Real Estate Data for the Rest of Us

Dreaming Big: Americans Still Yearning for Larger Homes

43% of adults would prefer homes bigger than where they currently live, but attitudes differ by age. Baby boomers would prefer to upsize rather than downsize by only a small margin, while the gap among millennials is much wider, with GenXers falling in between. Would-be downsizers outnumber upsizers only among households living in the largest homes.

Last year, we found that Baby Boomers were especially unlikely to live in multi-unit housing. At the same time, we noted that the share of seniors living in multi-unit housing rather than single-family homes has been shrinking for decades. These findings got us thinking about how the generations vary in house-size preference. So we surveyed over 2000 people at the end of last year to figure out if boomers have different house-size preferences than their younger counterparts. And that led us to ask: What size homes do Americans really want?

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Most Americans are not living in the size home they want

As a whole, Americans are living in a world of mismatch – only 40% of our respondents said they are living in the size home that’s ideal. Furthermore, over 43% answered that the size of their ideal residence is somewhat or much larger than their current digs. Only 16% told us that their ideal residence is smaller than their existing home. However, these overall figures mask what is going on within different generations.

It’s natural to think that baby boomers are the generation most likely to downsize.  After all, their nests are emptying and they may move when they retire.  As it turns out though, more boomers would prefer to live in a larger home than a smaller one: 21% said their ideal residence is smaller than their current home, while 26% wanted a larger home – a 5-percentage-point difference. Clearly, boomers don’t feel a massive yearn to downsize. On the contrary, just over half (53%) said they’re already living in their ideally sized home. Nonetheless, members of this generation are more likely to want to downsize than millennials and GenXers.

In fact, those younger generations want some elbow room. First, the millennials. They’re looking to move on up by a big margin: just over 60% told us their ideal residence is larger than where they live now – the largest proportion among the generations in our sample. By contrast, only a little over 13% of millennials said they’d rather have a smaller home than their existing one – which is also the smallest among the generations in our sample. The results are clear: millennials are much more likely to want to upsize than downsize.

The next generation up the ladder, the GenXers, are hitting their peak earning years and many in this group may be in a position to trade up. Many aren’t living in their ideally sized home. Just 38% said where they live now is dream sized. Nearly a majority (48%) said their dream home is larger, while only 14% of GenXers would rather have a smaller home.  This is the generation that bore the brunt of the foreclosure crisis. So, some of this mismatch could be because a significant number of GenXers lost homes during the housing bust and may now be living in smaller-than-desired quarters. But a much more probable reason is that many GenXers are in their peak child-rearing years. With kids bouncing off the walls, the place may be feeling a tad crowded.

Even the groups that seem ripe for downsizing don’t want smaller homes

Of course, age doesn’t tell the whole story about why people might want to downsize. It could be that certain kinds of households, – such as those without children, and living in the suburbs or in affordable areas – might be more likely to live in larger homes than they need. But our survey shows that households in these categories are about twice as likely to want a larger than a smaller home. For those with kids especially, the desire to upsize is strong: 39% preferred a larger home versus 18% who liked a smaller home.  For those living in the suburbs, the disparity is even greater – 42% to 16%. And even among those living in the most affordable zip codes, where ideally-sized homes might be within the budgets of households, 40% of our respondents preferred larger homes versus 20% who said smaller.

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Are all households more likely to upsize than downsize?

At this point you might be asking, “Are there any types of households that want to downsize?” The answer is yes. But only one kind of household falls into this category – those living in homes larger than 3,200 square feet.  Of this group, 26% wanted to downsize versus 25% that wanted to upsize – a slight difference. But, when we looked overall at survey responses based on the size of current residence, households wanting a larger home kicked up as current home size went down. We can see this clearly when we divide households into six groups based on the size of the home they’re living in now. Among households living in 2,600-3,200 square foot homes, 37% prefer a larger home versus 16% a smaller home; in 2,000–2,600 square foot homes, its 34% to 18%; 38% to 18% in 1,400–2,000 square foot homes; 55% to 13% in 800–1,400 square foot homes; and 66% to 13% in homes less than 800 square feet. This makes intuitive sense.  Those living in the biggest homes are most likely to have gotten a home larger than their ideal size. And those in the smallest homes are probably the ones feeling most squeezed.

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The responses to our survey show significantly more demand for larger homes than for smaller ones. But the reality, of course, is that households must make tradeoffs between things like accessibility, amenities, and affordability when choosing what size homes to get. The “ideal” sized home for most Americans may be larger than where they’re living now. But that spacious dream home may not practical.  As result, the mismatch between what Americans say they want and what best suits their circumstances may persist.

Survey Methodology

This survey was conducted online within the United States between November 6-10, 2014 among 2,008 adults (aged 18 and over) by Harris Poll on behalf of Trulia via its Quick Query omnibus product. Figures for age, sex, race/ethnicity, education, region and household income were weighted where necessary to bring them into line with their actual proportions in the population. Propensity score weighting was used to adjust for respondents’ propensity to be online.

All sample surveys and polls, whether or not they use probability sampling, are subject to multiple sources of error which are most often not possible to quantify or estimate, including sampling error, coverage error, error associated with nonresponse, error associated with question wording and response options, and post-survey weighting and adjustments. Therefore, the words “margin of error” are avoided as they are misleading. All that can be calculated are different possible sampling errors with different probabilities for pure, unweighted, random samples with 100% response rates. These are only theoretical because no published polls come close to this ideal.

Respondents for this survey were selected from among those who have agreed to participate in our surveys. The data have been weighted to reflect the composition of the adult population. Because the sample is based on those who agreed to participate in the online panel, no estimates of theoretical sampling error can be calculated.


For Home Prices, The Rebound Effect Is Over. Long Live Job Growth

Local home prices are no longer rising because of the rebound effect. Markets that had a more severe housing bust aren’t where home prices are climbing fastest anymore. The markets with the fastest employment growth now have the largest price increases.

Jed Kolko, Chief Economist
February 10, 2015

The Trulia Price Monitor and the Trulia Rent Monitor are the earliest leading indicators of housing price and rent trends nationally and locally. They adjust for the changing mix of listed homes and show what’s really happening to asking prices and rents. Asking prices lead sales prices by approximately two or more months. As a result, the Monitors reveal trends before other price indexes do. Here then is the scoop on where prices and rents are headed.

Asking Prices Rose a Modest 0.5% Month-Over-Month in January

Nationwide, asking prices on for-sale homes climbed 0.5% month-over-month in January, seasonally adjusted — the smallest monthly gain since August. Year-over-year, asking prices rose 7.5%, down from the 9.3% year-over-year increase in January 2014. Asking prices increased year-over-year in 94 of the 100 largest U.S. metros.

January 2015 Trulia Price Monitor Summary

% change in asking prices # of 100 largest metros with asking-price increases % change in asking prices, excluding foreclosures
seasonally adjusted
0.5% N/A 0.7%
seasonally adjusted
2.9% 82 3.4%
Year-over-year 7.5% 94 8.0%
Data from previous months are revised each month, so current data reported for previous months might differ from previously reported data.

The Rebound Effect is Over—Job Growth Drives Price Gains

The biggest home price increases are not necessarily in markets that had more severe housing busts. But the metros where home prices are now rising fastest are, almost without exception, the ones with faster job growth. Why? A growing economy fuels housing demand. Among the 10 metros with the biggest year-over-year price increases, nine had at least 2% year-over-year job growth. Only Detroit made the price growth top 10 despite tepid job gains. Plus, among these 10 metros with fastest price growth, four – Houston, Indianapolis, Denver, and Austin – had notably mild housing busts, with price declines from the peak of the bubble to the trough of the recession of less than 10%. Their price gains today don’t reflect a rebound after a sharp fall.

Where Asking Prices Increased Most in January

# U.S. Metro Y-o-Y % asking price change, Jan 2015 Y-o-Y % job growth Peak-to-trough price decline in housing bust
1 Atlanta, GA 16.2% 3.3% -26%
2 Cape Coral-Fort Myers, FL 15.4% 6.3% -56%
3 Deltona-Daytona Beach-Ormond Beach, FL 13.9% 2.3% -50%
4 Oakland, CA 13.8% 2.7% -39%
5 Houston, TX 13.8% 3.2% -4%
6 Indianapolis, IN 13.1% 2.0% -7%
7 Denver, CO 13.0% 3.7% -8%
8 North Port-Sarasota-Bradenton, FL 12.9% 4.3% -51%
9 Austin, TX 12.7% 4.0% -4%
10 Detroit, MI 12.6% 0.3% -40%
Note: among 100 largest metros. Job growth is as of 2014 Q2, from the Quarterly Census of Employment and Wages; peak-to-trough price decline is from the Federal Housing Finance Agency. To download the list of asking home price changes for the largest metros: Excel or PDF.

On the flip side, nearly all 10 markets with the slowest price gains (including six with declines) have had relatively sluggish job growth. Only Columbia, SC, had strong job growth but weak home price gains.

Where Asking Prices Decreased Most Or Increased Least in January

# U.S. Metro Y-o-Y % asking price change, Jan 2015 Y-o-Y % change in employment Peak-to-trough price decline in housing bust
1 Little Rock, AR -3.4% -0.1% -4%
2 Akron, OH -3.3% 1.6% -16%
3 Baltimore, MD -1.5% 0.6% -22%
4 Cleveland, OH -1.0% 0.3% -19%
5 Albany, NY -0.3% 0.6% -6%
6 Columbia, SC 0.0% 3.7% -12%
7 Winston-Salem, NC 0.6% 1.6% -10%
8 Allentown, PA 0.9% 1.3% -21%
9 Newark, NJ 0.9% 0.3% -19%
10 El Paso, TX 1.2% 1.4% -8%
Note: among 100 largest metros. Job growth is as of 2014 Q2, from the Quarterly Census of Employment and Wages; peak-to-trough price decline is from the Federal Housing Finance Agency. To download the list of asking home price changes for the largest metros: Excel or PDF.

Across all 100 largest metros, the relationship between job growth and home prices is strong. The correlation is 0.56 and statistically significant.


This isn’t new. Throughout the recovery, home prices have risen faster in markets with stronger job growth. What is notable is that the link between job growth and home prices is strengthening, as shown by the increasing statistical correlation between job growth and home prices (see chart below). But what’s really new is that the rebound effect has melted away. The correlation between the peak-to-trough price decline during the housing bubble and price changes in the recovery peaked in mid-2013 and has plunged since then. Now, the correlation between the peak-to-trough decline and the January 2015 year-over-year change is just 0.18, which isn’t statistically significant. (Nor is there a statistically significant relationship between the peak-to-trough declines and recent price gains after taking job growth into account.)

This is big news. For much of the recovery, the rebound effect was more closely tied to local price gains than job growth was. But today, things have reversed: Job growth is now much more important than the rebound effect. As home prices have increased and gotten close to long-term normal levels, and as investors and foreclosure sales have become a smaller part of housing activity, fundamental drivers of housing demand — like job growth—have taken over again.


Rents, Too, Are Rising Most Where the Job Growth Is

Nationwide, rents rose 6.5% year-over-year in January. The three large rental markets with the steepest rent increases – Denver, Oakland, and San Francisco – all have had job growth of 2% or more. In general, metros with faster job growth have larger rent increases, though some Sunbelt markets like Riverside-San Bernardino, Houston, and San Diego have had impressive job growth with more limited rent increases.

Rent Trends in the 25 Largest Rental Markets

# U.S. Metro Y-o-Y % change in rents, Jan 2015 Y-o-Y % change in employment
1 Denver, CO 14.2% 3.7%
2 Oakland, CA 12.1% 2.7%
3 San Francisco, CA 11.6% 4.5%
4 St. Louis, MO 10.1% 1.4%
5 Phoenix, AZ 10.0% 2.1%
6 Portland, OR 9.0% 2.9%
7 New York, NY 8.9% 2.3%
8 Baltimore, MD 8.3% 0.6%
9 Philadelphia, PA 8.3% 1.1%
10 Los Angeles, CA 7.5% 2.3%
11 Miami, FL 7.4% 2.5%
12 Tampa-St. Petersburg, FL 7.3% 2.4%
13 Cambridge-Newton-Framingham, MA 7.2% 1.5%
14 Orange County, CA 7.1% 2.0%
15 Chicago, IL 7.0% 1.9%
16 Newark, NJ 6.7% 0.3%
17 Atlanta, GA 6.6% 3.3%
18 Seattle, WA 6.4% 3.1%
19 Dallas, TX 6.2% 3.8%
20 Boston, MA 5.5% 1.7%
21 Riverside-San Bernardino, CA 4.5% 4.3%
22 Washington, DC 3.4% 0.3%
23 Minneapolis-St. Paul, MN 3.3% 1.7%
24 Houston, TX 3.2% 3.2%
25 San Diego, CA 3.2% 2.3%
Note: Job growth is as of 2014 Q2, from the Quarterly Census of Employment and Wages

The next Price and Rent Monitors are scheduled to be released on Tuesday, March 10.


The Trulia Price Monitor and the Trulia Rent Monitor track asking home prices and rents on a monthly basis, adjusting for the changing composition of listed homes, including foreclosures provided by RealtyTrac. The Trulia Price Monitor also accounts for regular seasonal fluctuations in asking prices in order to reveal underlying price trends. The Monitors can detect price movements at least three months before the major sales-price indexes. Historical data are revised monthly. Thus, historical data presented in the current release are the best comparison with current data. Our FAQs provide the technical details.


Where the Spring Housing Season Starts Early

In January and February, the housing market is in full bloom in Florida and Arizona. But upstate New York, New England, and some Pacific markets blossom later.

Jed Kolko, Chief Economist
February 5, 2015

Nationally, the spring housing seasons kicks off in March and stays strong into August before going into its winter lull. But the seasonality of the market – like everything else about housing – is local.

Using data on properties viewed on Trulia’s website from mid-2011 to mid-2014, we determined the seasonal pattern for home searches in each local market (see note). For the U.S. overall, January and February search activity is 2% above the annual average, rising to 10-15% above the annual average in the peak months from March­ to July. It then falls far below the annual average in November and December. However, each local market has its own seasonal rhythm.

Winter Sun Brings Out the Home Seekers

The housing season starts earliest on Florida’s west coast. In January and February, home search activity is 22% above the local annual average in Cape Coral-Fort Myers and 17% above in North Port-Sarasota-Bradenton. Three other Florida metros and the Arizona metros of Phoenix and Tucson are also in the top ten. All but Kansas City are in the Sunbelt.

Top 10 Metros Where Winter Home Searches are Above Annual Average

# Metro Jan/Feb home search activity relative to local annual average
1 Cape Coral-Fort Myers, FL +22%
2 North Port-Sarasota-Bradenton, FL +17%
3 Tucson, AZ +11%
4 Deltona-Daytona Beach-Ormond Beach, FL +10%
5 West Palm Beach, FL +10%
6 El Paso, TX +9%
7 Fort Lauderdale, FL +8%
8 Phoenix, AZ +8%
9 Kansas City, MO +7%
10 Charleston, SC +7%
Note: Among the 100 largest metros.

In several smaller metros as well, January/February search activity is 15% or more above the local annual average. All are in Florida, including Punta Gorda, Naples, Ocala, and Port St. Lucie. In the Sunshine State, the early-bird special isn’t just for dinner – it’s also for housing.

The metros where winter home searches are slowest relative to the annual average are in upstate New York (Syracuse, Buffalo, Rochester), New England (Cambridge-Newton-Framingham, Hartford), and the cooler, wetter winter markets on the Pacific (Seattle, San Francisco).  Honolulu and Houston are on the list of metros that wake up later, too. But the dips are just in the mid-to-low single digits. No markets are as far below the annual average for home search activity in January and February as the west coast of Florida is ahead.

Top 10 Metros Where Winter Home Searches are Below Annual Average



Jan/Feb home search activity relative to local annual average
1 Cambridge-Newton-Framingham, MA -5%
2 Seattle, WA -5%
3 Long Island, NY -4%
4 Honolulu, HI -4%
5 San Francisco, CA -3%
6 Syracuse, NY -3%
7 Houston, TX -3%
8 Buffalo, NY -3%
9 Rochester, NY -3%
10 Hartford, CT -3%
Note: Among the 100 largest metros.

Weather is the most important reason why the housing season starts earlier in some markets. Metros with strong search activity early in the year tend to have warm, dry winters (in much of Florida, for instance, the rainy season is June through September). And most, though not all, of the markets that sleep in a little later into the year have winters that are cold, wet, or both.

But weather isn’t the only factor. Another is that the markets that start early have had more foreclosures in recent years. Foreclosures are often bought by investors, and their housing demand is less seasonal. Investors tend not to hibernate as much as conventional buyers do in the winter months.

Home Seekers in Pricier Markets Take Their Time

The early-bird markets tend to have one other thing in common—greater affordability. The median price per square foot is lower on average in markets where the housing season starts early, after adjusting for differences in climate and foreclosure inventory. Not only do lower-cost metros have an early start to the housing season, but, even within metros, search activity picks up more at the start of the year in more affordable neighborhoods than in more expensive neighborhoods. Higher-priced neighborhoods tend to have slightly shorter house-hunting seasons, concentrated more in March through June.

Homeowners looking to sell in lower-priced neighborhoods and early-bird markets will see interest even this early in the year. But sellers in higher-cost areas are likely to have a slightly narrower window to catch peak buyer traffic.

Note: Search activity is based on properties viewed on from July 2011 to June 2014. Data from the full years of 2011—2014 were used to adjust for the upward trend in the data in order to reveal seasonal fluctuations. The housing crisis affected the seasonal pattern of home prices and other housing activities, so the search patterns observed in the recent past could change in the future.


Are We Past the Flipping Point?

House flipping activity tends to pick up as year-over-year price changes exceed 10 percent. Now that home prices have slowed in the recovery, even arms-length flips will become rarer.

Flipping is the practice of buying a house and reselling in a short period. Both professional and casual investors flip houses.  And like other asset trading, successful flipping involves buying at a discount, selling at a premium, or both—all of which is sensitive to changes in housing prices. We at Trulia Trends set out to explore at what point in the housing market price cycle flipping activity pick ups.

Selling houses at a premium generally requires price growth. The more prices are rising, the more profitable it is to flip. Thus, when prices are rising faster, flippers have greater opportunity to come out ahead. However, flipping activity can also accelerate when prices fall. That’s because foreclosures provide investors opportunities to get properties at discounted prices. To tease out how price increases affect flipping activity, we focus exclusively on nondistressed sales—transactions not involving foreclosures or short sales—of homes that had last sold within the previous 12 months.


A chart of home-price-change and flip percentages nationwide from the first quarter of 2000 to the third quarter of 2014 shows flipping sped up as prices rose and slowed as prices fell. In the first quarter of 2000, flippers turned over about 4% of all U.S arm’s length transactions, that is, transactions in which the buyer and seller are independent parties. By the third quarter of 2005, the percentage of flips had increased to nearly 7% of all arm’s length transactions. Between these two quarters, year-over-year increases in the FHFA national house price index went from just over 6% to almost 11%. Then, as prices plunged during the housing crash, the percentage of flips fell in 2011 to a low of 2% of all arm’s length transactions.

To better understand flipping, we grouped average flips at the metro level between 2000 and 2014 by level of year-over-year price changes. We found the flipping point—the point at which flipping activity picks up—to be around 10% year-over-year price growth. Metros that experienced year-over-year price declines had flipping rates between 1% and 2%. By contrast, metros that had 10% or more price growth on average had flipping activity between 4% and 7%.



But this is old news. What has happened with flipping lately? To get a current look, we plotted price changes and flip percentages for the 100 largest metros over the past year. We found a strong positive relationship. Metros with the highest flipping activity generally had above-average home price gains.



Six of the top 10 metros for flipping from the third quarter of 2013 to the third quarter of 2014 were in California. Nashville, Knoxville, Fort Lauderdale, and Cambridge-Newton-Framingham rounded out the list. Nearly all our top-ten flipper metros experienced above-median price gains in 2014 and most posted price growth near or above double digits. Still, we aren’t anywhere near the levels of frenzied flipping seen during the housing bubble, despite similar year-over-year price gains in many metros. All of the metros that registered year-over-year price growth above the critical 10% point had flipping activity around 3% to 4%. That’s on par with average flipping at that price growth range between 2000 and 2014—in other words, flipping is occurring at a historically normal rate. Moreover, we’re predicting slowing price growth in 2015 and flipping should moderate. You might say, we’re not expecting the housing market to flip out anytime soon!

Top 10 Metros With the Most Arms-Length Flips, 2013-2014

# U.S. Metro % Flips, 2013Q3 – 2014Q3 % YOY Price Change, 2013Q3 – 2014Q3
1 San Jose, CA 4.0% 11.1%
2 San Francisco, CA 3.8% 11.7%
3 Knoxville, TN 3.6% 3.2%
4 Bakersfield, CA 3.5% 10.9%
5 Nashville, TN 3.4% 8.2%
6 Orange County, CA 3.3% 8.2%
7 Fort Lauderdale, FL 3.3% 13.3%
8 Ventura County, CA 3.3% 9.3%
9 Cambridge-Newton-Framingham, MA 3.3% 5.8%
10 Riverside-San Bernardino, CA 3.3% 14.4%
Note: Median year-over-year price growth is 5.11%. Year-over-year price changes are calculated using the FHFA quarterly house price index.

Note on methodology:

We differentiate between two different types of property flips: a “traditional flip,” which is the purchase of a house at market rate and selling it at a higher market rate price, because of improvement to the property and/or rising prices; and a “clearance flip,” which is the purchase of a distressed house at a discount because of a forced sale, such as a foreclosure, followed by a resale at market rate. We do not include clearance flips in our measure of flipping since our goal is to estimate the relationship between prices and flipping in a relatively normal, non-distressed housing market. As a result, our definition of a flip is a property that sold twice in a twelve-month period, in which both transactions are considered arm’s length.

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Urban Headwinds, Suburban Tailwinds

Although home prices are rising faster in urban neighborhoods, population is growing faster in suburban neighborhoods. Consumer preferences and the aging of the population are tailwinds for suburban growth; so are falling oil prices if they stay low long-term.

Jed Kolko, Chief Economist
January 22, 2015

How fast have cities and suburbs grown recently? During last decade’s housing bubble, suburbs and rural areas grew much faster than cities. But briefly, early in the housing recovery, it looked like the long suburbanization of America might go into reverse. For a moment in 2011, urban counties grew faster than suburban and rural counties. Since then, old patterns have returned. Suburbs are now gaining population faster than urban neighborhoods, even though home prices are rising faster in cities than in suburbs. And the trend seems likely to continue. Trulia’s latest consumer survey and projected demographic shifts point to future headwinds for urban growth.

To compare cities and suburbs, we classify individual neighborhoods as urban or suburban based on whether or not most households live in detached single-family homes (see note on methods and data sources). This definition better reflects how residents describe their neighborhoods than do official city boundaries. That’s because many neighborhoods within big-city limits consist overwhelmingly of single-family homes and feel more suburban than urban.

Prices Rising Faster in Cities, but Population Growing Faster in Suburbs

In the recovery, the home-price rebound has been stronger in urban neighborhoods. Most of the overbuilding during last decade’s bubble involved single-family suburban homes, and the single-family vacancy rate remains elevated. Comparing urban and suburban neighborhoods in the 100 largest metros over the past four years, urban home prices have consistently risen faster, or fallen less, than prices in suburbs. And, for the year 2014, the urban median asking price per square foot rose 8.1% versus a 5.7% gain in suburban neighborhoods.


However, during most of this period, suburban population growth has been faster than urban growth. In 2014, suburbs grew 0.96% and cities 0.85%, according to Postal Service data on occupied homes receiving mail. That’s not a huge difference, but it continues the trend of suburbs outpacing urban areas.


How can prices rise faster in urban neighborhoods even as suburbs lead in population growth? One reason is that most new construction takes place outside urban neighborhoods. Cities have less open land, and often more onerous regulations limiting new construction. It’s true that in 2014 multiunit buildings—typically located in urban neighborhoods—accounted for the highest share of overall construction since 1973. Still, in urban neighborhoods, fewer new units are built relative to the size of the housing stock. Limited construction holds back urban population growth and worsens urban affordability, even when—as rising prices show—housing demand in cities is strong. But what does the future hold?

Dreaming of the Suburbs and Beyond

In November, Trulia asked more than 2,000 American adults whether they lived in an urban, suburban, or rural area, and where they wanted to live in five years. We didn’t define urban, suburban, and rural, but instead left them open to interpretation. (See note.) Rural areas were the winner. Just 21% of respondents said they were rural residents, but 28% said they would like to be living in a rural area in five years.


Urban residents feel the tug of the suburbs. For every 10 suburbanites who said they wanted to live in an urban area in five years, 16 urban dwellers said they wished to live in the suburbs. Even among young adults aged 18-34— who are more likely to live in urban areas than older adults are—more wanted to move from city to suburbs than the other way around, though the sample size was small.

To put it another way: Urban residents were the least likely to want to live in a similar area in five years. Two-thirds (67%) of urbanites wanted to live in an urban area in five years, compared with 80% of suburbanites and 83% of rural residents who wanted to live in areas like where they were.

Urban living generally declines with age, which partly explains why people are less likely to want to live in cities in five years. But will the large millennial generation give urban areas a sustained boost? In other words, will the U.S. population’s changing age profile be a tailwind or headwind for cities?

The Demographic Urban Headwind

Today, urban neighborhoods are getting a demographic jolt. The largest segment of the big millennial group—folks in their early 20s—are entering the peak age for urban living. Some 26% of 22-24 year-olds live in urban neighborhoods, rising to 27% for 25-29 year-olds. Older people are far less likely to live in urban neighborhoods. Just 17% of the largest segment of baby boomers—those in their early 50s—live in such neighborhoods, a third lower than the share of early twentysomethings.

Comparing the 2009-2013 period with 2000, city living held up for people aged 25-44. Meanwhile, the share of adults 18-24 living in urban neighborhoods declined because they became more likely to live with their parents. But the most dramatic change in urban demographics concerned older adults. People age 45 and older were less likely to live in urban neighborhoods in 2009-2013 than in 2000. And the share of seniors in their 70s and early 80s living in urban neighborhoods fell more than 10 percent. In fact, for decades, seniors have become more likely to live in single-family homes. Increasingly, cities may be for the young. But that’s because oldsters are getting less urban, not because youngsters are getting more so.


What then will happen as the two largest groups age—those in their early 20s and those in their early 50s? As millennials get older, many will follow a familiar path: They’ll partner up, have kids, and move to the suburbs. Urban living starts to decline after ages 25-29 and drops to its lowest level at ages 65-69. On the other hand, the baby boomer leading edge is nudging 70, when urban living starts to rise again. The return of older boomers to cities will offset some of the millennial suburban migration.

Nevertheless, the aging population is overall a slight headwind for cities. Suppose the propensity of each age group for urban living remains at 2009-2013 levels. In that case, over the next few decades, the changing age distribution projected by the Census Bureau would lead to a slow, but steady decline in the share of adults living in urban neighborhoods. To be sure, the decline will probably be small—around a tenth of a percentage point per decade. Still, it suggests that aging boomers returning to cities might not fill all the homes suburbanizing millennials leave behind.

What could boost urban growth? For starters, more construction in city neighborhoods. That would allow higher urban population growth, while slowing price increases. Also, higher energy prices would encourage people to live closer to work and in smaller homes. By the same token, the recent drop in oil prices, if sustained, would have the opposite effect, becoming yet another tailwind for suburbs and headwind for cities. Finally, cultural attitudes could shift in favor of renting and urban living. But is that likely? Today, the vast majority of young renters aspire to own. Homeownership remains core to the American Dream. The future of the suburbs looks bright.


To compare “city” versus “suburb,” we classify neighborhoods as urban or suburban based on how dense or spread out the housing is, as we have done in previous Trulia Trends posts. Using Census data, we define urban neighborhoods as ZIP codes (technically, ZCTAs — ZIP Code Tabulation Areas) where a majority of the housing is apartments, attached townhouses, or other multi-unit buildings; suburban neighborhoods are those where a majority of the housing is single-family detached houses. We used this methodology rather than simply identifying the biggest city in a metro as “urban” and treating the rest of the metro as the “suburbs,” as other reports on cities-versus-suburbs often do. The problem with using city boundaries is that many neighborhoods outside of the biggest city are actually much more urban than some neighborhoods within a city’s boundary. For instance, our definition classifies Hoboken, NJ, Central Square in Cambridge, MA, and Santa Monica – which are all very dense – as urban neighborhoods, even though they’re outside the city boundaries of New York, Boston, and Los Angeles, respectively.

The urban vs. suburban comparisons of prices and population growth cover the 100 largest metros, with individual ZIP codes classified as urban or suburban. Price changes are year-end year-over-year changes in median asking price per square foot of homes listed on Trulia. Population changes are the year-end year-over-year change in the U.S. Postal Service’s count of addresses receiving mail, reported monthly by ZIP code.

The consumer data is based on a survey of 2,008 American adults conducted for Trulia on November 6-10, 2014, by Harris Interactive. Respondents were asked whether they lived in an urban, suburban, or rural area, without being provided a definition. Self-reported urban locations aligned better with our housing-stock-based definition of urban versus suburban than with official city boundaries, though race, for instance, had a statistically significant relationship with self-reported neighborhood urban classification, even after accounting for the housing stock, household density, and city population.

Data on urban residence by age group is based on the ZCTA population as reported in the 2000 decennial Census and the 2013 five-year American Community Survey, covering the years 2009–2013. Population projections by single year of age from 2014 to 2060 are also from the Census Bureau.