Multi-level differentiation of short-term rental properties: A deep learning-based analysis of aesthetic design

Huihui Zhang, Florian J. Zach, Zheng Xiang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

Abstract

This study aims to test the effects of differentiation on short-term rental performance along the dimension of aesthetic design. Online platforms display listing cover photos as search results, thus making aesthetic design a key element of differentiation. We hypothesize opposite impacts in two geographical scopes, local- and city-level, which answers an important question in differentiation literature of whom to compare to. Based on the assumption that localized competition has asymmetric influences, we introduce competition intensity as moderator. Hypotheses are tested with 96,196 listings from April 2021 to March 2022 in the Texas Airbnb market. We quantify aesthetic design by probability distribution scores over four design styles predicted by a pre-trained machine learning model. This study identifies differentiation benefits at local-level but discounts at city-level. Furthermore, it shows market intensity strengthens benefits and mitigates discounts regardless of the geographic scope. Finally, implications for aesthetic design as a strategic tool are discussed.

Original languageEnglish
Article number104832
JournalTourism Management
Volume100
Early online dateSept 2023
DOIs
Publication statusPublished - Feb 2024
Externally publishedYes

Keywords

  • Aesthetic design
  • Conformity
  • Deep learning
  • Differentiation
  • Localized competition
  • Short-term rental

ASJC Scopus subject areas

  • Development
  • Transportation
  • Tourism, Leisure and Hospitality Management
  • Strategy and Management

Fingerprint

Dive into the research topics of 'Multi-level differentiation of short-term rental properties: A deep learning-based analysis of aesthetic design'. Together they form a unique fingerprint.

Cite this