Research

This paper studies the causal impact of street noise on housing prices. It focuses on a very dense urban environment and its entire soundscape, using granular data on listed flats and street noise. We employ a combination of hedonic price and fixed effects model, exploiting the regular grid shape of the Eixample district, in Barcelona. Our results indicate that doubling the perceived street noise generates an average depreciation of 3.4% on sales and 2% on rents. We show that the lower semi-elasticity with which the rental market adjusts for the negative externality generates a higher turnover of tenants in louder streets. Moreover, we collect several pieces of evidence which suggest that the effect is not driven by sorting by neighbors. Lastly, we use our results to perform two cost-benefit analyses of policies which help reducing noise.

In the context of increasing urban bike use, this paper examines the impact of cycling infrastructure—cycle lanes and bike-sharing stations—on housing prices. To address potential identification challenges linked to the location of these infrastructures, we leverage the staggered implementation of cycle lanes and the opening of bike-sharing stations in a grid-shaped neighborhood of Barcelona. Using unique geolocated data from 2007 to 2019, we analyze housing price variations across concentric rings of increasing distance from each dwelling. By examining origin-destination trips from the bike-sharing service and employing least-cost path computation, we estimate bike traffic on each street segment. This serves as a proxy for bike-friendliness and connectivity within the wider cycling network, allowing us to assess whether cycling traffic affects housing prices to varying extents. Our findings show that cycling infrastructure and traffic positively influence both rents and sales, particularly near dwellings, with the effect diminishing over distance.

This paper investigates how residential building height influences land surface temperatures in dense urban areas of Spain, using a novel dataset that integrates high-resolution satellite imagery with detailed cadastral information. The findings reveal a concave relationship: surface temperatures increase with building height, peaking at an average of five floors. Beyond this height, the marginal warming effect declines, resulting in temperature levels similar to those of much lower buildings—yet achieved at significantly higher population densities. The pattern is largely driven by impervious surfaces such as pavement and concrete, which absorb heat during the day and release it at night, intensifying the Urban Heat Island effect. Importantly, the results show that the key driver is not the footprint of the buildings themselves but the characteristics of the surrounding areas. Overall, the paper uncovers an overlooked mechanism through which building height can mitigate the costs of density, and highlights the space around buildings as a promising target for temperature-reducing interventions.