Tax on Vacant Houses and Housing Price Bubble in Shiraz
Bibliographic record
Abstract
Aim and Introduction The housing sector is one of the important economic sectors that, in addition to consumer demand, also faces demand from speculators due to its high capital return rate and low risk level. Speculators, motivated by the desire to profit from future price increases, refrain from offering their houses for sale, resulting in a housing vacancy. The presence of vacant houses reduces the housing supply and can lead to the formation of a housing price bubble. Imposing taxes on vacant houses is one of the government's tools to address this issue. The aim of this study is to examine the impact of taxes on vacant houses on the housing price bubble in the city of Shiraz. Methodology In this research, an agent-based model is used, considering four active agents in the housing market: sellers, buyers (including sellers and buyers with personal consumption and speculative motivations), developer, and real estate agencies, to investigate the dynamic processes of the housing market. To forecast the housing prices in Shiraz over an eight-year period, statistics and information by the beginning of 2022 have been incorporated into the model, and three different percentages of speculative buyers, including 30%, 50%, and 70% of the total buyers, along with different tax rates of 10%, 15%, 20%, and 25% have been considered. Findings The results of the research show that by applying a tax rate of 10%, if 70% of buyers are speculators, the highest growth rate of the housing price bubble was observed; that the decreasing growth rate was equal to 18%, that is, with application of tax on empty houses, the housing price bubble of Shiraz city in 2022 to the end of 2031 decreased by almost 19%, and after that the application of the tax rate of 15% in these conditions was approximately 17% which reduced the housing price bubble. But when the number of regular buyers is more than speculative buyers (30% of buyers are speculative), the application of different tax rates on vacant houses shows the least reduction effect on the housing price bubble. Therefore, when 70% of the buyers in the market are present in the market with the motive of personal consumption, the number of transactions is low. Since ordinary buyers will re-enter the market with a slight probability, and the majority of transactions are made by the 30% of buyers who are speculative, so applying the tax on vacant houses in the first year will cause a number of speculater to leave the market and the number of transactions will be less than before the tax was applied. In fact, mobilisation of the current stock of housing due to the tax may not have been high enough to affect prices which is consistent with Sego (2019). Furthermore, the results indicate that increasing the tax rate on vacant houses does not necessarily lead to a further reduction of housing price bubble. When more than half of the housing market is in the hands of speculators, their power in transactions would be greater, and the increase in tax rate in the form of an increase in price will intensify the housing price bubble which could mean more transactions between traders. In fact, traders add tax to the price of the property, and increasing tax rates, in return worsens the bubble. So, here selecting the optimal tax rate becomes critically important. When less than half of the housing market is in the hands of speculators, the power of speculators will decrease as a result, which leads to further weakening of the price bubble. However, to a lesser extent when more than half of the market is in the hands of speculators, the price bubble will decrease. Discussion and Conclusion The research results indicate that the implementation of different tax rates, despite varying numbers of speculators, can lead to a reduction in the housing price bubble in the city of Shiraz, although the effectiveness may vary under different conditions. Moreover, it can create an appropriate income for the government, which can reduce the class gap by allocating and optimally directing the resulting resources towards the supply of housing for low-income groups. But the government should be careful in choosing the tax rate. It is necessary to set the tax rate on empty houses in such a way that renting the house or offering it in the market is more economical than keeping it empty by traders. In addition to the tax rate, choosing the tax base is also crucial. As mentioned in the text of the research, some countries consider the value of the property as the tax base instead of the rental income, or a fixed annual tax is collected
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".