
Property Rental Price Prediction Using the Extreme Gradient Boosting Algorithm
Author(s) -
Marco Febriadi Kokasih,
Adi Suryaputra Paramita
Publication year - 2020
Publication title -
ijiis: international journal of informatics and information systems
Language(s) - English
Resource type - Journals
ISSN - 2579-7069
DOI - 10.47738/ijiis.v3i2.65
Subject(s) - renting , gradient boosting , boosting (machine learning) , property (philosophy) , property market , computer science , operations research , algorithm , econometrics , artificial intelligence , business , real estate , economics , finance , engineering , philosophy , civil engineering , epistemology , random forest
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%.