City’s house pricing prediction based on machine learning algorithms

Zihao Liu; Yuxuan Wang

doi:10.1117/12.2658679

23 November 2022 City’s house pricing prediction based on machine learning algorithms

Zihao Liu, Yuxuan Wang

Proceedings Volume 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022); 124542P (2022) https://doi.org/10.1117/12.2658679
Event: International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 2022, Hohhot, China

Abstract

According to a related report, housing prices in New York will have similar price changes in the future as they do now, with minor swings and a modest increase overall. This study hopes to take a more original view, which means, less popular sides would be considered, and through machine learning to predict the trend of house prices in New York. The information of data was about house prices and real estate in New York. The first step is called conversion for data type, converting the string data in the document to the data format called INT. The second step is normalization, the values are mapped from 0-1. The proposed prediction of this article study uses three algorithm methods: Support Vector Machine (SVM), Linear regression, Neural network. And then the article will go for the hyperparameter configuration analysis. The paper concludes that practically every aspect of the data affects house prices through the comparison of the three forecasting models. Multiple linear regression models provide unstable outcomes, and the outcomes always vary from time to time. The neural network model's prediction results are relatively stable, and when the hyperparameters are kept constant, the prediction results are roughly the same. The most stable outcomes are predicted by the SVM model.

Citation Download Citation

Zihao Liu and Yuxuan Wang "City’s house pricing prediction based on machine learning algorithms", Proc. SPIE 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 124542P (23 November 2022); https://doi.org/10.1117/12.2658679

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