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Recent advancements in large language models have spurred significant developments in Time Series Foundation Models (TSFMs). These models claim great promise in performing zero-shot forecasting without the need for specific training, leveraging the extensive "corpus" of time-series data they have been trained on. Forecasting is crucial in predictive building analytics, presenting substantial untapped potential for TSFMS in this domain. However, time-series data are often domain-specific and governed by diverse factors such as deployment environments, sensor characteristics, sampling rate, and data resolution, which complicates generalizability of these models across different contexts. Thus, while language models benefit from the relative uniformity of text data, TSFMs face challenges in learning from heterogeneous and contextually varied time-series data to ensure accurate and reliable performance in various applications. This paper seeks to understand how recently developed TSFMs perform in the building domain, particularly concerning their generalizability. We benchmark these models on three large datasets related to indoor air temperature and electricity usage. Our results indicate that TSFMs exhibit marginally better performance compared to statistical models on unseen sensing modality and/or patterns. Based on the benchmark results, we also provide insights for improving future TSFMs on building analytics.