Monitoring Market Rents in Metro Vancouver Transit Locations 1scarp-hrg.sites.olt.ubc.ca/files/2019/08/Market... · Metro Vancouver 2040 Regional Growth Strategy3 Context Strategy

Monitoring Market Rents in Metro Vancouver Transit Locations │1_

School of Community And Regional Planning


This project was completed as a requirement of the Affordable Housing Policy and Planning course (PLAN 530) at the

University of British Columbia’s School of Community and Regional Planning in partnership with Metro Vancouver.

The project was designed to provide an opportunity for students to collaborate with a community partner on a

research topic that is current, practical, and relevant to housing issues in British Columbia.

The research findings and recommendations in this report are those of the authors and they do not necessarily

reflect the views of The University of British Columbia or Metro Vancouver.


TABLE OF CONTENTS

Context 4

Purpose 5

Limitations 6

Census Survey (Statistics Canada) 7

Rental Market Survey (CMHC) 7

Process 8

Advantages & Disadvantages 9

Current Practices 9

Legality 13

Web Scraping Pilot Program 15

Collaborative Rental Housing Data Platform 18


Figure 1. Metro Vancouver 2040 Regional Growth Strategy3

Context

Strategy 4.1 of Metro Vancouver 2040: Shaping our Future, Metro Vancouver’s regional growth

strategy, illustrates the organization’s goal to provide diverse and affordable housing choices to its

residents.1 In order to accomplish this goal, Metro Vancouver uses the combined cost burden of

housing and transportation as one of the key measures to track their progress on its Metro 2040

Dashboard.2

However, the current methodology of determining housing cost by census data has several

limitations. First, Statistics Canada conducts the national census survey every five years. Such

infrequency significantly decreases the accuracy of current housing cost due to a discrepancy gap of

up to six years, including the data processing time. It impedes the process of capturing up-to-date

information on rental housing stock. Second, census data are granular data that Statistics Canada

rounds up or excludes values in small geographical areas to protect the residents’ personal

information. Third, the scope of the data is confined to the neighbourhood level and it is impossible to

access each housing unit’s information. For instance, the average rental value can only be released at

a census tract level or a larger geographic area. The geographic data constraint limits the potential of

conducting microscale analyses with the use of each dwelling unit’s specific location and attributes.

The limitations above hinder the ability of local government

planners and decision-makers to retrieve actual housing

costs in the market. Therefore, it is highly necessary to

develop a better procedure for collecting rental market

values at a more frequent and appropriate scale. The

resulting methodology will improve the understanding of

market rents in such a rapidly growing region of Metro

Vancouver, especially nearby current and future rapid-

transit locations. In addition, the improvement in data

access is critical to executing an equitable transit-oriented

development, ensuring that such development induces the

desired societal benefits and minimizes unfavourable

effects. As a result, government officials can deliver

appropriate policy responses from the outcomes.3

1,3 http://www.metrovancouver.org/services/regional-planning/PlanningPublications/RGSAdoptedbyGVRDBoard.pdf 2 http://www.metrovancouver.org/metro2040


Figure 2. Research Process

Purpose

The census data’s limitations on capturing market rental costs affect Metro Vancouver’s ability to

assist municipalities in monitoring housing status and developing policies. Currently, there is no

standard reporting of average market rents near transit locations, and no clear data source to track

rental charges, particularly in the secondary rental stock. The purpose of this research is to explore,

develop, and propose methodological tools to compile and measure asking market rents in Metro

Vancouver. These alternative tools will accurately and comprehensively compile current asking prices

of primary and secondary rents in the region, in particular, near transit locations. Accordingly, local

governments and other stakeholders can use the recommended methodologies to better coordinate

future developments in the region. The information collected by the proposed tools will be essential

for planners and decision-makers to preserve and encourage affordable housing, employment,

childcare, and access to recreation and services near rapid-transit areas.

This report was prepared in three phases (Figure 2). During the data collection phase, the team

examined academic literature, conducted informal interviews (Figure 3), and undertook Q&A

correspondence via email.

The research team communicated with six key informants, conducted three informal interviews,

and examined three legal cases on web scraping. In addition, the team surveyed a personal blog

that demonstrates a ‘how-to guide’ and its applications of web scraping. The literature review

indicated a limitation of academic study on the legality of web scraping. During the communications

with key informants, the team asked them different questions subject to their expertise. The

interviewees were asked to provide a detailed methodology of their current practice, such as

survey, analysis, and research. In addition, researchers who regularly use web scraping were

asked to provide their opinion on the legality of web scraping.

In the data analysis phase, the team compiled all the information and summarized the findings. Then,

this report aimed to answer two key questions: (1) what are current and potential methodologies to


Figure 3. Organizations and Occupations of Key-informants

compile market rents near transit locations; and (2) what are the administrative and legal constraints

associated with these methodologies?

During the final phase, the team created recommendations on potential methodologies based on the

research findings. The team suggested a web scraping pilot program and a collaborative data

platform to monitor the cost of rental housing in the Metro Vancouver region. Lastly, the report was

revised based on the feedback provided by the course instructors.

Limitations

This report was conducted over four months, from January to April 2019. All researchers who use web

scraping were reluctant to reveal in-depth information of their research (e.g. full instruction of their

research method such as computer codes). Although they agreed to share their publicly available

analyses and reports (e.g. blog posts), they wanted to remain discreet due to the ambiguous legality of

web scraping. Additionally, several respondents showed concerns that web scraping may be

challenged in the courts that web scraping may violate terms and conditions of some websites, which

is a significant barrier for conducting collaborative research and publishing results. Lastly, some of

the information gathered in this research was technical and perhaps can be better understood by

professionals in the corresponding field (e.g. software programming, law, finance).


Census Survey by Statistics Canada

Statistics Canada conducts a national census survey to collect information on all individuals and

households in Canada. The survey has extensive coverage across the country with a high response

rate; the response rate of the most recent census in 2016 was over 98%.4 It collects information of

each dwelling’s various characteristics (e.g. owned or rented, price of monthly rent, number of rooms,

condition for repairs, subsidized housing or not).

However, Statistics Canada conducts the survey every five years. As a result, users may have to rely

on the data that was collected more than five years ago. Each entry of information is aggregated into

groups and compressed at a Census Tract level. Although the census has excellent coverage,

however, its housing data contains a limited amount of rental type information. For instance, all

dwellings are categorized into either subsidized or non-subsidized and the census does not determine

whether the dwelling is a purpose-built rental or a secondary market rental. Moreover, the census

only collects 25% sample data of all dwellings in Canada. Therefore, it is difficult to retrieve accurate

and current rental market information.

Rental Market Survey by Canada Mortgage and Housing Corporation

Canada Mortgage and Housing Corporation (CMHC) conducts an annual rental market survey,

predominantly by phone interviews with minimal site-visits. Less than 5% of the sampled units are

site-visited and the information gathered through site-visits are not formally collected.5 Its primary

purpose is to verify the building’s status (e.g. demolished, condo or non-market, and vacancy). CMHC

often knocks on doors to ask tenants for the name and number of the person who collects the rent.

However, this is a small percentage of the entire data collection since CMHC does not conduct an in-

depth survey. Overall, it collects information about the rental price, availability, turnover rate, and

vacancy rate across Canada. CMHC conducts the survey every year, which is more frequent than

census; however, it is still not frequent enough to accurately capture rental data, which often

fluctuates throughout the year. Also, the rental market survey by CMHC only collects purpose-built rental information. Therefore, it excludes most of the

rental units in the housing market. The survey only targets privately initiated

structures with at least three rental units, further excluding sublet units by

individuals or rented units by private investors. Moreover, the information of the

collected data is only released at a city scale, not by neighbourhood or location.

4 https://www12.statcan.gc.ca/census-recensement/2016/ref/response-rates-eng.cfm 5 Suzanne Milburn: Manager of Programs West, Canada Mortgage and Housing Corporation


As described above, the census by Statistics Canada and the rental market survey by CMHC have

substantial limitations to collect information on up-to-date market rental units. Therefore, an

alternative methodology is necessary to compile more accurate and consistent data of the rental

market in Metro Vancouver. In both the academic and the industry sector, web scraping is becoming a

more popular methodology to analyze a large amount of real-time data with high accuracy.

Multiple academic studies defined the meaning of web scraping and examined its technology. Daniel

Glez-Peña, a software engineering researcher, defined web scraping as “the process of extracting

and combining contents of interest from the web in a systematic way. By using scraping software,

the user can create a large database from multiple listings on online web pages.”6 Vargiu and Urru

described web scraping as “the set of techniques used to automatically get some information from a

website instead of manually copying it. The goal of a web scraper is to look for certain kinds of

information, extract, and aggregate it into new web pages. In particular, scrapers are focused on

transforming unstructured data and saving them in structured databases.”7

Process

The overall process of web scraping begins with data collection. Using

programming languages such as Java or Python, the user can construct

programming codes to automatically extract data of a particular section

in each page or listing.8 For instance, the user can write codes to extract

each rental listing’s address, size, number of bedrooms information and

compile them into a single spreadsheet. This spreadsheet may

continuously update itself if the user imposes a time limit.

However, the same listings often get posted more than once, possibly on multiple websites. Therefore,

the deduplication process that verifies the same listings and records them only once is critical.

Deduplication is conducted either manually by a human or automatically by computer codes. Then,

listings that do not meet a quality standard, such as missing values and outliers are filtered out.

Finally, the resulting inventory is used for analysis, such as calculating the median rental price of an

area.

6 Glez-Peña, D., Lourenço, A., López-Fernández, H., Reboiro-Jato, M., & Fdez-Riverola, F. (2013). Web scraping technologies in an API world. Briefings in bioinformatics, 15(5), 788-797 7 Vargiu, E., & Urru, M. (2013). Exploiting web scraping in a collaborative filtering-based approach to web advertising. Artif. Intell. Research, 2 (1), 44-54. 8 Data Scientist, Flipboard and Quantitative Rhetoric


Advantages and Disadvantages

Web scraping provides an opportunity to collect more up-to-date and comprehensive information on

market rents. By automatically compiling thousands of online listing data from multiple online

platforms, it saves the human labour of manually collecting listing data, which is efficient in both in

terms of cost and time. Also, the gathered data can easily be managed within a single spreadsheet.

However, web scraping also has limitations. First, not all listings contain the same level of data and

may exclude detailed information depending on each listing. Also, since websites are subject to

maintenance and modification, data gathering may be disrupted as a result. Lastly, but most

importantly, the legality of web scraping is still in a grey zone in Canada, meaning that its legal

permissibility is not fully defined and may vary on a case-by-case basis.9

Current Practices

The following examples demonstrate the current practices of web scraping in the academia and

the industry.10

PadMapper

PadMapper is a private rental housing platform

website, which produces a national Monthly

Canadian Rent Report by web scraping their

website’s listings data. Every month, it records

and publishes the median asking rents of all one-

bedroom apartments available in the top 25 most

populous Canadian cities.11 Its methodology can be

summarized as the following: first, ZumperPro

tool compiles active listings data based on a

combination of proprietary listings from

numerous brokers; then, it aggregates the data

with the information from PadMapper’s

established connections with multiple renters

and landlords.12 Consequently, every listing posted

on PadMapper is verified to create an accurate set of an inventory. Then, the resulting inventory is

used to calculate the median rents of each municipality. Lastly, PadMapper creates a public map

based on the summarized information (Figure 4).13 Since this is a part of PadMapper’s business model,

the company did not share more detailed information about its methodology and data sources.

9 PhD Candidate at the University of British Columbia 10 All key informants agreed upon sharing their name, organization, and the information used for this report. 11 https://blog.padmapper.com/category/rental-data/ 12 https://blog.padmapper.com/2017/09/11/our-methodology-empowering-the-renter-with-data/; Chrystal Chen: Marketing Manager at Zumper/PadMapper 13 https://blog.padmapper.com/canadian-rent-trends

Figure 4. PadMapper Map of May 2019 Canadian Rent

Report13


Figure 5. Map of Asking Rents of One-bedroom Units in 800m Radius Around SkyTrain Stations (March to May 2018)

Quantitative Rhetoric

A private data analyst at Quantitative Rhetoric publishes a Monthly Rental Report that provides a

statistical overview of median rental prices in the City of Vancouver.14 It displays the housing price

index and the median rental price in Vancouver based on the web scraped data from a significant

secondary market rental site (the author did not reveal the exact name). To achieve this, the author

deduplicates the aggregated database of multiple listings using the indicators of URL, number of beds,

number of bathrooms, and locations. The author then inspects the output on the first few reports to

examine its validity; and performs diagnostics such as rent distribution to ensure the quality of the

data. Finally, the author excludes outliers by applying hard cutoffs and medians for further robust

analysis.

MountainMath

MountainMath’s private data analyst created a set of maps to illustrate rental listing information in

Metro Vancouver visually.15 The author first collected rental listings data posted between from March

to May 2018 on a major online platform website alongside with TransLink’s SkyTrain station location

data. Then, the author produced three maps to demonstrate the results: (1) median rental price of one-

bedroom units in Metro Vancouver (Figure 5), (2) median rental price of two-bedroom units (Figure 6),

and (3) number of one-bedroom rental listings (Figure 7) within 800 metres around SkyTrain stations. Figure 5:

14 http://quantitativerhetoric.com/category/vancouver-real-estate.html 15 https://doodles.mountainmath.ca/blog/2018/06/21/skytrain-rents/


Figure 6. Map of Asking Rents of Two-bedroom Units in 800m Radius Around SkyTrain Stations (March to May 2018)

Figure 7. Map of One-bedroom Monthly Listings in 800m Radius Around SkyTrain Stations (March to May 2018)

These maps by MountainMath demonstrate the monthly listing prices of asking rents near SkyTrain

Stations in Metro Vancouver between the months of March and May in 2018. A future analysis of

Monitoring Market Rents in Metro Vancouver Transit Locations may include examining asking rents

near other types of rapid-transit stations (e.g. B-lines) and its change over time (e.g. before and after

installation of a new rapid transit line).


Statistics Canada

The federal agency of Statistics Canada produced Measuring Private

Short-term Accommodation in Canada report in March 2019.16 This report

provides an overview of the data sources and methods used to examine

the private short-term accommodation market in Canada. It explores the

temporal record of the short-term rental status and the economic impact

across Canada by statistical analyses. These analyses were conducted

based on the short-term rental information of total revenue generated,

fees paid, reserved days, the percentage of listing types, the percentage

of unit types, and more.

The report’s methodology section states, “Statistics Canada acquired data from a third-party market

research firm (AirDNA LLC) that specializes in providing data analytics for private short-term

accommodation rental platforms. The acquired data included web scraped listing information, in addition to

derived or modelled revenue data for all properties within the geographic boundaries of Canada.”

Then, the data was edited for consistency by removing duplicate records and filling in missing

information such as incorrect provincial classification. The number of listings collected was compared

to the estimates published by researchers or academics who scraped their listing information by

themselves. This procedure was followed by preliminary data estimates derived from Airbnb, the

market’s largest firm. The procedure contained the data aggregation of the generated revenue to

include the host and guest fees charged by the intermediary platform. Assumptions were made to

estimate these guest fees paid since Airbnb did not provide this information.

In summary, Statistics Canada did not web scrape the data itself but obtained data from a third-party

market research firm, AirDNA LLC, to measure private short-term accommodation in Canada. In the

process of selecting AirDNA LLC third-party market research firm to conduct this study, Statistics

Canada followed its standard Government of Canada procurement policies.17 Although Statistics

Canada does not have additional information about other available market research firms that utilize

web scraping, this report reinforces the potential possibility of legally conducting web scraping

analysis through a third-party.18

16 https://www150.statcan.gc.ca/n1/en/pub/13-605-x/2019001/article/00001-eng.pdf?st=3pjPF54N 17 https://www.canada.ca/en/services/business/doing-business/how-to-sell/procurement-policies.html 18 https://www150.statcan.gc.ca/n1/pub/13-605-x/2019001/article/00001-eng.htm


Legality

Academic Research (Craigslist vs. 3Taps)

The availability of literature on the legality of web scraping is limited. However, there is an academic

paper that specifically explored the legal issue of web scraping regarding the collection of

information on rental housing markets in the United States.19

Its authors describe three major legal considerations in utilizing web scraping methodology,

copyright, trespassing, and archives, through a legal dispute between Craigslist and 3Taps.20 3Taps,

an online exchange platform for an exchange of goods, services, and information, was accused of

web scraping Craigslist’s rental listing data in 2013 for competitive commercial purposes and

displaying them on their web site. As a result, three significant statements were made. First, the

Federal District Court of Northern California decided that it is not a violation of copyright to scrape

publicly available data such as Craigslist listings. Also, research is a non-commercial fair use that

neither repackages nor relists the data. Second, 3Taps was sued only after Craigslist sent a cease-

and-desist letter and blocked their IP addresses. The judge ruled that 3Taps trespassed on

Craigslist’s servers specifically by ignoring the cease-and-desist letter and using a proxy to violate

the restrictions that forbid them from accessing the servers. Third, other organizations such as the

Internet Archive scrape and snapshot Craigslist’s web pages along with various other websites.21

Researchers can collect rental listings from these snapshots instead of from Craigslist directly,

though they may lack specific details. As a result, 3Taps lost the case and agreed to pay Craigslist

$1,000,000 over ten years, and permanently stop scraping content from the website.

Trader Corporation vs. CarGurus

In 2017, Trader Corporation sued CarGurus for scraping their listings and photos.22 The main arguments

were:

1. Were the Trader vehicle photos protected by copyright and were the photos owned by Trader?

2. Did CarGurus infringe Trader's copyright?

3. Should statutory damages be applicable, what is the appropriate amount?

The Ontario Superior Court of Justice found the ownership of over 150,000 photos to Trader and confirmed that CarGurus infringed Trader’s copyright by using them for commercial purposes. As a result, CarGurus had to delete all their web scraped photos and make an agreement to not to reproduce Trader’s photos in the future. 19 Boeing, G., & Waddell, P. (2017). New insights into rental housing markets across the united states: web scraping and

analyzing craigslist rental listings. Journal of Planning Education and Research, 37(4), 457-476. 20 http://www.dmlp.org/sites/dmlp.org/files/2012-07-20-Craigslist%20Complaint.pdf 21 http:// archive.org/ 22 https://ca.practicallaw.thomsonreuters.com/w-007-6508?transitionType=Default&contextData=(sc.Default)&firstPage= true&bhcp=1&ignorebhwarn=IgnoreWarns


Century 21 vs. Zoocasa

In 2011, Century 21 Canada sued Zoocasa for scraping their online real estate listings.23 The British

Columbia Supreme Court concluded that Zoocasa, a search engine and aggregator of real estate

listings, violated the terms of use of Century 21, which forbids the copying or reuse of its real estate

listings.

Zoocasa was accused of breaching Century 21’s terms of use as well as both trespassing and violating

Century 21’s copyright by web scraping information and reproducing them on Zoocasa’s website. The

British Columbia Supreme Court ruled that real estate listings aggregator Zoocasa indeed violated

Century 21’s terms of use by putting the company’s listings on its site. The court stated that the terms

of use on a website are legally binding even if there is no opt-in provision for web surfers to indicate

that they agree to the conditions. Although Zoocasa only had to pay $1,000 as a fine, this legal case

confirms that a website’s fine print can be enforced even if its users are not asked to accept the terms

by explicitly clicking them.

Conclusion

According to the literature review, web scraping has been legally punished when it was used for

commercial purposes. However, arguments varied case-by-case and there was no litigation where

a private company sued a government authority. The Federal District Court of Northern California

stated that rearranging and publishing publicly available data is not a violation of copyright,

especially when data is used for non-commercial purposes. Also, Statistics Canada produced a

report on short-term accommodation through a private market research firm that web scraped

Airbnb. However, web scraping methodology should be approached cautiously, and researchers

should not violate the website’s terms of use in conducting such study.

23 https://www.stikeman.com/en-ca/kh/canadian-technology-ip-law/that-a-wrap-bc-supreme-court-enforces-website-terms-of-use-and-validates-browse-wrap-agreements-in-century-21-v-zoocasa


Metro Vancouver is interested in identifying options to improve access to market rental housing

information, in particular near rapid transit. The current measure based on Statistics Canada’s census

data is highly infrequent and has limits on examining detailed information. As an alternative, web

scraping from online marketplace platforms is becoming more popular among private data analysts

and organizations to collect data and produce monthly reports on rental housing. For instance,

PadMapper compiles active housing listings data by web scraping then produces monthly rent reports

by aggregating datasets in the largest 25 Canadian cities. Similarly, Statistics Canada produced a

report which measured private short-term accommodations in Canada by using third-party web

scraping services. However, automatic web scraping often has been disputed as a legitimate research

method as it may violate websites’ terms of use. Therefore, to enhance gathering more

comprehensive and accurate market rental data, the team suggests two methodologies:

A) Web Scraping Pilot Program B) Collaborative Rental Housing Data Platform

A) Web Scraping Pilot Program

If Metro Vancouver seeks a timely and effective method to scan the housing market at any given

time to compile rental data, then the ordinary web scraping method is a viable option. It provides a

compiled list of rental listings in real-time. The compiled database would create a list of units with

universal features found on all listings, such as the number of bedrooms and bathrooms, property

size, price, location, type of housing, availability, and more. The database can then be used for

further analysis which can guide municipalities to create policy responses.

The method described above contains multiple benefits. Web scraping is a digitalized and automatic

process which is cost-effective, current, and easy to manage, in comparison to telephone or paper

surveys. This method can reduce the administrative burden of data collection. Furthermore, better

access to real-time data also provides more in-depth insight into areas of inequality through detailed

investigation of housing and transportation costs. The Housing and Transportation Cost Burden Study

by Metro Vancouver is an excellent example, where it demonstrates how the accuracy and

comprehensiveness of housing information can impact residents’ affordability and livability.24

However, this methodology may require more time than traditional surveys to manage data

accuracy, such as filtering out duplications and false listings.

24 http://www.metrovancouver.org/services/regional-planning/PlanningPublications/HousingAndTransportCost BurdenReport2015.pdf


There are two other important considerations in this option:

1. Legality of Web Scraping

Online platform websites such as PadMapper and Craigslist have strict

restrictions on their uses. If Metro Vancouver uses web scraping in its future

analyses, it should carefully review each website’s terms of use (also called

terms of service or terms and conditions). Alternatively, Metro Vancouver may

request that a third-party firm conduct research similar to that done by

Statistics Canada on the topic of short-term accommodation. Whereas each

website’s terms of use vary, the following sections describe an example of how

a private housing listings platform website, PadMapper, states its acceptable

uses.

Acceptable uses Several sites, including PadMapper, allow visitors to access, view, use, download, and print its

site content subject to the following conditions:25

1. You may use the Services, and download, access and print the Site Content, only in

reasonable limited quantities for your personal, non-commercial use;

2. You may not modify the Site Content; 3. Any displays or print outs of the Site Content must be marked "© PadMapper, Inc. 2012-2016.

All rights reserved,” and

4. You may not remove or alter any copyright, trademark or other proprietary rights notices

that have been placed in the Site Content.

Not acceptable uses However, the website also states the following to restrict web scraping its content:

“You shall not: Harvest or otherwise collect any data, information or Site Content from the Website, including by using manual or automated software, devices, or other processes to "crawl,” "scrape” or "spider” any page of the Website or Services to copy, obtain, propagate, distribute or misappropriate any User Data or Site Content.”

Similarly, Craigslist states: “You agree not to copy/collect CL content via robots, spiders, scripts,

scrapers, crawlers, or any automated or manual equivalent (e.g. by hand)” unless it is licensed by

theme in a written agreement.26 Although the terms and conditions may seem counter-intuitive,

some of its terms may not be legally binding; however, a lawyer may have more accurate

information as to the enforceability of these terms.

25 https://www.padmapper.com/tos 26 https://www.craigslist.org/about/terms.of.use.en-us


2. Multidisciplinary Team

Another challenge is the capital cost of establishing the web scraping system and maintaining it. As

websites change over time and organizations restructure their policy of use, different roles are required to

ensure that the collected data is accurate, and volumes of data extraction are consistent. Below (Figure

8) is a simplified process model of what it would look like if Metro Vancouver were to develop its own

web scraping system:

The first action would be to assemble a multidisciplinary team to mitigate some of the risks and

unknowns. Based on the research on existing practices, a team would require the following members at

a minimum:

• Planner (data to request)

• IT analyst (internal data storage)

• Data scientist (data manipulation)

• Legal counsel (laws & regulations)

Once the team is complete, the planner can lead the team by creating a list of features to be

extracted from rental listing websites, based on the team’s objectives. Then, programmers can design

and build a system to import information from multiple websites. Here, the team may include not only

the major platforms such as Craigslist and Kijiji, but also other media platforms that are in other

languages (e.g. Chinese, Punjabi, Farsi, Korean, Spanish, Japanese, and more) and different types of

platforms (e.g. Facebook, institutional websites, media platforms, blogs) depending on the availability

of time and resources. After a comprehensive database is created and deduplicated, planners and

data scientists can conduct analyses from the compiled information and visualize the results.

Metro Vancouver Regional Planning

Figure 8. Simplified Process for a Potential Metro Vancouver Web Scraping Project


Figure 9. Simplified Example Framework of the Collaborative Platform

B) Collaborative Rental Housing Data Platform

The Collaborative Rental Housing Data Platform is an advanced system of the first recommendation,

the Web Scraping Pilot Program. This platform goes beyond web scraping various listing websites,

and it seeks to create user agreements and licensing strategies with websites or data companies. By

creating a partnership among multiple stakeholders, the proposed collaborative platform will engage

with other data providers to combine external data (e.g. transportation, occupation, health, and built

environment) with rental housing data. As a result, Metro Vancouver and municipal partners can

acquire a better understanding of the combined cost burden of housing and transportation.

For instance, trends and patterns of multiple datasets (e.g. rental market, redevelopment pattern,

land use changes, walkability, health levels, origin-destination travel patterns, job locations, and

more) can aid planners and decision-makers to examine the following questions:

• Are rents nearby transit-oriented locations increasing at a faster rate than in

other neighbourhoods that are away from rapid transits? • How do rents vary along rapid transit corridors? • What are the community impacts caused by the construction of rapid transit in

different neighbourhoods?

A collaborative platform would help Metro Vancouver to answer these questions. This collaborative

platform also allows Metro Vancouver to share their burdens across multiple disciplines

associated the resourcing impacts with data collection and analysis (cost, resources, time) with

other partners.


During this process, the collaborative platform (Figure 9) must address four key concerns:

• What matters the most? What are the main goals/objectives of these analyses? • What are the minimum requirements to achieve these goals? How in-depth and

comprehensive should the platform be? • How will the data be used? Who are the potential users? • How will the platform monitor the system and its data?

These questions can be best answered by a group of professionals who are involved with the

collaboration. By coordinating discussions and providing a wide range of support, Metro Vancouver

could gather more comprehensive and in-depth information on the primary and secondary rental

market in the region. However, organizing such a collaborative platform might be a challenge of

organizational management, time, and financial support.

king Market Rents intro Vancouver: The cost of market rental housing acts as a key measure to fulfill Metro Vancouver ’s goal of providing

diverse and affordable housing choices in the region. As the housing availability and cost data change

every day, more accurate and frequent data source than the traditional survey is required. Through

literature review and informal interviews, this report compared the advantages and disadvantages of

census survey, CMHC survey, and web scraping. As a result, the research team recommends two options

of A) Web Scraping Pilot Platform and B) Collaborative Rental Housing Data Platform. For a short-term and

quick analysis, we recommend option A. However, a multidisciplinary and extensive data platform of option

B will provide more significant benefits in the long run. The fundamental basis of both options is web

scraping. The current legality of web scraping varies depending on websites and cases; however, Statistics

Canada has recently published a report on short-term rental accommodations by using third-party web

scraping services, which indicates increased acceptance of this methodology. Collecting and monitoring

asking market rents in Metro Vancouver will encourage more equitable, affordable, and sustainable growth

in the region, in particular, areas along rapid transit corridors.


https://upload.wikimedia.org/wikipedia/commons/5/5b/TransLink_SkyTrain_departs_Stadium-Chinatown_station_in_Vancouver%2C_British_Columbia%2C_Canada.png

https://youtu.be/OTyxsjBEscY

https://www.google.ca/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&ved=2ahUKEwj-5bbrysnjAhUlMnwKHQdsAboQjRx6BAgBEAU&url=https%3A%2F%2Fcommons.wikimedia.org%2Fwiki%2FFile%3ATransLink_SkyTrain_departs_Stadium-Chinatown_station_in_Vancouver%2C_British_Columbia%2C_Canada.png&psig=AOvVaw1zsQ1NzrB_LQFH7t_p0ajf&ust=1563921232894097

Documents

Monitoring Market Rents in Metro Vancouver Transit Locations 1scarp-hrg.sites.olt.ubc.ca/files/2019/08/Market... · Metro Vancouver 2040 Regional Growth Strategy3 Context Strategy