Information Extraction (IE) is a key task in Natural Language Processing (NLP) that transforms unstructured text into structured data. This study compares human annotation, rule-based systems, and Large Language Models (LLMs) for domain-specific IE, focusing on real estate auction documents. We assess each method in terms of accuracy, scalability, and cost-efficiency, highlighting the associated trade-offs. Our findings provide valuable insights into the effectiveness of using LLMs for the considered task and, more broadly, offer guidance on how organizations can balance automation, maintainability, and performance when selecting the most suitable IE solution.