Predictive Analytics and Macroeconomic Influence: A Detailed Exploration of the Toronto Housing Market Dynamics
Bibliographic record
Abstract
The housing market, serving as a pivotal entity in economic matrices, inherently exhibits a particular complexity. This research sets foot into a comprehensive investigation of the Toronto housing market, unraveling its intertwining associations with macroeconomic variables while attempting to predict future trends based on such exploration. Backed by a robust data set from January 2011 to July 2023 of the Canadian economy, this study employs correlation models (Spearman and Pearson) and ARIMA to generalize and perform housing price predictions. Preliminary findings via correlation analysis signal a substantial linkage between housing prices and macroeconomic indexes of GDP, employment, and exchange rates. The ARIMA model application, underscored by a p-value of approximately 0.042 from the Ljung-Box test, provides a valid, future price estimation. In the intrinsic puzzle of the housing market, this research offers an alternative understanding from a macroscopic lens. This research, while showcasing predictive prowess, also stands as a testament to the multifaceted nature of the housing market and advocates for the ongoing refinement and diversification of predictive models in navigating its complexities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".