International Journal of Strategic Property Management ISSN 1648-715X print / ISSN 1648-9179 online

2011 Volume 15(3): 295–311 doi:10.3846/1648715X.2011.617857

EFFECTS OF QUALITY OF LIFE ON THE PRICE OF REAL ESTATE IN CITY

Marija Burinskienė 1, Vitalija Rudzkienė 2 and Jūratė Venckauskaitė 3  1 Department of Urban Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, E-mail: [email protected] 2 Department of Business Economics, Mykolas Romeris University, Ateities g. 20, LT-08303 Vilnius, Lithuania E-mail: [email protected] 3 Department of Urban Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania E-mail: [email protected]

Received 14 December 2010; accepted 24 March 2011

Abstract. The urban development covers the consensus of a wide range of activities and is aimed at equivalent coordination of the impact of economic, social and environmental factors on planning. Due to different attitudes and interests planners and scientists find it rather compli- cated to come to a common and widely acceptable attitude towards urban development patterns through harmonization of forms and development principles. The paper includes the theoretical attitudes, related to future insights, into the urban development conceptions. Moreover, assess- ing the impact of the economic paradigms on urban visions, the paper analyses the influence of development concepts on the qualities of development forms as well as on the creation of future urban alternative visions, compatibility with the principles of sustainable development and proper quality of life. The price of real estate is addressed as one of the key social indicators and the ones that reflect the quality of life. The theoretical provisions, with the help of expert evaluation, are verified taking the districts of Vilnius as an example and considering qualities and forms, population, jobs, area, noise level and pollution of individual districts on the basis of different components. Conceptual modelling principles are applied to determine critical values of the indicators that characterize the sustainable development. Based on them, a system is created that helps to define features of individual urban districts, potential development alter- natives and achievable allowed marginal values of the quality of life.

Keywords: Sustainable development; Quality of life; Price of real estate; Urban develop- ment

1. Introduction the same. Moreover, some new ones have oc- In 1972, the United Nations Conference on curred as a result of the urbanisation process Human Settlements (Habitat) was the first one development and the globalisation of economic to address human living conditions and issues and other activities. Today, with the increase of settlements (including cities). Since then, in life expectancy, the decrease in birth rate, the issues have not gone: they have remained increase in divorce rate and the potential for

Copyright © 2011 Vilnius Gediminas Technical University (VGTU) Press Technika http://www.tandfonline.com/loi/tspm20/ 296 M. Burinskienė et al. less pollution in our post-industrial society, difficult questions relating to what should be the city has once more become man’s natural incorporated as significantly influencing „qual- habitat. Compact polycentric cities are the only ity of life“. One reason for this is that there are sustainable form of development and should be large numbers of diverse definitions of quality designed to attract people (Turskis et al., 2006; of life (Fahy and Cinneide, 2008). Zagorskas and Burinskienė, 2005; Zagorskas The development of technology is gradu- and Holmberg, 2008; Zagorskas et al., 2007; ally changing human conscious, priorities and Zavadskas et al., 2006, Zavadskas et al., 2010). judgment on the quality of life. As a result, the If urban regeneration will be not implement in shifting conception of housing reflects changes cities – buildings and public spaces, education, in the price of flats. Housing prices is one of the health, employment, social inclusion and eco- key indicators revealing the level of economic nomic growth – will be undermined. development of cities and areas as well as the Hence, to improve the quality of life in mod- quality of life (Burinskienė and Rudzkienė, ern cities, a special attention is paid to the 2004; 2005; 2007a,b). analysis and evaluation of social consequences of the urban development: what is the ideal 2. Effects of the living city? How does it look like? What factors deter- environment on prices of real mine that it is a place where inhabitants feel estate good and safe, where it is convenient to work, live and spend their leisure time? In recent years, scholars have been in- The modern city is the most desirable place creasingly focused on the real estate market to live. It is a place where buildings are in a consisting of a system of market mechanisms healthy, safe and attractive environment to with its typical features and laws, and real es- live and to work in; a place with good com- tate market research. The real estate market munications, where housing is built and main- is one of the most significant parts of any na- tained in a sustainable manner by applying tional economy, comprising about half of the new cooperation forms between end-users and total planet assets (Hu and Pennington-Cross, the building process in a broad sense. The ap- 2001). plication of new technology will allow achiev- The real estate market has a number of ing the desirable quality of life in cities. qualities, of which the most important are low According to American architect Richard liquidity and cyclical nature. Cycles of the real Rodgers, sustainable urban development is de- estate market are not fully coincident with pendent on three factors; the quality of archi- general economic cycles. The decline of the real tecture, social well-being and environmental estate market starts and ends earlier than the responsibility. The compact sustainable city is general economic decline. Furthermore, prices multi-cultural with a hierarchy of density, has of real estate tend to increase steadily in the a mix of uses and tenures, is well connected long-term (Galinienė et al., 2006; Brown and with a coherent public transport, walking and Liu, 2001). cycling infrastructure, is well designed both As it is already known, the key factors con- in terms of public spaces and building, and trolling the market are demand and supply, is environmentally responsive (Rogers, 1997). and their ratio determined by the dynamics of Measuring quality of life in cities typically prices. In the real estate market, demand de- involves the development of indicators which pends on income of the population, changes in cover themes such as crime, healthcare, cost of the population, turnover of transport technolo- living, etc. Formulating indicators gives rise to gies and preferences of the population, dynam- Effects of Quality of Life on the Price of Real Estate in Vilnius City 297 ics of relations among social groups, crediting solid and compact structural territorial units conditions and opportunities. In short periods, (Motieka, 2009). supply may change faster than demand as the Even though many authors have analysed real estate demand is not very elastic in short the impact of high quality environment on the periods. Supply is determined by a number of real estate value (Boyle and Kiel, 2001; Colwell indicators: construction volume, ratio between et al., 2000), there are not many research on the cost of objects and the selling price, chang- the aesthetic impact of the living environment es in construction technologies, prices for land on the price of real estate. What concerns the plots and infrastructure. effects of the quality of the visual living envi- However, a system of indicators determin- ronment on the price of real estate, attention ing real estate demand and supply in one or should be paid to the effects of different visual another region or city is never complete as it is factors, such as positive effect of the view to difficult to take into consideration such aspects water (Benson et al., 1998), negative impact as national and cultural factors, behavioural of electric power transmission lines (Hamilton stereotypes of the population that decide pri- and Schwann, 1995). While analysing real es- orities of market participants and effect the tate operations in the New Zealand, the schol- rate and price of the real estate demand and ar S. C. Bourassa established that attractive supply. In a small region, the real estate de- neighbouring houses increase the price by even mand depends on a number of factors such as 37% (Bourassa et al., 2004). Similarly, poorly- infrastructure, ecological state, variety of cul- maintained houses reduce prices of neighbour- tural and domestics objects. Nevertheless, the ing houses. Moon et al. (2010) analyses how determinant factor is location, which reflects to to set an appropriate price of skyscrapes in in differences of prices in different regions. consideration of its landmark value in differ- The image of the modern city is conditioned ent area. by the distribution of industrial companies, For the purpose of the investigative part, main roads, aesthetic picture. Also, some re- Vilnius, the capital of Lithuania, was taken as searchers supposes that consumers’ marginal an example. The city of Vilnius is the biggest willingness to pay for environmental landscape city in Lithuania, covering the area of 401 km². attributes decreased during the 2008 reces- At the beginning of year 2010, Vilnius had the sion compared to the 2000–2006 real estate population of 560 thousand. The selection was boom (Cho et al., 2011). Real estate special- conditioned by the available large statistical ists note that the attractiveness of historical database (qualitative indicators) used for the city centres as a residential place has been de- comprehensive plan of the city of Vilnius as creasing. Areas with unique architecture and well as a database of the representative survey high level of comfort in residential premises of residents carried for the purpose of the said are becoming the most attractive (prestigious) comprehensive plan. Furthermore, Vilnius is ones. Low air pollution is considered to be one special as its , which started its for- of the main factors for attractiveness. How- mation as back as the Middle Ages, is one of ever, the key factor determining the quality the biggest in the Eastern Europe (its area is of the living environment is still the location more than 120 ha) and influences the central of the district. It is important to mention that part of the whole city. Political centre of the the increase in the quality of the environment Grand Duchy of Lithuania from the 13th to requires systemic, focused attempts to organ- the 18th century, Vilnius has had a profound ise functionally- and socially-integrated urban influence on the cultural and architectural de- and territorial structure as well as to form velopment of the Eastern Europe. Despite inva- 298 M. Burinskienė et al. sions and partial destruction, it has preserved area) started in Vilnius. During the imple- an impressive complex of Gothic, Renaissance, mentation of the sovietisation policy, foreign Baroque and classical buildings as well as its workers were displaced to new factories built medieval layout and natural setting. These in Vilnius, the division of the soviet army was were the reasons why the historical centre of dislocated in Šiaurės Miestelis, the territory of Vilnius was included in the UNESCO World Vilnius. Heritage List in 1994. Following the recovery Now, the development of Vilnius is cha- of the economic situation, the Old Town is in- otic – elemental and poorly-managed territo- tensively being restored and renewed. Green rial development in suburban areas, the city areas around the Old Town have increased the is dispersible with low density of the built-up value of the place even more, thus maintaining areas and low population density in urbanised high prices of real estate. territories. The central part of Vilnius may be Four states of the development of Vilnius characterised by poor quality of the living en- planning may be identified: partial urban vironment, heavy traffic, high concentration planning up to 1795, the annexation of Lithu- of jobs, pollution, conflict between pedestrians ania to the ; centralised plan- and transport. It also has peripheral issues, ning when the state was a part of the Tsarist such as undeveloped physical and functional until 1918 when Lithuania restored its structure, engineering and social infrastruc- independence; brief period of 1918–1944 when ture, insufficient public transport. Although the owner of the city changed five times in the differences in the quality of life are becom- 30 years (the Republic of Lithuania, Poland, ing more obvious, trends of changes in prices , Germany, Soviet Union again); of real estate remain similar. Nevertheless, centralised planning during the soviet period the price for 1 sq. m. differs by 2.5 times. Such in 1944–1990 (Čiurlionienė, 2008). In the lat- situation is illustrated by the chart below. The ter period, the construction of multi-apartment chart presents the dynamics of prices of real buildings and new districts (Naujamiestis, estate in the Old Town and Pilaitė (the last Žirmūnai, , Karoliniškės, Viršuliškės, multi-storey district that is built in Vilnius) Pašilaičiai, Fabijoniškės - 8% of recent Vilnius (Figure 1).

12000

10000

8000 2

/m Centras I 6000 Lt Pilaitė I 4000

2000

0 2005 2006 2007 200820092010 Year

Figure 1. Changes in prices of real estate in the Old Town and Pilaitė (JSC “Ober-House” monitoring data) Effects of Quality of Life on the Price of Real Estate in Vilnius City 299

3. Research on particularities the workplace; 11. Well-attended environment; of different districts 12. No noise; 13. No drug-addicts; 14. Close to in Vilnius a hospital; 15. Close to a pharmacy; 16. Good facilities for sports; 17. Many cultural estab- Major criticisms levelled at quality of life lishments; 18. No alcoholics in sight; 19. No projects are that the indicators typically reflect homeless in sight; 20. Close to a workplace; 21. expert opinion about what constitutes quality Beautiful architecture of buildings; 22. Well- of life and that citizens’ perceptions of the com- attended parks. munities and environments in which they live Other scientists (Viteikienė and Zavadskas, are ignored (Moller, 2001). The involvement of 2007; Zavadskas et al., 2007a) have also ana- the public in environmental policy-making is lysed the system of these 22 indicators. The proffered as a means of empowering citizens, expert survey was used to identify indicator enhancing institutional capacity and increas- values, while the multi-purpose method of ing social responsibility (Fahy and Cinneide, evaluation CORPAS (Complex Proportional As- 2008; Staniūnas, 2008; Čiegis and Gineitienė, sessment) was applied to prioritise residential 2008). This research aims at learning why districts by their sustainability. Burinskienė differences of the quality of life are becoming and Rudzkienė have evaluated the impact of more evident in zones, residential areas and certain indicators on housing changes in differ- individual districts of Vilnius, even though ent districts of the city, developed an empirical they contradict to the principles of sustain- correlation-regressive model and identified gen- able development and promote such negative eral groups of districts of the city by using the processes as emigration, compulsory mobility, cluster method (Burinskienė and Rudzkienė, degradation of different parts of the city, so- 2006). Jakimavičius and Burinskienė propose cial segregation. For this purpose, it has been and discuss accessibility and other indicators- decided to investigate the database of Vilnius based urban transport system analysis and residents’ survey. GIS (geographic information systems) calcula- In January–February 2005, a representa- tion method for indicating problematic trans- tive survey of Vilnius residents was carried portation zones in Vilnius city (Jakimavičius out in order to supplement the information and Burinskienė, 2009). base of the comprehensive plan of Vilnius 2005 Other scholars have also thoroughly ana- with data that were not in official sources of lysed Vilnius in respect of sustainable devel- statistics and to learn residents’ opinion on opment. Thus, for instance, Kaklauskas et al. their living area, job opportunities, reasons of (2009) have compared Vilnius with cities of oth- potential migration, etc. (JSC „Rait“, 2005). In er developed countries, found their differences total 2,575 residents of Vilnius at the age of and proposed how to improve sustainability of 16–74 evaluated the environment of 41 resi- Vilnius. Having analysed the statistical data dential areas in Vilnius. The respondents used on morbidity of Vilnius residents, the authors five-point scale to evaluate 22 statements de- together with Turskis identified the impact scribing the quality of a residential area: 1. of air pollution on human health, possibility Close to the city centre; 2. Extensive supply to get certain diseases and the price of real of trade services; 3. Close to a school; 4. Close estate (Zavadskas et al., 2007b). Raslanas et to a kindergarten; 5. Extensive supply of en- al. (2006) have analysed and compared prices tertainments; 6. Clean air; 7. Nice environ- of flats in Vilnius and in London using math- ment; 8. Safe; 9. Good communication with ematical statistical methods. They established the city centre; 10. Good communication with and compared the determining factors. 300 M. Burinskienė et al.

The authors of the article believe that the estimate the present and future situation most analysis of prices of real estate should take exactly as well as to present substantiated and into account both the opinion of residents and exact conclusions. socio-economic factors that allow achieving the For the research multivariate analysis tech- desirable quality of life. Many researches are niques were used to reveal the key informa- based either on residents’ surveys or on data tion contained in residents’ survey, and these of statistical sources. However, the relation analyses were applied in three stages. First, between these two types of data, i.e. how data principal component analysis (PCA) was used of residents’ surveys are related to and reflect to identify the variables that accounted for the statistical data provided by EUROSTAT and maximum amount of variance within the data other official sources, is rarely examined. This in terms of the smallest number of uncorre- research aims at combining the data received lated variables (components). Second, factor by the survey of Vilnius residents carried out analysis was conducted on these remaining 9 variables in order to reduce them to a smaller by JSC “Rait” in 2005, and official statistical number of underlying factors. data on transport districts that are marked in The aim of factor analysis is to explain the the comprehensive plan of the city of Vilnius. outcome of p variables in the data matrix X using fewer variables, the so-called factors. 4. Methodologies of the These factors are interpreted as latent (unob- multivariate statistical served) common characteristics of the observed analysis x ⊂ Rn. In the factor analysis every observed xx= (, x )T can be written as: To describe the social-economic processes 1 n and phenomena, a great volume of initial data k xa=+fjε ,,=≤1 nk; n , (1) and indicators are used that characterize the jj∑ ll j  l=1 development of a process, therefore it is very important to select the most important ones where: fl for l = 1, …, k denotes the factors; εj of them and to consider a small amount of in- is the residual of xj on the factors. Given the dicators or their groups. Frequently the initial assumption that the residuals are uncorrelated data are transformed so that to ensure the across the observed variables, the correlations minimal loss of information. among the observed variables are accounted Most of these indicators take the form of for the factors. time series. This causes some difficulty con- And finally, the multi-dimensional regres- nected with the establishment of interrelation sion analysis was performed to assess the structure of these indicators. In addition, many mutual relationship of variables and forecast social and human-initiated events deal with in- their changes. For this we will make use of a complete or limited by nature information and linear equation: a complex structure of interdependencies. That k =+ββ+=ε is why the use of multivariate analysis meth- yxij0 ∑ ij i ,,in1 ,, (2) ods for analysis of the social-economic process j=1 is not only justified but also indispensable. where: β are unknown parameters; εi is a random Multivariate statistical methods from the variable with the mean μ = 0 and variance δ2. majority of possible probabilistic-statistical The statistical methods above helped to ver- models enable us to make a grounded choice ify two hypotheses based on urban models of of a model that suits best to the initial statisti- Rogers (1997) with separated home, work and cal data that characterize the real behaviour of leisure areas. Respective features described the set of objects under consideration and can each area: Effects of Quality of Life on the Price of Real Estate in Vilnius City 301

Hypothesis I. Home, work and leisure en- quality of the environment: close to the city cen- vironments that are typical for a certain resi- tre; clean air, nice environment; safe, good com- dential district highly affect prices of flats in munication with the city centre; well-attended such district and their changes. 5 variables out environment; no noises; no drug-addicts; no al- of those analysed were related to the home en- coholics in sight; no homeless in sight; beauti- vironment: extensive supply of trade services; ful architecture of buildings. Theoretically, all close to a school; close to a kindergarten; close these variables could affect the price of real es- to hospital; close to a pharmacy. The work tate in the residential districts analysed. environment of the district is measured by 2 A great number of variables aggravate the variables in the residents’ survey: good com- data analysis and the determination of quali- munication with the workplace and close to ties and relations typical for a group. The the workplace. These two variables have also number of data was reduced by the principal impact on the price of flats in the district. The component analysis method that reduces the leisure area is evaluated by 4 variables, that number of variables with minimal information is, extensive supply of entertainments; good losses by replacing the information present in facilities for sports; many cultural establish- many qualities with a smaller number of vari- ments; well-attended parks. ables. When analysing the variables, the suit- Hypothesis II. Other factors such as pres- ability of data (variables) to the analysis was tige of the district, nice environment, etc. have checked by the Kaiser-Meyer-Olkin measure of a great impact on the price of flats in a residen- sampling adequacy test (KMO) (Kaiser, 1960). tial district and the pattern of their changes. KMO of the variables of the “home” group The verification of hypothesis II included the is 0.76, meaning that the data are suitable comparison of official statistical data and gen- for the analysis. The analysis of eigenvalues eral data of the residents’ questionnaire survey. identified one distinct factor which explains In the survey, the biggest group of qualities (11) 78% of the variance change (Total Variance consisted of general variables related to the Explained) (Figure 2).

Scree Plot

4

3

2 Elgenvalue

1

0

1 2 3 4 5 Component Number

Figure 2. Screen plot of variables from the “home” group 302 M. Burinskienė et al.

After one significant factor was determined, no alcoholics in sight, no homeless in sight). its factor scores were calculated and replaced This factor is related to health & safety. the initial variables: The second factor consists of 4 variables n (close to the city centre, good communication ' == Fbji∑ jizm,,j 1 ..., , (3) with the city centre, well-attended environ- i=1 ment, beautiful architecture of buildings). This ′ factor reflects the attractiveness of the city where: Fj – factor value j; zi – standardised centre and aesthetics of the district. value of the variable i; bij – coefficient values. Similarly, variables of the “work” area were replaced by one variable which explained 93% 5. Case study. Reflection of of the total variance, while variables of the Vilnius residents’ opinion “leisure” area were replaced by one variable in the price of flats explaining 69% of the total variance. Variables of the “environment” area were 5.1. Assessment of the relation between suitable for factor analysis (KMO = 0.748), the residents’ opinion and changes in however, here two factors were distinguished, the price of real estate in Vilnius explaining 69% of the total variance change. Five factors distinguished from the research The rotation helped to determine the order of data (home, work, leisure, safety & health, the variables (Table 1). centre & aesthetics) were analysed by relating Table 1. Correlation of variables with the factors them with average prices of flats in different distinguished transport districts of Vilnius in 2005–2010. The Rotated component matrix a period of 2005–2010 was special as the econom- Component ic crisis of that time determined the reduction of the “price bubble”. Although prices were go- 1 2 ing down in all Vilnius districts (Figure 3), the Close to the city centre –.311 .780 reduction differed in different districts. Clean air .783 –.150 In solving the question whether changes in the values of one variable tend to be associated Safe .882 –.178 with changes in the others, we can use differ- Good connection with the city –.375 .843 ent statistics. The best-known is Pearson’s centre sample coefficient of correlation Well–attended environment –.070 .673 ∑ XY − mXY No noise .861 .003 ρ= , (4) Xm22− XY22− mY No drug–addicts .851 –.260 ()∑∑()

No alcoholics in sight .694 .047 where: XY; are the means of the variable X No homeless in sight .689 –.482 and Y, respectively. The sample coefficient of multiple deter- Beautiful architecture of .404 .754 buildings mination is denoted by R2 and represents the relationship between more than two variables. Extraction method: principal component analysis. It equals the proportion of the total variation Rotation method: Varimax with Kaiser normalisation. in the values of the dependent variable Y, a Rotation converged in 3 iterations. which is explained by multiple regression of Y As Table 1 shows, the first factor consists of 6 on XX12, , ... Xn , and possibly additional inde- variables (clean air, safe, no noise, no drug-addicts, pendent variables XXnn++1, 2, ... Effects of Quality of Life on the Price of Real Estate in Vilnius City 303

8000,00

7000,00 2 6000,00 T/m L 5000,00 RE, RE,

of of 4000,00

price price 3000,00

erage erage 2000,00 Av

1000,00

0,00 2005 2006 2007 2008 2009 2010

Year Figure 3. Average price of flats in Vilnius in 2005–2010 (JSC “Ober-House” monitoring data)

2 The significance of the unique distribution ∑(YYˆ− ) R2 = , of each indicator is examined by calculating 2 (5) ∑(YY− ) the sample coefficient of partial correlation r. Since the sample under investigation is rath- ˆ where: Y are estimated values of Y and Y is er short the hypothesis H0: r = 0 was tested. the mean. As a test statistic we use Fisher’s statistic 11+ r The coefficient of multiple correlation in- zr( )= ln . dicates a fraction of the total variation in Y 21− r accounted for the regression equation and is The results of the examination of the im- denoted as a square root of the multiple coef- pact of the factors distinguished on the price ficient of determination RR= 2 . of real estate in Vilnius districts in 2005 de- A partial coefficient of determination is a termined that two factors – work and health & measure of the strength of the relationship be- safety – have no impact on changes in prices in tween the dependent variable and independent districts (at the 5% significance level). variable, when the linear effect of the rest of Other factors proved to be significant. the variables is being eliminated. The general The change in prices in 2005 can be fore- formula for this coefficient is presented as: casted using the following regression equa- tion: 22 RR••− r2 = ijkw... ituw... , (6) ij( kn... )•tu ... w 2 y = 2646.15 – 589.1x + 972.18x + 632x ,(7) 1 − Rit• uw... 2005 1 2 3

2 where: R2 are coefficients of multiple determi- where: y2005 – prices of flats in 2005 (LTL/m ); nation. x1 – factor of the home environment; x2 – lei- The square root of the partial coefficient of sure factor; x3 – factor of centre & aesthetics. To verify the significance of the regression determination ri( jk ... n )• tu ... w is called a partial correlation coefficient. equation, the sample coefficient of multiple 304 M. Burinskienė et al.

2 2 determination R is used. Having calculated where: y2009 – prices of flats in 2009 (LTL/m ); the value of this criterion for all the data of 32 – factor of centre & aesthetics. years analysed, we obtain R2 = 0.71. Hence, it may be stated that this model explained 71% I quarter of 2010 (R2 = 0.38) of the changes in the price of district flats in y2010 = 3756.3+886.0x3, (12) 2005. The significance of the regression coef- ficients, i.e. the hypothesis H : β = 0 is veri- 0 where: y2010 – prices of flats in the first quar- 2 bj ter of 2010 (LTL/m ); x – factor of centre & fied by the criterion t = where 3 j T −1 1/2 sXX[( ) ]jj aesthetics. s – the standard error of residuals. When the 5.2. Disclosure of factors “centre & hypothesis Ho: β = 0 is true, statistics t has Student‘s t-distribution with n – k – 1 degrees aesthetics” and “home” by statistical of freedom. For all coefficients |t| > 2.6 and data of the comprehensive plan of the city of Vilnius p < 0.02, therefore, all coefficients are statisti- cally significant at the 5% significance level. The forecast of changes in the price of real The regression analysis method was also estate using statistical data requires the rela- employed in forecasting price changes in 2006, tion between values of the “centre & aesthet- 2007, 2008, 2009 and 2010 with the help of the ics” factor and the data provided in the com- factors above. The equations were as follows: prehensive plan of the city of Vilnius. The arti- cle analysis the following statistical data: 2006 (R2 = 0.38) Population density in districts, population/ha y2006 = 5517.5 – 943.7x1 + 1654.1x3, (8) 2004 Density of workplaces in districts, qty/ha 2 2005 where: y2006 – prices of flats in 2006 (LTL/m ); Length of streets km x1 – factor of the home environment; x3 – fac- tor of centre & aesthetics. Noise in the daytime Dba Area of built-up territories, 2005 % 2 2007 (R = 0.48) Area of built-up territories, 2009 % Area of green territories, 2005 % y2007 = 6773.85 – 1040.2x1 + 1836.5x3, (9) Area of green territories, 2009 % 2 where: y2007 – prices of flats in 2007 (LTL/m ); Distance to the centre km x1 – factor of the home environment; x3 – factor of centre & aesthetics. The analysis of the relation between the “centre & aesthetics” factor and the statisti- 2008 (R2 = 0.52) cal data showed that most of the changes of this factor may be explained by two variables: y2008 = 6711.0 – 857.2x1 + 1712.7x3, (10) distance to the centre dc and the difference of 2 the green area Δ2009-2005: where: y2008 – prices of flats in 2008 (LTL/m ); x – factor of the home environment; x – factor 1 3 x = 1.59 – 0.202d – 0.118 Δ , (13) of centre & aesthetics. 3 c 2009 – 2005 where: x – factor of centre & aesthetics; d – 2009 (R2 = 0.35) 3 c distance to the centre; Δ2009 – 2005 – the differ- y2009 = 4431.4 + 895.4x3, (11) ence of the green area in 2005 and 2009. Effects of Quality of Life on the Price of Real Estate in Vilnius City 305

The equation (13) explains 66.5% of the Cluster 3 consists of eleven Vilnius dis- changes in the factor of centre & aesthetics tricts (most of which are residential districts (determination coefficient is R² = 0.665, all with multi-apartment houses of 40–30 years equation coefficients are significant with the old). Šeškinė is a fairly new suburb located in significance level of 0.001). the north of Vilnius, built in 1977 as a micro- The relations of this equation are illustrat- district. It is a largely residential suburb. ed in the charts below (Figure 4). Karoliniškės is a microdistrict and elderate. Furthermore, the analysis revealed certain The district was started to be built in year values of the “centre & aesthetics” factor (Ta- 1971. Fabijoniškės and Justiniškes are the ble 2). Vilnius transport districts were grouped newest districts of Vilnius municipality, built by these values (Figure 4). in the late 1980s to early 1990s. Almost all Cluster 1 consists of four districts in buildings are Soviet-built human habitats. The the heart of Vilnius: Centre II, Old Town, districts have good a recreation area with a Žvėrynas, Naujamiestis. It is the oldest part little forest, park, and a lake. of Vilnius and the Old Town – one of the larg- Cluster 4 consists of two districts: Pilaitė est surviving medieval old towns in Northern I and ; Naujininkai is one of the Europe. In 1994 the was in- Vilnius’ neighbourhoods that is situated in the cluded in the UNESCO World Heritage List south-west of the city and lies between the Vil- (No. 541) in recognition of its universal value nius Airport and the railway station. and originality. These districts are the oldest Cluster 5 consists of 4 peripheral Vilnius and prestigious neighborhoods in Vilnius. To districts (Dvarčionys, N. Vilnia, etc.), except the northwest lies Vingis Park. It remained Žirmūnai I. is a neighborhood mostly residential with very few industrial en- in eastern Vilnius, situated along the banks terprises. Territory has a great number of gov- of the Vilnia River. The district has a popu- ernment and educational institutions, finance lation of about 32,800. 34% of the population and insurance companies, as well as health are Poles. Žirmūnai is the most populous ad- care institutions. ministrative division (elderate) in Vilnius. It Cluster 2 consists of six Vilnius districts: is also a neighbourhood Vilnius, encompassing Centre I, , Santariškės, Baltupiai, the city district of the same name, built in the Šnipiškės, Žirmūnai II. Antakalnis is one of the 1960s. Žirmūnai was important to the indus- oldest historical suburbs of Vilnius City. It is trial sector in the USSR; since that time, this located in the eastern section of Vilnius, along function has been replaced or supplanted by the right bank of the River. Šnipiškės is newer businesses, including some of Lithua- located on the north bank of the river Neris, nia’s leading companies. it is the site of Vilnius’ new business district. Cluster 6 consists of two most “green” dis- Several skyscrapers and the new Europa Tow- tricts in Vilnius: and A. , that er business center have been erected. Although is, the remaining peripheral Vilnius districts. until recently the area was just a small vil- Verkiai, is an elderate in Vilnius and also the lage north of Vilnius proper, it continues to be name of a settlement, historically situated expanded with plans for modern commercial north of Vilnius but today a part of Vilnius and apartment complexes. Santariškės and city municipality. Verkiai elderate is a north- Baltupiai belongs to Verkiai – the northern- ernmost elderate of Vilnius. It occupies 5,565 most elderate in Vilnius and situated beside ha and has 30,000 inhabitants. It is rich in . architectural monuments and historical sites. 306 M. Burinskienė et al.

2,50 " " 2,00

1,50 1,00

s Aesthetic 0,50 & &

e e 0,00 tr 0246810 12 14 -0,50 en

"C -1,00 -1,50

Faktor -2,00 -2,50 The distance to the centre

2,50 " " 2,00 1,50 1,00

s Aesthetic 0,50 & &

e e 0,00 tr -8 -6 -4 -2 -0,50 02468101214 en

"C -1,00 -1,50 -2,00 Faktor -2,50

The diffrence of the green area in 2005 and 2009

Figure 4. General shift in changes of the “centre & aesthetics” factor, the distance to the centre and green areas

Table 2. Values of the “centre & aesthetics” factor in Vilnius districts

Rating of the Name “Centre & Group Rating of the Name “Centre & Group district aesthetics” district aesthetics” factor factor 1 Centre II 2.08 1 16 Viršuliškės 0.24 3 2 Old Town 1.99 1 17 Fabijoniškės 0.12 3 3 Žvėrynas 1.98 1 18 Vilkpėdė 0.12 3 4 Naujamiestis 1.16 1 19 Justiniškės 0.05 3 5 Centre I 0.97 2 20 0.03 3 6 Antakalnis 0.89 2 21 Lazdynai 0.02 3 7 Santariškės 0.79 2 22 Pilaitė I –0.1 4 8 Baltupiai 0.78 2 23 Naujininkai –0.41 4 9 Šnipiškės 0.56 2 24 Užusienis –0.58 5 10 Žirmūnai II 0.5 2 25 N. Vilnia –0.6 5 11 Šeškinė 0.4 3 26 Žirmūnai I –0.63 5 12 Karoliniškės 0.35 3 27 Ž. Paneriai –0.68 5 13 Valakupiai 0.34 3 28 Dvarčionys –0.72 5 14 Pilaitė II 0.3 3 29 Verkiai –1.39 6 15 Pašilaičiai 0.28 3 30 A. Paneriai –2.02 6 Effects of Quality of Life on the Price of Real Estate in Vilnius City 307

2 Variables connected by statistical relations x1 = –0.681 – 0,037f – 0.0002f , (14) may be also identified in the home environ- ment factor which had a negative effect on where: x1 – factor of the home environment; prices in 2005–2008, i.e. prevented them from f – population density in districts. increasing. This factor is explained the best by The equation (14) explains 70% of the the variable “population density in districts” changes in the factor of home environment (de- from the data provided in the comprehensive termination coefficient is R2 = 0.70, all equa- plan of the city of Vilnius. The said variable is tion coefficients are significant with the signifi- related to the factor by non-linear relations: cance level of 0.05) (Figure 5).

Figure 5. Vilnius districts grouped in six clusters 308 M. Burinskienė et al. environmental" "Home "Home Factor Factor

Population density Figure 6. Relation between population density in districts and the factor of home environment

6. Conclusions 3. Factor analysis was employed to cut the quantity of data. The analysis replaced 1. The assessment of Vilnius by the level of the information present in many quali- sustainability was carried out using the ties by one variable. The analysis of ei- data of a representative survey of the genvalues distinguished one distinct fac- capital residents, where residents used five-point scale to evaluate 22 statements tor in home, work and leisure groups. describing the quality of life. These fac- The biggest group, that is, the group of tors were grouped into three areas char- the quality of the environment, had two acterized by similar qualities. The home factors distinguished. The first one was area was represented by 5 variables, the related to health & safety, while the work area by 2 variables, the leisure area second one reflected the attractiveness by 4 variables. The last group with 11 of the city centre and aesthetics of the qualities consisted of general variables district. Hence, five factors were distin- related to the quality of the environ- guished from the available data: home, ment. work, leisure, safety & health, centre & 2. The authors of the paper put forward 2 aesthetics. These factors were further hypotheses to identify the factors deter- analysed by relating them to the average mining the changes in the price of real price of flats in Vilnius transport districts estate in different Vilnius districts. The in 2005–2010. first hypothesis suggested that the price 4. It was established that in 2005–2010 two of real estate was conditioned by the factors, namely work and health & safe- home, work and leisure environments, ty, had no impact on changes in the price while the second one said that the chang- of flats in Vilnius transport districts. It es in the price of real state are highly was determined that, in different years, affected by the prestige of the residential the price of flats was influenced by the area. following factors: Effects of Quality of Life on the Price of Real Estate in Vilnius City 309

six clusters consisting of the following Year Factor components: 2005 home, leisure, centre & aesthetics •• Cluster 1 consists of four districts in 2006 home, centre & aesthetics the heart of Vilnius: Centre II, Old 2007 home, centre & aesthetics Town, Žvėrynas, Naujamiestis; 2008 home, centre & aesthetics •• Cluster 2 consists of six Vilnius districts: 2009 centre & aesthetics Centre I, Antakalnis, Santariškės, Bal- 2010 centre & aesthetics tupiai, Šnipiškės, Žirmūnai II; (1 qtr.) •• Cluster 3 consists of eleven Vilnius districts (most of which are residential The conclusion was made that two varia- districts with multi-apartment houses bles had the greatest impact on the price of flats in Vilnius residential districts. of 40–30 years old); The first variable was related to the •• Cluster 4 consists of two districts: “home” factor which had negative effect Pilaitė I and Naujininkai; in 2005–2008. The second variable was •• Cluster 5 consists of five peripheral the joint factor of “centre & aesthetics” Vilnius districts (Dvarčionys, N. Vil- with the positive effect which remained nia, etc.); during the changes in the “price bubble” •• Cluster 6 consists of two districts: Ver- (2009–2010). kiai and A. Paneriai, that is, the re- 5. It was determined that in terms of the maining peripheral Vilnius districts. principles of sustainable development the residents usually associated the quality of life with the concept of the quality of References the closest living environment and the Benson, E. D., Hansen, J. L., Schwartz, A. L. and centre & aesthetics, i.e., the concept of Smersh, G. T. (1998) Pricing residential ameni- prestige. According to the residents, ties: the value of a view, Journal of Real Es- the factor “centre & aesthetics” means tate Finance and Economics, 16(1), pp. 55–73. prestigious and high quality living en- doi:10.1023/A:1007785315925 vironment. Such environment is Vilnius Bourassa, S. C., Hoesli, M. and Sun, J. (2004) What’s districts that are the closest to the city in a view, Environment and Planning A, 36(8), centre and characterised by larger green pp. 1427–1450. doi:10.1068/a36103 areas. The factor “home” had the great- Boyle, M. A. and Kiel, K. A. (2001) A survey of est negative effect on districts where the house price hedonic studies of the impact of average population density was 40–120 environmental externalities, Journal of Real per ha. Estate Literature, 9(2), pp. 117–144. after the resultant factors were identi- Brown, S. J. and Liu, C. H. (2001) A global per- fied, the analysis was continued to prove spective on real estate cycles. Springer. or deny the hypotheses put forward. The doi:10.1007/978-1-4419-8642-9 analysis found out that according to the Burinskienė, M. and Rudzkienė, V. (2004) Com- capital residents the price of real estate parison of spatial-temporal regional develop- ment and sustainable development strategy in was mostly effected by the prestige of the Lithuania, International Journal of Strategic Vilnius district – prices remained high Property Management, 8(3), pp. 163–176. next to the protected areas. doi:10.1080/1648715X.2004.9637515 6. The research determined the precise val- Burinskienė, M. and Rudzkienė, V. (2005) Nekilno- ues of the factor “centre & aesthetics”, jamojo turto rinkos kainą įtakojantys veiksniai. and Vilnius districts were grouped into In: International conference proceedings “Park 310 M. Burinskienė et al.

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SANTRAUKA

GYVENIMO KOKYBĖS POVEIKIS NEKILNOJAMOJO TURTO KAINOS VERTINIMUI VILNIUJE

Marija Burinskienė, Vitalija Rudzkienė, Jūratė Venckauskaitė

Subalansuota miestų plėtra apima įvairių veiklos sričių konsensusą, siekiant lygiavertiškai derinti ekono- minių, socialinių, ekologinių veiksnių įtaką planavimui. Dėl skirtingų požiūrių ir interesų planuotojams bei mokslininkams nelengva rasti bendrą, visiems priimtiną požiūrį į miestų plėtros šablonus, derinant formas ir plėtros principus. Straipsnyje analizuojama plėtros konceptų įtaka plėtros formų savybėms, ateities miesto alternatyvių vizijų kūrimui, atitikčiai darnios plėtros principams ir tinkamai gyvenimo kokybei. Kaip vienas pagrindinių socialinių ir gyvenimo kokybę atspindinčių rodiklių nagrinėjama nekilnojamojo turto kaina. Te- orinės nuostatos, pasitelkiant ekspertinį vertinimą, tikrinamos Vilniaus miesto rajonų pavyzdžiu (Lietuvos Respublikos sostinė – 560 tūkst. gyv.), atsižvelgiant į atskirų 40 transportinių rajonų savybes ir formas, gyventojų, darbo vietų skaičių, teritorijos plotą, triukšmo lygį, užterštumą pagal skirtingus komponentus. Darnią plėtrą charakterizuojančių rodiklių kritinėms reikšmėms nustatyti taikomi konceptualaus modelia- vimo principai. Jais remiantis sudaroma sistema, padedanti nustatyti atskirų miesto rajonų plėtros savybes, galimas vystymosi alternatyvas, pasiekiamas leistinas ribines gyvenimo kokybės reikšmes.