When a person acquires real estate, she/he also acquires a set of rights, including possession, control and transfer rights. In order to achieve capital appreciation investment in real estate involves the commitment of funds to property with an aim to generate income through rental or lease. However, the real estate income can be highly unpredictable and consequently investment in real estate is very risky as it is the case with investment in equity. One of the main reasons for the collapse of even the largest financial institutions, in the recent times, leading to the recession of major economies of the world is due to the collapse of the real estate business. This global crisis could have been prevented if the potential risk factors leading up to it had been identified at an early stage. But the risk assessment associated with this strategic investment was not properly followed. Thus, in order to be able to increase chances of investments in real estate returning profitable margins, it is critical that risk factors are identified and a proper investment strategy put into place.
Mughees Shaukat in his paper (2010) defined real estate as land & Buildings associated with it. This definition is used because land & associated buildings are finite and can be used for transaction purpose. Procuring the Real Estate is used as a hedge for inflation. In his paper, he classified real estate into six categories: Residential – Family housing, Apartments. Commercial – Shopping centers, Malls, Clinics and Multiplexes etc. Industrial – manufacturing plants, warehouses, etc., used by businesses for production and storage of goods. Office – Office buildings, Towers. Hospitality – Hotels and Resorts. Land – Typically includes land without any buildings or agricultural land.
A Sirmans, G Stacy (2001) in his paper observed the return patterns and classified returns in broad categories. He also concluded that the returns are dependent upon the market efficiency and argued that the microeconomic variables may help in measuring the variations in Real Estate Returns. 1.2.1 Returns from Housing Property: Case etal (1990) in their paper studied the single family home prices. His findings were the returns on investment are dependent on the location. For example, the metropolitan areas housing (flats/single housing) will have a better return than the rural areas. Also it is dependent on other factors such as income, population growth and material/construction cost. 1.2.2 Diversification and portfolio optimization benefits: Liang etal (1995) defined diversification and portfolio optimization benefit as the difference between the return on the portfolio and the required rate of return for the investments. Seiler etal (1999) on the same subject concluded that the return on real estate requires mixed portfolio. Some researchers questioned the wisdom of Seiler and remarked “do the returns from real estate behave like stocks & bonds?”. However, the research by other researchers shows that the returns have low or negative correlation with other types of investment returns so the real estate adds significant diversification benefits to a mixed portfolio. 1.2.3 Inflation and real estate returns: It is a common belief that the performance of real estate value depends on the inflation. Wurtzebach etal (1991), studied on the impact of inflation on the value of assets. In their study, they showed that real estate does provide an inflation hedge. They concluded that when market imbalance occurs the risk increases and the returns suffer regardless of inflation. Rubens etal (1989) in their paper stated that Real Estate hedge against inflation depends on the type of real estate. Other studies also showed that mix of commercial and residential properties provide a better inflation hedge.
Any investment requires certain strategies to handle risks. Real Estate investment is of no exception. The risk of investment in properties depends on the type, location and the status of the property in terms of its development. Giliberto, (1993) in his paper classified the risks as Low Risk-properties in major metropolitan cities with stable long-term cash flow. For example, leased office buildings in metropolitan cities. This is because tenants are usually signed to multi-year leases. Moderate Risk- properties with less predictable cash flows, either as a result of their location, or their status. For example, a shopping center in the process of being leased up or an older mall in need of complete renovations and re-leasing; etc. or Hospitality properties as their cash flow is less predictable because the guests in a hotel sign up very short-term leases when reserving a room. High Risk- properties with limited or no cash flow, because of their current status where the investments tend to be in longer term which requires a greater degree of skill in execution. They also tend to provide the greatest return over the long term.
There have been lots of academic studies and articles on the ways of increasing the efficiency of Real Estate Investment. The academicians have studied both at micro and macro level. The ideas of real estate risk management comes under the domain of financial transactions with regard to properties buy or sell at an opportune time where it could provide high yield. Real estate investment exposed to different risks. Some of the researchers have advocated for the creation of Real Estate Investment Trust (REIT). They also advocated the selection and portfolio construction criteria should be based on operating efficiency. Markowitz (1952) developed Modern Portfolio Theory (MPT) , which is based on simple assumption that the risk is defined as volatility (price fluctuations). As per MPT, the investors are willing to take more risks when there is a chance for more profit. Initially the researchers and academicians found this logic is compelling as it is easy to understand & it also makes perfect sense. The MPT is based on certain assumptions. Some of the important assumptions are: The investor do not consider buying / selling cost, tax , dividend and capital gains while making investment decisions Market liquidity is infinite. The investors are aware of all the risks; for more volatility the investor will look for more return. Selling of assets is only motivated for higher rate of return in a shorter time span. Politics and investor psychology have no effect on the markets. Now the question became how to determine the expected returns, volatility and correlations between them? Markowitz assumed these questions. His recommendation was to keep or watch the market data for some time. However, he had hoped better methods which will take into account more information. But after 60 years still today the researchers do not have a definite methodology to tackle this problem. 1.4.1 Arguments by other researchers on MPT Jose Castro (2008) in his paper argued that that the risk of a portfolio can be minimized without reducing the expected return when there is a low correlation between the fixed assets. The reason he cited that the risk is quantifiable but can be classified in two segments parts: ‘Systematic risk, which can’t be ignored, ‘Diversifiable risk’ which is the component of an asset that is not linked with the market, which is called “Idiosyncratic Risk”. Young & Graff (1995) while assessing Real Estate Risks used MPT in their research and observed that the strategic risks on investment can be minimized by combining different stocks for investment. For example, Webb and O’keefe (2002) suggested that 10-20% of total stocks should be of real estate. The rest should be into bonds, currencies, international stocks & bonds. Brown (2000) in his paper extensively worked on the use of MPT which can be used in Risk Assessment. Brown was the first one who introduced the concept of three tier real estate based on public & private use. Simons et al (2002) took this research one step further by reviewing literatures on international direct real estate investment and tried to analyze how the real estate portfolios are being analyzed. Anderson (2003) extensively studied the works of other researchers (Anderson etal 2000) on the risk of Real Estate Investment both at the macro and micro level. They defined that at the macro level the competition in the real estate market makes the risk minimum. Whereas at micro level, the risk is greater if the market is inefficient. They then advocated that the strategic risk may be minimized by establishing diversified real estate portfolios. Brown (2004) investigated the risk in real estate investment by conducting theoretical and empirical analysis of risk and returns accruing to individuals who were involved in real estate investments. He claimed that the returns are not normally distributed and that private real estate investors compensate for the distributional burdens imposed by market upon them by carefully assessing and controlling unavoidable non-systematic risk. Hutchison etal (2005) in their paper argued that the risks in real estate investment is attributed to the valuation of property done by the valuers. This means, the risk assessment measures need to be more rigorous to minimize the risk of investment. Therefore, they suggested that an investor is exposed to many risks notably valuation accuracy & valuation variance. The authors also suggested that the real estate investment has certain forms of risks in terms of valuation of the property. They recommended that investment risk will be minimized by improving the valuation methods. The authors then defined “investment risk as the probability that the cash flows and the resulting target rate of return will not be realized”. Earlier both empirically and theoretically researchers have analysed risks and returns. Area of common interest is performance of investment and the associated risk which is found in both commercial and individual real estate. Researchers do agree that real estate do provide an inflation hedge. It is also a general belief that real estate assets provide a protection blanket against any sorts of negativity that may creep in due to unexpected inflation. The question now comes how to minimize the risks of investment? Nitish (2006) in his paper cited that choosing a prime location will be the main component for minimizing investments. To substantiate his argument he developed a price model based on the structure, location and rent of various cities in Germany. However, it does not fully assess the elements of risk fully. Even though MPT is the major breakthrough in the financial world some real estate researchers have tried to use this theory with some mixed reservations, because in MPT the risk is not properly represented by volatility. Another reservation is that portfolio selection & building too often rely on the past performance. In other words, MPT requires three types of data. They are: Future return potential of each portfolio Correlation of each portfolio with another Volatility of each portfolios H. Mohd Ali (2006) in his research paper has done extensive investigation and found that MPT without major modifications cannot be used as is risk assessment in real estate. Cotter etal (2006) in their paper argued that the inclusion of REIT equity options will increase the fund flow. They observed that real estate prices are subject to inflation and requires hedging against inflation. Their model on REIT daily volatility has become very useful tool in managing real estate risk. McGreal etal (2009) have made an extensive study in risk management in real estate for both UK & USA. Their summarization is that there is a need for diversifying the investment portfolio in order to reduce the risk. Diavatopoulos etal (2010) examined the effect of real estate risk management by examining the characteristics of REIT equity options. Their analysis showed that in 1996 only 5% of REIT had traded options increases to 35% by 2006. This proves that the REIT traded option has become a useful tool in real estate risk management.
There have been lots of studies undertaken by the researchers on the fluctuations or volatilities of the real estate market in order to minimize the risk in real estate investment. Hung et al (2009) in their paper studied the relationship between the different type of volatilities by using a mathematical model (GARCH-in -model) on the returns using the concept of REIT. Their findings were: “Momentum returns has asymmetric volatility Momentum returns are higher when the volatility is higher REIT with lowest past returns has higher risks than those with higher past returns. There is a positive relation between asset returns and aggregate market volatility”. In essence, when the volatility is high the investors require higher returns for their investment to mitigate the inflationary pressures.
As noted above the standard practice in real estate risk management has been to work with REIT indices mixed with various equity indices. However, this does not consider the basic question on when to buy a real estate property and when to sell the same.
The activities of risk management in real estate investment are largely similar to the activities of financial methods. However, by surveying the available literature & material it can be concluded that the following factors are ignored. 1.7.1. Large commercial properties have been widely studied. Little has been done on medium sized real estate risk analysis (see Brown (2004)). 1.7.2.Most of the research papers have also devoted their tools and their methodologies relate to stock market investments. However, none of them pinpoint the risks of investment which can be used by any investor to: Identify the sources of risk Measure and monitor those risks Devise an approach to control, mitigate or hedge The extreme important effects of real estate asset on savings and portfolio choice are undeniable. Moreover, the effect of real estate on consumption is significant and larger than the effect of financial assets (Fei 2009). Furthermore, the volatility of real estate price is one of the key determinants of the options value realized by mortgage default and prepayment. The risks of real estate market will affect mortgage and mortgage related securities market and consequently as well as the institutional investors since they usually hold a notable part of their funds invested in mortgage related securities. Quite recently the financial market turmoil driven by the sub-prime mortgage and credit crisis has indicated again that the variations in housing market are extremely crucial to overall economy, the mortgage capital markets and the welfare of the society. In light of the above, the thesis addresses the questions when and what percentage of the share of a real estate project needs to be procured, when and what share of a real estate project to be sold, and when to completely withdraw from a real estate project. The scope of the paper will be limited to issues related to real estate owners requiring partnership mode of operations. The related issues that will be discussed are (a) the pattern of the volatility of real estate market, and (b) the growth pattern of the real estate investment process that will be developed in the paper.
The aim of this research is to develop a dynamic risk sharing model which can be applied by any individual investor who would like to know when and how much share of the real estate property to purchase and when and how much of the same to sell so that the risk is properly mitigated and yet earn a good margin.
Develop an in-depth understanding of all the aspects in real estate risk management process.
Construct a functional model based on the information gained through the available published /unpublished literature which will be used to update the estimates of the risk element in real estate investment. This model, an alternative to MPT Model, will help an investor to design Operating Rules and implement the same.
Propose a methodology & management process which can be used for using the model in calculating the risk.
The data that is required to develop the Operating Rules in real estate management is the time series data on real estate prices. The basic unit of return measurement is the incremental difference between current year’s and previous year’s prices, ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. (1) where x(t) is return in the beginning of year t, P(t) is price in the beginning of year t and P(t-1) is price in the beginning of year t-1. The investor in financial assets has very little to do with how current year’s price of a stock differs from last year’s. Private real estate offers the opportunity for an investor to “have a say” in its value by adding his own management to the equation. Thus, by adding entrepreneurial labor to his investment, the private real estate investor/operator may positively influence P(t) and achieve a greater return. Of course, this means that a portion of the return we observe represents a return on the investor’s time as well as on his invested capital. Such a reality adds yet another reason to the list describing why real estate is different. Unfortunately, there is no standard time series data on P(t) available in the form required for this study. The basic reason, amongst many others, is design variations that can be noted for different real estate properties. Prices of real estate vary from property to property even in the same geographic locality. There is no standard price pattern available for one to develop the Operating Rules. Pyhrr et al (1990) discussed developing dynamic investment strategy under alternative inflation cycle scenarios since real asset prices and inflation are highly correlated; but their discussion was not supported by proper data. Furthermore, their analysis is limited to comparing the outcomes of various investment periods. Any attempt in this direction has to be property specific. However, basic indicators for movement of real estate price are based on macroeconomic factors of a country or a geographical region. In this regard many authors developed indices especially in the context of mixed-asset portfolios. From these indices one can get an idea of the trend of the prices and corresponding variability in addition to measures of covariance between real estate prices and stock prices. Accordingly, an attempt will be made in this thesis to develop a mathematical model for Pt, which will be used for simulating the effects of various Operating Rules. The model parameters will be estimated using varieties of results available in the literature in conjunction with methods based on value judgments to incorporate entrepreneurial labor mentioned above. In view of the above, an important consideration will be to enhance the core competencies in risk management. The first step in this regard is to develop a risk-shared dynamic mathematical portfolio selection model for real estate business to be used by private real estate investors. Using this model and simulation select Operating Rules to be adopted by an investor. Based on the outcome of the simulation, a strategy for implementation for the selected Operating Rules will be developed. This will then lend immunity to any private investor against market instability, and other macro factors which influence the real estate market to a certain extent. This will help the investors who would like to share the risk with his partners as well as earn a good margin. Real-life data will be collected and will be used to test and validate the model. Last, strategies for implementation will be developed.
In order to test and validate the mathematical model that will be proposed as an alternative to the MPT risk model, real world data needs to be gathered. The nature of the data to be collected is shown in chapter 5. Appropriate statistical methods and techniques like sampling, regression, analysis of variance, etc., whichever applicable, will be used to estimate the parameters of the model and test the reliability of the same. Next, appropriate non-linear mathematical programming technique that deals with dynamics of decision making will be used to derive the operating decision rules. Extensive simulation will be carried out to test the sensitivity of the parameters involved in the mathematical model and establish the limits of the operating policies. The statistical analysis will eventually be used to test the validity of the mathematical model using the European countries as empirical data. Furthermore, the rules for updating the estimates of the parameters from time to time will take into account the global perspectives that will be developed. In addition, strategies for the using the operating decision rules will be developed.
Chapter 1 deals with real estate investment scenario along with risks that are associated with real estate investment. This chapter also aims and objectives, methodology and research approach. Chapter 2 presents a review of the existing literature detailing the hypotheses used and statistical and mathematical models used by the researchers in the past. Chapter 3 presents the detailed description of the methodology how the data will be collected which will be the back bone of dynamic risk-sharing mathematical model. Chapter 4 presents the nature of the data collected and the methodology used for estimating the various parameters used in the mathematical model and test their sensitivity using appropriate hypothesis testing techniques. Chapter 5 presents the operating rules derived by using appropriate non-linear mathematical programming technique. Chapter 6 presents the sensitivity analysis of the parameters and the limits of the operating decision rules using simulation methodology. Chapter 7 gives the strategies to be adopted for using the operating decision rules and the procedures for updating the parameters from time to time as required. Finally, conclusions and future direction of studies are presented in Chapter 8. LIST OF ORIGINAL PAPERS The following papers have been reviewed: Mughees Shaukat, (2010). “The Benefits and Importance of Commercial Real Estate”, MPRA Paper Number 28268, posted 20, January 2011. Sirmans, G Stacy (2001). “Returns and Risk on Real Estate and Other Investments: More Evidence”. Journal of Real Estate Portfolio Management. Vol. 7, No. 3, 2001. Case and Shiller (1990). “Returns and risk on real estate and other investments: More evidence” Journal of Real Estate Portfolio Management. July 1, 2001 Liang etal (1995), Seiler etal (1999), Wurtzebach etal (1991), Giliberto, S. M., “Measuring Real Estate Returns: The Hedged REIT Index”, Journal of Portfolio Management, Spring 1993 Jose Castro (2008) Young and Graff (1995). “Real estate portfolio analysis under conditions of non-normality: The case of NCREIF”.Journal of Real Estate Portfolio Management Webb, B and O’Keefe, J. (2002) The Case for Global Real Estate, Working paper published by UBS Global Asset Management Simons et al (2002), R.I. Anderson, (2003), Journal of Real Estate Portfolio Management. R.I. Anderson and T.M. Springer (2005). “Investor Perception of Retail Property Risk: Evidence from REIT Portfolios”, Journal of Shopping Center Research, 12: 104-120 Roger J. Brown, (2004). “Risk and Private Real Estate Investments” Journal of Real Estate Portfolio Management. N.E Hutchison (2005), Nitish (2006), Hishamuddin Mohd Ali (2006). “Modern Portfolio Theory: Is There Any Opportunity for Real Estate Portfolio?” Cotter, J . and S . Stevenson. Multivariate Modeling of Daily REIT Volatility. Journal of Real Estate Finance and Economics, 2006, 32:3, 305-25. Stanley McGreal, Alastair Adair, James R. Webb (2009).” Optimal Diversification in U.S./U.K. Private Real Estate Only Portfolios: The Good, the Bad, and the Uncertain”. Journal of Real Estate Portfolio Management .1083-5547 Diavatopoulos, D., J. Doran, and D. Peterson. The Information Content in Implied Idiosyncratic Volatility an d th e CrossSection of Stock Returns: Evidence from the Option Markets. Journal of Futures Markets, 2008, 28:11, 1013-16. Szu-Yin Kathy HungA andA John L. Glascock (2009).” Volatilities and Momentum Returns in Real Estate Investment Trusts”. The Journal of Real Estate Finance and Economics. Volume 41, Number 2,A 126-149″ Fei 2009 (Refer Dr.Sinha’s Chapter 3) Pyhrr et al (1990) (Refer Dr.Sinha’s Chapter 3)
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