Houses

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

df = pd.read_csv('MORTGAGES.csv')

#Amortisation of mortgage payments, assumes 20% down payment by borrower
#Change 360 to 180 to model 15-year mortgages
df['RATE'] = df['RATE'] / 1200
df['PAYMENT'] = (0.8 *df['MEDPRICE'] * df['RATE'])/(1-np.exp(-360*np.log(1+df['RATE'])))

#Index=100 at 2012-01-01
inf = df[['PCEPI']].copy()
pay = df[['PAYMENT']].copy()

#list-of-tuples
data = []

#The payments are deflated and put into a corrosponding tuple
#Change 359 to 179 for 15-year mortgages
i = 0
while (i < 597):
  cost = (100 * pay.iat[i, 0] / inf.loc[i:i+359])
  i = i + 1
  data.append(cost)

#The payments are summed and the list of tuples is converted to a pandas series
data = [PCEPI.sum() for PCEPI in data]
s = pd.concat(data, ignore_index=True)

#The 20% down payment is added to the series
s = s + 20 * df['MEDPRICE'] / df['PCEPI']

#Converts series to DataFrame, attaches the real price of purchasing in cash, and sets date as index.
dfpt = s.to_frame()
dfpt['rprice'] = 100* df['MEDPRICE'] / df['PCEPI']
dfpt['DATE'] = df['DATE']
dfpt = dfpt.set_index('DATE', drop=True).rename_axis(None)
dfpt.columns = ['Real Cost', 'Real Price']

#aesthetics
ax = dfpt.plot(figsize=(14,7), xticks= [0,117,237,357,417,477,596])
plt.xticks(rotation=45)
plt.axvline(x=237, color='green', linestyle=':')
plt.axvline(x=417, color='green', linestyle='--')
ax.set_xlim(left = 0, right=597)

## End ##
## SAVE THE BELOW AS MORTGAGES.CSV##