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Record W2971134310 · doi:10.1007/s11146-019-09720-0

Energy Efficiency Information and Valuation Practices in Rental Housing

2019· article· en· W2971134310 on OpenAlexaff
Andrea Chegut, Piet Eichholtz, Rogier Holtermans, Juan Palacios

Bibliographic record

VenueThe Journal of Real Estate Finance and Economics · 2019
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsUniversity of Guelph
FundersHorizon 2020Nederlandse Organisatie voor Wetenschappelijk OnderzoekEuropean Commission
KeywordsValuation (finance)RentingProperty valueEfficient energy useDatabase transactionResidential propertyTransaction costActuarial scienceBusinessEnergy (signal processing)EconomicsEconometricsFinanceStatisticsReal estateMathematicsEconomic geographyDatabasePolitical scienceLawComputer scienceEngineering

Abstract

fetched live from OpenAlex

The consensus in the academic literature is that energy efficiency is associated with transaction value premiums, but it is not clear to what extent property appraisers take account of this. We decompose external appraisals of rental housing by international valuation firms in England and the Netherlands in two waves, keeping the samples of valued homes constant between these years. We find a notable change in the behavior of external property appraisers. In England, energy performance does not impact assessed values in 2012, while estimation results for 2015 show a significant discount in assessed values for D-, E- and F- relative to C-labeled dwellings. For the Netherlands, we do not observe a significant relationship between energy efficiency and assessed values in 2010, but in 2015 we find that more energy efficiency leads to higher external valuations.

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)0.003
metaresearch head score (Gemma)0.022
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0000.001
Research integrity0.0000.000
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 head0.011
GPT teacher head0.226
Teacher spread0.216 · 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

Citations31
Published2019
Admission routes1
Has abstractyes

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