Financing Green Home Energy Adoption
Real estate generates 40% of global carbon emissions – 11% from new construction and 29% from the energy usage of existing buildings (International Energy Agency 2023). To help achieve decarbonization of the real estate sector, over 30 states have authorized private lenders to originate government-backed loans for investments in energy-efficient retrofitting of commercial or residential properties. A key innovation of these lending programs is that they help democratize access to financing to close the green home investment gap by allowing homeowners to obtain loans without requiring that they attain a minimum credit score.
This project will evaluate publicly-backed specialized loan programs and compare them to alternative methods to encourage property owners to make upgrades such as solar panel installs or energy efficient HVAC systems. Which borrowers and types of properties are more likely to benefit from these loan programs, and do these policies have the potential to amplify existing inequalities through spillovers to home values in the surrounding neighborhoods?
I am seeking a team or RAs to work towards the creation of a nationwide database of loans granted for investment in green home improvements. The bulk of the RA work involves web scraping, submitting written data requests to governmental offices, and formatting property addresses across different databases to track homeowners and building upgrades over time.
Requisite Skills and Qualifications
The data acquisition portion of this project involves compiling records from municipal tax authorities and related government websites. This may require a large number of web searches, web scraping (i.e. batch downloading files and text, or converting PDFs to tabular format in Excel), submitting written data requests via email to the relevant public official(s), and cleaning the raw data to ensure consistency across jurisdictions. Therefore, attention to detail and professional writing skills are crucial to the execution of the research.
Prior experience with web scraping is preferred. I am especially interested in candidates who have experience geo-locating data points using tools such as Google Maps API and software packages like tigris in R. However, familiarity with these tools is not a requirement.
Basic proficiency in statistical/econometric software packages such Stata or R is required for aspects of the project which involve cleaning the source data collected.
I welcome applicants with interests in other fields besides economics – including but not limited to – finance, urban studies, public health, computer science, mathematics/statistics, physics, and political science.