Purpose – The purpose of the study was to develop a baseline decription of the current state of corporate real estate management within South Africa. Design/methodology/approach – A combined online/paper questionnaire was administered on the top 200 firms listed on the Johannesburg Stock Exchange. Findings – Majority of the firms have no formally organised real estate unit. A few have only recently organised formal real estate units as a department of the company, reporting mostly to the CEO even though the latter seldom gets involved in Corporate Real Estate decision. The CFO appears to be the one in charge of corporate real estate decisions in most of the firms. Majority of the firms own, rather than lease their corporate real estate due to location, transport advantages, minimisation of the risk of rent changes and community links that are advantageous to their business effort. The principal reason for leasing was the flexibility it affords in relocating in future. Other issues covered include the cost treatment of using CRE, the importance of CRE management, in-house management and outsourcing of CRE services and the criteria for selecting an external CRE management service provider. Business Implications – Service providers need to take into account specific criteria used by respondent firms in the selection of who manages their corporate real estate. Originality/value – This paper provides the first comprehensive description of corporate real estate management on the Johannesburg Stock Exchange.

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Over the course of the COVID-19 pandemic, housing prices have risen sharply and ubiquitously, with the highest jumps frequently occurring in previously sleepy markets like Boise City, Idaho (FHFA, 2021). One explanation touted in the media and in "YIMBY" activist circles is the restrictive effect of land use regulation on housing supply. Although economic theory generally accords with this explanation, attempts to quantify the effects of land use regulations on housing supply have faced significant conceptual and practical challenges. Conceptually, land use regulations are difficult to measure because regulations are multidimensional, dynamic and political, among other challenges. Practically, there is no national database of land use regulations, so researchers have typically gathered their own data and created their own measures of regulatory stringency, either directly—typically by reading and interpreting hundreds of pages of legalese per city or surveying thousands of urban planners—or indirectly—by connecting land use regulations to a different, more easily measured, quality like time required for a permit or percentage of permits accepted, or inferring effects from natural experiments. Methodological differences between time periods studied, types of regulations measured, numbers and types of jurisdictions included, and level of spatial analysis have frustrated efforts to unify the lessons of each study into a coherent whole (Gyourko and Molloy 2015). What is needed is a way to quantify and analyze land use regulations that is: a) Easily calculated from readily available open-source data b) Comparable within and across geographic areas at multiple scales c) Comparable within and across geographic areas over time This thesis explores an original measurement that meets the criteria above: regulatory utilization, which is the used proportion of a regulatory limit. It defines Ru and demonstrates its calculation from municipal GIS and administrative data. It explores the advantages and disadvantages compared to current approaches. And it demonstrates a method for combining many different Ru values into two aggregate metrics: density utilization and bulk utilization. The next section relates these aggregates to 3 important topics in real estate economics: real options, price elasticity of supply, and land leverage. It continues by suggesting applications in identifying and interpreting neighborhood change, calculating a "build score" (similar to a "walk score") for parcels, and estimating the impact of policy reforms. Directions for future research are outlined in the conclusion. / Master of Urban and Regional Planning / Over the course of the COVID-19 pandemic, housing prices have risen sharply in many cities, with the highest jumps frequently occurring in previously sleepy markets like Boise City, Idaho (FHFA, 2021). One explanation given in the media and in activist circles is that local regulations are causing a shortage by making it very difficult to build more housing in popular areas. This is a sound economic argument in theory but proving it requires a way to measure how restrictive, a.k.a. "stringent", these regulations are so researchers can compare cities. But each city has its own unique code with hundreds of pages of regulations. These rules can change over time, and different cities may use the same word in different ways. Even compiling these rules can be challenging because there is no national source of information. Researchers have been gathering their own data and inventing new measurements for decades. Some collect and read the regulations themselves, but this limits how many cities they can study at once. Others send out surveys to thousands of urban planners or real estate developers, but these provide a spotty and limited view. Still others tried to measure something simpler like the time or number of steps it takes for someone to get a building permit, but these might be different for many reasons (efficiency, number of staff, etc.) so these too are unreliable. Overall, the differences and disagreements between studies have prevented scholars from drawing definitive conclusions about the effects of these regulations on housing construction and prices (Gyourko and Molloy 2015). I argue that an ideal measurement of regulatory stringency would be: a) Easily calculated from open-source data available online b) Comparable within and across geographic areas c) Comparable over time in the same area(s) This thesis explores an original measurement that meets these three criteria that I call "regulatory utilization." I start by defining land use regulations and describing how economists think they affect markets for jobs, homes, and land. Next, I explain several challenges that researchers face when trying to measure these regulations and examine the main approaches that have been used in the past. Then I define my own measurement. I demonstrate how to calculate it from the open data that many cities publish on their websites, and I compare it to past approaches. I show how it relates to important topics in real estate economics and consider practical applications: to sense neighborhood change over time, inform homeowners about their redevelopment options, and help politicians and activists estimate the impacts of potential zoning changes. I conclude by summarizing and suggesting areas for further study.

This thesis was born out of the efforts of the Department of the Navy to better leverage the available land it controls to financially support the needs of the shore infrastructure. My directive was to design a process that could be universally applied to all installations around the world to improve the speed and efficiency of decision makers in the enhanced use leasing process. The leading example of this process is the Broadway Complex in downtown San Diego, California where 12 acres of underutilized waterfront property has been leased to a private developer in return for a new headquarters building and years of maintenance for that facility. My process takes into account the general means and methods of that real estate action but also considers that no where else in the world is such a large piece of prime real estate available for such a project. The model that succeeded in San Diego can not be universally applied to other locations due to the unique challenges and requirements of not only the installation but the surrounding community. The process I have designed can be applied to every location but must be tested by installation staffs to ensure the enhanced use lease process is feasible with consideration of security requirements, community needs, and then financial viability for a private developer. This is best shown through a simplified procedure and tested as a case study of an actual installation to ensure the necessary requirements are met without impact to Navy operations and training. / Master of Science / Being the sea-going service for the US military, the Navy has a large inventory of waterfront property in some expensive locations. The primary example for my thesis is the Broadway Complex in downtown San Diego, California. For decades, the 12 acre parcel was mostly parking lots in one of the most expensive real estate markets in the country. The Navy decided to explore the possibility of leasing out the land for a long duration to a private development company in exchange for something the developer was very good at, new construction or major renovation projects. The Navy created a system of leasing out land in return for construction work. After this process was completed, the Navy has tried to locate the next piece of valuable land that could go through the same transformation. This thesis examples the idea of creating a tool or inventory of desirable land to allow real estate professionals to quickly sort through it and start working on the next lease to a private company. Through this investigation, the best method to examine Navy property and decide whether it is desirable for private development, is to ask the local public works office to examine their own surroundings and future plans. The idea of a tool to collect these piece of land using general features such as location, size, nearby property values, isn't enough to understand what a property is worth to a private business. The conclusion of this thesis comes down to the fact that local knowledge is absolutely necessary to properly examine land value. This conclusion is supported by applying a new method that can be applied everywhere but must be tested on each base to find the next Broadway Complex redevelopment project that will greatly benefit the Navy and that specific base.

by Man Suk-lan, Christina, Yeung Mei-fong, Elsa. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 50). / ACKNOWLEDGEMENT --- p.i / ABSTRACT --- p.ii / TABLE OF CONTENTS --- p.iii / Chapter / Chapter 1. --- INTRODUCTION --- p.1 / Scope of the Project --- p.1 / Background --- p.2 / Birth of Austerity Measures --- p.4 / Aftermath --- p.8 / Chapter 2. --- METHODOLOGY --- p.9 / Marketing Situation Analysis --- p.9 / Primary Data Collection --- p.9 / Secondary Data Collection --- p.10 / Data Analysis --- p.10 / Limitations And Obstacles --- p.11 / Chapter 3. --- THE AUSTERITY MEASURES --- p.13 / Details of Austerity Measures in Property Mkt --- p.13 / Immediate Impact of Austerity Measures --- p.15 / Chapter 4. --- LOCAL GOVERNMENT ATTITUDES --- p.20 / General Situation --- p.20 / Northern China --- p.21 / Southern China --- p.21 / Remote Areas in China --- p.22 / Chapter 5. --- STAGE I-IMPACT AND COUNTERMOVE --- p.24 / First Austerity Measures --- p.24 / Impact to the Property Market-Supplier Side --- p.25 / Impact to the Property Market-Demand Side --- p.29 / Countermoves by Developers --- p.32 / Chapter 6. --- STAGE II-IMPACT AND COUNTERMOVE --- p.36 / Second Austerity Measures --- p.36 / Impact --- p.37 / Countermoves --- p.39 / Chapter 7 . --- CONCLUSION --- p.42

Wei Peiling. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 79-82). / Abstracts in English and Chinese. / Abstract --- p.i-ii / Acknowledgement --- p.iii / Table of Contents --- p.iv-v / List of Tables --- p.vi-vii / List of Figures --- p.viii / Chapter Chapter 1. --- Introduction --- p.1 / Chapter Chapter 2. --- Literature Review --- p.4 / Chapter 2.1 --- Review of Literatures on Price relations in Real Estate Markets --- p.4 / Chapter 2.2 --- Review of Literatures on Price relations in Stock Markets --- p.7 / Chapter 2.3 --- Review of Literatures on Industrial Real Estate Market --- p.8 / Chapter 2.4 --- Review of Literatures on Measurement of Real Estate Prices --- p.11 / Chapter Chapter 3. --- Data Description --- p.14 / Chapter 3.1 --- Economic Property Research Center Dataset --- p.14 / Chapter 3.2 --- Sample Selection and Classification --- p.14 / Chapter 3.3 --- Summary of Statistics --- p.15 / Chapter 3.4 --- Chapter Summary --- p.17 / Chapter Chapter 4. --- Methodology --- p.18 / Chapter 4.1 --- Measurement --- p.18 / Chapter 4.1.1 --- Rate of Return (ROR) as a measure of price level --- p.18 / Chapter 4.1.2 --- Number of transactions as a measure of trading volume (Vol.) --- p.19 / Chapter 4.2 --- Unit Roots Test --- p.20 / Chapter 4.3 --- Partial Autocorrelation Function --- p.21 / Chapter 4.4 --- Bootstrap Method --- p.23 / Chapter 4.5 --- Granger Causality Test --- p.23 / Chapter Chapter 5. --- Empirical Results --- p.25 / Chapter 5.1 --- Sale Market --- p.25 / Chapter 5.1.1 --- Relationships between Prices --- p.25 / Chapter 5.1.2 --- Relationships between Trading Volumes --- p.27 / Chapter 5.2 --- Rental Market --- p.28 / Chapter 5.1.1 --- Relationships between Rents --- p.28 / Chapter 5.1.2 --- Relationships between Trading Volumes --- p.29 / Chapter 5.3 --- Chapter Summary --- p.31 / Chapter Chapter 6. --- Concluding Remarks --- p.33 / Appendix 1. Summary of Previous Literatures on Industrial Real Estate --- p.37 / Appendix 2. Tables --- p.41 / Appendix 3. Figures --- p.65 / Appendix 4. Definition of property types --- p.77 / Appendix 5. Example of Perl program used to extract the data from EPRC data sheets --- p.78 / Bibliography --- p.79

International Telemetering Conference Proceedings / November 04-07, 1991 / Riviera Hotel and Convention Center, Las Vegas, Nevada / In this paper we present the REAL/STONE Real-Time Tester, a tool for performance evaluation of real-time UNIX operating systems. The REAL/STONE Real-Time Tester is a synthetic benchmark that simulates a typical real-time environment. The tool performs typical real-time operations, such as: (a) reads data from an external source and accesses it periodically, (b) processes data through a number of real-time processes, and © displays the final data. This study can help users in selecting the most-effective real-time UNIX operating system for a given application.