Home building industry sage George Casey takes on one of housing finance's gorilla-in-the-room issues: big data and the affordability conundrum

Challenging conventional wisdom and accepted practice can be an interesting undertaking and might be good for the soul. It might also lead to understanding and new opportunity.

It can also lead to confusion and head scratching.

In the past, I have noted: “beware apparent truths that are easily financed”, as blind acceptance without nuance and thought can lead to deep trouble. The two explored previously were “to own your own home is the American Dream” and “you must have a college education to get ahead.” Blind adherence and going into debt chasing those apparent truths has created and continues to create major issues for our population and economy.

Over the past couple of weeks, I have been reading a lot locally and nationally about affordability of housing, both for-sale and for-rent. It seems that in many markets, more than half of the households are “housing burdened” and are, therefore, suffering affordability problems.

The fact that they are still somehow living somewhere and somehow surviving, albeit less than comfortably, seems to get lost in the discussion.

I know of Gen-Xrs and Millennials who are paying over 50% of their income for housing and still seem to have money for taxis, Uber rides, smart phones and extended data plans, Netflix and Pandora subscriptions, takeout meals, and beer with friends a couple nights a week.

I have yet to see a gun to their head forcing them to do this.

I also see seniors on fixed incomes cutting costs and necessities in order to age in place in their current home, despite rising rents, taxes, and utilities that seem to go up much faster than their incomes, irrespective of what the consumer price index seems to say.

What seems crazy is that people who are spending the 50% of income on rental housing and seeming to get by cannot qualify for a mortgage, because they exceed the magic 30% ratio that seems to govern or, at least, highly influence their ability to qualify.

Where the 30% limit come from anyway? Every place I read an article about affordability or inhibitors to home ownership, the 30% seems to pop up as a truth handed down from Moses as the secret 11th commandment.

A little research shows that the genesis of the 30% standard goes back to the United States National Housing Act of 1937; nearly 80 years ago, during the (last) Depression. As part of a national public housing program to serve families in the “lowest income group”, it was deemed that paying over 30% of income for housing expenses created a “burden.”

In 1969 (nearly a half-century ago), the “Brooke Amendment” to the 1968 Housing and Urban Development Act established a rent threshold of 25% of family income for public housing rents. This was raised to 30% in 1981 (35 years ago) and remains there.

The 30% rule in the post-1969 period began to migrate to the for-sale mortgage market in cases where the Federal government was purchasing or insuring loans, first under FHA and VA programs, and later into Fannie Mae and Freddie Mac.

Initially, this was not much of an issue, as large portions of mortgage loans were held by Savings and Loan institutions and, later, private capital securitized pools. However, beginning with the S&L meltdown in the 1980’s, Federal programs began taking ever-larger shares of the mortgage market and the 30% rule became a de-facto standard. Now, Federal programs comprise 90% of mortgage activity, according to some sources.

So, why are a set of standards, developed over three-quarters of a century ago for renters in the lowest income group, now the standard for both public housing renters and nearly all home purchasers, irrespective of income?

A lot has happened in the intervening period and perhaps we should really be looking at this imbedded standard that really is impacting a major segment of our economy in a way never intended or anticipated.

When left to the free market, we can see that the market responds in the rental sector. Supply and demand are at play in setting rents and people trade off unit age, proximity to employment, unit size and amenities, and other factors. Some choose to pay a higher portion of income for convenience, while others do not. In many markets, developers are adding rental units to catch up with demand.

Recent reports of labor migrating to apartment construction in lieu of single family should not be surprising. When the portion of income spent on housing becomes unconstrained by rule, wages can adjust and capital will flow.

40 or 50% of income can support more production than 30%.

Study after study shows a proclivity toward eventual ownership of housing by Millennials and Gen-X’rs. But, the arbitrary 30% qualification rule forces many of them into renting, even though in many cases they could afford to buy with the same 50% they are actually spending.

The truth is that the simple and arbitrary rules of several generations ago were set in a time that data was not ubiquitous and the ability to mine data was limited by the number of green-eye shaded folks with pencils and paper that could fit in a building.

We now carry sufficient computing power and data to solve this problem, if we wished.

This past weekend, during an 8-hour drive home from Thanksgiving in New Jersey

with my son and daughter-in-law, we thought nothing about our GPS adjusting our route several time, correcting for delays on our intended route caused by construction or accidents miles and miles ahead. Data from a variety of sources, including the actual experience of automobiles on the road, feed into the algorithms in the GPS units and effectively limit the speed limit on roads where there are issues. This information causes the GPS to seek a faster route, using its own artificial intelligence based on where I want to go and what my speed is.

Ubiquitous data, algorithms, and computing power that we all take for granted get applied to my individual location and situation to create the best outcome for me and my passengers based on where we wish to go. Thousands of others are getting their individualized solutions at the same time and we all are updated nearly instantaneously.

If we can do this for travel, why can’t we do this for mortgage qualification?

Houses have individualized utility and energy consumption characteristics. Individual municipalities have differing income and property tax profiles. Commuting costs vary based on house and job locations. One kid at home costs more than none, two more than one, etc. FICO scores show how one handles their current level of income and spending. Some eat out and spend more on entertainment while others might eat at home and save.

It seems to me that somebody out there has to be able to take the individualized circumstances, income, and expense patterns and create an individualized limit on what amount of mortgage debt one can handle that has to be better than the one-size-fits-all old way.

And in that ability to apply 21st century technology, data, and computing power to forge an individualized solution might lie at least one of the keys to open up ownership to those who want to own and are actually handling costs in excess of what home ownership costs would be.

The stumbling block is obviously whether the Feds choose to move into a non-1930’s paradigm. Even if they do not, the better way could open the way for private capital to begin to return to the mortgage arena, financing those who are able and truly qualified using modern evaluation techniques. Who knows, the Feds might be forced to follow.

I think it is long past time to rethink this issue. Our industry, our economy, and the future of tens of thousands of our citizens could benefit.