HouseCanary applies the power of data science to derive accurate property valuation
How much is a house worth? Ask five real estate experts and you will get five different answers. Then take a look at Zillow’s “Zestimate,” and you’ll get a sixth — and probably an uneasy feeling that “this doesn’t look right.” Unfortunately, that’s the reality of the US residential real estate market in 2016.
At $27 trillion, US Residential Real Estate is the most valuable asset class not only in our nation, but in all the world. Add to that the fact that a house is typically someone’s largest investment — and their greatest potential source of net worth or liability. Given the size and importance of this investment, why is there no standard and trusted way to accurately value a home? Why isn’t there a Kelley Blue Book for houses? Granted, the housing market is vastly different than the automobile market – there are a couple thousand combinations of car models and trims; conversely, there are 120 million houses in the U.S., all with distinct features and characteristics that effect individual values, not the least of which is location. But even so, shouldn’t there be some way to figure it out?
Cracking the Code on Property Valuation
That is precisely the dilemma that HouseCanary brought data together to solve. Our premise was simple — accurate property valuation will drive better real estate decisions. And better real estate decisions, mean better market health for the individuals and institutions at virtually every phase of the residential real estate transaction. To do this right, we need to combine the best data with the best models. So, that’s what we did.
Today, HouseCanary has the most accurate, most comprehensive valuations for residential real estate — a statement backed up by ongoing testing and by our customers. HouseCanary is used and trusted by some of the biggest brands in real estate and financial services. We have created property valuation tools for real estate that range between 0% error and 3.6% error.
Our valuation algorithm incorporates all available market data to estimate the most likely value that a property would currently sell for in an arm’s-length transaction. We strive to put a value on every single family home, condo and townhouse in the United States.
HouseCanary’s property valuation models are built based on machine learning-algorithms that arrive at a solution by learning patterns from large amounts of data in order to make predictions. The algorithmic process is iterative and often performs many iterations of error minimization in order to produce a robust and highly accurate prediction.
Producing the most trusted real estate valuations requires that we provide transparency to our performance and approach. That’s why we have published our valuation technical brief, to share the details of our methodology and performance.
Looking ahead: Accurate Valuations that help people make better real estate decisions.
We are at the beginning of a revolution in real estate. Soon determining a property value won’t depend on someone’s hunch or a “guestimate.” Instead, you’ll be able to:
- Look at a house.
- Know most factors affecting its value in seconds.
- Have an accurate understanding of the home’s value.
- Make better decisions as a result of better understanding value.
Welcome to a world of pricing truth and transparency with HouseCanary.
Learn more by downloading our valuation technical brief, where you will find out how we:
- Identify the data included in the valuation algorithm
- Provide context for the logic behind the valuation algorithm
- Define key model outputs