Applied Quantitative Analysis for Real Estate

✍ Scribed by Sotiris Tsolacos, Mark Andrew

• Publisher: Routledge
• Year: 2020
• Tongue: English
• Leaves: 327
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Contents
List of figures
List of tables
Preface
Acknowledgements
1 Introduction
1.1 Motivation and rationale for this book
1.2 Broad themes covered in the book
1.3 Book online resource
2 Real estate data
2.1 Introduction
2.2 Segments of the real estate market
2.3 User/occupier market data
2.4 Investment market
2.5 Indirect investment – property funds
2.6 Final remarks
3 Data, common manipulations and descriptive statistics in analysis
3.1 Introduction
3.2 Data representations
3.3 Data structures
3.4 Mathematical symbols, operations and rules
3.5 Indices/indexes
3.6 Applications: preparing data for analysing investment performance
3.7 Descriptive statistics
3.8 Applications: analysing investment performance
3.9 Concluding remarks
4 Random variables, correlation, estimation and hypothesis testing
4.1 Introduction
4.2 Random variables and probability distributions
4.3 The normal and standard normal probability distributions
4.4 Measures of association: covariance and correlation
4.5 Samples and sampling distributions
4.6 Estimation
4.7 Hypothesis testing
4.8 Concluding remarks
5 Simple regression analysis
5.1 Introduction
5.2 Regression versus correlation – the difference
5.3 Population regression function (PRF): key concepts
5.4 The sample regression function (SRF): key concepts
5.5 The ordinary least squares estimator (OLSE)
5.6 Sampling variability of OLS estimators
5.7 Significance of regression coefficients
5.8 Analysis of variance (ANOVA)
5.9 Overall performance of the model – goodness of fit
5.10 Assumptions of the classical linear regression model (CLRM)
5.11 The issue of bias and efficiency – properties of CLRM
5.12 A simple regression model of US commercial prices
5.13 Forecasting
5.14 Concluding remarks
6 Multiple regression
6.1 Introduction
6.2 Multiple regression model: an overview
6.3 Coefficient interpretation in multiple regression
6.4 Coefficient of determination: the adjusted R-squared
6.5 The F-test of multiple restrictions in the model
6.6 Model specification – dynamics and lags in the real estate market
6.7 Attributes of a good model
6.8 Example: building a multiple regression model for Hong Kong office rents
6.9 Using the F-test to test for restrictions
6.10 Omitted variables
6.11 Standardised coefficients
6.12 Concluding remarks
Appendix 6A: F distributions
7 Regression diagnostics
7.1 Introduction
7.2 E(ui) = 0
7.3 Homoscedastic errors
7.4 Uncorrelated error terms: E(ui,uj) = 0
7.5 Regressors not correlated with disturbances: E(ui,xi) = 0
7.6 Inappropriate functional form (non-linearities)
7.7 Residuals are normally distributed: ui ~ N(0, σ2)
7.8 Multicollinearity
7.9 Structural breaks and parameter stability
7.10 Concluding remarks
8 Stationarity
8.1 Introduction
8.2 Stationarity
8.3 Random walks
8.4 Implications of non-stationarity
8.5 Inducing stationarity
8.6 Unit root and stationarity tests
8.7 Practical considerations in real estate analysis
8.8 Concluding remarks
9 Forecast evaluation
9.1 Introduction
9.2 Objectives in real estate forecasting
9.3 Forecast approaches
9.4 Sources of error in real estate forecasting
9.5 Forecast evaluation tests
9.6 Application of forecast evaluation tests – ex post forecasts
9.7 Dynamic ex ante forecasts and further testing – US property prices
9.8 Directional forecast evaluation
9.9 Qualitative forecasts and real estate forecasting in practice
9.10 Concluding remarks
10 ARMA models
10.1 Introduction
10.2 AR and MA processes
10.3 ARMA specification
10.4 Example
10.5 Concluding remarks
11 Vector autoregressions
11.1 Introduction
11.2 VAR specification
11.3 Specifying a VAR: an application to City of London office market
11.4 VAR diagnostics
11.5 Impulse response functions – City of London office market VAR
11.6 Variance decompositions
11.7 Granger causality tests
11.8 VAR forecasting – London office market
11.9 VAR advantages and limitations
11.10 Concluding remarks
12 Epilogue
Appendix A: statistical tables
References
Index