Menu Content/Inhalt
August 2019
29 30 31 1 2 3 4
5 6 7 8 9 10 11
12 13 14 15 16 17 18
19 20 21 22 23 24 25
26 27 28 29 30 31 1

Search on Site

Events Print
previous year previous month next month next year
See by year See by month See by week See Today Search Jump to month
Guillaume Camille Chapelle, SciencesPo Print
Tuesday, 14 May 2019, 14:00 - 15:15

Guillaume Camille Chapelle, SciencesPo

Can Big Data increase our knowledge of local rental markets? Estimating the cost of density with rents 

Abstract : In this paper, we argue that the cost of agglomeration should preferably be measured with rents since the cost of housing based on prices is forward looking and might depend on parameters likely to vary with city size. As access to rental data is usually limited, we create a new data set regularly scraping two major French real estate websites. Comparing our data set with the French Housing survey only available at the department and regional level, we show that internet-based estimates are not biased as they do not systematically differ from surveys. We then use our data set to create a comparable rent measure for every urban area in France. We show that rent/price ratios are lower in large agglomerations resulting in a lower elasticity of housing cost with respect to city size when measured with rents instead of prices as in the seminal contribution of Combes, Duranton, and Gobillon (2018). This result is of particular importance when computing the net benefits of density which appear larger and also positive for renters. 


Location: R42.2.113
Contact: Nancy De Munck - This e-mail address is being protected from spam bots, you need JavaScript enabled to view it