This paper explores the interplay between cultural organi- zations, safety incidents, and pricing within the context of New York City’s Airbnb hospitality landscape. By analyzing data from Airbnb list- ings, crime incidents, and cultural organizations, the research investigates how these factors influence the attractiveness and pricing of accommo- dations. Using machine learning models and visualization techniques, the paper identifies key trends, including the significant impact of cul- tural organizations on pricing and the trade-offs between affordability, diversity, and safety in districts like Queens and Bronx. The Gradient Boosting Regression model achieved the highest accuracy in predicting listing prices, with a Median Absolute Error of 23. 91. These findings highlight how cultural and safety factors contribute to shaping guest ex- periences and pricing dynamics in urban hospitality. The paper provides actionable insights for stakeholders in the hospitality sector, including hosts, travelers, and urban planners, and suggests directions for future work on localized predictors in hospitality services.
Dishant Tandel Tandel (Wed,) studied this question.