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Informationen zum Autor 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. Zusammenfassung 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. 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 ...
