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Record W1491544437

The reaction speeds of prices and transaction volume in the Finnish housing market to demand shocks

2009· preprint· en· W1491544437 on OpenAlexaboutno aff
Elias Oikarinen

Bibliographic record

VenueRePEc: Research Papers in Economics · 2009
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsShock (circulatory)Predictive powerDemand shockMonetary economicsDatabase transactionQuarter (Canadian coin)EconometricsTransaction dataVolume (thermodynamics)
DOInot available

Abstract

fetched live from OpenAlex

This article provides new empirical evidence on the dynamics of price movements and transaction volume in the housing market using data from Finland. While the previous related literature studies the reactions of sales volume and prices to an interest rate shock only, we investigate the responses to income and credit shocks as well. Based on an estimated vector-error correction model, the response of prices to demand shocks is found to be substantially slower than that of sales. The effect of a demand shock on sales peaks within a quarter from the shock. The results show that the differences in the reaction patterns to demand shocks can create the kind of strong positive co-movement between price movements and sales and the kind of negative correlation between price level and sales that has been found in a number of housing markets. It is also found that the direct predictive ability of the fundamentals with respect to housing price growth and sales is overwhelmed by the predictive power of the lagged observations on price changes and sales themselves.

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)0.009
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.032
GPT teacher head0.272
Teacher spread0.240 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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Same venueRePEc: Research Papers in EconomicsSame topicHousing Market and EconomicsFrench-language works237,207