Gestión de los lodos

Our proposed bubble methodology/model excels over conventional techniques at assessing bubbles in housing markets. It is borne from the theoretical framework of irrational bubbles and rests on the belief that a housing bubble is a situation in which all the speculative activities of market participants (i.e. individuals, investment firms, financial institutions, and builders) undergo an approximate synchronisation, leading to an irrational and synchronous increase in a wide range of relevant variables. The advantage of our method can be summarised as follows.

First, it is apparent that the assessment of fundamental values (FV) in housing markets is rarely done, since existing FV conventional techniques are subject to several serious limitations, as discussed in the literature (Orrel and McSharry 2009, Glaeser and Gyourko 2007, Stiglitz 1990, Case and Shiller 1989, Shiller 1992, Smith and Smith 2006). As for the ‘‘housing market’’ specifically, Robert Shiller (1992) made clear that price determinants of housing are far more complicated and unknown than they are for other assets. This issue makes the process of calculating fundamental value in housing even more problematic, as expressed by Krainer and Wei (2004, p.1): ‘‘We see that housing bubbles are difficult to detect because fundamental value is fundamentally unobservable.’’ Another profound gap in conventional techniques is that the existing housing bubble models lack a basic hierarchical structure for the selected variables. Neither has there been any attempt to assign a weight to each variable based on the housing bubble theory followed. Finally, fundamental value approaches lack a clear and objective interpretation procedure when assessing the presence of bubbles. This is because such models tend to accept the presence of a housing bubble if house prices are not reconciled with variations of macroeconomic variables, or if the price

change cannot be explained by both fundamentals and ‘reasonable’ shifts (Shen et al. 2005). This issue was made evident by Taipalus (2006, p.11) who questioned, ‘‘How can one say that the real estate prices are in a bubble?’’ He then stated that this question is the same as asking, ‘‘How can one say that prices are detached from their fundamentally justified level?’’ However, the notion of how much actual prices must deviate from the so-called fundamental value or for how long in order to detect a bubble is open to different interpretations and thus to a profound bias. Similarly, Meltzer (2002) explained that the reason that a bubble hypothesis is difficult, if not impossible, to test (using fundamental value methods) is that expectations are measured relative to the hypothesis associated with rational expectations (i.e. that investors exploit all the available information). Thus, the bubble phenomenon is what remains unexplained by the hypothesis and therefore by the model.

Unlike the existing fundamental value approaches, our proposed bubble methodology/model gives rise to a new approach for estimating bubbles that emphasises the bubble component itself (via modelling the symptoms of bubbles) rather than comparing actual and fundamental values. 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, I use the inherent symptoms of bubbles to directly explain the bubble component in the UK housing market. Therefore, our approach extends the concept of housing bubbles to a new measure that could be described as a proxy of a bubble index. More specifically, this is achieved by employing an algorithm- based model, which in turn relies on a wide range of data coming from different sources and covering a long period of time. This approach is also in line with the statement of Milton Friedman (as quoted by Hamermesh 2000 p. 376): ‘‘I have long had relatively little faith in judging statistical results by formal tests of significance. I believe that it is much more important to base conclusions on a wide range of evidence coming from different sources over a long period of time.’’ Another benefit of our model is that it incorporates a basic hierarchical order for the selected variables. This process has been undertaken to determine variable best explains the presence of the phenomenon if its velocity is positive over an economic curve, thereby comprising a bubble force factor. This position is in line with the thesis’ general subject of analysis (i.e. the speed of growth). Another crucial advantage of our proposed method is based on the way in which results are interpreted. In contrast to fundamental value models, our method considers that a housing bubble exists if the output of the model is above a certain well-defined and well-documented empirical threshold (i.e. rule). This inherent feature in our model leaves less room for bias when assessing housing

bubbles, thus making it a more objective measure for interpretation purposes. Moreover, since this study already provides the variables’ ‘‘weights’’ to potential users of the model, along with the level of the bubble rule/threshold, the remaining process for applying the model involves only two simple steps:

1) Simple data transformations of easily accessible data 2) Straightforward application of a simple algorithm

As such, it can be argued the model offers a feasible process for frequent future bubble testing. This can be considered an additional advantage of the model compared to existing models, which include time consuming tests and complicated procedures with several data adjustments in some cases (as reviewed in the literature), making frequent testing an infeasible process for ‘‘ordinary people.’’

Second, the existing fundamental value methods make use of several idealistic assumptions about market participants in assessing bubbles. For instance, by relying first on the efficient market hypothesis and subsequently on rational bubble theory, conventional methods consider that market participants make decisions according to an optimal use of ‘‘market’’ information and that they use rational rules in doing so (Diappi 2013). Also, in implementing such approaches, researchers also assume that market participants know that the market is overvalued but have no incentive to leave it because they expect the bubble component to grow and compensate them appropriately (i.e. the bubble premium) (Hardouvelis 1998, Harrison and Kreps 1978, Flood and Hodrick 1990). The idea that participants in housing markets are so optimizing, calculating and willing to update their views based on new investment information has been called into question by the influential work of Case and Shiller (2003, 1989, 1988). The authors found that the actual housing market paints a very different picture from what has been thought. They concluded that real estate markets are far from rational, as they are populated by amateurs who make infrequent transactions on the basis of limited information and with no serious experience in gauging the fundamental value of the houses they are trading. Case and Shiller (2003) also found that during housing bubbles, market participants are not aware that the market is overvalued or bubbly when they form their investment decisions. Keynes (1936) also explained that asset markets operate in an environment in which market participants may not be governed by an objective view of the fundamentals but by “what average opinion expects average opinion to be.’’ This led Krugman (2009), in one of his most novel articles, to assert that the ‘‘widespread belief

that markets are efficient and rationally constructed had blinded many if not most economists to the emergence of the biggest financial bubble in history.’’ With this in mind Meltzer (2002) explained that the rational bubble hypothesis is empirically empty in terms of evidence and that rational price expectations cannot be empirically observed. The efficient market hypothesis and, to an extent, assumptions of rational bubbles, which were once at the forefront of economics, are now accepted as being outdated. Currently, the new area of research on housing bubbles offers a remarkable alternative to the EMH and to rational bubbles (Case and Shiller 2003). This alternative is irrational bubble theory, which uses more realistic assumptions when assessing bubbles.

Based on the above, it can be considered that our method utilizes more realistic assumptions about how market participants interact in housing markets. Principally, in contrast with the existing outdated theories and assumptions, our method touches upon the irrational bubble theory and additionally on the realistic and logical assumption that during housing bubbles, all speculative activities of the market participants (i.e. individuals, investment firms, financial institutions, and builders) achieve an approximate synchronisation. This assumption has been validated empirically by employing a correlation analysis in Section 7.1.5 (and Section 6.2.2). Furthermore, our method does not take into account any assumptions about rational price expectations from the market participants’ point of view, which is a key subject of contention in the existing bubble debate.

Third, in the existing bubble identification models, various assumptions about the bubble’s potential movement path or the probability of collapse have been applied (Cameron et al. 2006, Zhou and Sornette 2003, Himmelberg et. al 2005, McCarthy and Peach 2005). The general limitations associated with statistical and econometric forecasting have been well documented by Philips (2004), Chen and Wang (2004) and Vasigh et al. (2013). As was once said by an early statistician, Simpson (1952), ‘‘The only thing certain about the future is that it is uncertain.’’ Compared to the various existing bubble tests in the literature, our method’s advantage is that it does not require any forecasting or assumptions about the potential bubble movement path because it is a purely diagnostic model (it is only concerned with assessing the present bubble situation in the UK housing market).