“People want these larger homes”

I’m quoted in a recent Zillow story titled “Upsizing on the Upswing: The Big Decision More Homebuyers are Making“:

The data corresponds with what sociologists are seeing firsthand, says Brian Miller, an associate professor of sociology at Wheaton College, just outside Chicago. Miller, who studies cities, suburban migration and culture, argues that several factors could be impacting the shift in housing trends, including the strength of the national economy.

“I see a lot about tiny houses and micro apartments in Seattle, San Francisco, and New York — these cities who are really grappling with housing issues and trying to fast-track 200- or 400-square-foot apartments,” Miller says. “And yet the overall pattern across America is that people want these larger houses.

“The economy has gotten better over the last few years,” he continues, with a nod to cities like Dallas, one of the hottest housing markets in the country. “It seems it’s enabled people to [buy large houses] again.”

Popular culture may be influencing this decision as well, Miller adds, pointing to how homes are depicted on television, in both the reality and scripted genres.

“The typical home on TV is huge. Think about the ‘Friends’ apartments, which were impossibly large,” he says. “I’m thinking of HGTV shows I’ve seen over the past few years, where the dining room seats 10 or 12. I don’t have those parties, but if you’re watching HGTV, it just seems like everything is huge.”

I think the larger story goes like this: Americans tend to like large homes and even major financial issues, such as the bursting of the housing bubble, may not be enough to reverse that trend. This does not mean the desire for large homes will continue forever. Yet, major changes need to occur to the economic system and/or enduring values need to shift for Americans as a whole to embrace smaller homes.

Related topics:

McMansions are back.

There are a limited number of tiny houses in the United States.

Zillow defines McMansions but doesn’t really capture their essence

The recent Washington Post analysis on the return of the McMansions depends on Zillow’s definition of a McMansion:

(Since a “McMansion” is in the eye of the beholder, Zillow doesn’t have a targeted way of tracking them nationwide. For this article and the video above, they approximated the category by focusing on houses built after 1980 that were greater than 3,000 square feet but less than 5,000 square feet. They also looked for houses located on streets where the homes are similarly sized, on similarly sized lots, and built within six years of each other, to isolate cookie-cutter communities.)

This definition has several key aspects:

  1. A time period after 1980. The term McMansions arises in this era.
  2. A certain square footage. Once a home is too large, it is no longer a McMansion.
  3. The large homes are built as part of a development of similar homes.

This definition of a McMansion would seem to primarily capture suburban McMansions. Indeed, the analysis spends more time discussing general suburban trends than it does McMansions:

Many casual onlookers have forecast the death of the suburbs in recent years, especially as younger renters and buyers turn an eye to city centers. Skylar Olsen, a senior economist at Zillow, says that young people today have far more interest in living in urban environments. “That’s where jobs had been growing fastest over the course of this economic recovery over the past five years,” says Olsen…

Their decision is also supported by cheap energy costs, which make it affordable to commute. in mid-June, the nationwide average price of regular gasoline was $2.32 a gallon. Like the McMansion and the pickup in the housing market, it’s another source of deja vu. After remaining elevated for years, oil prices are now roughly the same as they were June 2000, when adjusted for inflation.

This definition leads to two major problems with defining what homes are McMansions:

  1. Not all suburban houses are McMansions. It may be easy to conflate the two – the majority of McMansions are likely located in the suburbs – but they are not the same.
  2. The Zillow data provides little insight into the architecture of the home. A home of that time period and square footage in a cookie-cutter neighborhood is not necessarily garish or poorly proportioned. Such homes might be more likely to be McMansions but it is not a guarantee.

Zillow may be limited in the architectural data they can access. For example, they may be able to know how large the garages are on these homes but it doesn’t really know how exactly these garages are presented. Yet, painting McMansions with a broad brush may not be very accurate and fall into the trap of painting most of suburbia as filled with McMansions.

American homes grow in size yet lots shrink

Zillow finds that American homes continue to grow larger even as their lots shrink:

Nationally, the median size of a new house is now 2,600 feet, a full 500 square feet (or almost 25 percent) more than it was just 15 years ago.

Yet the median lot size is now 8,600 square feet, down 1,000 square feet (or about 10 percent) over the same period:

Zillow continues to find interesting patterns in real estate data. So what could be behind this trend? Both the land and the home (materials, labor) cost developers and builders money. Thus, smaller lots with bigger houses can reduce land costs even as the home price might stay similar or increase because the home is growing. Or, perhaps this is also the result of land regulations from municipalities. Small lots could be preferred by some places because subdivisions and residential properties then take up less space.

One of the common complaints about McMansions is that the big house are on small lots. Yet, this may be necessary for some housing in order to (1) make housing more affordable (lower the costs for land) and (2) to limit damage to the environment (use less land and open land for more green space or open space).

The perils of analyzing big real estate data

Two leaders of Zillow recently wrote Zillow Talk: The New Rules of Real Estate which is a sort of Freakanomics look at all the real estate data they have. While it is an interesting book, it also illustrates the difficulties of analyzing big data:

1. The key to the book is all the data Zillow has harnessed to track real estate prices and make predictions on current and future prices. They don’t say much about their models. This could be for two good reasons: this is aimed at a mass market and the models are their trade secrets. Yet, I wanted to hear more about all the fascinating data – at least in an appendix?

2. Problems of aggregation: the data is analyzed usually at a metro area or national level. There are hints at smaller markets – a chapter on NYC for example and another looking at some unusual markets like Las Vegas – but there are not different chapters on cheaper/starter homes or luxury homes. An unanswered questino: is real estate within or across markets more similar? Put another way, are the features of the Chicago market so unique and patterned or are cheaper homes in the Chicago region more like similar homes in Atlanta or Los Angeles compared to more expensive homes across markets?

3. Most provocative argument: in Chapter 24, the authors suggest that pushing homeownership for lower-income Americans is a bad idea as it can often trap them in properties that don’t appreciate. This was a big problem in the 2000s: Presidents Clinton and Bush pushed homeownership but after housing values dropped in the late 2000s, poorer neighborhoods were hit hard, leaving many homeowners to default or seriously underwater. Unfortunately, unless demand picks up in these neighborhoods (and gentrification is pretty rare), these homes are not good investments.

4. The individual chapters often discuss small effects that may be significant but don’t have large substantive effects. For example, there is a section on male vs. female real estate agents. The effects for each gender are small: at most, a few percentage points difference in selling price as well as slight variations in speed of sale. (Women are better in both categories: higher prices, faster sales.)

5. The authors are pretty good at repeatedly pointing out that correlation does not mean causation. Yet, they don’t catch all of these moments and at other times present patterns in such a way that distort the axes. For example, here is a chart from page 202:

ZillowTalkp202

These two things may be correlated (as one goes up so does the other and vice versa) but why fix the axes so you are comparing half percentages to five percentage increments?

6. Continuing #4, I supposed a buyer and seller would want to use all the tricks they can but the tips here mean that those in the real estate market are supposed to string along all of these small effects to maximize what they get. On the final page, they write: “These are small actions that add up to a big difference.” Maybe. With margins of error on the effects, some buyers and sellers aren’t going to get the effects outlined here: some will benefit more but some will benefit less.

7. The moral of the whole story? Use data to your advantage even as it is not a guarantee:

In the new realm of real estate, everyone faces a rather stark choice. The operative question now is: Do you wield the power of data to your advantage? Or do you ignore the data, to your peril?

The same is true of the housing market writ large. Certainly, many macro-level dynamics are out of any one person’s control. And yet, we’re better equipped than ever before to choose wisely in the present – to make the kinds of measured judgments that can prevent another coast-to-coast bubble and calamitous burst. (p.252)

In the end, this book is aimed at the mass market where a buyer or seller could hope to string together a number of these small advantages. Yet, there are no guarantees and the effects are often small. Having more data may be good for markets and may make participants feel more knowledgeable (or perhaps more overwhelmed) but not everyone can take advantage of this information.

Chief economist for Zillow says “homeownership is not for everyone”

The chief economist for Zillow suggests we need alternatives to homeownership for low-income American residents:

All this leaves us with a conundrum: Overall, homeownership is a tremendous boost to millions. But in some specific cases, it simply does not deliver as advertised. Depending on circumstances, homeownership is not for everyone. And our steadfast belief that homeownership is always the better option has led us to worry less about the one-third of Americans that rent,leading to a crisis in affordable rental housing.

Please don’t get me wrong. None of this is to say that lower-income Americans should not aspire to homeownership, nor be given opportunities to access its tremendous benefits. But we also need to be steely-eyed about the realities and foster a wider diversity of options on housing, crafting innovative solutions that address the reality we face, not the one we imagine.

If we truly believed this, we could do different things. We could focus on the creation and maintenance of more affordable rental housing. We could find innovative new ways to build wealth, aside from homeownership. Given the prevalence of single-family rentals in the aftermath of the recession, we could explore the feasibility of renting-to-own on a wider scale. We could narrow and sharpen our focus on addressing the fundamental sources of inequality that drive differences in homeownership in the first place.

Yet, even with the strong negative effects of the recent economic crisis/housing bubble, I wonder if it is easier to promote homeownership than it is to advance other policies. Here are several reasons why this might be the case:

1. Americans really do seem to prize homeownership. Homeownership is closely tied to the American Dream, making this issue both politically and culturally important. As far as I know, every president since the 1920s has promoted homeownership. Suggesting that everyone can’t access the American Dream can be problematic.

2. Renting may be a good short-term solution but because of the status conferred to homeowners, renters receive the opposite sentiments: transient, less committed to their community, more prone to social problems, etc. Plus, how many wealthier residents want to live near cheaper rental housing?

3. Speaking of cheaper housing, affordable housing is a very contentious issue. Where will these units be built? Wealthier neighborhoods and communities want little to do with affordable housing. Which developers will go this route rather than chasing bigger profits with larger and more expensive housing units?

4. Getting at the fundamental issues behind the the differences in homeownership is a huge task. Which shall we tackle first – Race? Social class? Residential segregation? Large disparities in wealth? Unequal access to resources?

Perhaps the price to be paid in housing bubbles is more palatable to those in charge than the other options…

Deciphering the words in home listings; “quaint” = 1,299 square feet, “cute” = 1,128 square feet

An analysis of Zillow data looks at the text accompanying real estate listings:

Longer is almost always better, though above a certain length, you didn’t get any added value — you don’t need to write a novel. Over 250 words, it doesn’t seem to matter. Our takeaway was that if you’ve got it, flaunt it. Descriptive words are very helpful. “Stainless steel,” “granite,” “view” and “landscaped” were found in listings that got a higher sales price than comparable homes.

And there are words you should stay away from, especially “nice.” We think that in the American dialect, you say “nice” if you don’t have anything more to say. And then there are the words that immediately tell a buyer that the house is small: When we analyzed the data, we found that homes described as “charming” averaged 1,487 square feet, “quaint” averaged 1,299 square feet, and “cute” averaged 1,128. All of them were smaller than the average house in our sampling.

The impact of words seems to vary by price tier. For example, “spotless” in a lower-priced house seemed to pay off in a 2 percent bonus in the final price, but it didn’t seem to affect more pricey homes. “Captivating” paid off by 6.5 percent in top-tier homes, but didn’t seem to matter in lower-priced ones.

There are certainly codes in real estate listings that are necessary due to the limited space for words. But, as the article notes some of the words are more precise than others. If someone says they have stainless steel appliances, the potential buyer has a really good idea of what is there. But, other words are much more ambiguous. Just how “new” are some big-ticket items like roofs or flooring or furnaces? The big data of real estate listings allows us to see the patterns tied to these words. Just remember the order for size: cute is small, quaint is slightly larger, and charming slightly bigger still.

If I’m Zillow, is it time to sell this info to select real estate professionals?

Zillow off a median of 8% on home prices; is this a big problem?

Zillow’s CEO recently discussed the error rate of his company’s estimates for home values:

Back to the question posed by O’Donnell: Are Zestimates accurate? And if they’re off the mark, how far off? Zillow CEO Spencer Rascoff answered that they’re “a good starting point” but that nationwide Zestimates have a “median error rate” of about 8%.

Whoa. That sounds high. On a $500,000 house, that would be a $40,000 disparity — a lot of money on the table — and could create problems. But here’s something Rascoff was not asked about: Localized median error rates on Zestimates sometimes far exceed the national median, which raises the odds that sellers and buyers will have conflicts over pricing. Though it’s not prominently featured on the website, at the bottom of Zillow’s home page in small type is the word “Zestimates.” This section provides helpful background information along with valuation error rates by state and county — some of which are stunners.

For example, in New York County — Manhattan — the median valuation error rate is 19.9%. In Brooklyn, it’s 12.9%. In Somerset County, Md., the rate is an astounding 42%. In some rural counties in California, error rates range as high as 26%. In San Francisco it’s 11.6%. With a median home value of $1,000,800 in San Francisco, according to Zillow estimates as of December, a median error rate at this level translates into a price disparity of $116,093.

Thinking from a probabilistic perspective, 8% does not sound bad at all. Consider that the typical scientific study works with a 5% error rate. An eight percent error rate suggests the estimate is right 92% of the time. As the article notes, this error rates differs across regions but each of those have different conditions including more or less sales and different kinds of housing. Thus, in dynamic real estate markets with lots of moving parts including comparables as well as the actions of homeowners and homebuyers, 8% sounds good.

Perhaps the bigger issue is what people do with estimates; they are not 100% guarantees:

So what do you do now that you’ve got the scoop on Zestimate accuracy? Most important, take Rascoff’s advice: Look at them as no more than starting points in pricing discussions with the real authorities on local real estate values — experienced agents and appraisers. Zestimates are hardly gospel — often far from it.

Zillow can be a useful tool but it is based on algorithms using available data.