Leveraging Machine Learning to Study Immigrant Renting via Online Advertisements in Vancouver

Project Lead

Sub-Theme: Housing and Migration

This sub-theme explores the impact of the rapid increase in immigration on the housing market in various regions of Canada and the challenges that immigrants, refugees and international students encounter in securing housing.

Objective

Mirroring global trends, a growing share of immigrants to Canada are renting, but the evidence landscape on immigrant renting is patchy at best. In general, rental markets present unique research challenges, making it difficult to get an accurate reading of rental housing activity. Recent studies have leveraged online rental listings for new insights, but the vast majority consider dominant-language platforms only. In places with substantial immigrant communities, such as many Canadian cities, this narrow focus is highly problematic.

To address this gap, this project will examine Vancouver’s rental market using online listings from two platforms: one English, and one Chinese.

Research Questions

Does information supply differ across platforms, and to the extent that it does, how do differences online compare to actual settlement patterns?
Do listings on the Chinese language platform cater to recent immigrants to Canada?

Methodology

The project employs a combination of computational methods (including deep learning), qualitative content analysis, quantitative text analysis, statistical analysis, and geospatial analysis.

Status

This project is active and currently in the data analysis phase.

Outcomes

Past events and presentations:

“Where do migrants rent in Vancouver: Mapping rental listings on the Chinese Craigslist”, presented by Julia Harten, School of Community and Regional Planning, University of British Columbia, Vancouver, Canada, May 1, 2025