-Decided on libraries to import. Includes pandas, json, requests, plotly express, hvplot, geopandas, and url.
-Read in a nested json and cvs files of residential properties for sale in PHX and surrounding areas, and sorted through the atrributes to get our dataframes in a clean format.
-Using urlopen, we read other json files into a sperate dataframe to plot and compare with our other data.
-Analyzed and plotted the data using plotly, hvplot, and geopandas.
-Plots included a map of hotspots, a histogram of price, and other maps showing differences in price between neighborhoods.
You can run this locally with the jupyterlab file, as long as you are in a pyviz enviroment. There is also a powerpoint attached detailing our findings in an easy-to-interpret manner.
Our goal was to give investors an in depth analysis of the booming Arizona real estate market. Some of the questions we wanted to answer are as follows.
Question 1: If you were to invest today, which zip codes have the lowest cost investment per square ft. = (Best Investment)
Question 2: Identification of Low Risk, High Yield (Best Investment) Property categorized by Property Type, Bedroom size, community_ammenities.
Question 3: Is the housing market in az healthy and sustainable?
Our answers to each question are below, with the corresponding number
Answer 1: Glendale, Avondale, Cave Creek.
Answer 2: We found multiple low risk high yield investments in many areas, grouped by different "value buckets".
Answer 3: Yes. We have high diversity, low unemployment, and a growing rate of home ownership.
CREDITS Reading in data- Ashton, Jeremy Cleaning Data- Ashton, Jeremy Analysis - JJ, Ashton, Jeremy, Tim Plots- JJ Power Point- Tim