MétaCan
Menu
Back to cohort
Record W1583640411

The Risk-Return Relationship in Housing Markets: Financial Risk Versus Consumption Insurance

2008· article· en· W1583640411 on OpenAlexaff
Lu Han

Bibliographic record

VenueSSRN Electronic Journal · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHedgeConsumption (sociology)Risk–return spectrumEconomicsIncentiveAsset (computer security)Capital asset pricing modelFinancial assetFinancial economicsFinanceMicroeconomics
DOInot available

Abstract

fetched live from OpenAlex

Standard risk-return tradeoff theory cannot explain why housing return varies with risk positively in some markets but negatively in some other markets. This paper addresses this issue by incorporating two unique features of housing into a standard consumption-based asset pricing model: (1) intertemporal hedging incentives and (2) a kinked housing supply function. The model nests two competing effects of price risk on housing return: a financial risk effect associated with owning risky housing asset, and a consumption insurance effect associated with using the current house to hedge against future housing cost risk. The empirical findings confirm several equilibrium predictions implied by the model. In particular, the variation in housing risk-return relationship across markets is driven by both local households' hedging incentives and housing supply constraints.

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)0.002
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.005
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.217
Teacher spread0.190 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueSSRN Electronic JournalSame topicHousing Market and EconomicsFrench-language works237,207