Applied Quantitative Analysis for Real Estate

Applied Quantitative Analysis for Real Estate
  • eBook:
    Applied Quantitative Analysis for Real Estate
  • Author:
    Sotiris Tsolacos
  • Edition:
    1 edition
  • Categories:
  • Data:
    September 14, 2020
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    326 pages
  • Format:
    PDF, ePUB

Book Description
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.


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

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