By: Scott Rhode   This is the second of a three-part blog series focused on the residential solar market looking at; 1) the history of solar technology, 2) current trends and financing mechanisms, and finally 3) overcoming market and regulatory challenges with Experian’s help. Lets discuss the current trends in solar and, more importantly, the mechanisms used to finance solar in the US residential market.  As I discussed in the last blog, the growth in this space has been astronomical.  To illustrate this growth, there was a recent article in The Washington Post by Matt McFarland, highlighting that solar-related jobs are significantly outpacing the rest of labor market in terms of year over year growth.  The article states that since 2010 the number of solar-related jobs has doubled in the US, bring the total number of jobs in this industry to 173,807.  While this is still smaller in comparison to other sectors of our economy, it underscores how much growth has occurred in a short amount of time. So what is driving this explosive growth?  There are a few factors that should be considered; however, in the residential solar market, financing, is the main catalyst.  As you might expect, there are a variety of financial products in the market giving the consumer lots of choices. First, there are traditional loans like home improvement loans, home equity loans, or energy efficiency loans offered by a bank, credit union, or specialty finance company.  For homeowners that do not choose to secure their loan against their property, there are a variety of specialty lenders that will offer long-term, unsecured loans that only file a UCC against the panels themselves.  For these types of offerings, some specialty lenders will even have special credit plans for the 30% Solar Investment Tax Credit so that the homeowner can have a deferred interest plan with the expectation that once they get the tax credit from the federal government, they will pay off the special plan and all of the deferred interest will be waived.  If the customer does not pay in full, the plan rolls to their regular loan plan and the customer has a higher cost of financing. Second, there is a lease product which offers zero to little down and a monthly payment that is less than the savings that the homeowner will experience on their utility bill.  Of all the financing options, the lease has been the biggest driver of growth since it offers an inexpensive, no-hassle way to get all the benefits of going solar without breaking the bank.  What is unusual to most people that are unfamiliar with this concept is the term of the lease, which is typically 20 years.  However, when you consider that most manufacturers warrant their panels for 25 and many have a usable life of 40 years, this term does not seem all that unusual.  The benefits of this program look something like this: The homeowner has an average electric utility bill of $350 / month A solar company quotes a customer a savings of $200 / month in the form of a net metering energy credit, so their bill after solar is now $150 / month The lease payment for the installed solar array, metering equipment, and monitoring software is $150 / month The homeowner’s net saving is an average of $50 / month with nothing out of pocket Over the life of the lease, energy prices will increase which will mean more savings over time so long as there are not escalators in the contract that exceed the increase in energy prices The lessor “owns” the equipment and is responsible for maintenance, performance, and insurance With this product comes complexity.  Many companies offering this program do not have the cash or the appetite to take on massive debt, so they partner with Tax Equity investors to make this transaction possible.  Because of the 30% ITC and accelerated depreciation, this transaction is very favorable for a Tax Equity investor like Google, US Bank, or Bank of America Merrill Lynch.  There are a number of structures they can use; however, the Sale-Leaseback structure is the easiest and most efficient way to fund the transaction.  While this is not “known” to the end customer, it is important because the Tax Equity Investor effectively owns the asset and has the final say in setting credit policy.  This transaction does require that the developer have a stake as well; however, many of the developers go to the debt market for “back leverage” on their stake so that they can reduce the impact to their balance sheet. This complexity carries a cost, as the cost of capital is higher than most traditional loan products from established financial services firms.  That said, the fact that the lease allows the customer to monetize the tax credit and accelerated depreciation in the amount financed, balances out the higher costs of capital.  In the next blog we will touch more on the challenges this product, in particular, has in the market. Last, but not least, there is another mechanism gaining popularity in the market.  This concept is known as community solar.  One of the obstacles of the lease and Tax Equity arrangement is that the lease is only available to single family residence homeowners and, if they have multiple homes, only the homeowner’s primary residence.  That means that people who rent, own a condo, own a vacation home, or own a small business do not qualify for this type of lease.  As a result, community solar has become a great option. With community solar, the panels are put in an ideal location for maximum exposure to the sun and they often produce 10-15% more power than panels on a rooftop.  Portions of this solar farm can be sold, rented, or sublet to consumers regardless of their living situation.  As the panels produce electricity, that power gets sold to the local utility and the customer gets money from that utility that shows up as a credit on their next bill.  In this structure, the customer is not required to put money down in most cases and they are signing up for a specific term. Like a rooftop lease, this structure often has a Tax Equity investor that funds the project.  Again, this allows them to take the 30% ITC and accelerated depreciation which, in turn, gets monetized and lowers the costs of construction. In the final installment of this blog series, I will discuss some of the challenges that this market faces as the ITC expiration date approaches and the market becomes more mature. Leasing is driving the market, so if the ITC does not get renewed, the market will need to have a plan in place to find other innovative ways to keep solar affordable so more consumers can realize the benefits of going solar.   Solar Financing — The current and future catalyst behind the booming residential solar market (Part 1)

By: Scott Rhode This is the first of a three-part blog series focused on the residential solar market looking at; 1) the history of solar technology, 2) current trends and financing mechanisms, and finally 3) overcoming market and regulatory challenges with Experian’s help. Most people tend to think of the solar industry as a recent, and not so stable, market phenomenon.  However, the residential solar industry is still gaining traction as component prices come down. For more than two thousand years man has been trying to harness the sun’s energy and power. In fact, architects and city planners in early civilizations would also look to the sun when designing dwellings, buildings and bathhouses, so that they could capture as much of the sun’s energy to heat their homes and the water they used.  Our ancestors knew that the sun, unlike any other resource, was a consistent and powerful source of energy that fueled life. Fast forward to the late 19th and early 20th centuries where renowned scientists in the US and across the globe started looking at ways to harness the sun’s energy to generate electricity, and the birth of the modern solar industry was here.  By the mid 1950’s, US architects were trying to incorporate the power of the sun in their designs so that heating the water and commercial office space could be done without heavy use of electricity.  One architect, Frank Bridgers, was so successful in using this technology that his building still continues to operate this way today.  In addition, many companies like Bell Labs, Western Electric, and the US Signal Corp Laboratories started to develop photovoltaic cells that power the panels that we use today. These early cells, operating at 7-11% efficiency (This is the measurement of how efficient the cell is at converting solar radiation to electricity), gave life to solar powered electronics, lights, and panels used by the burgeoning space program to power satellites orbiting earth.  In reaction to the growing possibilities and the broader oil crisis in the late 1970’s, the US Department of Energy created what would later become the National Renewable Energy Laboratory enabling the federal government to use its resources to help grow the industry and foster technological innovations to improve cell efficiency. Throughout the 1980’s, 90’s, and early 2000’s, the industry starts to take root with utilities and mainstream energy providers as they look to the sun to diversify their energy sources away from coal, gas, and oil.  This adoption leads to a push by the US Department of Energy to have “One million Solar Roofs” in the US so that individual home owners can realize the benefits of going solar.  Soon, retailers like Home Depot started selling panels in their stores for customers to install themselves for “off-grid” properties or other uses.  While this allowed a homeowner to use solar, costs are still so high that solar is only available to a select few and, as a result, not competitive with traditional methods of producing energy. In order to incent homeowners to invest in solar, the US Government created the Solar Investment Tax Credit in 2005.  This tax credit allows homeowners to get a credit of 30% of the fair market value of the system they have installed on their roof.  As a result of this and local incentives from municipalities and utility companies, residential solar installations have grown 1,600% over the last ten years, representing an annual CAGR of 76%.  In fact, through the first half of 2014, 53% of all new electric capacity is from solar, making it the fastest growing source of energy in the market.* Since this tax incentive is unlikely to be renewed after it expires, the industry set out to solve the cost issue in order to manufacture and produce highly efficient and durable panels for individual Consumers that could bring the costs to produce down to parity with traditional power.  In this endeavor, the manufactures have poured significant resources into research and development, pushed their manufacturing processes towards ever higher levels of efficiency, and used the latest technology to significantly reduce costs to produce panels that now range from 18-23% cell efficiency.  Since 2010 the average price of a panel has come down by 64% and the industry continues to push to find ways to make solar more affordable.  This is especially important given that the tax credit expires on December 31st of 2016. In the next blog in the series, I will talk about solar financing and the current industry trends.  Financing, as you would expect, has been and will continue to be critical to growth in this space so that more homeowners can afford to move to solar as their primary energy source.  As such, the methods used to acquire, originate, and serve these customers must evolve in order for the industry to sustain the impressive growth rates mentioned earlier in this blog. Solar Financing – The current and future catalyst behind the booming residential solar market (Part II)

By: John Straka For many purposes, national home-price averages, MSA figures, or even zip code data cannot adequately gauge local housing markets. The higher the level of the aggregate, the less it reflects the true variety and constant change in prices and conditions across local neighborhood home markets. Financial institutions, investors, and regulators that seek out and learn how to use local housing market data will generally be much closer to true housing markets. When houses are not good substitutes from the viewpoint of most market participants, they are not part of the same housing market.  Different sizes and types and ages of homes, for example, may be in the same county, zip code, block, or even right next door to each other, but they are generally not in the same housing market when they are not good substitutes.  This highlights the importance of starting with detailed granular information on local-neighborhood home markets and homes.  To be sure, greater granularity in neighborhood home-market evaluation requires analysts and modelers to deal with much more data on literally hundreds of thousands of neighborhoods in the U.S. It is fair to ask if zip-code level data, for example, might not be generally sufficient. Most housing analysts and portfolio modelers, in fact, have traditionally assumed this, believing that reasonable insights can be gleaned from zip code, county-level, or even MSA data. But this is fully adequate, strictly speaking, only if neighborhood home markets and outcomes are homogenous—at least reasonably so—within the level of aggregation used. Unfortunately, even at zip-code level, the data suggests otherwise.  Examples All of the home-price and home-valuation data for this report was supplied by Collateral Analytics. I have focused on zip7s, i.e. zip+2s, which are a more granular neighborhood measure than zip codes. A Hodrick-Prescott (H-P) Filter was applied by Collateral Analytics to the raw home-price data in order to attenuate short-term variation and isolate the six-year trends. But as we’ll see this dampening still leaves an unrealistically high range of variation within zip codes, for reasons discussed below. Fortunately there is an easy way to control for this, which we’ll apply for final estimates of the range of within-zip variation in home-price outcomes.  The three charts below show the H-P filtered 2005-2011 percent changes in home-price per square foot of living area within three different types of zip codes in San Diego county. Within the first type of zip code, 92319 in this case, the home-price changes in recent years have been relatively homogenous, with a range of -56% to -40% home-price change across the zip7s (i.e., zip+2s) in 92319. But the second type of zip code, illustrated by 92078, is more typical. In this type of case the home-price changes across the zip7s have varied much more. The 2055-2011 zip7 %chg in home prices within 92078 have varied by over 40 percentage points, from -51% to -10%. In the third type of zip code, less frequent but surprisingly common, the home-price changes across the zip7s have had a truly remarkable range of variation. This is illustrated here by zip code 92024 in which the home price outcomes have varied from -51% to +21%, or a 71 percentage point range of difference—and this is not the zip code with the maximum range of variation observed! All of the San Diego County zip codes are summarized in the bar chart below. Nearly two-thirds of the zip codes, 65%, have more than 30 percentage points within-zip difference in the 2005-2011 zip7 %changes in home prices. 40% have more than a 40 percentage point range of different home-price outcomes, 23% have more than a 50 percentage point range, and 13% have more than a 70 percentage point range of differences. The average range of the zip7 within-zip code differences is a 37 percentage point median, 41 percentage-point mean. These high numbers are surprising, and are most likely unrealistically high. Summary of Within-Zip (Zip+2 level) Ranges of Variation in Home-Price Changes in San Diego: Percentage of Zips by Range Across Zip+2s in Home Price/Living Area %Change 2005-2011 Controlling for Factors Inflating the Range of Variation Such sizable differences within a typical single zip code clearly suggest materially different neighborhood home markets. While this qualitative conclusion is supported further below, the magnitudes of the within-zip variation in home-price changes shown above are quite likely inflated. There is a tendency for a limited number of observations in various zip7s to create statistical “noise” outliers, and the inclusion of distressed property sales here can create further outliers, with cases of both limited observations and distress sales particularly capable of creating more negative outliers that are not representative of the true price changes for most homes and their true range of variation within zip codes.  (My earlier blog on June 29th discussed the biases from including distressed property sales while trying to gauge general price trends for most properties.) Fortunately, I’ve been able to access a very convenient way to control for these factors by using the zip7 averages of Collateral Analytics’ AVM (Automated Valuation Model) values rather than simply the home price data summarized above. These industry-leading AVM home valuations have been designed, in part, to filter out statistical noise problems.  The bar chart below shows the still significant zip7 ranges within San Diego County zip codes using the AVM values, but the distribution is now shifted considerably, and more realistically, to a much smaller share of the zip codes with remarkably high zip7 variation. Compared with the chart above, now just 1% of the zips have a zip7 range greater than 60 percentage points, 5% greater than 50, and 11% greater than 40, but there are still 36% greater than 30. To be sure, this distribution, and the average range of zip7 differences—which is now a 25 percentage-point median, 26 percent age-point mean—do show a considerable range of local home market variation within zip codes. It seems fair to conclude that the typical zip code does not contain the uniformity in home price outcomes that most housing analysts and modelers have tended to simply assume. The difference between the effects on consumer wealth and behavior of a 10% home price decline, for example, vs. a 35 to 50% decline, would seem to be sizable in most cases. This kind of difference within a zip code is not at all unusual in these data. How About a Different Type of Urban Area—More Uniform? It might be thought that the diversity of topography, etc., across San Diego County (from the sea to the mountains) makes its variation of home market outcomes within zip codes unusually high. To take a quick gauge of this hypothesis, let’s look at a more topographically uniform urban area: Columbus, Ohio. When I informally polled some of my colleagues asking what their prior belief would be about the within-zip code variation in home price outcomes in Columbus vs. San Diego County, there was unanimous agreement with my prior belief. We all expected greater within-zip uniformity in Columbus. I find it interesting to report here that we were wrong. Both the H-P filtered raw home-price information and the AVM values from Collateral Analytics show relatively greater zip7 variation within Columbus (Franklin County) zip codes than in San Diego County.  The bar chart below shows the best-filtered, most attenuated results,  the AVM values. 5% of the Columbus zips have a zip7 range greater than 70 percentage points, 8% greater than 60, 23% greater than 50, 35% greater than 40, and 65% greater than 30. The average range of zip7 within-zip code differences in Columbus is a 35 percentage point median, 38 percentage-point mean. Conclusion These data seem consistent with what experienced appraisers and real estate agents have been trying to tell economists and other housing analysts, investors, and financial institutions and policymakers for quite a long time. Although they have quite reasonable uses for aggregate time-series and forecasting purposes, more aggregate-data based models of housing markets actually miss a lot of the very real and material variation in local neighborhood housing markets.  For home valuation and many other purposes, even models that use data which gets down to the zip code level of aggregation—which most analysts have assumed to be sufficiently disaggregated—are not really good enough. These models are not as good as they can or should be. These facts are indicative of the greater challenge to properly define local housing markets empirically, in such a way that better data, models, and analytics can be more rapidly developed and deployed for greater profitability, and for sooner and more sustainable housing market recoveries. I thank Michael Sklarz for providing the data for this report and for comments, and I thank Stacy Schulman for assistance in this post.

By: Kari Michel The topic of strategic default has been a hot topic for the media as far back as 2009 and continues as this problem won’t really go away until home prices climb and stay there. Terry Stockman (not his real name) earns a handsome income, maintains a high credit score and owns several residential properties. They include the Southern California home where he has lived since 2007. Terry is now angling to buy the foreclosed home across the street. What’s so unusual about this? Terry hasn’t made a mortgage payment on his own home for more than six months. With prices now at 2003 levels, his house is worth only about one-half of what he paid for it. Although he isn’t paying his mortgage loan, Terry is current with his other debt payments.   Terry is a strategic defaulter — and he isn’t alone. By the end of 2008, a record  1 in 5 mortgages at least 60 days past due was a strategic default. Since 2008, strategic defaults have fallen below that percentage in every quarter through the second quarter of 2010, the most recent quarter for which figures are available. However, the percentages are still high: 16% in the last quarter of 2009 and 17% in the second quarter of last year. Get more details off of our 2011 Strategic Default Report What does this mean for lenders? Mortgage lenders need to be able to identify strategic defaulters in order to best employ their resources and set different strategies for consumers who have defaulted on their loans. Specifically designed indicators help lenders identify suspected strategic default behavior as early as possible and can be used to prioritize account management or collections workflow queues for better treatment strategies. They also can be used in prospecting and account acquisition strategies to better understand payment behavior prior to extending an offer. Here is a white paper I thought you might find helpful.

By: John Straka The U.S. housing market remains relatively weak, but it’s probably not as weak as you think. To what extent are home prices really falling again? Differing Findings Most recent media coverage of the “double dip in home prices” has centered on declines in the popular Case-Schiller price index; however, the data entering into this index is reported with a lag (the just released April index reflects data for February-April) and with some limitations.  CoreLogic publishes a more up-to-date index value that earlier this month showed a small increase, and more importantly, CoreLogic also produces an index that excludes distressed sales.  This non-distressed index has shown larger recent price increases, and it shows increases over the last 12 months in 20 states. Others basing their evidence on realtors’ listing data have concluded that there was some double dip last year, but prices have actually been rising now for several months (See Altos).  These disparate findings belie overly simplistic media coverage, and they stress that “the housing market” is not one single market, of course, but a wide distribution of differing outcomes in very many local neighborhood home markets across the nation. (For a pointed view of this, see Charron.) Improved Data Sources Experian is now working with the leading source of the most granular and timely home market analytics and information, from nationwide local market data, and the best automated valuation model (AVM) provider based on these and other data, Collateral Analytics. (Their AVM leads in accuracy and geographic coverage in most large lender and third party AVM tests). While acknowledging their popularity, value, and progress, Collateral Analytics President Dr. Michael Sklarz questions the traditional dominance of repeat-sales home price indexes (from Case-Shiller etc.).  Repeat-sales data typically includes only around 20 to 30 percent of the total home sales taking place. Collateral Analytics instead studies the full market distribution of home sales and market data and uses their detailed data to construct hedonic price indexes that control for changing home characteristics.  This approach provides a similar “constant quality” claim as repeat-sales—without throwing away a high percentage of the market observations. Collateral Analytics indexes also cover over 16,000 zip codes, considerably more than others. Regular vs. Distressed Property Sales Nationwide, some well-known problem states, areas and neighborhoods continue to fare worse than most others in today’s environment, and this skewed national distribution of markets is not well described by overall averages. Indeed, on closer inspection, the recent media-touted gloomy picture of home prices that are “falling again” or that “continue to fall” is a distorted view for many local home markets, where prices have been rising a little or even more, or at least remaining flat or stable.  Nationwide or MSA averages that include distressed-property sales (as Case-Shiller tends to do) can be misleading for most markets. The reason for this is that distressed-property sales, while given much prominence in recent years and lowering overall home-price averages, have affected but not dominated most local home markets. The reporting of continued heavy price discounts (twenty percent or significantly more) for distressed sales in most areas is a positive sign of market normality.  It typically takes a significantly large buildup of distressed property sales in a local area or neighborhood home market to pull down regular property sale prices to their level.  For normal or regular home valuation, distressed sales are typically discounted due to their “fire sale” nature, “as is” sales, and property neglect or damage. This means that the non-distressed or regular home price trends are most relevant for most homes in most neighborhoods. Several examples are shown below. As suggested in these price-per-living-area charts, regular (non-distressed) home-sale prices have fared considerably better in the housing downturn than the more widely reported overall indexes that combine regular and distressed sales(1). Regular-Sale and Combined Home Prices in $ Per Square Foot of Living Area and Distress Sales as a Pct of Total Sales In Los Angeles, combined sale prices fell 46 percent peak-to-trough and are now 16 percent above the trough, while regular sale prices fell by considerably less, 33 percent, and are now 3 percent above the trough.   Distressed sales as a percent of total sales peaked at 52 percent in 2009:Q1, but then fell to a little under 30 percent by 2010:Q2, where it has largely remained (this improvement occurred before the general “robo-signer” process concerns slowed down industry foreclosures).  L.A. home prices per square foot have remained largely stable for the past two years, with some increase in distressed-sale prices in 2009. Market prices in this area most recently have tended to remain essentially flat—weak, but not declining anew, with some upward pressure from investors and bargain hunters (previously helped by tax credits before they expired). Double-Dip: No. In Washington DC, single-family home prices per square foot have been in a saw- tooth seasonal pattern, with two drops of 15-20% followed by sizable rebounds in spring sales prices. The current combined regular & REO average price is 17 percent below its peak but 13 percent above its trough, while the regular-sale average price is just 12 percent below the peak and 10 percent above its trough. Distressed sales have been comparatively low, but rising slowly to a peak of a little over 20 percent in 2010, with some slight improvement recently to the high teens. Single-family prices in DC have remained comparatively strong; however, more of the homes in DC are actually condos, and condo prices have not been quite as strong, with the market data showing mixed signals but with the average price per square foot remaining essentially flat.  Double-Dip: No. In the Miami area, the combined average home price per square foot fell by 48 percent peak to trough and is now just 1 percent above the 2009:Q2 trough. The regular-sale average price already experienced an earlier double-dip, falling by 32 percent to 2009:Q2, then stabilizing for a couple of quarters before falling another 9 percent relative to the peak; since 2010:Q3 this average has been choppy but basically flat, now 3 percent above that second trough. Prices in Miami have been among the weakest in large metro areas, but average prices have been largely flat for the past year, without any sharp new double dip. Distressed sales as a percent of the total peaked at 53 percent in 2009:Q1, but then fell to a little under 30 percent by 2010:Q2; since then there has been some return to a higher distress share, in the mid to upper 30s (but all of these figures are about 10 percentage points lower for condos).   New Double-Dip: No. The Dallas area has seen some of the strongest prices in the nation. The combined price per square foot had an earlier peak and fell by 31 percent peak to trough, but it is now 33 percent above the trough. The regular-sale average price fell briefly by 22 percent peak to trough, but it has since risen by 32 percent from the 2009:Q1 trough to where it is now 3 percent above the peak. The increases have occurred in a saw-tooth seasonal pattern with spring prices the highest, but prices here have been largely rising considerably. Distress sales as a percent of the total peaked at 22 percent in 2009:Q1 but have largely fallen since and now stand at just 11 percent.   Double-Dip: No. Here You Can See 47 More Examples of Where Double-Dips Are and Are Not: »         Pacific West »         Southwest »         Mountain West »         Midwest »        Northeast »         Mid Atlantic »         Southeast  To summarize this information and gain a little more insight into the general area conditions for most homes and individuals in the U.S., we can add up the number of homes and the total population across the counties examined.  To be sure, this information is not a rigorous random sample across homes, but I have tried to include and show the details of both stronger and weaker metro-area counties throughout the U.S. As shown in the tables below, the information used here has covered 51 metro-area counties, including a total population of over 15 million homes and nearly 75 million individuals(2).  These results may be regarded as suggestive of findings from a more thoroughgoing study. Based on these reviews of the market price averages and other data, my assessment is that a little over half of the counties examined are not currently or recently experiencing a double-dip in home prices. Moreover, these counties, where home prices appear to be at least flat or relatively stronger, encompass almost two-thirds (65%) of the total affected U.S. population examined, and nearly three-fifths (58%) of the total properties covered by the data studied. Conclusion This is, on balance, good news. But there are remaining concerns. One is the continued high, or more recently rising, shares of distressed sales in many markets, and the “shadow inventory” of distressed sales now being held up in the current foreclosure pipeline. But it is also interesting to see that many of the reductions in the distressed-property shares of total sales in high-stress areas occurred before the foreclosure processing slowdowns. Another interesting observation is that most of the recent double-dips in prices have been relatively mild compared to the previous original peak-to-trough meltdown. While, to be sure, there are plenty of reasons to remain uncertain and cautious about U.S. home prices, home markets in general do vary considerably, with significant elements of improvement and strength as well as the continuing weaknesses. Despite many reports today about “the beleaguered housing market,” there really is no such thing … not unless the report is referring to a very specific local market.  There definitely are double dips in many areas, and reasons for continuing overall concern. But the best available evidence suggests that there are actually double-dip markets—most relatively moderately so, stable markets, and stronger markets, with markets affecting a majority of homes and individuals actually in the stable and stronger categories.  Note: In a next installment, we’ll look at some more granular micro market data, to explore in greater depth the extensive variety of home-price outcomes and market conditions in weak pockets and strong pockets across various local areas and home markets. This will highlight the importance of having very good information, at sub-county and even sub-zip code levels, on local-neighborhood home markets. Source of Home Price and Market Information: Collateral Analytics HomePriceTrends. I thank Michael Sklarz for providing the extensive information for this report and for comments, and I thank Stacy Schulman for assistance in this posting. __________________ (1) Based on analysis by Collateral Analytics, price/living sq ft is a useful, simple “hedonic” measure which typically controls for around 70 percent or more of the changing characteristics in a housing stock and home sale mix. Patterns in home prices without dividing by the square footage are generally similar, but not always. (2) The property inventory counts are from Collateral Analytics, while the population estimates are from the 2010 U.S. Census.

By: Kari Michel In January, Experian announced the inclusion of positive rental data from its RentBureau division into the traditional credit file. This is great news for an estimated 50 million underbanked consumers - everyone from college students and recent graduates to immigrants - to build credit with continuous on-time rental payments. With approximately 1/3 of Americans renting, lenders who are using VantageScore will benefit from the inclusion of RentBureau data into the score calculation.  VantageScore from Experian is able to both enhance its predictive ability for those that can already be scored as well as provide scores for those that previously could not be scored. With the inclusion of RentBureau data, 34% of thin file consumers increased their score from an ‘F’ (VantageScore 501 – 599) to a ‘D’ (VantageScore 600 – 699). For those consumers that did not have a prior credit history, 70% of them were able to be scored after the inclusion of RentBureau data into the credit repository.  As a result, fewer consumers will get a “no hit” returned to lenders during a credit inquiry. Lenders will now have a comprehensive understanding of a consumer’s total monthly obligations to assist with offering credit to emerging consumers.

As our newly elected officials begin to evaluate opportunities to drive economic growth in 2011, it seems to me that the role of lenders in motivating consumer activity will continue to be high on the list of both priorities and actions that will effectively move the needle of economic expansion. From where I sit, there are a number of consumer segments that each hold the potential to make a significant impact in this economy. For instance, renters with spotless credit, but have not been able or confident enough to purchase a home, could move into the real estate market, spurring growth and housing activity. Another group, and one I am specifically interested in discussing, are the so called ‘fallen angels’ - borrowers who previously had pristine track records, but have recently performed poorly enough to fall from the top tiers of consumer risk segments. I think the interesting quality of ‘fallen angels’ is not that they don’t possess the motivation needed to push economic growth, but rather the supply and opportunity for them to act does not exist. Lenders, through the use of risk scores and scoring models, have not yet determined how to easily identify the ‘fallen angel’ amongst the pool of higher-risk borrowers whose score tiers they now inhabit. This is a problem that can be solved though – through the use of credit attributes and analytic solutions, lenders can uncover these up-side segments within pools of potential borrowers – and many lenders are employing these assets today in their efforts to drive growth. I believe that as tools to identify and lend to untapped segments such as the ‘fallen angels’ develop, these consumers will inevitably turn out to be key contributors to any form of economic recovery.  

With the issue of delayed bank foreclosures at the top of the evening news, I wanted to provide a different perspective on the issue and highlight what I think are some very important, yet often underestimated risks hidden within this issue. For many homeowners, the process of becoming delinquent and eventually going into default is actually a cash-flow positive experience. The process offers these borrowers temporary “free rent,” whereby a major previous monthly commitment is no longer a monthly obligation, freeing up cash for other purposes, including paying other bills. For those consumers who are managing cash flow issues each month, the lack of a mortgage commitment immediately allows them to meet other commitments more easily - making payments on credit cards and car loans that may have previously also become delinquent. From the perspective of a credit card or auto lender, the extended foreclosure process is a short-term positive – it allows a borrower who had previously struggled to remain current to now pay on time and in the short-run, contributes to portfolio health. Although these lenders will experience an improvement in delinquency rates, the reality is that the credit risk is simply dormant. At some point, the consumer’s mortgage will go into foreclosure, and which point the consumer will again be under pressure to continue meeting their obligations. The hidden and significant risk management issue is the misinterpretation of improved delinquency rates. Halting foreclosures means that an accumulating number of consumers are going to enter into this delayed stage of ‘free rent’, without any immediate prospect of having to make a rent or mortgage payment in the near future. In fact, according to Bank of America, “the average foreclosed borrower has not made a payment in 18 months”. This extended period of foreclosure delay will naturally result in a larger number of consumers being able to meet their non-mortgage obligations – but only while their free-rent status exists. A lender who has an interest in the “free rent” consumer is actually sitting on a time-bomb. When foreclosures stop or slow to a rate that is less than consumers entering it, that group will continue to grow in size - until foreclosures start again – at which point thousands of consumers will be processed and will have to start managing rent/housing payments again. Almost immediately, thousands of consumers who have had no problems meeting their obligations will have to start making decisions about which to pay and which not to pay. So, this buildup of rent-free mortgage holders presents a serious risk management issue to non-mortgage lenders that must be addressed. Lenders who have a relationship with a consumer who is delinquent on their mortgage may be easily fooled into thinking that they are not exposed to the same credit risk as mortgage lenders, but I think that these lenders will quickly find that consumers who have lived rent-free for over a year will have a very difficult time managing this transition, and if not diligent, credit card issuers and automotive lenders may find themselves in trouble. _____________________ http://cnews.canoe.ca/CNEWS/World/2010/10/08/15629836.html

With the news from the Federal Reserve that joblessness is not declining, and in fact is growing, a number of consumers are going to face newly difficult times and be further challenged to meet their credit obligations. Thinking about how this might impact the already struggling mortgage market, I’ve been considering what the impact of joblessness is on the incidence of strategic default and the resulting risk management issues for lenders. Using the definitions from our previous studies on strategic default, I think it’s quite clear that increased joblessness will definitely increase the number of ‘cash-flow managers’ and ‘distressed borrowers’, as newly jobless consumers face reduced income and struggle to pay their bills. But, will a loss of income also mean that people become more likely to strategically default? By definition, the answer is no – a strategic defaulter has the capacity to pay, but chooses not to, mostly due to their equity position in the home. But, I can’t help but consider a consumer who is 20% underwater, but making payments when employed, deciding that the same 20% that used to be acceptable to bear, is now illogical and will simply choose to stop payment? Although only a short-term fix, since they can use far less of their savings by simply ceasing to pay their mortgage, this would free up significant cash (or savings) for paying car loans, credit cards, college loans, etc; and yet, this practice would maintain the profile of a strategic defaulter. While it’s impossible to predict the true impact of joblessness, I would submit that beyond assessing credit risk, lenders need to consider that the definition of strategic default may contain a number of unique, and certainly evolving consumer risk segments. __________________________ http://money.cnn.com/2010/08/19/news/economy/initial_claims/index.htm

By: Staci Baker With the increase in consumer behaviors such as ‘strategic default’, it has become increasingly difficult during the past few years for lenders to determine who the most creditworthy consumers are – defining consumers with the lowest credit risk. If you define risk as ‘the likelihood of [a consumer] becoming 90 days or more past due’, the findings are alarming. From June 2007 to June 2009, Super Prime consumers (those scoring 900 or higher) in the U.S. have gone from an average  VantageScore® credit score* of 945 to 918, which increased their risk level  from approx. 0.12% to 0.62% - an increase of 417% for this highly sought after population!  Prime and near prime risk levels increased by 400% and 96% respectively.  Whereas subprime consumers with few choices (stay subprime or improve their score), saw a slight decrease in risk, 8% - increasing their average VantageScore® credit score from 578 to 599. So how do lenders determine who to lend to, when the risk level for all credit tiers increases, or remain risky?  In today’s dynamic economy, lenders need tools that will give them an edge, and allow them to identify consumer trends quickly.  Incorporating analytic tools, like Premier Attributes, into lender’s origination models, will allow them to pinpoint specific consumer behavior, and provide segmentation through predefined attribute sets that are industry specific and target profitable accounts to improve acquisition strategies. As risk levels change, maintaining profitability becomes more difficult due to shrinking eligible consumer pools.  By adding credit attributes, assessing credit risk both within an organization and for new accounts will be simplified and allow for more targeted prospects, thus maximizing prospecting strategies across the customer lifecycle and helping to increase profitability. * VantageScore®, LLC, May, 2010, “Finding Creditworthy Consumers in a Changing Economic Climate”  

I recently attended a conference where Credit Union managers spoke of the many changes facing their industry in the wake of the real estate crisis and economic decline that has impacted the US economy over the past couple of years.  As these managers weighed in on the issues facing their businesses today, several themes began to emerge – tighter lending standards & risk management practices, increased regulatory scrutiny, and increased competition resulting in tighter margins for their portfolios. Across these issues, another major development was discussed – increased Credit Union mergers and acquisitions. As I considered the challenges facing these lenders, and the increase in M&A activity, it occurred to me that these lenders might have a common bond with an unexpected group –American family farms.  Overall, Credit Unions are facing the challenge of adding significant fixed costs (more sophisticated lending platforms & risk management processes) all the while dealing with increased competition from lenders like large banks and captive automotive lenders.  This challenge is not unlike the challenges faced by the family farm over the past few decades – small volume operators having to absorb significant fixed costs from innovation & increased corporate competition, without the benefit of scale to spread these costs over to maintain healthy lending margins. Without the benefit of scale, the family farm basically disappeared as large commercial operators acquired less-efficient (and less profitable) operators. Are Credit Unions entering into a similar period of competitive disadvantage? It appears that the Credit Union model will have to adjust in the very near future to remain viable. With high infrastructure expectations, many credit unions will have to develop improved decisioning strategies, become more proficient in assessing credit risk –implementing risk-based pricing models, and executing more efficient operational processes in order to sustain themselves when the challenges of regulation and infrastructure favor economies of scale. Otherwise, they are facing an uphill challenge, just as the family farm did (and does); to compete and survive in a market that favors the high-volume lender.

By: Staci Baker As more people have become underwater on their mortgage, the decision to stay or not stay in their home has evolved to consider a number of influences that impact consumer credit decisions.  Research is revealing that much of an individual’s decision to meet his credit obligations is based on his trust in the economy, moral obligation, and his attitude about delinquency and the effect it will have on his credit score. Recent findings suggest that moral obligation keeps the majority of homeowners from walking away from their homes.  According to the 2009 Fannie Mae National Housing Survey (i) – “Nearly nine in ten Americans (88%), including seven in ten who are delinquent on their own mortgages, do not believe it is acceptable for people to stop making payments on an underwater mortgage, while 8% believe it is acceptable.”  It appears that there is a sense of owning up to one’s responsibilities; having signed a contract and the presumed stigma of walking away from that obligation. Maintaining strong creditworthiness by continuing to make payments on an underwater mortgage is motivation to sustain mortgage payments.  “Approximately 74% of homeowners believe it is very important to maintain good credit and this can be a factor in encouraging them not to walk away (ii).”  Once a homeowner defaults on their mortgage, their credit score can drop 150 to 250 points (iii), and the cost of credit in the future becomes much higher via increased interest rates once credit scores trend down. Although consumers expect to keep investing in the housing market (70% said buying a home continues to be one of the safest investments available (iv)) they will surely continue optimizing decisions that consider both the moral and credit implications of their decisions. i     December, 2009, Fannie Mae National Housing Survey ii  4/30/10, Financial Trust Index at 23% While Strategic Defaults Continue to Rise, The Chicago Booth/Kellogg School Financial Trust Index iii  http://www.creditcards.com/credit-card-news/mortgage-default-credit-scores-1270.php iv  December, 2009, Fannie Mae National Housing Survey    

Since 2007, when the housing and credit crises started to unfold, we’ve seen unemployment rates continue to rise (9.7% in March 2010 *)  with very few indicators that they will return to levels that indicate a healthy economy any time soon. I’ve also found myself reading about the hardship and challenge that people are facing in today’s economy, and the question of creditworthiness keeps coming into my mind, especially as it relates to employment, or the lack thereof, by a consumer. Specifically, I can’t help but sense that there is a segment of the unemployed that will soon possess a better risk profile than someone who has remained employed throughout this crisis. In times of consistent economic performance, the static state does not create the broad range of unique circumstances that comes when sharp growth or decline occurs. For instance, the occurrence of strategic default is one circumstance where the capacity to pay has not been harmed, but the borrower defaults on the commitment anyway. Strategic defaults are rare in a stable market. In contrast, many unemployed individuals who have encountered unfortunate circumstances and are now out of work may have repayment issues today, but do possess highly desirable character traits (willingness to pay) that enhance their long-term desirability as a borrower. Although the use of credit score trends, credit risk modeling and credit attributes are essential in assessing the risk within these different borrowers, I think new risk models and lending policies will need to adjust to account for the growing number of individuals who might be exceptions to current policies. Will character start to account for more than a steady job? Perhaps. This change in lending policy, may in turn, allow lenders to uncover new and untapped opportunities for growth in segments they wouldn’t traditionally serve. *  Source: US Department of Labor. http://www.bls.gov/bls/unemployment.htm

In the past few days I’ve read several articles discussing how lenders are taking various actions to reduce their exposure to toxic mortgages – some, like Bank of America, are engaging new principal repayment programs.*  Others, (including Bank of America) are using existing incentive programs to fast-track the approvals of short-sales to stunt their losses and acquire stronger lenders on existing real-estate assets. Given the range of options available to lenders, there are significant decisions to make regarding the creditworthiness of existing consumers and which treatment strategies are best for each borrower, these decisions important for assessing credit risk, loan origination strategies and loan pricing and profitability.  Experian analysis has uncovered the attributes of borrowers with various borrowing behaviors: strategic defaulters, cash-flow managers, and distressed borrowers, each of whom require a unique treatment strategy. The value of credit attributes and predictive risk scores, like Experian Premier Attributes and VantageScore® credit score, has never been higher to lenders. Firms like Bank of America are relying on credit delinquency attributes to segment eligible borrowers for its programs, and should also consider that more extensive use of attributes can further sub-segment its clients based on the total consumer credit profile. Consumers who are late on mortgage payments, yet current on other loans, may be likely to re-default; whereas some consumers may merely need financial planning advice and enhanced money management skills. As lenders develop new methods to manage portfolio risk and deal with toxic assets on their portfolios, they should also continue to seek new and innovative analytics, including optimization, to make the best decisions for their customers, and their business. *  LA Times, March 25, 2010, ‘Bank of America to reduce mortgage principal for some borrowers’

A recent January 29, 2010 article in the Wall Street Journal * discussing the repurchasing of loans by banks from Freddie Mae and Fannie Mac included a simple, yet compelling statement that I feel is worth further analysis. The article stated that "while growth in subprime defaults is slowing, defaults on prime loans are accelerating." I think this statement might come as a surprise to some who feel that there is some amount of credit risk and economic immunity for prime and super-prime consumers – many of whom are highly sought-after in today’s credit market. To support this statement, I reference a few statistics from the Experian-Oliver Wyman Market Intelligence Reports: • From Q1 2007 to Q1 2008, 30+ DPD mortgage delinquency rates for VantageScore® credit score A and B consumers remained flat (actually down 2%); while near-prime, subprime, and deep-subprime consumers experienced an increase of over 36% in 30+ rates. • From Q4 2008 to Q4 2009, 30+ DPD mortgage delinquency rates for VantageScore® credit score A and B consumers increased by 42%; whereas consumers in the lower VantageScore® credit score tiers saw their 30+ DPD rate increase by only 23% in the same period Clearly, whether through economic or some other form of impact, repayment practices of prime and super-prime, consumers have been changing as of late, and this is translating to higher delinquency rates. The call-to-action for lenders, in their financial risk management and credit risk modeling efforts, is increased attentiveness in assessing credit risk beyond just a credit score...whether this be using a combination of scores, or adding Premier Attributes into lending models – in order to fully assess each consumer’s risk profile. *  http://online.wsj.com/article/SB10001424052748704343104575033543886200942.html