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Financial Anxiety, Ongoing Uncertainty Keeping Sellers on the Sideline

Financial Anxiety, Ongoing Uncertainty Keeping Sellers on the Sideline

  • About a third of homeowners who are considering selling in the next three years cite life being too uncertain right now (34%) and financial uncertainty (31%) as reasons they aren’t selling.
  • Nearly 40% of these potential sellers say they anticipate a higher sale price if they wait.

Despite a market tilted decidedly in their favor — with demand sky-high and inventory at rock bottom — potential home sellers are largely staying on the sidelines, citing a variety of personal financial, lifestyle and/or health concerns as major reasons.

Only 1% of homeowners recently surveyed by Zillow said their homes were currently listed for sale. Among the 99% whose homes are not on the market, more than a quarter (26%) said they were concerned they would not be able to find or afford a new home once their current home was sold, the most commonly cited reason for not selling. General life uncertainty (22%) was the second-most common reason, followed by anticipation of a more favorable sale price if they wait (21%).

Reasons for not selling vary by age group: More than a third (35%) of Gen Z and Millennial homeowners said their plans for or completion of a home renovation was a main reason to stay put, compared to just 21% and 14% of Gen X and older homeowners, respectively. Younger homeowners were also more likely to cite concern about COVID-19 (20% of Gen Z and Millennial and 18% of Gen X) as a reason for not selling than Boomer and Silent Generation homeowners (11%).

Some homeowners realistically may simply enjoy their current home and have no need or desire to sell and move any time soon. But among homeowners considering selling within the next 3 years, 39% said they anticipate a better price if they wait. Among those who are not currently considering selling, but may be open to it after 3 years, 39% cite concern that they won’t be able to find or afford a new home.

Concern over finding or affording a new home among those more likely to sell in the near-term was a bit more muted, but still very real: Almost a third (31%) of homeowners considering selling in the next three years say their plans are paused because they are concerned about finding or affording a new home. The findings are a clear example that selling a home can sometimes cut both ways: According to the 2020 Zillow Consumer Housing Trends Report, almost two-thirds (63%) of sellers are also buyers. But while these dual-track homeowners may be able to sell their home for top dollar, they will also turn around and enter an extremely competitive buyers’ market where homes are going under contract in 12 days.

Given the historically low mortgage interest rates that many home buyers and homeowners can enjoy these days, it may come as little surprise that 15% of homeowners report a recent refinance as a reason for staying put — but their planned uses for their newfound financial flexibility are very different. Among respondents that cited a recent refi as a reason to stay put, a majority (54%) said they would use their savings to pay off debt. Exactly half said they would use the savings for home improvements or saving for retirement.

General life uncertainty, especially given the ongoing pandemic, high unemployment and volatile economic conditions, is a main factor keeping more than a third (34%) of those considering selling in the next three years out of the market. Among these homeowners hesitant to put their home on the market now, a quarter said they weren’t selling because they were concerned about their household’s health and safety during the pandemic.

Financial anxiety, again likely attributable at least in part to the pandemic, is another big factor keeping those that might sell sooner on the sidelines: 31% of homeowners considering selling in the next three years said a currently uncertain or precarious financial situation is a reason to stay put. More than a quarter of these would-be sellers (27%) reported a recent change in employment with a decrease in hours or pay, and 17% said they or their spouse/partner were laid off or involuntarily unemployed. Among sellers who may be considering putting their home on the market in the next three years, 6% said they are currently taking advantage of mortgage forbearance programs that allow them to delay or defer monthly mortgage payments.

Methodology

In this brief, “homeowners” refer to household decision makers who own their primary residence and have not moved in the past year.

Zillow Group Population Science collected a nationally representative sample of more than 1,000 homeowners (household decision makers that own their home and did not move in the past year). From September 29th to October 5th, 2020 the survey asked homeowners questions about their plans to sell and recent life events. Among those who did not have their home listed for sale (99% of the sample), the survey also asked why they were not currently selling.

To achieve national representativeness, quotas for age, ethnicity/race, education, income, region, relationship status, and sex limited oversampling of any given demographic group. In addition to quotas, ZG Population Science used statistical raking to weight the sample to the US Census Bureau American Community Survey 2018 sample of homeowners. Weighting used the same variables as the quotas. Margins of error are at a 95% confidence interval.

For more information from our partners at Zillow, check out their blog.

What are the Top Affordable U.S. Suburbs With a City Feel?

What are the Top Affordable U.S. Suburbs With a City Feel?

Increased opportunities to work remotely are pushing more Americans to rethink how and where they want to live. But even if there’s less of a need to live as close to urban job centers, traditional urban amenities — think restaurants, nightlife, museums and sports venues — remain a big draw and demand for city living remains high. As a result, many buyers may seek places that balance the space and affordability of the suburbs, while still maintaining that big-city feel.

A new “Cityness Index” created by Zillow and Yelp Inc. helps identify the U.S. suburbs that best strike that balance. Key metrics include housing affordability compared to the nearest big cities and to the country at large, housing availability, the mix and diversity of businesses — including restaurants, nightlife and the arts — and consumer reviews and check-ins.

Zillow data shows extremely strong housing demand driven both by incredibly low mortgage interest rates and more millennials and Gen Zers reaching prime home buying age. This desire to move is also reflected in a recent Zillow survey of people newly working from home because of the pandemic, with almost two-thirds of respondents saying they would consider moving if they had the flexibility to work from home occasionally. The opportunity to telework could also give almost 2 million current renters an opportunity to relocate and buy a home in a more affordable area. There very well may be a “Great Reshuffling” on the horizon.

“At Yelp, we’re seeing consumer interest and requests for quotes for categories like movers, packing services and mortgage lenders increase in major metro areas, compared to the same time period last year,” said Yelp Trend Expert Tara Lewis. “For city dwellers who don’t want to sacrifice great amenities like restaurants, art galleries and nightlife, but are dreaming of a little more space and a more affordable lifestyle, these suburbs offer a similar variety of great local businesses.”

This demand is also driving up prices and depleting inventory. But using this data, it is possible for young people looking to buy and find space for their expanding families to bid farewell to big city premiums without giving up the feel of a city in amenity-rich suburbs.

Hundreds of suburbs nationwide were scored on the index, and we identified the highest scorer in each of the 50 largest U.S. metros. Waterbury, Conn., near New Haven and Hartford, topped our list, beating out Lowell, Mass. for the top spot. Filling out the top five are Joliet, Ill.; Sunrise, Fla.; and Pasadena, Texas.

Zillow and Yelp’s top 10 affordable suburbs with a city feel:

10 affordable suburbs with a city feel

1. Waterbury, Connecticut

  • Cityness Index Score: 67.6
  • Typical home value: $139,304

Nestled between New Haven and Hartford, Waterbury has a quintessential New England feel. Downtown Waterbury boasts the historic Palace Theater, the Mattatuck Museum for art lovers and a University of Connecticut satellite campus.

Housing affordability in Waterbury is another advantage — the typical Waterbury home is 30% less expensive than the typical home in New Haven, and 46% less expensive than the typical U.S. home.

2. Lowell, Massachusetts

  • Cityness Index Score: 64.7
  • Typical home value: $323,576

Located near the New Hampshire border about 25 miles northwest of Boston, Lowell offers a great balance of city-like amenities with suburban conveniences. Residents can immerse themselves in the history of America’s Industrial Revolution at Lowell National Historical Park or take a casual stroll along the Merrimack River, which winds through downtown Lowell.

Home values in Lowell are about half those in Boston. Brockton, Mass., also scored highly in our Cityness Index, demonstrating the number of affordable, livable city/suburb options available in the Boston area.

3. Joliet, Illinois

  • Cityness Index Score: 63.8
  • Typical home value: $155,018

Joliet is perhaps best known for being home to the Chicagoland Speedway, but there’s plenty more to this Chicago suburb.

Nearby Aurora almost made the list, but Joliet’s housing affordability advantage gave it the edge. The typical home in Joliet is about 40% less expensive than the typical Chicago home.

4. Sunrise, Florida

  • Cityness Index Score: 60.7
  • Typical home value: $243,078

Minutes away from the beach and the Everglades, natural beauty is never far when you’re in Sunrise. For sports fans, Sunrise is home to the NHL’s Florida Panthers and several golf courses.

Multiple Miami suburbs scored well in our Cityness Index, including Delray Beach and Boca Raton, but none beat Sunrise’s mix of affordability and city-like amenities. The typical home in Sunrise is 36% less expensive than in the city of Miami.

5. Pasadena, Texas

  • Cityness Index Score: 60.5
  • Typical home value: $168,080

About 15 miles southeast of downtown Houston you’ll find Pasadena, home to one of the largest urban wildlife preserves in the U.S.

Pasadena offers an affordability edge over Houston, with the typical home value 14% lower.

6. Lancaster, California

  • Cityness Index Score: 59.3
  • Typical home value: $320,494

Lancaster is located near the north edge of the greater Los Angeles area, within the Antelope Valley. Tens of thousands of visitors flock to the area each year for the California Poppy Festival to view more than 1,700 acres of the state’s official flower.

Housing affordability relative to the area shot Lancaster into the top 10 — the typical Lancaster home value is less than half that in the city of Los Angeles.

7. Hampton, Virginia

  • Cityness Index Score: 58.6
  • Typical home value: $188,373

With the Chesapeake Bay to the north and east and the busy harbor at Hampton Roads to the south, Hampton residents have plenty of options for water and beach activities.

Typical home values in Hampton are almost 60% less than the nearby city of Virginia Beach and 36% less than the U.S.

8. Marietta, Georgia

  • Cityness Index Score: 58.4
  • Typical home value: $318,069

Marietta is about 20 miles northwest of Atlanta, and offers residents easy access to Interstate 75 and everything Atlanta has to offer. This suburb’s spot in the top 10 is due in large part to its affordability. Although typical home values in Marietta are slightly higher than the city of Atlanta, it’s still relatively affordable compared to other nearby suburbs including Canton and Smyrna.

Known for its abundance of nature and parks, the culinary scene rivals that of Atlanta, according to Yelp.

9. Norman, Oklahoma

  • Cityness Index Score: 58.2
  • Typical home value: $180,833

Home to the University of Oklahoma, Norman offers a small-town atmosphere with a vibrant nightlife and plentiful coffee shops and parks. Yelp users also give the local restaurant scene high marks. Homes in Norman are generally more expensive than those in Oklahoma City, but remain more affordable than the typical U.S. home.

10. Tempe, Arizona

  • Cityness Index Score: 57.9
  • Typical home value: $327,963

Just east of Phoenix and home to Arizona State University, Tempe is known for a bustling performing arts scene. Locals and tourists alike also enjoy soaking in the surrounding scenery while kayaking and paddleboarding at Tempe Town Lake.

Tempe home values are up 10% from last year, indicating a strong housing market. Listings are also up nearly 3%, offering extra housing options available on the market.

Methodology

The Cityness Index is designed to highlight cities with vibrant amenities and relatively affordable housing, which result in a city-like environment in a more-affordable suburban region. Any city not included in the official name of a metropolitan area, as defined by the U.S. Census, were counted as a suburb in this analysis. The highest-scoring suburb in 50 of the largest U.S. metro areas were considered, with the highest scores among those making it into the final top 10 list of the most city-like suburbs.

There were four individual Yelp indicators evaluated for each suburb to determine its cityness.

  1. A mix of businesses similar to major cities
  2. A diversity of restaurant and nightlife businesses
  3. A diversity of arts businesses
  4. A high level of consumer activity

Among these, the mix of businesses indicator was given most weight.

The mix of businesses were found by comparing the distribution of open businesses across its major business categories, such as home services and restaurants, in each candidate city with the average distribution in several of the largest U.S. cities. Diversity of business type was measured across two sectors: (1) restaurants, food, and nightlife; and (2) the arts. Suburbs were compared by how many unique types of businesses, such as piano bars or diners, are present. Consumer activity was measured as the number of page views, reviews, and photos per business.

Zillow analyzed five main variables. The most impactful variables were targeting affordability of these suburbs compared to the principal cities in the metropolitan area and the US overall:

  1. Ratio of typical home values in the suburb compared to the principal cities, defined as those named in the official Census MSA name (e.g., Dallas, Fort Worth and Arlington home values were used for comparison in the Dallas-Fort Worth-Arlington metro)
  2. Ratio of typical home values in the suburb compared to the national median

The other housing related variables were targeting the availability of for sale, new for sale, and rental inventory:

  1. Ratio of new for sale inventory in the suburb compared to the principal cities
  2. Ratio of rental inventory in the suburb compared to the principal cities
  3. Ratio of existing for sale inventory in the suburb compared to the principal cities

In each of the Zillow variables, multi-unit housing was given extra weight in calculating inventory to emphasize housing availability in more dense, city-like parts of town.

The collection of Yelp indicators and Zillow variables were each given 50% of the overall weight for the final index. Scores reflect data from June 2020 to August 2020.

Freedom to Telecommute Could Add Almost 2 Million Potential Buyers to the Market

Freedom to Telecommute Could Add Almost 2 Million Potential Buyers to the Market

  • A switch to more telework could give 1.92 million U.S. renters (4.5% of renter households) the option to leave the metropolitan areas where they currently live and buy a starter home in a cheaper locale.
  • Starter homes are more expensive than the nation as a whole in 37 of the 50 largest U.S. metros.
  • Fleeing from a metro’s central city to it’s suburbs is not as broadly beneficial. The markets where the largest share of renters in the center city would gain the power to buy if they looked outside city limits are San Francisco, Seattle, Washington, D.C., and Boston.

Almost 2 million U.S. renters that currently can’t comfortably afford to buy an entry-level home in their current metro area could potentially afford the nation’s typical starter home if they took advantage of increased telework options and moved to a less-expensive locale.

Zillow analyzed renter households for whom monthly payments on a starter home in their metro are unaffordable, but would be affordable on the typical U.S. starter home. Those households were then assigned a probability of being able to telecommute based on income, the worker’s industry and occupation. Millennials, between 26 and 40 years old, represent almost half of the 1.92 million renter households who could afford homeownership if given the flexibility to work from home, the largest generational group to potentially benefit from these new options.

Nationwide, the typical starter home is currently valued at $131,740. But similar starter homes in 37 of the nation’s 50 largest metro areas — home to the lion’s share of the country’s jobs — are more expensive than in the country at large, often by a wide margin. As a result, owning even a modest home (and taking advantage of the wealth-building opportunities that can bring) is out of reach for many households as long as they need to be within commuting distance of a physical workplace.

Rethinking the Relationship Between Work & Home

Close to half (43.6%) of U.S. workers are in occupations in which teleworking is at least theoretically feasible, though less than a quarter of these workers actually telework. But the ongoing pandemic has shaken up how workers and their employers alike think about the relationship between work and home. Over the past six months, many companies have found that their workforce can function better remotely than originally thought.  If telework becomes more of a norm, and businesses allow it where possible, this could give millions of Americans more choice over their home and home finances.

Among the country’s largest metros, the San Francisco Bay Area is home to the most renters who could maybe leave and buy a home elsewhere if telework became the norm — perhaps unsurprisingly, given how expensive the area is relative to both the U.S. and most other large metros. In the San Francisco and San Jose metro areas, 22% and 25.2% of local renters, respectively, would be able to leave the area and buy a home in a cheaper local if telework were an option — almost a quarter million renters total. Los Angeles (17.2% of renters could leave and buy a starter home elsewhere), San Diego (15.4%) and Denver (14.6%) round out the top 5 list of large markets in which the largest share of renters could afford a home elsewhere.

But while homes in most of the nation’s largest 50 metros are more expensive than the U.S. at large, home values for starter homes in 13 of these areas are less than the U.S. median — leaving residents in those areas little incentive to leave and buy a starter home elsewhere.

From the City to the Suburbs

Still, despite whatever financial advantages may be in play, many renters may rightly choose not to move for any number of personal reasons — they simply might prefer to rent in a bustling city like New York, rather than own in a sleepier rural area in another state. And while it may make sense on paper to move far from a given area to be able to afford homeownership, practically speaking it can be very difficult to completely uproot and move away from family, friends and valued local cultural institutions (sports teams, schools, museums etc.).

As such, in many cases, it may be far more likely that current residents can’t or won’t flee and cut the cord with their hometown entirely, and instead exchange it for an extension cord — moving from the commute-friendly center city to farther-flung suburbs, but still maintaining ties. But the affordability benefits in moving from the city to the suburbs, rather than from one metro area to an entirely new, cheaper one, are less-pronounced.

A starter home is worth more in a metro’s namesake city than it is in the metro as a whole in only 20 of the nation’s 50 largest metropolitan areas (and in just 11 of the 27 metros where income data was available on occupations at the city level). In cities including Minneapolis, Phoenix and Denver, a starter home is more affordable than in the larger metro area, leaving city residents with no real price incentive to leave for the suburbs. And relatively affordable starter homes (within the context of the metro) are what separate Los Angeles and San Jose from San Francisco, and Portland from Seattle. In San Francisco and Seattle a large share of renters currently living in the city could telework and buy a starter home outside the city (10.4% and 8.4% respectively). In Los Angeles and Portland it’s a much smaller share (0.8% and 1.6% respectively).

Methodology

A home is assumed to be not affordable for those households in which expected monthly payments on a starter home (assuming a 30-year, fixed-rate mortgage with a 3.0% interest rate and 20% down, plus estimated taxes, insurance, HOA dues) are greater than 30% of household income. We compared the bottom-tier (referred to here as “starter/entry-level”) Zillow Home Value Index for the United States and for individual large metros. Many cities are not identified in ACS microdata and were excluded from the city-level analysis.

Households were assigned a probability of being able to telecommute by income weighting individual earner probabilities. Individual probabilities were derived from an intersection of the probabilities by worker’s industry and occupation presented in this BLS analysis of American Time Use Survey data. The denominator is the total number of renter households.

Urban Rent Slowdown May Signal Renters are Edging Toward the Suburbs

Urban Rent Slowdown May Signal Renters are Edging Toward the Suburbs

  • U.S. rent growth has slowed this spring as heavy unemployment hit renters harder than homeowners.
  • Rent price growth in urban ZIP codes has slowed more than those in suburban areas since February, one outcome of unemployment affecting urban renters particularly hard and a possible signal that preferences are shifting in favor of the suburbs.
  • The split between urban areas and the suburbs is largest in Dallas-Fort Worth, Sacramento, San Francisco and the greater New York metro.
  • Conversely, urban rent growth has been stronger than the suburbs in several metros, led by Kansas City, Detroit, Baltimore and Riverside.

While the for-sale market has shaken off the early impact of the coronavirus pandemic and resumed its torrid pre-pandemic pace, rent growth hit the brakes this spring. Rent prices in urban areas have slowed more than those in suburban areas, a possible signal that renters’ preferred location is tilting toward the suburbs.

Rents were chugging along at a stable pace into the early part of this year, but the spike in unemployment has hit renters more severely than homeowners, and millions have moved back in with parents or grandparents, impacting demand for rentals. That’s caused the rate of rent growth to slow from February to June.

During that period, rent price growth has slowed more in urban ZIP codes than in the suburbs — annual rent growth has slowed two percentage points in urban areas, compared to 1.4 percentage points in suburban areas. That is a subtle split, but it goes against the trend seen just before COVID-19 hit the U.S., indicating the shift was influenced by the pandemic. Contributing to this are urban renters who have lost their jobs, are missing rent payments or are moving home in greater numbers than their suburban counterparts, and suburban rentals may now be more appealing for renters who no longer need to commute or are temporarily unable to enjoy some of the amenities of urban living.

Renters usually have more flexibility than homeowners given their relatively short lease terms, and rent prices are often quicker to move as a result. Search traffic data does not yet show home shoppers are more interested in suburban homes than in past years, and both areas are seeing similar home-value growth, time on market, sales above list price and rate of newly pending sales. Survey results, however, indicate working remotely is causing many to reconsider their options. If this early shift in the rental market is indicative of a more widespread change in preferences, similar changes to the for-sale market could follow, but the economic impact on urban renters may be playing a larger role.

It’s important to separate how much of the trend is coming from shifting tastes as opposed to the economic reality that renters face. It may be tempting to conclude that urban renters who have been cooped up without outdoor space and unable to visit their favorite local bar are ready to commit to suburban life, and that is likely true for many. But that narrative ignores the fact that urban areas have been affected by job loss more so than suburban and rural areas, particularly renters who are disproportionately employed in the industries most affected.

This split between urban and suburban rent growth was present in more than half of large U.S. metros studied. The biggest gaps were in Dallas-Fort Worth, Sacramento, San Francisco and the greater New York metro.

Not all markets are following this pattern. Urban rent growth has been stronger than suburban growth in some metros, and that difference is biggest in Kansas City, Detroit, Baltimore, Riverside and St. Louis. Rents in both urban and suburban areas of Kansas City are accelerating, but urban rents are to a greater degree. Baltimore rent growth was softening before the pandemic, and has continued on that trajectory.

METROPOLITAN AREA YOY RENT GROWTH – URBAN AREAS (JUNE 2020) SLOWDOWN SINCE FEB. IN URBAN AREAS (PERCENTAGE POINT DIFFERENCE) YOY RENT GROWTH – SUBURBAN AREAS (JUNE 2020) SLOWDOWN SINCE FEB. IN SUBURBAN AREAS (PERCENTAGE POINT DIFFERENCE) DIFFERENCE IN SLOWDOWN BETWEEN URBAN AND SUBURBAN AREAS (PERCENTAGE POINT DIFFERENCE)
United States 1.60% -2.00% 2.40% -1.40% -0.60%
New York, NY 0.10% -3.80% 2.00% -1.30% -2.50%
Los Angeles-Long Beach-Anaheim, CA 1.20% -2.50% 1.20% -2.10% -0.40%
Chicago, IL 1.40% -1.30% 1.30% -1.70% 0.40%
Dallas-Fort Worth, TX 0.00% -3.70% 2.50% -0.50% -3.20%
Philadelphia, PA 2.20% 0.10% 1.80% -1.10% 1.20%
Houston, TX 0.00% -1.90% 0.40% -0.90% -1.00%
Washington, DC -0.10% -3.00% 1.00% -1.80% -1.30%
Miami-Fort Lauderdale, FL 1.80% -0.90% 2.30% -0.90% 0.00%
Atlanta, GA -0.50% -2.00% 4.20% 0.00% -2.00%
San Francisco, CA -2.20% -3.90% 0.80% -1.30% -2.70%
Detroit, MI 4.40% 1.40% 2.00% -0.80% 2.20%
Riverside, CA 4.90% 1.00% 3.80% -0.80% 1.80%
Phoenix, AZ 6.30% -3.20% 6.00% -2.80% -0.40%
Seattle, WA 1.90% -4.30% 2.00% -3.40% -1.00%
Minneapolis-St Paul, MN 2.30% -1.50% 1.70% -2.30% 0.70%
San Diego, CA 2.80% -1.90% 1.50% -2.20% 0.40%
St. Louis, MO 4.20% 0.80% 3.20% -0.90% 1.70%
Tampa, FL 2.20% -2.10% 3.70% -0.90% -1.20%
Baltimore, MD 1.40% -0.20% 0.50% -2.30% 2.10%
Denver, CO 0.50% -3.30% 0.80% -2.60% -0.70%
Pittsburgh, PA 1.60% -3.80% -1.60% -2.40% -1.50%
Portland, OR 2.30% -1.10% 2.60% -1.90% 0.80%
Charlotte, NC 3.40% -0.70% 3.00% -1.60% 0.90%
Sacramento, CA 3.50% -2.70% 3.80% 0.30% -3.00%
San Antonio, TX 1.50% -2.00% 1.90% -1.30% -0.70%
Orlando, FL 0.40% -3.60% 1.50% -2.40% -1.20%
Cincinnati, OH 4.20% -0.80% 3.00% -1.80% 1.00%
Cleveland, OH 4.90% 1.70% 2.80% 0.10% 1.70%
Kansas City, MO 3.70% 1.20% 2.50% -1.20% 2.40%
Las Vegas, NV 2.40% -3.90% 2.00% -3.40% -0.40%
Columbus, OH 3.80% 0.20% 2.90% 0.10% 0.10%
Indianapolis, IN 5.40% -1.00% 3.60% -0.70% -0.30%
San Jose, CA -0.90% -4.20% -0.70% -3.80% -0.40%
Austin, TX -0.10% -3.20% 1.80% -2.80% -0.40%
No Bargains in Sight as Home Prices Show Little Impact from Coronavirus

No Bargains in Sight as Home Prices Show Little Impact from Coronavirus

  • The median sale price was up 4.6% year-over-year in May, to $263,408.
  • Most major metros saw a slight deceleration in sale price growth from April to May
  • A resurgence of more-expensive listings, low price cuts, and record-low days on market are all expected to sustain upward pressure on sales prices.

The median price of U.S. homes sold in May was $263,408, up 4.6% year-over-year. But May was also the second straight month in which annual growth was slower than the month prior — definitively snapping an almost year-long period of continuous acceleration that began in April 2019 and peaked in March at 5.5%.

Annual growth in median sale price was slower in May than in April in 31 of the nation’s 50 largest metros, though the deceleration was generally small. The biggest slowdown was in Providence, down 2 percentage points in May from April (from almost 9.2% to just slightly more than 7.1%). San Jose was at the opposite end — annual growth in the heart of Silicon Valley was 2.1 percentage points faster in May than in April (to 5.3% from 3.2%).

The data make clear that despite the nationwide shockwaves generated by the coronavirus pandemic, home prices haven’t been hit to the same degree as other sectors of the economy — at least for now. And because closed sales obviously lag active listings, we can expect sales prices to reflect the relative stability and growth of median list prices that we’ve seen over the past few months. Sale prices increased year-over-year in May in all 50 of the nation’s largest metros.

Still, the pandemic has had a small but noticeable impact on prices. In mid-April, inventory of available homes to buy hit its low-point as stay-at-home orders temporarily paused home transaction activity. Initially, listings of the most-expensive homes fell the most, skewing the distribution of homes actually available for sale toward those in lower price ranges. It’s likely we’re seeing the impact of the closed sales of those lower-priced homes reflected in May’s deceleration in the median sale price.

Recent data point to continued stability in sales prices. By early June, as inventory recovered from April lows, listings at more expensive price points surged back to levels close to last year’s, while lower-priced listings remained depressed. Sellers are also consistently holding firm on their asking prices — just 4.1% of active listings in the last week of June had undergone a price cut, compared to 5.6% of listings a year earlier. And with homes typically selling just 20 days after hitting the market — the lowest level ever recorded by Zillow — sellers have little incentive to slash prices. All of these upward pressures on list prices will likely carry through to sales prices in the coming months.

As spring turns to summer, buyers currently in the market and expecting to score a bargain from desperate sellers may be in for a rude surprise. Zillow listing metrics clearly point to the fact that, despite challenges posed by the pandemic, it remains a very competitive homebuying season — ever-low inventory is no match for buyers’ pent-up demand. Steady sales prices are a final confirmation of this trend.

Experts: Spring’s Missing Home Sales Will be Added to Coming Years

Experts: Spring’s Missing Home Sales Will be Added to Coming Years

  • In a survey of 106 economists and real estate experts conducted by Pulsenomics and Zillow, 41% of panelists expect the U.S. recovery will follow a ‘U’ shape, with the recession lasting several quarters before returning to growth.
  • Once the pandemic begins to subside, experts agree, there will be an increase in demand for suburban and rural living.
  • On average, panelists expect home values to decrease 0.3% in 2020, a sharp decline from expected growth of 3.3% when surveyed three months before.

When coronavirus turned the economy upside down, anxiety and uncertainty about the future initially kept many homebuyers and sellers at bay. Inventory and sales have picked up over the past month, though, and a panel of housing experts and economists say the U.S. housing market hasn’t lost those missing springtime transactions for good.

The Zillow Home Price Expectations Survey, sponsored by Zillow and conducted quarterly by Pulsenomics LLC, asks more than 100 economists, investment strategists and real estate experts for their predictions about the U.S. housing market. The Q2 survey focused on the impact of coronavirus on the market and expected recovery patterns, and also asked for predictions on how the pandemic will shape home-buying decisions in the future.

Coronavirus and subsequent stay-at-home orders resulted in lower-than-expected transaction volume during what was primed to be a busy spring home shopping season. While it was thought the spring buying season could shift to the fall, the pandemic effects are poised to continue into summer and only 10% of the survey panelists said they believe those transactions will materialize later in 2020. More than twice as many experts (22%) said they expect a “double up” during next spring’s shopping season, and the vast majority predicted that recovery will be spread out over the next several years.

ZHPE results, Q2 2020 coronavirus

This prediction is in line with how the experts expect the U.S. economy to recover overall. Forty-one percent said they think economic recovery will follow a ‘U’ shape, and 33% say it will be a bumpy, multi-year return back to trend growth. Both patterns are characterized first by a sharp decline and then match how experts see transaction volume recovering, with the consensus generally being a more gradual journey back to normal.

Prices nationally are now projected to fall 0.3 percent this year according to the panel-wide average forecast — down from an expected increase of 3.3 percent just three months ago.

“This is the first time since 2012 that the panel-wide price outlook has turned negative, and the quarter-to-quarter swing in expectations is the largest we’ve seen in more than a decade,” said Terry Loebs, founder of Pulsenomics. “Longer term, the outlook for home values nationwide is mixed — price projections for 2022 and beyond actually inched higher from levels recorded prior to the Covid-19 outbreak. However, nearly seven in ten experts now indicate that their five-year forecast has downside risk. Last quarter, fewer than four in ten panelists foresaw downside — of course, that was before the Covid-19 crisis, its economic devastation and unprecedented government response.”

Zillow’s own forecast calls for a 1.8% drop in home prices by October 2020, expecting home prices to return to Q4 2019 levels by the Q3 2021. While predictions on home prices continue to steepen, the outlook on pending home sales continues to become more optimistic, and Zillow now shows sales hit bottom in April with a 44% drop, and are on their way back up, compared to the original forecast of a 60% dip.

Experts’ forecasts on the future of housing vary widely at this early stage of the recovery. Zillow’s own forecast has become more optimistic as we ingest new data and watch pending sales pick up faster than expected. What does seem more consistent in this wisdom of crowds is that full recovery is a couple years away — much faster than in the last housing downturn — and remote work will eventually work its changes on the housing market.

Experts also said that where people choose to live will look a little different once the pandemic subsides. Panelists predict future homebuyers will show more interest in suburban and rural areas, at the expense of urban density. Previous Zillow research has indicated that a future that sees more people working from home could make the suburbs more appealing, and the panelists echoed the likelihood of this demand. Although panelists believe there will be a shift in location preference after coronavirus, they also say buyers will want larger homes equipped with home offices moving forward. Stay-at-home orders quickly emphasized the need for more space while stuck at home, and panelists think more space will be a determining feature for future home-buyers.

Even with lower sales volumes compared to 2019, the U.S. housing market has shown resilience during the pandemic and has already begun to rebound. Pending sales are up 40.8% in the past month, and new home sales in April were up 0.6% from March.

Year      Average Home Value Growth Expectations – Q1 2020 Survey Average Home Value Growth Expectations – Q2 2020 Survey
2020 3.3% -0.3%
2021 2.7% 0.9%
2022 2.7% 2.9%
2023 3.0% 3.3%

 

Methodology

This edition of the Zillow Home Price Expectations Survey surveyed 106 experts between April 28, 2020 and May 15, 2020. The survey was conducted by Pulsenomics LLC on behalf of Zillow, Inc. The Zillow Home Price Expectations Survey and any related materials are available through Zillow and Pulsenomics.

Web Visits to For-Sale Listings Rebounding as Spring Unfolds

Web Visits to For-Sale Listings Rebounding as Spring Unfolds

  • Page views on for-sale listings on Zillow fell as much as 19% year-over-year in mid-March, but have rebounded sharply since then.
  • Traffic on listings in some metros have recovered more quickly, including Los Angeles, Houston, Dallas and Atlanta.

Web traffic to for-sale home listings on Zillow fell off dramatically in mid-March as the U.S. coronavirus outbreak began in earnest and stay-at-home orders were expanded, effectively shuttering large parts of the economy. But by mid-April, overall visits to for-sale homes had rebounded to levels — perhaps surprisingly — that are actually slightly higher than a year ago.

In early March, the market was still looking forward to the impending busy spring home shopping season. Market fundamentals were largely strong, and traffic to for-sale listings was higher than it was at the same time a year ago. But starting March 11, web traffic began to slide. Even in a month jam-packed with bad news, that date stands out. On March 11, the World Health Organization officially classified the coronavirus outbreak as a global pandemic; President Trump announced a travel ban from Europe as part of a televised national address; and the National Basketball Association cancelled the rest of its season. Over the next week and a half, culminating on March 22, traffic to homes listed for sale on Zillow dropped by almost a fifth compared to the comparable week in 2019.

But not every market dropped in tandem. In Seattle, home to Zillow headquarters and site of some of the first known U.S. cases of community transmission of the novel coronavirus, traffic was below-normal as early as February.

The New York metro area, now home to the nation’s biggest outbreak, has experienced some of the biggest daily traffic declines: It fell more than 30% for the 7-day period ending March 22, and remained down about 24% in the first week of April. The most recent available data, for the 7 days ended April 13, show New York still down about 8%. Late-March/early-April traffic to listings in the Boston area, also currently grappling with one of the nation’s largest outbreaks, fell more than 20% by the week ending March 20 and remained below that depressed level through April 8, when it began to recover toward normal levels.

San Francisco experienced a more acute drop than Boston, down about 28% for the week ending March 22 and subsequent 7-day windows through March 26. But it recovered steadily soon after that, and in the week ending April 13 verage traffic was higher than the same week in 2019.

The traffic dropoff was less severe in Los Angeles, with a nadir 20% lower than 2019 for the week beginning March 16, the same day the LA Department of Public health issued an order prohibiting gatherings of 50 or more. But traffic to Los Angeles-area listings on Zillow subsequently rebounded quickly, and has actually been substantially higher year-over-year through the first two weeks of April.

It’s a similar story in Minneapolis, which was experiencing strong year-over-year growth in traffic in the days prior to March 11, before dropping off sharply. Minneapolis’ biggest one-week drop in traffic also came on the week starting March 16, the day Minnesota Governor Tim Walz ordered restaurants, bars and other public gathering areas closed. But by mid-April, Zillow traffic was up by double-digits in the twin cities.

While traffic to listings in some markets still remains down from a year ago, the national total has rebounded significantly, up 13% year-over-year for the week ending April 13. In fact, in 30 of the 35 largest metro areas, web traffic to for-sale listings was up year-over-year during the second week of April. Of those large markets, only the Pittsburgh, Detroit, Philadelphia, Boston, and New York City metro areas continue to show depressed traffic.

It’s impossible to know precisely what’s driving this recent spike in traffic: It could be coming from optimistic buyers hoping to get an early jump on their plans as soon as restrictions are lifted, or simply from aspirational viewers stuck at home and seeking an escape through real estate. Early data on home sales show significant drops in mortgage purchase applications in March, so one simple explanation could be that many of the home shoppers who would have bought in March are still on the sidelines, keeping tabs on the market alongside everyone who began eyeing Zillow listings in April.

We do know this April turnaround does not reflect positive economic news. In the four weeks from March 15 through April 11, roughly 22 million Americans filed claims for unemployment assistance, or almost 15% of all workers who were otherwise employed as recently as the middle of March. The total scale of the economic slowdown is not yet clear, but it is almost certain that more people will soon be out of work for an indeterminate period of time.

Methodology

All page view events of for-sale homes on Zillow.com and the Zillow app are tabulated by day and the listing’s ZIP code, and then aggregated to day and MSA. Figures are presented as rolling 7-day trailing averages to smooth out daily noise. Page views exclude real estate agents and other professional users on Zillow. Year-over-year comparisons are done after offsetting 2019 data by 2 days, in order to compare the same days of the week, e.g. we compare Sunday, March 29, 2020 with Sunday, March 31, 2019.

 

For more tips from our partners at Zillow, check out their blog!

Information From Past Pandemics, And What We Can Learn: A Literature Review

Information From Past Pandemics, And What We Can Learn: A Literature Review

The United States has officially entered a bear market, with major financial indices falling by more than 20% since the beginning of the year. The market has fallen in response to a mix of information, including global community spread of the Novel Coronavirus COVID-19, a travel ban for Europeans into the US, and general uncertainty about a fiscal response to the virus.

Zillow Research conducted a deep dive into past research and data on the economic effects of global pandemics to help provide perspective on what the future could hold under various scenarios. We found the following main quantitative patterns:

  • During epidemics such as the 1918 influenza or the 2003 SARS outbreaks, economic activity fell sharply during the epidemic (a 5-10% temporary hit to GDP or industrial production over the course of the epidemic) but snapped back quickly once the epidemic was over.
  • This pattern differs from a standard recession, which is a situation in which economic activity falls for 6-18 months and then recovers more slowly.
  • During SARS, Hong Kong house prices did not fall significantly, but transaction volumes fell by 33-72% as customers avoided human contact (“avoidance behavior” like avoiding travel, restaurants, and public gatherings). After the epidemic was over, transactions snapped back to normal volumes.
  • During the current episode in China, early news reports indicate that home prices have so far not fallen but transactions have nearly ceased.
  • During standard recessions, home prices and transaction volumes may fall but this is not always the case (e.g. the 2001 recession).
  • Before February 2020, leading economic indicators (job openings, the yield curve, interest rate spreads, and sentiment indicators) were giving mixed signals about the risk of a standard recession this year, with betting markets (PredictIt, 2020) giving probabilities ranging from 30% in December 2019 to 15% in January 2020, rising to 44% as of March 1. PredictIt defines a recession as at least two consecutive quarters of falling GDP.
  • It is difficult to precisely forecast the probability of an epidemic-related downturn and/or how such a downturn could provoke a standard recession because this depends on how COVID-19 progresses and how this progress interacts with preexisting recession risks and policy responses (ranging from doing nothing to shutting down entire cities for months at a time).

Digging Deeper – Insights From Historical Data and From the Literature

Empirical research into the SARS and 1918 influenza pandemics both indicate a significant loss in output during the time of the pandemic. Hong Kong lost 5.1% of monthly output during the 5 months of the SARS epidemic (or 1.75% of annualized GDP) and the US lost between 7% and 9.5% of monthly industrial production during the 1918 influenza epidemic, with an effect on annual GDP of 0.5%. The effects vary by sector–the epidemics led to people curtailing unnecessary social activities and curtailing human contact, which led to larger falls in services and (semi-)durable goods, while the effect on manufacturing is influenced by trade spillovers.

Since consumers wish to avoid nonessential human contact, the 2003 SARS pandemic led to a temporary fall in monthly real estate transactions from 33% to 72% vs. baseline for the duration of the epidemic, while real estate prices held steady.

Meanwhile, during the current episode in China, news reports and early data provided by Goldman Sachs (2020) indicate a near-shutdown in the volume of Chinese real estate transactions, although there is not yet a clear effect on real estate prices.

AUTHOR(S) SCENARIO DATA/MODEL MAIN FINDINGS
Zillow Economic Research (2020) Hong Kong, SARS, 2003 Aggregated macro data 1.75% loss in annualized GDP, or 5.1% monthly loss at peak. Quick recovery to trend after end of pandemic. 1.3% increase in unemployment; unemployment recovered within 3 quarters. Statistically insignificant 1.9% fall in home prices, count of transactions down by an average of 33% for duration of pandemic.
Lee and McKibbin (2012) Multiple countries, SARS, 2003 Theoretical model 2.63% loss in annualized GDP for Hong Kong, 1.05% loss for China. Size of loss depends on policy response.
Wong (2008) Hong Kong, SARS, 2003 Micro data on 44 housing estates 1.6% fall in home value, 2.8% in infected areas. 72% fall in transactions volume.
Siu and Wong (2004) Hong Kong, SARS, 2003 Disaggregated macro data Shift to at-home consumption, away from travel, restaurants, and entertainment. Trade was mainly unaffected.
James and Sargent (2006, 2006a) Canada and US mild flu pandemic Aggregated macro data Loss of Canadian industrial production of 1.2% at peak of epidemic (Oct 1957). 0.3% to 1.1% of annualized GDP. Coincided with a recession.
CBO (2006) US, mild flu pandemic Theoretical model 1% loss of annualized GDP.
Keogh-Brown et al. (2010) UK, mild flu pandemic Theoretical model 0.6%-2.5% loss of annualized GDP, depending on how customers shift their consumption behavior.
James and Sargent (2006) US, severe flu pandemic Aggregated macro data 1918 flu saw annual GDP impact of 0.5%, with loss of 7% of monthly industrial production at peak (Oct 1918). Coincided with drawdown surrounding end of World War I and a recession.
CBO (2006) US, severe flu pandemic Theoretical model 4.25% loss of annualized GDP.
McKibbin and Sidorenko (2006, 2006a) US, severe flu pandemic Theoretical model 5.5% loss of annualized GDP.
Cooper (2006) US, severe flu pandemic + trade disruption Theoretical model 6% loss of annualized GDP, of which 1.75% is due to trade disruption.
Zillow Economic Research (2020) US, severe flu pandemic, 1918 Aggregated macro data 9.5% loss in industrial production in October 1918 (peak of epidemic) vs. July 1918, but less reliable data on other sectors.
Kennedy, Thompson, and Vujanovic (2006) Australia, severe flu pandemic Theoretical model 6% loss of annualized GDP.
Douglas, Szeto, and Buckle (2006) New Zealand, severe flu pandemic Theoretical model 5-10% loss of annualized GDP.
Keogh-Brown et al. (2010) UK, severe flu pandemic Theoretical model 4.5%-6% loss of annualized GDP, depending on how customers shift their consumption behavior.

Case study: SARS in Hong Kong (2003)

The SARS epidemic began in the Guangdong province of China in November 2002. In February 2003, the first confirmed cases appeared in Hong Kong. The epidemic peaked in March and April 2003 and trailed off during May and June, until Hong Kong was removed from the WHO’s list of affected areas on June 23.

The chart below shows how real GDP and unemployment evolved before, during, and after the SARS epidemic. GDP data are shown as a percent relative to a Q4 2001 baseline. Both datasets are obtained from the Hong Kong Monthly Digest of Statistics, various issues.

Hong Kong GDP growth during the SARS outbreak

Until the onset of SARS in February, GDP was growing and unemployment was falling, consistent with an economic expansion. Then, GDP fell precipitously throughout the duration of the epidemic (by our estimation, 5-6% below trend in April and May), and unemployment rose from 7.4 percent to 8.7 percent, for a 1.3 percent increase. Once the epidemic subsided, GDP snapped back to its pre-epidemic trend, while unemployment took until the winter to recover. Altogether, the total gap between actual and trend GDP during this period is consistent with a loss of 1.75% of annual GDP as a result of SARS, which when spread over 4 months instead of 12, represents a fall in monthly GDP of 5.1%.

This loss is slightly smaller than (but of the same order of magnitude as) the model-based projections of Lee and McKibbin (2012), who predict a larger effect of the disruptions to economic activity caused by the epidemic. Lee and McKibbin simulate such an epidemic using a theoretical model (the “G-cubed” model), and they predict a loss of 2.63% of annual GDP for Hong Kong as a result of the SARS epidemic, versus a loss of 1.05% of annual GDP for China. Lee and McKibbin find that their larger loss prediction is driven by the behavior of macro policy in their model. If macro policy responds effectively to an epidemic, then the loss in output would be smaller than if it did not respond.

We also have data on the behavior of real residential real estate prices and the volume of secondary residential transactions. The chart below shows a real residential real estate price index compiled by the Bank for International Settlements (BIS) (2020), as a percent relative to a Q4 2001 or November 2001 baseline. It also shows raw transaction counts of secondary residential real estate transactions, not seasonally adjusted, from Midland Realty (2020).

Hong Kong real estate market during the SARS outbreak

By the time that SARS hit in February 2003, the Hong Kong real estate market had already experienced a downward trend in transactions and in a real residential price index. Between February and May 2003, transactions were 33% below their January 2003 value, before returning to normal by July. We note that this fall is difficult to distinguish from the preexisting downward trend. Meanwhile, real property prices fell to 1.9% below trend in May and then recovered, although this fall is difficult to distinguish from other real estate price swings that are unconnected with SARS.

Elsewhere in the literature, Wong (2008) comes to similar conclusions with respect to house prices. She finds, based on transactions data covering 44 housing estates, that the onset of SARS coincides with a 1.6% decrease in house prices versus a pre-SARS trend (which is comparable with our 1.9%). Importantly, she also finds that the onset of the SARS epidemic coincides with a 72% reduction in transaction volumes for these estates. She explains this pattern (small price reductions coincided with a large reduction in volume) as customers adopting a “wait and see” approach, whereby they avoid nonessential interactions with other people, instead waiting until the end of the epidemic to defer their transactions. This avoidance behavior is noted by Jonas (2013) as a major transmission mechanism from pandemics to economic risk.

Looking beyond real estate, Siu and Wong (2004) examine disaggregated macro data from the SARS episode, and they find that the travel, tourism, durable and semi-durable retail, and entertainment sectors were strongest hit, while production and exports were less affected. This pattern is also consistent with customers avoiding nonessential interactions, although the effect of the crisis on production and exports depends on the extent of the crisis in trading partners, and whether or not that crisis affects supply chains.

Theoretical and Empirical Evidence from the Influenza Literature

Beyond the SARS literature, there is an extensive literature on the past and likely effects of an influenza epidemic. The Congressional Budget Office (CBO) (2006) summarizes much of this literature, giving a predicted loss caused by a severe flu epidemic (similar to 1918) of about 4.25% of annual GDP and an estimated loss caused by a mild epidemic (similar to 1957 or 1968) of about 1%. In both cases, the CBO predicts that economic activity would snap back quickly after the epidemic ended, which is consistent with the data from the SARS epidemic in Hong Kong. However, since these theoretical models are mainly constructed using annual aggregates, the models do not make any specific predictions about monthly or quarterly aggregates.

Theoretical studies of influenza pandemics mostly land at losses in excess of 5% of annual GDP. For instance, a study by Kennedy, Thompson, and Vujanovic (2006) simulates a pandemic with ⅓ the mortality rate of the pandemic using a theoretical model. They find a reduction to Australian GDP of about 6%. Similarly, Douglas Szeto and Buckle (2006) predict that a severe pandemic would reduce New Zealand GDP by 5-10%. Meanwhile, McKibbon and Sidorenko (2006) predict that a severe pandemic would reduce US GDP by 5.5%, while Cooper (2006) simulates the CBO’s scenario but with disruptions to trade, and finds a 6% decline instead of a 4.25% decline in GDP. For the UK, Keogh-Brown et al. (2010) simulate mild and severe pandemics and find GDP losses of 0.6% to 2.5% for the mild scenario and 4.5% to 6% for the more severe scenario.

Contrasting with the theoretical studies, the empirical study of James and Sargent (2006) predicts that a severe flu pandemic would reduce Canadian GDP by 0.3 percent to 1.1 percent. James and Sargent base their estimates on macro data from US flu pandemics in 1918, 1957, and 1968. They find that the severe 1918 pandemic reduced annual GDP by 0.5% in 1918, with smaller effects from the other two mild pandemics. James and Sargent also cite data from the SARS outbreak, finding that while SARS severely affected tourism, travel, and services in the short run, it did not harm Hong Kong’s productive capacity in the medium run. In a similar vein, Garrett (2007) documents severe localized effects of the 1918 pandemic in places such as Little Rock, where merchants reported a 40-70% decrease in sales during the pandemic, and Memphis, where a pandemic-induced labor shortage disrupted operations. Altogether, these disruptions corresponded with a fall in a monthly industrial production index from 123.4 in July 1918 to 112.2 in October 1918 (-9.5%). The underlying data are reported by Persons (1931) and would correspond with a 2.4% fall in annual GDP for a three-month pandemic, given that industrial production is ordinarily more volatile than GDP. In addition, the Federal Reserve Bulletins from the time report significant disruptions to retail trade (up to one-third of the workforce out at any specific time) and especially to nonessential gatherings.

Altogether, the theoretical literature on influenza has given somewhat larger output losses than historical data, although the empirical literature and historical data indicate that output losses vary according to geography (harder-hit areas have higher output losses) and sector (nonessential services being hardest hit). Furthermore, trade disruptions can make the impact of the epidemic larger than it would otherwise have been.

Early Indications from the COVID-19 Outbreak in China

While official data are still not yet ready for January or February 2020, unofficial data reported by Brown (2020) at Marketwatch indicate that Chinese house prices remained stable from December to January (+0.27%) although the volume of transactions has fallen by 90 to 98% from normal. This episode illustrates a particularly strong “wait and see” pattern similar to what happened during the SARS outbreak–customers are not going to walk-throughs or closing on transactions in person. Data in upcoming weeks will tell us how long this outbreak lasts in China.

Additionally, a report by Hatzius et al. (2020) at Goldman Sachs shows detailed activity data from China during the current episode. The Hatzius report corroborates the Brown report–property transactions and transportation have nearly ceased due to avoidance behavior (some of it driven by a public policy response) while the consumption of coal fell by only 30% year over year, since people still need to heat their homes.

………………..

Appendix: Data Sources for Hong Kong Analysis

  • Monthly GDP: GDP is officially measured on a quarterly basis–we took seasonally adjusted growth rates from the Hong Kong Monthly Digest of Statistics, various issues. We first took logarithms and then interpolated it to a monthly basis using our own interpolation algorithm based on Fernandez (1981). We therefore urge caution in interpreting month-to-month movements.
  • Monthly unemployment: We took seasonally adjusted unemployment rates from the Hong Kong Monthly Digest of Statistics, various issues. The unemployment rate is presented in the Digest as a 3-month centered moving average.
  • Monthly real residential real estate prices: We took quarterly unadjusted real residential real estate prices from the St. Louis Fed’s FRED website. The original source of these data is the Bank of International Settlements (2020). We seasonally adjusted these data ourselves, took logarithms, and then interpolated it to a monthly basis using our own interpolation algorithm. We therefore urge caution in interpreting month-to-month movements.
  • Monthly real estate transactions: We took raw secondary transactions volumes directly from the online datasets published by Midland Realty (2020).

References

Bank for International Settlements (BIS), 2020, via FRED Database. “Selected residential property price series – data documentation”. Source: National sources, BIS residential property price database (http://www.bis.org/statistics/pp.htm). FRED URL: https://fred.stlouisfed.org/series/QHKR628BIS

Brown, Tanner, 2020. “Coronavirus slows China’s property market to a crawl — and even the most robust real-estate app is no match.” Marketwatch, Feb. 21, 2020, retrieved on Feb. 28, 2020. URL: https://www.marketwatch.com/story/coronavirus-slows-chinas-property-market-to-a-crawl-and-even-the-most-robust-real-estate-app-is-no-match-2020-02-18

Census and Statistics Department, Hong Kong Special Administrative Region, 2020. Hong Kong Monthly Digest of Statistics, various issues.

Congressional Budget Office (CBO), 2006. “A Potential Influenza Pandemic: An Update on Possible Macroeconomic Effects and Policy Issues.” Manuscript, Congressional Budget Office. URL: https://www.cbo.gov/publication/17785

Cooper, Sherry, 2006. “The Avian Flu Crisis: An Economic Update.” Manuscript, BMO Nesbitt-Burns.

Douglas, James, Kam Szeto, and Bob Buckle, 2006. “Impacts of a Potential Influenza Pandemic on New Zealand’s Macroeconomy.” Policy Perspective Paper 06/03, New Zealand Treasury. Retrieved February 28, 2020. URL:

https://treasury.govt.nz/publications/ppp/impacts-potential-influenza-pandemic-new-zealands-macroeconomy-pp-06-03-html

Federal Reserve Bulletin, various issues, via Thomson Reuters. “References to ‘influenza’ in the monthly Federal Reserve Bulletin during 1918 and 1919.” Retrieved on Feb. 28, 2020. URL: https://fingfx.thomsonreuters.com/gfx/ce/7/8626/8607/INFLUENZA%20REFERENCES%20IN%20THE%20FEDERAL%20RESERVE%20BULLETIN%201918-19.pdf

Fernández R.B. 1981. “A methodological note on the estimation of time series,” The Review of Economics and Statistics 63, pages 471-478. URL: https://www.jstor.org/stable/1924371?seq=1

Garrett, Thomas A., 2007. “Economic Effects of the 1918 Influenza Pandemic.” Manuscript, Federal Reserve Bank of St. Louis. Retrieved on Feb. 27, 2020. URL: https://www.stlouisfed.org/~/media/files/pdfs/community-development/research-reports/pandemic_flu_report.pdf

Goldman Sachs, 2020. “A Larger Virus Hit and Another Round of Rate Cuts.” US Economics Analyst, March 1, 2020. Retrieved March 2, 2020. URL:  https://research.gs.com/content/research/en/reports/2020/03/01/31bfffb7-f94a-4c0e-b0d6-49b1468aed2f.html

Hatzius, Jan, Daan Struyven, David Choi, and David Mericle, 2020. “A Viral Global Slowdown.” Global Economics Analyst, Goldman Sachs Economic Research. Retrieved on March 1, 2020. URL: https://research.gs.com/content/research/en/reports/2020/02/28/ae384520-6a4b-415d-a6e6-6fa28e8e25ee.html

Kennedy, Steven, Jim Thompson, and Petar Vujanovic. “A Primer on the Macroeconomic Effects of an Influenza Pandemic.” Working Paper 2006-11, Treasury of Australia. Retrieved on Feb. 27, 2020. URL: https://pdfs.semanticscholar.org/f605/da3a347548d5635e425a5531fdb64cd19c8d.pdf?_ga=2.70573072.1412815931.1583204112-1096427715.1583204112

James, Steven, and Timothy Sargent, 2006. “The Economic Impact of an Influenza Pandemic.” Mimeo, Economic Analysis and Forecasting Division, Department of Finance, Government of Canada. Retrieved on Feb. 27, 2020. URL: https://www.publicsafety.gc.ca/lbrr/archives/cn000034577651-eng.pdf

James, Steven, and Timothy Sargent, 2006a. “The Economic Impact of SARS and Pandemic Influenza.” In: SARS in Context: Memory, History, Policy, ed. Jacalyn Duffin and Arthur Sweetman. McGill-Queen’s Press. Retrieved on Mar. 1, 2020. URL: https://www.google.com/books/edition/SARS_in_Context/xAibijIszawC?hl=en&gbpv=1&printsec=frontcover

Jonas, Olga, 2013. “Pandemic Risk.” World Development Report Background Paper, the World Bank. Retrieved on Feb. 27, 2020. URL:  http://siteresources.worldbank.org/EXTNWDR2013/Resources/8258024-1352909193861/8936935-1356011448215/8986901-1380568255405/WDR14_bp_Pandemic_Risk_Jonas.pdf

Keogh-Brown, Marcus, Simon Wren-Lewis, W. John Edmunds, Philippe Beutels, and Richard D. Smith, 2010. “The Possible Macroeconomic Impact on the UK of an Influenza Epidemic.” Health Economics 19(11), pages 1345-1360. Retrieved on Feb. 28, 2020. Working paper version URL: https://www.gtap.agecon.purdue.edu/resources/download/3828.pdf

Lee, Jong-Wha, and Warwick J. McKibbin, 2012. “The Impact of SARS,” in China: New Engine of World Growth, Garnaut, Ross, and Ligang Song, eds. ANU Press. Retrieved on Feb. 28, 2020. URL: https://www.jstor.org/stable/j.ctt24h9qh.10?seq=1#metadata_info_tab_contents

McKibbin, Warwick J., and Alexandra Sidorenko, 2006. “Global Consequences of Pandemic Influenza.” Manuscript, Brookings Institution, Lowy Institute for International Policy. Retrieved on Feb. 27, 2020. URL: https://www.brookings.edu/research/global-macroeconomic-consequences-of-pandemic-influenza/

Midland Realty, 2020. “Statistics of Properties Transactions in Land Registry – Last 12 Months.” Retrieved on February 28, 2020. URL: https://en.midland.com.hk/land-registry-record/12months.html

Persons, W.M., 1931. Forecasting Business Cycles. John Wiley, New York, pages 93-143. Data available in the NBER Macrohistory database, via the St. Louis Fed FRED database. Retrieved on Feb. 28, 2020. URL: https://fred.stlouisfed.org/series/M1204BUSM363SNBR

PredictIt, 2020. “Will there be a recession in Trump’s 1st term?” Retrieved March 2, 2020. URL: https://www.predictit.org/markets/detail/4292/Will-there-be-a-recession-in-Trump’s-1st-term

Siu, Alan, and Y.C. Richard Wong, 2004. “The Economic Impact of SARS: The Case of Hong Kong.” Asian Economic Papers 3:1, pages 62-83. Retrieved on Feb. 27, 2020. URL: https://hub.hku.hk/bitstream/10722/88855/1/content.pdf

Wong, Grace, 2008. “Has SARS Infected the Property Market? Evidence from Hong Kong.” Journal of Urban Economics 63(1), pages 74-05. Retrieved on Feb. 27, 2020. URL: https://www.sciencedirect.com/science/article/pii/S0094119007000095

Home Value Growth Expected to Re-Accelerate Just in Time For Home Shopping Season (January 2020 Market Report)

Home Value Growth Expected to Re-Accelerate Just in Time For Home Shopping Season (January 2020 Market Report)

  • The typical home in the U.S. is worth $245,193, up 3.8% from a year ago.
  • There were 1,500,262 homes listed for sale in January, down 8% from a year ago.
  • Typical U.S. rent grew 2.3% year-over-year, to $1,602.

Annual U.S. home value growth slowed for the 21st consecutive month in January, but you have to squint to spot the difference. Paired with inventory that is hovering near record lows, the nearly two-year slowdown in the housing market may come to an end right as home shopping season kicks off.

U.S. home values grew 3.8% year-over-year to a Zillow Home Value Index of $245,193, less than one-hundredth of a percentage point slower than the previous month (before rounding: 3.77% year-over-year growth in December, 3.76% in January), according to the January Zillow® Real Estate Market Report. Annual home value appreciation has slowed in each month since April 2018, but this is the smallest month-over-month drop during that period.

Among the nation’s 50 largest markets, annual home value growth in January was fastest in Memphis (6.9%), Phoenix (6.7%) and Birmingham (6.3%). Growth was slowest in San Jose (-2.9%), New York (+.8%) and San Francisco (1%). Annual growth in San Francisco, while very modest, broke a streak of annual declines that dated to May 2019.

Annual home value growth in 27 of the 50 largest U.S. metro markets was faster in January compared to December, and was flat in one additional market. In other words, the slowdown has already reversed itself in many places, and it may be only a matter of time before that reversal will begin showing up in faster national appreciation numbers.

At the same time as the ongoing slowdown in home value appreciation has largely bottomed out, the number of homes listed for sale remains incredibly low. Inventory increased from record lows a month earlier, but was down 8% year-over-year in January — the biggest annual drop since March 2018. There were 1,500,262 homes on the market last month, up 4,295 from the previous month but down 130,310 year-over-year.

Inventory was down year-over-year in 47 of the nation’s 50 largest metro markets. For-sale inventory fell the most from a year ago in Seattle (-27.6% year-over-year),  Phoenix (-24.5%) and San Diego (-23.1%). The three large markets in which inventory rose year-over-year in January were: San Antonio (+7.7%), Detroit (+6.4%), and Chicago (+0.3%).

This persistently low inventory is a key reason why home value growth is expected to speed up once again. The economy has remained strong, mortgage rates are low and buyers will be competing for a limited number of homes this home shopping season. Inventory appeared to have hit bottom and was on an upswing a year ago, rising year-over-year in every month between September 2018 and April 2019. But in hindsight, that “growth” was illusory, largely a result of a temporary stock market dip, prolonged government shutdown and a surge in mortgage interest rates that spooked buyers and/or prodded on-the-fence sellers to list for fear of losing out if a prolonged slump developed, pushing inventory up.

But as the economic storm clouds on the horizon in early 2019 cleared up, we saw buyers return in droves, taking advantage of ultra-low mortgage rates. This first look at 2020 data suggests we could see the most competitive home shopping season in years, as buyers are already competing over near-record-low numbers of homes for sale. That is likely to mean more multiple-offer situations, and that buyers will have a harder time finding the perfect fit for their families. The good news for buyers is that low mortgage rates are helping to make homeownership more affordable, and home builders are responding to the hot housing market by starting construction on more homes than at any time since 2007.

Rent growth remained stable, with the typical U.S. rent now $1,602/month, up 2.3% year over year and just $1 more than last month. Annual rent growth has hovered between 1.7% and 2.4% in every month over the past year. Rent was up year-over-year in 47 of the nation’s top 50 markets. Annual rent growth was fastest in October in Phoenix (up 7.9% YoY), Pittsburgh (+7%) and Cincinnati (+5.7%).

Looking for the Housing Markets Most Likely to Outperform in 2020? Look South

Looking for the Housing Markets Most Likely to Outperform in 2020? Look South

A collection of relatively affordable, sun-belt markets are among those in which home value growth in 2020 is most expected to outperform the national average, according to a panel of experts recently surveyed by Zillow.

As part of the Q4 2019 Zillow Home Price Expectations Survey, sponsored by Zillow and administered by Pulsenomics, a panel of more than 100 U.S. economists and real estate experts was asked to rate their 2020 expectations for home value growth compared to the nation in 25 large markets nationwide. On average, panelists said they expected U.S. home values to grow by 2.8% in 2020. In order to create a score for each of the 25 markets analyzed, the share of panelists saying they expected a market to outperform that average was weighed against the share saying they expected it to underperform.

Austin, Atlanta and Charlotte, scored highest among the panelists, with scores of 76, 59 and 51, respectively. A whopping 83% of respondents said they expected Austin to outperform, undercut slightly by the 7% that said they expected the Texas state capital to underperform. And even though Charlotte received a lower overall score than Austin, it was the only market among the 25 analyzed in which none of the panelists said they expected it to underperform.

Of the 14 markets that received a positive score (a higher share of panelists said they expected the market to outperform than underperform), 11 were in Texas or elsewhere in the Southwest or Southeast. Portland, Minneapolis and Denver were the only non-Southern markets to make the list of those expected to outperform.

Seattle was the only market to receive a neutral score, with an even 40% of panelists each saying they expected it to underperform and overperform, and the balance saying they expected Seattle’s housing market in 2020 to perform about on par with the national average.

A group of pricey California markets topped the list of those most expected to underperform, with the worst scores tallied in San Francisco (-40), San Jose (-38) and Los Angeles (-35). Of the 10 markets that received negative scores, six were in California (the three mentioned above, plus Riverside, Sacramento and San Diego). Cincinnati, Columbus, Miami and Oklahoma City rounded out the list of 10 markets most expected to underperform.

And panelists didn’t just expect those large California markets to underperform, but maybe still grow slightly – in many cases, they said they expected the typical home values in those places to outright fall, ending 2020 lower than where they began the year. Panelists, 42 total, who thought at least one major metro would see falling home values in 2020 generally agreed that California markets would make that list. A majority (57%) of these panelists expect home values to fall in San Francisco, and half said they expected the same in San Jose. More than a third (38%) said they expected home values in L.A. to fall in 2020, followed by 29% in both San Diego and Riverside, and 24% in Sacramento.

Nashville was the only market analyzed in which no panelists said they expected home values to fall in 2020.

markets expected to fall