Semantic Convolutional Neural Network model for Safe Business Investment by Using BERT

Abstract

The real estate market creates one of the significant business domains for investors, but a wise investment in real estate is more important for low-income people who have just one home as their life-time investment. So during the economic recession, many homeowners lose their homes because of two major factors: one, they could not pay their mortgages, and second, the house could not be rented to cover the mortgages. So real estate investment can be secured if the house could be an excellent rental property. Information from real estate websites can be a rich source of knowledge essential to detect the house's potential as a rental property. This work uses a natural language processing approach to propose a new real estate investment model based on online textual information. For the first time, we apply a transfer learning approach based on multiple online resources to recognize the house as valuable rental property in the real estate market. Bidirectional attention models based on transformers (BERT) are used to extract features for semantic convolutional neural network models to secure real estate investment. This research has three main points: (1) using transformers to implement semantic CNN based on Airbnb, Zillow data (2) performance evaluation of traditional Machine learning models with our new transfer learning model for rent prediction. (3) a new public data set for more than 5 million houses in the U.S based on semantic information that can be used for real estate market research. This research offers a new model for safe investment in the real estate market based on the transfer learning approach.