Geospatial Airbnb Analysis in London

CONTEXT AND OBJECTIVE

Analyze how Airbnb listing prices in London vary with location, distance to the city center and the context of nearby comparable listings.

DATA AND DIAGNOSIS

• Segmentation of each listing as cheap, stable or expensive within its own room type using the 25th and 75th percentiles.
• Calculation of geographic distance to Charing Cross and comparison of prices by neighborhood and distance bands from the city center.
• Construction of neighborhood variables with KNN (K-Nearest Neighbors / nearest neighbors) to measure median nearby price and the local share of expensive comparable listings.
• Mapping of price, distance and local context to identify spatial concentrations and territorial patterns.

DELIVERY AND LEARNING

The analysis combines relative price segmentation, distance to the city center and neighborhood metrics to build a spatial reading of the market. The maps make it possible to compare neighborhoods, detect areas with higher concentrations of expensive listings and understand how each property behaves relative to nearby listings of the same type.