Elamur Research⌕
☰
home / papers / 2026 / Social Construction of Urban Space: Using LLMs to Identify Neighborhood Boundaries From Craigslist Ads

Social Construction of Urban Space: Using LLMs to Identify Neighborhood Boundaries From Craigslist Ads

Paper · 2026 · Adam Visokay, Ruth Bagley, Chris Hess et al.

Summary

The paper investigates how real-estate advertisers reshape the map of Chicago through the neighborhood names they attach to rental listings. The authors argue that official boundaries miss how neighborhoods are perceived and marketed. They used daily-scraped Craigslist rental ads (December 2018 to June 2024) and compared two ways of extracting the neighborhood each ad claims for its unit, as opposed to neighborhoods it merely names: an expert-designed string-matching baseline and zero-shot LLM annotation. Both were scored against 200 hand-labeled listings. Using the LLM labels, they estimated a "social center" for each neighborhood and examined, with topic modeling, how ad wording changes with distance from that center. Their geospatial analysis found three kinds of mismatch:

  • competing boundary definitions,
  • legitimate claims by units on borders,
  • "reputation laundering" toward distant, prestigious areas.

Ads located farther from a neighborhood's social center contain less place-specific language and more boilerplate. LLMs gave only a modest accuracy gain over the expert baseline but made large-scale analysis practical. This offers urban sociology a scalable way to observe contested space.

Key points

  • Data: Chicago Craigslist rental ads were collected with the Helena web scraper from December 2018 to June 2024. The largest collection gap ran from August to early October 2019. The data are not released, in line with Craigslist's terms of service.
  • Evaluation: The authors compared a zero-shot LLM against string matching on a gold set of 200 author-annotated listings. When assigning gold labels, the listing's title takes highest priority, followed by its body text and finally its neighborhood field. Annotation cost was kept below $100.
  • Reference maps: Ads were compared with Chicago's 77 official community areas and with Zillow neighborhood boundaries. The authors acknowledge that no authoritative neighborhood catalog exists.
  • Three mismatch patterns:
    • conflicting spatial definitions
    • valid claims by border units to adjacent neighborhoods
    • "reputation laundering"
  • Language and distance: Across distance deciles from each LLM-derived social center, place-based language declines while generic text grows. Peripheral units also emphasize different amenities than central ones.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of WashingtonSeattle · United States353 works · 200,043 citations🎓University of California, BerkeleyBerkeley · United States175 works · 227,440 citations🎓University of Illinois ChicagoChicago · United States61 works · 20,107 citations🎓Northwestern UniversityEvanston · United States41 works · 2,194 citations🎓Kennesaw State UniversityKennesaw · United States4 works · 2 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Social Sciences · 1,772 worksSociology and Political Science · 577 worksTransportation · 6 worksUrban Studies · 1 work

Cite

Cite as Adam Visokay, Ruth Bagley, Chris Hess, Ian Kennedy, Kyle Crowder, Rob Voigt and Denis Peskoff. 2026. Social Construction of Urban Space: Using LLMs to Identify Neighborhood Boundaries From Craigslist Ads. Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.nlpcss-1.18

@inproceedings{visokay2026social,
  title = {{Social Construction of Urban Space: Using LLMs to Identify Neighborhood Boundaries From Craigslist Ads}},
  author = {Adam Visokay, Ruth Bagley, Chris Hess, Ian Kennedy, Kyle Crowder, Rob Voigt and Denis Peskoff},
  booktitle = {Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science},
  year = {2026},
  doi = {10.18653/v1/2026.nlpcss-1.18},
  url = {https://doi.org/10.18653/v1/2026.nlpcss-1.18}
}

References

53 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Kenan Alkiek, Anna Wegmann, Jian Zhu, and David Jurgens. 2025. Neurobiber: Fast and interpretable stylistic feature extraction. arXiv preprint arXiv:2502.18590. — [arXiv:2502.18590](https://arxiv.org/abs/2502.18590) find
  2. Abdulkareem Alsudais. 2020. Incorrect data in the widely used inside airbnb dataset. Decision Support Systems, 141:113453. find
  3. Taylor Berg-Kirkpatrick, David Burkett, and Dan Klein. 2012. An empirical investigation of statistical significance in nlp. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 995–1005. find
  4. Max Besbris, Ariela Schachter, and John Kuk. 2021. The unequal availability of rental housing information across neighborhoods. Demography, 58(4):1197– 1221. find
  5. Douglas Biber. 1991. Variation across speech and writing. Cambridge University Press. find
  6. Abeba Birhane, Atoosa Kasirzadeh, David Leslie, and Sandra Wachter. 2023. Science in the age of large language models. Nature Reviews Physics, pages 1– 4. find
  7. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993–1022.
  8. Geoff Boeing and Paul Waddell. 2017. New insights into rental housing markets across the united states: Web scraping and analyzing craigslist rental listings. Journal of Planning Education and Research, 37(4):457– 476. find
  9. Sarah E. Chasins, Maria Mueller, and Rastislav Bodik. 2018. Rousillon: Scraping distributed hierarchical web data. In Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology, pages 963–975. find
  10. Eric Chyn and Lawrence F. Katz. 2021. Neighborhoods matter: Assessing the evidence for place effects. Journal of Economic Perspectives, 35(4):197–222. find
  11. Megan Evans and Barrett A. Lee. 2020. Neighborhood reputations as symbolic and stratifying mechanisms in the urban hierarchy. Sociology Compass, 14(10):1– 15. find
  12. George Galster and Erin Godfrey. 2005. By words and deeds: Racial steering by real estate agents in the us in 2000. Journal of the American Planning Association, 71(3):251–268. find
  13. Edward L. Glaeser, Michael Luca, and Erica Moszkowski. 2020. Gentrification and neighborhood change: Evidence from yelp. National Bureau of Economic Research. find
  14. Igal Hendel, Aviv Nevo, and François Ortalo-Magné. 2009. The relative performance of real estate marketing platforms: Mls versus fsbomadison. com. American Economic Review, 99(5):1878–1898. find
  15. Chris Hess and Sarah E. Chasins. 2022. Informing housing policy through web automation: Lessons for designing programming tools for domain experts. In CHI Conference on Human Factors in Computing Systems Extended Abstracts, pages 1–9. find
  16. Chris Hess, Rebecca J. Walter, Ian Kennedy, Arthur Acolin, Alex Ramiller, and Kyle Crowder. 2023. Segmented information, segregated outcomes: Housing affordability and neighborhood representation on a voucher-focused online housing platform and three mainstream alternatives. Housing Policy Debate, 3… find
  17. Randolph Hohle. 2023. Rusty gardens: stigma and the making of a new place reputation in buffalo, new york. American Journal of Cultural Sociology, 11(2):193–219. find
  18. Matthew Honnibal and Ines Montani. 2017. spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. To appear. find
  19. Geoff Boeing, Max Besbris, Ariela Schachter, and John Kuk. 2021. Housing search in the age of big data: smarter cities or the same old blind spots? Housing Policy Debate, 31(1):112–126. find
  20. Biqing Huang and Ronald Rutherford. 2007. Who you going to call? performance of realtors and non-realtors in a mls setting. The Journal of Real Estate Finance and Economics, 35:77–93. find
  21. Dallas Card, Peter Henderson, Urvashi Khandelwal, Robin Jia, Kyle Mahowald, and Dan Jurafsky. 2020. With little power comes great responsibility. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9263–9274, Online. Association for Computational… find
  22. Jackelyn Hwang and Robert J. Sampson. 2014. Divergent pathways of gentrification: Racial inequality and the social order of renewal in chicago neighborhoods. American Sociological Review, 79(4):726–751. find
  23. Ronda Kaysen. 2024. Apartment rent renewal rates are rising. The New York Times. find
  24. Ian Kennedy, Chris Hess, Amandalynne Paullada, and Sarah Chasins. 2020. Racialized discourse in seattle rental ad texts. Social Forces, 99(4):1432–1456. find
  25. Richard Kirk. 2024. Legitimising displacement: Academic discourse, territorial stigmatisation and gentrification. Urban Studies, 61(13):2492–2512. find
  26. Elizabeth Korver-Glenn and Sarah Mayorga. 2024. A good reputation: how residents fight for an American barrio. University of Chicago Press. find
  27. Maria Krysan and Kyle Crowder. 2017. Cycle of Segregation: Social Processes and Residential Stratification. Russell Sage Foundation, New York. find
  28. Agneta Kullberg, Toomas Timpka, Tommy Svensson, Nadine Karlsson, and Kent Lindqvist. 2010. Does the perceived neighborhood reputation contribute to neighborhood differences in social trust and residential wellbeing? Journal of Community Psychology, 38(5):591–606. find
  29. Anita Minh, Nazeem Muhajarine, Magdalena Janus, Marni Brownell, and Martin Guhn. 2017. A review of neighborhood effects and early child development: How, where, and for whom, do neighborhoods matter? Health & Place, 46:155–174. find
  30. Burt L. Monroe, Michael P. Colaresi, and Kevin M. Quinn. 2017. Fightin’ words: Lexical feature selection and evaluation for identifying the content of political conflict. Political Analysis, 16(4):372–403. find
  31. David Newman, Arthur Asuncion, Padhraic Smyth, and Max Welling. 2009. Distributed algorithms for topic models. Journal of Machine Learning Research, 10:1801–1828. find
  32. NYC Office of Special Enforcement. 2023. Short-term rental registration and verification by booking services. find
  33. Gabriel Otero, Quentin Ramond, María Luisa Méndez, Rafael Carranza, Felipe Link, and Javier Ruiz-Tagle. 2024. The damages of stigma, the benefits of prestige: Examining the consequences of perceived residential reputations on neighbourhood attachment. Urban Studies, 61(3):462–494. find
  34. Jeffrey Nathaniel Parker. 2019. That Kind of Neighborhood: Creating, Contesting, and Commodifying Place Reputation. Ph.D. thesis, The University of Chicago. find
  35. M. Permentier, G. Bolt, and M. Van Ham. 2011. Determinants of neighbourhood satisfaction and perception of neighbourhood reputation. Urban Studies, 48(5):977–996. find
  36. Kirsten Robertson and Antony Doig. 2010a. An empirical investigation of variations in real-estate marketing language over a market cycle. Housing, Theory and Society, 27(2):178–189. find
  37. Kirsten Robertson and Antony Doig. 2010b. An empirical investigation of variations in real-estate marketing language over a market cycle. Housing, Theory and Society, 27(2):178–189. find
  38. Robert J. Sampson, Jeffrey D. Morenoff, and Thomas Gannon-Rowley. 2002. Assessing "neighborhood effects": Social processes and new directions in research. Annual Review of Sociology, 28(1):443–478. find
  39. Ariela Schachter, John Kuk, Max Besbris, and Lydia Ho. 2024. What’s in a name? place misrepresentation and neighbourhood stigma in the online rental market. Urban Studies, 61(16):3050–3068. find
  40. Youngme Seo, JongHo Im, and Brian Mikelbank. 2020. Does the written word matter? the role of uncovering and utilizing information from written comments in housing ads. Journal of Housing Research, 29(2):133– 155. find
  41. Patrick Sharkey and Jacob W. Faber. 2014. Where, when, why, and for whom do residential contexts matter? moving away from the dichotomous understanding of neighborhood effects. Annual Review of Sociology, 40(1):559–579. find
  42. Mahesh Somashekhar, Chris Hess, Ian Kennedy, and Kyle Crowder. 2024. How do real estate actors advertise in mixed-income neighborhoods? the importance of home security. Socius, 10:23780231241260253. find
  43. Remy Stewart, Chris Hess, Ian Kennedy, and Kyle Crowder. 2023. Move-in fees as a residential sorting mechanism within online rental markets. Cityscape (Washington, DC), 25(1):239. find
  44. Forrest Stuart, Charles R. Collins, Bocar Wade, Rebecca D. Gleit, and Caylin Louis Moore. 2024. Where do neighbourhood reputations come from? analysing chicago community areas using a systematic neighbourhood reputation score, 1985–2020. Urban Studies, pages 00420980241297088. find
  45. Emma Tran, Kim Blankenship, Shannon Whittaker, Alana Rosenberg, Penelope Schlesinger, Trace Kershaw, and Danya Keene. 2020. My neighborhood has a good reputation: Associations between spatial stigma and health. Health & Place, 64:102392. find
  46. United States Census Bureau, the. 2023. American community survey 5-year data (2009-2021). find
  47. Mohammadzaman Zamani and H. Andrew Schwartz. 2017. Using Twitter language to predict the real estate market. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 28–33, Valencia, Spain. Association for Computati… find
  48. Sarah Zelner. 2015. The perpetuation of neighborhood reputation: An interactionist approach. Symbolic Interaction, 38(4):575–593. find
  49. Caleb Ziems, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, and Diyi Yang. 2024. Can large language models transform computational social science? Computational Linguistics, 50(1):237–291.
  50. Livia Hollenstein and Ross Purves. 2010. Exploring place through user-generated content: Using Flickr to describe city cores. Journal of Spatial Information Science, 1:1–18. find
  51. Anon Plangprasopchok and Kristina Lerman. 2009. Constructing folksonomies from user-specified relations on Flickr. In Proceedings of the 18th International Conference on World Wide Web, pages 781– 790. find
  52. Mikael Brunila, Jack LaViolette, Sky CH-Wang, Priyanka Verma, Clara Féré, and Grant McKenzie. 2023. Toward a Critical Toponymy Framework for Named Entity Recognition: A Case Study of Airbnb in New York City. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, p… find
  53. Tuomo Hiippala, Anna Hausmann, Henrikki Tenkanen, and Tuuli Toivonen. 2019. Exploring the Linguistic Landscape of Geotagged Social Media Content in Urban Environments. Digital Scholarship in the Humanities, 34(2):290–309. find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/visokay-2026-social-construction-urban-space-llms/