EconPapers    
Economics at your fingertips  
 

Real Estate Price Prediction

Rabia Naz ()
Additional contact information
Rabia Naz: Department of Software Engineering, University of Sargodha, Sargodha, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 3, 1031-1044

Abstract: Real estate price predictions are critical for stakeholders, including investors and developers, because they have a considerable impact on investment decisions and market stability. In order to fill in the shortcomings in earlier approaches, this work presents a novel methodology by utilizing Deep Learning(DL) and Machine Learning (ML) techniques to improve real estate price forecast accuracy. We used the "House Prices 2023 Dataset" from Kaggle, which contains 168,000 entries of Pakistani property data. Our methodology included extensive data preparation, feature engineering, and the use of various algorithms, including Linear Regression, Gradient Boosting, Random Forest, Convolutional Neural Networks (CNN), and K-Nearest Neighbors (KNN). The models were tested using MSE, RMSE, R-squared, and accuracy. KNN outperformed the other models, with a lower RMSE of 13.79 and a higher R-squared value of 0.85, indicating improved predictive accuracy. RF also produced impressive results, with an accuracy of 80%. Handling complicated feature interactions, guaranteeing model scalability, and controlling hardware resources were all challenges that suggested possibilities for future improvement. As a result, our research offers a solid foundation for raising forecasting accuracy in fluctuations in the market and emphasizes the possibility of utilizing ML approaches for better real estate price prediction.

Keywords: Real Estate; Machine Learning; Deep Learning; Market Dynamics; Investment Analysis (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journal.50sea.com/index.php/IJIST/article/view/951/1498 (application/pdf)
https://journal.50sea.com/index.php/IJIST/article/view/951 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:abq:ijist1:v:6:y:2024:i:3:p:1031-1044

Access Statistics for this article

International Journal of Innovations in Science & Technology is currently edited by Prof. Dr. Syed Amer Mahmood

More articles in International Journal of Innovations in Science & Technology from 50sea
Bibliographic data for series maintained by Iqra Nazeer ().

 
Page updated 2025-09-19
Handle: RePEc:abq:ijist1:v:6:y:2024:i:3:p:1031-1044