CORRESPONDENCE - WS-1 OPPOSITION

Home Browse Search

college shares than the control group. As Figure 9 and Figure 10 show, this is not a function of the areas in which treated individuals lived in 1993. In this figure, we fix the location of those treated by rent control at their 1993 locations, but allow the control group to migrate as seen in the data. If rent -controlled renters were equally likely to remain in their 1993 apartments across all locations in San Francisco, we would see the sign of the treatment effects on each neighborhood characteristic to be the same as in the previous regression. Instead, we find strong evidence that the out -migration of rent -controlled tenants came from very selected neighborhoods. Had treated individuals remained in the 1993 addresses, they would have lived in census tracts which had significantly higher college shares and higher house prices than the control group. This evidence is consistent with the idea that landlords undertake efforts to remove their tenants or convince them to leave in improving, gentrifying areas. 4.2 Parcel and Landlord Effects We continue our analysis by studying the impact of rent control on the structures themselves. In particular, we examine how rent control impacts the nature of the tenants who live in the buildings, as well as its impact on investments that landlords choose to make in the properties. We run a similar specification to that above: Ykt = bzt + Ak +,dt * Tk + ckt, (2) where k now denotes the individual parcel and Ak represent parcel fixed effects. The variable Tk, denotes treatment, equal to one if, on December 31, 1993, the parcel is a multifamily building with less than or equal to four units built between the years 1900 and 1979. The 6,zt variables once again reflect zipcode by year fixed effects. Our outcome variables Ykt now include the number of renters and owners living in the building, whether the building sits vacant, the number of renovation permits associated with the building, and whether the 16