Enhancing Airbnb Price Predictions with Location-Based Data: A Case Study of Istanbul

• Autorzy: Akalın Özgün , Isiklar Alptekin Gülfem

Identyfikatory

DOI

10.15439/2024F7603

• Konferencja: Federated Conference on Computer Science and Information Systems (19 ; 08-11.09.2024 ; Belgrade, Serbia)
• Języki publikacji: EN

Abstrakty

EN

Airbnb, a prominent online marketplace, facilitates short- and long-term rentals by connecting customers with property owners offering entire apartments or private rooms. Accurate price prediction is crucial for both the platform and rental property owners. Previous studies have primarily focused on statistical methods and pre-processing techniques, with limited exploration of the impact of location attributes. This paper aims to enhance price prediction models for Airbnb listings by incorporating location data. Utilizing data from InsideAirbnb for Istanbul, we implemented various data pre-processing techniques and enriched the dataset with location-specific information. Our findings show that incorporating these location-based features significantly improved model performance, increasing the adjusted R2 metric by 22.5%. This enhancement was achieved by using location-related index values and public transportation data provided by the Istanbul Metropolitan Municipality. These advancements can help property owners optimize rental prices and assist urban planners in making informed decisions about city infrastructure development.

Słowa kluczowe

EN

price prediction; regression; XGBoost; location; Airbnb

PL

przewidywanie cen; regresja; XGBoost; lokalizacja; Airbnb

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2024
• Tom: Vol. 39
• Strony: 207--–212
• Opis fizyczny: Bibliogr. 20 poz., il., tab., wykr.

Twórcy

autor

Akalın Özgün

• Computer Engineering, Galatasaray University, Istanbul, Turkey

• https://orcid.org/0009-0001-2067-856X

autor

Isiklar Alptekin Gülfem

• Computer Engineering, Galatasaray University, Istanbul, Turkey

• https://orcid.org/0000-0003-0146-1581

Bibliografia

• 1. Airbnb: https://news.airbnb.com/about-us/
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• 4. Wang, Haoqian. “Predicting Airbnb listing price with different models.” Highlights in Science, Engineering and Technology 47 (2023): 79-86.
• 5. Yang, Siqi. “Learning-based Airbnb price prediction model.” 2021 2nd International Conference on E-Commerce and Internet Technology (ECIT). IEEE, 2021.
• 6. Lektorov, A., Abdelfattah, E., and Joshi, S. “Airbnb Rental Price Prediction Using Machine Learning Models,” 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 2023, pp. 0339-0344.
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• 9. Peng, Ningxin, Kangcheng Li, and Yiyuan Qin. “Leveraging multi-modality data to Airbnb price prediction.” 2020 2nd International Conference on Economic Management and Model Engineering (ICEMME). IEEE, 2020.
• 10. InsideAirbnb: https://insideairbnb.com/get-the-data
• 11. 34 Dakika Istanbul: https://34dakika.istanbul/map
• 12. Istanbul Metropolitan Municipality https://data.ibb.gov.tr/en/dataset
• 13. Lewandowska, Alexandra. “XGBoost meets TabNet in Predicting the Costs of Forwarding Contracts,” 2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS) (2022): 417-420.
• 14. Podlodowski, Ł. and Kozłowski, M. “Predicting the Costs of Forwarding Contracts Using XGBoost and a Deep Neural Network,” 2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS) (2022): 425-429.
• 15. Sammut, Claude, and Geoffrey I. Webb. “Mean absolute error.” Encyclopedia of Machine Learning 652 (2010).
• 16. Wright, Sewall. “Correlation and causation.” Journal of Agricultural Research 20.7 (1921): 557.
• 17. Miles, Jeremy. “R-squared, adjusted R-squared.” Encyclopedia of Statistics in Behavioral Science (2005).
• 18. Schwarzová, Lucie. Predicting Airbnb Prices with Neighborhood Characteristics: Machine Learning Approach. Diss. Tilburg University, 2020.
• 19. Chica-Olmo, Jorge, Juan Gabriel González-Morales, and José Luis Zafra-Gómez.” Effects of location on Airbnb apartment pricing in Málaga.” Tourism Management 77 (2020): 103981.
• 20. Luo, Yanjie, and Mizuki Kawabata. “Airbnb pricing and neighborhood characteristics in San Francisco.”, Available at: https://tinyurl.com/w53w277s, 2018.

Uwagi

1. This research has been financially supported by Galatasaray University Research Fund, with project ID: FBA-2024-1258.

2. Main Track: Short Papers

3. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).