Real estate generates many documents, measurements, and decisions. It does not automatically produce reliable, interoperable data available at the right time. Between the promise of a PropTech solution and its daily use lies an integration effort often underestimated.
A PropTech solution is adopted when it connects with a precise business decision, relies on sufficiently reliable data, removes a real source of friction and respects professional responsibilities. A sound pilot tests the entire chain: collection, verification, interpretation, decision-making and feedback.
A convincing demonstration is not yet a business practice.
PropTech solutions can accelerate data collection, bring dispersed information closer together, automate checks, simulate scenarios, or make a portfolio more readable.
The demonstration often follows an ideal path: available data, consistent formats, trained users, stable processes. The reality on the ground presents leases drawn up at different times, competing reference frameworks, missing information, shared responsibilities and urgent decisions.
The question is therefore not only what can the solution do? You must ask under what conditions will our organization be able to obtain, understand, and use this result?
RICS emphasizes that the challenges of sectoral digitalization relate as much to change management, leadership, skills and culture as to the availability of technologies. Its 2024 survey also stresses the quality, structure and coherence of data in the built environment.
Technology becomes useful when it reduces the effort required to make a better decision, not when it adds another screen to the process.
Four frictions often determine a project’s fate.
The data exists, but not in a usable form
Information may exist in a PDF, a local spreadsheet or a property manager’s memory without being available to the system. The first step may be less about deploying AI than about defining a reference dataset, mandatory fields and responsibility for updates.
The new tool is added rather than replacing existing ones.
If users have to enter data twice, export and then re-import it, or retain the old file as a precaution, the cost of use increases. A solution that performs well in isolation can degrade the overall process.
The decision remains ambiguous
A dashboard can multiply indicators without clarifying what needs to be decided. Business teams need thresholds, priorities and escalation rules, not just visualisations.
Trust is not built
An estimate with no visible source, an unexplained score or an overly frequent alert will be bypassed. Trust develops when users can understand the data, test its consistency and flag an exception.
Connect each data point to a decision and an owner.
A simple approach is to start from the decision rather than from a catalogue of features.
| Decision | Required data | Human control |
|---|---|---|
| Prioritise work | Status, costs, risks, obligations, usage | Verify assumptions and local constraints |
| Compare assets | Shared reference framework, history and market context | Interpret the gaps and quality of sources |
| Manage occupancy | Sensors, calendars, seasonality | Protect privacy and explain the limits |
| Track ESG performance | Consumptions, areas, periods, factors | Check the scope and missing data |
| Prepare a valuation | Transactions, characteristics, condition and market | Maintaining professional judgment and responsibility |
In its work on the digitalization of transactions, RICS highlights that data standards, governance and professional oversight are essential, and that digital tools should enhance rather than replace human judgment.
This linkage avoids two pitfalls: collecting data without a defined use and making decisions based on indicators whose creation no one controls.
Test the complete chain within a controlled scope.
The pilot must include difficult cases. A solution tested only on clean data produces a technical proof, not a usage proof.
Ten questions before buying or expanding a PropTech.
- Which decision or specific pain point do we want to improve?
- Who will use the result, when and in which tool?
- What data is required, and who guarantees its quality?
- Which work actually disappears?
- What new work appears: oversight, configuration, maintenance or support?
- How will exceptions be handled?
- Is the result explainable and traceable?
- What technical or contractual dependencies are we creating?
- How will we measure quality, not just speed?
- Who will be able to stop or correct the system?
The real estate sector does not need to oppose historical expertise and innovation. It needs to connect them. A robust PropTech makes practices more transparent, data more reliable and decisions better supported, while keeping limits and responsibilities visible.
Reference sources
- RICS, Tech Partner Programme Survey 2024
- RICS, How data is changing decision-making in real estate investment
- RICS, response on the digitalisation of real-estate transactions, 2026
FAQ
What is a PropTech company?
The term refers to technologies and innovations applied to real estate and the built environment: data, business software, platforms, sensors, automation, digital services, or artificial intelligence.
Why does data quality hold some projects back?
Incomplete, heterogeneous or hard-to-link data increase manual checks and reduce confidence in the results. The tool may then add an interface without simplifying the decision.
How to choose a PropTech pilot?
Choose a frequent decision, a limited scope, identified users, accessible data, and a measurable outcome. Integrate exceptions and the cost of control from the outset.
Should professional judgment be replaced?
No. Technology can structure information and inform a decision. Accountability, understanding of context and assessment of limitations must remain clearly assigned.
