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.

We estimate how two complementary forms of cycling infrastructure, cycle lanes and bike-sharing stations, capitalize into housing prices in Barcelona. We distinguish the presence of infrastructure from its use, which we capture recovering bike traffic across the entire street network from bike-sharing trip records. We combine geolocated sale and rental listings with digitized records of every cycle-lane segment and bike-sharing station and estimate hedonic price gradients across concentric 50-meter rings rather than a single proximity indicator. We find an asymmetry by infrastructure type and tenure. Sales respond more to stations and rents respond more to cycle lanes; station effects stay positive and fade with distance, whereas lane effects are concentrated nearby and turn negative farther out. We read this through a road-space trade-off that varies with resident characteristics. When we move from the provision of infrastructure to its use, we find that doubling local traffic raises sale prices far more than physical presence alone. This indicates that residents capitalize a well-connected, well-ridden network rather than mere adjacency. Capitalization is positive but modest, too small to support strong gentrification concerns.

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.