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of the printing and typesetting industry. Lorem Ipsum has been the industry’s standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum
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This is second question in our five-part series on the FFIEC guidance and what it means Internet banking. If you missed the first question, don't worry, you can still go back. Check back each day this week for more Q&A on what you need to know and how to prepare for the January 2012 deadline. Question: What does “multi-factor” authentication actually mean? “Multi- Factor” authentication refers to the combination of different security requirements that would be unlikely to be compromised at the same time. A simple example of multi-factor authentication is the use of a debit card at an ATM machine. The plastic debit card is an item that you must physically possess to withdraw cash, but the transaction also requires the PIN number to complete the transaction. The card is one factor, the PIN is a second. The two combine to deliver a multi-factor authentication. Even if the customer loses their card, it (theoretically) can’t be used to withdraw cash from the ATM machine without the PIN. _____________ Look for part three of our five-part series tomorrow.
This first question in our five-part series on the FFIEC guidance and what it means Internet banking. Check back each day this week for more Q&A on what you need to know and how to prepare for the January 2012 deadline. Question: What does “layered security” actually mean? “Layered” security refers to the arrangement of fraud tools in a sequential fashion. A layered approach starts with the most simple, benign and unobtrusive methods of authentication and progresses toward more stringent controls as the activity unfolds and the risk increases. Consider a customer who logs onto an on-line banking session to execute a wire transfer of funds to another account. The layers of security applied to this activity might resemble: 1. Layer One- Account log-in. Security = valid ID and Password must be provided 2. Layer Two- Wire transfer request. Security= IP verification/confirmation that this PC has been used to access this account previously. 3. Layer Three- Destination Account provided that has not been used to receive wire transfer funds in the past. Security= Knowledge Based Authentication Layered security provides an organization with the ability to handle simple customer requests with minimal security, and to strengthen security as risks dictate. A layered approach enables the vast majority of low risk transactions to be completed without unnecessary interference while the high-risk transactions are sufficiently verified. _____________ Look for part two of our five-part series tomorrow.
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.
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With the National Automobile Dealers Association (NADA) Show set to kickoff later this week, it seemed fitting to explore how the shifting dynamics of the used vehicle market might impact dealers and buyers over the coming year. Shedding light on some of the registration and finance trends, as well as purchasing behaviors, can help dealers and manufacturers stay ahead of the curve. And just like that, the Special Report: Automotive Consumer Trends Report was born. As I was sifting through the data, one of the trends that stood out to me was the neck-and-neck race between Millennials and Gen X for supremacy in the used vehicle market. Five years ago, in 2019, Millennials were responsible for 33.3% of used retail registrations, followed by Gen X (29.5%) and Baby Boomers (26.8%). Since then, Baby Boomers have gradually fallen off, and Gen X continues to close the already minuscule gap. Through October 2024, Millennials accounted for 31.6%, while Gen X accounted for 30.4%. But trends can turn on a dime if the last year offers any indication. Over the last rolling 12 months (October 2023-October 2024), Gen X (31.4%) accounted for the majority of used vehicle registrations compared to Millennials (30.9%). Of course, the data is still close, and what 2025 holds is anyone’s guess, but understanding even the smallest changes in market share and consumer purchasing behaviors can help dealers and manufacturers adapt and navigate the road ahead. Although there are similarities between Millennials and Gen X, there are drastic differences, including motivations and preferences. Dealers and manufacturers should engage them on a generational level. What are they buying? Some of the data might not come as a surprise but it’s a good reminder that consumers are in different phases of life, meaning priorities change. Over the last rolling 12 months, Millennials over-indexed on used vans, accounting for more than one-third of registrations. Meanwhile, Gen X over-indexed on used trucks, making up nearly one-third of registrations, and Gen Z over-indexed on cars (accounting for 17.1% of used car registrations compared to 14.6% of overall used vehicle registrations). This isn’t surprising. Many Millennials have young families and may need extra space and functionality, while Gen Xers might prefer the versatility of the pickup truck—the ability to use it for work and personal use. On the other hand, Gen Zers are still early in their careers and gravitate towards the affordability and efficiency of smaller cars. Interestingly, although used electric vehicles only make up a small portion of used retail registrations (less than 1%), Millennials made up nearly 40% over the last rolling 12 months, followed by Gen X (32.2%) and Baby Boomers (15.8%). The market at a bird’s eye view Pulling back a bit on the used vehicle landscape, over the last rolling 12 months, CUVs/SUVs (38.9%) and cars (36.6%) accounted for the majority of used retail registrations. And nearly nine-in-ten used registrations were non-luxury vehicles. What’s more, ICE vehicles made up 88.5% of used retail registrations over the same period, while alternative-fuel vehicles (not including BEVs) made up 10.7% and electric vehicles made up 0.8%. At the finance level, we’re seeing the market shift ever so slightly. Since the beginning of the pandemic, one of the constant narratives in the industry has been the rising cost of owning a vehicle, both new and used. And while the average loan amount for a used non-luxury vehicle has gone up over the past five years, we’re seeing a gradual decline since 2022. In 2019, the average loan amount was $22,636 and spiked $29,983 in 2022. In 2024, the average loan amount reached $28,895. Much of the decline in average loan amounts can be attributed to the resurgence of new vehicle inventory, which has resulted in lower used values. With new leasing climbing over the past several quarters, we may see more late-model used inventory hit the market in the next few years, which will most certainly impact used financing. The used market moving forward Relying on historical data and trends can help dealers and manufacturers prepare and navigate the road ahead. Used vehicles will always fit the need for shoppers looking for their next vehicle; understanding some market trends will help ensure dealers and manufacturers can be at the forefront of helping those shoppers. For more information on the Special Report: Automotive Consumer Trends Report, visit Experian booth #627 at the NADA Show in New Orleans, January 23-26.
The automotive industry is constantly changing. Shifting consumer demands and preferences, as well as dynamic economic factors, make the need for data-driven insights more important than ever. As we head into the National Automobile Dealers Association (NADA) Show this week, we wanted to explore some of the trends in the used vehicle market in our Special Report: State of the Automotive Finance Market Report. Packed with valuable insights and the latest trends, we’ll take a deep dive into the multi-faceted used vehicle market and better understand how consumers are financing used vehicles. 9+ model years grow Although late-model vehicles tend to represent much of the used vehicle finance market, we were surprised by the gradual growth of 9+ model year (MY) vehicles. In 2019, 9+MY vehicles accounted for 26.6% of the used vehicle sales. Since then, we’ve seen year-over-year growth, culminating with 9+MY vehicles making up a little more than 30% of used vehicle sales in 2024. Perhaps more interesting though, is who is financing these vehicles. Five years ago, prime and super prime borrowers represented 42.5% of 9+MY vehicles, however, in 2024, those consumers accounted for nearly 54% of 9+MY originations. Among the more popular 9+MY segments, CUVs and SUVs comprised 36.9% of sales in 2024, up from 35.2% in 2023, while cars went from 44.3% to 42.9% year-over-year and pickup trucks decreased from 15.9% to 15.6%. 2024 highlights by used vehicle age group To get a better sense of the overall used market, the segments were broken down into three age groups—9+MY, 4-8MY, and current +3MY—and to no surprise, the finance attributes vary widely. While we’ve seen the return of new vehicle inventory drive used vehicle values lower, it could be a sign that consumers are continuing to seek out affordable options that fit their lifestyle. In fact, the average loan amount for a 9+MY vehicle was $19,376 in 2024, compared to $24,198 for a vehicle between 4-8 years old and $32,381 for +3MY vehicle. Plus, more than 55% of 9+MY vehicles have monthly payments under $400. That’s not an insignificant number for people shopping with the monthly payment in mind. In 2024, the average monthly payment for a used vehicle that falls under current+3MY was $608. Meanwhile, 4-8MY vehicles came in at an average monthly payment of $498, and 9+MY vehicles had a $431 monthly payment. Taking a deeper dive into average loan amounts based on specific vehicle types—as of 2024, current +3MY cars came in at $28,721, followed by CUVs/SUVs ($31,589) and pickup trucks ($40,618). As for 4-8MY vehicles, cars came in with a loan amount of $22,013, CUVs/SUVs were at $23,133, and pickup trucks at $31,114. Used 9+MY cars had a loan amount of $19,506, CUVs/SUVs came in at $17,350, and pickup trucks at $22,369. With interest rates remaining top of mind for most consumers as we’ve seen them increase in recent years, understanding the growth from 2019-2024 can give a holistic picture of how the market has shifted over time. For instance, the average interest rate for a used current+3MY vehicle was 8.0% in 2019 and grew to 10.2% in 2024, the average rate for a 4-8MY vehicle went from 10.3% to 12.9%, and the average rate for a 9+MY vehicle increased from 11.4% to 13.8% in the same time frame. Looking ahead to the used vehicle market It’s important for automotive professionals to understand and leverage the data of the used market as it can provide valuable insights into trending consumer behavior and pricing patterns. While we don’t exactly know where the market will stand in a few years—adapting strategies based on historical data and anticipating shifts can help professionals better prepare for both challenges and opportunities in the future. As used vehicles remain a staple piece of the automotive industry, making informed decisions and optimizing inventory management will ensure agility as the market continues to shift. For more information, visit us at the Experian booth (#627) during the NADA Show in New Orleans from January 23-26.
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