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DataNexx

Data category

Real Estate & Property Data

Listings with final prices and time on market, valuation reports, leases and maintenance histories.

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Real estate data for AI is the private record of how properties really perform: final sale and lease prices, valuation reports, lease terms, vacancies and maintenance histories. AI teams use it to build valuation, property-search and building-operations models. Listing sites show asking prices; your records show what was agreed. DataNexx helps owners, managers and agencies license these records without tenant or buyer identities.

Why it matters for AI

Recorded, judged, resolved.

Public listings show asking prices; private records show what properties actually sold or leased for, how long it took and what they cost to run. Tenant and buyer details are removed before anything is licensed.

Typical data

  • Asking vs final price
  • Time on market
  • Valuation reports
  • Property attributes
  • Lease terms & renewals
  • Vacancies
  • Maintenance requests
  • Building energy data
  • Floor plans & photos

Representative workflow

Illustrative

  1. 01Property attributes
  2. 02Valuation
  3. 03Listing & asking price
  4. 04Offers & negotiation
  5. 05Final price
  6. 06Lease or sale
  7. 07Occupancy & maintenance

Who typically holds it

  • Brokerages and agencies
  • Valuation and appraisal firms
  • Property and facility managers
  • Developers and landlords

Potential AI applications

  • Automated valuation models
  • Floor-plan and listing understanding
  • Maintenance and energy forecasting
  • Property-search assistants

Which systems hold real estate data?

Brokerages keep deals in a CRM and a transaction management tool. Valuation firms keep reports and comparables. Property managers run a property management system with leases, rent rolls and work orders. Building systems log energy use and equipment faults.

The value is in outcomes. An asking price linked to the final price and the time on market tells a model far more than a listing alone.

  • Asking price, final price and days on market
  • Valuation reports with the comparables used
  • Lease terms, renewals and vacancies
  • Maintenance requests with cost and time to fix

What do AI teams build with real estate data?

Teams train automated valuation models on real estate datasets built from agreed prices rather than asking prices. They train floor-plan and listing models on plans and photos. They forecast maintenance and energy use from years of work orders and meter data.

Property-search assistants also need real outcomes. They learn which homes actually leased, at what rent and how fast.

Long histories help most. Several years of rents, vacancies and repairs show how a building ages and how a market moves through a cycle.

What privacy and fairness issues apply to property data?

Leases and sale files name tenants and buyers, and often hold income, credit or payment details. These are removed before anything is licensed. Photos are checked for faces, family details and personal items.

Fairness matters too. In the US, the Fair Housing Act bars discrimination in housing. Models trained on property data are checked so that no proposed use targets protected groups. Listing photos and plans may belong to a photographer or a listing service, so their rights are checked first.

How does a property business prepare a first description?

A short description is enough to start. You do not send files yet.

  • Property types: residential, office, retail or industrial
  • Number of years and regions covered, roughly
  • Which systems hold the records
  • Who owns the photos and floor plans

Example record (illustrative)

Illustrative record: a fictional, de-identified apartment lease. Not a real tenant.
FieldWhat it holds
Property typeTwo-bedroom apartment in a mid-rise building
Asking rentAdvertised monthly rent, banded
Agreed rentAgreed rent after one counter-offer, banded
Days vacantDays between the previous lease end and the new start
Maintenance logThree work orders in year one, each with cost and time to fix
RedactionsTenant names, unit number and street address removed

Frequently asked questions

Why is private real estate data more useful than listings?

Listings show asking prices. Private records show the agreed price, the time it took and what the property cost to run.

Are tenant and buyer details shared?

No. Names, contact details, income and payment data are removed before anything is licensed.

Can we license listing photos?

Only if you hold the rights. Photos often belong to a photographer or a listing service, so ownership is checked first.

Who usually holds this data?

Brokerages, valuation firms, property and facility managers, developers and landlords with long portfolios.

What does it cost to take part?

Nothing. There is no fee to the data owner. Compensation comes from the licence with the AI company.

Is building energy and maintenance data useful?

Yes. Meter readings and work orders over several years help models forecast repairs and energy use for similar buildings.

Sources

Trust & compliance

Data licensing without losing control.

Your data stays yours until you decide otherwise. We assess before anything is shared, and we treat rights and privacy as conditions of a transaction — not afterthoughts.

No raw data for an initial evaluation

Nothing moves without authorization

Ownership is not the same as licensing rights

Specialist review where it is needed

Scoped records, never bulk exports

How we handle rights, privacy and confidentiality

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