Theoretical evidence

Minsky (1977) and Kindlerberger (1978) considered the role of money and credit in determination of asset prices. According to theirs hypothesis, the expansion in credit can lead to boom and bust on housing market and subsequent financial crisis.

3.2.1 Kinderberger’s framework

Kindlerberger (1978) stressed the importance of “displacement” and “euphoria” events as an initial factor that might lead to booms and busts. His framework can be modelled in the following way:

Figure 6: Kindleberger’s framework on a financial crisis

Displacement Euphoria/

Manias Financial distress Panic Contagion Lender of the

Last Resort

Source: own creation

Prior to boom, there might be some “displacement events” that lead to credit expansion.

For instance, expansion of automobile production, financial liberalization, revolution in information technology, an unanticipated change in monetary policy, or some outside “positive”

shock to the macroeconomic system. For the recent boom, financial liberalization (deregulation), financial innovations (Nesvetailova, 2007, Dore, 2008), securitization (Minsky, 2008), new regulation rules, historically low interest rate (Bordo, 2008), shadow banking system (Shiller, 2008) were pointed out as displacement events for the recent credit expansion and housing boom.

9However, the correlation does not prove that it is linearly correlated: other factors (GDP, unemployment, and interest rate) might have an influence on housing prices growth and lending growth.

10 Other theoretical approaches are bank runs, financial regulations, the monetarist approach, rational expectations, uncertainty, credit rationing, asymmetric information and agency cost, and dynamics of dealer markets. These are the approaches to financial crisis, however, they can be transposed to understand asset price developments.

46 The displacement events provided new profitable investment opportunities. It created an investment boom financed by bank money. The displacement events then led to state of euphoria.

The “euphoria” phase in the economy, a boom phase, is characterized by excessive borrowing, increased interest in investments and sparked asset prices even further. So, the main characteristic of euphoria is over-optimism. Over-optimism leads to excessive risk taking, to the belief that asset prices will always go up, and to a high degree of speculative activity among investors.

However, during the euphoria, investors have difficulty in distinguishing sound and unsound prospects (Bordo, 2008), which, in the future, increases the chances of defaults. At some point, the euphoria and mania can be distressed by some “negative” events (for example, the bankruptcy of Lehman Brothers in October 2008, resulting in a crisis worldwide). The negative events further lead to a panic, such as fire sale of assets and de-leveraging (Adrian and Shin, 2008), declining net worth, bankruptcies, bank failures. Because of the systemic risk, the turmoil spreads (the contagion effect) worldwide. Only The Lender of The Last Resort is expected to halt the contagion effect by supplying as much money as may be necessary to stabilise the market. Thus, the three capital injections (three bank rescue packages) in form of bank guarantees in Denmark were made to stop the panic. On 5^th of October, 2008, the Danish Contingency Association concluded an agreement on financial stability (Bank Rescue Package 1) with the Danish Government, securing an unlimited guarantee to all depositors contributing up to DKK 35 billion (Danmarks Nationalbank, 2009). The Bank Rescue 2, of 3^rd of February, 2009, injected app.

DKK 75 billion to banking institutions and app. DKK 25 billion to mortgage-credit institutes.

The Bank Rescue Package 3 was adopted in March 2010, aimed at amending the legislation relating to failing financial institutions in order to secure a fast and efficient liquidation.

3.2.2 Minsky’s framework

Minsky (1997) stressed that pro-cyclicality in credit lead to asset price boom and bust. Thus, the supply of credit in good times and the decline in the supply of credit in less optimistic economic times result in asset prices fluctuations. Also, credit pro-cyclicality increases the likelihood of financial (housing) crisis (Minsky, 1977).

The pro- cyclicality in credits characterized by following states:

Figure 7: Minsky’s framework on the financial crisis

Hedge Financing Speculative

Financing Ponzi Financing

Source: own creation

47 Hedge financing occurs when a firm’s cash flows (from net operating profit) exceed cash flows commitments (interest rate payments) to serve debt over a long period. Speculative financing entails cash flow payments over a short period that exceeds cash flow receipts (interest rate payment). Ponzi finance occurs when a firm has interest rate payment higher than its net income¹¹

A banker operates on the basis of expectations of cash flows (Minsky, 2008). Therefore, there is an increase in credit supply during hedge financing because of an improved profitability, and a decline during Ponzi financing because of a declined ability to collect interest payments of the underlying debt.

.

Therefore, in Hedge Financing stage, the credit supply increases, leading to increased demand for housing and an increase of the price, and there is the opposite affect in Ponzi Financing stage.

Also, academics discuss “credit” dynamics as one the most important variables in housing demand and therefore, housing prices. Some of the empirical findings will be presented in the following.

3.2.3 Empirical evidence and other studies on bank credit and property prices

There are several variables of credit dynamics that might have some effect on housing prices. Some of the dynamics are presented here: credit conditions, development of interest rate and credit growth.

Muellbauer and Murphy (2008) stress that credit availability is one of the main demand-driven factors, (together with income, housing stock, demography, interest rate and lagged appreciation).

When credits are more available to households, there will be more incentive to enter the housing market. Therefore, the growth in credit leads to a growth in demand for housing and, thus, to higher prices.

A study by de Greef and Haas (2000) documented that the amount of mortgage credit measured not only by its price (interest rate), but also by its volume, reflected the excess in housing and credit demand in Netherland in the period between 1993 and 1999. They found that there is a positive correlation between the level of outstanding mortgage debt as a percentage of GDP and housing prices. Their findings show that 88 per cent of house price increase can be explained by growth in mortgage lending for the corresponding period.

11 Please note, that Ponzi finance is not the same as Ponzi schemes/pyramid. The former term describe the relationship between the operating income and the debt service payments of individual borrowers, which is affected by the level of indebtedness, interest rate. The latter term involves promises to pay an interest rate of 30 or 50 per cent a month, however, it requires new depositor every month in order to keep this promise. The common feature in these definitions is a need of new capital injection in order to keep up with high interest payments. However, operating on Ponzi Financing stage is legal, while on Ponzi pyramid is not.

48 Miles (1994) also emphasized the importance of the households’ credit channel to housing market, and therefore, to the housing prices and the level of owner-occupation. However, he stressed the effect of changing credit conditions allied with more optimistic expectations of future income as factors contributing to the increased demand for housing. Thus, relaxed credit standards result in higher prices, while tightened credit standards result in lower prices. The evidence was based on observing that countries with lower credit availability (for example, Germany) experienced lower housing price fluctuations.

Another important characteristic of credits is interest charges, as a function of interest (mortgage rate) and the amount borrowed and the term of agreement (Finlay, 2009). A range of empirical studies demonstrated the correlation between interest rate and housing prices. The studies are presented in appendix 19A.

Thus, studies concluded that credit growth, its cost and conditions are important variables that determine housing prices. Therefore, credit expansions (credit boom) lead to housing booms.

From another side, housing price increase may lead to increased credit demand. A study by Goodhard and Hofmann (2007) measured how housing prices (housing wealth) affected the credit demand for 16 industrialized countries over the period 1980 till 1998 using quarterly data. They found that property prices appear to be an important determinant of the long-run borrowing capacity of the private sector (because borrowing capacity is a function of their collateralizable net worth).

Therefore, property prices might explain the long-run movements of bank lending. Thus, housing booms lead to credit booms.

According to Goodhard and Hofmann (2007), “This potential two- way causality between bank lending and property prices may give rise to mutually reinforcing cycles in credit and property markets” (p.148).

In addition, the credit boom and bust are not new to history. Norway, Finland and Sweden also experienced such real estate boom. The underlying causes of those booms were massive credit availability driven by irrational expectations (Allen and Gale, 2009). The Japanese bubble in real estate and stock markets in 80’s and 90’s provides a good example when expansion in credit leads to a bubble, which caused severe financial distress (see Goodhard and Hofmann, 2007, p.98;

Kindleberger, pp.126-135; Allen and Gale, p.380).

Thus, during the boom times, there was evidence (both, theoretical and empirical) of a coming financial and housing instability, as were stressed by Kindleberger (2005) and Minsky (1982).

However, as was stated by the Economist (2009), Minsky’s hypothesis was neglected by the regulators and by markets, and the economy is now paying the price. So, a relationship of credit and

49 prices is not new, “a critical determinant of asset prices is, thus, the amount of credit that is provided” (Allen and Gale, 2007, p. 237).

In the following, I analyze credit dynamics in Denmark, represented by legal rules on mortgage lending (3.3.1), credit policies (3.3.2), credit growth (3.3.3), credit supply conditions (3.3.4) and the underlying risks (3.3.5) as a background to understand the credit affordability concept.