AI Realtor: Towards Grounded Persuasive Language Generation for Automated Copywriting

TL;DR

This paper presents an agentic framework using large language models to generate grounded, persuasive, and factual real estate marketing copy aligned with user preferences, outperforming human-written descriptions.

Contribution

Introduces a novel agentic framework with modules for grounding, personalization, and factual marketing, advancing automated, targeted real estate copywriting.

Findings

Generated descriptions preferred over human-written ones in user studies.

The approach maintains factual accuracy while enhancing persuasiveness.

Systematic experiments validate the effectiveness of the framework.

Abstract

This paper develops an agentic framework that employs large language models (LLMs) for grounded persuasive language generation in automated copywriting, with real estate marketing as a focal application. Our method is designed to align the generated content with user preferences while highlighting useful factual attributes. This agent consists of three key modules: (1) Grounding Module, mimicking expert human behavior to predict marketable features; (2) Personalization Module, aligning content with user preferences; (3) Marketing Module, ensuring factual accuracy and the inclusion of localized features. We conduct systematic human-subject experiments in the domain of real estate marketing, with a focus group of potential house buyers. The results demonstrate that marketing descriptions generated by our approach are preferred over those written by human experts by a clear margin while maintaining the same level of factual accuracy. Our findings suggest a promising agentic approach to automate large-scale targeted copywriting while ensuring factuality of content generation.