Sub-City Real Estate Price Index Forecasting at Weekly Horizons Using Satellite Radar and News Sentiment

TL;DR

This paper develops a method to forecast neighborhood-level real estate prices weekly by combining satellite radar data, news sentiment, and transaction history, significantly improving long-term prediction accuracy.

Contribution

It introduces a multimodal forecasting framework that integrates satellite radar, news sentiment, and transaction data for sub-city real estate price prediction at weekly horizons.

Findings

Sentiment and satellite data become crucial beyond 14 weeks forecast horizon.

Full multimodal model reduces mean absolute error by 35% at 26-34 weeks.

Nonparametric models outperform deep learning in this setting.

Abstract

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.