用卫星雷达和新闻情绪预测迪拜小区房价，每周更新，长期效果显著。
Sub-City Real Estate Price Index Forecasting at Weekly Horizons Using Satellite Radar and News Sentiment
- 融合卫星雷达回波、新闻情绪和交易历史，构建周频小区房价指数。
- 34周后预测误差降低35%，从4.48降至2.93，情绪与雷达数据起关键作用。
- 非参数模型优于深度学习，适合此类高频小规模预测任务。
可靠的房地产价格指标通常以城市为单位、频率较低，难以支持街区尺度监测和长期规划。本文研究是否可通过结合卫星雷达的物理开发信号与新闻文本的市场叙事，实现亚城市层面、周频的房价指数预测。基于迪拜土地局2015-2025年超过35万条交易数据，构建了19个亚城市区域的周度价格指数，并评估了2至34周前的预测表现。框架融合区域交易历史、哨兵-1号合成孔径雷达（Sentinel-1 SAR）后向散射、结合词法语气与语义嵌入的新闻情绪，以及宏观经济背景。结果呈现强烈的时间依赖性：在10周内，仅使用价格历史即可达到多模态模型水平；但超过14周后，情绪与SAR信号变得至关重要。在长周期（26-34周），全模态模型将平均绝对误差从4.48降至2.93（降低35%），且在各区域均具统计显著性。非参数学习器在此数据环境下持续优于深度架构。研究确立了周频亚城市指数预测的基准，并证明遥感与新闻情绪可显著提升战略相关时间跨度下的预测能力。
原文摘要 · Abstract (English)
Reliable real estate price indicators are typically published at city level and low frequency, limiting their use for neighborhood-scale monitoring and long-horizon planning. We study whether sub-city price indices can be forecasted at weekly frequency by combining physical development signals from satellite radar with market narratives from news text. Using over 350,000 transactions from Dubai Land Department (2015-2025), we construct weekly price indices for 19 sub-city regions and evaluate forecasts from 2 to 34 weeks ahead. Our framework fuses regional transaction history with Sentinel-1 SAR backscatter, news sentiment combining lexical tone and semantic embeddings, and macroeconomic context. Results are strongly horizon dependent: at horizons up to 10 weeks, price history alone matches multimodal configurations, but beyond 14 weeks sentiment and SAR become critical. At long horizons (26-34 weeks), the full multimodal model reduces mean absolute error from 4.48 to 2.93 (35% reduction), with gains statistically significant across regions. Nonparametric learners consistently outperform deep architectures in this data regime. These findings establish benchmarks for weekly sub-city index forecasting and demonstrate that remote sensing and news sentiment materially improve predictability at strategically relevant horizons.
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