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Record W4415483338 · doi:10.1080/10835547.2025.2565078

Effects of Cost of Mortgage on House Prices: The Role of the Maturity Structure of Mortgage Contracts

2025· article· en· W4415483338 on OpenAlexaboutno aff
Rafael Barros de Rezende

Bibliographic record

VenueJournal of Real Estate Portfolio Management · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsMaturity (psychological)Mortgage insuranceMortgage underwritingWork (physics)Order (exchange)

Abstract

fetched live from OpenAlex

This paper examines the implications of the maturity structure of mortgage contracts for the effects of the cost of mortgage on house prices. Theoretical results indicate that economies with larger shares of variable interest rate mortgage contracts present faster responses of house prices to changes in the cost of mortgage, as well as higher interest rate sensitivities of cost of mortgage and house prices. Lower mortgage rate levels, higher loan-to-value ratio, and higher effects of cost of mortgage on house prices also increase these sensitivities. Empirical results validate the theoretical ones. Local projections estimated for the US, UK, Sweden, Canada, Finland and France show negative and significant responses of house prices to shocks on the cost of mortgage, with responses being faster in economies with larger shares of variable interest rate mortgage contracts. Estimates of the interest rate sensitivity of the cost of a mortgage are also in line with theory and increase as the mortgage rate declines. This sensitivity is attenuated when the central bank shifts its focus of monetary policy towards longer-term rates or when the effective lower bound is perceived to be lower.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.203
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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