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The thesis tries to explain different nature of the dynamics during the upward and downward part of the last house price cycle in Spain, characterized by important rigidities. Covered bonds are introduced as an instrument which may accelerate a house price boom, while it may also serve as a source of correction to overvalued house prices in downturn. In a serious economic stress, lack of investment opportunities motivates investors to buy the covered bonds due to the strong guarantees provided, which may in turn help to revitalize the credit and housing markets. To address such regime shift, house price dynamics is modelled within a framework of mutually related house price, credit and business cycles using smooth transition vector autoregressive model. Linear behaviour of such system is rejected, indicating the need to model house prices in a nonlinear framework. Also, importance of modelling house prices in the context of credit and business cycles is confirmed. Possible causality from issuance of covered bonds to house price dynamics was identified in this nonlinear structure. Finally, potential threat to financial stability resulting from rising asset encumbrance both in the upward and downward part of the house price cycle was identified, stressing the need to model impact of the covered bonds on house prices in a situation when Basel III liquidity requirements motivate towards use of this instrument.1 2
Keywords: E32, G21, G23, E44, E58, C32
JEL code: House price dynamics, credit cycle, rigidities on housing market, covered bonds, securitization, smooth transition vector autoregressive models, asymmetric behaviour
In many domestic housing markets, behaviour of house prices around the last financial crisis revealed important rigidities on the downward part of the house price cycle, with differences in adjustment across countries. While part of these differences may be explained by distinct indebtedness of households and subse- quent deterioration on credit markets, adjustment mechanism in those countries where the indebtedness grew the most still remains largely unexplained.
One of the hypothetical explanations of the distinct dynamics of house price adjustment across countries might be the diverse use of covered bonds along the cycle. Their possible corrective effects during the downward part of the cycle have been recently discussed on theoretical grounds, but they have not been empirically examined yet. On the other hand, their possible enhancement effects on the upward part of the cycle have been completely neglected in light of the extensive use of the mortgage backed securities, which were typical of the U.S., but much less of some other countries like Spain, for example.
Although the usage of both of these instruments - the mortgage backed securities and the covered bonds - were associated with house price bubbles, in light of the development after the house price reversal, the nature of the underlying mechanism linking these instruments and house prices seems to be rather distinct, in fact. While in the past, then, covered bonds played an important role in several countries only, the new liquidity regulatory proposals under the Basel III framework, which favour usage of the covered bonds, may extend issuance of them among other economies. For this reason, empirical investigation of potential effects of the covered bonds on house prices should be definitely getting more attention.
There are several features that make Spain a convenient country to address such question. The housing market in Spain experienced an unprecedented upswing in prices in years preceding the financial crisis, with booming con- struction activity and household indebtedness rising sharp. Despite of a sig- nificant excess of dwellings, however, house prices long hesitated to fall when the bubble burst. At the same time, both sides of these cycles, the peak and the trough, were accompanied by increasing issuance of covered bonds. Should therefore the covered bonds had some impact on the house price dynamics, its importance for credit financing in Spain makes this country a unique case to study.
Regardless of the source of potential rigidities in the market with housing, modelling house prices remains largely in the linear framework. Existing at- tempts to put house prices into nonlinear regime switching models have been limited to univariate or at least uniequational framework, most of all. In the theoretical literature, some authors nevertheless argue that given the interre- lations between house prices and business cycle, nonlinearities in house prices might well explain nonlinerities in macroeconomic variables like the output, which have been largely modelled apart. (Balcilar et al., 2012)
To address these problems, Chapter 2 departs from Hejlová (2011), when it reviews house prices in Spain in the context of mutually reinforcing powers between business, credit and house price cycles and explains reason why such approach is important. It then presents possible sources of rigidities on the downward part of the house prices cycle and explains motivation for assuming covered bonds as a potential source of the corrective mechanism.
Chapter 3 then introduces covered bonds in detail, emphasizing differences between these and the mortgage backed securities. Discussion of possible threats to financial stability explains why it is important to assume covered bonds in the context of house price and business cycles, and explains differ- ences from the mortgage backed securities again in this context. It contains important reasons for why covered bonds and house prices are modelled as to mutually effect each other in the following model and has implications for the final discussion of the possible impacts of cover bonds at the end of the work. The key hypothesis behind the role of covered bonds in house price dynamics in a situation of a housing bubble and increased indebtedness of households, as well as reasons why house prices may hardly be modelled in linear framework, are presented at the end of the chapter. Such hypothesis is then supported by data analysis for Spain as well as panel of selected countries to ensure that the model takes from the data indeed.
Chapter 4 first reviews theoretical background of the nonlinear, regime switching models. Given the scarce empirical implementation on house prices and economic time series in general, examples in which such models proved useful are included, with special attention to the range of possibilities which a researcher has for modelling. Procedure for smooth transition vector autoregression used in the empirical analysis is then described in detail. Finally, model for explaining house price dynamics in the framework of credit cycle, extended by the use of covered bonds, is presented. Improvements between various models in terms of fit are commented on.
Finally, it is concluded with the financial stability threats resulting from the usage of the covered bonds, which follow from the previous theoretical parts of the work and the empirical analysis.
The contribution of the present work is twofold. First of all, it attempts to testing the mechanism behind the usage of covered bonds and its impact on house prices in an empirical manner. which has not been dealt with so far. Secondly, it approaches to modelling house prices in an nonlinear framework extended to a vector of variables, where one of them is the covered bonds. For this purpose, a unique set of data on covered bonds obtained from the Asociación Hipotecaria Española, which is not publicly available, were used.
House prices have proved to be an important determinant of the overall eco- nomic performance, when the business cycle booms accompanied by a housing bubble have been identified as those that frequently lead to more costly busts (Borgy et al., 2009). In light of the recent financial crisis, the mutually enforcing powers between business, credit and house price cycle have been emphasized. Nevertheless, modelling house prices remains a crucial task: spillovers of house prices to the business cycle are important for a proper conduct of the monetary policy, while implications of possible financial imbalances between house prices and credit cycle are of particular concern for the financial stability issues. In Hejlová (2011), the transmission mechanism between real economy, housing market and financial sector has been analyzed in detail and so were discussed numerous determinants that might have been crucial in case of Spain. For purpose of this work, the relations will be briefly reviewed to give framework to the research topic and so will be mentioned the most important drivers of the house price dynamics. The context given thereby will be threfore based on results of Hejlová (2011), supplemented by results reported by other authors that have dealt with the topic so far.
The last boom phase of the Spanish economy was not only of higher am- plitude, but also of a longer duration than the booms previously experienced. There were several important factors that allowed for increased consumption as a whole. First, significant growth of gross domestic product was accompanied by an increase in real wages. Secondly, both incorporation of women into labour market and better conditions for employability of young workers accounted for an increase in disposable income of households, as the basic unit for decision making about house purchase. Since there has always been an extremely high propensity to primary home ownership in Spain and popularity of secondary housing, the favourable economic development, sometimes referred to as the Golden Age of Spanish economy, lead to increased number of purchases of housing of both types.
Positive price signals from the real estate market translated into economic activity through increased construction. Construction represented the most dynamic part of Spanish economy and gave rise to another significant increase of employment. Thus, the economy became construction industry based, what finally allowed for the mutually reinforcing relationships between real economy, credit and housing markets to interact.
Nominal interest rates determined by central bank combined with high a- verage inflation in Spain and fiscal subsidies on mortgages, under which inter- est payments were tax deductible, made financing of house purchase by credit affordable. Moreover, “credit is not only procyclical in Spain, but actually am- plifies the cycle.” (Herrero and de Lis, 2008) Dramatic upswing in real estate prices caused that value of the collateral, by which mortgages were backed, raised and allowed for further increase in mortgage lending. At the same time, overall conditions of borrowers were significantly improved, caused by economy being in its boom phase, what lead to general over-optimism of banks at the time of mortgage granting. Banks were strongly motivated to lend by increas- ing profitability, since there were both lower loan loss provisions and loan losses incurred.
This all meant that the borrower’s financial conditions improved to some extent artificially, since the boom of the economy was partially triggered by the housing bubble being built up. So did not values of the collateral thoroughly reflect the correct intrinsic value of the real estate. In case of significant drop in housing prices, borrowers’ conditions would deteriorate (partly because a lot of people were employed in the construction industry) and in case of non- repayment, the actual values of collateral would be found much lower than estimated. Thus, high indebtedness in connection with house price bubble posed serious financial stability problems.
With purpose of carrying out prudential regulation and with the aim to reduce procyclicality of lending as well as to eliminate feedback effect from the credit granting to the real economy, dynamic provisioning was introduced in 2000 by the Bank of Spain. The dynamic provisioning was designed on the premise that lending mistakes (such that materialize in the downturn) are prevalent in the upturn. In this way, it complemented the already existing general and specific provisioning by a statistical component, counting with losses that “have not yet been identified in specific loans” (Saurina, 2009). Such a tool should increase loan loss provisioning in the boom phase of the economy and reduce the feedback effect of lending, since otherwise in the boom phase “some negative NPV projects financed to be later found impairment of the loan or default of the borrower, while in recessions, banks suddenly turn conservative and tighten credit standards well beyond positive net present values” (Saurina and Jiménez, 2006). Probably the major advantage of the dynamic provisioning was the automatic accumulation and depletion of the provisioning fund, since the level of provisioning is determined by estimate of the risk in a cyclically neutral year. (based on Saurina, 2009)
To gain approval of the IFRS, statistical provisions became a part of the general provisions fund in 2005. However, until the proposal of the Basel III, which introduced countercyclical tools, Spain was the only example in the world of application of such anticyclical regulatory tool. (Hilbers et al., 2005)
Up to now, research on house price determinants and house price dynamics in Spain has been twofold. One stream of investigators follow the asset market approach and thus base their research on microeconomic decision making model between purchasing housing as an asset and paying for it as a service. In this way, intertemporal equilibrium equation is searched based on information from both house prices and prices of rents. The second stream of investigators carry out more macroeconomic type of models, in which they let the housing market interact with the credit market. These in most cases included indicators of real economy and credit market conditions and lead to autoregression models or cointegration analysis with error correction representation.
From the perspective of this study, works by Gimeno and Mart´ınez (2006) and Valverde and Fernández (2010) are the most relevant to study. Both of them concentrate on the cointegration between house prices and loans for house purchase and corrective powers between the two as represented by a VECM. The latter work is in fact extension of the former, when it brings additional information about structural breaks. Analysis by Esteban and Altuzarra (2008) then represents another macroeconomic type of models, in which the value added consists of employing additional demand and supply determinants, as well as assessing their relative importance to the last house price increase. The work will thus be presented first, to make an introduction to the house prices determinants in Spain. The rest of the research on Spain has concentrated on asset market approach and overview of existing studies of this type is included at the end, mainly for the review’s completeness.
Esteban and Altuzarra (2008) offer an encompassing discussion of macroe- conomic determinants of house prices in Spain, that were quite scarce before in terms of complexity. Authors emphasize that the increase in prices was de- mand driven and offer a comprehensive classification of demand for housing, useful for any kind of house price analysis. Apart from favourable financial conditions, authors determine the demographic trend and social changes in Spain as the main drivers of these dynamics. While the former was caused by increased natality and significant inflow of immigrants, the former accounted for atomisation of traditional families in Spain. Finally, the foreign investment into real estate was, according to the authors, another important driver, which was also reflected in the model presented in Hejlová (2011). Authors argue that this caused excessive supply of secondary housing in the coastal areas and not in the cities where the demand for primary housing was pressing on the local markets.
Regarding the assumptions about supply of housing, Esteban and Altuzarra (2008) use three-equational model where, most importantly, one of the equa- tion models number of housing starts on real housing prices and number of households. Authors follow the macroeconomic approach to modelling but in comparison with the following studies, they employ a wider range of proxies for both demand and supply factors, mainly the supply factors of the housing market. Using cointegration analysis, they find that house prices are in long run positively and significantly dependent on real disposable income, debt ser- vice burden and real residential investment, while the effect of mortgage rate is negative and significant. Number of housing starts is positively explained by the number of households and house prices, from where comes their important conclusion about demand driven house prices in Spain.
Considering the analysis of Esteban and Altuzarra (2008) as a departure study for selection of drivers of the house price dynamics, the work by Hejlová (2011) addressed the issue of adequacy of supply to meet demand in a timely manner using so denominated upper estimate of effective demand3. The results support the analysis of Bank of Spain concerning the supply of new housing, which evidenced that the quantity of finished dwellings exceeded transactions with the same, and does not therefore suggest supply pressures on a house price increase. Having concluded that the aggregate supply of housing could not rep- resent a limit for the demand for housing, which would in its consequence drive the house prices up, it was continued with analysis of whether spatial mismatch between supply and demand was not present. Cross-sectional analysis of house prices across regions (comunidades autónomas) was estimated including set of proxies for the households’ disposable income, dummy variable taking value of 1 for coastal areas and indicators of demand pressures4. The relation between rising number of households and increase in stock of housing was not found to explain differences in house prices significantly. On the other hand, regional analysis suggests that the frequency of housing transactions with respect to existing stock of housing played significant role in explaining the house price differences. Given the importance of transactions during the house price boom and the fact that the absolute number of transactions far exceeded the demand, the analysis supports the hypothesis of the role of speculative demand on such behaviour of house prices in Spain and makes possible implications on evolution of house prices in the financial crisis aftermath.
Gimeno and Mar´ınez (2006) present in fact the first attempt to implement credit market into a model of house prices in Spain, covering the period until 2004. Valverde and Fernández (2010) already cover a time span until 2008, and thus benefit from analysing the house prices during the whole period of boom. Authors of the first paper employ house prices, loans on house purchase, labour income and nominal interest rates in a model with two cointegrating re- lationships based on Eagle-Granger procedure. Valverde and Fernández (2010) use similar variables, but try to avoid problem of omitted variable bias by in- cluding proxy for a credit quality as represented by mortgage credit default rates. Apart from this, authors widen the set of variables used by Gimeno and Mart´ınez (2006) by real interest rates and inflation to capture the effect of the membership in the European Monetary Union, and price-to-rent ratio (as an alternative to house prices) to measure degree of the house prices overvaluation, as used in the financial types of models. Although they use Johansen cointegration procedure, they arrive to detect equal number of cointegrating relationships as the former authors. In both models, variables are used in per households form to capture the wealth effect of the unit which is determinant for the decision making about a home purchase.
In the long run, Gimeno and Mart´ınez (2006) find that house prices depend positively on income and level of credit financing. For identification of the long run relationships, authors impose restriction of zero coefficient to the nominal interest rates in the house price equations, so that changes in financing costs as well as in other credit market conditions are reflected in volume of mortgage loans. Level of indebtedness then positively depends on income and effect of nominal interest rates is found negative. House prices and credit aggregate are found to be positively dependent on each other in the long run. Valverde and Fernández (2010) present in fact similar results for the long-run relationships.
Regarding the adjustments after departure from the long-run equilibrium values, Gimeno and Mart´ınez (2006) show that in the long run, house prices are corrected when the level of indebtedness exceeds its equilibrium value. Authors also point out at significant corrective power of nominal interest rates in re- establishing equilibrium on the credit market. Valverde and Fernández (2010), who do not impose any restriction on nominal interest rates in the house prices equation in case of house price disequilibrium, confirm results reached by the previous authors, but bring additional evidence about larger relative corrective power of nominal interest rates with respect to mortgage lending.
Both authors then proceed with more short-term analysis by vector error correction representation. In the short run, mortgage credit is positively influenced both by house prices and salary income, while interest rates have a negative impact on it. (Varverde and Fernández, 2010) House prices are then negatively influenced by interest rates. Both papers suggest the same results regarding the impact of lagged disequilibria on contemporaneous values. While correction for both house prices and mortgage credit long-term disequilibrium has effect on house prices in the short term, disequilibrium of house prices has no effect on mortgage credit in the short run.
Regarding the possibilities of including credit market conditions into the modelling, Valverde and Fernández (2010) note that in a situation of infor- mation asymmetries present in the market, interest rates or lagged values of house prices are important representatives of borrowers’ expectations. On the other hand, as discussed in Gimeno and Mart´ınez (2006), interest rates can- not completely capture effects of liberalization of financial markets in Spain, increased competition and changing business model of the banks. Borrowing capacity has thus been increased by factors such as lengthening of maturity of the mortgages. For this reason, inclusion of volume of mortgage credit granted may be also important. Valverde and Fernández (2010) then emphasize im- portance of testing for structural breaks in borrowing capacity. Importantly, they suggest presence of structural shock in lending before 2001, due to factors connected with approval of legislation on securitization and dramatic decrease in real interest rates. Regime shift was thus estimated using dummy variable multiplying the mortgage credit prior to 2001, coefficient of which was esti- mated as significant. Finally, model presented above was estimated for the two sub-samples separately, finding that house prices and interest rates have stronger effect on mortgage lending after the credit conditions were eased.
Hejlová (2011) departs from these works with the aim to capture the mutu- ally reinforcing powers between real economy, housing and credit market and assess the role of anticyclical prudential tools within this framework. It pro- poses simple model, where the three markets are represented by gross domestic product, volume of credit on housing and house prices (in both real and nominal terms). Limited by short time span of publicly available data, it employs less variables than the previous models, but takes advantage of including volume of dynamic provisioning and foreign investment in real estate as endogenous and exogenous variable, respectively (as discussed further based on Esteban and Altuzarra, 2008). The time series used cover the period between 1996 and 2010. On one hand, this brings problems with stationarity, since these vari- ables were growing really significantly those years and the series were not found stationary without differencing them twice, as opposed to the respective series used by the previous authors that were I(1), as reported. On the other hand, the period analysed covers even the drop in house prices towards the end of the sample and thus offers additional information about the adjustment process. Regarding the nature of the data, cointegration analysis is first carried out. It is realized that when the sample is restricted only to the period covering the house price increase, more cointegrating relationships are found in the data. Finally, using the vector error correction representation, asymmetry of adjust- ment of house prices downwards was found, raising needs for further theoretical explanation. This confirms one of the previous findings, which suggested that mutually reinforcing powers between the real economy, credit and housing mar- ket function correctly in the upturn, whereas in the downturn, rigidities may be often present.
Turning briefly to the financial type of models, Pagés and Maza (2003) ap- proach to modelling both long- and short-run interactions between house prices and other variables based on financial approach. They include determinants as used in the decision making models between housing purchase and renting, namely the income, user cost of housing (“defined as nominal interest rate less the expected future rate of change of nominal house prices,” (Pagés and Maza, 2003) the stock of housing and the stock exchange return. In line with the pre- vious discussion, limitation of such approach consists of ignoring other factors of credit availability than interest rates. Using uniequational model of house prices, they show that in the long run, house prices are only dependent on real income, while the nominal interest rates are not found significant. Impos- ing a unitary restriction on income elasticity, interest rates are found to have negative impact on house prices.
While in the previous model, the discount factor was proxied by the user cost of housing, Ayuso and Restoy (2006) use an intertemporal asset pricing model including both housing services and consumption which are allowed to interact, and so the discount factor is also determined. Their empirical analysis thus goes behind the asset pricing model when it introduces present value of the rental payments in sense of housing as a service, as opposed to the concept of housing as an asset, in which the discount factor is equal to the return on alternative investment. (based on Ayuso and Restoy, 2006) Using vector autoregression model for three countries (Spain, U.K and the U.S.), authors concentrate on estimating equilibrium value of house prices in each country and determining the implicit under- or overvaluation, and thus do not go into estimating short run dynamics. Interestingly, authors estimate the degree of house price overvaluation to be around 20% and attribute this “to the sluggishness of supply in the presence of large demand shocks in this market and/or the slow adjustment of observed rents to the conditions prevailing in the housing market.” (Ayuso and Restoy, 2006)
The case of Ireland is often put into comparison with Spain as another example of a house price bubble that was built prior to the last financial crisis. In comparison to Spain, however the real house prices dropped more significantly and rapidly in the first years following the bubble bust. In Spain, the most frequent declaration concerning the house price bubble is that it is of speculative nature and there is clear evidence of vast quantity of dwellings that are empty for the majority of the year. How then comes that such an excess of supply has not driven house prices down?5
The role that the credit financing played in creating the house prices bubble suggests that holders of those dwellings, which were bought for speculative purposes, suddenly turned reluctant to sell their real estate, since offering it for a significantly lower price would leave them with negative equity. On the other hand, expectations of the potential buyers about house price burst lead to reluctance to buy for existing prices. Mortgages were often used to finance not only primary, but also secondary housing or purchases which were motivated by speculative reasons. This draws attention to rigidities on house price market in Spain and rises question about the role of rental market in returning these house prices back to the level determined by economic fundamentals.
Rigidities on housing market, which may in turn cause asymmetric behaviour of house prices on the downward side of the cycle, have been recently widely addressed on theoretical grounds. The main motivation for research is the so called house price puzzle, when there has been found “a strong posi tive correlation between house prices and sales volume.” (Genesove and Mayer, 2001) There have been two main theoretical contributions explaining such rigidity: the equity constraints and aversion to loss.
On one hand, households tend to buy their own housing when they expect prices to rise and so avoid paying more in the future. (Balcilar et al., 2012) On the other hand, equity constraint is described as a situation when owners of mortgage financed housing are reluctant to accept market price when housing prices are on decline, since this could cause problems with the asset repayment.
(Engelhardt, 2003) Given that periods of house price overvaluation are typically accompanied by highly leveraged households on market-wide scale, the market may be found stuck very easily.
Aversion to loss as a more striking explanation argues with about general unwillingness of sellers to realize nominal losses by selling their housing bellow the initial purchase price (Genesove and Mayer, 2001). After all, “nominal loss aversion, whereby households are averse to realizing nominal housing market losses and, hence, treat gains and losses asymmetrically, is a characteristic of preference.” (Engelhardt, 2003) One may argue that the asking and bidding prices should always meet, so that the adjustment of both should naturally come. In reality, only a small correction of the original asking prices is doc- umented by the final transaction prices. Instead, “homes tend to sit on the market for long periods of time with asking prices, and many sellers eventually withdraw their property without sale.” (Genesove and Mayer, 2001) As a result, households are less likely to transact, causing rigidities on the downward part of the house price cycle. (Balcilar et al., 2012) Genesove and Mayers (2001) point out that this is an especially puzzling feature, because “the moves are local, so that the typical seller is also a buyer in the same market.” This issue is also discussed in Hejlová (2011), where finding the equilibrium is supposed to lie in the rental market. The previous authors report that the volume sold may fall by 50% between the peak and through of the house price cycle. Genesove and Mayer (2001) also explore differences in behaviour of households and in- vestors, finding that the owner-occupants tend to set higher reservation prices to avoid potential loss. On the other hand, low equity is reported to be more important for the investors, supposedly because they can calculate return on a wider portfolio of assets. This implies that the percentage of speculative de- mand by institutional investors also enters the price dynamics importantly. To capture these hypotheses, Muellbauer and Murphy (1997) suggest modelling nonlinearity in the predicted rate of return. Finally, it is argued that the area of inactivity increases around the equilibrium the bigger are the transaction costs, which are rather typical of housing market in general.
In Spain, however, rigidities on both demand and supply side of the housing market call attention. Problems of the housing market in Spain and overview of possible solutions to such situation, which have been recently discussed, are the following:
1. Vacant houses are not put on the market or do not find a buyer for the offered price.
2. On the demand side, accessibility of home ownership is limited in serious economic downturn; young households cannot afford buying a primary home, since the unemployment rate is extremely high; on the other hand, to avoid overcrowding of public universities as a result of high unemploy- ment, the government has risen enrolment fees into courses significantly, which makes the situation of starting up one’s own living even more dif- ficult.
3. On the other hand, the vacant houses are not rented either, since in Spain, protection of the subtenant is so strong that it creates motivation for the owners to keep it empty, rather that to face any problems connected to collecting the payments.
4. The general consensus on the way how to boost activity on housing mar- ket in Spain is therefore to reduce number of vacant dwellings; first, it should be done through introducing more flexibility on the rental mar- ket (so called “desahucio”6 ); secondly, since adjustment of house prices definitely requires the frequency of transactions with real estate to raise, increase in prices of rent would make decision making between renting and purchasing a primary home more prone to the second option.
At the end, however, there must be something that helps the system get back to the equilibrium, or at least explain different adjustment across the countries, in which house price bubbles were credit financed to similar extent.
The real estate and financial crisis in Spain has also been widely compared to the crisis in the USA, in which a crucial role was played by the subprime mortgages. However, in the academic literature, it has not been pointed out enough on the differences between the two and not much attention has been devoted to the phenomenon of the securitization in Spain, which is in fact what probably stays behind the capacity of mortgage credit to grow in this country. Recently, there has been appearing broad evidence on specifics of securitization in Spain, which make the country a unique case to study. In this way, it is the covered bonds, which are in Spain referred to as cédulas hipotecarias, and not the mortgage backed securities, impact of which is particularly relevant to explore in case of Spain.
According to Valverde, Rosen and Rodr´ıquez (2011), the upswing of secu- ritization in Spain has been realised from insignificant scope in 1990s to be largely spread in the loan portfolio right prior to the crisis. In its very peak between 2005 and 2007, Spain became the second largest issuer of securitized assets, following the U.K. At the same time, private sector exposure has been aligned with this phenomenon for the most part. (Álvarez, 2008)
As opposed to the relationship between real, credit and house price cycles, which is largely covered in the literature, the role of securitizations in this mu- tually enhancing system has not been very much addressed yet. Introducing securitisation into existing models might nevertheless add information to ex- plaining causality from the housing prices to the credit, the so called feedback effect. Such a relationship has been largely reported in the research, however, the causality has not been clarified robustly, yet. The overall effect was finally underlined by the inherent procyclicality of the bank lending, that brought along loosening of lending conditions as a result of the overly favourable eco- nomic conditions.
In contributing to the empirical notions about the impact of usage of covered bonds, this work may help to solve such uncertainty by introducing covered bonds as a factor that might have, due to its capacity to provide cheap and liquid financing at all times including the downturn, accounted for part of dynamics of the house prices after the reversal. Key to this are the basic specific features of the covered bonds, as well as some less commonly known pitfalls connected to their use.
1 Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nábrež´ı 6, 111 01 Prague 1, Czech Republic,
2 I am grateful to PhDr. Michal Hlaváček, Ph.D., supervisor of the thesis from the Institute of Economic Studies, Charles University in Prague. My further acknowledgements go to Luis Pedauga, Ph.D. for providing me with his codes for LSTVAR, which I further modified, and his voluntary tutorship during my stay at the University of Granada in 2012. I also thank to Asociación Hipotecaria Española for providing me with data.
3 Upper estimate of effective demand was calculated as a sum of yearly differences of both primary and secondary home ownerships as well as primary home tenants. Such an estimate of effective demand is thus conservative, since usually some of the secondary homes may be provided by commercial sector.
4 Number of existing dwellings per transaction and number of new families per stock of finished dwellings.
5 Remarks that the present author made are based on information gathered while studying the relevant subjects at the University of Granada, Spain, during academic year 2011/2012; these were particularly Econom´ıa Urbana given by Pedro E. Barillao and Sistema Financiero by José Mar ıa Alcón.
6 Set of legal arrangements, which should make moving the tenants out a dwelling in one’s property easier.
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