The millennial generation has reached peak home buying age, covering a range of about 25-40 years old. Just this year this cohort at 72.1 million passed the baby boom generation in sheer size[1] although this is a bit less than the 78.8 million peak reached by boomers in the late 90’s[2]. Millennial presence or lack of it in the housing market has been the subject of voluminous commentary, ranging from extreme optimism derived from the magnitude of the population bulge to caution related to affordability and impediments to building savings stemming from student loan debt[3] and high rental and child care costs[4]. What can big data tell us about this?

A 2018 study by the Federal Reserve showed that Millennials had lower incomes and assets and higher debt than previous cohorts at a similar age[5]. Our data sets can provide some useful, but far from conclusive, insights into these trends since the onset of the Covid-19 pandemic. Covid-19 is both a cyclical and structural shock. On the cyclical side mortgage rates have fallen to record lows as the Federal Reserve responded quickly and decisively to the health crisis. On the structural side, residents of urban areas have picked up and fled to less dense locations, resulting in a sharp increase in house prices nationally. According to Redfin these price gains have been led by those in rural and suburban areas, rendering these areas less affordable, while urban areas remain out of reach for most.

​The Global Financial Crisis (GFC) of a dozen years ago was at its core a housing crisis. More specifically, it was a single-family mortgage crisis as imprudent lending led to a huge surge in home sales in a bubble that simply could not be sustained. The resulting collapse led to the biggest economic shock since the Great Depression. Until, perhaps, now. The other segment of residential housing, the rental market, skated through almost unscathed. While almost nine million people lost their jobs, including renters, millions of families lost homes and had to turn to rent, keeping the multifamily market afloat.

In our third look at 2019 HMDA[1] characteristics we look at mortgage originations by income bracket. Lending to low- and moderate-income households is an important regulatory requirement of banks[2]. The definition of “low” and “moderate” depends on the local area in which the bank operates. HMDA data is well-suited to regulators looking to track the performance of the institutions they oversee and allows banks to benchmark their performance against their competition. If banks need to add low- to moderate-income loans to their portfolio to meet requirements, HMDA can provide direction regarding which institutions might be a source of product that meets needed characteristics.

Below we present a quick high-level example. HMDA data operates down to the census tract level, but for our purposes here let’s look at two distinct states: California and Oklahoma. In 2018, median income in the two states was $70,500 and $54,400, respectively[3]. According to Zillow data[4], the median house prices in California and Oklahoma that year were $550,000 and $122,000 respectively. Clearly housing is relatively unaffordable for households at or below median income in California compared to Oklahoma. So it is not surprising that the homeownership rate in Q2 2018 for California, at 54.3%, is substantially below that of Oklahoma, at 69.1%[5].
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Confirming this, the following table from 2018 and 2019 HMDA show that there is a substantially greater share of lower- and moderate- income loans available in Oklahoma than in California. Interestingly this share declined in 2019 relative to 2018, particularly for Oklahoma. It is not clear whether this is due to fundamental factors or technical issues related to an increase in the share of “N/A” responses between the two years.

​Finally, to be consistent with prior posts we look at the share of conforming loans originated by banks that are sold to the GSEs, broken down by income brackets:

​A few interesting observations pop up. First, in California the loans that banks keep on their book are almost entirely made to the highest-income households. For Oklahoma, it’s a mixture of highest income and lowest income. This suggests that policy requirements regarding serving poorer communities plays a relatively greater role in Oklahoma than California.

As is the case for residential mortgages, every month Ginnie Mae publishes data on loan-level delinquencies for its commercial real estate programs. The structure is a bit different than for single family, with different categories (the dominant one being FHA multifamily, but also hospitals and nursing homes). In this short post we look at recent performance for FHA multifamily and nursing homes.
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Traditionally, multifamily DQs for FHA are low because these loans are concentrated in affordable housing, where there is a persistent condition of excess demand. The costs of eviction are low and new tenants are ready to move in. But this is not necessarily the case in the COVID-19 era as the economic impact falls most heavily on the lower income working class, so there are fewer people who can afford affordable housing without support from government income programs such as jobless benefits.