Real Estate Analysis in the Information Age

Techniques for Big Data and Statistical Modeling
1. Auflage 2017
ISBN: 978-1-138-23290-7
Verlag: Taylor & Francis Ltd (Sales)

Buch, Englisch, 180 Seiten, Format (B × H): 173 mm x 244 mm, Gewicht: 136 g

The creation, accumulation, and use of copious amounts of data are driving rapid change across a wide variety of industries and academic disciplines. This ‘Big Data’ phenomenon is the result of recent developments in computational technology and improved data gathering techniques that have led to substantial innovation in the collection, storage, management, and analysis of data.

Real Estate Analysis in the Information Age: Techniques for Big Data and Statistical Modeling focuses on the real estate discipline, guiding researchers and practitioners alike on the use of data-centric methods and analysis from applied and theoretical perspectives. In it, the authors detail the integration of Big Data into conventional real estate research and analysis. The book is process-oriented, not only describing Big Data and associated methods, but also showing the reader how to use these methods through case studies supported by supplemental online material. The running theme is the construction of efficient, transparent, and reproducible research through the systematic organization and application of data, both traditional and 'big'. The final chapters investigate legal issues, particularly related to those data that are publicly available, and conclude by speculating on the future of Big Data in real estate.

Zielgruppe

Academic, Professional, and Professional Practice & Development

Autoren/Hrsg.

Winson-Geideman, Kimberly

Krause, Andy

Lipscomb, Clifford A

Evangelopoulos, Nick

Fachgebiete

• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Immobilienwirtschaft
• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Anlagen & Wertpapiere

Inhaltsverzeichnis

Introduction

Section 1 Concepts

Chapter 1 Traditional Real Estate Data – the what, where, when and how

Chapter 2 Big Data

Section 2 Data management and related issues

Chapter 3 Managing real estate data

Chapter 4 Cleaning real estate data

Chapter 5 Building a transparent and repeatable workflow

Chapter 6 The process of gathering ‘Big’ real estate data

Section 3 Modeling and Analysis

Chapter 7 Software tools for real estate analysis

Chapter 8 Mapping and exploratory data analysis

Chapter 9 Analyzing spatio-temporal changes in properties

Chapter 10 Statistical techniques to identify data error and outliers

Chapter 11 Pricing models

Chapter 12 Analysis of unstructured text

Section 4 Concluding remarks

Chapter 13 The legalities of Big Data

Chapter 14 The future of Big Data

APPENDICES Two case studies

Case Study 1: residential property valuation

Case Study 2: analysis of social media content

Über Autor(innen)

Kimberly Winson-Geideman is Senior Lecturer in Property at the University of Melbourne, Australia.

Andy Krause is Principal Data Scientist at Greenfield Advisors, USA.
Clifford A. Lipscomb is the Vice Chairman and Co-Managing Director at Greenfield Advisors, USA.

Nicholas Evangelopoulos is Professorof Business Analyticsat theUniversity of North Texas, USA.