A Model for Monthly, Local-Level Airbnb Changes Using Public Dataset

Abstract

This study presents a novel methodological framework for estimating monthly variations in Airbnb listings across Seoul's administrative districts from 2017 to 2019, leveraging publicly available datasets. The proposed model integrates key factors such as housing supply, lodging unit density, proximity to tourist attractions, and retail sector activity, using negative binomial regression to quantify their impact on Airbnb dynamics. Our analysis demonstrates significant variations in these effects across different districts, with the model achieving superior statistical accuracy (average Pseudo R² of 0.7922) compared to baseline approaches. These findings underscore the utility of using accessible, timely data for capturing short-term rental market trends and highlight the model's potential applicability in diverse urban tourism settings. The research contributes valuable insights for tourism management, helping to develop policies that align with sustainable tourism growth and local economic development. The results indicate that proximity to major tourist destinations and the number of foreign visitors are among the most influential factors in determining Airbnb activity, highlighting the strong connection between short-term rentals and tourism demand. Lodging unit density and retail composition also play significant roles in shaping the distribution of Airbnb listings, suggesting that tourism infrastructure and commercial activity are key determinants of short-term rental market expansion. The study finds that certain districts experience high Airbnb concentrations due to their tourism appeal, whereas others exhibit minimal short-term rental activity, reflecting the heterogeneous nature of urban tourism accommodation demand. Our monthly regression analysis captures dynamic fluctuations in Airbnb listings, addressing the limitations of traditional models that rely on annual data. This research has critical implications for tourism stakeholders, including policymakers, destination managers, and hospitality businesses. By incorporating real-time, publicly available datasets, our approach facilitates responsive and data-driven decision-making to manage the short-term rental market effectively while fostering sustainable tourism growth. Future studies can extend this model to other urban contexts to explore how different socio-economic and regulatory environments influence short-term rental patterns. The adaptability of this methodology makes it a valuable tool for ongoing research in tourism analytics and policies.