Housing and mortgage choices are among the largest financial decisions households make and they substantially impact households’ liquidity. This paper explores how monetary policy affects aggregate demand by influencing these portfolio choices. To quantify this channel, I build a heterogeneous-agent life-cycle model with long-term mortgages and endogenous house prices. I find that, although only a small fraction of households adjust their housing and mortgage holdings in response to an expansionary monetary policy shock, these households account for over 50 percent of the increase in aggregate demand. Mortgage refinancing explains approximately four-fifths of the contribution, whereas adjusted housing choices account for one-fifth—uncovering a new transmission channel. I also show that the different pass-through of the policy rate to short and long mortgage rates drives the difference in the house-price and aggregate demand response between economies with adjustable-rate as compared to fixed-rate mortgages.

We study the severity of liquidity constraints in the U.S. housing market using alife-cycle model with uninsurable idiosyncratic risks in which houses are illiquid, butagents can extract home equity by refinancing their mortgages. The model implies thatfour-fifths of homeowners are liquidity constrained and willing to pay an average of 13cents to extract an additional dollar of liquidity from their home. Most homeownersvalue liquidity for precautionary reasons, anticipating the possibility of income declinesand the need to make mortgage payments. The model reproduces well the observedresponse of consumption to tax rebates and mortgage relief programs and predicts largewelfare gains from policies aimed at providing temporary liquidity relief to homeowners.

Long-term longitudinal dataset with information on generational links and socioeconomic and health conditions of individuals over time. The central foci of the data are economic and demographic, with substantial detail on income sources and amounts, wealth, savings, employment, pensions, family composition changes, childbirth and marriage histories, and residential location. Over the life of the PSID, the NIA has funded supplements on wealth, health, parental health and long term care, housing, and the financial impact of illness, thus also making it possible to model retirement and residential mobility. Starting in 1999, much greater detail on specific health conditions and health care expenses is included for respondent and spouse. Other enhancements have included a question series about emotional distress (2001); the two stem questions from the Composite International Diagnostic Interview to assess symptoms of major depression (2003); a supplement on philanthropic giving and volunteering (2001-03); a question series on Internet and computer use (2003); linkage to the National Death Index with cause of death information for more than 4,000 individuals through the 1997 wave, updated for each subsequent wave; social and family history variables and GIS-linked environmental data; basic data on pension plans; event history calendar methodology to facilitate recall of employment spells (2001). The reporting unit is the family: single person living alone or sharing a household with other non-relatives; group of people related by blood, marriage, or adoption; unmarried couple living together in what appears to be a fairly permanent arrangement. Interviews were conducted annually from 1968 through 1997; biennial interviewing began in 1999. There is an oversample of Blacks (30%). Waves 1990 through 1995 included a 20% Hispanic oversample; within the Hispanic oversample, Cubans and Puerto Ricans were oversampled relative to Mexicans. All data from 1994 through 2001 are available as public release files; prior waves can be obtained in archive versions. The special files with weights for families are also available. Restricted files include the Geocode Match File with information for 1968 through 2001, the 1968-2001 Death File, and the 1991 Medicare Claims File. * Dates of Study: 1968-2003 * Study Features: Longitudinal, Minority Oversampling * Sample Size: 65,000+ Links * ICPSR Series: http://www.icpsr.umich.edu/icpsrweb/ICPSR/series/00131 * ICPSR 1968-1999: Annual Core Data: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/07439 * ICPSR 1968-1999: Supplemental Files: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03202 * ICPSR 1989-1990: Latino Sample: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03203

This project aims to assess changes in housing affordability or housing cost burdens pre- and post-pandemic. It explores the effects of homeownership, racial disparities, and other demographic characteristics. The main source of data is the Panel Study of Income Dynamics (PSID): Household-level data on housing costs, socioeconomic, and demographic characteristics.

A dataset, 1970-2009, containing equivalently defined variables for the British Household Panel Study (BHPS), the Household Income and Labour Dynamics in Australia (HILDA), the Korea Labor and Income Panel Study (KLIPS) (new this year), the Panel Study of Income Dynamics (PSID), the Russia Longitudinal Monitoring Survey (RLMS-HSE) (new this year), the Swiss Household Panel (SHP), the Canadian Survey of Labour and Income Dynamics (SLID), and the German Socio-Economic Panel (SOEP). The data are designed to allow cross-national researchers not experienced in panel data analysis to access a simplified version of these panels, while providing experienced panel data users with guidelines for formulating equivalent variables across countries. The CNEF permit researchers to track yearly changes in the health and economic well-being of older people relative to younger people in the study countries. The equivalent file provides a set of constructed variables (for example pre- and post-government income and United States and international household equivalence weights) that are not directly available on the original surveys. Since the Cross-National Equivalent File 1970-2009 can be merged with the original surveys, PSID-CNEF users can easily incorporate these constructed variables into current analyses. The most recent release of the Equivalent File includes: * BHPS data from 1991 to 2005 on over 21,000 individuals and approximately 6,000 households. * GSOEP data from 1984 to 2007 on over 20,000 individuals and approximately 6,000 households in Germany. * HILDA data from 2001 to 2006 on over 19,000 individuals and 7,000 households. * PSID data from 1980 to 2005 on over 33,000 individuals and approximately 7,000 households. * SHP data from 1999 to 2006 on 12,900 individuals and 5,000 households. * SLID data from 1993 to 2006 on over 95,000 individuals and approximately 32,000 households. With one exception, the CNEF country data are available on CD-ROM from Cornell University for a fee. The Canadian SLID data are not distributed on the CD but are available to CNEF registered researchers through special arrangements with Statistics Canada. Complete instructions for obtaining CNEF data may be accessed on the project website. * Dates of Study: 1980-2007 * Study Features: International, Longitudinal * Sample Size: ** BHPS: 21,000+ ** PSID: 33,000+ ** SLID: 95,000+ ** GSOEP: 20,000+ ** HILDA: 19,000+ ** SHP: 12,900+ NACDA link: http://www.icpsr.umich.edu/icpsrweb/NACDA/studies/00145/detail

The Panel Study of Income Dynamics–Social, Health, and Economic Longitudinal File (PSID-SHELF) provides an easy-to-use and harmonized longitudinal file for the Panel Study of Income Dynamics (PSID), the longest-running nationally representative household panel survey in the world.PSID-SHELF accentuates the PSID's strengths through (1) its household panel structure that follows the same families over multiple decades; and (2) its multigenerational genealogical design that follows the descendants of panel families that were originally sampled in 1968, with immigrant sample refreshers in 1997–1999 and 2017. Every individual who has ever been included in the PSID's main study is included in the PSID-SHELF data, with over 80,000 people observed, some of them across more than 40 survey waves (1968–present). The current version of PSID-SHELF includes 41 waves of survey data, ranging from 1968 to 2019.The file contains measures on a wide range of substantive topics from the PSID's individual and family files, including variables on demographics, family structure, educational attainment, family income, individual earnings, employment status, occupation, housing, and wealth—as well as the essential administrative variables pertaining to key survey identifiers, panel status, sample weights, and household relationship identifiers. PSID-SHELF thus covers some of the most central variables in PSID that have been collected for many years. PSID-SHELF can easily be merged with other PSID data products to add other public-use variables by linking variables based on a survey participant’s individual and family IDs.Despite a focus on longitudinally consistent measurement, many PSID variables change over waves, e.g., thanks to new code frames, topcodes, question splitting, or similar. PSID-SHELF provides harmonized measures to increase the ease of using PSID data, but by necessity this harmonization involves analytic decisions that users may or may not agree with. These decisions are described at a high level in the PSID-SHELF User Guide and Codebook, but only a close review of the Stata code used to construct variables in the data will fully reveal each analytic decision. The Stata code underlying PSID-SHELF is openly accessible not only to allow for such review but also to encourage users, as they become more comfortable with PSID, to use and alter the full code or selected code snippets for their own analytic purposes. PSID-SHELF is entirely based on publicly released data and therefore can be recreated by anyone who has registered for PSID data use.Despite careful and multiple code reviews, it is possible that the code used to produce PSID-SHELF contains errors. The authors therefore encourage users to review the codes carefully, to report any mistakes and errors to us (psidshelf.help@umich.edu), and take no responsibility for any errors arising from the provided codes and files. Current VersionPSID-SHELF, 1968–2019, Beta Release 2023.01Recommended CitationsPlease cite PSID-SHELF in any product that makes use of the data. Anyone who uses PSID-SHELF should cite the data or the PSID-SHELF User Guide and Codebook—and, as required by the PSID user agreement, the main PSID data.PSID-SHELF data:Pfeffer, Fabian T., Davis Daumler, and Esther M. Friedman. PSID-SHELF, 1968–2019: The PSID’s Social, Health, and Economic Longitudinal File (PSID-SHELF), Beta Release. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor],

This paper considers optimal taxation of housing capital. To this end, we employ a life-cycle model calibrated to the U.S. economy, where asset holdings and labor productivity vary across households, and tax reforms lead to changes in house and rental prices, interest rates, and wages. We find that the optimal property tax in the long run is considerably higher than today, partly due to the relatively inelastic demand and supply of housing. A higher property tax also reduces house prices and causes a reallocation from housing to business capital, which in turn decreases interest rates and increases wages. These equilibrium effects allow for an improved consumption smoothing over the life cycle. However, most current households would incur substantial welfare losses from an implementation of a higher property tax, since house prices fall, and a majority own their home.Hence, when accounting for transitional dynamics, it is not clear that a higher property tax is feasible or preferred.

Data and code to replicate empirical results in "Collateralized Marriage."Abstract: Marriage rates have become increasingly stratified by homeownership. We investigate this in a household model where investments in public goods reduce future earnings, and thus divorce risk creates inefficiencies. Access to a joint savings technology, like a house, "collateralizes" marriage, providing insurance to the lower earning partner and increasing specialization, public goods, and value from marriage. We use idiosyncratic variation in housing prices to show that homeownership access indeed leads to greater specialization. The model also predicts that policies that erode the marriage contract in other ways will make wealth a more important determinant of marriage, which we confirm empirically.Data includes compiled Census / American Community Survey Data, PSID data, SIPP data, ATUS data, and FHFA housing price data, all of which are publicly available.