The Price of Quietness: How a Pandemic Affects City Dwellers' Response to Road Traffic Noise

2026-07-20

AI Summary

This study investigates how the COVID-19 pandemic altered urban residents’ sensitivity to road traffic noise and their willingness to pay for housing. Leveraging a quasi-natural experiment induced by the pandemic, the authors analyze nearly 47,000 rental transactions and over 10,000 property listing texts in Singapore from 2006 to 2022—the first large-scale housing market assessment of changes in the value of quietness. Employing difference-in-differences estimation, supported by parallel trends and permutation placebo tests, and integrating machine learning–based textual analysis, the study finds that post-pandemic exposure to road noise immediately reduced rents by 3.8%, with the penalty widening to 12.7% (approximately USD 186.7 per month) in the following year. The results indicate a roughly 10% increase in the intensity of preference for quieter environments, highlighting mechanisms linked to the rise of remote work and increased delivery-related traffic.

Abstract

Using the outbreak of COVID-19 in Singapore as a quasi-natural experiment, we investigate tenants' changing responses to road traffic noise in the rental housing market, using 46,980 transaction records between 2006 and 2022. Our difference-in-differences estimates show that road traffic noise decreases housing rents by 3.8% immediately after the pandemic outbreak and further declines by 12.7% in the subsequent year-equivalent to 186.7 US dollars per month. The results are robust to parallel trend analysis, permutation placebo tests, and tests using alternative distance thresholds or distance to the nearest main road. Then, we adopt a machine learning text analysis of 10,425 rental housing advertisements, showing that tenants' preference for quietness increases by approximately 10% from 2019 into 2020. The new work-from-home business model and rising traffic from delivery services can explain for this pattern. To the best of our knowledge, this is the first paper using a large volume of transaction records to quantify city dwellers' willingness to pay for quietness in the COVID-19 context. Our results have policy implications for other nations and post-pandemic era on the interaction among urban planning, transport networks, and human settlements, and shed light on the pathway to achieve sustainable development goals.

Problem

• road traffic noise
• urban housing
• pandemic impact
• quietness preference
• sustainable development

Innovation

• quasi-natural experiment
• difference-in-differences
• machine learning text analysis
• willingness to pay for quietness
• urban noise pollution