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luni, 23 decembrie 2013

Machine Learning for SEOs

Posted: 22 Dec 2013 03:16 PM PST

Since the Panda and Penguin updates, the SEO community has been talking more and more about machine learning, and yet often the term still isn't well understood. We know that it is the "magic" behind Panda and Penguin, but how does it work? Why didn't they use it earlier? What does it have to do with the periodic "data refreshes" we see for both of these algorithms?

I think that machine learning is going to be playing a bigger and bigger role in SEO, and so I think it is important that we have a basic understanding of how it works.

Disclaimer: Firstly, I'm no expert on machine learning. Secondly, I'm going to intentionally simplify aspects in places and brush over certain details that I don't feel are necessary. The goal of this post is not to give you a full or detailed understanding of machine learning, but instead to give you a high-level understanding that allows you to answer the questions in my opening paragraph should a client ask you about them. Lastly, Google is a black box, so obviously it is impossible to know for sure exactly how they are going about things, but this is my interpretation of the clues the SEO community has stitched together over time.

Watermelon farming

Machine learning is appropriate to use when there is a problem that does not have an exact answer (i.e. there isn't a right or wrong answer) and/or one that does not have a method of solution that we can fully describe.

Examples where machine learning is not appropriate would be a computer program that counts the words in a document, simply adds some numbers together, or counts the hyperlinks on a page.

Examples where machine learning would be appropriate are optical character recognition, determining whether an email is spam, or identifying a face in a photo. In all of these cases it is almost impossible for a human (who is most likely extremely good at these tasks) to write an exact set of rules for how to go about doing these things that they can feed into a computer program. Furthermore, there isn't always a right answer; one man's spam is another man's informative newsletter.

Explaining Machine Learning with Will Critchlow at SearchLove 2013 in London. I like watermelons.

The example I am going to use in this post is that of picking watermelons. Watermelons do not continue to ripen once they are picked, so it is important to pick them when they are perfectly ripe. Anyone who has been picking watermelons for years can look at a watermelon, give it a feel with their hands, and from its size, colour and from how firm it feels they can determine whether it is under-ripe, over-ripe or just right. They can do this with a high degree of accuracy. However, if you asked them to write down a list of rules or a flow chart that you or I could use to determined whether a specific watermelon was ripe, then they would almost certainly fail - the problem doesn't have a clean cut answer you can write into rules. Also note that there isn't necessarily a right or wrong answer - there may even be disagreement among the farmers.

You can imagine that the same is true about how to identify whether a webpage is spammy or not; it is hard or impossible to write an exact set of rules that work well, and there is room for disagreement.

Robo-farmers

However, this doesn't mean that it is impossible to teach a computer to find ripe watermelons; it is absolutely possible. We simply need a method that is more akin to how humans would learn this skill: learning by experience. This is where machine learning comes in.

Supervised learning

We can set up a computer (there are various methods, we don't need to know the details at this point, but the method you've likely heard of is artificial neural networks) such that we can feed it information about one melon after another (size, firmness, color, etc.), and we also tell the computer whether that melon is ripe or not. This collection of melons is our "training set," and depending the complexity of what is being learnt it needs to have a lot of "melons" (or webpages or whatever) in it.

Over time, the computer will begin to construct a model of how it thinks the various attributes of the melon play into it being ripe or not. Machine learning can handle situations where these interactions could be relatively complex (e.g. the firmness of a ripe melon may change depending on the melon's color and the ambient temperature). We show each melon in the training set many times in a round robin fashion (imagine this was you; now that you've noticed something you didn't before you can go back to previous melons and learn even more from them).

Once we're feeling confident that the computer is getting the hang of it, then we can give it a test by showing it melons from another collection it has not yet seen (we call this set of melons the "validation set"), but we don't share whether these melons are ripe or not. Now the computer tries to apply what it has learnt and predict whether the melons are ripe or not (or even how ripe they may or may not be). We can see from how many melons the computer accurately identifies how well it has learnt. If it didn't learn well we may need to show it more melons or we may need to tweak the algorithm (the "brain") behind the scenes and start again.

This type of approach is called supervised learning, where we supply the learning algorithm with the details about whether the original melons are ripe or not. There do exist alternative methods, but supervised learning is the best starting point and likely covers a fair bit of what Google is doing.

One thing to note here is that even after you've trained the computer to identify ripe melons well, it cannot write that exhaustive set of rules we wanted from the farmer any more than the farmer could.

Caffeine infrastructure update

So how does all this fit with search?

First we need to rewind to 2010 and the rollout of the Caffeine infrastructure update. Little did we know it at the time, but Caffeine was the forefather of Panda and Penguin. It was Caffeine that allowed Panda and Penguin to come into existence.

Caffeine allowed Google to update its index far faster than ever before, and update PageRank for parts of the web's link graph independently of the rest of the graph. Previously, you had to recalculate PageRank for all pages on the web at once; you couldn't do just one webpage. With Caffeine, we believe that changed and they could estimate, with good accuracy, updated PageRank for parts of the web (sub-graphs) to account for new (or removed) links.

This meant a "live index" that is constantly updating, rather than having periodic updates.

So, how does this tie in with machine learning, and how does it set the stage for Panda and Penguin? Lets put it all together...

Panda and Penguin

Caffeine allowed Google to update PageRank extremely quickly, far faster than ever before, and this is likely the step that allowed them finally apply machine learning at scale as a major part of the algorithm.

The problem that Panda set out to solve is very similar to the problem of determining whether a water melon is ripe. Anyone reading this blog post could take a short look at a webpage, and in most cases tell me how spammy that page is with a high degree of accuracy. However, very few people could write me an exact list of rules to judge that characteristic for pages you've not yet seen ("if there are more than x links, and there are y ads taking up z% of the screen above the fold..."). You could give some broad rules, but nothing that would be effective for all the pages where it matters. Consider also that if you (or Google) could construct such a list of strict rules, it would become easier to circumvent them.

So, Google couldn't write specific sets of rules to judge these spammy pages, which is why for years many of us would groan when we looked at a page that was clearly (in our minds) spammy but which was ranking well in the Google SERPs.

The exact same logic applies for Penguin.

The problems Google was facing were similar to the problem of watermelon farming. So why weren't they using machine learning from day one?

Training

Google likely created a training set by having their teams of human quality assessors give webpages a score for how spammy that page was. They would have had hundreds or thousands of assessors all review hundreds or thousands of pages to produce a huge list of webpages with associated spam scores (averaged from multiple assessors). I'm not 100% sure on exactly what format this process would have taken, but we can get a general understanding using the above explanation.

Now, recall that to learn how ripe the watermelons are we have to have a lot of melons and we have to look at each of them multiple times. This is a lot of work and takes time, especially given that we have to learn and update our understanding (we call that the "model") of how to determine ripeness. After that step we need to try our model out on the validation set (the melons we've not seen before) to assess whether it is working well or not.

In Google's case, this process is taking place across its whole index of the web. I'm not clear on the exact approach they would be using here, of course, but it seems clear that applying the above "learn and test" approach across the whole index is immensely resource intensive. The types of breakthroughs that Caffeine enabled with a live index and faster computation on just parts of the graph are what made Machine Learning finally viable. You can imagine that previously if it took hours (or even minutes) to recompute values (be it PageRank or a spam metric) then doing this the thousands of times necessary to apply Machine Learning simply was not possible. Once Caffeine allowed them to begin, the timeline to Panda and subsequently Penguin was pretty quick, demonstrating that once they were able they were keen to utilise machine learning as part of the algorithm (and it is clear why).

What next?

Each "roll out" of subsequent Panda and Penguin updates was when a new (and presumably improved) model had been calculated, tested, and could now be applied as a signal to the live index. Then, earlier this year, it was announced that Panda would be continuously updating and rolling out over periods of around 10 days, so the signs indicate that they are improving the speed and efficiency with which they can apply Machine Learning to the index.

Hummingbird seems to be setting the stage for additional updates.

I fully expect we will see more machine learning being applied to all areas of Google over the coming year. In fact, I think we are already seeing the next iterations of it with Hummingbird, and at Distilled we are viewing the Hummingbird update in a similar fashion to Caffeine. Whilst Hummingbird was an algorithm update rather than an infrastructure update, we can't shake the feeling that it is setting the foundations for something yet to come.

Wrap-up

I'm excited by the possibilities of machine learning being applied at this sort of scale, and I think we're going to see a lot more of it. This post set out to give a basic understanding of what is involved, but I'm afraid to tell you I'm not sure the watermelon science is 100% accurate. However, I think understanding the concept of Machine Learning can really help when trying to comprehend algorithms such as Panda and Penguin.

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Seth's Blog : "In an abundance of caution"

"In an abundance of caution"

Do we have a caution shortage?

Is it necessary to have caution in abundance?

When a lawyer or a doctor tells you to do something in an abundance of caution, what they're actually doing is playing on your fear. Perhaps we could instead for an abundance of joy or an abundance of artistic risk or an abundance of connection. Those are far more productive (and fun).

Also: The things we have the most abundance of caution about are rarely the things that are actual risks. They merely feel like risks.

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duminică, 22 decembrie 2013

Mish's Global Economic Trend Analysis

Free Shipping, Lenient Return Policies, Rampant Returns; UPS Expects 15% Jump in Returns; Reflections on Retailer Wages

Posted: 22 Dec 2013 07:49 PM PST

It's no secret that online shopping is soaring. From a customer standpoint, what's not to like about the shop-at-home, no gasoline, no sales tax, low-price-guarantee, and lenient return policy model of online retailers?

For those who know what they want, as well as those willing to wait a couple days to get it once they find it, the answer is nothing.

And as sales (or alleged sales) increase, so do returns. This has sellers sending emails that suggest clothing sizes based on return history, hoping to break return habits.

UPS Expects 15% Jump in Returns

Please consider Rampant Returns Plague E-Retailers

Behind the uptick in e-commerce is a little known secret: As much as a third of all Internet sales gets returned, according to retail consultancy Kurt Salmon. And the tide of goods flowing back to retailers is rising. Shipper United Parcel Service Inc. UPS +0.15% expects returns to jump 15% this season from last year, making them a significant and growing cost for retailers.

The stakes get even higher during the holidays, when return volume peaks. So this year, chains are digging through past transactions to weed out chronic returners, train shoppers to make better decisions or stem buyer's remorse.

Fashion discounter Rue La La, owned by Kynetic LLC, is testing a program that gives customers access to their own purchasing history, and also access to sizing data across its customer base, to help them make better purchases the first time around.

For instance, a customer who has continuously bought the same brand of dress shirts in both a small and a medium might see a note pop up saying: "Are you sure you want to order the small? The last five times you ordered both sizes, you only kept the medium," Chief Executive Steve Davis said.

The biggest cause of returns is size. To help shoppers choose better sizes, Macy's Inc. and Nordstrom Inc. JWN +1.58% are working with analytics startups such as True Fit Corp. that crunch data to show customers how clothes and shoes will fit them in real life. The companies match up garment specifications and other data from retailers with information provided by shoppers about their favorite clothing items, to generate sizing and fit recommendations.

Retailers are zeroing in on high-frequency returners like Paula Cuneo, a 54-year-old teacher in Ashland, Mass., who recently ordered 10 pairs of corduroy pants in varying sizes and colors on Gap Inc. GPS +0.73% 's website, only to return seven of them. Ms. Cuneo is shopping online for Christmas gifts this year, ordering coats and shoes in a range of sizes and colors. She will let her four children choose the items they want—and return the rest.

Reflections on Wages

That stores can take a huge jump in returns and still make a profit says a lot about the viability and cost pressures on the store-front model with greeters, retail clerks, sales associates and floor assistance.

It also says a lot about why wages are where they are.

Mike "Mish" Shedlock
http://globaleconomicanalysis.blogspot.com

Christmas Sales Decline at Target in Wake of 40 Million "Compromised Credit" Cards; Stolen Cards for Sale on Black Market

Posted: 22 Dec 2013 04:13 PM PST

The final weekend of Christmas sales is the worst possible time to have a security breach, but that's what happened at Target. Sales are down at least three percent following a security breach in which hackers stole millions of credit cards.

Let's back up a bit and review what happened.

40 Million Credit, Debit Cards Compromised

On December 19, Target disclosed that hackers gained access to as many as 40 million credit and debit cards used by customers of Target during the height of the holiday shopping season, in one of the biggest data breaches in history.

The Washington Post has details in Target says 40 million credit, debit cards may have been compromised

Company officials offered few details on the intrusion, which reportedly began the day before Thanksgiving and lasted until Sunday this week. Security experts said that the kind of information stolen – including names, card numbers, expiration dates and three-digit security codes – could allow criminals to make fraudulent purchases almost anywhere in the world.

The breach highlighted vulnerabilities in the massive, interconnected shopping systems used for billions of dollars of retail transactions every day. Customers at Target's nearly 1,800 stores in the United States were potentially affected, though those who shopped online were not, the company said.

"Whatever money Target thought they were going to get during the holiday season just got flushed down the data-breach toilet," said John Kindervag, an analyst and data security expert at Forrester, a research firm. He estimated that Target will have to spend at least $100 million to cover legal costs and to fix whatever went wrong.

Kindervag said the company will owe money to card brands, like Visa and American Express, that have to reimburse customers for fraudulent transactions. Target, based in Minneapolis and one of the nation's largest retailers, also faces the risk of enduring damage to its reputation, according to analysts and consumer advocates.

The number of serious data breaches appears to be rising. This month, JPMorgan Chase disclosed that 465,000 of its card users' data had been stolen after an attack on its the Web site for its prepaid card.

Stolen Cards for Sale on Black Market

Fox News reports Debit and credit cards stolen in Target breach reportedly for sale in underground black markets

Credit and debit card accounts stolen during a security breach involving retailer Target have reportedly flooded underground black markets, going on sale in batches of one million cards.

The cards are being sold from around $20 to more than $100 each, KrebsOnSecurity reports.

The security news site said it spoke to a fraud analyst at a major bank who said his team was able to buy a portion of the bank's accounts from an online store advertised in cybercrime forums as a place where thieves can buy stolen cards.

The Target data theft is the second-largest credit card breach in U.S. history, exceeded only by a scam that began in 2005 involving retailer TJX Cos. That incident affected at least 45.7 million card users.

On Friday, Target reiterated that the stolen data included customer names, credit and debit card numbers, card expiration dates and the embedded code on the magnetic strip found on the backs of cards, Target said.

Angry Target customers expressed their displeasure in comments on the company's Facebook page. Some even threatened to stop shopping at the store.

Target hasn't disclosed exactly how the breach occurred but said it has fixed the problem.

Traffic at Target Stores Down 3-4%

In the wake of the data breach, Traffic at Target Stores Down 3-4%

The number of transactions at Target slipped 3% to 4% compared with the final weekend before Christmas last year, estimates retail consultancy Customer Growth Partners LLC.

By contrast, transactions at other retailers were strong.

A spokeswoman for Target declined to comment specifically on this weekend's results, saying the retailer reports sales on a quarterly basis. Target tried to limit the damage by offering a 10% discount to all customers in its U.S. stores over the weekend, and analysts said that effort helped.

"This is the worst possible time something like this could happen," said Craig Johnson, president of Customer Growth Partners. His firm estimates that U.S. retail sales on Saturday totaled $17 billion, exceeding those on Black Friday by $2 billion.

The breach began Nov. 27 and wasn't halted until Dec. 15.

J.P. Morgan Chase & Co. slapped daily spending limits on debit-cards that had been used at Target stores during the period in question. Citigroup Inc., meanwhile, in some cases may lower limits, block transactions and reissue cards for debit-card holders if it sees suspicious activity, a person familiar with the matter said Sunday.

Mike "Mish" Shedlock
http://globaleconomicanalysis.blogspot.com

Seth's Blog : "Am I supposed to like this?"

"Am I supposed to like this?"

If we think we are, we probably will.

We're more likely to laugh at the comedy club. More likely to like the food at a fancy restaurant. More likely to feel like it's a bargain if we're at the outlet store.

Am I supposed to applaud now? Be happy? Hate that guy? Use a fork?

Judgments happen long before we think they do.

And successful marketers (and teachers and leaders) invest far more into "supposed to" than it appears.

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sâmbătă, 21 decembrie 2013

Mish's Global Economic Trend Analysis

Australia's Alleged Conservatives Surrender to Unions; GM Australia vs. GM US; Currency Madness Everywhere

Posted: 21 Dec 2013 06:13 PM PST

Here is a link from a couple of weeks ago from my "Down Under" friend "Bisbane Bear" regarding Australia politicians surrendering to unions for absolutely no reason, and for no possible benefit.

For US readers not following Australian politics, Conservatives won a landslide victory in the September election with Labor Party percentage vote the lowest in 100 years.

Conservatives could have and should have demanded change. Instead Minister Ian Macfarlane decided to create a panel to advise on unions, and one of the three appointees was a huge Labor Party advocate.

Surrender to the Weak

In response, News.Com.Au commented "Surrender to the weak and willful will cost us all".

INDUSTRY Minister Ian Macfarlane made an incomprehensible decision this week. After talks with Coca-Cola Amatil, a taxpayer-funded three-person panel was created to advise on a request from SPC Ardmona for assistance. The panel's charter includes "workplace practices, productivity" and "product range".

Labor Party heavy and former ACTU secretary Greg Combet is one of the appointees - he is going to advise on enterprise bargains with the unions.

After the announcement of the SPC panel, between fielding calls from the bewildered and outraged, I scrabbled for an explanation for Macfarlane's reckless move. I have been writing on these issues since 2007 and have never found reason to criticise the Coalition, but this action is naive, irresponsible and indicates the government is captured by the big business-big union nexus.

Australia is at a crossroads. For 20 years, regardless of the legislation, about half of our companies have been incrementally enterprise bargaining themselves into bankruptcy, while the other half have not.

Increasingly, many of those that have bargained are on the verge of ruin. A growing number will be seeking government subsidies during the next few years.

It is not always easy to say no to unions, and occasionally you must negotiate, but no company can be forced to make an enterprise agreement. Any business can simply pay its workers the modern award wage.

About 20 per cent of Australian workers are paid only the award wage, while roughly 30 per cent are paid a rate above the award, with the award remaining as the legal minimum.

Many companies have never bargained with their workers, while others stop bargaining after seeing its adverse effects.

Roughly 50 per cent of Australian workers are employed by companies that have chosen to enterprise bargain. Enterprise bargaining agreements are binding contracts that sit over and above the award, like gold-plated awards. Agreements include extra productivity restrictions, often doubling, tripling or even quadrupling the total employment cost of a workforce. These agreements have the force of law. The employer cannot change them without an employee vote.

Enterprise bargaining is the main reason that Holden, Toyota, Simplot and SPC Ardmona are in strife. These companies seek government subsidies because they need money for the inflated wages and conditions they have agreed to pay but cannot afford.

During this election term the list of companies needing help will grow. No one in their wildest dreams would have envisaged a Coalition government would create a crack team of taxpayer-funded faux toecutters to run the ruler over and advise on the internal workings of distressed companies.

Let me just cut to the chase here. Companies that are financially distressed because of unaffordable enterprise bargaining agreements should be instructed to lodge a form with the Fair Work Commission to have their agreement dissolved, at their own cost. All of their workers and unions should sign the form and be returned to the award wage before any of them even consider putting their hand out for money.

For a government to have a policy other than this is to reward sections of the business community for doing the wrong thing, for being foolish, irresponsible and weak. A government should never send the signal to business that taking the easy way out - giving in to the unions and paying workers wages it cannot afford - will result in financial assistance.

Conservative in Name Only

I asked Brisbane Bear a few questions including "Why would Macfarlane toss such a negotiation offer to unions in the first place?"

He responded ...

Hello Mish

Macfarlane is a Country politician. They are the worst type of socialists. He is conservative in name only.

These food companies that have their hands out are based in the farm belts or food growing regions. Huge US companies like Simplot own many Australian food brands. If they close down these small towns are decimated.

We have award wages in this country. They are the basis wages and working conditions which are the bare minimum. We have a system whereby companies can do an enterprise bargaining agreement (EBA) at individual companies.

Companies in strong union dominated industries have written very lucrative EBA's in recent years during the mining boom and have subsequently pushed wages and benefits thru the roof.

These companies can't possibly afford these wages and now they are losing money hand over fist.
The idea these car makers or any other manufacturer can pay whatever the unions demanded and now think they can simply get taxpayers money to help subsidize these wages is ridiculous.

I have argued for about 3 or 4 years that wages are too high and working conditions too generous.
I sign off most letters to the papers by saying "they can't afford these wages and neither can we".

"We" being the rest of us in the real world trying to run a business with these high wages and generous working conditions and no free money from the taxpayer to offset them.

Businesses in trouble don't need to go broke or close down. They just have to go back to paying award wages. That would mean pay cuts of 50% or more in most cases.

Looking for madness? Baggage handlers for QANTAS earn up to $85k per annum.

When these protected companies and industries cave in to unions, these new wages and working conditions become the benchmark and these wages flow onto every other sector. It is not sustainable and the repercussions are being felt right now.

GM has decided to shut down their Holden plant in Australia. 50,000 jobs are directly and indirectly on the line. BP announced 300 jobs going in Australia saying our costs are way too high.

There will be hell to pay as these jobs disappear in the 100,000's. Our property bubble has been blown off the back of these outsize wages.

Hope that helps make it a clearer picture.

Regards
Brisbane Bear

GM Closes Australia Plants, Toyota to Follow

On December 11, Yahoo!Finance reported General Motors to close Australian plants by 2017

Auto giant General Motors said Wednesday it will close its Holden plants in Australia by 2017, prompting Toyota to review its operations as unions warned the car industry was finished.

Holden's decision to move to a national sales company, costing 2,900 jobs, comes after Ford said in May it would stop making vehicles at its unprofitable Australian factories in 2016, with the loss of 1,200 jobs.

With Mitsubishi closing its Adelaide plant five years ago, only Toyota Australia -- which employs more than 4,000 workers -- will be left making cars in the country.

Even that appeared uncertain, with the Japanese auto firm immediately announcing a review of its own position in Australia.

"This will place unprecedented pressure on the local supplier network and our ability to build cars in Australia," Toyota Australia said in a statement about Holden's closure.

The Australian Manufacturing Workers Union said it expected Toyota to follow Holden's lead.

"It's now highly likely that Toyota will leave Australia. In fact it's almost certain," AMWU national vehicles division secretary Dave Smith told reporters.

"It's a very bleak day indeed."

GM chief Dan Akerson said the decision to shutter Holden's Australian operations reflected a "perfect storm of negative influences the automotive industry faces in the country."

"This includes the sustained strength of the Australian dollar, high cost of production, small domestic market and arguably the most competitive and fragmented auto market in the world," he said.

Reflections on GM Australia and GM U.S.

How did GM U.S recover?

In bankruptcy GM shed a mountain of debt. Equally important, if not more important, unions agreed to work rule and pay changes. The same thing needs to happen in Australia, across the board: in manufacturing, in restaurants, and in retail stores of all kinds.

Instead of pushing that agenda industry minister Macfarlane wants to ask unions what needs to be done.

I can tell you the answer in advance: the unions will seek still more handouts in return for trivial rule changes and little if any pay structure changes.

Currency Wars

The Reserve Bank of Australia is in on the act as well.

As with Japan striving to sink the yen, Australia's central bank wants to sink the Australian dollar. Of course the ECB wants a lower euro, and the Fed wants a lower US dollar.

Madness is everywhere.

Mike "Mish" Shedlock
http://globaleconomicanalysis.blogspot.com

Expect Higher Mortgage Loan Rates in 2014; New "QM" Rules May Mean Less Lending

Posted: 21 Dec 2013 11:27 AM PST

In regards to Average 30-Year Mortgage Rate Hits 4.47% (Not Counting Fees); Affordability Check Michael Becker at WCS Funding Group just pinged me with his thoughts on why mortgage rates will go up in 2014 even if treasury rates stay flat.

Michael writes ...

Hey Mish,

I was just reading your post on rising mortgage rates and I can confirm that mortgage rates are approaching the highs reached earlier this year in early September.

Additionally, they are set to go higher in 2014 regardless of whether or not the yield on the 10 year Treasury continues to rise. The agency that oversees Fannie and Freddie, the FHFA, has announced another increase in their guarantee fees or g-fees and increases in their loan-level price adjustments or LLPA.

The former are charges to lenders for guaranteeing mortgage backed securities and the latter are risk based adjustments to pricing on mortgages. The new increases in both will be charged to borrowers and will increase mortgage rates as much as .375% for many borrowers.

The FHFA has stated the reason for these increases is to encourage private money, non-Fannie or Freddie, to return to the mortgage market. While that is a good idea and many in the industry would like to see that happen, it's hard to see that happening with the new Qualified Mortgage (QM) rules issued by the Consumer Finance Protection Bureau CFPB starting on January 10, 2014.

Without going into much detail these new rules will restrict lending in the future and I believe discourage private money from entering the mortgage market.

So with rising rates, increased fees making rates even higher than they would be otherwise, and mortgage credit being further restricted it's hard to see how real estate will continue to recover in 2014 as affordability decreases.

Regards,

Michael Becker
WCS Funding Grp.

New "Qualified Mortgage" Rules May Mean Less Lending

The Chicago Tribune reports It'll take time to see effect of new mortgage rules

New regulations governing home loans take effect Jan. 10, but it's likely to take a few months to see how much they really alter a prospective borrower's ability to get a mortgage.

Combined with other tweaks made in the past few months, the changes will mean new terminology and revamped paperwork for lenders to understand and then explain to borrowers in 2014. They also could lead to less lending, experts say.

The goal of the new mortgage rules from the Consumer Financial Protection Bureau is to better protect borrowers from the lax underwriting that wreaked havoc on people and the housing market. The regulations are designed to ensure a borrower's "ability to repay" a mortgage while also offering lenders protection from borrower lawsuits so long as they make safer so-called qualified mortgages.

"I think the mainstream borrower is going to be OK," said Bob Walters, chief economist at Quicken Loans. "Lenders will go through a period of adjustment. There will be some upset in the first half of the year as people digest the rules."

The borrowers most likely to be affected are those on the lower and higher ends of the lending spectrum.

The rules bar some loan products that all but disappeared during the housing crisis — interest-only loans, balloon-payment loans and mortgages with terms that extend past 30 years — from being considered qualified mortgages.

Under another part of the rule, a borrower's overall debt can make up no more than 43 percent of gross income. The effect of that provision will be muted, however, because, at least temporarily, it does not apply to loans that will be purchased by Fannie Mae or Freddie Mac or backed by the Federal Housing Administration. Those agencies continue to account for the overwhelming majority of new mortgage loans.

However, Fannie Mae, Freddie Mac and the FHA all are looking to limit their exposure, and thereby the taxpayer's exposure, in the housing market.

The FHA last month decreased its maximum loan limits for 2014. The Federal Housing Finance Agency, which regulates Fannie Mae and Freddie Mac, this month said it was considering reducing the maximum loan size it may buy.

Housing experts say one effect of that rule could be that consumers looking for loans in the $100,000 to $150,000 range may find fewer lenders from which to choose. That's because a loan has to go through the same amount of paperwork and underwriting, regardless of whether it's for $100,000 or $400,000.

"Lenders may not do those loans," said Ken Perlmutter, president of Perl Mortgage. "It's just as much work, and you can't change the fees."

That 3 percent cap also could affect a borrower's ability to buy down their interest rate by paying points upfront, as well as restrict the ability of people with lower incomes and risky credit, who typically have paid higher fees, to receive a mortgage.

Jumbo mortgages also could become harder to receive because they too must meet the 43 percent debt-to-income ratio to be considered a qualified mortgage. However, Perlmutter said he already is seeing investors step in who are interested in purchasing mortgages that fall outside the government's regulations.

2014 Summary

More Consumer Protections
Loans Harder to Get
More Fees
All things equal, 0.375 Percentage Point Hike in Mortgage Rates

Actual amount of increase or decrease of mortgage loan rates in 2014 will depend on treasury rates, but the base assumption (if treasury yields remain unchanged) is a hike in mortgage rates of 0.375 percentage points, with some loans harder to get irrespective of rates.

Mike "Mish" Shedlock
http://globaleconomicanalysis.blogspot.com

Working Together on Behalf of the American People

Featured

Weekly Address: Working Together on Behalf of the American People

In his weekly address, President Obama highlights the bipartisan budget agreement that unwinds some of the cuts that were damaging to the economy and keeps investments in areas that help us grow, and urges both parties to work together to extend emergency unemployment insurance and act on new measures to create jobs and strengthen the middle class

Click here to watch this week's Weekly Address.

Seth's Blog : Noise-tolerant media

Noise-tolerant media

Twitter is the noisiest medium in history. Do you actually believe that Taylor Swift has 33,000,000 million (and counting) people eagerly waiting for her next tweet, ready to click on whatever she links to?

In fact, less than one in a thousand people who 'get' one of her tweets will click. Most of the 33 million won't even read it, making the word 'get' worthy of quotation marks.

And yet Twitter works just fine at this level. That's because it immerses the user in waves of media, a stream of ignorable content that people can dip into at will. More noise makes it better, not worse.

Email was wrecked by many marketers for many people, because email isn't structured for noise. Noise is the enemy. Instant messages, because there is no easy accessible API, isn't overwhelmed, but it too is noise-intolerant. Texts you don't want to get are a huge hassle.

The simple rule is that the easier it is to use a medium, the faster it will become noisy, and the noisier it is, the less responsive it is.

You can play at Facebook and Twitter, and make them work. But they will only work if treat them like a cocktail party, as an opportunity to eavesdrop and layer general connection and value and insight. No, it's not an ideal direct marketing medium. It's a metropolis.

• Email to a friend •

luni, 23 decembrie 2013

Machine Learning for SEOs

Machine Learning for SEOs