The ABC Data Exchange
Housing and Homeownership
Homeownership and residential stability are linked to many positive benefits like higher high school graduation rates and social/civic engagement.
Owning a home is a way to build assets and wealth, and it can be used as equity in case of unexpected expenses to prevent major disruption to well being.
Literature Review Highlights
Homeownership relates to the following positive factors (not necessarily causal):
- Higher high school graduation; [1]
- Provides residential stability; [2]
- A hedge against rising rent; [3]
- Homeownership is related to variables indicating social capital (voting, being part of local organizations, working to solve local problems, etc) and the relationship might be causal. The authors claim the causal relationship might be because homeowners are more stable; [4]
- Children exhibit fewer behavioral problems. [5]
Literature Review References
[1] Aaronson, D. (2000). A Note on the Benefits of Homeownership. Journal of Urban Economics, 47(3), 356-369.
[2] Id.
[3] Sinai, T., & Souleles, N. S. (2005). Owner-occupied housing as a hedge against rent risk. The Quarterly Journal of Economics, 120(2), 763-789.
[4] DiPasquale, D., & Glaeser, E. L. (1999). Incentives and social capital: Are homeowners better citizens?. Journal of Urban Economics, 45(2), 354-384.
[5] Haurin, D. R., Parcel, T. L., & Haurin, R. J. (2002). Impact of homeownership on child outcomes (pp. 427-446). Brookings Institution Press.
Major Findings
White residents, residents with higher educational attainment, and residents with higher household incomes were more likely to live in an owned home than their respective counterparts.
The percentage of African American residents living in an owned home jumped from 37% to 58% from 2019 to 2021.
More renters experienced housing cost burden than homeowners.
Homeownership
Homeownership typically represents those who live in homes that are owned free and clear or those who own their own home via a mortgage or loan. Owning a home, or other assets such as savings accounts, investments, and vehicles provides financial security that can protect households from unexpected expenses and decrease the likelihood of experiencing poverty [1]. While homeownership does not indicate overall wealth, or even the value of homes, they do indicate ownership of a valuable asset, which could provide financial security [1]. View data notes for this measure.
Data Visualization
Homeownership (Forsyth County, 2021)
Use the dropdown menu below to view data on different groups.
Key Takeaways
There was a significant increase of Forsyth County residents living in owned homes from 2018 to 2021.
In 2018, 59% of residents lived in an owned home compared to 69% of residents in 2021.
Residents with higher educational attainment were more likely to live in an owned home.
In 2021, 80% of residents with a Bachelor’s Degree lived in an owned home compared to about 65% of residents with a high school diploma or less.
Residents with higher incomes were more likely to live in an owned home.
Residents with a household income of over $100,000 had a homeownership rate of 88% compared to 38% for residents with an income below $20,000.
Major racial/ethnic disparities were present.
About 80% of White residents lived in an owned home compared to 58% and 55% of Black and Hispanic/Latino residents, respectively.
Older adults were more likely to live in an owned home.
In 2021, 84% of adults 65 years and over lived in an owned home. Residents aged 18-34 were the least likely to live in an owned home at 53%.
Data Notes
Homeownership
Data Notes
Homeownership in this context is measured at a person-level rather than household-level, therefore, results reflect the number of individuals that live in an owned home (free and clear or mortgage) and not the number of households that own their home.
The margin of error for the Hispanic/Latino estimates are high across all years and should be interpreted with caution.
References
- Cramer, R. & Shanks, T. (2014). The assets perspective: The rise of asset building and its impact on social policy. New York, NY: Palgrave Macmillan.
Data Source(s)
- 2009-2019, 2021 American Community Survey, 1-Year Public Use Microdata Samples. U.S. Census Bureau, 2021.
Housing Cost Burden
When a significant portion of a household’s income is devoted to housing expenses, there is less money available to cover other basic needs such as food, health care, and transportation, a situation which may result in financial insecurity [1,2]. Housing cost burden measures housing expenses as a percentage of household income for homeowners and renters. The threshold for housing cost burden is when a household spends more than 30% of their income on housing expenses. Expenses include mortgage or rent payments, utilities, property taxes, insurance, and other fees. View data notes for this measure.
Data Visualization
Housing Cost Burden of Homeowners (Forsyth County, 2021)
Use the dropdown menu below to view data on different groups.
Data Visualization
Housing Cost Burden of Renters (Forsyth County, 2021)
Use the dropdown menu below to view data on different groups.
Key Takeaways
About 21% of homeowners experienced a housing cost burden in 2009 compared to 15% in 2021.
Renters had higher rates of housing cost burden than homeowners.
In 2021, 15% of homeowners experienced housing cost burden compared to 45% of renters.
Residents with higher incomes had lower rates of housing cost burden.
Residents with at least an Associate’s Degree or more were less cost burdened than residents with less than an Associate’s Degree.
Data Notes
Housing Cost Burden
References
- Cramer, R. & Shanks, T. (2014). The assets perspective: The rise of asset building and its impact on social policy. New York, NY: Palgrave Macmillan.
- Schwartz, M. & Wilson, E. (2007). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. Retrieved from: https://www.census.gov/housing/census/publications/who-can-afford.pdf
Data Notes
Both educational attainment and household income categories for the housing cost burden measures were collapsed into two and three categories, respectively, in order to provide more reliable estimates. Additional data notes for each measure is as follows:
Cost burdened renters:
- For people living in cost burdened renting households, caution is still needed in interpreting the results for those that fall in the $30,001-$60,000 income category in years 2009, 2010, 2013, 2017, and 2021 given the high margin of errors for those estimates.
- The margin of errors for those 65 years old and older are high, estimates should be interpreted with caution. Further, estimates for those 18 years old and younger have high margins of errors across all years except 2010, 2012, 2014, and 2017. Estimates for the non-listed years should be interpreted with caution.
- Hispanic/Latino estimates for some years were unreliable and excluded from the data visualization. The remaining estimates have large margin of errors and should be interpreted with caution.
Cost burdened homeowners:
- For people living in cost burdened households that own their home, caution is still needed in interpreting the results for those that fall in the $0 to $30,000 income category in the years 2010, 2012, 2018, 2019, and 2021 given the high margin of errors for those estimates
- Hispanic/Latino estimates for a few years were unreliable and excluded from the data visualization.
- The remaining estimates have large margin of errors and should be interpreted with caution.
Data Source(s)
- 2009-2019, 2021 American Community Survey, 1-Year Public Use Microdata Samples. U.S. Census Bureau, 2021.
About the ABC Data Exchange
Data Notes
Educational Attainment
References
- Haskins, R. (2011). Fighting poverty the American way. Anti-Poverty Programs in a Global Perspective: Lessons from Rich and Poor Countries, Social Science Research Center, Berlin, [Record of a Symposium]. June 20-21, 2011. Berlin, Germany. Retrieved from https://www.brookings.edu/wp-content/uploads/2016/06/0620_fighting_poverty_haskins.pdf
- Acs, G. (2011). Downward mobility from the middle class: Waking up from the American dream. Retrieved from http://www.pewtrusts.org/~/media/legacy/uploadedfiles/pcs_assets/2011/middleclassreportpdf.pdf?la=en
- Furchtgott-Roth, D., Jacobson, L., & Mokher, C. (2009). Strengthening community colleges’ influence on economic mobility. Retrieved from http://www.frbsf.org/economic-research/files/Jacobson.pdf
- Sharkey, P., Bryan, G. (2013). Mobility and the metropolis: How communities factor into economic mobility. Retrieved from http://www.pewtrusts.org/~/media/legacy/uploadedfiles/pcs_assets/2013/mobilityandthemetropolispdf.pdf
- Ross, C., & Wu, C. (1995). The links between education and health. American Sociological Review, 60(5), 719-745. Retrieved from http://www.jstor.org/stable/2096319
- Lochner, L. (2007). Education and crime. Retrieved from http://economics.uwo.ca/people/lochner_docs/educationpolicycrime_nov12.pdf
Data Source(s)
- 2009-2019 American Community Survey, 1-Year Public Use Microdata Samples. U.S. Census Bureau, 2020.
Welcome to the ABC Data Exchange (ABCDE), part of the Asset Building Coalition's website. This resource is made up of six interconnected web pages which, together, offer in-depth data and context on the issue of Asset Poverty in Forsyth County, North Carolina.
The landing page of the ABCDE introduces the issue of Asset Poverty, and the impact it has on individuals and families in our community. Five additional, topic-specific data deep dive pages offer a broader range of data and contextual information.
The Asset Building Coalition's purpose and goals in producing this web-based resource are to:
- Build a hub of local information on financial wellbeing and asset poverty to be used as a tool for community stakeholders.
- Educate and raise awareness locally about asset poverty, its upstream causes, and the effects it has on individuals and our community overall.
- Highlight key issues, challenges, and disparities among core measures of asset poverty to catalyze local conversations about innovative and equitable solutions.
The Asset Building Coalition has produced this content in partnership with Forsyth Futures, a registered 501(c)(3) organization that provides action-oriented data analysis and reporting services to organizations within Forsyth County. If you have questions about this content, please contact [email protected].
Some measures in the ABC Data Exchange are based on modeled estimates
Some measures contained in the Data Exchange use estimates that are not solely based on local data. These estimates use local demographic and other information to predict local estimates based on analyst modeling of state-level data. That is, estimates are calculated at a state level that is then adjusted to local demographics.
Specific measures that are based on modeled estimates are clearly labeled within the data exchange.
The Impact of COVID-19
In 2020 and 2021, the COVID-19 pandemic dramatically reshaped how our community functions and it disrupted many aspects of our day-to-day lives. Many people experienced impacts to health and safety, increased stress, and many lost some or all of their ability to earn income. These and many other impacts on our community have tested our resolve, impacted our well being, and almost certainly changed the circumstances around our financial wellbeing in ways we don’t yet understand.
All of the data contained in this web-based informational resource is pre-pandemic; for more specific information, view the data notes that are available for each measure. These measures will be updated with 2020 data in late 2021, once that data becomes available.
If you have questions about the data contained in this informational resource, please contact [email protected].