Laserfiche WebLink
period, averaging between $2300 and $6600 per person each year, with aggregate benefits <br />totaling over $393 million annually. These effects are counterbalanced by landlords reducing <br />supply in response to the introduction of the law. We conclude that this led to a city-wide <br />rent increase of 7% and caused $5 billion of welfare losses to all renters. We discuss how the <br />substantial welfare losses due to decreased housing supply could be mitigated if insurance <br />against large rent increases was provided as a form of government social insurance, instead <br />of a regulated mandate on landlords. <br />Our paper is most related to the literature on rent control. Recent work by Autor et <br />al. (2014) and Sims (2007) leverages policy variation in rent control laws in Cambridge, <br />Massachusetts to study the property and neighborhood effects of removing rent control <br />regulations. Our paper studies the effects of enacting rent control laws, which could have <br />very different effects than decontrol. De -control studies the effects of removing rent control <br />on buildings which remain covered. Indeed, we find a large share of landlords substitute away <br />from supply of rent controlled housing, making those properties which remain subject to rent - <br />control a selected set. Further, we are able to quantify how tenants use and benefit from <br />rent control, a previously unstudied topic due to the lack of the combination of appropriate <br />data, natural experiments and estimation methods. <br />There also exists an older literature on rent control combining applied theory with cross- <br />sectional empirical methods. These papers test whether the data are consistent with the <br />theory being studied, but usually cannot quantify causal effects of rent control. (Early <br />(2000), Glaeser and Lut.tnrer (2003), Gyouko and Linneman (1989), Gyourko and Linnernan <br />(1990), Moon and S'totsky (1993) Olsen (1972)). <br />Our estimation methods build on the dynamic discrete choice literature. Previous work <br />using dynamic demand for housing and neighborhoods has required strong assumptions <br />about how agents form expectations and how all neighborhood characteristics evolve over <br />time (Bishop and Murphy (2011), Kennan and Walker (2011), Bayer et al. (2016), Davis et <br />al. (2017), Murphy (2017)). We relax these assumptions by building on Scott (2013). His <br />5 <br />