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Record W3213055981 · doi:10.1515/bejm-2021-0099

Revisiting the Link between House Prices and Monetary Policy

2021· article· en· W3213055981 on OpenAlexaboutno aff
Shiu‐Sheng Chen, Tzu‐Yu Lin

Bibliographic record

VenueThe B E Journal of Macroeconomics · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsMonetary policyHouse priceEconomicsInflation (cosmology)Monetary economicsShock (circulatory)Interest rateInflation targetingLiberalizationInternational economicsMarket economy

Abstract

fetched live from OpenAlex

Abstract This paper revisits the link between house prices and monetary policy using a data set on house prices provided by the Bank for International Settlements. It is found that a loose monetary policy unambiguously results in a rise in real house prices, and such an increase is statistically significant for 19 of the 20 countries studied here. Empirical results also show that for some countries (Belgium, Canada, Switzerland, Denmark, the Netherlands, Sweden, and South Africa), the interest rate shock can explain a large percentage of real house price movements. The response of house prices to monetary policy shocks varies between countries, and the strength of the relationship between house prices and monetary policy can be associated with financial liberalization. On the other hand, evidence shows that interest rate shock plays an important role in explaining recent house price hikes for Australia, Spain, Ireland, the Netherlands, the US, and South Africa. In particular, during 2002–2006, on average 24% of the house price hikes in the US can be attributed to monetary policy shocks. Finally, we also find evidence that central banks react to the housing market, particularly in those countries adopting a policy of inflation targeting.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.230
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2021
Admission routes1
Has abstractyes

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