Population change means fewer Asians are living in Chinatowns, but more Asians now own properties within them.

For over 150 years, Chinatowns have played an important role in large US cities. But, with increasing gentrification and urban change, Chinatowns are changing. In new research focusing on Chinatowns in Boston and Philadelphia, Arthur Acolin and Domenic Vitiello find that while the number of Asian residents has fallen, the share of Asian ownership has risen. Ethnic neighborhoods have important ..

Borrowing Constraints and Homeownership

This paper identifies the impact of borrowing constraints on homeownership in the U.S. in the aftermath of the 2008 financial crisis. While homeownership declines and tightened credit are evident, the role the tightening of credit has had on the probability of individual households to become homeowners has not been previously identified. The homeownership rate in 2010-2013 is estimated to be 2.3 percentage points lower than if the constraints were set at the 2001 level

Homeownership and Nontraditional and Subprime Mortgages

This article documents the growth and geographic distribution of nontraditional mortgages (NTMs) and subprime mortgages during 2000-2006, and examines the association between these products and homeownership at the county level between 2000 and 2012. It finds a significant relationship between the origination of NTM and subprime mortgages during the boom and changes in the number of homeowners (positive during the 2000-2006 period and negative during the 2006-2012 period) but no significant relationship with the change in the homeownership rate. Looking at specific categories of the population, the results indicate a positive relationship between the presence of NTMs and subprime mortgages and increased numbers of homeowners for young households as well as for low income and minority households, but the relationship is smaller than for the general population. Overall, the relationship between NTMs and homeownership is stronger than the relationship between subprime mortgages and homeownership during the boom and it is less negative during the bust

A Renter or Homeowner Nation?

Between the 1940s and the 1960s, the U.S. homeownership rate increased by nearly 20 percentage points, from mid-40 to mid-60 percent. The self-amortizing 30-year, fixed-rate mortgage, introduced by the Federal Housing Administration/Veterans Administration (VA—now the U.S. Department of Veterans Affairs) transformed the United States from a nation of renters to a nation of homeowners (Acolin and Wachter, 2015; Fetter, 2013)

Pursuing healthy homeownership: an evaluation of the neighborhood health trajectories of shared equity homeowners

BackgroundShared equity homeownership - a model in which low- and moderate-income households purchase homes at affordable prices on the condition that the houses remain affordable upon resale - has been shown to produce several health-enhancing housing outcomes. These include permanent affordability, housing stability, and modest wealth-building. However, studies suggest low- and moderate-income households may sacrifice neighborhood quality when becoming homeowners, which can undermine the health benefits of homeownership. To understand the health impacts of the shared equity homeownership model more fully, it is important to evaluate participants' neighborhood health trajectories - how their neighborhood health environments change when they move into homeownership - and how these trajectories compare to those of similar households entering traditional homeownership and those continuing to rent.MethodsWe conducted difference-in-differences analyses comparing changes in neighborhood health characteristics (walkability, food access, socio-economic vulnerability, and life expectancy) for US households moving into shared equity homeownership between 1997 and 2017 compared to households moving into traditional homeownership and those continuing to rent. Shared equity homeowner data was obtained through the Grounded Solutions Network HomeKeeper National Data Hub and households from the Panel Study of Income Dynamics served as matched controls for the analysis. All data on neighborhood characteristics were obtained from publicly available, census tract-level datasets.ResultsCompared to households entering traditional homeownership, households entering shared equity homeownership experienced a relative increase in walkability (difference-in-differences 1.07, p = 0.004), increase in food access (0.13, p < 0.001), increase in socio-economic vulnerability (0.06, p = 0.02), and similar life expectancy. Compared to households moving between rental units, households entering shared equity homeownership experienced similar trajectories in terms of walkability and food access but experienced a relative increase in socio-economic vulnerability (0.06, p = 0.01) and decrease in average neighborhood life expectancy (-0.64, p = 0.01).ConclusionsHouseholds entering shared equity homeownership avoid the sacrifices in neighborhood walkability and food access that are associated with moving into traditional homeownership, but they experience increased neighborhood socio-economic vulnerability. While understanding the net impact of these factors on individual and household health requires further study, these results can inform the siting and design of shared equity homeownership units to maximize the health benefits of the model

Small-area estimates from consumer trace data

Background: Timely, accurate, and precise demographic estimates at various levels of geography are crucial for planning, policymaking, and analysis. In the United States, data from the decennial census and annual American Community Survey (ACS) serve as the main sources for subnational demographic estimates. While estimates derived from these sources are widely regarded as accurate, their timeliness is limited and variability sizable for small geographic units like towns and neighborhoods. Objective: This paper investigates the potential for using nonrepresentative consumer trace data assembled by commercial vendors to produce valid and timely estimates. We focus on data purchased from Data Axle, which contains the names and addresses of over 150 million Americans annually. Methods: We identify the predictors of over- and undercounts of households as measured with consumer trace data and compare a range of calibration approaches to assess the extent to which systematic errors in the data can be adjusted for over time. We also demonstrate the utility of the data for predicting contemporaneous (nowcasting) tract-level household counts in the 2020 Decennial Census. Results: We find that adjusted counts at the county, ZIP Code Tabulation Areas (ZCTA), and tract levels deviate from ACS survey-based estimates by an amount roughly equivalent to the ACS margins of error. Machine-learning methods perform best for calibration of county- and tract-level data. The estimates are stable over time and across regions of the country. We also find that when doing nowcasts, incorporating Data Axle estimates improved prediction bias relative to using the most recent ACS five-year estimates alone. Contribution: Despite its affordability and timeliness compared to survey-based measures, consumer trace data remains underexplored by demographers. This paper examines one consumer trace data source and demonstrates that challenges with representativeness can be overcome to produce household estimates that align with survey-based estimates and improve demographic forecasts. At the same time, the analysis also underscores the need for researchers to examine the limits of the data carefully before using them for specific applications

Redefining Resilience as a Public Health Response to Stress

Thesis (Ph.D.)--University of Washington, 2023Stress, a transdiagnostic risk factor linked to both physical and psychological illness, is important to population health and health equity. Promoting population resilience, or the capability for populations to “bounce back” in the face of stressors, may act as a public health antidote to stress-related illness by bolstering a population’s capability to cope and adapt following varied stresses.Despite extensive research into resilience at the individual level, little is known about its potential at the population level. This dissertation leverages the COVID-19 pandemic to advance the understanding of population resilience. Specific aims are: 1) Develop a structural model of population health as an alternative to the biomedical model and propose “structural resilience” as a population health outcome; 2) Compare the biomedical and structural models in the context of population-level distress during the COVID-19 pandemic; 3) Explore population psychological distress as a potential measure for the construct of “structural resilience.