Work

Using Large Language Models to Predict Neighbourhood Change

Year: 2025

Type: conference-paper

Abstract: This paper uses a large-language model (LLM) to read user-contributed text data – in this case property listings on Airbnb – and assign a score to indicate the degree to which the property’s neighbourhood is potentially undergoing gentrification. Preliminary results for a case study in Bristol are evaluated. These scores will ultimately be used as an input in a model of dynamic spatio-temporal neighbourhood change with the aims of (i) quantifying the impact that neighbourhood reputation has on historical house prices and (ii) predicting the emergence of new house price bubbles.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15231203

Authors: Malleson, Nick, Quang, Thanh Bui, Nguyen Thi Thuy, Hang, Hoang Huu, Phe, Kieu, Minh, Asher, Molly, Wang, Yiyu, Comber, Alexis

Institutions: University of Leeds, The Alan Turing Institute, Vietnam National University, Hanoi, VNU University of Science, VNU Vietnam Japan University, University of Auckland

Language: English

Topic: Housing Market and Economics

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDG: Sustainable cities and communities

Open Access status: green

OpenAlex ID: W6930881464

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