Real-GPT: Efficiently Tailoring LLMs for Informed Decision-Making in the Real Estate Industry

In recent times, large language models (LLMs) such as ChatGPT and LLaMA have gained significant attention. These models demonstrate remarkable capability in solving complex tasks, drawing knowledge primarily from a generalized database rather than niche subject areas. Consequently, there has been a growing demand for domain-specific LLMs tailored to social and natural sciences, such as BioGPT or BloombergGPT. In this study, we present our own domain-specific LLM focused on real estate, based on the parameter-efficient finetuning technique known as Low-rank adaptation (LoRA) applied to the Mistral 7B model. To create a comprehensive finetuning dataset, we compiled a curated 21k self-instruction dataset sourced from 670 scientific papers, market research, scholarly articles and real estate books. To assess the efficacy of Real-GPT, we devised a set of ca. 5,000 multiple-choice questions to gauge the real estate knowledge of the models. Despite its notably compact size, our model outperforms other cutting-edge models. Consequently, our developed model not only showcases superior performance but also illustrates its capacity to facilitate investment decisions, interpret current market data, and potentially simplify property valuation processes. This development showcases the potential of LLMs to revolutionize the field of real estate analysis and decision-making.