jetpack domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /home/urbanana/public_html/wp-includes/functions.php on line 6114Since interest rates began to rise in early 2022, housing affordability in Canada has worsened. And while the Bank of Canada’s recent and expected rate cuts may improve affordability, a review from Desjardins Economic Studies concluded that a return to pre-pandemic levels is unlikely.
To recap: housing prices surged from 2009 to 2017, with a brief moderation in 2018 due to stress test changes. Between April 2020 and February 2022, ultra-low interest rates caused housing prices to skyrocket. The subsequent rapid rate hikes worsened affordability, with higher mortgage payments making already expensive housing even more out of reach.
The bank’s report offers some hope for prospective homebuyers, however, as Desjardins predicts a slight buyer opportunity in late 2024 or early 2025 year, if interest rates decline as expected. However, this forecast relief comes with some caveats.
The Desjardins Affordability Index (DAI) measures housing affordability in Canada’s regional markets. The index compares the decline in mortgage payments from falling interest rates against the anticipated rise in housing values due to lower rates. Given that any decline in interest rates is expected to be moderate in the short term, mortgage payment relief will be minimal and likely offset by increasing housing values. DAI also expects muted income growth — the other oft neglected variable in determining affordability — therefore the likelihood of improved affordability relief is only moderate, if at all.
The report’s findings may help explain the outward migration from Ontario to the western provinces. Despite recent affordability erosion in Alberta, housing prices in that province remain significantly lower than in other Canadian jurisdictions. Considering that average household incomes are higher in Alberta compared to Ontario, the western migration is understandable.
Housing affordability will likely be a key issue in next year’s federal elections. Election years usually bring promises of measures to improve affordability. Extending the amortization period, for example, which currently stands at 25 years in Canada, may feature in the manifestos of leading contenders.
Desjardins’ analysis warns that extending the amortization period could worsen affordability in the medium term, as rising housing values would negate the immediate benefits of longer amortizations. While a few short-term beneficiaries might purchase before prices escalate, most prospective buyers won’t. Therefore, current and future policymakers should leave amortization periods unchanged.
Housing advocates in Canada have criticized the rapid increase in international students and non-permanent residents (NPRs), arguing that this surge has driven up housing demand. In 2022 alone, Canada’s population grew by a million, with the current annual growth rate rivalling that of some African countries.
The report estimated a planned reduction in NPRs by 25 to 35 per cent by the end of 2026 but found no evidence that this would improve affordability. Their nuanced findings highlight that NPRs are more active in the rental market, with limited impact on the resale market. However, a more significant side effect of reduced population growth could be a reduction in housing supply, as some NPR workers are employed in construction. This lower supply could exacerbate the demand-supply imbalance.
The report also simulated the impact of an eighties-style recession on housing affordability. Surprisingly, their conclusions mirrored our previous discussions on the unintended consequences of recessions restoring affordability. The report noted that price declines and lower mortgage payments would be accompanied by massive layoffs and income losses. It warned that “those hoping for a recession should weigh their homebuying ambitions against immense longer-term economic and social costs.”
If a recession, or slowing population growth, or extended amortization periods cannot improve affordability, what can? The answer lies in building more houses — many more. Desjardins asserts that “increasing housing supply is the only sustainable long-run solution.” We concur, as does the Canada Mortgage and Housing Corporation, which estimates the country needs 5.8 million new homes built within the next decade to restore housing affordability.
Canada’s policymakers have grappled with housing affordability for a while now, often resorting to perceived quick fixes. However, there are no quick solutions to this issue. The only answer is to increase supply, which requires a concerted effort from both the public and private sectors and a consensus that building more homes is imperative.
]]>Meta-analysis is a statistical technique for combining the results from multiple similar studies. The talk will provide a brief introduction to meta-analysis and will demonstrate how to perform meta-analysis in Stata. The -meta- command offers full support for meta-analysis, from computing various effect sizes and producing basic meta-analytic summaries and forest plots to accounting for between-study heterogeneity and potential publication bias. Examples demonstrating how to conduct meta-analysis within Stata will be provided. These examples will focus on the interpretation of meta-analysis under various models, meta-regression, subgroup analysis, small-study effects and publication bias, and various types of forest, funnel, and other plots.
]]>You can read the Column in the December 06 issue of the Financial Post.
]]>Causal Inference by Empirical Balancing Calibration Weighting
When: November 23, 2020. 1:30 PM (EST)
To Register, please visit https://tinyurl.com/causal-inference-webinar.
Abstract:
The webinar introduces ATE, an R package for estimating the average treatment effects using calibration estimators. The webinar presents the theory of calibration estimators in the context of estimating the average treatment effect, including the treatment effect on the treated, treatment effect for multiple treatment arms and their corresponding variances.
The webinar also presents a hands-on tutorial demonstrating the ATE package. The tutorial will demonstrate how to install the ATE package, import and process data, and estimate the average treatment effects. Participants are encouraged to follow the tutorial using R (available at https://cloud.r-project.org/). A web-based instance of R can be also be used (https://rstudio.cloud).
Presenter: Asad Haris, Ph.D.
Post Doctoral Fellow
Dept. of Epidemiology, Biostatistics & Occupational Health
McGill University
This Statistics for Data Science course is designed to introduce learners to the basic principles of statistical methods and procedures used for data analysis. After completing this course learners will have practical knowledge of crucial topics in statistics including – data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA (analysis of variance), regression and correlation analysis. You will take a hands-on approach to statistical analysis using Python and Jupyter Notebooks – the tools of choice for Data Scientists and Data Analysts.
]]>Causal Inference for Complex Observational Data with Stata
When: November 02, 2020. 1:30 PM (EST)
To Register, please visit https://tinyurl.com/causal-inference-webinar.
Abstract:
Observational data often present unique challenges. The treatment status or exposure of interest is often not assigned randomly. Data are sometimes missing not at random (MNAR) which can lead to sample selection bias. And many statistical models for MNAR data must account for unobserved confounding. This talk will demonstrate how to use standard maximum likelihood estimation to fit extended regression models (ERMs) that deal with the common issues either alone or simultaneously.
Presenter: Chuck Huber, Ph.D. [StataCorp]
Chuck Huber is Director of Statistical Outreach at StataCorp and Adjunct Associate Professor of Biostatistics at the Texas A&M School of Public Health. In addition to working with Stata’s team of software developers, he produces instructional videos for the Stata YouTube channel, writes blog entries, develops online Net Courses and gives talks about Stata at conferences and universities.
Most of his current work is focused on statistical methods used by behavioural and health scientists. He has published in the areas of neurology, human and animal genetics, alcohol and drug abuse prevention, nutrition and birth defects. Dr. Huber currently teaches introductory biostatistics at Texas A&M where he previously taught categorical data analysis, survey data analysis, and statistical genetics.
]]>Fetching, visualizing, and analyzing Statistics Canada’s data using R.
October 19, 2020. 6:30 – 8:00 PM (EST)
The webinar will demonstrate how to:
Presenter: Jens von Bergmann, Ph.D. [MountainMath Software and Analytics]
Jens von Bergmann holds undergraduate degrees in Physics and Computer Sciences and a Ph.D. in Mathematics. He taught for several years at the University of Calgary, University of Notre Dame and Michigan State University before founding MountainMath to work on his passion for data analysis and visualization.
To Register, please email liam.donaldson@ryerson.ca.
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