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As real estate forms a significant part of the asset portfolios of most investors and lenders, it is crucial that analysts and institutions employ sound techniques for modelling and forecasting the performance of real estate assets. Assuming no prior knowledge of econometrics, this book introduces and explains a broad range of quantitative techniques that are relevant for the analysis of real estate data. It includes numerous detailed examples, giving readers the confidence they need to estimate and interpret their own models. Throughout, the book emphasises how various statistical techniques may be used for forecasting and shows how forecasts can be evaluated. Written by a highly experienced teacher of econometrics and a senior real estate professional, both of whom are widely known for their research, Real Estate Modelling and Forecasting is the first book to provide a practical introduction to the econometric analysis of real estate for students and practitioners.
Foundations of Real Estate Financial Modelling is specifically designed to provide an overview of pro forma modelling for real estate projects. The book introduces students and professionals to the basics of real estate finance theory before providing a step-by-step guide for financial model construction using Excel. The idea that real estate is an asset with unique characteristics which can be transformed, both physically and financially, forms the basis of discussion. Individual chapters are separated by functional unit and build upon themselves to include information on: Amortization Single-Family Unit Multi-Family Unit Development/Construction Addition(s) Waterfall (Equity Bifurcation) Accounting Statements Additional Asset Classes Further chapters are dedicated to risk quantification and include scenario, stochastic and Monte Carlo simulations, waterfalls and securitized products. This book is the ideal companion to core real estate finance textbooks and will boost students Excel modelling skills before they enter the workplace. The book provides individuals with a step-by-step instruction on how to construct a real estate financial model that is both scalable and modular. A companion website provides the pro forma models to give readers a basic financial model for each asset class as well as methods to quantify performance and understand how and why each model is constructed and the best practices for repositioning these assets.
To fully function in today’s global real estate industry, students and professionals increasingly need to understand how to implement essential and cutting-edge quantitative techniques. This book presents an easy-to-read guide to applying quantitative analysis in real estate aimed at non-cognate undergraduate and masters students, and meets the requirements of modern professional practice. Through case studies and examples illustrating applications using data sourced from dedicated real estate information providers and major firms in the industry, the book provides an introduction to the foundations underlying statistical data analysis, common data manipulations and understanding descriptive statistics, before gradually building up to more advanced quantitative analysis, modelling and forecasting of real estate markets. Our examples and case studies within the chapters have been specifically compiled for this book and explicitly designed to help the reader acquire a better understanding of the quantitative methods addressed in each chapter. Our objective is to equip readers with the skills needed to confidently carry out their own quantitative analysis and be able to interpret empirical results from academic work and practitioner studies in the field of real estate and in other asset classes. Both undergraduate and masters level students, as well as real estate analysts in the professions, will find this book to be essential reading.
A guide to modeling analyses for financial and sports gambling markets, with a focus on major current events Addressing the highly competitive and risky environments of current-day financial and sports gambling markets, Forecasting in Financial and Sports Gambling Markets details the dynamic process of constructing effective forecasting rules based on both graphical patterns and adaptive drift modeling (ADM) of cointegrated time series. The book uniquely identifies periods of inefficiency that these markets oscillate through and develops profitable forecasting models that capitalize on irrational behavior exhibited during these periods. Providing valuable insights based on the author's firsthand experience, this book utilizes simple, yet unique, candlestick charts to identify optimal time periods in financial markets and optimal games in sports gambling markets for which forecasting models are likely to provide profitable trading and wagering outcomes. Featuring detailed examples that utilize actual data, the book addresses various topics that promote financial and mathematical literacy, including: Higher order ARMA processes in financial markets The effects of gambling shocks in sports gambling markets Cointegrated time series with model drift Modeling volatility Throughout the book, interesting real-world applications are presented, and numerous graphical procedures illustrate favorable trading and betting opportunities, which are accompanied by mathematical developments in adaptive model forecasting and risk assessment. A related web site features updated reviews in sports and financial forecasting and various links on the topic. Forecasting in Financial and Sports Gambling Markets is an excellent book for courses on financial economics and time series analysis at the upper-undergraduate and graduate levels. The book is also a valuable reference for researchers and practitioners working in the areas of retail markets, quant funds, hedge funds, and time series. Also, anyone with a general interest in learning about how to profit from the financial and sports gambling markets will find this book to be a valuable resource.
To fully function in today’s global real estate industry, students and professionals increasingly need to understand how to implement essential and cutting-edge quantitative techniques. This book presents an easy-to-read guide to applying quantitative analysis in real estate aimed at non-cognate undergraduate and masters students, and meets the requirements of modern professional practice. Through case studies and examples illustrating applications using data sourced from dedicated real estate information providers and major firms in the industry, the book provides an introduction to the foundations underlying statistical data analysis, common data manipulations and understanding descriptive statistics, before gradually building up to more advanced quantitative analysis, modelling and forecasting of real estate markets. Our examples and case studies within the chapters have been specifically compiled for this book and explicitly designed to help the reader acquire a better understanding of the quantitative methods addressed in each chapter. Our objective is to equip readers with the skills needed to confidently carry out their own quantitative analysis and be able to interpret empirical results from academic work and practitioner studies in the field of real estate and in other asset classes. Both undergraduate and masters level students, as well as real estate analysts in the professions, will find this book to be essential reading.
The fully revised and updated version of the leading textbook on real estate investment, emphasising real estate cycles and the availability and flow of global capital Real Estate Investment remains the most influential textbook on the subject, used in top-tier colleges and universities worldwide. Its unique, practical perspective on international real estate investment focusses on real-world techniques which measure, benchmark, forecast and manage property investments as an asset class. The text examines global property markets and real estate cycles, outlines market fundamentals and explains asset pricing and portfolio theory in the context of real estate. In the years since the text’s first publication, conditions in global real estate markets have changed considerably following the financial crisis of 2008-2009. Real estate asset prices have increased past pre-crisis levels, signalling a general market recovery. Previously scarce debt and equity capital is now abundant, while many institutions once averse to acquiring property are re-entering the markets. The latest edition – extensively revised and updated to address current market trends and practices as well as reflect feedback from instructors and students – features new content on real estate development, improved practical examples, expanded case studies and more. This seminal textbook: Emphasises practical solutions to real investing problems rather than complex theory Offers substantial new and revised content throughout the text Covers topics such as valuation, leasing, mortgages, real estate funds, underwriting and private and public equity real estate Features up-to-date sections on performance measurement, real estate debt markets and building and managing real estate portfolios Includes access to a re-designed companion website containing numerous problems and solutions, presentation slides and additional instructor and student resources Written by internationally-recognised experts in capital management and institutional property investing strategies, Real Estate Investment, Second Edition: Strategies, Structures, Decisions is an indispensable textbook for instructors and students of real estate fund management, investment management and investment banking, as well as a valuable reference text for analysts, researchers, investment managers, investment bankers and asset managers.
The highly prized ability to make financial plans with some certainty about the future comes from the core fields of economics. In recent years the availability of more data, analytical tools of greater precision, and ex post studies of business decisions have increased demand for information about economic forecasting. Volumes 2A and 2B, which follows Nobel laureate Clive Granger's Volume 1 (2006), concentrate on two major subjects. Volume 2A covers innovations in methodologies, specifically macroforecasting and forecasting financial variables. Volume 2B investigates commercial applications, with sections on forecasters' objectives and methodologies. Experts provide surveys of a large range of literature scattered across applied and theoretical statistics journals as well as econometrics and empirical economics journals. The Handbook of Economic Forecasting Volumes 2A and 2B provide a unique compilation of chapters giving a coherent overview of forecasting theory and applications in one place and with up-to-date accounts of all major conceptual issues. - Focuses on innovation in economic forecasting via industry applications - Presents coherent summaries of subjects in economic forecasting that stretch from methodologies to applications - Makes details about economic forecasting accessible to scholars in fields outside economics
Financial models in Excel allow investment analysts and other finance professionals to take the laborious number crunching out of financial analysis and forecasting. Models help them to gain meaningful insights into the way that a business is working and focus attention on areas to improve bottom-line results. They can also be used as powerful tools to test the potential impact of various risks on business performance. In this brand new guide, financial modelling expert Paul Lower presents step-by-step instructions for seven spreadsheet models that will help the user to gain a better understanding of the financial data coming out of a business. These seven models can be used to: 1. Assess how a business is performing on key financial indicators. 2. Produce sales and cost forecasts. 3. Create a cash flow forecast. 4. Understand the impact of product price changes on profitability. 5. Assess potential investment decisions. 6. Check the sensitivity of key financial measures to risk events. 7. Produce a business valuation. The book also includes downloadable spreadsheets of the author’s original Excel models and introductory chapters about best practice when modelling in Excel. With this suite of seven tools, a financial analyst will be equipped to use Excel to achieve a deep understanding of a business and its financial data.
This book gathers papers addressing state-of-the-art research in the areas of machine learning and predictive analysis, presented virtually at the Fourth International Conference on Information and Communication Technology for Intelligent Systems (ICTIS 2020), India. It covers topics such as intelligent agent and multi-agent systems in various domains, machine learning, intelligent information retrieval and business intelligence, intelligent information system development using design science principles, intelligent web mining and knowledge discovery systems.