A Study of Relation Between Housing Price Bubble and Macroeconomic Factors - Evidences from Suite in Taipei City and New Taipei City
Bibliographic record
Abstract
[[abstract]]台灣近幾年一直處於高房價低所得低利率狀態，政府實施許多打房政策並未抑制房價上漲，低所得導致首次購屋的購買者只能先從房屋產品類型中總價較低的套房下手，低利率使得投資成本下降投資者紛紛進場。本研究探討2007年第三季至2015年第四季套房房價泡沫及泡沫價格與總體經濟因素之關係。 實證結果發現由所得與租金兩個不同變數推估出台北市泡沫價格走勢，在2014年第一季所得與租金推估出泡沫價格走勢皆達到高峰，而所得與租金推估出泡沫價格占房價比例分別為22.11%與20.82%。另外，由所得與租金兩個不同變數推估出新北市泡沫價格走勢，在2009年第一季所得推估出泡沫價格走勢皆達到高峰，而其占房價比例為6.63%，而在2007季租金推估出泡沫價格走勢皆達到高峰而其占房價比例為59.3%。就泡沫價格與總體經濟因素關係而言，貸款利率、失業率、貨幣供給年增率與經濟成長率皆為影響台北市與新北市泡沫價格之因素。 Taiwan has been in a high housing prices, low income and low interest rates in recent years so the government implemented a number of policies to lower prices but did not let prices fall. While the low income makes the first purchase buyers can only start with the housing product type in the lower total price of the suite to start. Low interest rates make the investment cost down investors have entered the market. This study examined relation between suite housing bubble price and macroeconomic factors in the third quarter of 2007 to the fourth quarter of 2015. The empirical results show that the difference between the income and the rent of two different variables to estimate the trend of the bubble in Taipei City, in the first quarter of 2014 and rents estimated the bubble price trend reached a peak, and the income and rent estimated the bubble price of housing prices The proportion was 22.11% and 20.82%. In addition, the two different variables from the income and rent to estimate the New Taipei City bubble price trend, in the first quarter of 2009 to estimate the bubble price trend reached a peak, and its share price ratio of 6.63%, while in 2007 rent It is estimated that the bubble price trend reached its peak and its proportion of 59.3%. In terms of the relationship between the bubble price and the overall economic factors, the loan interest rate, the unemployment rate, the money supply growth rate and the economic growth rate are all factors that affect the bubble price of Taipei City and New Taipei City.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".