Natural disasters create victims, and some of these victims may be unable or unwilling to continue paying their mortgages. These events often lead to a spike in defaults and put billions in mortgage balances at risk of being lost. We quantitatively assess the risk these events can have on a portfolio.
Extreme climate-related events are a test for most mortgage models; we demonstrate how to assess the impact of a storm like Hurricane Ida on mortgage losses. Using Moody’s Analytics Portfolio Analyzer’s extreme events credit forecasting capabilities, along with historical Federal Emergency Management Agency (FEMA) data, we can quantify and refine the impact of a major natural disaster on local mortgages, presenting in this case Ida’s impact on Louisiana. We forecast probability of default at the state level and extrapolate down to the county level, while assessing the impact of the storm for different degrees of severity.
Increased climate-related default risk will affect lenders, servicers, investors, and insurers more often and with less predictability. Large hurricanes such as Katrina, Harvey, Sandy, and Irma resulted in elevated defaults shortly afterward. Generating early estimates of a storm’s impact on credit risk provides many useful applications. Lenders and insurers can improve investor relations by signaling losses before they occur. A bank officer can increase reserves in anticipation instead of reacting. Alternatively, a risk manager can use estimates to determine an appropriate accommodation program.
Ida presents an opportunity to quantify default risk from a climate-related event. Unfortunately, many organizations cannot produce such estimates today. Traditional default models omit climate-related natural disasters, as events are few, their impacts are local, and each event differs from the next one.
Using a new approach, we incorporate climate factors into a mortgage performance model and demonstrate the impact a storm such as Hurricane Ida has on a representative sample of Louisiana mortgages. To better understand credit risk associated with climate-related events, we measure the impact of natural disasters on mortgage losses using loan-level mortgage data, along with a FEMA dataset that contains more than 13,000 events in the United States. Our framework predicts different responses based on event type, location, and storm severity, as measured by property damage. By making assumptions on Ida’s severity, we demonstrate that this storm will significantly alter mortgage loan performance. Based on historical patterns, during the next few months we forecast Louisiana mortgages to see:
» 12–18% of mortgage borrowers miss at least three payments
» $10 billion–14 billion of mortgage loans in trouble
» Default risk most elevated in Southeast Louisiana
We use historical FEMA data to quantify the impact of extreme events on default rates. By applying appropriate statistical assumptions, we can forecast the probability of default at the state level and extrapolate down to the county level. Under our approach, we can assess the impact of a storm for different degrees of severity, as measured in total state-level residential, commercial, and public property damage. A storm’s severity is particularly relevant because estimates are often available within days after an event.
The impact of extreme events on mortgage defaults captured by the model is the temporary one-month spike caused directly by the event. It is important to note that not all troubled borrowers will default. Because many lenders adopt leniency measures after major catastrophes, and some local and federal support is offered, cure rates tend to also increase, so many borrowers do not default.
State-level impact of Hurricane Ida by severity level
To simulate a portfolio, we use a new module in Mortgage Portfolio Analyzer at the state level. Incorporating recent data from Equifax, we determine a baseline historical default rate, along with balances at specific risk levels. Next, we adjust the probability of default for this particular storm. Moody’s Analytics estimates the total Louisiana state-level property damage from Hurricane Ida to be between $14 billion and $20 billion. For our analysis, we take the $16 billion mark as a conservative yet realistic estimate.
As seen in figures 1 and 2, Ida’s estimated default rate comes close to Hurricane Katrina in 2005. Zero severity property damage means there is no additional increase in default rates relative to historical averages. This severity level is estimated to increase short term, average state-level default rates from 0.07% to between 11.6% and 18.4%, with a conservative estimate of 13.8%. Consequently, we can approximate between 64,000 and a little over 100,000 mortgages will be at risk of a default in Louisiana over the next few months. Those at-risk loans hold balances between $9.5 billion and $15.1 billion, with a conservative estimate of balances at risk of $11.4 billion.
County-level impact of Hurricane Ida by severity level
It takes time to understand a storm’s county-level impact. In practice, state-level estimates are often available shortly after a storm hits. Nevertheless, historical patterns can help us understand mortgages and borrowers at the greatest risk. We use Moody’s Analytics Four Twenty Seven climate risk scores to adjust forecast results from the state level to the local level. These scores assess the sensitivity of a specific location to various climate risks (cyclones and hurricanes, flooding, heat stress, etc.). Using these risk scores enables us to consider the inherent local risk for major types of extreme events. Consequently, risky regions more exposed to climate risk are simulated to have
higher default rates and mortgage losses. In this case, we use the scores to disaggregate the state-level projections to the county level. Figure 3 depicts the distribution of Four Twenty Seven hurricane scores and the corresponding impacts on mortgage default rates. The maps show the projections based on a $16 billion property damage severity level. We see that southeastern parts of Louisiana, more exposed to storm risk, are expected to suffer more than other regions when a hurricane hits. For a severity level of $16 billion, the hardest-hit areas in the south could see default rates spike to 15.5%, while the lowest numbers in the north measure 9.8%.