1st Edition

Real Estate Analysis in the Information Age Techniques for Big Data and Statistical Modeling

    180 Pages
    by Routledge

    180 Pages
    by Routledge

    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.


    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


    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 Professor of Business Analytics at the University of North Texas, USA.