FannieMae AniMae TideSafe Housing Solutions

Logo
Housing Prices alongside Hurricane Katrina
HeatMap of Houston

Inspiration

Our inspiration came from the resilience of communities in the face of natural disasters. We wanted to understand and illustrate how catastrophic events like Hurricane Katrina and Harvey affect the housing market, and how data visualization can shed light on recovery and preparation efforts.

What it does

FannieMae AniMae TideSafe Housing Solutions provides an in-depth analysis and visual representation of housing market fluctuations in response to Hurricanes Katrina and Harvey. By comparing data from Houston, Texas, and New Orleans, Louisiana, we offer insights into the resilience and recovery of these communities.

How we built it

We built our project by sourcing data from Zillow and climate risk scores from the Climate On Demand API. We used Python for data manipulation, analysis with Pandas, and visualization with Seaborn and Matplotlib to create compelling graphs that tell the story of these housing markets over time.

Challenges we ran into

We faced challenges in data collection and normalization, ensuring accurate representation of the market pre- and post-disasters. Integrating housing data with climate risk scores posed a unique challenge in correlating disparate datasets.

Accomplishments that we're proud of

We are proud of our ability to transform complex data into clear, insightful visual narratives. Our work highlights the potential of data-driven approaches in understanding and preparing for climate-related challenges.

What we learned

We learned about the profound impact of natural disasters on housing markets and the importance of data in driving recovery initiatives. We also gained insights into climate resilience and the power of visual storytelling.

What's next for FannieMae AniMae TideSafe Housing Solutions

The next step is to expand our analysis to other regions and disaster types, incorporate real-time data for up-to-the-minute analysis, and develop predictive models to help communities prepare for and mitigate the effects of future disasters.

View our presentation: https://docs.google.com/presentation/d/1pBgy8f8rc8Z5vf-R6YNpY4m46ZZbdc2KC5ZjpCYnT0Q/edit?usp=sharing

Built With

api
matplotlib
pandas
python
seaborn
zillow

Submitted to

BisonBytes
- Winner FinTech track

Created by

I worked on the research for finding the APIs that would be suitable for our projects and finding the most efficient ways to integrate them and pull data from them

Sharon0motosho Omotosho
I was apart of the research for finding solutions to help those affected by flood related disasters or damages. I created a Google Form to those who were willing to volunteer a room/rooms to help those affected with having a place to stay for the meantime. I included insurance providers that cover flood related costs and damages and a link with over 100+ companies who give back to communities and people in need. This link provided a form people can fill out to get the help and resources they need.

Trishma Garcon
I worked on the research aspect of the project. I collected the data for the solutions and case studies. I also made some of the google documents that had resources for those affected by the Hurricane. Additionally, I created the name for our project (Tide Safe Housing Solutions) and was part of the team that created the presentation.

Kayla Lodge
I was responsible for data manipulation, analysis and visualization.

Aayushka Budhathoki

Updates

Aayushka Budhathoki started this project — Mar 31, 2024 11:51 AM EDT

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