Available online at ScienceDirect. Procedia Engineering 172 (2017 )

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1 Available online at ScienceDirect Procedia Engineering 172 (2017 ) Modern Building Materials, Structures and Techniques, MBMST 2016 Determinants of Housing Market Fluctuations: Case Study of Lithuania Laura Tupenaite*, Loreta Kanapeckiene, Jurga Naimaviciene Dept. of Construction Economics and Real Estate Management, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Saultekio al. 11, Vilnius, Lithuania Abstract The article aims to distinguish the most important determinants of housing market fluctuations in Lithuania for period of 2005 to distinguish the most significant determinants of housing market fluctuations, expert survey performed and significances of determinants by using the Analytic Hierarchy Process (AHP) method identified. Research reveals that prices movements in als as well as housing market indicators The The Authors. Authors. Published Published by Elsevier by Elsevier Ltd. Ltd. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the organizing committee of MBMST Peer-review under responsibility of the organizing committee of MBMST 2016 Keywords: housing market; prices; fluctuations; determinants; AHP; Lithuania. 1. Introduction The past decade has seen many of the most persistent and severe housing booms since the 1970s. According to Agnello and Schuknecht [1], only Japan, Germany and Belgium do not report housing booms in the past decade. 1 years and resulted in an above-trend increase of house prices by 67%, prices in France increased by over 50% over nine years, similar trends were observed in Spain and the UK. In other countries the magnitude of house price increases beyond trend ranged from 22% to 67% and the duration from 3 to 11 years [1]. * Laura Tupenaite. Tel.: address: laura.tupenaite@vgtu.lt The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the organizing committee of MBMST 2016 doi: /j.proeng

2 1170 Laura Tupenaite et al. / Procedia Engineering 172 ( 2017 ) The enlargement of the EU has influenced development of economy and the housing market in the Baltic States. Notably, in the period of Lithuania experienced very strong economic growth. Countries were significantly influenced by favorable lending and expansion of private sector credit. Due to global financial crisis Lithuania experienced the period of the most dramatic boom in housing prices over the last decade, which was followed by economic downturn, and consequently, the burst of prices bubble. However, in 2014 country already demonstrated housing market recovery and prices growth. Lithuania was one of the few countries in the EU to experience positive prices growth since the period of crisis. Such movements of the housing prices attracted attention of the Bank of Lithuania, State Enterprise Center of Registers, other authorities and private business enterprises; however, the scientific research on the most important determinants to explai researched market cycle in the Baltic countries for period of , in more recent study Azbainis et al. [4] constructed conceptual model of Lithuanian real estate development in and revealed the tangible and iple movements in the Baltic States for the period from 2000 to This research aims to distinguish the most important determinants of housing prices fluctuations in Lithuania for period of ten years ( ). Authors believe that findings of the research will provide insights on further developments of the housing market. 2. Literature Review The international literature on booms and busts in housing market is extensive. The traditional literature analyses the determinants of housing prices and more recently focuses on the macroeconomic and policy implications, related et al. [8], [9], [10]; Nuuter et al. [11], among many others). Single and cross-country studies usually find that housing markets and the macroeconomics are strongly interrelated at country-level and internationally correlated [12]. Studies suggest that at national and regional levels, housing prices are strongly influenced by the economic cycles and therefore driven by fundamentals like GDP and income growth, [14]). Fadiga and Wang [15] evaluated the dynamics in four USA regional housing markets. They found that unemployment, federal funds rate, corporate default risk, economic expansion, unanticipated inflation in the construction market are the key factors that affect both the short-run and the long- countries over a period of 30 years and found that a 1% increase in economic activity raises the demand for houses and thus house prices over the long run by 0.6%. Stevenson [16] studied the price behavior among regional housing markets in Ireland. He found the movements in regional house prices can be attributed primarily to key economic fundamentals. ggested that two of the key drivers frequently cited in the recent run up in house prices have been rising income levels and the benign interest rate environment faced by many countries. Financial variables such as interest rate, money and mortgage supply have been found related to housing prices developments by many authors (see e.g. Tse et al. [17], Su et al. [18], Ngo [19]). One can agree that favorable mortgage conditions are one of the most important catalysts of housing booms, while contrary financia Norwegian, Swedish and British house prices reacted immediately and strongly to monetary policy shock. According ouse prices after 2006 in many countries can be explained by higher risk-aversion and tightening of credit constraints following the general economic downturn, especially the global Some of the authors also emphasize the influence of irrational factors, such as buyer expectations and speculative behavior on housing prices (e.g. Engsted and Pedersen [21], Lind [22], Kaklauskas et al. [8], [9], [10], etc.). When housing prices are increasing rather quickly and if there are strong expectations of future price increases, then some people might see an opportunity for quick profits by buying an apartment or house and selling it again rather soon. This speculative behavior might then further increase demand and prices. In many historical asset market bubbles this type of behavior has been observed, even if it cannot be expected to be central on the housing market where transaction

3 Laura Tupenaite et al. / Procedia Engineering 172 ( 2017 ) costs are high [22]. Engsted and Pedersen [21] have documented that changing expectations of future housing returns has been the main driver of housing market volatility in the OECD area in the last 40 years. Literature analysis suggests that in assessment of housing prices determinants both rational and irrational factors should be taken into account. 3. Changes of Residential Property Prices in Lithuania In 2005 the growth of the real estate market in Lithuania had reached a record high. Average increases in the prices in one year. Enormous increase in prices continued through period of Lithuania entered recession by 2009 as the GDP registered felt down to 14.8%. Unemployment rate increased sharply and reached 13.8%. Gross wages were also lower than the previous years due to weaken domestic economy. Inflation rate meanwhile fell sharply. To the end of 2009 housing prices fell from their peak by almost 40% first quarter of Li-o-y basis, losing 37.2% of its value since the peak. First quarter of 2009 saw the house price index of Lithuania down by 20.0%. Economy of Lithuania made a slow recovery in Housing prices also recovered albeit at a slower rate or stagnant in major cities. Official House Price Index for Lithuania grew up by 1.3% on y-o-y basis. Despite recording a down by 1.9% (q-o-q % change) in the first quarter, the house price index rebounded by the second quarter, thus marking the recovery of house price index (Fig. 1). Since spring 2010, the real estate price changes were insignificant, compared to the preceding period of decline for more than two years. Compared to the highest level, housing prices were almost two times lower. Real estate prices which have remained broadly unchanged also in 2011 and low interest rates had a positive impact on the housing affordability. The activity in the market was gradually recovering. Fig. 1. Average prices of flats in major cities of Lithuania (Euro/sq.m) (data source: State Enterprise Center of Registers). In the first half of 2012, trends in the economic slowdown, which had been observed at the end of 2011, continued to intensify in Lithuania. Similar trends were in the real estate market: the year of 2012 has started with a optimism; though, halfway the year, the market took run, and many market segments recorded growth in transactions, some of them even increase in prices. However, at the end of the half of the year, positive trends began to decline. Although the prices edged up by an average of just 1.2% in 2013 versus 2012, the increase was recorded in new construction flats segment about 1,400 of new apartments were sold 48 pct. more than in the first half-year of During the first half-year the sales of 29 new projects with more than 1,200 flats were started in Lithuania. In comparison with the corresponding period of the previous year, the amount of new apartments, offered for the market, was twice less. The price level has increased in all housing segments within a year, especially in capital Vilnius. In the first quarter of 2014, the fastest growth in the real estate market activity was recorded in the housing segment. The number of single-family houses and flats, which changed hands in that period, soared by 43% in y-o-y terms

4 1172 Laura Tupenaite et al. / Procedia Engineering 172 ( 2017 ) (seasonally adjusted; by 15% in q-o-q terms), mostly as a result of the rapid economic growth, the search for alternative investment opportunities in the prolonged environment of low interest rates (amid low yields on risk-free assets), the expected change of the national currency and, presumably, efforts to legalize some of the money circulating in the shadow economy. Summarizing results at the end of 2014, the number of transactions of flats was 11% higher than in Decline of housing market activity was observed at the beginning of In Q almost 31% fewer purchase and sales transactions for flats were recorded than a year ago, average 1,900 transactions for apartments were concluded per month. However, according to data of the Association of Lithuanian Banks, in Q2 2015, the main credit establishments of the country granted mortgage loans for EUR 224 million, which is a 49% increase compared to Q The total value of the loans granted in Q was the highest since Despite geopolitical challenges, the overall economic situation in Lithuania remained positive and this continued to encourage both the construction sector and steady growth in housing demand. During the first three months of the year 2015 average prices of flats increased only by 1.6%. No major changes in prices were recorded in the Q2 and Q3 of the year 2015, a certain growth in prices that is worth mentioning was recorded in Vilnius only where average prices of flats reached EUR 1,282 per sq.m. In Q average prices of flats reached highest level since the beginning of the Determinants of Housing Prices Fluctuations in Lithuania The aim of research was to establish the set of the most important determinants of housing market fluctuations in Lithuania for period of Basing on findings from literature review, the hierarchical system of determinants was formed. Due to large number of determinants and hierarchical nature of the system, Analytic Hierarchy Process (AHP) method by Saaty [23] as a research method was selected. This method was proved as efficient and was widely used by many authors for different real estate and related tasks solutions. Martins et al. [24] used integrated cognitive mapping with the AHP to calculate time-on-the-market indices in the residential real estate market. Adnan et al. [25] employed AHP procedure to analyze the relative importance of the main factors chosen by the main sectors of tenants at top grade office buildings. [26] used AHP as a tool to rank different critical success factors for construction projects in Lithuania. [27] applied TOPSIS Grey (Technique for Order Preference by Similarity to Ideal Solution with grey numbers) and AHP methods for the case study of upgrading the old vernacular building. 28] used AHP model to evaluate the performance of banks. Ecer [29] proposed a hybrid model of AHP and COPRAS-G methods for evaluating the website quality of banks. The modified algorithm of AHP 30], which was used for this research, is presented in Fig. 2. Very important issue in this research was to select the experts having appropriate expertise in Lith market fluctuations. Eleven experienced experts who fully satisfied the requirements were selected for the survey: five experts from Vilnius Gediminas University, four from real estate enterprises and two from public sector authorities. The questionnaires of judgment matrices where prepared and provided to experts. Judgment matrices, filled by 1 m m cij j1 qi 1 m m m ckj k1 j1 (1) and

5 Laura Tupenaite et al. / Procedia Engineering 172 ( 2017 ) m m max cij qi, i1 j1 (2) where: k number of experts; m number of attributes; c i i th determinant; q i significance (weight) of the i th determinant; eigenvalue. Fig. 2. Proposed algorithm for determination of weights of criteria by pairwise comparison [30]. The consistency ratio (CR) of each matrix was checked: CI CR, (3) RI where: RI random consistency index and CI consistency index calculated as follows [23]: max m CI. m 1 (4) The determined significances of determinants are presented in Figure 3. It can be observed that out of the six economic indicators highest significances experts attributed to interest rates and newly issued mortgage loans ( and , respectively), another significant factor, representing the general growth of prices is inflation (the determined significance is ). housing supply (0.2501), and real estate transactions that indicate activity of the housing market (0.2501).

6 1174 Laura Tupenaite et al. / Procedia Engineering 172 ( 2017 ) assigned to the return on investment indicator (0.3868). Significances of consumer expectations and the construction sector confidence were assessed as and , respectively. While comparing the groups of indicators, experts remarked that the most significant indicators to explain housing prices movements were economic indicators (significance ), the second most important market indicators (significance ), and finally, irrational indicators (significance ). The trend of economic development of the Lithuania has not changed the domestic demand, which is mostly stimulated by private consumption, is growing and one can expect that this factor will remain as the main cause of economic development in As a result of economic growth, the number of employed people is increasing as well as growth of average wage is foreseen. Expectations of households are also becoming more positive both with regard to the national economy and with regard to the situation in the real estate market (today, only a small number of households expect prices to drop or are pessimistic about the prospects of real estate market). DETERMINANTS OF HOUSING MARKET FLUCTUATIONS RATIONAL INDICATORS (q = ) IRRATIONAL INDICATORS (q = ) Economic indicators (q = ) Market indicators (q = ) Customer expectations (q = ) Speculation (q = ) GDP (q = ) Inflation (q = ) Earnings per capita (q = ) Unemployment rate (q = ) Interest rates (q = ) Housing mortgages (q = ) New housing supply (q = ) Building permissions (q = ) Construction price index (q = ) Housing transactions (q = ) Housing affordability (q = ) Construction sector confidence (q = ) Fig. 3. Determinants of housing market fluctuations in Lithuania. As suggested by the findings from this research, prices are not expected to change at a fast pace in the nearest future. Moreover, any disproportionate developments in housing prices due to credit growth are curtailed by the Responsible Lending Regulations, which, starting from 1 November 2015, also took account of the ultra-low interest rates of recent years. 5. Conclusion and Further Research Research revealed that the housing market fluctuations in Lithuania are mostly explained by economic indicators; the most significant among them interest rates and newly issued mortgage loans as well as inflation. Moreover, housing market indicators should also be assessed, including the most significant ones: construction cost index, new housing supply, and real estate transactions. Importance of irrational factors assessed as less important. Findings of this research are in line with majority of findings by international studies. The calculated significances of determinants will be used in future research for more accurate and integrated assessment of housing prices movements in Lithuania. Acknowledgements Authors are thankful to State Enterprise Center of Registers (Lithuania) for housing data support.

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