The eviction of tenants is a pressing problem, which is prevalent among low-income renters in the USA, and has devastating consequences. Despite the presence of various measures to combat evictions, identifying high-need regions and tenant groups is highly challenging in many regions due to a lack of access to eviction records (partly because of some infrastructural/policy constraints). In response to this information gap, this paper proposes a solution driven by Machine Learning (ML) to monitor eviction status at various spatial resolutions using Airbnb data when ground-truth eviction data is inaccessible. In particular, we begin by demonstrating the potential of utilizing Airbnb data to build ML-driven methods for distinguishing different neighborhoods across different spatial resolutions with respect to eviction status. We then proceed to develop an ML model capable of learning eviction status levels from Airbnb data, even in the absence of ground-truth labels. Empirical evidence is presented, showcasing the model's performance on par with several robust fully-supervised ML models that had access to ground-truth labels during training. Finally, we conduct a set of cross-region tests to comprehensively study the generalizability of the achieved performance across various unseen regions in the USA that were not used during model training. The code of this project can be accessed via https://github.com/maryam-tabar/Airbnb-Eviction.