This dissertation is composed of three chapters on macro finance. The first chapter documentsthe rise of residential institutional investors, and explores its implications. The second chapter proposes a new tool to filter non-linear dynamic models. The third chapter assesses the relationship between optimal deposit insurance financing and banking fragility. Chapter 1 presents evidence for the rise of institutional investors. Residential institutionalinvestors increased their market share of rental houses from 17% in 2001 to 28% in 2018. Along with this change, survey data show that the annual house operating-cost premium of institutional investors relative to homeowners fell. To measure how these reduced costs affected the housing bust in 2007-2011, I build a general equilibrium heterogeneous agent housing model featuring corporate investors and two types of dwellings. The corporate-cost reduction moderated the house price fall, amplified the drop in the homeownership rate, and mainly benefited existing owners. Chapter 2 proposes a new tool to filter non-linear dynamic models that does not require usto fully specify the model. We can use a flexible statistical model and a known measurement equation to back out a hidden state if the following two conditions are met. First, the state must be sufficiently volatile or persistent. Second, the possibly non-linear measurement must be sufficiently smooth and map uniquely to the state absent measurement error. We illustrate the method through simulation studies and an application to a small open economy model with an occasionally binding constraint. Chapter 3 present a quantitative model of deposit insurance. We characterize the policymaker’soptimal choices of coverage for depositors and premiums raised from banks. Premiums contribute to a deposit insurance fund that lowers taxpayers’ resolution cost of bank failures. We find that risk-adjusted premiums reduce moral hazard, enabling the policymaker to increase deposit insurance coverage by 3 percentage points and decrease the share of expected annual bank failures from 0.66% to 0.16%. The model predicts a fund-to-covered-deposits ratio that matches the data and declines in taxpayers’ income due to taxpayers’ risk aversion.