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Open-source smartphone database offers a new tool for tracking coronavirus exposure

Researchers from Berkeley Haas and four other universities have released a trove of smartphone tracking data in an open-source database—a powerful tool for studying how people are changing their movement patterns and potential exposure levels during the coronavirus pandemic.

Asst. Prof. Victor Couture
Victor Couture

The Covid-19 Exposure Indices, created by Berkeley Haas Asst. Prof. Victor Couture and researchers from Yale, Princeton, the University of Chicago, and the University of Pennsylvania in collaboration with location data company PlaceIQ, is aimed at academic investigators studying the spread of the pandemic. The data sets allow researchers to visualize how people can potentially be exposed to those infected with the virus, based on cell-phone movements to and from businesses and other locations where a great deal of the exposure happens.

Couture hopes that researchers may start to find correlations between the disease and certain venues and travel patterns. Looking forward, the data also could be useful in anticipating the movement patterns that predict where future outbreaks could reemerge once restrictions are lifted. “The end goal is to identify how changes in exposure rates within different types of venues and for different demographic groups impact the number of cases,” says Couture.

Couture and his collaborators are the first academics to release open-source smartphone location data. They are part of a much bigger movement of researchers, companies, and institutions making data easily and freely available to study the pandemic. For instance, Apple, Google, Foursquare, and other big companies have also released movement data. Couture hopes that by being transparent about their data source, methodology, and potential biases, they can make available data that is suitable for peer-reviewed research.

“We’re in the midst of an unprecedented sharing of data from the academic and technical communities,” Couture says. “We hope everyone can use this data to influence better policies during the coronavirus pandemic.“

We’re in the midst of an unprecedented sharing of data from the academic and technical communities. We hope everyone can use this data to influence better policies during the coronavirus pandemic.

The smartphones in our pockets are all equipped with GPS, and depending on privacy settings, many popular applications record our geolocation. Data providers buy and aggregate location information from different applications, and build databases that record the movement and visits to various venues for millions of devices.

Couture and his colleagues have so far released two indices derived from this smartphone movement data: the location exposure index (LEX), showing our movements between states and counties, such as people flying or driving across the country; and the device exposure index (DEX), showing how many people (as measured by their devices) we are coming into close proximity with outside the home. For instance, if you drive to Walmart and 100 other phones are at that same location, and then you drive to a drugstore with 50 people with phones inside before returning home, your DEX exposure level would be 150 people. More detailed data that includes types of venues (grocery stores, drugstores, big-box stores, parks) will be released soon.

An application of the data is shown below. This animated time-lapse map from January 23 to April 2 shows how people’s movements dropped much more quickly in some areas than in others, such as in the South and Southeast of the United States. This is represented by color changes from yellow to green to dark purple as the average device exposure within a county went down.

Device Exposure Index gif

The data can’t tell researchers how well people are practicing social distancing at any given location, and therefore their specific risk of catching the coronavirus. But it can start to reveal how successful various policies—such as states of emergencies and shelter-in-place orders—have been. “When you prescribe rules of movement, when you tighten them, and when you relax them—you can correlate that with what people are actually doing and which groups and areas are reducing their exposure,” says Couture.

“It often looks like people start to reduce their exposure when a state of emergency is declared, but when the actual shelter in place order comes in, it has less impact on movement,” says Couture. An example is the analysis charted below for New Haven-Milford, Connecticut, which showed that the announcement of the state of emergency itself appeared to be a powerful mechanism that convinced people to reduce their movements, and hence their exposure, to the virus.

The exposure indices Couture and colleagues have compiled also include demographic information that can show which groups are at greater risk for exposure, such as minorities and low-income people. Exposure levels vary over time: The top chart in the set below shows that people living in New Haven, Connecticut neighborhoods with median incomes in the top 10% initially had higher exposure rates than those in the bottom 10%.  After a state of emergency was declared, exposure levels in high-income neighborhoods declined much faster than in low-income neighborhoods. This may be because lower income people are more likely to work on the front lines in essential business, and have less ability to reduce their exposure than those who can afford to shelter at home.

The bottom panel shows the ratio of the device exposure index (DEX) for people living in neighborhoods with median income levels in the bottom 10% relative to those in the top 10%. A value higher than 1 signals that exposure is higher in low-income than in high-income neighborhoods

Tracking device expose by income

These patterns could prove helpful in showing which locations and demographics have the potential for greater exposure under different types of orders when policies have been gradually lifted.

As another example, the graphic below shows that metropolitan areas with more jobs that can be done from home had larger reductions in potential exposure to the coronavirus. Again, this correlation suggests that who works from home depends on who can afford to do so while remaining gainfully employed.



Eventually, the explosion of location data has the potential to tease out even deeper stories explaining not just the hidden patterns behind our unique location in the world, but also the unexpected things that larger groups and types of places share in common. “One of the things we want to encourage is careful work to identify the causations behind these correlations,” says Couture.

The Covid-19 Exposure Indices were created by Jonathan Dingel of the University of Chicago’s Booth School of Business, Kevin Williams of the Yale School of Management, Jessie Handbury of the Unviersity of Pennsyania’s Wharton School of Business, Allison Green of Princeton University, and Victor Couture at Berkeley Haas.

Looming nightmare in mortgage industry, experts warn

Powerful lightning storm front passes over residential houses
A powerful lightning storm front passes over residential houses. Photo credit: Stuart Monk/Adobe Stock

Berkeley Haas Professors Nancy Wallace and Richard Stanton were some of the few voices to forewarn of the massive risk posed by shoddy practices in the mortgage industry prior to the 2008 financial crisis.

Unfortunately, history seems to be repeating itself.

Berkeley Haas Prof. Nancy Wallace
Prof. Nancy Wallace

More than two years ago, Wallace and Stanton again began raising the alarm that the mortgage landscape that emerged from the last crisis is dominated by “nonbank” lenders who operate with little of their own capital or access to emergency cash. It was another disaster waiting to happen, they warned, and called for increased oversight.

No one predicted a shock the size and speed of the coronavirus pandemic, but it’s now upon us, and Wallace fears the worst. Millions of laid-off Americans won’t be able to make mortgage payments, and have been given a temporary payment reprieve by the federal rescue package. This plummeting cash flow could quickly push fragile nonbanks into bankruptcy. And since so many of the loans they service are backed by the U.S. government, that’s who will be left holding the bag.

“The $2.2. trillion (coronavirus relief act) was the largest in history, but we’re talking about liabilities that are orders of magnitude bigger,” Wallace says. “Solutions are going to have to involve trillions of dollars. It could be the bailout of all bailouts.”

“Solutions are going to have to involve trillions of dollars. It could be the bailout of all bailouts.”

Berkeley Haas Prof. Richard Stanton
Prof. Richard Stanton

Wallace says this new crisis will begin to show itself within the next 30 days, as people forgo their monthly payments and the highly leveraged nonbanks face margin calls from the brokers they’ve borrowed from—commercial banks like JP Morgan Chase and Wells Fargo Bank and investment banks such as Morgan Stanley. They need cash to pay these lenders, and they don’t have it. The nonbanks have already begun asking for a rescue.

We asked Wallace, who has studied real estate industry financial dynamics for the past three decades, to explain this looming mortgage crisis.


What are nonbanks, and who are the biggest players?

Mortgages are originated and serviced by two types of institutions. Traditional lenders are the highly regulated banks, funded with deposits or Federal Home Loan Bank advances. They tend to have multiple lines of business. Nonbank lenders, in contrast, are lightly regulated and get their funding through short-term credit. Usually their only line of business is originating and servicing residential mortgages.

Some of the biggest players are Quicken Loans, Mr. Cooper Group, and Freedom Mortgage. They include about 1,088 smaller companies as well.

When did you become aware of the risks posed by nonbanks?

The standard narrative of the 2007-2010 housing crisis centers on the collapse of the housing bubble that was fueled by low interest rates, easy credit, low regulation, and subprime mortgages. However, we found nonbanks played an overlooked role, defaulting on their credit agreements and contributing to the collapse.

Why and how have nonbanks grown?

After the financial crisis, the traditional banks were put under heavy regulation. Because of the stringent capital requirements and the fact that they lost a lot of money servicing defaulted mortgages, most of the big banks scaled back their residential mortgage businesses. A number of large banks sold off loan servicing rights, and the nonbanks stepped in.  The growing market share of the nonbanks came about in part because they were very nimble with new platform lending technology—like Quicken, with the eight-minute mortgage.

Nonbanks originated 20% of single-family home loans in 2007, and that had grown to half of loans by 2016. Today they service about two-thirds of home loans. The bigger problem is they tend to have a high proportion of the riskier loans to low- and moderate-income people, which are backed by the U.S. government. We’re talking trillions of dollars. As of February 2020  they originated 88% of the loans sold to Ginnie Mae, which is part of the Department of Housing and Urban Development and has a $2.1 trillion portfolio. And 61% of loans sold to the GSEs (government sponsored enterprises) Fannie Mae and Freddie Mac, which have a combined residential single family loan portfolio of about $4.9 trillion.

The bigger problem is they tend to have a high proportion of the riskier loans to low- and moderate-income people, which are backed by the U.S. government. We’re talking trillions of dollars.

How do nonbanks get their money, and how big is their debt exposure?

They rely on short-term lending known as warehouse lines of credit. These credit lines are usually provided by larger commercial and investment banks. It’s difficult to get data because most nonbank lenders are private companies which are not required to disclose their financial structures. That was the subject of our Brookings paper, which was the first public tabulation of the scale of warehouse lending to nonbanks. We found there was a $34 billion commitment on warehouse loans at the end of 2016, up from $17 billion at the end of 2013. That translated to about $1 trillion in short-term “warehouse loans” funded over the course of one year.  As of year end 2019, there was $101 billion of warehouse commitments on the books of warehouse lenders.

Last year was a banner year. Nonbanks originated nearly a trillion dollars of mortgages that were securitized by Fannie Mae, Freddie Mac, and Ginnie Mae—the largest origination volume since 2006. However, the high levels of refinancing due to historically low interest rates had a significant negative impact on the value of the mortgage servicing rights held by nonbanks.

If nonbanks are so big and borrow so much money, why aren’t they regulated like banks?

The simple answer is they have a very powerful lobby, the Mortgage Bankers Association. What the industry leaned on was that they were saving the mortgage market because the banks didn’t want to hold mortgages anymore. Nonbanks were happy to promise that they would service 30-year loans and pay the bondholders, whether or not they received borrower principal and interest payments, but there are no mechanisms in place to hold them to that promise. They were gambling that the market wouldn’t crash.

What the industry leaned on was that they were saving the mortgage market because the banks didn’t want to hold mortgages anymore.

The nonbanks have actively resisted paying for any form of liquidity insurance or supporting any credible oversight similar to banks. Their regulator, the Conference of State Bank Supervisors (CSBS), does not have high-quality loan-level data for the mortgage industry. That’s why they recently asked our team—Paulo Issler, Christopher Lako, Richard Stanton and me, here at the Real Estate and Financial Markets Lab in the Fisher Center for Real Estate and Urban Economics—to perform detailed data breakdowns and analysis for them. They do not have the data to perform this analysis themselves.

Did anything change after your 2018 paper, co-written with Federal Reserve economists, which called for greater oversight?

Ginnie Mae started trying to require higher capital and liquidity thresholds as well as stress tests, requiring them to show how they would handle an economic shock. They had an initiative called Ginnie Mae 2020, but they were getting major pushback from the industry. In addition, the Conference of State Bank Supervisors has been trying hard to standardize the reporting rules, but they have no data, and they have little power.

Under the $2.2 trillion emergency CARES Act (Coronavirus Aid, Relief and Economic Security), mortgage servicers are required to allow borrowers to delay payments for as long as a year. What do you expect will happen now?

I think the situation is extremely serious, a looming nightmare. We’ve had 16 million people file for unemployment in three weeks. We know that most Americans can’t even withstand a $400 shock to their finances. Millions of people won’t be able to make their mortgage payments. They’ve been told to call their lenders and tell them they can’t pay, and the phones are ringing off the hook.

I think the situation is extremely serious, a looming nightmare.

The immediate problem for the nonbanks is the risk to their warehouse lines of credit, and the fact that the nonbank loan servicers still have to make payments to the mortgage-backed security bondholders, even if people don’t pay their mortgages. Margin calls have been in the level of tens of millions of dollars and the creditors are demanding cash. Not making your margin calls on lines of credit is a serious problem and could trigger default. Nonbanks are also facing millions of dollars of margin exposure from short sales of mortgage-backed securities. These onerous margin calls, some as large as $100 million for a single institution, are what’s leading their lobbyists, the Mortgage Banking Association, to go to the Securities and Exchange Commission and demand that the brokers be forbidden from exercising their margin rights. It’s ridiculous, because the brokers—big banks like Goldman Sachs and Morgan Stanley—have every right to play hardball. The SEC has turned down the request.

Why does this pose such a threat to the U.S. government, and ultimately, to taxpayers?

Most of these loans are guaranteed by the U.S. government through Ginnie Mae, Fannie Mae, and Freddie Mac. The nonbank lenders have been given some forbearance, and will eventually receive compensation for the payment shortfalls they are experiencing, but they have a timing problem. In the meantime they still have to make timely payments of interest and principal—for 120 days to the Fannie and Freddie MBS bondholders, and, in the case of those who owe to Ginnie Mae mortgage-backed security bondholders, until they go bankrupt. I’m not sure some of them have the liquidity to last even 30 days, and many won’t be able to do it for three months, much less a year. We are going to see bankruptcies, and substantial loss in lending capacity as we did in 2007, when we lost two-thirds of lending capacity. This might be worse because unemployment may be worse.

They can’t keep pushing the envelope and then expect to be rescued. They don’t want to follow any of the rules that banks follow, and then they want to be treated  like banks when liquidity shocks occur. It’s just wrong.

Will any of the stimulus measures passed so far help?

The nonbanks are already asking for a bailout, but none of the federal relief efforts so far have included them. The MBA tried to get some protection in the CARES Act, which had $450 billion in loans and loan guarantees from the Fed and Treasury. But they were excluded for a reason—because these firms have pushed every boundary and rejected every form of oversight. Thus far, they have also been excluded from the actions the Fed has been taking, including a new round of quantitative easing, and participation in the Term Asset-Backed Securities Loan Facility, which is a way to provide liquidity. Ginnie Mae has now created an assistance program to provide loans to its nonbank counterparties who are unable to cover the principal and interest payments to bondholders.  Fannie Mae and Freddie Mac have refused to provide such assistance to their nonbank counterparties, because they are still under conservatorship status from the 2008 crisis and face their own capital shortfalls.

So some kind of bailout is nearly inevitable?

To save the market, the nonbanks will have to be bailed out either by the Fed or by the U.S. Treasury. This will be very difficult under restrictions put in place concerning nonbank bailouts under the Dodd-Frank Act.  The cost is going to be very high. In my opinion, there has to be a quid pro quo from the industry in the form of significant future fees in return for such extraordinary support—they can’t keep pushing the envelope and then expect to be rescued. They don’t want to follow any of the rules that banks follow, and then they want to be treated  like banks when liquidity shocks occur. It’s just wrong.

Real estate & economics forecast: a recession is on the horizon

San Francisco skyline: A recession is on the horizon

Unemployment is at its lowest rate in 50 years, there are almost 7 million job openings nationwide, and consumer confidence is at its highest level in a decade. There’s more venture capital flowing than at any point since 2000, and commercial real estate loan origination is at its highest point ever. So, what could go wrong?

Lots, said Kenneth Rosen, faculty director for the Haas School’s Fisher Center for Real Estate & Urban Economics, who foresees a recession within the next 18 to 24 months.

“We’ve had a sugar high for the economy, and it will wear off,” said Rosen, delivering his annual forecast at the 41st Annual Real Estate & Economics Symposium in San Francisco this week.  “We’re in the last stages of a very good recovery, but we’re buying this time by spending a lot of money that we don’t have.”

Kenneth Rosen
Kenneth Rosen

Even so, Rosen does not foresee a crash in the real estate market anything like 2004 to 2007 or the late 1980s, and there’s still plenty of “dry powder”—or cash available for investment, he said. While real estate is overvalued compared to historical prices, it’s not overvalued compared to everything else, he said.

“This is not a very dramatic forecast, but the risks have risen dramatically and this is just the beginning of volatility,” he said. “There will be correction, and the Bay Area is likely to have a bigger correction than the rest of the country, because we’ve gone up so much.”

“A sledgehammer to break open a walnut”

Rosen pointed to rising interest rates, a ballooning national deficit, increasingly restrictive immigration policies in a tight labor market, and the escalating trade war with China as trouble signs. With $250 billion in tariffs imposed and $257 billion more threatened by President Trump, Chinese retaliation is to be expected.

“We certainly have problems with China, but tariffs are an exceptionally a blunt tool. It’s like using a sledgehammer to break open a walnut,” said Rosen, who is also chairman Rosen Consulting Group, a real estate market research firm. “No one wins in a trade war.”

The combined economic stimulus of tax cuts and increased spending has overheated the economy, and left few tools in policy makers’ arsenal to recover from the next recession, he said.  A gridlocked Congress would not be able to pass a spending increase bill, or another tax cut. “In a full employment economy, the deficit should be zero or positive, so we should be at a balanced budget today. We’re doing the opposite. We’re not going to be ready for the next big downturn.”

While he had plenty of critiques of President Trump and his administration, he said Congress is also at fault. “There are no fiscal hawks any more. There’s no one who believes in balanced budgets.”

Red-hot economy

In the immediate future, however, the economy remains red hot. There’s a boom in job creation nationwide, especially throughout the West. California as a whole is a bit lower than the rest of the region, but it still had 1.9 percent growth in job creation year-over-year. Unemployment in San Francisco hovers just over 2 percent.

“The tech cities are going strong: Seattle, Austin, Silicon Valley, Denver, San Francisco, and Oakland are still very strong. The only thing constraining these places is the fact that housing is so expensive that they can’t get people to come. We’d be growing faster if we can solve some of those issues.”

Real estate forecast

Nationwide, retail is still struggling while industrial properties are hot, with vacancy rates at their lowest point since the 80s. “It’s a red-hot sector because of e-commerce, which is driving the demand for this space and bypassing retail network.”

In terms of commercial real estate, Rosen predicts cap rates—or the rates of return on commercial property—are expected to rise after historic lows. He warned the crowd of 300 real estate and  finance professionals in the room to not be too smug. “We’ve had big periods of appreciation in the last decade because cap rates went down. Don’t think it’s because you’re smart—it’s because they repressed interest rates. It’s going to reverse. We’ll have headwinds.”

His advice? “As a lender, I’d say now’s the time to be cautious. Don’t lend to inexperienced people. You’ve got to be able to hold through the next six years—don’t think you’re going to miss this recession,” he said. “As an investor, it’s now is the time harvest or hedge, don’t wait for the next cycle.”

Nationally there’s strong demand for office space, but it’s nothing like previous booms. And vacancy rates have stopped going down because businesses need less space per worker—thanks to open floor plans and co-working spaces.

“The WeWorks of the world are 50 percent more dense than traditional office spaces,” he said.

Although the rental market peaked several years ago and home ownership is creeping back up, rental vacancy rates are low. The proportion of young adults living with their parents has begun to drop from a high of 32 percent in 2016. In the Bay Area, apartment vacancy rates are below 4 percent, and rents are rising again after appearing to top out.

California—a victim of its own success?

California homes have become increasingly unaffordable–just 30 percent of people can afford a median priced home, compared with 50 percent nationally. In the Bay Area, prices were up almost 10 percent year-over-year in September. Recently, however, there are signs that home prices may be beginning to drop—whether it’s due to rising interest rates or people leaving the region.

All this leads Rosen to believe California may soon be a victim of its own success.

“We may be the cause of our own demise. With high housing prices and congestion, people are going to move elsewhere,” he said. “You’ve seen reports that up to a third of people are thinking of relocating in the next five years. Add to that the higher taxes that we keep on voting on ourselves, and I think we could be in a situation where we’ve hit our peak moment and it’s just a question of how fast we can slow down.”

Minority homebuyers face widespread statistical lending discrimination, study finds

UC Berkeley study finds Minority Homebuyers Face Widespread Statistical Lending Discrimination

Face-to-face meetings between mortgage officers and homebuyers have been rapidly replaced by online applications and algorithms, but lending discrimination hasn’t gone away.

A new University of California, Berkeley study has found that both online and face-to-face lenders charge higher interest rates to African American and Latino borrowers, earning 11 to 17 percent higher profits on such loans. All told, those homebuyers pay up to half a billion dollars more in interest every year than white borrowers with comparable credit scores do, researchers found.

The findings raise legal questions about the rise of statistical discrimination in the fintech era, and point to potentially widespread violations of U.S. fair lending laws, the researchers say. While lending discrimination has historically been caused by human prejudice, pricing disparities are increasingly the result of algorithms that use machine learning to target applicants who might shop around less with higher-priced loans.

“The mode of lending discrimination has shifted from human bias to algorithmic bias,” said study co-author Adair Morse, a finance professor at UC Berkeley’s Haas School of Business. “Even if the people writing the algorithms intend to create a fair system, their programming is having a disparate impact on minority borrowers—in other words, discriminating under the law.”

First-ever dataset 

A key challenge in studying lending discrimination has been that the only large data source that includes race and ethnicity is the Home Mortgage Disclosure Act (HDMA), which covers 90 percent of residential mortgages but lacks information on loan structure and property type. Using machine learning techniques, researchers merged HDMA data with three other large datasets—ATTOM, McDash, and Equifax—connecting, for the first time ever, details on interest rates, loan terms and performance, property location, and borrower’s credit with race and ethnicity.

The researchers—including professors Nancy Wallace and Richard Stanton of the Haas School of Business and Prof. Robert Bartlett of Berkeley Law—focused on 30-year, fixed-rate, single-family residential loans issued from 2008 to 2015 and guaranteed by Fannie Mae and Freddie Mac.

This ensured that all the loans in the pool were backed by the U.S. government and followed the same rigorous pricing process—based only on a grid of loan-to-value and credit scores—put in place after the financial crisis. Because the private lenders are protected from default by the government guarantee, any additional variations in loan pricing would be due to the lenders’ competitive decisions. The researchers could thus isolate pricing differences that correlate with race and ethnicity apart from credit risk.

The analysis found significant discrimination by both face-to-face and algorithmic lenders:

  • Black and Latino borrowers pay 5.6 to 8.6 basis points higher interest on purchase loans than White and Asian ethnicity borrowers do, and 3 basis points more on refinance loans.
  • For borrowers, these disparities cost them $250M to $500M annually.
  • For lenders, this amounts to 11 percent to 17 percent higher profits on purchase loans to minorities, based on the industry average 50-basis-point profit on loan issuance.

“Algorithmic strategic pricing”

Morse said the results are consistent with lenders using big data variables and machine learning to infer the extent of competition for customers and price loans accordingly. This pricing might be based on geography—such as targeting areas with fewer financial services—or on characteristics of applicants. If an AI can figure out which applicants might do less comparison shopping and accept higher-priced offerings, the lender has created what Morse calls “algorithmic strategic pricing.”

“There are a number of reasons that ethnic minority groups may shop around less—it could be because they live in financial deserts with less access to a range of products and more monopoly pricing, or it could be that the financial system creates an unfriendly atmosphere for some borrowers,” Morse said. “The lenders may not be specifically targeting minorities in their pricing schemes, but by profiling non-shopping applicants they end up targeting them.”

This is the type of price discrimination that U.S. fair lending laws are designed to prohibit, Bartlett notes. Several U.S. courts have held that loan pricing differences that vary by race or ethnicity can only be legally justified if they are based on borrowers’ creditworthiness. “The novelty of our empirical design is that we can rule out the possibility that these pricing differences are due to differences in credit risk among borrowers,” he said.

Overall decline in lending discrimination

The data did reveal some good news: Lending discrimination overall has been on a steady decline, suggesting that the rise of new fintech platforms and simpler online application processes for traditional lenders has boosted competition and made it easier for people to comparison shop—which bodes well for underserved homebuyers.

The researchers also found that fintech lenders did not discriminate on accepting minority applicants. Traditional face-to-face lenders, however, were still 5 percent more likely to reject them.



Read the full paper.

Berkeley Haas Media Relations: Laura Counts, [email protected], (510) 643-9977

Berkeley Law: Prof. Robert Bartlett, [email protected]