88 M. Renigier-Biłozor, R. WiśniewskiANALYSIS OF THE EFFICIENCY OF SELECTED RE MARKETS IN POLANDData from various real estate markets in Poland for 2008–2010 are presented in Table1 with reference to population statistics. The analyzed data constitute a benchmarkfor measuring the size and efficiency of real estate markets in selected Polish cities, andit accounts for: population, unemployment rate, average gross monthly wages, area insquare kilometers, number of real estate transactions separately for land plots and apartments,and the average price per sq. m. of apartment area. The data have been used toanalyze real estate market efficiency. At this stage of the analysis, the choice of data wasdictated by the ease of acquisition and the availability of the relevant information. Thereal number of transactions on a given local market (city) proved to be most problematic.It could be postulated that the level of difficulty with acquiring the relevant data wasreversely proportional to city size (population and area). According to the authors, theabove theory is supported by the following arguments:––––lack of data gathering systems in the public domain,lack of data sorting algorithms in units and departments responsible for data accumulation,lack of advanced systems for updating, processing and releasing data,the units and departments responsible for gathering public information are reluctantto create access to the data.If the efficiency of Polish real estate markets were to be evaluated based on the criterionof data availability, the majority of Polish cities would receive low or very lowmarks. Access to information is an important, yet not the only factor determining marketefficiency. Two indicators were computed based on the assumption that the acquired dataare credible:1. PO/RET – population per 1 real estate transaction,2. HA/GW – housing area in square meters that can be purchased with an average grossmonthly wage.The two indicators can be used to perform a simplified classification of the efficiencyof selected real estate markets in Poland. The first indicator, PO/RET, indicates the size ofthe local population per 1 real estate transaction, and the higher its value the lower the efficiencyof the local market. The second indicator, HA/GW, is a price to income ratio thatmeasures the affordability of real estate, and the higher its value, the higher the efficiencyof the real estate market. The value of the second indicator illustrates the correlation betweenreal estate prices and incomes on the local market.Real estate markets are ranked according to the adopted indicators in Tables 2 and3. An analysis of Table 2 data indicates that a given market’s place in the ranking is notdetermined by the size of the city, its population or the unemployment rate. The rankingis topped by medium-sized cities with a population nearing 100,000 – Zielona Góra,Koszalin and Słupsk. Table 3 suggests a certain trend, namely that real estate prices aremore affordable in smaller cities, in this case – Ciechanów, Działdowo and Kętrzyn. Ananalysis of both tables shows a certain analogy as regards similar positions occupied byBydgoszcz, Łódź, Suwałki and Ełk.Acta Sci. Pol.
Table 1. Efficiency of real estate markets in PolandTabela 1. Sprawność rynków nieruchomości w PolsceNo. City PopulationUnemploymentrateGrossmonthly wagein PLNNo. of transactions Average Population/No. ofArea inkm 2 Landprice transactionsApartmentsplotsPLN/m 2 [PO/RET]Average wage//Average price per m 2[HA/GW]1 Olsztyn 176457 4.5 2830 88.33 224 717 4765 188 0.592 Słupsk 97331 9.2 2667 43.15 91 816 3783 107 0.703 Suwałki 69448 13.4 3645 66.00 270 124 4433 176 0.824 Ciechanów 45270 5.7 2994 32.51 131 182 2503 145 1.205 Wrocław 632162 5.0 3415 292.82 159 2661 6740 224 0.516 Działdowo 21644 6.6 2546 11.47 17 60 2401 281 1.067 Inowrocław 76137 20.4 2789 30.42 11 25 3443 2115 0.818 Gdańsk 456591 5.1 4053 261.68 26 1728 6215 260 0.659 Kraków 755000 4.6 3424 326.00 127 2298 7260 311 0.4710 Koszalin 106987 4.7 2932 98.33 258 805 4112 101 0.7111 Kętrzyn 27942 27.5 2423 10.35 9 31 2345 698 1.0312 Toruń 193115 8.3 3175 115.75 49 252 4666 642 0.6813 Gołdap 13514 5.7 2361 17.20 4 5 2432 1501 0.9714 Poznań 554221 3.3 3669 261.85 83 1292 5800 403 0.6315 Łódź 742387 9.5 3159 293.25 251 2165 4666 307 0.6816 Bydgoszcz 357650 7.3 2830 175.98 61 1235 4125 276 0.6917 Zielona Góra 117503 7.5 3060 58.00 12 615 3446 91 0.8918 Ełk 57579 12.2 2584 21.00 72 252 2990 178 0.8619 Elbląg 127954 16.5 2521 38.94 87 821 3894 141 0.6520 Białystok 294685 11.6 3145 102.00 74 324 4660 740 0.67Source: Own research based on: http://www.stat.gov.pl/cps/rde/xbcr/gus/PUBL_PBS_transakcje_kupna_sprzedazy_nieruch_2008.pdf; http://www.mi.gov.pl/2-492414ae09dd9-1793287-p_1.htm; www.money.pl; www.egospodarka.pl, www.gratka.pl, www.oferty.net.pl, http://www.stat.gov.pl/cps/rde/xbcr/gus/PUBL_ik_obrot_nieruchomosciami_2009.pdf, information given by municipal housing departments.Źródło: Opracowanie własne na podstawie: http://www.stat.gov.pl/cps/rde/xbcr/gus/PUBL_PBS_transakcje_kupna_sprzedazy_nieruch_2008.pdf; http://www.mi.gov.pl/2-492414ae09dd9-1793287-p_1.htm; www.money.pl; www.egospodarka.pl, www.gratka.pl, www.oferty.net.pl, http://www.stat.gov.pl/cps/rde/xbcr/gus/PUBL_ik_obrot_nieruchomosciami_2009.pdf, informacji udzielonych przez pracowników urzędów miast.
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Oeconomia 10 (1) 2011, 35-43EDUCATI
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Oeconomia 10 (1) 2011, 159-169CHANG
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Changes in rural women’s movement
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Ewa SzymańskaTourism function of M