Administrative data are information collected by government or private sector organizations as part of their ongoing operations. Examples include records of births and deaths, taxation records, records about the flow of goods and people across borders, and data collected by satellites.

Like most other statistical agencies, Statistics Canada uses administrative data in lieu of or to complement survey data, and to support statistical operations. Using administrative data responsibly means the agency is able to improve data quality, meet new and ongoing information needs, reduce data collection costs and save time for Canadians who respond to our surveys. Administrative data are especially helpful to obtain data pertaining to populations or topics that may be difficult or costly to obtain by survey. Statistics Canada does this in a transparent manner.

Authority to obtain administrative data

The Statistics Act grants us authority to obtain administrative data. Statistics Canada ensures:

• privacy by collecting only the information we need to produce statistics and research, and limiting access based on the need to know
• security by keeping data safe from unauthorised access and use
• confidentiality by not releasing information that could directly or indirectly identify individuals, households, or businesses
• transparency in all our processes.

Use of administrative data in statistical programs

Administrative data enable Statistics Canada to fill information needs about Canadian society, the economy and environment. The use of administrative data reduces respondent burden, and improves quality and timeliness of data.

Statistical uses and benefits of administrative data include:

• Determining what we need to measure and the best means to do so
• Reducing response burden through the use of existing data
• Processing data to meet the highest quality and accuracy standards
• Benchmarking to validate the quality and accuracy of complementary data.

For additional information, visit Statistics Canada's Survey Methods and Practices Manual.

Principles for the Statistical Use of Administrative Data

1. To reduce the duplication of information collected from Canadians, and in accordance with the United Nations Fundamental Principles of Official Statistics and the Recommendation of the Council on Good Statistical Practice of the Organisation for Economic Co-operation and Development, Statistics Canada makes use of administrative data in the production of official statistics.
2. The statistical uses of administrative data are kept functionally separate from the administrative uses of such data through strong legal, policy and organizational safeguards. In particular, the confidentiality of all identifiable administrative data obtained by Statistics Canada for statistical purposes is protected by the Statistics Act and the data are only accessed within Statistics Canada based on demonstrated needs.
3. In collaboration with data providers, Statistics Canada uses its mandate under the Statistics Act to access administrative data for statistical purposes, and to influence the information collected by departments of government (including the provinces and territories), in order to enhance the usefulness of administrative data for statistical purposes and reduce duplication.
4. Statistics Canada's objective in using administrative data for statistical purposes is to achieve a better overall balance among cost, respondent burden and data quality, where quality refers to the relevance, accuracy, timeliness, accessibility, interpretability, and coherence of statistical outputs.
5. Administrative data are used for many different statistical purposes: replacing or complementing direct data collection to reduce costs and respondent burden; achieving efficiencies in statistical operations, such as the creation of survey frames, design of survey samples, imputation, estimation, and the measurement of the quality of other data; and developing and providing access to new data products (tabular or analytical), such as by integrating administrative data with other data.
6. Statistics Canada carefully considers the impact on privacy in using identifiable administrative data for a purpose that was not envisaged at the time of original data collection, particularly when administrative data are to be linked to other data.
7. Statistics Canada considers the possibility of using administrative data already available before undertaking new data collection activity.
8. Statistics Canada establishes and maintains ongoing, mutually beneficial relationships with data providers, both to ensure the long-term supply, usefulness and quality of administrative data, and to identify and influence future changes to administrative data sources that may affect statistical uses.
9. Statistics Canada assesses the fitness for use of administrative data sources and, in collaboration with data providers, identifies ways to improve the quality of the administrative data. In particular, the processes for collecting and producing the administrative data within the data provider's organization and the effects on the proposed statistical use must be well understood, and the quality of the administrative data must be documented.
10. Statistics Canada is transparent in its use of administrative data. It communicates the benefits of reduced respondent burden from using data already collected as well as the measures taken to protect the confidentiality of the data.

Fit for use assessment of administrative data

Statistics Canada aims to develop, produce and disseminate accurate and timely information that represents its data users and stakeholders. Administrative data products are therefore assessed against established criteria, such as relevance, timeliness, accuracy, accessibility, interpretability and coherence, to determine statistical usability. Statistics Canada's Quality Assurance Framework provides an overview of the approach used to support statistical program areas as they strive to achieve an appropriate balance between the various dimensions of quality and related factors, including the evolving needs of users, costs and respondent burden.

Microdata linkage is an internationally recognized statistical method that maximizes the use of existing information by linking different files and variables to create new information that benefit Canadians. Statistics Canada performs microdata linkages to support the design, maintenance, evaluation, research and redesign of ongoing data collection and methodological studies within Statistics Canada, as well as to provide statistical information in aggregate or anonymous format in support of research studies.

Statistics Canada takes your privacy and the confidentiality of your data very seriously. Several steps are taken during the process of microdata linkage to ensure that your personal information is kept confidential at all times.

Microdata can include units of a population, such as individuals, households or businesses. We first link the different data records by using the variables they have in common. In order to protect your confidentiality, all personal information are removed, so that the linked files are anonymized, or de-identified. In addition, synthetic versions rather than original data are often generated and accessed by researchers. This ensures your private information remains confidential while allowing researchers the access required to develop policies to help the Canadian public. The data is next aggregated or compiled to produce non-confidential published products. These confidential files are restricted to only Statistics Canada and deemed employees who are sworn to secrecy, and subject to fines and/or imprisonment, should they reveal confidential information.

Statistics Canada does recognize that researchers require access to microdata at the individual business, household or person level for research purposes. To preserve the privacy and confidentiality of individuals, businesses and organizations, and to encourage the use of microdata, Statistics Canada offers a wide range of options through a series of online channels, facilities and programs. More details are available at Access to microdata.

The Social Data Linkage Environment (SDLE), the Business – Linkable File Environment (B-LFE), and the Longitudinal Employee and Business Analytical Files (LEBAF) at Statistics Canada promote the innovative use of existing administrative and survey data, and expand the potential of data integration across multiple domains through the creation of linked analytical data files without the need to collect additional data from Canadians.

As part of its governance over microdata linkages, Statistics Canada has pre-approved specific types of linkages. The linkages involved are those where the privacy risks and situations of potential conflict of interest are low and where procedures to mitigate risk to confidentiality and privacy are in place.

All other microdata linkages must undergo a prescribed review and approval process, which involves the submission of well documented proposals. When such linkages include personal information, a summary of the approved microdata linkage is posted on Statistics Canada's website.

Microdata linkage must adhere to Statistics Canada's Directive on Microdata Linkage, which is designed to ensure the public value of each linkage truly outweighs any intrusion on privacy that it represents. For more information on the microdata linkage process and a summary of the approved microdata linkages see Microdata Linkage at Statistics Canada.

If you have any questions about microdata linkage, please contact the Departmental Privacy Officer at the following address:

Pierre Desrochers
Departmental Privacy Officer
Office of Privacy Management and Information Coordination
Strategic Data Management Field
Statistics Canada / Government of Canada
Pierre.Desrochers3@statcan.gc.ca / Tel: 613-894-4086

According to the Canada Open Government website, open data is defined as structured data that is machine-readable, freely shared, used and built on without restrictions.

Smart cities and governments are increasingly making use of data when looking to implement problem-solving measures to provide efficient and effective services to constituents. Open data invites innovation, not only through government channels, but also through grassroots organizations, individuals, and businesses. The benefit of open data is that any user can access and make use of it freely. Individuals, formal and informal organizations, or enterprises can use the data and other information to research and innovate on any number of topics.

Statistics Canada has recently embarked upon an exploratory initiative which aims to enhance the use and harmonization of open building data from government sources, for the purpose of contributing to the creation of a complete, comprehensive and open database of buildings in Canada. The outcome of this exploratory work led to the creation of the Open Database of Building (ODB), a centralized and harmonized repository of building data made available under the Open Government Licence - Canada.

This initiative originates from insights taken from the Statistics Canada pilot project on data crowdsourcing, which used OpenStreetMap as a platform for integrating data on building footprints. In addition to the possible benefits of crowdsourcing, the project highlighted the potential of integrating open data from municipal, regional, and provincial governments to meet the needs of official statistics.

In this information era, it is more important than ever to provide Canadians with reliable and timely data in order to enable informed decision-making.

Statistics Canada is using web scraping to gather data efficiently. Web scraping is a process by which information is collected and copied from the Internet for analysis.

The use of web scraping is part of a broader effort to reduce burden on businesses and organizations while continuing to provide high-quality, timely data in a cost-effective manner.

In the spirit of openness and transparency, Statistics Canada is committed to respecting the following best practices when conducting web scraping.

Statistics Canada will:

• Transparency
  • carry out web scraping activities in a transparent, consistent and ethical manner;
  • notify the relevant companies that web scraping activities will be taking place;
  • publish the results of the web scraping activities on its website;
  • conduct all web scraping activities on Statistics Canada authorized computer equipment connected to its highly secure networks and secure the data on encrypted servers.
• Ethics
  • use web scraped data appropriately and responsibly in statistical programs in order to facilitate fulfilment of its mandate;
  • collect only data available to the public from businesses and organizations for use in its statistical and research programs;
  • take steps to minimize burden on the websites, such as scraping during off-peak hours and only as needed, and coordinating data requirements across statistical programs to avoid duplicating efforts;
  • use an application programming interface (API) when possible in lieu of web scraping;
  • limit collection to only what is necessary and proportional for the production of the required statistical outputs.

Statistics Canada will not:

• scrape personal information about individuals from any website;
• scrape personal information that could establish a profile of individuals;
• resell web scraped data or use them for commercial purposes;
• scrape any information that will not be used to produce statistical outputs.

Useful links

• Web scraping in the COVID-19 pandemic
• Frequently asked questions

As part of Statistics Canada's modernization, crowdsourcing has become an innovative way to collect valuable information for statistical purposes. Crowdsourcing involves collecting information from a a large community of users. It relies on the principle that individual citizens are experts within their local environments. Crowdsourcing surveys further permit us to benchmark and validate the data with other sources of complementary data to ensure that the results are of good quality. A couple of examples are outlined below.

The OpenStreetMap (OSM) crowdsourcing pilot project, which was completed in March 2018, crowdsourced geographic information by mapping the building footprints in the Ottawa, Ontario and Gatineau, Quebec areas. The network and experience of this pilot project helped to launch the Building Canada 2020 initiative (BC2020), aimed at mapping all building footprints of Canada on OSM by the year 2020.

Statistics Canada recently embarked on an innovative project on crowdsourcing the price of the cannabis sector before legalization. This initiative continues to collect information on a relatively new market and helps to monitor prices in a confidential and non-intrusive manner. Statistics Canada has asked the general public to provide the price of their last purchase of dried cannabis; this information is collected anonymously and is disseminated to the public through a dedicated application and web page.

Investment, Science, and Technology Division

Table of Contents

1. Introduction
2. Key definitions
   1. Price base date
   2. Volume state date
   3. Residential property
   4. Non-residential property
   5. Properties subject to municipal, provincial, territorial and federal payment-in-lieu
3. Input data
   1. Data sources
   2. Unit reported
4. Auxiliary Data
   1. Multiple Listing Service data
   2. Fédération des chambres immobilières du Québec
   3. Building permit and investment in construction data
   4. Census of Population
   5. Census of Agriculture
   6. List of CSDs from the Data Integration Infrastructure Division
5. Classification
   1. Geography
   2. Property type
6. Imputation for missing data
   1. Imputation of residential values
   2. Imputation of non-residential values
7. Price adjustments
   1. Choice of Source Data Vintage
   2. Residential Price adjustment
      1. Overview of price adjustment methodology
      2. Calculating weighted monthly average resale price
      3. Residential price index for Nunavut
   3. Non-residential price adjustment
8. Volume adjustments
   1. Residential volume adjustments
   2. Non-residential volume adjustments
9. Removals and adjustments in accordance with typical property assessment and taxation practices
   1. Removal of CSDs on account of First Nations and other Aboriginal Groups
   2. Exclusion of exempt residential property
   3. Exclusions of schools, churches and hospitals
   4. Removal of properties subject to provincial-territorial and municipal payments-in-lieu of taxes
   5. Adjustments in the Northwest Territories and Nunavut
   6. Removal of machinery and equipment values in Alberta, Northwest Territories and Nunavut
10. Other Adjustments
    1. Mixed-use properties
    2. Estimation of farm residence values
11. Quality control
    Annex 1. List of CSD types representing First Nations and other Aboriginal Groups

1. Introduction

The Property Values Program produces annual estimates of assessment values of properties at current prices across Canada. Finance Canada uses these estimates to determine fiscal capacity with respect to property taxes for the Equalization program and the Territorial Formula Financing (TFF) program. ^{Footnote 1} In order to ensure comparability of the data, a number of adjustments are made, including: coding property categories to a common classification; adjusting to a common price base date and volume state (or stock) date; and imputation of missing property values in some areas. Additionally, other removals and adjustments are carried out in order to produce estimates of assessment values at current price that meet the requirements to determine fiscal capacity.

This document presents these adjustments in more detail.

2. Key definitions

a. Price base date ^{Footnote 2}

The price base date (also called the valuation date) corresponds to a fixed point in time as of when a property is valued.

b. Volume state date

The volume state date is the fixed point in time as of when the stock of properties is recorded, which also corresponds to the date where all properties are represented in an assessment roll data file.

c. Residential property

Defined as all types of property categorized as residential for assessment purposes in the majority of provinces and territories. It includes single and multi-unit properties, farm residences, cottages and vacation homes, mobile homes, and vacant lands which are lawfully usable for residential purposes.

d. Non-residential property

Defined as all types of property categorized as non-residential for assessment purposes in the majority of provinces and territories. It includes industrial, commercial and institutional properties, engineering construction and mining properties, and vacant lands which are lawfully usable for non-residential purposes.

Agricultural properties ^{Footnote 3} (not including farm residences, which are part of residential property) as well as machinery and equipment properties are excluded from final estimates.

e. Properties subject to municipal, provincial, territorial and federal payment-in-lieu

Defined as municipal, provincial, territorial and federal government-owned property for which owners remit payment-in-lieu of tax to municipal governments or local taxation authorities for receiving municipal services. A payment-in-lieu of taxes is made to compensate a local government for some or all of the tax revenue that it loses because of the nature of the ownership or use of a particular piece of real property. Usually, no property tax is collected for buildings owned by government.

3. Input data

a. Data sources

Assessment data are collected from provincial, territorial and municipal assessment entities and are based on municipal assessment rolls. Data providers agree to provide the data on a regular basis either through formal agreements or responding per data request.

Starting in January 2018, assessment roll microdata is gradually being received from every jurisdiction, to replace the use of assessment roll aggregate data. The objective is to receive microdata from each province and territory within two years.

b. Unit reported

Data are reported either at the municipality level, or at property or sub-property level.

4. Auxiliary Data

a. Multiple Listing Service data

Multiple Listing Service (MLS) data are produced by the Canadian Real Estate Association (CREA). The data are obtained via Haver Analytics, a company that is the sole distributer of CREA MLS data. MLS data are for resale homes and are comprised of dollar volume sales and number of units sold by real estate board. Data are available for all provinces and territories with the exception of Québec and Nunavut.

b. Fédération des chambres immobilières du Québec

The Fédération des chambres immobilières du Québec (FCIQ) produces home resale data for several real estate boards within Quebec as well as provincial figures.

c. Building permit and investment in construction data

Data on the number of residential and non-residential building permits issued, investment in construction completion, by type of work (e.g., new unit, conversion, etc.), is obtained from Statistics Canada's Building and Demolition Permits (BDP) and Investment programs. The data are produced monthly, by jurisdiction.

d. Census of Population

Data from Census of population are available every five years. Between census years, yearly values, referred to as "Intercensal" values, are derived using linear interpolation. ^{Footnote 4} These values are used at various stages of the production cycle such as for the imputation of missing values and for the estimation of farm residences.

e. Census of Agriculture

Similar to the Census of population, data from Census of Agriculture are available every five years. Yearly values ("Intercensal" values) are also derived using linear interpolation and used during the production cycle. Census of Agriculture values are used to estimate the values of farm residences in Ontario, Saskatchewan and British Columbia, provinces where such values are embedded in totals or are missing.

f. List of CSDs from the Data Integration Infrastructure Division

The list of Census Subdivisions (CSD) is produced, maintained and updated annually by the Data Integration Infrastructure Division at Statistics Canada.

5. Classification

a. Geography

The municipalities covered by the collected data are assigned to Census Subdivisions (CSDs) updated annually by Statistics Canada's Data Integration Infrastructure Division, using the Standard Geographical Classification system. The assignment of CSDs is revised yearly to reflect changes (municipal amalgamations, legal status changes, etc.) that occur during the year.

CSDs containing First Nations or other autonomous or self-governing areas are out of scope for Fiscal Arrangements purposes (see Annex 1); consequently, estimates are not produced for these CSDs.

b. Property type

Property type concordance tables are developed at the provincial and territorial level to classify properties according to property attributes found on the input data. With the arrival of assessment roll microdata, the classification of properties is more precise using new property attributes now present on the source files. New types of properties are introduced in the classification to better represent the data sources.

6. Imputation for missing data

There exist municipalities or regions that are not assessed by provincial or territorial assessment bodies, and therefore no property taxes are levied. As a result, assessment values are missing for some jurisdictions, mostly in unorganized areas. ^{Footnote 5} Additionally, on occasion, some municipalities submit their assessment values to assessment bodies later than when the data are required. Missing property assessment values for these municipalities are imputed.

For taxation year 2017, there were 153 jurisdictions with missing data that were imputed, 143 of which were in Newfoundland-and-Labrador, 7 were in Northwest Territories and 3 were in Saskatchewan.

a. Imputation of residential values

The imputed residential value for a CSD is calculated by multiplying the number of private dwellings by the average value of owner-occupied dwellings for the CSD from the intercensal Census of Population file.

In order to produce an imputed value that best reflects the desired price base and volume state dates:

• the number of private dwellings value is taken from the yearly intercensal file of the same year as the volume state date of the raw file; and
• the average value of owner-occupied dwellings is taken from the yearly intercensal file or derived from assessed values of the same year as the price base date of the raw file.

The resulting imputed values are then processed and adjusted ^{Footnote 6} using the same methodology as for raw values.

b. Imputation of non-residential values

Unlike the imputation for residential property values where dwelling values from intercensal files can be used to estimate the value of residential properties, no similar direct indicator is available for non-residential properties. Therefore, non-residential values are imputed using data of CSDs with similar Census population counts within the same province or territory.

Ratios of the total non-residential values over the total population are calculated using data from CSDs for each population class (see table 1 below) for each province and territory. These ratios ^{Footnote 7} are then applied to the population count of the missing CSD to derive the imputed non-residential value. Most of the missing CSDs are from rural areas.

7. Price adjustments

Due to differences in assessment practices and frequency of revaluation practices, data received do not always align with the target price base date of July 1 of the year preceding the taxation year.

a. Choice of Source Data Vintage

In order to minimize price adjustments, the data from the file whose price base date most closely aligns with the target price base date is used to produce the estimates of a given taxation year. In the event that two input files have the same time interval between their price base date and the target price base date, the file with the closest volume state date is selected.

b. Residential Price adjustment

Price adjustments for residential properties are derived using monthly sales data for the resale housing market from MLS and FCIQ. Nunavut is the only region for which resale housing data does not exist; therefore, a residential price index is constructed for this territory.

i. Overview of price adjustment methodology

For a given province or territory, the price adjustment calculations are done at two geographic levels:

• CSDs within a Census Metropolitan Area (CMA)
• CSDs outside CMAs (or rest of the province)

For Newfoundland and Labrador, ^{Footnote 9} Prince Edward Island, Yukon and Northwest Territories, there are no data available at the CMA level; therefore, the price adjustment calculations are performed on the provincial and territorial totals.

ii. Calculating weighted monthly average resale price

In order to smooth seasonal fluctuations that can exist in monthly data, weighted monthly average prices are used in the calculation of the residential price adjustment. For a given month, twelve consecutive months of data (period beginning six months before and ending five months after the month) are used.

For a given month k, the formula for calculating the weighted monthly average is as follows:

${\text{Weighted\_Monthly\_Average}}_k\text{=}\frac{\sum _{k-6}^{k+5}{\text{ResDollarVol}}_k}{\sum _{k-6}^{k+5}{\text{ResUnitSold}}_k}$

Where ResDollarVol is the total dollar value of the monthly residential sales and ResUnitSold is the monthly total number of residential units sold.

The residential price adjustments are performed at the CSD level. To arrive at the price adjusted assessment value, the ratio of the weighted monthly average for the month of the target price date over the weighted monthly average for the month of the price base date is calculated. The ratio that is applied to the assessment value of a given CSD is dependent on if the CSD is located in a CMA, and is dependent on the province or territory that it is located in.

iii. Residential price index for Nunavut

As resale data do not exist for Nunavut, Statistics Canada uses data for the region of northern Quebec (NQC) ^{Footnote 10} as a proxy for this territory. ^{Footnote 11} The property assessment data are provided by the provincial Government of Quebec.

The Nunavut residential index is calculated using an unweighted average of residential and non-residential property values reported. ^{Footnote 12}

An annual series is generated and converted into a monthly series by adding one twelfth of the dollar difference between two observations to each successive month between observed values (linear interpolation), creating a monthly index. Residential price-adjustments are then applied to Nunavut property values using the same algorithm (for ratios) designed for resale data.

c. Non-residential price adjustment

Unlike residential properties, non-residential properties (more specifically industrial, commercial, and industrial (ICI)) are not often for sale. It is therefore comparatively more difficult to find appropriate market indicators to use for non-residential price adjustment. To overcome this, the correlation between residential and non-residential price changes was analysed.

A regression analysis was performed and a model was constructed using aggregate raw data from four provinces: Prince Edward Island, New Brunswick, Quebec and British Columbia. The reasons for using these specific four provinces are twofold: (1) these provinces evaluate their property stock on an annual basis, ^{Footnote 13} and (2) they report data for both assessment values and numbers of properties. This level of detail allowed the derivation of the annual non-residential price movements.

Based on the regression analysis using data from the four provinces mentioned above, the conclusion was to use the model coefficient of 0.73336 as a discount factor to the residential series and also to the residential price index in Nunavut. ^{Footnote 14} The resulting series, generated by applying the discount factor to the residential series, is used for the price adjustment of non-residential values for all provinces and territories.

8. Volume adjustments

Volume adjustments ensure that properties reflect a common volume state date of January 1st of the taxation year. For assessment data that reflects a volume state date earlier or later than the target volume state date, the value of all completed construction that occurred in the period between the two dates is estimated using Statistics Canada's monthly Building and Demolition Permits (BDP) Program or from the Investment Program and then added or subtracted, as the case may be, from the total property values. This methodology is used for both residential and non-residential property values.

a. Residential volume adjustments

For residential properties, the volume adjustment is calculated by estimating the construction that was completed in between the volume state date and the target volume state date using the number of permits from the BDP survey and monthly resale values, or from using the investment in construction completion values.

Based on an analysis of residential construction data, the assumption is made that a residential property being newly built can be considered to be a substantially finished (assessable) unit approximately three months after a building permit is issued. Therefore, for a given month, the BDP data used for the volume adjustment is that of the given month minus three (so for example for June we would use the March BDP data).

For each month falling in the period between the volume state date and target volume state date, the number of permits is multiplied by the average monthly resale value from MLS/FCIQ (for all provinces and territories other than Nunavut), to obtain a monthly volume adjustment value. The monthly volume adjustment values are summed for each month to arrive at the total volume adjustment for the period. For Nunavut, the average assessment value for Northern Québec is used.

Similarly, construction completion values represent the total investment in construction available upon completion of construction. Monthly values that fall between the volume state date and the target volume state date are summed for an estimated total volume adjustment for the period.

Although the two methods are comparable, volume adjustments calculated using investment in construction completion values are slightly more accurate than those calculated using building permit values. When investment in construction completion values are available, they are used in the calculations over the use of building permit values. Residential volume adjustments account for approximately 2% of total values.

b. Non-residential volume adjustments

For non-residential construction, the assumption is that a property undergoing construction can be considered to be a substantially finished (assessable) unit approximately eight months after a building permit is issued. As non-residential construction projects vary significantly in scope, size, and values, using an average value is not recommended; therefore, the volume adjustment methodology for residential properties cannot be applied to the non-residential.

Instead of using the number of permits, the total value of all permits issued, by month, is used. Consequently, "total values of the non-residential permits" that were issued during the period (using an 8-months lag to allow for construction to be completed) is used to obtain the volume adjustment value. This estimate of new property construction value is used to adjust the total estimates.

Instead of using the number of permits, the total value of all permits issued, by month, is used. Consequently, "total values of the non-residential permits" that were issued during the period (using an 8-months lag to allow for construction to be completed) is used to obtain the volume adjustment value. This estimate of new property construction value is used to adjust the total estimates.

9. Removals and adjustments in accordance with typical property assessment and taxation practices

a. Removal of CSDs on account of First Nations and other Aboriginal Groups

Census subdivisions containing First Nations reserves, and autonomous or self-governing areas are removed as they are deemed out of scope. Such CSDs are identified based on their CSD type. ^{Footnote 15} For taxation year 2017 estimates there were 18 CSDs that were classified as out of scope and removed from final estimates. Of the CSDs that were removed, 34 had assessment values, representing 0.04% of total assessment values across Canada.

b. Exclusion of exempt residential property

In some provinces, certain properties are identified as exempt from property taxes as presented in the input files received from the assessment bodies. Any value associated with these properties are excluded from estimates for the purposes of fiscal arrangements.

c. Exclusions of schools, churches and hospitals

The most important non-residential properties which are generally exempt from property taxes are schools, churches and hospitals (S/C/H).

Some provinces and territories provide detailed breakdowns of S/C/H in their assessment data. For these provinces and territories, the exact proportion of S/C/H is removed from the final estimates.

For provinces and territories where the S/C/H breakdowns are not available, the proportion of the S/C/H assessment values relative to total assessment values for non-residential properties is estimated by calculating and applying the proportion of S/C/H property values from a similar reporting province or territory. It should be noted that values for engineering and mining properties are excluded from the total assessment value for non-residential properties used in the calculation of the S/C/H proportions.

The list of provinces and territories used in the calculation of estimated S/C/H proportion depends on data availability and can change from one year to the next as microdata is received.

d. Removal of properties subject to provincial-territorial and municipal payments-in-lieu of taxes

Instead of regular property taxes, federal, provincial or municipal government usually remit a payment in lieu of taxes (PILT) for their exempt properties. However, only federal PILT property represents fiscal capacity for the consolidated provincial-territorial-municipal-local sector; provincial, territorial and municipal (PTM) PILT properties are excluded.

When breakdowns of values of PILT properties are not available, as is the case for a number of provinces and territories, these values are estimated. The estimation of PM-PILT values takes into account the S/C/H values, some of which are also PTM-PILT properties, which have already been removed. Only the "remaining" PILT values are estimated and removed.

Although the estimation methodology using aggregate assessment roll data is successful in estimating the remaining proportion to remove, the arrival of assessment roll microdata allows for a more precise estimation of remaining PILT proportions to remove.

e. Adjustments in the Northwest Territories and Nunavut

Unlike in provinces and the Yukon, property assessments in the Northwest Territories and Nunavut do not consistently follow market value standards.

Land values within the municipal taxation areas (Iqaluit in Nunavut; Yellowknife, Fort Simpson, Fort Smith, Hay River, Norman Wells and Inuvik in NWT), reflect full market value, while land values in the remainder of the two territories (i.e. in the General Taxation Areas) are, according to the data provider, based on average regional development costs.

Improvements (i.e. buildings) in both territories are assessed based on depreciated Edmonton construction costs, using Alberta's depreciation schedule. The value so determined for Yellowknife is then multiplied by a factor of 1.35, which is set out in regulations. According to the assessment data provider, this was done to reflect Yellowknife's actual construction costs relative to Edmonton's. Yellowknife's assessed building values therefore approximately reflect market value. ^{Footnote 16}

Outside of Yellowknife, in the two territories, a discount factor of 0.666 has been applied to building values initially assessed at depreciated Edmonton construction costs. This factor is also set out in regulations and, according to the assessment data provider, was introduced to encourage development. Upon data entry, this embedded 0.666 scaling factor is removed from the building values in the Northwest Territories outside of Yellowknife and Nunavut.

f. Removal of machinery and equipment values in Alberta, Northwest Territories and Nunavut

Property values for machinery and equipment (M&E) components in the non-residential category are deemed to be out of scope.

The data received from Northwest Territories and Nunavut contain a sizeable share of M&E components in the non-residential total. They are mainly embedded in the following three non-residential classes: mineral, transmission and hydrocarbon. The M&E components are removed by multiplying the reported improvement values by a deflationary factor for each of the previously mentioned three non-residential classes. These factors are provided yearly by the respondents. This treatment ensures that only real property values are included in final estimates, and that the M&E components are excluded.

In Alberta, property values for the M&E components are reported separately by the data providers and are excluded from the final estimates.

10. Other Adjustments

a. Mixed-use properties

Some properties are used for both residential and non-residential purposes. In cases where no further breakdowns are available, the values of mixed-use properties are redistributed between residential and non-residential property types according to the existing distribution of total residential and non-residential property values by CSD. In cases where further breakdowns are available, mostly in jurisdictions where microdata was received, the values are assigned according to the exact breakdown. The mixed-use properties represent 0.16% of the total valuation of properties in Canada.

One of the most common cases of mixed-use type properties are of a building consisting of ground level commercial with one or more floors of residential units above.

b. Estimation of farm residence values

In Ontario and British Columbia, farm residence values are embedded in the general agricultural category, and not identified separately. Because the farm residence values are to be classified under the residential category, farm residence values are estimated, removed from the Agriculture category and reclassified to the residential category.

In some jurisdictions in Saskatchewan, farm residences are not assessed and therefore their values are not included in the data received for Saskatchewan. Since the majority of provinces include farm residence values in their assessment values, farm residences for these jurisdictions in Saskatchewan are estimated and added to the Saskatchewan total residential values.

To estimate farm residence values, auxiliary data from Statistics Canada's Census of Population and the Census of Agriculture are used. As more recent auxiliary data become available, estimates are revised yearly.

Farm residence value is estimated by multiplying the average rural house price by the number of farms. The average rural house price is derived from the Census of Population by taking the weighted average value of owner-occupied dwellings of CSDs with a population of less than 10,000. The number of farms is taken from the Census of Agriculture.

11. Quality control

Statistics Canada's quality assurance framework requires an assessment of data relevance, accuracy, timeliness, accessibility, interpretability and coherence. The quality of the raw input data collected from provincial, territorial and municipal assessment departments and agencies cannot be evaluated in this framework. However, confrontational analysis is performed to compare the source data to existing statistical programs and public information such as annual reports obtained from Provincial websites. Any irregularities identified are carefully reviewed and analyzed before the official release of the data.

Total adjusted residential estimates, for both taxable and exempt properties, are compared to Statistics Canada's Census of Population. The coherence of the values is examined by census coverage analysis, which compares the source data to private dwelling counts and values found in Statistics Canada's Census of Population.

Annex 1. List of CSD types representing First Nations and other Aboriginal Groups ^{Footnote 17}

The following are the list of CSD types representing First Nations and other Aboriginal groups presented by province and territory. As of December 31 2017 there are 18 CSDs that are of one of these CSD types.