/san_fransisco_real_estate

An application of technology to real-estate markets, is an innovative domain in the fintech industry. Assume that you’re an analyst at a proptech company that wants to offer an instant, one-click service for people to buy properties and then rent them. The company wants to have a trial of this offering in the San Francisco real-estate market.

Primary LanguageJupyter Notebook

Table of Contents

  1. Calculate and plot the housing units per year.

  2. Calculate and plot the average prices per square foot.

  3. Compare the average prices by neighborhood.

  4. Build an interactive neighborhood map.

  5. Compose your data story.

IMPORTANT: I am able to plot on iPython Notebook, however the plots are not visible when I published the Notebook on GitHub. More on this issue

1. Calculate and Plot the Housing Units per Year

  • Use the groupby function to group the data by year.
  • Aggregate the results by the mean of the groups.
  • Use the hvplot function to plot the housing_units_by_year DataFrame as a bar chart.
  • Make the x-axis represent the year and the y-axis represent the housing_units.
  • Style and format the line plot to ensure a professionally styled visualization.
    • What’s the overall trend in housing units over the period that you’re analyzing? 1

2. Calculate and Plot the Average Sale Prices per Square Foot

  • Group the data by year, and then average the results.
  • What’s the lowest gross rent that’s reported for the years that the DataFrame includes?
  • Create a new DataFrame named prices_square_foot_by_year by filtering out the “housing_units” column.
  • The new DataFrame should include the averages per year for only the sale price per square foot and the gross rent.
  • Use hvPlot to plot the prices_square_foot_by_year DataFrame as a line plot.
  • This single plot will include lines for both sale_price_sqr_foot and gross_rent.
  • Style and format the line plot to ensure a professionally styled visualization.
    • Did any year experience a drop in the average sale price per square foot compared to the previous year?
      • If so, did the gross rent increase or decrease during that year? 2

3. Compare the Average Sale Prices by Neighborhood

  • Create a new DataFrame that groups the original DataFrame by year and neighborhood. Aggregate the results by the mean of the groups.
  • Filter out the “housing_units” column to create a DataFrame that includes only the sale_price_sqr_foot and gross_rent averages per year.
  • Create an interactive line plot with hvPlot that visualizes both sale_price_sqr_foot and gross_rent.
  • Set the x-axis parameter to the year (x="year").
  • Use the groupby parameter to create an interactive widget for neighborhood.
  • Style and format the line plot to ensure a professionally styled visualization.
    • For the Anza Vista neighborhood, is the average sale price per square foot for 2016 more or less than the price that’s listed for 2012? 3

4. Build an Interactive Neighborhood Map

  • Read the neighborhood_coordinates.csv file from the Resources folder into the notebook, and create a DataFrame named neighborhood_locations_df.
  • Be sure to set the index_col of the DataFrame as “Neighborhood”.
  • Using the original sfo_data_df Dataframe, create a DataFrame named all_neighborhood_info_df that groups the data by neighborhood.
  • Aggregate the results by the mean of the group.
  • Review the two code cells that concatenate the neighborhood_locations_df DataFrame with the all_neighborhood_info_df DataFrame.
  • Using hvPlot with GeoViews enabled, create a points plot for the all_neighborhoods_df DataFrame.
  • Use the interactive map to answer the following question:
    • Which neighborhood has the highest gross rent, and which has the highest sale price per square foot? 4

5. Compose Your Data Story

  • Based on the visualizations that you created, answer the following questions:
    • How does the trend in rental income growth compare to the trend in sales prices? Does this same trend hold true for all the neighborhoods across San Francisco?
    • What insights can you share with your company about the potential one-click, buy-and-rent strategy that they're pursuing? Do neighborhoods exist that you would suggest for investment, and why?

File: San Fransisco Real Estate Market Analysis