Social Construction of Urban Space: Understanding Neighborhood Boundaries Using Rental Listings
This work addresses how urban space definitions are contested in real estate, though it is incremental in applying NLP to a known social science problem.
The researchers analyzed Chicago Craigslist rental listings from 2018-2024 to understand how neighborhood boundaries are socially constructed through language, revealing three patterns of mismatches between institutional boundaries and neighborhood claims in listings.
Rental listings offer a unique window into how urban space is socially constructed through language. We analyze Chicago Craigslist rental advertisements from 2018 to 2024 to examine how listing agents characterize neighborhoods, identifying mismatches between institutional boundaries and neighborhood claims. Through manual and large language model annotation, we classify unstructured listings from Craigslist according to their neighborhood. Geospatial analysis reveals three distinct patterns: properties with conflicting neighborhood designations due to competing spatial definitions, border properties with valid claims to adjacent neighborhoods, and ``reputation laundering" where listings claim association with distant, desirable neighborhoods. Through topic modeling, we identify patterns that correlate with spatial positioning: listings further from neighborhood centers emphasize different amenities than centrally-located units. Our findings demonstrate that natural language processing techniques can reveal how definitions of urban spaces are contested in ways that traditional methods overlook.