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DECISION TREE-BASED GRADIENT BOOSTING: ALGORITHM TO APPROACH HOUSE PRICE PREDICTION IN JAKARTA, BOGOR, DEPOK, TANGERANG, BEKASI (JABODETABEK)
(1) National Central University
(2) Innopolis University
(*) Corresponding Author
Abstract
The house sale prices are a particular concern for some people, whether sellers or buyers, for personal use or investment. Commonly, the buyer comes from newly-married couples, parents, or investors. Compared to years ago, the recent price is more expensive due to some conditions over the time. Forecasting is a method to see at which price the house may fit the market price with certain features. Through this study, we complement the previous research about house prices and analyze the results. Besides, here we also break down the algorithm and sketch the steps so that it eases the reader to understand the method. Exploratory data analysis is also done to see and analyze the characteristics of the dataset. Applying decision tree-based gradient boosting, we run the algorithm into datasets in Jakarta, Bogor, Depok, Tangerang, and Bekasi (Jabodetabek) consisting of house price and its features. We see that the RMSE value is Rp277.369.397 and the MAPE is 17,3%. With that value of accuracy we could mention that gradient boosting is quite competitive compared with other methods and able to give its best prediction over house prices.
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DOI: https://doi.org/10.26714/jsunimus.12.2.2024.1-9
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