Modernization, smaller homes, and social class

I wanted to come back to a post from earlier this year where an economist argues that modern conveniences mean people can save money by living in smaller houses:

DR. SHILLER: Big houses are a waste. People are still in a mode of thinking about houses that is kind of 19th century. As we modernize, we don’t need all this space. For example, we don’t need elaborate kitchens, because we have all kinds of delivery services for food. And maybe you don’t need a workshop in your basement, either. You used to have a filing cabinet for your tax information, but now it’s all electronic, so you don’t need that, either. And bookshelves, for people who read a lot. We have electronic books now, so we don’t need bookshelves anymore…

DR. SHILLER: Having a big house is a symbol of success, and people want to look successful. People have to know about your achievements. How do you know, really? Who knows what people are doing in their day job? But you do see their house…

DR. SHILLER: When it comes to housing, there are books about this in the last 20 years—including “The New Small House”—that talk about designing houses to look impressive as well as function with a smaller scale.

Just like we’re developing Uber and Lyft and Airbnb using existing resources more efficiently, we can also build houses that are better at serving people’s needs without being big.

All of this could indeed be true. Many of the items people purchased just a few years ago may not be necessary. However: some of the services mentioned above seem to be tied to social class and age. Which people in society are getting all of their food delivered? How many people are doing all their taxes and bill paying online? Who needs space to store books, clothes, toys, electronics, gym equipment, etc.? Imagine a few scenarios of who might trade stuff for a smaller home:

  1. A downsizing well-off couple who wants to move to the big city now that they are empty-nesters.
  2. A recent college graduate who cannot afford a large residence but wants to spend money on cultural options and food.
  3. A professional who works long hours and does not want to care for a large residence.
  4. People who live the majority of their life through the Internet and their smartphone.

On the flip side, imagine people who might still want a larger home:

  1. A suburban couple with a child on the way who want more space for their kids.
  2. A young worker who has saved a little money, wants to put down roots in a community, and invest in something that will probably rise in value over the decades.
  3. People who like to have friends and family visit or who want to gain some extra income through hosting people.
  4. Numerous Americans who think a larger home is a better deal given that they can use the space, they like to buy stuff, and/or think that their home will appreciate in value.

In sum, I could imagine those who choose to buy smaller homes might be doing it for class/education/taste based reasons rather than just because they want a more efficient home. Those with more education might value a big home less. I would guess it will take time for many American residents to come around to the way of thinking that a smaller home is more efficient. In the meantime, there are still many forces still pushing people to buy larger homes.

Amenities, ROI on housing, and social class

A recent piece linking amenities to higher return on investment for housing left unnamed a key factor: social class.

It turns out that if Trader Joe’s is nearby, your house might be worth more than if it were close to other grocery chains. The average return on investment, or ROI, for Trader Joe’s-adjacent homes is 51 percent, 10 percentage points more than the runner-up, Whole Foods (41 percent), and almost 20 percentage points more than Aldi (34 percent)…

“When we overlay points of interest (like transit, shopping, and amenities) on top of prices, we see trends in the distance to these features,” Marshall says. “In urban areas, ClearAVM has found that access to public transit has a large correlation with higher property prices. We have found the same with access to restaurants, coffee shops and groceries in urban and suburban areas.”…

Some of the positive location amenities that can impact home values and equity include high-ranking schools, hospitals, shopping centers, green spaces and being near the waterfront (think oceans and lakes), as well as access to highways and main thoroughfares.

Negative location markers include things like high-traffic and high-noise areas, crowded commercial properties, high-tension power lines or other utility easements, a poorly maintained home or neighborhood, and not being near the appealing attractions mentioned earlier, Hunt says.

While I don’t doubt these factors do influence housing values, there is a common factor that helps join them all together: the social class of residents. Grocery stores, like many other businesses, figure out where to locate at least in part on looking at the residents who live nearby. Whole Food’s is generally not going to move to a community where residents do not have the resources to pay their prices. Aldi, in contrast, appeals to a different market. Going further, think of the differences in locations between Walmart and Target, McDonald’s and Chipotle, Dollar Stores versus chain drug stores, and more.

A number of the items on the list of “positive location amenities” are also closely connected to social class. High-ranking schools tend to be in wealthier communities. The same is true of shopping centers and higher property values mean only certain kinds of residents can afford homes on the waterfront.

This does not mean that there is not more affordable housing in these areas with positive amenities. There may be. But, I would guess the zip codes connected to the higher-class grocery stores tend to be wealthier and more educated zip codes overall. The habitus of social class extends even to what grocery stores people prefer, the desired appearance of nearby homes, and close amenities that help reinforce their social class, practices, and tastes.

Maybe modernist houses will appeal to millennials – in certain circumstances

Architects and cultural critics often like modernist homes even as Americans largely do not prefer them. But, perhaps millennials will select modernist homes:

“For a while people were just tearing them down, but people are seeking them out now — they’re the anti-McMansion,” says Ellen Hilburg, co-founder of the real estate resource Mid Century Modern Hudson Valley. “For some people, it’s a nostalgia factor. But Millennials are discovering them, too. It’s an aesthetic that appeals to people who are aware and environmentally conscious.”

There are a number of pieces of this story that suggest preferences for modernist homes are tied to particular traits of the homeowner or observer:

1. A higher social class.

2. Higher levels of education.

3. Rejection of consumerism and the implied materialism and conformity that goes with it.

4. An interest in the “cool” factor of a home.

5. Living in a community – such as a wealthy, middle to upper-class suburb – where modernist homes are present and accepted.

Putting these categories together, there may indeed be a slice of Americans who prefer modernist homes. But, this also sounds like a taste connected to cultural capital, to invoke Bourdieu. In other words, expressing a preference for modernist design is connected to social class and education that not all Americans have access to.

Google Street View, machine learning, and social patterns

I have wondered why more researchers do not make use of Google Street View. Here is a new study that connects vehicles in neighborhoods with voting patterns and demographics:

Abstract: The United States spends more than $250 million each year on the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed several years. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may become an increasingly practical supplement to the ACS. Here, we present a method that estimates socioeconomic characteristics of regions spanning 200 US cities by using 50 million images of street scenes gathered with Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22 million automobiles in total (8% of all automobiles in the United States), were used to accurately estimate income, race, education, and voting patterns at the zip code and precinct level. (The average US precinct contains 1,000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographics may effectively complement labor-intensive approaches, with the potential to measure demographics with fine spatial resolution, in close to real time.

And a little more explanation from a news source:

The researchers created an algorithm to identify the brand, model and year of every car sold in the US since 1990.

The types of cars also provided information about the race, income and education levels of a neighborhood, the study said.

Volkswagens and Aston Martins were associated with white neighborhoods while Chryslers, Buicks and Oldsmobiles tended to appear in African-American neighborhoods, the study found.

This study seems to do two things that get at different areas of research:

  1. Linking lifestyle choices to voting behavior as well as other social traits. Researchers and marketers have done this for decades. For example, see this earlier post about media consumption and voting behavior. This hints at the work Bourdieu who suggested class status is defined by cultural tastes and lifestyles in addition to access to resources and power.
  2. Connecting different publicly available big data sets to find connections. Google Street View is available to all and election outcomes are also accessible. All it takes is a method to put these two things together. Here, it was a machine learning algorithm by which different kinds of vehicles could be identified. It would take humans a long time to connect these pieces of data but algorithms, once they correctly are identifying vehicles, can do this very quickly.

Of course, this still leaves us with questions about what to do with it all. The authors seem interested in helping facilitate more efficient national data-gathering efforts. The American Community Survey and the Dicennial Census are both costly efforts. Could machine learning help reduce the effort needed while providing accurate results? At the same time, it is less clear regarding the causal mechanisms behind these findings: do people buy pick-up trucks because they are Republican? How does this choice of a vehicle fit with a larger constellation of behaviors and beliefs? If someone wanted to change voting patterns, could encouraging the purchase of more pick-up trucks or sedans actually change voting patterns (or are these more of correlations)?

Comparing the McMansions of Matt Ryan and Tom Brady

Relive some of the excitement of Super Bowl by comparing the McMansion of Matt Ryan in Duluth, Georgia versus Tom Brady’s homes:

I’d say Matty Ice picked himself the most conventional McMansion possible…

But what his house, or houses? I bet he has no taste…This house in Brookline, Massachussets? You’re kidding me. It’s kind of tasteful. Okay, it’s great, it’s perfect…What about the house in LA? I bet that’s hideous…You know what though, Tommy Boy? You are not McMansion material…

The winner here is Matt Ryan for keeping it real.

All those hours of coverage of the big game and you didn’t see important information like this. Both clearly have large homes but there are notable differences. This analysis suggests this comes down to personal taste but I think there are some other factors at work:

  1. Brady operates in different locations where expectations about large homes may be different. Compared to the Atlanta area, are there were fewer McMansions in Brookline (probably) or in the Los Angeles area (maybe not but there are also more legitimate mansions)?
  2. Brady operates in a different social circle than Ryan. With his model wife, Brady has to fit in with a range of famous people while Ryan is with the football crowd. Both have plenty of money but there is a difference in social class and taste a la Bourdieu.
  3. Both grew up in suburban areas: Ryan in Exton, Pennsylvania (outside Philadelphia) and Brady in San Mateo, California (Bay Area). This could influence both wanting to live in suburban areas now.
  4. Ryan is younger than Brady and perhaps he hasn’t had the time or experience to move to a more “mature” home.

Overall, I suspect many pro athletes have homes critics would call McMansions.

The cultural bubbles of popular TV shows tell us what exactly?

This is a cool set of maps of the popularity of 50 different TV shows across zip codes in the United States. But, what is the data and what exactly can it tell us? Here is the brief explanation:

When we looked at how many active Facebook users in a given ZIP code “liked” certain TV shows, we found that the 50 most-liked shows clustered into three groups with distinct geographic distributions. Together they reveal a national culture split among three regions: cities and their suburbs; rural areas; and what we’re calling the extended Black Belt — a swath that extends from the Mississippi River along the Eastern Seaboard up to Washington, but also including city centers and other places with large nonwhite populations.

Some quick thoughts:

  1. Can we assume that Facebook likes are an accurate measure? How many people are represented per zip code? Who tends to report their TV show preferences on Facebook? Why not use Nielsen data which likely has a much smaller sample but could be considered more reliable and valid?
  2. How exactly does television watching influence everyday beliefs and actions? Or, does it work the other way: people have certain beliefs and behaviors and they watch what confirms what they already like? Sociologists and others that study the effects of television don’t always have data on the direct connections between viewing and other parts of life. (I’m not suggesting television has no influence. Given that the average American still watches several hours a day, it is still a powerful medium even with the rise of
  3. The opening to the article both suggests TV viewing and the related cultures fall along an urban/rural divide but then also split across three groups. The maps display three main groups – metro areas, rural areas, and areas with higher concentrations of African Americans. I would want to know more about two areas. First, political data – and this article wants to make the link between TV watching and the 2016 election – suggests the final divide is really in the suburbs between areas further out from the big city and those closer. Can we get finer grained data between exurbs and inner-ring suburbs? Second, does this mean that Latinos and Asian Americans aren’t differentiated enough to be their own TV watching cultures?
  4. The introduction to this article also repeats a common line among those that study television:

In the 1960s and ’70s, even if you didn’t watch a show, you at least probably would have heard of it. Now television, once the great unifier, amplifies our divisions.

We certainly are way into the cable era of television (and probably beyond with all of the options now available through the Internet and streaming) but could we argue instead that the earlier era of fewer channels and viewing options simply papered over differences? As numerous historians and other scholars have argued, the 1950s might have appeared to be a golden era but most of the benefits went to white, middle class, suburban families.

In other words, I would be hesitant to state that these TV patterns are strong evidence of three clearly different cultures in the United States. Could these television viewing patterns fit in with other cultural tastes differentiating various groups based on class and race and ethnicity? Yes, though I’d much rather see serious academic work on this developing Bourdieu’s ideas and encompassing all sorts of consumption items treasured by Americans (homes, vehicles, sports fandom, making those hard choices like Coke and Pepsi or McDonald’s and Burger King or Walmart and Target). Also, limiting ourselves to geography may not work as well – this approach has been tried by many including in books like The Big Sort or Our Patchwork Nation – as it did in the past.

High performing school districts driving residential segregation

A new sociological study suggests schools are helping lead to residential segregation:

Study author Ann Owens, an assistant professor of sociology at USC Dornsife College of Letters, Arts and Sciences, examined census data from 100 major U.S. metropolitan areas, from Los Angeles to Boston. She found that, among families with children, neighborhood income segregation is driven by increased income inequality in combination with a previously overlooked factor: school district options.

For families with high income, school districts are a top consideration when deciding where they will live, Owens said. And for those in large cities, they have multiple school districts where they could choose to buy homes.

Income segregation between neighborhoods rose 20 percent from 1990 to 2010, and income segregation between neighborhoods was nearly twice as high among households that have children compared to those without…

She recommended that educational leaders should consider redrawing boundaries to reduce the number and fragmentation of school districts in major metropolitan areas. They also should consider designing inter-district choice plans and strengthening current plans within districts to address inequities.

Generally, wealth and race leads to residential segregation but it is interesting to see through what mechanisms this works. As Bourdieu (and others) suggested, schools tend to reproduce existing social stratification and here they work to reify desirable housing locations.