With many homeowners locked into their properties by low mortgage rates, and listings for existing homes are generally low, the onus for property availability in desirable areas falls on the homebuilders with new homes. The newly released HMDA data shows the population is flowing towards areas of higher climate risk[1], with the understanding that our research covers new homes with a mortgage only. HMDA data does not come with client risk measurements. However, FHFA provides a list of census tracts as “Designated Disaster Areas (DDAs)”, which are located “in a county designated by the federal government as adversely affected by a declared major disaster under the Federal Emergency Management Agency’s (FEMA) administration, where housing assistance payments were authorized by FEMA”[2].We incorporate these into our HMDA Analyzer, which contains lender and borrower information. By so doing, we can perform complex analyses of mortgage origination in many dimensions. We first take a look at the heat map for 2023 DDAs provided by FHFA[3], noticing that DDAs are concentrated in coastal states, especially Florida and Texas: In terms of lenders, we identify eight homebuilding firms that report to HMDA. The table below shows purchase mortgage volumes in dollar terms in 2023 everywhere and in DDAs: In 2023, just over 38% of purchase mortgages by volume were produced in designated disaster areas. On this basis, about 5¼ % of total originations were produced by homebuilders while almost 6½ % of all DDA mortgages were underwritten by these entities. So, the builders are overrepresented here. There is a rather broad range of DDA shares among the group of eight builders. DHI, KB Home and Taylor Morrison have ratios above 50%, while Pulte and TBI fall under 45%, with NVR way out on the tail of the distribution with 16.4%. Using the HMDA data, we can look for characteristics of these lenders that correlate with these differences. The FHFA map shows the densest part of DDAs are in Florida and Texas. Intuitively, the share of originations in these two states is highly correlated with the exposure to DDAs: Next, we look at the relationship between DDA share, loan size, and median income for two-borrower households (as the income of two-borrower mortgages is typically much higher than that of one-borrower, we would apply this control): The results do not support a particular correlation between these two characteristics (loan size and income) and the share of loans in DDAs. The concentration in DDAs appears to be much more a purely geographic consideration than anything else. One reason could be that in-DDAs homes are more likely to be destroyed by natural disasters, and insurance companies only cover replacements built on the same site. However, we believe the overexposure in DDA areas by homebuilders is caused more by people favoring today’s “nice weather” but overlooking long-term “climate risk”. For some time, market participants have been assessing the potential consequences of an “Urban Doom Loop” popularized by Columbia Professor Stijn Van Nieuwerburgh where the financial fallout from empty office buildings leads to reduced revenues for city governments that in turn reduces public services less office attendance[4]. What we face now is a Sun Belt Doom Loop where households seeking lower housing costs and sunshine find themselves moving to areas experiencing more frequent climate events. The loop is exacerbated by builders happy to accommodate the spike in demand. The potential ramifications in terms of household and financial markets balance sheets are immense. This is a very complex subject, but we hope to indicate in this brief post how combining huge mortgage data sets with other publicly available data can offer analysts a cost-effective way of monitoring the situation. [1] https://www.redfin.com/news/climate-migration-real-estate-2024/
[2] https://www.fhfa.gov/sites/default/files/2024-06/DDA_methodology_2024.pdf [3] https://www.fhfa.gov/sites/default/files/2023-10/dda_map_2023.pdf [4] The "Urban Doom Loop" Could be Coming to a City Near You | Columbia Business School |
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