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A new analysis released today from RealtyTrac shows that single-family rental property owners in 48% of all U.S. counties are at above average risk for default. The RealtyTrac Rental Property Risk Report gauges the relative default risk of single-family rental homes, almost 90% of which are owned by mom-and-pop investors who own fewer than 10 properties. The financial impact of COVID-19, resulting job losses, and government-imposed eviction moratoria have all contributed to reduced on-time rental payments which, in turn, can lead to potential default among these smaller investors, many of whom are highly leveraged based on loan-to-value (LTV) ratio data. According to the research, the average risk score among the country’s 3,143 counties is 50.2, with 1,514, or 48%, at above-average risk. When looking at the largest 100 counties – based on the total number of properties – the average risk score is 43.6, with 53% at above-average risk. Among these large counties, Florida, New York, and California counties accounted for 44% of the 25 most at-risk counties. New York (Erie, Kings, Monroe, and New York Counties) and Florida (Collier, Lee, Polk, and Marion) each had four counties in the top 25 ranking, and California (Kern, Riverside, and San Bernardino) had three. Mohave County in Arizona was rated as the most at-risk of the 100 largest counties in the country. “The job losses in a handful of severely impacted industries due to the COVID-19 recession have disproportionately affected renters,” said Rick Sharga, RealtyTrac executive vice president. “Federal, state, and local governments have responded by enacting eviction bans to protect tenants, but in doing so have inadvertently put many landlords at risk. And the longer the eviction bans are in place, the higher the likelihood that these landlords are going to default on their mortgages, declare bankruptcy, or be forced to sell off properties at distressed pricing, which could have a negative impact on local housing markets.” The RealtyTrac Rental Property Risk Report, using real estate and mortgage records from ATTOM Data Solutions, analyzed data from the 3,143 counties across the United States against three criteria to determine which counties might be the most at-risk of single-family rental properties going into default: the percentage of properties in the county that were rental units; the unemployment rate in the county; and the degree to which rental properties were leveraged (the loan-to-value ratio). A weighted average was created using those criteria on a scale of 0-100, with 100 representing the highest potential risk. Counties with a high percentage of rental properties, high unemployment rates, and high LTV ratios had a higher risk score; while counties with a low percentage of rental properties, low unemployment rates, and low LTV ratios were considered less at risk. Of the 100 largest counties, Mohave County in Arizona had the highest risk score at 77.2, due to a high percentage of rental properties (79%) and a higher-than-average unemployment rate (8.7%). Salt Lake County in Utah had the lowest risk score at 17.2, reflecting the county’s relatively low percentage of single-family rental homes, low LTV ratios and low unemployment rate. “While it’s completely appropriate that the government has taken steps to protect tenants from eviction during a global pandemic, it’s also completely unrealistic to assume that landlords can bear 100% of the financial burden of missed rent payments,” Sharga noted. “There’s a misperception that most landlords are corporations or institutional investors. The fact is that almost 90% of single-family rental landlords are smaller investors who own fewer than 10 properties, are often highly leveraged, and simply don’t have the financial strength to weather this storm. And financial failure by these investors has implications for both their tenants and the communities where their rental properties are located.” Six States Account for a More Than a Quarter of Highest-Risk Large Counties Of the 100 largest counties with higher-than-average risk scores, those located in six states accounted for 27%: Florida (7), New York (5), California (4), Ohio (4), Texas (4) and Illinois (3). Four states had two counties each with above-average risk scores – Arizona, Connecticut, Maryland, and Michigan. No other state had more than one of the 100 largest counties with an above-average risk score. The average unemployment rate for all of the 100 largest counties with above-average risk scores was almost a full point higher than the national average (7.62%). But while unemployment rates were one of the three criteria used to assess risk, there wasn’t always a direct correlation between a state’s unemployment rate and above-average risk scores. While California (9.0%) and New York (8.2%) had two of the highest unemployment rates in the country, Florida, which had the highest number of at-risk counties among the 100 largest, had an unemployment rate below the national average (6.1% vs. 6.7%), as did Ohio (5.5%). Regionally, the Midwest has the highest number of large counties with above-average risk scores with 12, followed by the Northeast with 11, the Southeast with 10, the West with eight, and the South with six. “Despite the pandemic, default activity is at its lowest level in decades, and the government and mortgage industry are working together to prevent unnecessary foreclosures and evictions,” Sharga said. “But there needs to be a concerted effort to backstop the landlords as well, or numerous counties across the country are going to see rising levels of foreclosures on rental properties and needless financial distress.” About RealtyTrac Founded in 1996, RealtyTrac publishes the largest database of foreclosure property information in the U.S. along with other real estate and mortgage data used by real estate investors and professionals to find, analyze and purchase residential and commercial distressed properties. RealtyTrac is owned and operated by ATTOM Data Solutions, a leading provider of publicly recorded tax, deed, mortgage and foreclosure data as well as proprietary neighborhood and parcel-level risk data for more than 150 million U.S. properties.
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