Winnipeg Trivia – Real Estate

One of the ideas that came to mind while browsing the Winnipeg Building Index was to establish the “extremes” of Winnipeg houses – the oldest, largest, smallest, most expensive, and so forth. There are any number of questions that can be considered, but I have limited myself here to a fairly standard ten items.

One of the obstacles of determining those kinds of facts is having adequate data. Thankfully, the City of Winnipeg has created an Open Data Portal (data.winnipeg.ca) to “share with citizens, businesses and other jurisdictions the greatest amount of data possible while respecting privacy and security concerns” (Winnipeg City Council, 2012, p. 1). The Open Knowledge Foundation (OKF) defines “open”, in relation to data, as something that “anyone can freely access, use, modify, and share for any purpose (subject, at most, to requirements that preserve provenance and openness)” (2007, para. 3). This definition is a summary of a document published by the OKF, The Open Definition, currently version 2.1 (OKF, 2015). Further to that, Van Loenen, Vancauwenberghe, Crompvoets, and Dalla Corte (2018, p. 2) argue that data is “critical for a well-functioning society” and as such “access to the data should be optimised.” They go on to suggest that one way to achieve this goal is by making data freely available, namely, “open data.” To which end, Van Loenen et al. (2018, p. 2) note that in recent years, governments in particular have been setting up open data initiatives, making their ‘primary input and output’ available as open data on the Internet.

The data used to arrive at the ten facts below is from the assessment parcel dataset. Given that the dataset is updated every so often, it should be noted that the assessment parcel dataset I used was downloaded on May 16, 2019.

 

Smallest House

442 William Newton Avenue, the smallest house

442 William Newton Avenue

The Guinness Book of World Records has an entry for the “Smallest House in Great Britain” (also known as Quay House). The total floor area is 8.3 ft by 6 ft, presumably on each floor, for a total of 99.6 sq. ft (McWhirter, 1988, p. 111). The 1986 Guinness Book of World Records describes an even smaller house (the “Smallest Residence [in the world]”) belonging to Alexander Wortley, a British naval veteran, who lived in a “green painted box in the garden of David Moreau in Langley Park, Buckinghamshire, England” (McWhirter, 1986, p. 252). The dimensions of this box were 5 x 4 x 3 ft, “with an extension for his feet” (McWhirter, 1986, p. 252).

The smallest house in Winnipeg, on the other hand, is 442 William Newton Avenue, at 312 sq. ft. The house is a bungalow and was built in 1934.

 

Largest House

570 Park Boulevard West, the largest house

570 Park Boulevard West

The largest house in Winnipeg is 570 Park Boulevard West at 10,733 sq. ft. Located in the Tuxedo neighbourhood, the house was built in 1964 and is zoned as an estate.

This is fairly cozy compared to the largest (non-palatial) house in the world, called “Antilia”, which is 400,000 sq. ft. in a tower 525 feet tall (over 27 stories), with 3 helipads, a gym, a soccer field, and several other amenities (Crabtree, 2019, pp. 6-7). The house is owned by Indian billionaire Mukesh Ambani and is located in South Mumbai, India.

 

Smallest Lot

659 Minto Street

659 Minto Street

The smallest lot in Winnipeg is 1,005 sq. ft and is located at 659 Minto Street in the Minto neighbourhood. The house, built in 1925, is 787 sq. ft.

 

Narrowest Lot

641 Pritchard Avenue

641 Pritchard Avenue

The narrowest lot in Winnipeg, with a width of 16.51 ft, is 641 Pritchard Avenue, located in the William Whyte neighbourhood. A rental ad placed in the Winnipeg Free Press in 1953 referred to it as a “small furnished cottage”.

 

Least Expensive House

6 Rover Avenue

6 Rover Avenue

The CMHC defines overvaluation in the housing market as arising from house prices that “are elevated compared to price levels supported by personal disposable income, population, interest rates, and other fundamentals” (2019, p. 2). This can be due to speculation driving prices up or when prices decrease slowly in response to “deteriorating housing market conditions” (2016, para. 24). Overvaluation refers to prices in MLS listings, which are a reflection of market value, though market value tracks reasonably well with assessed value (given the former is used to calculate the latter).

The CMHC considers Winnipeg to have moderate overvaluation (2019, p. 6). Despite this, there are a number of homes whose assessed values are very low compared to the average assessed (and sale) price of all homes in Winnipeg. The lowest is 6 Rover Avenue, whose assessed value is $36,100. This is less than the property taxes of some homes in Winnipeg. The house was built in 1909 and is located in the North Point Douglas neighbourhood.

 

Most Expensive House

550 Park Boulevard West

550 Park Boulevard West

The most expensive (i.e. highest assessed) house in Winnipeg is 550 Park Boulevard West, with an assessed value of $4,377,000.00. The house was built in 1956 and is located in the neighbourhood of Tuxedo.

This is a bargain compared to “Antilia” – aside from being the largest house in the world it is also the most expensive; according to Thomas Johnson, the director of marketing at Hirsch Bedner Associates, one of the companies consulted in the design process, a potential dollar figure is $2 billion USD (Woolsey, 2008, para. 4).

 

Fewest Rooms

411 Desalaberry Avenue

411 Desalaberry Avenue

The single detached house with the fewest rooms appears to be 411 Desalaberry Avenue, with two rooms inside the 511 sq. ft space. The house is located in the Chalmers neighbourhood and was built in 1955.

The dataset is not entirely trustworthy though, when not critically analyzed. For example, 30 houses are tied with one room listed. None of the houses have one room – these appear to be errors (real estate listings for many of them indicate at least three rooms). Beyond that, a 1500 sq. ft house is unlikely to have two rooms (and a 6000 sq. ft house is especially unlikely to have one room). The dataset also had a 13-way tie for houses with two rooms though, again, some results are unlikely to be correct (e.g. two storey houses). The list of two-room houses was checked and only 411 Desalaberry had a plausible claim. 411 Desalaberry is also interesting in that it was previously a restaurant (called “Baron’s Lunch”), as noted in a Winnipeg Tribune article from February 29th, 1964 (“Window broken: man held”, 1964).

 

Most Rooms

1063 Wellington Crescent

1063 Wellington Crescent

The house with the most rooms is likely 1063 Wellington Crescent with 24 rooms in a space cited variously in a range of 18,000 to 27,000 sq. ft.

On the other hand, the world record holder, St. Emmeram Castle (or Schloss Thurn und Taxis), has over 20 times this with 517 rooms in a space of 231,000 sq. ft. (Matthews & McWhirter, 1995, p. 218). It was was originally a Benedictine monastery founded in approximately 739 in Regensburg, Bavaria, Germany (Hourihane, 2012, p. 170) but was granted to the Princes of Thurn und Taxis in 1812 and subsequently converted into a residence, which is its current use.

 

Highest Street Number

6683 Betsworth Avenue

6683 Betsworth Avenue

The highest street number for a single detached home is 6683 Betsworth Avenue, in the Westdale neighbourhood of Charleswood.

In contrast, the highest street number in the world could be 986039 Oxford-Perth Road, located in Wilmot township, Waterloo, Ontario, Canada, just south-west of Kitchener, Ontario (Marshall, 2019, para. 5). This address is, incidentally, a short distance down the road from the unincorporated hamlet of Punkeydoodles Corners (which is arguably second only to Saint-Louis-du-Ha! Ha!, Quebec on the list of strangest place names in Canada).

 

Lowest Street Number

1 Palk Road

1 Palk Road

The lowest street number in Winnipeg is “1”, which is shared by a total of 227 addresses across the city. One address that stands out is 1 Palk Road, which is also the only address on Palk Road. The dataset contains “0” street numbers (e.g. “0 Dominion Street”), but these did not appear to actually exist.

The lowest street number in the world is possibly Minus Two Woodend Lane, Cam, Dursley, Stroud, Gloucestershire, England (Plowman, 2018, para. 14).

 

References

CMHC (Canada Mortgage and Housing Corporation). (2016, May 12). Housing market assessment (HMA). Retrieved from https://www03.cmhc-schl.gc.ca/hmip-pimh/en/TableMapChart/HMAMethodology

CMHC (Canada Mortgage and Housing Corporation). (2019). Housing market assessment Canada. Ottawa, Canada: CMHC. Retrieved from https://assets.cmhc-schl.gc.ca/sf/project/cmhc/pubsandreports/housing-market-assessment/2019/q3/housing-market-assessment-canada-68456-2019-q03-en.pdf

Crabtree, J. (2018). The billionaire Raj: A journey through India’s new gilded age. New York, NY: Tim Duggan Books.

Hourihane, C. (Ed.). (2012). The Grove encyclopedia of medieval art and architecture (Vol. 1). Oxford, UK: Oxford University Press.

Marshall, S. (2019, June 10). Punkeydoodle’s Corners and the world’s highest numbered address [Blog post]. Retrieved from https://seanmarshall.ca/2019/06/10/punkeydoodles-corners-and-the-worlds-highest-numbered-address/

Matthews, P., & McWhirter, N. (1995). The Guinness book of records, 1995. New York, NY: Bantam Books.

McWhirter, N. (1986). 1986 Guinness book of world records. Toronto, Canada: Bantam Books.

McWhirter, N. (1988). Guinness book of world records 1989. New York, NY: Sterling Pub. Co.

OKF (Open Knowledge Foundation). (2007, June 28). The open definition. Retrieved from https://opendefinition.org/

OKF (Open Knowledge Foundation). (2015, November 19). Open definition 2.1. Retrieved from https://opendefinition.org/od/2.1/en/

Plowman, P. (2018, August 4). UK address oddities! [Blog post]. Retrieved from http://www.paulplowman.com/stuff/uk-address-oddities/

Small furnished cottage cottage for rent. (1953, September 10). Winnipeg Free Press, p. 29.

Ubaldi, B. (2013). Open government data: Towards empirical analysis of open government data initiatives. OECD Working Papers on Public Governance, No. 22. Paris, France: OECD Publishing. doi: 10.1787/5k46bj4f03s7-en

Van Loenen, B., Vancauwenberghe, G., Crompvoets, J., & Dalla Corte, L. (2018). Open data exposed. In B. Van Loenen, G. Vancauwenberghe, J. Crompvoets, & L. Dalla Corte (Ed.), Information Technology & Law Series: Vol. 30 Open data exposed. The Hague, Netherlands: TMC Asser Press.

Window broken: man held. (1964, February 29). The Winnipeg Tribune, p. 22.

Winnipeg City Council. (2012, May 22). Minute no. 417, report – alternate service delivery committee, item no. 2 open and accessible data. Winnipeg, MB: City of Winnipeg. Retrieved from http://clkapps.winnipeg.ca/dmis/ViewPdf.asp?SectionId=312124

Woolsey, M. (2008, April). Inside the world’s first billion-dollar home. Forbes Magazine. Retrieved from https://www.forbes.com/2008/04/30/home-india-billion-forbeslife-cx_mw_0430realestate.html.

Visualizing Data From The Winnipeg Building Index

I have long browsed the Winnipeg Building Index (WBI), and have enjoyed the information and photos presented. I thought it would be interesting to see the information contained inside of it presented in a more visual way, e.g. in a plot, on a map. The end goal I had in mind was an animated heatmap of the geographic coordinates of the buildings in the index by decade. The idea behind the entire exercise was to practice web scraping and data visualization.

To collect the data, I used Python and a web scraping library called Beautiful Soup.

Once the data was collected, it was cleaned up. For example, there were many different year formats present in the approximately 2550 items collected:

  • 1906 (circa)
  • 1905 – 1906
  • 1950-1951
  • 1903 (1912?)
  • 1908?
  • 1885, 1904
  • 1946-
  • 1880s
  • 1971 circa

For years, the last complete (4 digits) and plausible (1830 < year < 2015) year found was used as the year for each building. Addresses were slightly less varied. For example, most suitable addresses (i.e. geocodable) took the form of “279 Garry Street” or “Main Street at Market Avenue” – simply a street (e.g. “Colony Street”) in the address column was removed. There were also a few addresses that don’t appear to actually exist. (The names of buildings aren’t important for this stage – though, they will be for a later project.) Once the data was cleaned up, it was saved again as a CSV file.

As there are a number of things that can be done with this data, I decided to do a simple task at first. Since most buildings had years associated with them, I decided to visualize the number of buildings in the WBI for each decade. To do this, I imported the ‘year’ column into plot.ly and used the histogram plot type. After that, I created 19 ‘buckets’ corresponding to each decade from the 1830s to 2010s. The result is below:

Frequency Distribution of Years by Decade - made in plot.ly

Frequency Distribution of Years by Decade – made in plot.ly

There are other interesting things that can be determined. For example, the most common streets that appear in the WBI. To find that out, I wrote a simple script that removed numbers from addresses, added them to a Counter object, and then used the most_common() method to determine the most common streets. The result is below (the legend is ordered, with Main Street being the most common):

The ten most common streets in the resulting dataset.

The ten most common streets in the resulting dataset (most common is at the top).

Following this, I imported the data into QGIS as a csv file using MMQGIS. Once loaded, the addresses were then geocoded using the Google Maps API (via MMQGIS). Geocoding is a somewhat slow process at about 160 addresses processed per minute. The result was a shapefile layer:

All geocoded points, over a Stamen Toner base map.

All geocoded points, over a Stamen Toner base map.

The points have a large amount of overlap, which means the above image does not give a good sense of the actual density of building locations. To visualize density, I used QGIS to create a heatmap from the shapefile layer. The result is below (red areas are higher density):

A heatmap of all points.

A heatmap of all points.

And as an overlay over the points themselves on a map of Winnipeg:

A heatmap of all points overlaid over the original points.

A heatmap of all points overlaid over the original points.

With the data loaded into QGIS, I was also able to answer other questions – for example, determining the highest density areas. To do that I drew polygons in the densest areas (as seen in the heatmap) and used the ‘Points in polygon’ tool to count the total number of points (geocodable addresses) that were inside. Some of the highest density areas were:

  1. Exchange District – 146 addresses*
  2. Armstrong Point – 121 addresses
  3. University of Manitoba – 65 addresses

(*using the boundaries for the National Historic Site)

Adding polygons for counting points.

Adding polygons for counting points.

The last task was to create the animated heatmap. To do that, the years associated with each point (geocoded address) were categorized by decade (i.e. 1830-1839, 1840-1849, etc) and assigned a decade code (0-18). After that, separate layers were made for each decade using the query builder (that is, a set of points associated with each decade code). After that, a heatmap was produced for each layer and exported as an image. The exported images were imported into Adobe Premiere Pro and animated. The resulting video is the following:

 

Links:

Winnipeg Building Index: http://wbi.lib.umanitoba.ca/WinnipegBuildings/

Beautiful Soup: http://www.crummy.com/software/BeautifulSoup/

QGIS: http://qgis.org/

MMQGIS: http://michaelminn.com/linux/mmqgis/

plot.ly: http://plot.ly/

Web Scraping: http://en.wikipedia.org/wiki/Web_scraping

Stamen Toner map: http://maps.stamen.com/toner/