Rationale for choosing the selected variables

The house price is the foremost variable used to begin to answer the question of whether the market is undergoing a bubble. Not surprisingly, all contributors to the literature on detecting housing bubbles have incorporated house prices in their analyses. It has been used either as a benchmark for fundamental value or user cost or as an important variable in a descriptive and ratio analysis. Generally, a rapid increase in house prices is a necessary first component to define a situation as a housing bubble. This can be justified by the fact that most available definitions of housing bubbles put emphasis on house prices in describing the phenomenon:

i) “any significant increase in the price of an asset…that cannot be explained by the fundamentals” (Kindleberger & Aliber, 2005, p.30)

ii) ‘‘a situation where an asset’s price exceeds the fundamental value of the asset” (Barlevy, 2007, p.46)

iii) “if the reason the price is high today is only because investors believe that the selling price will be high tomorrow—when ‘fundamental’ factors do not seem to justify such a price—then a bubble exists” (Stiglitz, 1990, p.13)

iv) ‘‘an unsustainable price surge that does not fit with societal fundamentals’’ (Reed and Wu 2010, p.41)

v) “a situation in which the market prices of an asset (such as stocks or real estate) rise far above the present value of the anticipated cash flow from the asset” (Smith & Smith, 2006, p.2)

5.3.1.2 Debt-Burden Ratio

Affordability is a core element in detecting overheating property markets (Himmelberg et al., 2005; Dolphin & Griffith, 2011). A large number of studies have employed affordability ratio analysis as a tool for bubble detection (Case & Shiller, 2003; McCarthy & Peach, 2004; Hou,

2009; Shen et al., 2005; Dreger & Zhang, 2010; Weeken 2004). Yan (2011, p.16) explains the collapse of a bubble in terms of its instability:

‘‘Think of a ruler held up vertically on your finger: this very unstable position will lead eventually to its collapse as a result of a small (or an absence of adequate) motion of your hand or due to any tiny whiff of air. The collapse is fundamentally due to the unstable position; the instantaneous cause of the collapse is secondary.’’

Looking at affordability helps to uncover such an unstable position. As Higgins (2013) states, since prices within a bubble “expand above the value that is warranted by normal returns and demands, they cannot continue to rise.” With this in mind, demand can be measured using affordability indices, which should therefore not be neglected when examining the possibility of a housing price bubble.

The debt-burden ratio (D/B ratio) is one of the main indicators of housing affordability. This ratio measures total home ownership costs, taking mortgage payments as a percentage of a typical household’s monthly (pre-tax) income. If the ratio rises too far above what is regarded as normal, households become increasingly dependent on rising property values to service their debts. Simply put, debt-to-income ratio considers the ability of households to meet their mortgage liabilities (Tsai, 2013). Similarly, Farlow (2004) notes that the debt- burden ratio is relevant for identifying purchasing power in the housing market and explaining the sustainability of property prices. A rule of thumb is that 25% (or sometimes 30% or higher) of household monthly income being spent on housing is considered affordable and sustainable (Ndubueze, 2009). However, each market has its own rule of thumb. For example, Glindro, Subhanij, Szeto, and Zhu (2008) assert that average levels for a particular market provide a good rule of thumb for affordability. Bordo and Jeanne (2002) state that debt burden is closely related to the expected price of assets, while Tsai (2013) argues that housing has a unique characteristic: as prices increase, expectations of further price increases stimulate demand and mortgage expenses grow accordingly, thus reducing affordability. Dolphin and Griffith (2011) assert that D/B ratio measurements fully reflect the long-term performance of nominal interest rates and act as an indicator by which to track the money illusion. The literature suggests that the debt-burden ratio can be useful in terms of providing an indication of how stable the ability of households is to service their loans and how willing new households are to enter the market. These are key determinants in uncovering any instability or abnormality in housing prices.

5.3.1.3 Gross Lending

The lending parameter is frequently discussed in the housing bubble literature. Several authors have explained the vital contribution of real estate lending to house prices and to banks’ capital assets (Herring & Watcher, 2002; Kindleberger & Aliber, 2005; Hou, 2010). Zhou and Sornette (2003) argue that a housing bubble is part of a more general credit bubble. In this framework, a housing bubble cannot exist without the existence of a credit bubble. Similarly, Hou (2009) notes that the rapid growth of mortgage loans contributes to higher housing demand since credit transforms the purchasing power of house buyers. According to a large number of studies, two factors can explain the tendency of housing bubbles to recur. One is disaster myopia, which is linked to human cognitive issues, while the other is the presence of perverse incentives (Caprio et al., 1998; Tversky & Kahneman, 1974). Both of these factors were discussed in Section 3.8. As for the presence of perverse incentives, this is directly linked with the gross lending parameter. As a result, gross lending is seen as an important indicator for the early detection of housing bubbles (Herring & Watcher, 1999, 2002; Cornand & Gimet, 2011; Hunter et al., 2005; Stroh et al., 2008; Haldane, 2009). However, few studies have incorporated variables related to lending. Some, like the studies of Cameron et al. (2006), Case and Shiller (2003) and Allen and Gale (2000), have used nominal or real mortgage rates to represent the lending variable, while others (e.g. Hou, 2009) have used the average percentage growth in mortgage lending in monetary terms to denote the lending aspect of the phenomenon. Alternatively, McCarthy and Peach (2005) examined the lending aspect indirectly by employing debt-burden ratios in their analysis. The above discussion demonstrates that lending plays an important role in terms of the formation and identification of housing bubbles and should therefore be used as an individual variable.

5.3.1.4 Housing Completions

Housing completions are a core element in terms of measuring the supply of housing in the market. The UK has had a well-documented undersupply of housing. According to some studies, undersupply is one of the main factors responsible for the historic UK housing bubbles (Dolphin & Griffith, 2011; Kuenzel & Bjornbak, 2008). Ermish (1990) argues that supply inelasticity becomes a crucial determinant of the duration of a bubble. When housing supply is elastic, new construction quickly comes on line as prices rise, which causes the

bubble to unravel quickly. Despite this, Mueller and Laposa (1994, cited in Pyhrr et al., 1999) suggest that oversupply is one of the four phases in property cycles—together with recession, recovery and expansion—while at the same time is the inflection point that triggers a gap between demand and supply. However, during oversupply conditions, most participants do not recognize that this transition has occurred, since the market still looks good. Pugh and Dehesh (2001) and Reed and Wu (2010) explain that the lag (gap) between demand and supply exists because real estate cannot be produced instantaneously, as ordinary consumer goods can. During the oversupply phase, supply growth is higher than demand. At such weak equilibrium points, house prices are very vulnerable to collapse. Housing supply is not only informative in terms of the length and the performance of housing cycles, but also plays a crucial role in tracking the expectations of market participants and housing bubbles. The literature suggests that housing supply is an important indicator of overheated housing markets and hence this variable must be included when the presence of a housing bubble is examined. For instance, Mueller and Pevnev (1997) and Baum and Hartzell (2012) note that building development activity in housing markets increases during a boom. Developers are always more incentivized to invest in the construction sector (i.e. supply) when prices are rising (or when they exceed construction costs). In support of this, Holcombe and Powel (2009) and Baum and Hartzell (2012) note that the quantity of houses built often helps in measuring bubbles and can be described as a quantity dimension of bubbles. This study accepts that housing completions ideally describe the supply parameter of housing markets. All of the above considerations provide a solid justification for using this variable in this analysis.

5.3.1.5 Income

Income has been extensively used in housing bubble research, either as part of an affordability indicator or as an individual variable (McCarthy & Peach, 2004; Cameron et al., 2006; Baker, 2006; Hou, 2009; Shen et al., 2005; Dreger & Zhang, 2010; Case & Shiller, 2003; Weeken, 2004). Income is considered a fundamental factor in bubble theory (Case & Shiller, 2003; Dolphin & Griffith, 2011; Hou, 2009). The significance of income to housing bubble theory can be better understood by looking at Kindleberger and Aliber’s (2005) definition of a bubbles as “any significant increase in the price of an asset…that cannot be explained by the fundamentals.” This can be interpreted as follows: if house prices can be explained by market fundamentals like income, then no bubble exists. Alternatively, if house

prices cannot be explained by changes in income then a bubble exists. Notably, Case and Shiller (2003), McCarthy and Peach (2004), Dolphin and Griffith (2011) and Hou (2009) all consider income to be a fundamental factor in housing bubbles; hence, it cannot be ignored when attempting to identify housing bubbles. Cameron et al. (2006) and Hou (2009) note that income is a core demand shifter, influencing the consumption of housing from the demand side. A review of the literature on housing bubble theory suggests that income has been used almost as often as house prices. In fact, income is perhaps the second most widely used variable (after house prices), particularly if both of the possible forms of use, as affordability indicator or as a single variable, are taken into account. For these reasons, this study necessarily includes the income variable in order to produce reliable conclusions.

5.3.1.6 House-Price-to-Income Ratio

The house-price-to-income (HP/I) ratio is the basic affordability measure for housing in a given region or country. It is the ratio of median or average house prices to the median or average familial disposable income, and can be expressed as a percentage or as years of income. The relationship of the HP/I ratio to housing bubbles is important, since the ability of households to purchase houses is, in large part, dependent on their earnings (Dolphin & Griffith, 2011). This ratio can be used to measure whether housing prices are too high (McCarthy & Peach, 2004) and seem to be suited to advanced economies with well- established housing markets (Ndubueze, 2009). Based on the assumption that house prices and incomes share some common trends in the long term, aggregate demand for homes is proposed to be a stable function of the average income. An extreme price-income ratio indicates that a higher percentage of income is required to purchase a house. This implies the existence of ‘overvalued’ houses (Himmelberg et al., 2005; Girouard et al., 2005; Finicelli, 2007). According to Flood (2001), ratios between 3:1 and 5:1 are regarded as normal and are therefore the “best measure of pressure on the housing market,” while Reed and Wu (2010) note that the accepted affordability standard is normally 3:1. Nevertheless, for each market there are different rules of thumb (Hancock, 1993; Freeman, Chaplin, & Whitehead, 1997; Lerma & Reeder, 1987; Ndubueze, 2009). For instance, Dolphin and Griffith (2011) and Glindro et al. (2008) propose that values well above the historical average can be used as good proxies for the presence of bubbles. Generally, the issues mentioned above offer a valid justification for incorporating such a measurement in this study.