Enhancing Regional Airbnb Trend Forecasting Using LLM-Based Embeddings of Accessibility and Human Mobility

2025-11-18

AI Summary

To address the worsening housing affordability crisis exacerbated by short-term rental platform expansion, this paper proposes the first multi-step spatiotemporal forecasting framework for regional Airbnb market trends—including revenue, booked nights, and booking volume. Methodologically, it innovatively leverages large language models (LLMs) to generate context-aware, dynamic regional embeddings from structured urban data, integrating heterogeneous sources such as listing attributes, urban accessibility, and human mobility patterns. These embeddings are then fed into a hybrid architecture jointly modeling spatial and temporal dependencies using RNNs, LSTMs, and Transformers. Evaluated on a real-world Seoul dataset, the framework achieves an average 48% reduction in RMSE and MAE over conventional baselines, significantly improving prediction accuracy for 1–3-month horizons. It effectively identifies oversupplied accommodation zones, thereby enabling data-driven, fine-grained urban governance and evidence-based housing policy formulation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The expansion of short-term rental platforms, such as Airbnb, has significantly disrupted local housing markets, often leading to increased rental prices and housing affordability issues. Accurately forecasting regional Airbnb market trends can thus offer critical insights for policymakers and urban planners aiming to mitigate these impacts. This study proposes a novel time-series forecasting framework to predict three key Airbnb indicators -- Revenue, Reservation Days, and Number of Reservations -- at the regional level. Using a sliding-window approach, the model forecasts trends 1 to 3 months ahead. Unlike prior studies that focus on individual listings at fixed time points, our approach constructs regional representations by integrating listing features with external contextual factors such as urban accessibility and human mobility. We convert structured tabular data into prompt-based inputs for a Large Language Model (LLM), producing comprehensive regional embeddings. These embeddings are then fed into advanced time-series models (RNN, LSTM, Transformer) to better capture complex spatio-temporal dynamics. Experiments on Seoul's Airbnb dataset show that our method reduces both average RMSE and MAE by approximately 48% compared to conventional baselines, including traditional statistical and machine learning models. Our framework not only improves forecasting accuracy but also offers practical insights for detecting oversupplied regions and supporting data-driven urban policy decisions.

Problem

• Forecasting regional Airbnb trends using LLM-based embeddings
• Predicting revenue and reservation patterns with mobility data
• Improving accuracy for urban planning and policy decisions

Innovation

• LLM-based embeddings for regional Airbnb forecasting
• Integrating accessibility and mobility into time-series models
• Sliding-window approach predicting 1-3 month trends