Algorithmic Landlords, Market Competition, and the Influence Network of Price Decision

• In Event: 40860 - Section on Economic Sociology Roundtables
  In Refereed Roundtable (60 minute): Table 1. Algorithmic economies

Abstract

The growing adoption of algorithmic rent-setting tools by corporate landlords has reshaped the rental housing market, raising concerns about price inflation, market competition, and housing affordability. This study examines the role of algorithm-driven landlords in shaping rent price dynamics, investigating whether they act as price setters who initiate market-wide changes or merely follow broader trends. Using a comprehensive dataset of weekly rent observations, ownership records, and neighborhood characteristics in Milwaukee County from 2018 to 2023, we employ descriptive and network-based methodologies to analyze pricing behaviors and rent diffusion patterns. Our findings reveal that algorithmic landlords exhibit distinct pricing strategies, characterized by frequent, synchronized rent adjustments that contribute to market segmentation and price convergence. Leveraging Graph Attention Networks (GAT), we construct an influence network that captures the diffusion of rent changes, identifying key price-setting landlords and tracing the hierarchical spread of rent inflation across socioeconomic and racialized market segments. Our results suggest that algorithmic landlords play a central role in shaping rental market trends, accelerating rent homogenization and disproportionately impacting lower-income communities. This study provides critical insights into the implications of algorithm-driven pricing on market competition, housing inequality, and regulatory policy.

Authors

• Eunsung Yoon, Princeton University

• Sarah Johnson, Princeton University

• Carl E. Gershenson, Princeton University