The aim of this research is to investigate corporate behavior, including investment and financing decisions, when corporations face uncertainty and flexibility/inflexibility, and to explore the effects of this behavior on real option valuation. It expands the real options analysis framework into two paths: the first is to add institutional details, portfolio aspects and financial structure into the classical real option model; the other is to extend the real option model into a firm valuation model with corporate investment and financing decisions. Two types of theoretical models are developed. The first set of theoretical models follows the framework of binomial option pricing. Three binomial option pricing models are constructed to represent real estate development in Hong Kong, in which developable land has different flexibility in accordance with covenants in typical land lease contracts. First, the firm may have contractually limited time to complete the development following conversion of urban fringe/agricultural land into commercial/residential land after paying a negotiable “land premium”; second, it may buy land from the market without development time constraints; or thirdly it may buy land at public auction also with contractual development time constraints. The three binomial models deal with the flexibility/inflexibility in these land development circumstances imposed by institutional arrangements. Interaction effects from cost-saving through co-development and potential price increases through agglomeration effects from co-location of multiple options are included in the binomial models. The financial structure of the firm is also seen to influence real option values, because capital structure could imply different capital costs in the exercise of the real options, which is ignored in traditional real option theory. In addition to the traditional factors in financial option pricing models, numerical examples show that interaction effects and capital structure influence real option values and their investment thresholds. The second set of theoretical models aims to value both real flexibility and financial flexibility dynamically and simultaneously. Financial flexibility in the firm, which is seen as an important factor in the capital structure decision, is itself seen as analogous to a real option in project valuation, as the firm can use some debt capacity to invest in the opportunity but still preserve unused capacity for future opportunities. The thesis argues that the firm owns the financial flexibility to adjust its debt through sale of its existing assets or to use these as loan collateral. The firm with more collateralizable assets would have larger debt capacity, use more debt and invest more through the flexible utilization of debt capacity. Two empirical tests are conducted to confirm the findings of the theoretical models, structured into three principal hypotheses: firstly, real option value is not only determined by embedded flexibility, but also by the existing corporate asset structure through interactions and the firm’s ability to trade or collateralize its existing assets (properties); second, real option value and real option execution/investment is directly influenced by external financing decisions due to financial frictions and constraints; and thirdly, financial flexibility is expected to increase corporate investment through the collateral channel and lower cost of capital. The first empirical study uses project level land auction price data in Hong Kong to investigate the relationships between real options and firm fundamentals including interactions, constraints and financial structure. Three sub-hypotheses associated with principal hypotheses one and two about real option valuation are derived from the theoretical binomial option models. Sub-hypothesis one states that real option value is expected to increase if the firm has more properties that may interact with the property underlying a real option; two states that real option value is expected to decrease if the firm faces capital budgeting constraints when allocating resources among competing projects and high financing costs caused by financial frictions; and three, real option value is expected to increase if the firm has sufficient internal funds and financial flexibility to finance the opportunity. The empirical results support the sub-hypotheses. It is shown that the option premium embedded in the land price is not only related to real flexibility such as waiting to invest (as identified in traditional real option theory), but it also increases or decreases with the direct interactions with other properties, their competition for firm resources and the firm’s financial status. This links for the first time project level predictions of real option theory to firm fundamentals. The second empirical test investigates investment decisions and the influence of the firm’s financial flexibility. It tests principal hypotheses two and three about the effects of financial constraints, frictions and flexibility. Through the firm valuation model, sub-hypotheses four to six are derived. Sub-hypothesis four states that utilization of external funds relaxes financial constraints and induces investment in profitable opportunities; while the higher opportunity cost of external funds raises the investment threshold which reduces investment and lowers real option value. Sub-hypothesis five states that the firm with more collateralizable assets has lower investment thresholds in comparison with the firm relying on external capital with higher financing cost, and is divided into two: 5.1 states that the firm with more collateralizable assets would use more debt financing (because the collateralizable assets create low-risk debt capacity); and 5.2 states that the firm with more collateralizable assets would have access to debt at lower interest rates. Sub-hypothesis six states that existing debt reduces internal funds and debt capacity of the firm, leading to less investment and a higher investment threshold due to potentially high capital cost of external financing. Hong Kong real estate company data is employed to test the implications from the firm valuation model. The results can be summarized as follows: (1) debt level influences investment decisions, in which debt firstly relaxes the financial constraint for investment and then imposes financial frictions; (2) collateralizable assets increase investment by creating debt capacity if the firm faces financial constraints; (3) the firm has the ability to use more long term debt if it owns more collateralizable assets; (4) interest rates charged for long term debt reduces in the firm with more collateralizable assets; (5) financially constrained firms have to depend on substantial collateralizable assets to issue debt and invest. In sum, there is support for the functioning of a “collateral channel” at the firm level. / published_or_final_version / Real Estate and Construction / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / toc / Real Estate and Construction / Doctoral / Doctor of Philosophy

Many countries such as Australia, Ireland, Netherlands, United Kingdom (UK), Spain, United States of America (USA) and South Africa (SA) among others have experienced an increase in housing prices, since the late 1990s. In SA, the abrupt increase in residential property prices, particularly during the period 1999 to 2007, resulted in an improvement in the level of households’ net wealth position. Empirical investigations, mainly from developed countries, provide evidence indicating that a house price increase has a significant impact on the households’ wealth, and thus house price gains increase housing collateral for homeowners which make it possible for them to take out equity in the form of refinancing or selling of the house to finance consumption. With the above in mind, this study investigates the relationship between aggregate expenditure on consumption by households and residential house prices in South Africa. Following the permanent-income/lifecycle hypothesis (PI-LCH), this study applies the vector error model (VECM) into the 1980:Q1 to 2007:Q4 quarterly data sample. The overall finding of the study indicates there is indeed a long-run positive relationship between housing prices and consumption in South Africa. / Thesis (M.Com.)-University of KwaZulu-Natal, Westville, 2010.

An ongoing debate in the corporate finance world concerns the question of a firm's optimal capital structure. More specifically, is there a way of dividing a firm's capital into debt and equity so as to maximize the value of the firm? From a practical standpoint, this question is of utmost importance for corporate financial officers. Yet, the academic literature has not been very helpful to provide clear guidance on practical issues. Except for a few theoretical models, there is a lack of literature concernmg how companies should decide their leverage ratios in practice. These models are unfortunately not applicable in real practice because of their inability to provide managers with a precise optimal leverage level. The purpose of this study concerns the practical matter of deciding the appropriate capital structure and the possibility of improvement for the companies. Specifically: How do the case companies decide their capital structure? Are their current capital stmctures optimal or is there room for improvement? To be able to examine these questions it was necessary to investigate companies that are as comparable as possible within the same industry. Different industries were identified based on the Johannesburg Stock Exchange industry classification and were analyzed for comparability issues. The real estate industry was found to experiences very similar business and has an opportunity to take more debt due to the nature of its asset structure. Three companies were selected from the property segment of the real estate industry based on their leverage ratios and companies with highest, medium, and lowest leverages in the industry were selected. Gold-edge was found to be the highest levered company in the industry, while Samrand and Putprop were found to be average and least levered in the industry respectively. The findings indicate that none of the companies uses capital structure models when deciding their capital structure. The case companies' capital structure indicates that Gold-edge's current capital structure is considered as close to optimal as possible while Putprop and Samrand current capital structure are not optimal and there is room for improvement. / Thesis (MBA)-University of Natal, Durban, 2003.

by Tong See Wai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 78-80). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgements --- p.iii / Table of Contents --- p.iv / List of Tables --- p.vii / List of Figures --- p.viii / Chapter 1. --- Introduction --- p.1 / Chapter 2. --- Background and Data --- p.5 / Chapter 2.1 --- Linkage between Hong Kong's real estate market and stock market --- p.5 / Chapter 2.2 --- Review of Hong Kong mortgage lending policy --- p.6 / Chapter 2.3 --- Corporate Profiles --- p.7 / Chapter 2.3.1 --- Cheung Kong (Holdings) Limited --- p.7 / Chapter 2.3.2 --- Hang Lung Development Company Limited --- p.7 / Chapter 2.3.3 --- Henderson Land Development Company Limited --- p.8 / Chapter 2.3.4 --- New World Development Company Limited --- p.8 / Chapter 2.3.5 --- Sino Land Company Limited --- p.8 / Chapter 2.3.6 --- Sun Hung Kai Properties Limited --- p.9 / Chapter 2.3.7 --- Swire Pacific Limited --- p.9 / Chapter 2.4 --- Sources of Data --- p.10 / Chapter 3. --- Literature Review --- p.12 / Chapter 4. --- Methodology --- p.17 / Chapter 4.1 --- Nominal Rate of Capital Gain --- p.17 / Chapter 4.2 --- Internal Rate of Return --- p.19 / Chapter 5. --- Results --- p.25 / Chapter 5.1 --- Empirical Findings --- p.25 / Chapter 5.1.1 --- Results from Aggregate Data --- p.25 / Correlations of Returns --- p.26 / Average Rate of Capital Gain --- p.26 / Chapter 5.1.2 --- Results from Disaggregated Data --- p.27 / Graphs --- p.27 / Correlations of Returns --- p.28 / Property Developers --- p.28 / Housing Estates --- p.28 / Testing the Significance of Correlation --- p.29 / Cross Correlation Matrix on Stock --- p.30 / Regression --- p.30 / Average Returns on Housing and Stock --- p.31 / Unleverage --- p.31 / Leverage --- p.31 / Comparison of the Average Unleveraged Return with the Average Leveraged Return --- p.31 / Housing --- p.31 / Stock --- p.32 / Comparison of the Average Return calculated from Aggregate Data with that calculated from Disaggregated Data --- p.32 / Housing --- p.32 / Stock --- p.33 / Chapter 5.2 --- Discussion --- p.33 / Chapter 5.2.1 --- Correlation of Returns --- p.33 / Chapter 5.2.2 --- Average Return --- p.35 / Chapter 5.2.3 --- Hedging --- p.39 / Chapter 6. --- Conclusion --- p.40 / Tables --- p.41 / Figures --- p.58 / References --- p.78