RELATIONSHIP BETWEEN RESIDENTIAL HOUSING PRICE AND RENT ACROSS DIFFERENT REGIONS IN VANCOUVER AND TOKYO
Bibliographic record
Abstract
This paper analyzes the relationship between residential housing price and rent across different regions in Vancouver and Tokyo.All data sets are collected in time series from 2005 to 2019.Data for Vancouver's housing price is obtained from the average annual residential housing prices directly collected from the "MLS HPI Home Price Comparison" data base offered by Real Estate Board of Greater Vancouver.For rental market, we directly collected our data from Canada Mortgage and Housing Corporation (CMHC) through their "Rental Market Survey (RMS)" including average rents for all types of residential units, availability for each type of unit and their vacancy rates accordingly.All Vancouver data sets were collected in Canadian dollars per unit on average.Both price and rent data for Tokyo was collected from a local Japanese Real Estate information provider called Tokyo Kantei Co. Ltd.We picked Tokyo and the other three regions around Tokyo (Yokohama, Saitama and Chiba).All Tokyo data were collected in average price (JPY) per square metre for both price and rent.We mainly used discount cash flow analysis through Excel Solver for estimating the discount rate for residential housing market across the 12 regions in the Greater Vancouver Area and the 4 regions in Tokyo area to test if the estimated cost of finance matches the actual mortgage rate offered by local financial institutions.We estimated the annual discount rate for each region by assuming that one investor buy the property at the end of 2005 and sell it by the end of 2019, who will also receive annual rent as consecutive cash inflows for 14 years.The second part of analysis focused on using panel regression model to test the relationship between change in price and change in rent for both cities.We also include the lag period due to the late response in rent change compared to the change in price of the same unit.The main finding of our study is that the estimated annual discount rate for all regions did not match the actual mortgage rates perfectly, but there is a similar pattern and movement in general.For panel regression, we obtained significant results for fixed and random effect tests on Vancouver data, both of which indicating a positive relationship between the price change and the rent change, with one-year lag in rent change.For Tokyo, we were not able to get a significant result to conclude a strong correlation between housing price change and rent change across 4 regions in Tokyo Area.However, the lag period test could suggest that the housing market is more sensitive to recent rent growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".