A Natural Quasi-Experiment of the Monetary Policy Shocks on the Housing Markets of New Zealand during COVID-19
Bibliographic record
Abstract
It is hard to experimentally test the impacts of monetary policy shocks on housing markets as it is very unlikely for a central bank to change monetary policies swiftly twice within a short period of time for exogenous reasons. However, during the pandemic, the central bank of New Zealand changed its policies 180 degree in 2 years, from an unprecedented low interest rate and a relaxed mortgage policy in 2020 to a 13-year record high interest rate and a tightened mortgage policy in 2022. Among the OECD members, New Zealand is the country that increased the interest rate the earliest and also the country that had its house prices fall the earliest. It provides natural quasi-experiments to test the monetary policy hypothesis empirically by the two policy changes as treatments on house prices. This study conducts a time series regression analysis on the housing markets of New Zealand to test the hypothesis in the pre-COVID and the COVID periods, ranging from 2016 Q2 to 2022 Q3. The results confirm that mortgage rates have a negative and significant effect on house price changes after controlling for the economic growth factor and the housing supply factor, no matter whether the monetary policy switches to expansionary or contractionary mode. The robustness test results of the housing markets show that a 1% fall/rise in the mortgage rate caused a 5.6% increase/decrease in house prices, ceteris paribus, in the COVID period. The results also do not support the housing supply hypothesis in New Zealand.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".