Rays of Research on Real Estate Development

Jaime Luque

• Publisher: Business Expert Press
• Year: 2016
• Print ISBN: 9781631576003
• eBook ISBN: 9781631576010
• Topic: Business
• Subtopic: Real Estate
• Index: Business

Real estate development accounts for one of the major economic sectors in most countries, yet during the last two decades research on this important topic has been scattered. This textbook brings together some of the most important results on this subject. The book is written in a pedagogical way and covers crucial aspects of this industry such as growth management and real options, land use regulations, mixed housing developments, taxes, externalities, housing affordability problems, land prices and uncertainty, public infrastructures, and housing supply. This book is an excellent source for an advance course in real estate development that attempts to cover important contributions in this area. The has included multiple choice questions to test students' assimilation of the material.

PART I

Growth Management and Real Options

CHAPTER 1

Volatility, Competition, and Investments

In collaboration with Alexandra Piechowicz and Nicholas Sanders

The possibility of failure deters many individuals from investing time or money into something, in particular when the probability of an attractive return is low or the projected revenue is truly uncertain. For example, a rational individual would not pay $50,000 for a lottery ticket if his or her chances of winning $75,000 were one-in-a-million. Risking $50,000 is likely not worthwhile when the potential return on his or her investment is only 50 percent and the likelihood of losing the investment is almost inescapable. Here, the expected benefit (multiplying the probability of success by the $75,000) is $37,500, whereas the cost for the lottery ticket is $50,000. In essence, purchasing the lottery ticket poses a risk far greater than the expected return. While the aforementioned example is an extreme and unlikely circumstance, rational thinking and analytical deliberation are essential to the many realms of large capital investment where uncertainty is inevitable.

Bulan, Mayer, and Somerville (2009) explore the relationship between market volatility and irreversible investment in real estate development in Vancouver, Canada, examining how idiosyncratic and systematic risk and competition affect firms’ decisions to undertake certain projects. The goal of the authors is to demonstrate the existence of a negative relationship between idiosyncratic risk and irreversible asset investment with a focus on the real options model. In addition, the authors prove a negative relationship between market volatility and real estate investment consistent with capital asset pricing model (CAPM). Bulan, Mayer, and Somerville (2009) attempt to show that competition within development reduces the impact of return volatility on investment. They seek to establish that competition is a driver for investment and that delays in investment tend to occur during a market downturn in which competition is reduced. The authors analyze data collected between 1979 and 1998 and compare their results to existing literature specific to competition and the real options framework.

The data that the authors analyze was collected from 1,214 strata projects built between January 1979 and February 1998 in Vancouver, Canada. This nearly twenty-year period exhibited four boom-and-bust cycles (1982, 1986, 1991, and 1996) during which the growth of strata projects consistently surpassed the growth of single-family developments. The authors provide information about the number of projects in a given calendar year and the average size of the projects for that year in units per project. Using the sale prices of all strata transactions between 1979 and 1998—obtained from the British Columbia Assessment Authority (BCAA)—the authors compile a repeat sales index for each of the seven sections of the city—three unique and four consolidations on the basis of similarities. Using a combination of autoregressive models and GARCH models, the authors compute expected price appreciation, project-specific discount rates, systematic risk, return volatility, and the magnitude of competition.

The author’s primary model for analysis is the real options model. The theory supporting the model is that firms should apply a higher user cost to new investments in real estate when returns are volatile, reflecting the option to delay that is lost when investment occurs. The authors believe that this model is valid because the traditional CAPM model already predicts lower investments in volatile markets. Higher volatility is linked to greater nondiversifiable risk and thus a higher required rate of return from investors. Thus, the authors find clear support for the negative relationship between investment and idiosyncratic risk in existing models and intuition. The second component of the real options model is the effect of competition on investment. Local competition posed by proximate potential developments can erode the value of the option to delay irreversible investment and make firms more likely to develop by lowering the cost of forfeiting their option.

The authors first find that coefficients for neighborhood price-level indexes and volatilities, building type, project size variables, and neighborhood fixed effects generally conform to the real options model. Price coefficients are expectedly greater than one, indicating that the developers choose to develop a parcel more quickly when neighborhood prices are higher. The coefficient on the volatility of condo returns is less than one suggesting that developers wait longer when volatility is higher, even when controlled for price levels. One standard deviation increase in condo return volatility decreases the monthly hazard rate, reflecting the probability of development as a function of time alone, by 13 percent which leads to a 9-percent decrease in prices. The authors then controlled for market risk by multiplying the neighborhood condo risk rate by the volatility of the TSE 300 Index. Here, one standard deviation increase in the average market volatility across neighborhoods leads to an 8-­percent decline in the hazard rate, whereas an equivalent standard deviation increase in idiosyncratic volatility leads to an 11-percent decrease in the hazard rate.

One additionally significant variable is the risk-free interest rate. One percentage point increase in the risk-free rate leads to a 52-percent ­decline in the monthly hazard rate. However, the drift rate, which measures expected capital appreciation, was found to be insignificant and independent of the hurdle rate, which conforms to the real options model.

Further results show how volatility correlates with pricing and development through examining expected price appreciation. The statistically significant coefficient on expected price appreciation is above one while the coefficient on negative expected price appreciation is less than one. This demonstrates that holding price constant, development is more likely when prices are rising faster and when prices are falling faster since the negative coefficient produces a positive effect on the hazard rate when combined with negative price changes. The authors attribute this contradiction to the fact that rising prices can help developers overcome liquidity constraints and pursue a larger number of projects leading to a higher hazard rate. Conversely, developers race each other to build when prices are quickly falling due to an iteration of the prisoner’s dilemma in which both developers choose to build rather than being beaten to the market, which causes a cascade of development and falling prices.

The authors next address the second component of their hypothesis regarding the relationship between competition and the hazard rate. Their results show that volatility has a smaller impact on option exercise in areas that face greater potential competition. As more competitors surround a project, its hazard rate of construction becomes less sensitive to volatility. Given the mean number, 23, of potential projects, measured as the number of potential projects within 4 years and 1 kilometer, 1 standard deviation increase in return volatility leads to a 13-percent decline in new construction. However, if the number of competitors increases by 50 percent, the equivalent standard deviation increase in volatility leads to only a 9-percent decline. The authors additionally found that competition was not significantly related to any other variable than volatility and the coefficient on competition in isolation was insignificant.

The authors conclude that builders delay development during periods of greater exposure to idiosyncratic and market risk. They also conclude that competition in the local market significantly reduces the effect of option exercise in respect to volatility. Given these two conclusions, the authors find sufficient support for the real options model because the interaction between volatility and competition does not appear to affect the user cost of a reversible investment. The authors expand their discussion to address the common conception that developers irrationally overbuild. However, the authors’ results show that rational and strategic decision-making processes cause developers to start more projects as prices fall. A more salient conclusion discussed is that the real options model has major implications for how developers time their investment. If competition decreases in recession, the real options model implies that developers now find more value in delaying their irreversible investment. Conversely, boom times leads to higher competition, which decreases the value of the option and delaying investment. The relationship between volatility, competition, and investment timing leads to a broader conclusion that understanding the real options model can help explain the cyclical component of real estate investment across the economy.

Bulan, Mayer, and Somerville (2009) rely heavily on the real options model to support the arguments in their paper. Another scholar, Francesco Baldi—who acknowledges real options as imperative to real estate development and investment—proposes a new methodology that quantifies the values of said options based on a portfolio view to complement the real options model (see Baldi 2013). Baldi identifies real options as a combination of timing—immediate and deferrable—and scaling—up and down—strategies. This flexibility is extremely important to real estate development as unforeseen circumstances may derail the progress of an investment; real options help developers adjust to these new conditions so that they can maximize and retain projected profits. The valuation framework that Baldi proposes consists of “binomial trees”—bottom, intermediate, and upper—that represent the three stages of development, respectively, preconstruction, completion of the first stage, and the final completion of the project. The value—based on expected land appraisal—of a real option varies depending on when—during which of the aforementioned stages—the decision is made. Flexibility and the availability of real options are especially favorable to developers as market volatility increases; changes to the itinerary may be crucial for profit maximization. All in all, real options are best taken into consideration through a thorough portfolio approach when appraising a development. An extensive analysis of market conditions, development progress, and expected property values can ensure that investors will receive a desirable payoff.

Bulan, Mayer, and Somervile (2009) identify several weaknesses in their analysis. The first arises from strata data, which is filed when a building is very close to completion. Therefore, there is no reliable start date for construction which is relevant when evaluating option exercise, or when the choice whether or not to invest occurs. To compensate, the authors added a lag time of 1 year to the date indicated on the strata plan. This lag time is an estimation based on an observation that 59 percent of new multifamily projects are completed within 1 year from the construction start date. A second weakness is found within the project-specific discount rate. The model used to find the rate makes the assumption that real estate market is in perpetual equilibrium. The authors believe that this model is inconsistent with the market history marked by periods of disequilibrium. Instead of using the project-specific discount rate, a substitute is implemented from the CAPM model in which the project-specific rate is estimated by simply adding a market risk premium to the risk-free rate. A third weakness is the existence of dual options inherent in the sequential nature of real estate development. Developers can invest and disinvest by repurposing or terminating projects before final completion. Since strata plans are filed very close to completion, projects that were terminated or repurposed will not be included in strata data and excluded from analysis. To compensate for this weakness, the authors artificially censor the data by truncating the sample from 1994 when there was a downturn in development.

The authors offer the solid foundation of a reliable model to apply to real estate development in macroeconomic analysis. It offers a convincing perspective on the cyclical nature of the market and can help explain how developers time their investments. Given this basis, researchers could apply this model to other markets other than Vancouver to establish generalizability and determine how results differ across markets using the real options model. The research, here, was conducted prior to the subprime crisis, so it would be relevant to examine whether the unprecedented downturn has affected developer’s investment behavior and if the real option model has different implications in today’s altered environment.¹ A longitudinal study using the real options model across a diverse sample of markets across the United States would likely provide useful insights into today’s resurging market and the reasons for development given current risk and competitive landscapes.

Multiple Choice Questions

1. 1. Many scholars argue that market power is essential to attaining greater revenue when volatility is increased, even positing that one firm’s strategic advantage may discourage other firms from developing. However, Bulan, Mayer, and Somervile (2009) use the results of external research to explain that competition is favorable to a market in which uncertainty is high. Which of the following is true regarding competition in real estate development?
   a. Real estate is a perfectly competitive, homogenous market in which locations are perfect substitutes for each other
   b. Holding price constant, development stagnates when prices rise quickly in a competitive market
   c. Holding price constant, development stagnates when prices fall quickly in a competitive market
   d. None of the above

Explanation: The correct answer is (d): none of the above statements are correct. (a) is incorrect because although homogeneity is necessary for ...

Table of contents

• Cover
• Half Title Page
• Title Page
• Copyright Page
• Dedication
• Contents
• Preface
• Part I Growth Management and Real Options
• Part II Land Use Regulation
• Part III Mixed Housing Development and Neighborhoods
• Part IV Taxes
• Part V Housing Supply
• Part VI Selected Topics
• Bibliography
• Index