We assess the impact of Airbnb on residential house prices and rents: using a data set of Airbnb listings from the entire United States and an instrumental variables estimation strategy, we show that Airbnb has a positive impact on house prices and rents.
Using a novel dataset which merges real estate listings with real estate transactions in San Francisco from 2007-2009, we present new evidence that foreclosures causally depress nearby home prices. We show that this decrease occurs only after the foreclosed home is listed for sale, which suggests that the effect is due to the additional housing supply created by foreclosure rather than from neglect of the foreclosed property. Consistent with a framework where a foreclosed home simply increases supply, we find that new listings of foreclosed homes and nonforeclosed homes each lower sales prices of homes within 0.1 miles of the listing by 1 percent. * We thank Leah Brooks, Karen Pence, Hui Shan, and seminar participants for helpful suggestions. We thank Lindsay Relihan for excellent research assistance, and Nate Howard, a real estate agent at Louise Beck Properties, who provided valuable insights into the residential real estate market and MLS data. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors.† Federal Reserve Board of Governors, Washington DC.
Using a novel dataset which merges real estate listings with real estate transactions in San Francisco from 2007-2009, we present new evidence that foreclosures causally depress nearby home prices. We show that this decrease occurs only after the foreclosed home is listed for sale, which suggests that the effect is due to the additional housing supply created by foreclosure rather than from neglect of the foreclosed property. Consistent with a framework where a foreclosed home simply * We thank Leah Brooks, Karen Pence, Hui Shan, and seminar participants for helpful suggestions. We thank Lindsay Relihan for excellent research assistance, and Nate Howard, a real estate agent at Louise Beck Properties, who provided valuable insights into the residential real estate market and MLS data. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors.
We present an empirical dynamic discrete choice model of life insurance decisions designed to bypass data limitations where researchers only observe whether an individual has made a new life insurance decision but but do not observe the actual policy choice or the choice set from which the policy is selected. The model also incorporates serially correlated unobservable state variables, for which we provide ample evidence that they are required to explain some key features in the data. We empirically implement the model using the limited life insurance holding information from the Health and Retirement Study (HRS) data. We deal with serially correlated unobserved state variables using posterior distributions of the unobservables simulated from Sequential Monte Carlo (SMC) methods. Counterfactual simulations using the estimates of our model suggest that a large fraction of life insurance lapsations are driven by i.i.d choice specific shocks, particularly when policyholders are relatively young. But as the remaining policyholders get older, the role of such i.i.d. shocks gets less important, and more of their lapsations are driven either by income, health or bequest motive shocks. Income and health shocks are relatively more important than bequest motive shocks in explaining lapsations when policyholders are young, but as they age, the bequest motive shocks play a more important role.
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