🤖 AI Summary
This study addresses the lack of efficient, automated methods for structuring heterogeneous real estate questionnaire documents. To this end, the authors propose an end-to-end information extraction framework that first categorizes documents into structural types using K-Means clustering and text classification. Subsequently, it leverages the DeepSeek-R1 large language model enhanced with prompt engineering to accurately extract 35 predefined attributes from complex document formats—including checkboxes and scanned images—and outputs them as structured JSON. Evaluated on a dataset of 2,781 documents, the method produced 2,766 unique property records. Downstream validation demonstrated a Jaccard similarity of 0.82, marking the first high-precision, scalable solution for structured information extraction from such challenging real estate documentation.
📝 Abstract
Real estate property listings expose structured metadata through the API. Still, the richest property-level information (i.e., legal status, structural condition, utility supplies, heating systems) sits in attached questionnaire documents that no automated system currently processes at scale. These documents are heterogeneous. Some are digitally generated with selectable text, others are scanned physical forms. There are even more complex layouts that contain checkbox annotations that defeat conventional text extraction. In this paper, we present an end-to-end pipeline for acquiring, classifying, and extracting structured data from selectable text documents. The pipeline was applied to 3965 questionnaire documents collected from a live property platform via reverse-engineered REST APIs. First, we classified each document into one of three structural categories (text_only, scanned, and special_char), then extracted 35 predefined property attributes from eligible documents using DeepSeek R1 as the Large Language Model, prompted to return a structured JSON object. All 2781 submitted documents were processed successfully, producing a final dataset of 2766 unique property records. Downstream validation confirmed the data quality. Cosine similarity matching achieves a Jaccard consistency score of 0.82, and K-Means clustering produces interpretable market segments with a silhouette score of 0.2088. Results show that the proposed extraction from each property document is both feasible and reliable at this scale.