Listing Performance Analysis

Date: 2026-01-14 Analysis Period: 2024-01-01 onwards (last 3 months for decay analysis)

Executive Summary

This analysis covers three key aspects of listing performance:

Time on Market - How long listings take to close by category/city
View Decay - How views decrease as listings age
Contact Conversion Decay - How view-to-contact rates change over time

Key Findings

Metric	Finding
Time on Market	Improved 50-60% from early 2024 to late 2025
View Decay	75% drop by Day 90, Day 2 is peak
Conversion Decay	Drops from 8.5% to 5.2% (-39%) over 90 days
Critical Window	First 2 weeks generate ~50% of all contacts

1. Time on Market Analysis

Overview

Time on market measures days from published_at to close_time for listings that have been closed.

By Category (Median Days)

Category	Closed	Median	P25	P75	P90
Room for Rent	4,594	27	8	80	145
Big Flat for Rent	17,791	34	14	70	121
Apartment for Rent	166,552	39	15	82	144
Villa for Rent	23,256	46	17	98	159
Land for Sale	68,011	89	33	148	235
Big Flat for Sale	20,623	88	37	149	235
Villa for Sale	75,864	88	37	155	249
Apartment for Sale	68,130	104	53	180	269

Insight: Rentals move 2-3x faster than sales

By City (Median Days)

City	Closed	Median	P25	P75
الرياض (Riyadh)	302,701	53	19	111
الظهران (Dhahran)	2,638	58	23	117
الهفوف (Hofuf)	4,208	69	25	127
جدة (Jeddah)	72,524	76	30	143
الدمام (Dammam)	23,352	78	28	151
مكة المكرمة (Makkah)	8,787	94	35	167

Insight: Riyadh is the fastest market

Monthly Cohort Trends

Apartment for Rent (Median Days by Cohort)

Cohort	Riyadh	Jeddah	Dammam
Jan 2024	51	62	57
Jul 2024	39	39	33
Jan 2025	33	37	33
Oct 2025	32	35	30

Trend: Convergence to ~30-35 days across all cities

Villa for Sale (Median Days by Cohort)

Cohort	Riyadh	Jeddah	Dammam
Jan 2024	108	152	141
Jul 2024	94	142	164
Jan 2025	18	60	21
Oct 2025	52	49	30

Trend: Major improvement from 108-152 days to 30-52 days

Query: Time on Market by Category

SELECT
  c.name_en AS category,
  COUNT(*) AS closed_listings,
  ROUND(AVG((l.close_time - l.published_at) / 86400), 1) AS avg_days,
  ROUND(
    median ((l.close_time - l.published_at) / 86400),
    1
  ) AS median_days,
  ROUND(
    quantile (0.25) ((l.close_time - l.published_at) / 86400),
    1
  ) AS p25_days,
  ROUND(
    quantile (0.75) ((l.close_time - l.published_at) / 86400),
    1
  ) AS p75_days,
  ROUND(
    quantile (0.90) ((l.close_time - l.published_at) / 86400),
    1
  ) AS p90_days
FROM
  sadb_listings l FINAL
  JOIN sadb_categories c FINAL ON l.category = c.category_id
WHERE
  l.close_time > l.published_at
  AND l.published_at > 0
  AND l._peerdb_is_deleted = 0
  AND (l.close_time - l.published_at) / 86400 BETWEEN 1 AND 730
  AND toDate (l.published_at) >= '2024-01-01'
GROUP BY
  c.name_en
HAVING
  closed_listings >= 100
ORDER BY
  closed_listings DESC

Query: Time on Market by City

SELECT
  d.city_name,
  COUNT(*) AS closed_listings,
  ROUND(AVG((l.close_time - l.published_at) / 86400), 1) AS avg_days,
  ROUND(
    median ((l.close_time - l.published_at) / 86400),
    1
  ) AS median_days,
  ROUND(
    quantile (0.25) ((l.close_time - l.published_at) / 86400),
    1
  ) AS p25_days,
  ROUND(
    quantile (0.75) ((l.close_time - l.published_at) / 86400),
    1
  ) AS p75_days
FROM
  sadb_listings l FINAL
  JOIN sadb_districts d FINAL ON l.district_id = d.district_id
WHERE
  l.close_time > l.published_at
  AND l.published_at > 0
  AND l._peerdb_is_deleted = 0
  AND (l.close_time - l.published_at) / 86400 BETWEEN 1 AND 730
  AND toDate (l.published_at) >= '2024-01-01'
GROUP BY
  d.city_name
HAVING
  closed_listings >= 1000
ORDER BY
  closed_listings DESC

Query: Monthly Cohort Analysis by Category + City

SELECT
  toStartOfMonth (toDate (l.published_at)) AS cohort_month,
  c.name_en AS category,
  d.city_name,
  COUNT(*) AS closed_listings,
  ROUND(
    median ((l.close_time - l.published_at) / 86400),
    1
  ) AS median_days
FROM
  sadb_listings l FINAL
  JOIN sadb_categories c FINAL ON l.category = c.category_id
  JOIN sadb_districts d FINAL ON l.district_id = d.district_id
WHERE
  l.close_time > l.published_at
  AND l.published_at > 0
  AND l._peerdb_is_deleted = 0
  AND (l.close_time - l.published_at) / 86400 BETWEEN 1 AND 365
  AND toDate (l.published_at) >= '2024-01-01'
  AND c.name_en = 'Apartment for Rent'
  AND d.city_name IN ('الرياض', 'جدة', 'الدمام')
GROUP BY
  cohort_month,
  c.name_en,
  d.city_name
HAVING
  closed_listings >= 50
ORDER BY
  d.city_name,
  cohort_month

2. View Decay Analysis

Overview

Views decrease significantly as listings age. Day 2 is typically the peak (not Day 1, due to indexing delays).

Overall View Decay Pattern

Listing Age	Avg Views	Median Views	% of Peak
Day 1	28.9	18	73%
Day 2	39.6	25	100%
Day 3	33.7	21	85%
Day 7	26.8	16	68%
Day 14	21.7	13	55%
Day 30	17.2	10	43%
Day 60	12.3	7	31%
Day 90	9.6	5	24%

View Decay by Category

Category	Week 1	Week 2	Wk 3-4	Mo 2	Mo 3	Decay
Apartment Rent	39.8	29.9	24.5	19.8	16.8	—58%
Villa Rent	25.1	18.3	14.9	11.7	10.2	—59%
Villa Sale	20.4	14.8	12.3	10.6	8.6	—58%
Land Sale	11.3	7.5	6.0	4.9	4.0	—65%
Apartment Sale	11.2	8.9	8.0	6.0	4.7	—58%

View Decay by City

City	Week 1	Week 2	Wk 3-4	Mo 2	Mo 3	Decay
الدمام	43.5	32.4	26.2	18.5	14.7	—66%
الرياض	31.7	23.3	19.0	14.7	11.4	—64%
جدة	27.2	21.0	17.5	13.5	10.6	—61%
مكة	23.9	19.7	16.6	12.4	7.2	—70%

Query: View Decay by Listing Age

SELECT
  s.day_time - toInt32 (l.published_at / 86400) AS listing_age_days,
  COUNT(DISTINCT s.id) AS listings_count,
  SUM(s.views) AS total_views,
  ROUND(AVG(s.views), 2) AS avg_views_per_listing,
  ROUND(median (s.views), 1) AS median_views
FROM
  sadb_stats_listings_stats s FINAL
  JOIN sadb_listings l FINAL ON s.id = l.id
WHERE
  toDate (s.day_time) >= today () - INTERVAL 3 MONTH
  AND l.published_at > 0
  AND s.day_time >= toInt32 (l.published_at / 86400)
  AND l._peerdb_is_deleted = 0
  AND l.status = 1
  AND (s.day_time - toInt32 (l.published_at / 86400)) BETWEEN 0 AND 90
GROUP BY
  listing_age_days
ORDER BY
  listing_age_days

Query: View Decay by Category (Bucketed)

SELECT
  c.name_en AS category,
  CASE
    WHEN listing_age_days BETWEEN 0 AND 7  THEN '1. Week 1'
    WHEN listing_age_days BETWEEN 8 AND 14  THEN '2. Week 2'
    WHEN listing_age_days BETWEEN 15 AND 30  THEN '3. Week 3-4'
    WHEN listing_age_days BETWEEN 31 AND 60  THEN '4. Month 2'
    ELSE '5. Month 3'
  END AS age_bucket,
  ROUND(AVG(views), 2) AS avg_views,
  ROUND(median (views), 1) AS median_views
FROM
  (
    SELECT
      s.id,
      l.category,
      s.day_time - toInt32 (l.published_at / 86400) AS listing_age_days,
      s.views
    FROM
      sadb_stats_listings_stats s FINAL
      JOIN sadb_listings l FINAL ON s.id = l.id
    WHERE
      toDate (s.day_time) >= today () - INTERVAL 3 MONTH
      AND l.published_at > 0
      AND s.day_time >= toInt32 (l.published_at / 86400)
      AND l._peerdb_is_deleted = 0
      AND l.status = 1
      AND (s.day_time - toInt32 (l.published_at / 86400)) BETWEEN 0 AND 90
  ) t
  JOIN sadb_categories c FINAL ON t.category = c.category_id
WHERE
  c.name_en IN (
    'Apartment for Rent',
    'Villa for Sale',
    'Land for Sale',
    'Apartment for Sale',
    'Villa for Rent'
  )
GROUP BY
  c.name_en,
  age_bucket
ORDER BY
  c.name_en,
  age_bucket

3. Contact Conversion Decay Analysis

Overview

Conversion rate (contacts / views) also decays over time, but less dramatically than raw views.

Overall Conversion Decay

Listing Age	Avg Views	Avg Contacts	Conversion %
Day 0	20.9	2.42	11.6%
Day 2	39.6	3.37	8.5%
Day 7	26.8	1.80	6.7%
Day 14	21.7	1.40	6.4%
Day 30	17.2	1.02	6.0%
Day 60	12.3	0.67	5.4%
Day 90	9.6	0.50	5.2%

Decay Comparison

Metric	Week 1	Month 3	Decay
Views	30 avg	10 avg	—67%
Contacts	2.3 avg	0.5 avg	—78%
Conversion %	7.5%	5.3%	—29%

Conversion by Category

Category	Week 1	Week 2	Wk 3-4	Mo 2	Mo 3	Decay
Apartment Rent	8.0%	7.0%	6.6%	6.2%	6.1%	—24%
Villa Rent	7.2%	6.1%	5.8%	5.6%	5.6%	—22%
Land Sale	8.0%	5.6%	4.8%	4.2%	3.9%	-51%
Apartment Sale	4.5%	4.2%	4.3%	3.8%	3.9%	—13%
Villa Sale	4.3%	3.1%	3.0%	2.7%	2.5%	-42%

Insight: Rentals maintain conversion better, Land sales have steepest decay

Conversion by City

City	Week 1	Week 2	Wk 3-4	Mo 2	Mo 3	Decay
مكة	8.7%	7.8%	6.7%	6.6%	5.1%	—41%
الرياض	7.9%	6.7%	6.4%	5.9%	5.5%	—30%
الدمام	7.5%	6.7%	5.9%	5.3%	6.3%	—16%
جدة	7.2%	6.0%	5.6%	5.2%	4.8%	—33%

Query: Conversion by Listing Age

SELECT
  s.day_time - toInt32 (l.published_at / 86400) AS listing_age_days,
  SUM(s.views) AS total_views,
  SUM(s.calls + s.msgs + s.whatsapp) AS total_contacts,
  ROUND(
    100.0 * SUM(s.calls + s.msgs + s.whatsapp) / NULLIF(SUM(s.views), 0),
    2
  ) AS conversion_pct,
  ROUND(AVG(s.views), 1) AS avg_views,
  ROUND(AVG(s.calls + s.msgs + s.whatsapp), 2) AS avg_contacts
FROM
  sadb_stats_listings_stats s FINAL
  JOIN sadb_listings l FINAL ON s.id = l.id
WHERE
  toDate (s.day_time) >= today () - INTERVAL 3 MONTH
  AND l.published_at > 0
  AND s.day_time >= toInt32 (l.published_at / 86400)
  AND l._peerdb_is_deleted = 0
  AND l.status = 1
  AND (s.day_time - toInt32 (l.published_at / 86400)) BETWEEN 0 AND 90
GROUP BY
  listing_age_days
ORDER BY
  listing_age_days

Query: Conversion by Category (Bucketed)

SELECT
  c.name_en AS category,
  CASE
    WHEN listing_age_days BETWEEN 0 AND 7  THEN '1. Week 1'
    WHEN listing_age_days BETWEEN 8 AND 14  THEN '2. Week 2'
    WHEN listing_age_days BETWEEN 15 AND 30  THEN '3. Week 3-4'
    WHEN listing_age_days BETWEEN 31 AND 60  THEN '4. Month 2'
    ELSE '5. Month 3'
  END AS age_bucket,
  SUM(views) AS total_views,
  SUM(contacts) AS total_contacts,
  ROUND(100.0 * SUM(contacts) / NULLIF(SUM(views), 0), 2) AS conversion_pct,
  ROUND(AVG(views), 1) AS avg_views,
  ROUND(AVG(contacts), 2) AS avg_contacts
FROM
  (
    SELECT
      s.id,
      l.category,
      s.day_time - toInt32 (l.published_at / 86400) AS listing_age_days,
      s.views,
      s.calls + s.msgs + s.whatsapp AS contacts
    FROM
      sadb_stats_listings_stats s FINAL
      JOIN sadb_listings l FINAL ON s.id = l.id
    WHERE
      toDate (s.day_time) >= today () - INTERVAL 3 MONTH
      AND l.published_at > 0
      AND s.day_time >= toInt32 (l.published_at / 86400)
      AND l._peerdb_is_deleted = 0
      AND l.status = 1
      AND (s.day_time - toInt32 (l.published_at / 86400)) BETWEEN 0 AND 90
  ) t
  JOIN sadb_categories c FINAL ON t.category = c.category_id
WHERE
  c.name_en IN (
    'Apartment for Rent',
    'Villa for Sale',
    'Land for Sale',
    'Apartment for Sale',
    'Villa for Rent'
  )
GROUP BY
  c.name_en,
  age_bucket
ORDER BY
  c.name_en,
  age_bucket

Query: Conversion by City (Bucketed)

SELECT
  d.city_name,
  CASE
    WHEN listing_age_days BETWEEN 0 AND 7  THEN '1. Week 1'
    WHEN listing_age_days BETWEEN 8 AND 14  THEN '2. Week 2'
    WHEN listing_age_days BETWEEN 15 AND 30  THEN '3. Week 3-4'
    WHEN listing_age_days BETWEEN 31 AND 60  THEN '4. Month 2'
    ELSE '5. Month 3'
  END AS age_bucket,
  SUM(views) AS total_views,
  SUM(contacts) AS total_contacts,
  ROUND(100.0 * SUM(contacts) / NULLIF(SUM(views), 0), 2) AS conversion_pct
FROM
  (
    SELECT
      s.id,
      l.district_id,
      s.day_time - toInt32 (l.published_at / 86400) AS listing_age_days,
      s.views,
      s.calls + s.msgs + s.whatsapp AS contacts
    FROM
      sadb_stats_listings_stats s FINAL
      JOIN sadb_listings l FINAL ON s.id = l.id
    WHERE
      toDate (s.day_time) >= today () - INTERVAL 3 MONTH
      AND l.published_at > 0
      AND s.day_time >= toInt32 (l.published_at / 86400)
      AND l._peerdb_is_deleted = 0
      AND l.status = 1
      AND (s.day_time - toInt32 (l.published_at / 86400)) BETWEEN 0 AND 90
  ) t
  JOIN sadb_districts d FINAL ON t.district_id = d.district_id
WHERE
  d.city_name IN ('الرياض', 'جدة', 'الدمام', 'مكة المكرمة')
GROUP BY
  d.city_name,
  age_bucket
ORDER BY
  d.city_name,
  age_bucket

Business Implications

1. Listing Refresh Strategy

Listings not converting by Day 30 are getting ~40% of peak traffic
Consider automated refresh prompts at Day 14 and Day 30

2. Pricing Guidance

If no contacts by Week 2, listing may be overpriced
Sales listings (especially land) need pricing attention early

3. Category-Specific Strategies

Rentals: High volume, maintain conversion - focus on response time
Land Sales: Urgency-driven buyers - price aggressively early
Villa Sales: Longer cycle expected - patience but monitor conversion

4. City-Specific Insights

Riyadh: Fastest market, most competitive - quick response essential
Makkah: High initial interest but fast decay - capture early
Jeddah: Slower decay but lower baseline - quality over speed

Data Notes

Analysis Period: 2024-01-01 onwards (regulatory changes make earlier data incomparable)
View/Conversion Analysis: Last 3 months only for recency
Time on Market: Filtered to 1-730 days (excludes outliers)
day_time format: Days since Unix epoch (not Unix timestamp)
published_at format: Unix timestamp (seconds)