Neighborhood characteristics and resistance to the impacts of housing abandonment

ABSTRACT

Housing abandonment is often disproportionally distributed across a city. Past research has determined that a number of neighborhood socioeconomic and spatial characteristics are associated with housing abandonment. However, past research has not examined the degree to which the impact of housing abandonment varies with specific neighborhood characteristics. Therefore, this research examines whether the impact of housing abandonment on nearby property values varies among different neighborhoods, whether the impact changes at unequal rates over time, and, if so, what accounts for such variability. Focusing on Baltimore neighborhoods between 2001 and 2010, this research found considerable variability in the magnitude of impact of housing abandonment among neighborhoods and that the magnitude of impact changed at unequal rates among neighborhoods over time. Neighborhood crime rate was the strongest predictor of the variability in the magnitude of impact of housing abandonment among neighborhoods. Neighborhood unemployment rate, housing unaffordability, proportion of properties with housing violations, and foreclosure rates also influenced the variability in the impact of housing abandonment.

Notes

1. Baltimore Neighborhood Indicators Alliance–Jacob France Institute created these CSA boundaries in order to measure and track data over time. The boundaries of Baltimore neighborhoods expanded and contracted over time; therefore, they do not support the ability to measure outcomes in a place consistently over time. Another advantage of using CSAs comes from the fact that CSAs align with U.S. census tracts, allowing additional outcomes to be easily added and tracked.

2. As suggested by a reviewer, tallying the number of local organizations without considering how active and effective such organizations are may not be the most appropriate measurement of the level of social organization among neighborhoods. These research data do not include information on how active and effective the 800 organizations included in the data set are; therefore, the raw number of organizations is considered along with the rate of people registered to vote or who voted to create a level of social organization for each neighborhood.

3. Exploratory factor analysis revealed that the number of CDCs was not strongly interrelated and did not represent the underlying neighborhood social organization factor. The correlation matrix showed that the number of CDCs lacked sufficient correlation with other variables.

4. Educational attainment data were available but were not included in the final model because of the multicollinearity issue. The educational attainment variable at the neighborhood level was highly correlated with other neighborhood disadvantage variables including median household income and poverty rate.

5. Construction of accurate repeat sales data that do not violate the repeat sales methodology assumption that property and neighborhood characteristics have not changed between sales is critical. First, the repeat sales methodology includes only true market transactions. Therefore, nonrepresentative transactions such as non-arm’s-length transactions are excluded. Second, the data are filtered to eliminate any possible flipped properties and outliers—properties with abnormal price change—that may violate the repeat sales methodology assumption. Examining Baltimore residential property sales data, transactions with less than a one-year holding period showed abnormally high price appreciation, indicating that such transactions are likely flipped properties. Thus, any transactions with a holding period shorter than one year were eliminated from the data set. Finally, in order to identify outliers, quarterly price appreciation was calculated for all transactions for abnormality, and less than 1% of the total remaining transactions that were identified as outliers were eliminated from the data set.

6. Additional governmental programs were created to improve neighborhood quality: Project 5000 and Targeted Enforcement Toward Visible Outcomes, HOME, Community Development Block Grant Program, neighborhood stabilization programs, and other types of municipal investments. However, data on these programs were unavailable or incomplete and thus were not included in this research. Omitting these programs in this research likely underestimated the magnitude of the impact of government intervention in each of the neighborhoods.

7. The author appreciates the reviewer for suggesting this critical note of caution.

Additional information

Notes on contributors

Hye-Sung Han

Hye-Sung Han is Assistant Professor of Urban Affairs at the University of Missouri–Kansas City. Han received her PhD in city and regional planning from the University of North Carolina at Chapel Hill. Her main area of research focuses on housing abandonment and foreclosures, housing and community development, neighborhood revitalization, and housing policy. She has published articles in Housing Policy Debate and Housing Studies.