The Downfall of Zillow: How Machine Learning Failure Cost Them Millions
Zillow, one of the largest online real estate companies in the world, has been facing a major setback over its use of machine learning algorithms in property value estimation. The algorithm that was designed to automatically adjust home value estimates failed to perform accurately, leading to a significant decrease in website traffic and a loss of millions of dollars for the company.
Background
Zillow is a website that offers a myriad of real estate services. Home buyers, sellers, and real estate agents use it to find and list properties. Zillow’s main attraction is the “Zestimate,” an online feature that provides home value estimates based on public data and machine learning algorithms. The company claims that the “Zestimate” provides homeowners with an accurate estimate of their home’s worth, while enabling buyers to make informed purchase decisions. However, an error in Zillow’s algorithm meant that the Zestimate’s estimates were significantly off the mark.
Machine Learning Failure
Zillow’s machine learning algorithm was supposed to use public data, such as the size of homes, their location, and nearby schools, to estimate home values. However, the problem with the algorithm is that it was biased towards certain types of homes and regions, resulting in inaccurate estimates for many of the property listings. This led to incorrect home valuations, making it nearly impossible for sellers to sell their properties at fair prices.
Financial Impact
The financial implications of the machine learning failure were significant for Zillow. The company took a hit to its stock price and lost millions of dollars in revenue. Additionally, the website’s traffic took a plunge as many potential buyers and sellers lost trust in the overall accuracy of the site.
The Future of Machine Learning in Real Estate
Real estate companies are increasingly using machine learning algorithms to support their online businesses. The industry is expected to grow by leaps and bounds, with many predicting that machine learning and AI will soon take over. However, this failure by Zillow highlights the importance of more rigorous testing, proofing, and beta testing to prevent these types of issues in the future.
Conclusion
Machine learning and AI are still in the early stages of growth, and there will be many more successes and failures to come. The failure of Zillow’s algorithm to accurately estimate home values has highlighted the need for more rigorous testing and proofing of AI-based systems. Nevertheless, it does not mean that machine learning cannot transform the property industry. The failure of Zillow’s machine learning algorithm demonstrates that enhanced testing and analytics are crucial for success in real estate and other machine learning-based industries.
