You’ve heard it said before: purchasing real estate is one of the biggest financial decisions you will make in your life. That is more true now than ever. According to a recent report by ICE Mortgage Technology, the average American homeowner has $299,000 in equity, $193,000 of which is “tappable” and can be withdrawn.
Understanding the current value of such an impactful asset is incredibly important, whether you are considering selling your home, estimating your home’s tax burden, or calculating your net worth and investment portfolio.
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But figuring out your home’s value isn’t always easy, and guessing can cost you when so much equity is at stake. That’s where automated valuation models or AVMs come in. They use data and market trends to give you a quick estimate, so you have a better idea before making crucial real estate decisions.
In this guide, we’ll help you understand the ins and outs of AVMs, including how they are calculated and when they are (or aren’t) appropriate to use when making decisions in real estate.
To show you how to make the most of AVMs in the current real estate landscape, we spoke with Sandi Bates, a top real estate agent in American Fork, Utah, who has over two decades of experience.
What is an automated valuation model (AVM)?
An automated valuation model is a type of machine learning algorithm used by various online tools and websites to estimate the value of a real estate property. It uses a wide array of publicly available and user-submitted data, such as property type, size, general location, and comparable sales (when available), to provide an immediate value estimate, often available as quickly as a click of a button.
Several consumer-focused AVMs are available through sites like Zillow, Trulia, and HomeLight. The estimates from these online value estimators are ballpark figures based on the best available data, which means you should be careful when comparing them to other valuations, such as a comparative market analysis (CMA) that an experienced real estate agent can provide, or a property appraisal, which requires a licensed appraiser.
How do automated valuation models work?
Generally speaking, there are two ingredients to a value report produced by an AVM: a massive amount of data about as many real estate properties as possible, and a proprietary algorithm based on machine learning and regression analysis.
Which types of data are used in automated valuation models?
In general, AVMs work with two types of data: a calibration dataset that trains the model on real property values, and an ever-growing dataset that helps it estimate each home more precisely.
The calibration dataset acts as the model’s foundation, including a sample of properties and trusted information, like recently sold home prices, so the AVM can learn which features really influence a home’s value. This ‘source of truth’ helps the model weigh all the other available data to make reliable predictions. Think of it as teaching the system what really drives a home’s price.
The ongoing dataset then continually pulls in as much current information as possible from public records, surveys, and online activity. By combining what it learned from the calibration data with this expanding pool of information, the AVM’s algorithm can generate accurate estimates for each property.
Over time, as new data is added, the model becomes smarter and better at reflecting the latest market trends.
Examples of property data used by an AVM:
- Property size (acreage)
- Home size (square footage)
- The number of rooms (bedrooms, bathrooms, etc.)
- General location (state, city, zip code, and sometimes even neighborhood)
- Home quality characteristics (air conditioning, pool, garage size, etc.)
- User-submitted information (ex. HomeLight’s simple home value quiz)
- Property price history
- Property tax valuation history
- Property historical sales information
How algorithms used in automated valuation models work
The algorithms behind AVMs use techniques like machine learning and regression to turn large amounts of data into accurate home estimates. These terms can sound technical, but they’re easier to understand than you might think.
Regression is a method that looks at one set of data to predict another. Using a calibration dataset, the algorithm learns how different property details relate to home values, creating a formula that can predict a home’s price when given the right information.
Machine learning takes this a step further. Instead of just relying on a fixed formula, the algorithm keeps learning as new data comes in. Over time, it recognizes patterns and adjusts itself, getting better and more accurate at predicting property values as the dataset grows.
Why do different AVM tools produce different estimates for the same property?
You’ve probably noticed that the same home can show different estimates depending on which AVM tool you use. Each tool pulls from a mix of public records and proprietary data, and each uses its own method to calculate values. Differences in how the models are built and the types of extra data they include, like user-submitted surveys or website activity, explain why the estimates don’t always match.
Why do people use automated valuation models in real estate?
The main appeal of AVMs comes down to two things: speed and cost. Unlike other valuation methods, AVM estimates are nearly instant and usually free. In just a couple of minutes, you can visit a website, enter some basic information, and get a value estimate for your home. While other free home valuation methods exist, like a comparative market analysis (CMA), AVMs are the only option that delivers a quick estimate without any cost.
Even knowing that an AVM isn’t always perfectly accurate, there are plenty of reasons to use one. Homeowners thinking about selling often turn to AVMs for a starting point in planning their next steps.
“They’re useful if people are curious or just thinking about selling and want to know what their home might be worth,” says Bates, “or if they aren’t closely following the market and just need a general idea.”
Apart from getting ready to sell, there are several other scenarios where a quick value estimate is helpful for homeowners. Here are some of them:
- Gauging your borrowing power through a home equity loan or cash-out refinance
- Planning your investments by figuring out how your home fits into your overall portfolio
- Dividing assets during a divorce or separation
- Understanding the tax impact of homeownership
- Adjusting your insurance to make sure your coverage matches your home’s value
- Preparing your estate plan and figuring out how your property impacts your family’s assets
What are the drawbacks of using automated valuation models?
Most of AVMs’ drawbacks come from one simple problem: the data isn’t always perfect. While these tools use huge datasets, some information can be missing, hard to collect, or just plain inaccurate. That means the estimate can give a decent ballpark, but it might miss unique details that really affect your home’s true value.
Bates cites several specific examples of missing data that cause AVM estimates to be inaccurate. “In non-disclosure states, such as Utah, [AVMs] lack actual data from the sale of a house, including the sales price.” Refer to this map of multiple non-disclosure states.
“We’ve also seen these estimates significantly undervalue homes,” continues Bates, “because the public record often doesn’t include the basement square footage in its property information.”
There are multiple other limitations to AVM estimates that can affect their accuracy:
- The property condition, including necessary repairs and overall cleanliness
- Recent upgrades or improvements, such as a new kitchen, roof, or bathroom
- Specific location factors, such as its position on a busy street, access to public transportation, school district, zoning, and particular views or walkability to amenities like a park or beach
- Major changes in the area or community, such as local laws or news coverage
- Announcements that cause hype, such as large employers coming to the area
Even with these limitations, AVM estimates can still be very helpful to use as a starting point or springboard when exploring the value of your home. As a rule of thumb, the more standard your home is to the other homes in your area, the more you’ll be able to trust an AVM estimate.
What’s the difference between AVM and other valuation methods?
The main difference between AVMs and other ways to value a home is how the estimate is made. AVMs use algorithms and big datasets to give you a quick number, often in just minutes. A CMA is done by a real estate agent who looks at similar homes nearby and recent sales to give a more personalized estimate.
A professional appraisal goes even further, with a licensed appraiser checking out your home in person and factoring in details an algorithm or agent might miss. AVMs are fast and free, while CMAs and appraisals take more time but give a clearer picture if you’re making big financial decisions.
Here’s a breakdown of some of the differences among valuation methods:
- AVM — low cost, low quality, and doesn’t take much time
- CMA — low cost, higher quality, but may take time to contact and get from an agent
- Appraisal — high cost, high quality, and can take time or be quick depending on need and market conditions
When quality and accuracy are required, such as in the cases of mortgage loan approvals or for other official purposes, it’s important — and often required — to get an appraisal. Estimating property value has historically been a manual process, and in many cases, it’s as much of an art as a science when considering unique factors that affect home values.
How can I request an estimate using an automated valuation model?
Several websites provide free online estimates, but not all of them have the same access to data or tools to make their numbers as accurate as possible.
HomeLight’s Home Value Estimator includes a short questionnaire that can increase the accuracy of your home valuation estimate. These include simple information such as the property’s condition and the year it was built. We’ll pair your answers with housing market data from multiple trusted sources to get a real-world home value estimate in less than two minutes.
An experienced agent is a great option to determine your home’s value
An online home value estimate from HomeLight is a great starting point, but we recommend getting a full CMA from a top real estate agent as a next step.
“There are no two homes exactly alike,” explains Bates, “so getting an agent with access to the right data and knowledge of the local area is best.” An experienced local agent can factor in the elements that AVMs might miss to get you a more accurate understanding of your home’s value and help you sell it for the right price.
Use HomeLight’s Agent Match tool to connect with a top local professional in your area. These experts can combine the art and science of home value estimates.
AVMs help, but experts matter
Automated valuation models are a handy tool to get a quick, ballpark idea of your home’s value. How accurate they are depends on the data they can access and how well their machine learning has learned to make good predictions. As more data becomes available and algorithms keep improving, AVMs will only get better and more useful over time.
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But at the end of the day, there will always be factors that these models just can’t account for. For an official valuation, it’s best to work with a licensed appraiser. When you are ready to take action and sell your home, you should consult with an experienced local agent to find the best value for your home.
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