HouseTS: A Large-Scale, Multimodal Spatiotemporal U.S. Housing Dataset and Benchmark

TL;DR

HouseTS is a comprehensive multimodal dataset for U.S. housing market analysis, enabling improved long-term forecasting and interpretability through diverse data sources, benchmarks, and scalable annotations.

Contribution

The paper introduces HouseTS, a large-scale, multimodal spatiotemporal housing dataset with benchmarks and interpretability tools, filling gaps in existing resources.

Findings

Benchmarking 16 models on long-horizon forecasting tasks.

Demonstrating the utility of aerial imagery and multimodal data.

Providing scalable interpretability annotations for housing data.

Abstract

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.