A model for Real Estate Price Prediction using Multi-Level Stacking Ensemble Technique

Nnadozie, L. and Matthias, D. and Bennett, E.O. (2022) A model for Real Estate Price Prediction using Multi-Level Stacking Ensemble Technique. European Journal of Computer Science and Information Technology, 10 (3). pp. 33-45. ISSN 2054-0957 (Print), 2054-0965 (Online)

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Abstract

Recent research and economic publications have shown the impact of real estate investment on the over economy of Nigeria. It is therefore crucial to employ machine learning technique to predict the price for real estate properties. Real estate price analysis and prediction will assist in establishment of real estate policies and can also be used to aid real estate property stakeholders to come up with informative decisions without bias or prejudice. Thus, it is imperative to develop a model to improve the accuracy of real estate price prediction. The goal of this research is to develop a model using a multi-level stacking ensemble technique to predict price of real estate property. The dataset utilized for the study was collected from transactions done by real estate firms in Port Harcourt and it consist of a total of 1053 rows with twelve features. The base model used includes Random Forest(RF), Extreme Gradient Boosting Algorithm(XGBoost), Light Gradient Boosting Machine(LightGBM), Decision Tree regression and ElasticNet Regression. Various combinations of the base models were stacked using StackingCVRegressor. The final model was developed by combining the best performing stacked models and evaluated using R-Square, Mean Absolute Error(MAE), Root Mean Square Error(RMSE), Mean Square Error(MSE) and Training time. The proposed model outperformed the various individual base model with R-square of 0.985203, MSE of 0.013438, RMSE of 0.115923, MAE of 0.063411 and training time of 0.599398. The result show that multi-level stacking significant improve the accuracy of a model. Again, it was observed stacking improve the performance accuracy of a model at the cost of computational time. Stacking by using blending function for the proposed model significantly reduced the computational time for training the model to 0.599398 second when compared to using StackingCVRegressor with training time of 107.054931 seconds. Therefore, multi-level stacking ensemble technique can be employed to improve the predictive accuracy of a prediction model. Future work can be done by increasing the dataset and also increasing the number of features.

Item Type:	Article
Subjects:	T Technology > T Technology (General)
Depositing User:	Professor Mark T. Owen
Date Deposited:	28 May 2022 14:28
Last Modified:	28 May 2022 14:28
URI:	https://tudr.org/id/eprint/499

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