Seeing an asking price is easy. Understanding what that price means is harder. The Resales Dataset in MLS RealtyHub brings structured secondary-market offer data into a professional market intelligence environment, helping agents, developers, valuers and consultants compare relevant properties without treating one listing as proof of the wider market.
A Listing Shows an Offer, Not the Whole Market
A single resale listing answers a narrow question: what is one owner asking today? It does not show whether the property is typical for its area, how comparable homes are positioned or how the resale segment relates to new development. When professionals rely on isolated offers, they can mistake an exception for a pattern.
The practical problem is not a lack of numbers. It is a lack of context. Relevant records need to be reviewed together, with differences in location, property type, area and other characteristics kept visible. The Resales Dataset gives this work a structured starting point inside MLS RealtyHub.
What the Resales Dataset Contains
The Resales Dataset is a live data product dedicated to the secondary property market. Its value lies in the dataset itself: structured records that professionals can use as evidence when examining resale supply and asking prices. It is not an automated valuation, a research conclusion or an API by definition. Records include:
- main cadastral fields;
- property characteristics;
- property area;
- a description of the property;
- the price at which the property is offered.
These fields help a professional identify relevant offers and compare their observable characteristics. The level of detail may differ between records, so users should check which attributes are present before making a comparison.
Daily Refreshes Keep the Evidence Current
MLS RealtyHub updates the Resales Dataset daily. This cadence gives users a regularly refreshed view of secondary-market offers and reduces the need to rebuild a working set from disconnected sources each time they begin an analysis.
A daily dataset update does not mean that every underlying offer changes every day. It also does not mean that every field has been independently reverified during each refresh. Cadence describes how often the dataset is updated, not a guarantee about the completeness, accuracy or continued availability of every record.
Four Questions to Ask Before Comparing Properties
Better analysis starts with a narrower question. Before using the data, define the decision the comparison must support and the segment that matters. A broad collection of resale offers may look impressive, but a smaller set of genuinely comparable properties is usually more useful.
1. Are the Properties in the Same Microsegment?
Start with the location and property category relevant to the assignment. Then narrow the set using characteristics such as area and other available property attributes. The goal is not to find the largest number of records. It is to avoid comparing properties whose differences make the result misleading.
2. Are the Price Measures Equivalent?
The price field in the Resales Dataset is the price at which a property is offered. Treat it as an asking price. Do not present it as a completed transaction price, a rental price or a user-verified comparable unless another data source establishes that status.
3. Is the Record Detailed Enough for the Decision?
Check whether the available cadastral fields, area, description and property characteristics give enough context for the intended comparison. If an important attribute is missing or unclear, keep that uncertainty visible. A structured dataset does not remove the need for professional judgement.
4. What Can the Evidence Support?
Use the resulting set to describe observable resale supply and price positioning. Avoid extending the conclusion into a guaranteed valuation, investment return or future market forecast. The evidence should define the strength of the conclusion, not the other way around.
A Practical Resale Comparison from Question to Next Step
Consider an agent preparing a discussion with a client who is choosing between a resale property and a unit in a new development. The agent needs more than two attractive examples. They need a consistent way to examine how relevant resale offers are positioned before explaining the alternatives. The workflow is straightforward:
- Define the client’s relevant resale segment, including location, property type and a usable area range.
- Review matching records in the Resales Dataset and check the available cadastral and property characteristics.
- Separate asking prices from any rental, completed-transaction or user-verified comparable data used elsewhere in the analysis.
- Remove records that are not sufficiently comparable or clearly note the differences that remain.
- Summarise the observed resale price range and property characteristics, together with the limits of the available evidence.
- Compare that resale context with separately sourced primary-market information without merging the two datasets into one category.
The output is not a system-generated verdict. It is a more disciplined evidence set that the agent can use in a professional conversation. The agent remains responsible for selecting the records, interpreting the differences and explaining what the data can and cannot establish.
A Second Lens for Developers
A developer can use the same resale evidence for a different question: how are existing properties positioned within a relevant microsegment? Reviewing resale offers by location, area and other available characteristics can add context around the competing choices visible to buyers.
This is an analytical input, not proof of absorption, demand or achieved sales. Those conclusions require their own data and method. The Resales Dataset should therefore support a defined comparison, not be treated as a complete market model.
Keep Four Kinds of Evidence Separate
Market analysis becomes unreliable when different price concepts are blended. MLS RealtyHub’s Market Intelligence, Data and GIS module includes several data contexts, but each one answers a different question.
- Resale asking data shows how secondary-market properties are offered.
- Rental asking data concerns advertised rent, not sale value.
- Completed transaction data concerns registered or otherwise confirmed transactions and should not be inferred from an asking price.
- User-verified comparable data reflects information a user has verified and chosen to work with. It is not automatically equivalent to every record in a market dataset.
This separation matters because two records can describe the same type of property while measuring different events. Clear labels make the analysis easier to review and reduce the risk of presenting an asking figure as an achieved result.
Where the Dataset Fits in MLS RealtyHub
The Resales Dataset belongs to the Market Intelligence, Data and GIS module. That module brings together market data, spatial context and analytical tools for professional use. The dataset has a narrower role: it supplies structured evidence about secondary-market offers.
MLS RealtyHub provides the data environment, while the professional controls the question and interpretation. The platform is not a broker, intermediary or party to a transaction. It does not replace a valuer’s method, due diligence or the judgement required to advise a client.
Access Depends on the Way You Need to Work
Users working in property valuation can access a raw file export. Agents and developers can work with processed market data intended for everyday professional analysis. Available permissions and delivery methods depend on the intended workflow.
Access to a dataset within a professional workflow does not automatically include unrestricted raw download, API access or a ready-made integration. Teams that need a particular delivery method should discuss access and scope with MLS RealtyHub before planning their process around it.
What Is Confirmed and What Remains Open
The Resales Dataset is live and refreshed daily. Its fields provide a practical base for examining offered properties on the secondary market. Before using it for a particular analysis, establish whether its scope fits the question, including:
- the sources used for the relevant records;
- geographic coverage and any excluded areas;
- historical depth and record-retention period;
- how records are collected, cleaned, matched or removed;
- the completeness and verification standard of the fields used;
- the level of detail, schema and export formats needed;
- package eligibility, permissions and delivery options beyond the valuation-user export.
These details determine which questions the data can answer reliably. Users should not assume complete market coverage, access to every possible source, legal accuracy or a complete historical series.
Build the Comparison on Evidence You Can Explain
If your team needs structured resale data for pricing, valuation or market analysis, discuss the coverage, fields and delivery method required for your workflow with MLS RealtyHub.