15. โข EXRโs stock price follows the SPY with a 67% coefficient of
determination.
Company
Overview
Industry &
Competitors
Risk
Analysis
Financial
Analysis
Valuation Conclusion
EXRโs Stock Growth Compared to S&P
16. โข Over the last two years, the 100-day stock price moving average is
below the 50-day stock price moving average until the effects of
the November stock price drop become dominant.
โข Long-term the stock has shown strong growth in price.
โข The stock price is still recovering from the November drop.
Company
Overview
Industry &
Competitors
Risk
Analysis
Financial
Analysis
Valuation Conclusion
Technical Analysis
17. โข The six month chart shows the possible rebounding from that
November drop.
โข The 10-day moving average shows that there may be a possibility of
a short-term holding profit.
Company
Overview
Industry &
Competitors
Risk
Analysis
Financial
Analysis
Valuation Conclusion
Moving Averages
18. Year 31-Dec-08 31-Dec-09 31-Dec-10 31-Dec-11 31-Dec-12 31-Dec-13
Assets:
Real estate assets, net $ 1,938,922 $ 2,015,432 $ 1,935,319 $ 2,263,795 $ 2,991,722 $ 3,265,292
Investments in real estate ventures $ 136,791 $ 130,449 $ 140,560 $ 130,410 $ 106,313 $ 116,035
Cash and Cash equivalents $ 63,972 $ 131,950 $ 46,750 $ 26,484 $ 30,785 $ 33,600
Restricted Cash $ 38,678 $ 39,208 $ 30,498 $ 25,768 $ 16,976 $ 18,528
Receivables from related parties and affiliated real estate
joint ventures $ 11,335 $ 5,114 $ 10,061 $ 18,517 $ 11,078 $ 12,091
Other assets, net $ 42,576 $ 50,976 $ 49,549 $ 52,550 $ 66,603 $ 72,693
Total Assets $ 2,291,008 $ 2,407,556 $ 2,249,820 $ 2,517,524 $ 3,223,477 $ 3,518,239
Growth 11.5% 5.1% -6.6% 11.9% 28.0% 9.1%
Historical data was taken from SEC filings and Internet resources, run
through statistical software, and used for forecasting.
became
Company
Overview
Industry &
Competitors
Risk
Analysis
Financial
Analysis
Valuation Conclusion
Data Conversion
19. โข Total Assets = 20983 + (Years Since IPO x 257534)
โข Total Revenue = -50060 + (Total Assets x 0.143243)
โข Net Income = -84716 + (Total Assets x 0.059286)
Asset Regression Coefficients Standard Error t Stat P-value
Intercept 20983.16 243118.3 0.086308 0.933342
Year 257533.7 27096.91 9.504174 1.24E-05
$0
$1,000,000
$2,000,000
$3,000,000
$4,000,000
$5,000,000
$6,000,000
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
TotalAssets
(thousands)
Year
Company
Overview
Industry &
Competitors
Risk
Analysis
Financial
Analysis
Valuation Conclusion
Regression Equations for Forecasting
32. $-
$500,000
$1,000,000
$1,500,000
$2,000,000
$2,500,000
$3,000,000
$3,500,000
$4,000,000
1 2 3 4 5 6 7 8 9 10
$-
$1,000,000
$2,000,000
$3,000,000
$4,000,000
$5,000,000
$6,000,000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Total Assets Forecast
Since EXRโs IPO, there has been a steady
increase of total assets. In all the
regressions run, time showed the most
direct correlation with an R Squared of
0.919, t Statistic of 9.5, and P-value of
less than 0.0001.
By using the regression model,
we were able to forecast total
assets using the following
equation:
Total Assets = 20983 + (257534 x
Years since IPO)
Total Assets Since IPO
33. The correlation between revenue
and assets proved exceptionally
high for past periods with an R
Squared of 0.99, a t Statistic of
28.34, and a P-Value lower than
0.000000003. $-
$50,000
$100,000
$150,000
$200,000
$250,000
$300,000
$350,000
$400,000
$450,000
$500,000
$-
$100,000
$200,000
$300,000
$400,000
$500,000
$600,000
$700,000
$800,000
Revenue Forecast
We were able to forecast
Revenue for upcoming
years by using the
following equation: Total
Revenue = -50060 + (Total
Assets x 0.143243).
Revenue and Asset Correlation
34. Net Incomeโs Correlation
Coefficient in regards to Assets for
historical data was 0.9226, t
Statistic is 9.766, and P-Value is
than 0.00001.
$(50,000)
$-
$50,000
$100,000
$150,000
$200,000
$250,000
Net Income Forecasted
$(40,000)
$(20,000)
$-
$20,000
$40,000
$60,000
$80,000
$100,000
$120,000
$140,000
$160,000
Net Income = -84716 + (Total
Assets x 0.059286)
Net Income and Asset Correlation
35. โข Regression models similar to the one used for assets
were used to forecast liabilities and equity.
โข Growth rates were calculated each year, including
forecasted years, for assets, liabilities and equity.
โข Those growth rates were used to forecast individual
accounts within each section of the balance sheet.
โข Example: Cash and Cash Equivalents historical data was
input, the last reported number was then multiplied by
1+calculated growth rate for assets.
Balance Sheet Equations
36. โข The correlation coefficient for historic operational
cash flows and net income is 0.923, t Statistic of
6.9, and P-Value of 0.002.
โข Thus, forecasted net income was used to forecast
operational cash flows.
โข Those forecasted values were used to determine
the growth rate for operational cash flows.
โข Those growth rates were applied to individual
accounts within the operational cash flows
section of the Summary of Cash Flows Statement.
Operational Cash Flow Calculations
37. โข The regression process used to forecast revenue
was also used to expenses and income from
operations.
โข The calculated growth rates from those
forecasted accounts was again used to forecast
the relevant subordinate accounts.
โข Example: 2015 Acquisition Related Expense =
forecasted 2014 Acquisition Related Expense x
Calculated growth rate for 2015 Total Expenses.
Statement of Operations Calculations
Editor's Notes
Coefficient of Determination (Adjusted R Squared 0.9889)