Behavioural Science and Housing Decision Making

A Case Study Approach

Helen Bao

• Publisher: Routledge
• Year: 2020
• ISBN:
  9780429648670
• Edition: 1
• Topic: Architecture
• Subtopic: Urban Planning & Landscaping

This book takes a behavioural approach to examine six important housing questions: tenure decision, gentrification, place attachment, housing bubbles, housing wealth, and residential satisfaction.

Using experimental and field data, the book demonstrates the effects of six behavioural biases and heuristics (i.e., anchoring and reference dependence, loss aversion, mental accounting, endowment effect, herd behaviours, and social comparison) on these housing decisions. The first part of the book introduces the questions and provides a behavioural science toolbox before the second part adopts a real-world case study approach. Real data sets and suggested answers are provided, and the cases come from the UK, USA, and China. Background information is given in each case to facilitate the understanding of the case data and question, as well as the discussions on the results.

This book is ideal supplementary reading on a variety of courses such as housing studies, economics, real estate, research methods, and for students and academics who are interested in the application of behavioural science in housing decisions.

1

Introduction

Decisions that are made less frequently with limited experience are more prone to behavioural biases and heuristics. This is particularly true for housing decisions. It is, therefore, important to study the applications of behavioural science in housing decision making.

In this book we cover six important housing issues, that is, tenure decision, gentrification, place attachment, housing bubbles, housing wealth, and residential satisfaction. Using experimental and field data, we demonstrate the effects of six behavioural biases and heuristics (i.e., anchoring and reference dependence, loss aversion, mental accounting, endowment effect, herd behaviours, and social comparison) on these housing decisions. We adopt a case approach by using a real case for each topic. Data sets and suggested answers are provided; information on econometric methods to analyse the case data is also included. The cases come from the UK, USA, and China. Background information is given in each case to facilitate the understanding of case data and question, as well as the discussion of results.

The book is unique in three aspects. First, the case approach provides opportunities to study both the abstract concepts of behavioural biases and heuristics, as well as the design and implementation of behavioural studies. Second, the cases come from both developing and developed countries. Therefore, students can appreciate how behavioural effects on housing decisions may vary according to social, economic and political settings. Last, the textbook is versatile by covering housing decision, behavioural science, and some key statistics methods in one package. The book can be used in the teaching of courses on housing policy, urban studies, behavioural economics, and even quantitative research methods at both the undergraduate and postgraduate levels.

This book lies at the intersection of behavioural science and housing studies. The intended audience are those who have knowledge in one of the two areas, and are interested in venturing into the other relatively unknown territory. The approach is practical: we use six case studies to illustrate how behavioural insights can be applied to address important housing issues. We discuss only the theories and models that are necessary to facilitate the analysis of case. In each of the case studies, we use data collected from a wide range of sources and analyse the data with a battery of econometric methods.

In the next chapter, we will introduce the six housing topics to be covered in the case studies. For each topic, we review the literature and point out potential areas for the application of behavioural insights. In Chapter 3, we will present a behavioural toolbox, in which anchoring and reference dependence, loss aversion, mental accounting, endowment effect, herd behaviours, and social comparison are included.

As this book is neither a housing study book nor a behavioural science book, we keep both Chapter 2 and Chapter 3 concise, covering essential readings and recent development on each topic. These two chapters do not assume any prior knowledge on the topics. They introduce the concepts and theories in layman’s words and serve as background readings for the cases in Chapters 4 through 9 (case chapters hereafter). As a result, readers from the housing study field may skim through or even skip Chapter 2, but spend more time on Chapter 3 before studying the cases. Readers with behavioural background may do the opposite. The allocation of the six cases among these housing and behavioural topics can be found in Table 1.1. Some topics are covered in more than one chapter. For example, housing bubbles and related topics (i.e., housing cycles) are discussed in Chapters 6 and 7, while Chapter 5 can be used in classes on both gentrification and place attachment.

Table 1.1 Housing issues and behavioural topics covered in each case chapter

In empirical analysis the type of data determines the statistics methods that should be used. Our cases use cross-sectional, time series, and panel data. Therefore, there is a wide range of statistics methods adopted in the case chapters. The data types and statistics methods in each case chapter are summarised in Table 1.2. Because this is not a statistics or research methods textbook, the case chapters do not contain any technical details of or the use of statistics software for these statistics methods. The assumption is that students either have working knowledge about these quantitative methods already, or the training to use these methods is provided separately.

Table 1.2 Types of data and econometrics methods used in each case chapter

The data used in the case chapters are collected from a wide range of sources. Table 1.3 classifies the six cases according to data sources and geographic regions. The majority of the cases use data from the UK. Online data is a category that includes either data generated by third-party websites (i.e., Google Search Volume Index, Facebook likes) or researchers themselves (i.e., online experiment carried out at Amazon Mechanical Turk, or an online survey via Qualtrics.com). This is an increasingly popular data source among social scientists. We believe that it deserves a category of its own, and would like to encourage readers of this book to explore the potential and possibility of using online data in their studies.

Table 1.3 Data sources of cases in each case chapter

	China	UK	USA
Survey data	Chapter 4 Chapter 5	Chapter 8 Chapter 9
Experiment data		Chapter 6
Official statistics		Chapter 7	Chapter 7
Online data		Chapter 6 Chapter 7	Chapter 7

The structure of this book and the materials covered in it offer great flexibility to use the book for different purposes. In Table 1.4, we provid...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• List of illustrations
• Acknowledgements
• 1. Introduction
• 2. Six housing questions
• 3. Behavioural science toolbox
• 4. Housing provident fund and homeownership
• 5. Mega-events and gentrification
• 6. Endowment effect and house price determination
• 7. Market sentiment and housing bubbles
• 8. Housing wealth and energy consumption
• 9. Social comparison and residential satisfaction
• 10. Conclusions
• Index