This research is based upon work supported by the Urban Institute through funds provided by the Robert Wood Johnson Foundation. We thank them for their support but acknowledge that the findings and conclusions presented in this report are those of the author(s) alone, and do not necessarily reflect the opinions of the Urban Institute or the Robert Wood Johnson Foundation.
Intuitively one might guess that housing conditions could cause poor health outcomes. Similarly, if one does not have stable housing, it could also lead to undue stress and therefore impact health.
We understand that the level of disinvestment in Hartford has yielded deleterious outcomes for city residents, but to date, we have not seen locally-specific research that connects the relationship between housing conditions, health outcomes and neighborhood disparities.
In our research, we examined neighborhood level health data with local open data sources, enabling the exploration and examination of the relationship between health and housing in a concrete way for Hartford’s most disinvested neighborhoods.
We then created housing indices, one on housing conditions and one on housing stability. This lets us compare the differences in health outcomes amongst the most and least stable census tracts in the city and amongst the best and worst quality census tracts in the city.
18 health indicators were included in this analysis. These data were made available by the Centers for Disease Control at the census tract level for the 500 largest cities in the United States. Data are provided on health outcomes, unhealthy behaviors, and preventative health measures. Many of the health measures exhibit similar patterns.
In Clay Arsenal, the Northeast, and Upper Albany, as well southern Frog Hollow, Sheldon-Charter Oak, and Barry Square neighborhoods, more than 1 in 5 adults report poor physical health. The distribution of estimates for people reporting poor mental health transcend the North/South division.
The ribbon of high levels of poor mental health through the middle of the city shows a concentration of poor mental health reported in the Northeast neighborhood and a corresponding concentration in the South End neighborhoods of Frog Hollow and Barry Square, though the concentrations in the southern portion of the city are more geographically dispersed.
Source: CDC 500 Cities Health Outcomes, Physical health not good for ≥14 days among adults aged ≥18 years.
Source: CDC 500 Cities Health Outcomes, Mental health not good for ≥14 days among adults aged ≥18 years.
Using publicly available data, we constructed two housing indices. One examines Housing Stability: housing finances and tenure. The other examines Housing Conditions: aspects of housing quality that focuses on the physical quality of the housing stock as part of the built environment.
Each tract could receive a potential score from 7 to 35. The most unstable tract received a score of 11 and the most stable was 31.
Barry Square, Clay Arsenal, Frog Hollow, Northeast, and Upper Albany are the neighborhoods with highest housing instability.
Each tract could receive a potential score from 2 to 30. For Housing Conditions Index, the highest census tract scored 29, while the lowest scored 2.
Asylum Hill, Clay Arsenal, Northeast, and Upper Albany neighborhoods have some of the most deleterious housing conditions.
Neighborhoods with poor housing conditions are also areas with poor health outcomes.
Someone living in a highly unstable tract was 34 or 36% more likely to report being in poor mental or physical health than someone living in a tract with a high housing stability score. Smoking and COPD were both strongly related to a tract’s housing stability, as were diabetes, obesity and lack of physical activity.
Using the available point data for housing code violations and foreclosures, we were able to highlight physical concentrations of instability and poor conditions at a more granular level than was possible at the census tract level.
We identified areas of statistically significant concentrations of specific events, including housing code violations, essential services violations, and foreclosures. These allowed us to further drill down into individual neighborhood housing condition challenges to identify areas for targeted investment.
Foreclosures and Essential Services highly correlated with poor health.
Foreclosures were unevenly distributed throughout Hartford neighborhoods, however. Census tracts 5012, 5041, 5028, 5017, and 5015 had foreclosure rates more than twice the city average. Three of these high foreclosure tracts were in the north end of the city, and the remaining two were centered on Park Street in the Frog Hollow and Parkville neighborhoods.
Interestingly, these neighborhoods have very low homeownership rates compared with the city, suggesting that distressed landlords were more likely to experience foreclosure than owner-occupants. These high foreclosure neighborhoods also have higher rates of all negative health indicators under study. Higher foreclosures rates are significantly correlated with poorer health outcomes and preventative measures in nearly all the health indicator estimates under review.
Source: City of Hartford, Foreclosure/Lis Pendens.
Source: City of Hartford, annual average number of foreclosures (2011–2015) Foreclosure/Lis Pendens normalized by Parcels.
Residents can call the City Public Health Department to report housing code violations such as: interior rodents, bed bugs, and emergency service needs (roof collapse, fire, no heat). The map below shows areas of statistically significant high concentration of calls.
Source: City of Hartford, Housing Code Cases, accessed June 2018.
Essential services calls are among the most serious housing code violations, because they indicate that a property lacks heat or water service, making the property uninhabitable in the short term.
Areas in red show where there are statistically significant concentrations of calls for essential services, indicating poor housing conditions. A large cluster exists in the north—starting in Asylum Hill, extending into Upper Albany, slightly into Clay Arsenal and a little bit in the North End. A smaller but significant cluster in the center of Frog Hollow, and a third large cluster in Barry Square that extended slightly north into the South Green neighborhood.
Source: City of Hartford, Housing Code Cases, accessed June 2018.
Source: City of Hartford, Housing Code Cases, essential services code violation complaints per residential unit, 2011-2015, accessed June 2018.
We were curious to see if there was overlap between the location of affordable housing units and calls for essential services. We downloaded data on affordable housing from the National Housing Preservation Database and recognize that it does not include all types of affordable housing units but gives a portion of the picture.
The maps below visually show that there is some overlap between affordable housing locations and calls for essential services. The next step, in order to really identify the overlap, will be to actually compare the addresses in the two datasets which we plan on doing by mid-December.
Source: City of Hartford, Housing Code Cases, essential services code violation complaints per residential unit, 2011-2015, accessed June 2018.
Source: National Housing Preservation Database, Publicly Supported Housing Inventory, accessed November 16, 2018.
Since we found a strong correlation between health and foreclosures and also health and essential service calls, we wanted to ensure that these were unique problems that we identified and not the same properties. We linked the foreclosure data and essential service calls data and found that these are two unique problems residents face, requiring separate interventions.
There are about ~790 properties listed in 2011–2015 in Foreclosure dataset. During the same 5-year period, there were ~1,650 essential services calls in Hartford.
We linked the two datasets and found that nearly 60 foreclosed properties made at least one essential services call—that is about 7% of all foreclosed properties. At the same time, only about 90 (or 5%) of ~1,650 essential services calls were made from foreclosed properties.
Another indicator we included to measure Housing Stability was the eviction rate. Interestingly, the foreclosure rate was a better predictor of negative health outcomes than the eviction rate, although both variables measure forced residential instability. The eviction rate had lower correlations with all health indicators under review than the foreclosure rate, although several correlations were still statistically significant.
The map below shows neighborhoods with high concentrations of evictions (in red) and areas with low eviction rates (in blue).
Average annual eviction rate from 2011-2015, measured by total legal evictions divided by the count of residential units. Source: Eviction Lab.
When evaluating the relative impact of each measure, it is important to note that the formal eviction rate does not track the prevalence of more common informal or “DIY” evictions, but rather only measures evictions that had entered the legal process. Certain vulnerable populations, especially undocumented renters, may be more likely to vacate an apartment prior to a formal eviction filing.
A visual inspection of the distribution of high eviction rates suggests that some areas of high instability according to other measures—specifically tracts in Frog Hollow, Parkville, and Barry Square—had lower eviction rates than neighborhoods such as Asylum Hill and North Meadows (a largely industrial neighborhood). This may indicate racialized eviction patterns in the city, since both Asylum Hill and the north end more generally are predominately African American, while south end neighborhoods have high concentrations of Latinx residents. Alternatively, it may suggest a different spatial pattern between DIY and formal evictions, with formal evictions concentrated in African American communities and DIY evictions common among immigrant and migrant communities. Further research into the prevalence of informal, DIY evictions would be necessary to determine the explanation for this difference.