Buying and Selling Homes – a DataPal Use Case
Our team have been looking lately into the life event of buying, or selling, a home because this is a real-life scenario, the improvement of which proves the concept enshrined in DataPal.
Our Scenario
We wanted to explore how a new approach to the provision and flow of information, using the DataPal principles and platform, can addresses the challenges which characterise a very frustrating and opaque process for many stakeholders.
We talked to individuals and families we know about their experiences buying or selling properties, and we drew upon the considerable expertise of estate agents and lawyers in our professional network.
Prompted by one contributor’s observation, namely that:
‘People often begin the process without knowing, or having, all of the required information they will need along the way’
We concluded that there is an opportunity to improve this is several ways.
Regarding the information required from both buyer and seller perspective, its whereabouts will range from scattered across paper documents, held by agencies on our behalf, or digitised in an app or organisation web site. As such it may well not be easily accessible.
And many of those items are things that people won’t have had need of before; so there are new requirements as well as better accessing and presenting data we already have.
Our Method
So we started to map out every distinct job to be done - from start point through to conclusion – and (importantly) from both Supply and Demand perspectives.
We adapted a process mapping method to capture the analysis.
We included residential Buyer and Seller and all other contributors such as estate agents, surveyors and lawyers distinguishable by their distinct colours.
The result is a detailed schematic – the highest level is shown below.
Buying and selling your homes - no less than 59 subsidiary processes
Reasearch based on the current process in Scotland
It illustrates no less than 59 subsidiary processes and their various information flows all of which need to execute accurately to deliver the outcome.
This method enables us to explore, consider and confirm:
• have we captured everything – from trigger to conclusion?
• where are the many essential information flows and exchanges?
• what are the biggest blockers or unpredictable steps – those with the propensity to complicate things, or simply cost time and money?
That is the first step in making a benefits case for robust data intermediary services, such as those devised by DataPal, working for the buyer or seller to take much of the strain.
Building on this, we are close to identifying candidate processes which are simple enough to be completed by AI-powered agents, IF (note that’s a big IF !) the information needed were to be both accessible and guaranteed authentic.
What This Means for Process and Stakeholders
Thinking ahead to how the whole landscape could be impacted if this use case were operationalised, we envisage several game-changing outcomes:
Huge leaps forward from that ability to understand when the needs of the two parties intersect; alerting all stakeholders to exactly what data is required, when, and which of the parties is responsible for providing and verifying it.
Deliver transparency and clarity on the overall journey :
Set out the series of ‘jobs to be done’/ steps that the buyer must take and build a schedule for that.
Set out the equivalent steps that a seller must take and build a schedule for that.
Derive metrics like ‘estimated completion date’ for each sub-process; and thus for the entire end-to-end scenario.
See the ‘happy path’, in which every step happens efficiently, on schedule for both parties.
Also, know the risk of any ‘unhappy paths’ when one or more sub-processes fails to meet schedule, or indeed fails to complete at all.
Feed data into ‘whole of market’ research and understanding.
Importantly, support a better understanding and acknowledgement of those key processes where the needs of the parties do not “align” quite so readily, such as in price or terms negotiation.
Here is where regulated professionals really demonstrate their expertise – the human touch and creative thinking which makes the difference, and thereby delivers true value to, their clients.
This can also make a big difference to the emotional stress of the decisions needed by both seller and buyer when circumstances demand.
Once the data inputs to, and outputs from, a process are known, we see many opportunities for automation and agentification; i.e. the use of AI enabled software agents. Agents can and will operate on behalf of both parties with specific briefs/scope and guardrails.
An additional benefit of introducing smart personal data services into the overall home buying/selling industry would be that data built during the process lives on afterwards and each property builds its own more comprehensive and traceable data-set.
Conclusion
Proving the Concept with this first Use Case confirms our hypothesis that personal data services and AI-powered agents, fiduciary to each relevant party, have huge potential to support people through, and significantly streamline, the home buying/ selling process.