Final Project: The University Housing Affordability in Four Canadian Cities

This project will analyses the housing affordability for university students in four Canadian cities, Vancouver, Calgary, Toronto, and Montreal. For each city, one university within the jurisdiction thereof will be selected. The assessment of housing affordability will be based on two factors, the surrounding CTs of each university, the median income thereof, and the median monthly rental cost thereof.

Abstract

It has been established that housing affordability around universities is a crucial factor for students in determining which university to attend. What percentage of their income spend on (rent) housing is of foremost concern. This project will examine the housing affordability for university students in Vancouver, Calgary, Toronto and Montreal and determine the most affordable university (in terms of housing), among the University of British Columbia, University of Calgary, University of Toronto, and McGill University. Four housing affordability maps were created to visualize the rent to income ratio in residential areas with access to public transit in each of the four cities. Data on CT boundary, median yearly household income and median monthly rent were collected from Statistic Canada, and data on zoning district boundary and public transit station were gathered from the municipal agency of each city and Geofabrik (aka. Open Street Map). The way which we define the housing affordability index suggests that the lower the housing affordability index is, the higher the housing affordability. For the four maps in this report, interpretations are drawn and discussions are made regarding the housing affordability around universities in these four cities of interest. From our analysis, we discover that the University of Calgary is the most affordable university to attend and the University of British Columbia is the most unaffordable university to attend in terms of housing affordability.

Description of Project, Study Area, and Data

For local families which wish to send their children to attend a university, they must at least be able to afford to pay for the dwellings with public transit accesses to the university (UBC Student Housing Demand Study Final Report, 2009). The problem of housing affordability emerges around universities due to the large demand from students who wish to live in an accessible distance to public transportation (UBC Student Housing Demand Study Final Report, 2009). Since the cost of attending a university is greatly influenced by the affordability of housing (UBC Student Housing Demand Study Final Report, 2009), it is necessary and important to examine the housing affordability around universities in order to determine which is the most affordable Canadian university to attend.

In this project, we will be analyzing the housing affordability for university students in four different Canadian cities, Vancouver, Calgary, Toronto, and Montreal. In particular, we will conduct an analysis for the housing affordability for the students of the University of British Columbia, University of Calgary, University of Toronto, and McGill University. This analysis contains mainly two elements, the residential areas in these four cities that are accessible to public transportation and the housing affordability thereof. These two elements together will be referred to as Housing Affordability (This term will be defined later in detail).

In terms of our data, we will be using the 2016 Canadian Census data in this project because the ‘long form’ in Census data included socioeconomic data such as income, dwelling type, and value. Specifically, we will be using spatial data on Canada CT boundary and tabular data on median yearly household income and median monthly rent within the CT boundaries of Vancouver, Calgary, Toronto and Montreal. In addition, we will also be using data on land-use and public transit stations in each of the cities.

In sum, in this study, we will analyze the housing affordability for Vancouver, Calgary, Toronto, and Montreal. Based on that, we will determine the most affordable university (in terms of housing), among the University of British Columbia, University of Calgary, University of Toronto, and McGill University.

The methodology of Analysis

a. Acquiring Data

In our analysis, we acquired the following data. First, we collected the CT boundary data from Statistics Canada. Then, we downloaded the median yearly household income and median monthly rent tabular data collected by Statistics Canada during the 2016 Census, via CHASS, which is run by the University of Toronto. Next, we acquired zoning district boundary data of Vancouver, Calgary, and Toronto from the municipal agency of each city. Then, we collected zoning district boundary data of Montreal from Open Street Map, a website published by Geofabrik. Next, we acquired public transit station data of Vancouver, Calgary, and Toronto from Open Street Map. Lastly, we collected public transit station data of Montreal from Ville de Montréal agency.

b. Parse Filter

After we acquired all the data, we parsed and filtered our data using selection, export, and clip.

For the CT boundary data, we selected the CT cells that are within the jurisdiction of Vancouver, Calgary, Toronto, and Montreal. Then, we exported the CT layer for each city as a new layer. Next, we joined the tabular data of monthly rent (rent) and yearly income (income) to the exported CT layers with respect to CTUID. For the zoning district boundary data of those four cities, we selected the residential zones and exported them as new layers. For the projection of the layers, we changed the projection of each layer respective to the UTM Zone of each city. For the public transit station data of those four cities, we clipped these layers to the zoning district boundary layers.

c. Mine

We performed the following analysis with the filtered data. First, we created a new field called RentToIncome in the attribute table for each exported CT boundary layer by using the field calculator. In field calculator, we calculated the rent to income ratio by first dividing the yearly income into monthly income. Then, we divided the monthly rent by the monthly income. Second, we performed three proximity analysis. We created a 1-kilometre buffer to each transit station on the clipped transit station layer. Then, we dissolved the buffers to obtain layers for areas within 1-kilometre proximity to a public transit station. We also clipped the residential areas to areas within 1-kilometre proximity to a transit station. In addition, we clipped the layers of CT boundary with rent to income ratio to layers of residential areas with access to public transit. Layers of areas of interest for those four cities are obtained.

Lastly, we created vector points representing universities. In order to do so, we found the closest transit stations to these four universities and select them. Then, export as a new layer. Then, we used the exported vector point as the location of the universities.

d. Represent

In order to represent our data for the purpose of comparison, we symbolized the layers of areas of interest using the rent to income ratio layer of residential areas with access to public transit by using the manual breaks. The manual break contains five classes and the breaking values are 0.15, 0.20, 0.25, 0.30, and 1.0. We also symbolized the original CT boundary layers into grey colour and used them as based layers.

e. Table of dataset

Table 1: Original Dataset of Vancouver

Table 2: Original Dataset of Calgary

Table 3: Original Dataset of Toronto

Table 4: Original Dataset of Montreal

Discussion and Results

1.Discussion about Key Terms

Firstly, we have three key terms for our analysis:

#1 Housing Affordability Index

In our case, the housing affordability index is defined as the ratio of monthly rental cost to monthly household income. The lower housing affordability index is, the higher the housing affordability. Housing Affordability Index is calculated based on CT cells.

The housing of an area is considered as unaffordable if the ratio is higher than 0.3 (Mason, Baker, Blakely, & Bentely, 2013).

We use the median household income to predict the income of university students since statistically the income of university students is considered as part of household income under the definition of household given by Statistics Canada (2017). We use median instead of average or mode to assess an area’s income level since statistically median does a better job to reflect the central tendency of datasets than average and mode. The housing affordability for university students is therefore considered as the ratio between median rental cost and median income. We used rental cost since, in this analysis, we are measuring the housing affordability of university students who rent their dwellings.

#2 University

In this analysis, the term “university” may refer to any of these four universities, University of British Columbia, University of Calgary, University of Toronto, and McGill University. Each university will be represented as a vector point and labelled with appropriate names in the maps.

We represent universities as vector points since, in our analysis, the location of universities is not very significant. We did our proximity analysis based on public transit network, rather than the location of universities. This will be illustrated in more detail in the area of interest part.

#3 The Area of Interest

The area of interest is that area which we consider students may live, in order to attend a university.

The areas of interest are defined as follows:

Locate within the 1 Kilometer buffer around public transit stations; and
Locate within the residential area in municipal jurisdiction of either City of Vancouver, City of Calgary, City of Toronto, or Ville de Montréal; and
Be covered by the CT boundaries defined by Statistics Canada (for determining the availability of data); and
Be a residential area
The data about household income and the rental cost thereof are not suppressed by Statistics Canada (for determining the availability of data)

We define the area of interest mainly by three factors, 1-kilometer buffered areas around public transit stations, residential areas, and areas with both income and rental data because of following reasons. First, from our experience, most university students don’t own a car. Therefore, in order for them to get to school, they must live somewhere near a public transit station. Since four cities all have relatively well developed public transit network, we are assuming that as long as someone has access to public transit, he/she is able to arrive at a university in a reasonable time. Since the definition for a reasonable time may vary among different people, we would not arbitrarily set a standard for it. But in order to determine whether an area has access to public transit or not, we decided to a set 1-kilometre buffer around each public transit station. The areas fall within the buffers would be considered as having access to public transit. We choose 1-kilometer as the buffer distance since from our experience, 1-kilometre is a reasonable walking distance for most people. Second, we choose residential areas since those are the areas that people usually live. We may have some people living on farmlands, but they are certainly not the majority. From our experience, most people, including university students, live in residential areas. Therefore, we set residential areas as one of our criteria for the area of interest. Third, we decided that the availability of data over one area should also be considered as one of the criteria for defining the area of interest. The reason for this is obvious. If we do not have the income or rental cost data about an area, there is no way for us to do any analysis about it.

2.Discussion about the Housing Affordability for each City

We will first find out the median and mean of the housing affordability index of the area of interest in these four cities. Then, we will compare these medians and means to find out which city is the most affordable one. The university which locates within the most affordable city would be considered as the most affordable university to attend.

Based on our area of interest and our analysis, we calculated the means and medians of the housing affordability index for each city.

Vancouver: Mean = 0.216507; Median = 0.200037
Calgary: Mean = 0.181674; Median = 0.183399
Toronto: Mean = 0.208975; Median = 0.207851
Montreal: Mean = 0.188757; Median = 0.186553

Housing Affordability of Vancouver

Our area of interest covers 120 CT cells in Vancouver. The median of Vancouver’s housing affordability index is 0.200037 and the mean is 0.216507. Following the way which we define the area of interest, we know that for students who want to attend the University of British Columbia, very likely they need to spend more than 20% of their income on rent (housing).

Although legally the University of British Columbia is not located within the jurisdiction of the city of Vancouver, we still consider UBC as a university in Vancouver because of its geographical proximity to the jurisdiction of the city of Vancouver. However, because of this jurisdictional issue, the housings immediately around UBC are not taken into our consideration. This includes the student residences provided by UBC.

Housing Affordability of Calgary

Our area of interest covers 207 CT cells in Calgary. Both the median and mean for our area of interest in Calgary are around 0.18, which indicate that for students who want to attend the University of Calgary, on average or by the median, they would need to spend around 18% of their income on rent (housing).

Housing Affordability of Toronto

Our area of interest covers 565 CT cells in Toronto. Both the median and mean for our area of interest in Toronto are around 0.20. Following the way which we define the area of interest, we know that for students who want to attend the University of Toronto, it is likely that they would need to spend about 20% of their income on rent (housing).

Housing Affordability of Montreal

Our area of interest covers 555 CT cells in Montreal. Both the median and mean for our area of interest in Montreal are around 0.19 (overriding). Following the way which we define the area of interest, we know that for students who want to attend McGill University, they would probably need to spend about 19% of their income on rent (housing).

3. Overall Analysis and Conclusion

Based on our analysis for each individual city above, we can see that among these four cities, students who wish to attend the University of Calgary have the lowest housing affordability index, which indicates that among these four cities, or average or by median, the students who wish to attend the University of Calgary could spend the smallest portion of their income on rent (housing). Therefore, we can say that the housing in Calgary is the most affordable one of these four cities for university students. Since we have defined that the university which locates within the most affordable city would be considered as the most affordable university to attend and Calgary has been found to be the most affordable, we can conclude that the University of Calgary is considered as the most affordable one to attend.

In contrast, the University of British Columbia is considered the most unaffordable university to attend in terms of housing affordability. As indicated above, UBC is considered as a university in Vancouver, therefore we assess the affordability for attending UBC by assessing the housing affordability of Vancouver. Our analysis of the housing affordability of Vancouver shows that the students who wish to attend UBC spend the highest portion of their income on rent, in comparison to the other three cities. Thus, we can conclude that Vancouver is the most unaffordable one.

Although we only did analysis for one university for each city, due to the way we define our area of interest, the results of our analysis can be used for assessing any university which is located within the jurisdiction of these four cities since our analysis is based on access to public transit stations rather than the proximity to a university. Therefore, although we only selected one university for each city for our analysis, the results of our analysis are also applicable for assessing the affordability of attending other universities within the jurisdictions of either Vancouver, Calgary, Toronto, or Montreal.

Error and Uncertainty

In our analysis, error and uncertainty may stem from the data itself and our interpretations of the data. The data itself may cause error and uncertainty from three aspects, the accuracy of data, the age of data, and the reliability of providers.

In terms of the accuracy of data, the data we have acquired for this project comes with different projections. In order to fix this problem, we had to reproject almost all of them.

These reprojections will definitely cause some distortions, which are the source of error and uncertainty. These distortions are magnified since we performed proximity analyses. For example, the distortion created by the reprojection of public transit station layers were carried to the buffered layers. As buffered layers were used to determine residential areas within

1-kilometre proximity to a public transit station, due to the distortion, some residential areas may fall within 1-kilometre proximity to a public transit station in reality but were not covered by our buffers. Vice versa, some areas covered in our buffers may actually locate out of 1-kilometre proximity to a public transit station.

In terms of the age of data, we used the CT boundaries, median household income and rental cost from the 2016 census, whereas the zoning and public transit stations data were collected by municipal agencies and Geofabrik in 2018. Since the census data were collected 2 years ago, the CT boundaries, median household income, and the rental cost will have some degrees of discrepancy with the reality.

In terms of the reliability of the data providers, we have found some inconsistencies between data provided by Geofabrik and some municipal agencies. Geofabrik and City of Calgary both provide data regarding the zoning district of Calgary. These two organizations both claim that their data were collected in 2018. However, the zoning district data from these two organizations differ greatly. The data provided by Geofabrik covers only a few areas within the jurisdiction of the city of Calgary, whereas the zoning data from the city of Calgary covers the whole of its jurisdiction. In the end, we decided to use the zoning data from the city of Calgary because of better data completeness and trustworthiness. This data discrepancy made us question the reliability of Geofabrik as a data provider. We used a lot of data provided by Geofabrik since we are unable or having difficulties to find these data elsewhere. For example, we used the data about the zoning district of Montreal from Geofabrik since Ville de Montréal (2016) published all of their data and relevant information in French and we were unable to understand all contents. Since we used some data from Geofabrik and we have discovered some cases that the data provided by Geofabrik are different from others, it is possible that other data provided by Geofabrik may have the same problem.

The second problem comes from the way which we collect our data. In this project, we are analyzing the housing affordability for university students by using the data of median household income. However, using median household income to measure the income of university students is not very accurate. From our experience, university students usually

have their income lower than the median household income because many university students only work part-time and are considered as one person household because most of them are

not married. The other problem is that we didn’t account the fact that many students are still depending upon the money from their families and their families may reside somewhere outside of a CT cell which we are measuring.

The third problem is that the statistics we used for assessing housing affordability in the area of interest may be inaccurate. This inaccuracy also arises from the way which we define the area of interest and the way which we calculate housing affordability. Since the housing affordability ratio is calculated based on rental cost and income level, which are based on all the residents living within a CT cell. However, our area of interest may only partially cover a CT cell because of the way which we have defined it. Therefore, the actual median income and median rental cost in our area of interest may be different from the data contained in a CT cell. This discrepancy will definitely create some errors and uncertainties.

Further Research/recommendations

Based on our analysis, we will propose two recommendations for further research on housing affordability around Canadian universities. The first recommendation is to do more research on residential areas on the UBC campus. In our analysis of housing affordability around UBC, we did not take into account of the residential area in UBC because UBC is considered as University Endorsement Land, and is not included in the municipal jurisdiction of the city of Vancouver. As a result, when we acquired our data on Vancouver zoning district and residential areas, we only acquired data on the residential areas within the jurisdiction of Vancouver. However, there is numerous residence in UBC which are available for university students. Dormitories are arguably the most convenient and affordable residence for UBC students. In addition to dormitories, there are also other apartments and houses in UBC. Therefore, we suggest to future researchers that more research should be done on the affordability of dormitories and other residences on the UBC campus. One thing to be careful is that sometimes the rental costs of apartments on UBC campus are more expensive than other locations due to large student demand.

A second recommendation for future research would be to take into consideration university tuitions and cost of living. These are also important factors in determining which Canadian university is the most affordable to attend. Based on our experience, tuition fees sometimes varied among students. For example, international students are required to pay higher tuitions than Canadian students. Within Canadian students, tuitions for students attend university in their local province are cheaper than students who attend university in another province. In addition to tuition fees, the cost of living in each city varied. In Vancouver, for example, the cost of living is relatively high compared to a city like Calgary. Thus, this factor should be taken into account when students are choosing universities.

Appendix I: Bibliography

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https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-cata logue/.

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Vancouver, British Columbia: City of Vancouver. Retrieved December 1, 2018, from https://data.vancouver.ca/datacatalogue/zoning.htm.

Mason, K. E., Baker, E., Blakely, T., & Bentley, R. J. (2013). Housing affordability and mental health: Does the relationship differ for renters and home purchasers? Social Science & Medicine,94, 91-97. doi:10.1016/j.socscimed.2013.06.023

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Karlsruhe, Germany: Geofabrik. Retrieved December 1, 2018, from https://www.openstreetmap.org/export#map=11/49.2709/-122.9521.

Open Street Map. (2018b). British Columbia Public Transit Stations [Map]. Karlsruhe, Germany: Geofabrik. Retrieved December 1, 2018, from https://download.geofabrik.de/north-america/canada/british-columbia.html.

Open Street Map. (2018c). Ontario Public Transit Stations. In Ontario Public Transit Stations. Karlsruhe, Germany: Geofabrik. Retrieved December 1, 2018, from https://download.geofabrik.de/north-america/canada/ontario.html.

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Statistics Canada. (2016b). Median Income and Rent for Vancouver, Calgary, Toronto, and Montreal [Chart]. In Median Income and Rent. Toronto, Ontario: Canadian Census Analyser / Analyseur de recensement canadien Faculty of Arts & Science, University of Toronto. Retrieved December 1, 2018, from http://datacentre.chass.utoronto.ca/census/

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https://www12.statcan.gc.ca/census-recensement/2011/dp-pd/prof/help-aide/N3.cfm? Lang=E

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https://www12.statcan.gc.ca/census-recensement/2016/ref/dict/households-menage00 7-eng.cfm

UBC Student Housing Demand Study Final Report[PDF]. (2009, December). Vancouver: UBC Campus & Community Planning.

Ville de Montréal. (2016). STM – Tracés des lignes de bus et de métro – CKAN. Retrieved November 22, 2018, from

http://donnees.ville.montreal.qc.ca/dataset/stm-traces-des-lignes-de-bus-et-de-metro