{"id":498,"date":"2020-04-24T09:16:00","date_gmt":"2020-04-24T13:16:00","guid":{"rendered":"https:\/\/ibuyer.com\/blog\/?p=498"},"modified":"2023-11-08T03:51:24","modified_gmt":"2023-11-08T08:51:24","slug":"the-ibuyer-code","status":"publish","type":"post","link":"https:\/\/ibuyer.com\/blog\/the-ibuyer-code\/","title":{"rendered":"The iBuyer Code \u2014 Big Data keys to unlock the secrets of selling your home"},"content":{"rendered":"\n

The iBuyer revolution<\/strong> is underway and it\u2019s already reshaping how investors buy homes, how homeowners sell homes, how quickly real estate transactions happen, and the prices transactions happen at\u2014 which raises many questions: Is this a good thing for homeowners? When is the best time to sell to an iBuyer? What types of homes are iBuyers looking to buy? Are iBuyers paying a fair value? Perhaps more generally, can we decode the artificial intelligence which is driving their buying decisions?<\/p>\n\n\n\n

Our research uncovers the fundamentals of the largest national iBuyers in real estate today: Do iBuyers like Opendoor and Zillow and Offerpad want a two bedroom home in Kansas? Do their ai models prefer purchasing homes in the Spring like human buyers, or do they exploit human behavior to purchase houses below market value, during different times of year, in different places, and for different reasons?<\/p>\n\n\n\n

It\u2019s All About the Data<\/h2>\n\n\n\n
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To provide answers, we dive deep into the data \u2014 reverse engineering the ai by analysing over 20,000 known iBuyer transactions during all of 2019 \u2014 drawing insights to put the power of Big Data<\/strong> in the hands of the little guy, homeowners.<\/p>\n\n\n\n

iBuyer.com publishes iValuation instant offer home valuations<\/a> for millions homes across the country, and uses machine learning models which consume dozens of data inputs directly, and thousands more indirectly, for each individual home. To calculate the iValuation, iBuyer.com uses sophisticated and proprietary algorithms that incorporate data from county and tax assessor records, as well as valuation\u2019s from 3rd party data providers which incorporate direct and indirect feeds from hundreds of multiple listing services and brokerages.<\/p>\n\n\n\n

Measuring iBuyer Fairness<\/h2>\n\n\n\n

The ratio of the price an iBuyer pays for a home vs the price the iValuation says the home is worth, is a simple way to understand whether homeowners are getting a \u201cgood deal\u201d when selling to iBuyers. Throughout this paper we call this measure fairness<\/em><\/strong>.<\/p>\n\n\n\n

Our analysis consists of the big three national iBuyers: Opendoor<\/a>, Zillow<\/a> and Offerpad<\/a>. We study the data for all of 2019 to draw insights with respect not only to fairness<\/em>, but also geographic location, seasonality, and even bedroom counts \u2014 giving you unparalleled insight into the enigma of iBuyer purchasing behavior.<\/p>\n\n\n\n

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On the fairness axis (x-axis) in the chart above you can see a value of zero (0) which indicates the iValuation amount. To the right of 0 indicates cases where iBuyers paid a premium above the iValuation, and to the left of 0 indicates a discount below the iValuation (measured as a percentage %). The height of the bars indicates the count of homes purchased at that particular fairness (in decimal hundredths)<\/em>. Thus, for each national iBuyer, we can get a sense of total transaction volume in 2019, and generally speaking, how their offers compare to the iValuation for homes with 1 to 5 bedrooms.<\/p>\n\n\n\n

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Cash Offers on your home?\n You’re in the right place!<\/span><\/span>\n <\/h3>\n
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