This study investigates the heterogeneous effects of price fluctuations and crime happening nearby on listing performance in the short-term rental industry. Using a comprehensive dataset of Airbnb listings in Chicago from 2014 to 2024, the study examined how price surges and drops influence occupancy rates, Average Daily Rate (ADR), and revenue across different Airbnb property segments. Employing a combination of spatial analysis, tempo- ral analysis, causal forest, and machine learning methodologies, the study identified four distinct property clusters with varying sensitivity to property characteristics and review rat- ings. The findings reveal a strong association between price surges and increased occupancy rates. While this counterintuitive result may reflect a signaling mechanism, we caution that causality cannot be conclusively established. It is also possible that elevated demand may precede price changes. Hosts raise prices in response to unobserved shifts in demand — suggesting that these outcomes reflect correlated dynamics rather than strictly causal relationships. Price increases may operate as signals of quality or scarcity that elevate perceived value. Conversely, price drops generally result in revenue losses, highlighting asymmetric market responses to price adjustments. Additionally, the research find that neighborhood safety characteristics moderate these effects, with crime incidents having heterogeneous impacts across property segments. This research contributes to revenue management theory by demonstrating that strategic price positioning can leverage quality signaling effects, where consumers interpret higher prices as indicators of superior value in certain market segments. The findings provide practical insights for hosts and platform operators seeking to optimize pricing strategies in increasingly complex short-term rental markets.
Shuyi Zhang (Sun,) studied this question.