Machine learning-powered automation for real estate document processing: a case study

Purpose We demonstrate the practical application of machine learning (ML) techniques in document processing, addressing the increasing need for digitalization in the real estate industry and beyond. Our focus lies on identifying efficient algorithms for extracting individual documents from multi-page PDF files. Through the implementation of these algorithms, organizations can accelerate the digitization of paper-based files on a large scale, eliminating the laborious process of one-by- one scanning. Additionally, we showcase ML-powered methods for automating the classification of both digital and digitized documents, thereby simplifying the categorization process. Design/methodology/approach We compare two segmentation models that are presented in this paper to analyze the individual pages within a bulk scan, identifying the starting and ending points of each document contained in the PDF. This process involves extracting relevant features from both the textual content and page design elements, such as fonts, layouts and existing page numbers. By leveraging these features, the algorithm accurately splits multi-document PDFs into their respective components. An outlook is provided with a classification code that effectively categorizes the segmented documents into different real estate document classes. Findings The case study provides an overview of different ML methods employed in the development of these models while also evaluating their performance across various conditions. As a result, it offers insight into solutions and lessons learned for processing documents in real estate on a case-by-case basis. The findings presented in this study lay the groundwork for addressing this prevalent problem. The methods, for which we provide the code as open source, establish a solid foundation for expediting real estate document processing, enabling a seamless transition from scanning or inbox management to digital storage, ultimately facilitating machine-based information extraction. Practical implications The process of digitally managing documents in the real estate industry can be a daunting task, particularly due to the substantial volume of documents involved, whether they are paper-based, digitized or in digital formats. Our approach aims to streamline this often tedious and time-consuming process by offering two models as simplified solutions that encourage companies to embrace much-needed digitization. The methods we present in this context are crucial for digitizing all facets of real estate management, offering significant potential in advancing PropTech business cases. The open-source codes can be trained further by researchers and practitioners with access to large volumes of documents. Originality/value This study illustrates effective methods for processing paper-based, digitized and digital files, along with tailored ML models designed to enhance these methods, particularly within the real estate sector. The methods are showcased on two datasets, and lessons learned are discussed.