Firstly, I would like to thank, the Undergraduate Experiential Learning Opportunities Support Fund for enabling me to conduct this research assistant role. My Arts Undergraduate Research Internship (ARIA) research project was conducted with CurbCut, a group building a predictive housing model under the supervision of Dr. David Wachsmuth. This project aims to create sophisticated modeling tools for use by the federal government in understanding and predicting housing market trends across Canada. My interest in this ARIA opportunity stemmed from the intersection between statistical modelling techniques and addressing one of Canada's most pressing social issues. As someone passionate about mathematics and statistics, I was drawn to the prospect of applying machine learning and cutting-edge research methodologies to tackle the housing affordability crisis that affects millions of Canadians.
Going into this internship, I did not have specific learning objectives and instead anticipated that what I learned would be in the form of unknown unknowns. I gained insight into how large-scale, collaborative research projects are organized and managed, particularly in academic settings, as well as learned about the workflow behind translating theory in the form of mathematical models into tools that could inform real-world decision-making by government agencies.
The highlight of my ARIA was witnessing the impressive accuracy of our predictive model during testing phases. Our model successfully predicted housing trends with 95% accuracy for municipalities across Canada over a 10-year prediction period. This was validated by training the model on pre-2015 data and then comparing our predictions for the 2015-2024 period against actual observed housing data. Seeing this level of precision in real-world applications was rewarding and demonstrated the tangible impact that rigorous mathematical modeling can have on understanding complex social phenomena.
This ARIA experience has shaped my perspective on future career and education paths. The project has inspired me to seek out similar applications where advanced quantitative methods can address critical societal challenges. I am now seriously considering pursuing modeling positions in both public and private sectors, where I can continue applying similar mathematical techniques to real-world problems. The experience has also sparked my interest in consulting work, particularly in areas where data science and urban planning intersect. I can envision myself working with government agencies, urban planning firms, or research organizations that focus on evidence-based policy development.
The financial support provided by the ARIA program was instrumental in making this experience possible and maximizing its impact. The funding allowed me to focus entirely on my research without the need to maintain two part-time jobs, which would be necessary had this work been unpaid. This financial security meant I could dedicate my full attention and energy to learning, conducting research, and contributing meaningfully to the CurbCut project. The ability to be fully present and engaged in the research process enabled deeper learning and more substantial contributions than would have been possible if I had been juggling multiple work commitments.
I am deeply grateful to the anonymous donor whose generous contribution made this ARIA award possible. Their investment in student research experiences like mine creates opportunities for meaningful learning and contributes to important research that can benefit Canadian communities. The support has not only enhanced my academic and professional development but has also enabled me to contribute to research that may ultimately inform housing policy decisions affecting countless Canadians.
This ARIA experience has provided me with both technical skills and a clearer vision of how I might use my mathematical background to make meaningful contributions to society.
