Leveraging Airbnb Data to Predict Evictions in the United States under Lack of Ground-Truth Labels

The lack of access to eviction records in many regions poses significant challenges to the effective and efficient implementation of eviction prevention and diversion programs across the USA. To help better identify in-need regions nationwide in the face of existing data access challenges, this study proposes a Machine Learning (ML) driven solution that leverages Airbnb data to monitor eviction status at various spatial resolutions. In particular, this paper first shows the possibility of predicting the eviction status from Airbnb data using various types of fully-supervised ML approaches. After establishing the value of Airbnb data for this task, we then develop an ML-driven solution to learn the eviction status from Airbnb data when ground-truth labels are not available in the training data. Experimental results demonstrate that our method achieves performance comparable to the best-performing fully supervised ML approach. Cross-region tests further examine the generalizability to areas not observed during model training. The code is available at https://github.com/maryam-tabar/Airbnb-Eviction 1 .