Making policy is hard–affordable housing

During the latter 1970s, the city of Irvine, California, imposed a requirement that new developments include a limited percentage of “affordable” housing units. The first development subject to this requirement included 900–1,000-square-foot townhouses that were to be sold for around $50,000. This was significantly less than market rate for such housing. I had put “affrordable” in quotes because at this time, houses selling for that price were hardly low- or even moderate-income housing.

The units were to be sold to people having an income less than a specified maximum but high enough to qualify for financing. The maximum was high enough that some assistant professors at the University of California-Irvine were eligible. Given the high and escalating housing prices in Orange County, demand was extremely high. Buyers were to be selected using a lottery.

I was teaching a course on land use policy. Students were required to complete a final paper on a topic of their choosing. One student came in, told me she had read about the affordable housing requirement and the lottery and asked whether that would be a suitable topic for the paper. Of course it was.

A week or so later, the student came in again. After starting to research the policy, she discovered that she and her husband were eligible for the housing, and they had entered the lottery. After the lottery had taken place, she came to see me once more, very excited. She and her husband had won and would be purchasing one of the units, which were to be completed soon.

The student kept in touch after the term ended. She came to see me shortly after moving into their new house with an update. The affordable housing program placed no restrictions on resale. The day they moved in and ever since, she and her neighbors had real estate agents knocking on their doors, offering to sell or even buy the houses for at least $20,000 more than they had paid. Most were not accepting the offer, because if that had, they could do no better in the market-rate housing market in Orange County.

This first iteration of the “affordable” housing policy did result in the addition of a limited number of smaller, less expensive (around $70,000 at market prices) houses in Irvine. It also allowed a small number of households with incomes that could qualify to purchase a $50,000 house to buy these townhouses. I’ve always found it interesting to contemplate how similar a policy would have been that required the developer to build these smaller houses, sell them at market rates, presumably around $70,000, and then hold a lottery in which the winners would each get a check from the developer for $20,000. Especially if they were given priority in purchasing the houses, if they chose to do so.


Developing urban pattern measures

No single measure such as density can capture the complexity of urban patterns, including the distribution of housing units. For my research looking at 59 large urban areas from 1950 to 2010, I wanted to develop multiple measures of urban patterns to better characterize these areas.

Since around the turn of the century a significant amount of work has been undertaken to identify many variables to quantify urban patterns, mostly to assess levels of urban sprawl. I have raised questions about these multiple dimensions of sprawl efforts before on this blog (here and here).  I am not claiming now to be measuring sprawl, but these efforts provided many possible measures to consider. Too many, as some studies included literally dozens of variables with meanings and differences difficult to discern.

My objective was to identify a small set concepts that captured the most important aspects of urban patterns. I then selected a single variable for each concept that I felt was both among the best measures and, to the extent possible, was easy to understand and interpret.

The overall density of housing units in the urban area is the first, obvious measure.

The extent to which densities varied across census tracts came next. This is measured using the index of dissimilarity. This is a measure of the proportion of housing units that would have to be moved to other census tracts to produce a uniform distribution with equal densities in all tracts.

The centralization of housing units in the urban area, the extent to which more housing units were closer to the center, is an important aspect of the urban pattern. For this, I developed a measure I am calling the centralization ratio, which looks at the mean distance housing units are located from the center and is the proportional reduction of distance compared with the mean distance to the center if housing units were uniformly distributed.

Finally, while centralization is one form of clustering, multiple clusters of higher density housing units can exist at various locations in an urban area. This clustering is measured using Moran’s I, a measure of spatial autocorrelation. This is essentially a correlation coefficient between tract densities and the densities of the adjacent tracts.

More information on these measures, their rationale, and an empirical assessment can be found in my paper, “Developing Multiple Measures of Urban Patterns,” which can be downloaded here.

Nearly everything involves tradeoffs

Those who advocate for urban futures typically describe an ideal urban environment that will be superior to current urban areas. I think they are missing two important facts in doing so. One is that people are different and have different preferences. An urban environment that best meets the needs of one person will fail to be ideal for someone else. The second thing that is seldom addressed is that in making choices about the urban environment (and many other things), nearly every choice involves making tradeoffs among competing objectives. I’ll give a few examples of the latter.

The most commonly considered tradeoff, at least among urban economists, is that between accessibility to the center and space that is the foundation of the standard monocentric model. People can reduce transportation costs by choosing a residence closer to the center or they can have more space for the residence by living farther away. But the tradeoff with space involves not only costs of commuting to the center. There is a tradeoff between having a walkable neighborhood with multiple destinations within walking distance and space for the residence as well. This must be the case because higher residential densities can support higher densities of commercial and other activities. In lower-density areas, commercial activities will be more widely spaced to achive sufficient markets.

In the design of street patterns for residential areas, a tradeoff exists between connectivity and restricting through traffic. High street connectivity supports walkability (though there are other ways of achieving this as well) while restricting through traffic with cul du sacs and curving streets may increase safety, especially for smaller children. I discussed this in an earlier blog post.

Another transportation tradeoff involves the use of streets. Space for lanes used for motor vehicle traffic can be converted for dedicated transit use or bicycle lanes. This achieves very laudable objectives. But it also can slow automobile travel and increase congestion. Political conflict associated with making such tradeoffs can be very real. Los Angeles had reduced the number of traffic lanes in one section of the city to increase safety, including by the addition of bike lanes. This produced an outcry among both motorists and businesses in the area, led to lawsuits, and ultimately to a restoration of the traffic lanes.

Limiting the physical expansion of an urban area to reduce sprawl can achieve worthwhile objectives. But given the laws of supply and demand, reducing the supply of developable land may lead to higher housing prices. This might be ameliorated by other regulatory policies, but those too will involve yet more tradeoffs.

Los Angeles, utopia or dystopia?

As Los Angeles and Southern California began to grow by attracting large numbers from elsewhere in the United States, its environment and opportunities were sometimes characterized as being a utopia. More recently, as a range of problems have developed and increased, some have described the area as a dystopia. Obviously Southern California is not, and never has been, either. Rather, it has become a great example of the importance of making tradeoffs among competing objectives.

A major contributor to the idea of Los Angeles and Southern California as a utopia has been, of course, the natural environment. The year-round sunny climate is really very nice–especially if you left Indiana when it was 6 degrees F. and snowing! The Pacific Ocean and the nearby mountains contribute scenic beauty and abundament recreational opportunities. One time we literally went cross-country skiing in the morning, returned to Irvine, and went to the beach in the afternoon.

As the area was developing, abundant economic opportunities were available to the newcomers as well. With the capturing of water resources, agriculture was a profitable endeavor. Citrus growing was especially important–it wasn’t called Orange County for nothing. Industries were established that benefited from the good weather and sunshine, first the motion picture industry and later the aircraft industry. More industry followed both because of the growing market and the expanding labor force. And there are the ports of Los Angeles and Long Beach.

But the extremely large numbers of people pouring into the area created problems as well. Greater Los Angeles/Southern Califoria has grown to have a population of over 18 million. I am considering this to include Los Angeles and Orange Counties (the Los Angeles MSA), Riverside and San Bernardino Counties (the Riverside-San Bernardino MSA) and Ventura County (the Oxnard MSA).

Los Angeles is well-known for its notoriously poor air quality, the smog that is some of the worst in the county. High motor vehicle use combined with a topography and climate that can trap pollutants creates this problem (though it must be said that air quality is far, far better than at its nadir in the 1960s as a result of aggressive regulation). The automobile traffic also produces tremendous congestion on the freeways.

Continued population growth combined with the failure to expand the supply of housing at the same rate has led to very high housing prices. Barriers to the construction of more housing have been regulatory, physical (the mountains and the ocean), and sheer distance, as the barriers have channeled development farther and farther from the heart of the metropolitan area. And the high housing prices have contributed to a dramatic increase in the number of homeless persons.

Economic opportunities are fewer as well. Like other areas with significant manufacturing, jobs in this sector have been lost in recent decades. The problem was exacerbated in the aerospace and defense industry, which was negatively impacted by the end of the Cold War. Also on the the economic side, California is a high-tax state.

So what about the tradeoffs? During much of the last century, large numbers migrated to Southern California from elsewhere in the United States as the advantages were seen to outweigh the negatives. But their coming contributed to the growth of the problems. At some point, the growing disadvantages have come to slightly outweight the benefits, at least for some U.S. residents. From 2010 to 2015, net migration between Southern California and the rest of the U.S. was 225,000 out of the region. This was more than made up by the positive net international migration of over 370,000, however.

Why the differences between domestic and international migration? Domestic movers are giving greater weight to the problems. International migrants value the positives more highly, likely especially continued economic opportunity. And remember that even among those moving within the United States, this is net migration. People are still moving to Southern California. These movers presumably value the benefits, whether the physical environment or specific economic opportunities or something else, more highly than the problems. Those leaving, on the other hand, are saying good riddance to the smog, congestion, and high housing prices, which they feel have come to outweigh the climate, mountains, and ocean.

Most choices involve tradeoffs. The standard monocentric model of urban location has residents selecting a residence trading off accessibility to the center (lower transportation costs) with more space.

60 years of exurban growth

In an earlier post I described the tremendous variation in the sizes of exurban areas surrounding large urban areas in 2010, looking at both their land areas and numbers of housing units. In a subsequent post I looked at variation across census regions and factors associated with this. I have extended the analysis, looking at exurban areas from 1950 to 2010. The variation in size among the exurban areas was very large at every census year. For example, in 1950, the smallest area in terms of land area had only 5 square miles; the largest had near 2,000 square miles. The minimum number of housing units was under 700, the maximum was over 400,000.

The exurban areas exploded in size over this period. The mean land area across the 59 areas grew from 233 square miles in 1950 to over 1,700 square miles in 2010. Mean housing units jumped from just under 40,000 to nearly 240,000. The sizes of the exurban areas were closely related to the sizes of their urban areas throughout the period. But the exurban areas grew more rapidly in size than the urban areas.

I then looked at the sizes of the exurban areas relative to their urban areas, the ratios of exurban to urban land areas and housing units. In 1950, the mean ratios were far higher for exurban areas in the Northeast compared with the other three census regions. But by 2010, the relative sizes of exurban areas in the South had grown rapidly and the mean ratios had virtually caught up with the Northeast. The relative size of exurban areas in the West remained very low. Exurban areas in the Midwest ended up in the middle. As I found for 2010, the relative sizes of exurban areas having arid climates were significantly smaller than for other exurban areas throughout the period.

More information on the the growth and evolution of exurban areas from 1950 to 2010 can be found in my paper, “Exurban Areas Around Large Urban Areas in the U.S., 1950–2010,” which can be downloaded here.

This is not your father’s (or mother’s) suburb

I currently am living in Southern California, about 35 miles east of downtown Los Angeles. I frequently drive further east into the adjoining city of Rancho Cucamonga for shopping or other activities (recently, jury duty!). A normal person would take the freeway or the main east-west commercial artery, the old Route 66. But being interested in urban patterns, I occassionally will drive on other through streets that run mainly through residential areas. I had certainly seen numbers of apartment complexes, but on one of these explorations I was especially struck by the continuous line of apartments. Not exactly stereotypical suburbia.

First, a little background on Rancho Cucamonga. This is a very new city, largely developed since the 1970s. When I lived in Southern California in the mid–1970s, when driving through this area on the freeway, one passed through miles of vineyards. This was the last undeveloped gap between Los Angeles and San Bernardino to be filled in, as you can see on these maps for 1970 and 2010 of census tracts which I classified as urban for my research:

Urban census tracts in the Los Angeles area in 1970 (Rancho Cucamonga outlined in red).

Urban census tracts in the Los Angeles area in 1970 (Rancho Cucamonga outlined in red).

Urban census tracts in the Los Angeles area in 2010 (Rancho Cucamonga outlined in red).

Urban census tracts in the Los Angeles area in 2010 (Rancho Cucamonga outlined in red).

The popuation of the city of Rancho Cucamonga was 55,000 at the 1980 census and 177,000 according to the 2016 census estimate. No earlier populations are reported as the city was only incorporated in 1977!

Suburban residential development is generally associated with large expanses of single-family housing units on medium- to large-size lots. Some suburbs do have some apartments, but the general impression is that single-family housing dominates. In Rancho Cucamonga in 2015, only 62 percent of the housing units are detached single-family houses. So nearly 40 percent of the housing does not fit the suburban stereotype.

The overall population density for the city in 2016 was 4,430 persons per square mile, certainly more than many suburbs. But even this is misleadingly low. The city still has significant amounts of vacant land and has large areas developed with industrial uses, mainly for warehouse and distribution activities. (The broader area has developed as a huge distribution center, handling many of the millions of containers that come through the ports of Los Angeles and Long Beach.)

A better picture of the level of residential densities can be seen by looking at densities in individual census tracts, some of which will naturally be more residential and more completely developed than others. My research (using 2000 tract boundaries) has 9 census tracts within or predominantly within the City of Rancho Cucamonga. For several reasons, I have chosen to focus on housing units and housing unit densities rather than population densities for my research. The two census tracts with the highest housing unit densities in 2010 have densities of 3,330 and 2,835 units per square mile. Since population densities are more commonly used and are thus more familiar, these can be estimated by using the national average of 2.34 persons per housing unit. This gives estimated population densities for these tracts of 7,791 and 6,634 persons per square mile. The denser of the two tracts has a population density far greater than the overall density of the densest Urbanized Area in the U.S., Los Angeles. Both are denser than the New York Urbanized Area.

There is, of course, no standard criterion specifying what are “suburban” densities as opposed to “urban” densities. I believe that for a variety of reasons, no urbanists want to go there, as there would be no consensus. But an interesting article on the FiveThirtyEight website tried to get at this, reporting on work by the real estate website Trulia that surveyed people asking whether where they lived was urban, suburban, or rural. They also asked for ZIP codes and compared the responses to characteristics of the ZIP codes. The study found, perhaps not surprisingly, that the best predictor of how people described where they lived was the population density of the ZIP code. And the dividing line between suburban and urban was 2,213 households per square mile (or by my estimation of population density, 5,178 persons per square mile). Using that criterion, the two densest tracts in Rancho Cucamonga could easily be considered urban as opposed to suburban, and two more, with over 1,900 units per square mile, come close. (I need to acknowledge that their use of ZIP codes could have produced a lower value than if tracts could have been used.)

The bottom line is that this very recent development in Rancho Cucamonga does not look like the traditional idea of suburbia with single-family homes on spacious lots.

Exurban growth and urban growth

When looking at exurban areas in 2010, I also looked at the growth (or decline) of those areas from 2000 to 2010. Just as the size of the exurban areas varied tremendously (see this post), so did the changes over the decade. Change in land area ranged from a decline of 430 square miles to an increase of 1,270 square miles. Numbers of exurban housing units dropped by over 300,000 units in one area and increased by over 150,000 in another.

One question considered was whether the change in the size of an exurban area over the decade was related to the change in the size of the urban area. Two opposing hypotheses were considered. The first and more obvious (at least to me) was that there would be a positive relationship. Metropolitan areas growing more rapidly, with greater growth of their urban areas, might likewise expect to see more growth in their exurban areas. But faster urban growth could have the opposite effect. As an urban area grows, land surrounding the urban area that was previously exurban is converted to urban, reducing the amount of exurban land. If this happens more quickly than land at the periphery is added to the exurban area, exurban growth could be inversely related to urban growth.

I compared the change in exurban land area and housing units over the decade to the change in those same measures for the urban areas, calculating the correlation coefficients. The relationships were not statistically significant. Exurban growth was not related to urban growth. At least during the decade from 2000 to 2010, the two hypothesized effects apparently canceled each other out.

More information on the exurban areas in 2010 and changes from 2000 to 2010 can be found in my paper, “Exurban Areas Around Large Urban Areas in the U.S. in 2010,” which can be downloaded here.