Third party funded individual grant
Start date : 01.07.2019
End date : 30.06.2021
Private markets provide housing to the
poor primarily through the process of filtering. According to this
theory, a new home supplied to the market triggers a series of moves.
First, a household moves into the new unit, leaving vacant an older
unit. This in turn allows another household to move. In this way, a
number of households can move up the housing quality ladder. If the
process does not stop at some point, new housing supply reduces demand
for run-down, low-quality dwellings and thereby lifts pressure off the
bottom of the rent distribution. This improves housing conditions for
the poor. It may be, however, that property owners find it beneficial to
upgrade vacant moderate-quality units, so that rents at the lower end
remain unaffected. It is thus an empirical question which parts of the
rent distribution react to new housing supply.
This project aims to
investigate empirically how and when the supply of new housing units
affects the lower tail of the rent distribution, using instrumental
variable quantile regression. It also seeks to analyze heterogeneity in
the process by setting up a simple filtering model. The model shall
explicitly deal with the effects of moving costs and landlords’
propensity to upgrade moderate quality units. Its predictions shall be
tested empirically by exploiting differences over space in household
mobility and the propensity to upgrade.
A major innovation of the
project is the identification of exogenous housing supply shocks on the
local level through unforeseen weather events. In preliminary work, I
show that a particularly rainy July in a given location causes
substantial decreases in local end-of-year housing completions,
potentially because they prolong drying time of unfinished buildings.
Most of these unfinished units are completed about ten to twelve months
later. This implies that such weather shocks lead to sizable and
economically meaningful changes in local new housing supply.
Private markets provide housing to the
poor primarily through the process of filtering. According to this
theory, a new home supplied to the market triggers a series of moves.
First, a household moves into the new unit, leaving vacant an older
unit. This in turn allows another household to move. In this way, a
number of households can move up the housing quality ladder. If the
process does not stop at some point, new housing supply reduces demand
for run-down, low-quality dwellings and thereby lifts pressure off the
bottom of the rent distribution. This improves housing conditions for
the poor. It may be, however, that property owners find it beneficial to
upgrade vacant moderate-quality units, so that rents at the lower end
remain unaffected. It is thus an empirical question which parts of the
rent distribution react to new housing supply.
This project aims to
investigate empirically how and when the supply of new housing units
affects the lower tail of the rent distribution, using instrumental
variable quantile regression. It also seeks to analyze heterogeneity in
the process by setting up a simple filtering model. The model shall
explicitly deal with the effects of moving costs and landlords’
propensity to upgrade moderate quality units. Its predictions shall be
tested empirically by exploiting differences over space in household
mobility and the propensity to upgrade.
A major innovation of the
project is the identification of exogenous housing supply shocks on the
local level through unforeseen weather events. In preliminary work, I
show that a particularly rainy July in a given location causes
substantial decreases in local end-of-year housing completions,
potentially because they prolong drying time of unfinished buildings.
Most of these unfinished units are completed about ten to twelve months
later. This implies that such weather shocks lead to sizable and
economically meaningful changes in local new housing supply.