Property Rental Price Prediction Using the Extreme Gradient Boosting Algorithm

Marco Febriadi Kokasih, Adi Suryaputra Paramita


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

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2579-7069 (online)
International Journal of Informatics and Information Systems (IJIIS)
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