literature review on house price prediction

Effective House Price Prediction Using Machine Learning

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• Jincheng Zhou   ORCID: orcid.org/0000-0002-1995-4002 12 ,
• Tao Hai   ORCID: orcid.org/0000-0002-6156-1974 12 , 14 ,
• Ezinne C. Maxwell-Chigozie   ORCID: orcid.org/0000-0002-0422-1359 13 ,
• Afolake Adedayo   ORCID: orcid.org/0000-0002-4057-3861 13 ,
• Ying Chen 14 ,
• Celestine Iwendi   ORCID: orcid.org/0000-0003-4350-3911 13 &
• Zakaria Boulouard   ORCID: orcid.org/0000-0002-4891-3760 15  

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In recent times, there have been a surge in the housing business, such that prediction of houses is of utmost important both for the seller and the potential buyer. This has been influenced by several key indices. Many approaches have been used to tackle the issue of predicting house prices to help the house owners and real estate agents maximise their profit while the prospective buyers make better informed decision. This study focuses on building an effective model for the prediction of house prices. Since price is a continuous variable, it was expedient we used regression models. Some regression models like linear regression, Random Forest regressor (RF), Extreme Gradient Boosting Regressor (XGBoost), Support Vector Machine (SVM) regressor, K-Nearest Neighbor (KNN) and Linear regression were employed. The result showed that Random Forest Regressor showed a superior performance having an R2 score of 99.97% while SVM regressor performed poorly with an R2 score of −4.11%. The result proved that Random Forest regressor as an effective machine learning model to predicting house prices.

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Acknowledgements

This work was supported by Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education of Guizhou University (GZUAMT2022KF[07]), the National Natural Science Foundation of China (No.61862051), the Science and Technology Foundation of Guizhou Province (No.[2019]1299, No.ZK[2022]449), the Top-notch Talent Program of Guizhou province (No.KY[2018]080), the Natural Science Foundation of Education of Guizhou province(No.[2019]203) and the Funds of Qiannan Normal University for Nationalities (No. qnsy2019rc09). The Educational Department of Guizhou under Grant NO. KY[2019]067.

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School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou, 558000, China

Jincheng Zhou & Tao Hai

School of Creative Technologies, University of Bolton, Bolton, BL3 5AB, UK

Ezinne C. Maxwell-Chigozie, Afolake Adedayo & Celestine Iwendi

School of Information and Artificial Intelligence, Nanchang Institute of Science and Technology, Nanchang, China

Tao Hai & Ying Chen

LIM, Hassan II University of Casablanca, Casablanca, Morocco

Zakaria Boulouard

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Celestine Iwendi

Hassan II University Casablanca, El Mansouria, Morocco

Department of Information Systems, Comenius University, Bratislava, Slovakia

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Zhou, J. et al. (2023). Effective House Price Prediction Using Machine Learning. In: Iwendi, C., Boulouard, Z., Kryvinska, N. (eds) Proceedings of ICACTCE'23 — The International Conference on Advances in Communication Technology and Computer Engineering. ICACTCE 2023. Lecture Notes in Networks and Systems, vol 735. Springer, Cham. https://doi.org/10.1007/978-3-031-37164-6_32

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• DOI: 10.3390/ijgi12050200
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A Survey of Methods and Input Data Types for House Price Prediction

• M. Geerts , S. V. Broucke , Jochen De Weerdt
• Published in ISPRS Int. J. Geo Inf. 14 May 2023
• ISPRS Int. J. Geo Inf.

7 Citations

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Housing price prediction using machine learning algorithms in covid-19 times.

1. Introduction

1.1. house prices and machine learning, 1.2. house prices and covid-19, 2. materials and methods, 2.1. study area, information sources, and database, 2.2. descriptive statistics, 2.3. methodology, 3.1. model training and optimization, 3.2. model evaluation and selection.

• The difference in performance between the algorithms (in this case being minimal, varying between 0.9135 and 0.9192 (R² score));
• The need to select an algorithm that has no overfitting problems and generalizes well with unseen data (in this case, the xgbm and lgbm algorithms may be good candidates);
• The need to choose an algorithm with low prediction variability in the cross-validation process (low variance) (the gbr algorithm has had the lowest variability);
• The need to consider the necessary times for the training and optimization of the hyperparameters and whether they adapt to the project deadlines (in this case, the xgbm and lgbm algorithms are the best options);
• The need to consider the file sizes of the models required for deployment (in this case, the lgbm algorithm generates the smallest file and the rf and et algorithms generate the largest (77 to 112 times larger than the lgbm algorithm)).

3.3. Model Interpretation

4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest, abbreviations.

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Share and Cite

Mora-Garcia, R.-T.; Cespedes-Lopez, M.-F.; Perez-Sanchez, V.R. Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times. Land 2022 , 11 , 2100. https://doi.org/10.3390/land11112100

Mora-Garcia R-T, Cespedes-Lopez M-F, Perez-Sanchez VR. Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times. Land . 2022; 11(11):2100. https://doi.org/10.3390/land11112100

Mora-Garcia, Raul-Tomas, Maria-Francisca Cespedes-Lopez, and V. Raul Perez-Sanchez. 2022. "Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times" Land 11, no. 11: 2100. https://doi.org/10.3390/land11112100

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