A $1 trillion AI revolution in Real Estate is happening now. Who/How to profit from it?
By Alex Galt Founder Realiste.ai
5 Brief Theses on How AI Will Impact Real Estate:

  1. $1 trillion in investments will be redistributed among cities over the next approximately 5-10 years.

  2. Over 30% of realtors will change their jobs.

  3. Up to 100 million people globally will relocate in 5-10 years.

  4. I am convinced that over the next five years, artificial intelligence will act as a catalyst for creating approximately 100 (just a nice round number, not necessarily exactly 100) new billionaires, free humanity from many unnecessary actions, and facilitate the redistribution of resources into new areas vital for humanity. This topic deserves attention, and AI in real estate is a great opportunity. So please, read on.

  5. Tens of Thousands of new dollar millionaires will emerge soon.
I will present my arguments on this matter and invite discussion in the comments, where readers can point out potential errors or illogical conclusions.

I have gathered some really interesting materials on AI and real estate and will share my arguments and provide links to real-world AI models in real estate. I will continue to add materials over time, as well as new arguments and ways AI can be used in real estate (please write comments and attach new links - I will incorporate them into the article and credit the authors of these ideas or materials)

Artificial Intelligence is soaring and growing at a rate several times faster than the internet and mobile phones. Today, we'll discuss the international real estate market and AI, and how to leverage it. This will be a detailed breakdown of the topic.

Is AI Just Hype or a Mega Trend? This topic is thoroughly analyzed in the $80 billion COATUE fund report, which invests in AI (I will attach a link to this research at the bottom of the article). AI is proving to be so beneficial that it is here to stay in our lives and is likely to transform many markets within a couple of years.

After searching for information online, I realized that many articles about AI in real estate are just general words without deep meaning or practical application. All these years of working with AI have given me something interesting for you, and I think this information cannot be found anywhere but in this article. I decided to detail my eight years of experience in real estate AI and the conclusions I have reached.

I am convinced that over the next five years, artificial intelligence will act as a catalyst for creating approximately 100 (just a nice round number, not necessarily exactly 100) new billionaires, free humanity from many unnecessary actions, and facilitate the redistribution of resources into new areas vital for humanity. This topic deserves attention, and AI in real estate is a great opportunity. So please, read on.
Geographic arbitrage

To capture your interest now, I will give an interesting short example of AI power in real estate (and the details will be fully discussed in the middle of the article)

In London's cheapest area, Barking, the average prices are $6500 per square meter with low-quality real estate 1h away from the city center plus a high criminal area, 35%+ TAX, $6000 live spending/m) whereas the neighborhood "Hartland" (10 min from the city center, 0 criminal, 0% tax, $4000 live spending/m in Dubai offers business-class homes ( pool, doorman, gym, lobby) at $6300 per square meter. The cost difference between these places is VERY significant. The real difference between these two places in price/quality is 300% (meaning if sell in a London apartment you can buy 3 similar quality properties in Dubai). This means that any property owner in Barking could sell their property today and purchase three similar properties, rent out two of them, and potentially never have to work again (receiving rent).

Tasks AI Already Solves That Were Previously Impossible

For retail and institutional investors, as well as end buyers, one of the key questions in real estate remains relevant: what to buy and why, as well as when and for how much to sell? Today, property choice is often limited to knowledge of a single area, city, or market segment, as comprehensive information is UNAVAILABLE to anyone. Realtors, spending years studying certain markets, usually possess only a limited part of the information, not exceeding 10% of the total market data volume, and certainly can't accurately say which of the 10 cities is the best for investment and why.

For example, in the stock market, data is public and reliable. Data is regulated and visible to everyone - there are almost no advantages for anyone (there were in the early stages of stock market technology development, but now the advantages are very minimal).

If there were a possibility to use AI to analyze the real estate market, similar to the stock market, it could be a significant advantage, akin to foreseeing changes in the Nasdaq index a week before it becomes common knowledge.

AI in real estate is already starting to perform many functions at a human level (it's important to understand that even if it surpasses the average human in analytical tasks and information gathering, it's already significant progress), and in some tasks, it's better than 99% of experts. If AI works better than 99% of experts, its influence on the market will become monopolistic.

The difference between AI and a human is that it can perform tasks millions of times per second without sleep or geographical limitations, without subjective opinions and human errors. Having an efficiently working AI is equivalent to having a staff of thousands, as if it works better than 99% of experts and a thousand times more efficiently, people will increasingly rely on AI judgments first and then turn to humans (if they don't find a solution or answer).

A Brief Comment on What We Call AI: AI differs from a simple calculator in that it can perform tasks at a human level or even better. These tasks are not algorithmic: for example, a calculator can calculate better than a human, but cannot create a business plan considering all factors. Initially, AI will be compared for accuracy with humans, first with the average, then with professionals, and only after that will it be a comparison of machine against machine, because "human-level quality" of task resolution will be passed very quickly in almost any task in real estate

For example, here's an interesting slide about how AI surpassed humans in chess a long time ago, and humans are no longer competitors to AI in this field, but until 2005, this was still not widely believed

Below is information about the functions that AI already performs in the real estate sector, how effectively and accurately it does so, and whether it surpasses a human analyst (these assessments are based on my perception; experiments have not yet been conducted, but I believe that by 2024, public results will start to emerge)

https://exchange.realiste.ai Real Estate Search and Analysis of 100+ Cities, 20,000 Districts, Millions of Listings

https://my.realiste.io Real Estate Valuation and All Related Aspects
New Opportunities for People That Didn't Exist Before Freedom of Choice and Openness of Information

Most people buy real estate where they were born or where they have lived most of their lives. The lack of data on other locations and opportunities narrows people's choices to what they know and understand. Although at any given moment there are the top 10% of cities or locations in the world that are the best to live or invest in, logically 90% of people are unaware of this, otherwise, they would be buying real estate exactly there. Thus, in real estate, there exists a global geographical arbitrage worth trillions of dollars

Based on my estimates, at least 300 million people could potentially benefit from geographical arbitrage right now and enjoy the rest of their lives doing what they love without having to work again.

Identifying the most promising markets and opportunities can only be done by AI, as humans do not have access to such a vast amount of data for analysis. AI enables us to compare neighborhoods within a single city, projects, and even different cities, as well as various neighborhoods and projects in different cities, and most importantly, their growth prospects (while analysts may predict the growth of a city, they cannot accurately predict and compare 20,000 neighborhoods in 100+ cities to identify the best one).

Reducing errors and deception

There are situations where someone buys real estate and encounters unforeseen problems: real estate agents may withhold important information or intentionally not disclose all details, such as the upcoming construction of a railway nearby, even if this information was available. Purchasing properties with "hidden pitfalls" is becoming increasingly challenging, and selling such properties is even more problematic. Here is how an AI analysis of a specific project looks:

Selling undesirable real estate with such in-depth analysis and transparency of information will be extremely challenging.
Choosing the top 1% of growing locations or the best properties will become easier.

At the initial stages of AI implementation in the real estate market, its application will be relatively straightforward. However, as it becomes more widespread, the price difference between the best and worst real estate (where the "best" is often considered the most undervalued) will become less noticeable (although people believe that if the property is defect-free and priced reasonably, it's already excellent). Currently, the price difference between the top 10% and bottom 10% of residential complexes in one city can be as much as a 2-fold difference (one project increases by 30%, while another decreases by 30%, with the initial prices being the same).

In the future, fair pricing will be determined more accurately, and the opportunity to acquire high-quality real estate at very low prices will gradually diminish, starting with local markets and eventually comparing different cities. However, this opportunity is not widely known at the moment - use this advantage.

Investor Access to Emerging, More Promising Markets

Currently, there is no reliable data on which real estate market is best for investment. Large capital from Europe and the USA traditionally invests in local real estate markets, where they settle for a 4-5% annual return. At the same time, emerging markets can offer returns that are 3-5 times higher with relatively low risks. However, the lack of transparent information limits the flow of large investment capital into emerging markets that need it the most. This creates a significant imbalance in the value between well-known and traditionally less popular locations.

Reducing Real Estate Transaction Fees from an average of 10% for property exchange (sale and purchase) to 2-3%.

AI can serve more people and generally performs better than the average realtor, without requiring a 50% commission payment, as is necessary with human agents. With the simplification of the moving process within one city and between countries, people will be able to move more often, which, in turn, will lead to significant growth in the real estate market.

Currently, to sell a property in one city and move to another, a person usually has to pay about 10% of the transaction value in direct and indirect fees (5% for the sale and around 5% for the purchase of new property). Thus, the total expenses for the move are approximately 10%. If these costs are reduced to 3%, the secondary real estate market will become much more mobile, and the number of transactions will increase. The trend of remote work will also contribute to this process.
AI vs. Realtor ?

I have previously described arguments against real estate agents, and I will continue this topic now. Realtors are likely to be unable to compete with AI unless they resort to lobbying and banning or licensing AI (this may happen in the United States and Canada, while in other countries, realtor lobbying poses a weaker threat to AI).

From the perspective of real estate agency owners, their main goal, in addition to increasing sales, is to reduce costs. In this context, real estate agents in their agencies are the primary expense (earning commissions ranging from 30% to 80% of the agency's income). If there is a way to increase the profitability of the business by replacing realtors with AI or partially replacing the human process with AI and reducing commissions to realtors, they will do it.

AI already offers better advice and analytics than most realtors in terms of communication and responses to client questions. However, it is not yet widespread in the market. But AI products (such as AI chatbots or voice assistants) will begin to emerge in mass in 2024.

Realtors may follow the fate of stockbrokers, continuing their activity but focusing on serving wealthy and VIP clients. Their impact on the market and income will likely decrease tenfold in the near future, with these resources being redistributed in favor of companies providing AI services in real estate.

And most importantly, this could stimulate significant market growth (in terms of total volume and the number of transactions) by tens of percent in the next five years, benefiting realtors who embrace AI early. Those who resist and oppose these changes are likely to either leave the market or focus on client categories that will adopt technology more slowly, such as elderly people who prefer using button phones.

As an illustrative example, consider the possibility of creating an electronic realtor on the Character.ai platform right now, without the need for coding and prior preparation. Such a realtor would be more objective and work in the client's interest, rather than being motivated by commissions.

The only thing it currently lacks is detailed knowledge of prices, projects, the local market, and its nuances. However, this information is already available through Realiste.ai. Thus, it can be assumed that a full-fledged smart AI realtor for the UAE market will emerge soon, and thanks to the Realiste.ai API, dozens of similar AI realtors will soon appear in different countries.

In conclusion, realtors who adopt AI tools now will be able to earn a substantial fortune in the near future. Those who resist and oppose these changes are likely to be forced out of the market or will focus on client categories to which technology will penetrate more slowly, such as elderly people who prefer using button phones.

How to Profit from This
Currently, only a small number of people are aware of the existence of AI in real estate, giving a significant advantage over the market and other participants. However, in a couple of years, these opportunities will become more commonplace, and the potential income from their use will become more modest. As more people begin to utilize these tools, the advantage of their individual use will diminish.

Below, I outline what I believe are the most promising opportunities for profiting in this field:

Real Estate Investments In real estate, AI opens up two intriguing investment strategies that were previously nearly inaccessible. Each of these strategies has the potential to grow capital from $1 million (insert your own amount) to 10 times that amount (up to $10 million) over a period of 5 to 10 years.

Strategy 1 - Buying at a Discount and Reselling at Market Price: You can use AI to identify real estate properties that are selling at a discount or considered overvalued. After purchasing such real estate and waiting for its value to increase, you can resell it at market price, generating a profit.

The essence of the strategy is to find real estate that is priced 15% below the market value: 10% for profit and 5% to cover transaction costs and taxes. Regardless of market fluctuations, the goal is to sell such real estate at its market value. The faster the sale occurs, the better: if the cycle takes not a year but 6 months, you can increase the amount from $1 million to $10 million in 25 transactions, or in 12 years. With the use of financial leverage, this can be achieved in 12 transactions and in 5-6 years. Real estate can be found and sold without making improvements; you can calculate the value after renovations, taking into account investments - this is not essential. The key point is that AI can calculate this more efficiently and is already actively doing so.

Please note that taxes and expenses related to the sale or search for such real estate should be added on top of your calculations (I didn't mention this because different markets have different expenses and taxes).

Here is the link to the calculations https://docs.google.com/spreadsheets/d/1hfe0tb_8vizU7OSkSB1fSMtJbQ22nP3ddHjTXmbGMPs/edit?usp=sharing
Artificial intelligence can and is already being used to create a real estate pipeline with discounts. This is achieved through automated valuation models (AVMs), where each property entering the market undergoes an assessment and enters the pipeline. More detailed principles of how this system works are described here.

However, after five years of working on AI development, we have come to the conclusion that such a business model may seem dull. It requires a lot of movement and activity and is often more suitable for small companies. When working with a substantial amount of data, a large number of human resources is needed. Moreover, such models are rarely funded by banks, and non-banking capital is not always available in many countries around the world. For example, in Russia or the UAE, there are no financial products similar to Hard Money Loans, which represent a $30 billion industry in the United States.

Pros of the strategy:

  1. It is possible and works in any market, especially where there are no or deferred capital gains taxes (e.g., the 1031 exchange in the United States or in the UAE before the introduction of the 9% profit tax in 2023).

  2. Particularly effective in markets with well-developed financing options for such deals, such as the United States, England, Australia, and others.

Cons of the strategy:

  1. Continuously searching for such properties without software assistance can be challenging since there are not many property owners willing to sell at a 15% discount.

  2. It requires a high volume of transactions to grow capital, which can be resource-intensive.

Strategy #2: Smart Market and Project Selection

The second strategy that can lead to an increase in capital from 1 million to 10 million dollars involves making the right choices for just two projects over 10 years. This means selecting a market where the growth over 5 years would be approximately as follows:

  1. 15% in the first year,

  2. 15% in the second year,

  3. 11% in the third year,

  4. 7% in the fourth year,

  5. 5% in the fifth year.

For example, Dubai's real estate market has been demonstrating an average annual price growth of 15% since 2020. Currently, in 2024, the growth is around 11%. Some projects in Dubai have shown growth of 400% since 2020. The best projects within the top 10% over the last 4 years have exhibited an average annual growth rate of more than 30%. This Dubai example illustrates that finding a market and project with high growth rates is entirely possible with the right choices while avoiding risks related to defaults or property destruction.

Taxes and expenses related to the sale or search for such property should be added on top of these calculations (I didn't mention them because different markets have different expenses and taxes).

Here is a link to the calculations: https://docs.google.com/spreadsheets/d/1hfe0tb_8vizU7OSkSB1fSMtJbQ22nP3ddHjTXmbGMPs/edit?usp=sharing
Under such a strategy, it's crucial to make two correct project selections over 10 years. The actions involve:

  1. Buying one property and, after a year, evaluating it to gain extra funds from its increased value. You then purchase another property in the same market (or project) and continue this process for five years.

  2. On the fifth year, you sell everything and invest in market #2 (or project #2) and repeat the process for another five years.

The key point is to make the right choices for two markets, and the rest becomes a matter of technique.

Here's a brief explanation: In developed real estate markets, you can take out a loan to purchase a property and then order a reappraisal after a year. If the property's value has increased, this allows you to free up funds for purchasing the next property in the same project. Thanks to previous experience, you are already familiar with the project, making it easier to buy new properties with the released funds. Furthermore, having neighbors willing to help with off-market property purchases often enables you to save 5% to 15% off the market value.

Other strategies, such as renovation, construction, subdividing, or changing the property's use, often involve a significant amount of manual labor, paperwork, and additional complexities. This strategy focuses on the accessibility of all markets worldwide and the right choice, which doesn't require significant effort and isn't dependent on government authorities that can disrupt even the most promising investments.

Can AI significantly increase the chances of successfully selecting the market and project? Absolutely. By analyzing all markets and projects, AI can compare prospects in different locations, which is not feasible for humans due to the vast amount of information. AI can also predict when the market's potential is diminishing and it's time to exit a project. This becomes noticeable in advance on the AI's dashboard and forecasts regarding market and project prospects.

Important Notes:

In both the first and second strategies, it's crucial to:

  • Make the right project selection.

  • When buying during the construction phase, avoid defaults (choosing markets with escrow accounts is one option).

  • Consider entrusting additional due diligence of the developer to AI, which can reduce these risks but not eliminate them entirely.

  • Refinance in a timely manner and buy additional properties in the same project when necessary.

AI startups

The distinction between a startup and a conventional business lies in the fact that a startup can experience growth of over 100 times within a relatively short period, unlike what we typically associate with a business. A startup is essentially a business but with the potential to grow exponentially, like it's on steroids. In a regular business, such a growth dynamic is almost never feasible (for instance, it's easier for a startup selling software subscriptions to grow 100-fold in 5 years than to open a hundred restaurants instead of one).

The most advanced startups, those currently valued higher than others by the market, emerge when there is a shift in market paradigms and user behavior, or when new technologies emerge and proliferate (such as the internet, smartphones, and now, artificial intelligence).

The largest slice of the real estate market that an AI-based company can capture includes:

  1. Classifieds Market

  2. Realtor Market (Buying/Selling): AI can streamline and enhance the buying and selling process in the real estate industry.

  3. Market Analytics and Forecasting: AI can provide superior data analysis and market forecasting capabilities.

This is because AI is poised to perform these tasks better and faster than humans in the near future. Following these areas, there are other domains where AI can make significant inroads, such as document generation (replacing lawyers), security, building design (replacing architects), and construction optimization.

In the world, there are 1,200 cities with a population of over 0.5 million people, and in each of these cities, real estate agents capture over 90% of the property sales market. These are staggering numbers. However, since real estate agencies are widely dispersed, and the service primarily relies on individual agents rather than the brand, the companies that are currently very large have been around for over 40 years. On average, a real estate agency as a company globally earns no more than $3 million in annual commissions (meaning there are hundreds of thousands of real estate agencies). Moreover, no real estate agency in the world, in any city, commands a market share of more than 10%, illustrating just how fragmented the market is.

Classified ad platforms generate approximately 10-20% of their revenue from real estate agent commissions because their primary clients are real estate agents who publish a multitude of ads, often with fake listings and lowered prices to attract buyers. Typically, in each country, there are about a dozen real estate classified ad platforms that are very similar to each other, but the number one platform actually makes money, while all the others get crumbs (the "winner takes all" principle). Meanwhile, user behavior is changing - users want to see the best offers for them immediately, rather than calling all listings, some of which are fake. However, classified ad platforms cannot offer such functionality because one buyer usually contacts about ten real estate agents on average, and all these agents pay for advertising (similar to Tinder - if a person finds their perfect match on the first day, they won't continue using Tinder and will leave). Therefore, classified ad platforms need to provide good functionality to users but not too good to ensure they don't find the best offer right away.

AI startups better than SAAS or Fintech?

AI startups in the next 5-10 years will surpass the value of startups from the SAAS and FinTech era and create new value worth over $1 trillion. I believe that real estate will account for no less than 10% of this entire value due to the market volume and the value of AI in this sector. Here's the difference:

AI companies, unlike SaaS, have assets that can be localized or segmented and sold separately from the core business. This is not possible in SaaS and FinTech.

Over the past 20 years, all venture investors have fallen in love with Software as a Service (SaaS) startups due to the predictability of future revenues (if a person or company adopts a solution and links their card, the chances are high that they will keep paying for a very long time). FinTech became popular because what everyone uses (banks, accounts, transfers, payments) was extremely inconvenient and annoying for everyone and certainly not tailored for mobile devices, which were growing in popularity.

SaaS models and FinTech had one major drawback - the cost of acquiring 1 user was often only paid off after a couple of years (you can read in great detail about this). Therefore, these startups required large amounts of capital, and they have always been unprofitable and are likely to remain unprofitable for a very long time.

AI startups have extremely low customer acquisition costs today, and most users come organically (not through advertising) because the market is still far from saturated, and demand is growing exponentially due to expectations of significant time savings from routine tasks, which users actually experience from day one of using AI.

Important: AI startups offer product customization and localization. As an example, part of the strategy of my company, Realiste.ai:

The sales volume of the developer Emaar (they built the Burj Khalifa) is expected to be around $10 billion in 2023. If such a developer increases its sales margin by 4% from this volume, it will generate an additional $400 million in net profit per year.

If there exists an AI model capable of achieving this, the cost of such a model would be approximately $400 million (EMAAR buys it, returns its investment within 1 year, and receives a profit in the following year, and so on - a great investment). Can EMAAR create such a model for themselves? Can they buy or commission one specifically for themselves? They will definitely try to buy/create/obtain one (especially when everyone is talking about AI), but the chances that the commissioned model will cover all aspects and lead to increased margin are unlikely. The product is new; it's not like ordering CRM software from developers. The first to implement it will gain a significant market advantage, and then that advantage will diminish because other players will also adopt it, ultimately becoming as commonplace as CRM software.

A working AI model for the real estate market in Dubai can significantly increase a developer's margin: recommending what type of apartments to create in a new project, setting prices, determining the project's class, and even specifying the sizes of the apartments to maximize profits based on demand. During sales, it can automatically adjust prices (smart pricing) for apartments based on competitors' prices and demand (similar to algorithms in e-commerce or airline ticket sales, for example).

Every major company will rely on an internal model (selling such a model by subscription would be extremely difficult). The most interesting thing is that a developer like Emaar focuses on the Dubai market, and by selling such a model and the company that created it in the Dubai market, you can replicate the success in another hundred of the world's largest cities since large development companies usually concentrate their main market in one city.


  1. In this field, many unicorns are likely to emerge (the real estate market is the largest market in the world), and everyone understands the problems in this field. The key is to have a working solution. I suspect that the chances of becoming a unicorn are higher in this area than in any other AI-related field.

  1. The main challenge for an investor will be identifying who actually has a working, successful model and who simply mentions AI in their presentation and website (there will soon be many AI "tourist" companies, much more than in blockchain or "Uber for" industries). My recommendation is to test any product on a sample of 100+ different cases (for example, evaluate 100 different properties or take 100+ different recommendations and manually check and evaluate the product's quality). If a company hasn't created anything yet but plans to enter the AI real estate space, you already know what questions to ask (if they don't have answers, it means they don't fully understand what they are dealing with and how challenging it can be, and therefore the risk of failure increases significantly).
  2. Startup shares are illiquid (you have to wait a long time to sell shares and get cash). Liquidity events in a startup include: 1) IPO (7-10 years), 2) acquisition by a strategic buyer (2-3 years), 3) investment rounds (every 1-2 years), 4) company share buyback (5-6 years and usually before IPO or in cases of substantial income).
Investing in Real Estate Developers and Funds

With information obtained from AI, it is entirely possible to successfully invest in the stocks of real estate developers and real estate funds (until AI becomes widely adopted, at which point the advantage may diminish).

If there is a strong correlation between their official metrics (and there is), you can predict their performance.

For example, Emaar's shares have grown from 2.22 dirhams per share to 7.42 dirhams (a 338% increase from the price on December 1, 2023).

At Realiste, we plan to hire several analysts, provide them with our data, and allocate a couple of million dollars for such investments in 2024.

How AI Works in Real Estate

Reliable data is scarce almost everywhere, except for the real estate markets in the United States and Canada. Due to the high level of noise in the data, it's challenging to simply train a model, and a data synthesis method needs to be developed first if obtaining the data is not possible. For example, how can you determine the market price of transactions when there's no access to registries? Even if access exists, registry data often includes non-realistic transaction prices. AI can help here, but first, an accurate valuation model needs to be created, and then market data needs to be collected over time. After all, if you know that a year ago an apartment was sold for $1.1 million but lack context, the data is useless for valuation. Valuation requires context that can only be obtained over time. You can take listings from websites and predict when and at what price properties will be sold, and then compare the prediction accuracy to the actual results. But how can you distinguish real listings from fake ones, which can make up to 80% of listings on classified ads websites? It's difficult but possible.

Finding suitable teachers is challenging because each city is unique (and experts may disagree on property valuations and forecasts, so they need to be validated before they are allowed to teach the AI), and a model trained on one city won't work for another due to its specific characteristics. These city-specific features need to be taken into account during AI training. Sometimes, AI discovers unique features that teachers were unaware of. It's also possible to develop a city labeling system to account for over 90% of a city's features using a single algorithm: assign quality ratings to all houses, properly label districts, identify noisy areas, parks, water bodies, infrastructure, as well as zoning and about 30-40 other labeling layers to capture essential information about the city.

Conclusion: AI technologies are publicly accessible and can be implemented to some extent even by a school student. However, the key to creating AI that outperforms humans in the real estate market is data collection and preparation, addressing numerous small but important challenges, such as cleaning classified ad data from fake listings, and training models with the guidance of teachers.

Here is a brief excerpt from the Realiste patent describing part of the work with teachers and the iterative process of one part of the training. The faster iterations with feedback from teachers (or users) occur, the quicker (and therefore cheaper) the model can be trained.

Here I will leave a space and later I will provide a link to (which I am writing in parallel and will publish separately) on how AI works in real estate in detail. In the meantime, you can check out some not very detailed but interesting information here:


Can AI be blindly trusted today?

Modern AI capabilities can be compared to the functionality of early 2000s car navigation systems. While they are already working and becoming increasingly popular, users have not yet fully adapted to them, and sometimes these systems can make mistakes, such as directing into traffic jams. However, people are increasingly turning to them for advice before a trip.

By 2023, it's becoming difficult to find a taxi driver who relies solely on their memory rather than a navigator. In most cases (95 out of 100), navigators successfully guide to the intended destination via the most optimal route. Nowadays, more than 90% of trips, including daily commutes, are planned using navigators.

Thus, ignoring the data and insights provided by AI in real estate transactions seems unreasonable if these capabilities are known. It is recommended to verify the results suggested by AI with experts or based on common sense before making decisions regarding buying or selling.

In my estimation, by 2025, more than 50% of users will already be informed about AI in real estate. By 2030, not using AI in real estate decision-making will be as unusual as driving without a navigator is today. By that time, the quality of AI recommendations and insights will either be on par with the top 1% of experts or even surpass them.

Here are some links where Realiste.ai predicted in advance what would happen in the real estate market. If you're interested, you can search in the media section in Zen with the tag Realiste.

What are the limitations of AI in real estate today?

AI currently lacks precise data on what is inside a property (this can only be determined from photos with sufficient accuracy, for example, AI can estimate the quality of repairs in a property on a 5-point scale with 80% accuracy). There is limited data on where the windows of properties are facing (we've tried a couple of ideas, but haven't found a universal method suitable for all homes, and we continue to search). There's also limited or no data on who the neighbors are (it should be acknowledged that at the property selection stage, this is not so important and can be checked independently before buying).

What's coming soon, and how will the market change?

As of today, the real estate market is only in the early stages of digitalization, with less than 1% of transactions and analytics being digitized. A significant portion of processes and transactions still relies on human knowledge and skills.

The real estate market resembles the stock market of the 1980s: many participants, large cash flows, but most processes are still manual. Like stocks, real estate is used for capital preservation and growth, actively attracting retail investors. However, unlike the stock market, most property buyers have emotional attachments to their investments, as these are objects used in everyday life.

The history of the stock market shows that increasing transaction speed and analytical capabilities were rewarded during its formation. It can be assumed that the real estate market will follow a similar path. Speed and digitization will develop rapidly, bringing new technologies and solutions to the market.

The real estate market is also similar to e-commerce in that real estate properties can be purchased online, as is already happening in China through blogger streams. Real estate serves a dual purpose: as a means of use and as an instrument for capital preservation and growth. Unlike the stock market, most real estate buyers make choices based not only on numbers and forecasts but also on emotional attachment to the location, the appearance of the property, and the overall feeling it provides.

Here's a short list of products that will enter the market in 2024 and become very successful by 2026:

  1. AI Smart Pricing

  2. Construction Simulator

  3. Online Exchange

  4. Portfolio Manager

  5. Mortgage Valuation

  6. Real Estate Exchange

  7. Moody's-style Analytics

  8. Rental Search

  9. Both Residential and Commercial Real Estate

  10. AI Realtor Bots (will flood the market in 2024)

Here are a couple of GIFs of some of our products that are currently in closed beta and will be released in 2024.

About Realiste (Realiste.ai)

The company's goal is to create super intelligence (smarter than any human, real estate agent, or analyst, with access to 10,000 times more data and covering 100 times more cities) in the real estate market. The idea is to provide this AI to users for free (open access to some features, full functionality to be provided to B2B partners), while earning revenue through B2B channels (commissions from sales, integrating AI into businesses). The business model resembles that of Google. Currently, Realiste's platform includes data for 114 cities worldwide, 20,000 annotated districts, 20 macroeconomic indicators, and in the UAE, there are 20 layers of data and cross-sections (rental ROI, prices, green area, forecasts, etc.), 15 ratings for over 200 developers and 700 projects. Every day, over 2 million ads pass through the Realiste engine. All this data is updated either daily or weekly (for macro data), and all of it is free, used by our AI to provide forecasts and recommendations to clients. Our products are currently used by developers and banks, and there are free products for users. Realiste currently holds the world's largest real estate database (excluding the United States).

Brief Development History

I started betting on algorithms against human expertise in 2015 when I encountered the limitation of my investment analytics being confined to knowledge of just one district. Real estate agents were similarly restricted to one location (the same person could not provide accurate information about two different districts or two cities), and in 90% of cases, the expertise of real estate agents in investments was lacking. At that time, I had a real estate agency and was investing in land plots. Initially, like many others, it was a complex Excel spreadsheet with hundreds of variables. Then, with the help of developers, we transformed it into products. By 2019, we were putting all our efforts into training our first ML model.

We developed parallel models for different cities: New York (and 10 other cities in the US), Moscow, and Hong Kong as we traveled the world and studied the experiences of these locations.

From 2015 to 2020, we attempted to attract investments into this idea and were unsuccessful in securing VC investments anywhere (we pitched about 250 times, even multiple times to some because they forgot about us, and I pitched to one guy from the largest VC fund in the US three times in five years).

At the beginning of the pandemic, we returned from the US to Moscow and launched an open product in Moscow (users could find apartments in Moscow at 15% below market prices). This product attracted many users, and it was these users who invested money in the company.

A total of 60 angel users of the platform invested over 6 million dollars from 2020 to 2023 and continue to invest in the company. We came up with the idea of integrating AI into banks and developers in Russia, and we spent two years trying to do so. In two years, we integrated AI into various products in the 20 largest companies and earned only 10 million rubles (which wasn't worth the effort, of course). Meanwhile, users continued to use our platform and invest money in us.

Then the war in Ukraine happened, and we decided to leave for Saudi Arabia (because we always wanted to build an international business, and it was no longer possible to do so from Russia). We abandoned everything we had done in Russia (there wasn't much to leave behind, and integrating AI into banks and developers wasn't what a startup should be doing in Russia). At that moment, we were offered to sell 15% of the company in Dubai (where we had no initial plans to develop) for $700,000 and launch a product in Dubai. We launched the product in Dubai, came to adapt, and realized that Dubai was precisely the market where AI should be applied today. From October 2022 to October 2023, we sold 350 apartments in Dubai worth $130 million and earned $7 million in revenue in the first year. The company's valuation in Dubai was estimated at over $50 million by investors, and the 15% stake was worth more than $7.5 million. This investor is Maxim @kuchinmaksim (Telegram), who later joined the company and founded a department within the company to work with real estate agents (they can use our infrastructure and conduct transactions, generating $1.3 million in revenue in the first year).

Public track record is here : https://deals.realiste.ai/

The local success in Dubai encouraged both current and new investors to open cities and reserve locations for themselves (if it worked so successfully in Dubai, it could work just as well in Berlin, Moscow, Jakarta, and so on). Currently, Realiste's models are deployed in 114 cities, SEED investments have been secured in 15 cities, transactions are taking place in 5 cities (Dubai, Abu Dhabi, Sharjah, Ras Al Khaimah, Bali), and in 2024, the plan is to secure SEED investments in 40 cities (ranging from $200,000 to $500,000), conduct transactions in 15 cities, and have models deployed

Current Business Metrics for the First Year of Operations in the UAE:

  1. Sales: Sold properties worth $121 million, primarily to Russian-speaking clients. It's important to note that the Russian-speaking market declined by 4-5 times from March 2023 onwards, but our sales remained at a steady level. If the market hadn't declined, we could have achieved sales four times greater. Unfortunately, we didn't have the chance to shift towards English-speaking clients in 2023, but we are targeting five languages for 2024 in the UAE.

  2. Revenue: Earned $7 million, reaching self-sufficiency in the UAE.

  3. Online Transactions: Achieved $5 million in sales without the involvement of real estate agents, purely through online transactions. AI helped with analysis, and the support team assisted clients with transactions. These sales were conducted remotely, with no real estate agents participating. This achievement is remarkable, as few believed it was possible. Thanks to having all data and expertise on the platform, the need for a real estate agent's expertise, especially in investments, was eliminated. This allowed us to close deals through customer support and remote sales. The margin for such a business is 63% after accounting for marketing and sales expenses.

  4. Additional Revenue: Generated an additional $1.3 million through a scheme where real estate agents utilize the platform and our infrastructure to close their deals. In this scheme, we pay real estate agents 91% of the commission, retaining 9% for ourselves.
Initially, the team consisted of 120 individuals in the middle of the year, but it has now been reduced to 60 people thanks to the optimization of various processes using artificial intelligence.

Plans and Future Products: Currently, we provide API services and are beginning to offer White Label solutions. In Dubai, we are exploring the possibility of selling our developed AI strategies, although this is still in the discussion phase (we are already receiving interest from interested strategists). We plan to replicate the success we achieved in Dubai in several other locations in 2024, leveraging our experience, lack of competition, and significant market opportunities. We are also initiating collaborations with major companies for the integration of our AI (Artificial Intelligence as a Service, AIaaS). Our goal for 2024 is to increase revenue from $7 million to $35 million by expanding language groups and increasing the marketing budget. We have learned how to hire salespeople in large numbers, so that is not a problem. Additionally, we aim to start generating revenue from other locations at a level exceeding $1-2 million per year.

If You're Interested in Launching a Similar Project Anywhere in the World: Please follow the link at map.realiste.aifor further discussions. We are currently raising SEED funding and seeking co-founders in over 100 cities.

For Professional Investors Interested in Investing in Our Company: Contact information is provided below.
Some extra info:

I would like to explore the key challenges of the real estate market and also discuss lesser-known examples from various fields where digitalization has progressed faster than in real estate (e.g., e-commerce and the development of the stock market). Examples from other sectors and some lesser-known facts from related fields will help us better understand the development of AI in real estate and foresee where it will be in 5 years, allowing us to capitalize on this.

The real estate market is slow, and there is a GAP (discrepancy) between market events and participants' reactions, which can extend up to 6-12 months.

The real estate market is characterized by its sluggishness, especially when compared to the stock market. It's not only slow but also relatively predictable. For instance, events such as changes in mortgage rates or economic crises usually reflect on demand within 1-2 months. However, during crises, especially in developing countries, there tends to be a trend of investing in real estate as a protective tool to preserve capital (meaning real estate sales don't drop but instead rapidly increase in the initial stages of a crisis). In contrast, changes in real estate prices following a demand drop occur within 4-6 months in developing countries (take Russia, Argentina, or UAE, for example) and within 2-3 months in developed markets (like the USA, where the market reaction is quicker due to greater transparency). Meanwhile, stock market prices can change by 50% in a single day.

By analyzing changes in real estate demand, one can accurately predict price trends in the real estate market for the next 4-6 months. If you're ahead of other market participants, it's almost impossible to lose: during a crisis, you need to be the first to sell properties, even at a discount, and at the beginning of a market rise, to understand this before others. This 'GAP' can be verified by comparing property price charts of a city with sharp changes in mortgage rates or with changes in the stock market. The entire market's sluggishness provides an advantage to those who react faster during a crisis. The key question is: how to understand what's happening before everyone else?

Most property owners are convinced that real estate values always increase, and this belief is reinforced by strong emotional attachment.

The emotional bond with real estate is strengthened by significant life events and the fact that people spend over 50% of their time in living spaces. This emotional attachment and belief in the constant increase in property values make the market resistant to sharp price fluctuations. Rarely do real estate prices drop more than 30% in a single year, a situation influenced by the limited number of listings on the market, supporting its stability.

The average annual real estate turnover is up to 2% of the total market volume, with approximately the same percentage of new housing being built annually. This means that a complete cycle of ownership change in the market occurs roughly once every 85 years. Additionally, the demolition of properties is rare, with up to 1% of housing being removed from circulation annually. This suggests that the volume of real estate in the market is gradually increasing. Therefore, the popular saying, "Buy land, they're not making it anymore," doesn't always align with the realities of earning from real estate. There is still plenty of land, and its value is often determined by surrounding infrastructure and nearby buildings. The market's low turnover and owners' emotional attachment make it more predictable than the stock market. Essentially, if one gathers as much data as possible and understands people's behavioral strategies, does this open up opportunities for low-risk, high-reward trading in the market?

Real estate has arguably created more millionaires than any other sector in the economy (hundreds of millions own a house worth more than $1m).

Although exact data is lacking, a general analysis of various factors suggests a certain logic. If we consider the total number of millionaires worldwide, real estate likely stands as the field that has produced more dollar millionaires than any other industry or asset type. Why? Because real estate is the world's largest asset, and its use is widespread across all demographics. Unlike the internet, which is used by no more than 95% of the world's population, everyone uses real estate, making it a unique and all-encompassing asset. Someone aiming to make millions of dollars is more likely to achieve this in the real estate market than in stocks, business, or any other sphere.

The average property purchasing process takes about 3 months, and from the point of initiating a property transaction to its entry in the transaction register, it can take up to 6 months (typically around 3 months).

The primary path of a real estate buyer starts with online searches, where the main traffic goes to listing boards. Developers of new constructions invest in advertising their projects. In the first month, a client usually contacts around 15 sellers, both through listings and specialized websites. This is followed by a period of 1-2 months, involving calls, messaging in WhatsApp, and visiting properties. Then, a down payment is made, or a booking form is signed for new constructions. Two weeks later, funds are transferred, followed by a two-week procedure for registering the deal. Government sources typically update transaction data about a month later. In the case of new constructions in Dubai, it can take an average of three months from payment to registry update. Therefore, from the first call of a buyer to the completion of the deal, it usually takes an average of three months, and about 5-6 months for this information to become available to global analysts. By analyzing the number of calls and requests, one can predict what will happen with demand and how this will be reflected in the registries in six months.

The food chain in real estate operates as follows:

The residential real estate food chain starts with the landowner, who often acquires it through inheritance or by law. This owner sells the land to a developer, whose financing is either through the founder's personal funds or through external investors. The developer designs the project and obtains the necessary documentation, often forming Joint Ventures where large investors and funds acquire a stake in the project, expecting up to a 25% annual increase.

As soon as the project gets visualizations and layouts, investors step in, buying entire floors for subsequent retail resale. Before the project begins, the developer tries to gather as many pre-orders (EOIs) as possible, involving retail investors buying 1-2 apartments. The final apartment prices at the project launch are set based on the volume of pre-orders.

Both retail investors and end-buyers participate in the project launch phase. Investors purchase real estate with different goals: for quick resale, for sale after a few years, or for rental to generate income from value appreciation and rental payments. When the building is completed, investors enter the market, renting out apartments for long-term or short-term leases, along with end-buyers purchasing homes on mortgages.

The next stage involves renters subletting or renting out properties daily, and those working in the secondary market looking for quick resale deals. Each stage has various financing options from banks and non-financial organizations, with more developed markets offering more financing options.

In one project on a single market, there can be up to 20 different real estate investment strategies with varying conditions and terms. Each participant seeks a return on capital ranging from 2x (less risky participants) to 5x (more risky participants) over the central bank rate. Current practice includes manual analysis of hundreds of projects in one city to determine the most promising for a particular project, using tools like Excel for data collection and analysis. Surprisingly, most projects eventually find their investors and buyers at all stages, although the question remains: are all these projects successful, or do the top 10% take everything, leaving the remaining 90% to pay for this success? It's likely that not all projects, and not even 50%+, can be more profitable than the bank financing rate at all stages of the project

The Real Estate Lending Market

Real estate financing, both from banks and non-banking organizations, is based on two key factors: 1) the value of the property and its liquidation value; 2) the borrower's income and their ability to repay the loan.

In lending, it is acknowledged that the loan amount usually doesn't equal the property's full value. To secure a loan, borrowers often need to have 10% or 30% of the property value as their own funds. This ratio, known as LTV (loan-to-value), should be the primary criterion in loan issuance.

Banks and lenders can always reclaim their funds by selling the mortgaged property through court, although this can be time-consuming, and sometimes there are social restrictions. Eventually, the property can be sold for about 70% of its value, and the lender recovers their money plus interest. The main task for the lender is to accurately assess the property's value and react to its depreciation over time through reassessment.

However, most banks focus on borrowers, missing a significant portion of the market, including investors who improve properties and find tenants. In the USA alone, the real estate financing market is developed and represented by a short-term loan industry worth $35 billion. The situation in the rest of the world is different: real estate is often financed through mortgages and is mainly available to stably employed staff of large companies.

How Real Estate Prices Are Formed and Non-Standard Cases

Real estate prices largely depend on their location, especially proximity to the city center. This trend is confirmed by analyzing 114 cities worldwide, except for resort cities and places where properties are mainly purchased by non-local residents.

The second most significant factor is the class or quality of the property, followed by parameters like the property's size, then hundreds of other criteria, including floor, layout, view, the presence of renovations, etc. Simplifying, location and proximity to the city center form about 70% of a property's value, while all other factors collectively make up about 30%.

However, there are exceptions that make up about 10% of all properties worldwide. These non-standard cases fall into two groups: 1) The reputation of the house or area - for example, an area known for housing celebrities, wealthy people, or government officials, may be more expensive, even if it's far from the center; 2) Community and amenities - in Dubai, for instance, proximity to the center is almost irrelevant, but the quality of amenities and infrastructure plays a significant role. This is because most buyers in Dubai are not locals (90%), for whom there's no attachment to work or historical ties to the city center.

Pricing in Resort Real Estate and New Cities

In real estate markets primarily bought by non-locals, such as in Dubai, pricing often depends not so much on location but on the quality and features of the community, such as cottage settlement characteristics, amenities, and the audience. The main driving force behind price growth in such markets is often an active advertising campaign by real estate agencies and developers. Their efforts to promote the market among potential buyers can significantly increase demand and, consequently, property prices.

For example, in Dubai, without intensive marketing campaigns, real estate sales volumes could be 80% lower. In cities dominated by foreign buyers, such as resort towns, new cities, or cities without a long history, demand for real estate is mainly determined by the volume of marketing efforts by developers and real estate agencies. The more it's talked about, the more active the purchases are.

External events can also significantly impact the market. For instance, the conflict between Russia and Ukraine and subsequent mobilization actions led to an increase in demand for real estate in Dubai, which in turn doubled the value in some areas like Bluewaters, Palm, Bulgari Residences, Dubai Hills, and tripled the volumes of sales and rentals in a year.

Therefore, predicting the growth in value of real estate in such locations using traditional methods applicable, for example, in London, may be ineffective.

How the Stock and Share Market Has Changed from the 1980s to Today (useful to know because the real estate market is following the same path)

Before the 1980s, the stock market functioned without electronic trading systems. Trading was done by the Open Outcry method, where traders shouted and used hand signals to indicate their intentions to buy or sell, and communication with clients was maintained by phone. After the client's decision on the deal, the process could take from a few minutes to fifteen minutes.

Since the 1980s, the stock market has undergone active development and acceleration. Today, almost all stock trading is electronic, with transactions occurring in fractions of a second. Companies sought to outperform competitors by obtaining market information and making decisions faster, where even one second could provide a significant advantage in algorithmic trading.

The development and increased transparency of the stock market have led to a significant increase in its volume over the last 40 years due to attracting private investors and increasing capital in funds. This confirms that retail customers are inclined to enter markets with transparent rules of the game: the more transparent the market, the larger it is, and all participants usually benefit from its openness and transparency.

The second conclusion is that the speed of information retrieval and reaction in the era of technological development in the market allows earning billions. Although this advantage may disappear over time, the most capital is created when some have speed, and others do not.

Google was not the first internet search engine but outperformed everyone. Why? (useful to know because the real estate market is following the same path)

Google was not the first internet search engine, but it surpassed all competitors. Early search engines like AltaVista, AOL, and Yahoo were useful at the beginning of the internet's mass spread, providing access to information instead of long searches in libraries or through the yellow pages. However, as people became accustomed to the internet, they began to desire search results in seconds, not minutes.

Google stood out with its efficient search capabilities, allowing users to find the information they needed much faster than other search engines. This led to the joke that if you need to hide something, bury it on the second page of Google results, as most users never went beyond the first page, finding optimal results there.

Although Google's search technology was not unique in itself, the company quickly gained popularity. In 2000, Google was only eighth in traffic volume, while leading companies considered it a niche player. However, they ignored an important aspect - Google's potential in improving search quality. This could technically be implemented, but it did not fit their business models.

Google's key to success was its innovative business model with targeted advertising, launched two years after its founding. Unlike Yahoo and others who earned from selling banner ads with pay-per-view, Google focused on the quality of search and targeting ads, attracting advertisers and users. For other companies, improving search quality could have destroyed their revenues, so they did not bet on it and eventually lost. This lesson will be useful when discussing access to listings and ways to monetize users on online platforms.

Amazon survived the dot-com crash, and dozens of e-commerce companies did not (useful to know because it's not customary to buy real estate online, but that won't be for long)

During the rapid growth of the internet audience and the dot-com bubble, numerous companies launched the sale of various products online. These were sites like Pets.com (pet products), eToys.com (toys), Webvan.com (food), and Amazon, which started exclusively with books.

I believe Amazon's conscious choice to start with books played a significant role in their success. While toys, pet supplies, and food could easily be found in any store, a specific book had to be searched throughout the city. Amazon offered a solution to this problem, providing a selection 50-100 times greater than even the largest bookstores, along with convenient database search. This earned the company a loyal audience even during the dot-com crash.

Amazon's choice of product for online sales was deliberate, focusing on books. This helped them gain popularity and recognition. Only after a successful start with books did Amazon gradually expand its range, adding similar products like CDs and tapes. When a new market is developing, such as online real estate sales, choosing the wrong product category can be a critical mistake. The initial surge of interest from innovators may be followed by indifference from the majority of buyers, and the company may not gain fans in time. This lesson will be useful when discussing real estate sales online without realtors

Data Sources in Real Estate and Their Reliability

The effectiveness of AI in real estate directly depends on the availability and quality of data. In some countries, like the USA, extensive data such as MLS systems for advertising real estate, transaction registries, school ratings, crime statistics, census data, and more are available. However, even in these cases, data can be outdated or updated slowly and with difficulty.

In many other countries, like Russia, Germany, Indonesia, there are no public transaction registries. Data in government registries are often unreliable as people may understate transaction values in official documents to avoid taxes or for other reasons, and some or all of the transaction amount may be conducted in cash. Even data on mortgage transactions don't always reflect the real picture, as property valuations are often inflated to obtain larger loans.

Listing boards, while available in most countries, often contain inaccurate or false information. Realtors, especially newcomers, may create fake listings to attract clients and build a potential buyer database.

Thus, while the application of AI in the real estate market is possible, it first requires the development of technologies for collecting, processing, and even creating data to fill existing gaps. We will discuss how this can be implemented next.

How the Realtor Market Works in Developing Countries

On any real estate market, most transactions (ranging from 55 to 99% depending on the market) involve realtors. Real estate agencies or companies hire employees, train them, and in 99 out of 100 cases, their pay comes in the form of a share of the commission from transactions, usually split 50/50 between the agency and the agent. Agencies provide their employees with necessary resources, including client acquisition, office space, websites, business cards, etc. Agents, in turn, process requests and close deals. The extent of realtors' involvement in transactions varies by region: in Moscow, for example, the share of agency transactions in the new construction segment is about 30%, while in Dubai or New York, this figure exceeds 95%. The question arises: is it possible to completely eliminate realtors from the property sales process and move transactions online? This question is relevant as buying real estate for many is a critically important decision requiring human participation and support, both psychologically and procedurally. Real estate transactions are often complex and convoluted, and a small mistake can have serious consequences. The future role of realtors in real estate sales remains an open question.

Great, now that we have briefly covered all related fields and problems of how the real estate market operates, we can move on to AI in real estate and speculate on how the market will develop with this technology.

FAQ and Likely Comments:

Why did you write this article and disclose this information? I share parts of this material with employees, investors, partners in various countries, real estate agents, and more on a daily basis. I calculated that if I spend 20 hours on this article, I'll save myself 300 hours per year (the ROI is over 10X, so I decided to do it). If this information benefits our competitors, that's not a concern. In this field, the most important thing is to move faster and faster, and competitors can start by using our API to save years of their lives on what I've already accomplished. Plus, I believe this article will spread to people interested in the topic in various languages and attract the right talented individuals to our company.

Is it easy to create AI in real estate? No, it's not easy at all. It's easy to create AI when you have abundant, clean data. However, in real estate, data is scarce and often messy. You need to invent methods to solve these problems first. If it were easy, everyone would already have done it.

Isn't this just a retrospective analysis of historical data? Some might say this, even claiming they work in AI at OpenAI or Facebook and that creating such a system would only take a couple of weeks. I've heard it many times over 8 years. Please, tell me a company where I can go and see how it works. If it's that easy, folks, don't sit around. This field holds over $100 billion; you should get to work on it urgently.

But analyzing past prices won't predict future prices. Exactly! Predicting future prices or assessing real estate solely based on historical data is not the path to a good product. You can derive correlations and some insights about market behavior from the past, but predicting and evaluating based only on this data won't work.

What will real estate agents say? They might argue that AI won't affect them. They believe that for people, buying real estate is a significant decision that requires a real expert to explain and guide them through the process. True professionals will always be in demand.

Real estate agents should embrace AI. Real estate agents should urgently delve into AI because, firstly, it can earn them much more money. Secondly, those who don't adapt will end up working with clients who are 70+ years old or those who still use basic phones very soon.

What will old-school real estate investors say? They might ask about taxes and dividends, and mention that the costs of sales and real estate agents were not included in the calculations. Friends, we work in multiple markets, and taxes and expenses vary. My goal was to provide a high-level description of the market's workings and opportunities for investors. If you believe something was overlooked, feel free to use my data and make your own calculations.

My Contacts:

Links to Interesting Materials Used in This Article:

https://www.datocms-assets.com/65181/1698850844-the-saas-glossary-2023.pdf to the SaaS Glossary 2023
The content provided on this website, including any articles, opinions, or other information, is for informational purposes only and does not constitute financial advice. The material is solely the opinion of the author(s) and should not be interpreted as an endorsement or recommendation for any financial decision or action. Readers are advised to conduct their own research and consult with a qualified professional before making any financial decisions. The website and its authors assume no responsibility for any actions taken based on the information provided herein