Euphoria Turkey
Cdztattoo Barreto
Consequently, we utilize a new algorithmic technique as proposed by Pitros and Arayici (2016) to put in the epicenter of analysis the synchronized performance and the acceleration rate of certain indicators to assess the probability of a bubble. This dating algorithm model does not treat as its epicenter of analysis the variable representing house prices, or any other sole variable in bubble analysis. Instead, it has been developed and it places emphasis on estimating the magnitude of the phenomenon as a whole. Therefore, it purely represents the bubble without commingling with any fundamental aspect of pricing. Hence, what differentiates our method from most conventional bubble techniques is that instead of using fundamental variables to explain prices that in turn will somehow explain bubbles, we use the inherent symptoms of bubbles to explain directly the bubble component in the Turkish housing market (Pitros, 2016; Pitros & Arayici, 2016).
euphoria turkey
The contribution of this study is twofold. Firstly, to the best of our knowledge, this paper is the first study that attempts to identify housing bubble in Turkey by relying on the irrational bubble theory (also see, Pitros et al., (2016) for the preliminary version of this study). Secondly, this is the first study in Turkey to apply an algorithmic approach to assess the bubble risk for the period of 2006 and 2018, an alternative to the mainstream (rational and) fundamental value approach; in which its unreliability and its serious limitations has become increasingly obvious (see, among others, Stiglitz, 1990; Shiller, 1992; Krainer & Wei, 2004; Glaeser & Gyourko, 2007; Orrell and McSharry, 2009; Budd, 2011). This is an important step because the algorithmic approach has been solely constructed to serve the purpose of analyzing bubbles as a phenomenon, instead of focusing on the premises of forecasting/estimating what the fundamental housing prices should be in the marketplace and then comparing them with the actuals. In this respect, all previous quantitative studies to examine the bubble question in the Turkish housing market have been approached by exploiting the premises of the efficient market hypothesis; rational bubble theory and in extent they have relied on some sort of fundamental value method.
Defining housing bubble is difficult by every means. Theoretical/empirical literature suggest that the definition/measurement methodologies differ in housing bubble (Chen et al., 2013; Joebges et al., 2015). Below, we review the dynamics and empirical outcomes of bubble models by broadly utilizing/updating Pitros (2016) by also developing a housing bubble literature review for Turkish housing market.
As the frequently used conventional housing bubble detection methodology, the descriptive statistic-ratio approach makes use of several statistical tools and ratios and may also involve surveys. Most often, these ratios are expressed as affordability measures (e.g., house-price to income, house-price to rent, mortgage payment to income, etc.) (see, among others, Case & Shiller, 2003; Hlavček & Komrek, 2009; Hou, 2009; Bourassa et al., 2019). However, the extensive use of ratios tends to misjudged the bubble assessment particularly if the ratios are used alone.
As the final category, the algorithmic modeling approach explicitly integrates irrational bubble theory and thus it fulfills the need as raised by Greenspan (2015) of incorporating nonrational intuitive human responses in the models. One interesting study that uses an algorithmic approach to test housing bubbles is that of Bordo and Jeanne (2002). In their study, they identified housing boom if the three-year moving average of the growth rate of the inflation-adjusted asset price falls outside a specified range. The width of the range is defined taking into account the historical average growth rate and volatility of the asset price. Another algorithmic approach is that of Pitros and Arayici (2016). The authors suggested that the presence of the housing bubble is confirmed once the output of the model is equal or above the pre-established threshold of 0.85. For detecting housing bubble, the methodology adopted by Tajani et al. (2019) involves a genetic algorithm to identify the best functional relationships among the variables selected, and the data sample has been obtained by considering the main variables identified in the reference literature.
Overall, housing bubble studies for Turkish housing market have used rational bubble approaches and essentially focused on house price variable. Therefore, our study fills the gap in the existing literature by being the first to exploit the irrational bubble theory for Turkish housing market and the first to apply an algorithmic approach after Pitros et al. (2016) as the preliminary empirical exercise of this study.
Finally, as a general concern, like other studies in the bubble literature, our data, variable selection/construction, and methodology may also not fully address the complexity of housing market (see, Grybauskas and Pilinkienė 2018). There are probably unknown number of variables that move in the same velocity with the selected variables during each phase of the market (i.e., recession, recovery, expansion or bubble) (Pitros, 2016). Also, representative power of the selected variables maybe limited due to some inherent constraints. For example, it is difficult argue that aggregate level house prices reflect locational/neighborhood nuances or housing completions fully show existing supply without a lag. Some similar arguments may be also possible for the remaining employed variables from different perspectives.
Table 1 reports the numerical data sets used in this study for the period between 2006 and 2018. The year 2006 is not available in the Table since it represents the commencing year for calculating year-over-year (YoY) percentage change. All variables are measured in their annual YoY percentage change; except house price-to-income (HPI) ratio, which is employed in its annual nominal value form.
By using Pitros (2016) as the reference, below we are introducing the key parts of the model that are necessary for its application. Those include the: hierarchy of the variables, specific time frame of analysis, data transformation, measurement process, main multiplier, rule and finally the algorithmic model.
The purpose of the hierarchy of the variables in the model is to assign to each data point its proper amount of explanation over the phenomenon. With reference to Pitros and Arayici (2016), the study follows the previously established weights (see Table 2).
Here, it is worth to highlight that the income as a sole variable in the model has a weight of 0% (see, Pitros & Arayici, 2016). The weights of importance of the variables have been identified by relying on the following question: Which variable better explains the existence of the phenomenon; if its value increases rapidly? For example, rapid speeds of growth in house prices provide a stronger assumption of the presence of a bubble than does a rapid increase in income. Following this example, if the income is increasing rapidly-positively this performance explains that any general housing market euphoria is not bubbly but rather is supported and explained by the income increases. However, income usefulness in the model is utilized when its three year moving average performance is negative/declining (i.e., using the Model II in that case). Since, whereas the more negative/declining the income is (and all other variables remain constant-positive) the even greater the probability of a bubble becomes. Turning into more details, if all bubbly variables are increasing positively-rapidly (houses prices, D/B ratio, gross lending, and housing completions) and the variable of income is declining, then all of these positive euphoria in the market could be even more securely and sensibly explained and justified by the bubble phenomenon itself. Apart from its importance in the Model II case, the income variable is well recognized and relevantly integrated in the main multiplier of the model which is another key part and its details are seen below.
The measurement process for each of the model input (variable) is inherently related to aforementioned elements: the weights, the specific time frame of analysis and the data transformation. As explained above, for each variable, a different weight has been established. The equation used to measure the weighted moving average for each variable (i.e., α, β, γ, and δ) is the following. It is worth to mention that this process is the last step for making the selected data sets suitable for integration in the algorithmic process.
With reference to the original study, Pitros and Arayici (2016), the measurement process for the main multiplier is applied using nominal values of the house price-to-income (HPI) ratio and by utilizing the below equation without accounting for any weight. Thus, the main multiplier is measured on its simple three-year moving average value and is denoted by λ, with Eq. (5) as follows:
Following the quantification of the bubble rule in Pitros and Arayici (2016), this study also adopts the threshold of 0.85. Therefore, it is recommended that values that are equal or above the selected threshold of 0.85 indicate that a high risk of a housing bubble is present and thus it is associated with positive bubble diagnosis.
The original proposed method implies two models based on the proper explanations between the income and the bubble phenomenon. In this respect, Eq. (6) is used when the three-year moving average of income is positive, and Eq. (7) is employed when the three-year moving average of income is negative, although it is very rare for income to exhibit a negative performance on the basis of a three year moving average. The rationality for this relies on the view that the higher the growth of income (all other variables remain constant-positive) the less the probability of a bubble, whereas the more negative the income is (all other variables remain constant-positive) the greater the probability of a bubble. Therefore, when the three-year moving average of income is positive, its value is divided by \(\frac1\varepsilon \) to reflect lower significance as income rises. However, when the three-year moving average of income is negative, we use this approach: \(\sqrt (\varepsilon )^2 \), to convert its negative value to a positive number, thus reflecting in the model higher significance as income declines. Via this approach, the model thus ensures that the relationship between income and the bubble phenomenon is captured proportionately.
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