The real estate industry is no stranger to technological advancements, and the integration of artificial intelligence (AI) is taking it to new heights. AI in real estate appraisal is transforming the way properties are valued, enhancing accuracy, speed, and efficiency. With the ability to analyze vast amounts of data and make predictions based on sophisticated algorithms, AI is poised to revolutionize the appraisal process, benefiting both appraisers and property owners. Today we have Allan Weiss, CEO and Founder of Weiss Analytics. They created Automated Valuation Models to estimate property value without the need for a human appraiser and it automates property valuation report.
AI in Appraisals – A Coming Revolution? with Allan Weiss of Weiss Analytics
Listeners we’re in for a real treat. Today we’re going to be talking about where appraisals are going. And we’re going to talk about this from a number of angles. And there’s no one better to talk about property valuations than our guest. And I’m talking about none other than Allan Weiss. Allan was the former co-founder of the Case-Shiller report, the gold standard when it comes to everything about the housing values in the various markets. We’ve all known about Case-Shiller we’ve heard it, we’re gonna be talking to the guy that created it. And he’s doing some really exciting things. So Allan, really excited to have you on the podcast, giving us an update on the whole world that appraisals. Thank you, sir.
You’re welcome. Great to be here, David.
Let’s get Yeah, we’ve had the privilege of interviewing you before. But for those that didn’t hear that interview, and by the way, listeners, you could go to the link in the show notes. And you’ll see the previous interviews that we’ve done the previous episodes, with our interviews with Allan, and I would like to encourage you to listen to those. But Allan, for those that are not going to take the time to go listen to that. Give us a little bit more of your background. How in the world, did you get interested in the whole valuation space, the real estate market in general, what was your interest? Where did it all started?
Yeah, I traced it back to my 20s. Actually, I ended up buying a three family home in Cambridge mass with a friend. And we were renovating it, we sold one of the units to another friend of ours. And I was just hearing little rumors back then, which was in the 1990, that prices were weakening and falling and I was worried about it. And I looked around and it didn’t seem like there was any good way to find out anything. There wasn’t much information. And people would speak in very broad generalities without being able to tie it to any facts. There was the National Association, median sale price, but it was very broad, and I didn’t feel like was very informative. So I just had this mindset that everybody could use better information than I ended up going to business school. And I still had this bug in my head and one thing led to another, they ended up meeting up with a professor there, Bob Shiller, who had her just created a new home price index with Professor case of Wellesley College. So Bob Shiller was at Yale professor case was at Wellesley College, I was a graduate student at Yale. And Bob was kind enough to let me work with these indexes, which hadn’t even been published yet. One thing led to another, graduated, was still in my mind that there was enough good information out in the marketplace, there were other uses we thought of for the indexes. So we decided to form this company, and we began working on it. That’s how he started.
What a great successful story that lived a good life cycle, you were in with that for 10 years, if I’m not mistaken. And then you ended up selling that and you haven’t stopped your interest in property values and monitoring them talk about what Weiss analytics is today, as a result of this passion you’ve had.
Sure, but took a kind of hiatus, we sold the business in 2002. And at the time, I thought I was all done with that kind of work, which was fine, I would do something else. In the meantime, my kids were little and I had the privilege of getting to stay home and be a stay at home dad. But then the meltdown happened and the recovery started to happen. And it became apparent to me that there were still holes in the information. Because, for example, in our town, we were seeing that the very expensive houses were continuing to fall, and the small houses were rising in value. And I thought that’s really interesting, because Case-Shiller Weiss, we would have been lucky to have one index for all of Medfield, Massachusetts where I lived. And I realized that the high end would not have been reflected accurately in this one index, even if we had one, and the low end wouldn’t have been reflected, because it was probably in the middle. So it wouldn’t have informed anyone. And that kind of struck me is really significant, especially when markets are turning out. Does anyone know what’s going on? These are forces really big decisions in the dark. So I worked out a way to produce really accurate price indexes down to the house level. To answer this question, how do you know what’s going on? When markets are shifting in different kinds of houses are moving in different directions? It was a bear of a problem, but we ended up solving it. And that became the basis for the advantages Weiss analytics brings to the marketplace.
That’s very exciting. I can’t wait to get into that a little bit later. And listeners, you’re gonna want to stay all the way through this interview, because we’re gonna get into how this is changing and transforming our industry. But let’s talk now about the appraisal process. We all know that the average age of the average appraiser in America is going up in the upper 50s or late 60s. If I’m not mistaken. I don’t know that I have that quite accurate, but I recall that data is high and we’re not adding to the ranks, appraisals, we’re seeing a horrific attrition rate. That’s all public information, well known to every one of our listeners. And so it’s causing for a bit of a crisis in it. And now we bring in artificial intelligence, we’re seeing what can be done. And that’s where I want to go with this interview for a little bit. Allan, I want to talk about what does AI mean for appraisals? What is the potential here.
I think the industry is coming at it from two sides, both of which are helpful to a degree on the one side, are fully automatic valuations, which the industry calls AVMs, or Automated Valuation models. And those use AI to make themselves more accurate simply analyzing data in a deeper sort of way. And to some degree, those are being used as a substitute for appraisals or to aid appraisals to make them go a little faster for the appraiser to have a starting point for the value. But to the extent those are used more, it’s impacting the appraisal industry in the mortgage industry. And then on the other side, you have the full blown appraisals. And they’re starting to be aided by what I think of as appraisal workbenches, where there’s various kinds of automation that helps the appraiser do the more mundane aspects of the work more quickly fill in forms, finding good data, finding good comps, and so on. But still leaving the appraiser completely aware and free to bring their judgment to bear. As we develop AI that gets deeper into the process. I think it will continue to help appraisers focus on where they can bring their judgment to bear where they can bring their unique market knowledge to bear so that the appraisals are done more consistently, with less drudgery, and more efficiency.
And get a little bit into explaining this from a layman’s term standpoint of how this works, Allan.
Sure. we’ve all heard a lot about chat GPT and large language modules. And that’s not exactly what we’re talking about here yet. That’s the thing that’s gotten the most attention open AI and that sort of thing where you can ask it any question and it’ll spit out an answer that looks like a human produced it or you can ask the crazy questions like, I want you to give me this answer written in the style of a Shakespearean sonnet or something. Yeah, generative AI, which is producing language and so that’s one kind of AI, that’s become very prominent and very useful in a number of applications, not so much, obviously, in appraisals, it’s not quite as obvious. Then there’s another kind of AI, which I think is more value when dressing to numbers, like appraisals. And that tends to be performed by what are called neural networks, which are setups of computers that are designed to imitate the human brain. The idea is that we have neurons in our brain, which fire when they get certain signals from other neurons. And then that, in turn, affects other neurons. So you have this sort of ripple effect. And there’s some number of billions of neurons in our brain. And what I’ve heard is that the number of connections of neurons in our brain is a larger number that all the atoms in the universe, wow, you’ve got all these neurons connecting. So our brains are really good at picking up on patterns that are very complicated. Under this circumstances, if it’s raining, and it’s dark, and it’s cold, and it’s windy, and so on, I should do this. But if you slightly change the circumstances, it’s a different pattern, I should do that. So it all evolves so we could survive. And that kind of pattern recognition also is useful in mathematics, and in a form of Applied Math like doing an appraisal. So this is something that lends itself to a neural network application. And you can begin to get an idea of how it could work. For example, if you give a neural network a large number of appraisals, so that every word has been translated into computer form, so the appraiser could, rather the AI can see this was an address and it had these features. And the appraiser chose these particular comps. And the AI can see all the other comps to that might have chosen, it can develop patterns, pattern recognition of what are good comps versus not good comps, by making observations about what millions of appraisers have done. And then it can begin to detect these patterns. And then when given the next house, it can apply these patterns and say, Well, the most likely comps given what it’s I’ve been trained on would be these five or these 10. It may not be perfect. The appraiser can look at it and decide it’s right or not, but it may give the appraiser a leg up in making some initial choices. So that’s a kind of neural network where you simply given a huge training set and it discovers the patterns in either selecting comps or adjusting the dollar value of the comps by any number of feet features like square feet and so on. And it basically is applying the knowledge and behavior of millions of appraisers to imitate how they work.
What’s so interesting about what you were just talking about is this administration in some of the housing leaders are saying there’s a bias in our appraisals, does this dark help taking out? Is that going to be one of the benefits of using AI in appraisals? Is it going to duplicate AI possible bias that might be there? Or is it going to eliminate that?
That’s a great question. And it’s being examined very closely now. And there’s a couple of ways of looking at it. So on the one hand, you have a neural network that’s being trained by appraisers, by appraisal behavior, it’s going to be emulating a bad appraiser. To the extent that there is bias and appraisals, I don’t know if there is or there isn’t. But to the extent that there is and you’re relying upon appraisal data, that’s possible. Now, it’s also possible to use measures that prevent that, for example, if you have a large data set, and you’re not just imitating how appraisers work, but you’re comparing your results to what actual sales are. And if you do that, very meticulously and very granularly, across the country, you ought to be able to take out the bias, because the goal of anyone producing an appraisal workbench, or an AVM is to be as accurate as possible, because they’re going to be able to sell it more and make more money, they want to be accurate. And any sort of bias, whether it’s by the kind of person living in the house, or big houses, little houses, AVM producers, anyone trying to help, appraisers work more efficiently and predicate trying to do it in the most accurate way possible, does not want to have any bias, because it just takes less accurate. And it’s not that hard to get rid of bias, once you have it by simply means like compare all the valuations I produced against all the actual sale prices, then if I tend to be too high or too low, if the median difference is higher or lower than zero, then I’m biased, high or low. And what we do is we’re constantly at every stage of every evaluation product, checking for bias and getting rid of it, because it helps us be more accurate. So I don’t think there’s anyone motivated to have bias in there. And we have objective ways of getting rid of it. So I think the best scenario for training any sort of system is get the best, you can add of appraisals at a sale prices, all the information you can with the intent of also eliminating bias if it happens to be there. And bias creeps in for any number of reasons, you could just have a tendency for some in some market to be too high or too low. Because if any one of 1000 random reasons, we’re not really paying attention to why it’s happening. We’re just trying to get rid of it. So I don’t think it’s inevitable that these automated systems are biased. And I think they can be easily tested for lack or presence of bias. And therefore I think they’re probably more useful tool in checking for and assuring there is no bias than they are a problem. Because everyone’s motivated to get rid of it. And it’s easy to check.
Yeah, there’s no question everyone wants bias out. And no matter where you’re at whether it exists or doesn’t exist, it’s an interesting argument, but it is being raised more and more. But how will this affect market participants? I’m thinking appraisers, originators, homeowners or regulators what AI in what you’re developing could have a pretty major effect on everything. Am I correct?
That’s true. I think the world is going to take it in stages, I don’t think there’s gonna be any extremely rapid change, especially in a highly regulated cautious industry, like the mortgage industry. On the one hand, we have AVMs that are used in some cases for low risk loans, he locks that sort of thing. There are certain rules as to where you could use them. If you want to have the HELOC securitized, then you need to follow the rules of the rating agencies. And they have certain safeguards they put in place. If you want to use an AVM in a GSE loan, you’re not going to be able to because they require an appraisal and if they give you the ability to skip the appraisal, it’s because they’ve run their own analytics. So I see perhaps a gradual adoption of more full-blown AVMs on the low risk side. And then I see a gradual adoption of appraisal tools that make appraisers a more efficient and be more standardized in how they work. So it gets rid of the drudgery. The end of the day the appraiser can not sign off on it by regulation and law unless they have all the information as deep as they want to go, that they can have the conviction that they’ve been able to bring all their professional standards to bear and only then can they sign off on it. Whether it’s an officially an appraisal or just a valuation. The appraiser has to be part of the system. The appraiser has to be comfortable, it knows how it works and is in agreement where it needs to be able to change the appraisal. That’s why it’s really a workbench, you get some tools, it helps you get along, you can see various judgments that the AI is recommending, but you need to be able to go back and drill in and see the underlying data. And that has to be part of it, that transparency has to be part of it. As things go by, I think that appraisal will get increasingly comfortable as everyone else in the industry learns how to make them comfortable, that more of the process will be automated. And the benefits are, you get what comes along with the appraisal then is an objective, high number driven evaluation of the accuracy with an appraiser. If you have 5000 Different appraisers, they may go about it slightly differently, they may be more or less expert in a given market, what the AVM technology can do sitting alongside it has come up with an objective score. It doesn’t necessarily have the right number as to what the value is. But it knows if there’s a presence of a lot of rich data or less data. So it can give you an objective score of how accurate is this appraisal likely to be? Is it high risk or low risk. And then the appraisal of big financial institution that GSA whoever is taking some risks can weigh in and say, given that, I want you to check more, or I don’t need you to check as much it creates an efficiency because you’re automatically assessing the accuracy.
Efficiency is when you start realizing efficiency this brings is pretty exciting the potential of it, can you give us some other examples of how the use of AI is affecting our industry. And where you see this going,
I think where it’s going is to bring more information to bear or analytics to bear to make things go more rapidly. Another step along that path is to bring in photographs of the property that were taken now, either by a third party inspector or possibly even by the homeowner have a homeowner with an app on their phone, which assures that they are literally located in the address of the subject house because you have a GPS. And it’s geo fenced as they describe it. So like what’s really at that place, then we have AI, we have AI today that will analyze let’s say a video taken of the entire house and a walkthrough. And if exciting, yeah, it’s very interesting. And it’s fun, and I think it is efficient. So the AI figures out, that’s the kitchen, that’s bedroom one, that’s the bathroom and so on. And then it can very accurately score both the quality and condition of each room. And then those scores can be compared to standards in the local market and brought into the AVM to make the AVM more accurate. In some cases, that all leave the value alone. If it’s in better shape and with higher quality than average, it’ll increase the value. And similarly if it’s in poor shape, it’ll decrease the value. And it’s expressed in exactly the same scoring system that appraisers use one to six, well we can do is put on the appraisal workbench, here are the photos, here’s the AI score the photos, here’s the problems, the AI found, you can agree or disagree, you can change them around, or just say yeah, that looks right. And then gets input into the appraisal calculation. And it just all goes faster. So therefore, one of the interesting changes will be the involvement of the homeowner in the process through this app, they don’t have to use it. But they can be told, Hey, if you help us out, this will go faster, maybe you’ll get a slightly lower rate, because we don’t have to pay someone else to come out and take the photographs. And so I think that’s an important advance. And then further on down the road, I think we’ll see appraisals, getting less expensive, being performed faster, and being trusted more when enhanced with AI. But always having the human in the loop have always having the expert in the loop that’s critical. I don’t see that ever going away just means that the way the human is in the loop, the way the appraiser is in the loop might be a little bit different. And they’re going to be able to spend more time on the aspects of an appraisal, which you really can’t have a machine do. So what regulators want to see what opinion leaders want to see is let’s figure out what the AVM doesn’t know can’t know. What can the human bring to bear that only they can see and have them fill in those gaps. Also, where it’s difficult to make a judgement where there is lack of information that humans steps in, where it’s easier where the analytics tell us it’s more accurate, they can step back. I think this will make the mortgage process faster bearer less expensive, and an overall better value for everybody involved. So I see it as a plus. But I think we do need a period of education and awareness in this appraisal industry because people don’t know what AI is people don’t know what is possible. And so I think the people who are creating these sorts of tools like us need to pay a lot of attention to how do we make this easy to understand trustable usable, so that we make the people in the industry who are responsible, the appraisers comfortable, that’s an essential step that I don’t think there’s been enough investment in. That’s what we have our eye on.
What you’re saying is really exciting. But it’s almost bringing about a bit of a revolution, or at least it seems to in the way it’s bringing about some really positive and much needed changes. Love to get your thoughts on these positive changes.
Sure. I think the information is super useful for people like in the earlier part of my career, I felt like we were all in the dark, what’s going on with the market? How do I know things are going up or down? How do I make smart decisions. So for example, in terms of AI, using computer vision to pick up on whether a house is in good shape or not, that’s super useful. So it can pick up on mold, it can pick up on cracks, it can start to make suggestions about what to do about it, you’re saving money, you’re avoiding hazards, you’re saving money directly and fixing up your house and maintaining it. Your homeowners insurance policy is going to be protected. Your it’ll help with the rates of insurance, it can pick up on this new theme I’ve been reading about home hardening. So I don’t necessarily want to spend money making my house nicer. My first job is to make sure that I can remain habitable under harsher conditions. I see AI becoming homeowners partners, and making these sorts of smart decisions at just as I wanted 30 years ago to find information about what’s going on with the price so I could make smarter decisions. To me, that’s what it’s all about is there’s money at stake, there is a primal need to have shelter. And if we can do a better job with these tools. That’s a net plus for everybody. Absolutely.
What is needed for this to go?
That’s a great question. So I think that people bringing about change in a conservative industry, like the mortgage industry need to pay very close attention to every stakeholder, whether it’s the GSEs, government agencies, appraisers, there needs to be a lot of dialogue and a lot of transparency. There’s a lot of discussion going on right now about how should AVMS be regulated? How should AVMs be regulated? How do we address issues of bias? I think those questions they need to be taken very seriously. I think that the people who are working these analytical systems need to relate really well to all the stakeholders and develop this concept that I need to make everybody buy in and comfortable or it’s not going to work, I can’t just say, this is really smart, you should trust it, no one’s going to do that, no one’s going to trust it because someone says so or because some AI tool seems like it can imitate a human, they’re going to trust it because they get it. So we have to do a really good job at figuring out how to make it transparent and comfortable and intuitive. And that’s not easy to do. Because these tend to be black boxes. And it’s a little spooky, how smart these things can be. So we need to be able to figure out how to maintain the accuracy and not sacrifice the accuracy, and increase the transparency. So people really get it to everyone’s comfortable.
Very excited when I hear about AI and appraisals and all the data you’re gathering. And the benefits that could come out of this is you’re going to be able to predict down to the house level down to a street level to neighborhood future values and where these values could be going. And by seeing this opportunity accurately.
Absolutely. One advantage that we have looking at future prices and houses compared to the stock market is economists already know that prices in the housing market are not particularly efficient meaning if there’s information like interest rates went up or something happened in this neighborhood, the prices don’t jump overnight. There’s like a slow process. Because there’s a slow process. If you can observe that process, you can predict it because it doesn’t jump and then next second. And all of a sudden all that information is in the price that happens over time. It’s not really like the stock market that way. So the only challenge is really is to measure what’s going on. And that’s the basic idea behind our house specific indexes. It can precisely measure the month by month change of every house. So we’ve created visualizations that show you a map. The dots are where the houses are, and they’re color coded. So it shows the rate of appreciation or depreciation of each house in that month. So if the dot is dark green, that house is rising rapidly in that month, if the dot is dark red house is falling rapidly in that month, so we can literally show people a weather map so to speak a market weather map of an area we can put a circle around their house, and you’ve chosen the trends in their area, and they can see is everything uniformly rising. So if I’m thinking about when should I sell, I can be confident that I can wait a while and reap the benefits of continuing to hold the property, or am I seeing green turn to gray turn to pink turned to red coming at my house, that means a market of higher risk. And you don’t have to believe some black box because you could now see every single house and you can see the pattern for yourself. And almost with this kind of a tool, almost anybody can get a sense of how risky a market is. For example, if you’ve got expensive houses falling and inexpensive houses rising, you’ll see that now we overlay onto that the AI of the condition of every house, and the value of every house will even we haven’t done that step yet. But you’ll have even more insight. So this goes to this whole idea of AI helping the individual homeowner, at the end of the day, most of the risk is held by the homeowner, not the lender, they own the equity lender owns the debt. So everybody knows that the equity holder has more risk. My whole mindset is how do we get the best possible information into the hands of the people who are impacted, we’ve developed all these tools for institutions, I think the biggest impact will be getting it in the hands of homeowners, which will help the institutions because if the homeowner is making smart decisions about when I should buy what I should sell how I should in turn that helps the lenders, the whole idea is to give people great information, so that they can make better informed decisions. And we’re talking about decisions that are very big, and can help affect the wealth of an individual family and 10s of 1000s of dollars easily by making smarter decisions. That’s what these maps do. And that’s what this AI does. So I think it’ll help everyone
I just see the opportunity if you’re a first time homebuyer and you’re going into a highly leveraged position, very little equity, so you carry an enormous risk. And this, you are going to be able to make a greater decision of buying a home with a higher probability of greater appreciation than buying a home. Now on the flip side of that, you may be an investor that’s looking for those homes, that there’s a fixer upper component going to Chip and Joanna Gaines on the face famous fixer upper movie. And many of those and other shows like that, they’re going to be looking for those opportunities where they can lift the value by putting an investment into it, there’s just so many different things that this brings to consumers, and really to professionals, as well as the lenders that are involved in this thing. At this point, appraisals have been all about assessing from a lender standpoint, you need it if you’re buying the home to confirm the value. But it’s been weighted more this kind of shifts to where the value is now, at least equally shared are the benefits are going to be equally shared with the buyer on a going forward basis.
Yeah, that’s an excellent point. That’s true. In the past, what we’ve had is people who hunt for a home and negotiate a price, the appraisal comes way at the end, just basically to protect the lender and either it’s a thumbs up or thumbs down, usually thumbs up. But all this AI doesn’t cost much to run, it can be front loaded in the process. So a homeowner can describe the kind of house they want. You can have a kind of concierge AI agent for the homeowner scanning the MLS, finding good deals as determined by the criteria that the homeowner has, right want to drop? Or do I want something in great shape all this AI that can analyze photos, and the condition of houses can equally be applied to the MLS photos, and then you run the valuation. And you’ll look at the asking price. And you can find a good fixer awkward, and you can find one that’s not so good. So all these things make the whole market run better.
And more efficient and more effective. That’s the opportunity start exploring. Allan, let’s run through the products that you’re offering right now, if you could give us the name and a brief description of what they do.
Sure thing. So the first product is called Valpro. That’s the plain vanilla AVM our form of it, which does have some unique capabilities, including how specific indexes to adjust the comps. Because if you think about it, all valuations have as their foundation, prior sales of comps. And you have to be able to bring all those prior sales which occurred sometime in the past to the present day where you can’t use them on a level playing field and really build your models. So we have an advantage that all the sales prices are brought to the present day with our house specific indexes which are the most precise time adjustments possible. So that gives us a leg up and from there we filled five different valuations meld them together and that’s the Valpro product.
Okay, and who is best use suited to use that product that is designed for who primarily.
It was designed for underwriters who make their own rolls and want to use it for lower risk loans like HELOCS or low LTV. And that’s one of the use cases to our surprise. It’s all So being used by a very large property and casualty insurance company to look at their portfolio and see when houses have some sort of claim on them, what is the value because they want to make sure they don’t overpay. So one large insurance company gives us the address of every house as the claim comes in, we run it that day. To give them that perspective, we did not expect to see that application just showed up.
So that’s Valpro. That’s the first product. Second product?
The second product is called AVV, which stands for automated vision valuation. That’s a product where we have photos of the house either from the client, in the case of mortgage servicing, it might come from property press, we received the photos, and we do AI analysis on them to score the condition and quality of every roof and the exterior that makes the valuation more accurate. In the case of mortgage servicing, they’re concerned that the value is lower, and we can pick up on that or it could be higher. The second product is AVV, automated vision evaluation, which is an AVM valve growth plus condition score photos. And those are the two products that are out in the marketplace generating revenue today,
And the second is AVV Is that correct?
AVV automated vision valuation.
Okay, so for the automated visual valuation product who is best suited to be using that product.
Initially, what we’re finding is lenders or others who are concerned that the value is lower because the house is not in good shape. So mortgage servicers, lenders to fix and flippers seem to be the two initial applications, we also see them becoming useful in the appraisal process because you have the photos, you have them score a grazer needs to do that job. And if that can be partially automated, it can be both a check on the appraiser and help the appraiser work more efficiently. So those are the first two. The third product is what we call appraisal workbench. And essentially, it takes Valpro plus the photos, AVV and it unpacks it. And so what we can give an appraiser is here’s the guts, here’s what happened inside, here’s how we got to it. We looked at all these comps, we selected these comps, we made these adjustments, we showed them every step of the way how we did it. And at any step on that process, they can make an adjustment, they can say I don’t agree I want this comp and not back calm. I want this judgment and not that adjustment. If they want to bring their expertise to bear, this becomes the beginning of workbench. And it’s both using the Valpro technology. Unpack so you can see the inside and also the AVV technology. So we put on their desktop photographs condition scored, so they can be in control. But they have a great starting point. That third product is not yet finished. It’s not in the marketplace, but we are starting to test it. And people are starting to ask for even ahead of time,
What about the future of AVMs and the Valpro. And this suite of products as it relates to the agencies, Fannie Mae, Freddie Mac, if they accept them, there’s going to be a much greater probability of use what progress you make on that.
Yeah, so they have their own technology, they made an announcement, maybe it was eight months ago, that they were going to start to create a uniform data structure for handling photographs, and valuation so that they could bring in photographs and they could perform their AI, they can’t do it alone. There are some very good producers of technology. And what I foresee happening is a kind of partnership. It may not be branded anything but Fannie or Freddie, but I foresee a partnership in technology, which has already really begun so that each of the elements that are best done by some other party are brought in and it democratizes the process, and we gain all the benefits, both with the GSEs and the non GSE loans that way.
What’s so exciting about this is the product you’re developing, and the benefit is going to have but there is an opportunity beyond just being a user of this product, a benefactor of this product, you’re creating an opportunity for people to invest in your company is the other side of it. If people hear this and go, Man, this is a great opportunity. I would love to get a piece of the ownership while you’re allowing that you’re creating an opportunity for that to happen now. Am I correct?
That’s right, we’re on a growth path. And we’ve got our products up and running. We launched most of them just in 2023. They’re already making money. So we’ve got our new valuation product Pro 2.0. We’ve got the AI product which analyzes photos, which is called AVV or automated vision valuation. Both products are about being used by large financial institutions, insurance companies, mortgage servicing companies, they’re making money. And we think that we’re on a growth curve, and we are raising money so we can grow faster, cover our basic expenses, and then we expect to be both helping the industry helping the homeowner, and making money along the way, quite significantly. So yes, we are raising money so we can grow, cover our expenses and grow faster. That’s exactly right.
If they want to learn more about the product, as well as the investment opportunity, where’s the best place for them to go, Allan?
Sure, you can just go to weissanalytics.com. And you can click on various buttons that tell you about the product. And there’s also a button that says for accredited investors only. We can’t sell our securities to anybody but accredited investors that can just fill out a form and we’ll be in touch with them.
Very good. Allan, thank you so much for joining me here on the podcast, sharing this exciting update on the appraisals and give us a little glimpse of where it’s going. And also for those of us who like investing. This is a huge opportunity. It seems like to create upside, especially when you look at this is your first run. This is your third run at the whole appraisal industry. The thing is how successful you are with Case-Shiller, folks, I would say give this like a serious consideration. Here’s someone who was outrageously successful in their first venture. And he’s only improving it. This is really exciting. Allan, thank you so much for being here today. Thank you for taking time.
Thank you, David. Wonderful. Appreciate it.
You bet. All right, listeners, go to our website, check out the links in the website, and our show notes. I’m excited to share this and be sharing this with your appraiser community, those that are out there, your investor community if you’re a lender listening to so many of our people listening to this on our podcast, our lenders, loan officers, and our people underwriters. Share this with your staff. How can we use this? How can we get on this and be ahead of it, it’s going to create a competitive advantage. For those that do and again, there is an investment opportunity check out the links below. Thank you so much everyone for listening to this podcast. Allan again. Thank you for being here.
Hey listeners, this hot topic would not be possible without our sponsors. I want to say a special thank you to our sponsors – Total expert, Finastra, Byte software, Lender homepage, Angel AI, Truv, The Mortgage Bankers Association of America, Lender Home Page, The Mortgage collaborative, iEmergent, Modex, Mobility MMI.io and knowledge Coop. There’s so many good sponsors here and we’re so grateful for each one of them. Be sure to check out each of those sponsors and their spots on our website Lykken on Lending under the sponsorship page. Thank you.
Important Links
- Weiss Analytics
- For investment: click this link – Weiss analytics investment opportunity
- First interview of Allan Weiss
- LinkedIn – Allan Weiss
Allan is co-founder of Case Shiller Weiss, creator of the Case-Shiller Index. He is a pioneer and thought leader in the field of home price analytics and finance innovations. He is currently the founder and CEO of Weiss Analytics.
He leverages his unique expertise in valuations, home price indexes, and AI-driven novel algorithms to produce custom and industry-leading valuation and forecasting solutions.
He is also the inventor of several patented and award-winning financial products and securities, such as Common Index Securities and Macro Securities.
Allan has a proven track record of entrepreneurial success. He is passionate about developing and applying cutting-edge technology and financial innovation to solve real-world problems and create value for his clients, partners, and stakeholders.