Stylistic Signals in Professional and Amateur Language Used in C2C Markets

This study was conducted with the approval of the Research Ethics Committee of Corvinus University of Budapest. The ethics approval was granted under file number KRH/23/2024.

Abstract

In consumer‐to‐consumer (C2C) markets, individuals must decide whether to sell property directly or rely on professional intermediaries. While digital technologies increasingly enable direct transactions, the expertise and strategic communication skills of intermediaries continue to play an important role. Our objective in this research is to connect expertise theory with signaling theory by examining whether professionals employ stylistic elements in real estate listings that traditionally convey quality and trustworthiness, and how these differ from those used by amateurs. To address this, we introduce topic‐prevalent n‐grams to examine stylistic patterns across a large dataset of more than 300,000 real estate advertisements in the United States. Two consecutive studies apply natural language processing (NLP) techniques to uncover systematic differences in wording, tone, and rhetorical strategies between professional brokers and amateur homeowners. A third study, which involved a scenario‐based experiment with US homeowners, illustrates how potential buyers perceive and respond to the different linguistic styles. The results show that professional brokers use language strategically to convey quality signals and enhance buyer engagement, while amateur homeowners often rely on emotional expression and negative phrasing, which can increase trustworthiness but result in less favorable responses overall. These findings highlight the central role of language style as a signal of expertise, influencing trust, perceptions of quality, and ultimately buyer decision‐making. Beyond offering empirical evidence for the impact of stylistic variation, the study contributes a methodological innovation by demonstrating how topic‐prevalent n‐grams can be used as a powerful tool for text classification and for advancing research on language, expertise, and consumer behavior.