Exploring Data-driven Real Estate Price Prediction in a Developing Country: The Case of Pakistan

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.房地产价格预测对于房产买卖的科学决策至关重要。目前，针对巴基斯坦房地产市场、且公开共享数据集与数据采集来源的数据驱动型房价预测研究仍较为匮乏。此外，巴基斯坦电子市场的移动端与网页应用程序也未搭载数据驱动的房地产价格预测功能。本研究采集了2020年至2024年间拉合尔市5万条房地产交易数据，数据来源于当地历史最悠久、使用最广泛的移动端与网页应用程序。本研究基于数据集原始值、经对数（log）与z分数（z-score）转换后的数据，以及数据集子集，对基于回归的机器学习（Machine Learning, ML）与深度学习（Deep Learning, DL）模型开展训练。其中，基于互信息筛选特征子集训练得到的集成机器学习模型（即极端随机树，Extra Trees）表现最优，其评估指标为：决定系数R²=0.96、均方根误差RMSE=0.09、平均绝对误差MAE=0.05、平均绝对百分比误差MAPE=0.008。基于对数转换后的完整数据集训练得到的深度学习模型（即双向长短期记忆网络，Bi-LSTM）同样取得了优异结果，其评估指标为：R²=0.89、RMSE=0.14、MAE=0.09、MAPE=0.01。特征值的对数转换有助于提升两类机器学习与深度学习模型的整体性能。