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EDITORIAL ...... 2
ARTICLES
The Elasticity of the Office Space Market in Poland: Looking for the London Effect Krzysztof Nowak ...... 5
Seniors’ Housing Preferences in the Rural Areas Anna Jancz ...... 19
A Synthetic Assessment of the Impact of Housing Management Variables on the Level of Development of Communes in Świętokrzyskie Voivodship in the Years 2009-2017 Paweł Dziekański, Urszula Karpińska ...... 36
An Analysis of Municipal Real Estate Resources in Poland Milena Bera, Monika Śpiewak-Szyjka ...... 56
The Evolution of Housing Policy Models in European Countries. A Theoretical Approach Ewa Kucharska-Stasiak, Magdalena Załęczna, Konrad Żelazowski ...... 69
OTHERS
Jubilee Conference of the Department of Investment and Real Estate of the Poznan University of Economics entitled “Real Estate Market Border” (Poznan, 9-10 May 2019) Katarzyna Kania ...... 86
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EDITORIAL
Dynamic changes in real estate worldwide, as well as the recent evolution of real estate economy pose serious challenges for policy makers and a research agenda. Growing empirical evidence coming from emerging and evolving markets questions several well-established theoretical models developed in the US and the EU. The need for quality research on real economics and investment based on unique evidence coming from Central and Eastern Europe requires further evolution of World of Real Estate Journal. Starting from this issue all papers published in the journal are in English to improve the dissemination of the research results. We hope this long-awaited change will foster the future development of WOREJ and attract new authors and readers. The issue contains five papers focusing on the real estate market, regional development and housing policy. Along with the articles, within the conference news section, the reader can find a report on a recent conference held in Poznan. Within this issue we are publishing two research papers focused on the real estate market. Despite differences in the scope and methodology, both papers address interesting and valid questions regarding recent trends on office and housing markets. The first paper entitled "Elasticity of the Office Space Market in Poland: Looking for the London Effect” was written by Krzysztof Nowak and investigates the office space markets in Warsaw, Krakow, Wroclaw and Poznan. The paper uses the Error Correction Model to estimate price and income elasticities of demand for office space. In the second paper entitled "Seniors’ Housing Preferences in Rural Areas” Anna Jancz focuses on demand for housing. Using survey data the author explores stated preferences of seniors in rural communes of the Greater Poland Province, and focuses on migration propensity. Additionally, the paper investigates seniors’ choices regarding location, as well as housing services and quality. Within the second thematic section on urban and regional development we are publishing two other articles related to regional development. In addition to the thematic similarity, these papers share the empirical approach, as they both apply taxonomic methods. The third paper written by Milena Bera and Monika Śpiewak, entitled “An Analysis of Municipal Real Estate Resources in Poland”, investigates similarities and differences among provinces in Poland, using selected clustering methods. The authors calculate the taxonomic development measure and cluster 16 provinces in Poland based on selected indicators related to municipal real estate resources. The fourth paper was written by Paweł Dziekański and Urszula Karpińska. The article entitled “A Synthetic Assessment of the Impact of Housing Management Variables on the Level of Development of Communes in the Świętokrzyskie Voivodship in the Years 2009-2017” uses TOPSIS taxonomic method based on selected housing management indicators. Last but not least, in the review paper entitled "The Evolution of Housing Policy Models in European Countries. A Theoretical Approach” Ewa Kucharska- Stasiak, Magdalena Załęczna and Konrad Żelazowski summarise the evolution of housing policy models in Europe and assess the effects of their implementation. In the regular conference news section Katarzyna Kania reports on the Jubilee Conference of the Department of Investment and Real Estate of the Poznan University of Economics and Business entitled “Real Estate Market Border” held in May 2019. Michał Głuszak Thematic Issue Editor
ADVISORY BOARD EDITORIAL BOARD
Head of the Council: Editor-in-Chief: Prof. Adam Nalepka Michał Głuszak, PhD Cracow University of Economics, Poland Thematic Editor: Prof. Ion Anghel Prof. Andrzej Jaki The Bucharest University of Economics Studies, Agnieszka Małkowska, PhD Romania Prof. Bartłomiej Marona Prof. Stanisław Belniak Cracow University of Technology, Poland Statistical Editors: Prof. Iwona Foryś Prof. Ryszard Borowiecki WSB University, Poland Language Editors: Krzysztof Kwiecień, MSc – (English) Prof. Marek Bryx Małgorzata Maciejas, MA – (Polish) Warsaw School of Economics, Poland Editorial Assistant: Prof. Krystyna Dziworska Karolina Orzeł, PhD WSB University in Gdansk, Poland
Tamara Floricic, PhD Jan Konowalczuk, PhD Eng. Juraj Dobrila University of Pula, Croatia Cracow University of Economics, Poland
Prof. Henryk Gawron Prof. Małgorzata Krajewska Poznan University of Economics and Business, Nicolaus Copernicus University, Poland Poland
Prof. Krzysztof Jajuga Prof. Maciej J. Nowak West Pomeranian University of Technology in Wroclaw University of Economics, Poland Szczecin, Poland
Prof. Ewa Kucharska -Stasiak Łukasz Strączkowski, PhD University of Lodz, Poland Poznan University of Economics and Business, Poland Prof. Ewa Siemińska Nicolaus Copernicus University, Poland Prof. Anna Szelągowska Warsaw School of Economics, Poland Prof. Daniela Špirková Slovak University of Technology in Bratyslava, Anna Wojewnik-Filipkowska, PhD Slovakia University of Gdansk, Poland
Prof. Zygmunt Szymla Konrad Żelazowski, PhD Cracow University of Economics, Poland University of Lodz, Poland
Prof. Anna Zhivkova Gospodinowa Varna University of Economics, Bulgaria
Jarosław Plichta, PhD Foundation of the Cracow University of Economics, Poland
The Elasticity of the Office Space Market in Poland: Looking for the London Effect Krzysztof Nowak1 1 University of Rzeszów, Poland, ORCID: http://orcid.org/ 0000-0003-0543-1670, [email protected]
ABSTRACT Purpose: The article concerns elasticities of demand on the office space market. The article presents indicated in the literature reasons for discrepancies in the magnitude of elasticities of demand on local office space markets. The purpose of the article is to verify the presence of the so -called London effect on the Polish office space market. Methods: The verification of the hypotheses stated in the study was carried out by comparison of the calculated price and income elasticities of demand on the office space markets in Warsaw, Krakow, Wroclaw and Poznan. For the purpose of the study two econometric models were formulated with the use of the Error Correction Model approach. Findings: The London effect was verified on the four major office space markets in Poland. To this end, two research hypotheses were formulated, stating that price elasticity of demand and income elasticity of demand on the office space market in Warsaw are lower in magnitude than on the markets in Krakow, Wroclaw and Poznan put together. Based on the study, the presence of the London effect on the four major office markets in Poland cannot be unambiguously confirmed. Research implications: Dependencies between the rent and the number of employees and the need for office space are different in Warsaw than on the office space markets in Krakow, Wroclaw and Poznan. Keywords: price elasticity of demand, income elasticity of demand, office market, error correction model JEL codes: R30, R33 Article type: research article DOI: 10.14659/WOREJ.2019.108.01 6 The Elasticity of the Office Space Market in Poland: Looking …
INTRODUCTION Price and income elasticities of demand are some of the basic features of office space markets that can indicate adjustments taking place on the markets. Price and income elasticities of demand can however significantly differ among local office markets. The so-called London effect reflects differences in a magnitude of demand elasticities between markets characterized by a vast amount of total office space compared with smaller markets in the UK. This finding can be applied to the office space market in Poland as it is also dominated by capital city. As of the end of 2015 existing stock of office space in Warsaw comprised over 60% of total stock on nine major local office space markets in Poland (Colliers International, 2016)1. This fact makes question regarding the relation between magnitude of elasticities of demand in Warsaw and other local markets so crucial. In the article the price and income elasticities of demand on major polish office space markets are presented. The main goal of the article is to verify the presence of the London effect on examined markets. To achieve that two hypotheses stated: 1. Price elasticity of demand is lower on the office space market in Warsaw than on other major polish office markets; 2. Income elasticity of demand is lower on the office space market in Warsaw than on other major polish office markets. To verify the above hypotheses two Error Correction Models were formulated based on the data from office markets in Warsaw, Krakow, Wroclaw and Poznan. We believe that the article will attract more attention to the analysis of dynamics of the office space market in Poland, which has been noticing rapid expansion since Poland’s accession to European Union in 2004.
LITERATURE REVIEW Price and income elasticities of demand are topics widely used in economics. Price elasticity of demand represents the relative change in demand for a given good as a result of the relative change in its price. In the same manner income elasticity of demand is given by the ratio of the relative change in demand for a given good to the relative change in income (Begg, Fischer & Dornbuch, 1997, p. 134). A low value of elasticity is often stated as one of the major features of real estate market (Kucharska-Stasiak, 2006). This refers to the stability of demand and especially supply in the short term, what is one of the main reasons of disequilibrium on real estate market (Kucharska-Stasiak, 2016,
1 Total stock of office space (thousand sq. m.) as of the end of 2015: Warsaw (4 660), Krakow (672), Wroclaw (596), Poznan (341).
Krzysztof Nowak 7 p. 6) and causes its cyclical fluctuations, which already have been studied for over a century now (Jadevicius, Sloan & Brown, 2017). Few articles on the dynamics of the commercial real estate market, mainly in the USA, were published in the 1980s. Some of them referred also to the issue of demand elasticities. The examples of such studies are the articles on office markets by Hekman (1985), Shilling, Sirmans and Corgel (1987) and Wheaton and Torto (1988). Different results obtained in particular studies, already then, pointed out the discrepancies between local office markets when it comes to demand elasticities. Hendershott, MacGregor and White (2002) based on their study covering retail and office property markets in eleven UK regions reported so-called London effect - „in London, demand elasticities for space with respect to both price (rent) and income are much lower in magnitude” (Hendershott, MacGregor & White, 2002, p. 59) than in other regions. This can be interpreted as an indicator of dependency according to which on property markets with relatively high total supply, demand may have a more rigid character than on smaller markets. In that case on large markets, changes in rent and changes in a number of employees (in FIRE2, business services sectors or total employment) or in other time series representing demand variables, can have a lesser impact on the need for office space. Differences in the magnitude of elasticities of demand were also considered by other authors. Stevenson (2007) conducted the study on four internal London office markets, which indicated that elasticities of demand were much higher in Docklands than in three other London office locations. He attributed that to the fact that in the first half of the 1990s the supply of office space in the Docklands was intensively increased and this site is largely treated as an alternative to the office market in City. McCartney emphasized that differences in magnitude of elasticities between office markets may be caused by various exposure to foreign direct investment. Additionally, he pointed out that crucial role can play the way how tenants perceive a given market, if it is the first-choice market with „distinct locational advantages” without equal replacement, like London or is it a secondary market like Dublin (McCartney, 2012, p. 208). Individual characteristics of particular office market indicating e.g. typical provisions of lease agreements, can in our opinion also play a key role when it comes to demand elasticities.
2 Finance, insurance and real estate.
8 The Elasticity of the Office Space Market in Poland: Looking …
RESEARCH METHODOLOGY In order to determine demand elasticities for major office space markets in Poland, we used an Error Correction Model (ECM) approach. Hendershott, MacGregor and Tse (2002) and Hendershott, MacGregor and White (2002) to the best of our knowledge were first to employ an ECM approach to study office market. ECM usually contains two equations3. The first equation describes relationships between variables in the long run, implicitly in the state of equilibrium. The second equation represents short-term adjustments, when „dynamics of the variables in the system are influenced by deviations from equilibrium” (Enders, 2004, p. 329). The latter equation is based on the increments, of the time series used in the first one. The approach is derived from equating demand to rented office space (Hendershott, MacGregor & Tse, 2002, p. 172):
휆1 휆2 퐷 = 휆0푅 퐸 (1) 퐷(푅, 퐸) = (1 – 푣)푆푈 (2) where: D – demand, R – rent, E – economic activity, λ1 – price elasticity of demand (expected to be negative), λ2 – income elasticity of demand (expected to be positive), SU – total supply of office space, v - vacancy rate.
According to Hendershott, MacGregor and White (2002, p. 62) taking logs and transforming equation (2) gives:
ln푅 = − γ0 + γ1ln 퐸퐴 + γ2ln푆푈 + err (3) where: EA – economic activity, err – error term, in which the Authors included the impact of a vacancy in the absence of data on v.
Hendershott, MacGregor and Tse (2002, p. 173) indicated that price
3 However, they can also be merged. Hendershott, MacGregor and Tse (2002) obtained more econometric plausible results for a single equation ECM than for regular two-equation ECM system, for the City of London office market.
Krzysztof Nowak 9 elasticity of demand is determined as the inverse of supply variable coefficient. In turn, income elasticity of demand shall be computed as a negative value of the coefficient of demand variable divided by the coefficient of the supply variable.
휆1 = 1/훾2 (4)
휆2 = − 훾1/훾2 (5)
Table 1 specifies time series used in the study. Time series mostly used in literature to determine demand variables are those of employment in the finance, insurance, and real estate sectors and the business services sector. However, there are also examples of other time series being used e.g. GNP at the national level (McCartney, 2012), GDP at the local level (Brounen & Jennen, 2009b), gross value added of business services sector (Mouzakis & Richards, 2007). In our study, two different time series were verified regarding the demand variable in both models. Based on the statistical significance and econometric fit of the models obtained4 we decided to use the time series of „Number of workers in the enterprise sector” in Model 1 and „Average employment in the enterprise sector” in Model 2. Time series of total office market stock in sq. m. was used to define the supply variable. As far as rent time series are concerned it should be mentioned that data used in Model 1 concerns geographic area of Krakow, Wroclaw, and Poznan. Data on rent in Model 2 refer to the area of Central Business District. This arises from the fact that we obtained two sets of rent time series for separate areas in Warsaw: Central Business District and Non-Central locations. We believe that the use of the first data set is more appropriate taking into consideration the fact that CBD is the area from which development of the office market in Warsaw began first. Moreover, the time series consists of prime rent in all four cities i.e. apply to space in class A buildings. One of the main features of such buildings is appropriate, central location, which in Warsaw is mainly the CBD area.
Table 1. Specification of time series employed in the study Model Model 1 Model 2 Krakow, Wroclaw, Poznan Geographic area Warsaw (panel data) Time range Q4 2008 – Q1 2016 Q1 2005 – Q1 2016 Demand time Average employment in the Number of workers in series enterprise sector the enterprise sector
4 Level of R2, number of variables statistically significant and a p-value of statistical significance.
10 The Elasticity of the Office Space Market in Poland: Looking …
Supply time Total office market stock in sq m. series Rent time series EUR / sq m. per month Source: own study. For the purpose of the study we formulated two separate ECM models. Long term equations of Model 1 and Model 2 are presented in Table 2. Model 1 was formulated based on panel data for the office markets in Krakow, Wroclaw, and Poznan altogether. Model 2 was built for the office market in Warsaw. The coefficients for parameters of both employment and stock are statistically significant and as expected positive and negative respectively. Values of R2 indicate both models are of a quite good econometric fit. Table 2. Long-term equations for ECM Model 1 and Model 2 Model 1 – Panel Data Model 2 – Warsaw Independent Standard t Standard t variables Coefficient Coefficient Error Statistic Error Statistic Constant 4.3182 0.7149 6.04* -22.9274 2.8759 -7.97* Employment 0.3224 0.1607 2.01** 5.4239 0.6028 9.00* Stock -0.2575 0.0178 -14.50* -0.7519 0.1067 -7.05* Adjusted 0.4069 0.9073 0.7611 0.6482 R2*** Number of 90 45 observation *variable statistically significant at p 0.01, **variable statistically significant at p 0.05, ***in case of Model 1 overall, between and within R2 are reported respectively. Source: own study.
In the appendix Tables from 4 to 7 present results of performed econometric tests. Breitung and ADF tests were conducted to assess stationarity of time series used in long term equations of Models 1 and 2 respectively. Furthermore, stationarity of time series that shall be used to form short term equations also were tested. Cointegration of the variables was verified by Westerlund test in the case of Model 1 and Johansen test in the case of Model 2. Moreover, Hausman test5 which was conducted to specify the appropriate form of Model 1 on panel data, indicated using fixed effects over random effects approach. Results of the tests entitled us to continue the study. The study was performed in the Stata 13.
5The Hausman test was performed in Stata 13 using the additional "sigmamore" command. This was due because the initial results of the Hausman test had an inappropriate, negative value. This may be a result of finite, relatively short time series. In that case, as recommended by Baum, Shaffer and Stillman (2003) the "sigmamore" command was used.
Krzysztof Nowak 11
RESULTS & DISCUSSION Implied price and income elasticities of demand in the case of Model 1 are equal to – 3.88 and 1.25 respectively. In Model 2 the values stand at -1.33 and 7.21. These values refer to possible adjustments that could occur in the studied office markets. If rents in Krakow, Wroclaw and Poznan increase by 1%, then we expect demand for office space to deteriorate by 3.88%. If employment in Warsaw rises by 1% we expect that demand for office space in this city shall increase by 7.21%. Table 3 presents the elasticities of demand from Models 1 and 2 compared to results obtained by other authors. The absolute value of price elasticity of demand for Model 1 together with the number obtained by Brounen and Jennen (2009b), is the highest in the presented group of nine models based on panel data. Most of the results regarding income elasticity of demand stand close to one, what is also the case of Model 1. Price elasticity of Model 2 is close in magnitude to results reported by McCartney (2012) and Stevenson (2007), i.e. studies of office markets in Dublin and in London’s Docklands respectively. In other papers price elasticity of demand ranged between -0.55 and -0.19, except for Ke and White (2013) who obtained the highest absolute values. When it comes to income elasticity of demand in the case of models based on regular time series, most authors presented values between 0.31 and 1.70. Once again numbers of Ke and White (2013) for Beijing and Shanghai, as well as Stevenson (2007) for London’s Docklands stood out. Nevertheless, the income elasticity of demand on the Warsaw office market is of the definitely highest magnitude. As expected, price elasticities of demand in both Polish Models are negative while income elasticities are positive. The differences in the magnitude of the elasticities between studied markets may be a little surprising. Model 1 is characterized by almost three times higher the absolute value of price elasticity of demand than Model 2. According to that, the first hypothesis of the study can be confirmed. However, when it comes to income elasticity of demand, the results differ from what was previously assumed. The value of income elasticity of demand is almost six-time higher in magnitude in Warsaw than on regional markets. This means that the second hypothesis cannot be confirmed. Therefore, also the London effect on the four major office space markets in Poland, cannot be fully proved. Thus, the occurrence of the London effect may be ascribed to the specificity of the office market in the UK and especially in London. Cheshire and Hilber (2008) listed quite a long list of supply constraints mainly of an administrative character, which can extend both time and costs of the investment process in the UK. Jones and Orr (1999) presented an interesting
12 The Elasticity of the Office Space Market in Poland: Looking … study on the impact of the constraints on changes in rent on office, retail and industrial space market. Jones and Orr (2004) argued that apart from the economic situation the office market is significantly affected by restrictions at the local level such as planning restrictions, building density, etc. In addition, areas of concentration of office space supply in London are largely built-up. These two factors could limit the inflow of new office space to the London market what together with stable, relatively high need for office space may rigidify demand. As a result, demand response to changes in the economic environment may be slow and of less magnitude. Another reason can be long term lease contracts in London. „Leases in the London office market are long (typically 25 years up to the early 1990s, falling to an average of 10–15 years by the end of the analysis period6) with five-year rent ‘upward only’ reviews (to market or unchanged depending on which is higher) and penalties that hamper lease surrender or sub-letting. Thus, occupied space is unlikely to adjust quickly to changes in either employment or market rent” (Hendershott, Lizieri & MacGregor, 2010, p. 83). On the polish office space market lease contracts are typically concluded for a few years. Lack of confirmation of the London effect may also suggest that it may be reserved for countries with a long history of office market. The office market in Poland started to develop after the political and economic changes initiated in 1989. The great impulse for development was Poland's accession to the EU in 2004 and related to it inflow of foreign investors. Although in the last few years the office market in Poland has been experiencing a rapid pace of development, it is still at its infancy in relation to main western European markets. This may affect the functioning of the market in terms of access to professional services and the institutional side of the market and by that also affect the adjustments taking place on it.
6 i.e. 2006.
Krzysztof Nowak 13
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Tabl *total supply *total of officespace as supply variable, **supply of office rented space as supply variable,***period 1977 1977 ****period Source: ownstudy
14 The Elasticity of the Office Space Market in Poland: Looking …
CONCLUSION Based on the study, the presence of the London effect on the four major office markets in Poland cannot be unambiguously confirmed. The absolute value of price elasticity of demand is higher in magnitude in case of Model 1 based on the office markets in Krakow, Wroclaw and Poznan, than in case of Model 2 for Warsaw. However, when it comes to income elasticity of demand the results are opposite. The main reasons for such results in our opinion are different characteristics between office markets in Poland and the UK and the specificity of the London market itself. Comparing magnitude of elasticities obtained one should bear in mind that time range of data used in the study is shorter in case of Model 1 than in Model 2. The three regional markets are on the earlier stage of development than Warsaw office space market, what plays a key role as far as availability of reliable data is concerned. Moreover, different time series have been used to determine demand variables in both models. In our opinion future studies shall account for discrepancies in demand elasticities between various segments of office buildings based on their particular characteristics e.g. different classes of buildings, different energy efficiency characteristics of buildings, etc.
REFERENCES Begg, D., Fischer, S., & Dornbuch, R. (1997). Mikroekonomia. Warszawa: PWE. Brounen, D., & Jennen, M. (2009a). Asymmetric Properties of Office Rent Adjustment. Journal of Real Estate Finance and Economics, 39(3), 336-358. https://doi.org/10.1007/s11146-009-9188-9. Brounen, D., & Jennen, M. (2009b). Local Office Rent Dynamics. A Tale of Ten Cities. Journal of Real Estate Finance and Economics, 39(4), 385-402. https://doi.org/10.1007/s11146-008-9118-2. Cheshire, P.C., & Hilber, C.A. (2008). Office Space Supply Restrictions in Britain: the Political Economy of Market Revenge. The Economic Journal, 118(529), 185-221. https://doi.org/10.1111/j.1468-0297.2008.02149.x. Colliers International (2016). Market Insights. Polska, raport roczny 2016. Retrieved on 10/09/2019, from: http://www.colliers.com/pl-pl/- /media/files/emea/poland/reports/2016/Colliers_International_Raport_R oczny_2016.pdf?smclient=4cfe4fad-00cc-481e-ac9f- 87660f616600&timeZoneId=CET.
Krzysztof Nowak 15
Englund, P., Gunnelin, Å., Hendershott, P.H., & Söderberg, B. (2008). Adjustment in Property Space Markets: Taking Long-Term Leases and Transactions Costs Seriously. Real Estate Economics, 36(1), 81-109. https://doi.org/10.1111/j.1540-6229.2008.00208.x. Enders, W. (2004). Applied Econometric Time Series. USA, NY: John Wiley & Sons. Farrelly, K., Głuszak, M., & Matysiak, G. (2014). Panel Modelling of European Office Market Rent Dynamics and Asymmetries. European Real Estate Society Conference: Bucharest, Romania. Hekman, J.S. (1985). Rental Price Adjustment and Investment in the Office Market. AREUEA Journal, 13(1), 32-47. https://doi.org/10.1111/1540- 6229.00339. Hendershott, P., Lizieri, C., & MacGregor, B. (2010). Asymmetric Adjustment in the City of London Office Market. Journal of Real Estate Finance and Economics, 41(1), 80-101. https://doi.org/10.1007/s11146-009-9199-6. Hendershott, P.H., MacGregor, B.D., & Tse, R. (2002). Estimation of the Rental Adjustment Process. Real Estate Economics, 30(2), 165-183. https://doi.org/10.1111/1540-6229.00036. Hendershott, P.H., MacGregor, B.D., & White, M. (2002). Explaining Real Commercial Rents Using an Error Correction Model with Panel Data. Journal of Real Estate Finance and Economics, 24(1-2), 59-87. https://doi.org/10.1023/A:1013930304732. Ibanez, M., & Pennington-Cross, A. (2013). Commercial Property Rent Dynamics in U.S. Metropolitan Areas: An Examination of Office, Industrial, Flex and Retail Space. Journal of Real Estate Finance and Economics, 46(2), 232-259. https://doi.org/10.1007/s11146-011-9347-7. Jadevicius, A., Sloan, B., & Brown, A. (2017). Century of Research on Property Cycles: A Literature Review. International Journal of Strategic Property Management, 21(2), 129-143. https://doi.org/10.3846/1648715X.2016.1255273. Jones, C., & Orr, A. (1999). Local Commercial and Industrial Rental Trends and Property Market Constraints. Urban Studies, 36(2), 223-237. https://doi.org/10.1080/0042098993574.
16 The Elasticity of the Office Space Market in Poland: Looking …
Jones, C., & Orr, A. (2004). Spatial Economic Change and Long-Term Urban Office Rental Trends. Regional Studies, 38(3), 281-292. https://doi.org/10.1080/003434042000211079. Ke, Q., & White, M. (2013). A Tale of Two Chinese Cities: the Dynamics of Beijing and Shanghai Office Markets. Journal of Real Estate Portfolio Management, 19(1), 31-47. Kucharska-Stasiak, E. (2006). Nieruchomość w gospodarce rynkowej. Warszawa: PWN. Kucharska-Stasiak, E. (2016). Rynek nieruchomości w procesie powstania nierównowag makroekonomicznych. Świat Nieruchomości, 96, 5-9. https://doi.org/10.14659/worej.2016.96.01. McCartney, J. (2012). Short and Long-Run Rent Adjustment in the Dublin Office Market. Journal of Property Research, 29(3), 201-226. https://doi.org/10.1080/09599916.2012.689990. Mouzakis, F., & Richards, D. (2007). Panel Data Modelling of Prime Office Rents: A Study of 12 Major European Markets. Journal of Property Research, 24(1), 31-53. https://doi.org/10.1080/09599910701297713. Shilling, J.D., Sirmans, C.F., & Corgel, J.B. (1987). Price Adjustment Process for Rental Office Space. Journal of Urban Economics, 22, 90-100. https://doi.org/10.1016/0094-1190(87)90051-9. Stevenson, S. (2007). Exploring the Intra-Metropolitan Dynamics of the London Office Market. Journal of Real Estate Portfolio Management, 13(2), 93-98. Wheaton, W.C., & Torto, R.G. (1988). Vacancy Rates and the Future of Office Rents. AREUEA Journal, 16(4), 430-436. https://doi.org/10.1111/1540- 6229.00466.
Krzysztof Nowak 17
APPENDIX Table 4. Results of Breitung stationarity test for variables of Model 1 Variable Statistic p value Number of laggs Rent 2.2470 0.9877 0 Employment 0.4878 0.6872 0 Stock 4.9550 1.0000 0 RentC7 -2.9163* 0.0018 0 Employment C8 -2.0321** 0.0211 1 StockC -2.6816* 0.0037 0 *Time series stationary at p 0.01, **Time series stationary at p 0.05, ***Time series stationary at p 0.1. Source: own study. Table 5. Results of Hausman test specifying form of the long term equation (random effects or fixed effects) and Westerlund cointegration test, for Model 1 Hausman test Westerlund test chi2(2) Prob>chi2 Statistic Value Z value P value 47.17 0.0000* Gt -3.593 -3.656 0.000** Ga -12.924 -2.249 0.012*** Pt -5.636 -3.020 0.001** Pa -9.562 -2.496 0.006** *Fixed efects form of the equation is appropriate for p 0.01, **Cointegration for p 0.01, ***Cointegration for p 0.05; Westerlund test performed for zero lags. Source: own study. Table 6. Results of the ADF stationarity test for variables of Model 2 Test Critical Critical Critical Number Variable statistic value 1% value 5% value 10% of lags Rent -1.571 -3.621 -2.947 -2.607 0 Employment -1.943 -3.621 -2.947 -2.607 0 Stock -0.904 -3.621 -2.947 -2.607 0 RentC -3.453** -3.628 -2.950 -2.608 0 EmploymentC -4.575* -3.628 -2.950 -2.608 0 StockC -7.379* -3.628 -2.950 -2.608 0 *Time series stationary at p 0.01, **Time series stationary at p 0.05, ***Time series stationary at p 0.1. Source: own study.
7Letter „C” for the next three variables means time series that shall be used in the short-term equation of the ECM model, formulated as changes of the time series used in the long-term equation. 8For the EmploymentC variable, the Breitung test was carried out including one lag. This is due to the fact that it contains aggregated series of total employment, which are not limited to selected sectors. Accordingly, this series may contain potential deterministic trends and impact of other factors.
18 The Elasticity of the Office Space Market in Poland: Looking …
Table 7. Results of Johansen cointegration test for variables of long term equation in Model 2 Number of Maximum Critical Trace Critical Critical Critical cointegrating eigenvalue value statistics value 5% value 1% value 1% vectors statistic 5% 0 56.7076 24.31 29.75 52.0300 17.89 22.99 1 4.6776*,** 12.53 16.31 4.5044*,** 11.44 15.69 2 0.1731 3.84 6.51 0.1731 3.84 6.51 *One cointegrating vector at p 0.01, **One cointegrating vector at p 0.05, ***One cointegrating vector at p 0.1. Johansen test for one lag. Source: own study.
Seniors’ Housing Preferences in the Rural Areas Anna Jancz1 1Poznań University of Economics and Business, Poland, [email protected]
ABSTRACT Purpose: To identify seniors' housing preferences Methods: A survey questionnaire conducted among seniors in rural communes of the Greater Poland Province Findings: What was characteristic of the group under the survey was their accommodation in single-family houses. Respondents were not inclined to migrate and they preferred to stay in their present house or flat and adapt it to suit their future needs connected with old age. People who would decide to move would mostly choose to live in a detached/terraced house (up to 100 m2) or a two-room flat in a low block of flats (up to four floors). As regards financing, they would prefer to exchange their old flat for a new one or sign a long-term rent agreement. Respondents would like to live in the countryside or in the outskirts of the city. They would also choose a location not too distant from their present place of living due to their family relations and neighbour friends. Respondents would also consider moving to a building adapted to the needs of senior residents – flats with facilities for disabled people and services available for seniors. The most valued services were medical nursing and rehabilitation care. Housing preferences of elderly people living in a rural area were difficult to determine, especially among men. During meetings of seniors those were women who participated most often. The research was time-consuming and expensive. The housing preferences survey for seniors should be extended to studies in other voivodships. Research implications: The research can be used by local governments and developers to create an offer of senior housing in Poland for different groups of elderly people. Keywords: housing needs; housing preferences; senior; housing market JEL codes: R31 Article type: research article DOI: 10.14659/WOREJ.2019.108.02 20 Seniors’ Housing Preferences in the Rural Areas
INTRODUCTION In Poland, people at the age of 60 or older account for 24% of the population (2019). According to the demographic forecast prepared by the Main Statistical Office (Główny Urząd Statystyczny – GUS) in 2009, the proportion of people of over 60 years of age will probably exceed 30% of the total population in 2035. At the same time, significant social changes are observed. The model of multi-generational family is disappearing and seniors may increasingly often become single people in need of support. For the sake of the research, senior (elderly person, person of advanced age) is a person who is 60 or more years of age. The same threshold was established by the act of 11 September 2015 and the resolution of the Council of Ministers no. 238 of 25 December 2013. The ongoing transformation of the society may lead to the need for creating a special housing offer for seniors, which would ensure them access to additional services, especially healthcare. This offer should, first of all, take into consideration seniors’ housing expectations and preferences. Thus, the housing preferences of seniors should be examined so that the real estate market in Poland could meet their expectations. The article presents a selected issue of satisfying seniors’ housing needs in the context of the real estate market. The aim of the paper was to identify seniors’ housing preferences. To this end, a survey questionnaire was used. The study was carried out among senior residents of rural areas – living in village of no more than five thousand inhabitants in Greater Poland Province. These villages are located in typically rural areas outside the influence of large cities. Nevertheless, the research findings reflect the housing preferences of seniors from a typical rural area in Greater Poland Province. It can be assumed that the group of respondents under study will not be interested in changing their present accommodation in order to improve their housing conditions (life model, tradition, cultural factors – typical for people from rural areas in Poland).
SENIOR AS A CONSUMER ON THE REAL ESTATE MARKET The issue of senior people’s housing preferences has rarely been the subject of research in Polish literature. The most updated information concerning the accommodation of elderly rural residents is partly provided by the National Census of Population and Housing (2011). On the basis of the census, the document Flats. The National Census of Population and Housing of 2011 was created (2013). Other studies in this respect include: Polacy wobec własnej
Anna Jancz 21 starości (Centre for Public Opinion Research – CBOS, 2012), Aspekty medyczne, psychologiczne, socjologiczne i ekonomiczne starzenia się ludzi w Polsce (Polish Gerontological Society, 2012), Raport na temat sytuacji osób starszych w Polsce (Błędowski et al., 2012), Budownictwo senioralne w Polsce. Perspektywy rozwoju (REAS, 2015), Portret społeczno-demograficzny seniorów (2016), Ekspertyza dotycząca zbadania potrzeb mieszkaniowych seniorów oraz wskazania istotnych problemów i deficytów w obszarze mieszkalnictwa senioralnego (2017). The last of the above studies was a key document contributing to the formulation and updating of the housing policy in Poland as it also took account of elderly people. What is more, local studies of seniors housing conditions have also been conducted (Bamzar, 2019; Chen, Tsaih & Li, 2019; Lubik & Kosatsky, 2019; Pearson et al., 2019; Saisan & Russell, 2012; Riedy et al., 2019; Tanaś, Trojanek & Trojanek, 2019). The research methods used were surveys carried out among both seniors and people who deal with the property market (with used statistical methods), identification senior buyers’ revealed preferences, case study for house of residence. An impulse to solve this problem was the research gap in Polish literature concerning the housing situation of the elderly. Previous studies did not take into consideration the detailed profile of the elderly, including such factors as the place of living (countryside/city), monthly disposable income, gender, education, etc. Thus, the housing preferences of one group of seniors – the inhabitants of rural areas in Greater Poland Voivodeship were identified. To this end, a survey, with the application of a questionnaire, was carried out. 312 people at the age of 60 or more participated in the study. The survey was carried out from September 2017 to April 2018. It took place in 13 different rural communes (Blizanów, Brodnica, Chrzypsko Wielkie, Gniezno (community), Koźminek, Krzyż Wielkopolski, Łobżenica, Miłosław, Nekla, Ostrów Wielkopolski (community), Sieraków, Sośnie, Witkowo). The respondents filled in the questionnaire without any help, during meetings organized by senior residents’ clubs or other commune organizations. The study involved slightly more women than men (58% women). Almost 60% of respondents were people under the age of 70. Every third respondent indicated that he had primary vocational education. Nearly 70% of respondents indicated that they are a pensioner. About 11% of respondents reported that they are a farmer and 8% of respondents that they work (full- time, entrepreneur). When describing elderly people’s households, one should note that they do not follow consumption trends as often as young people do, and they rarely use luxury goods. However, the constant technological progress and the ongoing globalization process makes it necessary and, first of all, possible for
22 Seniors’ Housing Preferences in the Rural Areas various social groups, including seniors, to use specially designed products and services which make their life easier (Bylok, 2006, pp. 92-93). For example, flats and houses are being adapted to the needs of the elderly and provide access to a variety of services, such as nurses or shopping delivery. The existing body of literature concerning the purchasing behavior of seniors provides information that they constitute a group that is the least interested in using innovative goods and services available in the market. This opinion is influenced by the stereotypical image of Polish people of advanced age (Carrigan & Szmigin, 2000). They are considered to be ailing people, who lead an inactive lifestyle and have no passion and are tired of living. Moreover, their financial situation is usually unsatisfactory (Badowska & Rogala, 2015, p. 13). At present, this situation is changing and may change even more. It is often dependent on the senior’s age, place of living, state of health, social status, level income and the amount of accumulated capital (Badowska & Rogala, 2015, p. 13). More and more seniors are trying to lead an active lifestyle and participate in the life of their local community. They are interested in new technologies and their application in everyday life, e.g. online shopping with home delivery. This change in elderly people behavior is caused by two factors. The first of them is progress in medicine, which helps people to remain healthy and makes it possible to treat chronic diseases more effectively. The other aspect is the role of the senior from the sociological perspective – younger generations do not see the elderly as authority any longer. Thus, the elderly person aims to prevent the phenomenon of social inclusion or to minimize its symptoms (Szewczyk, 2017, pp. 45-46; Rogala & Fojutowski, 2014, p. 131).
Generally, seniors prefer living in the current home and live there for as long as possible (Kramer & Pfaffenbach, 2016; Andersson & Abramsson, 2012). It is associated with attachment to the apartment, neighbours, family and low mobility (Vasara, 2015; De Jong et al., 2012). This situation is called ageing in place (Davey et al., 2004, REAS, 2015). Developers in Poland do not have much to offer to senior buyers. New flats and houses are usually suited to young people’s needs. This is reflected in the visualizations of housing states and their descriptions in advertising brochures. Images often show kids playing in front of a block of flats or young married couples walking on the pavement. The description often includes information about a playground or a nearby kindergarten. This is due to elderly people’s lack of interest in purchasing flats in the real estate market. Young buyers are a majority among buyers both in the primary and secondary market. This is confirmed by various studies, which
Anna Jancz 23 reveal that people under the age of 35 constitute the main group of the buyers of flats. In their research, Trojanek and Trojanek (2012) showed that it was people at the age of 25-30 who bought the biggest number of flats in the secondary market in Poznań in the years 2010-2011. People of 60 or more years of age, in turn, accounted for a small percentage. According to the study conducted by Celka and Strączkowski (2017) in the primary market in Poznań, people below 35 years of ages accounted for as much as 41%, while those at the age of 36-45 represented 32% of the total number of the buyers of flats. The proportion of people who have turned 55 was only 10% of the total. Results of research in the secondary market in Poznań in the years 2010-2016 (Tanaś, Trojanek & Trojanek, 2019) indicated that seniors most often bought flats of 30-60m2, often buy relatively new flats (up to 10 years old) or those located in the oldest buildings (over 50 years old), usually paid in cash. Seniors in Poland don’t live in modern building. Only 5% seniors live in modern apartments built after 1989. About 30% of seniors live in houses built in 1945-1970 (in cities this proportion is much higher). Importantly, only 1% of seniors in Poland live in nursing homes (REAS, 2015). Senior housing should be characterized by special solutions due to the mobility restrictions of the elderly. Currently, apartments are often not suitable for seniors. The fundamental goal of the implemented housing solutions for the elderly is to meet the universal needs of seniors, to which they belong (REAS, 2015): mobility accessibility, financial affordability, social integration, intergenerational solidarity, support, care. In turn, lack of adaptation is understood by: • existence of architectural barriers of the flat, such as e.g. thresholds in the doors, bathtub in the bathroom instead of a shower, • inappropriate height of furniture cabinets; • too large an area of the apartment and, consequently, high costs of maintaining the property, • location of the dwelling in the building, the floor too high and no elevator, • narrow staircase, • the presence of stairs at the entrance to the building.
24 Seniors’ Housing Preferences in the Rural Areas
SENIORS’ HOUSING PREFERENCES IN THE LOCAL REAL ESTATE MARKET The chapter discusses the results of research on housing preferences of seniors from rural areas of the Greater Poland Province. Most elderly people under study indicated that they were not willing to change of their place of residence. Almost 69% of the respondents said that they did not want to move from their current flat of the house. There were people, however, who would like to change their place of living (20.19% of the respondents) or were hesitant in this respect (11.22% of the total). It was people who ran a single-person or two-person households that were interested in finding a new flat or house. Those with the highest disposable income, and who lived together with their family (households with three or more people), usually preferred to stay in their current location. A decision to change a place of living may be influenced by a number of different factors. For the sake of this study, eight of them were distinguished. They are as follows: • the conditions of the transaction – the price of purchasing/renting/exchanging a flat, costs of purchasing/renting/exchanging a flat, and a method of payment required when concluding the transaction, • transport – location in a town with good transport links between the place of living and places needed by the buyer (efficient public transport), • type of building – defined by the type of housing (single-family building, multi-family building), the design and size of the building, construction technology, • facilities – in other words, conveniences in the building, such as monitoring, security service, a lift, a separate outdoor or indoor car park, and friendly shared space, e.g. a wide, well-lit stairwell, benches, a playground, the green belt, • the characteristics of the flat – floor area, the number of rooms, the layout of the flat, location in the building, the floor on which it is located, which directions of the world the windows face, as well as additional spaces, such as a balcony, a terrace, storage area or a cellar, • location – limited to the area of the housing estate, sometimes the street, and evaluated with reference to the size of green areas, recreational places, safety in the district, traffic intensity, as well as the view from the windows and the type of neighborhood with respect to the function of the area and the human factor,
Anna Jancz 25
• the infrastructure of the housing estate – defined by ensuring proper technical infrastructure – surfaced access roads, pavements - and service-commercial infrastructure – the sufficient number of shops, pharmacy, health center, church, cinema, etc., • social factors – interpreted as one’s attachment to the neighborhood and friends/neighbours living there and a barrier to accepting new neighbours, as well as closeness to one’s family when a move means increasing a distance to them. The respondents were also asked to assess the significance of each factor in terms of their impact on the choice of a new flat. Since their answers ranged from very important, though important, don’t know, rather unimportant to definitely unimportant, the ranking of factors has been prepared. To this end, weighted average was used, defining weights as numbers from five to one, according to the adopted scale of answers. The highest weight was attributed to the answer that a given factor is very important for the respondent. In turn, the lowest weight was assigned to the answer in which the respondent showed the lowest approval of a given factor. Figure 1 shows the order of factors from the most important to the least important when it comes to changing the place of living.
3.98 3.93 3.83 3.77 3.77 3.68 3.63
3.41
Figure 1. The significance of factors from the point of view of the choice of a new housing unit Source: Author’s own work on the basis of the study conducted. Among the abovementioned determinants, the respondents found the location of a new flat to be the most important (the significance of the factor
26 Seniors’ Housing Preferences in the Rural Areas was 3.98). The transaction conditions were considered to be almost as important (the significance of the factor at the level of 3.93). People in the group under study also paid attention to the vicinity of family and friends. The type of building was found to be the least relevant by the respondents (the significance of the factor was 3.41). In the next question, the respondents were asked to indicate the preferred type of housing, the number of rooms and the floor area of a flat in case they had to move. Their answers are presented in table 1. Table 1. Preferred housing conditions – type of housing, number of rooms, size of flat The percentage of Feature The variant of the feature indications low block of flats (up to four floors) 31.41% high block of flats (above four floors) 0.64% Type of tenement house 1.28% housing single-family house 52.88% terraced house 11.54% other 2.24% one room 1.92% Number of two rooms 46.47% rooms three rooms 30.77% four or more rooms 20.83% up to30 m2 4.81% 31-40m2 16.35% 41-50 m2 25.96% Size of a flat 51-60 m2 20.83% 61-100 m2 23.72% 101 m2 and more 8.33% Source: Author’s own work on the basis of the study conducted. Slightly more than half of the respondents would be willing to live in single-family house of the floor space of up to 100m2, if they were to move. Almost one third of senior people under study would decide to live in a low block of flats (up to four floors). When they chose a housing unit in a multi- family building, the respondents preferred two-room flats. Elderly people in the countryside were not interested in living in multi- family building – a high block of flats with more than four floors or a tenement house. They did not want to move to a small flat of up to 30m2 either. What determined the respondents’ choice of a new flat was its location: whether it was situated in the city center, outside the center or in a
Anna Jancz 27 rural area. As many as 46.15% of the people under survey would not like to change the place of residence and prefer living in the countryside. Almost 40% of the respondents indicated that they would choose a location outside the city center, while only 13.78% would decide to find accommodation in the city center. Then, all the participants of the survey were asked whether they would be interested in moving to a residential building designed exclusively for seniors, if they had to change a place of living (Fig. 2).
9.62% 17.95%
30.77% 16.35%
25.32%
definitely yes rather yes difficult to say rather not definitely not
Figure 2. Answers to the question: Would you consider moving to a flat for seniors in the future? Source: Author’s own work on the basis of the study conducted. The respondents differed in their views concerning the potential move to a building designed for seniors. The largest group indicated that they would rather not live in this type of building (30.77%). It should be pointed out, however, that one quarter of the respondents had no definite opinion on this matter, and slightly more than one third admitted they would consider this option if they had to seek new accommodation. The next question concerned a method of financing the purchase of a new housing unit. The respondents’ answers are presented in Figure 3.
28 Seniors’ Housing Preferences in the Rural Areas
3.85%
12.82%
7.69% 31.73%
43.91%
cash mortgage loan exchange for an old flat long-term rent other, what?
Figure 3. Answers to the question: How would you like to finance the purchase of a new flat? Source: Author’s own work on the basis of the study conducted. The respondents usually indicated that if they had to move to another flat, especially the one in a building designed for seniors, they would prefer to exchange their old flat for a new one or they would like to choose the option of long-term rent. They were generally reluctant to buy a new flat for cash (12.82% of the total) or to take out a mortgage loan (7.69% of the respondents). Relatively low interest in the mortgage loan is associated with a barrier to getting this loan for older people. Some of the respondents said that they would use the help if their children or a social organization, while others emphasized that they would not choose any of these methods as they ruled out the necessity of moving out. Table 2 shows the current types of flats for seniors. They are both presented in the literature on the subject and physically available, mainly abroad. The respondents were asked to indicate the preferred type of housing units for elderly people that they would be inclined to choose if they had to move. Their preferences in this respect were included in table 2 in the column entitled the variant of the feature and the percentage of indications.
Anna Jancz 29
Table 2. The preferred form of accommodation in the respondents’ opinion The Feature The variant of the feature percentage of indications the old flat adapted to suit the needs of an elderly 37.18% person (facilities for disabled people) a new flat in a multi-family buildings with facilities for 8.01%
disabled people a flat in the building with exclusively flats designed for 11.86% seniors, with facilities for disabled people, but without additional services a flat in the building with exclusively flats designed for 18.27% seniors, with facilities for disabled people and with additional services a terraced or detached house adapted to suit the 15.06% needs of an elderly person a flat with improved standard adapted to suit the 4.17% needs of an elderly person (facilities for disabled
Preferred form of a flat for seniors for flat a of form Preferred people) a shared room in a nursing home 0.96% a single room in a nursing home 3.21% other answers 1.28% Source: Author’s own work on the basis of the study conducted. In their earlier answers, the respondents stressed that they would not like to move when they become old. Thus, most of them indicated that they would like to stay in their current flat and only adapt it to suit the needs of an elderly person (facilities for disabled people). As many as 18.27% of the seniors under study said that they would be willing to move to a building with facilities for disabled people with additional services. The respondents also often indicated that they would prefer living in a terraced or detached house adapted to the needs of elderly or disabled people. Very few people admitted that they would like to share a room with another person in a nursing home. As far as the other answers are concerned, the respondents usually indicated that they would live with their children or expressed their indecision. The last question concerned the type of services that could be additionally provided in the buildings for seniors. The respondents were supposed to choose from among the following kinds of services: • catering, which would involve a canteen or a restaurant in the building or food delivery to the flat,
30 Seniors’ Housing Preferences in the Rural Areas
• medical-nursing – medical services consist in ensuring medical care by doctors and nurses for seniors living in the building, while nursing services involve help in everyday activities provided by specially trained people, e.g. cleaning a flat, hanging curtains or taking out the rubbish, • rehabilitation – the possibility of using rehabilitation services provided exclusively for the residents of the building, • culture – providing cultural offer in the building, including leisure activities for seniors. For example, a senior club may organize a trip to the cinema or the theatre, • transport – the possibility of transporting people, e.g. by taxi, using the caretaker’s help, • hairdresser and beautician – services of a hairdresser and a beautician provided to the residents of the building, with the possibility of having the service done in the flat, • security – hiring a security company, monitoring, a fence around the building and the common area of the property, • help in shopping – the possibility of having the caretaker order shopping and the establishment of a convenience store or a pharmacy in the premises. The respondents were asked to assess the significance of each of the above services in the housing construction for seniors. To this end, they evaluated each factor on the scale: very important, important, I have no opinion, rather unimportant, definitely unimportant. The most necessary services were selected on the basis of weighted average (just like it was done when ordering the determinants of the purchase of a new flat). The highest weight was assigned to the answer “very important”. The lowest weight, in turn, was attributed to the answer “definitely unimportant”. Figure 4 shows the types of services from the most to the least needed in the housing construction for seniors.
Anna Jancz 31
3.82 3.73 3.35 3.14 2.94 2.85 2.75 2.72
Figure 4. The types of services ranked from the most to the least important in the housing units where the respondents would move to Source: Author’s own work on the basis of the study conducted. The respondents most frequently indicated that they would appreciate access to medical and nursing services in their new place of living (the weight for medical and nursing services was 3.82). They also found rehabilitation services important (in this case their weight was at the level of 3.73). The respondents pointed out that, when choosing a new flat, they did not really care whether it was monitored or whether the services of hairdressers or beauticians were provided.
CONCLUSION The article discusses the housing preferences of seniors living in the rural areas of Greater Poland Province. What was characteristic of the group under the survey was their accommodation in a single-family house. The respondents were not inclined to migrate and they preferred to stay in their present house or flat and adapt it to suit their future needs connected with the old age. People who would decide to move would mostly choose to live a detached/terraced house (up to 100m2) or a two-room flat in a low block (up to four floors). As regards financing, they would prefer to exchange their old flat for a new one or sign a long-term rent agreement. The respondents would
32 Seniors’ Housing Preferences in the Rural Areas like to live in the countryside or in the outskirts of the city. They would also choose a location not too distant from their present place of living due to their family relations and neighbour friends. The respondents would also consider moving to a building adapted to the needs of senior residents – flats with facilities for disabled people and services available for seniors. The most valued services were medical-nursing and rehabilitation care. It should be pointed out that the seniors under survey are not ready to change their place of residence, which may be culturally determined. The ageing of the society, the vanishing of the multi-generational family model, and lack of family care may lead to their growing share in the real estate market in future. The research was carried out in the rural area of the selected province. The housing preferences survey for seniors should be extended to studies in other voivodships. Extending the research would help to identify the housing preferences of older people in regional and national terms. In addition, surveys should be repeated due to possible changing housing preferences of the elderly.
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34 Seniors’ Housing Preferences in the Rural Areas
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A Synthetic Assessment of the Impact of Housing Management Variables on the Level of Development of Communes in Świętokrzyskie Voivodship in the Years 2009-2017 Paweł Dziekański1, Urszula Karpińska2 1 Jan Kochanowski University in Kielce, Poland, ORCID: http://orcid.org/0000-0003-4065-0043, [email protected] 2 Cooperative Bank in Kielce ABSTRACT Purpose: Municipalities operate in a dynamic environment of individual internal and external resources. They form a network of interrelationships, occur at the same time and are interdependent. The development of communes is shaped by many different elements making up the demographic, economic, financial and environmental aspects, connected by the multidimensional space of the facility. The aim of the article is to assess the level of development of communes in a multidimensional context in the aspect variables of housing management, and to determine its spatial diversity using a synthetic measure. Methods: As the source material, data from the Local Data Base of the Central Statistical Office from 2009-2017 were used. The analyses were carried out in the system of 102 communes of Świętokrzyskie Province. The following had the greatest impact on the level of development of Świętokrzyskie Voivodeship communes: own income, the level of unemployment, the number of employed persons, economic entities, natural persons conducting economic activity, flats with water supply, a bathroom and central heating. Findings: In 2009, the TOPSIS synthetic measure of development ranged from 0.34 to 0.57 and in 2017 from 0.32 to 0.62. The synthetic measure of development Si in 2009 ranged from 0.26 to 0.61, and in 2017 from 0.24 to 0.67. Regardless of the method of determining the synthetic measure, the best units were Kielce, Sandomierz, Ostrowiec Św., Sitkówka-Nowiny. They are characterised by a developed industrial and tourist function and the highest level of expenditure on housing and the number of flats per 1,000 inhabitants. The weakest units were Bliżyn, Fałków, Gowarczów, Mirzec, Imielno, and Waśniów. They were characterised by a developed agricultural function. Research implications: The results obtained are an assessment of the housing policy implemented by local authorities. They can also serve councillors, students, local economy entities and institutions interested in local development. Keywords: synthetic measure, development of communes, determinants of development, housing economy JEL codes: R13, R58 Article type: research article DOI: 10.14659/WOREJ.2019.108.03 Paweł Dziekański, Urszula Karpińska 37
INTRODUCTION Local government units operate in a dynamic internal and external environment. The changeability of the environment and its consequences for the management of local resources, including finances, are the result of underfunding of many tasks performed on behalf of government administration and the transfer of new tasks without full secured funding. Including the growing legal and reporting requirements for the budget economy and the related search for rational ways of financing new investment projects (Wołowiec & Soboń, 2010; Wołowiec & Reśko, 2012, pp. 61-89). Income and expenditure instruments determine the scope and effectiveness of the impact of local government on local and regional development. They may be held by local authorities depending on the division of competences and powers between general government and local government institutions (Patrzałek, 2010, pp. 549- 556). Municipalities are independent and independent from other institutions, legal entities that perform state functions aimed at satisfying the needs of residents (Milczarek, 1999, p. 11). Actions taken at the local level are aimed at supporting the local development process, improving the quality of life of local communities, supporting the local labor market, building or improving the quality of existing technical infrastructure, improving the state of the natural environment, skillful use of resources located in the commune (Jabłońska, 2008, p. 245), and its specific, individual features economy, society and resources (Rynio, 2013, p. 357). Local activities are a complex phenomenon, which results from many factors that shape them. This multidimensionality can be modeled using synthetic techniques. They allow them to be examined taking into account several features at the same time, thus increasing the efficiency of the analysis (Chrzanowska, Drejerska & Pomianek, 2013). The municipalities' activities are carried out in a multidimensional space of functioning. They refer to endogenous and exogenous resources, the use of which is to ensure qualitative and quantitative changes in the local economy. Resources, including: economic, social and infrastructural resources used in economic and social relations are interdependent and occur at the same time. They should therefore be considered together. There is a correlation between fundamentals of municipalities operation and the level and living conditions of the inhabitants (Dziekański, 2018).
38 A Synthetic Assessment of the Impact of Housing Management …
LITERATURE REVIEW The process of community operations or its development takes place in a multidimensional space of functioning, which is filled with endogenous and exogenous resources. They form a network of interrelationships, and because acting for the benefit of a given community (qualitative and quantitative changes in the local economy) they are interdependent and should be considered together. Resources include natural, economic, social and infrastructural environments used in economic and social relations are interdependent and occur at the same time. They shape and build investment attractiveness and indicate key areas of business conditions. The degree of intensity of endogenous values of the region, as well as their structures and mutual couplings will shape the development process (Dziekański, 2018). Region (local government) are complex systems, which evolve over time showing a certain productive specialization, as well as an occupation of space which is translated into densities, land uses and disparate growths. These phenomena, although generally consolidated over time, can be affected by natural events, which cause disruptions and changes in the referred spatial or economic evolution (Prada-Trigo & Solis, 2018). The significance of the role of the local government sphere in the three-sphere system (economic, social, environmental) cannot be contested. With a great responsibility of ensuring the constant and regular delivery of services such as electricity, access to potable water, proper sanitation, and waste removal, municipalities interface daily with the people (Madumo, 2015). Urban region development is the basic condition of human social development, is also an important symbol of human civilization progress. A region of the healthy, harmonious and rapid development not only conducive to the residents to live and work in peace and contentment, but also help to promote the overall social and economic sustainable development (Chen, 2015). Local development, according to Paryska (2001, p. 47), is a process of socio-economic development, controlled and modified by local authorities, using local development factors for the implementation of specific interests. According to Brola (1998, p. 11), local development is a harmonized and systematic activity of the local community, local authority and other entities operating in the commune, aiming at creating new and improving the existing utility values of the commune. Szlachta (1996) defines regional development as a systematic improvement in the competitiveness of living standards of the inhabitants and an increase in the economic potential of the region. Sustainability of growth is also emphasized by Kudłacz (1999), while pointing to the economic potential of the territorial unit as a source of regional development.
Paweł Dziekański, Urszula Karpińska 39
Sources of development inequalities in communes (poviats, voivodships) result from the spatial diversity of, among others: natural conditions, transport accessibility, concentration of plants of various industries and services, access to investment capital, equipment with infrastructure, access to resources and knowledge. Development is a multidimensional process that involves transforming individuals' factors and resources into goods and services for the local economy. It is controlled and modified by local authorities. The factors of local development are not changeable in time, therefore they should be subject to continuous and ongoing analysis (Prusek & Kudełko, 2009). Development is perceived on many levels, and at the same time it is a focused process. Refers to specific activities of the community and local authorities, as well as other entities operating in the commune. It occurs as a result of the accumulation, creation and enlargement of the real dimensions of the social product with simultaneous changes in institutions and economic relations (Brol, 2006). Endogenous factors (local economic base) directed at servicing the internal needs of the region are recognized as a system of interacting elements. It is also an investment attractiveness understood as a set of regional location values that have an impact on achieving the investor's goals. The endogenous conditions of the individual are the basis for its development, are inherent in the local system and result from the location rent (from localized resources, economic and social potential or favorable environmental and spatial conditions as well as infrastructural possibilities). They constitute factors that are important for the economy of a given area, often of a specific, unique nature, corresponding only to a given local system (Korenik, 1999, p. 38; Zakrzewska-Półtorak, 2010, pp. 11-20). One of the main factors determining the development of municipalities is the level of their infrastructural equipment. Infrastructure, due to its functions (location, location, integration, activation) and unique features (durability, relationship with the area, accessibility), is one of the key and most effective factors determining the development of a given area (Dolata, 2015).
RESEARCH METHODOLOGY The aim of the article is to assess the level of development of communes in a multidimensional context in the aspec variables of housing management and to determine its spatial diversity using a synthetic measure. The study included own income per capita, expenditure on housing, those working in communes, entities entered in the register, natural persons conducting economic activity, migration balance, flats per 1000 inhabitants, flats equipped with water supply, bathroom, central heating and the average usable floor space of 1 flat. The source material was data from the Local Database of the Central Statistical Office for 2009-2017. Selected
40 A Synthetic Assessment of the Impact of Housing Management … years are associated with the beginning of the economic and financial crisis, the entry into force of the new Public Finance Act of 2009 and a marked improvement in the situation of local government units. The analysis was made in the system of 102 rural communes of the Świętokrzyskie province. A synthetic measure of the development of communes in the Świętokrzyskie Province was determined in successive stages (Dziekański, 2016). First, variables describing the development, infrastructure, demography and financial situation of the municipalities were selected. Selected variables were determined on the basis of substantive, statistical or data availability criteria and they have the character of stimulant and destimulant (Grabiński, Wydymus & Zeliaś, 1989; Wysocki, 1996; Zeliaś, 2000). Variables due to variation index (the threshold was taken as 0.10, variables with lower quasi-constants removed from the study) and over-correlated (according to the inverse correlation matrix method) were eliminated from the study (Śmiłowska, 1997; Młodak, Józefowski & Wawrowski, 2016). Diagnostic variables usually have different titers, which prevents them from being directly compared and thus added. The goal of unitarisation is: unifying the nature of variables, bringing different variables to mutual comparability, replacing different ranges of variability of individual variables with a fixed range, eliminating negative values from calculations. Then the set of variables was subjected to zero unitarisation using the following formulas:
x푖푗−min푖x푖푗 푧푖푗 = , gdy 푥푖 ∈ 푆 (1) max푖x푖푗−min푖푥푖푗
max푖x푖푗−x푖푗 푧푖푗 = , gdy 푥푖 ∈ 퐷 (2) max푖x푖푗−min푖x푖푗 where: S ─ stimulant, D ─ destimulant; I = 1, 2…n; J = 1, 2…n, xij ─ means the value of the j-t feature for the examined unit, max ─ the maximum value of the j-t feature, min ─ the minimum value of the j-t feature (Wysocki, Lira, 2005; Kukuła, 2000; Młodak, 2006).
Paweł Dziekański, Urszula Karpińska 41
In order to determine the synthetic measure according to the TOPSIS method, Euclidean distances of individual objects from the pattern and anti-pattern were calculated according to the formula (Wójcik-Leń et al., 2019; Behzadian et al., 2012): 1 2 푑+ = √ ∑푚 (푧 − 푧+) (3) 푖 푛 푗=1 푖푗 푗
1 2 푑− = √ ∑푚 (푧 − 푧−) (4) 푖 푛 푗=1 푖푗 푗 where: n ─ is the number of variables that make up the pattern or anti-pattern, zij ─ is the value of the uniformized feature for the unit being tested (Wysocki, 2010; Zalewski, 2012, pp. 137-145).
Next, the synthetic measure values were determined according to the TOPSIS method for individual objects based on the formula:
− 푑푖 푞푖 = − + , 푔푑푧푖푒 0 ≤ 푞푖 ≤ 1, 푖 = 1, 2, . . . , 푛; qi ∈ [0; 1]; (5) 푑푖 +푑푖 whereby: − 푑푖 means the distance of the object from the anti-template (from 0), + 푑푖 means the distance of the object from the template (from 1). Higher values of the qi measure indicate a more favorable financial situation and a higher level of commune development (Hwang & Yoon, 1981; Pietrzak, 2016).
The synthetic measure determined using the standard method is to average the normalized values of simple features, according to the formula:
1 푝 푆 = ∑ 푧 ; 푖 = 1, 2, … , 푝, (6) 푖 푝 푗=1 푖푗 where: Si ─ synthetic measure in the examined period, zij -─features of the structure of the synthetic indicator, p ─ number of features. The indicator takes the value from the range [0,1]. A value closer to unity means that the object is characterized by a high level of analyzed phenomenon (Dziekański, 2016; Mioduchowska-Jaroszewicz, 2013, pp. 127-140).
In the last stage of analyzes, in order to interpret the obtained measures, the division into quartile groups was used, where the size of the indicator in the first group
42 A Synthetic Assessment of the Impact of Housing Management … means a better unit and in subsequent groups - weaker units. The mutual compliance of the obtained results was also verified based on the correlation coefficient and a scatter chart of the synthetic measure and its changes was presented (Wysocki & Lira, 2005; Zeliaś & Malina, 1997). An analysis of the gravitational effect was also made, which consists in the assumption that the level of development of a given commune depends, among others from the gravitational factor. The gravitational factor connecting two municipalities is directly proportional to the product of the economic potential of these municipalities and inversely proportional to the square of the geographical distance separating these units. Facilities with high economic potential located close to each other have a stronger impact (Mroczek, Tokarski & Trojak, 2014; Filipowicz & Tokarski, 2015).
These interactions are described by the so-called individual and total gravitational effects. Individual gravitational effects between the i-th and j-poviats are described by the following equation (Filipowicz, Tokarski, 2015):
pit pjt 푔푖푗푡 = 2 (7) dij where: pit, pjt ─ value of the synthetic measure in the commune in the studied area, dij ─ distance connecting the capital of the commune and the capital of the commune j.
The total gravitational effect (for the i-th commune) is defined as the geometric mean of individual gravitational effects:
101 101 퐺푖푡 = √∏푗=1 ⋀ 푗≠푖 (g푖푗푡) (8)
Accordingly, it follows that:
101 101 P푖푡 √∏푗=1 ⋀ 푗≠푖(푝푗푡) 퐺푖푡 = 2 (9) 푑푖 where: 101 101 푑푖 = √∏푗=1 ⋀ 푗≠푖 푑푖푗 (10) is the geometric mean from the distance of the capital ofthe ith commune from the capitals of other communes (Filipowicz, Tokarski, 2015; Pooler, 1987).
Paweł Dziekański, Urszula Karpińska 43
RESULTS & DISCUSSION The Świętokrzyskie Voivodeship is an industrial and agricultural region. The main industries of the Świętokrzyskie region are: metallurgy, metal, machine, building materials, ceramic, foundry, and food. The economy of the Świętokrzyskie region is based on the mining industry in the field of building materials (limestone, dolomite, marl, gypsum, sandstone). The agricultural south is the base for the production of organic food (Jóźwiak, Jóźwiak & Strzyż, 2010, Report …, 2017,). The examined population of communes of the Świętokrzyskie Voivodship was divided into 4 quartile groups. In 2009, the TOPSIS synthetic measure of development ranged from 0.34 (Waśniów (2); Ostrowiec poviat) to 0.57 (Kielce (1), city with poviat rights, capital of the region; in the middle of the Kielce poviat) and in 2017 from 0.32 (Waśniów) to 0.62 (Kielce) (Tab. 1). In 2009, the synthetic measure of Si development ranged from 0.26 (Waśniów (2); Ostrowiec poviat) to 0.61 (Kielce (1), a city with poviat rights, the capital of the region; in the middle of the Kielce poviat) and in 2017 from 0.24 (Waśniów) to 0.67 (Kielce) (Tab. 2).
Table 1. TOPSIS synthetic measure quartile groups development and the level of expenditure on housing management in the communes of the Świętokrzyskie Voivodship in 2009 and 2017 Expenditure on Apartments per TOPSIS development housing(a) 1000 inhabitants (a) 2009 2017 2017/ 2009 2017 2009 2017 2009 Kielce (1) 0.57 0.62 0.09 66,54 55,55 320 345 Sandomierz (1) 0.44 0.44 0 A Sitkówka-Nowiny (2) 0.38 0.43 0.13 Ostrowiec Św. (1) 0.42 0.42 0 Chęciny (3) 0.35 0.36 0.03 26,24 35,37 319 340 Czarnocin (2) 0.35 0.36 0.03 B Działoszyce (3) 0.36 0.36 0 Kije (2) 0.35 0.36 0.03 Bejsce (2) 0.33 0.34 0.03 15,09 20,28 302 325 Bodzentyn (3) 0.32 0.34 0.06 C Brody (2) 0.31 0.34 0.1 Ćmielów (3) 0.33 0.34 0.03 Bliżyn (2) 0.34 0.33 -0.03 11,25 21,42 303 321 D Fałków (2) 0.32 0.33 0.03 Gowarczów (2) 0.33 0.33 0
44 A Synthetic Assessment of the Impact of Housing Management …
Mirzec (2) 0.33 0.33 0 Waśniów (2) 0.34 0.32 -0.06 Sorted by 2017, the best 4 units in the group were presented, the best and weakest unit in the surveyed population; A very good, B good, C poor, D poor level of development; (1) urban communes, (2) rural communes, (3) urban-rural communes; (a) the average value of the variable in the quartile group Source: own calculations of the authors based on the data from the Local Data Bank of Statistics Poland and Central Statistical Office. Table 2. Groups of quartile measures of synthetic Si development in relation to the housing economy of communes in the Świętokrzyskie Voivodship in 2009 and 2017 Si development Expenditure on Apartments per housing(a) 1000 inhabitants (a) 2009 2017 2017/2009 2009 2017 2009 2017 A Kielce (1) 0.61 0.67 0.1 66,62 54,19 317 340 Ostrowiec Św. (1) 0.41 0.41 0 Sandomierz (1) 0.41 0.41 0 Sitkówka-Nowiny (2) 0.34 0.4 0.18 B Bodzechów (2) 0.28 0.3 0.07 26,57 36,17 321 344 Bogoria (2) 0.27 0.3 0.11 Iwaniska (2) 0.29 0.3 0.03 Kazimierza Wielka (3) 0.31 0.3 -0.03 C Baćkowice (2) 0.26 0.28 0.08 16,57 31,01 305 322 Bieliny (2) 0.28 0.28 0 Brody (2) 0.24 0.28 0.17 Ćmielów (3) 0.27 0.28 0.04 D Bliżyn (2) 0.27 0.27 0 13,83 18,17 306 329 Gowarczów (2) 0.25 0.27 0.08 Imielno (2) 0.25 0.27 0.08 Mirzec (2) 0.25 0.27 0.08 Waśniów (2) 0.26 0.24 -0.08 Source: own calculations of the authors based on the data from the Local Data Bank of Statistics Poland and Central Statistical Office. The best units Kielce (1), Sandomierz (1), Ostrowiec Św. (1), Sitkówka-Nowiny (2) are the cities of the region or units located in the central part of the province. They are characterized by a developed industrial and tourist function. Units of groups A and B were also characterized by the highest level of expenditure on housing and the number of flats per 1000 inhabitants. The weakest units were Bliżyn (2), Fałków (2), Gowarczów, Mirzec (2), Imielno (2), and Waśniów (2). They were characterized by a developed agricultural function. Measures of spatial diversity indicate the relative stability of municipalities in terms of development. In 2017, compared to 2009, the results show stability according to the standard deviation (0.05-0.05 Si; 0.03-0.04 TOPSIS; 2009-2017). The diversity coefficient is also indicated by the classic coefficient of variation (0.17-0.17
Paweł Dziekański, Urszula Karpińska 45
Si; 0.09-0.10 TOPSIS; 2009-2017). The values of the 0.37-0.43 (Si) and 0.26-0.30 (TOPSIS) range in terms of development indicate slight changes in the studied area. Table 3. Diversification of the synthetic measure of development and elements of housing economy in communes of the Świętokrzyskie Voivodship in 2009 and 2017 2009 2017 2009 2017 2009 2017 2009 2017
Si TOPSIS Apartments per Expenditure development development 1000 inhabitants on housing average 0.29 0.3 0.35 0.36 314.46 336.33 35.58 37.36 median 0.28 0.29 0.35 0.35 309.8 331.8 12.32 23.71 standard deviation 0.05 0.05 0.03 0.04 40.63 46.9 61.4 43.36 quarter (quartile) 0.29 0.3 0.35 0.36 311.66 337.79 18.63 27.03 deviation classic coefficient of 0.17 0.17 0.09 0.1 0.13 0.14 1.73 1.16 variation positional coefficient 1.02 1.01 1.01 1.01 1.01 1.02 1.51 1.14 of variation min 0.24 0.24 0.31 0.32 223.4 222.7 0 0 max 0.61 0.67 0.57 0.62 438.3 481.9 355.18 251.95 the range 0.37 0.43 0.26 0.3 214.9 259.2 355.18 251.95 quartile range 0.04 0.04 0.03 0.03 49.88 58.78 29.44 33.4 skewness 3.33 4.03 3.43 3.96 0.32 0.34 3.05 2.36 measure of 17.99 25.7 19.6 25.5 0.18 0.53 10.38 6.69 concentration- kurtosis Source: own calculations of the authors based on the data from the Local Data Bank of Statistics Poland and Central Statistical Office. The Pearson correlation coefficient between the value of the synthetic measure of development in 2007 in relation to 2014 was 0.916 (TOPSIS) and 0.924 (Si). It can be assumed that the spatial diversity of the studied area was quite stable, and the units reacted similarly to changes in the economy. Outstanding units are: Kielce, Sandomierz, Sitkówka-Nowiny, Daleszyce, and Oleśnica. These are cities that have a developed industrial function. Units located in the Kielce poviat, whose economy is characterized by the mining and processing industry of mineral raw materials and the production of foodstuffs (Fig. 1).
46 A Synthetic Assessment of the Impact of Housing Management …
TOPSIS; y = 0.0138 + 0.9865*x; r = 0.9160; p = 0.0000; r2 = 0.8391 Si; y = 0.0236 + 0.9642*x; r = 0.9247; p = 0.0000; r2 = 0.8550 0,65 0,70 Kielce (1) Kielce (1) 0,65 0,60
0,60 0,55 0,55
0,50 0,50
0,45 Sandomierz (1) 0,45 2017 Sitkówka-Nowiny (2) 2017 Sitkówka-Nowiny (2) 0,40 0,40 Daleszyce (3) 0,35 Daleszyce (3) 0,35 0,30 Oleśnica (2) 0,30 0,25 Oleśnica (2)
0,25 0,20 0,28 0,30 0,32 0,34 0,36 0,38 0,40 0,42 0,44 0,46 0,48 0,50 0,52 0,54 0,56 0,58 0,60 0,20 0,25 0,30 0,35 0,40 0,45 0,50 0,55 0,60 0,65 2009 2009 Figure 1. The relation of the synthetic measure of development 2009 to 2017 of communes of the Świętokrzyskie province with the matching line Source: own calculations of the authors based on the data from the Local Data Bank of Statistics Poland and Central Statistical Office. The correlation coefficient between the TOPSIS and Si development measure was 0.974 in 2009, 0.983 in 2017. It indicates a stable spatial diversity of the development of communes in the Świętokrzyskie Province. Communes reacted similarly to changes in the economy (Fig. 2).
2009; y = -0.2123 + 1.4274*x; r = 0.9749; p = 0.0000; r2 = 0.9505 2017; y = -0.2001 + 1.3946*x; r = 0.9838; p = 0.0000; r2 = 0.9679 0,65 0,70 Kielce (1) Kielce (1) 0,60 0,65
0,60 0,55
0,55 0,50 0,50 0,45
Si 0,45 2017 0,40 0,40 0,35 0,35
0,30 0,30
0,25 0,25
0,20 0,20 0,28 0,30 0,32 0,34 0,36 0,38 0,40 0,42 0,44 0,46 0,48 0,50 0,52 0,54 0,56 0,58 0,60 0,25 0,30 0,35 0,40 0,45 0,50 0,55 0,60 0,65 TOPSIS 2017 Figure 2. The relation of synthetic measure of development of communes of the Świętokrzyskie voivodship in 2009 and 2017 Source: own calculations of the authors based on the data from the Local Data Bank of Statistics Poland and Central Statistical Office. The correlation coefficients presented in Table 4 show that the greatest impact on the level of development of communes in the Świętokrzyskie Voivodship had: own income, the level of unemployment, the number of employees, business entities, natural persons conducting economic activity, flats equipped with water supply, bathroom and central heating.
Paweł Dziekański, Urszula Karpińska 47
Table 4. Correlation of the synthetic measure of development and elements of housing economy with elements of structure for communes of the Świętokrzyskie voivodship in the years 2009-2017 Si development TOPSIS development
Si development 1 0.9784
TOPSIS development 0.9784 1 Own income 0.6074 0.5597 Expenditure on housing 0.4441 0.416 Property (investment) expenses 0.2268 0.2168 The level of unemployment 0.7726 0.7271 Number of persons employed 0.8249 0.7985 Business Unit(s) 0.7935 0.7283 Self-employed persons 0.7194 0.6552 Apartments equipped with water supply 0.8242 0.7908 Apartments equipped with bathroom 0.8263 0.7944 Apartments equipped with central heating 0.825 0.7929 Linear correlation coefficients for observations from sample 1-1020; Critical value (at a double- sided 5% critical area) = 0.0614 for n = 1020 Source: own calculations of the authors based on the data from the Local Data Bank of Statistics Poland and Central Statistical Office. The value of the correlation coefficient between the synthetic measure of development (Si and TOPSIS) was 0.976, which means that they described the diversification of economic development to a very similar degree and individuals reacted similarly to changes in the economy. Regression analysis allows you to create a linear model. When creating it, it must be decided, which variables will be the explained variable and which variables will be the explanatory one. The regression model describing the dependence of variables takes the form: f (TOPSIS / Si development) = ∑ (own income, housing expenditure, property expenditure (investment), unemployment rate, number of employees, business entities, natural persons conducting economic activity, housing resources, flats equipped with water supply, flats equipped with bathroom, flats equipped with central heating, flat area).
The results of the regression analysis for the synthetic measure show that the presented regression model allows explaining R = 0.910 (Si) and R = 0.846 (TOPSIS) of variations of variables. High values of F statistics (930.07 - Si; 503.52 - TOPSIS) and the corresponding level of probability p confirm the statistical significance of the linear model. The t-Student statistic value for the p parameter means that all parameters are statistically significant. The value of the coefficient of determination (R2=0.909(Si); 0.844 (TOPSIS)) indicates a good fit of the regression model to the data (Tab. 5 and 6).
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Table 5. KMNK estimation (Si measure) Rate Standard error t-Student's p-value const 0.0915155 0.0101963 8.975 <0.0001 constant 8.99887e-06 1.14401e-06 7.866 <0.0001 own income 5.57437e-05 6.44292e-06 8.652 <0.0001 expenditure on housing 1.85656e-05 1.05336e-06 17.63 <0.0001 property expenses 3.05539e-06 4.87607e-07 6.266 <0.0001 (investment) number of persons 0.000712679 0.000172442 4.133 <0.0001 employed business unit(s) 0.000427295 0.000191919 2.226 0.0262 self-employed persons 0.000156508 1.59073e-05 9.839 <0.0001 housing resources −9.76822e-06 3.26169e-06 −2.995 0.0028 apartments equipped 2.36407e-05 3.76830e-06 6.274 <0.0001 with water supply apartments equipped −1.38321e-05 3.12056e-06 −4.433 <0.0001 with bathroom apartments equipped 0.000741162 6.84928e-05 10.82 <0.0001 with central heating
Arithmetic mean of the 0.298284 Standard deviation of the dependent variable dependent variable 0.048784 Sum of residual squares 0.217505 Residual Standard Error 0.014689 Determining coefficient 0.910311 Corrected R-square R- square 0.909332 F (11, 1008) 930.0762 P-value for the F test 0.000000 Logarithm of credibility 2863.760 Akaike information criterion −5703.52 Bayesian information −5644.390 Hannan–Quinn information −5681.06 criterion criterion observations used 1-1020; dependent variable Si Source: own calculations of the authors based on the data from the Local Data Bank of Statistics Poland and Central Statistical Office.
Table 6. MNC estimation of TOPSIS measure Rate Standard error t-Student's p-value const 0.189326 0.00925561 20.46 <0.0001 Constant 4.63849e-06 1.03847e-06 4.467 <0.0001 own income 3.67194e-05 5.84850e-06 6.278 <0.0001 expenditure on housing 1.17342e-05 9.56177e-07 12.27 <0.0001 property expenses 2.74255e-06 4.42621e-07 6.196 <0.0001 (investment) number of persons 0.000338918 0.000156532 2.165 0.0306 employed business unit(s) 0.000415862 0.000174213 2.387 0.0172
Paweł Dziekański, Urszula Karpińska 49
self-employed persons 0.000145024 1.44397e-05 10.04 <0.0001 housing resources −1.72763e-05 2.96077e-06 −5.835 <0.0001 apartments equipped with 2.26276e-05 3.42063e-06 6.615 <0.0001 water supply apartments equipped with −5.46894e-06 2.83265e-06 −1.931 0.0538 bathroom apartments equipped with 0.000795276 6.21736e-05 12.79 <0.0001 central heating
Arithmetic mean of the 0.357363 Standard deviation of the 0.033798 dependent variable dependent variable Sum of residual squares 0.179222 Residual Standard Error 0.013334 Determining coefficient 0.846030 Corrected R-square 0.844350 R- square F (11, 1008) 503.5214 P-value for the F test 0.000000 Logarithm of credibility 2962.494 Akaike information criterion −5900.988 Bayesian information −5841.857 Hannan–Quinn information −5878.536 criterion criterion observations used 1-1020; dependent variable TOPSIS Source: own calculations of the authors based on the data from the Local Data Bank of Statistics Poland and Central Statistical Office. The gravitational factor connecting two municipalities is directly proportional to the product of the economic potential of these municipalities and inversely proportional to the square of the geographical distance separating these units. Therefore, municipalities with high competitive potential located close to each other interact more strongly than those located far away from each other.
50 A Synthetic Assessment of the Impact of Housing Management …
Figure 7. The result of the gravity analysis for the synthetic measure TOPSIS and Si Source: own calculations of the authors based on the data from the Local Data Bank of Statistics Poland and Central Statistical Office. The analysis allowed dividing the communes into 4 groups in 2017. The first group for the Si / TOPSIS measure consists of central communes with the highest development potential (26; among others Kielce (1), Sitkowka-Nowiny (2), Masłów (2), Morawica (3), Miedziana Góra (2), Bieliny (2), Zagnańsk (2), Busko-Zdrój (2), Starachowice (1), Skarżysko-Kamienna (1), Sandomierz (1); their level is determined by the geographical location in relation to the capital of the voivodship, industrial function, developed labor market). The second and third groups are 25 units. The weakest fourth group is 26 peripheral units (incl. Dwikozy (2), Zawichost (3), Bałtów (2), Ożarów (3), Włoszczowa (3), Sandomierz (1)).
CONCLUSION Municipalities strive to stimulate competitiveness or local socio-economic development based on local resources. An analysis of the spatial distribution of communal development indicates the expansion of communes with a high level of development around the region's capital (Kielce). The increase in distance from the capital of the province usually reduced the level of development. Characteristic for them is the effect of attracting e.g. human capital, social or economic potential. The development of communes is multidimensional. It is shaped by many different elements that make up the demographic, economic, financial and environmental aspects, as well as the connections between these elements of the multidimensional space of the object. The highest impact on the level of development of communes in the Świętokrzyskie voivodship had: own income, the level of unemployment, the number of employed, business entities, natural persons conducting business activity, flats equipped with water supply, bathroom and central heating.
Paweł Dziekański, Urszula Karpińska 51
In 2009, the TOPSIS synthetic measure of development ranged from 0.34 to 0.57 and in 2017 from 0.32 to 0.62. The synthetic measure of development Si in 2009 ranged from 0.26 to 0.61, and in 2017 from 0.24 to 0.67. Regardless of the method of determining the synthetic measure, the best units were Kielce (1), Sandomierz (1), Ostrowiec Św. (1), Sitkówka-Nowiny (2) are the cities of the region or units located in the central part of the province. They are characterized by a developed industrial and tourist function. They were characterized by the highest level of expenditure on housing and the number of flats per 1000 inhabitant. The weakest units were Bliżyn (2), Fałków (2), Gowarczów, Mirzec (2), Imielno (2), and Waśniów (2). They were characterized by a developed agricultural function. Housing is a field concerning the vital interests of residents of municipalities, important in social terms. It connects with local development not only financial but also political and economic connections. The marketization of municipal housing management could result in the reduction of municipalities' expenditure as a result of stopping subsidizing unprofitable institutions. The results obtained are an assessment of the housing policy implemented by local authorities. They can also serve councilors, students, local economy entities and institutions interested in local development. The value of the correlation coefficient between the synthetic measure of development (Si and TOPSIS) was 0.976. This indicates that the units described the diversification of economic development to a similar degree and reacted similarly to changes in the economy. The reasons for diversity are due to the internal potential of individuals, geographical location or endogenous resources, social, economic, natural and infrastructural potential.
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An Analysis of Municipal Real Estate Resources in Poland Milena Bera1, Monika Śpiewak-Szyjka2 1West Pomeranian University of Technology in Szczecin, Poland, ORCID: http://orcid.org/0000-0002- 1997-349X, [email protected] 2 West Pomeranian University of Technology in Szczecin, Poland, ORCID: http://orcid.org/0000-0001- 5527-0305, [email protected]
ABSTRACT Purpose: The objective of the article was to analyse municipal real estate resources in Poland. The objective was achieved through the classification of 16 provinces in Poland in terms of the state of the municipal real estate economy in Poland. The evaluation encompassed municipal real estate resources, taking into account their access to infrastructure. Method: The empirical part of the research was described with the use of data coming from a publication of the Central Statistical Office. The research referred to the year 2016. Heuristic methods, such as "brainstorming” and the Delphi method were used for the measurement and evaluation of the phenomenon. In order to rank the provinces with regard to their municipal real estate resources, a taxonomic development measure was calculated. Findings: The status of municipal real estate resources largely depends on the size of municipal real estate resources and the manner of their management by local authorities. It needs to be emphasised that in many provinces both municipal, district and province authorities try to limit municipal real estate resources because their main tenancy generates costs. Hence, local government authorities, using the right of pre-emption, frequently sell the real estate to tenants. There is no statistical data for the x4 variable, Research implications: The research should be continued in the following years, which will enable a comparative analysis. Keywords: municipal real estate, province, Poland, real estate JEL codes: R38 Article type: research article DOI: 10.14659/WOREJ.2019.108.04 Milena Bera, Monika Śpiewak-Szyjka 57
INTRODUCTION In nearly all countries, municipalities own or control substantial amounts of real estate, but few municipal governments think of their holdings as a “portfolio” whose composition might be modified to better serve public purposes (Kaganova & Nayyar-Stone, 2000). Nowadays many individuals struggle with a difficult financial situation and unemployment. Municipal resources are limited, therefore local authorities are developing a number of regulations and provisions in the form of a resolution regarding a long-term programme for managing the resource. In order to implement these modifications, the existing law concerning this subject should be adapted or changed (Gross & Zrobek, 2015, pp. 5-20). Considering the above, the main objective of the study was defined, which entailed an analysis of municipal real estate resources in Poland with the use of statistical data gathered by the Chief Statistical Office. The objective was achieved through the classification of 16 provinces in Poland in terms of the status of the municipal real estate in Poland. The evaluation encompassed municipal real estate resources, taking into account their access to infrastructure. The empirical part of the research was described with the use of data originating from the publication of the Chief Statistical Office. The research referred to the year of 2016 (only data for the variable X4 are aggregated every 2 years and were provided by the statistical office for 2017). Heuristic methods, such as „brain storming” and the Delphi method were used for the measurement and evaluation of the phenomenon. Using the brain storming method was proposed potential diagnostic variables regarding municipal real estate resource in Polish provinces in 2016. In order to rank the provinces on account of their municipal real estate resources, a taxonomic development measure was calculated.
LITERATURE REVIEW Due to its size, real estate owned by municipalities is an important segment of the real estate market (Marona & Van den Beemt-Tjeerdsma, 2018). Polish local governments maintain approx. 1.1 million municipal flats, located in various localities and types of buildings – ranging from old tenement houses to new municipal blocks of flats built in the recent years. They constitute 8 per cent of the housing stock in Poland (Chief Statistical Office, p. 18). The changes in the number and demographic structure of the urban population, which are
58 An Analysis of Municipal Real Estate Resources in Poland directly related to the deficit on the real estate market, are the one of exogenous determinants the future of the estates in Poland (Szafranska, 2013). The main objective of managing real estate owned by a commune is to effectively use communal real estate in the process of performing public tasks by the commune, i.e. meeting collective needs of the community by providing local public goods. Specific objectives of managing real estate differ depending on the functions performed by the real estate. Municipal real estate may serve to: • implement the commune’s statutory obligations (functions of an administrative and public utility character), • generate one-off or periodical revenue streams (sales), e.g. rent, lease, lending, • or implement investment projects or build up a reserve for the implementation of development objectives in the future (Trojanek, 2015). The social role of municipal real estate resources constitutes a consequence of locating the operation of those assets as a public good within the life space of members of municipal communities – homeowners’ associations. Real estate should provide optimum living conditions, which encompass multi-layered condition, starting from the provision of real properties designated for public roads to the provision of real properties allocated for residential purposes, involving the improvement of housing conditions of the local community, maintaining previous standards, raising them and chiefly – managing those resources. Real estate resources constitute one of the key structures in terms of real estate management. The definitions of the above concept are regulated by article 4.2 of the Land Management Act of 21 August 1997, referring this resource to public real estate, thus the real estate that belongs to local government units or to the State Treasury. According to the above mentioned regulation, a real estate resource is to be understood as “real estate that constitutes on object belonging to the State Treasury, a municipality, a district or a province” (Law, 1997). Thus, the resource comprises land real estate, understood as land along with its components, with the exclusion of buildings and flats, if they constitute a separate object of ownership (Źróbek, 2004). A question of housing constitutes a significant element in the system of issues that local government units deal with, since housing is perceived as a
Milena Bera, Monika Śpiewak-Szyjka 59 universal fundamental commodity. It directly satisfies the housing needs of low-income households. It can be said that the goals of a housing policy chiefly focus on the development of housing construction, maintaining the existing resources or raising their standards, lowering construction costs and maintaining flats as well as managing those resources, therefore, overall, it concentrates on the improvement of local community housing conditions. When analysing the above, it can be concluded that local government constitutes a fundamental entity being in charge of managing the local economy, responsible for ensuring proper housing to the society. Assuming the criterion of the social role of real estate resource, local authorities ought to realize any necessary investment undertakings of public services through the prism of that criterion, and they ought to ensure an optimum number of available real properties of various purposes in accordance with the needs of residents aligning with the consequences of social development. The functions assigned first of all ought to take into account the use for purposes satisfying the needs of the local community, however, employing the principle of rational management. The satisfaction of residents’ communal needs through the fulfilment of municipality’s own tasks, determining the level of local community living conditions by using real estate for that purpose and managing them in an optimum manner is carried out in line with legal regulations (Topczewska & Siemiński, 2003) Municipalities may also prepare other studies with which it is possible to manage municipality property, e.g. a plan of using municipal real estate resources (Cymermann ,2013). Having the resources of the right structure, and within those having the real estate of the right quality (in good technical condition, good location, etc.), is positively perceived by the society, since it proves the economic stability and significant development capabilities (Wiśniewski, 2008, pp. 82-86).
RESEARCH METHODOLOGY The objective of the study was to classify 16 Polish provinces in terms of the state of municipal real estate resources in 2016. It was conducted in order to gain an answer to the question in which of the provinces the above-mentioned status of the resource was at the highest level, and in which ones the situation was different. The analysed provinces were divided into three typological groups. The first group includes the provinces featuring the best status of
60 An Analysis of Municipal Real Estate Resources in Poland municipal real estate resource, the second group features the real estate of good status, and the third group of provinces featured poor condition of municipal real estate management. The study was conducted on the basis of statistical data coming from www.stat.gov.pl portal. In order to make a selection of diagnostic variables for the study, heuristic methods were employed, such as: • “brain storming”, • the Delphi method. “Brain storming” comes down to a free exchange of intuitive opinions. It occurs in the course of the so-called idea exchange session, whose participants, and at the same time issue experts, discuss an issue sitting together at the table. The result of “a brain storming” is a list of characteristics compiled after a free exchange of thoughts on the subject of the proposals put forward by way of their gradual “improvement” (Nowak, 1990). Another heuristic method deriving from “brain storming” is the so- called Delphi method. It is also a method of group thinking about a problem, but not during a joint session, but by correspondence (Nowak, 1990). In the examined year of 2016, the following potential diagnostic variables were proposed (Tab. 1). Table 1. Potential diagnostic variables regarding municipal real estate resource in Polish provinces in 2016 Number of flats Number of Arrears in the in which tenants municipal flats Usable surface Flats payments for are in arrears the of municipal constitutin living in with the construction of flats per one g municipal municipal payments in which was resident of a property resources municipal commenced in province resources 2017 in thou. in % of in % of in % of total PLN/municip municipal flats in m2/person total flats municipal flats al flat in total X1 X2 X3 X4 X5 Dolnośląskie 10.200 0.759 0.475 0.01 1.86 Kuyavia- 5.900 3.742 0.459 0.00 0.90 Pomerania Lublin 2.700 1.876 0.476 0.00 0.41 Lubusz 7.400 4.209 0.522 0.00 1.24 Lodzkie 8.200 0.849 0.434 0.00 1.37 Lesser Poland 2.800 2.529 0.446 0.02 0.40 Masovia 5.600 6.209 0.477 0.00 0.93 Opole 6.200 1.038 0.423 0.01 1.06 Subcarpathia 2.500 2.066 0.444 0.00 0.32
Milena Bera, Monika Śpiewak-Szyjka 61
Podlaskie 3.400 0.908 0.342 0.00 0.55 Pomerania 6.300 1.497 0.465 0.00 1.05 Silesia 9.400 3.198 0.542 0.01 1.66 Świętokrzyskie 2.700 3.358 0.538 0.01 0.37 Warmia-Masuria 6.000 1.110 0.503 0.01 0.94 Greater Poland 4.400 0.487 0.192 0.01 0.71 West Pomerania 8.100 2.715 0.494 0.01 1.46 Number of Number of Number of Consumption of Population residents in residents in residents in water from number of a municipal municipal municipal water mains by province per resources resources using resources the residents of 1 municipal using water sewerage using gas municipal flat supply system system resources system % of the total % of the total in % of the person/muni residents of residents of total residents in m3/resident cipal flat municipal municipal of municipal resources resources resources X6 X7 X8 X9 X10 Dolnośląskie 25.00 97.70 91.00 81.70 35.40 Kuyavia- 48.00 96.80 91.30 68.80 31.50 Pomerania Lublin 101.00 94.60 88.80 69.80 30.20 Lubusz 37.00 97.20 91.20 74.80 30.60 Lodzkie 30.00 95.20 86.90 59.90 34.70 Lesser Poland 105.00 95.40 88.30 73.60 36.80 Masovia 43.00 93.90 90.50 71.10 39.70 Opole 45.00 98.50 91.50 76.00 32.30 Subcarpathia 131.00 94.30 89.40 88.50 30.20 Podlaskie 78.00 96.70 91.60 44.20 29.20 Pomerania 43.00 98.80 94.90 70.50 35.40 Silesia 28.00 98.10 87.30 71.30 31.00 Świętokrzyskie 105.00 96.10 87.00 68.80 30.00 Warmia-Masuria 47.00 98.90 95.50 68.50 32.00 Greater Poland 67.00 97.90 92.00 68.10 35.60 West Pomerania 33.00 97.70 91.80 77.30 33.30 Source: own calculations on the grounds of statistical data from www.stat.gov.pl. Using the statistical data represented in Table 1. a statistical selection of variables was conducted with the use of Ward method. In Ward method the starting point for calculations is provided by a matrix of Euclidean distances between objects (variables) calculated on the basis of the following formula:
K 1/ 2 2 dij = (zik − z jk ) k=1 (1)
62 An Analysis of Municipal Real Estate Resources in Poland
Ward method enabled determining internally uniform subgroups of diagnostic variables. A discontinuation of agglomeration method according to the formula: W = x + 2s k (2) where: x and s are respectively the values of arithmetic mean and standard deviation calculated on the basis of the distance located at minimum distances in individual matrix rows. The figure 1 presents the results of the conducted method for Wk = 137.70
Figure 1. Ward dendogram for 2016; Wk = 137.70 Source: Own work with the use of STATISTICA 8 software package. The accumulations obtained with the use of Ward method in 2016 for Polish provinces: 1) X1, X2, X3, X4, X5, X10, 2) X6, 3) X7, X8, X9.
Milena Bera, Monika Śpiewak-Szyjka 63
In individual accumulations there are “similar” variables, i.e. from a given accumulation only one representative must be selected. Therefore, the following were selected: X1, X4, X6, X8. Hence, a final set of diagnostic variables will contain the following variables: X1 – Flats constituting municipal property in % of total flats, X4 – Number of municipal flats the construction of which was commenced in 2017 in % of municipal flats out of total number of flats, X6 – Province population number per 1 municipal flat in individuals/municipal flat, X8 – Number of residents of municipal resources using sewerage system in % of total residents of municipal resources.
RESULT & DISCUSSION In order to rank the provinces on account of their municipal real estate resources, a taxonomic development measure was calculated. In line with the stages of determining a taxonomic development measure, the nature of diagnostic variables was defined. It occurred that in the set of diagnostic variables that will be the basis for carrying out the classification only stimulants were distinguished. Thereby, there was no need for performing a transformation of destimulants into stimulants. Then all the variables (stimulants) were made comparable, using a variable transformation method – standardisation. Classic standardization causes the uniformity of all variables in terms of the changeability measured with standard deviation; it means eliminating variability as a basis of object differentiation (Gatnar & Walesiak, 2004). Variable standardization was conducted in accordance with the following formula with the use of STATISTICA 8 software, and the results of standardization were presented in the table below. x − x x = ik k ik s k (3) where:
xk and sk mean, respectively, an arithmetic average and standard deviation of k – th diagnostic variability.
64 An Analysis of Municipal Real Estate Resources in Poland
Table 2. Values of diagnostic variability after normalization transformation with standardization method for 2016 X1 X4 X6 X8 Lower Silesia 10.2 0.0051 25.00 91 Kuyavia-Pomerania 5.9 0.0041 48.00 91.3 Lublin 2.7 0.0048 101.00 88.8 Lubusz 7.4 0.0002 37.00 91.2 Lodzkie 8.2 0.002 30.00 86.9 Lesser Poland 2.8 0.0181 105.00 88.3 Masovia 5.6 0.0041 43.00 90.5 Opole 6.2 0.0127 45.00 91.5 Subcarpathia 2.5 0.0042 131.00 89.4 Podlaskie 3.4 0 78.00 91.6 Pomerania 6.3 0.0022 43.00 94.9 Silesia 9.4 0.0102 28.00 87.3 Świętokrzyskie 2.7 0.008 105.00 87 Warmia-Masuria 6 0.0148 47.00 95.5 Greater Poland 4.4 0.0134 67.00 92 West Pomerania 8.1 0.0061 33.00 91.8 Source: own work with the use of STATISTICA 8 software package.
Using standardized variables a synthetic variable ( zi ) was determined in accordance with the following formula: 1 K zi = zik K k=1 (4)
Then. the values of zi were normalized to a range of [0.1] according to the formulas:
zi = zi − min{zi } . (5) i
zi zi = . (6) max{zi} i The transformation defined with formula (5) causes a shift of the measurement scale of development measures to a zero point. which is important especially in the case of the measures that can assume negative values. The transformations designated with formula (6) lead to determination
Milena Bera, Monika Śpiewak-Szyjka 65 of an upper limit of development measures at a level equal to 1. which in conjunction with the transformation (5) means their normalization within the range of <0.1> with the extreme values of that range being represented in a
zi value set. As a result. the least developed object assumes the value of equal to 0. while the most developed object is assigned with a measurement value equal to 1. Therefore. the higher the degree of development of a given object is. the higher the value of the measure corresponding to it will be (Grabiński & Wydymus, 1983). The changes in the location of the provinces towards each other in a general classification can be found in Figure 2 below.
Wykres rozrzutu Z'' względem Z'' Arkusz1 1v*16c Z'' = 0+1*x 1,2
Warmińsko-mazurskie 1,0 Opolskie Małopolskie
0,8 Pomorskie Wielkopolskie Podkarpackie 0,6 Kujawsko-pomorskie Dolnośląskie Z'' Lubelskie 0,4 Mazowieckie Zachodniopomorskie Lubuskie 0,2 Śląskie Łódzkie Świętokrzyskie Podlaskie 0,0
-0,2 -0,2 0,0 0,2 0,4 0,6 0,8 1,0 1,2 Z'' Figure 2. Ranking of Polish provinces in terms of the status of municipal real estate management in 2016 (standardized variables) Source: own work with the use of STATISTICA 8 software package. The assignment of provinces to individual typological groups during data standardization is presented in Table 3 below.
66 An Analysis of Municipal Real Estate Resources in Poland
Table 3. The assignment of provinces to individual typological groups during data standardization in 2016 z Lp. Voivodeship variable i Typological group 1 Warmia-Masuria 1 1 group 2 Greater Poland 0.687102454 1 group 3 Lesser Poland 0.668387104 1 group 4 Opole 0.638003069 2 group 5 Lower Silesia 0.528725315 2 group 6 Pomerania 0.525606024 2 group 7 West Pomerania 0.509520311 2 group 8 Subcarpathia 0.397307089 3 group 9 Silesia 0.387933804 3 group 10 Kuyavia-Pomerania 0.323916694 3 group 11 Lubusz 0.233957712 3 group 12 Lublin 0.221068742 3 group 13 Świętokrzyskie 0.220389377 3 group 14 Masovia 0.214774312 3 group 15 Podlaskie 0.186769746 3 group 16 Lodzkie 0 4 group Source: own work.
• group I: the best state of municipal real estate management
( zi z + sz ) • group II: good state of municipal real estate management
( z + sz zi z ) • group III: worse state of municipal real estate management
( z zi z − sz ) • group IV: the worst state of municipal real estate management z z − s i z
CONCLUSION The analysis of the state of municipal real estate resources in Poland was conducted through the identification of real estate resources that individual provinces have at their disposal. All the characteristics adopted in the study featured strong variability. thanks to which they could effectively discriminate
Milena Bera, Monika Śpiewak-Szyjka 67 between the provinces. The results of the conducted study demonstrate that the Warmia-Masuria, the Greater Poland and the Lesser Poland Provinces featured the best municipal real estate management. They belonged to the first typological group. The Opole, Lower Silesia and the Pomeranian Provinces featured a good state of municipal real estate resource. The remaining provinces. i.e.: the Sub-Carpathian. Silesian. Kuyavia-Pomerania. Lubusz. Lublin. Holy Cross. Masovian and Podlaskie were assigned to the III typological group with poor state of municipal real estate management. The worst status of municipal real estate resources featured Łódź Provinces (IV group). The status of municipal real estate resources largely depends on the size of municipal real estate resources and the manner of their management by local authorities. It needs to be emphasised that in many provinces both municipal. district and province authorities try to limit municipal real estate resources. because their maintenance generates costs. hence frequently local government authorities. using the right of pre-emption. sell the real estate to tenants.
REFERENCES CSO Report. (2017). Housing Economy 2016.Warszawa. Cymerman, J. (2013). Lokalna polityka gospodarki nieruchomościami w kreowaniu wartości nieruchomości. Studia Ekonomiczne, 144, 203-216. Gatnar, E., & Walesiak, M. (2004). Metody statystycznej analizy wielowymiarowej w badaniach marketingowych. Wrocław: Wydawnictwo Akademii Ekonomicznej im. Oskara Langego we Wrocławiu. Grabiński, T., Wydymus, S., & Zeliaś, A. (1983). Metody prognozowania rozwoju społeczno-gospodarczego. Warszawa: PWE. Gross, M., & Źróbek, R. (2015). Good Governance in Some Public Real Estate Management Systems. Land Use Policy, 49, 352-364. https://doi.org/10.1016/j.landusepol.2015.08.017. Kaganova, O., & Nayyar-Stone, R. (2000). Municipal Real Property Asset Management: An Overview of World Experience, Trends and Financial Implications. Journal of Real Estate Portfolio Management, 6(4), 307-326. Marona, B., & van den Beemt-Tjeerdsma, A. (2018). Impact of Public Management Approaches on Municipal Real Estate Management in Poland
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and The Netherlands. Sustainability, 10(11), 4291. https://doi.org/10.3390/su10114291. Nowak, E. (1990). Taxonomic Methods for Classification of Social and Economic Objects. Warszawa: PWE. Simons, R.A. (1994). Public Real Estate Management and the Planner's Role. Journal of the American Planning Association, 60(3), 333-343. https://doi.org/10.1080/01944369408975591. Szafrańska, E. (2013). Large Housing Estates in Post-Socialist Poland as a Housing Policy Challenge. European Spatial Research and Policy, 20(1), 119-129. https://doi.org/10.2478/esrp-2013-0006. Topczewska, T., & Siemiński, W. (2003). Gospodarka gruntami w gminie. Warszawa: Difin. Trojanek, M. (2015). Strategic Municipal Real Estate Management. Journal of International Studies, 8(2), 9-17. https://doi.org/10.14254/2071- 8330.2015/8-2/1. Wiśniewski, R. (2008). Gospodarowanie gminnymi zasobami nieruchomości. Olsztyn: Wydawnictwo Uniwersytetu Warmińsko-Mazurskiego w Olsztynie. Źrobek, R. (2004). Nieruchomości i ich zasoby jako przedmiot opisu i analiz. Acta Scientiarum Polonorum. Administratio Locorum, 3(1), 5-20.
The Evolution of Housing Policy Models in European Countries. A Theoretical Approach Ewa Kucharska-Stasiak1, Magdalena Załęczna2, Konrad Żelazowski3 1University of Lodz, Poland, ORCID: https://orcid.org/0000-0002-9781-6537, [email protected] 2University of Lodz, Poland, ORCID: https://orcid.org/0000-0003-4385-6119, [email protected] 3University of Lodz, Poland, ORCID: https://orcid.org/0000-0003-3319-1627, [email protected] ABSTRACT Purpose: Housing policy is a component of social policy; it attempts to study and assess housing problems and ways of equalising opportunities in access to housing services. The article aims to present the development of housing policy models in Europe to indicate the directions of their changes, and to assess the effects of their implementation. Methods: A systematic review of the literature and a comparative analysis were used to distinguish the most popular housing policy classifications, as well as to systematise knowledge on the drivers of the evolution of housing models. Findings: The study indicates a multitude of classifications of housing policy models. They all have roots in Donnison's original work but differ in nomenclature and complexity resulting from valorisation rules. They reveal the importance of historical, social, cultural, and political conditions in the evolution of housing policy. For achieving the objectives of housing policy, the comprehensive model, in which the state participates in the process of satisfying housing needs, was proved to be more productive. The article is theoretical and does not verify the effectiveness of the presented housing policy models to a greater extent. Research implications: The article highlights the fundamental differences between the main models of housing policy and the mechanism of their evolution. Besides, it presents a critical stance on the concept of housing policy of post-socialist countries, which, due to strongly market-oriented solutions focused mainly on private ownership of housing, has not improved their housing situation significantly. Keywords: housing policy, housing policy models, socio-economic inequalities JEL codes: O18, R31 Article type: review article DOI: 10.14659/WOREJ.2019.108.05 70 The Evolution of Housing Policy Models in European Countries…
INTRODUCTION The problem of inequality has long been recognized in economic literature9 taking on different faces: economic, socio-political, cultural, and psycho-social inequalities. Their importance for development is more and more emphasized, which caused a significant increase in the literature analyzing this issue in foreign (Piketty & Saez, 2013; Piketty, 2015; Sen, 2009; Atkinson, 2013, Stiglitz, 2006, 2012) and Polish literature (among others Kłos & Szymańska, 2014, Podemski, 2009, Tusińska, 2014). One of the areas of inequality analysis is the housing sphere. Attention was paid not only to the social aspects of these inequalities but also to economic aspects, the importance of real estate, both land and residential units, but within the meaning of classical economy. It is emphasised that it is necessary to study not only the impact of changes in housing prices on wealth, but also effects of changes in the housing policy in relation to real income and broadly understood wealth of the whole society (Maclennan & Miao, 2017, p. 136). This means that housing issues will return to the area of macroeconomic issues ‒ distribution of national income, economic growth, and asset productivity, with emphasis on fixed assets. In the discussion on housing policy, as a research field, an issue that causes differences in views and also underlies disputes remains the degree of state interference in the mechanism of functioning of residential real estate markets. The concepts of this intervention and types of housing systems often referred to as housing policy models (Hoekstra, 2003), have been highlighted. A new approach to classifying models is based on the method of housing wealth accumulation regimes. Regardless of the criterion for distinguishing models, they provide a simplified version of housing policy, characteristic of a defined group of countries. Therefore, they allow identifying countries with similar concepts of housing development and the scope of public interventionism. One of the classifications distinguishes two types of models: theoretical, which focuses on regularities of housing development, taking the
9 In the eighteenth century, Rousseau, in his "Discourse on the Origin and Foundations of Inequalities between People", indicated that elemental equality between people had been violated. This contributed to the perpetuation of injustice. He distinguished into two types of inequality between people: inequalities conditioned by nature, resulting among others from age differences or personality traits, and "moral" or "political" inequalities. The latter are created by people and perpetuated by them or endured. They depend on variously assigned privileges, which some enjoy to the detriment of others); he pointed out that the progress of civilization and the development of human reason violated elemental equality between people and contributed to the consolidation of inequalities.
Ewa Kucharska-Stasiak, Magdalena Załęczna, Konrad Żelazowski 71 form of economic or statistical models, and institutional, exemplifying the adopted principles of housing policy (Cesarski, 2010, pp. 23-24). The subject of this paper is the institutional type models. These models have been and are evolving. Both the rules of classification and their valorization, i.e., the significance of individual models, are changing. The article is for review. Its purpose is (1) to trace the evolution of housing policy models in European countries, (2) to supplement and update the existing classification of their subject in national literature with new approaches, (3) to indicate the directions of these changes. The review provided the basis for formulating two research theses: (1) typologies of housing models are subject to constant changes, which is visible not only at the stage of theoretical concepts but also their applications, (2) market failure as a regulator of meeting housing needs causes an evolution. This evolution creates a movement towards a model in which the state, together with market powers, participates in fulfilling the housing needs (the comprehensive model). The method of critical analysis of literature on the subject was used.
TYPOLOGIES OF MODELS BASED ON THE DEGREE OF STATE INTERFERENCE IN THE MECHANISM OF THE FUNCTIONING OF HOUSING MARKETS Interest in housing policy models dates back to the 1960s and continues to this day. Several models (housing systems) have been identified. The first systematics for Western European countries was formulated by Donnison (1967), distinguishing three models: embryonic, social, and comprehensive. In the embryonic model, in which housing is treated as a subject of consumption, housing investments limit expenditure on fundamental areas of the economy. The state takes a passive role in meeting housing needs. This model has been identified in Greece, Portugal, and Spain. In shaping this model, cultural conditions have taken on an important role, including the model of a large, multi-generational family, which, with the inheritance institution, led to a large share of apartments used by the owners. In the social model, market conditions were decisive for satisfying needs; public support was directed to people unable to meet housing needs on a free market. This model occurred in Great Britain, Ireland, and Switzerland. In the comprehensive model, housing investments were an important growth- forming factor; the state took over responsibility for meeting the housing needs of all citizens. This model has been identified in Germany, the Netherlands, Denmark, Norway, Sweden, Iceland, and Finland.
72 The Evolution of Housing Policy Models in European Countries…
For centrally planned economies, Andrzejewski, cooperating with Donnison (Cesarski, 2013, p. 113), proposed a different division of housing policy models. He distinguished the administrative-subsidy and accumulation- intervention model. Both models were based on the concept of the non- productive function of investment outlays incurred for housing, which meant that these outlays were not included in the economic growth models. The administrative-subsidy model, of an authoritative nature, based on public ownership of resources, a centralized system of accumulation and allocation of funds for housing, was widely used in Central European socialist countries until the 1970s. Both the creation phase, the division phase, and the use phase were co-financed from the state budget. In this model, the price of a dwelling and the level of rent were low10; in the extreme case, the whole amount could be covered by the collective consumption fund (Andrzejewski, 1987, p. 486). The accumulation and intervention model also permitted other forms of housing rights. It allowed individual preferences to be taken into account (Andrzejewski, 1969, pp. 316-322). It began to be implemented in Poland, Czechoslovakia, and Hungary at the turn of the 1960s and 1970s. An important assumption was to base the development of housing construction on the growing share of population resources in financing housing, often in the form of housing cooperatives. Housing policy changed dynamically in the 1980s in western countries and in the 1990s in post-socialist countries. In the West, it was the effect of the so-called welfare state crisis11, beliefs about the unreliability of the state12; in post-socialist countries, it was mainly the effect of political transformation. The slogan "less state, more market" paved the way for a lively discussion of housing policy models, contributing to the extension of their classification. It was dependent on the methods used to analyze housing systems13. Two of
10 In Poland, by the Decree of the National Liberation Committee of 7.IX.1944 on housing commissions (Journal of Laws No. 4, item 18), rents for flats were frozen at the level of 1939. 11 The idea of a welfare state was developed in the 1950s and was popular until the 1980s. Since the 1980s, attempts have been made to limit the development of the welfare state. Among others, external factors were invoked, including economic globalization, which caused pressure to reduce costs, including social spending (cf. Szarfenberg, 2015). 12 The theory of state unreliability was developed within the framework of public choice theory, classified as institutional economics. It deals with research on the demand and supply of public goods. This theory, developed, among others by J. Buchanan focuses not on market flaws, but the flaws of government solutions regarding economic issues. The creators of this concept (Buchanan & Tullock 1962) see the drawbacks of solutions in the fact that individuals making public elections make them primarily based on their own interests (see Legiędź 2005). 13 Lis gives three methods for testing housing systems (Lis, 2005):
Ewa Kucharska-Stasiak, Magdalena Załęczna, Konrad Żelazowski 73 these analyzes: divergence analysis, called the indirect method, and convergence analysis, called the universal method, opened the way for rich classification (Kemeny & Lowe, 1998). In the indirect method, idealized patterns have been created against which individual housing systems are compared. Three main typologies of these models have been distinguished in the literature (Lis, 2005): • typology of J. Barlow and S. Duncan, • typology of J. Kemeny, • J. Doling's typology based on the G. Ambrose typology. In 1994, Barlow and Duncan, developing D. Donnison's classification (Cesarski, 2010, p. 26), distinguished four models: rudimentary, liberal, corporate, and social-democratic. The designation was based on criteria such as the level of housing subsidization, control, and regulation of apartment prices and rents, housing allocation, and organization of new housing. The rudimentary model is the equivalent of the embryonic model in the D. Donnison classification. It occurred in southern European countries, such as Greece, Spain, Portugal, Italy, especially in the south part. It was characterized by a high housing ownership level and a small stock of social housing. The liberal model had a market-oriented attitude, the role of the family was marginal, and the state also interfered and helped in a small way. The state was supporting only a small part of the very low-income society. Such a model was visible in the United States, Australia, to a lesser extent in Great Britain and Ireland. In the corporate model, the state played an active role at the stage of providing housing. This model was used in Austria, Germany, Italy, and Belgium. Social goals were strongly emphasized in the social democratic model. The purpose of resource allocation was to meet housing needs. The role of the family and the market was marginal; the role of the state was crucial; it interfered in determining prices and rents of apartments. Corporate
1) the particular method, which is empirical, treats each country as a unique system, which makes it impossible to apply the results of research under other housing systems. 2) the indirect method, which uses the divergence analysis, enables typologies of individual housing systems, resulting from cultural, ideological, political conditions of the economy, or which are the result of using existing theories of economics and/or sociology. This method makes it possible to translate research results into similar (according to the criteria used) housing systems. 3) the universal method using convergence analysis. It assumes that all states are perceived as subject to the same overarching imperative of change. After meeting certain assumptions, it is justified to transfer solutions between housing systems. The analysis of housing systems based on the indirect and universal method allows distinguishing housing policy models.
74 The Evolution of Housing Policy Models in European Countries… and social democratic models correspond to the comprehensive model of D. Donnison. The discussions on the social democratic model of housing policy emphasize the need to base it on: stabilization measures, both concerning the real estate market (suppressing price fluctuations with the help of various housing policy instruments, depending on the phase of the housing market cycle), as well as in the context of individual households (assistance in the process of investing in their future, primarily through education and professional development). Social solidarity mechanisms were developing. They were visible e.g., in care for the quality of the neighborhood, striving to control access to public rental housing and strengthening self-esteem by providing "regenerative social assistance" that enables housing choices in line with one's lifestyle (Clapham, 2006, pp. 55-76). Kemeny used the indirect method, i.e., the divergence analysis. The basis for distinguishing models is the role of public rented housing in the housing stock. He identified two models: dual and unitary models. In the dual model, which occurred mainly in the Anglo-Saxon countries, the state and private resources participated in satisfying the needs without competing with each other, because they had different recipients. The public sector supplied only the most vulnerable part of society with housing. In the unitary model, which occurred, among others in Sweden, the Netherlands, Germany, and Switzerland, the public sector, which competed with the private sector, played a significant role. A more comprehensive model approach is presented by the typology used by J. Doling, who distinguished the liberal, socialist, corporate, and Asian models. In the liberal model, housing was perceived as a private good; the role of the state was insignificant. The allocation of the resource was determined by the market, the share of ownership or private rental housing dominated. In the socialist model, identified in post-socialist countries, the initiator of development, the course of the process of building and distributing housing rights was the state, setting rents at a minimum. In the corporate model distinguished by J. Doling, which operated in Sweden, Denmark and the Netherlands, the state played a significant role in creating a new supply of residential real estate. In the Asian model, which appeared in Hong Kong and Singapore, Taiwan, Israel, and South Korea, the state was responsible for shaping the environment conducive to housing development; the market principles were subject to the construction and consumption stages. The use of a universal method based on convergence analysis seeks to identify universal principles of the development of housing systems regardless of cultural, political, and social differences. The application of the universal
Ewa Kucharska-Stasiak, Magdalena Załęczna, Konrad Żelazowski 75 method has become the basis for the World Bank experts (Angel and Mayo) to distinguish the housing market model for the countries of transformation (Lis, 2008, p. 30). The central axis of the model was basing the development of the housing sector on reducing the impact of the welfare state and promoting the privatization of the housing stock. In this model, the state assumed the role of the housing sector coordinator, which also improved its functioning. This model only provided for minor social assistance in the form of housing allowances and public housing, addressed solely to the most vulnerable market participants. The model promoted three instruments on the demand side (protection of property rights, development, and regulation of housing financing institutions and rationalization of housing subsidies) and three supply-side tools (regulations regarding spatial development, construction, and maintenance of technical infrastructure devices and organization of the housing sector) as well as one administrative instrument in the form of the development of institutions enabling and supporting the management of the housing sector (Lis, 2008, p. 27). The use of individual instruments was conditioned by the level of economic and institutional development14. The convergence method was also used to construct in the early 1990s an extensive housing system model for former centrally controlled economy countries. The authors of this model, Hegedus and Tosics (1996), showed that in Eastern European countries, similar assumptions were made regarding housing policy. Housing in these countries was treated as part of the social welfare sector and not as a part of the economy being assessed for economic efficiency. The deficit of financial resources allocated to housing intensified the housing deficit. The remedy was private ownership and admission of the market mechanism to housing. The development of housing cooperatives and individual housing was a result. The transition to a market economy was accompanied in these countries by conventional processes and problems in housing. There are also differences between the conditions for the development of post-socialist countries, which allowed the separation of three groups: the Baltic States, reformist countries of Central Europe and the countries of Southeastern
14 The effect of these assumptions was to distinguish four groups of countries: (1) countries with low national income (Tanzania, Bangladesh), (2) highly indebted countries with middle national income (Argentina, Brazil), (3) former countries with a centrally planned economy (countries Central and Eastern Europe, Southeastern Europe and the Baltic States, (4) a group of other middle-income countries (South Korea, Malaysia).
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Europe15. This division was used, among others, to study changes in the development paths of housing systems in post-socialist countries (Soaita & Dewilde, 2017). The authors concluded that despite the passage of years, existing groups continue to develop in their own way "running on parallel tracks." Mainly, a common element was the give-away privatization, which allowed the withdrawal of the broadly understood state from housing obligations towards citizens and caused a fundamental change in the ownership structure - a massive increase in the share of private owners, but without a significant increase in mortgage burdens. This phenomenon is one of the most characteristic elements of the housing markets of post-socialist countries. The differences concerned, among others, changes in the public housing stock for rent. At least two models of reforms of the public housing for rent were created (Lis, 2008, p. 34). In countries such as Bulgaria, Lithuania, Romania, Slovenia, and Hungary, a residual model of public housing stock has been created. It was characterized by a small share of public flats for rent and a slight increase in rents. The second model, called the holistic model, was characterized by a large share of this sector in the stock and high rent increases.
TYPOLOGIES OF MODELS BASED ON THE ACCUMULATION OF HOUSING WEALTH One of the significant approaches used in recent years to classify housing models is to base it on housing wealth accumulation regimes. This criterion does not exclude the rule used so far, based on the degree of state intervention, it is its complement. The main principles of the classification are the level of ownership and the method of financing. Seven models have been distinguished by classification evolution (Wind, Lersch & Dewilde, 2017): regulated rental model, privatized rental, regulated expansion, liberal growth, and family ownership, privatized ownership, and liberal ownership. The models define owners of housing property (their age and price paid for a dwelling), the size of benefits gained from the housing market growth, as well as the possibility of incurring losses resulting from the state of the market. For example, Germany and Austria used housing policy instruments aimed at supporting the tailored quantitative and qualitative structure of the rental segment, using a conservative system of financing the acquisition of property compared to other countries (Wind, Lersch & Dewilde, 2017). Also,
15 The first group includes Lithuania, Latvia, and Estonia; the second includes Poland, the Czech Republic, Slovakia, Hungary, Slovenia, Croatia, and the last includes Bulgaria and Romania, taking into account the countries currently belonging to the EU.
Ewa Kucharska-Stasiak, Magdalena Załęczna, Konrad Żelazowski 77 in the Scandinavian countries, solutions were used to enable the purchase of housing for the less well-off. In Denmark, the Netherlands, and France, thanks to the development of mortgage-based securities, an extension of the repayment period, and an increase in the loan amount concerning the value of the real estate, it was possible to purchase flats by at least middle-income people. A family ownership model that applies to selected Southern European countries is the traditionally supporting property model. In these countries, the role of the state in meeting the housing needs of citizens was limited; the social rental sector was small; the housing stock was privatized after World War II. Lack of a well-developed financing system, tolerance for building arbitrariness meant that family support was of fundamental importance (Allen, 2006). Despite the development of the financing system and reduction of tolerance for building arbitrariness, in the absence of a developed social rental sector, this model still condemns some potential buyers, in particular, young ones, to using family support. Hungary and Slovenia are classified as a privatized ownership model. The reason is the high level of ownership in the past. There was a need to use family support after the state withdrew from housing assistance and extensive privatization resulting in a high level of ownership. Spain is the only country representing the liberal ownership model. It was so classified because of the distinctness of the transformations Spain experienced in the 1990s. Liberal financing rules for the housing market were introduced there, which contributed to the increase in demand for flats and caused a construction boom (Gala et al., 2013). The current family ownership model has evolved into a model based on the financial system (liberal ownership). An essential factor that appeared in recent years and significantly influenced the real shape of housing models was the formulation by the European Commission of the definition of social housing (Report on social housing in the European Union, 2012). This definition narrowed to flats for the most vulnerable households, which meant the need to limit support recipients (Załęczna, 2014).
MODELS OF HOUSING POLICIES AND CHANGES IN HOUSING STOCK Models of housing policy are not only the subject of theoretical considerations; they are of application nature: they have a direct impact on housing conditions in both quantitative and qualitative terms. Taking the number of flats per 1000 inhabitants as an indicator of quantitative improvement can be seen that in
78 The Evolution of Housing Policy Models in European Countries… the years 1951-201116, the most significant development occurred in countries implementing the comprehensive model, i.e., the model with the active role of the state - see Table 1.
Table 1. Models of housing policy and changes in the housing stock in the years 1951-2017 The number of flats per 1000 Change Models/ states inhabitants (%) 1951 1971 2001 2011 1951-2011 2017* 1. Embryonic Greece 223 354 350 362 62.3 601 Portugal 241a 318b 340 371 53.9 573 Spain 225a 313b 352 390 73.3 542 Italy 229 325 372 397 73.3 551 2. Social UK 274 338 408 421 55.1 425 Ireland 239c 235b 342 355 48.5 416 3. Comprehensive Netherlands 235d 286 406 413 75.7 476 Denmark 329f 367b 444 446 35.5 490 Sweden 344f 430 b.d. 423 22.9 480 Finland 247a 317b 442 463 87.4 545 4. Administrative- subsidy and accumulation- intervention /market after 1990 Poland 236f 254 300 330 39.8 379 Czechoslovakia (after 293a 308b 369 390 33.1 454 1990 Czech Republic) Hungary 237f 321e 363 391 64.9 416 * the indicator for 2017 concerns the total number of dwellings a-1950 r, b-1970, c-1961, d-1956, e-1972, f-1960, g-1975, h-1973 Source: own work based on UN Demographic Yearbooks 1948, 1955, 1962, 1971, Compendium of Human Seettlements Statistics 1983 1995, 2001, 2011, Eurostat Census time series, ECB Structural Housing Indicators Statistics.
16 Due to the lack of data on dwellings inhabited during inter-census periods, the total number of housing units was presented in 2017, which, however, does not allow for a direct comparison. Therefore the percentage changes in the years 1951-2011 were calculated.
Ewa Kucharska-Stasiak, Magdalena Załęczna, Konrad Żelazowski 79
In countries such as Finland and the Netherlands, the indicator increased by 87.4% (from 247 to 463) and 75.7% (235 to 413), respectively. Both of these countries had the initial level of the indicator lower than the others in the studied group. The increase in the number of dwellings per 1000 inhabitants in countries with embryonic models that had lower output rates was also high: it was 73.4% in Italy, 73.3% in Spain, 53.9% in Portugal. This pace proved insufficient to catch up with this indicator in countries with a comprehensive model. Weaker growth occurred in countries with a social model with moderate state aid targeted only at the most vulnerable households. The distance between countries with a market economy and the systemic transformation was growing. Both socialist models have led to widening differences in the housing standard between countries with a centrally planned economy and states with a market economy, even with an identified embryonic housing policy model. Not only the primary quantitative but also qualitative indicators evidenced this phenomenon: the share of dwellings with water supply, the functionality of flats, their standard as well as technical condition, indicating the considerable size of the repair gap (Kucharska-Stasiak, 1990). The transition to a poorly defined market model in these countries did not significantly improve the situation, creating new problems caused by housing privatization, see Table 2.
Table 2. Indicator of housing ownership in selected European countries Models/ states Property ownership indicator 1960 1980 1990 2000 2010 2017 1. Embryonic Greece na 70 77 75 77,2 73,3 Portugal 45 52 58 75 74,9 74,7 Spain 53 73 76 84 79,8 77,1 Italy 46 59 64,2 71 72,6 72,4 2. Social UK 42 56 67,5 69 70 65 Ireland 57 71 76 76 73,3 69,5 3. Comprehensive Netherlands 30 42 47,5 53 67,2 69,4 Denmark 40 56 b.d. 61 66,6 62,2 Sweden 47 58 b.d. 53 67,3 65,2 Finland 57 61 65,4 68 74,3 71,4 4. Administrative-subsidy and accumulation- intervention /market after 1990
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Poland na 36 44 55 81,3 84,2 Czechoslovakia (after 1990 na 53 na 50 78,7 78,5 Czech Republic) Hungary na 71 na 92 89,7 85,2 Source: own work based on (Wind, Lersch, Dewilde 2017; CECODHAS, 2006; HYPOSTAT 2019).
Privatization of the housing stock on non-market terms, having the nature of giving-away, enabled the entry of "owners without money" - economically weak people, with low awareness of the effects of decisions taken, thus limiting the ability to accumulate funds for renovation activities. It is noticed that satisfying housing needs by entering into ownership has worsened the mobility of the workforce, making it more challenging to enter the housing market, especially representatives of the young generation17. The increasing concentration of the population in the most attractive locations causes an increase in demand for housing services, a demand that is price inelastic. The supply of capital and labor is becoming more and more flexible, but the supply of housing space is inflexible in the short term. The increase in housing demand and the slow response of the supply side cause prices to rise faster than income. This strikes households, the burden of housing costs is increasing, in particular for those in a worse financial situation (Housing Statistics 2018). There is no comprehensive and harmonized survey of households excluded from the housing market; data on the number of homeless in EU countries is fragmentary. The homeless is a growing problem - in Italy, in 2014, it was calculated that the homeless in cities over 250,000 there are 50.7 thousand inhabitants. In Spain, the total number of homeless people was in the range of 23-35 thousand, in the Netherlands 31 thousand, in Great Britain 78 thousand, in Sweden 33 thousand, in France 143 thousand, in Poland 41,000, 11,500 in the Czech Republic (Feantsa, 2018). Further challenges for housing policy models are present.
CONCLUSION Housing policy models are evolving: both the position of the models and their classification are changing. Discussions on model classifications, particularly in the field of terminology, remain valid (Cesarski, 2010, pp. 23-330). Despite the richness of model divisions, the classification proposed by Donnison seems to
17 Pressure on further sale of public housing stock has been recognized in Poland as a sectoral threat to further housing development. It was also pointed out that the historically and culturally embedded ownership paradigm creates such a threat (Strategia długofalowego rozwoju…2005, s.20).
Ewa Kucharska-Stasiak, Magdalena Załęczna, Konrad Żelazowski 81 be the most universal. Others do not constitute its negation; they are complementary to, developed by introducing additional criteria of valorization, such as the role of rented public housing, the dynamics of rent changes or accumulation of housing wealth. Presented changes in housing policy models lead to the following conclusions: • Political conditions played an important role in shaping housing policy models. The dissimilarity of regimes was the reason for formulating different models for market economies and centrally planned economies, evidencing the significant impact of political, economic concepts. • Within the individual political blocks, no single housing policy model was created. The sources of their diversity are also other factors, such as social or cultural conditions. Solutions adopted in Greece, Portugal, Spain ─ a model of a large, multi-generational family, which, with the inheritance institution, led to a large share of dwellings used by the owners ─ see p. 71. The level of economic and institutional development affected the instruments used in housing policy (see p. 71). • Despite the quantitative and qualitative changes taking place in the housing markets, the consequences of political conditions (attachment to ownership in transformation countries as a response to previous restrictions on private property), combined with social or cultural terms, will continue to shape the path dependence of these markets in individual countries to a significant extent. Despite their evolution, these models are stable within certain limits, and changes are noticed only over many years. Post-socialist countries are an exception because of the shock changes that have been applied. • Adoption in the transformation countries of a specific market model, based on private ownership and at the same time low level of affluence of the society, failed. The housing policy of post-socialist countries has strengthened existing inequalities. The continuation of the market model, preferring only ownership, leads to a violation of the balance between the public and private sectors, necessary for the proper functioning of the economy, threatens the achievement of the objectives of housing policy. • For achieving the objectives of the housing policy, the model in which the state, i.e., the comprehensive model, participates at the stage of satisfying housing needs is more effective. • Solving social problems in the area of satisfying housing needs requires a "new opening" of housing policy as a response to current socio-economic challenges. The controversy around the concept of meeting needs through ownership can mean the evolution of housing policy models
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towards the development of public or social housing (Polakowski et al., 2017). This direction in the evolution of models can be supported by the event of a sharing economy that will encourage, especially young people, to rent and not to own.
ACKNOWLEDGEMENTS AND FINANCIAL DISCLOSURE The work is part of the project that has received funding from The Polish National Science Centre under the grant agreement No UMO- 2016/21/B/HS4/00750.
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Jubilee Conference of the Department of Investment and Real Estate of the Poznan University of Economics and Business entitled “Real Estate Market Border” (Poznan, 9-10 May 2019) Katarzyna Kania1 1 Cracow University of Economics, Poland, ORCID: http://orcid.org/0000-0003-4116-5971 [email protected]
87 Katarzyna Kania
From 9th to 10th May 2019 in Poznan was held Conference entitled „Real Estate Market Border”. The conference was organized by The Investment and Real Estate Department of Poznan University of Economics and Business to celebrate 50 Jubilees existing of Department. The honorary patronage was taken over by His Magnificence prof. Maciej Żukowski, Rector of the University of Economics and Business in Poznan and Jacek Jaśkowiak, President of the City of Poznan. The main target of this event was to create a forum to exchange experiences and analyze the present situation on the real estate market. Moreover the hosts tried to encourage to deeper reflection on perpective and directions and form of development of this market. Majority of participants were from national universities and native departments but there were also the friends from international coperated academic and industry society. So the conference was adressed to wide associated group of academics and business representatives such as: • developers, investors, representatives of construction companies, • management staff of real estate agents, • property managers, real estate appraisers, • representatives of institutions financing the real estate market, • representatives of real estate consultancy companies • real estate scientists. On behalf of the conference’s organizers its participants and guests were welcomed by Profesor Piotr Bartkowiak – The Head of Department of Investment and Real Estate. The worlds of welcome were also adressed to the conference participants by the Vice-Rector of Poznan University of Economics and Business. Due to the fact that the conference had a jubilee character, many thanks and congratulations were given to the start of the conference which were continued during the Gala Dinner. As the first, former Head of the jubilarian - Professor Gawron presented the history of the foundation and development of the Department. As the next lectures introducing to the subject of the conference were presented by: • Prof. Marek Bryx from Warsaw School of Economics • Prof. Ewa Kucharska-Stasiak from University of Lodz • Prof. Magdalena Habdas from University of Silesian in Katowice The subject of the conference was related to the basic area of the functional real estate market - trade, management, valuation, financing, and real estate investments. To accomplish the objectives reffered to above , the formula of conference to be adopted was based on discusions which were diveded into thematic panels within the right order on the first and second day. The first panel entitled Limits of the Real Estate Market run by Phd Anna Mazurczak from Poznan University of Economics. The participants of this
“Real Estate Market Border” (Poznan, 9-10 May 2019) 88 discussion were: Bartosz Guss, Vice President of Poznan, Prof. Marek Bryx, Warsal School Of Economics , Prof. Henryk Gawron, Poznan University of Economics and Business, Andrzej Marszałek, President of the Branch of the Polish Association of Developers in Poznan, Sebastian Bedekier, Regional Director of Colliers International, Małgorzata Wojton, Leasing Manager Skanska S.A. The second panel was devoted to the development of Local Real Estate Markets conducted by Phd Sławomir Palicki from Poznan University of Economics and Business. The following guests were invited to the conversation: Prof. Stanisław Belniak from Cracow University of Economics, Phd Łukasz Straczkowski, Poznan University of Economics, Patryk Frąckowiak, Partner Celka&Frackowiak Ltd., Phd Paweł Nowacki, Investment Manager BBI Development Inc., Marta Politowicz, Regional Sales Manager, Echo Investment Inc., Tomasz Lewandowski, President of the Board of Municipal Residential Resources in Poznan On the second conference day, two more discussion panels took place. The first panel entitled Funding Sector and the Real Estate Market which was conducted by Phd Anna Mazurczak from Poznan University of Economics and Business. The following guests took part in the conversation: Prof. Gabriel Glowka, Warsal School of Economics, Phd. Eng. Eryk Glodzinski, Politechnika Warszawska, Prof. Krystian Pera, University of Economics in Katowice, Malgorzata Kosinska, President of the REIT Poland Association Second panel was devoted to Spatial Management and Cooperation With Local Governments in the Real Estate Sector and it was conducted by Phd Anna Bernaciak from Poznan University of Economics and Business. The invited interlocutors were: Phd Maciej Koszel, Poznan University of Economics, Prof. Anna Szelągowska, Warsal School of Economics, Prof. Eng. Adam Nadolny, Poznan University of Technology, Prof. Adam Nalepka, Cracow University of Economics, Piotr Sobczak, City Architect, Director of the Urban Planning and Architecture Department. In this place it is worth adding that there was also a session dedicated to property appraisers during which Phd Agnieszka Małkowska presented the problem of Pressure on Estimating the Value of Real Estate. Also during the conference, you could watch the prepared posters from the conducted research. The direct effect of this conference will be occasional book publication which organizers promissed to prepare after conference as soon as possible.
THE FOUNDER >> ISSN 1231-8841 >> eISSN 2450-534X PUBLISHER OF THE FOUNDATION World of he Foundation of the Cracow University of Economics, in addition ŚWIAT NIERUCHOMOŚCI >> 108 (2/2019) Tto its statutory purposes which include, among others: Conducting broad educational activity for the academic circles in social, economic and technical sciences, REAL ESTATE Initiating actions supporting the European integration and developing contacts and cooperation among societies, Funding scholarships and other kind of material assistance to students URBAN AND from poor families, including, in particular, students of the Cracow University of Economics coming from the country, REGIONAL DEVELOPMENT Improvement in the social and living conditions of disabled students and in the accessibility of classes organized by the Cracow University of INVESTMENTS Economics, it decides to enrich its economic activity by the following scope of services:
Conference offer Providing financial services in conference budget Developing the programme of an event and coordination during it Full scope of accommodation and catering services Preparing detailed post-conference reports
Publishing offer Publishing books and albums Professional graphic design Typesetting and proof services Quick print according to individual guidelines
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