Building Buyer

• 

  Exploratory Data # 1

• 

  Exploratory Data # 3

• 

  Exploratory Data # 2

Inspiration

Streamline the process for potential real estate investors or home buyers to find properties within budget in a specified zip code in the Southern California area.

What it does

Assists users in finding real estate properties within their budget in a specified zipcode. Generates the addresses and information of the five closest property costs to the budget. If the user wants to see properties slightly over the specified budget, the user is able to select an option to filter results. There is a choropleth map indicating the cost of properties in the Southern California area and zooms in to the specified zip code.

How we built it

We started off by creating a function to read the csv dataset into a Pandas DataFrame. From there we were able to use the DataFrame to manipulate the data and get useful information that we needed such as the maximum, minimum, and average total assessment value for a zip code. From this we developed a streamlit application to visualize the housing cost in the Southern California area and allow users to search by zip codes for buildings that they may want to buy given a budget. Additionally, on the web application, we plotted an interactive choropleth map of the housing cost by zip codes in the Southern California area that displays important cost information for a zip code and other information.

Challenges we ran into

We realized that there was a lot of missing data values that would make finding a good location for the user harder as it restricted user input on certain features. We had to find ways to handle this data but also clean certain data that was not very useful.

Accomplishments that we're proud of

We successfully built a functional web application that provides insightful visualizations for users to decide based on building cost and personal budget.

What we learned

We learned how to use Streamlit in combination with plotly to create a web application that was both interactive and useful to users. We also learned how to use pandas data frames to extract key feature details that we thought were necessary for our product.

What's Next for Affordable Building Group

More visual elements such as pictures of the property that would help users see the general area without having to look up the locations separately. More specification possibilities such as square footage or pool sizes.

Built With

• pandas
• plotly
• python
• streamlit