The location of an asset determines the proximity and accessibility of daily needs, activities, and amenities for its occupants, as well as the modes of transportation available for those people to carry out daily trips. 87% of daily trips in the US are completed by car and these trips account for 23% of greenhouse gas (GHG) emissions in the country. Therefore, location is a factor that dictates significant environmental impacts, specifically the GHG emissions of what is built where. When you consider the millions of real estate decisions that are made every year, from the acquisition of an asset or portfolio to renting an apartment or opening a new retail store, the result is a globally-scaled environmental challenge that drives Local Logic’s mission to use data and location intelligence to create more sustainable, livable cities.
Local Logic’s mission is super ambitious! We’ve quantified everything outside the four walls of any asset to optimize the triple bottom line because we believe that by making more informed real estate decisions, we can create more sustainable, livable cities. As we grow and scale as a company, we continue to improve our products, adding more data and more offerings, we also continue to add incredibly smart and mission-driven team members.
To tap into our people’s passion and ingenuity in solving this big, audacious problem, our Chief Product Officer and Co-founder, Gabriel Damant-Sirois hosted a hackathon last quarter. “By allowing the team to freely work on the challenge of how the built environment impacts transportation behavior and ultimately GHG emissions, we felt new ideas would be created and help us get closer to our mission,” Gab said. “We were not disappointed! All of the ideas were so good that they will get either directly in our roadmap, or be used as inspiration in the way we build our products.”
The hackathon brought forth a variety of solutions, creativity, fun, and urban-planning nerdiness from our teams. The solutions were considered based on their sustainable impact and feasibility of implementation. The panel of judges included external industry leaders, Masha Krol, Co-founder and CEO of Ampersand – derisking AI/ML models, Sam Vermette, Co-founder and CEO of Transit – an app providing transit information for over 200 cities worldwide, and Simon Chauvette, an organizational development consultant for tech startups.
Summary of the teams’ ideas
Team “Emission: Impossible” – Colin, Tansin, Rola, and Flo
The idea was to show homebuyers that homes in car-oriented suburbs, while often appearing cheaper than more centrally located homes, may actually be more expensive due to the differences in transportation costs. In total, the monthly costs of owning a car can be substantial compared to other forms of transportation, such as walking, biking, or mass transit. The team’s aim was to quantify emissions for different transportation modes and show residential consumers that they could save money — and pollute less — by living in a neighborhood where a car is not needed.
Team Park-Royal – Katia, Mich, Chris, and Charles
The team identified that most North American cities dedicate an extremely large proportion of space to cars, noting that the US has an estimated 8 parking spots for every car. Despite this oversupply, cities continue to require large parking ratios in their zoning. These regulations are not only wasteful and environmentally damaging, but they also come at a cost to developers.
The team’s proposed solution created a machine learning model that will leverage Local Logic’s demographics, scores, and POI data to allow developers to plan for parking more efficiently. By taking into account existing parking, transit stations, and proximity to amenities, the team proposed to help developers build data-driven and methodologically-sound business cases to build less parking and eventually reduce the GHG footprint of their projects.
Jane Jacobs’ Dream Team – Mitchell, Nick, and Cassandra
The team took a look at Local Logic’s current product offerings and customer feedback patterns to propose a concept that would extend and harmonize them, proposing additions to the Local Analytics Platform’s features that would allow customers to create narratives using not only our insights but their own sources of data, as well as being able to easily share and access those narratives in a variety of formats to meet their needs, from on-demand APIs to PDF reports.