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Record W4387667617 · doi:10.3368/le.100.2.071522-0056r

The Value of a Sea View: Hedonic Estimates Using 3D Simulation and Natural Language Processing

2023· article· en· W4387667617 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLand Economics · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsTrinity College
FundersEuropean Association of Environmental and Resource EconomistsIrish Research Council
KeywordsValue (mathematics)Measure (data warehouse)Computer scienceSample (material)Natural (archaeology)Property (philosophy)Hedonic pricingEconometricsGeographyMathematicsData miningMachine learningArchaeology

Abstract

fetched live from OpenAlex

<h3>Abstract</h3> This paper develops a novel measure of sea-view breadth and depth, based on a combination of GIS 3D simulation and Natural Language Processing (NLP) techniques. NLP identifies properties that have a sea view, while GIS measures its extent. It then estimates the value of a sea view using a hedonic housing price model and a sample of over 100,000 sales listings in Ireland, 2014-2019. We find that a sea view can add significant value to a property, all else being equal, with an average premium of 8.1% rising to 15% for a wide breadth of sea view.

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.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.024
GPT teacher head0.252
Teacher spread0.229 · 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