A lot of technology has evolved over the past decade. You may be familiar with self-driving cars, machine learning robots, and Siri. While these new technologies are getting all the fame and glory, there is another piece of tech that has remained relevant and highly useful. This specific type of tech can help realtors make more accurate predictions about house prices. I’m talking about the Automated Valuation Model (AVM). We all know that Artificial Intelligence is changing business. Right now anyone who can get it done wins. The automated predictions, getting the details of the investment, quick data analysis can be easily done with limited property attributes. There is no need to include human work in the process, all is done automatically in milliseconds after the users enter the property details.
AVM can help us predict real estate prices by creating automatic valuation. What we need to do is input the information (e.g., attributes of the property), then the model will learn and predict the price automatically. AVM is not a new technology, in fact, it has been used professionally for many years now. However, the model needs enough training examples (data, property attributes). In addition, the AVM models can be fitted with other data analysis tools, together creating a full report about a certain property.
How can such models be used in Real Estate? We will go through 6 points here:
- Fill in missing data.
In the real estate industry, there is a constant need to capture data for millions of properties using applications and APIs. Our company has found that some data, such as the number of bedrooms and bathrooms, are missing from listings. To improve these areas, we will fill in some of this missing data by training a machine learning model to do it automatically.
- Find out great property deals for investors.
When it comes to investing, you need to know how to find the opportunities that will lead you to success. Real estate is one of them, especially when it comes to rental properties. Getting the best deal on an undervalued property can really be a game changer. But how do you find such deals?
The answer here is AVMs. In milliseconds the property can be analyzed. Based on the model evaluation you can see what will be the ROI, how much is property really worth and how much can you make from it. Finding such deals is crucial and can be automated.
Such calculations can be done when the listing is added and automatically trigger notifications. This way we can get users’ interest. There are quite a lot of use cases that can rocket your revenue.
AVM is a well known term in the Real Estate domain. It is just like a simple formula that will give us the estimated or objective price of real estate. Using AVM we can determine how much to pay for a quality property under some known assumptions and circumstances. In the last few years, AVMs have gained popularity because of their speed and accuracy.
- Custom recommendations for clients using machine learning
Recommendation engines use product based modeling to recommend items to users and in the real estate industry, they’ve been proven very useful. For example, they can be used in real estate as a tool for the agents to easily recommend lettings to their clients. A user visits an agent’s website, what products are likely to be of interest to that user? That is exactly what the recommendation engine would take care of.
We can combine recommendation engines which can also be based on machine learning algorithms with Automated Valuation Models. That way we will be able to select the best deals per customer segment or even per customer. This gives us a huge lead against other systems in the Real Estate industry.
- Recommend listing updates for the seller
Machine learning can also be used to propose some updates to the listing. For example, we can score photos if the scores are bad and we can notify sellers to get better ones and update them. This can be impactful for AVM transaction price prediction. All the components from the house can be treated that way. The next thing we can use here is to propose better property descriptions also based on machine learning.
To make the updates we can use a set of data such as price history, user behavior, etc. Say we notice something like sellers often put the feature of “being close to public transportation” when they list their place, but it seems like users don’t care about that so much. Therefore we can propose an update from a machine learning perspective where the changes like “being close to public transportation” in the description are replaced by other related features. We can get this data from user behavior profiles or historical prices on the places (city).
- Sell all of the above features and even more
We have a lot of ideas for value driven Artificial Intelligence. It can upgrade your product, give more value to the users, automate a lot of things that agents are doing right now. Connecting the listings data with machine learning, data analysis, and static calculations it’s happening right now. The value added by machine learning and data analysis can be a premium product that users can buy or subscribe to. Connecting it with products has never been so simple.
What’s more, is that Artificial Intelligence in Real Estate can help agents and brokers. This technology can filter out relevant information that is useful for customers, instead of them sifting through irrelevant information, thus saving time.
Maybe you have some ideas on how to use Artificial Intelligence or Machine Learning in your product? Contact us and let’s find the best solution for your business.
Machine learning is omnipresent in every branch of our lives. The question that will be answered in this article is: How machine learning can impact our lives in terms of healthcare?
It’s obvious that using a processor, GPU and (probably soon) quant computers can help us detect cancer using images, assist in surgeries, give us advice based on our medical tests results and probably a lot more in the future. The world of AI is moving forward and the more data we use and analyze about healthcare the more we understand. Algorithms are improving, we are getting better and better. A few years ago it was hard to imagine self-driving cars. Right now we can see a technological shift which will change our lives. Autonomous vehicles are used right now on the streets. It’s true that some legal restrictions may appear, but we are evolving and it will change soon as technology grows and is tested better. The same situation is happening before our eyes – healthcare improvement. It is done using powerful machines which compute, think and give better results than humans. They are able to work 24 hours per day, 7 days per week, whole year with minimized downtime. Technology will replace human beings and we will be forced to change qualifications. It will not be the end of the world, though.
The technology helps us detect and cure diseases faster. Based on our medical results, genes and previous data analysis we will be able to improve our lives. Right now it’s shown that machine learning cancer detectors work better than experienced doctors. What will be achieved after that? The use of Artificial Intelligence in medicare will grow and in some situations outperform human beings. The algorithms will find health problems on the image – if any occurs, then the doctor will be notified and he or she will decide what to do. Machine learning can also show some abnormalities and deviations. It can propose drugs and it’s dosage. More autonomous systems will be created, maintained and constantly improved.
Based on that we can assume a few things about machine learning in healthcare:
- lowers human error
- detect diseases from blood, liquids, medicare images, x-rays, timestamped data gathered during a day (for example using a simple smartwatch)
- can assist humans
- discover new drugs
- find a right treatment
- automatically detect dangerous situation
The last thing to mention here is that AI will lead us to a better retirement. We can wear a smartwatch, have some cameras around the house or even both! For example with an AI the system is able to see if someone is lying on the floor or is in any dangerous situation. From a smartwatch we know some basic information like heart rate and blood pressure. These information is basic, but can save our lives. Such a system can notify an emergency directly or some other people – friends, family. They can check if everything is fine with the person. Such systems can save our lives.
Going deeper into technology we have a lot of methods to accomplish such tasks. Starting from fast cloud data processing, through algorithms, machine learning, neural networks, deep learning and ending with distributed, federated learning or agent systems. Right now we can build machine learning systems for all of the tasks mentioned in this article using these methods.
Data processing is always beneficial and needed. We can process streams and tones of bytes with current auto-scaling cloud systems in a few seconds. Anyway it’s a place to optimize our resources. This will have an impact on the next steps.
Deep machine learning can be used to perform any predictions, discovery, tasks and lower human error. It will analyse data and based on previous decisions the model will make its own ones. They can be validated and used.
There are plenty of solutions waiting for your needs. A great example of solving a problem with private data sharing is federated learning. Which trains a local model for each participant and then those models can be concatenated into a global model. Imagine a situation where we have a few big medicare companies and all of them willing to cooperate on a shared model which will detect cancer, but do not want to share the data itself. Using federated learning we can allow each of the companies to train their own model and later concatenate it into one global model which can be used by all of them. Sharing data without actually sharing it was never so simple!
All of those above lead to better healthcare.
As individuals we will be able to profit from better healthcare services.