Like the rest of the country, we have been spending a great deal of time recently thinking about hurricanes and natural disasters. This inspired a quick analysis on the intersection of hurricanes and what we do for a living.
When it comes to the effects of large scale natural disasters on housing market trends, home price indices are insufficient. This is because home price indices derive from transactions, traditionally from a paired sales methodology that requires the same home to trade twice in a given period. The below price chart from HouseCanary Pro shows the home price line for New Orleans. If you look closely at 2005, you will see that the effect of Hurricane Katrina was imperceptible at the MSA level. The homes that were most flood-affected simply did not transact, and the homes that did transact were riding the momentum from the peak of the national housing bubble.
Below the surface, however, the lack of transactions in the most damaged neighborhoods creates a meaningful selection bias that masks what is actually going on in the region. Below is a map that we created with our block-group-level home price data.;
To create this map, we snapshotted the number of home transactions in each block group during the month before Katrina: August 2005. We then counted the number of months it took each block group to return to the August 2005 level of transaction volume.
August is by no means the slowest real estate sales month of the year, but it’s also not peak selling season (which typically hits most markets in May and June). In the worst-affected neighborhoods in New Orleans, it took a full two years before we saw transaction volume return to where it was in August 2005;this is during the peak of the hottest national housing market in US history.
You can see that the neighborhoods that saw transaction volume fall off a cliff corresponded with the most flood-damaged neighborhoods, as one would expect.
So clearly, the MSA-level HPIs in the area were by no means accurate representations of value, because the calculation selected away from the worst-effected areas.
Long-term effects of natural disasters on hedonic consumer preferences
An academic paper in 1997 did a great job teasing out the long-term effects on the housing market of a damage-causing natural disaster. The gist of the paper is that before a natural disaster (in this case, an earthquake) occurred in the Bay Area within the lifetime of the marginal home buyer, consumers lacked information to accurately assess the risk of one home over another.
The result of this lack of data was human risk aversion, which incorrectly applied an “earthquake discount” evenly across the entire Bay Area (not entirely unlike how the market routinely applies a 15% haircut to BPO/broker price opinion valuations, due to lack of more specific collateral knowledge). After the earthquake, the home-owning population suddenly found itself in possession of very important data: which homes were left standing. Almost immediately, this data was incorporated into differential home prices between earthquake-safe homes and earthquake-unsafe homes.
If we examine pricing on a more granular level, we can learn more. You can see the combination of both the short-term and long-term effects in the ZIP-code level HPI chart below.
The Marina, a district of San Francisco built on landfill, was having a bit of a moment in the run-up to the ’89 earthquake. When the earthquake happened, neighboring Pacific Heights actually outperformed the rest of the country in a flattening housing market and into the coming recession, while the Marina simply traded flat for lack of transaction data (as in post-Katrina New Orleans). Once the Savings and Loan Crisis and recession forced sellers into the market, the damaged Marina homes were forced to seek liquidity, drastically underperforming Pacific Heights. As residents realized the differential value of a home built on bedrock versus on landfill, the wedge between the two neighborhoods continued to widen through the recovery.
Did you know HouseCanary has an API endpoint that shows the effects of past hurricanes? Learn more in our blog post.