Real Estate
19 columns
200 rows
8 formats

Property Listing Image Quality Data

This dataset provides detailed records of property listing images, including image quality scores, technical attributes, user engagement metrics, and listing performance outcomes. It enables PropTech firms to analyze how image quality influences user interactions and listing success, supporting improvements in visual marketing and platform optimization.

last updated 20 Aug 2025
by GoMask

The brief that made it

Correlating image quality with user engagement and conversion rates

Sample rows

preview · 8 of 200 rows · all 19 columns

listing_idstring	property_typestring	listing_performance_inquiriesinteger	listing_performance_conversionboolean	listing_location_citystring	image_idstring	image_typestring	image_quality_scorefloat	image_resolution_widthinteger	image_resolution_heightinteger	image_file_size_kbinteger	image_upload_datetimedatetime	user_engagement_viewsinteger	user_engagement_clicksinteger	user_engagement_favoritesinteger	listing_posted_datetimedatetime	listing_location_statestring	listing_location_countrystring	image_urlstring
APT001	apartment	13	true	San Francisco	IMG_INT_0001	interior	0.97	2992	1878	980	2023-05-14T10:37:41Z	1102	180	42	2023-05-12T09:00:00Z	CA	USA	https://images.example.com/APT001_livingroom.jpg
APT001	apartment	13	true	San Francisco	IMG_EXT_0002	exterior	0.91	3200	2390	1022	2023-05-15T08:24:05Z	1784	219	56	2023-05-12T09:00:00Z	CA	USA	https://images.example.com/APT001_exterior.jpg
APT001	apartment	13	true	San Francisco	IMG_FP_0003	floorplan	0.86	1200	1600	512	2023-05-15T09:02:35Z	1342	182	50	2023-05-12T09:00:00Z	CA	USA	https://images.example.com/APT001_floorplan.png
APT002	apartment	11	true	Toronto	IMG_INT_0004	interior	0.89	2048	1550	870	2023-07-02T11:15:27Z	1070	170	44	2023-07-01T08:00:00Z	ON	Canada	https://images.example.com/APT002_bedroom.jpg
APT002	apartment	11	true	Toronto	IMG_EXT_0005	exterior	0.95	3100	2180	1020	2023-07-03T13:47:11Z	1260	210	54	2023-07-01T08:00:00Z	ON	Canada	https://images.example.com/APT002_exterior.jpg
APT002	apartment	11	true	Toronto	IMG_FP_0006	floorplan	0.78	1600	1300	680	2023-07-03T14:21:54Z	950	144	41	2023-07-01T08:00:00Z	ON	Canada	https://images.example.com/APT002_floorplan.jpg
HS003	house	4	false	Austin	IMG_EXT_0007	exterior	0.72	2700	1950	790	2024-01-09T15:46:20Z	780	88	11	2024-01-08T08:00:00Z	TX	USA	https://images.example.com/HS003_exterior.jpg
HS003	house	4	false	Austin	IMG_INT_0008	interior	0.85	3500	2250	1120	2024-01-09T16:03:11Z	950	95	21	2024-01-08T08:00:00Z	TX	USA	https://images.example.com/HS003_kitchen.jpg
HS003	house	4	false	Austin	IMG_FP_0009	floorplan	0.74	1280	800	360	2024-01-09T16:15:40Z	540	54	7	2024-01-08T08:00:00Z	TX	USA	https://images.example.com/HS003_floorplan.jpg
HS004	house	6	true	Los Angeles	IMG_EXT_0010	exterior	0.94	2960	2000	990	2022-11-12T09:20:18Z	1120	110	25	2022-11-10T08:00:00Z	CA	USA	https://images.example.com/HS004_exterior.jpg
HS004	house	6	true	Los Angeles	IMG_INT_0011	interior	0.78	2650	1900	800	2022-11-12T09:48:42Z	1060	98	28	2022-11-10T08:00:00Z	CA	USA	https://images.example.com/HS004_livingroom.jpg
COMM005	commercial	1	false	London	IMG_MAP_0012	map	0.43	900	900	210	2024-04-15T07:25:00Z	92	3	0	2024-04-14T08:00:00Z	England	UK	https://images.example.com/COMM005_map.jpg
COMM005	commercial	1	false	London	IMG_INT_0013	interior	0.58	1200	1024	310	2024-04-15T08:11:23Z	110	8	1	2024-04-14T08:00:00Z	England	UK	https://images.example.com/COMM005_office.jpg
COMM005	commercial	1	false	London	IMG_EXT_0014	exterior	0.69	1100	850	295	2024-04-15T09:02:34Z	150	13	2	2024-04-14T08:00:00Z	England	UK	https://images.example.com/COMM005_exterior.jpg
COMM006	commercial	1	false	Manchester	IMG_MAP_0015	map	0.52	750	750	200	2023-03-10T08:12:00Z	80	2	0	2023-03-10T07:00:00Z	England	UK	https://images.example.com/COMM006_map.jpg
COMM006	commercial	1	false	Manchester	IMG_EXT_0016	exterior	0.81	1200	1100	360	2023-03-10T09:10:40Z	200	10	3	2023-03-10T07:00:00Z	England	UK	https://images.example.com/COMM006_exterior.jpg
LAND007	land	0	false	blank	IMG_MAP_0017	map	0.15	1	1	0	2022-01-01T00:00:00Z	0	0	0	2022-01-01T00:00:00Z	blank	blank	https://images.example.com/LAND007_map.jpg
LAND007	land	0	false	blank	IMG_EXT_0018	exterior	0.04	9999	9999	100000	2022-01-01T00:10:00Z	5000	1500	800	2022-01-01T00:00:00Z	blank	blank	https://images.example.com/LAND007_exterior.jpg
LAND008	land	0	false	blank	IMG_MAP_0019	map	0.1	600	800	50	2023-02-01T09:00:00Z	10	0	0	2023-02-01T08:00:00Z	blank	blank	https://images.example.com/LAND008_map.jpg
LAND008	land	0	false	blank	IMG_OTHER_0020	other	0.98	2560	1440	1200	2023-02-01T09:14:00Z	1200	220	80	2023-02-01T08:00:00Z	blank	blank	https://images.example.com/LAND008_other.jpg

What the 200 rows show

from the 200-row sample

Apartment (property type) stands out: mean listing_performance_inquiries is 11.1, against 1.9 for the rest.

48%listing_performance_conversion = true
3.5median listing_performance_inquiries
5image types
5listing location countries
14listing location states
0.77median image_quality_score

Mean listing_performance_inquiries by property_type200 rows

listing_performance_inquiries200 rows, in bands of 2

Median 3.5, from 0 to 14.

listing_location_city158 rows with a value · 42 left blank

Berlin20
London18
San Francisco17
Austin17
Toronto15
Dallas11
Riyadh10
Manchester9
Vancouver6
Frankfurt6

19 columns by typefrom the column list below

string 8
integer 7
float 1
datetime 2
boolean 1

Columns

19 columns in four groups

blueprint · 19 columns
column	type	description	example
Text 8 columns
`listing_id`	string	Unique identifier for the property listingunique	APT001
`image_id`	string	Unique identifier for the image associated with the property listingunique	IMG_INT_0001
`image_url`	string	Direct URL to the property image	https://images.example.co…
`image_type`	string	Type of image (e.g., exterior, interior, floorplan, map)exterior · interior · floorplan · map · other	interior
`property_type`	string	Type of property (e.g., apartment, house, commercial, land)apartment · house · commercial · land · other · optional	apartment
`listing_location_city`	string	City where the property is locatedoptional	San Francisco
`listing_location_state`	string	State or region where the property is locatedoptional	CA
`listing_location_country`	string	Country where the property is located5 countries · optional	USA
Numbers 8 columns
`image_quality_score`	float	Automated or manual score indicating the quality of the image (0.0-1.0)0 to 1	0.97
`image_resolution_width`	integer	Width of the image in pixels1 or more	2992
`image_resolution_height`	integer	Height of the image in pixels1 or more	1878
`image_file_size_kb`	integer	File size of the image in kilobytes0 or more · optional	980
`user_engagement_views`	integer	Number of times the image was viewed by users0 or more · optional	1102
`user_engagement_clicks`	integer	Number of times users clicked on the image0 or more · optional	180
`user_engagement_favorites`	integer	Number of times users favorited the listing after viewing this image0 or more · optional	42
`listing_performance_inquiries`	integer	Number of inquiries received for the listing0 or more · optional	13
Dates and times 2 columns
`image_upload_datetime`	datetime	Timestamp when the image was uploaded	2023-05-14T10:37:41Z
`listing_posted_datetime`	datetime	Timestamp when the property listing was postedoptional	2023-05-12T09:00:00Z
True or false 1 column
`listing_performance_conversion`	boolean	Indicates if the listing resulted in a successful transaction (sale/rent)optional	true

Use it for

A real estate dashboard
The listing_performance_conversion rate, listing_performance_inquiries by property_type and a breakdown of listing_location_city. Excel, Power BI or Tableau.
Why do the 67 apartment rows have a mean listing_performance_inquiries of 11.1?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Listings200
APT00113apartment
APT00113apartment
HS0034house
A software demo
Believable listings with image_id, image_url and image_type to fill a screen in front of a buyer.

This dataset200 rows19 columns

Yours1,000 rows19 columnslisting_location_city: UK only

blueprint · property-listing-image-quality-data

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 19 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Record image score for each listing
Include engagement metrics (clicks, inquiries)
Flag listings with poor images
Exclude listings without images

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: property-listing-image-quality-data

What should your data show?

No signup. No card.

Sample rows

What the 200 rows show

Columns

Use it for

A real estate dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Real Estate

What should your data show?