Predicting Real Estate Price Using Stacking-Based Ensemble Learning

Huiyi Zhao; Kainuo Wang

doi:doi:10.11648/j.ajist.20230702.14

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Predicting Real Estate Price Using Stacking-Based Ensemble Learning

Huiyi Zhao, Kainuo Wang

Published in American Journal of Information Science and Technology (Volume 7, Issue 2)

Received: 14 March 2023 Accepted: 23 April 2023 Published: 27 April 2023

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Abstract

As one of the leading researches focusing on modern economics, real estate industry not only affects people's well-being but also has a close relationship with the national economy and social stability. Nevertheless, there are numerous complex factors that influence real estate prices, which makes house price forecasting remain a classic and challenging problem in the field of data analysis. The development of data mining and machine learning has greatly facilitated the analysis and extraction of useful information from complex data sets and the building of models to make predictions. In this study, a stacking-based ensemble model is proposed to identify potential links between property prices and various factors so that the more accurate prediction of property prices can be made. Some base predictive models, including linear regression, support vector regression, ridge regression, least absolute shrinkage and selection operator, machine language programs, random forest regression, and gradient boosting regression are trained to individually predict the estate price in the experiment. Then, the stacking-based ensemble model is obtained by integrating competent base predictive models and optimized using Grid search. The experimental outcomes indicate that the proposed model is superior to base predictive models and can be more accurate in predicting house prices.

Published in	American Journal of Information Science and Technology (Volume 7, Issue 2)
DOI	10.11648/j.ajist.20230702.14
Page(s)	70-75
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Ensemble Model, Real Estate Price, Stacking, Prediction

References

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APA Style

Huiyi Zhao, Kainuo Wang. (2023). Predicting Real Estate Price Using Stacking-Based Ensemble Learning. American Journal of Information Science and Technology, 7(2), 70-75. https://doi.org/10.11648/j.ajist.20230702.14

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ACS Style

Huiyi Zhao; Kainuo Wang. Predicting Real Estate Price Using Stacking-Based Ensemble Learning. Am. J. Inf. Sci. Technol. 2023, 7(2), 70-75. doi: 10.11648/j.ajist.20230702.14

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AMA Style

Huiyi Zhao, Kainuo Wang. Predicting Real Estate Price Using Stacking-Based Ensemble Learning. Am J Inf Sci Technol. 2023;7(2):70-75. doi: 10.11648/j.ajist.20230702.14

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@article{10.11648/j.ajist.20230702.14,
  author = {Huiyi Zhao and Kainuo Wang},
  title = {Predicting Real Estate Price Using Stacking-Based Ensemble Learning},
  journal = {American Journal of Information Science and Technology},
  volume = {7},
  number = {2},
  pages = {70-75},
  doi = {10.11648/j.ajist.20230702.14},
  url = {https://doi.org/10.11648/j.ajist.20230702.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajist.20230702.14},
  abstract = {As one of the leading researches focusing on modern economics, real estate industry not only affects people's well-being but also has a close relationship with the national economy and social stability. Nevertheless, there are numerous complex factors that influence real estate prices, which makes house price forecasting remain a classic and challenging problem in the field of data analysis. The development of data mining and machine learning has greatly facilitated the analysis and extraction of useful information from complex data sets and the building of models to make predictions. In this study, a stacking-based ensemble model is proposed to identify potential links between property prices and various factors so that the more accurate prediction of property prices can be made. Some base predictive models, including linear regression, support vector regression, ridge regression, least absolute shrinkage and selection operator, machine language programs, random forest regression, and gradient boosting regression are trained to individually predict the estate price in the experiment. Then, the stacking-based ensemble model is obtained by integrating competent base predictive models and optimized using Grid search. The experimental outcomes indicate that the proposed model is superior to base predictive models and can be more accurate in predicting house prices.},
 year = {2023}
}

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TY  - JOUR
T1  - Predicting Real Estate Price Using Stacking-Based Ensemble Learning
AU  - Huiyi Zhao
AU  - Kainuo Wang
Y1  - 2023/04/27
PY  - 2023
N1  - https://doi.org/10.11648/j.ajist.20230702.14
DO  - 10.11648/j.ajist.20230702.14
T2  - American Journal of Information Science and Technology
JF  - American Journal of Information Science and Technology
JO  - American Journal of Information Science and Technology
SP  - 70
EP  - 75
PB  - Science Publishing Group
SN  - 2640-0588
UR  - https://doi.org/10.11648/j.ajist.20230702.14
AB  - As one of the leading researches focusing on modern economics, real estate industry not only affects people's well-being but also has a close relationship with the national economy and social stability. Nevertheless, there are numerous complex factors that influence real estate prices, which makes house price forecasting remain a classic and challenging problem in the field of data analysis. The development of data mining and machine learning has greatly facilitated the analysis and extraction of useful information from complex data sets and the building of models to make predictions. In this study, a stacking-based ensemble model is proposed to identify potential links between property prices and various factors so that the more accurate prediction of property prices can be made. Some base predictive models, including linear regression, support vector regression, ridge regression, least absolute shrinkage and selection operator, machine language programs, random forest regression, and gradient boosting regression are trained to individually predict the estate price in the experiment. Then, the stacking-based ensemble model is obtained by integrating competent base predictive models and optimized using Grid search. The experimental outcomes indicate that the proposed model is superior to base predictive models and can be more accurate in predicting house prices.
VL  - 7
IS  - 2
ER  -

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Author Information

Huiyi Zhao

School of Urban Design, Wuhan University, Wuhan, China
Kainuo Wang

School of Resource and Environmental Science, Wuhan University, Wuhan, China

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Plain Text BibTeX RIS

APA Style

Huiyi Zhao, Kainuo Wang. (2023). Predicting Real Estate Price Using Stacking-Based Ensemble Learning. American Journal of Information Science and Technology, 7(2), 70-75. https://doi.org/10.11648/j.ajist.20230702.14

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ACS Style

Huiyi Zhao; Kainuo Wang. Predicting Real Estate Price Using Stacking-Based Ensemble Learning. Am. J. Inf. Sci. Technol. 2023, 7(2), 70-75. doi: 10.11648/j.ajist.20230702.14

Copy | Download

AMA Style

Huiyi Zhao, Kainuo Wang. Predicting Real Estate Price Using Stacking-Based Ensemble Learning. Am J Inf Sci Technol. 2023;7(2):70-75. doi: 10.11648/j.ajist.20230702.14

Copy | Download

@article{10.11648/j.ajist.20230702.14,
  author = {Huiyi Zhao and Kainuo Wang},
  title = {Predicting Real Estate Price Using Stacking-Based Ensemble Learning},
  journal = {American Journal of Information Science and Technology},
  volume = {7},
  number = {2},
  pages = {70-75},
  doi = {10.11648/j.ajist.20230702.14},
  url = {https://doi.org/10.11648/j.ajist.20230702.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajist.20230702.14},
  abstract = {As one of the leading researches focusing on modern economics, real estate industry not only affects people's well-being but also has a close relationship with the national economy and social stability. Nevertheless, there are numerous complex factors that influence real estate prices, which makes house price forecasting remain a classic and challenging problem in the field of data analysis. The development of data mining and machine learning has greatly facilitated the analysis and extraction of useful information from complex data sets and the building of models to make predictions. In this study, a stacking-based ensemble model is proposed to identify potential links between property prices and various factors so that the more accurate prediction of property prices can be made. Some base predictive models, including linear regression, support vector regression, ridge regression, least absolute shrinkage and selection operator, machine language programs, random forest regression, and gradient boosting regression are trained to individually predict the estate price in the experiment. Then, the stacking-based ensemble model is obtained by integrating competent base predictive models and optimized using Grid search. The experimental outcomes indicate that the proposed model is superior to base predictive models and can be more accurate in predicting house prices.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Predicting Real Estate Price Using Stacking-Based Ensemble Learning
AU  - Huiyi Zhao
AU  - Kainuo Wang
Y1  - 2023/04/27
PY  - 2023
N1  - https://doi.org/10.11648/j.ajist.20230702.14
DO  - 10.11648/j.ajist.20230702.14
T2  - American Journal of Information Science and Technology
JF  - American Journal of Information Science and Technology
JO  - American Journal of Information Science and Technology
SP  - 70
EP  - 75
PB  - Science Publishing Group
SN  - 2640-0588
UR  - https://doi.org/10.11648/j.ajist.20230702.14
AB  - As one of the leading researches focusing on modern economics, real estate industry not only affects people's well-being but also has a close relationship with the national economy and social stability. Nevertheless, there are numerous complex factors that influence real estate prices, which makes house price forecasting remain a classic and challenging problem in the field of data analysis. The development of data mining and machine learning has greatly facilitated the analysis and extraction of useful information from complex data sets and the building of models to make predictions. In this study, a stacking-based ensemble model is proposed to identify potential links between property prices and various factors so that the more accurate prediction of property prices can be made. Some base predictive models, including linear regression, support vector regression, ridge regression, least absolute shrinkage and selection operator, machine language programs, random forest regression, and gradient boosting regression are trained to individually predict the estate price in the experiment. Then, the stacking-based ensemble model is obtained by integrating competent base predictive models and optimized using Grid search. The experimental outcomes indicate that the proposed model is superior to base predictive models and can be more accurate in predicting house prices.
VL  - 7
IS  - 2
ER  -

Copy | Download