Property Rental Price Prediction Using the Extreme Gradient Boosting Algorithm

Marco Febriadi Kokasih, Adi Suryaputra Paramita

Abstract


Online marketplace in the field of property renting like Airbnb is growing. Many property owners have begun renting out their properties to fulfil this demand. Determining a fair price for both property owners and tourists is a challenge. Therefore, this study aims to create a software that can create a prediction model for property rent price. Variable that will be used for this study is listing feature, neighbourhood, review, date and host information. Prediction model is created based on the dataset given by the user and processed with Extreme Gradient Boosting algorithm which then will be stored in the system. The result of this study is expected to create prediction models for property rent price for property owners and tourists consideration when considering to rent a property. In conclusion, Extreme Gradient Boosting algorithm is able to create property rental price prediction with the average of RMSE of 10.86 or 13.30%.


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Keywords


Rental Price; Prediction Model; Extreme Gradient Boosting; XGBoost.

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References


G. Zervas, D. Proserpio, and J. Byers, “A First Look at Online Reputation on Airbnb, Where Every Stay is Above Average,” SSRN Electron. J., pp. 1–22, 2018, doi: 10.2139/ssrn.2554500.

T. Chen and T. He, “xgboost: Extreme Gradient Boosting,” R Lect., no. 2016, pp. 1–84, 2014, doi: 10.1145/2939672.2939785>.This.

T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., vol. 13-17-August-2016, pp. 785–794, 2016, doi: 10.1145/2939672.2939785.

L. Markusheski, I. Zdravkoski, and M. Andonovski, “Data Mining Process Ljupce,” Ibaness Congr. Ser. Econ. Bus. Manag., pp. 71–79, 2019, [Online]. Available: https://www.researchgate.net/publication/332876172.

T. Hendrickx, B. Cule, P. Meysman, S. Naulaerts, K. Laukens, and B. Goethals, “Mining association rules in graphs based on frequent cohesive itemsets,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9078, no. 3, pp. 637–648, 2015, doi: 10.1007/978-3-319-18032-8_50.

D. R. Hardoon, S. Szedmak, and J. Shawe-taylor, “Canonical correlation analysis ; An methods,” Science (80-. )., vol. 16, no. 12, pp. 2639–64, 2003, [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/15516276.

M. S. Gal and D. L. Rubinfeld, “Data standardization,” New York Univ. Law Rev., vol. 94, no. 4, pp. 737–770, 2019, doi: 10.2139/ssrn.3326377.

P. Jesus, C. Baquero, and P. S. Almeida, “A Survey of Distributed Data Aggregation Algorithms,” IEEE Commun. Surv. Tutorials, vol. 17, no. 1, pp. 381–404, 2015, doi: 10.1109/COMST.2014.2354398.

P. R. Kalehbasti, L. Nikolenko, and H. Rezaei, “Airbnb Price Prediction Using Machine Learning and Sentiment Analysis,” 2019, [Online]. Available: http://arxiv.org/abs/1907.12665.

S. B. Kotsiantis and D. Kanellopoulos, “Data preprocessing for supervised leaning,” Int. J. …, vol. 1, no. 2, pp. 1–7, 2006, doi: 10.1080/02331931003692557.

R. B. Davis, S. Ounpuu, D. Tyburski, and J. R. Gage, “Davis_1991.pdf,” Human Movement Science, vol. 10. pp. 575–597, 1991.

E. Tang and K. Sangani, “Neighborhood and Price Prediction for San Francisco Airbnb Listings,” 2015.

C. C. Aggarwal, X. Kong, Q. Gu, J. Han, and P. S. Yu, “Active learning: A survey,” Data Classif. Algorithms Appl., pp. 571–605, 2014, doi: 10.1201/b17320.

H. Zheng, J. Yuan, and L. Chen, “Short-Term Load Forecasting Using EMD-LSTM neural networks with a xgboost algorithm for feature importance evaluation,” Energies, vol. 10, no. 8, 2017, doi: 10.3390/en10081168.

N. H. Trang, “Limitations of Big Data Partitions Technology,” J. Appl. Data Sci., vol. 1, no. 1, pp. 11–19, 2020.


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2579-7069 (online)
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