Bidirectional Transformer based on online Text-based information to Implement Convolutional Neural Network Model For Secure Business Investment

Abstract

Real estate investment decisions are critical for low-income people who have just one home as their life-time investment option. So during the COVID-19 pandemic, unemployment causes many homeowners with a low income to lose their homes because of two major factors: one, they could not pay their mortgages without a job, and second, their house could not be rented easily. Rent prediction in real-estate can guarantee the success of an investment. Online information from real estate websites plays a significant role in making a business decision to buy a home. This paper applies natural language processing models to introduce a new model for safe real estate investment based on online information. For the first time, we use a transfer learning model based on online information from various online resources to detect a profitable rental property. Bidirectional Encoder Representations from Transformers(BERT) are used to implement a semantic convolutional neural network model to predict real estate investment safety. This work introduces a new model for rent prediction based on the United States housing market. Our contribution is three-fold: (1) using natural language processing approach to use the semantics of online information on Airbnb, Zillow, Schools, Public transportation, and crime rate websites for rent prediction (2) We perform a comprehensive analysis of eager and lazy machine learning models as a traditional Machine learning models with our proposed new transfer learning model for rent prediction. (3) Creating a new public data set of semantic analysis for more than 5 million houses in the United States based on online information. This data set will be available for public research in natural language processing research for people analytic applications. This work introduces a new machine learning model to guarantee safe investment in the real estate market using a transfer learning approach based on online information.