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.
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Postgraduate and Professional
Illustrationen
65 s/w Abbildungen, 65 s/w Zeichnungen, 88 s/w Tabellen
88 Tables, black and white; 65 Line drawings, black and white; 65 Illustrations, black and white
Maße
Höhe: 250 mm
Breite: 175 mm
Dicke: 22 mm
Gewicht
ISBN-13
978-1-138-56132-8 (9781138561328)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Sotiris Tsolacos is Professor of Real Estate Investment at Cass Business School and the Programme Director for the MSc Real Estate Investment. Previously he was with Henley Business School. He earned his PhD in economics at the University of Reading. He has over 25 years of experience in the quantitative analysis of real estate markets. He has over 40 publications in this field and is the co-author of the book Real Estate Modelling and Forecasting. He has long industry experience. He was with JLL in London and the Director of European Research in Property & Portfolio Research/CoStar. He is the co-founder of KappaSigma Partners, a regional property consultancy firm. He works closely with the industry on a range of topical themes in real estate. His industry experience and continual engagement with the profession has greatly inspired the content of this book.
Mark Andrew is Senior Lecturer in Real Estate Finance and Investment and Programme Director for the MSc Real Estate at Cass Business School, City, University of London. He received his PhD from the University of Reading in 2001 and has taught non-cognate and cognate real estate students for over 15 years. He has published in highly ranked real estate and non-real estate journals, such as Real Estate Economics, Urban Studies and Environment and Planning A. He has undertaken a number of government commissioned reports on his research specialty in residential markets. He currently acts as a referee for a number of leading academic real estate journals and is a serving member on the IPF Research Steering Group.
Autor*in
University of Reading, UK
Cass Business School, UK
1. Introduction; 2. Real Estate Data; 3. Data, Common Manipulations and Descriptive Statistics in Analysis; 4. Random Variables, Correlation, Estimation and Hypothesis Testing; 5. Simple Regression Analysis; 6. Multiple Regression; 7. Regression Diagnostics; 8. Stationarity; 9. Forecast Evaluation; 10. ARMA Models; 11. Vector Autoregressions; 12. Closing Discussion; Appendix A: Statistical Tables; References; Index
