Predicting the Trend of Rental Housing Prices in Shenzhen Based on Stacking Regression Models

DOI

10.2991/978-94-6463-304-7_73How to use a DOI?

Keywords

Rent projections; unique thermal code; Data preprocessing; Stacking regression model

Abstract

Rent price prediction is an important research direction because it can help tenants, landlords, and real estate companies better understand market dynamics and formulate strategies. In this paper, we obtained and preprocessed the Shenzhen rent information from the Chain Home and Shell websites, and the processed data were divided into eighty-two divisions, and six single models were established, Multiple regression model, Ridge regression, LASSO regression, Multi-layer Perceptual Machine, Random Forest regression, and XGBoost regression for rent prediction comparisons, and finally, in order to improve the prediction performance of the model, finally, based on the aspect of improving the prediction performance in this paper, the Stacking regression model is used, a single model as the primary learner, and the linear regression model as the secondary learner to establish the Stacking integrated regression model as the prediction model of Shenzhen rent, the final training model has an MSE of 0.165, an R2 of 0.84, and an MRE of 1.179, which are better than the previous models in all the three evaluation indexes, which indicate that the The prediction performance of the model has been significantly improved, which brings great reference significance to home buyers, landlords and real estate companies.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Hongzhan Li
AU  - Hao Lin
AU  - Yuntao Jia
PY  - 2023
DA  - 2023/12/04
TI  - Predicting the Trend of Rental Housing Prices in Shenzhen Based on Stacking Regression Models
BT  - Proceedings of the 3rd International Conference on Digital Economy and Computer Application (DECA 2023)
PB  - Atlantis Press
SP  - 698
EP  - 710
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-304-7_73
DO  - 10.2991/978-94-6463-304-7_73
ID  - Li2023
ER  -