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Real Estate Investment Trust (REIT) Valuation Model Jamila Awad

Real Estate Investment Trust (REIT)Valuation Model

Author

Jamila Awad

Rights Reserved

JAW Group

Date

May 13, 2014

Paper: “Real Estate Valuation Model” (2014) Rights Reserved: JAW GroupAuthor: Jamila Awad JAW Group, 3440 Durocher # 1109Date: May 13, 2014 Montreal, Quebec, H2X 2E2, Canada Mobile: (1) 514 799-4565 E-mail: [email protected]


Executive Summary

The ever-evolving capital market integration and segmentation environment quests a REIT investment vehicle framework that cements a transparent trading platform between market operators and investors. The disquisition aspires to conceptualize a technical and theoretical framework that encapsulates economic as well as firm-specific factors into the parameters utilized in risk and return models. The narration is partitioned in five sections. The introduction shadows the concepts elaborated in the prescript. The second chapter asseverates the REIT structure and the capital market environment entangled to REIT investment vehicles. The third section umbrages the fundamental and technical arrangements integrated in modeling as well as evaluating REIT equity securities. The fourth division presents an empirical study of the REIT valuation model and describes the cascade steps to implement the protocol. Finally, the conclusion formulates the general findings derived from the research paper.



1.Introduction

The evolving interest in real estate investment mediums accentuates the necessity to cement robust valuation techniques for real estate financial instruments that safeguard market participants from perilous activities. Furthermore, the era of globalization expands real estate capital flows that enhance portfolio diversification. Empirical studies demonstrate that Real Estate Investment Trust (“REIT”) liquidity had significantly increased in the 1990s due to improvements in REIT market capitalizations, trading volumes, and lastly, organizational structure. However, the financial crisis of 2008 had a catastrophic impact on the REIT market.

REIT stocks depict financial instruments that securitize real-estate conglomerates trading in financial markets and adequately hedge in an environment with capital and liquidity constraints as well as steep inflation. In light, the financial crisis of 2008 induced institutional and individual investors to massively withdraw capital flows from REIT investment vehicles fearing spillover financial losses in capital markets. REIT stock prices that deviate from their Net-Asset-Value (“NAV”) lead to potential arbitrage opportunities. There are strong linkages between REIT and equity securities even though distinct differences still surface between them (Lee and Stevenson, 2007). REIT stock prices exhibit more than twice the volatility of direct property investment in the real-estate sector.

The proposed REIT valuation model protocol strives to deliver a technical and theoretical framework that encapsulates economic as well as firm-specific factors into the parameters utilized in risk and return models. The mathematical framework used is a regression model to adjust the beta coefficient in line with economic as well as firm-specific variables that greatly impact true systematic risk. A REIT portfolio inclines to hold a strong association with diversified property portfolios over the long run, although with much different property types and gearing characteristics that should be considered in investment decision-making.

Cyclical economic factors in a frictionless world greatly impact true fundamental REIT values (Stambaugh, 1999). The proposed technique therefore thwarts potential conflicts of interest arising from entities pricing the values of REIT underlying assets. In light, entities that value REIT investment vehicles blend public data accessible to all investors with private information available only for managers as well as insiders. The cherry-picking methodology implemented by firms designated to price REIT NAV as well as their commissions on trading business lead to an environment that induces potential conflicts of interest.

The disquisition is partitioned in five sections. The introduction shadows the concepts elaborated in the narration and articulates the goal of the protocol. The second chapter asseverates the REIT structure and the capital market environment entangled to REIT investment vehicles. The third section umbrages the fundamental and technical frameworks integrated in modeling as well as evaluating REIT equity securities. The fourth division presents an empirical study of the REIT valuation model and describes the cascade steps to implement the protocol. Finally, the



conclusion resumes the theorems as well as techniques integrated in the proposed modus operandi and formulates the general findings from the empirical study.



2.The REIT Architecture and its Market Environment

The second chapter articulates the REIT structure and the capital market environment entangled to REIT investment vehicles.

A REIT depicts a tax-exempt pass-through entity with broad based ownership that distributes a significant portion of its earnings and capital gains to investors. REIT conglomerates manage various types of properties ranging from industrial to residential location in an array of geographical locations. The REIT architecture enables investors with limited means to invest via equity securities in real estate asset classes, and thus, grasp opportunities linked to property investments. REIT stocks also purpose the needs of investors who seek an equity security purposing a diversified pool of real estate assets. In the U.S.A., REIT firms pay tax charges on capital gains that depend on the depreciation of the entity’s assets. The U.S.A. unlisted REIT on public stock exchanges that burgeoned in the early 2000s raised capital by intermediaries such as broker-dealer networks however follow the same SEC (“Securities and Exchange Commission”) policy guidelines as listed REIT. The shares of unlisted REIT are in most circumstances emitted at a fixed inception price with a constant dividend payout. In the U.S.A. jurisdiction, the degree of debt leveraging by REIT firms is superintended by bond-rating agencies. In addition, REIT entities are permitted to detain taxable REIT subsidiaries to grasp entrepreneurial activities while conducting administrative and fund management.

The blossoming integration of non-traditional REIT gained momentum in the years 2000s to achieve greater tax efficiency and increase shareholder’s wealth. The following axes portray non-traditional REIT investments: health care, data storage, and lastly, energy transmission businesses.

The beta coefficient depicts the sensitivity of each REIT unit price to price fluctuations in the stock market. Furthermore, a beta coefficient less than one reflects a REIT investment vehicle that holds an inferior risk movement in REIT stock compared to movements in the financial market index that trades the underlying. The volume value of REIT investments is equal to the total yearly amount traded in the security’s currency. The value of a REIT is characterized by the sum of the values of all trading prices multiplied by the number of shares relating to each price. The annualized dividend yield for American and Canadian REIT entities is expressed as a percentage of the security’s price. However, the annualized dividend yield for REIT firms headquartered outside North America is measured in percentage of the security’s price as the trailing annual actual dividend.

The REIT architecture as a medium of equity financial instrument serviced in the 1990s as a route to bundle capital in order to finance maturing mortgages and to cease opportunities in acquiring new properties in the real estate platform. The private real estate market in the 1990s as well as the capital constraint environment cemented the involvement of the securitization process of commercial as well as residential real estate assets in form of equity and debt. However, the real estate building saturation that occurred in the 1990s also triggered private real



estate entities to recapitalize their balance sheets with the following actions: entering into private equity joint ventures or raise equity through initial public offers (IPO). In essence, the market environment and survival constraints forced real estate private firms to go public.

Following, the successful entrance of these real estate private entities in the stock market, the prices of REIT securities in the U.S.A. increased drastically which forced the competing remaining private real estate firms to emulate and go public with IPOs. Investment bankers snatched opportunities to arbitrage private and public real estate markets during the prolonged episodes of low interest rates and bond yield at interesting prices. In light, the participation of investment banking operators interfered with the REIT true Net-Asset-Values (“NAV”) from premium to discount with the changing nature of REIT institutional ownership.

The clientele effect states that shares of closed-end funds that are held in majority by individual investors drive the underlying shares to trade at discount because they act upon sentiments (Lee and al., 1991). However, the clientele argument is invalidated with a significant ratio of REIT listed and unlisted stocks held by institutional investors who transact like Wall Street strategists. Precisely, since individual ownership is more concentrated in REIT shares than in direct property investment, then individual investor sentiment could only partially influence premium and discount to NAV because individuals and institutions hold different expectations in their utility curves. Furthermore, the growth of institutional ownership in the REIT market is dominantly linked to burgeoning REIT mutual funds (Kallberg and al, 2000). In fact, the rapid growth of open-ended REIT mutual funds also occurred during the emerging dot-com stock phenomenon. The REIT stock prices then collapsed with the start of the NASDAQ dot-com debacle. Ergo, the reasoned explanation to REIT stocks trading at NAV differential is rather that the detainers of the REIT funds are not the owners of the REIT underlying assets.

The securitization process therefore cements a robust public REIT investment medium and promotes commercial mortgage-backed securities (CMBS). These financial instruments have also bonded sound exchanges between private real estate assets and global capital markets. REIT entities became dynamic organizations that are proactively operating quality properties in both public and private real estate conglomerates. The following features characterize efficient REIT: diversified capital structure from private and public sources, sound internal management, expertise knowledge on property types, vertically integrated operating platforms, moderate debt leverage, and lastly, value growth-oriented. Furthermore, the dynamic operational REIT systems implemented is a key parameter to REIT stocks increasing demand by individual as well as institutional investors. The legislative REIT context aligns shareholder interests with REIT managers thus enhances the efficiency of REIT administrators operating decisions.

REIT entities in the U.S.A. jurisdiction are entitled to select between internal or external management. The internal administration route seems more appealing to a majority of REIT entities due to more integrated and actively managed operations. However, REIT firms also acquire income-producing properties to expand growth while other REIT organizations focus on the construction and development of new buildings. The vertical integration remains a



dominating feature for REIT entities to survive in hazardous economic conditions but also at times of changes in the relative valuations between the stock and the property market. In essence, vertical integration permits REIT organizations to profit by selling real estate units evaluated at NAV exceeding stock market valuation while holding ownership of operational management to gain geographical scope. However, the era of globalization forces REIT entities to concentrate on the specialization of property types and regions in order to secure profitable operations regardless evolving real estate markets and new real estate investment instruments.

Investors also acquire risk reduction in a REIT stock investment through the process of REIT specialization by retaining REIT stocks that hold underlying diversified real estate portfolios. Furthermore, sophisticated investors also enhance diversification by purchasing different types of REIT stocks. Institutional investors also seek REIT entities that are skillfully managed and thus boost the potentiality of shareholder wealth maximization.

In addition, non-traditional REIT organizations also developed unique niches in self-storage and manufactured housing. The increase in market capitalization in non-traditional as well as traditional REIT arises from growth through property acquisitions and development. The consolidation phenomena in REIT also enabled various REIT firms to grasp economies of scale and compete with other peer entities (Ambrose and al., 2005). On the one side, economies of scale are linked to major fixed expenses and, on the other side, diseconomies of scale are bonded to administering ever-expanding firms operating in various markets (Yang, 2001).

The global financial crisis and the credit risk correction that occurred in 2008 crunched REIT stock prices as well as shrunk the appraisal of REIT properties in various geographical locations. The implementation of rigid new regulations in capital markets and the economic expansion enabled to heighten REIT stock prices and real estate property estimates back to appealing levels. However, REIT organizations that specialize in regional segments or niche property types grow rapidly because they retain advantage in value-creating opportunities.

In conclusion, the REIT architecture and its market environment remain appealing features for individual as well institutional investors to participate in acquiring liquid real estate securities through REIT stocks while collecting dividends. The ongoing challenge for equity REIT remains to administer investment decisions and secure asset growth in uncertain economic conditions such as lagging production boom and hazardous unemployment rate. In light, recognition delay in economic downturns in the equity as well as debt market thus when investors and capital providers transact under uncertainty. In consequence, the risks linked to investing in REIT vary across property sectors and geographical markets.



3. Fundamental and Technical Frameworks

The third section umbrages the fundamental and technical frameworks integrated in modeling as well as evaluating REIT equity securities.

REIT investment vehicles are predominantly detained by institutional investors who seek to maximize shareholder wealth therefore not bonded to individual investor sentiment. REIT stock prices diverge from NAV thus permit arbitrage opportunities in capital markets. Aggregate NAV appear to be stationary and mean-reverting therefore leads to potential profitable trading strategies that capture market timing rather than cross-sectional stock selection. Closed-end funds portray investment mediums that transact securities distant from NAV due to various factors: tax liabilities, market segmentation, and lastly, management costs. The REIT financial instrument shares similarities with closed-end funds whereas market operators can generate profit from various trading strategies in managing stocks that pool illiquid real estate assets. However, REIT stocks trade at lower discount than closed-end funds due to higher dividends (Pontiff, 1996).

REIT organizations operate in similar environments as multinational corporations whereas REIT administrators raise capital from external markets and then invest funds in operating assets to foster stock value growth. REIT entities also depict attractive investment vehicles due to the double taxation avoidance in equity-financed mediums. However, the tax liability element interferes with price-to-NAV (“P/NAV”) valuation (Clayton and al., 2003). High P/NAV securities show low subsequent returns, and on the other hand, low P/NAV stocks expose high historical returns. In addition, stocks with hefty P/NAV tend to be dominating securities with significant market capitalization and that are slightly more liquid. Furthermore, REIT entities obtain an annual tax deduction for dividend distributed to shareholders. In fact, REIT are confined by law to earn at least 75% of income from real-estate related investments as well as gauge 95% of their income from their operating assets, dividends, and finally, from interest and capital gains. REIT are compelled to distribute 90% of their taxable income in form of dividends. However, depreciation allowances considerably reduce taxable income in the case of REIT investment vehicles which also jeopardizes the capability of a REIT entity to finance from internally generated funds. Finally, REIT entities are also ruled to invest 75% of their assets in real-estate, REIT shares, mortgages, government securities, and lastly, liquidity. The legal rigid REIT infrastructure as well as its equity-linked feature encourages REIT managers to soundly and efficiently administer the underlying assets.

The disquisition aspires to cement a REIT valuation model that prices REIT stock returns in line with fundamental value. The mathematical framework used is a regression model to adjust the beta coefficient in line with economic as well as firm-specific variables that greatly impact true systematic risk. In fact, cyclical economic factors in a frictionless world greatly impact true fundamental REIT values (Stambaugh, 1999). The proposed technique therefore thwarts potential conflicts of interest arising from entities pricing the values of REIT underlying assets. In light, entities that value REIT investment vehicles blend public data accessible to all investors



with private information available only for managers as well as insiders. The cherry-picking methodology implemented by firms designated to price REIT NAV as well as their commissions on trading business lead to an environment that induces potential conflicts of interest.

Firm-specific and external economic factors influence REIT price volatility (Knight et al., 2005). Various techniques have been tested in the past to value REIT stocks such as the Fama and French (1993) five-factor model as well as customized macroeconomic driven pricing methodologies (Chan and al., 1990). Furthermore, past experimental results have demonstrated that securitized real estate volatility is highly correlated with stock market risk in the short horizon compared to unsecuritized real estate volatility (Liow and Ibrahim, 2010). Past studies have also concluded that the maturity of the market plays a determining factor in anticipating REIT stock returns (Serrano and Hoesli, 2010). There seems to be a general consensus that active trading practices generate acute forecasts compared to conventional strategies. Liquidity issues greatly impact REIT investment vehicles compared to momentum returns (Hung and Glascock, 2010). The dynamic between monthly REIT data and market factors efficiently captures REIT returns (Cotter and Stevens, 2006).

Inflated volatility levels serve as tax purposes to firms by heightening the cost of debt/equity capital that negatively impact REIT firm values. Stock return volatility is closely entangled with macroeconomic variables and financial leverage which leads to enhanced stock return variability during financial market downturns (Fama and Schwert, 1977). The determinant of time-series return behavior relies on how investors and market operators interpret new information integration in security prices (Chen and Wei, 2007). The blossoming exchange traded-fund (“ETF”) market influences volatility in the REIT stock sector. Precisely, significant and positive changes in the risk associated to an index, such as the S&P 500, impact REIT stock returns. The impulse to transact public real estate is enhanced in down markets due to the narrowing of illiquidity in the private sector (Li, 2011).

The Capital Asset Pricing Model (“CAPM”) depicts an international reference framework to value the expected return on a security by segmenting the underlying into two main components: the systematic risk measured by the beta coefficient bonded to a risk premium return, and, the other component depicts the unsystematic volatility represented by a Treasury bond risk-free return. Empirical tests have demonstrated that REIT betas increase during periods of market distress therefore market operators are recommended to consider a REIT valuation model whereas the beta coefficient efficiently captures variables that play a role in systematic risk. The proposed model considers dividend and earnings data that help predict security market returns for time horizons extending to five years (Fama and French, 1988).

The Modigliani-Miller (“M&M”) methodology articulates that the volatility of REIT financial instruments is impacted by the following parameters: risk linked to the firm’s total value, debt price volatility, and lastly, Debt-to-Equity (“D/E”) ratio. Therefore, the leverage effect greatly influences the volatility of a REIT stock. There is also a positive correlation between inflation shocks with REIT return sigma which leads to the conclusion that unanticipated changes in



actual inflation impact REIT stock valuation. In precise terms, an unexpected drop in inflation induces a larger consequence on REIT return volatility than an unanticipated jump in inflation.

During the global financial crisis that occurred in 2008, empirical tests have demonstrated that REIT stock volatility jumps were nearly twice compared to common stock risk (Li, 2011). The recommended REIT valuation model encapsulates asymmetric effects of various economic and firm-specific factors with a beta coefficient that integrates these effects.

The CAPM model measures the expected return of a security with the following mathematical expression:

E(Ri,t) = αi + βi,t*(Rm,t – Rf,t) + εi,t

Variable nomenclature:

E(Ri,t): The expected return on a securityαi: The constant security return in excess of systematic riskβi,t: The beta coefficient of the securityRm,t: The market return rate at horizon tRf,t: The risk-free return at horizon tεi,t: The error term at horizon t

The expected return for a portfolio containing n securities can therefore be solved with the equation:

E(Rp) = ∑i=1

n

X i,t*E(Ri,t)


E(Rp): The expected return on a portfolio containing n securities. Xi,t: The weight of a security in the portfolio whereas the sum of all weights is equal to 1.E(Ri,t): The expected return of a security bundled in a portfolio.

The variance (Var) of the expected return on a security is measured by the following mathematical solution:

σ2p = ∑

i=1

n

X i,t * ∑j=1

n

X j,t * σi,j

σ2p = ∑

i=1

n

X i,t2 * σ2

i + ∑i=1

n

X i,t * ∑j=1

n

X j,t* σi,j



Var (E(Ri,t)) = βi,t2Var((Rm,t – Rf,t)) + Var(εi,t) + 2Cov(βi,t*(Rm,t – Rf,t),εi,t)


E(Ri,t): The expected return on a securityβi,t: The beta coefficient of the securityRm,t: The market return rate at horizon tRf,t: The risk-free return at horizon tεi,t: The error term at horizon t

(Rm,t – Rf,t) : The risk premium of a securityCov(.): The covariance between two variables.

Empirical studies have shown that the variance of American stocks can be partitioned in three main components (Liu and Mei, 1994): one third is attributed to the variance of changes in anticipated dividends, one third is characterized by the variance of unexpected stock return, and lastly, the remaining tierce is explained by the covariance of the two stated factors. The REIT return volatility also increases with the use of short-term debt which leads to a cascade of consequences such exposing the firm to rollover risk and heightening credit risk (He and Xiong, 2010).

Staggering inflation impacts REIT stock returns by inducing a weaker economy and reducing firm profits. Furthermore, steep inflation accentuates the overall riskiness of real estate assets. The effect of inflation shocks on REIT return volatility is steepened for property sectors such as hotel and industrial markets. Therefore, the proposed REIT valuation model captures the positive and negative asymmetric effects of inflation that are integrated into the beta coefficient.

In brief, the technical framework and the REIT market environment umbrage the general features and characteristics that need to be considered when modeling a valuation protocol for REIT stocks.



4. The REIT Valuation Model Implementation

This segment presents an empirical study of the REIT valuation model and describes the steps to implement the protocol.

4.1 The Instructions to Execute the REIT Valuation Protocol

The proposed technique adjusts the beta coefficient to adequately encapsulate economic and firm-specific factors that impact the true expected return on REIT equity securities. The correction of the beta coefficient and the reconciled anticipated return on the stock then enable to harmonize the true dividend. In essence, the modus operandi secures a robust framework that captures all the risks linked to REIT equity investments to cement a transparent transacting environment between market operators and investors.

The cascade instructions to execute the delivered REIT stock valuation model are:

1. Gather the following monthly data about retained REIT listed stocks and the affiliated financial index for a 48-months window frame: Stock and index closing prices, REIT market value, T-Bill 12-months, the inflation rate, the financial leverage index, unemployment rate, and lastly, the housing starts.

2. Calculate the descriptive statistics such as the standard type for the REIT stock and the affiliated financial index to measure the covariance between these two variables.

3. Compute the adjusted beta by expressing the systematic risk parameter with a rolling window based on 24-months. This step therefore corrects the cyclical and seasonal risks that interfere between the REIT stock and the market (that is represented by the financial index).

4. Integrate the following variables to examine linear ANOVA regressions at 95% confidence level: the adjusted beta (calculated in step 3) is the dependant variable while the independent variables are the REIT market value, the financial leverage index, the unemployment rate, the housing starts, and lastly, the inflation rate.

5. Assemble the linear ANOVA regression equation for each REIT stock and then incorporate the mean of each independent variable used in step 4 to obtain the corrected beta (ßα,β).

6. Calculate the REIT stock expected return (ROEα,β) with the following parameters integrated into the Capital Asset Pricing Model (CAPM): the corrected beta (ßα,β) obtained in step 5, historical risk premium for investing in publicly traded equity REITs, and lastly, the mean T-bill 12-months rate.

7. Compute the corrected dividend (Dα,β) with the following expression: Dα,β

= (ROEα,β - Gα,β)*REIT stock Price close monthly whereas Gα,β depicts the corrected growth rate.

8. Analyze the results to assess the risk-return relationship.



4.2Empirical Study with the Designed REIT Valuation Model

This sub-section describes the main findings and results following the implementation of the proposed REIT valuation technique. The data source is Compustat, Research Insight. The data window is from December 2008 until December 2012 with information on a monthly basis. The selected index is the S&P 500 used to compute the covariance between the market and each REIT stock.

The empirical study is performed with the following selected REIT stocks:

1- American Campus Communities Inc. (NYSE:ACC)2- Apartment Investment & Management Co. (NYSE: AIV)3- AvalonBay Communities Inc. (NYSE: AVB)4- BRE Properties Inc. (NYSE: BRE)5- Camden Property Trust (NYSE: CPT)6- Equity Lifestyle Properties Inc. (NYSE: ELS)7- Home Properties Inc. (NYSE: HME)8- Mid-America Apartment Communities Inc. (NYSE: MAA)9- Post Properties Inc. (NYSE: PPS)10- UDR Inc. (NYSE: UDR)

Figure IAdjusted beta coefficient for a rolling window 24-months from

December 2008 until December 2012 for the examined REIT stocks.

Dec08

Apr09

Aug09Dec0

9

Apr10

Aug10Dec1

0

Apr11

Aug11Dec1

1

Apr12

Aug12Dec1

2

00.5

11.5

22.5

3ACCAIVAVBBRECPTELSHMEMAAPPSUDR

Rolling window beta

Date



Please refer to appendixes 1 to 10 for elaborated information about ANOVA regressions for each REIT stock.

Table 1Descriptive Statistics following the implementationof the REIT Valuation Model for December 2012

REIT stock ticker

R2 ßα,β ROEα,β Dα,β

(in U.S. $)for Q4 2012

Stock PriceClose monthly

(in U.S. $)

Dividendpayout*

ACC 91,8% 0,9611 9,1210% 0,5499 46,13 1,3073%

AIV 89,6% 2,0651 15,7450% -0,1079 27,06 -0,2538%AVB 88,4% 1,1394 10,1908% 1,3098 135,59 0,9477%BRE 85,8% 1,2068 10,5952% 0,6791 50,83 1,2605%

CPT 93,8% 1,3894 11,6908% 0,7039 68,21 0,8830%ELS 91,4% 1,1187 10,0666% 0,3725 33,65 1,0989%HME 92,7% 1,4059 11,7898% 1,4169 61,31 1,9605%MAA 91,3% 1,4092 11,8096% 2,1264 64,75 2,7811%PPS 87,0% 1,3912 11,7016% 0,2947 49,95 0,5044%UDR 83,5% 1,1693 10,3702% -0,1836 23,78 -0,7449%

*Dividend payout obtained from Compustat used to adjust the calculation of the other variables.

The historical risk premium for investing in publicly traded equity REITs (as measured by the FTSE NAREIT Equity Total Return Index) in excess of the 10 year treasury from February 1, 1972 to December 31, 2012 is around 6%. The mean risk-free rate for the entire data window obtained from the Treasury bill 12-months rate is 3,3544%.

The Pearson squared coefficient (R2) for all ANOVA regressions exposes a strong linear relationship. The corrected beta coefficients range from 0,9611 (ACC) to 2,0651 (AIV) which infuses firm-specific and economic parameters. The REIT entities AIV and UDR had a negative payout in Q4 2012 following Research Insight information however corrected return on equity (ROEα,β) still remains positive because the underlying is used to be harmonized with the instructions of the protocol. Also, the dividend (Dα,β) for Q4 2012 does consider the dividend payout as formalized in the table.



5-Conclusion

Successful REIT stocks portray real-estate conglomerates that adequately manage underlying real estate properties and grow due to profitable operational growth which in return lead to reliable dividend distribution and enhance shareholder wealth. The REIT playground remains a highly competitive environment whereas individual investors and institutional ownership hold divergent preferences as well as requirements in their utility curves.

A significant proportion of REIT entities focus on internal growth through sound property administration, cost reduction, and lastly, by increasing residential or corporate occupancy. However, the REIT firms that concentrate efforts on external development complete the function by acquiring investments in other REIT. Although REIT investment vehicles have victoriously restructured their operational methodologies, some REIT entities still struggle in recapitalizing their balance sheets. Regardless the costly property infrastructure linked to REIT conglomerates, REIT organizations strive to face challenges in an era of globalization and blossom growth opportunities to enhance shareholder value.

The proposed REIT valuation model encapsulate economic and firm-specific parameters that greatly impact the systematic risk linked to a REIT investment. The execution of the REIT protocol exposes the importance to capture risk parameters in the beta coefficient and then adjust the true dividend.

In conclusion, a REIT valuation model enhances the transparency of a trading environment whereas investors and market operators examine a clear picture frame of all the risks entangled to REIT stocks regardless economic conditions and geographical locations.



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Appendix 1: ACC Regression

Descriptive Statistics

Mean Std. Deviation N

beta_corrected ,6756 ,33550 49

market_value_monthly 2266,5089 1108,14691 49

financial_leverage_index 2,1830 ,28073 49

unemployment_rate 8,9429 ,72629 49

housing_starts ,6321 ,10611 49

inflation_rate 1,6053 1,51635 49

Model Summaryb

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

1 ,958a ,918 ,909 ,10126

a. Predictors: (Constant), inflation_rate, unemployment_rate,

housing_starts, market_value_monthly, financial_leverage_index

b. Dependent Variable: beta_corrected

Coefficientsa

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig. 95,0% Confidence

Interval for B

B Std. Error Beta Lower

Bound

Upper

Bound

1

(Constant) -1,085 ,404-

2,689,010 -1,900 -,271

market_value_monthly ,000 ,000 -,417-

3,099,003 ,000 ,000

financial_leverage_index ,374 ,168 ,313 2,231 ,031 ,036 ,713

unemployment_rate ,124 ,033 ,269 3,819 ,000 ,059 ,190

housing_starts ,318 ,278 ,101 1,142 ,260 -,244 ,879

inflation_rate -,050 ,012 -,228-

4,166,000 -,075 -,026

a. Dependent Variable: beta_corrected



Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value ,1264 1,1522 ,6756 ,32152 49

Residual -,17105 ,31046 ,00000 ,09584 49

Std. Predicted Value -1,708 1,482 ,000 1,000 49

Std. Residual -1,689 3,066 ,000 ,946 49


Y= -1,085+ 0,374Xb +0,124Xc +0,318Xd - 0,050Xe

Y= -1,085+ 0,374*(2,1830) +0,124*(8,9429) +0,318*(0,6321) - 0,050*(1,6053) = 0,9611

Appendix 2: AIV Regression



beta_corrected 2,0641 ,84609 49






Model Summaryb


Square

Std. Error of the

Estimate

1 ,947a ,896 ,884 ,28809






Coefficientsa


Coefficients

Standardized

Coefficients


Interval for B

B Std.

Error

Beta Lower

Bound

Upper

Bound

1

(Constant) -4,733 1,088-

4,351,000 -6,927 -2,539

market_value_monthly -9,405E-005 ,000 -,107 -,945 ,350 ,000 ,000

financial_leverage_index 2,028 ,457 ,673 4,434 ,000 1,106 2,950


housing_starts ,778 ,681 ,098 1,142 ,260 -,596 2,151


2,556,014 -,176 -,021




Predicted Value ,8877 3,2042 2,0641 ,80094 49

Residual -,50999 ,42524 ,00000 ,27268 49


Std. Residual -1,770 1,476 ,000 ,946 49


Y= -4,733-9,405E-005Xa+ 2,028Xb + 0,254Xc+0,778Xd - 0,098Xe

Y= -4,733-9,405E-005*(2498,0597) + 2,028*(2,1830) + 0,254*(8,9429) + 0,778*(0,6321) - 0,098*(1,6053) = 2,0651



Appendix 3: AVB Regression









Model Summaryb


Square

Std. Error of the

Estimate

1 ,940a ,884 ,871 ,10470




Coefficientsa


Coefficients

Standardized

Coefficients


Interval for B


Bound

Upper

Bound

1

(Constant) -,972 ,460 -2,116 ,040 -1,899 -,045

market_value_monthly-4,730E-

005,000 -,564 -3,473 ,001 ,000 ,000



housing_starts 1,159 ,247 ,423 4,689 ,000 ,661 1,658

inflation_rate -,018 ,015 -,093 -1,159 ,253 -,049 ,013






Predicted Value ,7321 1,5472 1,1403 ,27357 49

Residual -,18601 ,22144 ,00000 ,09910 49


Std. Residual -1,777 2,115 ,000 ,946 49


Y= -0,972-4,730E-005Xa+ 0,539Xb +0,078Xc+ 1,159Xd - 0,018Xe

Y= -0,972-4,730E-005*(9861,8597) + 0,539*(2,1830) +0,078*(8,9429)+ 1,159*(0,6321) - 0,018*(1,6053) = 1,1394

Appendix 4: BRE Regression









Model Summaryb


Square

Std. Error of the

Estimate

1 ,926a ,858 ,842 ,08838






Coefficientsa


Coefficients

Standardized

Coefficients


Interval for B

B Std.

Error

Beta Lower

Bound

Upper

Bound

1

(Constant) -,739 ,389-

1,901,064 -1,523 ,045

market_value_monthly -9,983E-005 ,000 -,450-

2,703,010 ,000 ,000



housing_starts ,737 ,208 ,352 3,534 ,001 ,316 1,157

inflation_rate ,004 ,014 ,027 ,277 ,783 -,025 ,033




Predicted Value ,8742 1,4871 1,2025 ,20566 49

Residual -,17062 ,16739 ,00000 ,08365 49


Std. Residual -1,931 1,894 ,000 ,946 49


Y= -0,739 -9,983E-005Xa+0,486 Xb + 0,078Xc+0,737Xd +0,004Xe

Y= -0,739 -9,983E-005(2854,7872)+0,486 (2,1830) + 0,078(8,9429)+0,737(0,6321) +0,004(1,6053) = 1,2068



Appendix 5: CPT Regression









Model Summaryb


Square

Std. Error of the

Estimate

1 ,968a ,938 ,931 ,10355




Coefficientsa


Coefficients

Standardized

Coefficients


Interval for B

B Std.

Error

Beta Lower

Bound

Upper

Bound

1 (Constant) -1,088 ,470 -

2,315

,025 -2,036 -,140



market_value_monthly -9,075E-005 ,000 -,321-

2,698,010 ,000 ,000

financial_leverage_index ,689 ,217 ,492 3,173 ,003 ,251 1,126


housing_starts ,358 ,243 ,097 1,473 ,148 -,132 ,849


2,903,006 -,064 -,012




Predicted Value ,8242 1,8960 1,3851 ,38055 49

Residual -,21297 ,18461 ,00000 ,09801 49


Std. Residual -2,057 1,783 ,000 ,946 49


Y= -1,088-9,075E-005Xa+ 0,689Xb +0,129Xc+0,358Xd - 0,038Xe

Y= -1,088-9,075E-005*(3808,3659)+ 0,689*(2,1830) +0,129*(8,9429)+0,358*(0,6321) - 0,038*(1,6053) = 1,3894

Appendix 6: ELS Regression









Model Summaryb


Square

Std. Error of the

EstimatePaper: “Real Estate Valuation Model” (2014) Rights Reserved: JAW GroupAuthor: Jamila Awad JAW Group, 3440 Durocher # 1109Date: May 13, 2014 Montreal, Quebec, H2X 2E2, Canada Mobile: (1) 514 799-4565 E-mail: [email protected]


1 ,956a ,914 ,904 ,05320




Coefficientsa


Coefficients

Standardized

Coefficients


Interval for B


Bound

Upper

Bound

1

(Constant) ,587 ,232 2,535 ,015 ,120 1,055


4,724,000 ,000 ,000

financial_leverage_index ,188 ,096 ,307 1,964 ,056 -,005 ,380

unemployment_rate ,016 ,018 ,070 ,909 ,369 -,020 ,053

housing_starts -,070 ,129 -,043 -,541 ,592 -,329 ,190

inflation_rate ,014 ,008 ,124 1,863 ,069 -,001 ,029




Predicted Value ,4874 ,9836 ,7857 ,16371 49

Residual -,11546 ,14690 ,00000 ,05035 49


Std. Residual -2,170 2,761 ,000 ,946 49


Y=0,587+ 0,188Xb + 0,016Xc - 0,070Xd +0,014Xe

Y=0,587+ 0,188*(2,1830) + 0,016*(8,9429) - 0,070*(0,6321) +0,014*(1,6053) = 1,1187



Appendix 7: HME Regression









Model Summaryb


Square

Std. Error of the

Estimate

1 ,963a ,927 ,919 ,09135




Coefficientsa


Coefficients

Standardized

Coefficients


Interval for B


Bound

Upper

Bound

1 (Constant) -,600 ,433 -

1,386

,173 -1,472 ,273




2,880,006 ,000 ,000


unemployment_rate ,053 ,032 ,121 1,652 ,106 -,012 ,119

housing_starts ,245 ,215 ,081 1,140 ,261 -,189 ,679

inflation_rate -,007 ,013 -,031 -,494 ,623 -,033 ,020




Predicted Value ,5515 1,3977 1,0231 ,30880 49

Residual -,22694 ,21473 ,00000 ,08646 49


Std. Residual -2,484 2,351 ,000 ,946 49


Y= -0,600+ 0,636Xb + 0,053Xc+0,245Xd -0,007Xe

Y= -0,600+ 0,636*(2,1830) + 0,053*(8,9429)+0,245*(0,6321) -0,007*(1,6053) = 1,4059

Appendix 8: MAA Regression









Model Summaryb


Square

Std. Error of the

EstimatePaper: “Real Estate Valuation Model” (2014) Rights Reserved: JAW GroupAuthor: Jamila Awad JAW Group, 3440 Durocher # 1109Date: May 13, 2014 Montreal, Quebec, H2X 2E2, Canada Mobile: (1) 514 799-4565 E-mail: [email protected]


1 ,956a ,913 ,903 ,09677




Coefficientsa


Coefficients

Standardized

Coefficients


Interval for B


Bound

Upper

Bound

1

(Constant) -,598 ,438-

1,365,179 -1,481 ,285


4,161,000 ,000 ,000



housing_starts ,473 ,228 ,161 2,072 ,044 ,013 ,933


2,325,025 -,056 -,004




Predicted Value ,4968 1,3537 ,9157 ,29714 49

Residual -,15801 ,17774 ,00000 ,09159 49


Std. Residual -1,633 1,837 ,000 ,946 49


Y= -0,598+ 0,399Xb +0,099Xc+0 ,473Xd - 0,030Xe



Y= -0,598+ 0,399*(2,1830) +0,099*(8,9429) + 0,473*(0,6321) - 0,030*(1,6053) = 1,409

Appendix 9: PPS Regression









Model Summaryb


Square

Std. Error of the

Estimate

1 ,933a ,870 ,855 ,11548




Coefficientsa


Coefficients

Standardized

Coefficients


Interval for B

B Std.

Error

Beta Lower

Bound

Upper

Bound



1

(Constant) -2,122 ,546-

3,884,000 -3,223 -1,020

market_value_monthly 4,395E-005 ,000 ,110 ,649 ,520 ,000 ,000



housing_starts ,301 ,271 ,105 1,111 ,273 -,245 ,847

inflation_rate ,008 ,015 ,042 ,558 ,580 -,022 ,039




Predicted Value ,9396 1,7314 1,3912 ,28254 49

Residual -,21341 ,32795 ,00000 ,10930 49


Std. Residual -1,848 2,840 ,000 ,946 49


Y= -2,122+4,395E-005Xa+ 0,929Xb +0,135Xc+ 0,301Xd + 0,008Xe

Y= -2,122+4,395E-005(1702,1153)+ 0,929(2,1830) +0,135(8,9429)+ 0,301(0,6321) + 0,008(1,6053) = 1,3912

Appendix 10: UDR Regression











Model Summaryb


Square

Std. Error of the

Estimate

1 ,914a ,835 ,816 ,13272




Coefficientsa


Coefficients

Standardized

Coefficients


Interval for B

B Std.

Error

Beta Lower

Bound

Upper

Bound

1

(Constant) -2,366 ,627-

3,775,000 -3,630 -1,102

market_value_monthly -9,038E-006 ,000 -,049 -,255 ,800 ,000 ,000



housing_starts ,947 ,313 ,324 3,024 ,004 ,315 1,578


1,568,124 -,068 ,009




Predicted Value ,7111 1,5705 1,1663 ,28294 49

Residual -,23021 ,26693 ,00000 ,12561 49


Std. Residual -1,735 2,011 ,000 ,946 49




Y= -2,366-9,038E-006Xa+ 0,893Xb + 0,120Xc+0,947Xd - 0,030Xe

Y= -2,366-9,038E-006*(4172,6559) + 0,893*(2,1830) + 0,120*(8,9429)+0,947*(0,6321) - 0,030*(1,6053) = 1,1693


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