Abstract: A rent guarantee insurance (RGI) policy makes a limited number of rent payments to the landlord on behalf of an insured tenant unable to pay rent due to a negative income or health expenditure shock. We introduce RGI in a rich quantitative equilibrium model of housing insecurity and show it increases welfare by improving risk sharing across idiosyncratic and aggregate states of the world, reducing the need for a large security deposits, and reducing homelessness which imposes large costs on society. While unrestricted access to RGI is not financially viable for either private or public insurance providers due to moral hazard and adverse selection, restricting access can restore viability. Private insurers must target better off renters to break even, while public insurers focus on households most at-risk of homelessness. Stronger tenant protections increase the effectiveness of RGI.
Discussant: Michael Boutros, University of Toronto
Tess Scharlemann, Federal Reserve Board of Governors
Ishita Sen, Harvard University
Ana-Maria Tenekedjieva, Federal Reserve Board of Governors
Abstract: This study investigates the prevalence and severity of under-insurance among U.S. households using novel microdata linking homeowners insurance and mortgage information from 2013-2023 nationwide. We document widespread under-insurance on the intensive margin, particularly among borrowers in high climate risk states, with low credit scores, and high loan-to-value ratios. We examine the role played by household credit constraints by showing that households respond to rising premiums by both dropping coverage as well as increasing mortgage debt, suggesting that mortgage credit is used to finance insurance purchases. These results imply that rising premiums change mortgage risks by inducing households drop coverage as well as taking on higher loan balances. Lastly, we study the broader impacts of under-insurance on household financial resilience after natural disasters.
Abstract: We provide survey evidence that individuals believe there is substantial nonpayment risk in annuity, life insurance, and long-term care insurance (LTCI) products. Using simple statistical analysis we show that nonpayment beliefs predict insurance ownership and that the insurance ownership rate would be much larger if people believed there was zero nonpayment risk. To better quantify how nonpayment risk affects insurance ownership and how different features of insurance products affect consumer welfare, we develop an incomplete-markets life-cycle model of the demand for life insurance, annuities, LTCI, and a risk free bond. We incorporate features of real-world insurance products such as perceived nonpayment risk, high loads above actuarial fair prices, and quantity restrictions (e.g., age restrictions on purchases, short-selling constraints). Both high prices and nonpayment risk substantially decrease insurance ownership. Compared to our rational expectations baseline, the welfare loss from sub- optimally owning zero insurance is 0.4 percent in consumption equivalent units. If the products had no risk and were sold at actuarially fair prices, the welfare cost of zero insurance ownership is much larger at 7.9 percent. If subjective beliefs are wrong and payments are always made, correcting beliefs increases welfare by 4 percent.
Discussant: Sasha Indarte, University of Pennsylvania
