Land Rate Prediction Using Computational Intelligence

Predicting land rates is an essential challenge in the real estate industry. Several deep learning algorithms have been applied to forecast land prices. These three algorithms forecast land values using a dataset of real estate properties. The dataset includes information such as location, surrounding retailers, a nearby train station, and the size of the property. We trained our models on the dataset and evaluated their performance using the accuracy. Deep belief networks beat both multi-layer perceptron and radial basis function networks in forecasting land values, according to our findings. This is owing to its capacity to manage complicated, structured datasets as well as its ability to automatically handle missing values and outliers. As a result, we advocate using deep belief networks for land price prediction jobs in the real estate business.