Property Price Prediction

Computer Science Engineering

Predicting a price variance rather than a specific value is more realistic and attractive in many real-world applications. Price prediction can be thought of as a classification issue in this situation. However, the House Price Index (HPI) is a common tool for estimating the inconsistencies of house prices. Since housing prices are closely correlated with other factors such as location, city, and population, predicting individual housing prices needs information other than HPI. The HPI is a repeat sale index that tracks average price shifts in repeat transactions or refinancing of the same assets. Therefore, HPI is ineffective at predicting the price of a single house because it is a rough predictor based on all transactions. This study explores the use of Random Forest machine learning technique for house price prediction. Now, the urbanization process of India is accelerating. Urban land price is of great interest for the government to make reasonable policies and keep the healthy development of land market. Based on the data source of dynamic monitoring system and the statistical yearbook of Bengaluru city, we identified the related factors influencing the comprehensive land price of Bengaluru. Firstly, we identified the nine strongly correlative factors of land price of Bengaluru city. Secondly, we derived the land price for prediction. Thirdly, we compared the predicted land price with the real land price in the period of 2014–2015. Finally, the comprehensive land price of Bengaluru in the period of 2022–2023 was forecasted with random forests and neural network, respectively. According to the results, we found that the error of the random forests is much smaller than that of neural network. Thus, we utilized random forests to predict the comprehensive land price of Bengaluru in the period of 2022–2023. Our results showed that the comprehensive land price of Bengaluru in the next two years would be stable and rises slightly.

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