[USER (data scientist)]: Perfect! Finally, I want to create a table showing the annual number of affordable properties sold, average price, and percentage change for each year. Please generate a DataFrame to summarize annual metrics, including the number of affordable properties sold, average price, and percentage changes in both number sold and average price, for the Melbourne housing market.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import matplotlib.pyplot as plt
import pandas as pd
import pickle

melbourne_housing = pd.read_csv("melb_data.csv") 

# YOUR SOLUTION BEGIN:
<code1>
[GENERATE YOUR CODE]
</code1>
# YOUR SOLUTION END:

print(annual_metrics)

# save data
pickle.dump(annual_metrics,open("./pred_result/annual_metrics.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: You can create a new DataFrame to store the results. Here's the code to create the table: 
