Riyadh Sale Listing Price Delta by District × Category

Date: 2026-09-23 Period: Q1 2025 to Q3 2026, calendar quarters (Q3 2026 is partial: Jul 1 – Sep 22) Scope: Riyadh (city_id = 21), sale categories Data Sources: sadb_listings, sadb_categories, sadb_districts

Question: How did the asking price of new sale listings in Riyadh change quarter by quarter since Q1 2025, when each (district_id, category) cluster is compared only with itself?

Companion report: Riyadh Rent Listing Price Delta.

Executive Summary

• Riyadh sale asking prices fell 9.4% from Q1 2025 to Q3 2026 (volume-weighted average delta), or −11.9% on the weighted geometric median, across 188 clusters and 139,741 listings. Only 23% of clusters ended higher than they started.
• Prices fell in all six quarters. The sharpest drop was Q4 2025 (−5.1% QoQ). The decline has slowed since then (−0.3% to −1.3% per quarter) but has not reversed. YoY Q3 2026 vs Q3 2025: −7.3%.
• Big Flat for Sale fell the most: −13.3%, with 94% of clusters down. It dropped −4.4% in Q2 2026, a second leg down after Q4 2025. An Narjis, Al Arid, Tuwaiq and Al Janadriyah are each down about 20%.
• Apartment for Sale: −9.4%. Prices fell in every quarter from Q3 2025 on, and the largest districts are down 7–13%.
• Villa for Sale: −6.9%. Almost all of the drop came in 2025 (Q2 −3.8% and Q4 −4.3%). Villa prices have held steady for three quarters, and Q3 2026 is the first small uptick (+0.7%).
• Land for Sale: −11.1% average and −26.6% per m². The drop was concentrated in Q4 2025 (−12.2% QoQ). Banban (−36%), Uraidh (−25%) and Al Arid (−24%) fell the most.

Methodology

Choice	Value
Listings	city_id = 21, rega_licensed = 1, daily_rentable = 0, price > 0, not deleted
Quarter	Calendar quarter of create_time (Asia/Riyadh). Q3 2026 covers Jul 1 – Sep 22.
Outliers	Per cluster, prices outside the full-period p2–p98 range are dropped
Cluster panel	Balanced: only clusters with ≥ 10 listings in every one of the 7 quarters (188 sale clusters covering over 80% of Riyadh sale listings)
Weight	Cluster’s total listings over the period

A balanced panel means every quarter is measured on exactly the same clusters. A district dropping in or out of a quarter can’t show up as a price change.

Estimators for the Q1 2025 → Q3 2026 delta:

• A: weighted mean of average-price deltas. Σ wᵢ·(avg_Q3’26ᵢ/avg_Q1’25ᵢ − 1) / Σ wᵢ. This is the headline “weighted average price delta”.
• C: weighted geometric mean of median ratios. exp(Σ wᵢ·ln(med_Q3’26ᵢ/med_Q1’25ᵢ) / Σ wᵢ) − 1. This is symmetric for rises and falls and resistant to a few extreme clusters.
• D: same as C on meter_price (only meaningful for Land).

The quarterly index is Σ wᵢ·(pᵢ,q/refᵢ) / Σ wᵢ × 100, where refᵢ is the cluster’s full-period average. QoQ is index_q / index_q−1 − 1. Land uses the weighted geometric mean.

Limitations

• sadb_listings stores only the current price, with no price history. “Price in quarter Q” is the current asking price of listings created in Q, so price edits made after creation are attributed to the creation quarter.
• These are asking prices, not transaction prices.
• Q3 2026 is partial (it ends Sep 22). Its listing count is lower, but because the panel is balanced, prices are still compared on the same clusters.
• For Land, total price is dominated by plot size. Prefer estimator D (price per m²).

Weighted Price Delta by Category

Q1 2025 → Q3 2026, with a same-quarter YoY check (Q3 2026 vs Q3 2025, estimator A). Categories with ≥ 5 panel clusters are shown; the “All sale” row includes every category.

Category	Clusters	Listings	Weighted avg price (Q1’25 → Q3’26)	A: wavg mean Δ	C: wgeo median Δ	D: wgeo /m² Δ	YoY Q3 Δ	% clusters up
All sale	188	139,741		−9.4%	−11.9%		−7.3%	23%
Villa for Sale	67	55,504	3,388,638 → 2,680,075	−6.9%	−8.7%		−4.5%	22%
Apartment for Sale	38	30,887	974,331 → 884,603	−9.4%	−10.2%		−8.1%	18%
Big Flat for Sale	31	28,109	1,117,543 → 961,648	−13.3%	−13.7%		−10.8%	6%
Land for Sale	41	23,232	3,412,147 → 2,923,568	−11.1%	−18.7%	−26.6%	−9.5%	34%
Building for Sale	8	1,467	4,491,109 → 4,474,039	+7.2%	−2.7%		+13.0%	62%

Building for Sale has only 8 clusters, and its estimators disagree on sign. Lounge for Sale (3 clusters) is left out of the table.

SQL Query

WITH
  base AS (
    SELECT
      l.district_id,
      l.category,
      toStartOfQuarter(toDateTime(l.create_time, 'Asia/Riyadh')) q,
      toFloat64(l.price) price,
      toFloat64(l.meter_price) mp
    FROM
      sadb_listings l FINAL
    WHERE
      l._peerdb_is_deleted = 0
      AND l.rega_licensed = 1
      AND l.daily_rentable = 0
      AND l.price > 0
      AND l.rent_period NOT IN (1, 2)
      AND l.district_id > 0
      AND l.city_id = 21
      AND l.create_time >= toUnixTimestamp(toDateTime('2025-01-01 00:00:00', 'Asia/Riyadh'))
      AND l.create_time < toUnixTimestamp(toDateTime('2026-09-23 00:00:00', 'Asia/Riyadh'))
  ),
  bounds AS (
    SELECT
      district_id,
      category,
      quantile(0.02) (price) lo,
      quantile(0.98) (price) hi
    FROM
      base
    GROUP BY
      1,
      2
  ),
  trimmed AS (
    SELECT
      b.*
    FROM
      base b
      JOIN bounds x USING (district_id, category)
    WHERE
      b.price BETWEEN x.lo AND x.hi
  ),
  cq AS (
    SELECT
      district_id,
      category,
      q,
      count() n
    FROM
      trimmed
    GROUP BY
      1,
      2,
      3
  ),
  -- balanced panel: >= 10 listings in all 7 quarters
  panel AS (
    SELECT
      district_id,
      category,
      sum(n) w
    FROM
      cq
    GROUP BY
      1,
      2
    HAVING
      count() = 7
      AND min(n) >= 10
  ),
  cl AS (
    SELECT
      t.district_id district_id,
      t.category category,
      any(panel.w) w,
      avgIf (price, q = '2025-01-01') a0,
      avgIf (price, q = '2026-07-01') a1,
      avgIf (price, q = '2025-07-01') y0,
      medianIf (price, q = '2025-01-01') m0,
      medianIf (price, q = '2026-07-01') m1,
      medianIf (
        mp,
        q = '2025-01-01'
        AND mp > 0
      ) s0,
      medianIf (
        mp,
        q = '2026-07-01'
        AND mp > 0
      ) s1
    FROM
      trimmed t
      JOIN panel USING (district_id, category)
    GROUP BY
      1,
      2
  )
SELECT
  c.name_en category,
  count() clusters,
  sum(w) listings,
  round(sum(w * a0) / sum(w)) wavg_q1_25,
  round(sum(w * a1) / sum(w)) wavg_q3_26,
  round(100 * sum(w * (a1 / a0 - 1)) / sum(w), 1) A_delta,
  round(
    100 * (exp(sum(w * log(m1 / m0)) / sum(w)) - 1),
    1
  ) C_delta,
  round(
    100 * (
      exp(
        sumIf (
          w * log(s1 / s0),
          s0 > 0
          AND s1 > 0
        ) / sumIf (
          w,
          s0 > 0
          AND s1 > 0
        )
      ) - 1
    ),
    1
  ) D_sqm_delta,
  round(100 * sum(w * (a1 / y0 - 1)) / sum(w), 1) A_yoy_q3,
  round(100 * countIf (a1 > a0) / count()) pct_up
FROM
  cl
  JOIN sadb_categories c FINAL ON cl.category = c.category_id
WHERE
  positionCaseInsensitive(c.name_en, 'rent') = 0 -- sale categories
GROUP BY
  ROLLUP (c.name_en)
ORDER BY
  listings DESC

Quarter-over-Quarter Delta

Weighted quarterly index rebased to Q1 2025 = 100, balanced panel of (district, category) clusters. Hover a point for its value. * Q3 2026 is partial (Jul 1 – Sep 22).

Each value is the weighted price change from the previous quarter (e.g. Q1’26 = Q1 2026 vs Q4 2025). Cumulative is Q3 2026 vs Q1 2025 on the index. It is slightly larger than estimator A above because the index averages normalized prices before dividing, while A averages each cluster’s ratio.

Category	Q2’25	Q3’25	Q4’25	Q1’26	Q2’26	Q3’26*	Cumulative	YoY Q3
All sale	−1.3%	−2.3%	−5.1%	−1.3%	−0.3%	−1.3%	−11.2%	−7.9%
Villa for Sale	−3.8%	−1.2%	−4.3%	−0.7%	−0.6%	+0.7%	−9.5%	−4.9%
Apartment for Sale	+1.7%	−3.3%	−3.1%	−3.3%	−0.5%	−1.7%	−9.8%	−8.4%
Big Flat for Sale	+0.3%	−3.2%	−4.3%	−0.1%	−4.4%	−2.6%	−13.5%	−10.9%
Land for Sale	−0.2%	−2.0%	−12.2%	−1.1%	+4.9%	−4.1%	−14.4%	−12.5%

* Q3 2026 is partial (Jul 1 – Sep 22).

Quarterly index (100 = each cluster’s own full-period average):

Category	Q1’25	Q2’25	Q3’25	Q4’25	Q1’26	Q2’26	Q3’26*
All sale	107.1	105.7	103.3	98.0	96.7	96.4	95.1
Villa for Sale	108.1	104.0	102.8	98.4	97.7	97.1	97.8
Apartment for Sale	104.6	106.4	102.9	99.7	96.4	95.9	94.3
Big Flat for Sale	106.3	106.6	103.2	98.8	98.7	94.4	91.9
Land for Sale	107.5	107.3	105.2	92.4	91.4	95.9	92.0
Listings (all)	18,165	17,246	25,843	22,139	19,639	17,446	19,263

• Q4 2025 was the break. All four main categories fell 3–12% that quarter, straight after a Q3 2025 listing surge (+50% QoQ in volume).
• Apartment and Big Flat are still falling. Big Flat had a second leg down in Q2 2026 (−4.4%) and fell again in Q3 2026 (−2.6%).
• Villa has stabilized. Its index has held at about 97–98 for four quarters.
• Land dropped 12% in Q4 2025 and has moved sideways since then (a Q2 2026 rebound of +4.9% was followed by −4.1%).

SQL Query

-- Same base / bounds / trimmed CTEs as above
cq AS (
  SELECT
    district_id,
    category,
    q,
    avg(price) p,
    count() n
  FROM
    trimmed
  GROUP BY
    1,
    2,
    3
),
panel AS (
  SELECT
    district_id,
    category,
    sum(n) w,
    sum(p * n) / sum(n) ref
  FROM
    cq
  GROUP BY
    1,
    2
  HAVING
    count() = 7
    AND min(n) >= 10
),
j AS (
  SELECT
    cq.category AS cat,
    cq.q AS q,
    cq.p AS p,
    cq.n AS n,
    panel.w AS w,
    panel.ref AS ref
  FROM
    cq
    JOIN panel ON panel.district_id = cq.district_id
    AND panel.category = cq.category
    JOIN sadb_categories c FINAL ON cq.category = c.category_id
  WHERE
    positionCaseInsensitive(c.name_en, 'rent') = 0 -- sale categories
),
r AS (
  SELECT
    cat,
    q,
    if(
      cat = 2,
      100 * exp(sum(w * log(p / ref)) / sum(w)), -- Land: geometric
      100 * sum(w * p / ref) / sum(w)
    ) idx,
    sum(n) nq
  FROM
    j
  WHERE
    cat IN (2, 3, 6, 22)
  GROUP BY
    cat,
    q
  UNION ALL
  SELECT
    0 AS cat,
    q,
    100 * sum(w * p / ref) / sum(w) idx,
    sum(n) nq
  FROM
    j
  GROUP BY
    q
)
SELECT
  cat,
  arrayMap(
    x -> round(x.2, 1),
    arraySort(groupArray((q, idx)))
  ) idx_by_quarter,
  arrayMap(x -> x.2, arraySort(groupArray((q, nq)))) listings_by_quarter
FROM
  r
GROUP BY
  cat
ORDER BY
  cat
  -- QoQ = idx[i] / idx[i-1] - 1; Cumulative = idx[Q3'26] / idx[Q1'25] - 1; YoY = idx[Q3'26] / idx[Q3'25] - 1

Top Districts per Category

The 8 highest-volume panel districts per category. The columns are average asking prices (SAR) per quarter. Δ is Q3 2026 vs Q1 2025, and YoY is Q3 2026 vs Q3 2025.

Villa for Sale

District	Listings	Q1’25	Q3’25	Q3’26	Δ	YoY
Al Mahdiyah	9,427	2,491,478	2,567,059	2,514,004	+0.9%	−2.1%
An Narjis	5,133	4,125,112	3,815,762	3,453,870	−16.3%	−9.5%
Ar Rimal	4,777	2,122,554	2,245,994	2,045,608	−3.6%	−8.9%
Tuwaiq	3,315	1,297,740	1,324,428	1,321,175	+1.8%	−0.2%
Al Arid	3,054	3,437,694	3,351,355	2,937,453	−14.6%	−12.4%
Al Janadriyah	2,010	1,515,704	1,343,439	1,349,571	−11.0%	+0.5%
Al Munsiyah	1,716	2,946,684	2,952,515	2,894,343	−1.8%	−2.0%
Dhahrat Laban	1,630	2,145,552	2,249,157	2,142,331	−0.2%	−4.7%

Apartment for Sale

District	Listings	Q1’25	Q3’25	Q3’26	Δ	YoY
Al Mahdiyah	4,411	886,696	895,243	789,258	−11.0%	−11.8%
Ar Rimal	2,549	818,228	820,536	748,695	−8.5%	−8.8%
An Narjis	2,099	1,294,811	1,265,802	1,208,939	−6.6%	−4.5%
Al Malqa	1,642	1,470,987	1,429,489	1,303,010	−11.4%	−8.8%
Dhahrat Laban	1,611	806,650	759,945	739,388	−8.3%	−2.7%
Al Arid	1,490	1,236,978	1,226,420	1,113,846	−10.0%	−9.2%
Tuwaiq	1,479	672,599	683,507	585,452	−13.0%	−14.3%
Al Munsiyah	1,440	1,131,330	1,065,769	993,209	−12.2%	−6.8%

Big Flat for Sale

District	Listings	Q1’25	Q3’25	Q3’26	Δ	YoY
Tuwaiq	3,233	976,746	899,908	782,864	−19.8%	−13.0%
Ar Rimal	3,212	1,050,076	1,081,304	969,614	−7.7%	−10.3%
Al Janadriyah	2,870	914,105	840,339	735,563	−19.5%	−12.5%
Badr	2,153	669,646	660,853	626,855	−6.4%	−5.1%
An Narjis	1,933	1,852,898	1,860,421	1,488,862	−19.6%	−20.0%
Okaz	1,470	710,327	710,335	645,876	−9.1%	−9.1%
Ash Sharq	1,462	910,667	887,254	803,563	−11.8%	−9.4%
Al Arid	1,163	1,686,509	1,690,552	1,363,248	−19.2%	−19.4%

Land for Sale

District	Listings	Q1’25	Q3’25	Q3’26	Δ	YoY
Banban	3,533	1,401,573	1,194,519	896,453	−36.0%	−25.0%
Ash Sharq	2,087	895,614	899,341	797,322	−11.0%	−11.3%
Uraidh	1,873	585,303	532,878	438,425	−25.1%	−17.7%
An Narjis	1,634	5,523,819	4,744,834	4,642,952	−15.9%	−2.1%
Al Arid	1,563	5,756,923	5,592,257	4,372,459	−24.0%	−21.8%
Dahiat Namar	1,533	1,127,940	1,359,441	1,362,196	+20.8%	+0.2%
Al Mahdiyah	1,430	2,780,862	2,639,570	2,240,482	−19.4%	−15.1%
Ar Rimal	1,060	5,752,842	5,779,208	5,558,833	−3.4%	−3.8%

SQL Query

-- Same base / bounds / trimmed / cq / panel CTEs as the category query, with AND l.category IN (2, 3, 6, 22) in base
cl AS (
  SELECT
    t.district_id district_id,
    t.category category,
    any(panel.w) w,
    avgIf (price, q = '2025-01-01') a0,
    avgIf (price, q = '2025-07-01') y0,
    avgIf (price, q = '2026-07-01') a1
  FROM
    trimmed t
    JOIN panel USING (district_id, category)
  GROUP BY
    1,
    2
),
r AS (
  SELECT
    cl.*,
    row_number() OVER (
      PARTITION BY
        category
      ORDER BY
        w DESC
    ) rk
  FROM
    cl
)
SELECT
  category,
  d.district_name_en district,
  w listings,
  round(a0) q1_25,
  round(y0) q3_25,
  round(a1) q3_26,
  round(100 * (a1 / a0 - 1), 1) delta,
  round(100 * (a1 / y0 - 1), 1) yoy_q3
FROM
  r
  JOIN sadb_districts d FINAL ON r.district_id = d.district_id
WHERE
  rk <= 8
ORDER BY
  category,
  w DESC