SURF Mentoring
Potential projects/topics: Kinder Institute - Housing & Neighborhoods - Multiple Projects
(1) What is driving home insurance cost increases?
Student would assist in a research brief on home insurance costs in Harris County through descriptive quantitative data analysis and cleaning, ideally with R (tidyr/dplyr packages)
(2) What are barriers to developing affordable rental housing in greater Houston?
Student would assist on a research brief to barriers facing the development of Low Income Housing Tax Credit (LIHTC) in Harris County tracing issues related to zoning, neighborhood associations, and federal legislation. Descriptive qualitative data analysis and cleaning, ideally with R (tidyr/dplyr packages).
(3) Where are landlords committing property tax fraud?
Student would be a research assistant for a project on landlord property tax fraud in Harris County. They would also explore how low-income homeowners and homeowners of color might be less likely to receive property tax exemptions, for which they are eligible. Descriptive quantitative data analysis and cleaning, ideally with R (tidyr/dplyr packages). Project will involve extensive data merging.
(4) What are the causes and social consequences of Houston’s stray dog problem?
Student would assist in the early stages of a research project on the stray dog problem in Houston neighborhoods, measuring upstream causes of stray dogs related to street sweeping, trash collection, sewage mitigation, as well as the downstream effects such as impact of residents mobility, access to green spaces, transit, postal service, and other public services impacted by stray animals.
(5) What constitutes a healthy neighborhood and how might that be achieve? What are the impacts of neighborhood design and regulation (e.g., sidewalk access, tree canopy, stray animals) upon the health activities of individuals in particular geographic regions? How does this design the health and wellbeing of residents. Researchers will engage in qualitative interviewing and photoethnography to map some of these issues.
Potential skills gained: Descriptive quantitative data analysis and cleaning in R with dplyr/tidyr packages, Qualitative Interviewing, knowledge of tenant/landlord policy, and property tax policy, insurance policy, structural conditions producing high incidence of stray dogs
Required qualifications:
- Required skills: Multiple projects, some require no prior skills, some require Descriptive quantitative or qualitative data analysis and cleaning, ideally with R (tidyr/dplyr packages). Project will involve extensive data merging.
Direct mentor: Faculty/P.I., Other Research Associate
