Data & Analytics

As part of its guidance, the Office of the Comptroller of the Currency recommends that lenders perform regular validations of their credit score models in order to assess model performance.

Using a risk model based on older data can result in reduced predictive power.

By: Maria Moynihan Crime prevention and awareness techniques are changing and data, analytics and use of technology is making a difference. While law enforcement departments continue to face issues related to data - ranging from working with outdated information, inability to share data across departments, and difficulty in collapsing data for analysis -  a new trend is emerging where agencies are leveraging outside data sources and analytic expertise to better report on crimes, collapse information, predict patterns of behavior and ultimately locate criminals. One best practice being implemented by law enforcement agencies is to skip trace an individual much like a debt collector would.   Techniques involve using historic address information and individual connections to better track to a person’s current location. See the full write up from CollectionsandCreditRisk.com to see how this works. Another great example of effective use of data in investigations can be seen in this video, where one Experian client, Intellaegis of El Dorado Hills, CA, recently worked with local law enforcement to follow the digital data footprints of a particular suspect, finding her in in just five minutes of searching. p> And, yet another representation of improved data gathering, handling and sharing of information for crime prevention and awareness can be found on a site I was just made aware of by one of my neighbors - www.crimemapping.com. Information is collapsed across departments for greater insight into the crimes that are happening within a neighborhood, offering a more comprehensive option for the general public to turn to on local area crime activity. Clearly, data, analytics and technology are making a positive impact to law enforcement processes and investigations. What is your public safety organization doing to evolve and better protect and serve the public?     

Data quality should be a priority for retailers at any time of the year, but even more so as the holiday season approaches. According to recent research from Experian, organizations feel that, on average, 25 percent of their data is inaccurate and 12 percent of departmental budgets are wasted due to inaccuracies in contact data. During the 2013 holiday season, consumer spending is expected to increase by at least 11 percent. Retailers need to improve data quality early on in order to ensure that relevant holiday offers reach consumers and to take advantage of the expected increase in consumer spending. View our recent Webinar: Unique insights on consumer credit trends and the impact of consumer behavior on the economic recovery Source: View our data quality infographic: ’Twas the month before the holidays

The average bankcard balance per consumer in Q2 2013 was $3,831, a 1.3 percent decline from the previous year. Consumers in the VantageScore® near prime and subprime credit tiers carried the largest average bankcard balances at $5,883 and $5,903 respectively. The super prime tier carried the smallest average balance at $1,881.

Using data from IntelliViewSM, Credit.com recently compiled a list of states with the highest average bankcard utilization rates. Alaska took first place, with an average utilization ratio of 27.73 percent. This should come as no surprise since Alaska has recently topped lists for highest credit card balances and highest revolving debt.

When validating a model in the presence of overlay criteria, it is important to remember that any metrics computed at the aggregate portfolio level will not be indicative of the model's true performance. While traditional validation methodologies and portfolio metrics may provide directional insight into model performance, the overlay strategy is an additional variable that must be accounted for in each step of the validation analysis. An effective validation should include: Establishment of an appropriate base line Piece-wise validation of overlay segments An overlay strategy analysis Do you have model validation questions? Learn more and transform your business goals with Experian's Analytical Consulting Services. Source: VantageScore® Solutions LLC white paper: Validating a Credit Score Model in Conjunction with Additional Underwriting Criteria. VantageScore® is owned by VantageScore Solutions, LLC.

As part of its expanded guidance, the Office of the Comptroller of the Currency explicitly recommends that financial services firms utilizing predictive models and decision analytics run regular validations to gauge model efficacy. The VantageScore® credit score model was recently measured against the best credit score models from each of the three largest credit reporting companies (CRCs). When comparing KS values, there is exceptionally strong performance for mortgage originations, with the VantageScore® credit score model outperforming the CRC models in a range from 8 percent to 12 percent. The average range of outperformance is 3 percent to 4 percent across the board for most of the key industries. View the VantageScore® Webinar: Executing Effective Validations in 2011 and Beyond. Source: Executing Effective Validations, American Banker. VantageScore® is owned by VantageScore Solutions, LLC.

VantageScore® Solutions LLC polled risk professionals about how they are measuring score performance, and 60 percent of respondents said they are now using metrics beyond the Kolmogorov-Smirnov (KS) statistic value. One new metric is score consistency, which is defined as the ability to provide near-identical risk assessment of a consumer across multiple credit reporting agencies. In other words, this means having confidence that when a consumer gets a 700 from one agency, he or she is likely to get a 700 from another agency. The other metric that risk managers referenced was stability, which is defined as the ability of a model to retain its predictive accuracy across an extended time frame. Learn more about the VantageScore credit score® Source: VantageScore newsletter, April 2011 VantageScore® is owned by VantageScore Solutions, LLC

Experian® QAS®, a leading provider of address verification software and services, recently released a new benchmark report on the data quality practices of top online retailers. The report revealed that 72 percent of the top 100 retailers are using some form of address verification during online checkout. This third annual benchmark report enables retailers to compare their online verification practices to those of industry leaders and provides tips for accurately capturing email addresses, a continuously growing data point for retailers. To find out how online retailers are utilizing contact data verification, download the complimentary report 2012 Address Verification Benchmark Report: The Top 100 Online Retailers. Source: Press release: Experian QAS Study Reveals Prevalence of Real-Time Address Verification Increasing Among Top Online Retailers.

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: Mike Horrocks The realities of the new economy and the credit crisis are driving businesses and financial institutions to better integrate new data and analytical techniques into operational decision systems. Adjusting credit risk processes in the wake of new regulations, while also increasing profits and customer loyalty will require a new brand of decision management systems to accelerate more precise customer decisions. There is a Webinar scheduled for Thursday that will insightfully show you how blending business rules, data and analytics inside a continuous-loop decisioning process can empower your organization - to control marketing, acquisition and account management activities to minimize risk exposure, while ensuring portfolio growth. Topics include: What the process is and the key building blocks for operating one over time Why the process can improve customer decisions How analytical techniques can be embedded in the change control process (including data-driven strategy design or optimization) If interested check out more - there is still time to register for the Webinar. And if you just want to see a great video - check out this intro.

What happens when once desirable models begin to show their age? Not the willowy, glamorous types that prowl high-fashion catwalks. But rather the aging scoring models you use to predict risk and rank-order various consumer segments. Keeping a fresh face on these models can return big dividends, in the form of lower risk, accurate scoring and higher quality customers. In this post, we provide an overview of custom attributes and present the benefits of overlaying current scoring models with them. We also suggest specific steps communications companies can take to improve the results of an aging or underperforming model. The beauty of custom attributes Attributes are highly predictive variables derived from raw data. Custom attributes, like those you’ve created in house or obtained from third parties, can provide deeper insights into specific behaviors, characteristics and trends. Overlaying your scoring model with custom attributes can further optimize its performance and improve lift. Often, the older the model, the greater the potential for improvement. Seal it with a KS Identifying and integrating the most predictive attributes can add power to your overlay, including the ability to accurately rank-order consumers. Overlaying also increases the separation of “goods and bads” (referred to as “KS”) for a model within a particular industry or sub-segment. Not surprisingly, the most predictive attributes vary greatly between industries and sub-segments, mainly due to behavioral differences among their populations. Getting started The first step in improving an underperforming model is choosing a data partner—one with proven expertise with multivariate statistical methods and models for the communications industry. Next, you’ll compile an unbiased sample of consumers, a reject inference sample and a list of attributes derived from sources you deem most appropriate. Attributes are usually narrowed to 10 or fewer from the larger list, based on predictiveness Predefined, custom or do-it-yourself Your list could include attributes your company has developed over time, or those obtained from other sources, such as Experian Premier AttributesSM (more than 800 predefined consumer-related choices) or Trend ViewSM attributes. Relationship, income/capacity, loan-to-value and other external data may also be overlaid. Attribute ToolboxTM Should you choose to design and create your own list of custom attributes, Experian’s Attribute ToolboxTM offers a platform for development and deployment of attributes from multiple sources (customer data or third-party data identified by you). Testing a rejuvenated model The revised model is tested on your both your unbiased and reject inference samples to confirm and evaluate any additional lift induced by newly overlaid attributes. After completing your analysis and due diligence, attributes are installed into production. Initial testing, in a live environment, can be performed for three to twelve months, depending on the segment (prescreen, collections, fraud, non-pay, etc), outcome or behavior your model seeks to predict. This measured, deliberate approach is considered more conservative, compared with turning new attributes on right away. Depending on the model’s purpose, improvements can be immediate or more tempered. However, the end result of overlaying attributes is usually better accuracy and performance. Make your model super again If your scoring model is starting to show its age, consider overlaying it with high-quality predefined or custom attributes. Because in communications, risk prevention is always in vogue. To learn more about improving your model, contact your Experian representative. To read other recent posts related to scoring, click here.

This is the third and final post in an interview between Experian’s Tom Whitfield and Dr. Michael Turner, founder, president and CEO of the Policy and Economic Research Council (PERC)—a non-partisan, non-profit policy institute devoted to research, public education, and outreach on public and economic policy matters. In this post Dr. Turner discusses mandatory credit-information sharing for communications companies, and the value of engaging and educating state regulators. _____________________________ Does it make sense for the FTC to mandate carriers to report? Credit information sharing in the United States is a voluntary system under the Fair Credit Reporting Act (FCRA). Mandating information sharing would break precedent with this successful, decades-old regime, and could result in less rather than more information being shared, as it shifts from being a business matter to a compliance issue. Additionally, the voluntary nature of credit reporting allows data furnishers and credit bureaus to modify reporting in response to concerns. For example, in reaction to high utility bills as a result of severe weather, a utility provider may wish to report delinquencies only 60 days or more past due. Similarly, a credit bureau may not wish to load data it feels is of questionable quality. A voluntary system allows for these flexible modifications in reporting. Further, under existing federal law, those media and communications firms that decide they want to fully report payment data to one or more national credit bureaus are free to do so. In short, there is simply no need for the FTC to mandate that communications and media companies report payment data to credit bureaus, nor would there be any immediate benefit in so doing. How much of the decision is based on the influence of the State PUC or other legislative groups? Credit information sharing is federally regulated by the Fair Credit Reporting Act (FCRA). The FCRA preempts state regulators, and as such, a media or communications firm that wants to fully report may do so regardless of the preferences of the state PUC or PSC. PERC realizes the importance of maintaining good relations with oversight agencies. We recommend that companies communicate the fact of fully reporting payment data to a PUC or PSC and engage in proactive outreach to educate state regulators on the value of credit reporting customer payment data. There have been notable cases of success in this regard. Currently, just four states (CA, OH, NJ and TX) have partial prohibitions regarding the onward transfer of utility customer payment data to third parties, and none of these provisions envisioned credit reporting when drafted. Instead, most are add-ons to federal privacy legislation. Only one state (CA) has restrictions on the onward transfer of media and communications customer payment data, and again this has nothing to do with credit reporting. Agree, disagree or comment Whether you agree with Dr. Turner’s assertions or not, we’d love to hear from you. So please, take a moment to share your thoughts about full-file credit reporting in the communications industry. Click here to learn more about current and pending legislation that impacts communications providers.

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.