How Can Machine Learning and Automated Valuation Models (AVM) Help the Real Estate Industry?

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:

1. 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.

2. 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.

3. Valuation

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.

4. 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.

5. 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).

6. 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.