Bibliographic record
Abstract
The housing market in Canada has experienced notable price growths over the past two decades, especially in gateway cities such as Toronto between 2013 and 2017, which was characterized by rapid property value appreciation and high sales turnovers. The rapidly rising property prices have contributed to a housing crisis. To address the affordability issue, it is critical to understand the underlying causes of the exuberance. This thesis seeks to contribute to such knowledge. Chapter 1 provides a scoping review of academic and policy studies that examine the determining factors of the Canadian residential real estate price movements between 2000 and 2019. Using a text-mining search and processing algorithm on relevant articles, it shows that the fundamentals, policies and regulations, foreign ownership and immigration are among the top research priorities in relevant Canadian literature. These topics reflect the historical and socioeconomic environment in Canada. Chapter 1 further reviews these studies in detail and identifies research gaps that are addressed in Chapter 2 and Chapter 3 respectively. Despite the strong public and policy interests in foreign ownership, quantitative estimates of its influences have been limited, especially for the period of 2013 - 2017 when cross-boarder purchases were widely perceived as one of the main drivers of the sky-rocketing housing prices in some regions. Chapter 2 fills this gap by quantifying the influences of increased foreign real estate investment demand and low interest rates on the Toronto housing market. I use a general equilibrium model which is calibrated to Toronto statistical moments, and perform counterfactual exercises by feeding into the model the June 2013 - April 2017 levels of foreign investment shock and interest rate shock respectively. The results show foreign investment influx at the time raised home prices by 2.2% - 4.9%, whereas lower interest rates increased home prices by 9.0% - 20.8%. Between the two, the low interest rate effects dominated the foreign investment effects, contributing to over 80% of the composite influence. My findings suggest that foreign investment was not a major contributing factor to surging property prices in Toronto during the time examined, contrary to mainstream beliefs. In comparison, low interest rates accounted for a substantial fraction of the observed price appreciation. Furthermore, the results suggest that historically low interest rates worsen homeownership inequality by generating cross-sectional heterogeneous effects: while high-income households take advantage of low interest rates and drive up property demand, low-income households are disproportionately priced out of homeownership. Meanwhile, although there is evidence that expectations and sentiment are important in home price movements and they warrant attentions from policy makers and researchers, there lacks a measure of housing market sentiment that has a sufficiently long time series and available at the sub-provincial level. To address this gap, I construct a text-based sentiment index to approximate housing market expectations, which measures the relative tone in regional newspaper articles that focuses on residential real estate. I examine the relationship between the sentiment index and local home price movements and investigate whether the COVID-19 pandemic had an impact on this relationship. The results show that the media sentiment has statistically significant but short- lived predictive effects on future home price growth, while it is also influenced by cumulative price appreciations in the recent past. However, this relationship has significantly weakened between 2020 and 2022.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".