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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2506.00765 |
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| _version_ | 1866912889230589952 |
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| author | Wang, Shengkun Sun, Yanshen Chen, Fanglan Wang, Linhan Ramakrishnan, Naren Lu, Chang-Tien Chen, Yinlin |
| author_facet | Wang, Shengkun Sun, Yanshen Chen, Fanglan Wang, Linhan Ramakrishnan, Naren Lu, Chang-Tien Chen, Yinlin |
| contents | Accurate long-horizon house-price forecasting requires benchmarks that capture temporal dynamics together with time-varying local context. However, existing public resources remain fragmented: many datasets have limited spatial coverage, temporal depth, or multimodal alignment; the robustness of modern deep forecasters and time-series foundation models on housing data is not well characterized; and aerial imagery is rarely leveraged in a time-aware and interpretable manner at scale. To bridge these gaps, we present HouseTS (House Time Series), a multimodal spatiotemporal dataset for ZIP-code-level housing-market analysis, covering monthly signals from March 2012 to December 2023 across over 6,000 ZIP codes in 30 major U.S. metropolitan areas. HouseTS aligns monthly housing-market indicators, monthly POI dynamics, and annual census-based socioeconomic variables under a unified schema, and includes time-stamped annual aerial imagery. Building on HouseTS, we define standardized long-horizon forecasting tasks for univariate and multivariate prediction and benchmark 16 model families spanning statistical methods, classical machine learning, deep neural networks, and time-series foundation models in both zero-shot and fine-tuned modes. We also provide image-derived textual change annotations from multi-year aerial image sequences via a vision--language pipeline with LLM-as-judge and human verification to support scalable interpretability analyses. HouseTS is available on Kaggle, with code and documentation on GitHub. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00765 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HouseTS: A Large-Scale, Multimodal Spatiotemporal U.S. Housing Dataset and Benchmark Wang, Shengkun Sun, Yanshen Chen, Fanglan Wang, Linhan Ramakrishnan, Naren Lu, Chang-Tien Chen, Yinlin Artificial Intelligence Accurate long-horizon house-price forecasting requires benchmarks that capture temporal dynamics together with time-varying local context. However, existing public resources remain fragmented: many datasets have limited spatial coverage, temporal depth, or multimodal alignment; the robustness of modern deep forecasters and time-series foundation models on housing data is not well characterized; and aerial imagery is rarely leveraged in a time-aware and interpretable manner at scale. To bridge these gaps, we present HouseTS (House Time Series), a multimodal spatiotemporal dataset for ZIP-code-level housing-market analysis, covering monthly signals from March 2012 to December 2023 across over 6,000 ZIP codes in 30 major U.S. metropolitan areas. HouseTS aligns monthly housing-market indicators, monthly POI dynamics, and annual census-based socioeconomic variables under a unified schema, and includes time-stamped annual aerial imagery. Building on HouseTS, we define standardized long-horizon forecasting tasks for univariate and multivariate prediction and benchmark 16 model families spanning statistical methods, classical machine learning, deep neural networks, and time-series foundation models in both zero-shot and fine-tuned modes. We also provide image-derived textual change annotations from multi-year aerial image sequences via a vision--language pipeline with LLM-as-judge and human verification to support scalable interpretability analyses. HouseTS is available on Kaggle, with code and documentation on GitHub. |
| title | HouseTS: A Large-Scale, Multimodal Spatiotemporal U.S. Housing Dataset and Benchmark |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.00765 |