Real Estate Investment Trust Project

Projects Skill Path Projects
1

Here is my workings on the REIT project.

pip install numpy
import numpy

adj_closings_SBRA = numpy.loadtxt('SBRA.csv', skiprows=1, usecols=5, delimiter = ',')
print(adj_closings_SBRA )
adj_closings_EQR = numpy.loadtxt('EQR.csv', skiprows=1, usecols=5, delimiter = ',')
print(adj_closings_EQR )

def simple_rate_of_return(adj_closings):
    daily_simple_ror = numpy.diff(adj_closings)/adj_closings[:1]
    return daily_simple_ror

daily_simple_returns_sbra = simple_rate_of_return(adj_closings_SBRA)
print(daily_simple_returns_sbra)
daily_simple_returns_eqr = simple_rate_of_return(adj_closings_EQR)
print(daily_simple_returns_eqr)

average_daily_simple_return_sbra = numpy.mean(daily_simple_returns_sbra)
print(average_daily_simple_return_sbra)
average_daily_simple_return_eqr = numpy.mean(daily_simple_returns_eqr)
print(average_daily_simple_return_eqr)

def log_returns(adj_closings):
    log_adj_closings = numpy.log(adj_closings)
    daily_log_returns = numpy.diff(log_adj_closings)
    return daily_log_returns

daily_log_returns_sbra = log_returns(adj_closings_SBRA)
print(daily_log_returns_sbra)
daily_log_returns_eqr = log_returns(adj_closings_EQR)
print(daily_log_returns_eqr)

def annualize_log_return(daily_log_returns):
    average_daily_log_returns = numpy.mean(daily_log_returns)
    annualized_log_return = average_daily_log_returns * 250
    return annualized_log_return

annualized_log_return_eqr = annualize_log_return(daily_log_returns_eqr)
print(annualized_log_return_eqr)

daily_variance_sbra = numpy.var(daily_log_returns_sbra)
print(daily_variance_sbra)
daily_variance_eqr = numpy.var(daily_log_returns_eqr)
print(daily_variance_eqr)

daily_sd_sbra = numpy.std(daily_log_returns_sbra)
print(daily_sd_sbra)
daily_sd_eqr = numpy.std(daily_log_returns_eqr)
print(daily_sd_eqr)

corr_sbra_eqr = numpy.corrcoef(daily_log_returns_sbra, daily_log_returns_eqr)
print(corr_sbra_eqr)