusing on seven key attributes. Employing unsupervised learning techniques like K-Means Clustering combined with Principal Component Analysis (PCA), the study identifies three principal components and two distinct clusters, achieving a silhouette score of 0.64. By visualizing these clusters on a map, the research offers guidance to the London government for a deeper understanding of host behaviors and assists renters in selecting more suitable accommodations and hosts on Airbnb.