Prof. John Östh from Oslomet to Deliver a Lecture – 1 December
On 1 December, Prof. John Östh, a researcher from Oslo Metropolitan University (Oslomet), will deliver a lecture titled "Airbnb and hedonics, can Ai and Bullshiting show the way?" at the Faculty of Economic Sciences.
Prof. Östh is Head of Research for the Department for Built Environment at Oslomet. He also leads the Transport and Urban research and education team within the department. His research focuses on the intersections between spatial analysis, GIS, register data studies, accessibility and mobility studies as well as urban and population geographies. His recent publications are available on the on the following website: https://www.oslomet.no/en/about/employee/johnosth/.
The seminar will take place at 17:00 in room B111. Those interested in participating online are kindly asked to contact Dr Kateryna Zabarina: Y8r6E2_wRv[uW+%saxPhf9{]#[E3Ts}!If03Sd95wkj^1^o*h
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The Airbnb sector has experienced exponential growth over the past decade and has led to extensive research in fields such as hospitality sciences, urban geography, tourism economics, and information management. This paper contributes to quantitative research in the Airbnb sector by focusing on the integration of digital platform data at the neighborhood level. It explores innovative methodologies for analyzing urban attractiveness by combining insights from hedonic pricing models with large-scale digital data sourced through AI-based approaches. This novel framework compares user-based valuations of accommodations derived from hedonic pricing with subjective, AI-generated neighborhood descriptions, offering new perspectives on data quality and reliability in information systems. The study also critically examines the challenges of integrating AI-generated content in information science, referencing also ‘Garbage-in Garbage-out’ and ‘Bullshit-in Bullshit-out’ concepts. Employing a multi-scalar modeling approach, the research examines Airbnb pricing dynamics across several U.S. cities, starting with Manhattan (USA) as an illustrative case. A subsequent large-scale application to additional metropolitan areas utilizes a combination of hedonic price modeling, social media data, and AI-generated urban descriptions, including a Shapley decomposition analysis. This interdisciplinary integration provides actionable insights into neighborhood attractiveness and pricing mechanisms, while highlighting methodological and empirical contributions to the broader field of information management. By employing the relationship between AI-driven textual data and quantitative modeling, this research provides added value in analyzing urban information systems and their application to digital platforms.