Real Estate Price Valuation

This dataset provides comprehensive, transaction-level real estate data, including property attributes, sale prices, geolocation, and transaction details. It is ideal for training automated valuation models (AVMs), optimizing appraisals, and conducting in-depth investment and market analytics. The dataset's granularity and breadth make it valuable for both industry professionals and data scientists.

  • last updated 22 Jul 2025
  • by GoMask
The brief that made it

Automated property valuation model (AVM) development

Sample rows

preview · 8 of 200 rows · all 28 columns
transaction_idstringproperty_typestringbathroomsfloathas_garagebooleanaddress_statestringproperty_idstringtransaction_datedatesale_pricefloatlisting_pricefloatyear_builtintegerbedroomsintegerliving_area_sqftfloatlot_size_sqftfloatparking_spacesintegerhas_poolbooleanaddress_streetstringaddress_citystringaddress_postal_codestringaddress_countrystringlatitudefloatlongitudefloatseller_idstringbuyer_idstringagent_idstringtransaction_typestringfinancing_typestringdays_on_marketintegerproperty_conditionstring
TXN1001single-family2.5trueNCPROP20012021-05-1346500047800019874220088002false214 Oak Grove DrCary27513USA35.7896-78.7811S101B101A101saleconventional19good
TXN1002multi-family9trueCAPROP20022023-10-2813750001400000201512145004800018true2227 Fillmore StSan Francisco94115USA37.7898-122.4348S102B102A102saleconventional67excellent
TXN1003condo2falseFLPROP20032022-06-1638900041000020112110001true884 Sunrise Ave Apt 304Miami33139USA25.7807-80.1407S103B103A103saleFHA25excellent
TXN1004land0falseNAPROP20042020-11-0219500blankblank00170000falseLot 17B Willow LaneUnknown00000USA37.021-84.5077blankblankblankauctioncash310unknown
TXN1005townhouse2.5trueWAPROP20052023-03-1866800067500019993175030002false4885 Northgate PlBellevue98007USA47.6104-122.2007S104B104A104saleconventional14good
TXN1006apartment1falseMAPROP20062021-09-212650002700002006180000true1225 Summerhill Rd Apt 5CBoston02118USA42.3389-71.0723S106B106A105saleFHA12good
TXN1007multi-family16trueCAPROP20072024-02-14295000031000002020202300010000040true30 Mission StLos Angeles90012USA34.0505-118.2489S107B107A106saleother49excellent
TXN1008condo2falseFLPROP20082022-04-0929900031500020022102001true1011 Las Olas Blvd Unit 106Fort Lauderdale33301USA26.1194-80.1373S108B108A107saleconventional18good

What the 200 rows show

from the 200-row sample

Multi-family (property type) stands out: mean bathrooms is 16.6, against 1.9 for the rest.

  • 60%has_garage = true
  • 2.5median bathrooms
  • 5transaction types
  • 5financing types
  • 6property conditions
  • 349,450median sale_price
Mean bathrooms by property_type200 rows
010203.0sing…58 ro…1.9condo22 ro…2.3town…29 ro…1.3apar…24 ro…0.0land27 ro…16.6mult…32 ro…0.0other8 rows
bathrooms200 rows, in bands of 5
0851701651010543302035bathrooms →

Median 2.5, from 0.0 to 32.0.

address_state200 rows · top 10 of 34 values
  1. TX24
  2. CA22
  3. NA21
  4. NC14
  5. FL13
  6. WA12
  7. CO11
  8. IL9
  9. OH7
  10. AZ7
28 columns by typefrom the column list below
  • string 14
  • integer 4
  • float 7
  • date 1
  • boolean 2

Columns

28 columns in four groups
blueprint · 28 columns
columntypedescriptionexample
Text 14 columns
transaction_idstringUnique identifier for each property transactionuniqueTXN1001
property_idstringUnique identifier for the propertyPROP2001
property_typestringType of property (e.g., single-family, condo, townhouse, apartment, land)7 valuessingle-family
address_streetstringStreet address of the property214 Oak Grove Dr
address_citystringCity where the property is locatedCary
address_statestringState or province where the property is locatedNC
address_postal_codestringPostal or ZIP code of the property27513
address_countrystringCountry where the property is locatedUSA
seller_idstringUnique identifier for the seller (can be anonymized)optionalS101
buyer_idstringUnique identifier for the buyer (can be anonymized)optionalB101
agent_idstringUnique identifier for the real estate agent handling the transactionoptionalA101
transaction_typestringType of transaction (e.g., sale, foreclosure, auction, new construction)sale · foreclosure · auction · new_construction · othersale
financing_typestringType of financing used (e.g., cash, conventional, FHA, VA, other)cash · conventional · FHA · VA · other · optionalconventional
property_conditionstringGeneral condition of the property at sale (e.g., new, excellent, good, fair, poor)6 values · optionalgood
Numbers 11 columns
sale_pricefloatFinal sale price of the property in local currency0 or more465000
listing_pricefloatOriginal listing price of the property in local currency0 or more · optional478000
year_builtintegerYear the property was originally constructed1,800 to 2,100 · optional1987
bedroomsintegerNumber of bedrooms in the property0 or more · optional4
bathroomsfloatNumber of bathrooms in the property (can be fractional for half baths)0 or more · optional2.5
living_area_sqftfloatTotal interior living area in square feet0 or more · optional2200
lot_size_sqftfloatTotal lot size in square feet0 or more · optional8800
parking_spacesintegerNumber of dedicated parking spaces0 or more · optional2
latitudefloatLatitude coordinate of the property-90 to 90 · optional35.7896
longitudefloatLongitude coordinate of the property-180 to 180 · optional-78.7811
days_on_marketintegerNumber of days the property was listed before transaction0 or more · optional19
Dates and times 1 column
transaction_datedateDate when the property transaction occurred2021-05-13
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 garage60%119 of 200 rowsmean bathrooms by pro…3.0sing…1.9condo2.3town…1.3apar…

    A real estate dashboard

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

  • Why do the 32 multi-family rows have a mean bathrooms of 16.6?

    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.

Same 28 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 rows28 columns
Yours1,000 rows28 columnsaddress_street: UK only

blueprint · real-estate-price-valuation

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 28 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
  • Prices influenced by location and amenities
  • Seasonal trends in buying/selling
  • Historical appreciation/depreciation applied
  • Outlier transactions flagged for review
  • Neighborhood demographics impact value
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
real-estate-price-valuation

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