[USER (data scientist)]: Just one more thing. Can you tell me the average age of residential buildings that underwent alterations and additions in 2016-2017? Please generate a dataframe to explore and analyze the average age of buildings renovated in 2016-2017 within the 'melbourne_housing' dataset, and then serialize the average age result for future reference.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

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(average_age_renovated)

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure, let me calculate the average age of residential buildings that underwent alterations and additions in 2016-2017: 
