{"id":1208,"date":"2022-01-13T15:48:14","date_gmt":"2022-01-13T22:48:14","guid":{"rendered":"https:\/\/jeremywhittaker.com\/?p=1208"},"modified":"2022-01-14T09:54:31","modified_gmt":"2022-01-14T16:54:31","slug":"whats-a-better-investment-stocks-or-real-estate","status":"publish","type":"post","link":"https:\/\/new.jeremywhittaker.com\/index.php\/2022\/01\/13\/whats-a-better-investment-stocks-or-real-estate\/","title":{"rendered":"What’s a better investment stocks or real estate? Let’s write some Python code."},"content":{"rendered":"\n

I was recently chatting with a buddy about investing in real estate or stocks and which was better. I decided to use Python to analyze what that investment would look like. I used data from FRED <\/a>and yfinance<\/a> which is one of my favorite sources for historical data. Although they do not offer smaller timeframes. <\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

The first thing I’m going to do is set the dates I’m interested in. <\/p>\n\n\n\n

beginning_date = '1900-01-01'\ndate_today = datetime.now()\nend_date = date_today\n\n<\/code><\/pre>\n\n\n\n
I’m not going to use FRED and YFinance which are two Python plugins to download historical data. <\/p>\n\n\n\n
df = yf.download('^gspc',beginning_date,date_today,progress=True).drop(columns=['Open','High','Low','Close','Volume']).rename(columns={'Adj Close':'SP500'})\ndf = df.rename(columns={'Adj Close':'SP500'})\n\ndf['MSPUS'] = pdr.DataReader('MSPUS','fred',beginning_date,date_today)\ndf = df.rename(columns={'MSPUS':'Median_House_price'})\n<\/code><\/pre>\n\n\n\n
The stock market index symbol from Yahoo is ^gspc <\/a>as you can see in the code above. The median house price is MSPUS <\/a>from FRED. <\/p>\n\n\n\n
df = df.ffill(axis=0)\n\ndf['Median_House_price_pct_change'] = df['Median_House_price'].pct_change()*100\ndf['SP500_pct_change'] = df['SP500'].pct_change()*100\ndf = df.dropna()\n\ndf['Median_House_price_pct_change_cumsum'] = df['Median_House_price_pct_change'].cumsum()\ndf['SP500_pct_change_cumsum'] = df['SP500_pct_change'].cumsum()\ndf = df.dropna()\n\ndf['spread'] = df['SP500_pct_change_cumsum'].sub(df['Median_House_price_pct_change_cumsum'])\n\n\ndf.tail(20)<\/code><\/pre>\n\n\n\n
The Median house price data is quarterly so I’m using ffill to forward fill the data. <\/p>\n\n\n\n
What I’m essentially doing next is creating columns in my DataFrame for the following:<\/p>\n\n\n\n