Real Estate Price Estimation

This dataset provides comprehensive real estate transaction records, including detailed property attributes, sale prices, listing information, and anonymized participant identifiers. It enables robust market price estimation, trend analysis, and portfolio management for investors, analysts, and real estate professionals. The dataset's granular structure supports property valuation modeling and geographic market comparisons.

  • last updated 12 Jul 2025
  • by GoMask
The brief that made it

Market price prediction and property valuation modeling

Sample rows

preview · 8 of 130 rows · all 25 columns
transaction_idstringproperty_typestringbedroomsintegerhas_garagebooleanaddress_statestringproperty_idstringtransaction_datedatesale_pricefloatlisting_pricefloatyear_builtintegersquare_feetfloatlot_size_sqftfloatbathroomsfloatparking_spacesintegeraddress_streetstringaddress_citystringaddress_postal_codestringaddress_countrystringseller_idstringbuyer_idstringagent_idstringlisting_datedatedays_on_marketintegerhas_poolbooleanproperty_conditionstring
T00001single-family4trueKSP100012023-10-2146530047550019882200.585002.527429 Oak Crest DriveWichita67206USAS1023B2941AG1042023-08-1468falsegood
T00002condo2falseONP100022024-01-12659000670000201811000211206 Harbourfront AveTorontoM5V 2X4CanadaS2041B1032AG2012023-12-0538trueexcellent
T00003land0falseFLP100032022-05-17185000189000blankblank50000001564 Pine Meadow RdOcala34472USAblankblankblankblankblankfalseunknown
T00004commercial0trueTXP100042023-03-1525900002630000199918000720001480450 Commerce BlvdHouston77056USAS4619B4179AG3072022-12-0798falsegood
T00005townhouse3trueCOP100052023-11-193950003950002010150023002.513078 Maple RowDenver80207USAS6312B6292AG1282023-09-0179falsegood
T00006multi-family16trueILP100062024-02-1089900009350000198121000.52650010.586001 Heritage CtChicago60611USAS8077B1910AG1102023-10-02131falsegood
T00007condo2falseCAP100072023-08-282874002900002006935021993 Union SquareSan Francisco94108USAS3940B2140AG2092023-07-1049falsegood
T00008single-family5trueTXP100082023-05-14124000012900002017310014000438013 Willow SpringsAustin78745USAS5923B8931AG1132023-03-1263trueexcellent

What the 130 rows show

from the 130-row sample

Multi-family (property type) stands out: mean bedrooms is 11.5, against 1.9 for the rest.

  • 57%has_garage = true
  • 2median bedrooms
  • 2address countries
  • 5property conditions
  • 439,500median sale_price
  • 475,500median listing_price
Mean bedrooms by property_type130 rows
010203.8singl…33 rows1.4condo29 rows2.6townh…17 rows11.5multi…17 rows0.0land17 rows0.0comme…17 rows
bedrooms130 rows, in bands of 2
0255050422132351301018bedrooms →

Median 2, from 0 to 17.

address_state130 rows · top 10 of 32 values
  1. TX17
  2. ON15
  3. CA11
  4. QC8
  5. FL7
  6. NC7
  7. OK6
  8. OH5
  9. PA5
  10. BC4
25 columns by typefrom the column list below
  • string 12
  • integer 4
  • float 5
  • date 2
  • boolean 2

Columns

25 columns in four groups
blueprint · 25 columns
columntypedescriptionexample
Text 12 columns
transaction_idstringUnique identifier for each real estate transactionuniqueT00001
property_idstringUnique identifier for the property involved in the transactionP10001
property_typestringType of property (e.g., single-family, condo, townhouse, multi-family, land, commercial)7 valuessingle-family
address_streetstringStreet address of the property7429 Oak Crest Drive
address_citystringCity where the property is locatedWichita
address_statestringState or province where the property is locatedKS
address_postal_codestringPostal or ZIP code of the property67206
address_countrystringCountry where the property is located2 countriesUSA
seller_idstringUnique identifier for the seller (can be anonymized)optionalS1023
buyer_idstringUnique identifier for the buyer (can be anonymized)optionalB2941
agent_idstringUnique identifier for the real estate agent handling the transactionoptionalAG104
property_conditionstringGeneral condition of the property at sale (e.g., new, good, fair, needs renovation)6 values · optionalgood
Numbers 9 columns
sale_pricefloatFinal sale price of the property in local currency0 or more465300
listing_pricefloatOriginal listing price of the property in local currency0 or more · optional475500
year_builtintegerYear the property was originally built1,800 to 2,100 · optional1988
square_feetfloatTotal interior living area of the property in square feet0 or more · optional2200.5
lot_size_sqftfloatLot size of the property in square feet0 or more · optional8500
bedroomsintegerNumber of bedrooms in the property0 or more · optional4
bathroomsfloatNumber of bathrooms in the property (can be fractional, e.g., 1.5)0 or more · optional2.5
parking_spacesintegerNumber of parking spaces available with the property0 or more · optional2
days_on_marketintegerNumber of days the property was on the market before sale0 or more · optional68
Dates and times 2 columns
transaction_datedateDate when the property transaction occurred2023-10-21
listing_datedateDate when the property was listed for saleoptional2023-08-14
True or false 2 columns
has_garagebooleanIndicates if the property has a garageoptionaltrue
has_poolbooleanIndicates if the property has a swimming pooloptionalfalse

Use it for

  • has garage57%74 of 130 rowsmean bedrooms by prop…3.8sing…1.4condo2.6town…11.5mult…

    A real estate dashboard

    The has_garage rate, bedrooms by property_type and a breakdown of address_state. Excel, Power BI or Tableau.

  • Why do the 17 multi-family rows have a mean bedrooms of 11.5?

    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 transactions with property_id, transaction_date and sale_price to fill a screen in front of a buyer.

Not quite right?

Make it yours.

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This dataset130 rows25 columns
Yours1,000 rows25 columnsaddress_street: UK only

blueprint · real-estate-price-estimation

Behind this dataset

Same schema. As many rows as you need.

These 130 rows came out of a blueprint — 25 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
  • Properties must have verified location and size
  • Transactions under $20,000 flagged for manual review
  • Sales involving foreclosures must be labeled
  • Missing valuation data is imputed
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
real-estate-price-estimation

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