The Creation and (Spoiler) Sale of 'Hood Rank

A lot has happened since I posted that "We Bought a House!", and a lot of it has been pretty exciting, so I thought I would write this update to share the story.

One of the reasons I wrote that post was because of all the interest I was getting in my "Neighbourhoods" spreadsheet from friends and colleagues that I mentioned it to.  Many people had requested a version of the spreadsheet that they could use themselves to help them determine where to buy their own house.  Unfortunately, the spreadsheet wasn't the most user-friendly tool.  One of my hopes in writing the blog post was that, in reading about my method, interested people might be able to create their own customized analysis.  I'm not sure how many people ended up doing so, but the post definitely brought a lot more attention to my project than I was expecting (well beyond friends and family)!

Sending out a couple tweets brought even more attention:

This broader interest led to big name Edmonton open data guru, David Rauch, contacting me and inviting me to present about my neighbourhood ranking spreadsheet at an Open Edmonton event (now called BetaCity YEG).

This event was a great way to discover what was happening in the open data community locally and to connect with other people with similar aspirations!  At this meet-up, I was also encouraged to attend the annual Open Data Day Hackathon hosted by EPL.

All this interest and encouragement was definitely leading me to consider more seriously the prospect of building a web-app aimed at making it easy for anyone to do a similar neighbourhood ranking analysis based on their own personal priorities.  Not being a professional coder, I thought the Open Data Day Hackathon would be a great way to connect with a few people who were.  Not to mention the opportunity to further gauge the market for this type of application.

Eleven people ended up joining my team and contributing in some way to the project on the day of the hackathon!  By the end of the day I had great insight into tools that would help me take the idea to the next level, and was convinced it was worthwhile.   We had created many things related to my proposal, in nearly as many different directions as there were people on the team.

After the hackathon it was necessary for me to re-focus on my specific vision, and build an interactive web-application from scratch, using some of the tools I had learned about.  It wasn’t long before ‘Hood Rank, the site I had created, was featured on CTV News (twice), CBC Radio and CBC News, among others:

Later that year, I came across a new startup company with a tool similar to ‘Hood Rank.  I had an opportunity to chat with a couple of their lead people (at Startup Edmonton’s Launch Party) and discovered that their goals and aspirations were very closely aligned with mine: helping people use data and science to determine where to live in Edmonton based on their specific personal preferences.  This company also had many of the resources and expertise I was aiming to build into ‘Hood Rank, such as a successful real estate agent (Elisse Moreno, of RE/Max Excellence) and a team of people.

After a few months and meetings, I’m happy to announce that Home Tribe has officially purchased ‘Hood Rank!  The Home Tribe Match function is a great tool that uses answers to a series of simple questions to help people to not only find their ideal neighbourhood but also their perfect home.  I’m also happy to be working with Home Tribe on an ongoing basis to help bring even more attention to all sorts of cool data and the exciting insights we can glean from it!


We Bought a House!

Update (2016-03-30): The Creation and (Spoiler) Sale of 'Hood Rank

I tend to make a spreadsheet before purchasing anything over about $38 to make sure I'm getting the best product or at least the best value.  So when we started thinking about spending four orders of magnitude more than that, I started compiling what ended up being a few of the most data and formula heavy spreadsheets I've ever made.  Sound like fun?  In the following, I elaborate on just how much fun.

Global ("where to live.xls")

Long before seriously considering buying a house, I had made a few spreadsheets that helped me determine where to live.  While travelling in 2009 and 2010 I wondered if any of the places we visited in North America and Europe might be suitable places to settle down.  I rated a few things, such as the presence of various types of communities of which I would be interested in being a part, the potential for a good job, the mountains, certain types of weather, a low disaster risk, and 36 of my favourite friends and family members, based on how important each metric was to me, and the percentage chance that each existed (and would remain) in various cities.  After visiting the number of places I did, I was surprised to find Edmonton to be such a front-runner on my personal world stage for places to live, but this relatively quick spreadsheet helped illuminate why.

Local ("Neighbourhoods.xlsx")

After a few more years living in Edmonton, my wife and I started looking at the odd house in the area we were living at the time.  I felt that our choice of location might have been biased by the fact that we were already living there, so I started an in depth assessment of Edmonton's 387 neighbourhoods.  Thank you Edmonton for all the data that is available in the City of Edmonton Open Data Catalogue!  I couldn't find everything I needed from there, but it was a great start, and they even repaired one of the datasets for me that seemed somewhat broken (or at least less useful than it could have been).

I ended up assigning a 5 point rating for each of the following 9 requirements that were important to our family for each of the 387 neighbourhoods in Edmonton: 
  • Schools
  • Crime
  • Location
  • Grocery Store
  • Transit
  • Library
  • River Valley Proximity
  • Pool
  • Spray Deck
  • Playground (relatively unsuccessful, so I don’t count this one)
I then weighted each priority with a number based on family discussions and summed all the weighted ratings for each neighbourhood, resulting in a total number of points or a score, allowing me to compare it to all the other neighbourhoods (more on this below).

The school rating was probably the most important, given our two young kids and plans for more.  It was also the most complicated.  I ran into difficulty evaluating a large number of schools in any meaningful way.  It seems that one would probably need to visit a school and chat with some teachers to get a really good sense of its quality.  So initially I settled on a formula that provided a rating from 1 to 5 based on the total number of schools within 1 km of the neighbourhood centroid, the number of elementary schools within 1 km, the number of junior high schools within 1.5 km, and the number of high schools within 1.5 km.  This would at least provide us with as much choice as possible.  Once we started looking at specific houses, I checked the Fraser Institute’s school ranking system (based largely on grade 3 and 6 achievement tests) on the designated school for each house, but didn't give it too much weight in my decision.

Secondly, I compared crime rates between the neighbourhoods.  Unfortunately it was difficult to find an accurate crime related dataset in a useful format (hopefully this is added to the Open Data Catalogue in the future), so I had to rely on the Edmonton Police Crime Mapping site.  This site showed all the crimes in the last three years for each neighbourhood, but unfortunately not in an easily exportable format.  I had to click multiple times to access the information for each neighbourhood, then manually add and copy the total crimes for the past three years (for 2011, 2012 and 2013).  As such, I only ended up entering data for about 130 neighbourhoods rather than the full 387, but these were the front-runners anyway.  Initially, I scaled this data for each neighbourhood based on the total number of crimes in all three years.  For example, to get 5 points, a neighbourhood would need to have had less than 100 crimes in all of 2011, 2012 and 2013.  Although imperfect, at least it was something.  The biggest issue was that the neighbourhoods differed greatly in size and population, so this wasn’t really a fair rating across all neighbourhoods.  It was surprisingly difficult to find a dataset that provided neighbourhood size (without additional calculations), so I went with population.  Population probably makes more sense anyway because dividing the total number of crimes by population would come closer to estimating the chance that a crime will be committed against any one person (me, for example).

Next I added a location rating (above and beyond the various ratings based on proximity to important things that I discuss below) to give extra points to neighbourhoods that are close to my work and other places I tend to go often.  I settled on a centre point here because it seemed to be a pretty good average of all the good stuff in Edmonton.  A given neighbourhood would get a rating of 5 if it was within 3.5 km of this centre point, 4 within 5 km, 3 points within 7 km, etc..  Even with the location of each neighbourhood, it is surprisingly complicated to calculate the distance between two coordinates.  I ended up with a formula that looked something like:


Where W1 and W2 were the centre point coordinates and the latitude and longitude of the neighbourhood centroids were in columns U and V.  6371 km is the radius of the Earth, which I didn’t initially think I would need to buy a house.  As it turned out, most of my ratings needed this formula in some form or another.

Slightly more complex than the location rating, my grocery story rating was based on the distance to the nearest grocery store.  We really wanted to be able to quickly walk to the store to pick up any last minute food items.  As such, the centroid of the neighbourhood had to be within 0.7 km of a grocery store in order for the neighbourhood to get a rating of 5.  The difficult part was compiling locations of most grocery stores, especially since I needed the latitude and longitude.  I had some success cleaning up the source code from pages such as Save On Food’s Store Finder using a complicated procedure of finding and replacing.  Some of the data was also available through point of interest databases for GPS services and devices.  If neither of these options worked for a grocery chain (I’m looking at you Sobey’s), I was able to use a great tool called GPS Visualizer to convert a list of addresses into a list of location data.

A neighbourhood's transit rating was primarily based on proximity to an LRT station, and needed to be within 1 km to get a 5.  If it didn’t already have a 5, a neighbourhood would gain one extra point for being within 1 km of a transit centre.  I was previously able to find a dataset with all bus stop locations with type (apparently type is no longer listed as of this posting), which I whittled down to just LRT stations and transit centres.  Converting this data to the 5 point system was relatively similar to the above ratings.

Using the City of Edmonton’s Open Data Catalogue again, the proximity based ratings for libraries and spray parks were also relatively similar to the proximity ratings above.

Proximity to the river valley was a slightly more difficult dataset to find, so I ended up creating one.  Using Google Maps Engine I created a line of coordinates throughout the river valley and sub-valleys such as Whitemud Creek and Mill Creek.  I exported these as a KML file, then converted that to a CSV of location coordinates to import into my spreadsheet.

In addition to City of Edmonton pools I added the Meadows (which wasn’t on the city list yet), YMCAs, MacEwan U, U of A and NAIT pools to this dataset, then manually added additional data for “indoor” and “toddler-friendly”.  Neighbourhoods got higher rankings for being close to both indoor and toddler-friendly pools due to our family’s apparent tendency to be largely composed of toddlers.

For playgrounds, I would have ideally liked a rating based on how good and fun a playground is, but decided this wasn’t feasible without visiting each playground and rating it manually (a project for another year).  There are playgrounds all over the place, so instead I aimed to rate neighbourhoods based on how many playgrounds were within a certain distance of each neighbourhood.  Unfortunately, the City dataset appears to list each separate sand section within a playground as a separate playground (among other problems) so I wasn’t able to calculate this rating. In the end, I didn’t use a playground rating at all; instead, I made sure that the playgrounds near the houses we looked at were pretty good.

After compiling all of the above rankings for all of the neighbourhoods, I needed to combine them into a total score for each neighbourhood.  After some family discussion, I settled on the following weightings for each rating:

Grocery Store
River Valley Proximity
Spray Deck

Looking at these after the fact, more discussion and thought might have led us to change the bottom four, but I’m still happy with the top 5, which played a much bigger role in the total score of a neighbourhood anyway.  As you might expect, the formula for total score then became:

Neighbourhood Score = (schools ranking)*6 + (crime ranking)*5 + (location ranking)*5 + … + (pool ranking)*0.5

Drumroll please…after all that data and analysis, here are the top 96 neighbourhoods in Edmonton and their rankings!

RankingNeighbourhood (NH)Total RatingSchoolsCrimeLocationGrocery StoreTransitLibraryRiver ValleyPoolSpray Deck
2Malmo Plains115553454545
3Lendrum Place114544553335
5Royal Gardens113543554545
7North Glenora105.5534525545
8Rideau Park105.5543445344
10Central McDougall101.5505554355
11Fulton Place101.5453425545
22Queen Alexandra94.5405545445
25King Edward Park91.5424515545
26Meadowlark Park91.5523515445
28Windsor Park91.5055355543
30Empire Park90503555234
31Bonnie Doon88.5415325545
32Blue Quill88.5342452534
34Carter Crest88452405552
35Aspen Gardens87.5353322535
36West Meadowlark Park85.5523405445
38Jasper Park85503525535
42Spruce Avenue84.5404445245
44Sweet Grass84342343435
45Laurier Heights83154324525
50Gold Bar82153514545
51Forest Heights82424223535
52Patricia Heights82353303534
54Ramsay Heights80.5352314533
57Terwillegar Towne80451413351
65Boyle Street77.5105455545
66Lee Ridge77532304144
68Alberta Avenue76304345335
74Virginia Park74.5123445545
75Grandview Heights74.5054241524
78Sifton Park73.5321552253
86Blue Quill Estates71.5152332533
91Rundle Heights70312425554
95Callingwood North70322405555


After reading the paragraph above about grocery stores, you were probably thinking, “0.7 km to the closest grocery store?  But the neighbourhood itself is probably bigger than that!”  I too was worried, so I realized it was important to get a view of how much a neighbourhood rating could vary within any given neighbourhood.  In order to take a look within a specific neighbourhood, I converted the rows of my spreadsheet from the 387 or so neighbourhoods to a series of locations (latitude and longitude sets).  I was then able to import the scores for each of these locations (differing by less than 100 m) into a cool mapping tool called Open Heat Map to create the following maps.

I also used the City of Edmonton Neighbourhood Maps to take a look at the top neighbourhoods based on total points (ignore the fact that Royal Gardens is red):

Additionally, with some data graciously provided by Mike Ross I was able to add average house price and points/$10 000 to my spreadsheet.  Here are the neighbourhoods with the highest points per dollar:

Houses ("Houses.xls")

These maps and data, among other things, led my wife and me to focus our search primarily in Malmo, Royal Gardens and the surrounding areas, which of course necessitated the development of another spreadsheet.  This spreadsheet incorporated the “Neighbourhoods.xls” spreadsheet in its entirety (see above), but used the location of each house to calculate the location related rating instead of the location of the neighbourhood.  It also included an entire additional section for house specific statistics such as total surface area, number of bedrooms, yard size, and number of bathrooms.  For each house we viewed or that looked interesting, I calculated location points (similar to the neighbourhood points described above), house points (using house related ratings), total points, and total points per dollar based on asking price.

This spreadsheet also allowed me to track and take notes on houses we visited, and started to give me a view into market data by tracking the price at which houses sold and the number of days they were on the market (inspiring me to create the next spreadsheet).

I hadn’t initially intended to create a spreadsheet relating to market data; I really only wanted to learn a little bit about why people say things like “oh, now is a good time to buy…better hurry”.  I came across a few books about buying houses and managed to get through “Realty Check: Real Estate Secrets for First Time Canadian Home Buyers" by Sandra Rinomato, “Welcome Home: Insider Secrets for Buying or Selling your Property” by Sarah Daniels, and most interestingly, “Secrets of the Canadian Real Estate Cycle” by Don Campbell and others.  “Secrets of the Canadian Real Estate Cycle” gave me great insight into some of the variables that actually determine house prices in a given area, and even provided examples.  Unfortunately, these examples were often from Calgary, Vancouver or Toronto, which kept leading me to wonder, “what is the real estate cycle like in Edmonton?”  I would advise you to read or at least skim Don Campbell’s book in order for the rest of this section to make an optimal amount of sense.  The gist is that there are 16 or so key drivers that provide clues as to in what stage of the real estate cycle a given area is: recovery, boom or slump.  With good data going back in time for years, combining these drivers in an unbiased, scientific way can really help inform your real estate investment decisions.

So I started poking around to see if I could find some of the data that Don Campbell was talking about, but specific to Edmonton.  It was not in the least bit easy, but here’s what I found*:

*Much of the data has been averaged over each month or year to reduce noise.  For the Statistics Canada links, you will need to "Add/Remove" and "Manipulate" data using their form to find what I used.

Using Don’s method as best as I could, I came to the following conclusions about each of the key drivers of the real estate cycle in Edmonton in early 2014:

As a result, I inferred that we were at the end of the recovery phase and heading for a boom.  My family and I were probably going to buy a house anyway, so this didn’t really affect our decision, but it was very interesting and kind of fun.  If anyone knows more about this technique, please let me know how close I was in my assessment, or if I made any significant mistakes!


Finally it came time to buy the best house in the best place in the best neighbourhood in the best city in the world (for us); but could we afford it?  Obviously I had been working to gain a better understanding of mortgages long before this final stage, but the detailed mortgage spreadsheet I created really came in handy during our multiple offer bidding and negotiating situation.  I found the vast majority of online mortgage calculators to not provide enough information about how mortgages really work, often due to a bias towards the bank offering the calculator.  Plus, creating one myself gave me a deeper understanding and more confidence on how much more we could really afford to offer.

In addition to countless other financial spreadsheets, decision assistance spreadsheets and even an analysis of the cost of covering our house with bubble wrap, my efforts certainly seem to have paid off!  We moved into our new house in May and so far the experimental data has confirmed the above excel-based theoretical analysis that we are living in the best 586 m2 in the world** and we got a sweet deal on it!

**to live in, given our current priorities, life situation, and savings.