The Apartment Price Index

Gangnam-gu, Seoul had the highest average apartment sale price among districts with at least 30 sales, at ₩3.09bn from Jul to Sep 2026, according to Real Transaction Price of Apartments (Ministry of Land, Infrastructure and Transport).

What an apartment actually sold for, by district. Average transaction price across 2026-07 to 2026-09 (3 months), for the 213 of 269 districts with at least 30 sales in the window.

Source: Real Transaction Price of Apartments (Ministry of Land, Infrastructure and Transport) (국토교통부 아파트 매매 실거래가) — Ministry of Land, Infrastructure and Transport (국토교통부). 2026-07 to 2026-09 (3 months). Retrieved 2026-10-31.

Data change history

Change types and comparisons

A period change reflects a new period. Same-period data changes distinguish confirmed source corrections from reasons still under review. Changes to coverage or methods require care when comparing. More than one type can apply.

A single month, a year-to-date total, and a moving window cover different spans. Entering or leaving a top-N list alone does not establish a change in the population studied. Counts of observed stations or countries describe the saved data, not necessarily the source population.

Period change

Applied: 2026-10-31

Before: 2026-06~2026-08 · Period window · Retrieved 2026-10-17 · Data source

After: 2026-07~2026-09 · Period window · Retrieved 2026-10-31 · Data source

View values and ranks

Names without translations appear in their original Korean. Ranks follow the saved list; ranks outside it are not inferred.

Average transaction price

Changed items서울특별시 강남구 — Before: rank 1 · 308,910 10,000 KRW; After: rank 1 · 308,919 10,000 KRW
서울특별시 용산구 — Before: rank 2 · 266,793 10,000 KRW; After: rank 2 · 263,286 10,000 KRW
서울특별시 서초구 — Before: rank 3 · 263,161 10,000 KRW; After: rank 3 · 251,179 10,000 KRW
경기도 과천시 — Before: rank 4 · 231,429 10,000 KRW; After: rank 4 · 228,706 10,000 KRW
서울특별시 송파구 — Before: rank 5 · 212,670 10,000 KRW; After: rank 6 · 179,038 10,000 KRW
서울특별시 성동구 — Before: rank 6 · 183,357 10,000 KRW; After: rank 5 · 188,212 10,000 KRW
경기도 성남시 분당구 — Before: rank 7 · 162,753 10,000 KRW; After: rank 7 · 163,991 10,000 KRW
서울특별시 마포구 — Before: rank 8 · 148,487 10,000 KRW; After: rank 8 · 149,754 10,000 KRW
서울특별시 광진구 — Before: rank 9 · 142,942 10,000 KRW; After: rank 9 · 145,512 10,000 KRW
서울특별시 영등포구 — Before: rank 10 · 138,905 10,000 KRW; After: rank 10 · 138,794 10,000 KRW
서울특별시 강동구 — Before: rank 11 · 133,788 10,000 KRW; After: rank 13 · 129,313 10,000 KRW
서울특별시 동작구 — Before: rank 12 · 132,095 10,000 KRW; After: rank 11 · 130,201 10,000 KRW
서울특별시 양천구 — Before: rank 13 · 130,028 10,000 KRW; After: rank 12 · 129,940 10,000 KRW
서울특별시 중구 — Before: rank 14 · 123,664 10,000 KRW; After: rank 14 · 124,389 10,000 KRW
경기도 성남시 수정구 — Before: rank 15 · 123,368 10,000 KRW; After: rank 15 · 122,875 10,000 KRW
서울특별시 종로구 — Before: rank 16 · 122,705 10,000 KRW; After: rank 16 · 117,920 10,000 KRW
경기도 하남시 — Before: rank 17 · 111,444 10,000 KRW; After: rank 18 · 111,024 10,000 KRW
서울특별시 서대문구 — Before: rank 18 · 110,058 10,000 KRW; After: rank 17 · 111,468 10,000 KRW
서울특별시 동대문구 — Before: rank 19 · 108,398 10,000 KRW; After: rank 19 · 105,675 10,000 KRW
경기도 용인시 수지구 — Before: rank 20 · 100,811 10,000 KRW; After: rank 20 · 101,815 10,000 KRW
서울특별시 성북구 — Before: rank 21 · 99,806 10,000 KRW; After: rank 21 · 99,251 10,000 KRW
서울특별시 강서구 — Before: rank 22 · 96,314 10,000 KRW; After: rank 22 · 93,369 10,000 KRW
서울특별시 관악구 — Before: rank 23 · 89,812 10,000 KRW; After: rank 24 · 88,865 10,000 KRW
경기도 광명시 — Before: rank 24 · 89,590 10,000 KRW; After: rank 23 · 89,116 10,000 KRW
서울특별시 은평구 — Before: rank 25 · 88,248 10,000 KRW; After: rank 26 · 86,092 10,000 KRW
경기도 성남시 중원구 — Before: rank 26 · 85,706 10,000 KRW; After: rank 25 · 88,317 10,000 KRW
경기도 안양시 동안구 — Before: rank 27 · 83,326 10,000 KRW; After: rank 27 · 83,323 10,000 KRW
경기도 화성시 동탄구 — Before: rank 28 · 81,047 10,000 KRW; After: rank 28 · 80,565 10,000 KRW
경기도 수원시 영통구 — Before: rank 29 · 79,794 10,000 KRW; After: rank 30 · 77,041 10,000 KRW
서울특별시 구로구 — Before: rank 30 · 78,829 10,000 KRW; After: rank 29 · 78,354 10,000 KRW
경기도 구리시 — Before: rank 31 · 76,241 10,000 KRW; After: rank 32 · 74,394 10,000 KRW
경기도 의왕시 — Before: rank 32 · 75,664 10,000 KRW; After: rank 33 · 74,235 10,000 KRW
부산광역시 수영구 — Before: rank 33 · 73,248 10,000 KRW; After: rank 31 · 74,648 10,000 KRW
서울특별시 강북구 — Before: rank 34 · 71,755 10,000 KRW; After: rank 35 · 70,604 10,000 KRW
서울특별시 노원구 — Before: rank 35 · 69,518 10,000 KRW; After: rank 34 · 70,782 10,000 KRW
경기도 수원시 팔달구 — Before: rank 36 · 67,601 10,000 KRW; After: rank 36 · 68,860 10,000 KRW
서울특별시 금천구 — Before: rank 37 · 67,475 10,000 KRW; After: rank 37 · 66,820 10,000 KRW
인천광역시 연수구 — Before: rank 38 · 65,472 10,000 KRW; After: rank 38 · 66,435 10,000 KRW
경기도 용인시 기흥구 — Before: rank 39 · 64,397 10,000 KRW; After: rank 43 · 58,667 10,000 KRW
대구광역시 수성구 — Before: rank 40 · 62,578 10,000 KRW; After: rank 41 · 60,098 10,000 KRW
경기도 수원시 장안구 — Before: rank 41 · 61,948 10,000 KRW; After: rank 39 · 62,518 10,000 KRW
경기도 안양시 만안구 — Before: rank 42 · 61,757 10,000 KRW; After: rank 40 · 61,637 10,000 KRW
경기도 고양시 덕양구 — Before: rank 43 · 59,391 10,000 KRW; After: rank 42 · 59,018 10,000 KRW
부산광역시 해운대구 — Before: rank 44 · 58,514 10,000 KRW; After: rank 45 · 58,025 10,000 KRW
경기도 부천시 원미구 — Before: rank 45 · 57,883 10,000 KRW; After: rank 44 · 58,190 10,000 KRW
서울특별시 도봉구 — Before: rank 46 · 57,765 10,000 KRW; After: rank 46 · 57,741 10,000 KRW
경기도 남양주시 — Before: rank 47 · 57,268 10,000 KRW; After: rank 47 · 56,479 10,000 KRW
서울특별시 중랑구 — Before: rank 48 · 56,281 10,000 KRW; After: rank 48 · 56,053 10,000 KRW
경기도 고양시 일산동구 — Before: rank 49 · 54,726 10,000 KRW; After: rank 49 · 54,842 10,000 KRW
대구광역시 중구 — Before: rank 50 · 54,486 10,000 KRW; After: rank 50 · 54,148 10,000 KRW
경기도 부천시 소사구 — Before: rank 51 · 53,018 10,000 KRW; After: rank 53 · 52,938 10,000 KRW
부산광역시 남구 — Before: rank 52 · 53,008 10,000 KRW; After: rank 51 · 54,040 10,000 KRW
부산광역시 연제구 — Before: rank 53 · 52,244 10,000 KRW; After: rank 54 · 52,543 10,000 KRW
세종특별자치시 세종특별자치시 — Before: rank 54 · 51,449 10,000 KRW; After: rank 52 · 53,427 10,000 KRW
부산광역시 동래구 — Before: rank 55 · 51,438 10,000 KRW; After: rank 55 · 52,446 10,000 KRW
경기도 수원시 권선구 — Before: rank 56 · 51,116 10,000 KRW; After: rank 56 · 51,652 10,000 KRW
경기도 화성시 병점구 — Before: rank 57 · 50,843 10,000 KRW; After: rank 57 · 50,142 10,000 KRW
경기도 군포시 — Before: rank 58 · 49,338 10,000 KRW; After: rank 58 · 49,245 10,000 KRW
경기도 안산시 단원구 — Before: rank 59 · 49,058 10,000 KRW; After: rank 59 · 48,501 10,000 KRW
인천광역시 서해구 — Before: rank 60 · 48,151 10,000 KRW; After: rank 61 · 47,434 10,000 KRW
경기도 광주시 — Before: rank 61 · 47,914 10,000 KRW; After: rank 60 · 47,570 10,000 KRW
대전광역시 유성구 — Before: rank 62 · 47,557 10,000 KRW; After: rank 62 · 47,269 10,000 KRW
인천광역시 검단구 — Before: rank 63 · 46,920 10,000 KRW; After: rank 63 · 47,167 10,000 KRW
경기도 김포시 — Before: rank 64 · 46,378 10,000 KRW; After: rank 64 · 46,109 10,000 KRW
울산광역시 남구 — Before: rank 65 · 45,012 10,000 KRW; After: rank 65 · 45,142 10,000 KRW
부산광역시 서구 — Before: rank 66 · 43,006 10,000 KRW; After: rank 68 · 42,136 10,000 KRW
경기도 시흥시 — Before: rank 67 · 42,265 10,000 KRW; After: rank 66 · 43,259 10,000 KRW
경상남도 창원시 성산구 — Before: rank 68 · 41,840 10,000 KRW; After: rank 70 · 41,292 10,000 KRW
경기도 고양시 일산서구 — Before: rank 69 · 41,697 10,000 KRW; After: rank 69 · 41,622 10,000 KRW
경기도 안산시 상록구 — Before: rank 70 · 41,314 10,000 KRW; After: rank 72 · 40,423 10,000 KRW
울산광역시 중구 — Before: rank 71 · 41,261 10,000 KRW; After: rank 74 · 40,106 10,000 KRW
대구광역시 남구 — Before: rank 72 · 41,129 10,000 KRW; After: rank 79 · 39,579 10,000 KRW
경기도 용인시 처인구 — Before: rank 73 · 40,449 10,000 KRW; After: rank 73 · 40,309 10,000 KRW
대전광역시 서구 — Before: rank 74 · 40,229 10,000 KRW; After: rank 67 · 43,026 10,000 KRW
전남광주통합특별시 남구 — Before: rank 75 · 40,157 10,000 KRW; After: rank 71 · 40,535 10,000 KRW
경기도 파주시 — Before: rank 76 · 39,276 10,000 KRW; After: rank 82 · 38,458 10,000 KRW
경기도 화성시 효행구 — Before: rank 77 · 39,075 10,000 KRW; After: rank 77 · 39,821 10,000 KRW
인천광역시 부평구 — Before: rank 78 · 39,024 10,000 KRW; After: rank 75 · 40,050 10,000 KRW
부산광역시 강서구 — Before: rank 79 · 38,987 10,000 KRW; After: rank 76 · 39,846 10,000 KRW
경기도 오산시 — Before: rank 80 · 38,933 10,000 KRW; After: rank 78 · 39,615 10,000 KRW
부산광역시 금정구 — Before: rank 81 · 38,721 10,000 KRW; After: rank 85 · 37,368 10,000 KRW
부산광역시 부산진구 — Before: rank 82 · 38,448 10,000 KRW; After: rank 81 · 38,765 10,000 KRW
경상남도 창원시 의창구 — Before: rank 83 · 38,234 10,000 KRW; After: rank 93 · 35,608 10,000 KRW
전남광주통합특별시 동구 — Before: rank 84 · 38,196 10,000 KRW; After: rank 84 · 37,478 10,000 KRW
경기도 화성시 만세구 — Before: rank 85 · 37,994 10,000 KRW; After: rank 83 · 38,251 10,000 KRW
부산광역시 동구 — Before: rank 86 · 37,892 10,000 KRW; After: rank 80 · 38,957 10,000 KRW
충청북도 청주시 흥덕구 — Before: rank 87 · 37,863 10,000 KRW; After: rank 95 · 34,695 10,000 KRW
경기도 부천시 오정구 — Before: rank 88 · 37,176 10,000 KRW; After: rank 90 · 36,275 10,000 KRW
인천광역시 영종구 — Before: rank 89 · 37,175 10,000 KRW; After: rank 86 · 36,683 10,000 KRW
경기도 의정부시 — Before: rank 90 · 37,071 10,000 KRW; After: rank 88 · 36,582 10,000 KRW
경기도 양평군 — Before: rank 91 · 36,470 10,000 KRW; After: rank 89 · 36,507 10,000 KRW
울산광역시 북구 — Before: rank 92 · 36,393 10,000 KRW; After: rank 91 · 36,212 10,000 KRW
대구광역시 서구 — Before: rank 93 · 36,018 10,000 KRW; After: rank 87 · 36,644 10,000 KRW
인천광역시 남동구 — Before: rank 94 · 35,496 10,000 KRW; After: rank 96 · 34,661 10,000 KRW
대구광역시 동구 — Before: rank 95 · 35,103 10,000 KRW; After: rank 94 · 35,384 10,000 KRW
경상남도 진주시 — Before: rank 96 · 34,697 10,000 KRW; After: rank 97 · 33,818 10,000 KRW
제주특별자치도 제주시 — Before: rank 97 · 34,579 10,000 KRW; After: rank 92 · 35,815 10,000 KRW
대구광역시 달서구 — Before: rank 98 · 34,524 10,000 KRW; After: rank 101 · 32,395 10,000 KRW
경기도 평택시 — Before: rank 99 · 34,337 10,000 KRW; After: rank 98 · 33,648 10,000 KRW
부산광역시 북구 — Before: rank 100 · 33,598 10,000 KRW; After: rank 102 · 31,833 10,000 KRW
인천광역시 미추홀구 — Before: rank 101 · 33,385 10,000 KRW; After: rank 99 · 33,563 10,000 KRW
전남광주통합특별시 서구 — Before: rank 102 · 33,287 10,000 KRW; After: rank 100 · 32,948 10,000 KRW
전북특별자치도 전주시 덕진구 — Before: rank 103 · 33,146 10,000 KRW; After: rank 103 · 31,820 10,000 KRW
경상남도 창원시 마산회원구 — Before: rank 104 · 31,565 10,000 KRW; After: rank 105 · 31,395 10,000 KRW
대전광역시 중구 — Before: rank 105 · 31,320 10,000 KRW; After: rank 106 · 30,983 10,000 KRW
부산광역시 기장군 — Before: rank 106 · 31,248 10,000 KRW; After: rank 109 · 30,385 10,000 KRW
경기도 양주시 — Before: rank 107 · 30,679 10,000 KRW; After: rank 107 · 30,827 10,000 KRW
전남광주통합특별시 무안군 — Before: rank 108 · 30,630 10,000 KRW; After: rank 104 · 31,801 10,000 KRW
제주특별자치도 서귀포시 — Before: rank 109 · 30,299 10,000 KRW; After: rank 108 · 30,404 10,000 KRW
전남광주통합특별시 광산구 — Before: rank 110 · 30,054 10,000 KRW; After: rank 110 · 29,798 10,000 KRW
대구광역시 북구 — Before: rank 111 · 29,170 10,000 KRW; After: rank 112 · 29,002 10,000 KRW
충청남도 천안시 서북구 — Before: rank 112 · 29,152 10,000 KRW; After: rank 111 · 29,200 10,000 KRW
경상남도 창원시 마산합포구 — Before: rank 113 · 28,990 10,000 KRW; After: rank 116 · 27,573 10,000 KRW
대전광역시 동구 — Before: rank 114 · 28,978 10,000 KRW; After: rank 113 · 28,376 10,000 KRW
강원특별자치도 춘천시 — Before: rank 115 · 28,930 10,000 KRW; After: rank 114 · 28,288 10,000 KRW
인천광역시 계양구 — Before: rank 116 · 28,571 10,000 KRW; After: rank 115 · 28,155 10,000 KRW
전남광주통합특별시 북구 — Before: rank 117 · 28,167 10,000 KRW; After: rank 117 · 27,342 10,000 KRW
전북특별자치도 전주시 완산구 — Before: rank 118 · 28,107 10,000 KRW; After: rank 120 · 26,667 10,000 KRW
울산광역시 울주군 — Before: rank 119 · 27,609 10,000 KRW; After: rank 118 · 27,101 10,000 KRW
전남광주통합특별시 구례군 — Before: rank 120 · 27,365 10,000 KRW; After: rank 119 · 27,002 10,000 KRW
충청북도 청주시 상당구 — Before: rank 121 · 26,439 10,000 KRW; After: rank 121 · 25,936 10,000 KRW
대구광역시 달성군 — Before: rank 122 · 26,007 10,000 KRW; After: rank 128 · 24,972 10,000 KRW
충청남도 아산시 — Before: rank 123 · 25,917 10,000 KRW; After: rank 122 · 25,836 10,000 KRW
경상남도 양산시 — Before: rank 124 · 25,812 10,000 KRW; After: rank 126 · 25,530 10,000 KRW
경상북도 예천군 — Before: rank 125 · 25,317 10,000 KRW; After: rank 125 · 25,568 10,000 KRW
강원특별자치도 속초시 — Before: rank 126 · 25,085 10,000 KRW; After: rank 133 · 24,208 10,000 KRW
경기도 가평군 — Before: rank 127 · 25,027 10,000 KRW; After: rank 124 · 25,631 10,000 KRW
대전광역시 대덕구 — Before: rank 128 · 25,015 10,000 KRW; After: rank 131 · 24,504 10,000 KRW
울산광역시 동구 — Before: rank 129 · 24,958 10,000 KRW; After: rank 132 · 24,217 10,000 KRW
충청북도 청주시 청원구 — Before: rank 130 · 24,815 10,000 KRW; After: rank 134 · 24,134 10,000 KRW
강원특별자치도 강릉시 — Before: rank 131 · 24,805 10,000 KRW; After: rank 123 · 25,706 10,000 KRW
부산광역시 사상구 — Before: rank 132 · 24,694 10,000 KRW; After: rank 127 · 25,175 10,000 KRW
전남광주통합특별시 순천시 — Before: rank 133 · 24,555 10,000 KRW; After: rank 135 · 24,036 10,000 KRW
경상남도 김해시 — Before: rank 134 · 23,943 10,000 KRW; After: rank 136 · 23,351 10,000 KRW
경상남도 창원시 진해구 — Before: rank 135 · 23,616 10,000 KRW; After: rank 141 · 22,567 10,000 KRW
경상북도 경산시 — Before: rank 136 · 23,323 10,000 KRW; After: rank 139 · 22,793 10,000 KRW
경기도 이천시 — Before: rank 137 · 23,212 10,000 KRW; After: rank 129 · 24,892 10,000 KRW
전남광주통합특별시 나주시 — Before: rank 138 · 23,111 10,000 KRW; After: rank 130 · 24,560 10,000 KRW
인천광역시 제물포구 — Before: rank 139 · 23,053 10,000 KRW; After: rank 140 · 22,617 10,000 KRW
경기도 여주시 — Before: rank 140 · 22,940 10,000 KRW; After: rank 143 · 21,973 10,000 KRW
부산광역시 영도구 — Before: rank 141 · 22,300 10,000 KRW; After: rank 138 · 22,957 10,000 KRW
부산광역시 사하구 — Before: rank 142 · 22,206 10,000 KRW; After: rank 142 · 22,270 10,000 KRW
전남광주통합특별시 여수시 — Before: rank 143 · 22,169 10,000 KRW; After: rank 137 · 23,040 10,000 KRW
전북특별자치도 완주군 — Before: rank 144 · 22,002 10,000 KRW; After: rank 144 · 21,900 10,000 KRW
강원특별자치도 원주시 — Before: rank 145 · 21,904 10,000 KRW; After: rank 145 · 21,835 10,000 KRW
충청남도 서산시 — Before: rank 146 · 21,814 10,000 KRW; After: rank 150 · 20,768 10,000 KRW
경상북도 경주시 — Before: rank 147 · 21,797 10,000 KRW; After: rank 148 · 21,309 10,000 KRW
충청북도 충주시 — Before: rank 148 · 21,235 10,000 KRW; After: rank 149 · 20,840 10,000 KRW
충청남도 천안시 동남구 — Before: rank 149 · 21,081 10,000 KRW; After: rank 151 · 20,668 10,000 KRW
충청남도 당진시 — Before: rank 150 · 20,774 10,000 KRW; After: rank 147 · 21,453 10,000 KRW
경상남도 밀양시 — Before: rank 151 · 20,738 10,000 KRW; After: rank 155 · 19,283 10,000 KRW
충청북도 청주시 서원구 — Before: rank 152 · 20,725 10,000 KRW; After: rank 146 · 21,633 10,000 KRW
충청남도 계룡시 — Before: rank 153 · 20,546 10,000 KRW; After: rank 156 · 19,236 10,000 KRW
경상북도 포항시 남구 — Before: rank 154 · 20,185 10,000 KRW; After: rank 154 · 19,721 10,000 KRW
경상북도 김천시 — Before: rank 155 · 20,134 10,000 KRW; After: rank 152 · 20,179 10,000 KRW
경상북도 포항시 북구 — Before: rank 156 · 19,773 10,000 KRW; After: rank 158 · 18,940 10,000 KRW
경상북도 구미시 — Before: rank 157 · 19,309 10,000 KRW; After: rank 159 · 18,885 10,000 KRW
경상북도 안동시 — Before: rank 158 · 19,295 10,000 KRW; After: rank 157 · 19,219 10,000 KRW
경상남도 통영시 — Before: rank 159 · 19,294 10,000 KRW; After: rank 165 · 18,408 10,000 KRW
강원특별자치도 고성군 — Before: rank 160 · 19,285 10,000 KRW; After: rank 160 · 18,805 10,000 KRW
경기도 안성시 — Before: rank 161 · 18,879 10,000 KRW; After: rank 153 · 19,729 10,000 KRW
경상남도 거창군 — Before: rank 162 · 18,608 10,000 KRW; After: rank 166 · 18,399 10,000 KRW
경기도 포천시 — Before: rank 163 · 18,556 10,000 KRW; After: rank 169 · 17,851 10,000 KRW
전북특별자치도 고창군 — Before: rank 164 · 18,465 10,000 KRW; After: Outside saved list
경상북도 상주시 — Before: rank 165 · 18,392 10,000 KRW; After: rank 173 · 17,572 10,000 KRW
강원특별자치도 동해시 — Before: rank 166 · 18,295 10,000 KRW; After: rank 164 · 18,431 10,000 KRW
충청남도 예산군 — Before: rank 167 · 18,263 10,000 KRW; After: rank 181 · 16,585 10,000 KRW
전남광주통합특별시 목포시 — Before: rank 168 · 17,942 10,000 KRW; After: rank 170 · 17,831 10,000 KRW
전북특별자치도 군산시 — Before: rank 169 · 17,793 10,000 KRW; After: rank 167 · 18,328 10,000 KRW
부산광역시 중구 — Before: rank 170 · 17,761 10,000 KRW; After: rank 171 · 17,768 10,000 KRW
충청남도 논산시 — Before: rank 171 · 17,726 10,000 KRW; After: rank 182 · 16,539 10,000 KRW
경상북도 문경시 — Before: rank 172 · 17,632 10,000 KRW; After: rank 190 · 16,066 10,000 KRW
전남광주통합특별시 화순군 — Before: rank 173 · 17,464 10,000 KRW; After: rank 177 · 17,107 10,000 KRW
경상남도 사천시 — Before: rank 174 · 17,404 10,000 KRW; After: rank 178 · 17,095 10,000 KRW
전북특별자치도 부안군 — Before: rank 175 · 17,400 10,000 KRW; After: rank 183 · 16,507 10,000 KRW
충청남도 공주시 — Before: rank 176 · 17,373 10,000 KRW; After: rank 162 · 18,554 10,000 KRW
충청북도 옥천군 — Before: rank 177 · 17,348 10,000 KRW; After: rank 185 · 16,345 10,000 KRW
충청남도 보령시 — Before: rank 178 · 17,301 10,000 KRW; After: rank 168 · 18,182 10,000 KRW
강원특별자치도 홍천군 — Before: rank 179 · 17,194 10,000 KRW; After: rank 175 · 17,433 10,000 KRW
전남광주통합특별시 영광군 — Before: rank 180 · 17,183 10,000 KRW; After: rank 203 · 12,395 10,000 KRW
전남광주통합특별시 해남군 — Before: rank 181 · 16,915 10,000 KRW; After: Outside saved list
경상북도 영주시 — Before: rank 182 · 16,799 10,000 KRW; After: rank 163 · 18,442 10,000 KRW
충청북도 제천시 — Before: rank 183 · 16,683 10,000 KRW; After: rank 179 · 16,927 10,000 KRW
경기도 동두천시 — Before: rank 184 · 16,629 10,000 KRW; After: rank 187 · 16,266 10,000 KRW
충청남도 홍성군 — Before: rank 185 · 16,624 10,000 KRW; After: rank 188 · 16,171 10,000 KRW
강원특별자치도 양양군 — Before: rank 186 · 16,558 10,000 KRW; After: rank 180 · 16,655 10,000 KRW
인천광역시 강화군 — Before: rank 187 · 16,453 10,000 KRW; After: rank 174 · 17,492 10,000 KRW
충청북도 증평군 — Before: rank 188 · 16,255 10,000 KRW; After: rank 184 · 16,420 10,000 KRW
전북특별자치도 남원시 — Before: rank 189 · 16,254 10,000 KRW; After: rank 172 · 17,736 10,000 KRW
충청북도 진천군 — Before: rank 190 · 16,142 10,000 KRW; After: rank 176 · 17,111 10,000 KRW
강원특별자치도 평창군 — Before: rank 191 · 16,015 10,000 KRW; After: rank 161 · 18,681 10,000 KRW
충청남도 금산군 — Before: rank 192 · 15,820 10,000 KRW; After: rank 191 · 16,061 10,000 KRW
강원특별자치도 삼척시 — Before: rank 193 · 15,687 10,000 KRW; After: rank 186 · 16,282 10,000 KRW
전북특별자치도 정읍시 — Before: rank 194 · 15,568 10,000 KRW; After: rank 195 · 14,523 10,000 KRW
경상남도 거제시 — Before: rank 195 · 15,279 10,000 KRW; After: rank 194 · 14,760 10,000 KRW
충청남도 태안군 — Before: rank 196 · 14,808 10,000 KRW; After: rank 189 · 16,069 10,000 KRW
경기도 연천군 — Before: rank 197 · 14,599 10,000 KRW; After: rank 204 · 11,815 10,000 KRW
전북특별자치도 익산시 — Before: rank 198 · 13,847 10,000 KRW; After: rank 192 · 15,802 10,000 KRW
강원특별자치도 영월군 — Before: rank 199 · 13,438 10,000 KRW; After: rank 193 · 14,950 10,000 KRW
강원특별자치도 횡성군 — Before: rank 200 · 13,404 10,000 KRW; After: rank 206 · 11,366 10,000 KRW
전북특별자치도 김제시 — Before: rank 201 · 13,252 10,000 KRW; After: rank 196 · 13,040 10,000 KRW
충청북도 음성군 — Before: rank 202 · 13,101 10,000 KRW; After: rank 200 · 12,638 10,000 KRW
전남광주통합특별시 광양시 — Before: rank 203 · 13,029 10,000 KRW; After: rank 197 · 12,938 10,000 KRW
경상남도 함안군 — Before: rank 204 · 12,170 10,000 KRW; After: rank 198 · 12,878 10,000 KRW
경상북도 청도군 — Before: rank 205 · 12,158 10,000 KRW; After: rank 201 · 12,615 10,000 KRW
경상남도 창녕군 — Before: rank 206 · 11,686 10,000 KRW; After: rank 208 · 11,136 10,000 KRW
경상북도 영천시 — Before: rank 207 · 11,644 10,000 KRW; After: rank 202 · 12,482 10,000 KRW
강원특별자치도 정선군 — Before: rank 208 · 11,596 10,000 KRW; After: rank 211 · 9,256 10,000 KRW
경상북도 칠곡군 — Before: rank 209 · 11,372 10,000 KRW; After: rank 205 · 11,465 10,000 KRW
전남광주통합특별시 고흥군 — Before: rank 210 · 11,237 10,000 KRW; After: Outside saved list
강원특별자치도 태백시 — Before: rank 211 · 10,652 10,000 KRW; After: rank 210 · 10,379 10,000 KRW
경상남도 고성군 — Before: rank 212 · 9,590 10,000 KRW; After: rank 209 · 10,425 10,000 KRW
전남광주통합특별시 영암군 — Before: rank 213 · 6,959 10,000 KRW; After: rank 213 · 7,054 10,000 KRW
충청북도 단양군 — Before: Outside saved list; After: rank 199 · 12,716 10,000 KRW
전남광주통합특별시 보성군 — Before: Outside saved list; After: rank 207 · 11,211 10,000 KRW
강원특별자치도 철원군 — Before: Outside saved list; After: rank 212 · 8,725 10,000 KRW

Changed source figures

rows[0].deals — Before: 475; After: 384
rows[0].avg_manwon — Before: 308,910; After: 308,919
rows[0].avg_per_m2_manwon — Before: 3518.6; After: 3533.8
rows[1].deals — Before: 176; After: 154
rows[1].avg_manwon — Before: 266,793; After: 263,286
rows[1].avg_per_m2_manwon — Before: 2892.8; After: 2811.6
rows[2].deals — Before: 409; After: 343
rows[2].avg_manwon — Before: 263,161; After: 251,179
rows[2].avg_per_m2_manwon — Before: 2947.3; After: 3058.6
rows[3].deals — Before: 59; After: 51
rows[3].avg_manwon — Before: 231,429; After: 228,706
rows[3].avg_per_m2_manwon — Before: 2808.1; After: 2831.4
rows[4].deals — Before: 766; After: 214
rows[4].avg_manwon — Before: 212,670; After: 188,212
rows[4].avg_per_m2_manwon — Before: 2664.4; After: 2420.8
rows[5].deals — Before: 283; After: 804
rows[5].avg_manwon — Before: 183,357; After: 179,038
rows[5].avg_per_m2_manwon — Before: 2315.2; After: 2,700
rows[6].deals — Before: 1,075; After: 799
rows[6].avg_manwon — Before: 162,753; After: 163,991
rows[6].avg_per_m2_manwon — Before: 2086.1; After: 2115.2
rows[7].deals — Before: 406; After: 340
rows[7].avg_manwon — Before: 148,487; After: 149,754
rows[7].avg_per_m2_manwon — Before: 1964.9; After: 1980.1
rows[8].deals — Before: 203; After: 173
rows[8].avg_manwon — Before: 142,942; After: 145,512
rows[8].avg_per_m2_manwon — Before: 1871.1; After: 1907.7
rows[9].deals — Before: 651; After: 519
rows[9].avg_manwon — Before: 138,905; After: 138,794
rows[9].avg_per_m2_manwon — Before: 1883.7; After: 1859.9
rows[10].deals — Before: 699; After: 444
rows[10].avg_manwon — Before: 133,788; After: 130,201
rows[10].avg_per_m2_manwon — Before: 1815.7; After: 1881.1
rows[11].deals — Before: 515; After: 445
rows[11].avg_manwon — Before: 132,095; After: 129,940
rows[11].avg_per_m2_manwon — Before: 1873.6; After: 1680.5
rows[12].deals — Before: 555; After: 598
rows[12].avg_manwon — Before: 130,028; After: 129,313
rows[12].avg_per_m2_manwon — Before: 1680.8; After: 1800.6
rows[13].deals — Before: 164; After: 141
rows[13].avg_manwon — Before: 123,664; After: 124,389
rows[13].avg_per_m2_manwon — Before: 1778.8; After: 1765.5
rows[14].deals — Before: 276; After: 238
rows[14].avg_manwon — Before: 123,368; After: 122,875
rows[14].avg_per_m2_manwon — Before: 1629.7; After: 1640.6
rows[15].deals — Before: 93; After: 94
rows[15].avg_manwon — Before: 122,705; After: 117,920
rows[15].avg_per_m2_manwon — Before: 1506.3; After: 1,474
rows[16].deals — Before: 853; After: 464
rows[16].avg_manwon — Before: 111,444; After: 111,468
rows[16].avg_per_m2_manwon — Before: 1412.2; After: 1452.7
rows[17].deals — Before: 578; After: 735
rows[17].avg_manwon — Before: 110,058; After: 111,024
rows[17].avg_per_m2_manwon — Before: 1453.3; After: 1408.8
rows[18].deals — Before: 600; After: 475
rows[18].avg_manwon — Before: 108,398; After: 105,675
rows[18].avg_per_m2_manwon — Before: 1440.6; After: 1443.8
rows[19].deals — Before: 1,544; After: 1,293
rows[19].avg_manwon — Before: 100,811; After: 101,815
rows[19].avg_per_m2_manwon — Before: 1035.7; After: 1057.2
rows[20].deals — Before: 816; After: 679
rows[20].avg_manwon — Before: 99,806; After: 99,251
rows[20].avg_per_m2_manwon — Before: 1274.3; After: 1296.2
rows[21].deals — Before: 1,026; After: 912
rows[21].avg_manwon — Before: 96,314; After: 93,369
rows[21].avg_per_m2_manwon — Before: 1343.2; After: 1,391
rows[22].deals — Before: 471; After: 620
rows[22].avg_manwon — Before: 89,812; After: 89,116
rows[22].avg_per_m2_manwon — Before: 1206.4; After: 1252.4
rows[23].deals — Before: 704; After: 425
rows[23].avg_manwon — Before: 89,590; After: 88,865
rows[23].avg_per_m2_manwon — Before: 1254.6; After: 1184.5
rows[24].deals — Before: 753; After: 359
rows[24].avg_manwon — Before: 88,248; After: 88,317
rows[24].avg_per_m2_manwon — Before: 1164.8; After: 1315.5
rows[25].deals — Before: 426; After: 698
rows[25].avg_manwon — Before: 85,706; After: 86,092
rows[25].avg_per_m2_manwon — Before: 1,283; After: 1171.3
rows[26].deals — Before: 1,378; After: 1,139
rows[26].avg_manwon — Before: 83,326; After: 83,323
rows[26].avg_per_m2_manwon — Before: 1215.8; After: 1233.8
rows[27].deals — Before: 2,205; After: 537
rows[27].avg_manwon — Before: 81,047; After: 80,565
rows[27].avg_per_m2_manwon — Before: 991.1; After: 1050.9
rows[28].deals — Before: 1,553; After: 765
rows[28].avg_manwon — Before: 79,794; After: 78,354
rows[28].avg_per_m2_manwon — Before: 1035.3; After: 1092.4
rows[29].deals — Before: 858; After: 1,557
rows[29].avg_manwon — Before: 78,829; After: 77,041
rows[29].avg_per_m2_manwon — Before: 1,083; After: 1021.2
rows[30].deals — Before: 589; After: 359
rows[30].avg_manwon — Before: 76,241; After: 74,648
rows[30].avg_per_m2_manwon — Before: 1024.4; After: 925.7
rows[31].deals — Before: 532; After: 266
rows[31].avg_manwon — Before: 75,664; After: 74,394
rows[31].avg_per_m2_manwon — Before: 959.3; After: 996.3
rows[32].deals — Before: 405; After: 467
rows[32].avg_manwon — Before: 73,248; After: 74,235
rows[32].avg_per_m2_manwon — Before: 908.7; After: 933.1
rows[33].deals — Before: 315; After: 1,680
rows[33].avg_manwon — Before: 71,755; After: 70,782
rows[33].avg_per_m2_manwon — Before: 958.3; After: 1107.7
rows[34].deals — Before: 1,949; After: 283
rows[34].avg_manwon — Before: 69,518; After: 70,604
rows[34].avg_per_m2_manwon — Before: 1090.4; After: 982.7
rows[35].deals — Before: 701; After: 624
rows[35].avg_manwon — Before: 67,601; After: 68,860
rows[35].avg_per_m2_manwon — Before: 946.8; After: 962.8
rows[36].deals — Before: 334; After: 315
rows[36].avg_manwon — Before: 67,475; After: 66,820
rows[36].avg_per_m2_manwon — Before: 923.9; After: 931.7
rows[37].deals — Before: 1,477; After: 1,292
rows[37].avg_manwon — Before: 65,472; After: 66,435
rows[37].avg_per_m2_manwon — Before: 788.6; After: 793.4
rows[38].deals — Before: 1,632; After: 700
rows[38].avg_manwon — Before: 64,397; After: 62,518
rows[38].avg_per_m2_manwon — Before: 716.4; After: 841.3
rows[39].deals — Before: 921; After: 733
rows[39].avg_manwon — Before: 62,578; After: 61,637
rows[39].avg_per_m2_manwon — Before: 713.2; After: 866.9
rows[40].deals — Before: 760; After: 856
rows[40].avg_manwon — Before: 61,948; After: 60,098
rows[40].avg_per_m2_manwon — Before: 829; After: 690.3
rows[41].deals — Before: 836; After: 1,305
rows[41].avg_manwon — Before: 61,757; After: 59,018
rows[41].avg_per_m2_manwon — Before: 870.5; After: 791.2
rows[42].deals — Before: 1,413; After: 785
rows[42].avg_manwon — Before: 59,391; After: 58,667
rows[42].avg_per_m2_manwon — Before: 795.5; After: 677.7
rows[43].deals — Before: 916; After: 1,337
rows[43].avg_manwon — Before: 58,514; After: 58,190
rows[43].avg_per_m2_manwon — Before: 713.9; After: 791.8
rows[44].deals — Before: 1,392; After: 853
rows[44].avg_manwon — Before: 57,883; After: 58,025
rows[44].avg_per_m2_manwon — Before: 785.5; After: 712.7
rows[45].deals — Before: 765; After: 702
rows[45].avg_manwon — Before: 57,765; After: 57,741
rows[45].avg_per_m2_manwon — Before: 829.5; After: 832.2
rows[46].deals — Before: 2,799; After: 2,505
rows[46].avg_manwon — Before: 57,268; After: 56,479
rows[46].avg_per_m2_manwon — Before: 692.1; After: 679
rows[47].deals — Before: 907; After: 840
rows[47].avg_manwon — Before: 56,281; After: 56,053
rows[47].avg_per_m2_manwon — Before: 1,085; After: 1,096
rows[48].deals — Before: 523; After: 494
rows[48].avg_manwon — Before: 54,726; After: 54,842
rows[48].avg_per_m2_manwon — Before: 634.4; After: 637.4
rows[49].deals — Before: 389; After: 344
rows[49].avg_manwon — Before: 54,486; After: 54,148
rows[49].avg_per_m2_manwon — Before: 683.5; After: 679
rows[50].deals — Before: 664; After: 624
rows[50].avg_manwon — Before: 53,018; After: 54,040
rows[50].avg_per_m2_manwon — Before: 727; After: 679.9
rows[51].deals — Before: 627; After: 1,106
rows[51].avg_manwon — Before: 53,008; After: 53,427
rows[51].avg_per_m2_manwon — Before: 665.9; After: 702.5
rows[52].deals — Before: 539; After: 597
rows[52].avg_manwon — Before: 52,244; After: 52,938
rows[52].avg_per_m2_manwon — Before: 670.2; After: 711.5
rows[53].deals — Before: 967; After: 525
rows[53].avg_manwon — Before: 51,449; After: 52,543
rows[53].avg_per_m2_manwon — Before: 676.6; After: 680.8
rows[54].deals — Before: 598; After: 528
rows[54].avg_manwon — Before: 51,438; After: 52,446
rows[54].avg_per_m2_manwon — Before: 659.5; After: 667.6
rows[55].deals — Before: 1,649; After: 1,575
rows[55].avg_manwon — Before: 51,116; After: 51,652
rows[55].avg_per_m2_manwon — Before: 674.8; After: 678.4
rows[56].deals — Before: 1,154; After: 1,060
rows[56].avg_manwon — Before: 50,843; After: 50,142
rows[56].avg_per_m2_manwon — Before: 635.8; After: 626.9
rows[57].deals — Before: 1,323; After: 1,291
rows[57].avg_manwon — Before: 49,338; After: 49,245
rows[57].avg_per_m2_manwon — Before: 715; After: 735.3
rows[58].deals — Before: 676; After: 643
rows[58].avg_manwon — Before: 49,058; After: 48,501
rows[58].avg_per_m2_manwon — Before: 711.8; After: 704.5
rows[59].deals — Before: 799; After: 903
rows[59].avg_manwon — Before: 48,151; After: 47,570
rows[59].avg_per_m2_manwon — Before: 611.8; After: 580.9
rows[60].deals — Before: 1,007; After: 681
rows[60].avg_manwon — Before: 47,914; After: 47,434
rows[60].avg_per_m2_manwon — Before: 582.3; After: 605
rows[61].deals — Before: 1,021; After: 933
rows[61].avg_manwon — Before: 47,557; After: 47,269
rows[61].avg_per_m2_manwon — Before: 581.8; After: 578.2
rows[62].deals — Before: 803; After: 762
rows[62].avg_manwon — Before: 46,920; After: 47,167
rows[62].avg_per_m2_manwon — Before: 589.6; After: 585.8
rows[63].deals — Before: 1,162; After: 1,059
rows[63].avg_manwon — Before: 46,378; After: 46,109
rows[63].avg_per_m2_manwon — Before: 560.1; After: 555.6
rows[64].deals — Before: 869; After: 731
rows[64].avg_manwon — Before: 45,012; After: 45,142
rows[64].avg_per_m2_manwon — Before: 576.3; After: 570.7
rows[65].deals — Before: 220; After: 1,491
rows[65].avg_manwon — Before: 43,006; After: 43,259
rows[65].avg_per_m2_manwon — Before: 556.3; After: 599.7
rows[66].deals — Before: 1,573; After: 1,317
rows[66].avg_manwon — Before: 42,265; After: 43,026
rows[66].avg_per_m2_manwon — Before: 591.5; After: 559.8
rows[67].deals — Before: 1,010; After: 219
rows[67].avg_manwon — Before: 41,840; After: 42,136
rows[67].avg_per_m2_manwon — Before: 569; After: 557.8
rows[68].deals — Before: 744; After: 729
rows[68].avg_manwon — Before: 41,697; After: 41,622
rows[68].avg_per_m2_manwon — Before: 514.2; After: 509.6
rows[69].deals — Before: 381; After: 962
rows[69].avg_manwon — Before: 41,314; After: 41,292
rows[69].avg_per_m2_manwon — Before: 559; After: 565.2
rows[70].deals — Before: 384; After: 478
rows[70].avg_manwon — Before: 41,261; After: 40,535
rows[70].avg_per_m2_manwon — Before: 515.8; After: 484.5
rows[71].deals — Before: 175; After: 371
rows[71].avg_manwon — Before: 41,129; After: 40,423
rows[71].avg_per_m2_manwon — Before: 520.8; After: 549.8
rows[72].deals — Before: 708; After: 693
rows[72].avg_manwon — Before: 40,449; After: 40,309
rows[72].avg_per_m2_manwon — Before: 556; After: 554.3
rows[73].deals — Before: 1,317; After: 359
rows[73].avg_manwon — Before: 40,229; After: 40,106
rows[73].avg_per_m2_manwon — Before: 530.2; After: 505.9
rows[74].deals — Before: 508; After: 1,149
rows[74].avg_manwon — Before: 40,157; After: 40,050
rows[74].avg_per_m2_manwon — Before: 478.4; After: 587.9
rows[75].deals — Before: 1,191; After: 320
rows[75].avg_manwon — Before: 39,276; After: 39,846
rows[75].avg_per_m2_manwon — Before: 503.7; After: 488.3
rows[76].deals — Before: 505; After: 516
rows[76].avg_manwon — Before: 39,075; After: 39,821
rows[76].avg_per_m2_manwon — Before: 507.7; After: 525
rows[77].deals — Before: 1,203; After: 1,094
rows[77].avg_manwon — Before: 39,024; After: 39,615
rows[77].avg_per_m2_manwon — Before: 581.2; After: 506.3
rows[78].deals — Before: 362; After: 182
rows[78].avg_manwon — Before: 38,987; After: 39,579
rows[78].avg_per_m2_manwon — Before: 480.8; After: 494.4
rows[79].deals — Before: 1,044; After: 125
rows[79].avg_manwon — Before: 38,933; After: 38,957
rows[79].avg_per_m2_manwon — Before: 497.9; After: 552.6
rows[80].deals — Before: 338; After: 863
rows[80].avg_manwon — Before: 38,721; After: 38,765
rows[80].avg_per_m2_manwon — Before: 500.1; After: 537.6
rows[81].deals — Before: 945; After: 1,134
rows[81].avg_manwon — Before: 38,448; After: 38,458
rows[81].avg_per_m2_manwon — Before: 536.4; After: 497.3
rows[82].deals — Before: 406; After: 285
rows[82].avg_manwon — Before: 38,234; After: 38,251
rows[82].avg_per_m2_manwon — Before: 484.9; After: 480.3
rows[83].deals — Before: 327; After: 293
rows[83].avg_manwon — Before: 38,196; After: 37,478
rows[83].avg_per_m2_manwon — Before: 528.3; After: 518
rows[84].deals — Before: 310; After: 318
rows[84].avg_manwon — Before: 37,994; After: 37,368
rows[84].avg_per_m2_manwon — Before: 477.4; After: 490.4
rows[85].deals — Before: 126; After: 281
rows[85].avg_manwon — Before: 37,892; After: 36,683
rows[85].avg_per_m2_manwon — Before: 550.8; After: 466.6
rows[86].deals — Before: 1,023; After: 176
rows[86].avg_manwon — Before: 37,863; After: 36,644
rows[86].avg_per_m2_manwon — Before: 499.9; After: 490.2
rows[87].deals — Before: 158; After: 1,382
rows[87].avg_manwon — Before: 37,176; After: 36,582
rows[87].avg_per_m2_manwon — Before: 577; After: 526
rows[88].deals — Before: 301; After: 140
rows[88].avg_manwon — Before: 37,175; After: 36,507
rows[88].avg_per_m2_manwon — Before: 470.1; After: 466.9
rows[89].deals — Before: 1,481; After: 139
rows[89].avg_manwon — Before: 37,071; After: 36,275
rows[89].avg_per_m2_manwon — Before: 525.8; After: 574.8
rows[90].deals — Before: 147; After: 713
rows[90].avg_manwon — Before: 36,470; After: 36,212
rows[90].avg_per_m2_manwon — Before: 471.3; After: 431.7
rows[91].deals — Before: 736; After: 299
rows[91].avg_manwon — Before: 36,393; After: 35,815
rows[91].avg_per_m2_manwon — Before: 433.3; After: 495.1
rows[92].deals — Before: 202; After: 383
rows[92].avg_manwon — Before: 36,018; After: 35,608
rows[92].avg_per_m2_manwon — Before: 481.4; After: 460.4
rows[93].deals — Before: 958; After: 658
rows[93].avg_manwon — Before: 35,496; After: 35,384
rows[93].avg_per_m2_manwon — Before: 486.7; After: 456.5
rows[94].deals — Before: 722; After: 890
rows[94].avg_manwon — Before: 35,103; After: 34,695
rows[94].avg_per_m2_manwon — Before: 452.9; After: 464.7
rows[95].deals — Before: 891; After: 899
rows[95].avg_manwon — Before: 34,697; After: 34,661
rows[95].avg_per_m2_manwon — Before: 439; After: 484.4
rows[96].deals — Before: 339; After: 772
rows[96].avg_manwon — Before: 34,579; After: 33,818
rows[96].avg_per_m2_manwon — Before: 487.5; After: 431.4
rows[97].deals — Before: 1,178; After: 1,802
rows[97].avg_manwon — Before: 34,524; After: 33,648
rows[97].avg_per_m2_manwon — Before: 428.2; After: 466.7
rows[98].deals — Before: 1,975; After: 647
rows[98].avg_manwon — Before: 34,337; After: 33,563
rows[98].avg_per_m2_manwon — Before: 472.1; After: 477.3
rows[99].deals — Before: 736; After: 843
rows[99].avg_manwon — Before: 33,598; After: 32,948
rows[99].avg_per_m2_manwon — Before: 442.6; After: 425.2
rows[100].deals — Before: 722; After: 1,030
rows[100].avg_manwon — Before: 33,385; After: 32,395
rows[100].avg_per_m2_manwon — Before: 477; After: 408.7
rows[101].deals — Before: 848; After: 694
rows[101].avg_manwon — Before: 33,287; After: 31,833
rows[101].avg_per_m2_manwon — Before: 427.9; After: 424.2
rows[102].deals — Before: 1,017; After: 883
rows[102].avg_manwon — Before: 33,146; After: 31,820
rows[102].avg_per_m2_manwon — Before: 437.2; After: 425.1
rows[103].deals — Before: 373; After: 227
rows[103].avg_manwon — Before: 31,565; After: 31,801
rows[103].avg_per_m2_manwon — Before: 400.6; After: 389.1
rows[104].deals — Before: 463; After: 335
rows[104].avg_manwon — Before: 31,320; After: 31,395
rows[104].avg_per_m2_manwon — Before: 416.5; After: 400.5
rows[105].deals — Before: 369; After: 416
rows[105].avg_manwon — Before: 31,248; After: 30,983
rows[105].avg_per_m2_manwon — Before: 408.7; After: 406.2
rows[106].deals — Before: 768; After: 741
rows[106].avg_manwon — Before: 30,679; After: 30,827
rows[106].avg_per_m2_manwon — Before: 403.9; After: 410.4
rows[107].deals — Before: 251; After: 89
rows[107].avg_manwon — Before: 30,630; After: 30,404
rows[107].avg_per_m2_manwon — Before: 374.5; After: 411.7
rows[108].deals — Before: 106; After: 356
rows[108].avg_manwon — Before: 30,299; After: 30,385
rows[108].avg_per_m2_manwon — Before: 422.6; After: 398.5
rows[109].deals — Before: 1,314; After: 1,247
rows[109].avg_manwon — Before: 30,054; After: 29,798
rows[109].avg_per_m2_manwon — Before: 401.6; After: 399.6
rows[110].deals — Before: 873; After: 1,271
rows[110].avg_manwon — Before: 29,170; After: 29,200
rows[110].avg_per_m2_manwon — Before: 386.7; After: 410
rows[111].deals — Before: 1,323; After: 807
rows[111].avg_manwon — Before: 29,152; After: 29,002
rows[111].avg_per_m2_manwon — Before: 402.2; After: 386.4
rows[112].deals — Before: 389; After: 404
rows[112].avg_manwon — Before: 28,990; After: 28,376
rows[112].avg_per_m2_manwon — Before: 347; After: 407.2
rows[113].deals — Before: 457; After: 681
rows[113].avg_manwon — Before: 28,978; After: 28,288
rows[113].avg_per_m2_manwon — Before: 412.7; After: 371.3
rows[114].deals — Before: 706; After: 606
rows[114].avg_manwon — Before: 28,930; After: 28,155
rows[114].avg_per_m2_manwon — Before: 376.1; After: 458.4
rows[115].deals — Before: 618; After: 352
rows[115].avg_manwon — Before: 28,571; After: 27,573
rows[115].avg_per_m2_manwon — Before: 456.7; After: 336.7
rows[116].deals — Before: 1,249; After: 1,172
rows[116].avg_manwon — Before: 28,167; After: 27,342
rows[116].avg_per_m2_manwon — Before: 376.6; After: 368
rows[117].deals — Before: 1,120; After: 511
rows[117].avg_manwon — Before: 28,107; After: 27,101
rows[117].avg_per_m2_manwon — Before: 376.4; After: 370.4
rows[118].deals — Before: 549; After: 49
rows[118].avg_manwon — Before: 27,609; After: 27,002
rows[118].avg_per_m2_manwon — Before: 378; After: 325.3
rows[119].deals — Before: 45; After: 1,041
rows[119].avg_manwon — Before: 27,365; After: 26,667
rows[119].avg_per_m2_manwon — Before: 329.8; After: 361.4
rows[120].deals — Before: 539; After: 499
rows[120].avg_manwon — Before: 26,439; After: 25,936
rows[120].avg_per_m2_manwon — Before: 354.2; After: 350.2
rows[121].deals — Before: 588; After: 1,274
rows[121].avg_manwon — Before: 26,007; After: 25,836
rows[121].avg_per_m2_manwon — Before: 342.4; After: 364.8
rows[122].deals — Before: 1,262; After: 512
rows[122].avg_manwon — Before: 25,917; After: 25,706
rows[122].avg_per_m2_manwon — Before: 366; After: 348.4
rows[123].deals — Before: 1,053; After: 63
rows[123].avg_manwon — Before: 25,812; After: 25,631
rows[123].avg_per_m2_manwon — Before: 354.9; After: 328
rows[124].deals — Before: 99; After: 104
rows[124].avg_manwon — Before: 25,317; After: 25,568
rows[124].avg_per_m2_manwon — Before: 308.7; After: 312.4
rows[125].deals — Before: 202; After: 987
rows[125].avg_manwon — Before: 25,085; After: 25,530
rows[125].avg_per_m2_manwon — Before: 344.3; After: 352.3
rows[126].deals — Before: 65; After: 288
rows[126].avg_manwon — Before: 25,027; After: 25,175
rows[126].avg_per_m2_manwon — Before: 316.9; After: 360.2
rows[127].deals — Before: 360; After: 586
rows[127].avg_manwon — Before: 25,015; After: 24,972
rows[127].avg_per_m2_manwon — Before: 346; After: 335.3
rows[128].deals — Before: 354; After: 453
rows[128].avg_manwon — Before: 24,958; After: 24,892
rows[128].avg_per_m2_manwon — Before: 347.2; After: 338.5
rows[129].deals — Before: 527; After: 294
rows[129].avg_manwon — Before: 24,815; After: 24,560
rows[129].avg_per_m2_manwon — Before: 348.4; After: 323
rows[130].deals — Before: 520; After: 338
rows[130].avg_manwon — Before: 24,805; After: 24,504
rows[130].avg_per_m2_manwon — Before: 338.3; After: 338
rows[131].deals — Before: 332; After: 298
rows[131].avg_manwon — Before: 24,694; After: 24,217
rows[131].avg_per_m2_manwon — Before: 357.6; After: 343.3
rows[132].deals — Before: 810; After: 185
rows[132].avg_manwon — Before: 24,555; After: 24,208
rows[132].avg_per_m2_manwon — Before: 321.3; After: 343.6
rows[133].deals — Before: 1,416; After: 482
rows[133].avg_manwon — Before: 23,943; After: 24,134
rows[133].avg_per_m2_manwon — Before: 310.5; After: 347.1
rows[134].deals — Before: 324; After: 754
rows[134].avg_manwon — Before: 23,616; After: 24,036
rows[134].avg_per_m2_manwon — Before: 300.7; After: 320.8
rows[135].deals — Before: 616; After: 1,243
rows[135].avg_manwon — Before: 23,323; After: 23,351
rows[135].avg_per_m2_manwon — Before: 317.3; After: 303.8
rows[136].deals — Before: 462; After: 353
rows[136].avg_manwon — Before: 23,212; After: 23,040
rows[136].avg_per_m2_manwon — Before: 318.6; After: 305.3
rows[137].deals — Before: 309; After: 130
rows[137].avg_manwon — Before: 23,111; After: 22,957
rows[137].avg_per_m2_manwon — Before: 309.8; After: 319.2
rows[138].deals — Before: 194; After: 574
rows[138].avg_manwon — Before: 23,053; After: 22,793
rows[138].avg_per_m2_manwon — Before: 348.3; After: 312.6
rows[139].deals — Before: 178; After: 181
rows[139].avg_manwon — Before: 22,940; After: 22,617
rows[139].avg_per_m2_manwon — Before: 331.2; After: 340.7
rows[140].deals — Before: 146; After: 294
rows[140].avg_manwon — Before: 22,300; After: 22,567
rows[140].avg_per_m2_manwon — Before: 313; After: 292.1
rows[141].deals — Before: 591; After: 544
rows[141].avg_manwon — Before: 22,206; After: 22,270
rows[141].avg_per_m2_manwon — Before: 316.6; After: 318.4
rows[142].deals — Before: 402; After: 166
rows[142].avg_manwon — Before: 22,169; After: 21,973
rows[142].avg_per_m2_manwon — Before: 297.2; After: 317.8
rows[143].deals — Before: 265; After: 250
rows[143].avg_manwon — Before: 22,002; After: 21,900
rows[143].avg_per_m2_manwon — Before: 314.5; After: 319.1
rows[144].deals — Before: 1,074; After: 980
rows[144].avg_manwon — Before: 21,904; After: 21,835
rows[144].avg_per_m2_manwon — Before: 295.8; After: 293.3
rows[145].deals — Before: 481; After: 533
rows[145].avg_manwon — Before: 21,814; After: 21,633
rows[145].avg_per_m2_manwon — Before: 296; After: 302.5
rows[146].deals — Before: 514; After: 448
rows[146].avg_manwon — Before: 21,797; After: 21,453
rows[146].avg_per_m2_manwon — Before: 285.2; After: 273.7
rows[147].deals — Before: 599; After: 479
rows[147].avg_manwon — Before: 21,235; After: 21,309
rows[147].avg_per_m2_manwon — Before: 307.2; After: 280.7
rows[148].deals — Before: 717; After: 544
rows[148].avg_manwon — Before: 21,081; After: 20,840
rows[148].avg_per_m2_manwon — Before: 296.9; After: 299.3
rows[149].deals — Before: 477; After: 402
rows[149].avg_manwon — Before: 20,774; After: 20,768
rows[149].avg_per_m2_manwon — Before: 270.7; After: 284.7
rows[150].deals — Before: 148; After: 644
rows[150].avg_manwon — Before: 20,738; After: 20,668
rows[150].avg_per_m2_manwon — Before: 281.3; After: 292.5
rows[151].deals — Before: 604; After: 241
rows[151].avg_manwon — Before: 20,725; After: 20,179
rows[151].avg_per_m2_manwon — Before: 298.9; After: 276
rows[152].deals — Before: 124; After: 490
rows[152].avg_manwon — Before: 20,546; After: 19,729
rows[152].avg_per_m2_manwon — Before: 296.1; After: 297.6
rows[153].deals — Before: 337; After: 299
rows[153].avg_manwon — Before: 20,185; After: 19,721
rows[153].avg_per_m2_manwon — Before: 264.7; After: 259.5
rows[154].deals — Before: 252; After: 155
rows[154].avg_manwon — Before: 20,134; After: 19,283
rows[154].avg_per_m2_manwon — Before: 273.9; After: 268.6
rows[155].deals — Before: 618; After: 101
rows[155].avg_manwon — Before: 19,773; After: 19,236
rows[155].avg_per_m2_manwon — Before: 248.2; After: 297.3
rows[156].deals — Before: 1,257; After: 298
rows[156].avg_manwon — Before: 19,309; After: 19,219
rows[156].avg_per_m2_manwon — Before: 269.8; After: 286.8
rows[157].deals — Before: 298; After: 540
rows[157].avg_manwon — Before: 19,295; After: 18,940
rows[157].avg_per_m2_manwon — Before: 288.1; After: 242.5
rows[158].deals — Before: 202; After: 1,183
rows[158].avg_manwon — Before: 19,294; After: 18,885
rows[158].avg_per_m2_manwon — Before: 254.2; After: 266.8
rows[159].deals — Before: 31; After: 30
rows[159].avg_manwon — Before: 19,285; After: 18,805
rows[159].avg_per_m2_manwon — Before: 300.6; After: 303.5
rows[160].deals — Before: 488; After: 95
rows[160].avg_manwon — Before: 18,879; After: 18,681
rows[160].avg_per_m2_manwon — Before: 291.5; After: 306.8
rows[161].deals — Before: 64; After: 130
rows[161].avg_manwon — Before: 18,608; After: 18,554
rows[161].avg_per_m2_manwon — Before: 245; After: 280
rows[162].deals — Before: 183; After: 139
rows[162].avg_manwon — Before: 18,556; After: 18,442
rows[162].avg_per_m2_manwon — Before: 273.5; After: 262.5
rows[163].deals — Before: 31; After: 230
rows[163].avg_manwon — Before: 18,465; After: 18,431
rows[163].avg_per_m2_manwon — Before: 263.9; After: 272.3
rows[164].deals — Before: 79; After: 203
rows[164].avg_manwon — Before: 18,392; After: 18,408
rows[164].avg_per_m2_manwon — Before: 268.2; After: 249.5
rows[165].deals — Before: 228; After: 68
rows[165].avg_manwon — Before: 18,295; After: 18,399
rows[165].avg_per_m2_manwon — Before: 268.8; After: 245.9
rows[166].deals — Before: 123; After: 647
rows[166].avg_manwon — Before: 18,263; After: 18,328
rows[166].avg_per_m2_manwon — Before: 238.6; After: 245.8
rows[167].deals — Before: 586; After: 130
rows[167].avg_manwon — Before: 17,942; After: 18,182
rows[167].avg_per_m2_manwon — Before: 237.8; After: 256.9
rows[168].deals — Before: 735; After: 164
rows[168].avg_manwon — Before: 17,793; After: 17,851
rows[168].avg_per_m2_manwon — Before: 241.3; After: 264.8
rows[169].deals — Before: 36; After: 575
rows[169].avg_manwon — Before: 17,761; After: 17,831
rows[169].avg_per_m2_manwon — Before: 265.7; After: 241.1
rows[170].deals — Before: 148; After: 30
rows[170].avg_manwon — Before: 17,726; After: 17,768
rows[170].avg_per_m2_manwon — Before: 262.4; After: 258.4
rows[171].deals — Before: 57; After: 85
rows[171].avg_manwon — Before: 17,632; After: 17,736
rows[171].avg_per_m2_manwon — Before: 266.7; After: 251.1
rows[172].deals — Before: 84; After: 89
rows[172].avg_manwon — Before: 17,464; After: 17,572
rows[172].avg_per_m2_manwon — Before: 250.3; After: 260.6
rows[173].deals — Before: 235; After: 36
rows[173].avg_manwon — Before: 17,404; After: 17,492
rows[173].avg_per_m2_manwon — Before: 237.1; After: 254.2
rows[174].deals — Before: 46; After: 69
rows[174].avg_manwon — Before: 17,400; After: 17,433
rows[174].avg_per_m2_manwon — Before: 247.6; After: 265.9
rows[175].deals — Before: 155; After: 190
rows[175].avg_manwon — Before: 17,373; After: 17,111
rows[175].avg_per_m2_manwon — Before: 263.2; After: 262.2
rows[176].deals — Before: 53; After: 84
rows[176].avg_manwon — Before: 17,348; After: 17,107
rows[176].avg_per_m2_manwon — Before: 254.7; After: 249.2
rows[177].deals — Before: 137; After: 217
rows[177].avg_manwon — Before: 17,301; After: 17,095
rows[177].avg_per_m2_manwon — Before: 251; After: 234.5
rows[178].deals — Before: 73; After: 322
rows[178].avg_manwon — Before: 17,194; After: 16,927
rows[178].avg_per_m2_manwon — Before: 265.5; After: 249.8
rows[179].deals — Before: 47; After: 41
rows[179].avg_manwon — Before: 17,183; After: 16,655
rows[179].avg_per_m2_manwon — Before: 251.3; After: 282.7
rows[180].deals — Before: 40; After: 109
rows[180].avg_manwon — Before: 16,915; After: 16,585
rows[180].avg_per_m2_manwon — Before: 238.8; After: 223.2
rows[181].deals — Before: 145; After: 128
rows[181].avg_manwon — Before: 16,799; After: 16,539
rows[181].avg_per_m2_manwon — Before: 247.7; After: 245.5
rows[182].deals — Before: 304; After: 39
rows[182].avg_manwon — Before: 16,683; After: 16,507
rows[182].avg_per_m2_manwon — Before: 251.7; After: 241
rows[183].deals — Before: 261; After: 71
rows[183].avg_manwon — Before: 16,629; After: 16,420
rows[183].avg_per_m2_manwon — Before: 240.6; After: 241
rows[184].deals — Before: 144; After: 54
rows[184].avg_manwon — Before: 16,624; After: 16,345
rows[184].avg_per_m2_manwon — Before: 224.6; After: 252.7
rows[185].deals — Before: 49; After: 108
rows[185].avg_manwon — Before: 16,558; After: 16,282
rows[185].avg_per_m2_manwon — Before: 283.1; After: 261.6
rows[186].deals — Before: 30; After: 224
rows[186].avg_manwon — Before: 16,453; After: 16,266
rows[186].avg_per_m2_manwon — Before: 233; After: 236
rows[187].deals — Before: 87; After: 154
rows[187].avg_manwon — Before: 16,255; After: 16,171
rows[187].avg_per_m2_manwon — Before: 244.1; After: 220.2
rows[188].deals — Before: 88; After: 55
rows[188].avg_manwon — Before: 16,254; After: 16,069
rows[188].avg_per_m2_manwon — Before: 234.2; After: 250.4
rows[189].deals — Before: 187; After: 46
rows[189].avg_manwon — Before: 16,142; After: 16,066
rows[189].avg_per_m2_manwon — Before: 248.7; After: 247.1
rows[190].deals — Before: 89; After: 50
rows[190].avg_manwon — Before: 16,015; After: 16,061
rows[190].avg_per_m2_manwon — Before: 274.1; After: 246.9
rows[191].deals — Before: 48; After: 610
rows[191].avg_manwon — Before: 15,820; After: 15,802
rows[191].avg_per_m2_manwon — Before: 245.3; After: 225
rows[192].deals — Before: 114; After: 31
rows[192].avg_manwon — Before: 15,687; After: 14,950
rows[192].avg_per_m2_manwon — Before: 256.2; After: 250.9
rows[193].deals — Before: 204; After: 472
rows[193].avg_manwon — Before: 15,568; After: 14,760
rows[193].avg_per_m2_manwon — Before: 241.1; After: 196.9
rows[194].deals — Before: 516; After: 169
rows[194].avg_manwon — Before: 15,279; After: 14,523
rows[194].avg_per_m2_manwon — Before: 204.4; After: 231.3
rows[195].deals — Before: 62; After: 95
rows[195].avg_manwon — Before: 14,808; After: 13,040
rows[195].avg_per_m2_manwon — Before: 235.1; After: 196.6
rows[196].deals — Before: 36; After: 410
rows[196].avg_manwon — Before: 14,599; After: 12,938
rows[196].avg_per_m2_manwon — Before: 218; After: 193.7
rows[197].deals — Before: 742; After: 86
rows[197].avg_manwon — Before: 13,847; After: 12,878
rows[197].avg_per_m2_manwon — Before: 210.2; After: 189.1
rows[198].deals — Before: 33; After: 32
rows[198].avg_manwon — Before: 13,438; After: 12,716
rows[198].avg_per_m2_manwon — Before: 232.3; After: 218.4
rows[199].deals — Before: 46; After: 184
rows[199].avg_manwon — Before: 13,404; After: 12,638
rows[199].avg_per_m2_manwon — Before: 219.4; After: 192.1
rows[200].deals — Before: 88; After: 33
rows[200].avg_manwon — Before: 13,252; After: 12,615
rows[200].avg_per_m2_manwon — Before: 199.8; After: 188.2
rows[201].deals — Before: 207; After: 169
rows[201].avg_manwon — Before: 13,101; After: 12,482
rows[201].avg_per_m2_manwon — Before: 200.6; After: 199.1
rows[202].deals — Before: 429; After: 39
rows[202].avg_manwon — Before: 13,029; After: 12,395
rows[202].avg_per_m2_manwon — Before: 191.7; After: 190.6
rows[203].deals — Before: 83; After: 31
rows[203].avg_manwon — Before: 12,170; After: 11,815
rows[203].avg_per_m2_manwon — Before: 182.9; After: 181.6
rows[204].deals — Before: 31; After: 185
rows[204].avg_manwon — Before: 12,158; After: 11,465
rows[204].avg_per_m2_manwon — Before: 188.9; After: 165.3
rows[205].deals — Before: 51; After: 44
rows[205].avg_manwon — Before: 11,686; After: 11,366
rows[205].avg_per_m2_manwon — Before: 164.5; After: 189.3
rows[206].deals — Before: 177; After: 30
rows[206].avg_manwon — Before: 11,644; After: 11,211
rows[206].avg_per_m2_manwon — Before: 192.6; After: 182.3
rows[207].deals — Before: 61; After: 48
rows[207].avg_manwon — Before: 11,596; After: 11,136
rows[207].avg_per_m2_manwon — Before: 210.1; After: 159.1
rows[208].deals — Before: 195; After: 35
rows[208].avg_manwon — Before: 11,372; After: 10,425
rows[208].avg_per_m2_manwon — Before: 165.9; After: 156.1
rows[209].deals — Before: 31; After: 95
rows[209].avg_manwon — Before: 11,237; After: 10,379
rows[209].avg_per_m2_manwon — Before: 177.9; After: 177.1
rows[210].deals — Before: 109; After: 53
rows[210].avg_manwon — Before: 10,652; After: 9,256
rows[210].avg_per_m2_manwon — Before: 181.3; After: 178.7
rows[211].deals — Before: 37; After: 30
rows[211].avg_manwon — Before: 9,590; After: 8,725
rows[211].avg_per_m2_manwon — Before: 141.9; After: 146.5
rows[212].deals — Before: 78; After: 61
rows[212].avg_manwon — Before: 6,959; After: 7,054
rows[212].avg_per_m2_manwon — Before: 117.5; After: 119.7
insufficient[1].deals — Before: 29; After: 28
insufficient[2].deals — Before: 29; After: 26
insufficient[3].deals — Before: 28; After: 26
insufficient[4].deals — Before: 28; After: 25
insufficient[5].deals — Before: 27; After: 25
insufficient[9].deals — Before: 24; After: 23
insufficient[10].deals — Before: 23; After: 22
insufficient[13].deals — Before: 20; After: 19
insufficient[16].deals — Before: 16; After: 17
insufficient[17].deals — Before: 16; After: 17
insufficient[25].deals — Before: 11; After: 10
insufficient[27].deals — Before: 9; After: 8
insufficient[28].deals — Before: 9; After: 7
insufficient[29].deals — Before: 9; After: 7
insufficient[30].deals — Before: 8; After: 7
insufficient[31].deals — Before: 8; After: 7
insufficient[32].deals — Before: 7; After: 6
insufficient[33].deals — Before: 7; After: 5
insufficient[34].deals — Before: 6; After: 4
insufficient[35].deals — Before: 6; After: 4
insufficient[36].deals — Before: 5; After: 4
insufficient[37].deals — Before: 2; After: 3
insufficient[38].deals — Before: 1; After: 2
insufficient[39].deals — Before: 1; After: 2
insufficient[41].deals — Before: 0; After: 1

Changed basis

Stored regions — Before: [["강원특별자치도/강릉시","51150"],["강원특별자치도/고성군","51820"],["강원특별자치도/동해시","51170"],["강원특별자치도/삼척시","51230"],["강원특별자치도/속초시","51210"],["강원특별자치도/양구군",null],["강원특별자치도/양양군","51830"],["강원특별자치도/영월군","51750"],["강원특별자치도/원주시","51130"],["강원특별자치도/인제군",null],["강원특별자치도/정선군","51770"],["강원특별자치도/철원군",null],["강원특별자치도/춘천시","51110"],["강원특별자치도/태백시","51190"],["강원특별자치도/평창군","51760"],["강원특별자치도/홍천군","51720"],["강원특별자치도/화천군",null],["강원특별자치도/횡성군","51730"],["경기도/가평군","41820"],["경기도/고양시",null],["경기도/고양시 덕양구","41281"],["경기도/고양시 일산동구","41285"],["경기도/고양시 일산서구","41287"],["경기도/과천시","41290"],["경기도/광명시","41210"],["경기도/광주시","41610"],["경기도/구리시","41310"],["경기도/군포시","41410"],["경기도/김포시","41570"],["경기도/남양주시","41360"],["경기도/동두천시","41250"],["경기도/부천시",null],["경기도/부천시 소사구","41194"],["경기도/부천시 오정구","41196"],["경기도/부천시 원미구","41192"],["경기도/성남시",null],["경기도/성남시 분당구","41135"],["경기도/성남시 수정구","41131"],["경기도/성남시 중원구","41133"],["경기도/수원시",null],["경기도/수원시 권선구","41113"],["경기도/수원시 영통구","41117"],["경기도/수원시 장안구","41111"],["경기도/수원시 팔달구","41115"],["경기도/시흥시","41390"],["경기도/안산시",null],["경기도/안산시 단원구","41273"],["경기도/안산시 상록구","41271"],["경기도/안성시","41550"],["경기도/안양시",null],["경기도/안양시 동안구","41173"],["경기도/안양시 만안구","41171"],["경기도/양주시","41630"],["경기도/양평군","41830"],["경기도/여주시","41670"],["경기도/연천군","41800"],["경기도/오산시","41370"],["경기도/용인시",null],["경기도/용인시 기흥구","41463"],["경기도/용인시 수지구","41465"],["경기도/용인시 처인구","41461"],["경기도/의왕시","41430"],["경기도/의정부시","41150"],["경기도/이천시","41500"],["경기도/파주시","41480"],["경기도/평택시","41220"],["경기도/포천시","41650"],["경기도/하남시","41450"],["경기도/화성시",null],["경기도/화성시 동탄구","41597"],["경기도/화성시 만세구","41591"],["경기도/화성시 병점구","41595"],["경기도/화성시 효행구","41593"],["경상남도/거제시","48310"],["경상남도/거창군","48880"],["경상남도/고성군","48820"],["경상남도/김해시","48250"],["경상남도/남해군",null],["경상남도/밀양시","48270"],["경상남도/사천시","48240"],["경상남도/산청군",null],["경상남도/양산시","48330"],["경상남도/의령군",null],["경상남도/진주시","48170"],["경상남도/창녕군","48740"],["경상남도/창원시",null],["경상남도/창원시 마산합포구","48125"],["경상남도/창원시 마산회원구","48127"],["경상남도/창원시 성산구","48123"],["경상남도/창원시 의창구","48121"],["경상남도/창원시 진해구","48129"],["경상남도/통영시","48220"],["경상남도/하동군",null],["경상남도/함안군","48730"],["경상남도/함양군",null],["경상남도/합천군",null],["경상북도/경산시","47290"],["경상북도/경주시","47130"],["경상북도/고령군",null],["경상북도/구미시","47190"],["경상북도/김천시","47150"],["경상북도/문경시","47280"],["경상북도/봉화군",null],["경상북도/상주시","47250"],["경상북도/성주군",null],["경상북도/안동시","47170"],["경상북도/영덕군",null],["경상북도/영양군",null],["경상북도/영주시","47210"],["경상북도/영천시","47230"],["경상북도/예천군","47900"],["경상북도/울릉군",null],["경상북도/울진군",null],["경상북도/의성군",null],["경상북도/청도군","47820"],["경상북도/청송군",null],["경상북도/칠곡군","47850"],["경상북도/포항시",null],["경상북도/포항시 남구","47111"],["경상북도/포항시 북구","47113"],["대구광역시/군위군",null],["대구광역시/남구","27200"],["대구광역시/달서구","27290"],["대구광역시/달성군","27710"],["대구광역시/동구","27140"],["대구광역시/북구","27230"],["대구광역시/서구","27170"],["대구광역시/수성구","27260"],["대구광역시/중구","27110"],["대전광역시/대덕구","30230"],["대전광역시/동구","30110"],["대전광역시/서구","30170"],["대전광역시/유성구","30200"],["대전광역시/중구","30140"],["부산광역시/강서구","26440"],["부산광역시/금정구","26410"],["부산광역시/기장군","26710"],["부산광역시/남구","26290"],["부산광역시/동구","26170"],["부산광역시/동래구","26260"],["부산광역시/부산진구","26230"],["부산광역시/북구","26320"],["부산광역시/사상구","26530"],["부산광역시/사하구","26380"],["부산광역시/서구","26140"],["부산광역시/수영구","26500"],["부산광역시/연제구","26470"],["부산광역시/영도구","26200"],["부산광역시/중구","26110"],["부산광역시/해운대구","26350"],["서울특별시/강남구","11680"],["서울특별시/강동구","11740"],["서울특별시/강북구","11305"],["서울특별시/강서구","11500"],["서울특별시/관악구","11620"],["서울특별시/광진구","11215"],["서울특별시/구로구","11530"],["서울특별시/금천구","11545"],["서울특별시/노원구","11350"],["서울특별시/도봉구","11320"],["서울특별시/동대문구","11230"],["서울특별시/동작구","11590"],["서울특별시/마포구","11440"],["서울특별시/서대문구","11410"],["서울특별시/서초구","11650"],["서울특별시/성동구","11200"],["서울특별시/성북구","11290"],["서울특별시/송파구","11710"],["서울특별시/양천구","11470"],["서울특별시/영등포구","11560"],["서울특별시/용산구","11170"],["서울특별시/은평구","11380"],["서울특별시/종로구","11110"],["서울특별시/중구","11140"],["서울특별시/중랑구","11260"],["세종특별자치시/세종특별자치시","36110"],["울산광역시/남구","31140"],["울산광역시/동구","31170"],["울산광역시/북구","31200"],["울산광역시/울주군","31710"],["울산광역시/중구","31110"],["인천광역시/강화군","28710"],["인천광역시/검단구","28290"],["인천광역시/계양구","28245"],["인천광역시/남동구","28200"],["인천광역시/미추홀구","28177"],["인천광역시/부평구","28237"],["인천광역시/서해구","28275"],["인천광역시/연수구","28185"],["인천광역시/영종구","28155"],["인천광역시/옹진군",null],["인천광역시/제물포구","28125"],["전남광주통합특별시/강진군",null],["전남광주통합특별시/고흥군","12740"],["전남광주통합특별시/곡성군",null],["전남광주통합특별시/광산구","12330"],["전남광주통합특별시/광양시","12190"],["전남광주통합특별시/구례군","12730"],["전남광주통합특별시/나주시","12170"],["전남광주통합특별시/남구","12270"],["전남광주통합특별시/담양군",null],["전남광주통합특별시/동구","12210"],["전남광주통합특별시/목포시","12110"],["전남광주통합특별시/무안군","12810"],["전남광주통합특별시/보성군",null],["전남광주통합특별시/북구","12300"],["전남광주통합특별시/서구","12240"],["전남광주통합특별시/순천시","12150"],["전남광주통합특별시/신안군",null],["전남광주통합특별시/여수시","12130"],["전남광주통합특별시/영광군","12830"],["전남광주통합특별시/영암군","12800"],["전남광주통합특별시/완도군",null],["전남광주통합특별시/장성군",null],["전남광주통합특별시/장흥군",null],["전남광주통합특별시/진도군",null],["전남광주통합특별시/함평군",null],["전남광주통합특별시/해남군","12790"],["전남광주통합특별시/화순군","12760"],["전북특별자치도/고창군","52790"],["전북특별자치도/군산시","52130"],["전북특별자치도/김제시","52210"],["전북특별자치도/남원시","52190"],["전북특별자치도/무주군",null],["전북특별자치도/부안군","52800"],["전북특별자치도/순창군",null],["전북특별자치도/완주군","52710"],["전북특별자치도/익산시","52140"],["전북특별자치도/임실군",null],["전북특별자치도/장수군",null],["전북특별자치도/전주시",null],["전북특별자치도/전주시 덕진구","52113"],["전북특별자치도/전주시 완산구","52111"],["전북특별자치도/정읍시","52180"],["전북특별자치도/진안군",null],["제주특별자치도/서귀포시","50130"],["제주특별자치도/제주시","50110"],["충청남도/계룡시","44250"],["충청남도/공주시","44150"],["충청남도/금산군","44710"],["충청남도/논산시","44230"],["충청남도/당진시","44270"],["충청남도/보령시","44180"],["충청남도/부여군",null],["충청남도/서산시","44210"],["충청남도/서천군",null],["충청남도/아산시","44200"],["충청남도/예산군","44810"],["충청남도/천안시",null],["충청남도/천안시 동남구","44131"],["충청남도/천안시 서북구","44133"],["충청남도/청양군",null],["충청남도/태안군","44825"],["충청남도/홍성군","44800"],["충청북도/괴산군",null],["충청북도/단양군",null],["충청북도/보은군",null],["충청북도/영동군",null],["충청북도/옥천군","43730"],["충청북도/음성군","43770"],["충청북도/제천시","43150"],["충청북도/증평군","43745"],["충청북도/진천군","43750"],["충청북도/청주시",null],["충청북도/청주시 상당구","43111"],["충청북도/청주시 서원구","43112"],["충청북도/청주시 청원구","43114"],["충청북도/청주시 흥덕구","43113"],["충청북도/충주시","43130"]]; After: [["강원특별자치도/강릉시","51150"],["강원특별자치도/고성군","51820"],["강원특별자치도/동해시","51170"],["강원특별자치도/삼척시","51230"],["강원특별자치도/속초시","51210"],["강원특별자치도/양구군",null],["강원특별자치도/양양군","51830"],["강원특별자치도/영월군","51750"],["강원특별자치도/원주시","51130"],["강원특별자치도/인제군",null],["강원특별자치도/정선군","51770"],["강원특별자치도/철원군","51780"],["강원특별자치도/춘천시","51110"],["강원특별자치도/태백시","51190"],["강원특별자치도/평창군","51760"],["강원특별자치도/홍천군","51720"],["강원특별자치도/화천군",null],["강원특별자치도/횡성군","51730"],["경기도/가평군","41820"],["경기도/고양시",null],["경기도/고양시 덕양구","41281"],["경기도/고양시 일산동구","41285"],["경기도/고양시 일산서구","41287"],["경기도/과천시","41290"],["경기도/광명시","41210"],["경기도/광주시","41610"],["경기도/구리시","41310"],["경기도/군포시","41410"],["경기도/김포시","41570"],["경기도/남양주시","41360"],["경기도/동두천시","41250"],["경기도/부천시",null],["경기도/부천시 소사구","41194"],["경기도/부천시 오정구","41196"],["경기도/부천시 원미구","41192"],["경기도/성남시",null],["경기도/성남시 분당구","41135"],["경기도/성남시 수정구","41131"],["경기도/성남시 중원구","41133"],["경기도/수원시",null],["경기도/수원시 권선구","41113"],["경기도/수원시 영통구","41117"],["경기도/수원시 장안구","41111"],["경기도/수원시 팔달구","41115"],["경기도/시흥시","41390"],["경기도/안산시",null],["경기도/안산시 단원구","41273"],["경기도/안산시 상록구","41271"],["경기도/안성시","41550"],["경기도/안양시",null],["경기도/안양시 동안구","41173"],["경기도/안양시 만안구","41171"],["경기도/양주시","41630"],["경기도/양평군","41830"],["경기도/여주시","41670"],["경기도/연천군","41800"],["경기도/오산시","41370"],["경기도/용인시",null],["경기도/용인시 기흥구","41463"],["경기도/용인시 수지구","41465"],["경기도/용인시 처인구","41461"],["경기도/의왕시","41430"],["경기도/의정부시","41150"],["경기도/이천시","41500"],["경기도/파주시","41480"],["경기도/평택시","41220"],["경기도/포천시","41650"],["경기도/하남시","41450"],["경기도/화성시",null],["경기도/화성시 동탄구","41597"],["경기도/화성시 만세구","41591"],["경기도/화성시 병점구","41595"],["경기도/화성시 효행구","41593"],["경상남도/거제시","48310"],["경상남도/거창군","48880"],["경상남도/고성군","48820"],["경상남도/김해시","48250"],["경상남도/남해군",null],["경상남도/밀양시","48270"],["경상남도/사천시","48240"],["경상남도/산청군",null],["경상남도/양산시","48330"],["경상남도/의령군",null],["경상남도/진주시","48170"],["경상남도/창녕군","48740"],["경상남도/창원시",null],["경상남도/창원시 마산합포구","48125"],["경상남도/창원시 마산회원구","48127"],["경상남도/창원시 성산구","48123"],["경상남도/창원시 의창구","48121"],["경상남도/창원시 진해구","48129"],["경상남도/통영시","48220"],["경상남도/하동군",null],["경상남도/함안군","48730"],["경상남도/함양군",null],["경상남도/합천군",null],["경상북도/경산시","47290"],["경상북도/경주시","47130"],["경상북도/고령군",null],["경상북도/구미시","47190"],["경상북도/김천시","47150"],["경상북도/문경시","47280"],["경상북도/봉화군",null],["경상북도/상주시","47250"],["경상북도/성주군",null],["경상북도/안동시","47170"],["경상북도/영덕군",null],["경상북도/영양군",null],["경상북도/영주시","47210"],["경상북도/영천시","47230"],["경상북도/예천군","47900"],["경상북도/울릉군",null],["경상북도/울진군",null],["경상북도/의성군",null],["경상북도/청도군","47820"],["경상북도/청송군",null],["경상북도/칠곡군","47850"],["경상북도/포항시",null],["경상북도/포항시 남구","47111"],["경상북도/포항시 북구","47113"],["대구광역시/군위군",null],["대구광역시/남구","27200"],["대구광역시/달서구","27290"],["대구광역시/달성군","27710"],["대구광역시/동구","27140"],["대구광역시/북구","27230"],["대구광역시/서구","27170"],["대구광역시/수성구","27260"],["대구광역시/중구","27110"],["대전광역시/대덕구","30230"],["대전광역시/동구","30110"],["대전광역시/서구","30170"],["대전광역시/유성구","30200"],["대전광역시/중구","30140"],["부산광역시/강서구","26440"],["부산광역시/금정구","26410"],["부산광역시/기장군","26710"],["부산광역시/남구","26290"],["부산광역시/동구","26170"],["부산광역시/동래구","26260"],["부산광역시/부산진구","26230"],["부산광역시/북구","26320"],["부산광역시/사상구","26530"],["부산광역시/사하구","26380"],["부산광역시/서구","26140"],["부산광역시/수영구","26500"],["부산광역시/연제구","26470"],["부산광역시/영도구","26200"],["부산광역시/중구","26110"],["부산광역시/해운대구","26350"],["서울특별시/강남구","11680"],["서울특별시/강동구","11740"],["서울특별시/강북구","11305"],["서울특별시/강서구","11500"],["서울특별시/관악구","11620"],["서울특별시/광진구","11215"],["서울특별시/구로구","11530"],["서울특별시/금천구","11545"],["서울특별시/노원구","11350"],["서울특별시/도봉구","11320"],["서울특별시/동대문구","11230"],["서울특별시/동작구","11590"],["서울특별시/마포구","11440"],["서울특별시/서대문구","11410"],["서울특별시/서초구","11650"],["서울특별시/성동구","11200"],["서울특별시/성북구","11290"],["서울특별시/송파구","11710"],["서울특별시/양천구","11470"],["서울특별시/영등포구","11560"],["서울특별시/용산구","11170"],["서울특별시/은평구","11380"],["서울특별시/종로구","11110"],["서울특별시/중구","11140"],["서울특별시/중랑구","11260"],["세종특별자치시/세종특별자치시","36110"],["울산광역시/남구","31140"],["울산광역시/동구","31170"],["울산광역시/북구","31200"],["울산광역시/울주군","31710"],["울산광역시/중구","31110"],["인천광역시/강화군","28710"],["인천광역시/검단구","28290"],["인천광역시/계양구","28245"],["인천광역시/남동구","28200"],["인천광역시/미추홀구","28177"],["인천광역시/부평구","28237"],["인천광역시/서해구","28275"],["인천광역시/연수구","28185"],["인천광역시/영종구","28155"],["인천광역시/옹진군",null],["인천광역시/제물포구","28125"],["전남광주통합특별시/강진군",null],["전남광주통합특별시/고흥군",null],["전남광주통합특별시/곡성군",null],["전남광주통합특별시/광산구","12330"],["전남광주통합특별시/광양시","12190"],["전남광주통합특별시/구례군","12730"],["전남광주통합특별시/나주시","12170"],["전남광주통합특별시/남구","12270"],["전남광주통합특별시/담양군",null],["전남광주통합특별시/동구","12210"],["전남광주통합특별시/목포시","12110"],["전남광주통합특별시/무안군","12810"],["전남광주통합특별시/보성군","12750"],["전남광주통합특별시/북구","12300"],["전남광주통합특별시/서구","12240"],["전남광주통합특별시/순천시","12150"],["전남광주통합특별시/신안군",null],["전남광주통합특별시/여수시","12130"],["전남광주통합특별시/영광군","12830"],["전남광주통합특별시/영암군","12800"],["전남광주통합특별시/완도군",null],["전남광주통합특별시/장성군",null],["전남광주통합특별시/장흥군",null],["전남광주통합특별시/진도군",null],["전남광주통합특별시/함평군",null],["전남광주통합특별시/해남군",null],["전남광주통합특별시/화순군","12760"],["전북특별자치도/고창군",null],["전북특별자치도/군산시","52130"],["전북특별자치도/김제시","52210"],["전북특별자치도/남원시","52190"],["전북특별자치도/무주군",null],["전북특별자치도/부안군","52800"],["전북특별자치도/순창군",null],["전북특별자치도/완주군","52710"],["전북특별자치도/익산시","52140"],["전북특별자치도/임실군",null],["전북특별자치도/장수군",null],["전북특별자치도/전주시",null],["전북특별자치도/전주시 덕진구","52113"],["전북특별자치도/전주시 완산구","52111"],["전북특별자치도/정읍시","52180"],["전북특별자치도/진안군",null],["제주특별자치도/서귀포시","50130"],["제주특별자치도/제주시","50110"],["충청남도/계룡시","44250"],["충청남도/공주시","44150"],["충청남도/금산군","44710"],["충청남도/논산시","44230"],["충청남도/당진시","44270"],["충청남도/보령시","44180"],["충청남도/부여군",null],["충청남도/서산시","44210"],["충청남도/서천군",null],["충청남도/아산시","44200"],["충청남도/예산군","44810"],["충청남도/천안시",null],["충청남도/천안시 동남구","44131"],["충청남도/천안시 서북구","44133"],["충청남도/청양군",null],["충청남도/태안군","44825"],["충청남도/홍성군","44800"],["충청북도/괴산군",null],["충청북도/단양군","43800"],["충청북도/보은군",null],["충청북도/영동군",null],["충청북도/옥천군","43730"],["충청북도/음성군","43770"],["충청북도/제천시","43150"],["충청북도/증평군","43745"],["충청북도/진천군","43750"],["충청북도/청주시",null],["충청북도/청주시 상당구","43111"],["충청북도/청주시 서원구","43112"],["충청북도/청주시 청원구","43114"],["충청북도/청주시 흥덕구","43113"],["충청북도/충주시","43130"]]

How this index works, and its limits

1Gangnam-guSeoul384 salesper m² ₩35340k₩3.09bn
2Yongsan-guSeoul154 salesper m² ₩28120k₩2.63bn
3Seocho-guSeoul343 salesper m² ₩30590k₩2.51bn
4Gwacheon-siGyeonggi-do51 salesper m² ₩28310k₩2.29bn
5Seongdong-guSeoul214 salesper m² ₩24210k₩1.88bn
6Songpa-guSeoul804 salesper m² ₩27000k₩1.79bn
7Seongnam-si Bundang-guGyeonggi-do799 salesper m² ₩21150k₩1.64bn
8Mapo-guSeoul340 salesper m² ₩19800k₩1.50bn
9Gwangjin-guSeoul173 salesper m² ₩19080k₩1.46bn
10Yeongdeungpo-guSeoul519 salesper m² ₩18600k₩1.39bn
11Dongjak-guSeoul444 salesper m² ₩18810k₩1.30bn
12Yangcheon-guSeoul445 salesper m² ₩16810k₩1.30bn
13Gangdong-guSeoul598 salesper m² ₩18010k₩1.29bn
14Jung-guSeoul141 salesper m² ₩17660k₩1.24bn
15Seongnam-si Sujeong-guGyeonggi-do238 salesper m² ₩16410k₩1.23bn
16Jongno-guSeoul94 salesper m² ₩14740k₩1.18bn
17Seodaemun-guSeoul464 salesper m² ₩14530k₩1.11bn
18Hanam-siGyeonggi-do735 salesper m² ₩14090k₩1.11bn
19Dongdaemun-guSeoul475 salesper m² ₩14440k₩1.06bn
20Yongin-si Suji-guGyeonggi-do1,293 salesper m² ₩10570k₩1.02bn
21Seongbuk-guSeoul679 salesper m² ₩12960k₩0.99bn
22Gangseo-guSeoul912 salesper m² ₩13910k₩0.93bn
23Gwangmyeong-siGyeonggi-do620 salesper m² ₩12520k₩0.89bn
24Gwanak-guSeoul425 salesper m² ₩11850k₩0.89bn
25Seongnam-si Jungwon-guGyeonggi-do359 salesper m² ₩13160k₩0.88bn
26Eunpyeong-guSeoul698 salesper m² ₩11710k₩0.86bn
27Anyang-si Dongan-guGyeonggi-do1,139 salesper m² ₩12340k₩0.83bn
28Hwaseong-si Dongtan-guGyeonggi-do537 salesper m² ₩10510k₩0.81bn
29Guro-guSeoul765 salesper m² ₩10920k₩0.78bn
30Suwon-si Yeongtong-guGyeonggi-do1,557 salesper m² ₩10210k₩0.77bn
31Suyeong-guBusan359 salesper m² ₩9260k₩0.75bn
32Guri-siGyeonggi-do266 salesper m² ₩9960k₩0.74bn
33Uiwang-siGyeonggi-do467 salesper m² ₩9330k₩0.74bn
34Nowon-guSeoul1,680 salesper m² ₩11080k₩0.71bn
35Gangbuk-guSeoul283 salesper m² ₩9830k₩0.71bn
36Suwon-si Paldal-guGyeonggi-do624 salesper m² ₩9630k₩0.69bn
37Geumcheon-guSeoul315 salesper m² ₩9320k₩0.67bn
38Yeonsu-guIncheon1,292 salesper m² ₩7930k₩0.66bn
39Suwon-si Jangan-guGyeonggi-do700 salesper m² ₩8410k₩0.63bn
40Anyang-si Manan-guGyeonggi-do733 salesper m² ₩8670k₩0.62bn
41Suseong-guDaegu856 salesper m² ₩6900k₩0.60bn
42Goyang-si Deogyang-guGyeonggi-do1,305 salesper m² ₩7910k₩0.59bn
43Yongin-si Giheung-guGyeonggi-do785 salesper m² ₩6780k₩0.59bn
44Bucheon-si Wonmi-guGyeonggi-do1,337 salesper m² ₩7920k₩0.58bn
45Haeundae-guBusan853 salesper m² ₩7130k₩0.58bn
46Dobong-guSeoul702 salesper m² ₩8320k₩0.58bn
47Namyangju-siGyeonggi-do2,505 salesper m² ₩6790k₩0.56bn
48Jungnang-guSeoul840 salesper m² ₩10960k₩0.56bn
49Goyang-si Ilsandong-guGyeonggi-do494 salesper m² ₩6370k₩0.55bn
50Jung-guDaegu344 salesper m² ₩6790k₩0.54bn
51Nam-guBusan624 salesper m² ₩6800k₩0.54bn
52SejongSejong1,106 salesper m² ₩7030k₩0.53bn
53Bucheon-si Sosa-guGyeonggi-do597 salesper m² ₩7120k₩0.53bn
54Yeonje-guBusan525 salesper m² ₩6810k₩0.53bn
55Dongnae-guBusan528 salesper m² ₩6680k₩0.52bn
56Suwon-si Gwonseon-guGyeonggi-do1,575 salesper m² ₩6780k₩0.52bn
57Hwaseong-si Byeongjeom-guGyeonggi-do1,060 salesper m² ₩6270k₩0.50bn
58Gunpo-siGyeonggi-do1,291 salesper m² ₩7350k₩0.49bn
59Ansan-si Danwon-guGyeonggi-do643 salesper m² ₩7050k₩0.49bn
60Gwangju-siGyeonggi-do903 salesper m² ₩5810k₩0.48bn
61Seohae-guIncheon681 salesper m² ₩6050k₩0.47bn
62Yuseong-guDaejeon933 salesper m² ₩5780k₩0.47bn
63Geomdan-guIncheon762 salesper m² ₩5860k₩0.47bn
64Gimpo-siGyeonggi-do1,059 salesper m² ₩5560k₩0.46bn
65Nam-guUlsan731 salesper m² ₩5710k₩0.45bn
66Siheung-siGyeonggi-do1,491 salesper m² ₩6000k₩0.43bn
67Seo-guDaejeon1,317 salesper m² ₩5600k₩0.43bn
68Seo-guBusan219 salesper m² ₩5580k₩0.42bn
69Goyang-si Ilsanseo-guGyeonggi-do729 salesper m² ₩5100k₩0.42bn
70Changwon-si Seongsan-guGyeongsangnam-do962 salesper m² ₩5650k₩0.41bn
71Nam-guJeonnam-Gwangju Special Metropolitan City478 salesper m² ₩4850k₩0.41bn
72Ansan-si Sangnok-guGyeonggi-do371 salesper m² ₩5500k₩0.40bn
73Yongin-si Cheoin-guGyeonggi-do693 salesper m² ₩5540k₩0.40bn
74Jung-guUlsan359 salesper m² ₩5060k₩0.40bn
75Bupyeong-guIncheon1,149 salesper m² ₩5880k₩0.40bn
76Gangseo-guBusan320 salesper m² ₩4880k₩0.40bn
77Hwaseong-si Hyohaeng-guGyeonggi-do516 salesper m² ₩5250k₩0.40bn
78Osan-siGyeonggi-do1,094 salesper m² ₩5060k₩0.40bn
79Nam-guDaegu182 salesper m² ₩4940k₩0.40bn
80Dong-guBusan125 salesper m² ₩5530k₩0.39bn
81Busanjin-guBusan863 salesper m² ₩5380k₩0.39bn
82Paju-siGyeonggi-do1,134 salesper m² ₩4970k₩0.38bn
83Hwaseong-si Manse-guGyeonggi-do285 salesper m² ₩4800k₩0.38bn
84Dong-guJeonnam-Gwangju Special Metropolitan City293 salesper m² ₩5180k₩0.37bn
85Geumjeong-guBusan318 salesper m² ₩4900k₩0.37bn
86Yeongjong-guIncheon281 salesper m² ₩4670k₩0.37bn
87Seo-guDaegu176 salesper m² ₩4900k₩0.37bn
88Uijeongbu-siGyeonggi-do1,382 salesper m² ₩5260k₩0.37bn
89Yangpyeong-gunGyeonggi-do140 salesper m² ₩4670k₩0.37bn
90Bucheon-si Ojeong-guGyeonggi-do139 salesper m² ₩5750k₩0.36bn
91Buk-guUlsan713 salesper m² ₩4320k₩0.36bn
92Jeju-siJeju Special Self-Governing Province299 salesper m² ₩4950k₩0.36bn
93Changwon-si Uichang-guGyeongsangnam-do383 salesper m² ₩4600k₩0.36bn
94Dong-guDaegu658 salesper m² ₩4570k₩0.35bn
95Cheongju-si Heungdeok-guChungcheongbuk-do890 salesper m² ₩4650k₩0.35bn
96Namdong-guIncheon899 salesper m² ₩4840k₩0.35bn
97Jinju-siGyeongsangnam-do772 salesper m² ₩4310k₩0.34bn
98Pyeongtaek-siGyeonggi-do1,802 salesper m² ₩4670k₩0.34bn
99Michuhol-guIncheon647 salesper m² ₩4770k₩0.34bn
100Seo-guJeonnam-Gwangju Special Metropolitan City843 salesper m² ₩4250k₩0.33bn
101Dalseo-guDaegu1,030 salesper m² ₩4090k₩0.32bn
102Buk-guBusan694 salesper m² ₩4240k₩0.32bn
103Jeonju-si Deokjin-guJeonbuk State883 salesper m² ₩4250k₩0.32bn
104Muan-gunJeonnam-Gwangju Special Metropolitan City227 salesper m² ₩3890k₩0.32bn
105Changwon-si Masanhoewon-guGyeongsangnam-do335 salesper m² ₩4010k₩0.31bn
106Jung-guDaejeon416 salesper m² ₩4060k₩0.31bn
107Yangju-siGyeonggi-do741 salesper m² ₩4100k₩0.31bn
108Seogwipo-siJeju Special Self-Governing Province89 salesper m² ₩4120k₩0.30bn
109Gijang-gunBusan356 salesper m² ₩3990k₩0.30bn
110Gwangsan-guJeonnam-Gwangju Special Metropolitan City1,247 salesper m² ₩4000k₩0.30bn
111Cheonan-si Seobuk-guChungcheongnam-do1,271 salesper m² ₩4100k₩0.29bn
112Buk-guDaegu807 salesper m² ₩3860k₩0.29bn
113Dong-guDaejeon404 salesper m² ₩4070k₩0.28bn
114Chuncheon-siGangwon State681 salesper m² ₩3710k₩0.28bn
115Gyeyang-guIncheon606 salesper m² ₩4580k₩0.28bn
116Changwon-si Masanhappo-guGyeongsangnam-do352 salesper m² ₩3370k₩0.28bn
117Buk-guJeonnam-Gwangju Special Metropolitan City1,172 salesper m² ₩3680k₩0.27bn
118Ulju-gunUlsan511 salesper m² ₩3700k₩0.27bn
119Gurye-gunJeonnam-Gwangju Special Metropolitan City49 salesper m² ₩3250k₩0.27bn
120Jeonju-si Wansan-guJeonbuk State1,041 salesper m² ₩3610k₩0.27bn
121Cheongju-si Sangdang-guChungcheongbuk-do499 salesper m² ₩3500k₩0.26bn
122Asan-siChungcheongnam-do1,274 salesper m² ₩3650k₩0.26bn
123Gangneung-siGangwon State512 salesper m² ₩3480k₩0.26bn
124Gapyeong-gunGyeonggi-do63 salesper m² ₩3280k₩0.26bn
125Yecheon-gunGyeongsangbuk-do104 salesper m² ₩3120k₩0.26bn
126Yangsan-siGyeongsangnam-do987 salesper m² ₩3520k₩0.26bn
127Sasang-guBusan288 salesper m² ₩3600k₩0.25bn
128Dalseong-gunDaegu586 salesper m² ₩3350k₩0.25bn
129Icheon-siGyeonggi-do453 salesper m² ₩3390k₩0.25bn
130Naju-siJeonnam-Gwangju Special Metropolitan City294 salesper m² ₩3230k₩0.25bn
131Daedeok-guDaejeon338 salesper m² ₩3380k₩0.25bn
132Dong-guUlsan298 salesper m² ₩3430k₩0.24bn
133Sokcho-siGangwon State185 salesper m² ₩3440k₩0.24bn
134Cheongju-si Cheongwon-guChungcheongbuk-do482 salesper m² ₩3470k₩0.24bn
135Suncheon-siJeonnam-Gwangju Special Metropolitan City754 salesper m² ₩3210k₩0.24bn
136Gimhae-siGyeongsangnam-do1,243 salesper m² ₩3040k₩0.23bn
137Yeosu-siJeonnam-Gwangju Special Metropolitan City353 salesper m² ₩3050k₩0.23bn
138Yeongdo-guBusan130 salesper m² ₩3190k₩0.23bn
139Gyeongsan-siGyeongsangbuk-do574 salesper m² ₩3130k₩0.23bn
140Jemulpo-guIncheon181 salesper m² ₩3410k₩0.23bn
141Changwon-si Jinhae-guGyeongsangnam-do294 salesper m² ₩2920k₩0.23bn
142Saha-guBusan544 salesper m² ₩3180k₩0.22bn
143Yeoju-siGyeonggi-do166 salesper m² ₩3180k₩0.22bn
144Wanju-gunJeonbuk State250 salesper m² ₩3190k₩0.22bn
145Wonju-siGangwon State980 salesper m² ₩2930k₩0.22bn
146Cheongju-si Seowon-guChungcheongbuk-do533 salesper m² ₩3030k₩0.22bn
147Dangjin-siChungcheongnam-do448 salesper m² ₩2740k₩0.21bn
148Gyeongju-siGyeongsangbuk-do479 salesper m² ₩2810k₩0.21bn
149Chungju-siChungcheongbuk-do544 salesper m² ₩2990k₩0.21bn
150Seosan-siChungcheongnam-do402 salesper m² ₩2850k₩0.21bn
151Cheonan-si Dongnam-guChungcheongnam-do644 salesper m² ₩2930k₩0.21bn
152Gimcheon-siGyeongsangbuk-do241 salesper m² ₩2760k₩0.20bn
153Anseong-siGyeonggi-do490 salesper m² ₩2980k₩0.20bn
154Pohang-si Nam-guGyeongsangbuk-do299 salesper m² ₩2600k₩0.20bn
155Miryang-siGyeongsangnam-do155 salesper m² ₩2690k₩0.19bn
156Gyeryong-siChungcheongnam-do101 salesper m² ₩2970k₩0.19bn
157Andong-siGyeongsangbuk-do298 salesper m² ₩2870k₩0.19bn
158Pohang-si Buk-guGyeongsangbuk-do540 salesper m² ₩2430k₩0.19bn
159Gumi-siGyeongsangbuk-do1,183 salesper m² ₩2670k₩0.19bn
160Goseong-gunGangwon State30 salesper m² ₩3040k₩0.19bn
161Pyeongchang-gunGangwon State95 salesper m² ₩3070k₩0.19bn
162Gongju-siChungcheongnam-do130 salesper m² ₩2800k₩0.19bn
163Yeongju-siGyeongsangbuk-do139 salesper m² ₩2630k₩0.18bn
164Donghae-siGangwon State230 salesper m² ₩2720k₩0.18bn
165Tongyeong-siGyeongsangnam-do203 salesper m² ₩2500k₩0.18bn
166Geochang-gunGyeongsangnam-do68 salesper m² ₩2460k₩0.18bn
167Gunsan-siJeonbuk State647 salesper m² ₩2460k₩0.18bn
168Boryeong-siChungcheongnam-do130 salesper m² ₩2570k₩0.18bn
169Pocheon-siGyeonggi-do164 salesper m² ₩2650k₩0.18bn
170Mokpo-siJeonnam-Gwangju Special Metropolitan City575 salesper m² ₩2410k₩0.18bn
171Jung-guBusan30 salesper m² ₩2580k₩0.18bn
172Namwon-siJeonbuk State85 salesper m² ₩2510k₩0.18bn
173Sangju-siGyeongsangbuk-do89 salesper m² ₩2610k₩0.18bn
174Ganghwa-gunIncheon36 salesper m² ₩2540k₩0.17bn
175Hongcheon-gunGangwon State69 salesper m² ₩2660k₩0.17bn
176Jincheon-gunChungcheongbuk-do190 salesper m² ₩2620k₩0.17bn
177Hwasun-gunJeonnam-Gwangju Special Metropolitan City84 salesper m² ₩2490k₩0.17bn
178Sacheon-siGyeongsangnam-do217 salesper m² ₩2350k₩0.17bn
179Jecheon-siChungcheongbuk-do322 salesper m² ₩2500k₩0.17bn
180Yangyang-gunGangwon State41 salesper m² ₩2830k₩0.17bn
181Yesan-gunChungcheongnam-do109 salesper m² ₩2230k₩0.17bn
182Nonsan-siChungcheongnam-do128 salesper m² ₩2460k₩0.17bn
183Buan-gunJeonbuk State39 salesper m² ₩2410k₩0.17bn
184Jeungpyeong-gunChungcheongbuk-do71 salesper m² ₩2410k₩0.16bn
185Okcheon-gunChungcheongbuk-do54 salesper m² ₩2530k₩0.16bn
186Samcheok-siGangwon State108 salesper m² ₩2620k₩0.16bn
187Dongducheon-siGyeonggi-do224 salesper m² ₩2360k₩0.16bn
188Hongseong-gunChungcheongnam-do154 salesper m² ₩2200k₩0.16bn
189Taean-gunChungcheongnam-do55 salesper m² ₩2500k₩0.16bn
190Mungyeong-siGyeongsangbuk-do46 salesper m² ₩2470k₩0.16bn
191Geumsan-gunChungcheongnam-do50 salesper m² ₩2470k₩0.16bn
192Iksan-siJeonbuk State610 salesper m² ₩2250k₩0.16bn
193Yeongwol-gunGangwon State31 salesper m² ₩2510k₩0.15bn
194Geoje-siGyeongsangnam-do472 salesper m² ₩1970k₩0.15bn
195Jeongeup-siJeonbuk State169 salesper m² ₩2310k₩0.15bn
196Gimje-siJeonbuk State95 salesper m² ₩1970k₩0.13bn
197Gwangyang-siJeonnam-Gwangju Special Metropolitan City410 salesper m² ₩1940k₩0.13bn
198Haman-gunGyeongsangnam-do86 salesper m² ₩1890k₩0.13bn
199Danyang-gunChungcheongbuk-do32 salesper m² ₩2180k₩0.13bn
200Eumseong-gunChungcheongbuk-do184 salesper m² ₩1920k₩0.13bn
201Cheongdo-gunGyeongsangbuk-do33 salesper m² ₩1880k₩0.13bn
202Yeongcheon-siGyeongsangbuk-do169 salesper m² ₩1990k₩0.12bn
203Yeonggwang-gunJeonnam-Gwangju Special Metropolitan City39 salesper m² ₩1910k₩0.12bn
204Yeoncheon-gunGyeonggi-do31 salesper m² ₩1820k₩0.12bn
205Chilgok-gunGyeongsangbuk-do185 salesper m² ₩1650k₩0.11bn
206Hoengseong-gunGangwon State44 salesper m² ₩1890k₩0.11bn
207Boseong-gunJeonnam-Gwangju Special Metropolitan City30 salesper m² ₩1820k₩0.11bn
208Changnyeong-gunGyeongsangnam-do48 salesper m² ₩1590k₩0.11bn
209Goseong-gunGyeongsangnam-do35 salesper m² ₩1560k₩0.10bn
210Taebaek-siGangwon State95 salesper m² ₩1770k₩0.10bn
211Jeongseon-gunGangwon State53 salesper m² ₩1790k₩0.09bn
212Cheorwon-gunGangwon State30 salesper m² ₩1470k₩0.09bn
213Yeongam-gunJeonnam-Gwangju Special Metropolitan City61 salesper m² ₩1200k₩0.07bn

ShareX Reddit

Practical takeaways

Top-ranked Gangnam-gu (₩3.09bn) is 44 times the bottom-ranked Yeongam-gun (₩0.07bn).
Ranked by price per square metre instead, Gangnam-gu comes first, and Yangyang-gun climbs 25 places — the average transaction price follows the size of what was sold there.
Of all 269 districts, 56 had fewer than 30 sales in the window and are not ranked. They are listed below all the same.

Computed only from the table above — nothing added from outside this dataset.

Below the sample floor — 56 districts with fewer than 30 sales

These are not ranked because an average over a handful of sales is not a price level. They are listed with their counts rather than dropped.

Haenam-gunJeonnam-Gwangju Special Metropolitan City29
Uljin-gunGyeongsangbuk-do28
Boeun-gunChungcheongbuk-do26
Seocheon-gunChungcheongnam-do26
Yeongdong-gunChungcheongbuk-do25
Gochang-gunJeonbuk State25
Sunchang-gunJeonbuk State25
Jangseong-gunJeonnam-Gwangju Special Metropolitan City25
Buyeo-gunChungcheongnam-do24
Goheung-gunJeonnam-Gwangju Special Metropolitan City23
Inje-gunGangwon State22
Damyang-gunJeonnam-Gwangju Special Metropolitan City21
Jangheung-gunJeonnam-Gwangju Special Metropolitan City21
Wando-gunJeonnam-Gwangju Special Metropolitan City19
Cheongyang-gunChungcheongnam-do18
Goryeong-gunGyeongsangbuk-do17
Uiryeong-gunGyeongsangnam-do17
Yanggu-gunGangwon State17
Sancheong-gunGyeongsangnam-do15
Hadong-gunGyeongsangnam-do14
Hapcheon-gunGyeongsangnam-do14
Gangjin-gunJeonnam-Gwangju Special Metropolitan City13
Uiseong-gunGyeongsangbuk-do12
Bonghwa-gunGyeongsangbuk-do12
Imsil-gunJeonbuk State12
Hamyang-gunGyeongsangnam-do10
Hampyeong-gunJeonnam-Gwangju Special Metropolitan City9
Seongju-gunGyeongsangbuk-do8
Goesan-gunChungcheongbuk-do7
Yeongdeok-gunGyeongsangbuk-do7
Gokseong-gunJeonnam-Gwangju Special Metropolitan City7
Jindo-gunJeonnam-Gwangju Special Metropolitan City7
Cheongsong-gunGyeongsangbuk-do6
Muju-gunJeonbuk State5
Namhae-gunGyeongsangnam-do4
Jangsu-gunJeonbuk State4
Jinan-gunJeonbuk State4
Hwacheon-gunGangwon State3
Yeongyang-gunGyeongsangbuk-do2
Gunwi-gunDaegu2
Ulleung-gunGyeongsangbuk-do1
Sinan-gunJeonnam-Gwangju Special Metropolitan City1
Ongjin-gunIncheon0
Seongnam-siGyeonggi-do0
Suwon-siGyeonggi-do0
Anyang-siGyeonggi-do0
Bucheon-siGyeonggi-do0
Ansan-siGyeonggi-do0
Goyang-siGyeonggi-do0
Yongin-siGyeonggi-do0
Hwaseong-siGyeonggi-do0
Cheongju-siChungcheongbuk-do0
Cheonan-siChungcheongnam-do0
Pohang-siGyeongsangbuk-do0
Changwon-siGyeongsangnam-do0
Jeonju-siJeonbuk State0

Comments

1000 characters left

Names are not verified — anyone can use any name.

No comments yet — add what you saw, and others will have something to reply to.