Analysis of the housing market in Lithuania/Nekilnojamojo turto rinkos Lietuvoje analize.

ABSTRACT. Cointegration and Granger causality tests were used for the statistical analyses of the housing market in Lithuania. The relationship between the cost of housing and affordability on the one hand, and interest rates, GDP and average incomes on the other was not proven to exist using the given statistical methods. The period of increase in the cost of housing in Lithuania over the last five years is exceptional and difficult to explain using fundamental economic factors and their fluctuation trends alone. The cost of housing has made a clear departure from the economic (business) cycle; the economy has grown, however at a much slower rate than rising costs in the housing market. The reasons for this situation are record lows in interest rates, good conditions to gain financing, the liberalisation of financial markets, speculative attitudes in expectation of the introduction of the Euro, and a divide between the supply and demand of housing that is available. It should be noted that the evaluation of the influence of these factors on fluctuations in costs in the housing market is more hypothetical in nature.

KEYWORDS: Housing market; General factors influencing housing costs; Statistical methods

SANTRAUKA

Nekilnojamojo turto rinkos Lietuvoje statistinei analizei buvo naudojami kointegravimo ir Grangerio priezastingumo testai. Taikant esamus statistinius metodus nebuvo irodyta, kad egzistavo rysys tarp nekilnojamojo turto kainos ir iperkamumo, viena vertus, ir palukanu normu, BVP bei vidutiniu pajamu, kita vertus. Nekilnojamojo turto kainos Lietuvoje didejimo per pastaruosius penketa metu laikotarpis yra isskirtinis ir sunkiai paaiskinamas remiantis vien pagrindiniais ekonominiais veiksniais ir ju svyravimu tendencijomis. Nekilnojamojo turto kaina aiskiai nukrypo nuo ekonomikos (verslo) ciklo; ekonomika isaugo, taciau gerokai letesniu tempu nei augancios kainos nekilnojamojo turto rinkoje. Sios situacijos priezastys--rekordiskai mazos palukanu normos, geros salygos gauti finansavima, finansu rinkos liberalizavimas, spekuliaciniai poziuriai tikintis isivesti eura ir takoskyra tarp esamo nekilnojamojo turto pasiulos ir paklausos. Pazymetina, kad siu veiksniu itakos kainu svyravimo nekilnojamojo turto rinkoje ivertinimas yra labiau hipotetinis.

1. INTRODUCTION

Many authors have performed analyses of the cost of housing and affordability to clarify their causality and cointegration with various economical factors (see e.g. Coulson and Kim, 2000; Gallin, 2006; Kapopoulosa and Siokis, 2005; Sanders, 2005; Mayer, 1981; Benito, 2006; Richardson and Thalheimer, 1982; Nazem and Guy, 1982; Linneman, 1980; Brown et al., 1997; Pain and Westaway, 1997; Kenny, 1999; Ambrasas and Stankevicius, 2007; Belinskaja and Rutkauskas, 2007; Case and Shiller, 1990; Liu et al., 2002; Chiang et al., 2005; Keskin, 2008; Kryvobokov and Wilhelmsson, 2007; Luo et al., 2007).

As example, some research in affordable housing markets, demand and supply of housing and used statistical techniques are shortly presented.

International mortgage markets can play an important role in stimulating affordable housing markets and improving housing quality in many countries. Unfortunately, international mortgage markets are often less developed than in the United States. This lack of development often translates into lower homeownership rates or lower housing quality. The problems faced in international mortgage markets include but are not limited to (1) legal systems that delay foreclosure proceedings, (2) incomplete or weak financial institutions, (3) high inflation, and (4) cultural barriers to mortgage market development and homeownership (Sanders, 2005).

Housing policy-makers show increased interest in encouraging rehabilitation of the existing housing stock. But little is known about what factors influence the decision to invest, particularly in rental housing, making policy design difficult. Mayer (1981) presents an empirical analysis of individual landlords' housing rehabilitation decisions in one housing market. The analysis tests hypotheses about the impacts of detailed neighborhood, structure, and site characteristics on each owner's investment activity.

Benito (2006) considers empirical implications of the down-payment constraint for the UK housing market. It shows that, at the aggregate-level, models of the housing market with this constraint are consistent with a number of stylized facts. Benito (2006) then exploits variation across local housing markets and considers how leverage affects the response of house price inflation to shocks. The evidence, based on data for 147 district-level housing markets for the period 1993-2002, suggests that a large incidence of households with high leverage (loan-to-value ratios) raises the sensitivity of house prices to a shock.

Previous studies of UK house prices, developed from the demand and supply of housing or from the asset market approach have been poor in terms of robustness and ex-post fore casting ability. The UK housing market has suffered a number of structural changes, particularly since the early 1980s with substantial house price increases, financial market deregulation and the removal of mortgage market constraints through competition. Consequently, models which assume that the underlying data-generating process is stable and apply constant parameter techniques tend to suffer in terms of parameter instability (Brown et al., 1997). Brown et al. (1997) use the Time Varying Coefficient (TVC) methodology where the underlying data-generating process in the UK housing market is treated as unstable.

Pain and Westaway (1997) develop a new approach to the modelling of house prices in the UK, with housing demand being conditioned directly on consumers' expenditure rather than the determinants of expenditure. Conditioning on consumption ensures that the permanent income measure used in determining the level of consumption is consistently reflected in housing demand.

Kenny (1999) uses cointegration analysis in order to separately identify both the demand and supply sides of the Irish housing market. The analysis suggests that in the long-run the demand side of the market can be modelled using a stable relationship between house prices, the housing stock, income and mortgage interest rates. To model the supply side of the market, the empirical section of the paper tests the data for the existence of a stable ratio of house prices to construction costs (including land costs) which is consistent with 'normal profits' in the house building sector. Impulse response functions are employed in order to shed light on the issue of short-term dynamics about the identified cointegrating relationships. Interestingly, the dynamics implied by the VECM specification suggest significant constraints on the supply side of the market and the potential for house prices to overshoot their long-run equilibrium level following a sudden increase in housing demand.

Richardson and Thalheimer (1982) employ four different statistical techniques (geographic, AID, cluster and discriminant analysis) to define homogeneous groupings of houses within an urban area. The major conclusions of the study are that there are no discernible differences among the four methods and that predictions made ignoring the grouping information are as accurate as those obtained by grouping.

Nazem and Guy (1982) performed an empirical study of the housing market using the statistical method of Markov Process. The first phase of the study is devoted to measuring the filtering process in a selected neighborhood by estimating probabilities of transition from one income group to another, over the period 1949-1969 using four-year intervals. The estimated transition probabilities are then used to forecast occupancy structure for different periods and the suitability of applying the Markov Process for long term policy analysis in housing is examined. The final phase of the study includes an examination of steady state occupancy structure by various income categories of household.

An attempt is made to develop a systematic statistical methodology for the analysis of the urban housing market. The standard estimation procedures used for fitting hedonic price functions for the urban housing market are reviewed, and several potentially serious sources of bias are noted. An alternative estimator which capitalizes property values into flows and also searches for the appropriate functional form which avoids these biases is developed (Linneman, 1980).

Our choice was quarterly data from the 1998: IV--2004: 111 period:

1) average cost of 1 [m.sup.2] of a given residence and affordability equal to the ratio of the averages of the cost and monthy income,

2) GDP, average monthly income and average annual interest rates on housing loans in litas.

The analysis starts with short explanation of real estate prices in Lithuania and its neighbouring countries. It is followed by a description of the factors potentially influencing real estate prices and the cointegration and Granger causality analysis. Lastly is a presentation of the conclusions reached.

2. DEVELOPMENT OF THE HOUSING MARKET