Kylie Davis: (00:01)

Welcome to the PropTech Podcast. It's Kylie Davis here and I'm delighted to be your host as we explore the brave new world where technology and real estate collide. I passionately believe we need to create and grow a sense of community between the innovators and real estate agents and sharing our stories is a great way to do that. So, the aim of each episode is to introduce listeners to a PropTech innovator who is pushing the boundaries of what's possible.

Kylie Davis: (00:28)

And to explore the issues and challenges raised by the tech and how they can create amazing property experiences for buyers, sellers, renters, and landlords. So my guest in this is Sarah Bell. The co-founder of Aire and the self-professed mother of Rita the real estate robot.

Kylie Davis: (00:46)

Sarah is a dear friend of mine who together with Ian Campbell is setting new benchmarks for the administration and CRM practises in real estate globally. But that actually sounds hideously dry and it only just touches on some of the things that Rita can do. So Sara, welcome to the PropTech Podcast.

Sarah Bell: (01:04)

Thanks so much for having me, Kylie. It's lovely to chat with you today.

Kylie Davis: (01:08)

Yeah, it's great. So look, you guys are one of the first to be doing AI in real estate. Tell us how that works. Tell us what it does.

Sarah Bell: (01:19)

So, look artificial intelligence it's one of those suitcase terms that encapsulates a really broad range of techniques. And I was thinking the other day, so many of us in real estate are still talking about what AI is instead of perhaps what it should be. So, artificial intelligence, there's a technical definition called ten-dollar words if you like and it specifically doesn't mention any narrow definition of AI, it doesn't mention a specific technique but really what it comes down to is that artificial intelligence is the ability of a computer to mimic human intelligence.

Sarah Bell: (02:06)

And there's some important things around that word mimicking, it's because artificial intelligence is designed to work through problems and learn and act in a way that feels intelligent but it's important to understand that computers and humans do actually think very, very differently. And therefore they're specialised at knowledge work that is very, very different.

Kylie Davis: (02:32)

So, give me some examples of how that plays out in real estate.

Sarah Bell: (02:36)

Yes, so look, one of the things that Rita does that's really, really time-consuming, not even possible for a human is to do, is to process all of the data in a CRM. She makes decisions about lots of different things within a CRM dataset including whether a note was made by a human or whether it was marketing, whether it was personalised about the client and she then kind of orders everyone in the CRM based on who she connected with and not.

Sarah Bell: (03:11)

Not only though is she doing that but she's also processing all of the information about what's happening in the marketplace with the sales and rental marketplace and looking at event triggers that might start conversations with those people as well as connecting to other kind of data that's transactional and relevant. And putting together a list for agents about who to call and what to say every day, whether that's first to the prospecting by management data improvement. And there's just no way that human beings could process that volume of information at that speed even if you had armies of on and off shore workers doing it. You just couldn't network it and make meaning in the way that Rita is able to.

Kylie Davis: (04:03)

So, I mean, because it sounds fairly simple doesn't it? It's basically look up somebody, work out when the last time was that we contacted them, work out what we should be talking to them about and I guess reach out to them. But when you think about the volume and the scale that Rita's able to do it at, how fast can she do these things? And how thoroughly?

Sarah Bell: (04:29)

I mean very, very quickly and the reality is is that what we've kind of just described is a really simple network of just sort of three points. But remember in your CRM there could be 50,000 people and you might have 5,000 people eligible for a particular conversation or a prospecting activity on a day. So it's not just about finding matches, it's actually about doing a much deeper analysis that will find for you out of that 5,000 the very best 20.

Sarah Bell: (05:04)

Because we're as human beings we're limited, we've got fixed capacity and as real estate workers, as knowledge workers, as providers of service, our capacity is fixed by time. And one of the things we hear from real estate agents all the time is that they don't do more so outreach, they don't do nurturing of their database because they don't have time.

Sarah Bell: (05:31)

So if you've only got time to call 20 people or five people, whatever your time allows is, you want to make sure that you're going to be speaking to the very best people for the very best reason at the very best time, each and every day. And using that time that you have not to do the data mining and all of that networking, which I think we've established we can't do anyway. But you want to be using that time to be in the most valuable part of your business and that's in front of customers.

Kylie Davis: (06:04)

So, I mean I always think that what Rita offers is really interesting because technology for the last sort of, I reckon, at least 10 years hasn't really delivered on its promise to make our lives easier. It's really just made our lives busier and more frantic and given us more things to do.

Kylie Davis: (06:23)

So for everything it took off our plates, it's added five other things that we had to do. But when you've got a Rita running in the background, I guess, managing your internal queries and then helping you target your team, it's starting to really deliver on that promise of tech.

Sarah Bell: (06:46)

That's right and I think, you know what, a lot what agents want to do in a business is stuff that comes intuitively and human intuition is what's at the core of every great example of artificial intelligence that I can think of. The issue is, is that we don't have time, we don't have the processing capability, we don't have all the information available to actually execute.

Sarah Bell: (07:12)

So as humans, we're all pretty clear on the why, right? We want to provide the great customer service, we want to make money, we want to kind of put down deep roots in a community and establish these real estate businesses where people recognise and value the expertise that we have to offer. And we're pretty cool on what the what is as well. Like real estate agents know what the job is but the how is where this all falls down.

Sarah Bell: (07:42)

And technology is kind of been pretty prescriptive traditionally about the customer journey and the very kind of rigid path that that has to follow. And we've kind of done a really good job actually of turning humans into robots if they work through their checklist.

Sarah Bell: (08:03)

In fact, I always laugh at that Google Duplex example, so Google Duplex is the voice interface technology that Google's developed and it has the Google Assistant ringing and having a voice conversation with a real assistant at a hairdressing salon. The most interesting part of that video is not that you can't differentiate the robot from the human, it's that you can't differentiate the human from a robot because she's kind of-

Kylie Davis: (08:34)

She goes into autopilot doesn't she when she's answering the phone.

Sarah Bell: (08:36)

She been customer service trained in an inch of her life and it's just this kind of terribly banal conversation that we keep having as customers and actually there's scope for us to just be so much more warm and empathetic and use our human skills of interruption and judgement to really value add where customers want it.

Sarah Bell: (09:03)

So I couldn't agree with you more. Traditional technology, AI couldn't exist without the back of that. If we didn't have data sitting beautifully and neatly in API first edging in CRMs, then we couldn't have artificial intelligence use that data.

Sarah Bell: (09:21)

But we have to make sure as humans that we're not just kind of taking blindly what technology's telling us to do or sitting there blindly and clicking buttons and following checklists, that we're actually leveraging the suggestions of intelligent technology to be more human, be more interactive, be more empathetic and understanding that it's not necessarily just information or just a goal that customers want.

Sarah Bell: (09:51)

The value is in all of that extra good stuff, all of the grey stuff that we know. All of those local factors and variables and all of that chaos that systems can't cope with but humans can. So work out kind of how you escalate and how you deal with the emotionality of being human and what actually is the service value of that. And then leverage technology to enable doing that well.

Kylie Davis: (10:20)

So I guess one of the things that Rita does beautifully is she examines your CRM and kind of helps you clean it up, as I understand. How dirty are the CRMs of the real estate businesses that you've been in? Or what were some of the key things that you found in them?

Sarah Bell: (10:43)

It's an interesting conversation because clean data means it's quite a subjective thing or it's sort of [crosstalk 00:10:46].

Kylie Davis: (10:47)

Is it like a clean house?

Sarah Bell: (10:49)

You know what I remember, I spent time in the front lines and I remember thinking that the definition of clean or an entry condition of what was really simple. Like if you can clean it, it's not clean right?

Kylie Davis: (11:01)

Oh yeah.

Sarah Bell: (11:01)

So it's sort pretty objective. But with data, I think-

Kylie Davis: (11:06)

It's like housework though it's never done.

Sarah Bell: (11:07)

It's never done. So with data, you're right, it is never done and there's no such thing as perfect data. But what is sitting in everybody's CRM is a bunch of good stuff and a bunch of dirty stuff. And sure we can clean the dirty stuff, some of that can be automated but a lot of it has to do with chaotic humans and what they want to do with it and they're not really sure.

Sarah Bell: (11:34)

But the important thing is identifying that good stuff that's sitting there because while it's in the CRM, and I see it all the time, you just have this kind of equivocal non-qualified segment of people.

Sarah Bell: (11:49)

So, for example, I've got a client they've got 80,000 people in their database and 25,000 of them are tagged in a segment as current buyers. We see this all the time and I often think to myself, if you have 25,000 current buyers then you don't need AI or anything else, you just buy a barbecue-

Kylie Davis: (12:11)

Sell them stuff.

Sarah Bell: (12:11)

Yeah, sell them stuff. Have a rolling auction outside the office every afternoon, it'll be fine. But the reality is, is that there's probably 800 or 1,000 actual current buyers looking and probably fewer still who are actually qualified and ready to purchase. And you've got to find them in the CRM because we're relying on these categories that rely on humans to keep them current and we really can't do that. At that sort of a volume at 25,000 people, you really can't maintain a current customer journey, so they go stale very quickly.

Sarah Bell: (12:49)

I mean out of everyone's customer journey and property is really different as humans, we're aspirational, we're idiosyncratic. And so one of the things AI can do is actually help us and manage so many different types of customer journeys in parallel, intelligently, based on behavioural triggers and other kind of trends that emerge from big data. And put the right person in front of the agent at the time for an optimised level of customer service. That's the goal.

Kylie Davis: (13:27)

Let's just take a short break and now a word from our sponsors.

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Kylie Davis: (14:42)

So I guess what we were talking about before which was the humans behaving as robots and robots behaving as humans. So we've got the AI looking into our CRM seeing, I guess, looking for the triggers in that that are more human than actually the labels that we put on it as human beings inside the database.

Kylie Davis: (15:06)

But then the worst-case scenario I guess would be if you then overlaid that with scripts and dialogues as you've identified your top 20 or your top 10 each day and gave your agents scripts and dialogues to kind of robotically go through pitching those leads.

Sarah Bell: (15:24)

So, there's the technique in AI called multi-agent AI and that's where like you and I don't need to have this conversation Kylie because my AI and your AI can have this conversation.

Kylie Davis: (15:36)

Fantastic.

Sarah Bell: (15:38)

If you think about the Google Assistant... I mean this conversation is human and beautiful and natural but if you go back to that example of booking that haircut or booking into an open home, the reality is is that everyone's mobile phone's Google Assistant could be able to book into the AI of the agency and you wouldn't have that human connection at all. Now some people think that's really good and certainly it's efficient.

Sarah Bell: (16:04)

But like I said at the beginning, I think we need to have conversations about what AI should be or what AI could be. And to do that, we need to understand AI more because my concern is that if we... Rita is engineered to be agent first, she is engineered to put the agent at the centre and to be enabling a supportive technology.

Sarah Bell: (16:27)

But what I kind of see, I guess in the wake of disruptive technology trends, is that we adopted all of this disruption and we put screens in between ourselves and clients. And we've missed out on things like the opportunity there to actually qualify buyers and find out what their situation is. Now we've missed out on opportunities to revisit buyers later on in their buyer journey because they just go into the vortex of darkness of current buyer tag and we never really talk to them again.

Sarah Bell: (17:00)

So I guess with AI, it should be facilitating the human conversations at the right time rather than having that kind of multi-agent system because that's kind of how real estate agents will be replaced. And we make a decision about whether or not we're going to be replaced.

Kylie Davis: (17:24)

So, tell me what your vision is for an ideal experience in real estate that is AI enabled.

Sarah Bell: (17:34)

So, it's quite interesting because this year, in 2019, I've been a tenant and the landlord of quite a few properties. I've sold a home and I bought my dream home.

Kylie Davis: (17:45)

It's been a busy year.

Sarah Bell: (17:48)

Yes. I've kind of worn all the hats as a consumer and it was really quite funny because there were definitely times where AI could have done it better but it's not what I wanted. What I guess I would have wanted from AI is certainly to not know it was there. So the purchase I made, over a million dollars and the house I sold was almost a million dollars and I was paying at both ends for a human service.

Sarah Bell: (18:24)

So, being involved in real estate as well, I didn't need a listing presentation. I didn't need... certainly I have access to property data in my job so I didn't really need a CMA so to speak. But what I did need from the agent was a deep understanding of what the current buyer cohort is looking for, maybe what features of properties buyers are matching most on. I wanted insights into buyer behaviour so that the advertising of the property that we were selling was hitting exactly. Knowing what buyers want generally but on the active cohort in real-time who were looking now.

Sarah Bell: (19:08)

In terms of the seller journey, that's the sort of information that I would want. And that comes from, I guess, a deep understanding of buyers and that can certainly be facilitated through AI. You could have chat surveys as buyers leave open homes, you could have these data analytics looking at demographic changes over time in the suburbs. We could be doing feature matching on things like APA legislation and home loans approved. There's a whole lot of cooperation. We keep looking at PropTech you and I, but there's actually a whole lot of cooperation that could happen between PropTech and Sintech to actually really look at the drivers of residential real estate in Australia.

Sarah Bell: (20:01)

So there's great AI out there that I used to look at the development potential of the property I bought and to demonstrate the development potential of the property I sold. We met a great guy at the PropTech Summit in Sydney who had an architecture 3D scanning AI that was not only looking at the development potential but could rapidly mock-up scenarios of what was possible and represent that data visually in a way that any human could understand without specialist technical knowledge.

Sarah Bell: (20:41)

And so I think the point of AI is to make the process much more simple and much more human and much more understandable, not to make it more complicated because technology's already done that.

Kylie Davis: (20:54)

Yeah, it's time to simplify it, not complicate it. So, tell me about some of the projects that you're working on because Rita just had her second birthday, she did. And you've now got some funding behind you so you've got a nice clear runway. What are some of the most interesting projects that you've worked on in the real estate space? So, if you're able to say, who've you been working with and what were some of the things that you discovered?

Sarah Bell: (21:24)

I'm not sure exactly what I'm allowed to talk about, to be honest.

Kylie Davis: (21:30)

Well, don't mention any clients then, just talk about the project generally.

Sarah Bell: (21:34)

There's a lot of spadework going on and I think... you know, we've had a lot of learnings in the last two years that are really interesting. And how they've been, I guess, validated or triangulated by what we see in the data, is pretty cool. So, one of the things that we hear all the time is that people want AI or big data or some kind of homogenised consumer data lake to tell them when people are about to sell.

Kylie Davis: (22:12)

The magic bullet.

Sarah Bell: (22:13)

The magic bullet, right. And you kind of have to play out in real life how that would work, right? What are the triggers? The triggers to sell that kind of force people into kind of active property mode are often very, very personal.

Sarah Bell: (22:33)

So, I was trying to work out what would be the absolute, like your safe bet silver bullets, that someone was going to think about selling. And they are just your ambulance-chasing specials. They are things like purchasing pregnancy tests or visiting funeral homes and stuff like that and the-

Kylie Davis: (22:53)

Changing your status on Facebook to it's complicated.

Sarah Bell: (22:56)

Googling divorce lawyers. People ask if we could do that and it's just... well technically there are some, you know, no technical but certainly a lot of regulatory barriers to doing that. The idea that you, without a relationship, are entitled to just be involved in somebody's very personal triggers of real estate is quite bizarre and quite phenomenal.

Sarah Bell: (23:26)

It sort of... it's almost too crazy and too yucky to think about. I don't know if you've ever been, probably for both of us in a previous life, but if you've ever kind of had someone just known too much about you at a party or something, it makes you resist and want to run away rather than run to them, right?

Kylie Davis: (23:50)

Yeah, yeah. It's not building trust.

Sarah Bell: (23:53)

No. And all of that data, what is more valuable than I guess breaching somebody's privacy, is looking at the trends that comes from big data and certainly that is half of the story. So, all of the aggregation of social factors that might make someone a statistical chance or have somebody approaching a some kind of statistically relevant trigger that they might be entering a real estate zone.

Sarah Bell: (24:32)

And if you were wanting to answer the question, is somebody ready to sell? Then that statistical inference would have a pretty good success rate. We know things like the average tenure of a home is 11 years, the average Australian marriage is nine years.

Kylie Davis: (24:50)

Oops.

Sarah Bell: (24:54)

And we know that these things do impact the sales transaction. So you can match enough social data to kind of predict medians in different suburbs and that's interesting and that's cool. But I don't think that that's the most helpful question because we know already that 5% in any given marketplace is going to sell in a year. So, what actually becomes cool is when you look at the data that exists in the relationship between the agent and the property owner. Because interestingly that is a much stronger coefficient or a much stronger inference where there is strong interactions, artefacts of the relationship, artefacts of the history of service. That's a much stronger relationship if you want to answer the question, is this person likely to list with me?

Sarah Bell: (25:50)

And if you're a real estate agent, you might think that the question you want to answer is when is somebody going to sell their house but actually the question that you want to answer is when does somebody that I know, going to enter the real estate zone and will they list with me? And a much more complex question requires a much more complex data model. And unless you have that relationship data, you're only ever going to get half the story and it's going to be a generic story that's available to everyone else. The relationships that real estate agents have are the key differentiator between them and the other coloured signs down the road.

Kylie Davis: (26:29)

Because I guess that's symptomatic of the real estate industry to date so far being really focused on the bang not the courtship stage. Like just wanting to get completely transactional immediately, isn't it? I know. Getting all transactional rather than thinking about... so our conversation around big data has always has been to data as an industry, how do I find out when someone's going to list their home. When what you're saying is that it should be about when is someone that I know going to get into that relationship zone so that I can offer to be of service to them.

Sarah Bell: (27:15)

Yes.

Kylie Davis: (27:15)

Because they already know me.

Sarah Bell: (27:17)

When am I relevant? When am I most relevant for this person?

Kylie Davis: (27:20)

When can I help?

Sarah Bell: (27:21)

Yeah, exactly because you know we talk about what is a customer's request for service from you as a service provider. That's kind of what you should be looking for and we know what inbound request for service look like. They look like, "Hi, can you come and tell me what my home's worth?" Or "I am thinking of selling." or there are also things, like, "Hi, please don't email be ever again." They're all customer inbound requests for service.

Sarah Bell: (27:50)

So we know what these inbound requests for service sound like and when they come in, then we know what to do with it right? But that's kind of not enough business, that passive thing and we're not building anything we're just reactive. But to be of service and to be truly relevant, an outbound request for service is where we anticipate the needs of a customer and be truly helpful at a time when they need us.

Sarah Bell: (28:27)

When you look at... and a really simple outgoing request for service would be if a house come on the street where you know a property owner and they might see the signboard, they might not be bothered to give you a ring and ask you about it. But you know them, you're their real estate professional, so are you going to leave it up to the other agent to advise them of the price or are you going to be proactive and offer them a comparison given that that new property's just dropped into the market and how does that shift the value of theirs?

Sarah Bell: (29:07)

So that would be an example of anticipating a passive request for service and it's one of the things that we either kind of matches up and puts in front of agents and it's super highly converting and it's a great way to not only generate some business or generate some appraisals. But it's also a defensive strategy or a defensive play to make sure that that relationship that you've gathered and you've nurtured and you've invested in isn't relinquished to the agent down the road that's put on that property, just through your kind of missing the ball or negligence of that relationship.

Kylie Davis: (29:51)

So I guess what we're hearing in this too is that you need to have something else to say to a client rather than, "Would you like to sell your home?" You need to be a little bit more multi-dimensional than just interested in that transaction.

Sarah Bell: (30:07)

Yeah, that's right. I'll jump back to our conversation about clean data. We've kind of collected names and phone numbers and addresses and stuff and that's been really good. But in terms of what we do with it, it's just sitting around like hoarded old newspapers in most CRMs that we look at because it's not actually being used.

Sarah Bell: (30:30)

So one of the things that we Rita looks at and is able to analyse, the metric that's unique to her, is she's able to look at the actual kind of personal engagement between the agent and the community of property owners and report back on that.

Sarah Bell: (30:44)

What we see quite consistently, sadly, is that there's often a huge proportion of the database that haven't had a personal contact sort of within six months. The other thing we track is lost opportunities and what we know from that is that after sort of a period of 90 days, if there hasn't been a personalised contact, we see a much greater risk or a high spike in that business potentially going to a competitor.

Sarah Bell: (31:15)

So, the relationship, the business case for nurture over time is so compelling based on the data that we see and I think for most agents it's just a matter of finding that list because the majority of data is... We call if the vortex of darkness or the limbo engagement sign but it is the equivalent of just stacks and stacks and stacks of newspapers that have been hoarded over time and we don't know what to do with them and they're kind of organised-

Kylie Davis: (31:48)

But we're too scared to throw them out.

Sarah Bell: (31:50)

You can throw them out. We've changed the way that we organise them five or six times but none of the times are really relevant. We've got so many tags that all of the segments are kind of segments of one. So, it's about kind of... Rita's job is to really make sure, to salvage those opportunities and those kind of relationships from that vortex of darkness. Even if it's been a long time between engagements at least reignite that conversation.

Kylie Davis: (32:30)

So, the best way to think of using Rita is not to help you really zero in on the absolute perfect algorithm to identify your next listing. But more to help you, at greater scale, understand who's already in your database, the background of the relationship that you've had with them and to identify times when it's the right time to reignite that relationship and to be back of service again. Would that be fair? And through that get more listings.

Sarah Bell: (33:02)

Yeah. And it's interesting. So I see, we've chosen the most unsexy white space to work in, right? Everyone's hot on why, everyone's hot on what, but no one wanted to tackle how. So, we've decided to tackle how. There's a lot of bright, shiny tools at the top of the funnel as well in terms of gathering data and marketing that agents really love, there's a lot of money spent on there. And in terms of the bottom of the funnel, like those hot listings that are about to drop now, here's the thing, agents are really good at that.

Sarah Bell: (33:39)

So, if you've got a seller that's about to drop in or you meet a buyer that has a property to sell, agents are very, very good at the short-game. But if you don't have enough listings to ensure that your listing volumes can continue just by virtue of the short-game, then you need to get listings from somewhere else. And where else are they going to come from but from the property owners in the community where you work?

Kylie Davis: (34:13)

True. So, I guess-

Sarah Bell: (34:14)

[crosstalk 00:34:14].

Kylie Davis: (34:14)

That's it.

Sarah Bell: (34:16)

I'd love to hear about it but-

Kylie Davis: (34:19)

Drop mike.

Sarah Bell: (34:20)

But this kind of middle of the funnel, I know these people. Now they're selling, the journey between you were a stranger and I've listed your house is unsexy and it's work. But what we want to make that work feel like is that you are totally supported, you are totally enabled and you're approaching every conversation with something relevant to say and being of service so that you build advocacy over time. And your position for the business whenever that person that you know falls part of that 5% that is selling in a year.

Kylie Davis: (35:01)

Let's just pause there for a moment and hear a quick word from our sponsors.

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Kylie Davis: (35:40)

The technology that we use every single day now has been around for about 15 years plus and we know in our waters that in fact it's not the shiny buttons that do the trick, it's actually the execution. You can give the same piece of tech to five different people and they'll use it five different ways and to five different degrees of success. But even though you say it's not the sexy bit of how we use it, I love that AI is actually now doing some of this heavy grunt work of actually helping us execute better rather than just being another bauble that we put out in the-

Sarah Bell: (36:24)

Back in the good old days, like in the human era of real estate, I was sort of taught real estate by my father-in-law who started the family business in 1979 back in the days when things were quick and dirty. He had this box of people, almost everyone in the area that he knew on his desk. That was the CRM and he called, you know, grabbed a giant handful of cards every day, called through them and put them at the back. And to be honest, it was an indisputably simple and elegant system. And he spoke to everyone so regularly and there was no mystery then as to why that business... it's just an independent family business, had that prolific market share for 35 years till we sold it.

Sarah Bell: (37:14)

But what happened is that we kind of took that focus of just even individualised attention on each one of those cards and kind of gave it up for this promise of technology that you could push a few buttons and with one push of a button you could send the same email to 50,000 people. And that sounded like a really efficient way to work and it is efficient if you want to take efficiency as in, with no quality kind of indication on it. And that's the nature of so much of our human behaviour actually is that once you put a simple number on things, we tend to cheat it. And I think that kind of bulk broadcasting capability that technology gave agents was a really good way for us to cheat out of the hard work. And it faked the quality of the relationships.

Kylie Davis: (38:14)

Exactly. So what are three quick ways that your clients are using you at the moment? Are they using you to clean up their CRM? Identify?

Sarah Bell: (38:30)

Understand the CRM. So, one of the first things we hear is, I've got all this data and I don't know what to do with it.

Kylie Davis: (38:36)

Come and help me stop being a weird, hoarding person.

Sarah Bell: (38:40)

Absolutely. And the very first thing that we do is we have an analytic session. Rita not only provides analytics on all of the contacts in the CRM, which you can drill down on by user. So each of the agents can kind of diagnose their mini business within your business, a very helpful tool. Even things like Rita will report on the geographical spread of your data and if you're an agent, that's really important because you need to know where you want to be leaving your shoe leather.

Sarah Bell: (39:12)

We've had agents that have a huge offline marketing spend in an area where they don't really know anyone and so just that kind of realignment and diagnosis. We have a look at the distribution of data throughout an agency, that's really important to understand kind of the power of the data or the potential of the yield of all of that newspaper that we've been hoarding.

Sarah Bell: (39:41)

Rita also is monitoring the market constantly. So those days of having to sit there and sort of scroll through the portals to make sure that you're on top of the deals or even drive around and sign count like they did in the old days. Rita's surfacing to agents the most relevant deals based on where their data actually is.

Kylie Davis: (39:59)

Wow.

Sarah Bell: (40:00)

And also calculating the yield of those listings whether they're one listing or [inaudible 00:40:08] listings and what we're finding out of the gates is that some customers who are complaining about a bad market are actually losing millions of dollars a year in GCI from people they already know, who they have a relationship with, who they're just simply not engaging with and that's going to other businesses.

Sarah Bell: (40:28)

So, that's probably... like I said, there's no two businesses the same, there's not two marketplaces that are the same, so just understanding the context of all of that is really important so that we know what the strategic imperative for redoing a business is.

Sarah Bell: (40:48)

And then she works as a personal assistant so she does all of that data networking and connection and mining. And most importantly she is optimising that, so running prioritisation algorithms through all of those relationships to make sure that we start with the best and cleanest relationships that are most likely to yield. And then we work through that over time, not contacting the same people too often and all of that spacing and analysis... she just does all of that for you.

Sarah Bell: (41:24)

For agents, they get an email every morning from Rita, here's people you should call. They don't think, they just do and they do a great job because that's why they got into real estate. No one got into real estate to process data. Maybe me, maybe you but no one else. And then she's also being used to provide an instant response to buyer inquiry through the portal.

Sarah Bell: (41:53)

So one of the things that I guess terrifies me, is someone vested in the industry is that your report that your did at CoreLogic for the 68% of buyers just thought that agents had little or no interest in helping them. And if you don't want to believe your report, you can believe the hard data from realestate.com who told us that 48% of buyer inquiry just simply wasn't even responded to.

Sarah Bell: (42:18)

We have customers that have done their own kind of market research on this and some of them have said that's it taken up to 16 days to get a response to a buyer inquiry when they've mystery shopped their competitors.

Sarah Bell: (42:32)

So, Rita can intelligently respond based on the customer inquiry, the information they actually want and have asked for. But more importantly, behind the scenes, she is augmenting the database with that inquiry, understanding that that person has come into real estate zone. She's connecting them to properties that they own or that they may own and then briefing the agent with all of that background knowledge so that when they do call the buyer, knowing that the buyer's already having instant response, but when they do call the buyer, they are in an informed position to properly qualify and provide a valuable service when they do speak with the buyer.

Kylie Davis: (43:12)

So, do you think Sara, two things... Do you think we'll have our own personal Rita in real estate as individual agents in the next couple of years. And supplementary to that, how could Rita disrupt or enhance franchising? So, taking all of these to this [inaudible 00:43:31].

Sarah Bell: (43:30)

So, the thing about Rita is that she's an assistant. So, whether your job is the managing director of a franchise or you're a buyers agent just starting out supporting a lead agent in a business, she understands what your job it and she gives the information that you need to know. So, that buyer example, that's how she will be supporting that developing agent.

Sarah Bell: (43:59)

And at a franchise level, and we do have some franchise level clients, the insights and the precision that they're able to make decisions. But not only make decisions, it's been quite interesting. What emerges from the data in not only the data that exists but how people are interacting with their own data and so what's come up is actually these precision opportunities for coaching. Because when we start to look at the results of these phone calls, just different trends emerge in different marketplaces.

Sarah Bell: (44:35)

Whether that's on a really granular level, Steve, in one office might have a really high success at getting appraisals from his Rita opportunities. And Megan might have less success. And so we can analyse what Steve's doing and what Megan's doing and for the business owners, we can see that Megan might not be using a channel that's successful. So Megan might not be calling people, Megan might be just emailing people. And if Megan could get more comfortable on the phone, then she might be able to replicate Steve's success.

Sarah Bell: (45:14)

In other offices, we've just been able to make just really minor tweaks, like what time of the day they're calling based on the success that emerges from agents. When you know the kind of conversations that agents are going to be having based on the Rita suggestions, you can really get some precision coaching around how those conversations can go. And as an agent, you can get really, really good at all of the different scenarios that happen and you can get really comfortable with that phone call rather than trying to have this purposeless, blind, followup phone call all the time that no one wants to make or receive.

Kylie Davis: (45:52)

So, I guess at the moment, Rita's handing off those insights to a human who's then doing the coaching. But is it possible that in the future, Rita will actually also be doing the coaching?

Sarah Bell: (46:07)

Maybe.

Kylie Davis: (46:07)

So, it's not will the robot replace real estate agents, will a robot replace the coaching industry?

Sarah Bell: (46:14)

Well, if you look at all the AI's geared up prediction right. So if you take probably the most prolific AI that I can think of is Netflix. It's geared at predicting whether or not you're going to like a TV show and it gives you a confidence score about that. And it matches all of these different variables and it's tracking for you how much you're watching it and all the rest of it.

Sarah Bell: (46:40)

So, yes certainly. The human doesn't have to execute the opportunity that Netflix gives them to watch a TV show but with that confidence score and the more and more information you can give to somebody to kind of help them interpret what that opportunity might mean, then yeah, we can seek to not just change behaviour but improve behaviour to again optimise it, make sure that agents are really doing the most effective, not efficient, effective activities at the best time, for the best reasons.

Kylie Davis: (47:23)

Very cool. Hey, we're probably going to have to wrap it up there because we have been going for ages.

Sarah Bell: (47:29)

Talk about AI.

Kylie Davis: (47:33)

And I can talk with you. It's been absolutely fascinating. Thank you so much, Sarah, I'm really looking forward to seeing what happens to Rita as she moves onto her third, fourth, fifth birthday. Thanks so much for sharing your time today.

Sarah Bell: (47:51)

Thanks for having me.

Kylie Davis: (47:52)

So, that was Sarah Bell, the co-founder of Aire and the mother of Rita, the real estate robot who is seeking not to replace real estate agents but to improve the quality of relationships that agents can have with sellers and buyers and to grow their business through connected and well-timed deliveries of service.

Kylie Davis: (48:11)

I really love the Aire story. Rita is primed to become one of the biggest successes of the Australian PropTech industry in my view and her star is only just on the ascendancy. She's recently received a million dollars in funding through PieLAB Ventures and the Queensland Government combined. And she really sits in that sweet spot of big data, automation, relationship building and I love that her goal is to create more human experiences and to get us back to being people again and connecting.

Kylie Davis: (48:43)

The agencies I know who are using it are reporting extraordinary process and structural efficiencies and these are translating into huge financial wins. And she really is being extraordinarily effective at identifying not necessarily the immediate listing but at identifying the relationships that you already have and then helping businesses restructure around those relationships.

Kylie Davis: (49:06)

So you can find more information about Rita and Aire in our show notes and if you have enjoyed this very first episode of the PropTech podcast, I'd love you to tell your friends, drop me a line or write us a review. I'd like to thank my audio support, Charlie Hollands, and our Sponsors Smidge Wines, official wines of the PropTech community and HomePrezzo, creating content from your data. We really couldn't do it without you.

Kylie Davis: (49:30)

So thanks, everyone. Until next week, keep on PropTeching.