We calculate the yearly average price for the price per square foot, gross rent and number of units present in the market. By plotting the histogram of the housing units, where each bin corresponds to a different year, we can see the increasing trend.

By using the same yearly average values we can state which year has the lowest gross-rent (year 2010). We drop the housing units column and we plot the price per square foot and gross rent data in the period; we can see the constant increase in both of the data throughout the period.

Groupying by neighborhood in addition to year, we can calculate the yearly average values related to each part of the city of San Francisco. By dropping the housing units column, we can plot the same graph as the last one but related to each neighborhood separately. It's possible to visualize different neighborhood's data by switching with the widget on the right of the chart.

We import into the notebook also the geographic information of the various neighborhoods in a separate dataframe and we can concatenate it with the dataframe containing the real estate market's information. With this synthesized information we can create an geographical map, displaying the market's data in terms of color and dimension of the dots related to the different locations.