Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)

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Machine Learning-based Models for House Price Prediction in Provincial Administrative Regions of China

Authors
Xiafei Ding1, *, †, Weiya Wang2, †, Yiqian Zhang3, †, Xiaoyuqian Zhong4, †
1Shandong University, Shandong, China
2Renmin University of China, Beijing, China
3Hubei University, Hubei, China
4American University, USA
†

These authors contributed equally.

*Corresponding author. Email: 201900820057@mail.sdu.edu.cn
Corresponding Author
Xiafei Ding
Available Online 26 March 2022.
DOI
10.2991/aebmr.k.220307.035How to use a DOI?
Keywords
Machine learning; House price prediction; Regression analysis
Abstract

House price is an intractable problem with numerous influence factors. In this paper, boosting and traditional algorithms are compared to screen out the optimal model for house price prediction in provincial administrative regions of China. Based on provincial house price data in China from 2000 to 2019, the data is preprocessed by doing statistical analysis, dealing the missing values as well as choosing characteristic features for analysis. Then the data is imported into different models, comparing the prediction effects to pick out the best and then optimizing the hyper-parameters. Using mean squared error (RMSE), root mean absolute percentage error (MAPE), R-square (R2), and explained variance score (EV) as evaluation indicators to appraise the models, the result presents that CatBoost is better than any other models, whose MAPE is 12.5%, R2 is 87.81% and EV is 90.5%. Then sub-sample test is used to examine the robustness, whose result shows that CatBoost is always effective. The empirical findings mainly show that CatBoost is effective in predicting house prices with complex variables and the feature importance graph generated by CatBoost presents that demand and macro environment factors can explain the major fluctuation of house price and that in macro environment factors, macro-economic and education indicators are obviously important than other macro indicators.

Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

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Volume Title
Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)
Series
Advances in Economics, Business and Management Research
Publication Date
26 March 2022
ISBN
978-94-6239-554-1
ISSN
2352-5428
DOI
10.2991/aebmr.k.220307.035How to use a DOI?
Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

Cite this article

risenwbib
TY  - CONF
AU  - Xiafei Ding
AU  - Weiya Wang
AU  - Yiqian Zhang
AU  - Xiaoyuqian Zhong
PY  - 2022
DA  - 2022/03/26
TI  - Machine Learning-based Models for House Price Prediction in Provincial Administrative Regions of China
BT  - Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)
PB  - Atlantis Press
SP  - 221
EP  - 229
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220307.035
DO  - 10.2991/aebmr.k.220307.035
ID  - Ding2022
ER  -