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| Format: | Recurso digital |
| Language: | English |
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2025
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| Online Access: | https://doi.org/10.5281/zenodo.17476038 |
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| _version_ | 1866902166450470912 |
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| author | Tahir, Touseef |
| author_facet | Tahir, Touseef |
| contents | <p>The price prediction is critical for making better decisions when buying or selling a real estate. Currently, there is a lack of data-driven price prediction studies on Pakistani real estate market, which publicly share their datasets and the sources of data collection. In addition, mobile and webapplications of Pakistani electronic markets are also missing the data-driven real estate price prediction. This study collected data of 50 thousand real estate properties of Lahore between 2020 and 2024. The data collected from oldest and most used mobile and web-application. The regression-based machine learning (ML) and deep learning (DL) models are trained on original values of dataset and transformed values (using log and z-score) and subsets of the dataset. The ensemble ML model (i.e., Extra Trees) trained on a subset of features that are selected using mutual information performed the best with evaluation measures of R2 = 0.96, RMSE = 0.09, MAE = 0.05, and MAPE =0.008. The DL model (i.e., Bi-LSTM) trained on logged transformed complete dataset achieved best results based on evaluation measures i.e. R2 = 0.89, RMSE = 0.14, MAE=0.09, MAPE=0.01. The log transformation of feature values helped to achieve best performances of both ML and DL models.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17476038 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Exploring Data-driven Real Estate Price Prediction in a Developing Country: The Case of Pakistan Tahir, Touseef <p>The price prediction is critical for making better decisions when buying or selling a real estate. Currently, there is a lack of data-driven price prediction studies on Pakistani real estate market, which publicly share their datasets and the sources of data collection. In addition, mobile and webapplications of Pakistani electronic markets are also missing the data-driven real estate price prediction. This study collected data of 50 thousand real estate properties of Lahore between 2020 and 2024. The data collected from oldest and most used mobile and web-application. The regression-based machine learning (ML) and deep learning (DL) models are trained on original values of dataset and transformed values (using log and z-score) and subsets of the dataset. The ensemble ML model (i.e., Extra Trees) trained on a subset of features that are selected using mutual information performed the best with evaluation measures of R2 = 0.96, RMSE = 0.09, MAE = 0.05, and MAPE =0.008. The DL model (i.e., Bi-LSTM) trained on logged transformed complete dataset achieved best results based on evaluation measures i.e. R2 = 0.89, RMSE = 0.14, MAE=0.09, MAPE=0.01. The log transformation of feature values helped to achieve best performances of both ML and DL models.</p> |
| title | Exploring Data-driven Real Estate Price Prediction in a Developing Country: The Case of Pakistan |
| url | https://doi.org/10.5281/zenodo.17476038 |