geographicreal-estatehousinghome-pricessale-to-listminneapolissaint-paul

Twin Cities Home Sales vs Last Asking Price: 48k Closed Sales with Beds, Baths, Size and Days on Market

Updated Oct 20, 2026

$99

Fixed price

Sample structure: 93.8 / 100
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Seller: Brick and Mortar
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Category
Geographic
Records
48,839 rows
Format
CSV
Update Frequency
Not documented
Collection Method
uploaded
PII
No flagged field names; not a privacy audit
File Size
~6.52 MB
Download links issued
0

Source, license and coverage

Supplier documentation. These claims are separate from the automated sample score. A listing edit date is not a data freshness date.

License
Brick & Mortar data licence: internal commercial use incl. model training, no redistribution
Source / creator
Public Realtor.com listing pages for the seven Twin Cities metro counties, collected 2026-10-12.
Collection method
How far below (or above) the ask do Twin Cities homes actually close? Across the 28,577 sales where the last ask survives, the median close is 0.994 of the ask. This file lets a buyer cut that by ZIP, price band, size, age, type and days on market. Collected from public listing pages in October 2026. On 20,262 rows the listing site had overwritten the ask with the sold price; those rows keep their sale but carry a NULL ask rather than a false 1.000 ratio.
Coverage start
Not applicable
Coverage end
Not applicable
Data last updated
2026-10-20
Update schedule
snapshot

Source documentation ↗

License terms ↗

Sample structure score: 93.8 / 100

This automated check describes the inspected sample, not factual accuracy, legal rights, representativeness, or the quality of the entire dataset. It is not a customer rating.

Assessed 10 sample records (CSV) on 2026-10-20. All records in the provided sample were checked.

CheckPointsEvidence
Populated cells43.8 / 50149 of 170 top-level cells contain a value. Null, absent and blank values count as missing; zero and false count as populated.
Consistent value types30 / 30149 of 149 populated cells match their column's most common observed type. Types are inferred, not checked against real-world truth.
Consistent record shape20 / 2010 of 10 records have the expected fields. CSV/TSV use the header width; JSON uses the union of observed keys.
Field-level findings and improvements

Check missing cells and mixed types below. Document intentional missing values or mixed types in your field descriptions. Do not fill legitimate unknowns with invented values just to increase this score.

FieldMissing cellsMost common typeOther populated types
sale_id0 / 10string0 / 10
sold_on0 / 10string0 / 10
sold_price0 / 10number0 / 10
last_list_price5 / 10number0 / 5
sale_to_list5 / 10number0 / 5
days_on_market10 / 10unknown0 / 0
property_type0 / 10string0 / 10
beds0 / 10number0 / 10
baths0 / 10number0 / 10
sqft0 / 10number0 / 10
lot_sqft1 / 10number0 / 9
year_built0 / 10number0 / 10
county0 / 10string0 / 10
city0 / 10string0 / 10
zip0 / 10number0 / 10
lat0 / 10number0 / 10
lon0 / 10number0 / 10
How the score is calculated, its limitations, and how to correct an assessment →

About this data

48,839 closed home sales (October 2025 to October 2026) across the seven-county Minneapolis-Saint Paul metro with sold price, date, property type, beds, baths, square feet, lot, year built, ZIP and, on 28,577 of them, the last asking price before close, so the sale-to-list ratio is a column. One CSV, 48,839 rows; column meanings in the schema. No street addresses, listing agents or brokerages. Coordinates rounded to 3 decimals (about 100 m) so a row places a sale in a block, not at a door.

Retrieve with your agent or Python

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Download the Python example
python3 retrieve-dataset.py 98de6d1c-ebc0-4310-8553-62b3b644cd85 --output dataset.bin

Schema

NameTypeDescription
sale_idstring
sold_onstring
sold_pricenumber
last_list_pricenumber
sale_to_listnumber
days_on_marketunknown
property_typestring
bedsnumber
bathsnumber
sqftnumber
lot_sqftnumber
year_builtnumber
countystring
citystring
zipnumber
latnumber
lonnumber

Sample Data

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For AI Agents

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    "databazaar": { "command": "npx", "args": ["databazaar-mcp"] }
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# 2. Your agent can then call:
search_datasets({ query: "Twin Cities Home Sales vs Last" })
// Found: 98de6d1c-ebc0-4310-8553-62b3b644cd85
buy_now({ dataset_id: "98de6d1c-ebc0-4310-8553-62b3b644cd85" })  // uses your saved card
Via REST API
# Authenticate with your API key:
curl https://api.databazaar.io/datasets/98de6d1c-ebc0-4310-8553-62b3b644cd85 \
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# Purchase:
curl -X POST https://api.databazaar.io/datasets/98de6d1c-ebc0-4310-8553-62b3b644cd85/buy \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"dataset_id": "98de6d1c-ebc0-4310-8553-62b3b644cd85"}'