Age Estimation of Real Estate Appearance Data using Divided Images and Deep Learning

Abstract

In recent years, a driving investment explosion in real estate tech, that combines real estate and information technology, has been recorded. Many studies are using the benefits of real estate big data to investigate the utilization of artificial intelligence for the application of image processing and deep learning in the real estate sector. Age is an indispensable factor in determining the value of a property. Furthermore, earthquake resistance, equipment, depreciation cost, the extent of deterioration, and the building price are some of the other factors to consider when estimating the value of the property as these factors also vary with the passage of time. As the age of a building is not disclosed because of the confidentiality of property taxes, it is possible to collect this information from the registry. However, this process is time-consuming and costly when looking for multiple properties. In this study, the input images divided, we estimated the age of a building from its appearance using bagging, which is an ensemble learning method. Also, we created classification model using the convolutional neural network technique, which is a deep learning method that recognizes objects from images. The experimental result shows accuracy rate of divided images and original images have similar performance. In conclusion, proposed method of my study is not inferior compared to estimation of using deep neaural network.