Who benefits from new housing supply?

谁从新住房供应中受益？

• 批准号: 409193813
• 负责人: Dr. Andreas Mense
• 金额: --
• 依托单位: Institut für Arbeitsmarkt- und Berufsforschung der Bundesagentur für Arbeit (BA)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/409193813?language=en
• 关键词: Who; benefits; 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. Thus, 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. However, depending on market conditions, it may be that property owners find it beneficial to upgrade vacant moderate-quality units. In that case, rents at the lower end remain unaffected. It is thus an empirical question which parts of the rent distribution react to new housing supply. The project’s main goal is 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 and data from Germany. A unit’s net rent is a perfect indicator for its quality if "Quality" is understood as a combination of all dwelling characteristics (including its location), as is common in the literature. 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. According to the data, 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. As a second goal, the project seeks to analyze heterogeneity in the filtering process. To understand better the sources of potential heterogeneity, I plan to develop a simple filtering model. The model shall deal explicitly with the effects of moving costs and landlords’ propensity to upgrade moderate quality units. Moving costs and rehabilitation are important because the filtering process implies a series of moves, and because it may stop when many units are rehabilitated before they are put on the market. Subsequently, the predictions of the theoretical model shall be tested empirically by exploiting differences over space in household mobility and in the propensity to rehabilitate.The project builds on three main data sources. It uses building completions, conversions, and demolitions data on the level of individual buildings, provided by the Statistical Offices of the German Länder. Data on individual rental dwellings was collected from three large German online market places in the years 2011—2017. The German Weather Service provides weather data in raster format. All data sets have a monthly time resolution.

私人市场主要通过筛选过程向穷人提供住房。根据这一理论，一个新的家庭提供给市场引发了一系列的动作。首先，一个家庭搬进了新的单位，留下一个空置的旧单位。这反过来又让另一个家庭搬家。这样，一些家庭可以在住房质量的阶梯上向上移动。因此，新的住房供应减少了对破旧、低质量住房的需求，从而减轻了租金分配底部的压力。这改善了穷人的住房条件。然而，根据市场情况，业主可能会发现改善空置的中等质量单位是有益的。在这种情况下，低端的租金不会受到影响。因此，租金分配的哪些部分对新的住房供应做出反应是一个经验问题。该项目的主要目标是利用工具变量分位数回归和来自德国的数据，从经验上调查新住房单元的供应如何以及何时影响租金分布的下尾。一个单位的净租金是一个完美的指标，其质量，如果“质量”被理解为所有居住特征的组合（包括其位置），这是常见的文献。该项目的一个主要创新是通过不可预见的天气事件确定地方一级的外来住房供应冲击。在初步工作中，我表明，一个特别多雨的7月在一个给定的位置导致大幅减少当地年底的住房完工，可能是因为他们延长干燥时间的未完成的建筑物。根据数据显示，这些未完成的单位大多数是在大约十到十二个月后完成的。这意味着这种天气冲击会导致当地新住房供应发生相当大的、具有经济意义的变化。作为第二个目标，该项目试图分析过滤过程中的异质性。为了更好地理解潜在异质性的来源，我计划开发一个简单的过滤模型。该模型应明确处理搬迁成本和房东倾向于升级中等质量单元的影响。搬迁费用和修复工作很重要，因为筛选过程意味着一系列搬迁，而且当许多单位在投放市场之前修复完毕时，这一过程可能会停止。随后，将利用家庭流动空间和复原倾向的差异，对理论模型的预测进行经验检验。它使用了德国各州统计局提供的关于单个建筑物水平的建筑完工、改建和拆除数据。个人出租住宅的数据是从2011-2017年德国三个大型在线市场收集的。德国气象局提供栅格格式的天气数据。所有数据集都有每月时间分辨率。