# Structured Data Extraction from Real Estate Documents using Clustering, Classification, and Large Language Models

arXiv: https://arxiv.org/abs/2607.06012 · HTML: https://mlsanity.com/paper/2607.06012/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 7 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://mlsanity.com/paper/2204.08387/) (2022)
- [A Survey of Deep Learning Approaches for OCR and Document Understanding](https://mlsanity.com/paper/2011.13534/) (2020)
- [ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images](https://mlsanity.com/paper/2306.03287/) (2023)
- [Utilizing Large Language Models for Information Extraction from Real Estate Transactions](https://mlsanity.com/paper/2404.18043/) (2024)
- [Hybrid OCR-LLM Framework for Enterprise-Scale Document Information Extraction Under Copy-heavy Task](https://mlsanity.com/paper/2510.10138/) (2025)

## Abstract (arXiv)

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.