{"id":491,"date":"2020-05-11T08:19:00","date_gmt":"2020-05-11T12:19:00","guid":{"rendered":"https:\/\/ibuyer.com\/blog\/?p=491"},"modified":"2023-11-02T05:06:36","modified_gmt":"2023-11-02T09:06:36","slug":"coronavirus-and-ibuyers","status":"publish","type":"post","link":"https:\/\/ibuyer.com\/blog\/coronavirus-and-ibuyers\/","title":{"rendered":"How AI Could Stabilize Real-Estate Prices During Coronavirus Panic"},"content":{"rendered":"\n

Most of the popular home valuations like the Zestimate use state-of-the-art machine learning technologies which, in recent years, have allowed ever more accurate predictions to become pervasive go-to reference points for both home buyers and sellers in residential real estate markets\u2014 and this could be good news for home owners during the coronavirus panic.<\/p>\n\n\n\n

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\u201cA couple of times a week, Nick Spencer checks the value of his four-bedroom house in Haddon Heights, N.J., on Zillow. He has no plans to move, describing the town, located about 10 miles from Philadelphia, as \u201cAmericana at its best,\u201d and his Cape Cod style home as \u201ca labor of love.\u201d

Yet there he is, clicking on Zillow every few days to see what the house he bought for $399,900 in 2006 is now worth. The last time he looked, the Zestimate \u2014 a Zillow algorithm that not only calculates current values for 110 million homes, but also predicts what they\u2019ll be worth in the future \u2014 pegged Mr. Spencer\u2019s home at $503,744. A little green arrow showed it up 1.7 percent from a month ago.\u201d<\/p>\nNYTimes<\/a><\/cite><\/blockquote>\n<\/div><\/div>\n\n\n\n

Perhaps it\u2019s a little high or low, but most people would agree that the Zestimate has become increasingly accurate in recent years. In fact, even if you think Zestimate is off in your area, chances are you think this in reference to one of its competitor valuations, which you consider more accurate \u2014 perhaps you prefer Trulia, or House Canary, or Realtor.com, or CoreLogic, or one of the many others out there? No matter which valuation you favor \u2014 odds are that it has become increasingly accurate over the past few years.<\/p>\n\n\n\n

But why? The answer is surprisingly simple, at least on the surface \u2014 and it all has to do with Big Data and Machine Learning.<\/p>\n\n\n\n

Machine Learning is undeniably one of the most influential and powerful technologies in today\u2019s world and we\u2019re far from seeing its full potential. There\u2019s no doubt, it will continue to make headlines for the foreseeable future and it has already been massively influential in industries ranging from healthcare to real estate and everything in between.<\/p>\n\n\n\n

In terms of real estate, it was June of 2019 that Zillow reported their signature home valuation tool \u2014 the Zestimate \u2014 was being upgraded to include new state-of-the-art machine learning technologies.<\/p>\n\n\n\n

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\u201cFrom a technical standpoint, incorporating computer vision and advanced machine learning models into the Zestimate algorithm enables us, for first time, to give consumers a more quantitative accounting of the qualitative aspects of their home.<\/p>\n\n\n\n

\u2026This release also marks the first time we are directly evaluating a home\u2019s list price, listing description, and how many days it\u2019s been on the market, as part of the Zestimate\u2019s calculations.\u201d<\/p>\nZillow<\/cite><\/blockquote>\n\n\n\n

In simple terms, machine learning is a tool for turning information into knowledge. In the past 50 years, there has been an explosion of data and this mass of data is useless unless we analyze it and find the patterns hidden within it. Machine learning techniques are used to automatically find the valuable (predictive) underlying patterns within complex data that we would otherwise struggle to discover. The hidden patterns and knowledge about a problem can be used to predict future events and perform all kinds of complex decision making.<\/p>\n\n\n\n

The critical thing to understand here is that machine learning algorithms are only as smart as the data they\u2019re trained with (which is usually pretty smart) \u2014 but in the case of real estate is skewed to reflect the previous decade\u2019s bull market.<\/p>\n\n\n\n

By now you\u2019re probably thinking \u201cOK so what does this mean for home prices during the coronavirus panic?\u201d<\/p>\n\n\n\n

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The Emperor Napoleon in His Study at the Tuileries<\/em><\/a> by Jacques-Louis David<\/a>, 1812 \u2014 wikipedia.com<\/a><\/figcaption><\/figure>\n\n\n\n
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\u201cNever awake me when you have good news to announce, because with good news nothing presses but when you have bad news, arouse me immediately, for then there is not an instant to be lost.\u201d<\/p>\nNapoleon Bonaparte<\/strong><\/cite><\/blockquote>\n\n\n\n

Like Napoleon, let\u2019s start with the bad news (for Sellers) \u2014 beginning with a Redfin coronavirus report which reads like a post-coronavirus \u201cState of the Real Estate Market\u201d:<\/p>\n\n\n\n

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Growth in Home Buying Demand Has Tanked<\/strong><\/p>\n\n\n\n

Many Open Houses Have Been Canceled<\/strong><\/p>\n\n\n\n

Industry-Wide, Listing Activations Are Up Slightly Over Past Seven Days<\/strong><\/p>\n<\/blockquote>\n\n\n\n

To be sure, most home sellers are not likely to love this news. Moreover, everybody knows the way of equities lately:<\/p>\n\n\n

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Dow Jones Average 2019\u20132020<\/figcaption><\/figure>\n<\/div>\n\n\n

And it\u2019s not far-fetched to think there could soon be a ripple effect in housing:<\/p>\n\n\n

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Stocks vs Real Estate 1970\u20132017<\/figcaption><\/figure>\n<\/div>\n\n\n

While the degree of correlation depends on the precise method of comparison between equities and real estate, it\u2019s intuitive to think there\u2019s a fairly strong correlation between the stock market and the real estate market. From the above chart it\u2019s easy to see that in 2008 at least, both asset classes dipped in tandem.<\/p>\n\n\n\n

So should we expect to see a similar result due to the recent coronavirus selloff? While we can\u2019t be certain of the outcome, there are a few reasons to think prices may be more resilient, or at least less correlated, than they were in the 2008 crisis.<\/p>\n\n\n\n

First and foremost, it\u2019s unlikely that most popular valuations like Zestimate are heavily weighting historical transactions prior to 2008 \u2014 this means that the machine learning algorithms have been \u201ctrained\u201d on datasets which are \u201coptimistically biased\u201d and which to date have amounted to ONE LONG BULL MARKET. In other words, these algorithms have never \u201clearned\u201d what a bear market even looks like.<\/p>\n\n\n\n

While some may argue that this \u201cblindspot\u201d means they\u2019re in reality \u201cnot too smart\u201d, others may see this as a form of consumer psychological resilience \u2014 meaning that even as the stock market crashes and erases their 401K or their employer decides to lay them off\u200a\u2014\u200athere is likely to be comfort found in knowing that the Zestimate remains remarkably stable (at least until it learns otherwise), which in the minds of consumers will only reinforce the notion that home values are still what they were a month ago, or even three to six months ago\u2026 And because both buyers and sellers alike habitually use these valuations as go-to starting points for their analysis, it\u2019s plausible that it could lead to a more stable real estate market, at least price-wise, than would otherwise exist.<\/p>\n\n\n

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