11 research outputs found
ΠΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΡΠ½ΠΊΠΎΠ² ΠΆΠΈΠ»ΠΎΠΉ Π½Π΅Π΄Π²ΠΈΠΆΠΈΠΌΠΎΡΡΠΈ ΠΊΡΡΠΏΠ½Π΅ΠΉΡΠΈΡ Π³ΠΎΡΠΎΠ΄ΠΎΠ² Π ΠΎΡΡΠΈΠΈ
Π‘ΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΠ΅ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΌΠ°ΡΡΠΎΠ²ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΡΠ½ΠΎΡΠ½ΠΎΠΉ ΡΡΠΎΠΈΠΌΠΎΡΡΠΈ ΠΆΠΈΠ»ΡΡ
ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΎΠ±Π»Π°Π΄Π°ΡΡ ΡΡΠ΄ΠΎΠΌ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΊΠΎΠ²: ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΠ΅ Π΄Π»Ρ ΠΊΠ°ΠΊΠΎΠ³ΠΎ-Π»ΠΈΠ±ΠΎ ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΡΠ΅Π³ΠΈΠΎΠ½Π° ΠΌΠΎΠ΄Π΅Π»ΠΈ Π½Π΅ Π³ΠΎΠ΄ΡΡΡΡ Π΄Π»Ρ Π΄ΡΡΠ³ΠΈΡ
ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ². ΠΡΠ΅ ΠΎΠ½ΠΈ Π±ΡΡΡΡΠΎ ΡΡΡΠ°ΡΠ΅Π²Π°ΡΡ ΠΈ ΡΡΠ΅Π±ΡΡΡ ΠΏΠΎΡΡΠΎΡΠ½Π½ΠΎΠΉ Π°ΠΊΡΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ, ΠΏΠΎΡΠΊΠΎΠ»ΡΠΊΡ Π½Π΅ ΡΡΠΈΡΡΠ²Π°ΡΡ ΠΏΠΎΡΡΠΎΡΠ½Π½ΠΎ ΠΌΠ΅Π½ΡΡΡΡΡΡΡ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΡΡ ΠΎΠ±ΡΡΠ°Π½ΠΎΠ²ΠΊΡ. ΠΠ½ΠΈ Π½Π΅ ΠΏΡΠΈΠ³ΠΎΠ΄Π½Ρ Π΄Π»Ρ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΡΡΠΎΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ Π±ΠΈΠ·Π½Π΅ΡΠ°. Π¦Π΅Π»ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠΎΠ·Π΄Π°Π½ΠΈΠ΅ ΡΠΈΡΡΠ΅ΠΌΡ ΠΎΡΠ΅Π½ΠΊΠΈ Π½Π΅Π΄Π²ΠΈΠΆΠΈΠΌΠΎΡΡΠΈ Π³ΠΎΡΠΎΠ΄ΠΎΠ² Π ΠΎΡΡΠΈΠΈ, ΠΏΡΠΈΠΌΠ΅Π½ΠΈΠΌΠΎΠΉ ΠΊ Π»ΡΠ±ΡΠΌ Π΅Π΅ ΡΠ΅Π³ΠΈΠΎΠ½Π°ΠΌ, ΠΏΡΠΈΡΠ΅ΠΌ Π½Π΅Π·Π°Π²ΠΈΡΠΈΠΌΠΎ ΠΎΡ ΠΏΠΎΡΡΠΎΡΠ½Π½ΠΎ ΠΌΠ΅Π½ΡΡΡΠ΅ΠΉΡΡ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΈΡΡΠ°ΡΠΈΠΈ. ΠΡΠ° ΡΠ΅Π»Ρ Π±ΡΠ»Π° Π΄ΠΎΡΡΠΈΠ³Π½ΡΡΠ° Π±Π»Π°Π³ΠΎΠ΄Π°ΡΡ ΡΠΎΠΌΡ, ΡΡΠΎ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ Π²Ρ
ΠΎΠ΄Π½ΡΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ ΠΏΠΎΠΌΠΈΠΌΠΎ ΡΡΡΠΎΠΈΡΠ΅Π»ΡΠ½ΠΎ-ΡΠΊΡΠΏΠ»ΡΠ°ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ² Π±ΡΠ»ΠΈ ΡΡΡΠ΅Π½Ρ Π³Π΅ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡ, ΡΠ°ΠΊΡΠΎΡ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΡΠ΄ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ², Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΡΡΠΈΡ
ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΡΡ ΡΠΈΡΡΠ°ΡΠΈΡ Π² ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΡΡ
ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
, Π² Π ΠΎΡΡΠΈΠΈ ΠΈ Π² ΠΌΠΈΡΠ΅. Π‘ΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π΄Π°Π½Π½ΡΠ΅ ΠΎ ΡΡΠ½ΠΊΠ°Ρ
Π½Π΅Π΄Π²ΠΈΠΆΠΈΠΌΠΎΡΡΠΈ Π Π€, Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΡΠ΅ Π΄Π»Ρ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ, Π±ΡΠ»ΠΈ ΡΠΎΠ±ΡΠ°Π½Ρ Π·Π° Π΄Π»ΠΈΡΠ΅Π»ΡΠ½ΡΠΉ ΠΏΠ΅ΡΠΈΠΎΠ΄ Ρ 2006 Π³. ΠΏΠΎ 2020 Π³., ΡΡΠΎ ΠΎΠ±ΡΡΠ»ΠΎΠ²ΠΈΠ»ΠΎ Π΅Π΅ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ²ΠΎΠΉΡΡΠ²Π°. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΏΡΠΈΠΌΠ΅ΡΠ° ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌΡ Π±ΡΠ»ΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Ρ Π²ΠΈΡΡΡΠ°Π»ΡΠ½ΡΠ΅ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΠ΅ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡ, ΠΊΠΎΡΠΎΡΡΠ΅, Π½Π°ΠΏΡΠΈΠΌΠ΅Ρ, ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ, ΡΡΠΎ Π² ΠΠΎΡΠΊΠ²Π΅ ΡΠ°ΠΌΡΡ Π²ΡΡΠΎΠΊΡΡ ΡΠ΄Π΅Π»ΡΠ½ΡΡ ΡΡΠΎΠΈΠΌΠΎΡΡΡ ΠΊΠ²Π°Π΄ΡΠ°ΡΠ½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΡΠ° ΠΈΠΌΠ΅ΡΡ ΠΎΠ΄Π½ΠΎΠΊΠΎΠΌΠ½Π°ΡΠ½ΡΠ΅ ΠΊΠ²Π°ΡΡΠΈΡΡ ΠΌΠΈΠ½ΠΈΠΌΠ°Π»ΡΠ½ΡΡ
ΡΠ°Π·ΠΌΠ΅ΡΠΎΠ² β 16 ΠΌ2. ΠΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½Π°Ρ ΡΡΠΎΠΈΠΌΠΎΡΡΡ Π΄Π²ΡΡ
ΠΊΠΎΠΌΠ½Π°ΡΠ½ΡΡ
ΠΊΠ²Π°ΡΡΠΈΡ Π΄ΠΎΡΡΠΈΠ³Π°Π΅ΡΡΡ ΠΏΡΠΈ ΠΈΡ
ΠΏΠ»ΠΎΡΠ°Π΄ΠΈ 90 ΠΌ2, ΡΡΠ΅Ρ
ΠΊΠΎΠΌΠ½Π°ΡΠ½ΡΡ
β 100 ΠΌ2, ΡΠ΅ΡΡΡΠ΅Ρ
ΠΊΠΎΠΌΠ½Π°ΡΠ½ΡΡ
β 110 ΠΌ2, ΠΏΡΡΠΈΠΊΠΎΠΌΠ½Π°ΡΠ½ΡΡ
β 120 ΠΌ2. ΠΠ»Ρ ΡΡΠ»ΠΎΠ²ΠΈΠΉ ΠΠΊΠ°ΡΠ΅ΡΠΈΠ½Π±ΡΡΠ³Π° ΡΡΠ΅Π΄ΠΈ Π΄Π²ΡΡ
ΠΊΠΎΠΌΠ½Π°ΡΠ½ΡΡ
ΠΊΠ²Π°ΡΡΠΈΡ Π½Π°ΠΈΠ±ΠΎΠ»ΡΡΡΡ ΡΡΠΎΠΈΠΌΠΎΡΡΡ ΠΊΠ²Π°Π΄ΡΠ°ΡΠ½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΡΠ° ΠΈΠΌΠ΅ΡΡ ΠΊΠ²Π°ΡΡΠΈΡΡ ΠΎΠ±ΡΠ΅ΠΉ ΠΏΠ»ΠΎΡΠ°Π΄ΡΡ 30 ΠΌ2, ΡΡΠ΅Ρ
ΠΊΠΎΠΌΠ½Π°ΡΠ½ΡΡ
β 110 ΠΌ2, ΡΠ΅ΡΡΡΠ΅Ρ
ΠΊΠΎΠΌΠ½Π°ΡΠ½ΡΡ
β 130 ΠΌ2, ΠΏΡΡΠΈΠΊΠΎΠΌΠ½Π°ΡΠ½ΡΡ
β 150 ΠΌ2. Π’Π°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ, ΡΠΈΡΡΠ΅ΠΌΠ° ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Π° Π΄Π»Ρ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΡΡΠΎΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ Π±ΠΈΠ·Π½Π΅ΡΠ°. ΠΠ½Π° ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΏΠΎΠ»Π΅Π·Π½Π° Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΡΠΌ ΡΡΡΡΠΊΡΡΡΠ°ΠΌ, Π·Π°Π½ΠΈΠΌΠ°ΡΡΠΈΠΌΡΡ Π²ΠΎΠΏΡΠΎΡΠ°ΠΌΠΈ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΡΠ½ΠΊΠΎΠΌ Π³ΠΎΡΠΎΠ΄ΡΠΊΠΎΠΉ Π½Π΅Π΄Π²ΠΈΠΆΠΈΠΌΠΎΡΡΠΈ, Π²ΠΎΠΏΡΠΎΡΠ°ΠΌΠΈ ΠΈΠΌΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ Π½Π°Π»ΠΎΠ³ΠΎΠΎΠ±Π»ΠΎΠΆΠ΅Π½ΠΈΡ, Π²ΠΎΠΏΡΠΎΡΠ°ΠΌΠΈ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΆΠΈΠ»ΠΈΡΠ½ΠΎΠ³ΠΎ ΡΡΠ½ΠΊΠ°
ΠΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΡΠ½ΠΊΠΎΠ² ΠΆΠΈΠ»ΠΎΠΉ Π½Π΅Π΄Π²ΠΈΠΆΠΈΠΌΠΎΡΡΠΈ ΠΊΡΡΠΏΠ½Π΅ΠΉΡΠΈΡ Π³ΠΎΡΠΎΠ΄ΠΎΠ² Π ΠΎΡΡΠΈΠΈ
Π‘ΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΠ΅ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΌΠ°ΡΡΠΎΠ²ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΡΠ½ΠΎΡΠ½ΠΎΠΉ ΡΡΠΎΠΈΠΌΠΎΡΡΠΈ ΠΆΠΈΠ»ΡΡ
ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΎΠ±Π»Π°Π΄Π°ΡΡ ΡΡΠ΄ΠΎΠΌ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΊΠΎΠ²: ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΠ΅ Π΄Π»Ρ ΠΊΠ°ΠΊΠΎΠ³ΠΎ-Π»ΠΈΠ±ΠΎ ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΡΠ΅Π³ΠΈΠΎΠ½Π° ΠΌΠΎΠ΄Π΅Π»ΠΈ Π½Π΅ Π³ΠΎΠ΄ΡΡΡΡ Π΄Π»Ρ Π΄ΡΡΠ³ΠΈΡ
ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ². ΠΡΠ΅ ΠΎΠ½ΠΈ Π±ΡΡΡΡΠΎ ΡΡΡΠ°ΡΠ΅Π²Π°ΡΡ ΠΈ ΡΡΠ΅Π±ΡΡΡ ΠΏΠΎΡΡΠΎΡΠ½Π½ΠΎΠΉ Π°ΠΊΡΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ, ΠΏΠΎΡΠΊΠΎΠ»ΡΠΊΡ Π½Π΅ ΡΡΠΈΡΡΠ²Π°ΡΡ ΠΏΠΎΡΡΠΎΡΠ½Π½ΠΎ ΠΌΠ΅Π½ΡΡΡΡΡΡΡ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΡΡ ΠΎΠ±ΡΡΠ°Π½ΠΎΠ²ΠΊΡ. ΠΠ½ΠΈ Π½Π΅ ΠΏΡΠΈΠ³ΠΎΠ΄Π½Ρ Π΄Π»Ρ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΡΡΠΎΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ Π±ΠΈΠ·Π½Π΅ΡΠ°. Π¦Π΅Π»ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠΎΠ·Π΄Π°Π½ΠΈΠ΅ ΡΠΈΡΡΠ΅ΠΌΡ ΠΎΡΠ΅Π½ΠΊΠΈ Π½Π΅Π΄Π²ΠΈΠΆΠΈΠΌΠΎΡΡΠΈ Π³ΠΎΡΠΎΠ΄ΠΎΠ² Π ΠΎΡΡΠΈΠΈ, ΠΏΡΠΈΠΌΠ΅Π½ΠΈΠΌΠΎΠΉ ΠΊ Π»ΡΠ±ΡΠΌ Π΅Π΅ ΡΠ΅Π³ΠΈΠΎΠ½Π°ΠΌ, ΠΏΡΠΈΡΠ΅ΠΌ Π½Π΅Π·Π°Π²ΠΈΡΠΈΠΌΠΎ ΠΎΡ ΠΏΠΎΡΡΠΎΡΠ½Π½ΠΎ ΠΌΠ΅Π½ΡΡΡΠ΅ΠΉΡΡ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΈΡΡΠ°ΡΠΈΠΈ. ΠΡΠ° ΡΠ΅Π»Ρ Π±ΡΠ»Π° Π΄ΠΎΡΡΠΈΠ³Π½ΡΡΠ° Π±Π»Π°Π³ΠΎΠ΄Π°ΡΡ ΡΠΎΠΌΡ, ΡΡΠΎ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ Π²Ρ
ΠΎΠ΄Π½ΡΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ ΠΏΠΎΠΌΠΈΠΌΠΎ ΡΡΡΠΎΠΈΡΠ΅Π»ΡΠ½ΠΎ-ΡΠΊΡΠΏΠ»ΡΠ°ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ² Π±ΡΠ»ΠΈ ΡΡΡΠ΅Π½Ρ Π³Π΅ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡ, ΡΠ°ΠΊΡΠΎΡ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΡΠ΄ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ², Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΡΡΠΈΡ
ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΡΡ ΡΠΈΡΡΠ°ΡΠΈΡ Π² ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΡΡ
ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
, Π² Π ΠΎΡΡΠΈΠΈ ΠΈ Π² ΠΌΠΈΡΠ΅. Π‘ΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π΄Π°Π½Π½ΡΠ΅ ΠΎ ΡΡΠ½ΠΊΠ°Ρ
Π½Π΅Π΄Π²ΠΈΠΆΠΈΠΌΠΎΡΡΠΈ Π Π€, Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΡΠ΅ Π΄Π»Ρ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ, Π±ΡΠ»ΠΈ ΡΠΎΠ±ΡΠ°Π½Ρ Π·Π° Π΄Π»ΠΈΡΠ΅Π»ΡΠ½ΡΠΉ ΠΏΠ΅ΡΠΈΠΎΠ΄ Ρ 2006 Π³. ΠΏΠΎ 2020 Π³., ΡΡΠΎ ΠΎΠ±ΡΡΠ»ΠΎΠ²ΠΈΠ»ΠΎ Π΅Π΅ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ²ΠΎΠΉΡΡΠ²Π°. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΏΡΠΈΠΌΠ΅ΡΠ° ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌΡ Π±ΡΠ»ΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Ρ Π²ΠΈΡΡΡΠ°Π»ΡΠ½ΡΠ΅ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΠ΅ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡ, ΠΊΠΎΡΠΎΡΡΠ΅, Π½Π°ΠΏΡΠΈΠΌΠ΅Ρ, ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ, ΡΡΠΎ Π² ΠΠΎΡΠΊΠ²Π΅ ΡΠ°ΠΌΡΡ Π²ΡΡΠΎΠΊΡΡ ΡΠ΄Π΅Π»ΡΠ½ΡΡ ΡΡΠΎΠΈΠΌΠΎΡΡΡ ΠΊΠ²Π°Π΄ΡΠ°ΡΠ½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΡΠ° ΠΈΠΌΠ΅ΡΡ ΠΎΠ΄Π½ΠΎΠΊΠΎΠΌΠ½Π°ΡΠ½ΡΠ΅ ΠΊΠ²Π°ΡΡΠΈΡΡ ΠΌΠΈΠ½ΠΈΠΌΠ°Π»ΡΠ½ΡΡ
ΡΠ°Π·ΠΌΠ΅ΡΠΎΠ² β 16 ΠΌ2. ΠΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½Π°Ρ ΡΡΠΎΠΈΠΌΠΎΡΡΡ Π΄Π²ΡΡ
ΠΊΠΎΠΌΠ½Π°ΡΠ½ΡΡ
ΠΊΠ²Π°ΡΡΠΈΡ Π΄ΠΎΡΡΠΈΠ³Π°Π΅ΡΡΡ ΠΏΡΠΈ ΠΈΡ
ΠΏΠ»ΠΎΡΠ°Π΄ΠΈ 90 ΠΌ2, ΡΡΠ΅Ρ
ΠΊΠΎΠΌΠ½Π°ΡΠ½ΡΡ
β 100 ΠΌ2, ΡΠ΅ΡΡΡΠ΅Ρ
ΠΊΠΎΠΌΠ½Π°ΡΠ½ΡΡ
β 110 ΠΌ2, ΠΏΡΡΠΈΠΊΠΎΠΌΠ½Π°ΡΠ½ΡΡ
β 120 ΠΌ2. ΠΠ»Ρ ΡΡΠ»ΠΎΠ²ΠΈΠΉ ΠΠΊΠ°ΡΠ΅ΡΠΈΠ½Π±ΡΡΠ³Π° ΡΡΠ΅Π΄ΠΈ Π΄Π²ΡΡ
ΠΊΠΎΠΌΠ½Π°ΡΠ½ΡΡ
ΠΊΠ²Π°ΡΡΠΈΡ Π½Π°ΠΈΠ±ΠΎΠ»ΡΡΡΡ ΡΡΠΎΠΈΠΌΠΎΡΡΡ ΠΊΠ²Π°Π΄ΡΠ°ΡΠ½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΡΠ° ΠΈΠΌΠ΅ΡΡ ΠΊΠ²Π°ΡΡΠΈΡΡ ΠΎΠ±ΡΠ΅ΠΉ ΠΏΠ»ΠΎΡΠ°Π΄ΡΡ 30 ΠΌ2, ΡΡΠ΅Ρ
ΠΊΠΎΠΌΠ½Π°ΡΠ½ΡΡ
β 110 ΠΌ2, ΡΠ΅ΡΡΡΠ΅Ρ
ΠΊΠΎΠΌΠ½Π°ΡΠ½ΡΡ
β 130 ΠΌ2, ΠΏΡΡΠΈΠΊΠΎΠΌΠ½Π°ΡΠ½ΡΡ
β 150 ΠΌ2. Π’Π°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ, ΡΠΈΡΡΠ΅ΠΌΠ° ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Π° Π΄Π»Ρ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΡΡΠΎΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ Π±ΠΈΠ·Π½Π΅ΡΠ°. ΠΠ½Π° ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΏΠΎΠ»Π΅Π·Π½Π° Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΡΠΌ ΡΡΡΡΠΊΡΡΡΠ°ΠΌ, Π·Π°Π½ΠΈΠΌΠ°ΡΡΠΈΠΌΡΡ Π²ΠΎΠΏΡΠΎΡΠ°ΠΌΠΈ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΡΠ½ΠΊΠΎΠΌ Π³ΠΎΡΠΎΠ΄ΡΠΊΠΎΠΉ Π½Π΅Π΄Π²ΠΈΠΆΠΈΠΌΠΎΡΡΠΈ, Π²ΠΎΠΏΡΠΎΡΠ°ΠΌΠΈ ΠΈΠΌΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ Π½Π°Π»ΠΎΠ³ΠΎΠΎΠ±Π»ΠΎΠΆΠ΅Π½ΠΈΡ, Π²ΠΎΠΏΡΠΎΡΠ°ΠΌΠΈ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΆΠΈΠ»ΠΈΡΠ½ΠΎΠ³ΠΎ ΡΡΠ½ΠΊΠ°
ANALYSIS OF THE INFLUENCE OF SPECIFIC FACTORS ON REAL ESTATE PRICES IN THE REPUBLIC OF SRPSKA
The work deals with the analysis of the real estate market and the specificities of the formation of real estate prices in the Republic of Srpska. The specificity is reflected, among other things, in the definition of the market value of real estate if the prices are known from the sales contracts entered in the Real Estate Price Register (formed on the basis of supply and demand for apartments), the formation of value zones (location factor), the value tables (relational tables and value levels), the additional factors of influence (factor of the position of the apartment in the building) and equations for estimating the value of the real estate. The analysis was done using the CAMA algorithm. The research results show that real estate prices from the Real Estate Price Register and real estate prices calculated according to the CAMA algorithm are 70% accurate, i.e. they are within the permitted deviation interval of +,- 10 %, which means that the CAMA algorithm can also be used for real estates that have not been registered in the Real Estate Price Register yet
The role of neural network for estimating real estate prices value in post COVID-19: a case of the middle east market
The main goal of this paper was to explore the use of an artificial neural network (ANN) model in predicting real estate prices in the Middle East market. Although conventional modeling approaches such as regression can be used in prediction, they have a weakness of a predetermined relationship between input and output. In this regard, using the ANN model was expected to reduce the bias and ensure non-linear relationships are also covered in the prediction process for more accurate results. The ANN model was created using Python v.3.10 program. The model exhibited a high correlation between predicted and actual house price data (R=0.658). In this respect, it was realized that the model could be effectively used in appraising real estate by investors. However, a major limitation of the model was realized to be a limited dataset for large and luxurious houses, which were not accurately predicted as data distribution between actual and predicted values became sparse for high house prices. A key recommendation made is that future research should include more variables related to luxurious houses and macroeconomic factors to increase the ANN model accuracy
Use of ANN model in economies
In this paper, the authors made their contribution by constructing a model for the forecast of average annual net earnings in the EU countries. The model is based on the artificial neural network (ANN) use and for the needs of its creation the authors have presented their proposal for a model entry β economic variables that determine earnings. Generally, implementing an economic policy aimed at preventing stagnation of earnings levels can be achieved by running a sustainable earnings policy and our model can be used as an acceptable tool in the function of keeping that policy
Parametric and non-parametric methods in mass appraisal on poorly developed real estate markets
Purpose: The objective of the article is to identify machine learning methods that provide the best real estate appraisals for small-sized samples, particularly on poorly developed markets. A hypothesis is verified according to which machine learning methods result in more accurate appraisals than multiple regression models do, taking into account sample sizes. Design/Methodology/Approach: Four types of regression were employed in the study: a multiple regression model, a ridge regression model, random forest regression and k nearest neighbours regression. A sampling scheme was proposed which enables defining the impact of a sample size in training datasets on the accuracy of appraisals in test datasets. Findings: The research enabled drawing several conclusions. First of all, the greater the training set was, the more precise the appraisals in a test set were. The conclusion drawn is that a reduction of a training set causes the deterioration of modelling results, but such deterioration is not substantial. Secondly, ridge regression model appeared to be the best model, and thereby the one most resistant to a low number of data. This model, apart from demonstrating the greatest resistance, additionally has the advantage of being a parametric, hence allowing inference. Practical Implications: Presented considerations are important, for instance in the case of valuations conducted for fiscal purposes, when it becomes necessary to determine the value of every type of real properties, even the ones featuring sporadically occurring states of properties. Originality/Value: The study contains modelling of the values defined by property appraisers, and not prices, as in the majority of studies. This decision enabled increasing the diversity of states of real estate properties, thereby including in the modelling process not just those real properties which are most typically traded.peer-reviewe
ANALYSIS OF THE INFLUENCE OF SPECIFIC FACTORS ON REAL ESTATE PRICES IN THE REPUBLIC OF SRPSKA
The work deals with the analysis of the real estate market and the specificities of the formation of real estate prices in the Republic of Srpska. The specificity is reflected, among other things, in the definition of the market value of real estate if the prices are known from the sales contracts entered in the Real Estate Price Register (formed on the basis of supply and demand for apartments), the formation of value zones (location factor), the value tables (relational tables and value levels), the additional factors of influence (factor of the position of the apartment in the building) and equations for estimating the value of the real estate. The analysis was done using the CAMA algorithm. The research results show that real estate prices from the Real Estate Price Register and real estate prices calculated according to the CAMA algorithm are 70% accurate, i.e. they are within the permitted deviation interval of +,- 10 %, which means that the CAMA algorithm can also be used for real estates that have not been registered in the Real Estate Price Register yet
Comparison of Various Machine Learning Models for Estimating Construction Projects Sales Valuation Using Economic Variables and Indices
The capability of various machine learning techniques in predicting construction project profit in residential buildings using a combination of economic variables and indices (EV&Is) and physical and financial variables (P&F) as input variables remain uncertain. Although recent studies have primarily focused on identifying the factors influencing the sales of construction projects due to their significant short-term impact on a country's economy, the prediction of these parameters is crucial for ensuring project sustainability. While techniques such as regression and artificial neural networks have been utilized to estimate construction project sales, limited research has been conducted in this area. The application of machine learning techniques presents several advantages over conventional methods, including reductions in cost, time, and effort. Therefore, this study aims to predict the sales valuation of construction projects using various machine learning approaches, incorporating different EV&Is and P&F as input features for these models and subsequently generating the sales valuation as the output. This research will undertake a comparative analysis to investigate the efficiency of the different machine learning models, identifying the most effective approach for estimating the sales valuation of construction projects. By leveraging machine learning techniques, it is anticipated that the accuracy of sales valuation predictions will be enhanced, ultimately resulting in more sustainable and successful construction projects. In general, the findings of this research reveal that the extremely randomized trees model delivers the best performance, while the decision tree model exhibits the least satisfactory performance in predicting the sales valuation of construction projects
Assessment of the Real Estate Market Value in the European Market by Artificial Neural Networks Application
Using an artificial neural network, it is possible with the precision of the input data to show the dependence of the property price from variable inputs. It is meant to make a forecast that can be used for different purposes (accounting, sales, etc.), but also for the feasibility of building objects, as the sales price forecast is calculated. The aim of the research was to construct a prognostic model of the real estate market value in the EU countries depending on the impact of macroeconomic indicators. The available input data demonstrates that macroeconomic variables influence determination of real estate prices. The authors sought to obtain correct output data which show prices forecast in the real estate markets of the observed countries