Airbnb fiyatlarının, konum ve fotoğraf verileri kullanarak tahminlenmesi

Until recently, temporary accommodation needs were met through businesses such as hotels. However, in recent years, housing rentals through Airbnb have become increasingly common. This model has several distinguishing features. First, the physical characteristics of rental properties (such as the number of rooms and available amenities) show significant variations, both compared to traditional accommodations and among themselves. Second, properties available for rent via Airbnb can be found in almost any location. Finally, the services offered vary compared to hotels. These differences necessitate different approaches to predicting prices. Identifying the factors that influence pricing accurately is crucial for profitability. This study, based on Airbnb listings in Istanbul, analyzes which factors influence property prices and to what extent, as well as determining the most successful price prediction method. In the research, in addition to basic information commonly included in Airbnb listings, factors like the integration of the location with public transportation, the extent to which daily needs can be met, and the analysis of listing photos were also considered. Various machine learning techniques were applied for price prediction, and the impact of each factor on pricing was calculated. The results indicate that while the physical characteristics of a property play a significant role, location-based information is also crucial. Proximity to public transportation stops, cultural activities, and educational facilities were identified as key factors. It was also determined that analyzing the listing photo using a large language model did not provide a meaningful contribution to price prediction.