Delinquencies Rise In The Second Quarter

The delinquency rate for mortgage loans on one-to-four-unit residential properties increased to a seasonally adjusted rate of 4.53 percent of all loans outstanding at the end of the second quarter of 2019, according to the Mortgage Bankers Association’s (MBA) National Delinquency Survey. The foreclosure inventory rate, the percentage of loans in the foreclosure process, was 0.90 percent last quarter – the lowest since the fourth quarter of 1995.  

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The delinquency rate was up 11 basis points from the first quarter of 2019 and 17 basis points from one year ago. The percentage of loans on which foreclosure actions were started in the second quarter rose by two basis points to 0.25 percent.

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“The unemployment rate remains quite low, but the national mortgage delinquency rate in the second quarter rose from both the first quarter and one year ago. The economy is slowing, and this poses the risk of further increases in delinquency rates,” said Marina Walsh, MBA’s Vice President of Industry Analysis. “Across loan types, the FHA delinquency rate posted the largest variance, increasing 29 basis points from last quarter and 52 basis points from a year ago.”

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Added Walsh, “Heavy rains and flooding, extreme heat, and tornadoes in certain states during the spring, may have also contributed to the increase in the delinquency rate, as some borrowers likely faced disruption or hardship.”

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Key findings of MBA’s Second Quarter of 2019 National Delinquency Survey:

  • Compared to last quarter, the seasonally adjusted mortgage delinquency rate increased for all loans outstanding. By stage, the 30-day delinquency rate increased four basis points to 2.62 percent, the 60-day delinquency rate remained unchanged at 0.81 percent, and the 90-day delinquency bucket increased seven basis points to 1.10 percent. 
  • By loan type, the total delinquency rate for conventional loans increased 15 basis points to 3.61 percent over the previous quarter. The FHA delinquency rate increased 29 basis points to 9.22 percent, while the VA delinquency rate decreased by 13 basis points to 4.24 percent over the previous quarter. 
  • On a year-over-year basis, total mortgage delinquencies increased for all loans outstanding. The delinquency rate increased by 16 basis points for conventional loans, increased 52 basis points for FHA loans, and increased 27 basis points for VA loans from the previous year.
  • The delinquency rate includes loans that are at least one payment past due, but does not include loans in the process of foreclosure. The percentage of loans in the foreclosure process at the end of the second quarter was 0.90 percent, down two basis points from the first quarter of 2019 and 15 basis points lower than one year ago. This is the lowest foreclosure inventory rate since the fourth quarter of 1995.
  • The serious delinquency rate, the percentage of loans that are 90 days or more past due or in the process of foreclosure, was at 1.95 percent – a decrease of 1 basis point from last quarter and a decrease of 35 basis points from last year. The serious delinquency rate was unchanged for conventional loans, down 2 basis points for FHA loans, and down 6 basis points for VA loans from the previous quarter. Compared to a year ago, the serious delinquency rate decreased by 35 basis points for conventional loans, 43 basis points for FHA loans and 22 basis points for VA loans.
  • The five states with the largest increases in their overall delinquency rate were affected by weather-related issues. This may have resulted in an increase in delinquencies over the previous quarter of the following magnitude: West Virginia (86 basis points), Mississippi (81 basis points), Alabama (73 basis points), Indiana (73 basis points), and New Mexico (65 basis points).

Study Shows Lender Failings

NestReady, a technology firm that develops platforms to put lenders at the center of the homebuying process, released its 2019 Marketing Technology Report today. The report is based on a survey of the use of digital marketing technologies by mortgage lenders this year.

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NestReady surveyed 500 of the largest mortgage lenders in the U.S. about 350 different digital marketing platforms in 11 categories. The categories included web analytics, audience data management (DMPs), media buying/demand-side platforms (DPSs), cross-channel retargeting, digital ad exchanges, A/B testing and content/conversion optimization, live chat, marketing automation, video platforms, tag management solutions and social media tools. When compared to other industries such as retail, the results concluded that lenders are significantly behind other industries in the use of the technology.

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“In today’s competitive age, lenders need to leverage the proper digital marketing tools and channels to attract and retain customers,” said Mauro Repacci, co-founder and CEO of NestReady. “By understanding how some of the larger lenders are using technology for their business growth, other lenders can learn how to improve their businesses and attract a larger customer base.”

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Some other survey findings from the top U.S. mortgage lenders include:

  • 60 percent use audience data platforms to help them understand consumers’ behavior and interests
  • 55 percent are leveraging cross-channel display advertising platforms, which enable them to run hyper-targeted campaigns with advanced bidding methods across multiple channels 
  • More than 56 percent use a tag management solution to deploy various marketing technologies across their websites from a centralized location
  • Just under 50 percent are leveraging a dedicated retargeting platform. Google and Bing retargeting dominate this category with 42 percent of surveyed lenders using at least one of them

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“It is important to look at the systems being used and seriously considered how we can increase efficiencies while providing better customer service,” Repacci said. “We need to learn from other industries when it comes to harnessing technology.” 

The Mortgage Technology Report, including detailed analysis and takeaways is available here.

Title Insurance And Settlement Services Provider Names New President

WFG National Title Insurance Company (WFG) a Portland-based, full-service provider of title insurance and real estate settlement services for commercial and residential transactions nationwide, announces that Brandon Baker has been named the new president of the company’s Dallas-Fort Worth division. 

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In this role Baker will manage the day-to-day administrative operations for the DFW market, including working with the WFG team to establish short and long-term goals, plans and strategies. He will also be looking to recruit additional revenue-producing employees to add to the company’s branches.

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 Baker comes to WFG from another local title company, where he was most recently a vice president and was responsible for strategic growth, fee attorney operationsand recruiting. Before that he held several executive sales positions. 

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“Joining WFG and leading the DFW office is a great opportunity to continue to develop in this marketplace,” Baker said. “With our current team and new recruits, we look to build on our relationships with the local real estate community to increase our market share.  Our ultimate goal is to create a better real estate experience for home buyers and sellers.”

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Rob Sherman, senior vice president and regional director at WFG, said the company concentrates on taking time and cost out of real estate transactions as it focuses on clients and their processes. 

“Brandon’s experience is a valuable asset to WFG,” Sherman noted. “He has demonstrated his leadership skills in maximizing growth through internal collaboration as well as in developing client relationships and cultivating talent within the industry. He’s a great addition to the team.”

CoreLogic Launches New Fraud Risk Score Model

CoreLogic announced its newest version of the Fraud Risk Score Model— version 4.0. Delivered within the LoanSafe product suite, the new model accounts for recent changes in mortgage fraud trends while leveraging new data assets. 

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Drawing experts from more than 30 top financial institutions, this year’s Mortgage Fraud Consortium was held in San Diego, California, and featured speakers from the FBI, Fifth Third Bank, Cognizant, Fannie Mae, Freddie Mac and more. The event revolved around the theme of current fraud trends and what they mean for the future market. Program discussions ranged from procedures to improve fraud detection to the impact eroding housing affordability has on fraud risk to how law enforcement officials are working to identify and combat the latest fraud schemes. Additional insights included case studies and best practices to help improve mortgage fraud prevention practices.

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“This year’s Mortgage Fraud Consortium was another success, providing leading industry professionals with the opportunity to learn about the latest mortgage fraud trends while collaborating on ways to reduce future risk,” said Bridget Berg, principal, Fraud Solutions at CoreLogic. “According to our latest research, the country has seen a 10 percent increase in fraud risk from Q1 2018 to Q1 2019. This continual increase reinforces the need for this annual event and we’re proud to continue helping mortgage loan providers mitigate risk and fight back against fraud.”

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During the event, CoreLogic shared details of the LoanSafe Fraud Manager roadmap and announced version 4.0 of its Fraud Risk Score Model. Integrated into the LoanSafe solution, the updated model provides more transparency into how the Fraud Risk score is calculated through an integration of alert predictive of fraud risk. The updated score was designed based on feedback from CoreLogic clients and will help make lenders more efficient in their fraud detection practices, ultimately saving them time and money. The latest version of the Fraud Risk Score will be released in the summer of 2019.

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CoreLogic is a global property information, analytics and data-enabled solutions provider. The company’s combined data from public, contributory and proprietary sources includes over 4.5 billion records spanning more than 50 years and providing detailed coverage of property, mortgages and other encumbrances, consumer credit, tenancy, location, hazard risk and related performance information. The markets CoreLogic serves include real estate and mortgage finance, insurance, capital markets, and the public sector. CoreLogic delivers value to clients through unique data, analytics, workflow technology, advisory and managed services. Clients rely on CoreLogic to help identify and manage growth opportunities, improve performance and mitigate risk. Headquartered in Irvine, Calif., CoreLogic operates in North America, Western Europe and Asia Pacific. 

AI-Based Textual Analysis

In today’s mortgage market you can’t pick up an industry publication or attend a trade show and not hear someone talk about Artificial Intelligence (AI).  Many of the claims talk about how AI will disrupt the mortgage industry or radically change how mortgages are originated or processed.

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Some of it is pure hype and some of it includes technology that can clearly streamline processes and reduce cost.  So how do you filter fact from fiction?  Let’s take a look at how AI-based textual analysis is actually at work in the mortgage industry and the different approaches some vendors are using.

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High-Level Approach

The most fundamental difference in approach between an AI textual analysis and the majority of other “advanced” document recognition technologies is that AI textual analysis treats variable layout documents as unstructured documents whereas most other prominent solutions treat them more as semi-structured documents.

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To illustrate the difference in methodology let us consider a customer wishing to process pay stub documents. An AI textual analysis implementation would typically be deployed with one set of completely generic rules designed to encompass all variations of the “Pay Stub” document type from any company.  Because alltext is evaluated by the AI engine, the rules can flex with the layout and verbiage changes just as a human does when reading the page. The solution is also capable of being configured to perform conditional processing for specific exceptions to generic rules (per-layout exceptions) but it is not typically necessary to do this.

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Most other modern advanced document recognition technologies treat document variations as semi-structured documents. These solutions typically either: 

a) Remember as many of the variations as is practical and process each variation with layout-specific templates for processing Or b) Apply a mix of layout-specific processing and some generic processing (usually the higher incidence layouts are processed with layout-specific or templated processing) 

The obvious advantage of the AI textual analysis generic approach is that, as new layouts appear or existing layouts change, the software is better equipped to deal with these new variations. This advantage was recently validated at an account with over 35,000 layout variations where rules had been in place for five years with not a single modification. An audit of this client’s processes after five years revealed an identical automation rate to that when the system was first deployed. This outcome was observed despite the fact that a significant portion of the originally dominant layouts had been transferred to EDI processingand were therefore bypassing the system now. 

As you can see the varying approaches often produce different levels of success.  But how can you determine which is best for your organization? Get a demo and hope that the solution is more than smoke and mirrors?  Up until now that is usually the case.  To overcome much of the confusion and disappointment, a better evaluation process in many cases may go a long way towards greatly minimizing the risks involved in choosing a vendor who can actually deliver AI-based textual analysis.

In order to quickly understand AI-based textual analysis and its capabilities, a blind test with several sample files should be considered the gold standard for an evaluation.  This is especially true when it comes to the challenges presented with the many and varying document types and quality levels of document images found in the mortgage industry.  Asking vendors if they are willing to perform a test on a never before seen sample set of typical loan files on site and in sight of your evaluation team, is a great first step in separating fact from fiction.

Ideally, an evaluation should be setup as a one day event to hedge against any vendor refining their results.  This kind of test is intended to demonstrate the validity of the vendors’ out-of-the-box capabilities so that prospects can be assured that they are considering a proven, robust and scalable solution ready to deliver productivity improvements in weeks rather than months or years.

For qualified opportunities, Paradatec will perform this process, which enables prospective clients to quickly understand the overall levels of automation, and speed improvements they will be able to achieve with their technology.  Download our whitepaper on AI based textual analysis

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Highest-Risk Cities for Floods, Hurricanes And Wildfires Underperformed Overall Market

ATTOM Data Solutions released its 2018 U.S. Natural Hazard Housing Risk Index, which found that median home prices in cities with the top 80th percentile for natural hazard housing risk have appreciated 40 percent on average over the last 10 years — 1.7 times the 24 percent home price appreciation in the overall U.S. housing market during the same time period.

For the report ATTOM indexed natural hazard risk in more than 3,000 counties and more than 22,000 U.S. cities based on the risk of six natural disasters: earthquakes, floods, hail, hurricane storm surge, tornadoes, and wildfires. ATTOM also analyzed housing trends in 2,616 cities and 440 counties — containing more than 53 million single family homes and condos — broken into five equal quintiles of natural hazard housing risk.

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“While combined natural disaster risk has not seemed to hobble home price appreciation over the past decade, the story is much different for some individual hazard risks — namely flood, hurricane storm surge and wildfire risk,” said Daren Blomquist, senior vice president at ATTOM Data Solutions. “Home price appreciation in the overall U.S. housing market was double the rate of appreciation in cities with the highest flood risk and triple the rate of appreciation in cities with the highest hurricane storm surge risk over the past 10 years. The broader market has also outperformed appreciation in cities with the highest wildfire risk during the last decade, although the gap is much narrower.”

Foreclosure rates elevated in highest-risk flood cities

Foreclosure rates were lower in cities in the top 80th percentile for natural hazard housing risk, and this was true for all individual natural hazard risk types except for flood risk. In cities in the top 80th percentile for flood risk, active foreclosures represented 0.61 percent of all properties, well above the foreclosure rate of 0.38 percent across all risk categories.

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“Weather is the largest external swing factor in U.S. economics and accounts for over $550 billion per year in lost revenue and up to 76,000 lost jobs,” said Mark Gibbas, president and CEO at WeatherSource, a technology company that provides global weather and climate data along with advanced analytics. “Weather can have an enormous impact on homeowners and the housing market.  When big weather events such as hurricanes, tornados and hail hit, many homeowners suffer financial hardship from various sources such as lost wages and losses due to inadequate insurance. And while the impact on homeowners can be severe, hurricanes like Harvey can change the landscape of the housing market region wide, including shifts in the number of available homes and shifts in home values.”

Cities with the highest flood risk also posted seriously underwater rates (loan-to-value ratio of 125 percent or higher) above the overall market average — 8.9 percent of all homes with a mortgage compared to 8.5 percent nationwide. Tornado risk was the only other individual natural hazard risk factor with seriously underwater rates above the market average in the highest risk cities — 10.0 percent of all homes with a mortgage.

Buyers paid a premium for homes in highest-risk cities in 2018

The report also shows that homebuyers so far in 2018 paid an average 1.0 percent premium above estimated market value for homes in cities with the highest natural disaster risk while homes in cities with the lowest natural disaster risk sold at an average 3.7 percent discount below estimated market value.

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The exception to this trend was in cities with the highest flood risk, where homes sold at an average 2.4 percent discount below estimated market value, cities with the highest tornado risk (2.2 percent discount below estimated market value), and cities with the highest hurricane storm surge risk (1.4 percent discount below estimated market value).

Counties and cities with highest natural hazard risk index

Among the 2,616 cities analyzed in the report with sufficient housing trend data, those with the top 20 highest natural hazard housing risk indexes were all located in the following metropolitan statistical areas: Oklahoma City, Oklahoma; San Diego, California; Clearlake, California; San Jose, California; Madera, California; Riverside-San Bernardino, California, Bakersfield, California; Houston, Texas, Santa Cruz, California; and Huntsville, Alabama.

Among the 440 counties analyzed in the report with sufficient housing trend data, those with the highest natural hazard housing risk indexes were Oklahoma County, Oklahoma (Oklahoma City); Monroe County, Florida (Key West); Santa Cruz County, California (Santa Cruz); Santa Clara County, California (San Jose); and Marin County, California (San Francisco).

Among those same 440 counties, those with the lowest natural hazard housing risk indexes were Milwaukee County, Wisconsin (Milwaukee); Muskegon County, Michigan (Muskegon); Cuyahoga County, Ohio (Cleveland); Kenosha County, Wisconsin (Chicago metro); and Monroe County, New York (Rochester).

Index Methodology

For its fifth annual Natural Hazard Housing Risk Index, ATTOM Data Solutions indexed more than 3,000 U.S. counties and more than 22,000 U.S. cities based on risk of six natural disasters: earthquakes, floods, hail, hurricane storm surge, tornadoes and wildfires. ATTOM also analyzed home sales and price trends in 440 counties and 2,616 cities with sufficient property data.

A risk index was created for each of the six natural hazards in each city and count with natural hazard data available. Each natural hazard index was divided into five categories of risk: Very High, High, Moderate, Low and Very Low based on a severity scale. Those six natural hazard indexes were summed to create a Total Natural Hazard Index. The maximum index for each category of risk is 60, and the maximum possible total index score is 360.

For the home sales and price trends analysis, the indexes in 735 counties and 3,441 cities were split into five equal groups (quintiles) matching the aforementioned five categories of risk.

Flood zone data is based on flood zones created by the Federal Emergency Management Agency (FEMA), and the level of risk was based on the percentage of homes in each county located in high-risk flood zones: A, A99, AE, AH, A.

Earthquake data is from the United States Geological Survey (USGS), and the level of risk was based on the probability of a magnitude 5.0 earthquake in each county.

Tornado data is from the National Oceanic and Atmospheric Administration (NOAA), and level of risk was based on the Destruction Potential Index (DPI) for each county. DPI is calculated using number of tornados, path of tornados in square miles, and intensity of tornados on the Fujita scale (FO to F5).

Wildfire data is from the United States Department of Agriculture Forest Service and Fire Modeling Institute, and risk level is based on the percentage of homes in each county located in “Very High” or “High” Wildfire Hazard Potential (WHP) areas.

Hurricane storm surge data is from FEMA and the National Hurricane Center (NHC), and risk level is based on the percentage of homes located in flood zones identified as having a risk of “storm-induced waves”: V and VE.

Hail data is from NOAA and the risk level is based on the average number of hail storms per year in each county with hail that exceeds 1-inch in size over the past 15 years.