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				col1.value as col1,
				col2.value as col2,
				col3.value as col3,
				convert(col4.value,SIGNED) as col4,
				convert(REPLACE(col5.value,' ',''),SIGNED) as col5,
				col6.value as col6,
				col7.value as col7,
				col8.value as col8,
				col9.value as col9,
				col10.value as col10,
				col11.value as col11,
				#__hp_properties.type as type,
				types.value as types
				from #__hp_properties
				left join  #__hp_properties2 as col1 on #__hp_properties.id=col1.property and col1.field=183
				left join  #__hp_properties2 as col2 on #__hp_properties.id=col2.property and col2.field=184
				left join  #__hp_properties2 as col3 on #__hp_properties.id=col3.property and col3.field=185
				left join  #__hp_properties2 as col4 on #__hp_properties.id=col4.property and col4.field=186
				left join  #__hp_properties2 as col5 on #__hp_properties.id=col5.property and col5.field=187
				left join  #__hp_properties2 as col6 on #__hp_properties.id=col6.property and col6.field=188
				left join  #__hp_properties2 as col7 on #__hp_properties.id=col7.property and col7.field=189	
				
				left join  #__hp_properties2 as col8 on #__hp_properties.id=col8.property and col8.field=195
				left join  #__hp_properties2 as col9 on #__hp_properties.id=col9.property and col9.field=193
				left join  #__hp_properties2 as col10 on #__hp_properties.id=col10.property and col10.field=178
				left join  #__hp_properties2 as col11 on #__hp_properties.id=col11.property and col11.field=194		
				
				left join  #__hp_properties2 as types on #__hp_properties.id=types.property and types.field=25				
				where #__hp_properties.published=1 and #__hp_properties.type=2 and (#__hp_properties.id in 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        (
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            [col5] => 2
            [col6] => 148 000
            [col7] => 5 156
            [col8] => tak
            [col9] => 
            [col10] => 
            [col11] => 
            [type] => 2
            [types] => Sprzedaż
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    [2683] => Array
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            [state] => 5126
            [col1] => Białystok / Dziesięciny
            [col2] => ul. Jarzębinowa
            [col3] => 3
            [col4] => 60
            [col5] => 10
            [col6] => 237 000
            [col7] => 3 927
            [col8] => tak
            [col9] => 
            [col10] => 
            [col11] => 
            [type] => 2
            [types] => Sprzedaż
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    [2679] => Array
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            [state] => 5124
            [col1] => Białystok / Nowe Miasto
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            [col3] => 2
            [col4] => 54
            [col5] => 3
            [col6] => 259 000
            [col7] => 4 726
            [col8] => tak
            [col9] => 
            [col10] => 
            [col11] => 
            [type] => 2
            [types] => Sprzedaż
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    [2680] => Array
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            [state] => 5121
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            [col3] => 2
            [col4] => 50
            [col5] => 2
            [col6] => 189 000
            [col7] => 3 713
            [col8] => tak
            [col9] => 
            [col10] => 
            [col11] => 
            [type] => 2
            [types] => Sprzedaż
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    [2677] => Array
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            [state] => 5118
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            [col2] => ul. Al. Piłsudskiego
            [col3] => 2
            [col4] => 46
            [col5] => 6
            [col6] => 208 000
            [col7] => 4 521
            [col8] => tak
            [col9] => 
            [col10] => 
            [col11] => 
            [type] => 2
            [types] => Sprzedaż
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    [2676] => Array
        (
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            [state] => 5117
            [col1] => Białystok / Leśna Dolina
            [col2] => ul. Niska
            [col3] => 2
            [col4] => 43
            [col5] => 0
            [col6] => 219 000
            [col7] => 5 022
            [col8] => tak
            [col9] => 
            [col10] => 
            [col11] => 
            [type] => 2
            [types] => Sprzedaż
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    [2673] => Array
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            [col5] => 2
            [col6] => 590 000
            [col7] => 6 982
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            [col10] => 
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            [col7] => 4 946
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            [col9] => 
            [col10] => 
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            [type] => 2
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    [2663] => Array
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            [col7] => 5 277
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    [2661] => Array
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            [col7] => 3 727
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            [col9] => 
            [col10] => 
            [col11] => 
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)
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