The nation’s foreclosure inventory is falling fast, and it’s presently the lowest it has been in 2007 according to CoreLogic. With nationwide foreclosures decreasing over the past 12 months from 42,000 in March 2015 to 36,000 in March 2016, that’s a full 67 percent decrease from the peak of the foreclosure inventory in September 2010. “Job and earnings growth have helped bring serious delinquency rates down in nearly every state,” observed CoreLogic chief economist Frank Nothaft. He added that nationally the economy added about 609,000 jobs in the first three months of 2016 alone and that average weekly earnings grew about two percent over the past 12 months, enough to traditionally keep pace with inflation.

Perhaps an even brighter sign that things are on the mend: in March 2016, the serious delinquency rate on mortgages was the lowest it had been since November 2007. At time of publication, about 3 in every 100 homes that were late on their mortgages were more than 90 days late, in foreclosure, or part of an REO portfolio at a bank. That is a massive improvement over recent years. “Delinquencies and foreclosure rates are now at pre-crash levels as the benefits of higher home prices, improving economic fundamentals, and years of cautious underwriting are being felt across the country,” said CoreLogic president and CEO Anand Nallathambi. He added that he believes this trend will continue and the serious delinquency rate will show “further substantive declines.”

Do you think that the housing market is truly on the mend?

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About the Author (Author Profile)

Carole Ellis is editor in chief of the Bryan Ellis Investing Letter. Under Carole’s leadership, the Bryan Ellis Investing Letter has grown to over 700,000 subscribers, making it one of the largest real estate newsletters in the world. Each day, Carole directly impacts the daily thinking and conversations of real estate investors worldwide by providing thought-provoking analysis and commentary on news topics relevant to serious real estate investors.

Carole has a strong background in research and in the management of respected publications. She holds a degree in English Literature from the University of Georgia, and has substantial research experience in plant biology. She is the former editor of and writer for the University of Georgia’s Research Magazine. She’s also the author of hundreds of articles and multiple books and home study courses published under the names of her clients, many of whom are well known, highly respected real estate entrepreneurs as well.

Carole makes her home in Kennesaw, Georgia with her husband Bryan and 4 children.

Comments (3)

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  1. G13Man says:

    on the mend , Yes

  2. Alex says:

    Housing on the mend? Statistics can tell the truth and statistics can lie…and while that sounds very contradictory, it is not.

    Problems With Statistics

    While statistics are extremely valuable, they are also notorious for being a means that people use to make false and misleading arguments.

    Faulty Statistics

    86% of statistics are made up on the spot, you know – the remaining 24% are mathematically flawed.- from an internet message board.

    An obvious problem with statistics is that they can be simply be fabricated. Of course this could be true with any claim, but because statistics use specific numbers, they have a quality of authority about them, and we may be a little less suspicious that a statistical claim is false than we would be for a more descriptive argument. Saying “83% of high school students admit cheating on tests” just sounds more authoritative than “most high school students admit they cheat on tests.”

    Other problems arise because of the way statistics are gathered. Some common problems are bad sampling (see Unfair Sampling) and biases introduced into polls and surveys.

    Bad sampling

    Most often, statistics are obtained by taking a sample from a larger group and assuming the whole group has the same characteristics as the sample. For example, if we ask 100 people who they are going to vote for in the next election, and 55 of them say they will vote for Murphy, we might assume that about 55% of all the voters will vote for Murphy. This is very useful, since we can’t possibly ask all the voters, but it has some important limitations.

    First, it would not be at all surprising to find that only 45% are really for Murphy, but just by luck we happened to talk to an unusually large percentage of Murphy supporters. This is the problem of sample size. The smaller the sample, the greater the influence of luck on the results we get.

    The other problem is that the way the people in the sample were picked might be biased toward a certain result. If Murphy supports spending lots of money for sports teams, and we sample people attending a football game, we might find an abnormally high percentage of Murphy supporters. On the other hand, if we sample people at the mall when most sports fans are at home watching a big game on television, we might find an abnormally low percentage of Murphy supporters. In either case, our results will be misleading.

    One kind of biased sample results when the people being sampled get to decide whether to respond. A television show might ask people to call in and vote on some issue. Not only might the people who watch that particular show be atypical of the overall population, but the people who are motivated to make the phone call might also be more or less likely to vote “yes” than people who don’t want to call. This is called “response bias” – people who choose to respond might on the average have different opinions than the people who don’t.

    Unfair poll questions

    Statistics based on polls can be faulty if the poll is constructed in such a way as to encourage a particular answer. If a question is worded “Do you feel you should be taxed so some people can get paid for staying home and doing nothing?” it is likely to get a lot of “no” responses. On the other hand, the question “Do you think the government should help people who are unable to find work?” is likely to get a lot more positive responses. Both questions could be about the same policy of providing unemployment assistance. Polls can easily be rigged to get a desired answer by the way the questions are phrased. Another way of rigging a poll is to have a series of questions designed to highlight the arguments for one side of an issue before presenting the question about how the poll responder feels about that issue. If it is important to know whether the results of some poll are reliable, one should try to find out exactly what was asked in the poll.

    Statistics that are true but misleading

    Even when statistics are technically accurate, particular statistical facts can be very misleading. I once heard a statistic that the rate of teenage pregnancy in a conservative religious group was higher than the national average. This seemed surprising until it became apparent that the reason wasn’t a high percentage of unwed mothers – it was a high percentage of women who got married while still in their teens.

    I recall hearing apparently conflicting claims about employment during a presidential election campaign a number of years ago. The challenger claimed that unemployment was up during the President’s term in office. The President’s campaigners said that employment was up! It turns out that both were true. The population had increased, and it turned out the number of people who were employed and the number of people who were unemployed had both increased.

    In sum, whether housing is on the mend or not is a matter if banking is safe and sound. If banking fails, housing fails. Banks are still too big to fail. We don’t have enough regulators.If foreclosures and bank repossessions hit 15 Million and we then add foreclosures without the title: short sales, we are not healed. So in sum, statistics lie, banking is not fixed and when you have 15 Million foreclosures in 2016, that’s too high.

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