Analysis of the Effectiveness of Predicting Housing Prices Based on Different Machine Learning Models

Wen Wang ()
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Wen Wang: Wuhan Textile University, School of Accounting

A chapter in Proceedings of the 2024 2nd International Conference on Management Innovation and Economy Development (MIED 2024), 2024, pp 433-438 from Springer

Abstract: Abstract With the growing housing price market, effectively predicting housing prices not only has an important impact on the economy but also is related to people’s living standards. However, the fluctuation of housing prices is affected by many factors, and in most cases, there is a non-linear relationship between housing price fluctuations and housing factors. In reality, there are differences in the effectiveness of different machine learning models in predicting home prices. Therefore, this paper uses multiple machine-learning models to explore how effective different machine-learning models are for house price prediction. In this work, the authors searched the Kaggle website for a data set of housing prices and housing factors in Bangalore, India. The housing attribute data includes the housing price, number of hardware facilities (number of bedrooms, number of swimming pools, number of sofas, etc.), and number of service facilities around the house (stadiums, shopping malls, etc.). Then, this data set was used to evaluate the house price prediction method of random forest, ridge regression, and XGboost. The results showed that the mixed model showed the best fit. This study can be used to select appropriate machine learning model predictions for home prices.

Keywords: Predicting Housing Prices; Machine Learning Models (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-542-3_50

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