Bibliographic record
Abstract
We show how using commonly reported measures of real estate market conditions can improve the accuracy of price predictions. The standard model of competitive markets asserts that excess demand causes price(s) to adjust to an equilibrium with little delay. In an illiquid market, such as the market for real estate, attaining an equilibrium may take significantly more time. That fact implies that data on market conditions would be informative. A better understanding of the adjustment process in a real estate market could also lead to buying or selling strategies which are better informed.This paper has two parts. The first part uses different types of models to focus on the conceptual distinction between an exogenous variable and an endogenous variable. Based on this distinction, we offer six hypotheses on why the effects of marketconditions might differ between cities. The second part uses vector auto-regression to study the persistence of several variables, with a particular focus on an inflation-adjusted price index and two popular measures of excess demand (the ratio of sales to new listings and “Months of Inventory”). Using monthly data on residential real estate markets in 31 Canadian cities, we find that excess demand affects prices contemporaneously, that changes in measured excess demand persist for a significant period of time and that they affect prices with a lag. We also find evidence of a statistically significant feedback effect, in some cities, from changes in prices to the measures of excess demand.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".