Well-connected real estate agents sell for more: network theory on 40,000 Atlanta transactions (Journal of Real Estate Research, 2025)

Real estate agents trade with each other. A listing agent on one deal is a buyer's agent on the next, and over thousands of transactions these pairings form a network. This paper, with Xiaojin Sun in the Journal of Real Estate Research (2025), applies network theory to that structure and asks whether an agent's position in it affects the outcomes of the transactions they handle.

Data and measure

The data cover more than 40,000 sales in Atlanta, Georgia. From the record of which agents transacted with which, we construct measures of each agent's connectedness in the trading network and relate them to sale prices and marketing time, controlling for property and location characteristics.

Results

Agents with stronger networks achieve higher sale prices: a one-standard-deviation increase in connectedness is associated with a 2.61 percent higher price. The channel appears to be information rather than bargaining power. Well-connected agents sell unique properties faster and price them more accurately, which is what one would expect if a larger network means better knowledge of likely buyers, comparable sales, and the true value of hard-to-compare homes.

Interpretation

The finding is a market-efficiency result as much as an agent-performance result. Unique properties are where information is scarcest, and that is where connected agents add the most. Networks move information through a market that lacks centralized price discovery, and the agents at the center of the network are the conduits.

Recognition

The paper received an American Real Estate Society Best Article Award. It connects to my work with Chris Cunningham and Kristopher Gerardi on estimating agent value-added, and to the broader question of how intermediaries process soft information in housing markets.

@article{shen2025more,
  author = {Lily Shen and Xiaojin Sun},
  title = {The More, the Merrier: Agent Connectedness in Real Estate Trading Networks},
  journal = {Journal of Real Estate Research},
  year = {2025},
  volume = {47},
  number = {2},
  pages = {149--169},
  doi = {10.1080/08965803.2025.2466957}
}

@misc{shen2026post_well_connected,
  author = {Lily Shen},
  title = {Well-connected real estate agents sell for more: network theory on 40,000 Atlanta transactions (Journal of Real Estate Research, 2025)},
  howpublished = {AI in Real Estate Finance, https://lilyshenai.github.io},
  year = {2026},
  month = {oct},
  url = {https://lilyshenai.github.io/posts/real-estate-agent-networks-connectedness/},
  note = {Accessed 2026-10-24}
}