Home Renovation Impact Dataset

This dataset provides detailed records of residential properties, including renovation history, sale transactions, and agent involvement, enabling robust analysis of how specific renovations affect home values and marketability. It is ideal for homeowners, agents, and analysts seeking actionable insights into renovation ROI, pricing trends, and listing performance.

  • last updated 20 Aug 2025
  • by GoMask
The brief that made it

Analyze the impact of renovations on property sale prices and time on market

Sample rows

preview · 8 of 200 rows · all 26 columns
property_idstringproperty_typestringbathroomsfloatrenovation_typestringaddress_streetstringaddress_citystringaddress_statestringaddress_postal_codestringaddress_countrystringbedroomsintegersquare_footageintegeryear_builtintegerrenovation_idstringrenovation_costfloatrenovation_datedatepre_renovation_valuefloatpost_renovation_valuefloatsale_idstringsale_datedatesale_pricefloatdays_on_marketintegeragent_idstringagent_namestringagent_contactstringlisting_statusstringrenovation_descriptionstring
P0001single-family3.5kitchen192 Oakridge LaneSpringfieldIL62704USA426501994R0001320002022-03-15370000415000S00012022-11-0542500018A0001Susan Moralessusan.morales@realtyplus.comsoldFull kitchen remodel including new appliances and cabinets.
P0002townhouse2.5blank85 Maplewood DriveVancouverBCV5K 0A1Canada314502014blankblankblankblankblankS00022023-01-1861000021A0002Laura Choilaura.choi@canadaproperties.casoldblank
P0003condo1.5windows703 Kingfisher StreetSeattleWA98103USA29702002R0002110002021-09-30340000355000blankblankblankblankA0003Erik Lindstromelindstrom@emeraldagents.comactiveReplaced all windows with energy-efficient models.
P0004condo2blank412 Easton AvenueTorontoONM4C 1B4Canada211002019blankblankblankblankblankblankblankblankblankA0004Megan Patelmegan.patel@ontariolistings.comactiveblank
P0005townhouse2.5roof15 Glenview CrescentDublinLDD02W086Ireland419502007R0003185002020-06-18410000445000S00032022-03-2545000013A0005Declan O'Sheadeclan.oshea@greenhomes.iesoldReplaced the roof and added new insulation.
P0006single-family4blank2708 Pine Ridge RoadDenverCO80205USA534002005blankblankblankblankblankblankblankblankblankA0001Susan Moralessusan.morales@realtyplus.comactiveblank
P0007multi-family6windows1185 Willow BendCalgaryABT2P 2M5Canada854001978R0004260002019-07-229800001035000blankblankblankblankA0006James Stewartj.stewart@albertarealty.compendingUpgraded windows in all units.
P0008condo1blank362 Birch Hill RoadLondonLDNSW1A 1AAUK16501999blankblankblankblankblankS00042023-05-1032800030A0007Hannah Taylorhtaylor@ukhomes.co.uksoldblank

What the 200 rows show

from the 200-row sample

Other (property type) stands out: mean bathrooms is 44.4, against 3.1 for the rest.

  • 2.5median bathrooms
  • 4listing statuses
  • 9address countries
  • 57address states
  • 4median bedrooms
  • 1,935median square_footage
Mean bathrooms by property_type200 rows
040803.0single…81 rows1.6condo36 rows2.2townho…35 rows6.4multi-…30 rows44.4other18 rows
bathrooms200 rows, in bands of 10
0100200192000000008050100bathrooms →

Median 2.5, from 0.0 to 99.9.

renovation_type77 rows with a value · 123 left blank
  1. kitchen18
  2. windows11
  3. landscaping9
  4. roof8
  5. basement8
  6. flooring8
  7. bathroom7
  8. exterior6
  9. other2
26 columns by typefrom the column list below
  • string 15
  • integer 4
  • float 5
  • date 2

Columns

26 columns in three groups
blueprint · 26 columns
columntypedescriptionexample
Text 15 columns
property_idstringUnique identifier for each propertyuniqueP0001
address_streetstringStreet address of the property192 Oakridge Lane
address_citystringCity where the property is locatedSpringfield
address_statestringState where the property is locatedIL
address_postal_codestringPostal code of the property62704
address_countrystringCountry where the property is located9 countriesUSA
property_typestringType of property (e.g., single-family, condo, townhouse)single-family · condo · townhouse · multi-family · othersingle-family
renovation_idstringUnique identifier for each renovation eventoptionalR0001
renovation_typestringType of renovation performed (e.g., kitchen, bathroom, roof, landscaping)9 values · optionalkitchen
renovation_descriptionstringDetailed description of the renovationoptionalFull kitchen remodel incl…
sale_idstringUnique identifier for each sale transactionoptionalS0001
agent_idstringUnique identifier for the listing agentoptionalA0001
agent_namestringFull name of the listing agentoptionalSusan Morales
agent_contactstringContact information for the listing agent (phone or email)optionalhtaylor@ukhomes.co.uk
listing_statusstringCurrent status of the property listingactive · pending · sold · off-marketsold
Numbers 9 columns
bedroomsintegerNumber of bedrooms in the property0 or more4
bathroomsfloatNumber of bathrooms in the property (can be fractional)0 or more3.5
square_footageintegerTotal finished square footage of the property0 or more2650
year_builtintegerYear the property was originally built1,800 to 2,1001994
renovation_costfloatCost of the renovation in local currency0 or more · optional32000
pre_renovation_valuefloatEstimated market value of the property before renovation0 or more · optional370000
post_renovation_valuefloatEstimated market value of the property after renovation0 or more · optional415000
sale_pricefloatFinal sale price of the property0 or more · optional425000
days_on_marketintegerNumber of days the property was listed before sale0 or more · optional18
Dates and times 2 columns
renovation_datedateDate when the renovation was completedoptional2022-03-15
sale_datedateDate when the property was soldoptional2022-11-05

Use it for

  • median bathroo…2.5200 rowsmean bathrooms by pro…3.0sing…1.6condo2.2town…6.4mult…

    A real estate dashboard

    Bathrooms by property_type and a breakdown of renovation_type. Excel, Power BI or Tableau.

  • Why do the 18 other rows have a mean bathrooms of 44.4?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable properties with address_street, address_city and address_state to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 26 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

1,000 rows: $1.99 · 10,000: $12.99One-time. No subscription. All prices

This dataset200 rows26 columns
Yours1,000 rows26 columnsaddress_street: UK only

blueprint · home-renovation-impact-dataset

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 26 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
  • Include renovation type and date
  • Track resulting sale price change
  • Flag high-ROI projects
  • Exclude owner-occupied properties
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
home-renovation-impact-dataset

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