Eric Ewing is being recognized for his devotion to delivering results that impact the public through his tenacity, measured approach and collaborative spirit to improve the lives of the public. While Eric was fulfilling his duties as the lead for the Data and Analytics Center of Excellence, Eric served as a senior advisor to the HUD Chief Data Officer.  Through his ability to make connections and relationships, he was able to lead the creation of the Housing and Lead Index (HaLI) Tool.  The HaLI Tool developed by HUD’s Office of Lead Hazard Control (OLHC) transforms how HUD selects recipients of grants for remediation of residential lead paint hazards, better supporting the decision-making process for both grant applicants assembling grant proposals and for HUD’s grant review process to ensure selection of programs that are optimally targeted to reach households with children most at-risk to lead poisoning.  

HUD’s OLHC offers millions of dollars annually in grant awards to identify and eliminate lead paint hazards in privately-owned housing. The funding directs critical support to units of state and local government (grantees) to evaluate and clean up lead-based paint hazards in their older housing developments.  

The HaLI Tool will improve grantees’ speed and targeting of lead remediation funds both in grant design and upon receipt, leading to more successful lead remediation efforts and an increase in return on investment for HUD’s grant dollars. The insights on lead risk gleaned through HaLI will have a compounding effect, as they will also benefit better targeting of lead remediation efforts funded from other sources.  

Secondly, HaLI and the LDPI will create a more objective, transparent, and efficient grant application process for HUD’s OLHC. HaLI will improve both the time required to review and award grants, while ensuring that grant funds are allocated to the areas with the most potential for high impact. The LDPI will also help to identify high-risk neighborhoods or areas that weren’t previously identified and were therefore underfunded. And, alignment of both relevant data layers (families in poverty, number of children) along with grant distribution by census tract will allow HUD to better quantify metrics for grant dollars, e.g. count of children in poverty in a given neighborhood and the amount of grant dollars distributed to that neighborhood in a given year. 

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Third, it will help HUD identify its own lead exposure risk for housing stock within the government’s jurisdiction, including risk from other environmental factors like lead Superfund sites. The outstanding liability of HUD’s own risk from housing properties within its jurisdiction is both unknown and potentially large. To support identification and remediation of lead hazards in these properties, the HaLI tool and data (including LDPI) in it can easily be used to identify those properties located in high-risk census tracts. The outcome will be a better quantification of and eventual reduction of HUD’s financial liability from lead paint, but also an increased public confidence in the quality and safety of publicly funded housing. For families and children living in these units, the long-term benefits will be immeasurable.  

Fourth, and most importantly perhaps, it will help raise awareness around lead risk in general and the availability of remediation resources that are available through HUD’s grants and community programs. The publicly accessible portion of the HaLI map application will not be restricted to just potential grant applicants. The hope is that citizens and community leaders concerned about risks in their own communities could use the tool for a better understanding of their risk and available resources. For example, in the future the map application could include information on grantees with active programs in a neighborhood, with information on how to seek assistance for testing and remediation.