A Study of Car and House Ownership in the Face of Increasing Commuting Expenses (CHOICE)

A Study of Car and House Ownership in the face of Increasing Commuting Expenses (CHOICE)

Elli Maria Papaioannou

A thesis submitted in conformity with the requirements for the degree of Masters of Applied Science Graduate Department of Civil Engineering University of Toronto

A Study of Car and House Ownership in the face of Increasing Commuting Expenses (CHOICE)

Elli Maria Papaioannou

Masters of Applied Science

Department of Civil Engineering University of Toronto

2014 Abstract

This research presents the design, implementation and results of a survey of Car and House Ownership in the face of Increasing Commuting Expenses (CHOICE). The CHOICE survey is a web-based survey that combines RP and SP components, and was designed to collect information of commuting mode choices, housing and neighbourhood preferences along with vehicle ownership choices of households with cross-regional commuters in the Greater Toronto Area. Investigations of the survey data revealed that for small increases in commuting costs people are willing to change to more efficient cars. As commuting costs reached higher levels, participants chose to relocate their home in order to commute shorter distances. This study provides evidence that vehicle ownership and especially residential location decisions are a complex process and are interrelated. The findings of this study show some of the possible reactions of households in the GTA in the face of extreme increases in transportation costs.

Acknowledgments

This two-year-long journey flew by and I am standing at the end of it today trying to think of all the challenges I faced, all the wonderful and bright people I met, all the things I learned, and all the friends I earned.

First, I would like to sincerely thank my supervisor, Khandker Nurul Habib, for his valuable guidance and genuine care. He is an enthusiastic teacher and a great person who treats his students as his children. I want to thank him for his help, advice and kindness. Many thanks to all my other professors for their important input: Amer Shalaby, Lloyd McCoomb, and especially Matthew Roorda for spending the time and energy as a second reviewer of my thesis.

Second, I want to thank Dimitris Panagiotakopoulos, the programmer who not only helped in coding the survey, but with his patience and experience also played a definitive role in the outcome of the study.

I also want to thank my friends and colleagues within the transportation group at the University of Toronto. Mohamed Salah Mahmoud offered his valuable help and support countless times throughout these two years, and I consider myself lucky for having the opportunity to learn from him. Many thanks go to Adam Weiss for offering his help during the development of the survey. Of course, these two years would not have been the same without my good friends and office- mates: Adam Wenneman and Nico Malfara; you two made my days in that office “brighter”.

Finally, I want to thank my family: my mother and my brother for their support and love, and especially my father who throughout my life has not only been a parent but my mentor and my biggest supporter. Many thanks to my “sisters”: Lindsey, Lida and Theofili for their love and countless sessions of skype-laughter. And last but not least, I want to thank my Toronto-people: Lee, Holly, Jane, Kaisa and Aitor who together with the rest of the crew made these two years the most memorable and adventurous journey of my life.

Elli Papaioannou

September 2014

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Table of Contents

Acknowledgments ...... ii

Table of Contents ...... iv

List of Tables ...... vii

List of Figures ...... ix

GLOSSARY ...... xi

1 Introduction ...... 1

1.1 Research Goals and Objectives ...... 3

1.2 Thesis Layout ...... 4

2 Literature Review ...... 6

2.1 Vehicle Ownership ...... 6

2.2 Transit-pass Ownership ...... 8

2.3 Residential Location Choice and Travel Costs ...... 9

2.4 Web surveys: Advantages and Disadvantages ...... 12

2.4.1 Errors in Surveys ...... 14

2.5 Revealed Preference (RP) and Stated Preference (SP) Data...... 16

3 A Survey of Car and House Ownership in the face of Increasing Commuting Expenses (CHOICE) and the Background of the Study Area ...... 18

3.1 Study Area ...... 18

3.1.1 The Greater Toronto Area ...... 20

3.1.2 The Transportation Tomorrow Survey ...... 23

3.2 Survey Sample Design and Target Population ...... 23

3.2.1 Sample Size Determination ...... 24

4 CHOICE Survey Design ...... 26

4.1 RP ...... 27

4.1.1 Housing Information ...... 27

4.1.2 Socio-demographic and Commuting Trip Information ...... 27

4.1.3 Vehicle Information ...... 29

4.2 SP ...... 31

4.2.1 Experimental Design ...... 31

4.2.2 SP1: Effects of Price Changes on Mobility Tool Ownership ...... 31

4.2.3 SP2: Residential Relocation as a Reaction to Increased Transportation Costs .... 37

4.2.4 Comments on the SP Component ...... 42

4.3 Software ...... 44

4.4 Recruiting ...... 44

4.5 Sample Distribution and Representativeness ...... 44

5 CHOICE Survey Results ...... 48

5.1 RP Descriptive Analysis ...... 48

5.1.1 Household Descriptive Analysis ...... 48

5.1.2 Descriptive Statistics of Individuals’ Characteristics ...... 51

5.2 SP1 Descriptive Analysis: Mobility Tool Ownership ...... 53

5.2.1 Fuel Consumption and Engine Type ...... 54

5.2.2 Vehicle Type ...... 55

5.2.3 Influence of Different Factors on Operation Costs ...... 56

5.2.4 Transit Pass Ownership ...... 57

5.2.5 Relocating Decision ...... 57

5.3 SP2 Descriptive Analysis: Residential Location Choice ...... 59

5.3.1 GTA Overview of SP2 ...... 60

5.3.2 Conclusion of Residential Location Choice Descriptive Analysis ...... 66

5.4 Dwelling Type Choice ...... 66 v

6 Modelling Mobility Tool Ownership and Residential Relocation ...... 68

6.1 Mobility Tool Ownership Model ...... 68

6.1.1 Data Preparation ...... 68

6.1.2 General Model Specification ...... 69

6.1.3 Empirical Analysis ...... 70

6.2 Trade-offs Between Mobility Costs and Residential Location ...... 72

6.2.1 Data Preparation ...... 72

6.2.2 General Model Specification ...... 72

6.2.3 Empirical Analysis ...... 73

6.2.4 Elasticities ...... 74

7 Modelling Dwelling Type Choice ...... 78

7.1 Dwelling Type Model ...... 78

7.1.1 Non Linear Interactions ...... 78

7.1.2 Utility Functions ...... 79

7.1.3 Results ...... 80

8 Conclusions and Future Work ...... 83

8.1 Summary ...... 83

8.2 Recommendations for Improving the CHOICE Survey ...... 85

8.3 Future Work ...... 86

References ...... 87

Appendix A: Survey Software ...... 93

Appendix B: Statistics of Current Home Location and Location Chosen in SP2 by Region ..... 112

Appendix C: Fixed and Variable Costs of all Vehicle Combinations in SP1 ...... 116

Appendix E: Transit Fare O-D Matrix for Scenario 1 ...... 123

List of Tables

Table 1: GTA Population Change 2006 to 20011 (TTS Data) ...... 20

Table 2: Attribute Characteristics of SP1 ...... 33

Table 3: Car Choice Characteristics ...... 34

Table 4: Number of Total Responses at each Part of the Survey ...... 45

Table 5: CHOICE Sample Representation of Household Attributes Based on TTS 2006 Data .. 45

Table 6: Sample Representation of Individual Attributes Based on TTS 2006 Data ...... 46

Table 7: Recruitment and Response Rate ...... 48

Table 8: Descriptive Analysis of Household Attributes ...... 49

Table 9: Descriptive Analysis of Individuals’ Characteristics ...... 52

Table 10: Home and Work Location Distribution of Sample ...... 53

Table 11: Estimation Results of Linear Regression Model for Operation Costs ...... 56

Table 12: Statistics of Housing Price in SP2 ...... 67

Table 13: Estimation Results of MNL for Mobility Choice in SP1 ...... 70

Table 14: Estimation Results of Binary Model for Leaving Current Home Location in SP1 ...... 74

Table 15: Residence Relocation Choice Elasticity with respect to Increasing Transportation Costs (derived from the results of the binary model in Section 6.2.3) ...... 76

Table 16: Estimation Results of MNL for Dwelling Type Choice in SP2 ...... 81

Table 17: Spatial Location Statistics of York Region Households ...... 112

Table 18: Spatial Location Statistics of City of Toronto Households ...... 113

Table 19: Spatial Location Statistics of Halton Region Households ...... 114 vii

Table 20: Spatial Location Statistics of Durham Region Households ...... 114

Table 21: Spatial Location Statistics of Peel Region Households ...... 115

Table 22: Variable and Fixed Costs of Vehicles in SP1 ...... 116

Table 23: Dwelling Type Price for One-bedroom Units by Neighborhood ...... 117

Table 24: Dwelling Type Price for Two-bedroom Units by Neighborhood ...... 118

Table 25: Dwelling Type Price for Three-bedroom Units by Neighborhood ...... 119

Table 26: Dwelling Type Price for Four-bedroom Units by Neighborhood ...... 120

Table 27: Dwelling Type Price for Five-bedroom Units by Neighborhood ...... 121

Table 28: Qualitative Characteristics of Neighbourhood ...... 122

Table 29a: Transit Fares for SP1 ...... 123

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List of Figures

Figure 1: Errors in surveys (figure reproduced from Groves (2005)) ...... 15

Figure 2: Greater Toronto and Hamilton Area ...... 19

Figure 3: GO Transit system map (source: GO Transit website) ...... 22

Figure 4: Survey design overview ...... 26

Figure 5: RP data model of CHOICE survey ...... 30

Figure 6: SP2 map overview ...... 38

Figure 7: Neighbourhood characteristics and housing prices in SP2 ...... 39

Figure 8: RP and SP data model connections ...... 41

Figure 9: Number of possible choice scenarios ...... 43

Figure 10: Reasons for moving from previous to current house ...... 50

Figure 11: Reasons for choosing current neighbourhood ...... 51

Figure 12: Fuel consumption, car ownership and engine types depending on fuel price in SP1 . 54

Figure 13: Vehicle type choice in SP1 ...... 55

Figure 14: Relocating percentage of GTA regions as a result of SP1 cost increases ...... 57

Figure 15: Home and work location concentrations of CHOICE respondents ...... 59

Figure 16: Home location, work location and SP2 home location choice of Durham residents .. 61

Figure 17: Home location, work location and SP2 home location choice of Peel residents ...... 62

Figure 18: Home location, work location and SP2 home location choice of York residents ...... 63

Figure 19: Home location, work location and SP2 home location choice of Halton residents ... 64

Figure 20: Home location, work location and SP2 home location choice of City of Toronto residents ...... 65

Figure 21: Residence relocation choice elasticity and weighted elasticity with respect to increasing transportation costs ...... 76

Figure 22: Welcome screen ...... 94

Figure 23: Help map for home and work location ...... 95

Figure 24: Part 1- Retrospective house information ...... 96

Figure 25: Part 2- Household sociodemographic information and typical commuting trip ...... 100

Figure 26: Part 3- Retrospective vehicle ownership ...... 103

Figure 27: Help tab for choosing vehicle category ...... 104

Figure 28: SP1- Mobility tool ownership ...... 105

Figure 29: SP2- Home location choice- Screenshot of main map ...... 107

Figure 30: SP2- Screenshot of the divisions in the selected region ...... 108

Figure 31: SP2- Screenshot of the districts in the selected division ...... 109

Figure 32: SP2- Screenshot of the neighborhoods in the selected district ...... 110

GLOSSARY

AFV Alternative Fuel Vehicles

CHOICE Car and House Ownership in the face of Increasing Commuting Expenses

CMA Census Metropolitan Area

DEFF Design Effect

GTA Greater Toronto Area

GTHA Greater Toronto and Hamilton Area

MLS Multiple Listing Service

MNL Multinomial Logit

NRCAN Natural Resources Canada

PD Planning District

RP Revealed Preference

RUM Random Utility Maximization

SEM Structural Equation Model

SP Stated Preference

SP1 Mobility Tool Ownership Stated Preference Experiment

SP2 Residential Location Choice Stated Preference Experiment

TAZ Traffic Analysis Zone

TTC Toronto Transit Commission

TTS Transportation Tomorrow Survey

1 Introduction

Transportation cost changes affect travel behaviour in many ways. These changes can affect short-term decisions and actions of people (e.g. departure time, trip frequency, mode choice), but they can also change people’s commitment to places and activities (e.g. change home location, change work location). According to transportation specialists (Meurs, 1990); (Bhat, 1998), vehicle ownership and residential location choice are two of the most important determinants of travel behaviour and affect among others the mode choice, the trip frequency, the trip distance, and the general travel patterns of people.

Identifying how travel cost changes can affect these decisions is of extreme importance to different parties for different reasons. In the short run and given a small increase in transportation costs, trip makers react by reducing the frequency or the distance of their trips, by altering departure time to avoid congested periods, and by switching to less expensive modes of travel. In situations of higher and longer-lasting increases in transportation costs people will decide to modify their mobility tools (vehicles and transit passes). However, humans are creatures of habit and many of their decisions are defined by it. In particular, when we are examining mode choice decisions, we observe that habit plays a significant role and people insist on choosing their usual mode instead of rationally comparing the alternatives (Idris, 2013). It is, therefore, difficult to capture behavioural changes for short term or small changes in cost.

The first time that researchers noticed a change in this apathy was after the crude oil shock in 2008 (Erath & Axhausen, 2010). The unusual and consistent increase in travel costs led many trip makers to abandon their private vehicles. At the same time, the automobile industry in Canada (which is closely linked to the automobile industry in the US due to the Automotive Products Trade Agreement1) noticed a significant drop in the sales of large vehicles. In 2008 the sales of pickup trucks dropped down by 17%, and large sport vehicles were down 29%. Meantime the sales of subcompact cars increased by 33%, and the Toyota Prius hybrid was among the most popular options with 23% increase in sales (NBC News, 2008).

1 The Canada—United States Automotive Products Agreement, commonly known as the Auto Pact or APTA, was an important trade agreement between Canada and the United States. It removed tariffs on cars, trucks, buses, tires, and automotive parts between the two countries, greatly benefiting the large American car makers. 1

This change reveals the demand for smaller and more efficient mode options in the face of the new cost circumstances. Taking this relationship a step further, one may argue that in case of permanent changes in transportation costs people will reconsider their long-term decisions and possibly change residential location.

These potential reactions could change the mobility and housing reality as we know it today. Altering, what we consider, medium and long-term decisions (vehicle ownership and residential location) would cause inconvenience and commotion to governments, individuals, agencies and private companies, and is, therefore, important to find answers to some of these questions.

Governments would be interested to know how people would travel given the new cost circumstances; with such information they would prepare alternative plans to adjust the urban system and provide solutions for different scenarios. At the same time public transit agencies would have forecasts to adjust their service and their network according to the future demand. Companies from the automobile industry would get a preview of the consumers’ preferences and would target their research and resources towards products that are probably going to be more popular among the public. Real estate agencies and construction companies might get information about the type of residence and the areas of the city that will be in high demand. Finally, individuals will have more accurate information about all these issues and they will be able to make educated decisions about their mobility options and their housing choices.

When examining these issues for the North American reality it is even more important to identify how major cost changes could affect individuals’ travel patterns and mobility-related decisions. The Greater Toronto Area (GTA) is the largest metropolitan area in Canada, and the fifth largest in North America with a population of about 6 million residents based on the 2011 Census. According to the population growth projections of the Ontario Ministry of Finance, the population of the GTA will reach 8.9 million people by 2036 (Ontario Ministry of Finance, 2013). The City of Toronto will grow an approximate 23% by 2036, and the other census divisions of the GTA (Halton, Peel, York and Durham) will experience a significantly faster growth (approximately 1.9 million people by 2036).

The residents of the GTA use primarily their private car for commuting trips and add traffic to the already congested street network. According to the Big Move (Metrolinx, 2008) – the

regions’ transportation master plan – commuters spend approximately 82 minutes in their cars and travel on average 26 km daily. The only metropolitan-wide transit service – GO Transit – is expensive, not easily accessible, and integrates poorly with the local transit agencies, resulting into a very low share of transit to the modal split (only 16.5% of the morning rush hour trips are taken in transit).

Taking all these issues into consideration and combining the possibility of another oil shock event in the future, it becomes apparent what questions this study is attempting to answer. It is of vital importance to understand how people will react, and what aspects of their every-day decisions they will change in order to face a new reality.

1.1 Research Goals and Objectives

With few exceptions most studies found in literature use aggregate or disaggregate revealed preference data for mobility tool ownership and residential location choices, and are, therefore, incapable of capturing reactions to cost ranges higher than those that were observed at the time. This study is trying to cover this gap by investigating the effects of long-term, extreme increases in transportation costs in the context of North American cities and regions.

In order to collect the data needed for this study a survey of Car and House Ownership for Increasing Commuting Expenses (CHOICE) was designed and conducted among a randomly selected sample in the Greater Toronto Area (GTA). The survey combined revealed and stated preference components, and aimed to collect information about the retrospective residential location, housing and vehicle ownership choices of the households. The stated preference component of the survey collected data of the effects of increasing transportation costs on mobility tool ownership levels and residential location choices. Such data could support investigations about the effects of economic recession, increasing traffic congestion and the possible implementation of pricing policies on the change in urban form (the relocation of households in and out of the GTA).

These investigations are very important for regional planning agencies to make informed decisions and/or evidence-based planning. Analyses and outputs of the CHOICE dataset could allow us to test alternative land use and travel demand policies. These could be used by local

agencies to examine if their short-term goals are met. In addition, the GTA’s regional transit authority – Metrolinx – could use such information to validate if their transportation demand management policies, and their numerous projects are effective and complying with the long- term objectives of the regions’ Master Plan (e.g: smart growth, mobility hubs, smart commuter program Metrolinx’s Big Move).

The collected data were used to develop a series of models in order to understand and interpret the decisions of GTA households in the past, and to form a better understanding about their intentions and willingness to accept higher costs before changing their current vehicle fleet, and/or residential location. Overall, the thesis aims to improve our knowledge on the link between transportation and land use. The data collected through the CHOICE survey will provide a step towards filling the apparent gap between travel demand modelling and land use modelling in practice.

1.2 Thesis Layout

The thesis consists of eight chapters. Chapter 1 presented a brief introduction of the problem, and the purpose of this study.

Chapter 2 attempts an overview of the research efforts and studies that dealt with the development of models for residential location and vehicle ownership. Main focus is given to studies that investigated these choices in combination with stated preference data and varying cost ranges. Additional literature is provided for advantages of combining revealed preference and stated preference data, and finally a review of the different survey collection methods is presented, highlighting the advantages and weaknesses of web-based surveys.

Chapter 3 introduces the CHOICE survey, the study area and the data collection process.

Chapter 4 includes a detailed description of the different parts of the survey and the experimental design, mainly focusing on the stated preference components of the survey and the design process.

Chapter 5 covers the descriptive analysis of all parts of the survey and is meant to be the first approach to understand the study area and the sample.

After the first analysis, a more thorough and analytical approach is taken in Chapters 6 and 7 with the construction of a series of models. These models cover the two SP parts and answer to the main questions of this study: how will people modify their mobility tools; when will people move; and where will they move.

Finally, Chapter 8 discusses the findings of this study and the interpretation of the analysis, and concludes with a detailed subsection for future work and recommendations to improve the survey for the second phase of the data collection.

2 Literature Review

This chapter starts with an overview of studies that investigated vehicle and transit pass ownership. Emphasis is given to studies that examined these choices using stated preference data for increasing travel costs. Subsequently, residential location studies are cited to review what has been found about the relationship between travel costs, and in particular commuting expenses, and house location. Finally, a brief overview of the advantages and shortcomings of web-based surveys is presented, followed by the strengths of combining different data sources for modelling.

2.1 Vehicle Ownership

In developing vehicle ownership models the main challenge that researchers faced, was to identify which mechanisms represent more accurately the true underlying choice process, and how these choices change in response to changing attributes. The first vehicle ownership models focused on different aspects of vehicle purchase, ownership and use. Most of these first models were based on aggregate data and explained vehicle ownership choices at a more generic level, such that of a traffic analysis zone, a region, a state or a country (Stanovnik, 1990). The biggest limitation of aggregate models lies in their incapability to be used in policy-sensitive analyses and their incompatibility with agent-based approaches in transportation modelling (Potoglou & Susilo, 2008).

These limitations led to a more microscopic-level approach and to the estimation of vehicle ownership models based on disaggregate data. The first disaggregate models were based on individual or household level data, and addressed the issues of the number and type of vehicles owned and the explanatory variables that influenced these choices; mainly socioeconomic variables, income, purchase price, and travel opportunities by other modes (Tardiff, 1984). Some examples of the first disaggregate vehicle ownership models can be found in Lave and Train (1979) who used multinomial logit methodology to model choice of car type; and in Hensher et al. (1989), who used a nested structure to model car type, size and quantity choices.

Nolan (2010) found that car ownership started becoming a necessity rather than a luxury over the years, and that the effect of income in explaining the variation of car ownership was declining. In 6

her study Nolan used six-year longitudinal data, and estimated the dynamic decisions of households regarding vehicle ownership. The findings indicated, that while higher income still appeared to affect car ownership in a positive way, the size and composition of the household, and the characteristics of the head of the household (age, gender, whether worker or not) were the most significant determinants in the model estimation.

While these early studies analysed vehicle choices in terms of number and type of vehicles, increasing concern about the environment and the energy consumption led to the inclusion of different engine technologies and fuel consumption in the research questions. Brownstone et al. (2000); Hensher and Greene (2001); Potoglou and Kanaroglou (2007) are some of the researchers that combined RP and SP data in their studies to evaluate the willingness for alternative fuel vehicles (for more information about RP and SP data refer to section 2.5). According to Brownstone et al. (2000), RP data are useful to acquire realistic vehicle size and type choices of respondents and SP data are useful for examining attributes that are not available in the market. Iten et al. (2005) developed an SP experiment to account for the make, type, engine size, fuel consumption and fuel efficiency class of the vehicle.

Similar to Iten et al. (2005), Potoglou and Kanaroglou (2007) collected SP data in Hamilton, Canada to examine the factors and incentives that were more likely to influence a household’s decision towards alternative fuel vehicles (AFV). Potoglou and Kanaroglou (2007) used a nested multinomial logit structure to investigate preferences for body type and engine technology. They found that age, gender, and household characteristics are factors affecting individuals’ willingness to pay for AFV. Interestingly, long-distance commuters were found to be more hesitant to adopt AFV. The main reason for this seems to be the shorter travel range of AFV as compared to gasoline/diesel fuelled vehicles. Other studies that used SP data to investigate willingness to switch to AFV technology can be found at Louviere et al. (2000); Alder et al. (2003); Ewing and Sarigollu (2000); and Dagsvik et al. (2002)

All of these models are able to forecast the demand for different fuel economy policies, and to model the future fuel consumption, but they have their limitations. First, they are incapable of capturing the shifts in car purchase caused by changes in fuel price, but most importantly they examine the choice of each vehicle as independent, even in the context of the same household.

Bhat and Pulugurta (1998) developed a multiple discrete continuous model, which accounts for multiple vehicles and use levels within the same decision making unit. They used SP data from a Swiss survey about household choices of fleet size and characteristics under different cost schemes. The results showed that households with members that commute farther tend to own more cars; however, they do not favour efficient cars. The authors concluded that inertia effects, in the context of downgrading the size and composition of fleet, cease in the face of drastic changes in cost. In contrast, minor cost changes lead to shifts from gasoline to diesel alternatives. These findings suggest that increasing costs will lead to more efficient alternatives and less energy consumption, but they will not affect the mobility of households (Jäggi et al., 2011).

More recently, Erath and Axhausen (2010) developed a structural equation model (SEM) to estimate the household’s consumption for different fuel types (gasoline, diesel, natural gas) in response to increasing costs. In order to develop the model they collected stated preference data from Swiss households in 2009 by using a novel survey approach. A three-stage questionnaire collected information about the mobility tools (vehicle and transit passes), the housing location and the housing costs of the households. Then the respondent was asked to add the desired mobility bundle for each commuting member based on different residential locations and for increasing gas price scenarios. Finally, in the last stage of the experiment the respondent had to indicate which combination from stage two (residential location and mobility tools) they would ultimately choose. Their investigation showed that older people, frequent public transit users, and people living in less accessible areas react more to higher transportation costs. In contrast, households with more adult members and households with big cars (luxury, sport, SUV) react less to higher costs.

2.2 Transit-pass Ownership

There are many studies that investigate vehicle ownership, but little can be found about public transit pass ownership and use. Even though it should be self-evident that transit pass ownership replaces, to a certain extent, vehicle ownership, there are very few studies that account for this interaction. Relevant studies found that the commitment to a mode affects significantly the use of the respective mode and that consequently strong substitution effects exist between vehicle and transit pass ownership (Simma & Axhausen, 2003).

This substitutive relationship becomes more evident when fuel prices increase significantly. König and Axhausen (2003) developed a web-based stated preference survey in the framework of the project “Mobiplan” that aimed to understand the travel behaviour of German and Dutch households. The participants had to state the mode choice and mode use based on real-time information about their commuting trip, and housing costs. The collected data were analysed by Darren et al. (2006) and confirmed that the substitution effects between car and transit ownership are significant, and ignoring such relationship may lead to bias in results.

Erath and Axhausen (2010) developed a more advanced version of the same experiment to account for varying price schemes. In one of the parts of the experiments the respondent had to choose the desired mobility bundle (cars and transit passes) for the household, given different travel costs. The results showed that increasing travel costs have a more distinct impact on the type of vehicle choice rather than the mode selected. Respondents were found more likely to downgrade the size and/or the engine technology of the vehicle rather than switch to public transit.

Triggered by the economic recession and the boosted oil prices of 2008, researchers and governments expressed increasing interest in studies that investigate travel behaviour in such occasions, so that they could prepare the system for similar events in the future. The topics in question include short-term reactions of the kind that König and Axhausen (2003) and Simma and Axhausen (2003) analysed in their studies, but also longer-term reactions that could lead to complete changes in travel patterns and in the structure of the urban form.

2.3 Residential Location Choice and Travel Costs

Increasing transportation costs could lead to complete changes in urban form. Unlike the large number of studies investigating the mode shifts and mobility tool ownership changes under increasing transportation costs, investigations about the residential location choice under the same circumstances are rarely encountered. However, residential location choice on its own has been extensively studied. Most residential location studies use longitudinal, revealed preference or stated preference data, whereas a large portion of the residential location studies focuses on the location choice as a result of the decision maker’s lifestyle (Beige, 2008); (Morrow-Jones, 2007). 9

Studies that are based on longitudinal data are incapable of capturing how travel cost changes influence the location choice. On the other hand, studies based on revealed preference (RP) data cannot directly capture information about the mobility costs, since the alternative/non-chosen locations are not sampled. Therefore, the mobility costs associated with the location of a household are subject to many uncertainties. Instead of using the mobility costs themselves, most studies using RP data include accessibility measures as an indirect indication of the mobility opportunities (locations with high access are related to lower travel costs). Bürgle (2006) concluded that only few travel-related variables influence the residential location choice; in particular those related to an individual’s commuting trip.

Employing SP data to analyse residential location choice is considered by many researchers (Hunt, 2001); (Earnhart, 2002); (Kim et al., 2003) to be the most powerful approach, because such formulations overcome the main constraints of RP (multi-collinearity issues and high data requirements). SP data overcome an additional constraint that RP data fail to address; they measure the impact of new policies and they capture behaviour and choices in hypothetical situations, such as changes in transportation costs, toll implementation, congestions pricing, introduction of new technologies etc. Studies from Hunt (2001) and Molin and Timmermans (2003) that used SP data found that in order for a household to downgrade residential unit, a drastic decrease in commute time to work would be required.

However, most studies are in agreement that residential location choice is mostly defined by neighbourhood attributes, housing characteristics, and household composition and demographics, rather than accessibility attributes (Weisbord et al., 1980); (Bhat & Guo, 2007); (Waddell, 2006). Building on that, many studies focused on the different neighbourhood characteristics and housing needs that households have with respect to their lifestyle. For example, households highlighting the accessibility to highways by car, or valuing the parking availability when choosing a residential location implies people heavily dependent on their motorized vehicles. On the other hand, people who value pedestrian and cycling infrastructure, or the availability of transit service close to their home, indicates more active, environment and health conscious individuals. (Walker & Li, 2007); (Mikiki & Papaioannou, 2014). Similarly, Mikiki and Papaioannou (2012) found that people, who are active and environmentally friendly, change more easily from car to other less polluting and energy consuming modes. 10

Finally, residential location choice has been looked at as a decision that is strongly defined by the life cycle stage of the household. Evidently, over a person’s lifetime different factors will have greater influence on the chosen residential location (Morrow-Jones, 2007). Households with children will most likely value the safety of a neighbourhood, the quality of schools, the distance to school, or spacious house units with yard. Older people will probably prefer neighbourhoods with easy access on foot or by transit to facilities, services and shopping areas (Cao et al., 2006).

Although various researchers have covered the area of modelling residential location choices, the influence of cost changes on the choice of residence is yet to be investigated. The crude oil price shock of 2008 revealed the first signs of willingness of households to reconsider their mobility tools –vehicles and public transit passes. In 2009 Erath and Axhausen (2009) collected information based on six scenarios, in order to evaluate the respondent’s propensity to move due to increasing travel costs. These scenarios encouraged the respondent to choose between various residential situations under different housing and transportation cost schemes. A multinomial logit model was used to question the willingness to accept higher mobility costs before changing residential location. The actual model examined the way people perceive transportation and housing costs and how income influences their cost perception. It further explored the relationship between travel time characteristics and residential location choice. Their results indicated that housing costs are perceived to be the most onerous; nevertheless, strong inertia was present before changing spatial location.

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Undoubtedly, the interactions and trade-offs among residential location choice, mobility tool ownership, and travel patterns are of multidimensional and complex nature. Most studies that can be found in the literature focus on one side of this relationship, leaving most of the interactions out of the equation. In particular, while previous research covers extensively the area of car ownership at the individual level, the household level is left unexamined. SP experiments that have been conducted in the past, collected data for a small range of fuel prices, leaving unanswered scenarios of extreme fuel price increases.

Most residential location studies conclude that neighbourhood attributes are more defining compared to travel attributes when choosing a location, but most of these studies cover a restricted range of travel costs. The example that can be found in the literature ((Erath & Axhausen, 2010) in sections 2.1-2.3) that investigated the interactions between mobility and housing costs under different cost schemes used a novel stated adaptation approach that proved to be successful. However, this study was based on Swiss data and applies to the European reality, where transit attracts a significantly higher percentage of trip makers, and where the land use and urban design differs notably from the North American situation.

This thesis aims to investigate the relationship that underlies between housing and commuting expenses. In particular, the following research questions are addressed:

 What are the preferences of each commuter regarding vehicle type and engine technology in each case?  What is the tipping point (trade off) between transportation costs and housing costs?  What factors play the most important role when choosing a new residence?

2.4 Web surveys: Advantages and Disadvantages

The proposed CHOICE survey designed for this study was conducted via the Internet and it would be beneficial to underline some of the advantages of using web-based techniques as well as the limitations that come with new approaches.

Evolving technology allowed people to respond via Internet and this method led to cost savings for the researcher and time saving for both the researcher and the respondent. According to researchers, (Berge et al., 1996); (Schmidt, 1997); (Zhang, 1999), the greatest time saving of web-surveys comes from the turnaround time, especially when compared to the delay and potential data loss of mail-surveys. Web-based surveys also reduce the input errors from coding the answers into a database (Zhang, 1999), since the collected information is directly stored in electronic format. The greatest advantage, however, is the design flexibility that web-surveys offer (Dillman, 2000); (Zhang, 1999). Web-surveys combined with a dynamic process that includes pop-up instructions, skip logic, and incorporation of attractive features, such as maps and graphics, may increase the respondent’s willingness and motivation to fill the survey

(Schmidt, 1997); (Zhang, 1999). From a research perspective web-surveys allow the embodiment of specialized software, algorithms and datasets that can improve the validity of the results and the accurate representation of the subject in question.

While the most commonly cited disadvantage of all surveys is the bias due to errors (refer to Section 2.4.1) that occur at the different life-cycle stages of the survey (Couper, 2000); (Dillman, 2001); (Dillman, 1998), there are some disadvantages that are solely associated with web- surveys. Among the survey errors (coverage error, sampling error, measurement error and nonresponse error), nonresponse errors are surprisingly common in web-surveys. Exogenous factors might interrupt the survey process (e.g. internet connection loss), or specific sociodemographic groups may be under-represented or excluded due to limited access to the Internet (people of low education or low income, older people, etc. (Zhang, 1999)). Typical response rates of travel surveys approximate 20%, but some studies suggest that response rates are lower in web-surveys compared to the traditional paper- and-pencil surveys (Crawford et al., 2001); (Sax et al., 2003); (Underwood et al., 2000). One common problem of web-surveys is the variability of the interface appearance. This problem arises due to the use of different web browsers, screen configurations and operating systems, and in practice may result in a problematic appearance or malfunction of the survey (Dillman, 2000; 2001). These problems may result in higher drop-outs (Smith, 1997); (Zhang, 1999), and, therefore, explain the lower response rates of web-surveys.

Web-surveys also deal with various ethical considerations. Data confidentiality and security constitute a general concern of surveys, but it becomes an even bigger challenge in web-surveys. In web-surveys it is more difficult to persuade the respondent about the integrity of the process, and to assert that the data will be safely and anonymously stored and handled. Finally, some people feel that web-surveys are crossing their personal boundaries. Some consider that receiving surveys via mailing lists is an invasion of privacy; and others are concerned about the degree of access that the researcher may have in their private information (e.g. researchers often collect supplementary information without the permission of the respondent: what browser was used to complete the survey, when they started and finished the survey etc.).

Finally, there is some technical difficulty in developing web-surveys. There are survey design software available in the market (Qualtrics, Surveymonkey,etc) that offer a variety of question types and applications, but they have limits in their flexibility and/or offer a very small portion of their tools in a free trial version, but they are very costly if full access to the application is required. Due to this restriction most web-surveys are developed by programmers and are customized to the needs of a specific survey topic. Over the long run this approach is not cost- effective since it requires the involvement and compensation of specialists, and the result is very case-specific and cannot be re-used for other purposes. The last technical difficulty is the storage location and administration requirement of web-surveys. Web-surveys require a database to store the information on and a server to host the survey. Servers are costly to acquire, are generally hard to configure, and the database setup is a very important part of the process, because it has to be ensured that all the information is properly stored.

2.4.1 Errors in Surveys

The most commonly cited disadvantage in web-surveys, and in all surveys, is the bias due to errors: coverage error, sampling error, measurement error and nonresponse errors. (Couper, 2000; (Dillman and Bowker, 2001); Dillman et al., 1998). Figure 1 illustrates the different sources of errors and the stage of the life cycle of the survey at which they are encountered.

Figure 1: Errors in surveys (figure reproduced from Groves (2005))

Coverage error in surveys occurs when there is a mismatch between the target and the frame population. Target population is the population that can be covered, in other words the population of inference, while frame population or sampling frame describes the set of population members that has the chance to be selected into the survey sample. Sampling errors result from the non-observational gap between sampling frame and the sample. Sampling error is dependent on the sample size; therefore, larger sample size reduces the sampling error and provides higher quality of data. However, a larger sample size does not eliminate the other sources of error. Measurement errors are the gap between the ideal measurement and the response obtained. Some researchers identified that respondents who completed a paper and pencil version of the same web-survey answered differently (Sax et al, 2003). Therefore,

suggesting that technology-related surveys experience larger bias due to inaccurate responses. Finally, nonresponse error concerns the non-observed gap between the sample and the respondent pool. In other words, nonresponse error arises when individuals in a sample are unwilling or unable to complete the survey. This error is surprisingly common in web-surveys..

2.5 Revealed Preference (RP) and Stated Preference (SP) Data

The CHOICE survey combined revealed and stated preference (RP and SP) components to collect information about housing and vehicle ownership of households in the GTA. This section offers a brief overview of each data type, their advantages and weaknesses, and the way they are used and/or combined in choice modelling.

RP methods describe the actual market behaviour of respondents based on measured variables; they are, therefore, limited to analysing the effects of existing factors and cannot provide direct information on new (non-existing) alternatives or examine hypothetical situations. SP data on the other hand are based on the intention or choices of respondents in hypothetical scenarios (Ben- Akiva et al., 1994). SP experiments have received increasing attention in the transportation field, where they are used to determine the influence of independent variables on the observed outcome (Louviere et al., 2000). In traditional SP experiments a number of choice scenarios are presented to the respondent. Each scenario describes some alternative choices with a set of predefined attributes, where the attributes can take a range of pre-selected levels, and the respondent is typically asked to choose one alternative. In general, the higher the number of attributes and levels involved the more complex it becomes for the respondent to comprehend and respond accurately to the experimental situation presented.

RP and SP data are usually collected in the form of discrete choice observations and provide choice models with data to investigate trade-offs among variables. The ultimate target is to develop models that can forecast future demand, either for existing or for non-existing products and situations. When trying to find relationships and explain non-existing situations/products/choices, RP data are inadequate and only SP data can cover this deficiency. SP data have numerous advantages over RP data: the attribute levels can be extended to cover a wider range; the correlation between attributes can be minimized; the attributes are free from measurements errors; and most importantly SP data can elicit people’s preferences for new 16

alternatives. However, SP data are subject to perception errors, and it still remains to be concluded how consistent they are with actual behaviour. (Ben-Akiva et al., 1994).

Evidently, each method has its advantages and its backdrops and can be respectively applied. However, when combining RP with SP data we create powerful models, where the utility functions have common preference parameters and usually express the trade-offs that the respondents carry out among the attributes they consider most important. When the RP and the SP data come from the same source it is likely that there will be correlation between the random component observations. Ben-Akiva and Morikawa (1990) developed a method that adjusts this correlation and also controls the correlation between different SP responses from the same individual. It is generally believed that stated preference surveys may yield biases and large random errors (Ben-Akiva and Morikawa, 1990). Therefore, using RP data that have similar characteristics as complementary to SP data can minimize or eliminate these deficiencies. Ultimately, joint RP/SP data are considered to increase the statistical efficiency of models and started giving new insight and explanatory power to the developed choice models.

3 A Survey of Car and House Ownership in the face of Increasing Commuting Expenses (CHOICE) and the Background of the Study Area

A survey of Car and House Ownership in the face of Increasing Commuting Expenses (CHOICE) was designed and conducted in the Greater Toronto Area (GTA). The CHOICE survey is a web-based survey designed to collect information about commuting mode choices, housing and neighbourhood preferences, along with vehicle ownership choices of households with cross-regional commuters in the GTA. The multidimensional nature of this topic requires an unconventional approach. The CHOICE survey is designed as a combination of two stated adaptation experiments that are pivoted on retrospective revealed preference information of commuting trips, car ownership and house ownership of the participating households. The survey is software-based and uses actual commuting costs and information of alternative mobility tool ownership, neighbourhood information and home type/cost in the face of increasing transportation costs.

This chapter starts by introducing the study area and other important information about the GTA. Then, the sampling technique and the sample size calculations are presented.

3.1 Study Area

The study area of the CHOICE survey is the Greater Toronto Area (GTA). The GTA covers an area of 7,124 km2 and consists of the City of Toronto (Etobicoke, York, Downtown, Toronto, East York and Scarborough), and the regions of Peel, Durham, Halton and York. The GTA experienced a population growth of more than 12% in 5 years, and as of 2011 has 6,057,400 residents. Figure 2 illustrates the boundaries of the GTA in addition to the City of Hamilton (GTHA).

Figure 2: Greater Toronto and Hamilton Area

The GTHA is served by nine separately-governed local transit agencies and only one regional transit provider, GO Transit (for more information about GO Transit, refer to Section 3.1.1.1). The poorly integrated transit system makes the cross-boundary trips inconvenient, unattractive and costly. This shortcoming becomes more evident if we take into consideration the large number of cross-boundary trips (approximately 25%) that take place in the GTHA. According to the Big Move2, a commuter spends on average 82 minutes travelling per day. More than 85% of all commuters use their private vehicle and travel approximately 26 km per day (Metrolinx, 2008). These figures combined with the expected population growth in the near future emphasize the importance of this study. The CHOICE survey targeted households located in the GTA with at least one cross-regional commuter.

2 The Big Move is the regional transportation plan published in 2008 by Metrolinx for the Greater Toronto and Hamilton Area. It makes specific recommendations for transit projects and investment strategies. 19

3.1.1 The Greater Toronto Area

The GTA consists of the City of Toronto and the regions of Peel, Durham, Halton and York. The City of Toronto is according to Statistics Canada home to 2,503,281 people, and is the largest City in Canada and one of the most populous cities in North America. After the amalgamation the City of Toronto consists of six districts: Etobicoke, York, Scarborough, Downtown Toronto, East York, and North York. The Region of Peel consists of three municipalities: Mississauga, Caledon and Brampton and is one of the biggest employment centres, after the City of Toronto. Durham Region is the largest region covering 2,523,63 km2 and consists of 8 municipalities/townships: Pickering, Ajax, Whitby, Oshawa, Clarington, Uxbridge, Scugog, and Brock. Halton Region includes the municipalities of Oakville, Halton Hills, Milton, and Burlington. Finally, York Region incorporates the cities of: Vaughan, Richmond Hill, King, Aurora, Newmarket, Georgina, Markham, Whitchurch-Stouffville, and East Gwillimbury.

Table 1 shows the population of each region and respective municipalities based on Transportation Tomorrow Survey (TTS) data for 2011 and 2006 (for more information about TTS refer to Section 3.1.2).

Table 1: GTA Population Change 2006 to 20011 (TTS Data)

Region Change TTS 2011 TTS 2006 City/Township % City of Toronto 2,616,800 2,445,900 6.99% Downtown 737,900 685,300 7.68% York 140,000 142,300 -1.62% East York 115,500 112,000 3.13% North York 649,000 604,400 7.38% Etobicoke 348,100 319,900 8.82% Scarborough 626,300 582,000 7.61%

Halton Region 502,000 422,700 18.76% Burlington 175,900 157,400 11.75% Oakville 182,600 159,700 14.34% Milton 84,400 52,900 59.55% Halton Hills 59,100 52,700 12.14%

Peel Region 1,297,600 1,119,100 15.95% Mississauga 713,900 648,400 10.10% Brampton 524,200 416,400 25.89% Caledon 59,500 54,300 9.58%

York Region 1,032,700 857,500 20.43% Vaughan 288,400 231,200 24.74% King 19,900 18,300 8.74% Aurora 53,300 45,100 18.18% Newmarket 80,000 72,300 10.65% Richmond Hill 185,500 158,000 17.41% Markham 301,900 249,000 21.24% Whitchurch-Stouffville 37,600 22,700 65.64% East Gwillimbury 22,500 20,500 9.76% Georgina 43,600 40,400 7.92%

Durham Region 608,300 539,500 12.75% Pickering 88,700 84,200 5.34% Ajax 109,600 87,700 24.97% Whitby 122,100 105,200 16.06% Oshawa 149,700 137,500 8.87% Clarington 84,600 74,800 13.10% Scugog 21,600 20,300 6.40% Uxbridge 20,600 18,200 13.19% Brock 11,400 11,600 -1.72% Greater Toronto Area 6,057,400 5,384,700 12.49%

3.1.1.1 Transit Authorities in the GTA

The GTA (without Hamilton) is served by seven local transit agencies: the Toronto Transit Commission (TTC) in the City of Toronto; Miway and Brampton Transit in Peel Region; York Region Transit/Viva; Durham Region Transit; Burlington Transit and Oakville Transit in Halton Region. There is only one regional transit provider, GO Transit, which serves all regions by bus and train. GO Transit fares depend on origin and destination zones, $5.20 being the minimum possible fare that a traveller could pay. The GO Transit fare system is integrated with six out of the seven local transit agencies, but not with the TTC. This creates inconvenience and additional

charges for most of the cross-regional commuters, as the majority of them travels to the City of Toronto. Figure 3 shows the GO Transit network, which mainly operates in the Greater Toronto and Hamilton area and extends into many communities in the Greater Golden Horseshoe. The GO Transit system covers 450 km by train and 2,760 km by bus, and carried more than 65 million passengers in 2012 (Metrolinx, 2014).

Figure 3: GO Transit system map (source: GO Transit website)

3.1.1.2 PRESTO Card

In 2007 Metrolinx and the government of Ontario started a trial for a smart card fare payment to integrate public transit systems in Southern Ontario. The full implementation began in 2009 and ever since more provinces and agencies are gradually joining the program. The PRESTO Card is an electronic fare payment system, which uses a smart-card technology and can be used with

most of the transit agencies that operate in the GTA. PRESTO is becoming popular among the cross-regional commuters, since it typically offers a discount fare compared to using cash fares.

3.1.2 The Transportation Tomorrow Survey

The Transportation Tomorrow Survey (TTS) is a survey that is conducted every five years in the GTHA and collects retrospective household trip diary for 5% of all households in the study area. The first TTS survey was conducted in 1986 and the most recent in 2011. In every survey a random sample of the study area is invited to complete the questionnaire. Until 2006 the survey was completed exclusively over the phone, but starting 2011 the respondents had the option to complete the survey on-line. The survey collects travel data for all household members (11 and older) for the day previous to the phone (or web) contact, in addition to some socio-economic and demographic information.

The CHOICE survey was designed to be consistent with TTS data so many of the questions and distributions defined in the following sections are based on TTS 2006 data. TTS 2006 was the most recent data at the time of the experimental design and implemenation of the survey.

3.2 Survey Sample Design and Target Population

The CHOICE survey intended to measure the trade-offs between mobility tool expenses and housing costs that households with cross-regional commuters are willing to make in the face of increasing transportation costs. At the time of the preparation of the CHOICE survey the TTS 2006 was the most recent survey, and consequently its data was used to determine the distributions of the survey sample. In more detail, the origin-destination matrices from TTS 2006 for all commuting trips were used to determine the total number of cross-regional commuters among the five regions. Households with at least one cross-regional commuter were invited to complete the survey. The survey population of the study (cross-regional commuters) is estimated 28,660 commuters distributed in the GTA. Given the large population and the limited resources it is evident that a sample survey is the only feasible alternative.

There are two types of sampling techniques: non-probability sampling and probability sampling. The first provides a fast and inexpensive way to collect the desired sample by using a non- random method; however, it does not ensure a representative sample of the population. The 23

second method is called probability sampling. When employing this method, we randomly select units from the population and avoid selection bias. Therefore, when using probability sampling it is possible to generalize the results to reflect the whole population (Franklin et al., 2003).

The sampling procedure followed for the CHOICE survey was the simple stratified random sampling based on the geographic home location of the population. The five regions of the GTA were the five strata that made up the sample size, as explained in the next section.

3.2.1 Sample Size Determination

The determination of the sample size is essential because it defines the precision of the estimates. The larger the sample size used in a study, the smaller the nonresponse errors, and therefore, the greater the precision of the estimates. There are many factors that should be accounted for when determining the sample size: the appropriate level of precision in terms of the margin of error, the sampling variance that is required, and finally the size of the survey population.

 Calculating the initial sample size: For the CHOICE survey we assumed a maximum population variability of P=0.5. Additionally, a margin of error e=0.05 at a 95% confidence interval is used to determine the sample. This means that there is a 5% chance that the randomly selected sample will not produce an estimate within the range of P±e (z=1.96).

() = (1)

1.96 ∗ 0.5(0.5 − 1) = = 384 0.5

 Adjust for the population N:

= = 384 (2)

 Adjust the sample size for the effect of the sample design. The design effect factor (DEFF) depends on the sampling strategy and for a random sampling procedure is usually DEFF=1, and for stratified simple design DEFF≤1:

= ∗ (3)

= 0.9 ∗ 384 = 345

 Adjust for response to determine the final sample size:

= = = 1725 (4) .

A typical response rate of surveys is about 20% (r=0.2). The calculations indicated that 1725 households had to be invited to participate in the CHOICE survey. Given a response rate of 0.2 approximately 345 valid complete records need to be collected. In order to make sure that this number would be reached after removal of incomplete or erroneous entrees, this number was augmented by 0.45 leading to a total of 500 complete responses.

4 CHOICE Survey Design

The CHOICE survey was conducted among a sample of households in the GTA who have at least one cross-regional commuter. The survey was designed to capture the collective decision making process of the household as a unit. However, the interviewee stated the choices for all household members, as this approach is organizationally simpler and is proven to be effective (Beckman et al., 2002); (Vrtic et al., 2007); (Erath and Axhausen, 2010).

The survey consists of two main parts; a revealed preference (RP) part with three sections, and a stated preference (SP) part with two sections. In the RP part the respondent had to indicate information about all household members, typical commuting trips, and retrospective information about housing and vehicle ownership. The information from the RP part was pivoted into the SP part with the two stated adaptation experiments; in SP1 the interviewee was asked to choose the preferred bundle of mobility tools for each member under different cost schemes; and in the SP2 the respondent had to indicate the desired alternative place of residence. Figure 4 illustrates the flow of the experiment.

Figure 4: Survey design overview

4.1 RP

This Section presents the RP parts of the survey, namely the housing information, vehicle ownership, and information about the commuting trip of each adult member. The screenshot of the questionnaire can be found in Figure 24, Figure 25, and Figure 26 in Appendix A.

4.1.1 Housing Information

In section 1 of the survey each respondent had to indicate the postal code of their residence and complete the following information about the current and the previous housing unit:

 Dwelling type (single detached, semi-detached, townhouse/row house, apartment/condo in detached duplex, apartment/condo in building with less than 5 stories, apartment/ condo in building with more than 5 stories)  Tenure (own, rent)  Moving dates (year, month)  Unit information (number of bedrooms, number of bathrooms, number of stories, area)  Reasons for moving  Rent cost/utilities for rental units  Asking price, receiving price, down payment for purchased units  Parking availability, rent/purchase price of parking spaces  Questions about the house searching procedure  Qualitative characteristics to support the choice of the specific house and neighbourhood  Type and price of units considered at the time of the move  Minimum required number of bedrooms

4.1.2 Socio-demographic and Commuting Trip Information

In section 2 of the survey the respondent was asked to fill information about the household members. The following information was filled for all members, aged 12 and above:

 Relationship to respondent, name or nickname for facilitating the experiment  Age  Education 27

 Employment status (full time worker, part time worker, student, working from home, full time homemaker, not employed, retired)

For members 18 years and older who are full time or part time workers the following information was asked:

 Industry category (as defined by the North American Industry Classification System Canada)  Postal code of job location  Years at work  Annual income  Driver’s license, years  Parking cost at work  Frequency of mode used for commuting trip per week (car as driver, car as passenger, public transit, park and ride, walk or bike, work from home)  Transit pass ownership and cost

For students 18 years and older:

 Student status (secondary, college, university)  Postal code of school location  Parking cost at school  Frequency of mode used for commuting trip per week (car as driver, car as passenger, public transit, park and ride, walk or bike, work from home  Transit pass ownership and cost (TTC, GO Transit, other agency)

The information about the origin and destination of each member’s commuting trip and the availability of driver’s license was used in the SP experiment. This information also determined the alternative options offered (members without a driver’s license could not choose a car in SP1) and defined the calculations of the commuting costs.

4.1.3 Vehicle Information

Section 3 of the survey was used to collect information about the vehicles that the household members currently own and/or use. Additionally, information about the vehicles that were replaced/ exchanged or traded off was asked in order to form a better understanding of the longitudinal retrospective choices of the same household. The respondents were asked to identify:

 Make, model and vintage (information retrieved from “Edmunds” website, which is a database with all the vehicles available in Canada and the United States after year 1990 (Older vehicles are not available in the database)  Member who owns or makes payments for the vehicle  Privately owned or leased  Fuel technology (gasoline, diesel, hybrid, electric, other)  Actual mileage  Principal driver  Year and type of purchase (new or used)  Reasons and method of replacing the vehicle  Vehicle category (subcompact/compact, midsize, full size, SUV, pickup truck, minivan)

Indicating the vehicle category correctly was really important because the same classification was used for the SP experiment. For this reason a help-button allowed respondents to visually compare the different categories and see information about each one (Figure 27 in Appendix A).

Figure 5: RP data model of CHOICE survey 30

4.2 SP

The following sections present the experimental design of SP1 and SP2. The screenshot of SP1 can be found in Figure 28 and screenshots of SP2 in Figure 29 to Figure 32 of Appendix A.

4.2.1 Experimental Design

The second part of the survey consisted of two experiments.

 SP1: A mobility tool experiment consisting of maximum four scenarios, and  SP2: A stated choice experiment for residential location.

In practice, during SP1 the respondent had to adjust the mobility tools of the household for different transportation costs. Once this cost exceeded their acceptable cost level they would move to SP2, where the respondent had to consider different residential locations.

4.2.2 SP1: Effects of Price Changes on Mobility Tool Ownership 4.2.2.1 Construction of the Experimental Plan

SP1 was a stated adaptation exercise where the respondent had to adjust the mobility tools for all household members given today’s home location and individuals’ work locations, but under new transportation cost schemes. The experimental plan involved four cost scenarios, but the respondent had the option to abandon after any of the four scenarios if he/she perceived the presented cost as extreme. After choosing the mobility tools and reviewing the total cost for the household, the respondent could choose to either stay in the current home location and consequently continue to the next cost scenario, or could choose to move home location in order to reduce the distances that some of the household members travel. This structure addresses a limitation that Erath and Axhausen (2010) identified in their study. They found that having an experimental structure that forces the respondent to change home location and choose the appropriate mobility bundle for the new home location was note easily perceived. The CHOICE survey allows the respondents to change home location at any stage of the experiment.

The four scenarios were generated using higher gas price values. At the time of the design stage of the experiment (September 2013) gas price in Toronto was approximately $1.25 per litre. The

objective of this experiment was to push the respondents to the cost they consider unacceptable and to capture the trade-offs between mobility tool ownership and residential costs for households with cross-regional commuters. Residential location choice is a choice that is complicated and influenced by various factors. Researchers identify numerous reasons for residential mobility; among them the distance to work has been singled out as an influential determinant (Morrow-Jones, 2007); (Lowry, 1964); (Waddell, 2002).

Taking into account the long-term commitment and the capital investment that is involved when someone chooses a specific house and a specific location, the proposed transportation cost increases in SP1 had to be significant to have an impact. Therefore, in order to generate the hypothetical scenarios, the current gas price was doubled, tripled and quadrupled. Similar cost increases for SP scenarios can be found in (Krumdieck et al., 2010); (Erath & Axhausen., 2010). Each value of gas price was used in the respective scenario of SP1, and it is the first component that was used in the calculations of the monthly commuting costs.

In addition to the auto costs, the new transit fares had to be determined for the four scenarios. The suggested increase for the transit fare, however, could not be of the same magnitude as the one used for the auto costs. Cross-regional transit fares are already very costly and a double, triple or quadruple ticket would bring them off-limits for most individuals. Therefore, a relationship had to be established to define how gas price affects the transit fare hikes.

Historical data for the price per barrel of gasoline and the cost for the Toronto Transit Commission monthly pass since 1990 were gathered, adjusted for the inflation rate of 2006 and used in different formulations to find which one explains best the relationship between gasoline price and transit fare. A third degree polynomial equation was the best fit, and determined the percentage increases for the three hypothetical scenarios. Table 2 shows the attributes used in SP1 and their levels.

Table 2: Attribute Characteristics of SP1

Variable Definition Levels

Car Cost Fuel price in CDN/l 1.25/2.5/3.75/5

Public Transit Price level compared to today's Costs price Today,+19%/+31%/+39%

4.2.2.2 Construction of the Choice Alternatives

SP1 used real time calculations to give direct feedback on the respondent’s mode choice. The software was programmed to customize the cost of the trip based on the distance of each respondent and for different combinations of variables. Therefore, all the costs associated with the mode alternatives had to be predefined and stored in the database in addition to information about the characteristics of each trip (time and distance by car and transit).

4.2.2.2.1 The Origin Destination Pair

In order to customize the commuting cost for each individual’s trip the EMME software was used. EMME is a complete travel demand modelling system used in urban, regional and national transportation forecasting. Data from the Transportation Tomorrow Survey 2006 (TTS) were used as inputs to calculate zone-to-zone travel times by car and public transit during the AM peak3. The TTS collects data at the level of traffic analysis zones (TAZ), an area based on population. The general population cannot identify the TAZ; therefore, the postal codes collected in the RP part of the survey were matched in real time to the respective TAZ. The values that corresponded to the time and distance of the specific origin-destination TAZ were retrieved from the EMME matrix and used in the calculations of the experiment.

4.2.2.3 Car Choice

When choosing a car the respondent had to indicate the car category and the fuel technology. The levels of each variable are shown in Table 3.

3 AM Peak is from 6:30- 9:00 33

Table 3: Car Choice Characteristics

Car Type Fuel Technology

(Sub)compact Gasoline

Midsize Hybrid

Large size Electric

SUV

Pickup Truck

The respondent was able to examine different combinations of car type and fuel technology, where each combination led to a different transportation cost for each participating household member. Different individuals have different preferences and different needs with respect to the size and the performance of the vehicle they choose to drive (Choo & Mohktarian, 2004). Also, different vehicles lead to very different costs depending on the size of the vehicle, the engine capacity, the fuel technology, the consumption rate etc. It would, therefore, be wrong to only offer one vehicle option in the experiment. The vehicle categories chosen for the experiment are some of the categories suggested by the Canadian Automobile Association (CAA) which have a visible difference in size and shape and would therefore be easily identifiable by the respondent, but they also have significant differences in costs.

Fixed costs for every combination of size and fuel technology that is available in market were determined using information from the CAA, and include:

 Maintenance fees and depreciation over a 5 year period  License fees  Insurance fees, and  Variable cost for each car type based on the consumption rate given in the CAA.

CAA did not provide cost information about the alternative fuel vehicles. Therefore, the characteristics of all the electric and hybrid models available in Canada were collected and the

average value of the respective characteristic was used instead (NRCAN, 2013). The choice set also included options for battery electric vehicles. In order to calculate the transportation cost for the electric car alternatives, the cost per kWh was used (OEB, 2013). The mid-peak charge rate of 10.9 cents/kWh was used for the experiment, based on the assumption that an individual would charge the vehicle overnight at home, when the off-peak charge is effective, and then will likely recharge it for a while at work, where the demand is high and the on-peak rate applies.

The software calculated the fixed and variable costs of the selected vehicles based on the origin- destination pair of each member and provided direct feedback with the cost for each individual and for the household as a total. The variable and fixed costs of all feasible combinations can be found in Table 22 in Appendix C.

4.2.2.4 Public Transit Pass

In the Greater Toronto Area there are seven different agencies that operate independently, and there is only one regional transit service, GO Transit, that connects the five regions. GO Transit has a distance based fare system, which means that different origin and destinations have fares that differ substantially. In 2009 the Ontario Ministry of Transportation together with GO Transit and 8 other municipal transit agencies implemented an integrated fare system called PRESTO Card (Section 3.1.1.2). When a transit rider uses a PRESTO Card, trips 1 to 35 have a reduced price (compared to a regular GO-Transit fare), rides 36-40 are reduced to approximately 13% of the initial fare, and after the 40th trip each additional trip from the same origin to the same destination is free of charge.

This fare calculation structure was used to determine the cost for 40 trips per month based on the origin and destination of each commuter, where the respective increases from Table 2 were applied in each scenario. In order to determine the cost for the monthly transit pass in SP1:

a) For the cross-regional trips, the GO Transit fare calculator was used b) For the within-the city/township trips, the local transit agency monthly pass was used.

An origin-destination matrix with the monthly transit fares was generated for each scenario. For the cross-regional trips, the calculations are based on the use of the PRESTO Card for 40 trips per month (five days a week, two times a day), and for trips within the region, the local transit 35

pass for each city was used. The exact fare for each origin-destination in scenario 1 can be found in Table 29a) and 29b) in Appendix E.

4.2.3 SP2: Residential Relocation as a Reaction to Increased Transportation Costs

After voluntarily abandoning the current home location, or after completing all four cost scenarios in SP1, the respondent had to complete SP2: the alternative residential location choice experiment. The respondent had to choose the region, the city or township, and last the neighbourhood of interest. Screenshots can be found in Figure 29, Figure 30, Figure 31, Figure 32 of Appendix A.

4.2.3.1 Construction of the Experimental Plan

During the preparation of SP2, various details had to be determined. The GTA had to be segregated into divisions that would geographically make sense, but that would also be easily identifiable by the respondents, and that would have somewhat homogeneous characteristics. Most of the official area boundaries, such as the traffic analysis zones, the census tracts, the forward sortation areas or the dissemination areas, are either not easily perceived, or they are too small and similar that would not justify showing them as separate alternatives.

Figure 6: SP2 map overviewFigure 6 shows a screenshot of the map that each respondent would view in SP2. The individual could click on the different regions and view a zoomed area of the cities that belong to each region. For the City of Toronto, a more detailed specification was provided. The City of Toronto is very diverse in terms of housing prices and neighbourhood characteristics, and was, therefore, treated at a more detailed level. As supported by other studies (Morrow-Jones, 2007), residential location choice is influenced by numerous factors, and is heavily dependent on the life cycle stage of the household. Besides housing price, there are other characteristics of a neighbourhood that will attract specific individuals or households.

Figure 6: SP2 map overview

4.2.3.1.1 Qualitative Characteristics of Neighbourhoods

Taking all these factors into account, the experiment included qualitative characteristics of neighbourhoods in addition to housing pricing (Figure 7). After choosing the specific area of interest, each respondent was able to review the following characteristics and decide if it is suitable for their household needs, or if they should continue searching: 38

-Walk score: A score from 0 to 10 that measured walkability based on walking routes to destinations such as grocery stores, schools, parks, restaurants, and retail (Manaugh & El- Geneidy, 2010); (Carr et al., 2010); (Duncan et al., 2011). A score of 0 means that most daily errands require a car, and a score of 10 indicates that most errands can be accomplished on foot.

-Neighbourhood safety: A score from 0 to 10 was used to indicate how safe a neighbourhood is. The data were retrieved from Wellbeing Toronto (2013), which uses a range of sources (City of Toronto); (GIC); (Statcan) to make thematic maps. Crime statistics (firearm incidents, assaults, sexual assaults, break and enters, robberies, vehicle thefts, murder, arson) per 10,000 residents from Toronto Police data were used to determine the neighbourhood safety index.

-School score: The 0 to 10 score described the performance of schools in different neighbourhoods of Toronto. The data were retrieved from Fraser Institute’s 2013 report card scores for both primary and secondary schools. The values shown in the experiment corresponds to the average of all schools across a neighbourhood (Fraser Institute).

The analytical scores for each neighbourhood can be found in Appendix D.

Figure 7: Neighbourhood characteristics and housing prices in SP2

4.2.3.1.2 Housing Pricing

Housing price is one of the most heavily weighted factors that confine the residential location choice. It usually sets the first constraint that will reduce the pool of available housing or neighbourhood options. For this reason, after choosing the desired geographical location and reviewing the qualitative characteristics of the neighbourhood, the respondent was presented with different housing units and had to make the decision that would conclude the survey (Figure 7).

-Housing Price: Average pricing of different dwelling types (detached, townhouse, apartment) and different number of bedrooms (one, two, three, four or more) for rent and for sale, was defined for each neighbourhood. All the available housing units for sale that were advertised by the Realosophy4 website in August 2013 were downloaded, categorized by dwelling type and number of bedrooms, and stored in a database. The same procedure was followed for the housing units that were for rent during September 2013 in the Multiple Listing Service (MLS) website.

After clicking on the desired city or neighbourhood, the respondent could review the qualitative characteristics and also the average housing price of dwelling types. Each respondent would review six dwelling units: a) a house for sale, b) a house for rent, c) a townhouse for sale, d) a townhouse for rent, e) an apartment for sale, and f) an apartment for rent. The prices that each respondent saw were real market prices for units that met the bedroom requirements of each household (refer to Section 4.1.1). If the respondent found a neighbourhood and a dwelling type that fulfilled the household’s needs, the survey terminated. If the respondent was not satisfied with the presented characteristics, he/she could return to the initial map and examine different neighbourhoods. The dwelling type price in each neighbourhood can be found in Appendix D.

This process was tracked and stored in the database in order to understand how individuals rank different neighbourhoods, and to allow us to create a dataset that could be used to model the house searching procedure. Figure 8 shows schematically how the SP parts of the survey were customized based on the RP answers and the external datasets.

4 Realosophy presents all listings that are available for sale in MLS and REALTOR 40

Figure 8: RP and SP data model connections

4.2.4 Comments on the SP Component

The SP experiment was an adaptation exercise, where the respondent was given the flexibility to either continue to the next stage or not. In SP1 the measure that each respondent had to consider was the monthly household commuting cost. Based on real time calculations the commuting cost of each member was shown. The respondent had to determine if the given total cost was acceptable, and continue to the next stage (scenario) with a higher gas price, or if this cost was excessive, and would ultimately change residential location in an attempt to cut back on commuting expenses.

The idea behind this is that in most North American cities the majority of offices and services is located in the city centre. Real estate and parking are more expensive in the central business district, while land is cheaper in the outskirts. This land-use structure encourages people to live where land is cheaper and to commute long distances to reach their work. However, this compromise is rational provided that the cost of living at a central location surpasses the cost of commuting long distances on a daily basis. In case of extreme increases in transportation costs, this balance would be interrupted, making households reconsider this trade-off, and could possibly lead to residential movements.

The traditional way that stated preference experiments work, is that a respondent is given a certain number of cards or scenarios, and each time is asked to choose one alternative based on the levels of the given attributes. When preparing the experimental design, the researcher has to define all the possible levels that the attributes may take. Based on the number of attributes and the possible levels of each attribute the experiment consists of numerous combinations, the scenarios.

The traditional stated preference methodology was not suitable for this experiment, as the desired household trade-offs could not be captured. Additionally, all the possible combinations for each member, when combined together at the household level, were leading to an extraordinary high number of scenarios. Furthermore, this number varied across participants based on the number of commuters that live in the same household. Figure 9 illustrates all the possible combinations in SP1.

Figure 9: Number of possible choice scenarios

Figure 9 shows that depending on the number of commuters per household the number of possible alternatives in the choice set increases exponentially. A household of two would have 256 alternative options among the different car types, fuel technologies and the option for a transit pass. A similar experimental design approach was used by Erath and Axhausen (2010) to investigate the mobility behaviour of households in Switzerland (for more information on their study refer to Sections 2.1, 2.2, and 2.3).

The SP1 experiment consisted of four scenarios. After each scenario the respondent had to review the new cost based on the selected mobility tools, or move residential location. This meant in practice that different respondents would abandon SP1 at different scenarios. As Erath and Axhausen (2010) concluded after their experiment, forcing the respondent to make drastic life changes, such as moving housing, is not easily perceived. The flexibility of the CHOICE survey allowed for a voluntary move instead of a forced choice, and also captured how far in the experiment different respondents would go.

4.3 Software

The CHOICE survey is a self-administrative web-based tool that was developed exclusively for the purpose of this study. The software employed the languages of php, Javascript and JQuery, was programmed to run on all web browsers (Internet explorer, Chrome, Firefox, Opera), and combined G.I.S. applications, EMME outputs and other interfaces. The software incorporated a dynamic process that included pop-up instructions, skip logic features, and attractive tools such as maps and graphics, which as cited by researchers may increase the respondents’ willingness and motivation to fill the survey (Schmidt, 1997); (Zhang, 1999).

Each interviewee had a unique ID and had the option to save parts of the survey and finish it later without losing any of the so far answered questions. Screenshots of the survey can be found in Appendix A. During the fieldwork, no problems were encountered with the software.

4.4 Recruiting

The survey distribution was commissioned to Research Now, a market research company that maintains a panel of 35,000 individuals in the GTA. Email invitations were sent to the respondents, who were asked to complete the survey, given that there was at least one cross- regional commuter in their household. As an incentive, Research Now awarded air miles (points to one of the most popular loyalty programs in Canada) to those who completed all parts of the survey.

All interviews were conducted between March and April 2014. During that time the respondents were able to contact the research group and ask for further instructions or clarifications.

4.5 Sample Distribution and Representativeness

The market research company was hired to collect a total of 500 complete responses. Incomplete responses were stored in the database as well, leading to a different number of complete responses for the different sections of the survey. Table 4 shows the number of total responses for each of the five sections of the survey.

Table 4: Number of Total Responses at each Part of the Survey

Number of complete responses Part 1 1090 RP Part 2 1013 Part 3 927 SP1 Part 4 646 SP2 Part 5 501

The sample was allocated to the five regions based on the percentage of cross-regional commuters that originate from each region, according to TTS 2006 data. The desired number of complete questionnaires was: 126 for the City of Toronto, 72 for Halton Region, 138 for York Region, 126 for Region of Peel, and 61 for Durham Region. Table 5 shows the sample representation of different households attributes based on the 901 households that completed (at least) part one, two and three of the survey. .

Table 5: CHOICE Sample Representation of Household Attributes Based on TTS 2006 Data Region Count Percentage TTS 2006 Durham 116 12.89% 7.00% Halton 87 9.67% 6.00% Peel 201 22.33% 16.00% Toronto 297 33.00% 32.00% York 199 22.11% 12.00%

Household Size Count Percentage TTS 2006 1 191 21.22% 21.00% 2 283 31.44% 33.00% 3 202 22.44% 18.00% 4 158 17.56% 18.00% 5 54 6.00% 10.00% 6 10 1.11% 7 2 0.22%

Household vehicles Count Percentage TTS 2006 0 98 10.89% 14.00% 1 313 34.78% 40.00% 2 369 41.00% 36.00% 3 120 13.33% 8.00% 4 2.00% 45

House Type (current) Count Percentage TTS 2006 Single Detached 555 61.67% 67.00% Semi Detached 61 6.78% Townhouse 112 12.44% 7.00% Apartment in duplex 16 1.78% 25.00% Apartment in less than 5 19 2.11% Apartment in more than 5 131 14.56% N/A 6

Table 6 shows the sample representation of individuals’ characteristics compared to TTS 2006 data (gender, age, employment status, driver’s license, and transit pass ownership). This analysis is based on the 901 households that completed all three parts of the RP sections and which amounts to 1394 individuals.

Table 6: Sample Representation of Individual Attributes Based on TTS 2006 Data Gender Count Percentage TTS 2006 Female 898 51.49% Male 846 48.51% 1744

Age Count Percentage TTS 2006 12-17 114 6.42% 0-10 13.00% 18-24 176 9.90% 11-15 7.00% 25-34 227 12.77% 16-25 11.00% 35-44 317 17.84% 26-45 28.00% 45-54 380 21.38% 46-64 24.00% 55-64 386 21.72% 65 or older 15.00% 65 or older 177 9.96% 1777

Employment Status Count Percentage TTS 2006 Full time 1032 58.08% 37.00% Part time 136 7.65% 8.50% Student 226 12.72% 22.50% Working from home 55 3.10% 3.00% Homemaker 44 2.48% Not employed 70 3.94% Retired 206 11.59% None 8 0.45%

Driver’s License

Count Percentage TTS 2006 No 201 14.42% 34.50% Yes 1193 85.58% 65.50% 1394 Transit Pass Count Percentage TTS 2006 No 1214 87.09% 93.00% Yes 180 12.91% 7.00% 1394

5 CHOICE Survey Results

This chapter features the descriptive analysis of the CHOICE survey data. A total of 501 households completed all five parts of the survey. However, the survey software allowed respondents to save their answers, and continue the survey at a different time. This resulted in different completion rates for the different parts of the survey. The analysis of this part is based on the respondents that completed the four first parts of the survey including SP1.

5.1 RP Descriptive Analysis

The first part of this section covers the analysis of the RP part of the survey (parts one, two and three). After the removal of erroneous or missing records 413 households and 757 individuals were left and formed the data basis of the analysis. . As seen in Table 7, which summarizes the recruitment and response rates, the total panel size of the research company reaches 35,000 individuals, which exceeds the sample size requirements calculated in Section 3.2.1. Also, the complete responses surpass the respective requirements.

Table 7: Recruitment and Response Rate Total Panel Size: 35,000 Total Responses: 1,090 Recruited: 5,682 Completes: 501 Recruitment rate (%): 16.23% Incompletes: 589 Qualified: 1,609 Completion rate (%): 45.96% Qualification rate (%): 28.32% Final Sample: 413 households, 757 individuals

5.1.1 Household Descriptive Analysis

Table 8 summarizes the descriptive analysis of all household attributes. Most of the participating households own their residence, and over 60% live in single detached units. More than 65% of the households own more than two vehicles. Household size is somewhat uniformly distributed with exception the category of five or more people, which participates with 8% in the total sample. On average, the sample shows that GTA households with cross-regional commuters are two or three person households, own more than one vehicle and have one to two commuters.

Regarding the housing unit, most participating households live in three bedroom houses, pay on average $1,086 per month for housing expenses (rent or mortgage payment), and purchased their unit before 1990 for more than $300,000.

Table 8: Descriptive Analysis of Household Attributes

Household Descriptive Statistics Variable Value Frequency Percentage House tenure Own 340 82.32% Rent 59 14.29% Other 14 3.39% Dwelling type House 274 66.34% Townhouse 61 14.77% Apartment 75 18.16% No. of vehicles 0 18 4.36% 1 152 36.80% 2 196 47.46% 3+ 47 11.38% Household Size 1 93 22.52% 2 111 26.88% 3 86 20.82% 4 87 21.07% 5+ 36 8.72% Household Averages Persons 2.69 Vehicles 1.66 Children 0.62 Commuters 1.87

Quantitative Household Statistics Mean St.dev Min Max House area (sqf) 1,761.38 931.27 200 9,000 Number of bedrooms 3.11 0.98 1.00 5.00 Monthly housing costs 1,086.33 806.82 0.00 10,000.00 Asking Price 337,879.41 181,751.57 5,000.00 1,595,000.00 Discount (Asking-Receiving Price) 9,680.00 31,894.35 0.00 360,000.00

5.1.1.1 Neighbourhood Preferences

Part 1 of the survey collected information about the respondent’s current and previous house. Additionally, information about the reasons for moving to their current unit, the reasons for choosing the specific unit, and the reasons for choosing the specific neighbourhood were asked to form a better understanding of the household’s attachment to the current location. Furthermore, this information would be important to understand what drives people to change

residential location, and how different housing attributes or neighbourhood characteristics affect these decisions.

Among the reasons for choosing the current unit, most respondents stated that they wanted a bigger or newer house, and that they wanted to switch from renting to owning. However, the third most selected reason for moving to a new house was to be closer to work, which confirms that people take into account the commuting distance when choosing their residential location. Figure 10 shows the reasons and the respective percentage distribution.

80% 70% 60% 50% 40% 30% Percentage 20% 10% 0%

Figure 10: Reasons for moving from previous to current house

Regarding the reasons for choosing the specific neighbourhood, most important ones were the price of the house and the attractiveness of the neighbourhood. The distance to work is the fifth most selected reason. Furthermore, we can see that the access to bus/transit is among the most important reasons for choosing the current home location. Figure 11 shows the percentage of respondents’ choices.

80%

70%

60%

50%

40%

Percentage 30%

20%

10%

Figure 11: Reasons for choosing current neighbourhood

5.1.2 Descriptive Statistics of Individuals’ Characteristics

This section summarizes the statistics of the 757 individuals. The information presented in Table 9 was collected in the second part of the survey and concerns members 12 year or older. Most of the household members belong to the age group 35-54, have higher education (bachelor’s degree and above), and hold a driver’s license. More than 80% of the sample are full time workers and are employed in professional, scientific and technical services. Finally, most individuals (30% of the sample) belong to the income category that earns between $50,000 and $75,000 per year.

Table 9: Descriptive Analysis of Individuals’ Characteristics

Variable Value Frequency Percentage Gender Male 379 50.07% Female 369 48.75% Missing 9 1.19% Age 12-17 22 2.91% 18-24 70 9.25% 25-34 106 14.00% 35-44 194 25.63% 45-54 204 26.95% 55-64 140 18.49% 65 or older 21 2.77% Education Elementary 2 0.26% Junior High 8 1.06% High School 133 17.57% Diploma 193 25.50% Bachelor 308 40.69% Masters or above 112 14.80% None 1 0.13% Full time 626 82.69% Employment status Part time 67 8.85% Students 64 8.45% Occupation Agriculture, forestry, fishing and hunting 1 0.13% (According to the Mining, quarrying, and oil and gas 1 0.13% North American extraction Industry Classification Utilities 13 1.72% System Canada Construction 25 3.30% (NAICS 2013) Manufacturing 60 7.93% Wholesale trade 18 2.38% Transportation and warehousing 25 3.30% Information and cultural industries 14 1.85% Finance and insurance 88 11.62% Real estate, rental and leasing 8 1.06% Professional, scientific and technical 92 12.15% services Management of companies and 16 2.11% enterprises Administrative and support, waste 35 4.62% management and remediation services Educational services 53 7.00% Health care and social assistance 51 6.74% Arts, entertainment and recreation 21 2.77% Accommodation and food services 7 0.92% Other services 122 16.12% Public administration 43 5.68% Students 64 8.45% Driver's license Yes 692 91.41% No 65 8.59% Personal income in 0-24,999 56 7.40% CDN$ 25,000-49,999 148 19.55% 50,000-74,999 243 32.10% 75,000-99,999 131 17.31% 100,000 or more 115 15.19% 52

5.1.2.1 Job Location

In the second part of the survey, information about the commuting trip of each member was collected. Table 10 shows the number of trips’ origin and destinations across the GTA regions. Most individuals (57%) work in different districts of Toronto (York, East York, North York, Scarborough, Etobicoke, and downtown Toronto). The second greatest work trip attractor is the Region of Peel with 16.5%, followed by York Region with 15.9%.

Even though the CHOICE survey targeted cross-regional (or between Toronto districts) trips, the origin and destination of all commuting members were collected. Table 10 shows that the region with the lowest intra-region trip rate is Halton with 22%. Halton Region is primarily a residential area with only 26% employment rate. This fact justifies the low number of commuting trips within the region’s boundaries. The region with the highest rate of cross-regional trips is Durham Region, with 63.5% of all trips crossing the boundaries and destining primarily to the City of Toronto and then to York Region.

Table 10: Home and Work Location Distribution of Sample

Work Home Toronto Durham York Peel Halton Total Toronto 156 5 32 22 5 220 Durham 61 22 10 3 0 96 York 117 2 61 5 1 186 Peel 82 0 12 71 18 183 Halton 23 1 5 23 16 68 Total 439 30 120 124 40 753

5.2 SP1 Descriptive Analysis: Mobility Tool Ownership

This section discusses the reaction of respondents to increasing transportation costs as captured in SP1. As presented in previous sections (Section 4.2), the respondent had the option to adjust the household mobility tools by choosing among different car size and fuel technology combinations or by selecting a transit pass. After the removal of erroneous entries (entries with critical information missing, such as trip distance, or no mobility tool chosen) the analysis was done on 413 households, which resulted into a total of 1758 choice situations. 53

5.2.1 Fuel Consumption and Engine Type

Figure 12 illustrates the changes in fuel consumption and engine technology preferences of households throughout the experiment. Interestingly, the average number of vehicles per household declines at a very slow rate and only after scenario two. This indicates that people are unwilling to change to a different mode despite the cost increases. However, after scenario two respondents start switching to more efficient vehicles, either smaller body size or alternative fuel technology.

As seen in the figure, the share of hybrid vehicles becomes bigger as costs triple and quadruple. This leads to a 13% reduction in the monthly consumption of the household. Generally, electric vehicles are not chosen; this indicates that despite the technological advancements, the drop in purchase price over the years, and their efficient performance electric vehicles are still considered unreliable and are not widely preferred.

1.4

1.2

1.0 90.8 91.7 hybrid

s per household per s 0.8 electric 78.9 78.8 0.6 gasoline

0.4

Number of carNumberof 0.2

0.0 1 2 3 4 Scenario of cost increase Averange household consumption [l/housedhold-month]

Figure 12: Fuel consumption, car ownership and engine types depending on fuel price in SP1

The analysis showed that the households that mostly reduced their total monthly consumption belong to the regions of Durham and Peel. These regions commute the longest distances according to our data collection and, therefore, faced the highest cost increases during the SP scenarios.

5.2.2 Vehicle Type

Looking at the preferences for specific vehicle types, we were not able to identify any major changes throughout the cost scenarios. As shown in Figure 13 subcompact vehicles dominate the respondents’ preferences throughout all cost scenarios. There is a minor increase of subcompact cars as the cost scenarios progress and their selection percentage reaches its peak at scenario three. Large size, SUV, and pick-up truck percentages show a less clear pattern. This is an interesting result, since these cars are the most inefficient and were the ones mostly affected by the fuel price increases imposed in SP1. A similar pattern was found in the study of Erath and Axhausen (2010), and could be justified by the fact that people who own larger vehicles (large size and SUV) have typically higher income and are consequently less affected by additional cost. On the other hand, pick-up trucks could presumably fulfil work requirements, and are therefore chosen despite the cost increases. Overall, it appears that there is strong inertia for downgrading the size of the vehicles. Respondents would rather change the engine technology, as shown in Section 5.2.1, than change the body type.

50% 45% 40% 35% Subcompact 30% 25% Mid-size 20% Large Percentage 15% 10% SUV 5% Pick-up 0% 1 2 3 4 SP Scenario

Figure 13: Vehicle type choice in SP1

5.2.3 Influence of Different Factors on Operation Costs

The influence of different trip attributes and cost factors on the commuting costs was tested by estimating a linear regression model. The explanatory variables used in the model include the monthly distance travelled from home to work, the fuel price, the consumption, the fixed cost per km of the chosen vehicle, and the average speed for the commuting trip of each individual. The dependent variable is the operational cost per km. The standardized regression coefficients are multiplied by the correlation coefficients of the independent variable with the dependent variable, and the summation of these values gives the total explained variance (Aigner, 1971). Table 11 lists the coefficients of the linear regression model.

Table 11: Estimation Results of Linear Regression Model for Operation Costs

Stand. Operational cost per km t-test Correlation Variance Explained Coeff.

Fixed cost per km 0.931458 133.02 0.864 80.48%

Total distance travelled per month -0.00574 -0.82 -0.5306 0.30%

Fuel price 0.171428 30.96 0.1936 3.32%

Average commuting speed -0.05969 -10.9 -0.1887 1.13%

Consumption of vehicle 0.043694 7.88 0.028 0.12%

Constant 5.35

R2 =0.92

As observed in the regression results, the greatest part of the operational commuting cost is explained by the fixed cost of the vehicle used. The fixed cost of a vehicle includes the monthly fees for registration and license, the insurance fee, and the cost of maintenance and depreciation (over a 5 year period). Next, but in a much smaller scale, the fuel price explains 3% of the total variance in the model. The influence of the consumption of the vehicle and total distance travelled per month are the least important. Finally, the model coefficients show that higher fuel price and higher fixed costs lead to higher operational cost per km, whereas longer commuting distances and faster commuting speeds lead to lower operational costs.

5.2.4 Transit Pass Ownership

Most of the respondents did not consider switching from private vehicle to transit for their commuting trip. This finding is in agreement with Erath and Axhausen’s study (2010), which found the same unwillingness of respondents to switch mode in the face of the new cost circumstances.

This finding is justified at a greater extent for the GTA considering the limited options of travel for cross-regional commuters. Cross-regional trips by transit are in many cases more expensive than using a private vehicle for the same trip, and add inconvenience and time for the trip maker. Therefore, the fact that only 17% of the respondents chose to commute by transit throughout the four SP1 scenarios, was a finding somewhat expected.

5.2.5 Relocating Decision

SP1 concluded each scenario by asking the respondent to choose between staying at the current home location or leaving. There were in total four scenarios, and the majority of the respondents (84.67%) decided to stay for all four scenarios, which means that they accepted the new higher cost in exchange of staying at their current house location. Figure 14 shows the percentage of respondents that chose to leave based on their current region of residence.

10%

8% Durham 6% Halton 4% Peel Percentage 2% Toronto York 0% 1 2 3 4 SP Scenario

Figure 14: Relocating percentage of GTA regions as a result of SP1 cost increases

The majority of all respondents, regardless where they live today, decided to leave after scenario two, when the gas price doubled. On the contrary, residents of York Region decided to leave at 57

scenario four. Figure 14 shows that there is not a consistent behaviour across the regions. In each scenario residents from different parts of the GTA decide to change homes. Durham residents abandoned their current home location during the first three scenarios. On the other hand, York residents kept a constant rate during the first three scenarios, but their majority changed homes in scenario four. Toronto, Halton and Peel residents had a more uniform distribution of voluntary residential relocation.

5.3 SP2 Descriptive Analysis: Residential Location Choice

In SP2 the participants identified the area where they would most likely move. All respondents had to fill this part, whether they voluntarily chose to move or not. Figure 15 illustrates the concentration of current home and work locations of cross-regional commuters in the GTA based on the data collected from the CHOICE survey. Home locations are uniformly distributed in the study area, with the highest concentrations at the borders of the City of Toronto with North York, and in Mississauga. Work locations, however, are concentrated closer to the city centre, with the highest density in the downtown core of Toronto.

Figure 15: Home and work location concentrations of CHOICE respondents

5.3.1 GTA Overview of SP2

In SP2 the respondents had to identify in what part of the GTA they would most likely move. The experimental design of SP2 included the fragmentation of the GTA in smaller geographical units; for the City of Toronto into 81 neighbourhoods; and for the surrounding regions into the 24 corresponding cities. This led to a total of 105 residential location alternatives. The exact screenshot of the experiment can be found in Appendix A.

The initial impression of the home relocation choice was not clear, because of the two-way exchange of population between the city and the surrounding regions. The following section presents region by region the home relocation choice of the participants.

5.3.1.1 Region of Durham

Figure 16 presents the current home location, the work location of all commuters/students, and the chosen planning district of the Durham households that participated in the survey. Currently, the majority of the participants (60.38%) are concentrated in the cities of Whitby and Ajax. The work location of individuals is illustrated by the black points, and is somewhat uniformly distributed across the regions of Durham, the southern part of North York, and the City of Toronto. The right-hand side of Figure 16 shows the location chosen in SP2, where it becomes obvious that the distribution disperses in a wider area. The cities of Whitby, Ajax and Clarington each received approximately 15% of the households that relocated, followed by the East End of Toronto (PD 15) that received 9%, and downtown Toronto with 7%.

Figure 16: Home location, work location and SP2 home location choice of Durham residents

5.3.1.2 Region of Peel

Participating households from the Region of Peel are primarily located in Mississauga (60.37%). Mississauga is a strong employment base with more than 417,000 jobs and is expected to grow by more than 15% by 2031, (Mississauga, 2014). Most of the residents of the Region of Peel work in Mississauga, Brampton, downtown Toronto or in the adjacent Halton Region.

The SP2 (right-hand side of Figure 17) showed that the relocation choice is evenly distributed across different cities and planning districts. While the City of Mississauga is still the most selected choice (28.57%), the City of Brampton, the City of Caledon and downtown Toronto are the next most popular locations. Halton Region, North York and some parts of Etobicoke attract

the remaining households, except for a few households that decided to move to Ajax and Oshawa.

Figure 17: Home location, work location and SP2 home location choice of Peel residents

5.3.1.3 York Region

Participating households from York Region are concentrated in the City of Markham (34.91%), and in the City of Vaughan (25.47%). The work locations of York Region commuters are gathered in the south part of York Region and along the corridor that leads to downtown Toronto (Planning Districts 11, 4 and 1). In SP2, most households relocated to the cities of Vaughan, Markham, and Richmond Hill, followed by the town of Newmarket and the three planning districts in the City of Toronto with the highest rate of work trips.

Figure 18: Home location, work location and SP2 home location choice of York residents

5.3.1.4 Halton Region

Compared to the so far presented regions, Halton residents appear to have the greatest difference between the before and after spatial distribution of home locations. The left-hand side of Figure 19 shows the home location density of the Halton households that were interviewed. 34% of the participants live in the town of Oakville. However, looking at the black points of Figure 19 it becomes apparent that Halton Region has a very large number of cross-regional commuters. The majority of work trips are destined to Mississauga, Brampton, and downtown Toronto. It is therefore easily justifiable that the SP home location choice (in the right-hand side of Figure 19) covers a wider area and expands to cities and regions farther than those immediately surrounding Halton Region. One quarter of all Halton households selected the town of Milton as an alternate home location, and approximately 16% chose the town of Oakville. Next most popular areas 63

include the City of Mississauga, the City of Burlington, the Town of Halton Hills, and downtown Toronto.

Figure 19: Home location, work location and SP2 home location choice of Halton residents

5.3.1.5 City of Toronto

The City of Toronto is divided into 6 districts: Scarborough, Etobicoke, North York, York, East York and downtown Toronto. The participating households from the City of Toronto were required to either have a cross- regional commuter or a member that would travel across districts to reach the work location. Approximately 15% of the participants currently live in the Planning Districts 11 and 8, which belong to the districts of North York and Etobicoke, respectively.

Since there are numerous business centres in the City of Toronto, work trips are directed to various areas. As presented in Figure 20 most of the trips are concentrated in the downtown core, but also in Scarborough, Etobicoke, and the central part of North York. In SP2 households spread across the GTA moving closer to the black points, which represent their work locations.

Figure 20: Home location, work location and SP2 home location choice of City of Toronto residents 65

5.3.2 Conclusion of Residential Location Choice Descriptive Analysis

Section 5.3.1.1 to 0 presented the home relocation choice of households from each GTA region. Most results of the SP2 showed that the residential location concentrations were redistributed in a more even pattern across regions, cities and planning districts. In general, households preferred relocating within the boundaries of their current region. Additionally, a pattern of “suburban” relocation choice seemed to dominate currently suburban residents. For example, in the case of Halton Region (Figure 19), households chose cities located in the south part of York Region, or in the Region of Peel, but avoided the City of Toronto itself, even though most work trips are destined there. This pattern is an indication that households prioritize reasons other than the commuting distance when selecting a specific location. The detailed distributions for the current and the chosen in the SP residential location can be found in Appendix B.

5.4 Dwelling Type Choice

In the last part of SP2 the respondent had to select the desired housing unit among six options based on the dwelling type, the preferred tenure and the price of the available options. The dwelling units presented to each respondent corresponded to the number of bedrooms that each household needed (as stated in part one of the survey) in the neighbourhood of their preference. Most households preferred to buy two-bedroom detached houses, followed by three and four- bedrooms units. Only 16.4% of respondents chose to rent a unit, and approximately half of the respondents chose townhouses or apartments.

When comparing these results with their current tenure and dwelling type (from Section 5.1.1), we identify a trend towards downgrading the type of the unit, but not a pattern of switching tenure (owning/ renting). Based on the RP results, 66.34% of the participating households live in detached houses. In SP2, 50.90% chose this type of unit, and the remaining respondents are distributed evenly between townhouses and apartments. Respondents preferred owning their residence; 83.60% chose to buy a unit in SP2, compared to 82.32% that actually owns the unit.

Based on the respondents’ choices, the average rent they are willing to pay is $1,702 per month and $586,222 to purchase a unit. Table 12 shows the statistics of the housing price based on the answers of the CHOICE survey.

Table 12: Statistics of Housing Price in SP2

Mean St.dev Min Max Number of bedrooms 2.6 0.97 1.00 5.00 Monthly housing cost for rent 1,702.98 627.57 775 4,200 Purchase price 586,222.20 410,743.73 106.950 3,975,000.00

6 Modelling Mobility Tool Ownership and Residential Relocation

The first part of this chapter is intended to capture the mobility tool ownership choices of individuals with respect to increasing commuting costs. For the analysis of this part only data from SP1 were used and only the response of the interviewee was considered. A multinomial logit model (MNL) was employed to identify the intention of respondents to downgrade vehicle, change mode, or do nothing in each cost scenario. The second part of this chapter presents a binary model based on household data for their decision to stay or leave during the stages of SP1. The models of this chapter were constructed with the software STATA version 10.

6.1 Mobility Tool Ownership Model

6.1.1 Data Preparation

The answers of each respondent for the SP1 scenarios two, three and four were compared to the choice selected in scenario one, and the cost difference was used in the model. Scenario one reflects today’s cost situation. In subsequent scenarios respondents had to choose their mobility tools again. Respondents who chose a smaller body type or changed from gasoline to alternative fuel vehicles downgraded their mobility tool to cut back costs. Those who chose a public transit pass instead of their private vehicle accepted the inconvenience and the additional travel time. Finally, those who kept the same mobility tool as in scenario one belong to the “do-nothing” category and are characterized by apathy and a bigger tolerance to increasing costs. Respondents who chose a bigger body type or switched to a less efficient car compared to scenario one were removed from the dataset. In total 774 observations were used for the model.

6.1.2 General Model Specification

Multinomial logit models have been broadly used to describe different choice situations in transportation and are derived from the fundamental random utility maximization (RUM) theory:

= (; ) + (5)

Where:

: is the utility that individual i obtains from alternative m

: is the systematic component of the utility

: is the vector of explanatory variables, including attributes of individual i and alternative m

β : is the vector of parameters and,

: is the random component of the utility

The current model considers three alternatives: (1) Do nothing/keep same mobility tools as in scenario one (reference), (2) downgrade mobility tool (choose smaller vehicle or more efficient technology), (3) switch mode (change to public transit).

The distribution of the random component of the utility is assumed to follow the independently and identically distributed extreme value type 1 property (IID). This assumption leads to the choice probability

= (6) ∑ ∈

Where:

: is the probability that individual i selects alternative m

: is the utility that individual i obtains from alternative m (i=1, …, I, m=1,…,N)

: is the choice set of alternatives N for individual i

6.1.3 Empirical Analysis

As discussed in the previous section an MNL was employed to investigate the reaction of respondents to increasing commuting costs. Many alternative structures were tested leading to the final formulation that is presented in Table 13. The model estimates the probability that an individual will choose any of the three alternative reactions (do nothing, downgrade, switch mode) in response to double, triple and quadruple commuting costs.

Table 13: Estimation Results of MNL for Mobility Choice in SP1

Variables Coef. Std. Error t-stats Alternative specific constant Downgrading vehicle -5.3078 0.7929 -6.69** Changing mode -5.11966 0.9859 -5.19** Total Cost Difference Downgrading vehicle 0.0150 0.0017 8.76** Changing mode 0.0177 0.0018 9.84** Job in Region of Durham Downgrading vehicle -3.872 1.4700 -2.63** Changing mode -5.4506 1.7418 -3.13** Household Income Downgrading vehicle 5.76E-06 2.92E-06 1.97* Changing mode -4.88E-06 5.82E-06 -0.84 Household without children Downgrading vehicle -0.8247 0.2439 -3.38** Changing mode -0.2672 0.3988 -0.67 More than one commuter Downgrading vehicle -0.986 0.3266 -3.02** Changing mode -0.92044 0.51936 -1.81 Consumption of vehicle chosen in scenario 1 Downgrading vehicle 45.1516 9.7087 4.65** Changing mode 30.2836 11.93215 2.54* Number of Observations 756 Log. Likelihood -343.84713 LR chi2(12) 313.59 Prob>chi2 0.00 Pseudo R2 0.3132 ** Significant at the 0.01 level * Significant at the 0.05 level

Most of the variables included in the model are significant at the 0.05 level (or 0.01). Most importantly the cost difference, or cost savings are perceived to have a positive influence for the choice of the participant. Higher commuting expenses lead the participants to either change vehicle or change mode to achieve a more inexpensive way of travel. Downgrading their vehicle means that they chose a smaller body type or a more efficient engine technology. Members who belong to households with higher income are more likely to change their vehicle instead of switching to public transit. Considering the inconvenience of cross-regional trips this is not a surprising finding. High-income households, in particular, are expected to have limited tolerance to inconvenience and delays.

Participants who belong to households with more than one commuter are less likely to modify their current vehicle compared to households with only one commuter. This is reasonable due to the more demanding schedule and needs of households with multiple commuters. Furthermore, individuals who currently own a vehicle with a high consumption rate are more likely to downgrade their vehicle in consequent scenarios compared to individuals who already drive efficient cars. The probability to change to transit is also higher for those who drive cars with high consumption.

Finally, the model includes a dummy variable to represent individuals who work in the Region of Durham. It appears that these individuals are less likely to downgrade their vehicles or change to transit compared to individuals from the other four regions. This may be explained in two ways: either the individuals who work in Durham Region belong to the income brackets that have the luxury to accept higher costs; or the low number of respondents who work in Durham Region combined with the poor transit service of the region creates an unclear pattern and makes these individuals appear more tolerant to cost increases.

Overall the model performed well with a McFadden R of 0.32. Unlike linear regression models, logistic regression does not have the exact equivalent of R, since it is based on a different estimation methodology. The McFadden R gives the relationship between the Log Likelihood of the estimated model and the Log Likelihood of the model with no parameters. According to Louviere et al. (2000) McFadden Rvalues of 0.2 to 0.4 are considered to be really good model fits.

6.2 Trade-offs Between Mobility Costs and Residential Location

This section presents a binary logit model that estimates the probability of leaving the current home location as a reaction to the increasing commuting costs. As described in previous sections, in SP1 the head of the household had to determine the mobility tools for the household. Then, the interviewee was asked to indicate whether they would stay at the current home location or not given the total commuting cost of each scenario.

6.2.1 Data Preparation

For this model all household commutes were aggregated to generate household level costs based on the choices they made in the different scenarios of SP1. All completed scenarios are considered separate records in the analysis, but with the same household characteristics (sociodemographic). In total 1382 records were used to estimate the binary model presented in the following section.

6.2.2 General Model Specification

In binary logistic models the prediction is measured as the possibility of an event occurring compared to the possibility that this event will never occur. The dependent variable refers to the logarithm of the odds ratio, and the mathematical expression can be written as a generalized linear model:

ln = + , + , + ⋯ + , (7)

In terms of odds the model can be written as:

= (,,⋯,) , (8)

or in terms of the probability of the outcome:

(,,⋯,) = (9) (,,,⋯,)

In the binary model of the following section we are calculating the probability of leaving the current home location due to the increased transportation costs. 72

6.2.3 Empirical Analysis

The parameter estimates of the binary model are presented in Table 14. Many alternative model formulations were tested and the one presented here provided the best fit and included variables whose coefficients delivered the expected signs and magnitude. The estimates presented in the table describe the choice to leave the current home location due to the imposed cost increases. It can be noticed that increasing car costs lead to higher probability of leaving the current home location. Public transit costs were not found to be significant. This is not surprising as public transit was not widely chosen as an alternative, and there is therefore not enough sample size to draw a conclusion.

As the scenarios proceed and the fuel prices rise, there is higher probability that households will decide to relocate. Furthermore, it seems that single parents and households consisting of a couple (two adults without children) perceive relocation the most negative among all other household types, and are less likely to leave their current home location. Households with only one commuter seem to be more flexible to relocate. This result indicates that for households with only one commuter the decision is simpler. On the contrary, households with more than one commuter have to consider more than one work destinations and a “middle ground” solution has to be found.

Finally, the model included the area (square footage) of the current house as an independent variable. This variable captures various other household characteristics, because the size of the household can be used as an indicator for: the housing expenses, the needs of the household members, the income of the family, and the location and type of the house (urban/suburban, apartment/ detached). While other combinations of variables were tested during the model formulation, the square footage of the house appeared to have the greatest significance in the model estimation. Households that live in bigger houses are less likely to change home location despite the increases of commuting expenses.

Table 14: Estimation Results of Binary Model for Leaving Current Home Location in SP1

Coef. Std. Error t-stats Variables Alternative specific constant Leave -3.7363 0.3812 -9.80**

Car Costs 0.0008 0.0003 2.71** Fuel Price 0.4684 0.0767 6.10** Couple without children -1.6296 0.3984 -4.09** Single Parent -1.1054 0.3621 -3.05** Household with one commuter 1.1677 0.3417 3.42** Area of current house -0.0004 0.0001 -2.69** Number of Observations 1382 Log. Likelihood -343.4036 LR chi2(12) 85.86 Prob>chi2 0.00 Pseudo R2 0.1111 ** Significant at the 0.01 level *Significant at the 0.05 level

In general, the model performed adequately with a McFadden R of 0.11. This value, while significantly lower than the one for the mobility tool model presented in Section 6.1, is satisfying, due to the multidimensional nature of this choice and the multilevel components that were involved in this experiment. Making the assumption that commuting costs play the only role in choosing house location would be a misguided simplification that does not comply with the findings of many researchers (see Section 2.3). However, this experiment showed that members’ commuting expenses when added up at the household level have a significant impact on a household’s decision to change residential location. The relocation choice is examined in the following chapter.

6.2.4 Elasticities

Based on the parameter estimates of the model presented in Section 6.2.3, the direct elasticity of the household transportation cost can be calculated. Direct elasticity measures the percentage

change in the probability of choosing an alternative of the choice set with respect to the percentage change in an attribute of this alternative.

Equation 10 calculates the point elasticity for the binary model:

= ∗ (10)

Where:

: is the elasticity of choosing alternative i with respect to a change in variable k

: is the probability of choosing alternative i for person q

: is the value of variable k of alternative i for person q

For linear formulations the utility term takes the form of Equation 11 for each individual:

= (1 − ) (11)

Usually, in order to acquire the aggregate elasticity Equation 11 is calculated for the sample average and (average estimated probability of choice of alternative). However, this is not generally correct because logit models are non-linear, and, therefore, the estimated logit function might not pass through the points defined by the sample averages. This mistake typically causes errors in estimating the change in choice probabilities with respect to changes in a variable (Louviere et al., 2000).

A better approach is to calculate Equation 11 for each individual q in the sample and then weigh each individual elasticity by the individual’s probability of choice. This technique is shown in Equation 12 and is called “the method of sample enumeration”. This method was used to calculate the direct weighted elasticities (Table 15 and Figure 21).

∑ = (12) ∑

: is an estimated choice probability

: is the aggregated probability of choice of alternative i

Table 15 shows the average direct elasticity of the sample with respect to increasing transportation costs. The point elasticity for each household record was calculated for each gas price scenario of the SP and then aggregated.

Table 15: Residence Relocation Choice Elasticity with respect to Increasing Transportation Costs (derived from the results of the binary model in Section 6.2.3) Transport Cost Elasticity Weighted Elasticity Probability Increase Factor 1 0.4082 0.4534 0.0315 2 0.5093 0.5605 0.0621 3 0.5420 0.6135 0.1012 4 0.5561 0.6082 0.1721

0.7

0.6 cation cation 0.5

0.4

0.3

choice probability choice 0.2 Elasticity 0.1 Weighted Elasticity Elasticity of residence reloresidence of Elasticity

0.0 1 2 3 4 Transport cost increase factor

Figure 21: Residence relocation choice elasticity and weighted elasticity with respect to increasing transportation costs

The elasticity of residential relocation choice was calculated for each of the cost increases imposed by the four scenarios in SP1. Table 15 shows that the weighted elasticity ranges between 0.45 and 0.61, while the average elasticity takes values 0.40 to 0.56. The elasticity sign is positive (correctly) showing that as transport costs increase, more households will choose to relocate to reduce the overall living costs. Similarly, the elasticity value increases with increasing transport costs, indicating that people become more sensitive to higher increases.

Elasticity of 0.45 means that in the case of 1% increase in transport cost at the current level, the probability of choosing to move to a new residence location will increase by 0.45%. For a transport cost that is twice higher compared to the current level the elasticity figure is 0.56, meaning that 1% increase in transport cost at that level will change the relocation choice probability by 0.56%

7 Modelling Dwelling Type Choice

This chapter is indented to investigate the complex procedure of residential location choice after completing SP1. This chapter presents a model for the housing unit choice of the participating households.

7.1 Dwelling Type Model

The last step of SP2 was to select among different dwelling type units in the indicated neighbourhood. This part captured the trade-offs that households would be willing to make in case of deciding to relocate. A multinomial logit model (MNL) was employed to capture the probability of choosing between different units and different tenures. A total of six alternatives were provided to each participant based on the necessary number of bedrooms in the selected neighbourhood:  House for sale  House for rent  Townhouse for sale  Townhouse for rent  Apartment for sale, and  Apartment for rent. The model was estimated with the software Biogeme version 2.3 and for its estimation all responses of SP2 were used. After the removal of erroneous entries 483 records were used.

7.1.1 Non Linear Interactions

According to the economic theory, people with lower income should be more sensitive in changes of costs. For this reason, the model includes non-linear interactions that describe the relationship between income and cost perception. The majority of modelling analyses allow for some interactions between the estimated parameters and socio-demographic attributes, but they usually achieve that by using separate parameter coefficients in the same model. However, previous researchers (Hess et al., 2008); (Erath & Axhausen, 2010); (Mackie et al., 2003) have used in their analysis a formulation to account for the continuous interactions between multiple variables. This interaction term is used in this context because of the averaging errors that occur

when grouping different income classes or when using the mean value of an income-class. The CHOICE survey collected income information for each individual at ranges of 25K. The mean of each range was used as a unique value for each individual, and thereafter, all individual incomes were summed to form the household income.

The Equation bellow was used to determine these effects:

, f(x, y) = β ∗ ∗ x (13)

Where:

β: is the parameter estimate of variable x x: is the independent variable, such as housing price y: is the observed value for a socio-demographic variable, such as income : is a reference value for this variable, most frequently the mean of the sample.

, : gives the elasticity of the utility based on the value of variable x with respect in changes in y

This formulation describes the sensitivity of a respondent to an attribute change with y. As Erath and Axhausen (2010) discuss, can take any value and has no effect on the model fit or the

estimate for ,. In our formulation we use the mean value; in this way we ensure that the

parameter estimates explain the sensitivity of x to the average of y in the sample. A negative value of λ indicates that individuals with higher income are less sensitive to cost. For households with income 80% higher than the average income, the fraction is 1.8. Assuming a λ parameter of -1, the sensitivity term is 0.55, which means that households with income 80% above the average are 45% times less cost sensitive than the average household.

7.1.2 Utility Functions

An MNL was used to identify what tenure and what dwelling type was more likely to be chosen by the participating households. The model formulation of MNLs can be found in Section 0. The final housing model considers the following elements:

 Perception of housing costs (rent cost/purchase price) 79

 Influence of income on cost perception

 Preference of dwelling type/ tenure depending on today’s dwelling type/tenure

The utility function of the suggested model takes the following form:

= + ∗ ∗ + ∗ + .

. ∗ + . ∗ + . ∗ +

. ∗ (14)

Where

: is the systematic utility of alternative i,

i: is the alternative (house for sale, townhouse for sale, apartment for sale, house for rent, townhouse for rent, apartment for rent)

j: is household j

Income j : is the annual income of household j

: is the average annual income in the sample

House cost i,j: is the monthly housing cost

: is 1, if the household’s actual housing unit is owned, else 0

: is 1, if the household’s actual housing unit is rented, else 0

: is 1, if the household’s actual housing unit is a single detached, else 0

: is 1, if the household’s actual housing unit is a townhouse, else 0

: is 1, if the household’s actual housing unit is an apartment, else 0

7.1.3 Results

As noted above an MNL was developed to investigate the dwelling type choice of the participants in the study area. The presented model estimates the probability that a GTA household will choose any given combination of housing type and tenure from the set of feasible

alternatives. After testing for alternative model structures the final model was developed and the model estimates are presented in Table 16 .

Table 16: Estimation Results of MNL for Dwelling Type Choice in SP2

Coef. Std. Error t-stats Variables Alternative specific constant House for sale 0.00 ------House for rent 0.595 0.182 3.26** Townhouse for sale -0.281 0.249 -1.13 Townhouse for rent -5.79 0.149 -38.86** Apartment for sale -0.904 0.265 -3.42** Apartment for rent 0.895 0.203 4.41**

Housing cost (rent/purchase price) -3.09e-007 1.49e-007 -2.07* Actual unit, house 0.639 0.257 2.48** Actual unit, townhouse 0.543 0.307 1.77 Actual unit, apartment 1.12 0.285 3.95** Actual tenure, own 3.14 0.188 16.73** Actual tenure, rent -1.15 0.225 -5.14**

λ own -0.313 0.335 -0.93 λ rent -0.00328 0.445 -0.01 Number of Observations 483 Log. Likelihood -619.417 Adjusted rho-square 0.269 ** Significant at the 0.01 level * Significant at the 0.05 level

As shown in Table 16 the final model was determined based on the inclusion of variables with the proper signs and statistical significance. Most of the variables in the model are significant at the 0.01 or 0.05 level. Only the perception for cost failed to derive a significant value for the 0.05 level; it had however the right parameter sign. The negative λ value indicates that individuals with higher income perceive costs as less onerous.

That factor aside, we observe that the cost parameter, either for renting or purchasing a unit, is negative and statistically significant. This indicates that costs have a negative influence on the utility of the decision maker and therefore decrease the probability of the respective alternative being chosen. 81

In addition, the model included dummy variables for the actual tenure and dwelling type of the respondents. A value of 1 indicated that respondents chose the type of house or tenure that they have today, and in other words are unwilling to change. Among the three dwelling types, those who live in apartments have the highest probability to re-select an apartment, followed by those who live in houses and last by those who live in townhouses.

Similarly, people who own their current dwelling type are more likely to choose to buy a unit in SP2. Surprisingly, people who are currently renting their units are less likely to rent again, which indicates that switching from renting to owning might be in their future plans.

Overall the model performed well with an adjusted R value of 0.27. As noted in previous sections, an R value of 0.2 to 0.4 is considered a very good fit for logistic regression models.

8 Conclusions and Future Work 8.1 Summary

This thesis presented some of the possible reactions of GTA households in response to extreme increases in transportation costs. The investigations concerned the medium and long-term decisions, which are reflected through the mobility tool ownership – vehicle fleet and transit passes – and the housing location and housing choices of GTA households. Most importantly, this study intended to identify the tipping point (trade off) between mobility costs for commuting purposes and housing costs.

The first findings of the analysis showed that the operational commuting costs mainly depend on the fixed costs of the vehicle chosen. This finding indicates that fuel price variations explain only 3% of the total costs for maintaining and operating household vehicles.

The analysis showed that for increasing commuting costs, people would adjust their mobility tools, mainly by changing the engine type and switching to more efficient technologies, such as hybrid and electric vehicles. However, it appeared that the majority of respondents were not willing to downgrade the size/type of their vehicle. This finding demonstrates strong inertia effects. Insisting on keeping the same vehicle might have to do with the performance for the specific car, and with the habit or pleasure that the driver gets from the vehicle. These factors seem to be more important compared to the costs that are associated with this vehicle.

The estimates of the developed MNL for the possible mobility tool reactions showed that individuals who belong to households with more than one commuter or households with children, are less likely to change their mode choice or type of vehicle despite the cost increases. Furthermore, respondents seemed overall unwilling to switch to transit for their commuting trip. Once again this finding is not surprising if we take into consideration the target population of the survey. Cross-regional trips by transit are not considered an inexpensive alternative. In many cases the transit fare is of the same magnitude as driving the same distance by car. The expensive fare, when combined with the additional travel time and inconvenience, makes transit a very unattractive solution.

The design of the survey allowed the respondents to voluntarily change home location, move closer to their work and reduce the commuting expenses of the household. The majority of the respondents insisted on staying at the current home location throughout the four cost scenarios. The weighted point elasticities for choosing to relocate with respect to increasing commuting costs range between 0.45 and 0.61. The positive sign of the result demonstrates that for increases in transport costs more households will choose to relocate to reduce the overall living costs. For a 1% increase of transport costs at the current gas price level ($1.25/litre), the probability of choosing to move to a new residence will increase by approximately 0.40%. Similarly, for a 1% increase in transport costs, given a hypothetical quadruple gas price value, the probability of households to relocate will increase by 0.61%. This finding shows that transport costs play some role in the home relocation decision; however this relationship has to be further investigated to arrive to more robust conclusions.

The binary model estimation showed that the household composition (single parent, no children, number of commuters) have some significance in the choice of a household to leave or stay. Commuting costs by car were significant, unlike public transit costs that appear to have no input in a household’s decision to relocate.

At the end of the experiment all participants had to indicate their preferred neighbourhood location. The analysis showed that while most participants moved from their current city, municipality or planning district, they preferred to stay within the boundaries of their region. In other cases it became apparent that households from the surrounding regions would much rather move to another region than move in the City of Toronto.

Finally, the dwelling type preferences of households showed that most of the participants insisted on staying at a dwelling type similar to what they have today. On the contrary most participants chose to purchase a house, regardless of being renters or owners today.

Overall, this study provided the first finding of GTA households with cross-regional commuters regarding their mobility tool ownership and housing type and location trade-offs. In order to have a more representative and in depth analysis some recommendations are provided in the following section.

8.2 Recommendations for Improving the CHOICE Survey

The CHOICE survey was conducted during March/April 2014 among a randomly selected sample in the GTA. The CHOICE survey provided rich information about GTA household choices. However, if it were to be implemented again, some minor improvements could lead to having a more complete picture of the households and their expenses. The points that follow in this section were identified during the data analysis, and could prove to be valuable additions to the already collected information.

In part one of the survey, (Section 4.1.1), there should be additional questions about housing costs. The current version asks for rent or mortgage payment, but during the data collection it became apparent that many households have paid off their loans, and seemingly have zero housing costs. In reality there are still maintenance costs, condo fees, utilities or taxes that were not asked.

In the second part of the survey (Section 4.1.2), we collected information about the transit pass ownership of each individual. However, there were no questions asked about single ride tickets. Apparently, many commuters use multiple modes during a week, and, therefore, choose to buy single ride tickets instead of a transit pass. In a future data collection, an additional question about the single ride fare of those who selected transit as one of their weekly commuting modes should be asked.

Finally, it proved to be challenging to connect the current commuting choices and preferences of individuals with the options selected in SP1. The problem was that the vehicle information was collected at the household level (how many cars, what type etc.), but during SP1 the vehicle/ transit pass was added for each respondent. It was, therefore, difficult to identify if the individual downgraded vehicle, changed mode etc. This is the reason why in the model presented in Section 6.1 the scenario one of the SP1 was used as the reference scenario and not the RP information. This is a part of the CHOICE survey that should be addressed in a different way if similar analysis is to be done.

8.3 Future Work

This thesis covered the first analysis of the data collected by the CHOICE survey. The survey provided a rich data source with information about retrospective, current and future preferences of GTA households for residential location, housing, and mobility tools in the face of higher transportation costs. This section offers some suggestions of future work that could be done with this dataset to build on top of the analysis presented in this thesis.

The topic that should be investigated in more detail is the residential location choice of households. More behavioural factors should be taken into consideration, as proved by the results of Section 6.2. Commuting expenses appear to play a small part of the residential location/housing expenditure decisions. The CHOICE survey provided information about the home location and dwelling type choice of participants in the past, and this data could be used to estimate some behavioural factors that guided their decision in the SP.

Furthermore, data from SP2 could be used to estimate a spatial location model for the GTA. The neighbourhoods or cities that each respondent viewed will create the alternative choice-set for each respondent and the qualitative factors combined with the housing prices could be used in a nested logit structure to account for all aspect of the residential location choice. Alternatively, a random choice-set could be created to make up for respondents who did not review enough locations before deciding the final one.

Finally, for a complete picture of this issue, short-term reactions should be investigated as well. Additional transportation options need to be investigated, such as change of departure time, more mode options (e.g. car-pooling, car sharing, park & ride, etc.). These should be included in the SP to identify how trade-offs occur at each stage of the decision-making.

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Appendix A: Survey Software

This appendix contains screenshots of the all parts of the survey. Each respondent was given a unique ID as seen on the top of Figure 22. With this ID the participant was able to save the responses and continue the rest of the survey later without losing any of the thus far answered questions.

Figure 22: Welcome screen

Figure 23 shows a screenshot of the help tool that was incorporated in the software and that allowed the respondents to pinpoint or search an address. Based on the point that each respondent indicated, the program matched the postal code to the respective TAZ and this information was used in SP1 for the travel time and cost calculations. When a respondent placed the marker on a location there was a chance that this specific point would have an incomplete postal code (as shown in Figure 23). In that case the closest full postal code would be used for the matching process.

Figure 23: Help map for home and work location

Figure 24: Part 1- Retrospective house information

Figure 24 shows an example of a questionnaire for someone who owns the current house and rented the previous house. It can be seen that the questions change dynamically based on someone’s answers.

Figure 25 shows the second section of the RP part which collected sociodemographic information of the household and detailed information about the working/studying adult members of the household. Respondents could give the name or the initial of the household members in order to facilitate following questions. Google maps were also embedded as shown in Figure 23.

Figure 25: Part 2- Household sociodemographic information and typical commuting trip

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Figure 26: Part 3- Retrospective vehicle ownership

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Figure 27: Help tab for choosing vehicle category

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Figure 28: SP1- Mobility tool ownership

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SP1, illustrated in Figure 28, started by showing a video with instruction on how to complete the following two parts of the survey. For each adult commuter of the household, as indicated in Part 2, there was a “column” with mode alternatives. In this example there are only two commuters, the interviewee and the husband. The program uses the home location from Part 1 and the work locations from part 2 and calculates the time by car and transit as shown in the Data box of Figure 28. For each member the respondent can add a car by combining the type (compact, midsize, large, SUV, pickup, or no car) and the fuel technology (gas, hybrid, electric), or add a transit pass. In the monthly cost box the breakdown of costs is shown, including the parking costs at work from Part 2.

Based on calculation of fuel consumption and trip distance the software shows the individual commuting cost per month as well as the total cost for the household. The respondents can modify the combinations until they reach a desired amount, and then they have to choose to either stay at the current home or change home location. If they decide to stay at the current home location they proceed to the next scenario. Otherwise, they continue to SP2 (shown in Figure 29).

Figure 29 shows the last part of the survey. The respondent starts by selecting the region of preference (Figure 29). After selecting a region the map zooms in the respective divisions (Figure 30), districts (Figure 31), and finally neighbourhoods (Figure 32). Then the respondent has to choose a neighbourhood and can review the qualitative information of the neighbourhood (walk score, crime, school rating) and the prices of six dwelling units. If the options are not satisfying the respondent can return to the main map and continue searching.

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Figure 29: SP2- Home location choice- Screenshot of main map

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Figure 30: SP2- Screenshot of the divisions in the selected region

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Figure 31: SP2- Screenshot of the districts in the selected division

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Figure 32: SP2- Screenshot of the neighborhoods in the selected district

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Appendix B: Statistics of Current Home Location and Location Chosen in SP2 by Region

Table 17: Spatial Location Statistics of York Region Households

York Region