Prices Analytical Series
Owned Accommodation in the Canadian Consumer Price Index: A Comparative Analysis of Measurement Approaches, Including an Exploration of Asking-Rent Rental Equivalence

Executive Summary

Introduction

In the context of the Consumer Price Index (CPI), owned accommodation (OA) refers to the cost of owning a home and how this cost changes over time. The measurement of OA in the CPI is an area of ongoing debate, with no current consensus on a preferred method as each has its own advantages and disadvantages.

This paper builds on previous work by extending the analysis of alternative approaches to measuring OA, with a particular focus on rental equivalence (RE). A key motivation is that the official rent index—commonly used as a proxy for RE—is primarily driven by the primary (purpose-built) rental market, which consists of privately developed structures intended for long-term rental supply. As a result, it may not fully capture dynamics in the secondary rental market, where a significant share of rental activity occurs. To address this gap, the analysis explores alternative treatments of OA in the Canadian context, including exploratory RE measures based on asking rents from online listings. This allows us to assess how housing inflation may have evolved over the past 20 years under different methodologies. The inclusion of asking-rent–based RE aims to better capture dynamics in the secondary rental market and provide more timely insights into how rent movements affect OA and overall inflation indicators.

Background

The method used to calculate the CPI follows accepted international standards. The CPI serves various purposes, including the indexation of payments (such as public and private pensions) and tax brackets, as a deflator of other economic aggregates, as a tool for setting monetary policy and as a gauge for the wellbeing of the economy. The approach adopted by Statistics Canada in calculating the CPI must balance these various uses of the CPI. OA is a key CPI component, representing more than 18% of the CPI basket of goods and services, and therefore has a significant impact on the all-items CPI. Statistics Canada’s current treatment of OA aligns with the conceptual basis that best satisfies the principal purpose of the CPI and reflects price change for the average Canadian. Housing affordability is illustrated in a number of Statistics Canada products, including the Housing Statistics Portal, indicators of household wealth and affordability, and distributions of household economic accounts.

Treatment of owned accommodation

There are four main approaches to OA: the acquisition/net acquisition approach, the payments approach, the rental equivalence approach and the user cost approach, with some countries excluding OA from the CPI entirely. The Canadian CPI uses a variant of these approaches that measures the change in the cost of owning (rather than buying) a home by tracking the ongoing costs of homeownership, including replacement cost, mortgage interest cost, property taxes, homeowners’ home and mortgage insurance, homeowners’ maintenance and repairs and other OA expenses, while excluding the principal mortgage payment and capital gains. Using this method, price changes in the housing market are represented in the CPI, either explicitly (replacement cost and mortgage interest cost) or implicitly (property assessment values affecting taxes, the replacement cost of houses affecting insurance premiums). In short, the Canadian CPI, like most others around the world, treats housing as a service people use and incorporates the price of that service into the index, rather than including the purchase price of an asset.

While each method has advantages and disadvantages and, globally, there is no consensus on a preferred method, there are situational factors in different countries that may impact the suitability of one or more of these approaches in a specific market. For instance, while the rental equivalence approach is the most commonly used worldwide, countries employing it typically feature less regulated and deeper rental markets. This includes many European countries, where the rental equivalence method is supported by extensive and high-quality rental data, and the United States, where changes in the financial markets and the emergence of new types of mortgage instruments in the early 1980s created additional complexities in accurately estimating the expenditure weight and monthly price movement of contracted mortgage interest costs. Australia and New Zealand employ the net acquisition approach, as both countries have robust data on building costs, which allow them to price a housing structure separately from land. No countries employ an acquisition approach and only Ireland uses a payment approach.

Analysis

In analyzing the impact of each approach on the OA index, the acquisition approach, followed by the net acquisition approach, resulted in the largest increases to the index over the last decade. The Canadian approach was third, closely followed by the payments approach but firmly above the rental equivalence (both using the official CPI Rent index and alternative rental equivalence measures based on asking rents from online listings) and user cost approaches. The Canadian approach to OA and other main methods produce analytical CPI series that generally follow similar long-term trends, particularly over the past decade, although they differ in the magnitude of growth observed during periods of strong housing market activity.

However, important differences emerge in terms of volatility. The acquisition approach exhibits significantly higher volatility than other methods, with pronounced peaks during housing booms and sharp declines during downturns. This heightened volatility is largely driven by the inclusion of an asset component, which introduces price movements that are not directly related to the consumption of housing services. As a result, the acquisition approach can generate excess volatility in measured inflation, making it less suitable for CPI purposes. This is why it is not adopted by countries for official CPI compilation, particularly for indexation, as it does not provide a stable or representative measure of housing costs for either homeowners or renters.

Conclusion

This analysis demonstrates that the official treatment of OA used in the Canadian CPI remains the best approach for Canada. This approach is aligned with the conceptual purpose of consumer price indexation, balances the various uses of the CPI by limiting volatility, can be produced using existing data sources and serves as a middle ground between the acquisition and net acquisition approaches, which have trended higher in the past decade, and the payments, rental equivalence and user cost approaches, which have trended lower.

For these reasons, there are no plans to make changes to the treatment of OA currently used for CPI calculation. Any subsequent changes to the CPI methodology would require in-depth consultations with Statistics Canada’s advisory committees and other national statistical organizations, as well as with external stakeholders, to ensure the ongoing quality, accuracy and utility of the CPI as a tool for both indexations, setting monetary policy and gauging the health of the economy. The goal remains to produce a CPI that is relatively stable, best serves its intended uses and better reflects the experiences of Canadians. Working with price experts, other national statistical organizations and partners ensures that the data and methods used in the calculation of the CPI are aligned with international standards and best practices.

Preface

As part of an ongoing research program into major challenges involved in the construction of consumer price indexes, this research paper is a collaborative project between Statistics Canada and the Bank of Canada. It focuses on constructing analytical price index series for Canada, using the main owned accommodation measurement concepts proposed by the International Consumer Price Index Manual and adopted by other countries.

This paper builds on earlier versions that have been shared and discussed with various research groups, including the Ottawa Group, United Nations Prices Sprint, Statistics Canada’s Price Measurement Advisory Committee, and many other stakeholders.  The current version includes new elements, such as the exploratory rental equivalence measure based on asking rents, and is being circulated to invite further feedback and discussion from these groups and other stakeholders. The views expressed in the papers are those of the authors and do not necessarily reflect the positions of the Consumer Prices Division, Statistics Canada, or the Bank of Canada.

1 Introduction

The Canadian Consumer Price Index (CPI) is an indicator that measures the price change of a fixed basket of consumer goods and services through time. This fixed basket contains goods and services that typical households purchase over a specified period. The prices of around 1,400 unique goods and services across the country are collected on a monthly basis.^Note  These collected prices are used to calculate price indexes and produce the CPI according to international standards and methods, which are regularly updated and reviewed by price statistics experts.

The CPI basket consists of eight primary categories: food; shelter; household operations and furnishings; clothing and footwear; transportation; health and personal care; recreation and education; and alcoholic beverages, tobacco and recreational cannabis.

Shelter, which includes rented accommodation and owned accommodation (OA), is one of the largest components, accounting for almost one-third of the CPI basket. Addressing what OA measures and how it is measured is among the most challenging aspects of developing consumer price indexes. There are several internationally recognized approaches to the treatment of OA; however, there is no international consensus on how price change for OA should be measured in the CPI. These diverse approaches are attributable to the complex nature of homeownership, which complicates the task of identifying and measuring price changes. They also stem from the varying needs of users with respect to the CPI.^Note 

The CPI is an important statistical indicator that is used not only for monetary policy, but also for many other uses and purposes. Governments, businesses and individuals use it to adjust specific contractual or legislated payments, such as wages, rents and social security. These adjustments are meant to maintain purchasing power. Consequently, the CPI excludes assets because they cannot be associated with the current consumption of a particular good or service. Conceptually, an owner-occupied dwelling can be considered as an asset or a consumption good—or both.

There are several approaches for the treatment of OA in the CPI. The rental equivalence approach gauges the cost of homeownership as if owners were renting their properties to themselves. The user cost approach considers the perceived costs of owning a home, while the payment approach assesses actual housing expenses.^Note  The (net) acquisition approach treats an owner-occupied dwelling like any other CPI household durable good, measuring the cost paid by a household to acquire a house (including or excluding the land). Finally, one approach considers OA as a pure investment and therefore excludes from the CPI any effect of price change related to the purchase and use of an OA.

In addition, the treatment of OA is an integral part of the discussion on how to explain the gap between perceived inflation (measured by the Bank of Canada’s Canadian Survey of Consumer Expectations) and measured inflation. The difference between the price of a house (or how much it costs to buy a house) and the cost of owning a house can be a source of confusion that affects consumers’ perception of inflation, potentially widening the inflation perception–measurement gap. While this source of confusion should not dictate how to appropriately measure OA, it may still have an impact on the credibility of the CPI as the official measure of inflation. Therefore, it is important to communicate the reasoning behind the adoption of a specific approach. The rest of this paper will address common questions that typically emerge on this topic:

• To what extent is the perception–measurement gap stemming from the conceptual difference between the cost of a house and the cost of housing?
• What would an analytical CPI look like under various OA measurement scenarios?

The purpose of this paper is to assess the advantages and limitations of available approaches to measuring (OA ) and to evaluate how analytical CPI inflation would differ under each approach, particularly in the context of the period of elevated inflation observed between 2022 and 2024. This analysis does not aim to reduce the gap between perceived and measured inflation through alternative constructions of the OA component.^Note  It also does not propose changes to the current measurement methodology or to the interpretation of the CPI with respect to housing inflation and affordability . The challenges Canadians encounter when entering the current housing market are illustrated in a number of Statistics Canada products, including the Housing Statistics Portal, indicators of household wealth and affordability, and distributions of household economic accounts.

This paper presents the construction of analytical CPI series for Canada, using the main OA measurement concepts proposed by the International Consumer Price Index Manual and adopted by other countries. This approach aims to evaluate the impact of different concepts and enable bilateral comparisons of inflation performance. Statistics Canada's analytical CPI series, based on four primary concepts of owned accommodation, are estimated for the period from January 2001 to December 2025. In the following section, the paper will present in detail the different approaches of measuring OA, along with their respective analytical CPI series. Each will be assessed in the Canadian context using several criteria, including the purpose of each approach, practical considerations and data limitations. Finally, considerations for the use of alternative OA approaches, including examining the gap between inflation perception and measurement, will also be discussed.

Annexes A and B include information on data sources and estimation methods for expenditure weights and price estimation for each OA approach.

2 Alternative approaches for cost of owned accommodation

In a market economy, measuring the price of rental housing services is relatively straightforward, because a consumer pays rent charged by a landlord for providing accommodation. By contrast, the landlord and occupier are the same person for owner-occupied housing, and there is consumption of housing services but no measurable rent. How statistical agencies should treat OA in their CPI is a complex and difficult question given the relative importance of OA services in the CPI and the different results when each approach is used to calculate the CPI.

Approach 1 — Acquisition approach

In the acquisition approach, OA services are considered a consumer good and treated similarly to other durable goods in the CPI. That is, all expenditures on a house purchase are attributed to the period of purchase, including structure and land, even though the use of the purchased house extends beyond that period. In other words, the full purchase price of the house would be captured in the month it is purchased, even though consumers are likely to finance this purchase with a mortgage and use this house over a longer period of time.

This approach includes property taxes, home insurance premiums, maintenance and repairs, and other expenses, as well as net purchases, of the housing unit, including renovations and alterations. Each component is associated with an expenditure weight that would be used for calculating the CPI. The expenditure weights for net purchases are calculated by taking the difference between the value of house purchases and the value of house sales during the reference period.

Intuitively, one could infer that the value of the net purchases should cancel out, but this is not the case. In fact, the positive value of net home purchases can be explained by the construction of new units that have been added to the market during the year and the withdrawal of units because of demolition, natural events or other reasons. It also includes the net purchases of the household sector from other institutional sectors, such as the corporate, government or non-profit sectors, including purchases of second-hand dwellings and buildings converted to residential dwellings.

Approach 2 — Net acquisition approach

Similar to the acquisition approach, the net acquisition^Note  approach also treats a dwelling as the purchase of a good, but it makes a distinction between the house and the land. It considers the cost of land as a capital good (asset), and thus out of scope for the CPI, and the cost of the structure (house) as consumption. The structure can be consumed over time (consumption of fixed capital), while the land generally appreciates in value. The separation of the cost of the land from that of the structure involves considerable practical difficulties. Therefore, accurately estimating the structure price and its expenditure weights is challenging.

For this approach, the expenditure weight for the OA component in the CPI basket corresponds to the purchases of new dwellings or dwellings purchased by households from sectors other than households (excluding land) and major repairs and maintenance. The weight would be based on the value of the structure and would not include interest payments associated with the financing. Last, it should be noted that the resale of houses from one household to another is outside the scope of the CPI. Such transactions do not constitute new additions to a household’s stock of houses. Including resale housing would lead to double counting the same housing units, because they were already accounted for in previous years when initially purchased as new units.

Dion and Sabourin (2011) emphasize the significance of a CPI that reflects the instantaneous utility derived from the purchase of goods and services, aligning with the concept of a cost of living index (COLI).^Note Note  They provide a detailed evaluation and analysis of OA approaches and the various needs and requirements of CPI users. They find that both the acquisition and net acquisition approaches are useful for measuring price inflation for the purpose of monitoring central bank monetary policy, because they instantly encompass the effect of house price increase in the CPI. However, the acquisition approaches are not consistent with uses of the CPI as an escalator for nominal income (cost of living indexing), because they do not consider the flow of services that are generated by an OA.

Approach 3 — Rental equivalence

A third approach measures the cost of owned accommodation (OA) by treating homeowners as renters of their own dwellings and estimating a monthly rental value the homeowner would pay to rent their own home. Because a market transaction does not occur, prices are not directly observable and must be imputed. In practice, statistical agencies derive these imputed prices either from observed rent data for comparable dwellings or through dedicated owner-equivalent rent surveys. As such, this approach relies entirely on imputed values to capture changes in the price of housing services.

In practice, statistical agencies estimate rental equivalent using rents from comparable dwelling units, incorporating both new and ongoing rental contracts. However, this may raise conceptual concerns, as homeowners would typically base their expectations on current market conditions, particularly new rental agreements.

In this paper, rental equivalent is estimated using asking rents for new tenants from the secondary Canadian rental market. This market primarily reflects listings from individual homeowners or small-scale landlords, with commercial rental providers excluded. As a result, asking rents for new tenants can serve as a useful proxy for the rental value that homeowners could expect for similar dwellings. However, it is important to note that asking rents are not transaction-based and therefore may differ from realized rents.

In fact, asking rents tend to be more responsive to current market conditions in both rental and housing markets, adjusting more quickly to shifts in demand and therefore provide a signal of indirect effects of housing prices. For example, increases in house prices that reduce affordability may push potential buyers into the rental market, raising demand for rental units and placing upward pressure on rents.

In the Canadian context, rental market dynamics are also influenced by immigration patterns. Empirical evidence shows that higher immigration inflows are associated with increased rental demand and rising rental prices, while reductions in immigration tend to moderate demand and slow rent growth (Hou, Koumaglo & Zhang, 2025; Caranci & Ercolao, 2025). In this context, asking rents—particularly for new tenants—provide a timely indicator of housing-related cost pressures, as they quickly reflect shifts in demand and capture the impact of immigration-driven changes in the rental market.

Under this approach, the OA expenditure weight in the CPI basket is based on the estimated rental value of owner-occupied dwellings in the reference year. These weights can be derived using two main methods: (1) self-reported rental values from owner-occupiers collected through household expenditure surveys, or (2) estimates based on multiplying owner-occupied dwelling stock counts by average rents for comparable dwelling units.

The advantage of this approach is that housing services for owner-occupied dwellings are treated similarly to shelter services in the rented accommodation market. Whether the occupant of the dwelling is a tenant or a homeowner does not prevent statisticians from comparing their aggregate accommodation expenditures across households.

The rental equivalence approach is suitable for a COLI, as it relies on estimates of the price of current consumption of accommodation services. While rental equivalence prices do not directly reflect housing price effects, they do capture indirect changes in house prices. This is because they are imputed from tenants’ rental prices (or closely tied to them), which are affected, at least in the long term, by house price changes. However, it is important to note that provincial and municipal regulations —such as rent controls —can limit the direct pass-through of house price changes to rental prices , particularly when rental equivalence is proxied using CPI rent, which includes both new and existing leases. This can introduce lagged adjustments in the index, as existing rents tend to adjust more slowly than market conditions.^Note  

Dion and Sabourin (2011) argue that these features may limit the usefulness of the rental equivalence index for monetary policy analysis. Separately, a key methodological challenge of the rental equivalence approach is the need to impute prices for owner-occupied dwellings, as these are not directly observed in the market. The extent of these limitations depends on the underlying data source. When rental equivalence is based on CPI rent, the presence of rent controls and the inclusion of existing leases can dampen responsiveness to current market conditions. In contrast, rental equivalence measures based on asking rents—focused on new listings—are less affected by such constraints and may better reflect contemporaneous market dynamics.

In addition, Hill et al. (2020) find that the rental equivalence approach is prone to missing the signs of a housing overvaluation. This is because such overvaluation is driven by the expectation of future capital gains rather than rising rents. As a result, this poses challenges for setting monetary policy.

Approach 4 — Payment approach

This approach, which measures actual cash flows, assumes that OA services are equivalent to the actual payments made by homeowners (such as mortgage interest payments and other operating expenditures). Opportunity costs (such as capital gains), imputed costs (such as depreciation) and investment costs (such as down payments and mortgage principal payments) are excluded.

The payment approach encompasses five^Note  of six components of Statistics Canada’s OA approach. The sole exception is replacement cost, which pertains to the portion of owner-occupied dwellings presumed to be consumed. This is represented by the worn-out structural portion of housing (depreciation of housing) or the amount a homeowner must spend to maintain the market value of the home. Unlike other owned accommodation components, it does not constitute an out-of-pocket expense. Therefore, being imputed, it is excluded from the OA cost under the payment approach.

It is essential to highlight that the term “replacement cost” is not synonymous with “homeowners’ maintenance and repairs.” The latter encompasses direct expenses incurred to restore the house’s physical condition and operation to a specified standard; thus, these costs are paid to keep the house in good working order or maintain its appearance.

Some economists argue that the payment approach appropriately serves the CPI’s primary use as an escalator for nominal income. Dion and Sabourin (2011) argue that the payment approach is not fully consistent with cost -of -living indexing since it ignores the replacement cost, one of the most important components of OA. It is also less desirable from a monetary policy standpoint, because it gives a prominent role to the mortgage interest cost, a component that is volatile and may send a misleading signal about the stance of monetary policy.^Note 

Approach 5 — User cost approach

The user cost approach is derived from the capital theory that assumes the user cost is an estimation of a home’s rental price based on the costs of owning a house. User costs encompass actual and imputed ongoing costs for OA. Therefore, a homeowner would incur interest costs and opportunity costs during the period of ownership (actual interest costs on mortgages or a forgone rate of return on owned funds that could otherwise have earned interest), replacement cost, and other operating costs (such as fees for maintenance and repairs, property taxes, and insurance premiums). Offsetting these expenses would be an expected capital gain (the expected selling price at time of disposal less the purchase price).

A simplified user cost^Note Note  method is defined as follows:
$$ut=pt\left(rt-gt+\delta t+\omega t\right)$$
$ut$ is the user cost for owner-occupied dwellings
$pt$ is the average housing price^Note 
$rt$ is the real interest rate
$gt$ is the real rate of capital gain on housing
$\delta t$ is the depreciation rate
$\omega t$ is the rate of operating and average transaction costs (including taxes).

This analysis uses five variants^Note  of the user cost approach:

• a simple user cost variant where real interest rate ${\overline{r}}_t$ and capital gain ${\overline{g}}_t$ are fixed and set equal to the average long-run natural rate of interest; a rate^Note  that would equate saving and investment in full employment condition.
• alternative variants^Note of the user cost where real interest rate ${\overline{r}}_t$ and capital gain ${\overline{g}}_t$ are variables; ${\overline{r}}_t$ is the weighted average real interest rate,^Note  and real capital gains are based on different horizon lengths of expectation formation, such as 0, 10, 25 and 30 years.

The user cost approach is consistent with COLI. However, there is a negative relationship between an expected housing appreciation and the user cost. That is, in a period of rising house prices, where $g_t$ (the real rate of capital gain on housing) becomes very high, there is a significant risk of obtaining a negative value for the estimate of the OA price using the user cost approach.

Rising home prices have direct effects on household wealth. However, the question becomes whether it is appropriate to capture this increase in wealth through a decline in the price index. It seems reasonable to assert that people may feel wealthier in a housing boom, not because the price of housing services has decreased, but because their comprehensive income, combined with the capital gains (from the rising value of their property), has increased. It seems unconventional to capture this increase in wealth statistically via a decline in the cost of housing services price index.

In addition, this approach may overstate the extent to which people are better off in a housing boom. The user cost approach seems to equate the increase in a house’s value (a form of wealth) directly with the cost of borrowing money (such as mortgage interest). While interest charges are actual cash payments that households must make, the increase in a house’s value is less concrete. It is a potential increase in wealth that may not be realized unless the homeowner sells the house at that increased value.

Approach 6 — Diewert and Nakamura’s opportunity cost approach

Given the constraints of the user cost approach and its underlying assumption, which views the user cost as an estimate of the market rental price, its validity as a measure for OA services has been challenged (Verbrugge, 2008). To address this issue, Diewert and Nakamura (2009) introduced an alternative methodology known as the opportunity cost approach.

This concept is the potential benefit an individual homeowner misses out on by not making the best decision:

• At the individual homeowner level, Diewert and Nakamura (2009) define the owned accommodation opportunity cost (OAOC) as the greater value between the cost to rent an equivalent dwelling (the rental equivalent $t$) and the owner or user costs.
• At the national level, Diewert and Nakamura (2009) define the OAOC index as a weighted expenditure share of the rental equivalence index and user cost index. The expenditure share weight of the rental equivalence index represents the estimated segment of owned properties where the cost to rent an equivalent dwelling surpasses the owner or user costs.

Diewert and Nakamura’s (2009) opportunity cost addresses the limitation found in the user cost approach, especially when there are elevated expectations for housing prices. In such cases, there is a pronounced risk of the user cost estimates returning a negative value. By taking the greater value between rental equivalence and user cost, the Diewert and Nakamura (2009) opportunity cost ensures values are never zero or negative. This approach is pertinent to homeowners with either positive or no home equity.

However, this approach presents issues associated with micro-aggregation stemming from differences between low-end and high-end dwellings. While the user cost and imputed rents are similar for low-end dwellings, the imputed rent is roughly half the user cost for high-end dwellings. This discrepancy arises because renters may not be able to afford the complete cost of ownership for premium properties.

In terms of CPI use, this characteristic of a combination of rental equivalence and user cost approaches makes it consistent with the cost -of -living indexing; however, Dion and Sabourin (2011) find it challenging in terms of monetary policy use. This method does not address the limitation of including an interest rate component of the user cost.

No statistical agency has adopted this approach because the necessary data for its implementation are unavailable. However, Bettina Aten^Note  (2018) and Bettina and Heston^Note  (2020) successfully estimated the OA price index using the opportunity cost approach applied on American Community Survey data.

Approach 7 — Statistics Canada’s owned accommodation approach^Note 

In the Canadian CPI, the OA index measures the impact of price changes on the cost of using a fixed stock of dwellings. By accounting for homeowners’ specific costs, this approach is consistent with the COLI concept.

Homeowners’ specific shelter costs in the Canadian CPI include the following components:

• replacement cost (or depreciation cost)^Note 
• mortgage interest cost
• property taxes
• homeowners’ home and mortgage insurance
• homeowners’ maintenance and repairs
• other OA expenses.

Changes in house prices affect all components of homeowners’ specific costs to some degree, either explicitly or implicitly. They directly affect the mortgage interest cost^Note  and replacement cost,^Note Note  as the house price is part of the calculation of price indexes for both components. House prices indirectly affect property taxes through property assessment values and homeowners’ insurance through the value of the replacement cost of houses. Dion and Sabourin (2011) find this approach to be an acceptable compromise between the monetary policy purpose and the escalation purpose of the CPI.

Table 1 presents a detailed breakdown of various housing-related components and how they are considered across different OA approaches. Each component is marked with a check mark (✓) if it is applicable to the respective approach. It is notable that no approach considers principal mortgage payments or down payments as part of its calculation.

3 International practice on the treatment of owned accommodation in the Consumer Price Index

The treatment of OA in the CPI varies across countries as there is no consensus about the best approach. The CPI manual, notably, does not prescribe a singular, universal approach for the treatment of OA in the CPI. It demands that statistical agencies make well-informed decisions that align with their CPI intended use and data availability.

The CPI manual states:

“Ideally, the approach chosen should align with the conceptual basis that best satisfies the main use of the CPI. However, the data requirements may be such that it is not feasible to adopt the preferred treatment. Also, the dual use of CPIs as both macroeconomic indicators and for indexation purposes can lead to clear tensions in designing an appropriate treatment for owner-occupied housing services costs that suits all needs.”^Note 

In selecting the most appropriate approach to measuring OA, statistical agencies use several criteria in their decision-making process:

• The chosen approach must align with the main purpose of the CPI, ensuring that the measure remains true to its intended purpose.
• It should adequately meet the needs of the CPI’s diverse users, from policy makers to the general public.
• The practicality of implementation is critical. Statistical agencies must consider the operational realities and constraints that come with data collection and processing.
• Finally, statistical agencies must acknowledge the data needs of each approach and confront any limitations that may arise.

Table 2 provides an assessment of various approaches to measuring OA in the CPI against two criteria in the Canadian context:

• their alignment with the purpose of the CPI and user needs, along with the assessment of analytical OA and CPI series against the six Statistics’ Canada quality dimension (relevance, accuracy, timeliness, accessibility, interpretability, and coherence).
• their practicality and data limitations.

The various approaches can result in fairly different weights in the CPI basket and contributions to CPI inflation. International practices (see Annex C) indicate that rental equivalence is the most prevalent approach to estimating OA in the CPI. Countries employing this approach often cater to a diverse range of user needs and typically feature less regulated and deeper rental markets (i.e., a more liquid market for the rental of single dwellings). For example, many European countries use rental equivalence, as the decision is supported by extensive and high-quality rental data. In U.S. the Bureau of Labor Statistics (BLS) has converted the CPI for All Urban Consumers (CPI-U) and the CPI for Urban Wage Earners and Clerical Workers (CPI-W) to a rental equivalence measure for homeowner costs, effective with data for January 1983 and the January 1985 data, respectively. These changes were influenced by professional judgments that shifted from a mixture of acquisition and payment approaches to a rental equivalence approach. Additionally, changes in the financial markets, characterized by the emergence of new types of mortgage instruments, which introduced additional complexities in accurately estimating the expenditure weight and monthly price movement of contracted mortgage interest costs.^Note 

The second most common approach is to exclude OA from the CPI entirely. This method is used by Argentina, Brazil, China, India, Indonesia, Korea, the Russian Federation, Saudi Arabia and Turkey. It is also used by the European Union’s Harmonised Indices of Consumer Prices. Yet Australia and New Zealand employ the net acquisition approach, as they both have robust data on building costs, which allow them to price a housing structure separately from land. Meanwhile, Sweden, Iceland and Canada employ variants of the user cost approach.

4 Comparison of analytical consumer price indexes for owned accommodation

Table 3^Note  shows the different expenditure shares for homeowners’ specific cost components across different analytical OA approaches, based on the 2024 CPI basket.^Note 

Over time, the expenditure share for the OA component of the CPI based on the rental equivalence approach is the least volatile between basket updates. The expenditure share based on the acquisition approach is the most volatile. Meanwhile, the expenditure shares based on Statistics Canada’s official approach, as well as the payment, user cost and net acquisition approaches, vary within the same range (Table 4).

Chart 1 presents a comparison between the year-over-year growth rate of the official OA price index and that of the estimated OA index using the rental equivalence approach. Two rental equivalence measures are presented: one uses the official CPI Rent index; the other uses average asking rents from online listings starting in January 2019.

The official rent index is derived from rents reported in the Labour Force Survey (LFS) rent module and adjusted for quality. It primarily reflects rents paid by sitting tenants in the primary rental market (e.g., apartments and purpose-built rental dwellings). Because only a small share of new rental contracts are captured, the index is less responsive to real-time developments in the secondary rental market.

In practice, statistical agencies estimate rental equivalence using observed rents adjusted for quality of comparable units, typically incorporating both new and ongoing rental contracts. However, this may not fully capture current market conditions, as homeowners’ expectations are more closely aligned with rents observed in new rental agreements.

To address these limitations, this paper develops an exploratory Rental Equivalence Index based on asking rent data. The average asking rent index captures more timely changes in the secondary rental market, focusing primarily on new rental contracts for dwellings owned by private homeowners. It therefore aims to exclude commercial rental properties that dominate the primary rental market, as these do not reflect the housing services consumed by owner-occupiers.

As such, the asking-rent–based measure better captures immediate market movements and the effects of tenant turnover in the secondary rental market. However, it estimates a simple average asking rent index and does not control changes in unit characteristics such as location, number of bathrooms, or utilities included. In addition, coverage of asking rent data is limited to selected major Canadian cities. Please see Annex E for further details on the data source and estimation method of the average asking rent index.

Finally, estimating a quality-adjusted asking rent index—using, for example, a hedonic method similar to Ning and Yang (2024)—would provide a more accurate measure of a rental equivalent index. This represents a key area for future development. Work is underway to improve the methodology in this direction, which would enhance comparability and better align the measure with the best international practices while addressing current data limitations.

Based on Chart 1, from January 2003 to December 2018 , using rent prices as a proxy for rental equivalent prices (which contribute to more than 95%^Note  of the expenditure weight for OA rental equivalence price index) resulted in lower inflation compared with the official approach.

From January 2020 onward,^Note  with both rental equivalence-based measures charted, notable divergence emerges in their year-over-year growth rates. In particular, growth in the rent index–based approach decelerated over the course of 2020, while the exploratory average asking rent–based approach continued to accelerate until mid-2021. Similar inflection points are observed later in the time series. These divergences reflect the asking-rent–based measure capturing sharper and more immediate movements in the secondary rental market, as it excludes existing contract rents and focuses solely on rents for new tenants.

While the gap between the official OA measure and rental equivalence narrowed in early 2023, trends diverge again thereafter. In particular, while asking-rent-based rental equivalence indicated stronger inflationary pressures through parts of 2023 and 2024, it suggests notably lower inflationary pressures by the end of 2025 relative to the official OA measure. This reflects its greater responsiveness to recent easing in rental market conditions.

These results suggest that although the rental equivalence approach excludes the direct impact of mortgage interest cost spikes, it does not systematically produce lower OA inflation. The inclusion of asking-rent-based rental equivalence enhances the analysis by capturing developments in the secondary rental market and providing a more timely indicator of changes in new rental contracts, which can significantly influence the measurement of owned accommodation.

Chart 1 start

Data table for Chart 1

Data table for chart 1
Table summary
This table displays the results of Data table for chart 1 Official owned accommodation (OA), Rental equivalence OA using official rent index and Rental equivalence OA using average asking rent index¹, calculated using 12-month percent change units of measure (appearing as column headers).

	Official owned accommodation (OA)	Rental equivalence OA using official rent index	Rental equivalence OA using average asking rent index Data table for chart 1 Note 1
	12-month percent change
Note ... not applicable Note 1 For the estimation of this index, asking rent data are used only from January 2019 onward; prior to that, the official rent series is applied. Return to note 1 referrer Source: Statistics Canada, Consumer Prices program.
2004
January	2.9	1.1	... not applicable
February	2.7	1.3	... not applicable
March	2.5	1.2	... not applicable
April	2.5	1.2	... not applicable
May	2.4	1.1	... not applicable
June	2.4	1.1	... not applicable
July	2.8	1.1	... not applicable
August	2.8	1.1	... not applicable
September	2.9	1.0	... not applicable
October	3.1	0.9	... not applicable
November	3.3	0.9	... not applicable
December	3.4	0.9	... not applicable
2005
January	3.3	1.0	... not applicable
February	3.1	0.9	... not applicable
March	3.2	0.9	... not applicable
April	3.2	0.8	... not applicable
May	3.4	1.0	... not applicable
June	3.4	0.9	... not applicable
July	3.2	0.9	... not applicable
August	3.1	0.9	... not applicable
September	2.9	0.9	... not applicable
October	2.8	0.8	... not applicable
November	2.7	0.8	... not applicable
December	2.7	0.8	... not applicable
2006
January	3.0	0.9	... not applicable
February	3.3	0.9	... not applicable
March	3.4	0.9	... not applicable
April	3.6	1.0	... not applicable
May	3.7	0.9	... not applicable
June	3.8	1.0	... not applicable
July	4.0	1.0	... not applicable
August	4.5	1.0	... not applicable
September	4.9	1.0	... not applicable
October	5.2	1.2	... not applicable
November	5.1	1.1	... not applicable
December	5.2	1.3	... not applicable
2007
January	5.0	1.2	... not applicable
February	4.9	1.2	... not applicable
March	5.0	1.4	... not applicable
April	4.9	1.4	... not applicable
May	4.7	1.4	... not applicable
June	4.9	1.5	... not applicable
July	4.8	1.5	... not applicable
August	4.9	1.6	... not applicable
September	4.8	1.7	... not applicable
October	4.8	1.6	... not applicable
November	5.1	1.7	... not applicable
December	5.0	1.7	... not applicable
2008
January	5.0	1.7	... not applicable
February	5.1	1.8	... not applicable
March	5.0	1.7	... not applicable
April	5.1	1.6	... not applicable
May	5.0	1.7	... not applicable
June	4.8	1.7	... not applicable
July	4.6	1.7	... not applicable
August	4.2	1.6	... not applicable
September	4.0	1.5	... not applicable
October	3.8	1.6	... not applicable
November	3.6	1.8	... not applicable
December	3.4	1.8	... not applicable
2009
January	3.5	1.8	... not applicable
February	3.0	1.6	... not applicable
March	2.4	1.6	... not applicable
April	1.9	1.6	... not applicable
May	1.5	1.6	... not applicable
June	1.1	1.7	... not applicable
July	0.8	1.6	... not applicable
August	0.5	1.7	... not applicable
September	0.1	1.7	... not applicable
October	-0.2	1.6	... not applicable
November	-0.6	1.4	... not applicable
December	-0.8	1.5	... not applicable
2010
January	-0.9	1.5	... not applicable
February	-0.9	1.4	... not applicable
March	-0.5	1.4	... not applicable
April	-0.1	1.4	... not applicable
May	0.3	1.3	... not applicable
June	0.6	1.3	... not applicable
July	1.3	1.3	... not applicable
August	1.2	1.2	... not applicable
September	1.4	1.1	... not applicable
October	1.7	1.2	... not applicable
November	1.9	1.2	... not applicable
December	1.9	1.1	... not applicable
2011
January	2.0	1.1	... not applicable
February	2.0	1.2	... not applicable
March	1.7	1.2	... not applicable
April	1.5	1.3	... not applicable
May	1.7	1.2	... not applicable
June	1.6	1.1	... not applicable
July	1.2	1.1	... not applicable
August	1.5	1.1	... not applicable
September	1.4	1.2	... not applicable
October	0.9	1.0	... not applicable
November	1.0	1.1	... not applicable
December	1.2	1.1	... not applicable
2012
January	1.2	1.2	... not applicable
February	1.2	1.2	... not applicable
March	1.3	1.3	... not applicable
April	1.7	1.3	... not applicable
May	1.5	1.3	... not applicable
June	1.3	1.4	... not applicable
July	1.2	1.4	... not applicable
August	1.0	1.5	... not applicable
September	1.0	1.5	... not applicable
October	1.1	1.6	... not applicable
November	0.9	1.6	... not applicable
December	0.7	1.7	... not applicable
2013
January	0.5	1.7	... not applicable
February	0.5	1.6	... not applicable
March	0.6	1.6	... not applicable
April	0.2	1.6	... not applicable
May	0.1	1.6	... not applicable
June	0.3	1.6	... not applicable
July	0.2	1.6	... not applicable
August	0.3	1.6	... not applicable
September	0.5	1.5	... not applicable
October	0.7	1.5	... not applicable
November	1.0	1.6	... not applicable
December	1.2	1.5	... not applicable
2014
January	1.5	1.5	... not applicable
February	1.5	1.4	... not applicable
March	1.5	1.4	... not applicable
April	1.9	1.5	... not applicable
May	1.9	1.4	... not applicable
June	1.8	1.5	... not applicable
July	2.1	1.5	... not applicable
August	2.1	1.4	... not applicable
September	1.9	1.5	... not applicable
October	1.7	1.5	... not applicable
November	1.6	1.4	... not applicable
December	1.7	1.4	... not applicable
2015
January	1.6	1.3	... not applicable
February	1.7	1.4	... not applicable
March	1.7	1.4	... not applicable
April	1.4	1.3	... not applicable
May	1.5	1.3	... not applicable
June	1.6	1.2	... not applicable
July	1.4	1.2	... not applicable
August	1.4	1.1	... not applicable
September	1.6	1.0	... not applicable
October	1.9	1.0	... not applicable
November	1.9	0.9	... not applicable
December	1.9	0.9	... not applicable
2016
January	1.9	0.9	... not applicable
February	1.8	0.9	... not applicable
March	1.7	0.8	... not applicable
April	1.8	0.7	... not applicable
May	1.8	0.7	... not applicable
June	2.1	0.6	... not applicable
July	2.2	0.6	... not applicable
August	2.4	0.6	... not applicable
September	2.3	0.6	... not applicable
October	2.3	0.6	... not applicable
November	2.6	0.5	... not applicable
December	2.4	0.5	... not applicable
2017
January	2.6	0.6	... not applicable
February	2.4	0.5	... not applicable
March	2.6	0.7	... not applicable
April	2.5	0.6	... not applicable
May	2.7	0.6	... not applicable
June	2.5	0.7	... not applicable
July	2.5	0.7	... not applicable
August	2.6	0.6	... not applicable
September	2.6	0.7	... not applicable
October	2.6	0.7	... not applicable
November	2.4	0.7	... not applicable
December	2.5	0.7	... not applicable
2018
January	2.5	0.8	... not applicable
February	2.8	0.9	... not applicable
March	2.6	0.9	... not applicable
April	2.7	1.0	... not applicable
May	2.6	1.1	... not applicable
June	2.6	1.1	... not applicable
July	2.7	1.2	... not applicable
August	2.7	1.4	... not applicable
September	2.8	1.3	... not applicable
October	2.6	1.4	... not applicable
November	2.7	1.5	... not applicable
December	2.6	1.4	... not applicable
2019
January	2.7	2.2	2.2
February	2.7	2.5	3.4
March	2.8	2.7	2.7
April	2.8	2.2	2.5
May	2.7	2.1	2.6
June	2.6	2.3	2.9
July	2.5	2.5	1.1
August	2.3	3.0	2.7
September	2.3	2.9	4.4
October	2.5	3.6	3.3
November	2.4	3.0	4.1
December	2.3	3.4	4.4
2020
January	2.2	2.4	3.8
February	2.2	2.7	3.2
March	2.3	2.5	3.0
April	2.4	2.3	4.4
May	2.2	1.5	4.4
June	2.2	1.8	5.9
July	2.0	1.2	7.6
August	2.0	1.3	6.2
September	2.1	1.6	4.7
October	2.2	0.9	5.5
November	2.2	1.5	6.4
December	2.3	0.5	6.0
2021
January	2.2	0.8	9.4
February	2.3	0.1	7.3
March	2.4	0.5	9.5
April	2.7	0.9	10.2
May	3.5	2.4	10.7
June	4.6	2.1	9.9
July	5.1	2.5	10.4
August	5.3	1.5	13.6
September	5.4	1.8	13.3
October	5.1	1.7	12.6
November	5.3	1.7	10.1
December	5.8	2.7	12.7
2022
January	6.1	3.0	10.0
February	6.2	4.1	12.5
March	7.0	4.0	11.2
April	7.6	4.4	11.2
May	7.3	4.3	10.1
June	6.7	4.2	10.5
July	6.3	4.9	11.2
August	6.2	4.7	9.2
September	6.5	4.3	9.2
October	6.8	4.7	10.6
November	6.7	5.9	10.7
December	6.5	5.8	9.4
2023
January	6.6	5.8	9.9
February	6.5	5.4	7.1
March	5.7	5.4	8.7
April	5.1	6.1	7.5
May	5.5	5.7	8.7
June	5.4	5.8	8.6
July	6.0	5.4	7.0
August	6.4	6.5	8.6
September	6.3	7.1	7.4
October	6.7	8.0	5.1
November	6.8	7.2	5.6
December	6.7	7.6	5.1
2024
January	6.7	7.7	4.6
February	6.7	7.9	7.0
March	6.8	8.2	6.5
April	6.6	7.9	5.4
May	6.2	8.6	4.5
June	6.3	8.4	3.4
July	5.9	8.2	4.4
August	5.5	8.5	1.1
September	5.1	7.8	1.7
October	5.0	7.0	2.8
November	4.6	7.3	1.2
December	4.5	6.9	2.7
2025
January	4.2	6.0	0.4
February	4.1	5.6	-2.8
March	3.8	4.9	-1.8
April	3.5	5.0	-1.3
May	3.2	4.3	0.7
June	2.8	4.5	0.8
July	2.5	4.9	0.5
August	2.3	4.3	0.6
September	2.1	4.6	0.3
October	2.0	5.1	0.3
November	1.7	4.5	-0.1
December	1.3	4.7	-2.9

Chart 1 end

Chart 2 compares the year-over-year growth rate of the official OA price index with that of the estimated OA using the acquisition approach. The construction price index^Note  accounts for more than 22.6% of the l OA price using the acquisition approach, and the home purchase price index^Note  accounts for almost 42.7 %. Over the same period, the increased influence of housing and construction prices in the OA price index has led to higher inflationary pressures, especially from mid-2020 to mid-2022, primarily because of factors such as higher demand and limited supply of building materials, COVID-19-pandemic-related supply -chain issues, and worker shortages. However, starting in late 2022 and continuing into early 2023, the 12-month price change using the acquisition approach – particularly those using CREA’s HPI – fell into negative territory, reflecting a slowdown in the housing market and the easing of supply-chain constraints. Throughout 2024 and 2025, these indexes remained relatively subdued, with continued low or slightly negative growth, largely due to persistent softness in the housing market.

Since there are several price measures of housing available from both Statistics Canada and external sources, three types of home purchase price indexes were used. These resulted in different OA acquisition approach indexes. The highest acquisition OA index (yellow line) uses the CREA HPI, which measures resale housing prices. By contrast, the lowest acquisition OA index (orange line) uses the New Housing Price Index, which measures the prices of newly constructed houses. The middle acquisition OA index (gray line) incorporates new and resale housing prices.

Chart 2 start

Data table for Chart 2

Data table for chart 2
Table summary
This table displays the results of Data table for chart 2 Official owned accommodation (OA), Acquisition OA – using NHPI¹, Acquisition OA – using NHPI¹ and CREA’s HPI² and Acquisition OA – using CREA’s HPI², calculated using 12-month percent change units of measure (appearing as column headers).

	Official owned accommodation (OA)	Acquisition OA – using NHPI Data table for chart 2 Note 1	Acquisition OA – using NHPI Data table for chart 2 Note 1 and CREA’s HPI Data table for chart 2 Note 2	Acquisition OA – using CREA’s HPI Data table for chart 2 Note 2
	12-month percent change
Note 1 NHPI represents the New Housing Price Index. Return to note 1 referrer Note 2 CREA's HPI represents the Canadian Real Estate Association's Home Price Index. Return to note 2 referrer Source: Statistics Canada, Consumer Prices program.
2004
January	2.9	4.9	5.7	5.9
February	2.7	4.8	5.7	5.9
March	2.5	5.1	5.9	6.1
April	2.5	5.4	6.2	6.4
May	2.4	5.4	6.1	6.3
June	2.4	5.5	6.3	6.5
July	2.8	5.5	6.2	6.4
August	2.8	5.4	6.0	6.1
September	2.9	5.2	5.9	6.1
October	3.1	5.2	5.7	5.8
November	3.3	5.1	5.7	5.9
December	3.4	5.0	5.8	6.1
2005
January	3.3	5.0	5.9	6.1
February	3.1	4.7	5.4	5.5
March	3.2	4.8	5.5	5.7
April	3.2	4.4	5.0	5.2
May	3.4	4.4	5.1	5.3
June	3.4	4.4	4.9	5.1
July	3.2	4.4	5.0	5.2
August	3.1	4.4	5.3	5.5
September	2.9	4.5	5.3	5.5
October	2.8	4.5	5.5	5.7
November	2.7	4.6	5.6	5.9
December	2.7	4.9	5.8	6.0
2006
January	3.0	5.5	6.2	6.4
February	3.3	5.8	6.6	6.9
March	3.4	6.2	7.1	7.3
April	3.6	6.8	7.7	7.9
May	3.7	7.1	8.1	8.3
June	3.8	7.5	8.4	8.7
July	4.0	8.1	8.8	9.0
August	4.5	8.9	9.3	9.4
September	4.9	9.0	9.4	9.5
October	5.2	8.6	9.0	9.1
November	5.1	8.3	8.6	8.7
December	5.2	8.0	8.4	8.6
2007
January	5.0	7.5	7.9	8.0
February	4.9	7.4	7.9	8.1
March	5.0	6.9	7.7	7.9
April	4.9	6.5	7.3	7.6
May	4.7	6.5	7.3	7.5
June	4.9	6.1	7.1	7.4
July	4.8	6.0	7.0	7.3
August	4.9	5.3	6.5	6.8
September	4.8	5.2	6.4	6.7
October	4.8	5.0	6.3	6.7
November	5.1	5.2	6.5	6.8
December	5.0	5.1	6.4	6.7
2008
January	5.0	5.2	6.3	6.5
February	5.1	5.1	5.9	6.1
March	5.0	4.8	5.3	5.5
April	5.1	4.3	4.7	4.8
May	5.0	3.4	3.6	3.7
June	4.8	3.0	2.9	2.9
July	4.6	2.5	2.2	2.1
August	4.2	2.1	1.5	1.4
September	4.0	1.9	1.0	0.7
October	3.8	1.5	0.1	-0.3
November	3.6	1.1	-0.6	-1.0
December	3.4	0.9	-1.4	-1.9
2009
January	3.5	0.3	-2.0	-2.6
February	3.0	-0.4	-2.7	-3.3
March	2.4	-0.9	-3.1	-3.7
April	1.9	-1.3	-3.2	-3.7
May	1.5	-1.2	-2.6	-2.9
June	1.1	-1.4	-2.0	-2.2
July	0.8	-1.1	-1.2	-1.2
August	0.5	-1.0	-0.5	-0.3
September	0.1	-0.7	0.4	0.7
October	-0.2	-0.2	1.7	2.2
November	-0.6	0.0	2.5	3.2
December	-0.8	0.4	3.7	4.5
2010
January	-0.9	1.1	4.5	5.4
February	-0.9	1.7	5.5	6.4
March	-0.5	2.5	6.4	7.4
April	-0.1	3.1	6.7	7.6
May	0.3	3.4	6.5	7.2
June	0.6	3.6	6.0	6.6
July	1.3	3.6	5.5	5.9
August	1.2	3.5	4.8	5.2
September	1.4	3.1	4.0	4.3
October	1.7	3.1	3.8	3.9
November	1.9	3.2	3.5	3.6
December	1.9	2.9	3.1	3.1
2011
January	2.0	2.7	3.0	3.1
February	2.0	3.0	3.2	3.2
March	1.7	2.6	3.0	3.1
April	1.5	2.6	3.2	3.3
May	1.7	2.7	3.5	3.8
June	1.6	2.7	4.0	4.3
July	1.2	2.5	4.1	4.5
August	1.5	2.7	4.5	4.9
September	1.4	2.8	4.7	5.2
October	0.9	2.3	4.2	4.7
November	1.0	2.4	4.4	4.9
December	1.2	2.5	4.4	4.9
2012
January	1.2	2.3	4.2	4.7
February	1.2	2.1	3.9	4.4
March	1.3	2.3	3.9	4.4
April	1.7	2.7	4.2	4.6
May	1.5	2.5	3.7	4.1
June	1.3	2.5	3.5	3.7
July	1.2	2.5	3.1	3.3
August	1.0	2.5	2.9	3.0
September	1.0	2.6	2.7	2.8
October	1.1	2.8	2.8	2.8
November	0.9	2.6	2.4	2.4
December	0.7	2.6	2.4	2.4
2013
January	0.5	2.6	2.2	2.1
February	0.5	2.6	2.1	2.0
March	0.6	2.4	2.0	1.9
April	0.2	2.1	1.6	1.5
May	0.1	2.1	1.7	1.6
June	0.3	2.0	1.8	1.8
July	0.2	1.9	2.1	2.1
August	0.3	2.0	2.3	2.4
September	0.5	1.9	2.6	2.7
October	0.7	1.6	2.7	3.0
November	1.0	1.7	3.1	3.5
December	1.2	1.6	3.1	3.5
2014
January	1.5	1.7	3.4	3.8
February	1.5	1.8	3.6	4.1
March	1.5	1.9	3.6	4.0
April	1.9	2.0	3.7	4.1
May	1.9	1.9	3.7	4.2
June	1.8	1.9	3.8	4.3
July	2.1	1.9	3.8	4.3
August	2.1	2.1	3.9	4.4
September	1.9	2.1	3.9	4.4
October	1.7	2.0	3.8	4.3
November	1.6	2.0	3.8	4.3
December	1.7	2.1	4.1	4.6
2015
January	1.6	1.8	4.0	4.6
February	1.7	1.7	3.9	4.5
March	1.7	1.8	4.1	4.7
April	1.4	1.5	4.2	4.9
May	1.5	1.6	4.4	5.1
June	1.6	1.7	4.7	5.5
July	1.4	1.8	5.0	5.9
August	1.4	1.8	5.3	6.2
September	1.6	1.8	5.4	6.4
October	1.9	2.1	5.8	6.8
November	1.9	2.1	6.0	7.0
December	1.9	2.1	6.1	7.2
2016
January	1.9	2.2	6.4	7.5
February	1.8	2.2	6.7	8.0
March	1.7	2.3	7.1	8.4
April	1.8	2.4	7.5	8.9
May	1.8	2.9	8.2	9.7
June	2.1	2.8	8.3	9.9
July	2.2	3.0	8.6	10.1
August	2.4	2.8	8.4	10.0
September	2.3	2.8	8.4	9.9
October	2.3	3.1	8.3	9.8
November	2.6	3.2	8.2	9.6
December	2.4	3.1	7.9	9.2
2017
January	2.6	3.1	7.8	9.2
February	2.4	3.1	8.1	9.5
March	2.6	3.2	8.5	10.1
April	2.5	3.4	8.3	9.7
May	2.7	3.3	7.3	8.5
June	2.5	3.5	6.6	7.4
July	2.5	3.5	5.7	6.2
August	2.6	3.7	5.3	5.7
September	2.6	3.8	5.0	5.3
October	2.6	3.6	4.7	5.0
November	2.4	3.5	4.4	4.7
December	2.5	3.7	4.4	4.6
2018
January	2.5	3.8	4.0	4.0
February	2.8	3.7	3.2	3.0
March	2.6	3.7	2.2	1.8
April	2.7	3.4	1.7	1.3
May	2.6	3.2	1.9	1.5
June	2.6	3.1	2.0	1.7
July	2.7	2.8	2.2	2.0
August	2.7	2.6	2.1	2.0
September	2.8	2.5	2.0	1.9
October	2.6	2.2	1.7	1.5
November	2.7	2.1	1.4	1.2
December	2.6	2.0	1.2	0.9
2019
January	2.7	2.0	0.9	0.7
February	2.7	1.9	0.6	0.3
March	2.8	1.8	0.5	0.1
April	2.8	1.7	0.4	0.1
May	2.7	1.4	0.2	-0.1
June	2.6	1.2	0.3	0.1
July	2.5	1.1	0.5	0.3
August	2.3	1.1	0.6	0.4
September	2.3	1.2	0.8	0.7
October	2.5	1.4	1.2	1.1
November	2.4	1.4	1.5	1.5
December	2.3	1.5	1.7	1.8
2020
January	2.2	1.5	2.2	2.3
February	2.2	1.6	2.6	2.9
March	2.3	1.7	2.9	3.2
April	2.4	1.7	2.4	2.6
May	2.2	1.8	2.6	2.8
June	2.2	2.0	3.3	3.6
July	2.0	2.4	4.1	4.5
August	2.0	2.7	4.9	5.4
September	2.1	3.4	5.6	6.1
October	2.2	3.8	6.0	6.5
November	2.2	4.3	6.6	7.2
December	2.3	4.6	7.3	8.0
2021
January	2.2	5.3	8.4	9.2
February	2.3	6.4	9.9	10.8
March	2.4	7.5	11.7	12.8
April	2.7	9.3	13.9	15.1
May	3.5	10.7	15.3	16.5
June	4.6	11.6	15.9	17.0
July	5.1	11.8	15.7	16.7
August	5.3	12.0	15.6	16.5
September	5.4	11.7	15.7	16.8
October	5.1	11.7	16.1	17.3
November	5.3	11.9	16.5	17.8
December	5.8	12.1	17.1	18.4
2022
January	6.1	12.4	18.3	19.9
February	6.2	12.0	18.4	20.2
March	7.0	12.3	17.4	18.8
April	7.6	11.6	15.7	16.8
May	7.3	10.7	13.3	14.0
June	6.7	9.9	10.7	11.0
July	6.3	9.3	8.6	8.5
August	6.2	8.6	6.7	6.2
September	6.5	8.1	5.1	4.4
October	6.8	7.3	3.4	2.4
November	6.7	6.5	1.7	0.6
December	6.5	5.7	-0.1	-1.5
2023
January	6.6	4.6	-2.7	-4.5
February	6.5	3.6	-4.4	-6.4
March	5.7	2.2	-5.0	-6.8
April	5.1	1.4	-4.1	-5.5
May	5.5	1.1	-2.5	-3.5
June	5.4	1.0	-0.8	-1.3
July	6.0	1.1	0.7	0.6
August	6.4	1.2	1.6	1.7
September	6.3	1.0	1.8	2.0
October	6.7	1.3	2.0	2.2
November	6.8	1.3	1.9	2.0
December	6.7	1.3	2.0	2.1
2024
January	6.7	1.4	2.1	2.3
February	6.7	1.5	2.3	2.5
March	6.8	1.5	2.2	2.3
April	6.6	1.5	1.1	1.0
May	6.2	1.4	0.1	-0.2
June	6.3	1.3	-0.4	-0.8
July	5.9	1.4	-0.7	-1.2
August	5.5	1.3	-0.8	-1.3
September	5.1	1.3	-0.6	-1.0
October	5.0	1.2	-0.1	-0.5
November	4.6	1.4	0.6	0.4
December	4.5	1.4	1.0	0.9
2025
January	4.2	1.4	1.3	1.3
February	4.1	1.4	0.8	0.7
March	3.8	1.4	0.4	0.2
April	3.5	1.0	0.1	-0.2
May	3.2	0.7	0.1	-0.1
June	2.8	0.7	-0.3	-0.5
July	2.5	0.4	-0.5	-0.7
August	2.3	0.2	-0.6	-0.8
September	2.1	0.2	-0.5	-0.7
October	2.0	0.2	-0.4	-0.5
November	1.7	0.1	-0.8	-1.0
December	1.3	-0.1	-1.1	-1.3

Chart 2 end

Chart 3 compares the year-over-year growth rate of official OA price index with that of the estimated OA using the net acquisition approach. The construction price index accounts for more than 26.0% of the OA price using the net acquisition approach, and the home purchase price index (structure only) accounts for almost 33.9% of OA using this approach. The increased influence of the house structure and construction prices in the OA price index has led to heightened inflationary pressures, particularly during periods of strong housing market activity. From 2011 to early 2020, the official OA and the net acquisition approach tracked closely together , reflecting a period of relative house price stability. However, starting in 2020, a significant divergence emerged, with the net acquisition index rising sharply ahead of the official OA index. This was driven by rapid increases in house prices and construction costs. In 2023, the net acquisition index declined sharply due to the slowdown in the housing market and the easing of supply-chain constraints. Throughout 2024 and 2025, the official OA index remained above the net acquisition index, exhibiting relatively stronger growth. In contrast, the net acquisition index stabilized at lower levels following its earlier peak and subsequent cooling, reflecting the slowdown in house price growth.

Chart 3 start

Data table for Chart 3

Data table for chart 3
Table summary
This table displays the results of Data table for chart 3 Official owned accommodation (OA) and Net acquisition OA, calculated using 12-month percent change units of measure (appearing as column headers).

	Official owned accommodation (OA)	Net acquisition OA
	12-month percent change
Source: Statistics Canada, Consumer Prices program.
2004
January	2.9	5.2
February	2.7	5.1
March	2.5	5.4
April	2.5	5.7
May	2.4	5.8
June	2.4	5.9
July	2.8	5.8
August	2.8	5.6
September	2.9	5.5
October	3.1	5.5
November	3.3	5.3
December	3.4	5.2
2005
January	3.3	5.2
February	3.1	4.8
March	3.2	5.0
April	3.2	4.5
May	3.4	4.5
June	3.4	4.3
July	3.2	4.2
August	3.1	4.3
September	2.9	4.3
October	2.8	4.4
November	2.7	4.4
December	2.7	4.8
2006
January	3.0	5.4
February	3.3	5.8
March	3.4	6.2
April	3.6	6.9
May	3.7	7.1
June	3.8	7.7
July	4.0	8.2
August	4.5	9.0
September	4.9	9.0
October	5.2	8.6
November	5.1	8.3
December	5.2	7.9
2007
January	5.0	7.3
February	4.9	7.2
March	5.0	6.7
April	4.9	6.2
May	4.7	6.3
June	4.9	5.8
July	4.8	5.7
August	4.9	5.0
September	4.8	4.9
October	4.8	4.8
November	5.1	5.0
December	5.0	5.0
2008
January	5.0	5.0
February	5.1	4.9
March	5.0	4.5
April	5.1	4.0
May	5.0	3.0
June	4.8	2.8
July	4.6	2.4
August	4.2	1.9
September	4.0	1.7
October	3.8	1.2
November	3.6	0.7
December	3.4	0.5
2009
January	3.5	0.0
February	3.0	-0.7
March	2.4	-1.1
April	1.9	-1.6
May	1.5	-1.4
June	1.1	-1.5
July	0.8	-1.3
August	0.5	-1.2
September	0.1	-0.7
October	-0.2	0.0
November	-0.6	0.3
December	-0.8	0.8
2010
January	-0.9	1.5
February	-0.9	2.2
March	-0.5	3.0
April	-0.1	3.8
May	0.3	4.1
June	0.6	4.2
July	1.3	4.3
August	1.2	4.1
September	1.4	3.5
October	1.7	3.5
November	1.9	3.5
December	1.9	3.1
2011
January	2.0	3.0
February	2.0	3.1
March	1.7	2.7
April	1.5	2.8
May	1.7	2.8
June	1.6	2.8
July	1.2	2.5
August	1.5	2.7
September	1.4	2.9
October	0.9	2.4
November	1.0	2.5
December	1.2	2.6
2012
January	1.2	2.4
February	1.2	2.2
March	1.3	2.4
April	1.7	2.7
May	1.5	2.5
June	1.3	2.6
July	1.2	2.6
August	1.0	2.6
September	1.0	2.7
October	1.1	2.9
November	0.9	2.7
December	0.7	2.9
2013
January	0.5	2.8
February	0.5	2.8
March	0.6	2.7
April	0.2	2.3
May	0.1	2.2
June	0.3	2.1
July	0.2	2.1
August	0.3	2.1
September	0.5	2.0
October	0.7	1.8
November	1.0	1.9
December	1.2	1.8
2014
January	1.5	2.0
February	1.5	2.1
March	1.5	2.1
April	1.9	2.4
May	1.9	2.3
June	1.8	2.3
July	2.1	2.4
August	2.1	2.4
September	1.9	2.5
October	1.7	2.3
November	1.6	2.3
December	1.7	2.4
2015
January	1.6	2.1
February	1.7	2.1
March	1.7	2.1
April	1.4	1.8
May	1.5	1.9
June	1.6	1.9
July	1.4	2.0
August	1.4	2.1
September	1.6	2.1
October	1.9	2.5
November	1.9	2.5
December	1.9	2.5
2016
January	1.9	2.7
February	1.8	2.6
March	1.7	2.7
April	1.8	2.8
May	1.8	3.3
June	2.1	3.2
July	2.2	3.4
August	2.4	3.3
September	2.3	3.3
October	2.3	3.4
November	2.6	3.4
December	2.4	3.3
2017
January	2.6	3.1
February	2.4	3.1
March	2.6	3.2
April	2.5	3.3
May	2.7	3.1
June	2.5	3.4
July	2.5	3.5
August	2.6	3.6
September	2.6	3.8
October	2.6	3.6
November	2.4	3.5
December	2.5	3.7
2018
January	2.5	3.8
February	2.8	3.8
March	2.6	3.8
April	2.7	3.6
May	2.6	3.5
June	2.6	3.3
July	2.7	3.0
August	2.7	2.8
September	2.8	2.6
October	2.6	2.3
November	2.7	2.2
December	2.6	2.1
2019
January	2.7	2.1
February	2.7	2.0
March	2.8	1.9
April	2.8	1.7
May	2.7	1.4
June	2.6	1.2
July	2.5	1.1
August	2.3	1.1
September	2.3	1.2
October	2.5	1.4
November	2.4	1.4
December	2.3	1.5
2020
January	2.2	1.5
February	2.2	1.6
March	2.3	1.8
April	2.4	1.8
May	2.2	1.8
June	2.2	2.1
July	2.0	2.4
August	2.0	2.8
September	2.1	3.5
October	2.2	4.0
November	2.2	4.5
December	2.3	4.9
2021
January	2.2	5.6
February	2.3	6.6
March	2.4	7.7
April	2.7	9.4
May	3.5	11.1
June	4.6	12.2
July	5.1	12.6
August	5.3	12.7
September	5.4	12.4
October	5.1	12.3
November	5.3	12.4
December	5.8	12.9
2022
January	6.1	13.2
February	6.2	13.0
March	7.0	13.4
April	7.6	13.0
May	7.3	12.0
June	6.7	11.0
July	6.3	10.2
August	6.2	9.5
September	6.5	9.1
October	6.8	8.4
November	6.7	7.7
December	6.5	6.8
2023
January	6.6	5.7
February	6.5	4.7
March	5.7	3.3
April	5.1	2.4
May	5.5	2.2
June	5.4	2.1
July	6.0	2.3
August	6.4	2.5
September	6.3	2.2
October	6.7	2.6
November	6.8	2.6
December	6.7	2.6
2024
January	6.7	2.7
February	6.7	2.7
March	6.8	2.7
April	6.6	2.6
May	6.2	2.4
June	6.3	2.3
July	5.9	2.3
August	5.5	2.2
September	5.1	2.1
October	5.0	2.2
November	4.6	2.3
December	4.5	2.3
2025
January	4.2	2.4
February	4.1	2.5
March	3.8	2.4
April	3.5	2.2
May	3.2	1.9
June	2.8	1.9
July	2.5	1.6
August	2.3	1.5
September	2.1	1.4
October	2.0	1.5
November	1.7	1.4
December	1.3	1.1

Chart 3 end

Chart 4 compares the year-over-year growth rate of official OA price index with that of the estimated OA using the payment approach. The only difference between the payment approach and the official OA series is the replacement cost, considered accrual cost, which is not included in the payment approach. As a result, the actual payments by homeowners, as estimated by this approach, show price pressures similar to the official OA. However, since the second quarter of 2018, the payment approach has shown greater volatility compared with the official OA. The difference between the two series is primarily caused by the growing influence of the mortgage interest cost,^Note  which can first be attributed to the rise in housing prices up until the first quarter of 2022, and subsequently to the increase in interest rates starting from the second quarter of 2022. In 2023, the year-over-year growth rate of the payment-based index peaked significantly above that of the official OA series, reflecting elevated borrowing costs. However, beginning in 2024, the year-over-year growth rate of the payment-based index began to decline more rapidly than that of the official OA index. This sharper decline is likely linked to the stabilization of interest rates at elevated levels, which remain substantially higher than those seen prior to 2022.

Chart 4 start

Data table for Chart 4

Data table for chart 4
Table summary
This table displays the results of Data table for chart 4 Official owned accommodation (OA) and Payment OA, calculated using 12-month percent change units of measure (appearing as column headers).

	Official owned accommodation (OA)	Payment OA
	12-month percent change
Source: Statistics Canada, Consumer Prices program.
2004
January	2.9	2.2
February	2.7	2.1
March	2.5	2.1
April	2.5	2.0
May	2.4	1.8
June	2.4	1.6
July	2.8	1.9
August	2.8	2.1
September	2.9	2.1
October	3.1	2.5
November	3.3	2.7
December	3.4	2.8
2005
January	3.3	2.7
February	3.1	2.5
March	3.2	2.7
April	3.2	2.6
May	3.4	3.0
June	3.4	3.2
July	3.2	3.0
August	3.1	2.8
September	2.9	2.6
October	2.8	2.3
November	2.7	2.2
December	2.7	2.2
2006
January	3.0	2.5
February	3.3	2.7
March	3.4	2.8
April	3.6	2.9
May	3.7	2.9
June	3.8	2.8
July	4.0	3.1
August	4.5	3.6
September	4.9	3.9
October	5.2	4.2
November	5.1	4.1
December	5.2	4.5
2007
January	5.0	4.3
February	4.9	4.4
March	5.0	4.5
April	4.9	4.5
May	4.7	4.3
June	4.9	4.5
July	4.8	4.6
August	4.9	4.5
September	4.8	4.7
October	4.8	4.8
November	5.1	5.1
December	5.0	5.0
2008
January	5.0	5.0
February	5.1	5.1
March	5.0	5.0
April	5.1	5.1
May	5.0	5.0
June	4.8	5.0
July	4.6	4.9
August	4.2	4.6
September	4.0	4.4
October	3.8	4.2
November	3.6	4.3
December	3.4	4.1
2009
January	3.5	4.3
February	3.0	3.9
March	2.4	3.5
April	1.9	2.9
May	1.5	2.7
June	1.1	2.4
July	0.8	2.0
August	0.5	1.6
September	0.1	1.1
October	-0.2	0.7
November	-0.6	-0.2
December	-0.8	-0.6
2010
January	-0.9	-0.9
February	-0.9	-0.9
March	-0.5	-0.8
April	-0.1	-0.6
May	0.3	-0.4
June	0.6	-0.3
July	1.3	0.4
August	1.2	0.3
September	1.4	0.4
October	1.7	1.0
November	1.9	1.4
December	1.9	1.5
2011
January	2.0	1.7
February	2.0	1.6
March	1.7	1.6
April	1.5	1.5
May	1.7	1.6
June	1.6	1.6
July	1.2	1.0
August	1.5	1.4
September	1.4	1.4
October	0.9	0.6
November	1.0	0.8
December	1.2	0.9
2012
January	1.2	0.9
February	1.2	0.9
March	1.3	1.0
April	1.7	1.5
May	1.5	1.2
June	1.3	1.2
July	1.2	1.0
August	1.0	0.7
September	1.0	0.7
October	1.1	0.8
November	0.9	0.6
December	0.7	0.3
2013
January	0.5	0.0
February	0.5	0.1
March	0.6	0.2
April	0.2	-0.3
May	0.1	-0.3
June	0.3	-0.2
July	0.2	-0.3
August	0.3	-0.1
September	0.5	0.1
October	0.7	0.4
November	1.0	0.8
December	1.2	1.1
2014
January	1.5	1.5
February	1.5	1.5
March	1.5	1.5
April	1.9	2.1
May	1.9	2.1
June	1.8	2.0
July	2.1	2.4
August	2.1	2.3
September	1.9	2.2
October	1.7	1.9
November	1.6	1.7
December	1.7	1.9
2015
January	1.6	1.7
February	1.7	1.9
March	1.7	2.0
April	1.4	1.7
May	1.5	1.8
June	1.6	2.0
July	1.4	1.7
August	1.4	1.8
September	1.6	1.8
October	1.9	2.3
November	1.9	2.2
December	1.9	2.2
2016
January	1.9	2.1
February	1.8	1.9
March	1.7	1.7
April	1.8	1.6
May	1.8	1.6
June	2.1	1.6
July	2.2	1.8
August	2.4	1.7
September	2.3	1.7
October	2.3	1.6
November	2.6	2.0
December	2.4	1.8
2017
January	2.6	1.8
February	2.4	1.9
March	2.6	2.0
April	2.5	1.9
May	2.7	2.0
June	2.5	1.9
July	2.5	1.8
August	2.6	1.9
September	2.6	2.0
October	2.6	2.0
November	2.4	1.8
December	2.5	2.0
2018
January	2.5	2.1
February	2.8	2.4
March	2.6	2.5
April	2.7	2.6
May	2.6	2.9
June	2.6	3.1
July	2.7	3.4
August	2.7	3.5
September	2.8	3.8
October	2.6	3.6
November	2.7	3.7
December	2.6	3.7
2019
January	2.7	3.9
February	2.7	4.0
March	2.8	4.0
April	2.8	4.1
May	2.7	3.8
June	2.6	3.7
July	2.5	3.7
August	2.3	3.6
September	2.3	3.4
October	2.5	3.6
November	2.4	3.5
December	2.3	3.4
2020
January	2.2	3.2
February	2.2	3.0
March	2.3	3.1
April	2.4	2.9
May	2.2	2.6
June	2.2	2.5
July	2.0	2.2
August	2.0	2.0
September	2.1	1.9
October	2.2	1.6
November	2.2	1.2
December	2.3	0.9
2021
January	2.2	0.6
February	2.3	0.3
March	2.4	0.1
April	2.7	0.0
May	3.5	0.3
June	4.6	1.1
July	5.1	1.5
August	5.3	1.7
September	5.4	1.7
October	5.1	1.6
November	5.3	1.8
December	5.8	2.6
2022
January	6.1	3.1
February	6.2	3.4
March	7.0	4.5
April	7.6	5.5
May	7.3	5.7
June	6.7	5.4
July	6.3	5.2
August	6.2	5.3
September	6.5	6.2
October	6.8	6.7
November	6.7	7.3
December	6.5	7.4
2023
January	6.6	7.7
February	6.5	8.0
March	5.7	7.5
April	5.1	7.4
May	5.5	8.0
June	5.4	8.4
July	6.0	9.2
August	6.4	10.0
September	6.3	9.8
October	6.7	10.5
November	6.8	10.8
December	6.7	10.8
2024
January	6.7	10.7
February	6.7	10.6
March	6.8	10.6
April	6.6	10.2
May	6.2	9.6
June	6.3	9.4
July	5.9	9.0
August	5.5	8.2
September	5.1	7.5
October	5.0	7.1
November	4.6	6.6
December	4.5	6.3
2025
January	4.2	5.9
February	4.1	5.6
March	3.8	5.1
April	3.5	4.7
May	3.2	4.4
June	2.8	4.2
July	2.5	3.7
August	2.3	3.5
September	2.1	3.2
October	2.0	3.1
November	1.7	2.8
December	1.3	2.3

Chart 4 end

Chart 5 compares the year-over-year growth rate of the official OA price index with that of the estimated OA using the user cost approach. This approach, which uses capital gain based on 30 years of horizon length of expectation and a weighted average rate of return, results in a more volatile OA index than the official approach. The monthly OA changes can vary between the minimum yellow line and the maximum grey line. These fluctuations are attributed to the different monthly changes in the components of the user cost, by applying different variants of the user cost calculation.

Chart 5 start

Data table for Chart 5

Data table for chart 5
Table summary
This table displays the results of Data table for chart 5 Official owned accommodation (OA), User cost (30 years) OA – using NHPI¹ and CREA’s HPI² ³, Maximum and Minimum, calculated using 12-month percent change units of measure (appearing as column headers).

	Official owned accommodation (OA)	User cost (30 years) OA – using NHPI Data table for chart 5 Note 1 and CREA’s HPI Data table for chart 5 Note 2 Data table for chart 5 Note 3	Maximum	Minimum
	12-month percent change
Note 1 NHPI represents the New Housing Price Index. Return to note 1 referrer Note 2 CREA's HPI represents the Canadian Real Estate Association's Home Price Index. Return to note 2 referrer Note 3 User cost estimates are estimates of expected capital gain based on 30 years of horizon length of expectation formation, applying the CREA's HPI and the NHPI. Return to note 3 referrer Note: The range indicates the spectrum of monthly price fluctuations based on different user cost variants. Source: Statistics Canada, Consumer Prices program.
2004
January	2.9	6.3	11.9	4.8
February	2.7	6.2	11.8	4.7
March	2.5	6.3	11.7	4.9
April	2.5	6.4	11.8	5.1
May	2.4	6.4	11.7	5.1
June	2.4	6.5	11.7	4.9
July	2.8	6.6	11.8	5.2
August	2.8	6.3	11.5	5.0
September	2.9	6.3	11.4	4.9
October	3.1	6.1	11.3	5.0
November	3.3	6.2	11.3	5.2
December	3.4	6.2	11.2	5.1
2005
January	3.3	6.2	11.2	5.0
February	3.1	5.8	10.7	4.7
March	3.2	5.8	10.7	4.7
April	3.2	5.4	10.2	4.4
May	3.4	5.5	10.2	4.5
June	3.4	5.3	10.0	4.6
July	3.2	5.2	9.7	4.2
August	3.1	5.3	9.7	4.3
September	2.9	5.2	9.5	4.1
October	2.8	5.2	9.3	3.9
November	2.7	5.3	9.4	4.1
December	2.7	5.3	9.3	4.0
2006
January	3.0	5.6	9.5	4.4
February	3.3	6.0	9.9	4.7
March	3.4	6.2	10.0	4.8
April	3.6	6.7	10.4	5.1
May	3.7	7.1	11.1	5.2
June	3.8	7.1	11.4	4.9
July	4.0	7.4	11.6	5.1
August	4.5	7.7	11.8	5.4
September	4.9	8.0	12.1	5.7
October	5.2	7.8	11.7	5.7
November	5.1	7.4	11.3	5.2
December	5.2	7.4	11.3	5.4
2007
January	5.0	7.0	10.8	5.0
February	4.9	6.9	10.9	4.8
March	5.0	6.8	10.9	4.8
April	4.9	6.5	10.6	4.5
May	4.7	5.1	10.2	4.2
June	4.9	4.4	10.2	4.2
July	4.8	3.5	10.0	3.2
August	4.9	2.5	9.6	2.2
September	4.8	1.7	9.3	1.3
October	4.8	2.0	9.4	1.6
November	5.1	1.4	9.3	0.8
December	5.0	0.4	8.9	-0.3
2008
January	5.0	-1.0	8.5	-1.8
February	5.1	-1.5	7.9	-2.4
March	5.0	-2.3	7.1	-3.2
April	5.1	-2.6	6.4	-3.6
May	5.0	-2.4	5.4	-3.5
June	4.8	-2.2	4.6	-3.4
July	4.6	-1.5	4.2	-2.8
August	4.2	-1.5	3.8	-2.7
September	4.0	-1.1	3.5	-2.4
October	3.8	-0.1	3.3	-1.7
November	3.6	0.7	3.2	-1.6
December	3.4	1.4	3.0	-1.6
2009
January	3.5	3.0	3.3	-1.2
February	3.0	3.0	3.0	-2.0
March	2.4	2.7	2.7	-2.5
April	1.9	1.6	1.6	-2.5
May	1.5	1.1	1.3	-2.3
June	1.1	0.4	1.3	-2.4
July	0.8	-0.9	1.3	-2.6
August	0.5	-1.9	1.4	-3.5
September	0.1	-3.1	2.1	-4.9
October	-0.2	-4.6	3.5	-6.5
November	-0.6	-6.6	4.5	-8.6
December	-0.8	-7.7	6.0	-9.9
2010
January	-0.9	-8.6	6.8	-10.9
February	-0.9	-9.0	7.8	-11.4
March	-0.5	-9.0	8.8	-11.6
April	-0.1	-8.0	9.0	-10.7
May	0.3	-7.1	8.7	-9.8
June	0.6	-6.4	7.9	-9.2
July	1.3	-4.4	7.2	-7.2
August	1.2	-4.5	6.3	-7.3
September	1.4	-4.1	5.5	-6.9
October	1.7	-2.9	5.1	-5.6
November	1.9	-2.2	4.8	-5.0
December	1.9	-1.9	4.7	-4.7
2011
January	2.0	-2.1	4.6	-5.1
February	2.0	-2.6	4.4	-5.6
March	1.7	-3.1	4.3	-6.3
April	1.5	-4.2	4.3	-7.5
May	1.7	-4.2	4.7	-7.3
June	1.6	-4.3	5.2	-7.2
July	1.2	-5.4	5.4	-8.3
August	1.5	-4.4	5.8	-7.0
September	1.4	-3.9	6.0	-6.3
October	0.9	-5.5	5.5	-7.8
November	1.0	-4.6	5.5	-6.7
December	1.2	-4.0	5.4	-5.8
2012
January	1.2	-3.3	5.3	-4.9
February	1.2	-2.6	4.9	-3.9
March	1.3	-1.7	4.7	-3.9
April	1.7	-0.2	4.9	-3.3
May	1.5	-0.4	4.4	-3.4
June	1.3	-0.4	4.0	-3.4
July	1.2	-0.5	3.6	-3.4
August	1.0	-0.6	3.3	-3.5
September	1.0	-0.5	3.1	-3.3
October	1.1	-0.2	3.0	-2.9
November	0.9	-0.2	2.9	-2.9
December	0.7	-0.4	2.9	-3.0
2013
January	0.5	-0.5	2.8	-3.0
February	0.5	-0.3	2.9	-2.6
March	0.6	-0.3	2.9	-2.4
April	0.2	-0.8	2.3	-2.8
May	0.1	-0.9	2.2	-2.8
June	0.3	-0.8	2.2	-2.5
July	0.2	-0.9	2.1	-2.6
August	0.3	-0.7	2.3	-2.3
September	0.5	-0.7	2.6	-2.1
October	0.7	-0.6	2.9	-2.1
November	1.0	-0.4	3.3	-1.7
December	1.2	-0.4	3.2	-1.6
2014
January	1.5	-0.2	3.5	-1.3
February	1.5	-0.2	3.6	-1.3
March	1.5	-0.2	3.7	-1.3
April	1.9	0.2	3.9	-0.9
May	1.9	0.1	3.9	-1.0
June	1.8	-0.1	3.9	-1.2
July	2.1	0.2	4.0	-0.8
August	2.1	0.1	4.1	-1.0
September	1.9	0.0	4.0	-1.0
October	1.7	-0.4	3.8	-1.5
November	1.6	-0.5	3.8	-1.6
December	1.7	-0.4	4.0	-1.5
2015
January	1.6	-0.5	4.0	-1.7
February	1.7	-0.6	4.0	-1.7
March	1.7	-0.6	4.1	-1.8
April	1.4	-1.0	4.1	-2.2
May	1.5	-1.0	4.2	-2.2
June	1.6	-1.0	4.5	-2.3
July	1.4	-1.3	4.6	-2.7
August	1.4	-1.2	4.9	-2.7
September	1.6	-1.3	5.0	-2.8
October	1.9	-0.9	5.4	-2.4
November	1.9	-1.0	5.6	-2.6
December	1.9	-1.1	5.7	-2.7
2016
January	1.9	-1.0	5.8	-2.6
February	1.8	-1.1	6.1	-2.7
March	1.7	-1.1	6.4	-2.6
April	1.8	-0.9	6.7	-2.5
May	1.8	-0.9	7.1	-2.4
June	2.1	-0.6	7.5	-2.0
July	2.2	-0.5	7.6	-1.9
August	2.4	-0.3	7.6	-1.7
September	2.3	-0.2	7.5	-1.5
October	2.3	-0.2	7.4	-1.4
November	2.6	0.3	7.5	-0.8
December	2.4	0.2	7.1	-0.9
2017
January	2.6	0.4	7.0	-0.6
February	2.4	0.5	7.0	-0.5
March	2.6	0.7	7.0	-0.2
April	2.5	0.7	6.5	-0.2
May	2.7	1.2	6.1	0.4
June	2.5	1.2	5.3	0.4
July	2.5	1.4	4.6	0.7
August	2.6	1.7	4.4	1.1
September	2.6	1.8	4.0	1.3
October	2.6	1.8	3.8	1.3
November	2.4	1.6	3.4	1.2
December	2.5	1.9	3.3	1.6
2018
January	2.5	1.8	3.0	1.5
February	2.8	2.0	2.9	1.8
March	2.6	1.8	2.6	1.6
April	2.7	1.8	2.6	1.6
May	2.6	1.6	2.3	1.4
June	2.6	1.4	2.1	1.2
July	2.7	1.3	2.1	1.1
August	2.7	1.1	1.9	0.9
September	2.8	1.2	1.9	0.9
October	2.6	0.8	1.6	0.6
November	2.7	0.8	1.5	0.5
December	2.6	0.7	1.4	0.5
2019
January	2.7	0.8	1.4	0.5
February	2.7	0.7	1.4	0.4
March	2.8	0.8	1.5	0.5
April	2.8	0.8	1.5	0.5
May	2.7	0.6	1.2	0.2
June	2.6	0.4	1.1	0.0
July	2.5	0.3	1.0	0.0
August	2.3	0.1	0.8	-0.3
September	2.3	0.1	0.8	-0.3
October	2.5	0.4	1.2	0.0
November	2.4	0.5	1.3	0.1
December	2.3	0.5	1.4	0.1
2020
January	2.2	0.6	1.7	0.1
February	2.2	0.5	2.0	0.1
March	2.3	0.8	2.3	0.4
April	2.4	0.9	2.2	0.5
May	2.2	0.9	2.3	0.5
June	2.2	1.1	2.7	0.7
July	2.0	1.0	3.1	0.6
August	2.0	1.2	3.6	0.7
September	2.1	1.5	4.1	1.0
October	2.2	1.8	4.5	1.3
November	2.2	2.1	5.0	1.6
December	2.3	2.4	5.6	1.8
2021
January	2.2	2.5	6.1	1.8
February	2.3	2.8	7.0	2.1
March	2.4	3.1	8.0	2.3
April	2.7	3.7	9.2	2.8
May	3.5	4.9	10.5	3.9
June	4.6	6.4	11.5	5.4
July	5.1	7.2	11.8	6.3
August	5.3	7.7	11.8	6.9
September	5.4	7.8	11.6	7.0
October	5.1	7.3	11.0	6.5
November	5.3	7.3	10.8	6.6
December	5.8	7.9	11.4	7.3
2022
January	6.1	8.3	11.6	7.7
February	6.2	8.3	11.4	7.9
March	7.0	9.2	12.1	8.8
April	7.6	9.8	12.7	9.5
May	7.3	9.0	11.3	8.9
June	6.7	7.9	9.9	7.8
July	6.3	7.1	8.8	6.9
August	6.2	6.3	7.8	6.1
September	6.5	6.1	7.5	6.0
October	6.8	5.7	6.7	5.6
November	6.7	5.2	6.0	5.1
December	6.5	4.3	4.8	4.2
2023
January	6.6	3.8	4.2	3.6
February	6.5	3.3	3.5	2.9
March	5.7	1.9	2.0	1.6
April	5.1	0.9	1.0	0.5
May	5.5	0.9	1.4	0.6
June	5.4	0.6	1.7	0.4
July	6.0	0.8	2.3	0.4
August	6.4	1.0	2.6	0.5
September	6.3	0.6	2.2	0.0
October	6.7	0.9	2.1	0.2
November	6.8	1.0	1.8	0.2
December	6.7	0.9	1.7	0.0
2024
January	6.7	0.8	1.7	-0.1
February	6.7	0.8	1.9	-0.2
March	6.8	0.9	2.1	-0.2
April	6.6	0.6	2.0	-0.6
May	6.2	0.4	1.9	-0.8
June	6.3	0.4	1.9	-0.7
July	5.9	0.2	1.9	-1.0
August	5.5	0.0	1.9	-1.2
September	5.1	-0.3	1.9	-1.5
October	5.0	0.0	1.8	-1.3
November	4.6	-0.3	1.9	-1.7
December	4.5	-0.2	2.0	-1.6
2025
January	4.2	-0.2	2.1	-1.6
February	4.1	-0.2	2.2	-1.6
March	3.8	-0.4	2.1	-1.8
April	3.5	-0.5	2.1	-1.8
May	3.2	-0.8	1.9	-2.0
June	2.8	-1.0	1.7	-2.2
July	2.5	-1.1	1.6	-2.3
August	2.3	-1.2	1.4	-2.4
September	2.1	-1.2	1.4	-2.3
October	2.0	-1.1	1.5	-2.2
November	1.7	-1.1	1.4	-2.2
December	1.3	-1.4	1.1	-2.4

Chart 5 end

As explored, different approaches result in a wide range of estimates for analytical OA price indexes (Chart 6 ). As a result, they also have varying impacts on total inflation, as measured by the change in the all-items CPI over time (Chart 7).

The analytical CPI using the payment approach continues to track the official CPI relatively closely over time, maintaining a stable trajectory with only moderate divergence during periods of growth in new housing prices (structure only). The user cost approach exhibits greater volatility, largely due to fluctuations in expected capital gains. This highlights the sensitivity of the user cost index to long-term housing market expectations.

In contrast, the acquisition and net acquisition approaches generate the highest index levels and are more immediately responsive to changes in house prices, new housing prices (structure only), and construction costs, thereby capturing inflationary pressures earlier than the other methods.

Meanwhile, the rental equivalence approach produced a relatively low index level prior to 2020, reflecting a generally stable rental market. In the post-COVID period, however, it increased, reflecting gradually strengthening rental market dynamics in both the primary and secondary rental markets. As a result, starting around March 2024, the rental equivalence index showed stronger growth, particularly when based on average asking rents. However, this trend reverses more sharply toward the end of the period. By late 2025, the asking-rent–based rental equivalence measure exhibits a steeper slowdown, indicating notably lower inflationary pressures relative to the official OA measure. This reflects its greater responsiveness to easing conditions in the rental market.

By contrast, the user cost index recorded the lowest growth. This primarily reflects the significant contribution of expected capital gains over a 30-year horizon, which acts to offset increases in other user cost components. In addition, the relatively stable growth in the rate of return index further moderated the overall user cost measure.

Recently, inflationary pressures as measured by most analytical OA approaches remained elevated compared to pre-pandemic periods. The payment and user cost approaches continued to reflect the influence of higher borrowing costs and long-term housing expectations. The acquisition and net acquisition approaches, which are more sensitive to house prices  and construction cost, have seen inflationary pressure moderate significantly since 2023, returning to levels consistent with the pre-pandemic period.

Chart 6 start

Data table for Chart 6

Data table for chart 6
Table summary
This table displays the results of Data table for chart 6 Official owned accommodation (OA), Payment OA, Rental equivalence OA, Rental equivalence OA using average asking rent index⁴, Net acquisition OA, Acquisition OA – using NHPI¹ and CREA’s HPI² and User cost (30 years) OA – using NHPI¹ and CREA’s HPI² ³, calculated using index (2003=100) units of measure (appearing as column headers).

	Official owned accommodation (OA)	Payment OA	Rental equivalence OA	Rental equivalence OA using average asking rent index Data table for chart 6 Note 4	Net acquisition OA	Acquisition OA – using NHPI Data table for chart 6 Note 1 and CREA’s HPI Data table for chart 6 Note 2	User cost (30 years) OA – using NHPI Data table for chart 6 Note 1 and CREA’s HPI Data table for chart 6 Note 2 Data table for chart 6 Note 3
	index (2003=100)
Note ... not applicable Note 1 NHPI represents the New Housing Price Index. Return to note 1 referrer Note 2 CREA's HPI represents the Canadian Real Estate Association's Home Price Index. Return to note 2 referrer Note 3 User cost estimates are estimates of expected capital gain based on 30 years of horizon length of expectation formation, applying the CREA's HPI and the NHPI. Return to note 3 referrer Note 4 For the estimation of this index, asking rent data are used only from January 2019 onward; prior to that, the official rent series is applied. Return to note 4 referrer Source: Statistics Canada, Consumer Prices program.
2003
January	98.6	98.9	99.5	... not applicable	97.7	97.6	97.3
February	98.9	99.2	99.5	... not applicable	98.3	98.2	97.9
March	99.2	99.3	99.5	... not applicable	98.3	98.3	98.2
April	99.5	99.6	99.7	... not applicable	98.8	98.8	98.7
May	99.8	99.9	99.8	... not applicable	99.3	99.2	99.1
June	100.0	100.1	99.9	... not applicable	99.6	99.6	99.6
July	100.0	100.0	100.0	... not applicable	100.0	99.9	99.9
August	100.1	100.0	100.2	... not applicable	100.4	100.4	100.4
September	100.3	100.1	100.3	... not applicable	100.8	100.8	100.9
October	101.1	100.9	100.5	... not applicable	101.9	102.0	102.2
November	101.2	100.9	100.5	... not applicable	102.4	102.5	102.6
December	101.4	101.1	100.6	... not applicable	102.7	102.8	103.1
2004
January	101.4	101.1	100.6	... not applicable	102.8	103.1	103.4
February	101.6	101.3	100.7	... not applicable	103.3	103.8	104.0
March	101.7	101.4	100.7	... not applicable	103.6	104.1	104.3
April	102.0	101.6	100.9	... not applicable	104.4	104.9	105.1
May	102.2	101.6	100.9	... not applicable	105.0	105.2	105.5
June	102.4	101.6	101.0	... not applicable	105.5	105.8	106.1
July	102.8	101.9	101.1	... not applicable	105.7	106.1	106.6
August	102.9	102.1	101.3	... not applicable	106.0	106.4	106.8
September	103.2	102.2	101.3	... not applicable	106.3	106.8	107.3
October	104.2	103.5	101.4	... not applicable	107.5	107.9	108.5
November	104.5	103.7	101.5	... not applicable	107.9	108.3	108.9
December	104.7	103.9	101.6	... not applicable	108.0	108.8	109.5
2005
January	104.7	103.9	101.6	... not applicable	108.2	109.2	109.8
February	104.7	103.8	101.6	... not applicable	108.3	109.3	110.0
March	105.0	104.1	101.7	... not applicable	108.7	109.8	110.4
April	105.3	104.3	101.7	... not applicable	109.1	110.1	110.8
May	105.7	104.7	101.9	... not applicable	109.7	110.6	111.2
June	105.9	104.9	101.9	... not applicable	110.0	111.1	111.7
July	106.1	105.0	102.0	... not applicable	110.2	111.5	112.1
August	106.1	105.0	102.2	... not applicable	110.6	112.0	112.4
September	106.2	105.0	102.2	... not applicable	110.9	112.4	112.8
October	107.1	105.8	102.2	... not applicable	112.1	113.7	114.1
November	107.3	106.0	102.3	... not applicable	112.6	114.4	114.7
December	107.6	106.2	102.4	... not applicable	113.2	115.1	115.3
2006
January	107.9	106.5	102.5	... not applicable	114.0	116.0	115.9
February	108.2	106.6	102.5	... not applicable	114.6	116.6	116.5
March	108.6	106.9	102.6	... not applicable	115.5	117.5	117.3
April	109.1	107.3	102.7	... not applicable	116.7	118.6	118.2
May	109.6	107.7	102.8	... not applicable	117.6	119.6	119.1
June	109.9	107.8	102.8	... not applicable	118.5	120.4	119.7
July	110.4	108.2	103.0	... not applicable	119.2	121.2	120.3
August	110.9	108.7	103.2	... not applicable	120.5	122.4	121.1
September	111.4	109.0	103.2	... not applicable	120.9	122.9	121.9
October	112.6	110.3	103.5	... not applicable	121.8	124.0	123.0
November	112.7	110.4	103.5	... not applicable	122.0	124.2	123.2
December	113.2	110.9	103.6	... not applicable	122.2	124.8	123.8
2007
January	113.3	111.1	103.7	... not applicable	122.4	125.2	124.0
February	113.6	111.3	103.8	... not applicable	122.8	125.9	124.6
March	114.1	111.7	104.0	... not applicable	123.2	126.5	125.2
April	114.5	112.1	104.2	... not applicable	123.9	127.3	125.9
May	114.7	112.4	104.2	... not applicable	124.9	128.3	125.2
June	115.2	112.7	104.4	... not applicable	125.4	129.0	125.0
July	115.7	113.2	104.6	... not applicable	126.0	129.7	124.6
August	116.3	113.6	104.8	... not applicable	126.5	130.3	124.2
September	116.8	114.1	105.0	... not applicable	126.9	130.8	123.9
October	118.0	115.6	105.2	... not applicable	127.7	131.8	125.5
November	118.4	116.0	105.2	... not applicable	128.1	132.3	124.9
December	118.8	116.5	105.4	... not applicable	128.3	132.7	124.3
2008
January	118.9	116.6	105.5	... not applicable	128.5	133.0	122.8
February	119.4	117.0	105.7	... not applicable	128.8	133.2	122.7
March	119.8	117.3	105.8	... not applicable	128.8	133.3	122.4
April	120.3	117.9	105.9	... not applicable	128.9	133.3	122.7
May	120.5	118.0	106.0	... not applicable	128.7	132.9	122.2
June	120.8	118.3	106.1	... not applicable	128.9	132.8	122.2
July	121.1	118.7	106.3	... not applicable	129.0	132.6	122.6
August	121.2	118.8	106.5	... not applicable	128.9	132.3	122.4
September	121.4	119.1	106.6	... not applicable	129.0	132.1	122.5
October	122.5	120.5	106.9	... not applicable	129.2	131.9	125.4
November	122.7	121.0	107.1	... not applicable	129.0	131.5	125.7
December	122.9	121.3	107.2	... not applicable	128.9	130.9	126.0
2009
January	123.1	121.6	107.3	... not applicable	128.5	130.4	126.5
February	123.0	121.6	107.4	... not applicable	127.9	129.6	126.4
March	122.7	121.4	107.5	... not applicable	127.4	129.1	125.7
April	122.5	121.3	107.6	... not applicable	126.8	129.0	124.6
May	122.2	121.2	107.7	... not applicable	126.9	129.5	123.5
June	122.1	121.1	107.9	... not applicable	126.9	130.1	122.6
July	122.0	121.1	108.0	... not applicable	127.4	131.0	121.6
August	121.7	120.8	108.3	... not applicable	127.5	131.7	120.1
September	121.5	120.5	108.4	... not applicable	128.1	132.7	118.7
October	122.3	121.3	108.6	... not applicable	129.2	134.2	119.6
November	121.9	120.7	108.7	... not applicable	129.4	134.8	117.5
December	121.9	120.6	108.8	... not applicable	129.9	135.7	116.3
2010
January	121.9	120.5	108.9	... not applicable	130.5	136.3	115.6
February	121.9	120.4	108.9	... not applicable	130.7	136.7	115.0
March	122.1	120.4	109.0	... not applicable	131.2	137.3	114.4
April	122.4	120.5	109.1	... not applicable	131.6	137.6	114.6
May	122.6	120.6	109.1	... not applicable	132.0	137.8	114.7
June	122.8	120.7	109.3	... not applicable	132.3	137.8	114.7
July	123.6	121.6	109.4	... not applicable	132.8	138.1	116.2
August	123.2	121.1	109.6	... not applicable	132.6	138.0	114.6
September	123.2	121.0	109.6	... not applicable	132.6	138.0	113.8
October	124.4	122.6	109.9	... not applicable	133.8	139.3	116.2
November	124.3	122.4	110.0	... not applicable	133.9	139.5	114.9
December	124.3	122.4	110.1	... not applicable	133.9	139.9	114.1
2011
January	124.4	122.5	110.1	... not applicable	134.4	140.4	113.2
February	124.4	122.4	110.2	... not applicable	134.7	141.1	112.0
March	124.3	122.3	110.3	... not applicable	134.8	141.5	110.8
April	124.3	122.3	110.4	... not applicable	135.2	142.0	109.8
May	124.7	122.6	110.5	... not applicable	135.8	142.7	109.9
June	124.7	122.6	110.5	... not applicable	136.0	143.3	109.8
July	125.0	122.8	110.6	... not applicable	136.2	143.8	109.9
August	125.0	122.8	110.8	... not applicable	136.3	144.1	109.7
September	124.9	122.7	110.9	... not applicable	136.4	144.5	109.4
October	125.4	123.3	111.0	... not applicable	137.0	145.2	109.8
November	125.5	123.4	111.1	... not applicable	137.4	145.6	109.6
December	125.7	123.5	111.3	... not applicable	137.4	146.0	109.5
2012
January	125.8	123.6	111.3	... not applicable	137.6	146.4	109.4
February	125.8	123.5	111.5	... not applicable	137.7	146.6	109.1
March	125.8	123.5	111.6	... not applicable	138.0	147.0	108.9
April	126.4	124.1	111.9	... not applicable	138.9	147.9	109.5
May	126.5	124.1	112.0	... not applicable	139.2	148.1	109.5
June	126.4	124.0	112.1	... not applicable	139.6	148.2	109.4
July	126.5	124.0	112.2	... not applicable	139.7	148.3	109.3
August	126.3	123.7	112.5	... not applicable	139.8	148.3	109.0
September	126.2	123.6	112.6	... not applicable	140.1	148.4	108.8
October	126.8	124.3	112.8	... not applicable	141.1	149.2	109.6
November	126.7	124.1	112.9	... not applicable	141.1	149.2	109.3
December	126.6	123.9	113.1	... not applicable	141.4	149.5	109.1
2013
January	126.4	123.6	113.2	... not applicable	141.5	149.5	108.8
February	126.5	123.6	113.4	... not applicable	141.6	149.7	108.7
March	126.6	123.8	113.5	... not applicable	141.7	149.9	108.6
April	126.6	123.7	113.7	... not applicable	142.1	150.3	108.7
May	126.6	123.7	113.7	... not applicable	142.3	150.6	108.5
June	126.8	123.8	113.8	... not applicable	142.4	150.9	108.5
July	126.7	123.6	114.0	... not applicable	142.6	151.3	108.3
August	126.7	123.5	114.2	... not applicable	142.7	151.7	108.2
September	126.9	123.7	114.3	... not applicable	142.8	152.2	108.1
October	127.7	124.8	114.5	... not applicable	143.7	153.2	108.9
November	128.0	125.2	114.7	... not applicable	143.8	153.8	108.9
December	128.0	125.2	114.8	... not applicable	143.9	154.1	108.7
2014
January	128.2	125.5	114.9	... not applicable	144.3	154.6	108.6
February	128.3	125.5	115.0	... not applicable	144.6	155.1	108.5
March	128.4	125.6	115.1	... not applicable	144.7	155.3	108.4
April	129.0	126.4	115.3	... not applicable	145.5	155.9	108.8
May	129.0	126.3	115.3	... not applicable	145.5	156.2	108.6
June	129.1	126.2	115.5	... not applicable	145.8	156.6	108.4
July	129.3	126.5	115.6	... not applicable	146.0	157.1	108.6
August	129.3	126.4	115.8	... not applicable	146.2	157.7	108.3
September	129.3	126.4	116.0	... not applicable	146.3	158.2	108.1
October	129.8	127.2	116.2	... not applicable	146.9	159.1	108.5
November	130.0	127.2	116.3	... not applicable	147.1	159.7	108.3
December	130.2	127.6	116.4	... not applicable	147.4	160.4	108.3
2015
January	130.3	127.6	116.4	... not applicable	147.3	160.8	108.0
February	130.5	127.9	116.6	... not applicable	147.6	161.2	107.9
March	130.7	128.1	116.7	... not applicable	147.7	161.7	107.7
April	130.9	128.5	116.8	... not applicable	148.1	162.4	107.7
May	131.0	128.6	116.8	... not applicable	148.2	163.0	107.5
June	131.2	128.8	116.9	... not applicable	148.6	164.0	107.3
July	131.1	128.6	117.0	... not applicable	148.9	164.9	107.1
August	131.2	128.7	117.1	... not applicable	149.2	166.0	107.0
September	131.3	128.8	117.2	... not applicable	149.4	166.8	106.7
October	132.3	130.1	117.3	... not applicable	150.6	168.3	107.5
November	132.4	130.1	117.4	... not applicable	150.8	169.2	107.2
December	132.7	130.3	117.5	... not applicable	151.1	170.2	107.1
2016
January	132.7	130.4	117.5	... not applicable	151.3	171.0	107.0
February	132.8	130.3	117.6	... not applicable	151.5	172.1	106.7
March	132.9	130.3	117.6	... not applicable	151.7	173.2	106.6
April	133.2	130.6	117.6	... not applicable	152.3	174.7	106.7
May	133.3	130.6	117.7	... not applicable	153.2	176.4	106.5
June	133.9	130.8	117.7	... not applicable	153.3	177.7	106.7
July	134.0	130.9	117.7	... not applicable	153.9	179.1	106.6
August	134.3	130.9	117.8	... not applicable	154.2	179.9	106.6
September	134.4	130.9	117.9	... not applicable	154.2	180.7	106.5
October	135.3	132.2	118.0	... not applicable	155.7	182.2	107.3
November	135.8	132.6	118.0	... not applicable	156.0	183.1	107.6
December	135.9	132.7	118.1	... not applicable	156.1	183.6	107.4
2017
January	136.1	132.8	118.1	... not applicable	156.0	184.4	107.4
February	136.0	132.7	118.2	... not applicable	156.2	186.0	107.2
March	136.3	132.9	118.3	... not applicable	156.6	187.9	107.4
April	136.5	133.1	118.3	... not applicable	157.3	189.1	107.4
May	136.9	133.2	118.4	... not applicable	158.0	189.3	107.8
June	137.2	133.3	118.5	... not applicable	158.6	189.3	108.0
July	137.4	133.3	118.5	... not applicable	159.3	189.2	108.1
August	137.8	133.4	118.5	... not applicable	159.8	189.5	108.4
September	137.9	133.6	118.7	... not applicable	160.2	189.7	108.4
October	138.8	134.8	118.8	... not applicable	161.2	190.9	109.3
November	139.0	135.0	118.8	... not applicable	161.5	191.2	109.3
December	139.3	135.3	119.0	... not applicable	161.8	191.7	109.4
2018
January	139.5	135.5	119.1	... not applicable	162.0	191.7	109.3
February	139.8	135.9	119.3	... not applicable	162.3	191.9	109.4
March	139.9	136.2	119.4	... not applicable	162.6	192.1	109.3
April	140.2	136.6	119.5	... not applicable	163.0	192.4	109.4
May	140.5	137.1	119.7	... not applicable	163.4	192.8	109.5
June	140.8	137.5	119.8	... not applicable	163.8	193.0	109.5
July	141.1	137.7	119.9	... not applicable	164.0	193.3	109.5
August	141.4	138.2	120.2	... not applicable	164.2	193.4	109.6
September	141.7	138.6	120.3	... not applicable	164.4	193.6	109.7
October	142.4	139.6	120.4	... not applicable	164.9	194.1	110.2
November	142.7	140.0	120.6	... not applicable	165.1	193.9	110.2
December	143.0	140.4	120.7	... not applicable	165.2	193.9	110.2
2019
January	143.3	140.8	121.7	121.7	165.3	193.5	110.2
February	143.6	141.3	122.2	123.4	165.6	193.1	110.2
March	143.9	141.7	122.6	122.7	165.7	193.0	110.2
April	144.2	142.1	122.2	122.5	165.8	193.2	110.3
May	144.3	142.4	122.2	122.8	165.8	193.3	110.1
June	144.4	142.6	122.6	123.3	165.8	193.7	109.9
July	144.6	142.9	122.9	121.3	165.8	194.1	109.9
August	144.6	143.1	123.8	123.5	166.0	194.5	109.7
September	144.9	143.4	123.8	125.6	166.4	195.1	109.8
October	145.9	144.6	124.7	124.4	167.2	196.4	110.6
November	146.2	144.9	124.2	125.5	167.3	196.8	110.8
December	146.3	145.2	124.7	126.0	167.7	197.3	110.7
2020
January	146.5	145.3	124.7	126.4	167.8	197.7	110.8
February	146.7	145.6	125.5	127.3	168.2	198.2	110.8
March	147.2	146.0	125.7	126.4	168.6	198.6	111.1
April	147.6	146.3	125.0	128.0	168.7	197.9	111.3
May	147.5	146.1	124.0	128.2	168.8	198.4	111.1
June	147.5	146.1	124.8	130.7	169.3	200.0	111.1
July	147.5	146.0	124.4	130.5	169.8	202.0	111.0
August	147.6	145.9	125.4	131.2	170.7	204.0	111.0
September	147.9	146.1	125.7	131.6	172.2	205.9	111.4
October	149.1	147.0	125.9	131.2	173.9	208.1	112.7
November	149.4	146.7	126.1	133.5	174.9	209.8	113.1
December	149.6	146.5	125.4	133.6	175.9	211.7	113.4
2021
January	149.7	146.2	125.7	138.2	177.2	214.3	113.5
February	150.0	146.0	125.6	136.6	179.3	217.8	113.9
March	150.7	146.1	126.4	138.3	181.6	221.8	114.6
April	151.5	146.3	126.1	141.0	184.6	225.4	115.4
May	152.6	146.6	126.9	141.9	187.5	228.8	116.6
June	154.3	147.7	127.3	143.6	189.9	231.8	118.2
July	154.9	148.1	127.5	144.1	191.2	233.7	119.0
August	155.4	148.4	127.3	149.0	192.5	235.8	119.6
September	156.0	148.7	128.0	149.1	193.6	238.3	120.1
October	156.7	149.3	128.1	147.7	195.4	241.5	120.9
November	157.3	149.4	128.2	147.0	196.7	244.4	121.4
December	158.3	150.3	128.7	150.6	198.5	247.9	122.4
2022
January	158.8	150.8	129.5	152.1	200.6	253.5	122.9
February	159.3	151.0	130.8	153.7	202.6	257.9	123.4
March	161.2	152.7	131.4	153.9	205.9	260.5	125.0
April	163.1	154.4	131.7	156.8	208.5	260.7	126.8
May	163.7	155.0	132.5	156.3	209.9	259.1	127.2
June	164.5	155.7	132.7	158.7	210.7	256.6	127.6
July	164.7	155.9	133.7	160.3	210.8	253.8	127.4
August	165.0	156.2	133.3	162.7	210.7	251.5	127.1
September	166.2	157.8	133.6	162.9	211.3	250.5	127.5
October	167.3	159.4	134.2	163.4	211.8	249.7	127.8
November	167.9	160.2	135.7	162.7	211.8	248.7	127.7
December	168.6	161.4	136.2	164.8	212.0	247.7	127.7
2023
January	169.3	162.3	137.0	167.2	212.1	246.7	127.6
February	169.7	163.1	137.8	164.7	212.1	246.5	127.4
March	170.4	164.2	138.5	167.3	212.6	247.4	127.5
April	171.4	165.8	139.7	168.7	213.5	250.0	127.9
May	172.6	167.4	140.0	169.9	214.5	252.6	128.2
June	173.5	168.7	140.4	172.3	215.1	254.5	128.4
July	174.5	170.2	141.0	171.5	215.6	255.6	128.5
August	175.5	171.8	141.9	176.7	215.8	255.7	128.4
September	176.6	173.3	143.1	175.0	215.9	254.9	128.2
October	178.4	176.2	145.0	171.7	217.2	254.7	129.0
November	179.3	177.5	145.5	171.8	217.3	253.3	129.0
December	180.0	178.7	146.6	173.2	217.6	252.6	128.8
2024
January	180.7	179.7	147.6	174.9	217.7	252.0	128.6
February	181.1	180.4	148.7	176.2	217.9	252.3	128.4
March	181.9	181.6	149.9	178.1	218.4	252.8	128.6
April	182.7	182.7	150.7	177.7	219.0	252.9	128.7
May	183.4	183.5	152.0	177.5	219.6	252.9	128.7
June	184.4	184.6	152.3	178.2	220.1	253.5	129.0
July	184.8	185.5	152.5	179.0	220.6	253.8	128.7
August	185.2	186.0	154.1	178.6	220.5	253.7	128.4
September	185.5	186.4	154.3	178.0	220.4	253.5	127.9
October	187.3	188.7	155.1	176.5	221.9	254.3	129.0
November	187.6	189.3	156.2	173.9	222.2	254.8	128.5
December	188.1	189.9	156.6	177.9	222.7	255.1	128.6
2025
January	188.3	190.3	156.5	175.6	223.0	255.2	128.4
February	188.4	190.6	157.0	171.3	223.4	254.3	128.1
March	188.8	190.9	157.2	174.9	223.6	253.8	128.1
April	189.1	191.3	158.3	175.4	223.8	253.0	128.0
May	189.2	191.6	158.6	178.7	223.8	253.0	127.7
June	189.6	192.3	159.2	179.7	224.2	252.9	127.7
July	189.5	192.3	160.0	179.8	224.1	252.7	127.3
August	189.4	192.4	160.7	179.7	223.7	252.3	126.8
September	189.4	192.4	161.5	178.6	223.5	252.1	126.4
October	191.0	194.6	163.0	177.0	225.3	253.4	127.6
November	190.8	194.6	163.3	173.7	225.4	252.9	127.1
December	190.6	194.2	164.0	172.8	225.1	252.4	126.8

Chart 6 end

The rental equivalence approach increased sharply through 2021 and 2022, reflecting strong rental market dynamics. However, the magnitude and timing of this increase differed across the two measures. The rental equivalence index based on average asking rents rose more rapidly and reached a higher peak, as it captures real-time movements in new rental listings and is solely based on new-tenant turnover rents in the secondary market. In contrast, the measure based on the official CPI Rent index increased more gradually, reflecting the slower adjustment of rents paid by sitting existing tenants in the primary rental market. Beginning in 2023 and continuing into 2024, both measures moderated as rental inflation eased. Although growth slowed compared with the peak period, both rental equivalence measures remained above their pre-pandemic levels.

Chart 7 start

Data table for Chart 7

Data table for chart 7
Table summary
This table displays the results of Data table for chart 7 Official owned accommodation (OA), Payment OA, Rental equivalence OA, Rental equivalence OA using average asking rent index⁴, Net acquisition OA, Acquisition OA – using NHPI¹ and CREA’s HPI² and User cost (30 years) OA – using NHPI¹ and CREA’s HPI² ³, calculated using 12-month percent change units of measure (appearing as column headers).

	Official owned accommodation (OA)	Payment OA	Rental equivalence OA	Rental equivalence OA using average asking rent index Data table for chart 7 Note 4	Net acquisition OA	Acquisition OA – using NHPI Data table for chart 7 Note 1 and CREA’s HPI Data table for chart 7 Note 2	User cost (30 years) OA – using NHPI Data table for chart 7 Note 1 and CREA’s HPI Data table for chart 7 Note 2 Data table for chart 7 Note 3
	12-month percent change
Note ... not applicable Note 1 NHPI represents the New Housing Price Index. Return to note 1 referrer Note 2 CREA's HPI represents the Canadian Real Estate Association's Home Price Index. Return to note 2 referrer Note 3 User cost estimates are estimates of expected capital gain based on 30 years of horizon length of expectation formation, applying the CREA's HPI and the NHPI. Return to note 3 referrer Note 4 For the estimation of this index, asking rent data are used only from January 2019 onward; prior to that, the official rent series is applied. Return to note 4 referrer Source: Statistics Canada, Consumer Prices program.
2004
January	1.3	1.1	1.0	... not applicable	1.6	1.7	1.7
February	0.7	0.6	0.5	... not applicable	1.0	1.2	1.2
March	0.8	0.5	0.5	... not applicable	1.1	1.2	1.2
April	1.7	1.5	1.4	... not applicable	2.1	2.2	2.1
May	2.4	2.3	2.2	... not applicable	3.0	3.1	3.0
June	2.5	2.4	2.2	... not applicable	3.0	3.1	3.1
July	2.3	2.2	2.0	... not applicable	2.8	2.9	2.9
August	1.8	1.7	1.5	... not applicable	2.3	2.4	2.3
September	1.8	1.6	1.5	... not applicable	2.2	2.3	2.3
October	2.3	2.2	2.0	... not applicable	2.8	2.8	2.8
November	2.4	2.3	2.1	... not applicable	2.8	2.9	2.9
December	2.1	2.0	1.8	... not applicable	2.5	2.6	2.6
2005
January	1.9	1.8	1.6	... not applicable	2.3	2.4	2.4
February	2.1	2.0	1.7	... not applicable	2.4	2.5	2.5
March	2.3	2.3	2.0	... not applicable	2.7	2.8	2.8
April	2.4	2.3	2.0	... not applicable	2.7	2.8	2.8
May	1.6	1.5	1.2	... not applicable	1.8	1.9	1.9
June	1.7	1.6	1.3	... not applicable	1.9	2.0	2.0
July	2.0	1.9	1.6	... not applicable	2.2	2.3	2.3
August	2.6	2.6	2.2	... not applicable	2.8	3.0	3.0
September	3.2	3.3	2.9	... not applicable	3.5	3.7	3.7
October	2.6	2.5	2.2	... not applicable	2.8	3.1	3.0
November	2.0	2.0	1.7	... not applicable	2.3	2.6	2.5
December	2.1	2.0	1.8	... not applicable	2.5	2.7	2.5
2006
January	2.8	2.8	2.5	... not applicable	3.2	3.4	3.2
February	2.2	2.1	1.8	... not applicable	2.6	2.8	2.6
March	2.2	2.0	1.7	... not applicable	2.6	2.8	2.6
April	2.4	2.4	2.1	... not applicable	3.1	3.3	3.0
May	2.8	2.7	2.4	... not applicable	3.4	3.7	3.4
June	2.4	2.3	2.0	... not applicable	3.1	3.3	3.0
July	2.3	2.2	1.9	... not applicable	3.1	3.2	2.9
August	2.1	1.9	1.5	... not applicable	2.8	3.0	2.6
September	0.7	0.4	0.1	... not applicable	1.4	1.6	1.2
October	1.0	0.7	0.3	... not applicable	1.6	1.7	1.4
November	1.4	1.1	0.8	... not applicable	2.0	2.1	1.8
December	1.7	1.4	1.0	... not applicable	2.1	2.3	2.0
2007
January	1.1	0.9	0.5	... not applicable	1.5	1.7	1.4
February	2.0	1.8	1.4	... not applicable	2.4	2.6	2.3
March	2.3	2.2	1.8	... not applicable	2.7	2.9	2.6
April	2.2	2.0	1.6	... not applicable	2.4	2.6	2.4
May	2.2	2.0	1.6	... not applicable	2.4	2.7	2.3
June	2.2	2.0	1.6	... not applicable	2.4	2.7	2.2
July	2.2	1.9	1.5	... not applicable	2.3	2.6	2.0
August	1.7	1.5	1.2	... not applicable	1.8	2.1	1.4
September	2.5	2.4	2.0	... not applicable	2.6	2.9	2.2
October	2.4	2.4	1.9	... not applicable	2.6	2.9	2.0
November	2.5	2.4	1.9	... not applicable	2.5	2.9	1.9
December	2.4	2.3	1.8	... not applicable	2.5	2.8	1.7
2008
January	2.2	2.0	1.6	... not applicable	2.3	2.6	1.3
February	1.8	1.7	1.3	... not applicable	1.9	2.2	1.0
March	1.4	1.2	0.8	... not applicable	1.4	1.6	0.4
April	1.7	1.6	1.2	... not applicable	1.6	1.8	0.8
May	2.2	2.2	1.7	... not applicable	2.0	2.1	1.4
June	3.1	3.1	2.6	... not applicable	2.8	2.8	2.4
July	3.4	3.4	2.9	... not applicable	3.1	3.0	2.9
August	3.5	3.5	3.0	... not applicable	3.1	3.0	3.0
September	3.4	3.4	2.9	... not applicable	2.9	2.7	2.9
October	2.6	2.6	2.2	... not applicable	2.1	1.8	2.1
November	2.0	2.0	1.6	... not applicable	1.4	1.1	1.5
December	1.2	1.2	1.0	... not applicable	0.7	0.3	0.8
2009
January	1.1	1.1	0.8	... not applicable	0.5	0.1	0.8
February	1.4	1.5	1.2	... not applicable	0.7	0.3	1.2
March	1.2	1.3	1.1	... not applicable	0.6	0.1	1.1
April	0.4	0.4	0.3	... not applicable	-0.3	-0.7	0.1
May	0.1	0.2	0.2	... not applicable	-0.4	-0.7	-0.1
June	-0.3	-0.1	-0.1	... not applicable	-0.7	-0.8	-0.4
July	-0.9	-0.9	-0.8	... not applicable	-1.3	-1.3	-1.3
August	-0.8	-0.6	-0.5	... not applicable	-1.0	-0.9	-1.0
September	-0.9	-0.8	-0.6	... not applicable	-1.0	-0.7	-1.2
October	0.1	0.2	0.4	... not applicable	0.1	0.5	-0.2
November	1.0	1.1	1.4	... not applicable	1.1	1.6	0.7
December	1.3	1.4	1.6	... not applicable	1.5	2.1	1.0
2010
January	1.9	2.0	2.3	... not applicable	2.3	2.9	1.6
February	1.6	1.7	2.0	... not applicable	2.1	2.8	1.3
March	1.4	1.4	1.7	... not applicable	2.1	2.8	1.0
April	1.8	1.9	2.1	... not applicable	2.6	3.2	1.5
May	1.4	1.4	1.6	... not applicable	2.1	2.6	1.0
June	1.0	0.8	1.1	... not applicable	1.6	2.0	0.5
July	1.8	1.8	1.9	... not applicable	2.4	2.7	1.5
August	1.7	1.6	1.7	... not applicable	2.3	2.5	1.4
September	1.9	1.8	1.9	... not applicable	2.3	2.5	1.6
October	2.4	2.4	2.3	... not applicable	2.8	2.8	2.2
November	2.0	1.9	1.9	... not applicable	2.3	2.3	1.7
December	2.4	2.3	2.2	... not applicable	2.6	2.6	2.2
2011
January	2.3	2.3	2.2	... not applicable	2.5	2.5	2.1
February	2.2	2.2	2.0	... not applicable	2.4	2.4	1.9
March	3.3	3.2	3.1	... not applicable	3.4	3.4	3.0
April	3.3	3.4	3.2	... not applicable	3.5	3.6	3.1
May	3.7	3.7	3.5	... not applicable	3.8	3.9	3.4
June	3.1	3.2	3.0	... not applicable	3.3	3.5	2.9
July	2.7	2.8	2.7	... not applicable	3.0	3.3	2.5
August	3.1	3.1	2.9	... not applicable	3.2	3.6	2.8
September	3.2	3.3	3.1	... not applicable	3.4	3.8	3.0
October	2.9	3.0	2.9	... not applicable	3.2	3.6	2.7
November	2.9	2.9	2.8	... not applicable	3.1	3.4	2.6
December	2.3	2.2	2.2	... not applicable	2.5	2.9	2.0
2012
January	2.5	2.5	2.4	... not applicable	2.6	3.0	2.3
February	2.6	2.6	2.6	... not applicable	2.7	3.1	2.4
March	1.9	1.9	1.9	... not applicable	2.1	2.5	1.7
April	2.0	1.9	1.9	... not applicable	2.1	2.5	1.7
May	1.2	1.3	1.3	... not applicable	1.5	1.8	1.1
June	1.5	1.5	1.5	... not applicable	1.7	1.9	1.3
July	1.3	1.2	1.2	... not applicable	1.4	1.6	1.0
August	1.2	1.3	1.4	... not applicable	1.6	1.7	1.2
September	1.2	1.1	1.2	... not applicable	1.4	1.5	1.0
October	1.2	1.1	1.2	... not applicable	1.5	1.5	1.0
November	0.8	0.8	1.0	... not applicable	1.2	1.2	0.7
December	0.8	0.8	1.0	... not applicable	1.2	1.2	0.7
2013
January	0.5	0.4	0.7	... not applicable	0.9	0.8	0.4
February	1.2	1.2	1.4	... not applicable	1.6	1.5	1.2
March	1.0	1.0	1.2	... not applicable	1.3	1.2	0.9
April	0.4	0.3	0.6	... not applicable	0.7	0.6	0.3
May	0.7	0.6	0.9	... not applicable	1.0	0.9	0.6
June	1.2	1.1	1.4	... not applicable	1.4	1.4	1.1
July	1.3	1.3	1.6	... not applicable	1.7	1.6	1.3
August	1.1	1.0	1.2	... not applicable	1.3	1.4	0.9
September	1.1	1.0	1.2	... not applicable	1.3	1.4	0.9
October	0.7	0.6	0.8	... not applicable	0.9	1.2	0.5
November	0.9	0.9	1.0	... not applicable	1.1	1.5	0.8
December	1.2	1.3	1.4	... not applicable	1.4	1.8	1.1
2014
January	1.5	1.5	1.5	... not applicable	1.6	2.0	1.3
February	1.1	1.1	1.1	... not applicable	1.3	1.7	0.9
March	1.5	1.5	1.5	... not applicable	1.6	2.1	1.3
April	2.0	2.1	2.0	... not applicable	2.2	2.5	1.9
May	2.3	2.3	2.2	... not applicable	2.3	2.7	2.1
June	2.4	2.4	2.2	... not applicable	2.4	2.8	2.1
July	2.1	2.1	2.0	... not applicable	2.1	2.5	1.9
August	2.1	2.1	2.0	... not applicable	2.2	2.6	1.9
September	2.0	2.1	2.0	... not applicable	2.2	2.6	1.9
October	2.4	2.4	2.3	... not applicable	2.4	2.8	2.1
November	2.0	1.9	1.9	... not applicable	2.0	2.5	1.7
December	1.5	1.4	1.4	... not applicable	1.6	2.1	1.2
2015
January	1.0	1.0	1.0	... not applicable	1.2	1.8	0.8
February	1.0	1.0	1.0	... not applicable	1.1	1.7	0.7
March	1.2	1.2	1.1	... not applicable	1.3	1.9	0.9
April	0.8	0.7	0.7	... not applicable	0.8	1.6	0.4
May	0.9	0.9	0.9	... not applicable	1.0	1.7	0.6
June	1.0	1.1	1.0	... not applicable	1.2	2.0	0.8
July	1.3	1.4	1.3	... not applicable	1.4	2.3	1.0
August	1.3	1.3	1.2	... not applicable	1.3	2.3	0.9
September	1.0	1.0	0.9	... not applicable	1.1	2.1	0.6
October	1.0	1.0	0.9	... not applicable	1.1	2.2	0.6
November	1.4	1.3	1.2	... not applicable	1.5	2.5	1.0
December	1.6	1.6	1.4	... not applicable	1.7	2.8	1.2
2016
January	2.0	2.0	1.8	... not applicable	2.1	3.2	1.6
February	1.4	1.4	1.2	... not applicable	1.5	2.7	1.0
March	1.3	1.3	1.1	... not applicable	1.5	2.8	0.9
April	1.7	1.7	1.5	... not applicable	1.9	3.2	1.4
May	1.5	1.5	1.3	... not applicable	1.8	3.2	1.2
June	1.5	1.4	1.2	... not applicable	1.7	3.1	1.1
July	1.3	1.1	0.9	... not applicable	1.5	2.9	0.8
August	1.1	1.0	0.9	... not applicable	1.4	2.8	0.8
September	1.3	1.3	1.1	... not applicable	1.6	3.0	1.0
October	1.5	1.4	1.2	... not applicable	1.7	3.0	1.2
November	1.2	1.1	0.9	... not applicable	1.4	2.6	0.9
December	1.5	1.4	1.1	... not applicable	1.7	2.8	1.2
2017
January	2.1	2.0	1.8	... not applicable	2.3	3.4	1.9
February	2.0	2.0	1.7	... not applicable	2.2	3.4	1.8
March	1.6	1.5	1.3	... not applicable	1.7	3.0	1.3
April	1.6	1.6	1.3	... not applicable	1.8	3.0	1.4
May	1.3	1.1	0.9	... not applicable	1.4	2.4	1.0
June	1.0	0.9	0.7	... not applicable	1.2	2.0	0.8
July	1.2	1.1	0.9	... not applicable	1.4	2.0	1.0
August	1.4	1.2	1.0	... not applicable	1.6	2.1	1.2
September	1.6	1.4	1.2	... not applicable	1.8	2.1	1.4
October	1.4	1.2	1.0	... not applicable	1.6	1.9	1.2
November	2.1	2.1	1.8	... not applicable	2.4	2.6	2.0
December	1.9	1.8	1.6	... not applicable	2.1	2.3	1.8
2018
January	1.7	1.6	1.4	... not applicable	2.0	2.1	1.6
February	2.2	2.1	1.8	... not applicable	2.4	2.3	2.0
March	2.3	2.2	1.9	... not applicable	2.5	2.2	2.2
April	2.2	2.1	1.9	... not applicable	2.4	2.0	2.1
May	2.2	2.3	2.0	... not applicable	2.4	2.1	2.1
June	2.5	2.5	2.1	... not applicable	2.6	2.3	2.3
July	3.0	3.1	2.7	... not applicable	3.0	2.8	2.8
August	2.8	2.9	2.6	... not applicable	2.8	2.7	2.6
September	2.2	2.3	2.0	... not applicable	2.2	2.1	2.0
October	2.4	2.6	2.2	... not applicable	2.4	2.3	2.2
November	1.7	1.7	1.5	... not applicable	1.6	1.4	1.4
December	2.0	2.1	1.8	... not applicable	1.9	1.7	1.7
2019
January	1.4	1.5	1.4	1.4	1.3	1.1	1.1
February	1.5	1.6	1.5	1.7	1.5	1.2	1.2
March	1.9	2.0	1.9	2.0	1.8	1.5	1.6
April	2.0	2.1	1.9	2.0	1.9	1.6	1.7
May	2.4	2.5	2.3	2.4	2.2	1.9	2.1
June	2.0	2.1	2.0	2.1	1.8	1.6	1.7
July	2.0	2.2	2.0	1.8	1.8	1.6	1.7
August	1.9	2.0	2.0	2.0	1.7	1.6	1.6
September	1.9	2.0	2.0	2.3	1.7	1.6	1.6
October	1.9	2.0	2.1	2.1	1.7	1.7	1.6
November	2.2	2.3	2.3	2.5	2.0	2.0	1.9
December	2.2	2.3	2.4	2.6	2.1	2.1	2.0
2020
January	2.4	2.5	2.4	2.7	2.2	2.3	2.1
February	2.2	2.3	2.3	2.4	2.1	2.3	2.0
March	0.9	0.8	0.9	1.0	0.8	1.0	0.6
April	-0.2	-0.2	-0.1	0.3	-0.2	-0.1	-0.5
May	-0.4	-0.5	-0.5	0.1	-0.4	-0.2	-0.7
June	0.7	0.6	0.6	1.4	0.7	0.9	0.4
July	0.1	0.1	0.1	1.3	0.3	0.6	-0.1
August	0.1	0.1	0.1	1.1	0.4	0.8	0.0
September	0.5	0.4	0.5	1.1	0.8	1.3	0.4
October	0.7	0.5	0.4	1.3	1.0	1.5	0.5
November	1.0	0.7	0.8	1.8	1.4	1.9	0.9
December	0.7	0.5	0.4	1.6	1.3	1.8	0.7
2021
January	1.0	0.8	0.9	2.6	1.8	2.4	1.1
February	1.1	0.8	0.7	2.2	1.9	2.7	1.1
March	2.2	1.9	1.8	3.6	3.2	4.1	2.3
April	3.4	3.1	3.0	4.9	4.6	5.6	3.5
May	3.6	3.2	3.4	5.1	5.1	6.0	3.8
June	3.1	2.6	2.6	4.2	4.6	5.5	3.3
July	3.7	3.1	3.2	4.8	5.1	5.9	3.9
August	4.1	3.6	3.4	5.8	5.6	6.4	4.4
September	4.4	3.8	3.7	6.0	5.8	6.7	4.7
October	4.7	4.2	4.0	6.3	6.2	7.2	5.0
November	4.7	4.2	4.0	5.7	6.2	7.3	5.0
December	4.8	4.4	4.2	6.3	6.3	7.5	5.2
2022
January	5.1	4.7	4.5	6.0	6.6	8.1	5.4
February	5.7	5.2	5.2	7.0	7.1	8.6	5.9
March	6.7	6.3	6.1	7.6	8.0	9.3	7.0
April	6.8	6.4	6.1	7.5	7.9	8.8	7.1
May	7.7	7.5	7.2	8.4	8.8	9.2	8.0
June	8.1	8.0	7.6	8.9	9.0	8.9	8.4
July	7.6	7.6	7.4	8.7	8.5	8.0	7.8
August	7.0	6.9	6.7	7.6	7.7	6.9	7.0
September	6.9	6.8	6.4	7.4	7.4	6.3	6.8
October	6.9	6.9	6.5	7.6	7.3	5.8	6.7
November	6.8	6.9	6.7	7.7	7.1	5.3	6.6
December	6.3	6.4	6.1	6.8	6.3	4.3	5.9
2023
January	5.9	6.0	5.7	6.5	5.7	3.2	5.4
February	5.2	5.4	5.1	5.4	5.0	2.2	4.7
March	4.3	4.5	4.3	5.0	3.9	1.4	3.7
April	4.4	4.7	4.6	4.9	3.9	1.8	3.7
May	3.4	3.6	3.4	4.0	2.7	1.3	2.5
June	2.8	3.0	2.9	3.4	2.2	1.3	1.9
July	3.3	3.5	3.2	3.5	2.6	2.1	2.4
August	4.0	4.4	4.0	4.5	3.3	3.0	3.1
September	3.8	4.2	4.0	4.1	3.1	2.8	2.8
October	3.1	3.4	3.4	2.9	2.4	2.2	2.1
November	3.1	3.4	3.2	2.9	2.3	2.1	2.0
December	3.4	3.7	3.6	3.1	2.6	2.5	2.3
2024
January	2.9	3.2	3.1	2.5	2.1	2.1	1.8
February	2.8	3.1	3.1	2.9	2.1	2.0	1.7
March	2.9	3.2	3.2	2.9	2.1	2.0	1.8
April	2.7	3.0	3.0	2.5	2.0	1.7	1.7
May	2.9	3.2	3.4	2.6	2.2	1.6	1.9
June	2.7	2.9	3.1	2.2	2.0	1.3	1.7
July	2.5	2.8	3.0	2.3	1.9	1.1	1.6
August	2.0	2.1	2.5	1.1	1.3	0.6	1.0
September	1.6	1.8	2.2	1.1	1.1	0.5	0.7
October	2.0	2.2	2.4	1.6	1.5	0.9	1.1
November	1.9	2.1	2.5	1.3	1.5	1.1	1.1
December	1.8	2.0	2.4	1.5	1.4	1.2	1.0
2025
January	1.9	2.1	2.3	1.2	1.5	1.4	1.1
February	2.6	2.8	2.9	1.3	2.3	1.9	1.9
March	2.3	2.5	2.6	1.3	2.1	1.6	1.7
April	1.7	1.8	2.1	0.8	1.5	1.0	1.1
May	1.7	1.9	2.0	1.3	1.5	1.1	1.1
June	1.9	2.0	2.2	1.5	1.7	1.2	1.3
July	1.7	1.8	2.2	1.3	1.5	1.0	1.1
August	1.9	2.0	2.3	1.6	1.7	1.2	1.4
September	2.4	2.5	2.9	2.0	2.3	1.6	1.9
October	2.2	2.3	2.8	1.8	2.1	1.5	1.8
November	2.2	2.4	2.8	1.9	2.2	1.5	1.9
December	2.4	2.6	3.0	1.5	2.4	1.7	2.1

Chart 7 end

5 Considerations for the Use of Alternative OA Approaches

OA price inflation can differ substantially depending on how it is measured. At the same time, there are many uses of the CPI that all contribute to its design. It is used by central banks to monitor monetary policy and keep inflation within a target range. It is also used in official indexation arrangements (e.g., for the escalation of pensions and tax allowances) and as the basis for most wage negotiations in the private and public sectors. Finally, it is used as a price deflator in national accounting and many economic analyses, as well as research conducted by business analysts and economists.

Ideally, the approach chosen to measure OA should align with the conceptual basis that best satisfies the principal purposes of the CPI. The methodology of the OA shelter services in the Canadian CPI therefore considers housing as a service that people use, as opposed to the purchase of an asset, and only incorporates the price of that service into the index. Its purpose is to measure the price-induced changes in the cost of using, rather than buying, a fixed stock of dwellings. Consequently, the Canadian CPI is not a direct measure of housing affordability. Alternative approaches for measuring housing inflation and affordability already exist. Various Statistics Canada products, such as the Housing Statistics Portal, indicators of household wealth and affordability, and a release on the distributions of household economic accounts, provide insights into the current state of housing affordability for Canadian households.

Thus, the CPI should not depart drastically from a desired COLI, which is a logical choice for income escalation and monetary policy purposes. The main advantage of applying a COLI is the fact that it is a welfare-oriented measure and is well suited to the Bank of Canada’s mandate to promote economic and financial welfare for Canadians. However, achieving such a target may not be that obvious in practice because of data limitations.

Further, it is essential to examine the impact on monetary policy from the user’s perspective, considering various factors. These factors include the perception–measurement inflation gap, the CPI’s sensitivity to house prices, the volatility resulting from each approach and the effectiveness of communication regarding these aspects. A discrepancy between the CPI and public perceptions thereof, excessive volatility or any potential misleading signals about the economy emanating from the CPI, such as a sharp increase in the mortgage interest cost, detract from the usefulness of a given approach. Such factors must be considered and addressed, because they can undermine trust in the CPI and its usefulness as an economic indicator. In fact, from the perspective of any statistical agency, the perception gap can be considered as a gauge of the credibility that the general public attributes to the CPI. It may also reflect the misconceptions of the public, highlight the need for educational support from these agencies and emphasize the importance of making CPI communication even more relatable. Thus, the perception gap reinforces the need for better communication—not a change in measurement.

The acquisition approach, which includes principal homes, secondary homes, other property (house and land) and investment on renovations, generates the highest inflation rates from late 2021 through mid-2022 and diverging significantly from consumer perceptions of inflation. The perception-measurement gap widened (Chart 8) further during periods of declining house prices, highlighting the approach’s sensitivity to housing market fluctuations. While this method is responsive to changes in the housing market, it does not reflect the actual cost of housing services consumed by owner-occupiers and tends to exhibit greater volatility (Table 5), limiting its suitability as a measure of owner-occupied housing costs in the CPI to be more volatile.

The net acquisition approach, while narrowing the perception gap relative to the acquisition approach (Chart 8), presents significant data challenges. For example, the data needed for some components, such as separate land and structure values and administrative data on housing transactions in the form of construction cost price indexes, are often available only on a quarterly basis. This limitation hinders the ability to meet the monthly frequency and timeliness requirements of the CPI. Timeliness being a measure of data quality , this approach would significantly compromise the quality of the CPI. Moreover, although it is less volatile than the acquisition approach, it still does not directly measure the cost of housing services, making it a less comprehensive reflection of inflation experienced by homeowners. (Table 5).

Chart 8 start

Data table for Chart 8

Data table for chart 8
Table summary
This table displays the results of Data table for chart 8 Consumer perceptions¹, Official owned accommodation (OA), Net acquisition OA and Acquisition OA – using NHPI² and CREA’s HPI³, calculated using 12-month percent change units of measure (appearing as column headers).

	Consumer perceptions Data table for chart 8 Note 1	Official owned accommodation (OA)	Net acquisition OA	Acquisition OA – using NHPI Data table for chart 8 Note 2 and CREA’s HPI Data table for chart 8 Note 3
	12-month percent change
Note 1 Consumer perceptions measured by the Bank of Canada’s Canadian Survey of Consumer Expectations. Return to note 1 referrer Note 2 NHPI represents the New Housing Price Index. Return to note 2 referrer Note 3 CREA's HPI represents the Canadian Real Estate Association's Home Price Index. Return to note 3 referrer Note: Quarterly inflation rates are calculated as the moving average of monthly inflation rates. Source: Statistics Canada, Consumer Prices program.
2014
Q4	2.4	1.9	2.0	2.5
2015
Q1	2.0	1.1	1.2	1.8
Q2	2.0	0.9	1.0	1.8
Q3	2.1	1.2	1.3	2.2
Q4	2.0	1.3	1.4	2.5
2016
Q1	2.1	1.5	1.7	2.9
Q2	2.0	1.6	1.8	3.2
Q3	2.0	1.2	1.5	2.9
Q4	2.0	1.4	1.6	2.8
2017
Q1	2.1	1.9	2.1	3.3
Q2	2.1	1.3	1.5	2.5
Q3	2.0	1.4	1.6	2.1
Q4	2.0	1.8	2.0	2.3
2018
Q1	2.4	2.1	2.3	2.2
Q2	2.6	2.3	2.5	2.1
Q3	2.5	2.7	2.7	2.5
Q4	2.3	2.0	2.0	1.8
2019
Q1	2.3	1.6	1.5	1.3
Q2	2.1	2.1	1.9	1.7
Q3	2.0	1.9	1.7	1.6
Q4	2.1	2.1	1.9	1.9
2020
Q1	2.1	1.8	1.7	1.9
Q2	2.0	0.0	0.0	0.2
Q3	2.0	0.3	0.5	0.9
Q4	2.0	0.8	1.2	1.7
2021
Q1	2.0	1.4	2.3	3.1
Q2	2.2	3.4	4.8	5.7
Q3	3.0	4.1	5.5	6.3
Q4	4.1	4.7	6.2	7.3
2022
Q1	5.0	5.8	7.2	8.6
Q2	6.1	7.5	8.6	9.0
Q3	8.0	7.2	7.9	7.1
Q4	7.9	6.7	6.9	5.2
2023
Q1	7.1	5.2	4.9	2.3
Q2	7.0	3.5	2.9	1.5
Q3	6.6	3.7	3.0	2.6
Q4	5.9	3.2	2.4	2.3
2024
Q1	5.3	2.8	2.1	2.0
Q2	5.2	2.7	2.0	1.5
Q3	4.8	2.0	1.4	0.8
Q4	3.9	1.9	1.5	1.1
2025
Q1	3.9	2.3	2.0	1.6
Q2	3.8	1.8	1.6	1.1
Q3	3.7	2.0	1.8	1.3
Q4	4.0	2.2	2.2	1.6

Chart 8 end

Although both rental equivalence approach measures—the one based on the official CPI Rent index and the alternative measure based on average asking rents—narrowed the gap with consumer perceptions of inflation during the 2021–2022 surge, the gap widened again after 2022. In 2021–2022, the asking-rent-based measure moved more sharply and tracked consumer perceptions more closely, reflecting its greater sensitivity to real-time developments in the secondary rental market. The official-rent-based measure increased more gradually, resulting in a slightly larger gap relative to perceptions during the peak period.

Beginning in 2023 and continuing through 2024–2025, consumer perceptions remained persistently higher than inflation measured under both rental equivalence approaches. The divergence is particularly evident in 2024–2025, when perceptions declined more slowly and remained elevated relative to both rent-based measures.

This suggests that, while rental equivalence is conceptually consistent in measuring the flow of housing services, it does not fully capture how households experience inflation. Perceptions are influenced not only by housing costs, but also by broader price developments, particularly large and frequent increases in essential goods such as food, which tend to weigh more heavily on households’ overall inflation experience (Statistics Canada, 2021 ). In addition, factors such as financing conditions and housing market sentiment may further shape how housing-related inflation is perceived.

Despite its conceptual soundness, the rental equivalence method can be fairly impractical as there is an unavailability of data,^Note  especially when the rental market^Note  is not well established or thin and market distortions such as rent controls^Note  are significant. In addition, the rental equivalence approach may overlook indications of housing exuberance, as they are more driven by anticipated future property value increases than by actual rent hikes (Hill et al., 2020). Overall, this approach does not reduce the gap with households’ perceptions of inflation (Chart 9).

Statistics Canada’s variant of the user cost approach is relatively consistent with measuring the cost of housing services. Data availability for the components of this approach is not a major concern, although there are some difficulties with data related to replacement costs and the mortgage interest cost index.^Note   Its performance in narrowing the perception-measurement gap is mixed. As shown in Chart 9, the Official OA approach CPI aligns more closely with consumer perceptions during certain periods, but this alignment weakens during periods of declining house prices and rising interest rates, particularly after 2022. This reflects the fact that the Official OA approach primarily captures the cost of operating a home , rather than the cost of purchasing a one, which is what consumers have in mind when forming their views about inflation. Finally, the post-2022 rapid increase in interest rates appears to be driving higher inflation. While the objective of monetary policy intervention is to control and curb inflation, this development poses a communication challenge for those overseeing monetary policy.

Chart 9 start

Data table for Chart 9

Data table for chart 9
Table summary
This table displays the results of Data table for chart 9 Consumer perceptions¹, Official owned accommodation (OA), Payment OA, Rental equivalence OA, Rental equivalence OA using Average Asking Rent Index⁵ and User cost (30 years) OA – using NHPI² and CREA’s HPI³ ⁴, calculated using 12-month percent change units of measure (appearing as column headers).

	Consumer perceptions Data table for chart 9 Note 1	Official owned accommodation (OA)	Payment OA	Rental equivalence OA	Rental equivalence OA using average asking rent index Data table for chart 9 Note 5	User cost (30 years) OA – using NHPI Data table for chart 9 Note 2 and CREA’s HPI Data table for chart 9 Note 3 Data table for chart 9 Note 4
	12-month percent change
Note ... not applicable Note 1 Consumer perceptions measured by the Bank of Canada’s Canadian Survey of Consumer Expectations. Return to note 1 referrer Note 2 NHPI represents the New Housing Price Index. Return to note 2 referrer Note 3 CREA's HPI represents the Canadian Real Estate Association's Home Price Index. Return to note 3 referrer Note 4 User cost estimates are estimates of expected capital gain based on 30 years of horizon length of expectation formation, applying the CREA's HPI and the NHPI. Return to note 4 referrer Note 5 The estimation of this index uses asking‑rent data beginning in January 2019. For earlier periods, the official rent series is applied. Return to note 5 referrer Note: Quarterly inflation rates are calculated as the moving average of monthly inflation rates. Source: Statistics Canada, Consumer Prices program.
2014
Q4	2.4	1.9	1.9	1.8	... not applicable	1.7
2015
Q1	2.0	1.1	1.1	1.1	... not applicable	0.8
Q2	2.0	0.9	0.9	0.9	... not applicable	0.6
Q3	2.1	1.2	1.2	1.1	... not applicable	0.8
Q4	2.0	1.3	1.3	1.1	... not applicable	0.9
2016
Q1	2.1	1.5	1.5	1.4	... not applicable	1.2
Q2	2.0	1.6	1.5	1.3	... not applicable	1.2
Q3	2.0	1.2	1.1	1.0	... not applicable	0.9
Q4	2.0	1.4	1.3	1.1	... not applicable	1.1
2017
Q1	2.1	1.9	1.8	1.6	... not applicable	1.6
Q2	2.1	1.3	1.2	1.0	... not applicable	1.1
Q3	2.0	1.4	1.2	1.0	... not applicable	1.2
Q4	2.0	1.8	1.7	1.5	... not applicable	1.7
2018
Q1	2.4	2.1	2.0	1.7	... not applicable	1.9
Q2	2.6	2.3	2.3	2.0	... not applicable	2.1
Q3	2.5	2.7	2.8	2.4	... not applicable	2.5
Q4	2.3	2.0	2.1	1.8	1.8	1.8
2019
Q1	2.3	1.6	1.7	1.6	1.7	1.3
Q2	2.1	2.1	2.3	2.1	2.2	1.9
Q3	2.0	1.9	2.1	2.0	2.0	1.6
Q4	2.1	2.1	2.2	2.3	2.4	1.8
2020
Q1	2.1	1.8	1.9	1.9	2.0	1.6
Q2	2.0	0.0	-0.1	0.0	0.6	-0.2
Q3	2.0	0.3	0.2	0.2	1.2	0.1
Q4	2.0	0.8	0.6	0.6	1.6	0.7
2021
Q1	2.0	1.4	1.2	1.1	2.8	1.5
Q2	2.2	3.4	3.0	3.0	4.7	3.5
Q3	3.0	4.1	3.5	3.4	5.6	4.3
Q4	4.1	4.7	4.3	4.1	6.1	5.1
2022
Q1	5.0	5.8	5.4	5.3	6.8	6.1
Q2	6.1	7.5	7.3	7.0	8.3	7.8
Q3	8.0	7.2	7.1	6.8	7.9	7.2
Q4	7.9	6.7	6.7	6.4	7.4	6.4
2023
Q1	7.1	5.2	5.3	5.0	5.6	4.6
Q2	7.0	3.5	3.8	3.6	4.1	2.7
Q3	6.6	3.7	4.0	3.7	4.0	2.8
Q4	5.9	3.2	3.5	3.4	3.0	2.2
2024
Q1	5.3	2.8	3.2	3.1	2.8	1.8
Q2	5.2	2.7	3.0	3.2	2.4	1.7
Q3	4.8	2.0	2.2	2.6	1.5	1.1
Q4	3.9	1.9	2.1	2.4	1.5	1.1
2025
Q1	3.9	2.3	2.4	2.6	1.2	1.6
Q2	3.8	1.8	1.9	2.1	1.2	1.2
Q3	3.7	2.0	2.1	2.4	1.6	1.5
Q4	4.0	2.2	2.4	2.9	1.8	1.9

Chart 9 end

The payment approach has the advantage of reflecting only actual transaction costs and not imputed costs, offering a more tangible representation of household expenses. However, a drawback is the non-inclusion of replacement costs; it could be argued that this diverges from adequately measuring the cost of housing services. Its performance in narrowing the perception-measurement gap is mixed. As shown in Chart 9, the payment approach CPI aligns more closely with consumer perceptions during certain periods. However, this alignment deteriorates during periods of rising interest rates, particularly after 2022, when increased borrowing costs amplified divergence between actual payments and perceived inflation.  Lastly, the user cost approach is consistent with the cost of operating a house; however, the imputation of opportunity costs and, more specifically, the assumptions on rates of interest and capital gains, can be arbitrary and challenging, resulting in a potentially excessively volatile price index series.

In summary, different approaches were analyzed based on varying criteria, and no single method satisfies all desired objectives for measuring housing costs and affordability, narrowing the perception gap, eliminating data availability challenges, and providing insights for monetary policy purposes.

6 Conclusion

The treatment of OA is one of the most difficult and controversial issues faced by CPI compilers. Statistical agencies^Note  usually implement a variant of the approaches listed in the Consumer Price Index Manual, but there is no consensus on a preferred method; each has its own advantages and disadvantages. The purpose of this analysis is not to propose a change in the measurement methodology used in the Canadian CPI or the interpretation of the CPI as a measure of housing inflation and affordability, but rather to illustrate for users the impacts of alternate measurement approaches and articulate the various advantages and disadvantages of each approach. This research serves as a complement to the CPI in assessing OA inflation by examining recent trends through the lens of various OA treatments and provides additional analytical value for policymakers.

The CPI serves various purposes, including the indexation of payments (such as public and private pensions) and tax brackets, as a deflator of other economic aggregates (e.g., gross domestic product) to obtain estimates at constant prices, and as a tool for setting and monitoring monetary policy. As with all other index components, the approach adopted by Statistics Canada in measuring OA prices seeks to find a balance between the various purposes the CPI serves. Ideally, the selected approach should align with the conceptual basis that best satisfies the principal purpose of the CPI and reflects price change for the average Canadian.

The Canadian CPI treats housing as a service that people use and incorporates the price of that service into the index, rather than including the purchase price of an asset. The purchase of a home is implicitly captured by a number of OA components. The CPI aims to consider the expenditures associated with owning a home, which are incurred by a significantly higher number of Canadians in a given month, rather than the expenditures associated with buying a home.

While the rental equivalence and user cost approaches are more aligned with measuring the cost of owning a house, the analytical CPI generated with a pure user cost approach (and its variants) was highly volatile because of the inconsistent impact of expected capital gain changes on the OA estimates.

The acquisition approach would have generated higher inflation, on average, before 2023. More recently, this approach would suggest that inflationary pressures are close to where they were before the pandemic. The acquisition approach could provide a signal of housing affordability for the purposes of policy. However, this approach is not well aligned with the cost of housing concept; its high volatility indicates that it is not an appropriate method for indexation purposes, to either homeowners or renters. Therefore, this approach is not used in any other countries for CPI calculations.

The official treatment of OA in the Canadian CPI, which is designed to measure the impact of price changes on homeowners’ ongoing costs, remains fit for purpose and aligned with the conceptual purpose of the CPI. This approach best balances the various uses of the CPI, provides a clear signal of trends in housing inflation while minimizing volatility in the all-items CPI, can be produced using existing data sources and serves as a middle ground between the acquisition and net acquisition approaches, which have trended higher in the past decade, and the payments, rental equivalence, and user cost approaches, which have trended lower. The Canadian approach satisfies Statistics Canada’s six quality dimensions (relevance, accuracy, timeliness, accessibility, interpretability, and coherence), meeting the needs of Canadian data users.

This analysis demonstrates that the official treatment of OA used in the Canadian CPI remains the best approach for Canada. Ultimately, Canada’s official treatment of OA prevails not due to a lack of alternatives, but because, after exploring these alternative approaches, it is deemed to be the best indicator for OA inflation in Canada. For these reasons, there are no plans to change the treatment of OA in the Canadian CPI. Further, recent trends in the housing market, and consumer inflation more generally, indicate that it is not the right time to consider significant changes to the calculation of the CPI. Any future changes will require in-depth consultations with the Price Measurement Advisory Committee, the Ottawa Group, and other national statistical organizations, as well as external stakeholders, to maintain the quality, accuracy, and utility of the CPI as a tool for both indexation, monetary policy and gauging the health of the economy.

Statistics Canada remains committed to prioritizing data quality, emphasizing accuracy and timeliness when measuring price change. The goal remains to produce a CPI that is relatively stable, best serves its intended uses and better reflects the experiences of Canadians. Working with price experts, other national statistical organizations and partners ensures that the data and methods used in the calculation of the CPI are aligned with international standards and best practices. The agency will continue to explore new potential sources of expenditure and price information for future research and to adopt the most appropriate methods to keep the CPI relevant for Canadians.

Annex A - Weights estimation

In this section, the Consumer Price Index (CPI) basket weights for all analytical owned accommodation (OA) approaches are estimated using Canadian data. Information on expenditure weights is essentially derived from the Survey of Household Spending (SHS) and Canadian System of National Accounts (CSNA) for the CPI baskets during the 2001 to 2021 period.

This section does not describe the official estimation of OA in the Canadian CPI (see the Consumer Price Index Reference Paper and the Shelter in the Canadian CPI: An overview, 2023 update). However, it will cover the calculation of components not included in the Canadian CPI, as well as the required adjustments to existing OA components to derive basket weights for all approaches.

OA under the payment approach includes all official components from the Canadian CPI except the replacement cost.

Acquisition approach

The acquisition approach includes six components:

• net home purchase
• investment on renovations
• property taxes
• home insurance premiums
• maintenance and repairs
• other OA expenses.

Net home purchase

The SHS includes information on the purchase price of homes (new or resale) bought in the survey year and the selling price of homes sold in the survey year. Expenditure weights of the net home purchase, including land, are estimated by calculating the difference between the purchase price of the home bought and the selling price of the home sold in the same year (see the formula below), including transactions on homeowners’ principal dwelling, secondary residences and other properties.

$$Nethomepurchase=\left[\begin{array}{l}valueofpurchasepriceofhomeboughtinsurveyyear-\\ valueofsellingpriceofhomesoldinsurveyyear\end{array}\right]$$

The estimation of the net home purchase by urban centre encompasses a risk of having an unreliable (and in some cases negative) expenditure weight on the net home purchase in some urban centres, because the proportion of sampled households that buy or sell a home may be small. As a result, it is important to apply regional expenditure weights for these urban centres and adjust for the difference in homeownership ratios between and across urban centres.

In addition, there is an important issue associated with the volatility of expenditure weights through baskets. This problem is attributable mainly to the cyclical nature of the housing market, so the expenditure weights can vary significantly from one basket to another, especially in a reference year considered to be a recession year. Thus, it would be important in these reference years to calculate the net home purchase as an average of expenditure weights across multiple years.^Note 

Investment on renovations

Two data sources^Note  are available for measuring household investment on home renovations. The first data source, the SHS reports the amounts of homeowners’ improvement and renovation spending. In addition, these amounts can be estimated using data on flows and stocks^Note  of fixed residential capital by type of asset, including residential investment on renovations at the national level. These residential investments cover all institutional sectors: households, non-profit institutions serving households, corporations and general governments. Therefore, the residential investments on renovations are multiplied by the ratio of households’ dwellings to total institutional sector residential dwellings.^Note 

$$\begin{array}{l} Householdresidentialinvestmentonrenovations=residentialinvestmentsonrenovations\times \\ \left(homeowners\text{'}residentialdwellingstotalinstitutionalsectorresidentialdwellings\right)\end{array}$$

For the other components of OA under acquisition approach, official expenditure weights for property taxes, homeowners’ insurance premiums, homeowners’ maintenance and repairs, and other OA expenses were used.

Net acquisition approach

The net acquisition approach to the owner -occupied housing index, as recommended by Eurostat for the Harmonised Indices of Consumer Prices, includes six components:

• self-built dwellings and purchases of new dwellings (excluding land)
• investment on renovations
• property taxes
• home insurance premiums
• maintenance and repairs
• other OA expenses.

Self-built dwellings and purchases of new dwellings (excluding land)

To estimate the net acquisition expenditure weights, data on flows and stocks^Note  of fixed residential capital by type of asset were used by province and territory. These data include residential investment on new construction and renovations by region and at the national level.

Residential investment on new construction is calculated through a model developed at Statistics Canada. In this model, the investment for a given month covers only the construction done on units started in that month and the construction done on all the other units under construction. This calculation uses the information on estimated values of construction starts and other dwelling conversions, collected by Statistics Canada’s monthly Building Permits Survey, the monthly number of construction starts and completions from the Canada Mortgage and Housing Corporation’s Starts and Completions Survey, and National Accounts quarterly data on land developers’ fees and sales taxes.

These residential investments cover all institutional sectors: households, non-profit institutions serving households, corporations and general government. To estimate the homeowner residential investment on new construction, the residential investment on new construction is multiplied by the ratio of homeowners’ dwellings to total institutional sector residential dwellings.^Note 

$$\begin{array}{l}Householdresidentialinvestmentonnewconstruction=\\ residentialinvestmentonnewconstruction\\ \times \left(homeowners\text{'}residentialdwellingstotalinstitutionalsectorresidentialdwellings\right)\end{array}$$

Official expenditure weights for property taxes, homeowners’ insurance premiums, homeowners’ maintenance and repairs and other OA expenses were either derived from the SHS or data from the CSNA.

Rental equivalence approach

Imputed rent of owner-occupied dwellings

Considered as the largest imputation in the gross domestic product (GDP) accounts, the rental equivalence expenditure weights were estimated using CSNA data on personal expenditure values for GDP imputed rent.^Note 

Homeowners’ maintenance and repairs

The weight of homeowners’ maintenance and repairs in the rental equivalence series includes expenditures usually incurred by tenants on materials and services for minor maintenance and repairs. Since 2005, the SHS has collected detailed information on maintenance and repairs made by homeowners. This information was used to estimate homeowners’ maintenance and repairs under the rental equivalence concept. Only materials and services for painting and wallpapering, interior walls and ceilings, electrical fixtures and equipment, and plumbing fixtures and equipment^Note  were used.

Homeowners’ insurance premiums

The estimation method and conceptual coverage have changed over time. Prior to 2000, gross insurance premiums were used, based on data from the household survey.

Under the rental equivalence approach, the homeowners’ insurance premiums expenditure weight in the rental equivalence approach is estimated by multiplying the average value of tenants’ insurance premiums per tenant by the ratio of owner to tenant households. This estimation is downward biased because tenant-occupied residences and owner-occupied dwellings are not similar. Owner-occupied dwellings are larger and more expensive, so this is adjusted by a factor that includes this difference.

Starting in 2000, the estimation shifted to using National Accounts data on property insurance. Under this approach, net premiums are used in the estimation of homeowners’ insurance within rental equivalence. Insurance premiums paid by homeowners are treated as payments for an insurance service. The output of insurance services is not equal to total (gross) premiums paid. Instead, it is measured as:

$$Netpremiums=premiumsearned+premiumsupplements-adjustedclaimsincurred$$

where:

Premiums earned exclude saving components;

Premium supplements represent investment income attributed to policyholders;

Adjusted claims reflect expected claims rather than actual claims, which can be volatile.

Net premiums paid by homeowners are estimated by multiplying total net premiums by the share of owner-occupied properties in total properties.

User cost approach

The different variants of estimating the user cost depend on the estimation of an expected capital gain and interest rate of return. However, in all user cost approach variants, the official expenditure weights of property taxes, homeowners’ insurance premiums, homeowners’ maintenance and repairs, and other OA expenses were used.

Expected capital gain

The capital gain is the most important element of the user cost. Its estimation method has an important effect on user cost estimated values, and thus their reliability.

To estimate the capital gain, the value of OA is calculated by multiplying the reported value of the home owned by the reported percentage of households that are homeowners, upon which an expected annual capital gain is applied.

$$\begin{array}{l}Expectedcapitalgain=\\ \left(valueofhomeowned\times homeownerpercentage\right)\times expectedrealannualcapitalgainrate\end{array}$$

The expected capital gain price change is a slowly changing function of past house price changes. The number of years over which these expectations are formed will have a significant effect on the result. Therefore, this analysis used various averages:

• In the simple user cost variant, the real expected capital gain is set equal to 2.5%, assumed to be the average long-run natural rate of interest, representing the real rate of interest that would equate saving and investment in full employment. In fact, Kichian (2015)^Note  estimates that in equilibrium, the real interest rate is between the values of 2.4% and 2.7%, from 1999 to 2005 in Canada.
• In the user cost (0) variant, the real capital gain is null. In the user cost (10) variant, user cost (25) variant and user cost (30) variant, the real capital gains are based on different horizon lengths of expectation formation. The geometric mean of the 10-year, 25-year and 30-year real capital gains are $g\left(10\right)$, $g\left(25\right)$ and $g(30)$ respectively.

Interest rate of return

The interest rate of return is also an important element of the user cost. Its estimation method has an important effect on the volatility of user cost indexes, which could make the use of this approach challenging for many CPI users.

To estimate the interest rate of return expenditure weights, the value of homes owned as reported by homeowners is calculated and then multiplied by an annual real interest rate of return. One type of rate for all user cost variants was applied, except for the simple user cost variant.

$$\begin{array}{l}Interestreturnoffunds=\\ \left(valueofhomeowned\times homeownerpercentage\right)\times realannualinterestrateofreturn\end{array}$$

For the simple user cost variant, the real interest rate $\overline{r}t$ is set equal to 2.5%, the average long-run natural rate of interest. For other user cost variants, $rt$ is calculated as the average real interest rate, using a weighted average of Government of Canada real yields and the mortgage real interest rate.^Note 

Annex B - Price index estimations and data sources

In this annex, the price index calculation of each component of these approaches is described for the Canadian context.

Acquisition approach

In terms of price indexes, the official price indexes for property taxes, homeowners’ insurance premiums, homeowners’ maintenance and repairs, and other OA expenses were used.

Net home purchase

Regarding the net home purchase, three types of housing price indexes were applied:

• New Housing Price Index

Statistics Canada’s New Housing Price Index (NHPI)^Note  measures the change over time in builders’ prices of newly built single and semi-detached homes and townhouses. It uses a matched-model approach with explicit quality adjustments. Monthly data are collected through a builders’ survey. However, this index has limitations because it does not include new or resale condominiums or resale houses.^Note  It could be argued that it would be better to price the series using a residential property price index that covers all types of houses sold in the Canadian housing market.

• Resale Residential Property Price Index^Note  (Canadian Real Estate Association price indexes)

The Canadian Real Estate Association (CREA) seasonally adjusted composite Home Price Index (HPI)^Note  is applied for the estimation of the Resale Residential Property Price Index. It is a measure used in Canada to track and analyze changes in housing prices over time. The CREA HPI is calculated using a hybrid modelling approach (multivariate regression analysis) that merges the repeat sales and hedonic price approaches. It considers various factors such as property type, location and other relevant attributes.

The index is calculated using a benchmark property or typical home in a given area, which serves as a reference point for comparison. The benchmark home is based on a set of specific characteristics and is assigned an initial value. Over time, the CREA HPI measures changes in the value of the benchmark property, allowing for comparisons and analysis of price movements.

• Monthly hybrid housing index

A hybrid housing price index was calculated as a weighted average of the CREA Resale Housing Price Index (80%) and NHPI (20%). These relative weights were derived from the Canada Mortgage and Housing Corporation’s Market Absorption Survey and the inventory of repeat sale transactions from Teranet Inc. and the Bank of Canada.

At the urban centre level, imputed prices from a higher geographic region were necessary, because of a low number of transactions or low new house construction activity, which can affect the reliability of the urban centres’ house price indexes.

Investment on renovations

For investment on renovations, the residential construction price index was used as a proxy for these types of expenditure prices. However, a transformation of these quarterly prices into monthly ones was conducted using the Chow-Lin method.

• The Chow-Lin method^Note 

The Chow-Lin method is a statistical technique used for temporal disaggregation, which involves deriving high-frequency data from low-frequency data. The main goal of the Chow-Lin method, known as temporal disaggregation, is to create a new time series that is consistent with the low-frequency data while keeping the short-term behaviour of the higher-frequency indicator series.

The Chow-Lin method aims to provide accurate disaggregation results by capturing the relationships and patterns observed in the available data. It is a widely used technique in the field of temporal disaggregation. It has been extensively researched and applied in various domains, providing a solid foundation for its reliability and practicality. One of the main limitations of the Chow-Lin method is its assumption of a linear relationship between the low-frequency and high-frequency data. In addition, the accuracy of disaggregated results heavily depends on the quality and reliability of the available low-frequency data. Finally, the Chow-Lin method may introduce smoothing effects on the disaggregated data. This can potentially obscure certain patterns or trends.

Net acquisition approach

For self-built dwellings and purchases of new dwellings (excluding land), the NHPI (house only) was used. This is possible because the NHPI program reports the price indexes of the house structure and land components using builders’ estimates collected in the survey separately.

For investment on renovations, the residential construction price index,^Note  transformed into monthly series using the Chow-Lin method, was used. Finally, for property taxes, homeowners’ insurance premiums, homeowners’ maintenance and repairs, and other OA expenses, official indexes are used.

Rental equivalence approach

Estimating rental equivalence price index using Official rent index

In this paper, the rental equivalence price index is imputed from the Canadian CPI’s rent price index. However, there are two main problems with these series.

First, it is difficult to align Canadian owner-occupied properties with comparable rental units. This issue is partly because the rent index often does not include higher-end residences and focuses mainly on apartment rentals.

Second, the rent price index encompasses both new and existing rental contracts. Yet homeowners usually base their estimates on new rental agreements. Studies by Francois (1989) and Kurz and Hoffmann (2004) indicate that estimated equivalent rents closely reflect the actual rents for new contracts. In fact, rental equivalence estimates rely on the hypothetical rent that homeowners would incur if they were to rent their properties, exclusive of furnishings and utilities. Thus, there is a prevailing tendency among owners to focus on rents established in new rental contracts, as supported by research from Francois (1989) and Frick and Grabka (2002).

To address limitations of using the official rent index, an Average Asking Rents of Canadian Secondary Rental Market is estimated, see Annex E for more information on data source, and estimation method.

Ideally, the most accurate way to calculate the rental equivalence price index would be through a survey targeting homeowners’ rental equivalence, although this method would be expensive.

The index of homeowners’ maintenance and repairs under the rental equivalence approach is based on prices of the types of maintenance and repair materials and services that are covered under the rental equivalence approach. The index of homeowners’ insurance premium is based on the tenants’ insurance premium price indexes^Note  adjusted to the furniture price index.

User cost approach

The different options of estimating the user cost vary only in the estimation of the expected capital gain, forgone rate of return on owned funds and mortgage interest cost. Therefore, in all user cost approach options, the official price indexes of property taxes, homeowners’ insurance premiums, homeowners’ maintenance and repairs, and other OA expenses were used.

Expected capital gain

In different variants of the user cost, where the real capital gains $g\left(10\right)$ ,$g\left(25\right)$ and $g(30)$  are the geometric mean of the 10-year, 25-year and 30-year real rates of capital gain, the monthly capital gain rate is defined as follows:

$$\begin{array}{l}monthlycapitalgainrat{e}_t^n=\\ {\left(\frac{monthlyhousingpricechang{e}_t-monthlyinflationrat{e}_t}{monthlyhousingpricechang{e}_{t-12\times n}-monthlyinflationrat{e}_{t-12\times n}}\right)}^{\frac{1}{12\times n}}\end{array}$$

$n$ corresponds to different scenarios: 10 years, 25 years and 30 years.

Real interest rate of return

The real interest rate of return index for any given month is calculated as follows:

$$r_t=\left[\begin{array}{l}30\%\times \left(GovernmentofCanadamarketablebonds10years,averageyields\right)\\ +70\%\times \left(mortgageinterestrate5yearfixedrate\right)\end{array}\right]-2.2\%$$

The expected inflation rate is assumed to equal 2.2% (average inflation rate of the 2001 to 2024 period).

Annex C - International practices: Treatment of owned accommodation in consumer price indexes

At the international level, the rental equivalence approach is the most commonly used approach to measuring owned accommodation (OA). The next most common practice is to exclude OA altogether.

Annex D - Distribution of expenditures on owned accommodation among its components according to different approaches and baskets

The distribution of expenditures on owned accommodation among its components varies depending on different approaches and baskets.

Annex E - Estimating Average Asking Rents on Canadian Secondary Rental Market

Data Sources

The asking rent data of Quarterly Rent Statistics (QRS) program^Note  are sourced from three major rental listing platforms in the Canadian rental market. These data consist of monthly extractions of active listings from 2019Q1 to 2025Q4, containing information on dwelling type, number of bedrooms and bathrooms, square feet, rental price, location, and limited descriptive attributes. Data are processed at the Census Metropolitan Area (CMA) and national levels.

Rental listings are geocoded by the Statistical Building Register codes that can be used for matching buildings from the primary rental market.

Data Cleaning and Preparation

A structured, multi-stage data cleaning process was implemented to ensure data quality, consistency, and relevance for rental equivalence estimation. Initial filtering removed extreme values, short-term and non-market listings, and duplicate observations across platforms and over time, retaining the most representative listing per dwelling-month.

Subsequent steps restricted the sample to relevant residential property types (houses, townhouses, and condominium apartments) and applied text-based filters to exclude atypical or non-comparable units (e.g., basement units or special-use properties). Additional linkage with administrative data (e.g., CMHC Rental Market Survey via the Statistical Building Register codes) was used to identify and remove listings associated with the primary rental market, thereby focusing the dataset on the secondary rental market.

This process results in a comprehensive dataset suitable for cross-source integration covering only secondary rental market as possible, and consistent estimation of rental equivalence using asking rents.

Categorization and Variable Definition

Observations were classified based on key structural characteristics of the dwellings, including:

• Dwelling type: condominium apartments, townhouses, and houses
• Number of bedrooms: standardized groupings (1–2 bedrooms; 3 or more bedrooms)

Automation and AI-Assisted Enhancements

To improve scalability and consistency in dwelling classification, this study tests large language models (LLMs) to address inconsistencies between unit and building types in online rental listings.

As a first step, the data were used to train and evaluate the models. A structured sample of listings—covering all dwelling types and ambiguous cases—was used to assess performance. Multiple models and prompts were tested, with GPT-3.5-turbo and a refined prompt delivering the most consistent results.

In the next step, the cleaned and edited data were integrated into the processing pipeline. LLMs are used to classify listings based on text descriptions, reconcile outputs with structured variables, and flag uncertain cases for review. This approach improves efficiency while maintaining data quality through targeted human validation.

Index Estimation Method of Average Asking Rent Index

A simple unadjusted index of average asking rents is first computed by dwelling type and CMA, then aggregated to Canada level using the 2021 census number of owner-occupied units as weights. It is worth noting that the online rental platforms have non-probability data that does not have a known selection mechanism and can cause selection bias (Aga et al., 2025).

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