The project aims to conduct calculations such as determining walk scores, access to amenities, travel times to hospitals, and in-depth analysis of rental and real estate data at the city and neighborhood levels. We may also develop a rental pricing assessment algorithm.

The primary objective of the project is to collect and normalize data sets related to city infrastructure and real estate. This includes analyzing walk scores, access to amenities, travel times to hospitals, and detailed rental and real estate data at both city and neighborhood levels. The data will then be used to build specialized language models that can provide insights through maps and chats.

The goal is to identify trends, problems, and opportunities to improve neighborhoods by offering valuable insights to city planners, builders, and other stakeholders.

The analyst will have the autonomy to build indexes they believe will impact decision-making, with the freedom to explore various directions to achieve the project's objectives.

What tasks will learners need to complete to achieve the project goal?

- Collect and normalize data sets related to city infrastructure and real estate.

- Build specialized language models that can provide inference on the data via maps and chats.

- Identify trends, problems, and opportunities to build better neighborhoods.

- Advise city planners, builders, and other stakeholders with the insights the data provides.

- Build indexes that will have an impact on decision making.

- Choose different directions that will lead to the goal.

- Work with LLMs and machine learning tech alongside our team of engineers and data scientists

• Direct access to our entire team of software engineers, data scientists, real estate professionals and others.
• Access to advanced LLM and machine learning models and platforms.
• Build connections in the real estate community in Canada for future work/project opportunities.