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pburkholder / sell-rent-monte-carlo

Learning to do monte carlo simulations to inform whether we sell or rent
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Some thoughts on statistical computing, Monte Carlo, and sell/rent

Context

Our family may be making some decisions down the line, one of them being: If move, do we rent our current residence or sell it? There's a fairly useful spreadsheet for doing the modelling at: https://www.journalofaccountancy.com/issues/2005/jun/sellit.html by Witmer and Kelley from 2005 Journal of Accountacy.

One of the things that occurred to me as I started to work with that model, though, is that you have to make fairly broad assumptions about: CPI inflation, market performance, rental market trends, real estate performance and so on. How do I prevent wishful thinking from coloring what numbers I use?

For fun, I started at the least useful part, but most interesting working with Monte Carlo simulations.

Tooling

I did some Googling to determine whether I should work in this R, Julia or Python. I've worked with R, which has me interested in Julia, but in the end I found that a good starting point was to work with Python, thanks to guidance like Simple Monte Carlo Simulation of Stock Prices with Python from Programming4Finance to work.

Getting Spyder installed on MacOS was a pain. This came in useful: http://engineeringterminal.com/computer-science/tutorials/scipy-setup-macos.html

There are no vim bindings that work for me, and panda_datareader was failing with cannot import name 'is_list_like' from 'pandas.core.common', so I have to import it like this:

import pandas as pd
# workaround until 0.7.0 of pandas_datareader is released.
pd.core.common.is_list_like = pd.api.types.is_list_like
import pandas_datareader.data as web

so in the end I was able to get the example from Programming4Finance on YouTube to work.

Neat tricks:

Import Case-Schiller Home Index from FREd:

import datetime as dt
import numpy as np
start = dt.datetime(1987,1,1)
end = dt.datetime(2018,6,1)

homes = web.DataReader('CSUSHPISA', 'fred', start, end)

Some notes on DataFrames vs Series:

DataFrame can have multiple keys, and is what's returned by DataReader, even if there's only one key:

homes = web.DataReader('CSUSHPISA', 'fred', start, end) # dataframe

Most stats are down on a series, so we can get the singular key with:

homes = web.DataReader('CSUSHPISA', 'fred', start, end)['CSUSHPISA'] # series

If we're only interested in the month-over-month percent returns:

homes = web.DataReader(
          'CSUSHPISA', 'fred', start, end
        )['CSUSHPISA'].pct_change() # series of Δs

The first element will be a NaN, so we should trim that:

homes = web.DataReader(
          'CSUSHPISA', 'fred', start, end
        )['CSUSHPISA'].pct_change()[1:] # series of Δs, less the first

for this work

The example from Programming4Finance is unrealistic for homes/rents because it uses random values each day, and stock data are nearly random, but home prices are strongly autocorrelated:

homes = web.DataReader(
          'CSUSHPISA', 'fred', start, end
        )['CSUSHPISA'].pct_change()[1:] # series of Δs, less the first
homes_ac=homes.autocorr(1)

We see that the autocorrelation is 0.93. Also fun to play with are lag_plots and autocorrelation_plot:

from pandas.plotting import autocorrelation_plot
from pandas.plotting import lag_plot
plt.figure()

data = pd.Series(0.7 * np.random.rand(1000) +  0.3 * np.sin(np.linspace(-9 * np.pi, 9 * np.pi, num=1000)))
plt.plot(data)
autocorrelation_plot(data)
lag_plot(data)
autocorrelation_plot(homes)
lag_plot(homes)

Meaning

What matters for a sell/rent decision is: