Thanks for listening to our second podcast! If you haven’t listened yet, you can do so using the player above or by searching for Mean, Median, and Moose in your favourite podcast app. We’ll be using these blog posts to provide images and links to some of the items we discuss during the podcast, and also to cover what we may have missed in our discussions. Today we’re talking about property taxes, municipality open data, and what you may have missed in the Statistics Canada dailies.
Property taxes are paid by every property owner in Canada to help their city or municipality pay for services like policing, fire fighters, parks, libraries, and storm drain maintenance. Occasionally you might hear someone complain that their property taxes are too high. But what does that mean exactly? A common argument made is that your property taxes are higher than another municipality’s. So, let’s examine the various ways property taxes can be compared and see if we can learn something about property taxes along the way.
Property tax, unlike income tax, is based on the value of your property, not what you earn. Renters pay it too, but indirectly as part of their rent. Attaching the amount you pay to the value of your property is intended to make the tax progressive. So, if you have a more expensive house relative to others in your city, it is assumed that you have the capacity to pay more in property taxes, and so you are charged more.
We’ll take a look at three ways to compare property taxes between municipalities: the tax rate (mill rate), the average tax revenue per household, and the taxes received for a representative house.
This is the percentage of your property value you pay each year to your municipality. What makes the measure interesting is that property values between cities are often detached from how much it costs to provide services in those cities. The costs of providing library services to someone doesn’t correlate very well to how much houses are valued at. As such you can have lower tax rates in areas where property values are higher. If you view property ownership primarily as an investment this can be an attractive prospect. But, when it comes down to it, when you compare municipalities that provide similar services solely on tax rates, it’s likely that the property values in the areas with lower tax rates are going to be much higher.
Look, for example, at the highest and lowest municipal tax jurisdictions in Ontario based on property tax rate:
If those cities are difficult to recognize, here’s all of the tax rates in Ontario on a map:
Many of the highest tax rate areas are in Northern Ontario where property prices are low. Similarly, many of the lowest tax rate areas are in the GTA and Ottawa – where property prices are relatively higher than the rest of the province. If you’d like to check out all the schedules for all the property taxes in Ontario cities, check out schedule 22 of the Ontario FIRs.
We have done a similar analysis for BC. In BC, the property taxes generally are higher than Ontario, but the services mix is also different. The most obvious example is that property taxes contribute to hospital funding in BC, but not so in Ontario. This is something to keep in mind when comparing property taxes even within a province – which services are provided are an important consideration.
Property Taxes Per Household
Another way to look at residential property taxes is to divide the total amount of revenue received by the number of households in a city. For Ontario, you can do this with the FIRs as well. This has the advantage of focusing on the amount people pay for their services instead of a rate. You can see how much residential property tax is paid in order to provide services to the average household. Let’s take a look at those rankings:
Interestingly, the picture is quite different from the tax rates. For example, Markham is ranked 17th lowest (out of 426 municipalities) in Ontario in terms of property tax rate. But it ranks 11th highest in terms of tax revenue per household. If you lived there you would be paying a relatively small amount of taxes compared to your property value, but the total amount you’re paying is also relatively high compared to if you lived elsewhere in Ontario. The problem with revenue per household measures is that comparisons between municipalities may be complicated if house values or housing stock differs greatly between the cities you are comparing. To illustrate the problem here, consider two imaginary cities. One has 90% apartment buildings while the other has 90% detached single family homes. Comparing property taxes between those two cities based on revenue per household could be problematic.
Taxes Paid for Representative House
If property tax rates are affected so much by a city’s property values, and revenue per household doesn’t take into account the nature of the housing stock in the comparative cities, what could help make cities comparable? One option is to compare the taxes for a “representative house”. Ontario doesn’t have this on hand, but BC does publish a representative house comparison as a part of its local government statistics. While we were unable to find a formal definition on the BC government website, some sources indicate that a “representative house” for the BC statistics is defined as the average of the detached single family homes within a city.
When looking at taxes paid for representative properties we again see the opposite effect as we saw just looking at tax rates. Highly urban areas known for high property values also show the most paid in property taxes for detached single family homes.
Open Data for Cities in Canada
In this section we’ll provide some links to the rankings and reports we mentioned in the podcast. We talked about the Public Sector Digest (PSD) open data rankings. You can find the rankings for 2017 and 2019 here. PSD paused the OCI program in 2018 to focus efforts on the launch of the first Geospatial Maturity Index (GMI), measuring the maturity of GIS programs across North America. The rankings are part of a larger report you can find here. If you’re more interested in rankings at the national level, take a look at the Open Data Barometer rankings here.
Edmonton’s fantastic open data portal includes wonderful data sets such as hens per neighborhood. Winnipeg’s open data portal is available at this link. We talked about their “trees per neighborhood” data set and their Capital Projects Explorer. We also talked about how even the excellent Open Ottawa site pales in comparison to these two stars.
Adventures in Edmonton’s Speed Camera Data
Doug talked about how the raw data is hard to interpret and work with directly. To answer the question “What is the speediest street in Edmonton”, you either have to go to an external API to get the street(s) at a latitude/longitude location or normalize a bear of a description column:
He came up with some SQL hacking and managed to get a pretty good result anyway:
It’s messy and you shouldn’t draw conclusions on the data, but knowing 18 locations where more than 50% of vehicles are exceeding the speed limit could help someone start to ask the right questions:
You can take a look at the data and it’s dictionary yourself at this page.
What You May Have Missed from Statistics Canada in September
Frazier and Doug talked turkey about some deep cuts into the Statistics Canada Dailies. You can find the poultry and egg statistics at this link.
The data on nurse overtime in the age of COVID-19 can be found here.
Weekly aircraft movements can be found here.
That’s it for this month! Be sure to rate and comment on the podcast so that more people can learn about us and open data in Canada! If you have any comments or want to reach out to us, feel free to drop us a tweet @MeanMedianMoose.