License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.16
URN: urn:nbn:de:0030-drops-189117
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18911/
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Brunila, Mikael ; Verma, Priyanka ; McKenzie, Grant

When Everything Is "Nearby": How Airbnb Listings in New York City Exaggerate Proximity (Short Paper)

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LIPIcs-GIScience-2023-16.pdf (3 MB)

Abstract

In recent years, the emergence and rapid growth of short-term rental (STR) markets has exerted considerable influence on real estate in most large cities across the world. Central location and transit access are two primary factors associated with the prevalence and expansion of STRs, including Airbnbs. Nevertheless, perhaps due to methodological challenges, no research has addressed how location and proximity are represented in the titles and descriptions of STRs. In this paper, we introduce a new methodological pipeline to extract spatial relations from text and show that expressions of distance in STR listings can indeed be quantified and measured against real-world distances. We then comparatively analyze Airbnb reviews (written by guests) and listings (written by hosts) from New York City in order to demonstrate systematically how listings exaggerate proximity compared to reviews. Moreover, we discover spatial patterns to these differences that warrant further investigation.

BibTeX - Entry

@InProceedings{brunila_et_al:LIPIcs.GIScience.2023.16,
  author =	{Brunila, Mikael and Verma, Priyanka and McKenzie, Grant},
  title =	{{When Everything Is "Nearby": How Airbnb Listings in New York City Exaggerate Proximity}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{16:1--16:8},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18911},
  URN =		{urn:nbn:de:0030-drops-189117},
  doi =		{10.4230/LIPIcs.GIScience.2023.16},
  annote =	{Keywords: spatial proximity, distance estimation, information extraction, named entity recognition, short-term rentals}
}

Keywords: spatial proximity, distance estimation, information extraction, named entity recognition, short-term rentals
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023

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