Seattle-Area Home-Flipping Tool

Defining Question

Flipping property has become…a business…in the United States. Sometimes it can be lucrative for those who invest hours into research. On the other hand, others can get lazy, think they can turn a quick buck, and make boneheaded decisions when they delve into the world of “house flipping”. This tool was created to make those latter people a bit wiser.

The dashboard focuses on the Seattle area, looking at sold property that was built between 1900 and 1992. The idea was to give people an idea of where the biggest opportunities were as far as maximizing profits on properties that could be renovated.

Data Collecting and Cleaning

The data for this dashboard was found on Kaggle, created by Andy Krause (here is the link). He also provided documentation regarding the labels for some of the variables in the dataset. Here is the GitHub link of those definitions. After collecting the data, I simply added the definitions that were provided, and then removed any rows that would not be needed for the dashboard (which was done through Excel). This is so the dashboard could run quickly.

Final Dashboard

The final dashboard, which you can see below, was done on Tableau. The top-left graph is a static graph that simply compare the median price per square foot for properties that were and weren’t renovated. This was a baseline graph to give the view an idea of investment opportunities. For example, house that were originally built between 1956 and 1970 had a significantly higher price once they have been renovated. The filters to the right allows customization as to property types. The heat map on the bottom left allows for understanding the prices in the area. The final graph shows the median price per square foot for properties based on the filters above (thus not static).

Furthermore, the data only includes house sales between 2018 and 2022, as properties purchased earlier could skew the results. This is also the main reason that median, instead of average, price was used, as some variables were inflated based on a small number of properties.