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Record W1993018897 · doi:10.1093/jeg/lbr007

The effects of land transfer taxes on real estate markets: evidence from a natural experiment in Toronto

2011· article· en· W1993018897 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Economic Geography · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsUniversity of TorontoOntario Brain Institute
Fundersnot available
KeywordsEconomicsWelfareReal estateProperty taxNatural experimentRevenueDeadweight lossMonetary economicsTax deferralAd valorem taxTax revenueTransfer (computing)Estate taxTax reformLabour economicsPublic economicsFinanceState income taxMarket economyGross income

Abstract

fetched live from OpenAlex

Taxes levied on the sale or purchase of real estate are pervasive but little studied. By exploiting a natural experiment arising from Toronto's imposition of a Land Transfer Tax (LTT) in early 2008, we estimate the impact of real estate transfer taxes on the market for single family homes. Our data show that Toronto's 1.1% tax caused a 15% decline in the number of sales and a decline in housing prices about equal to the tax. Relative to an equivalent property tax, the associated welfare loss is substantial, about $1 for every $8 in tax revenue. The magnitude of this welfare loss is comparable to those associated with better known interventions in the housing market. Unlike many possible tax reforms, eliminating existing LTTs in favour of revenue equivalent property taxes appears straightforward.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.017
GPT teacher head0.217
Teacher spread0.200 · 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