E88: Using AI to Predict the Future Cost of Rent

“EY: AI is definitely there. We try to use it a lot in our models and also, this is a growing field. So, there’s also, this is called like, causality, right? We should see those relationships, and there are some works to improve these types of models where you can also see the relationships. Let’s say, its current form, it’s really, really useful to make predictions. So, should we see that more in policy-making or in the real estate industry? 100%.”

[INTRODUCTION]

[0:00:31.1] ANNOUNCER: Welcome to another episode of Sync or Swim, brought to you by Rentsync. From operational challenges to marketing mastery, we uncover the strategies in technologies and all things PropTech. So, let’s dive in as we explore the trends, tactics and insights that define the future of multi-family investments. Sync or Swim starts now.

[INTERVIEW]

[0:00:52.9] GL: Okay. Hi, everyone, my name is Giacomo Ladas and I’m really pleased to welcome Erkan Yönder, an associate professor at Concordia’s John Molson School of Business, whose recent study has caught the attention of pretty much everyone in our industry, based on their prediction of where asking rents could reach, and I guess, we’ll talk about it in full but in short, the study used machine learning, which found that asking rents in Canada’s largest city such as Toronto and Vancouver could go as high as CAD 5,600 a month in Toronto by the year 2032, and in Vancouver, over CAD 7,700 a month.

So, Erkan, thank you for joining me today, we’re going to go into this a little bit and more detail but I’m really looking forward to the discussion today. Your articles and seeing you all over the place, it’s been really interesting and it’s a perfect fit for what we do here. So, it’s great to meet you to have this conversation.

[0:01:37.0] EY: It’s my pleasure. Thank you very much for inviting me.

[0:01:39.1] GL: So, I think let’s start from the beginning then, you know, for our listeners, what kind of was your inspiration for this research and how did it come to exist?

[0:01:47.0] EY: I mean, that’s a great question and that’s something really I want to discuss and kind of we missed out discussing this in the previous interviews a little bit. So, you know, I’m joining, like, or attending different industry events across Canada. Like, in Toronto, like Montreal or in some other places. I come across with some industry people and you know, in the end, they are academics. 

So, we do lots of research on the real estate market so that’s my specialization, I mainly work on real estate finance, and in some of those events, we came across with some people from Equiton, specifically, Aaron Pittman. I mean, I thank him for this work as well. So, we came up together and then we discuss, you know, what we can do together because we do some academic stuff and that’s really relevant too, to the industry, and then there are some questions that the industry is really interested in. 

And we sat down together, we developed a partnership with Equiton and Concordia University, and we started to discuss, you know, what to do, and of course, Equiton is also like a real estate company in the field. I said, “Okay, let’s focus on the Canadian rental market.” Because you know, it’s really interesting for everyone. There are many unknowns going on and of course, we wanted to look at it from like a massive perspective.

A real estate company perspective but also it attracted so much attention, you know, with respect to affordability problem or the immigration policy and such. So, the idea really came up by partnering, developing, and academy, a massive partnership and with that, that’s kind of, we came up with this idea.

[0:03:22.9] GL: No, that’s great and maybe, before we kind of get into the findings, can we maybe have like a little level playing field of start by explaining, what is the current state of Canada’s rental market and how does it compare with other G7 nations and you know, maybe particularly in terms of supply and demand, you know? Where do we sit and then we can kind of get into what did you see with your research?

[0:03:44.3] EY: I have two answers for this question. Like, one answer is like, you know, we had this preparation talk and then I saw your question, “Okay, I didn’t check the statistics for G7.” So I checked before getting to our meeting but I also have a personal experience, like, personal answer to that question. You know, I had my education in Netherlands and time to time, I come up with my supervisor and like, for a very long peer reporter, Piet Eichholtz.

And this summer, we came across, and every time we come together we ask ourselves, “Okay, what is the next big thing to research in real estate?” So, we have discussion almost every year, regularly, and I ask this time, and he said, “Affordability.” So, affordability is a problem, you know, for Canada, it’s a problem for the US, for many European developed countries. I mean, I’m from Turkey, it’s a very big problem in Turkey. 

So, it’s everywhere. So, it’s a really challenging question, and he said to me, what he doesn’t understand is, or we don’t understand is, you know, there is this affordability problem and there’s an easy answer. Why don’t we build more? So, it’s kind of a puzzle going on that. So, it’s not unique to Canada. So, it’s kind of everywhere but maybe Canada’s case is a little bit different from other G7 countries. 

Like, for instance, when I was thinking about this question, like for instance, there’s a report by Scotia Bank, I think released in 2021, and if you look at the passing, existing housing stock per person, Canada has the lowest number across G7 countries. Overall, as I said this is a global problem, but Canada has also very strong immigration policy. I mean, we can discuss this maybe, it’s contributing to the supply, sure, like supply, demand, mismatch. 

But it’s again, like, I wouldn’t say this is unique to Canada but maybe it’s a little bit bigger problem for Canada than other G7 countries.

[0:05:32.5] GL: No, it definitely is, and we’re seeing it all the time with, you know, the recent slowdown in international students coming in or to maybe soften demand a little bit. It’s number one thing that’s happening right now is how do we get more supply into the market or maybe if we’re not going to bring enough supply, we just lower demand and that seems to be the balance happening right now. 

So, you’ve probably spoken about this much but let’s get into your findings then. What exactly did you find? I gave a little bit of a lean-in in the introduction but what were your findings and what part of it was kind of surprising to you?

[0:06:03.0] EY: Okay, I want to start that I’m very happy that this is a podcast, you know? Sometimes I make long interviews and then some part of it is being released.

[0:06:11.0] GL: No, this is the long journey here.

[0:06:12.6] EY: I want to be very honest. So, first things first. I mean, our intention was not really to scare anyone or you know, to give scary numbers. To be honest, I mean, I’m a researcher in the end, I’m a professor at the University, and in the end, like predicting the future is not an easy task, let’s start with that. So, it’s really you know, every month, many analysts make predictions on stock prices on the economy, and then, like, they update all the time. 

So, first of all, this is like – and also, this is a very long-term forecast. In the report, we did like a 10-year forecast but the idea is this year. So, it’s not – I never really spoke about the exact dollar values because it’s really not my personal intention but in the end, you know, to make it better understandable, we release those numbers, but the time – the aim this year is really we want to look at the historical data. 

We are focusing on census subdivision, so we have more than 300 different locations across Canada that we collect the data, and we have different census datasets, we collected data from CMHC on completions, on different factors. So, we have a bunch of data and what machine learning model does or what we do in this project is like, you input those where there was different observations, and the model kind of detects the pattern. 

So, I think that’s the best way to read our studies. So, the model detects the pattern and they are government projections. So, what we wanted to do is like, our model determines each factor affects the rents, and we kind of use the government projections for the future and plot those future projections into our model, and that gives us the future ranks. So, that’s more or less what we wanted to do. 

So, to read the results in simple terms, there’s a pattern going on in Canada, especially with respect to immigration and with respect to different demographic factors, and these patterns indicate that if you continue to do things in the same way, we are heading to really high rental values. Maybe then, the affordability problem kicks in, like then it becomes very unaffordable. So, I think the focus should be really the pattern, the trends and the data. 

And then, the question becomes, “Okay, how can we change this trend in the data or the structure that we developed in the housing market? How can we can change this?” I think that’s the critical question that we wanted to address.

[0:08:36.9] GL: And yeah, that’s a really good point. Like, we’re not trying to scare anybody with these numbers but you can understand why someone might take it that way because you know, if you think someone living in, who wants to move to Vancouver in the next five to 10 years, you know, who is paying the CAD 7,500 a month to live there, right? So, would you call it more of like, maybe not a wake-up call or a warning? 

But maybe something like that, maybe that should be used as, look, it might not be as high as you predict or it might be higher. Who knows what? But if we continue this trend, the effects of it are going to be – they’re not small effects, right? It’s something needs to be done before we get to a point where we can’t go back.

[0:09:09.4] EY: Exactly, that’s what we want to do, Giacomo. It’s really our intention, it’s not – So, these numbers might not, like, happen and maybe not, I don't know but in the end – and by the way, this can happen, let’s also take it, discuss this a little bit, right? I mean, if you look at different markets in the US like New York, if you will get Hong Kong, I mean, also, if you would get the numbers in Canada, I was discussing with my wife yesterday.

So, when we first came to Montreal and we rented like a unit for CAD 2,000 was 2018, and now, we are talking about CAD 4,000 for that unit. I mean, it’s already happening in some way, right? So, we see it in the markets, so this can happen. So, it’s not – I cannot say it won’t happen, so this can happen but then the question becomes, “Can we change this?” I mean, what should we do? And also, our project is not related to or we don’t want to discuss the immigration policies. 

So, it’s not about discussing the benefits or the cross of immigration but it’s really about numbers. So, this is like, immigration, other factors create a demand and then we have some supply numbers and we use those supply numbers, and if you combine them in a model, and then if you do some projections with machine learning, this is the outcome. So, in the end, like, this is a possibility.

[0:10:27.4] GL: Yeah, I believe reports said that by 2027 and you know, Montreal rents could be somewhere like CAD 3,300 a month and then 2032, we are surpassing that CAD 4,000 a month rent and it’s interesting when we talked to people in Canada too because you know, I do a lot of these media hits because we post our rent reports of just what asking rents are currently, and you know, I talk to people from Vancouver, Montreal, Toronto, and the thing that I always make note is like, these are international destinations now.

You mentioned like, you know, what’s happening in New York. Well, Vancouver and Toronto, Montreal even specifically, like, this is where people want to live. So, you’re going to – I’m assuming then, with these predictions that there’s always going to be a natural inflow of people who want to live in these cities but then when you add all these external factors, just – you were saying supply can’t keep up. That’s where the issue I think comes in.

[0:11:14.8] EY: I connect to what you say from academic literature. I mean, we have incite that in our projects. So, this is another thing. We might maybe update the project based on the interest that you got. So, we’re kind of collecting feedback, thinking about how we can improve the model, use the data in a better way but at the same time, government updates the projections, so we are thinking about how we can implement them. 

But there’s one paper that I really like in the academic literature. So, it’s a top three finance publication. So, in that paper, the authors look into the London market and the way they structured the project is they’re looking at house prices and the way they structure is they, like for instance, there’s let’s say, I can give you an example from Turkey. So, let’s say there’s some political instability in Turkey. 

So, which means that Turkish people would like to leave Turkey and maybe to go to one of the G7 countries, so for a better living standards and so, in that paper, they are looking at the Turkish neighbourhoods in London. So, if something bad happens in Turkey, the house prices in London, in the Turkish neighbourhoods, they go up more than other regions. So, this has a meaning, this means that immigrants come to these big cities like Toronto, like Vancouver, and Montreal.

And then, they look at their peers, like maybe friends or relatives or other people like them, and they start to live, they prefer to live in those locations. So, which means that you know, the immigration, most likely, we will have the same pressure on the rents and house prices in Toronto, in Montreal, and in Vancouver, and that’s why those regions came up really large in our market as well. So, there’s still a tendency.

So, immigrants prefer to come to those locations that are known for them that they have, they can find people like them. So, there’s that trend, and I think this is not an ignorable path for immigration.

[0:13:06.7] GL: Yeah, no. It’s interesting you mentioned that too because I believe you had a quote earlier where you were speaking about how maybe certain areas and certain cities in the country need different rules and regulations almost, where you know, Toronto zoning areas should be a little bit different than London or something like that. Just because, you know, a city isn’t necessarily a city as a city in this country because so many of them are just a little bit more higher demand.

So, it’s almost like, we need some sort of specific zoning laws that are tailored to specific cities because we have to account for this increase in population, increase in demand. So, it’s interesting that you mentioned that before.

[0:13:43.1] EY: Yeah, and also, you know, maybe we’ll discuss more about the results but one reason we wanted to look into granular data, like to compare different locations across Canada is to see how the demand and rents are spread across Canada. So – and if you think about it, like, we should ask this simple question. Why people prefer to come to Toronto for instance? So, I gave one answer, right? There’s more immigrants in these locations. 

Then the question becomes, why do we have more immigrants in Toronto than many other regions across Canada? The reason is there are available jobs, there are more available jobs. So, business is mainly – is much larger let’s say in Toronto, in Montreal, and in Vancouver than other cities. So, the question becomes if, like, one result of our paper and kind of that’s the message we wanted to give that and I personally wanted to give this line, integrate demand for different locations. 

So, immigrants coming, everybody comes into Toronto, Montreal and Vancouver, and the question becomes, how can we spread and let’s make these people choose other locations to live in? The simple answer is we should create jobs in other regions as well. So, we should create more jobs in other regions, so that people might consider and like a different location than Toronto, Montreal, or Vancouver. 

And that’s the point we try to make, and that’s why we need local policies and government policies. We need to create some jobs, new jobs, new businesses in different locations than these major cities.

[0:15:15.4] GL: Yeah, no, that’s an interesting point because sometimes I talk to maybe some smaller publications across the country who live in smaller cities. You know, they ask this question that you know, they almost say, tongue-in-cheek but they go, Canada’s such a big country. Actual land size, right? It’s the second biggest country. How do we have a shortage, you know? 

And it’s kind of a – it’s not a silly question, but it’s like, yeah, that’s a very good point. Yeah, you make that point great, it’s like, there should be enough to go around where we have jobs in a lot of areas where it’s not just three or four major cities increasing that demand.

[0:15:47.7] EY: That works so many times, right? I mean, we can talk about, for instance, Austin Texas. Like California is the technology hub for the US, and we know that there has been some momentum towards Austin Texas for instance for high-tech companies. So again, like, first, the visitors moves into a new location and then people start to move to those locations to get their job, those jobs in different locations. 

So, it becomes really critical to expect the job creation as well and I think that’s really important to spread the demand. Otherwise, there’s this evidence that I shared with you and that we are all aware of, people come to the same locations. So, they prefer to work and living in the same locations as before.

[0:16:28.9] GL: That’s exactly it because you in your research does show that rent trends do, like, they vary wildly depending on regions, right? Like, I’m sure you saw some real estate differences you found between Toronto, Vancouver, Montreal, what else are these smaller regions as, it seems like those big cities are the ones that got all the media attention as well. You know, what’s going to happen to ours? 

So, no, I’m sure your research indicated not only is it the bigger cities but maybe there’s opportunities to come as well from other regions, which I think is more of a positive thing to take away from this. Not necessarily the doom and gloom of CAD 7,700 a month Vancouver rents, right? There’s enough variations between regions that is more of a call for opportunity than just a straight-up fear.

[0:17:07.6] EY: And also, if you look at the report close, like we put some heat maps. I mean, the heat maps, you’ll see that some places are really dark red, which means that rents are increasing a lot, and some locations are not. So then, this means that pressure goes on certain locations and not every location. So then, we need to find ways to spread the demand across from those highly hot regions to less hot regions.

And then, but we need to create some jobs and we need to create some policies to kind of direct the demand into different locations.

[0:17:39.6] GL: The one thing that I found really fascinating, and we’re seeing this all the time when I talk just anecdotally from people, everyone seems to say that they see cranes everywhere, right? They see new bills happening just by driving around their neighbourhoods, right? And you mentioned as well in your report that your finance indicated some sort of counterintuitive association between the increase in housing completions, and rising rents, right?

So, you mentioned that in other words, as supply increases, market rents are still going up, though you would think it wouldn’t be the case with the supply and demand issues, right? That’s kind of an interesting thing we see as well, and I’m not sure – I don’t want to explain it for you but that is definitely something that is kind of counterintuitive for people to think about, right?

[0:18:21.2] EY: Exactly. I mean, yeah, of course, right? If more units are available to people to rent and to live in, then of course, rents or prices should go down. I mean, that’s the logic. I mean, I really had a nice demand and supply figure. We first put them into the report and we removed it later on, like we didn’t want to make it too complex at the same time, it’s something – it’s an indicator. 

Let’s take it there simpler, this is an unhealthy relationship. I think the easiest way to say that if you increase supply if rents are going up or if there’s like a – in other words if there is a positive correlation between supply and rent, this is an unhealthy relationship. I mean, we should think this way and why is this an unhealthy relationship? Because we have really large excess demands. 

So, like the demand increase, increases, the increasing supply cannot catch up with the demand increases. So, the only way to fix this and we did kind know, we call it is like some convexity non-linearity new model. If I put it in simple words, we need to increase the supply so much that, that like small increases won’t solve the problem. We need to increase supply really much that this will become a healthy relationship. 

Otherwise, we’ll have this problem. One, of course, the other solution could be and I think right now that’s the government’s response, you could also decrease the demand, right? So, you can try to but still, there’s excess demand. Again, the question still becomes maybe we can cut the speed, like decrease the speed in the demand increase but there is still excess demand. So, already in the market we have – we don’t have enough units to cover the current demand in the market. 

So still, although we might cut the immigration numbers, we don’t build more, maybe if we fill the lack then slow down the increase but it won’t fix the problem at all. 

[0:20:19.8] GL: No, it’s been so undersupplied for so long. I believe CMAC, I think their estimate is over two million new units built, which is triple or quadruple Canada’s housing building rate they’ve ever had, right? That seems to be like an impossible number to get to but I think it indicates that we’re not close, right? You know, building a couple hundred new units in the city is we’re not close, right? So, I think that’s where it shows the strain. 

[0:20:46.0] EY: You made it perfect. It’s really kind of the point I’m trying to make. So, if you think that in the short term, it’s impossible, you know, to increase the supply that much, then we should think and look at the policy or the market in a different way. That’s why I bring in the idea that we should focus on different locations, maybe promote new locations that don’t have the rent to where housing crisis, like the price rises. 

Maybe that could happen to solve the problem. Is it an easy fix? No, again, there is the tendency that people want to live in Toronto. I mean, it’s not easy to change that issue. 

[0:21:22.6] GL: Yeah, and it’s kind of an unfortunate thing too about and you know, we were just in Vancouver and we spent a lot of time in Toronto but even geographically, it’s hard because you know Vancouver has limited space because you know, they’re in the mountains and the ocean and Toronto is such an old city, right? That it’s been building, building, building, it hasn’t really been able to catch up the infrastructure that they’re just simply harder just to build, right? 

You know, we spent time in Calgary recently that relatively newer city with tons of land to just expand, right? You can just see by visiting these cities where the constraints are and the regional variations that you brought up I think are great and that’s kind of where I’m assuming you see some opportunities for government investments or policymakers to really move forward in the private and public sector to kind of get things going because, with the supplier shortfall, it sounds like you do see a lot of opportunities still. 

[0:22:14.0] EY: Definitely. I mean, that’s like we don’t focus on that and to be honest, they don’t really go into details but that’s one side of the report, right? So, if you use the data in a good way, of course, like the models can be improved so our model can also be improved but in the end, this kind of indication, so maybe some opportunistic investment strategies. I mean, at the same time, like we should think in both phase, right? 

Opportunistic as an investment but at the same time, this type of investments could help fix the problem as well. So, I think it could be beneficial for the society but also for the investors at the same time. 

[0:22:48.8] GL: The one part that I really want to know about because I have really no knowledge about it is the actual AI side of things. So, obviously, these AI models were a really central component to your research. Could you maybe share a little bit in how AI enhanced the accuracy of your findings but also helped you forecast these trends? I think that’s a really – because everyone wants to know what AI can do but what can it do and how did it help? 

[0:23:12.5] EY: Okay, there are two things. So, we like a professor in real estate and I know I had maybe 20 papers on different topics in real estate, lots of things on writs, and commercial real estate markets but we use linear models a lot. Like, they’re standard linear models, we are really experts on that. We know how to use and the role of linear models and what I mean by linear, let me try to make that a little bit simpler. 

Like, just think proportionally increase, like if you increase let’s say immigration by one percent, let’s say rents are increasing by two percent. So, if you increase immigration by two, rents are increasing by four, so it’s proportional but machine learning model does this differently so it’s not proportional. So, it’s not linear, it’s not proportional, it’s complex. So, if you push a model to be proportional or linear, then you limit yourself. 

But if you allow the model to be complex and we don’t know how it calculates. So, because you plug in the data, the model does iterations and after those iterations, it makes like a complex calculation that we don’t know how, and in the end, it gives you the prediction. So, that complexity kind of help you improve or help you make better predictions on whatever you do. So, that’s the major purpose of using machine learning model. 

The caveat is you don’t see the relationship, so whatever I say if I tell and if I look at the report like forces one of the findings is like working from home increases rents, that’s a finding I can’t tell you from the linear model because it shows you the relationships. Again, the machine-learning model, it’s complex. So, I got that question from someone by email. I said, “I cannot tell you. I don’t know.” 

I mean, I don’t know what factor contributes to the problem in the machine learning model because – so that’s the tradeoff. You kind of lose the economy logic, though you can choose your inputs based on the logic but in the end, you don’t know the relationship but it can give you a better prediction. So, that’s all purpose for using AI and I can give you at least some evidence from the academic literature. 

I had another project for Global Risk Institute for Canadian pension funds, where we used a machine, a similar machine learning model to predict house prices in Quebec and for us is in that project what we found is that entering your prediction is like 30% less if you use an AI model than a typical linear model. So, that’s all the aim and there is some academic papers. I think the other percent is the same. 

So, there’s always like a – maybe it’s doing a 30% improvement in your prediction. So, that’s why we brought in the AI model and that’s why we did our projections using the AI model. That is not ChatGPT by the way. I mean, just to let you know. 

[0:26:04.1] GL: Yeah, that’s what most people think AI. It’s we’re not using ChatGPT to do this, right? 

[0:26:08.5] EY: I had that question also and that’s why I wanted to match in, yeah, what this is not. 

[0:26:12.7] GL: So, I can’t recreate this myself by using GPT. So, I’m assuming you see AI really in the future continuing to shape how we look at real estate research and policy planning, do you think this is something we should be leaning into more? Do you see it as something that’s here to stay? 

[0:26:28.9] EY: Relationships are important, right? Because to develop a policy, I should understand what contributes, I say in our case, to the rents. I really – I need to know that relationship. So, that’s fine. I mean, I try to do both in the project, so that’s landed on the standard traditional models but when it comes to projections and predictions, definitely AI kicks in and can help us a lot because, in the end, you only want to see where things are heading, right? 

So, in that respect, AI is definitely there. We try to use it a lot in our models and also this is a growing field. So, there is also physical like causality, right? We should see those relationships and there’s some words to improve this type of models where you can also see the relationships. Let’s say in its current form, it’s really, really useful to make predictions. So, should we see them more in policymaking or in the real estate industry? 100%.

[0:27:26.7] GL: No, I think that’s a great answer because it’s here to stay so why not use it as much as possible, right? And the more we use it, the more it kind of gets better, so that’s – 

[0:27:35.1] EY: And Giacomo, like I make that point a lot in my one-on-one conversations with some industry people also. I mean, think like this, so there’s let’s say a real estate company messing in the market. So, they have their own observations. There are things that they can see with their eyes, they have their personal experience in the real estate business, which academics or researchers miss on. 

Like, we sit on our data and try to understand data better. So, I think this is the best way to understand. So, there are things that industry people do pretty well, practitioners do well, and then there are things that you can really learn from the data that maybe you are not there. So, combining the two is the best option and I think I would say the same thing for AI. So, we have our own methodologies, traditional ways to do things. 

And AI can help us in certain aspect, especially when it comes to projections and predictions. So, why not have that information on the table and try to make more sophisticated decisions? 

[0:28:35.1] GL: No, I think that’s great and then unfortunately, you get the predictions and the projections and they’re these numbers that 2032, right? These monstrous scary numbers that I’ve shown some people on the office, I’ve shown some people just in my life who I know live in these cities and you know, they kind of ask the same thing too. It’s like, “Well, what happens if it gets to that?” 

Like, these projections they almost seem unobtainable in a way that how can anyone afford to live in these cities, right? That’s where I think yes, these numbers are – 

[0:29:02.3] EY: Can I ask you a question? 

[0:29:03.7] GL: Yeah. 

[0:29:04.1] EY: Let’s change the rules a little bit. I can ask you a question, just look at the median income in a couple of different location in Toronto, Montreal, or Vancouver. Look at the median income and look at the house price. Do you think that median income it can buy that house currently? 

[0:29:19.2] GL: No.

[0:29:20.9] EY: There’s that disconnection already, right? 

[0:29:23.3] GL: No. Yeah, so maybe renting is going to become a little bit more like homeownership where it’s just income is here not there, that’s possible, yeah. 

[0:29:29.5] EY: We had disconnect already that’s what I mean, so if you look at the annual income for a typical household and if you look at the house prices in that region, you know, there are regions if you check Toronto, there are regions that you see that the median income is around 100, the family is around CAD 100,000 and there the house prices are two, three million dollars. 

So, how possible it could be or how long would it take to buy a house with such an income in such a location? So, there’s already this disconnect but it’s going in the wrong direction for sure. I mean, it’s not – I don’t have an answer. I think we can think of it this way. If at some point, if people really cannot pay these rents, then people stop coming to Toronto or people will move somewhere else, right? They will leave Toronto. So, until we see that, I think there is this trend.

[0:30:24.1] GL: And maybe to kind of close us off a little bit, do you have any advice to give to landlords or renters who see something like this and you know, they want to navigate through these changing rental markets, you know? Is the advice simply maybe look to secondary markets where more affordable rents or is that you know, investor money to buy a house? Like do you have any advice based on what you’ve seen that maybe people should be doing? 

[0:30:45.5] EY: Maybe not like an advice but I always think about – maybe I gave that answer partially also but the way I see things is like what options do we have? I think that’s the way we should think. So, if rents are going this high if it becomes unaffordable, what options an individual has. I think we should really think about it. There are not many, right? One option is we need to pay higher income wages to these people so that they can start paying, which is not easy. 

Two, we can shrink the space. So, you know, I can give you an example from the Netherlands for instance. I mean, there are many houses, like single-family houses in the Netherlands and after 50, 100 years, they are divided into five, six different units. So, people like the space shrinks down and then that’s one thing people do. The second thing is again, it’s related to shrinking space, people can share space. 

So, two, people can come together and you know, share an apartment. These are undesirable outcomes but these are the options. The other option is you stop coming, you in fact moving somewhere out and you go somewhere else. Maybe one option is remote work so there is still there, the impact of remote work, which enables people to live in a suburb while working in a company in Toronto, downtown Toronto for instance. 

That’s another option. Other than that, there are not many options. I mean, in the end, this is how the market is being structured. So, I think maybe the profile might change, so maybe it could impact on position of immigrants. So, maybe that here immigrants talk to Congress so they already have the bouts to pay these rents and they come from Toronto. Maybe their wages or income won’t help to pay these rents enough. 

But maybe they already have the bouts to cover this, so no other options. This is like simple. Again, everything comes down to demand and supply relationship or the option is we have to build more and kind of that’s what we try to make and also, I do want to emphasize this, there is no government who could take such a burden on its own. I mean, we should really understand that we need property companies in the equation. 

We need to build more and government cannot do that on its own, so we need property companies to build this but then, how can property companies build this without capital? Then we need capital injected into this market. Again, the capital from the government won’t be enough to build this much, then we need institutional investors to come to these markets and invest in those property companies, invest directly, invest in writs so that we inject enough capital into this market then and so that we can build more. 

And then, there’s the role of the local government. Zoning should enable to increase the supply. So, we have zoning problems, we have regulation issues, so we should really think about those zoning restrictions at the same time. So, there are many dimensions as I said, you also need to think about the business side. In the end, people move to locations where there are jobs and businesses. 

So, then we should really think about how we can reshape businesses across different job revenues across Canada. I think that’s another solution. 

[0:34:00.8] GL: I think that was really, really well said and I think you touched on a good with it is a business, right? And developers probably, you know, they need to recoup money back of course, right? So, by making it more affordable for them whether that’s removing some sort of taxes for them, I know the federal government has removed HST over those who want to build purpose-built rentals but interest rates are also going down, right?

So, it’s really expensive to build, right? For these developers to build these purpose-built rentals and condos and homes, you know, it has to be more affordable for them to build too. It’s not necessarily renters and people who want to buy homes are on one side and then developers are on the other and they’re clashing. We all want the solution. We all want to be able to have something more affordable and I think you said it great when you said, “It’s not just going to be a one solution thing. It’s going to be everything coming together.”

[0:34:47.0] EY: And also, I mean, some people think that way. I mean, like, I even read the recent paper on this. They shouldn’t blame the investors here, right? So, we need the investors. So, it’s not – so investors might have a short-term impact on maybe with the rent increase, or so maybe it’s a possible one. In the long run, it’s a very simple solution, we need to build more, and in order to build more, we need capital investors, real estate companies, and local governments and the government involved in this problem.

[0:35:16.5] GL: No, I think that’s so well-said, and in for the future for our listeners, we’ll link everything we can but if anybody wants to read your research, learn a little bit more about you, what’s the best way to do so and to find this?

[0:35:28.6] EY: I’m still a professor at Concordia. So, I’m not moving anywhere. I’m at the finance department at John Molson School of Business. So, if you just search my name, you’ll reach out to my Molson profile and you can easily reach out to me by email for instance. Email is better, I don’t like to talk much, so I say email is better. You can reach out to me. First, email, then be the podcast together.

[0:35:50.8] GL: Well, there you go. We’ll make sure to link everything in the show notes for this. So, again, we really appreciate this. This was a great, great piece, we’ve been showing it all through our company and it’s really caught the eye of industry experts. So, I do want to thank you for spending some time, I know you’re quite the busy guy. So, again, thank you so much for all the work you did, we really appreciate this.

[0:36:08.2] EY: No, it’s the same here. Thank you very much. It was a pleasure to talk to you. Thank you.

[0:36:12.2] GL: Okay, thank you.

[END OF INTERVIEW]

[0:36:14.5] ANNOUNCER: Thank you for tuning in to another episode of Sync or Swim, brought to you by Rentsync. If you enjoyed today’s show, make sure to visit http://www.rentsync.com/podcast, for detailed show notes, key takeaways, and more. Thanks for listening. 

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