One Seoul, a 3.02× gap in price per pyeong.
Building the baseline for sales-launch and operating decisions.

Over the 24-month time series, all 5 districts trended upward. Average price 2024-01 vs 2025-12 — Gangnam 2.273B → 2.462B KRW (+8.3%), Seocho 2.121B → 2.198B (+3.6%), Songpa 1.561B → 1.805B (+15.6%), Mapo 1.187B → 1.397B (+17.7%), Nowon 0.558B → 0.627B (+12.4%). The absolute gap (Gangnam vs Nowon) widened from 1.715B to 1.835B, while the ratio gap narrowed slightly from 4.07× to 3.93×. ※ These C1 figures are a single-month comparison of 2024-01 vs 2025-12, whereas the C8 KPIs are a 2024 average vs 2025 average comparison (the figures differ due to the difference in basis, e.g. Mapo +17.7% vs +13.9%).

The 24-month average-price trend can be used for explaining the price position at sales launch, feeding the trend fit of a price-prediction model, and comparing per-district flows in buyer/seller price-adjustment meetings. This chart itself is historical descriptive; where extrapolation is needed, a separate forecasting step must be combined.

Songpa 9,579 > Nowon 9,438 > Gangnam 7,497 > Mapo 6,584 > Seocho 5,923. The most (Songpa) vs the least (Seocho) is a 1.62× gap. Gangnam and Seocho rank 1st and 2nd in average price but 3rd and 5th in volume — diverging into a high-price, low-turnover market.

Sales-network prioritization: a starting point for deciding to place staff in high-volume districts (Songpa, Nowon) and veterans in high-average-price districts (Gangnam, Seocho). Seocho's low volume may signal a listing shortage + waiting buyers, giving grounds to run a separate listing-supply analysis.

Weekday share by contract date — Sat 24.7% (9,657) > Fri 16.5% > Wed 13.9% > Thu 13.7% > Tue 13.6% > Mon 12.7% > Sun 4.9%. Saturday contract dates make up a quarter of the total, and Sunday is under 5%. The Sat/Fri concentration reflects a flow of weekday negotiation → weekend contract signing. ※ The API response's dealYear/Month/Day is the contract date, while the report date (rgstDate) is a separate column. There may be quarter-level differences from external official statistics.

Standardizing sales operations: place model-house and brokerage-office staff on Sat/Fri, while Sunday work is low-efficiency at a 5% share. Data fetch frequency: daily batches incur a 1–2 day Monday reporting lag, so weekly batches are more efficient. Because the pattern is stable across 24 months, it can serve as a fixed input for sales and operations unless transaction-reporting policy changes.

Large-unit (>135㎡) share — Seocho 12.3% > Gangnam 11.4% > Songpa 6.6% > Mapo 2.7% > Nowon 0.5%. Small-unit (<60㎡) share — Nowon 63.6% > Mapo 41.7% > Gangnam 30.3% > Songpa 29.0% > Seocho 22.9%. Of Nowon's 9,438 transactions, about 6,000 are small units, while only 1,359 of Seocho's 5,923 are small — the dominant size distribution clearly diverges by district.

Deciding the unit mix for a new sale: a 60~85㎡ mid-size offering in Nowon fits within an existing ~31% market share, whereas a 135㎡+ large offering in Nowon signals a thin buyer pool at a 0.5% market share. Listing recommendation algorithm: the per-district prior distribution can be used directly as a size-weighting parameter. Size distribution is stock information that does not change in the short term — the 24-month analysis can be reused as foundational reference for future material.

Gangnam and Seocho have the widest distributions — within the same district, transactions in the 500M–5B KRW range coexist. Nowon has a narrow distribution, with most concentrated in the 500M–900M central band. Mapo and Songpa sit in the middle between Gangnam and Nowon. Even at the same average price, a narrower distribution means higher price-prediction confidence.

Safety margin for setting sale prices: a Gangnam sale price of 2B ±500M sits within the IQR, while the same ±500M in Nowon is outside it. Price-prediction model oracle: narrow-distribution Nowon is likely to have a lower prediction RMSE. Judging asking-price reasonableness: if a new listing falls outside the district IQR, a negotiation message is prepared in advance. This is an observation point easily missed by analyses that look only at the average price.

Per-district area–price correlation — Nowon 0.799 > Gangnam 0.758 > Mapo 0.573 > Songpa 0.523 > Seocho 0.506. Build-year–price correlation — Mapo +0.475 > Nowon +0.279 > Seocho +0.186 > Songpa −0.087 > Gangnam −0.270. In Gangnam, build year and price are negatively correlated — a pattern where older buildings are on average more expensive (possibly reconstruction expectations or location/complex-composition effects). In Mapo it is +0.475 — a pattern where newer is more expensive. The price-formation structure runs in opposite directions across districts.

Diverging price-prediction models: a single-weight model produces mean-reversion errors on district-mix data. Per-district divergent models achieve higher accuracy. Diverging seller-negotiation messages: for older Gangnam stock, the grounds are "reconstruction potential or location premium"; for newer Mapo stock, "new-build premium." New-sale decisions: an observation point that, for new-build sales in Gangnam/Seocho, the sale price must be set in combination with location and reconstruction expectations.

Of all 39,021 transactions, brokered 37,868 (97.0%) · direct 1,153 (3.0%). Per-district direct-transaction ratio — Nowon 3.69% > Gangnam 3.44% > Seocho 3.16% > Songpa 2.51% > Mapo 1.82%. Diverging from common intuition — intra-family gifting and corporate title transfers in older outlying complexes may be a factor. The actual measurement puts Nowon first, at 2.0× Mapo. Direct transactions may include non-standard flows such as intra-family gifting, intra-corporate transfers, and trusts, so per-district differences register as an operational signal.

Separating the market-average-price calculation: filter with dealingGbn == 'direct' to isolate the "normal market price." Cautious use as a sale-price reference: districts with a high direct-transaction ratio can produce a flow where intra-family deals have little effect on the market price, so use them cautiously as a sale-price reference. Checking sales penetration: a district with a high direct-transaction ratio (Nowon) signals many transactions outside your own sales network — check for listing-information asymmetry. Because the 24-month cumulative pattern is stable, re-measuring this chart when policy changes (e.g. tighter gift tax) lets you quantify the market impact.

Volume change — Mapo +53.6%, Nowon +36.0%, Songpa +25.3%, Gangnam +9.2%, Seocho −1.4%. Average-price change — Mapo +13.9%, Gangnam +12.2%, Songpa +11.5%, Seocho +6.7%, Nowon +2.9%. Price per pyeong (10,000 KRW/pyeong) — Gangnam 9,160 → 10,559 (+15.3%), Mapo 5,639 → 6,437 (+14.1%), Songpa 6,755 → 7,608 (+12.6%), Seocho 9,191 → 9,617 (+4.6%), Nowon 3,189 → 3,336 (+4.6%). Volume growth and price growth diverge — Mapo rose on both at +53.6% volume and +13.9% price, while Seocho stalled at −1.4% volume.

A one-slide board summary: a simultaneous 5-district comparison in the form "2024–2025: Mapo/Songpa volume ↑ + price ↑ rising together, Gangnam price ↑ with volume moderate, Seocho volume stalled." Reallocating sales resources: add headcount in districts with rising volume + price (Mapo, Songpa), and shift districts with stalled volume (Seocho) toward securing listing supply. Tracking separately along the two axes (volume vs price) is a starting point for identifying the market phase (recovery, expansion, plateau, correction).

24-month average price per pyeong (10,000 KRW/pyeong) for the 5 districts — Gangnam-gu 9,890 > Seocho-gu 9,402 > Songpa-gu 7,230 > Mapo-gu 6,122 > Nowon-gu 3,274. A 3.02× gap between Gangnam and Nowon. The map shows the spatial relationship in one view: the Gangnam/Seocho cluster south of the Han River + Mapo north of the river + Nowon in the northeast.

Site-review report: inserting this single map uses the price-per-pyeong gap between adjacent districts to explain market position in IR material. Reusing the GeoJSON mapping: this case's SIG_CD-code-based district metadata can be reused as-is in an operational environment. New-branch location negotiation: convert the price-per-pyeong gap between adjacent districts into a "districts with inbound customer potential" message. The absolute price per pyeong rose in every district across 24 months, while the relative ranking among districts stayed stable — location premium is foundational reference that does not waver in the short term.