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Record W2301084507 · doi:10.14288/1.0095173

An analysis of the effects of M.U.R.B. legislation on Vancouver’s rental housing market

2010· article· en· W2301084507 on OpenAlexaffabout
Anne Patricia Wicks

Bibliographic record

VenuecIRcle (University of British Columbia) · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLegislationRentingRental housingBusinessPolitical scienceLaw

Abstract

fetched live from OpenAlex

The purpose of this paper is to examine the. impact of federal M.U.R.B. legislation on Vancouver's rental housing market, and to see what conclusions can be drawn about the effectiveness of this subsidy policy in achieving its objective, which was to increase the allocation of resources to the housing sector of the economy by stimulating the construction of rental units. It is the thesis of this paper that the M.U.R.B. legislation was not effective in achieving its objective, since the inelasticity of the land supply function, as imposed by junior levels of government through zoning and other supply constraints, prevented the rental market from responding to these incentives in the form of increased production. It is hypothesized that the real effect of the program was to create windfall gains for existing owners of multiple family zoned land at the time the legislation was passed. It is argued further that real estate markets are more efficient than they are generally given credit for, in the sense that tax shelter benefits associated with M.U.R.B. properties will be fully capitalized into the value of such properties, thus preventing M.U.R.B. investors from earning rates of return superior to those earned by owners of comparable non-M.U.R.B. properties. The paper begins with a brief history of M.U.R.B. legislation, and an analysis of the magnitude and cost of the program to the Canadian government. This is followed by a graphical analysis of the impact of M.U.R.B. legislation on the multiple family housing market, and a discussion and review of the literature pertaining to the theory of efficient markets, the capitalization of costs and benefits into value, and the various models of land value which have been formulated. Two theoretical models are then presented as the underlying basis for the empirical research in the paper. The first is a valuation function for apartment investments, where the dependent variable is the selling price of an apartment building; the second is a model of the determinants of multiple family land values, where the dependent variable is the price of a site. The two theoretical models are tested using multiple regression techniques. The data results provide evidence which contradicts the general case for the operation of the multiple family housing market, where renters should receive the full benefits of the M.U.R.B. program in the form of lower rents. The research shows that the future tax shelter benefits associated with M.U.R.B. properties are fully capitalized into the market values of completed M.U.R.B. buildings, and that M.U.R.B. investors do not earn rates of return superior to those of investors in non-M.U.R.B. apartment properties. The research suggests further that the expected M.U.R.B. tax shelter benefits were over-capitalized into higher land value premiums during the life of the program, thus creating windfall gains for existing landowners at the time the program was introduced. The results suggest that the full capitalization of M.U.R.B. benefits into both land and apartment block values resulted in all of the benefits of the subsidy policy not filtering through to renters. Some benefits most likely did reach renters, since it is unrealistic to assume no substitution in the production function for apartments, but the actual distribution of policy benefits between renters and existing landowners cannot be measured within the scope of this research.

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 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)0.000
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 score0.743
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.195
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 teacher head, 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

Citations0
Published2010
Admission routes2
Has abstractyes

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