Property Category Prediction Model using Random Forest Classifier to Improve Property Industry in Surabaya

Yosua Setyawan Soekamto (), Michelle Chandra, Trianggoro Wiradinata, Rinabi Tanamal and Theresia Ratih Dewi Saputri
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Yosua Setyawan Soekamto: Universitas Ciputra Surabaya
Michelle Chandra: Universitas Ciputra Surabaya
Trianggoro Wiradinata: Universitas Ciputra Surabaya
Rinabi Tanamal: Universitas Ciputra Surabaya
Theresia Ratih Dewi Saputri: Universitas Ciputra Surabaya

A chapter in Proceedings of the Business Innovation and Engineering Conference (BIEC 2022), 2023, pp 256-265 from Springer

Abstract: Abstract Urban planning is done not only to regulate residential areas, offices, retail spaces, and green spaces but also to ensure that people (community) who live in cities have a decent quality of life. Surabaya is a city that was built in the beginning of Indonesian civilization, so the arrangement of the city of Surabaya is a bit difficult and has an impact on housing costs. In reality, housing development is influenced by businesses in the residential development sector. This causes uneven house types to be built in accordance with the expectations of the government, which could impact the sustainability of Surabaya. This study is crucial because, from the data of Bank Indonesia, in supply and demand index for the property sector in Surabaya has not increased since 2019. Although property price has decreased since the fourth quarter of 2019 because of the Covid 19 pandemic, the demand index has not increased that well. This study intends to assist the process of classifying house types, so the government can make a selection on the house that will be built by the developer. 14 input attributes and 490 data from Surabaya property agencies were used in this study. In this study, random forest is used as the classification technique. The result of the classification model obtained an accuracy value of 89% and F1 score of 89%. A classification prediction model that can be used to determine property classification was found through this study.

Keywords: Classification; Data Science; Predictions; Property Industry; Random Forest; Sustainable City (search for similar items in EconPapers)
Date: 2023
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DOI: 10.2991/978-94-6463-144-9_24

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