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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.

 

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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

Timing and Price Uncertainty in Home Selling

Timing and Price Uncertainty in Home Selling

Timing and price uncertainty are among the most stressful aspects of the home selling process.

There’s no getting around it — selling a home is stressful.

Experiencing stress is a reality for 95% of sellers in the U.S., according to Zillow’s 2019 Consumer Housing Trends report.

Timing and price top the list of seller stresses

Breaking down the selling process into key sections, there are clearly more stressful aspects than others. Not surprisingly, the uncertainty around pricing and timing of a home sale are the most anxiety-inducing parts of the selling process.

At the top of that list is sellers not knowing whether their home will sell when they want it to – 56% of sellers find that uncertainty to be stressful. Doubts over whether a home will sell for the desired price is cited by 53% of sellers as stressful.

Next comes the double-whammy of worrying that an offer will fall through and the stress of making improvements and preparing a home for sale. And 51% of sellers say it’s stressful to time the sale of their old home with the purchase of a new one.

The younger the seller, the more stressful the process

Millennials and Gen Z sellers are more likely to say that any given aspect of selling is stressful, while stress seems to wane with more experienced sellers. For example, 55% of Gen Z sellers find it stressful to have to leave their home for tours and open houses, compared to just 21% of Silent Generation sellers.

That decrease in stress with older generations could be experience: simply having gone through the process before, and knowing what to expect. In fact, first-time sellers are more likely to say that most aspects of selling were stressful to them or their families.

The bottom line

Virtually no home seller is immune to stress in today’s market. While most stress surrounds the uncertainty of selling price and timing, there are many more aspects of the selling process that can make people uncomfortable, from making home improvements to judging the interest levels in potential buyers.

To read the full report click here, and for more helpful tips from our partners at Zillow, check out their blog!

The Era of the ‘DSIY’ Seller

The Era of the ‘DSIY’ Seller

There’s a good chance that when a seller contacts an agent today, they’ve already jump-started the selling process — painting rooms, sprucing up the garden, remodeling a bathroom or researching what similar homes have sold for.

And while sellers haven’t gone full-on do-it-yourself, they are decidedly DSIY — “do some of it yourself” — when it comes to presale home improvements, staging and even finding buyers via social media.

Despite their can-do spirit, though, most DSIY sellers (85%) still enlist an agent. They want a strategic partner who has the market knowledge, marketing skills and legal know-how to do the heavy lifting that will get the sale to closing, according to the Zillow Group Consumer Housing Trends Report 2018.*

What they do and don’t do

Half of DSIY sellers (50%) who eventually work with an agent tackle home improvements, 39% determine a listing price for their home and 25% have a home inspection done.

But there are some areas they shy away from before reaching out for help, including conducting buyer tours (only 17% do), promoting their home on social media (11%) and receiving offers (8%).

The agent as strategic partner

Like all good partnerships, once the seller reaches out to an agent, their relationship involves a division of labor. Some of the activities sellers participate in with and without their agent’s help:

  • 43% participate in deciding when to put their home up for sale.
  • 37% engage in some aspect of staging their home.
  • 35% make home improvement recommendations for selling.
  • 23% come up with the listing price on their own.
  • 36% work with their agent to determine the listing price.

But half of sellers (50%) say agents alone still do the heavy lifting that requires expertise:

  • 81% say their agent guides them through the sales process.
  • 78% say their agent organizes and submits paperwork associated with the sale.
  • 75% rely on their agent to lead contract negotiations.
  • 73% say their agent finds interested buyers.
  • 55% say their agent determines the listing price of their home.

Who are the DSIY sellers?

Generally, the younger the seller, the more involved they get in selling activities.

Millennials are so confident in their ability to get the ball rolling that 58% say they like to take the lead, compared with 40% of Gen Xers, 29% of baby boomers and 24% of silent gen sellers.

Millennials also are more likely to have photographs taken of their home (32%), help find interested buyers (22%), promote their home on social networks (38%) and have video or other media taken of their home (19%).

Seller involvement helps homes sell faster

The upshot for the DSIY seller? Sellers who work with an agent and participate in five or more selling activities close the sale almost two months sooner than sellers who do fewer than five activities. That’s 5.5 months on average for more active sellers versus 7.4 months on average for less active sellers.

How important is speed to sellers? A third (33%) of active DSIY sellers say they wish they had started even sooner.

The takeaway

  • Sellers understand that there’s work involved in selling, and many get things started themselves. Ask sellers lots of questions about their home-selling journey to date. This will build rapport and help you determine what you need to do for a client going forward.
  • Show your deep knowledge of the selling process by strategizing around a listing price — even if the seller has already decided on one. Explain how you can help get them to that number, or why the number is out of whack with the market.
  • Find the right balance between guiding and collaborating. A great agent is an educator and a facilitator, so make sure you continue to show your knowledge and educate the seller throughout the transaction.

For more helpful tips from our partners at Zillow Premier Agent, visit their blog!

*In 2018, millennials were ages 24-38, Gen Xers were 39-53, boomers were 54-73 and the silent generation was 74 and older.

The Needs and Wants of Parents Buying Homes

The Needs and Wants of Parents Buying Homes

Having children changes people, and for home buyers, that translates into vastly different wish lists and budgetary constraints. Once fans of happy hour and eating out, new parents find they’re lucky to make it to the grocery store and back. The desire to be trendy or able to walk to favorite shops gives way to the need to provide their child with safety, enrichment, and healthy habits. Ideas about commutes change as well. While a long commute can be a trade-off for a bigger or more affordable home, that time on the road for a parent with small children can mean a post-work crunch: Picking a child up from daycare, making and eating dinner and getting the little one to bed at a decent hour. Add to that the fact that peak child-raising years often coincide with peak career demands, and location becomes doubly important – and doubly tricky. As a result, the home-shopping process for buyers with children who are under 18 and living at home is more arduous: They are more likely to see an offer or mortgage financing fall through, and they’re more likely to go over budget. That’s despite attending more open houses and making more compromises to stick to their budgets. After they’ve hurdled the challenges, buyers with kids at home overwhelmingly love the homes they purchased.

Reconciling budgets, mortgages, and needs

Almost half of buyer households (45.9%) and a third of renters (33.1%) who moved in the past year have children who are under 18 and live at home. And 56.6% of buyers who had or adopted a child in the past year said it influenced their move, according to the Zillow Group 2018 Consumer Housing Trends Report. Buyers with children at home are more likely to go over budget (25.7%) than those without (21.2%), perhaps to attain the location and amenities on their long wish lists. Still, the majority of buyers with kids at home stay within their budgets (74.3%), often because they make compromises. Two-thirds (66.5%) made some type of compromise to stick with their budgets, considerably more than the 51.6% of buyers without kids. Among parents who made such a compromise, the top ones were increasing their commute (34.1%), purchasing a home without their desired finishes (32.7%) and purchasing a smaller home than planned (31.2%). The financial stretch for parents is visible in their down payments. In order to afford their homes, 54.7% of buyers with children at home had down payments of less than 20%, compared with 49.2% of buyers without kids at home. Parents also are more likely to worry about qualifying for a mortgage (64%) compared with non-parents (44%), and it turns out their concerns are warranted. Almost a third (31.5%) of buyers with children who eventually obtained a mortgage experienced a denial versus only 11.5% of buyers without kids at home.

All that, plus the kitchen sink

Most buyers with children under 18 hoped to buy single-family detached houses (83.7%), which tend to be more expensive than townhomes and condos. They also had long home and neighborhood wish lists. Parents place greater importance on nearly all home characteristics than buyers without kids living under their roofs, being more attached to everything from storage space to a particular number of bedrooms. Having a child’s needs in mind can add urgency – and parents of young children might be anticipating future needs without being sure which features will be most important as their children grow. Buyers with children are more likely to rate a private outdoor space as very or extremely important (75.3%) compared to buyers without children at home (65.1%). Similarly, a home’s potential to increase in value was highly important to 73% of parents compared to 61.5% of non-parents. To attain their long wish lists, buyers with kids are willing to expand their searches to include foreclosed homes, short-sales and homes for sale by owner.

Location, location….

Parents are less likely to give up on location. Every single neighborhood characteristic included in the study was more important to buyers with kids at home compared to those without. They’re often balancing the desire for a decent commute with wanting a particular school district and other child-focused needs. So, it makes sense that they are more likely to end up purchasing in the area they initially considered (71.8%) than buyers without kids at home (66.2%). Not surprisingly, purchasing in the preferred school district was very or extremely important to 66% of buyers with kids at home, compared to only 22.9% without. The commute was also highly important to parents (66.4%) compared to non-parents (43.3%).

For more helpful tips, visit the Zillow blog!

Home Values Dip Month Over Month for First Time in 7 Years

Home Values Dip Month Over Month for First Time in 7 Years

April Market Report

  • In April, the median home value fell 0.1% from March, the first time the market has posted a monthly decline in seven years.
  • A more stable metric — year-over-year appreciation — shows U.S. home values up just 6.1% from last April. That’s below annual growth of 7.5% in April 2018.
  • 16 of the largest 50 metros posted home value declines in April and have had flat or falling home values since January, raising our confidence that local home values there may have reached a peak.

The median U.S. home value fell 0.1% in April from March, the first monthly decline in seven years and another signal that the housing market continues to pump the brakes after several years of torrid growth.

The national housing market has been cooling for months, with annual gains slowing to 6.1% in April, down from April 2018 annual growth of 7.5%. The median home value now stands at $226,800, still above last April’s $213,700.

Home values in all but four of the country’s largest metro areas were flat or down from March to April. San Jose, Calif., posted the largest monthly decline, down 1.4%, the area’s sixth month-over-month decline in a row. April also was the second month in which San Jose’s median home value fell on an annual basis, down 2.7% to $1.19 million—still the priciest large market in the nation.

It’s important not to exaggerate the significance of month-over-month changes, which are always more volatile, following the adage that one point does not make a trend. A small percentage change in one month easily could rebound the following month, something that happens with housing data on a regular basis. That’s part of the reason market watchers prefer less-volatile quarter-over-quarter and year-over-year measures, which capture longer running trends.

Still, the data show it’s likely that home values in 16 of the largest 50 metros truly have peaked: Their home values are down this month and have been flat or falling for the quarter. They are San Jose, San Francisco, Pittsburgh, Los Angeles, Seattle, San Diego, New Orleans, Boston, Miami, St. Louis, Portland, Ore., Tampa, Virginia Beach, Baltimore, Philadelphia and Houston.

Home values in five of those markets—Philadelphia, Miami, Tampa, Virginia Beach and Baltimore—never returned to heights reached prior to the Great Recession more than a decade ago.

The number of U.S. homes for sale fell 1.7% year-over-year. Ten of the largest 50 metros posted double-digit inventory declines, led by Washington, D.C., (down 31.8%), Kansas City (down 24.1%), Oklahoma City (down 17.8%) and Baltimore (down 17.3%).

In a handful of the previously mentioned metros where home values appear to have hit a recent peak and are declining, inventory is also down in the double digits: Pittsburgh, where inventory dropped 12.2% year-over-year; New Orleans, down 13.1%; St. Louis, down 10.8%; Virginia Beach 13.8%; Baltimore 17.3%; Philadelphia 11.1%. That’s counterintuitive, and only time will tell what is happening in these markets.

Nationwide median rent continued to grow in April for the sixth consecutive month. The median U.S. rent rose 2.6% on an annual basis, to $1,477. Rents grew the fastest in Las Vegas (up 7.8%), Phoenix (up 6.7%) and Orlando (up 6.4%).

For more helpful information, check out the Zillow Blog!

Home Value Cooling Is More About Changes in Demand Than Supply (March 2019 Market Report)

Home Value Cooling Is More About Changes in Demand Than Supply (March 2019 Market Report)

The market has begun to cool, with the median home value nationally climbing in March by less than 7% annually (to $226,700) for the first time in more than two years.

Home values are growing especially slowly in some markets that until recently were among the country’s hottest. While values in most major metros continue to climb, but slowly, an exception is pricey San Jose, Calif.: Its home values fell year-over-year in March for the first time in seven years, falling 0.2% from March 2018 (the median home in the San Jose area was worth $1,209,700 in March, down from $1,212,100 a year ago).

While long-term housing demand continues to look strong, we’re seeing our first set of significant local housing slowdowns since the nationwide downturn in 2007. Other formerly white-hot markets that are jamming on the brakes include: San Diego, where home values grew just 1.3% annually in March (down from 8.6% year-over-year growth in march 2018); San Francisco and Los Angeles, both growing at 2% year-over-year, down from 9.5% and 7.6% last year; and Seattle, growing at a 2.6% annual pace, from 11.8% a year ago. Judging by their trajectory, it is possible home value appreciation will continue to soften and go temporarily negative in these markets as well.

It’s not surprising that the total inventory of homes available for sale during the month is simultaneously climbing. March was the seventh consecutive month of year-over-year inventory gains—the first time that’s happened since 2014.

However, these sister trends—home values falling and overall inventory climbing—are not a result of builders or homeowners putting more houses on the market, although it may feel that way to new buyers checking on available listings and seeing more from which to choose.

In fact, for the past four months, new for-sale inventory – the number of homes listed in a given month that were not on the market during the previous month – has fallen on an annual basis. In March, there were 6.1% fewer new listings than in March 2018; in February, 7.7% fewer; in January, 3.6% fewer; and in December, 2.8% fewer.

Although there have been months with annual increases—notably October, when new listings rose 13.4% from a year earlier—the trend is downward, a sign that even sellers are retreating from the housing market.

Without an influx of new listings, the boost in total inventory has been largely a result of changing demand:

  • Buyers are more willing to wait: February was the first month in four years (since February 2015) that the average number of days listings are on the market has risen. In February, listings nationally were on the market for an average of 96 days, up from 92 days a year earlier.
  • They’re also less willing to pay sellers’ asking prices: In March, 14.6% of listings had a price cut, up from 12.7% in March 2018.

This kind of slowdown story is typified by markets like Sacramento, Chicago, Riverside, Calif., Tampa, Fla., Dallas-Fort Worth, and Los Angeles—major metro areas where home value appreciation is significantly slower this year than last and overall inventory is up despite a drop in the number of new listings this March versus last March.

Slowdowns driven by an influx of new supply can be welcome news for certain types of housing markets, like rain after a long drought. And there are metros having that kind of breather–San Jose and Seattle, the two metro areas with the most abrupt slowdown in home value appreciation, are experiencing swells in overall inventory bolstered at least in part by increases in new listings. Denver, Boston, Detroit, and San Antonio have similar profiles, but with more gradual softening in price growth.

The Atlanta metro is an odd exception. Like the above, overall inventory and new listings are significantly higher, up 13.8% and 14.8% respectively, yet home value appreciation remains on par with last year, growing in the double digits.

On the other end of the spectrum, Indianapolis, Virginia Beach, and Austin continue to experience both increasing appreciation and declining inventories—a good reminder that the national trend no longer typifies all markets’ experience.

Non-coastal metros take off

While expensive coastal markets experience a home-value slowdown, values in inland markets including Indianapolis, Atlanta and Las Vegas are posting double-digit growth. Annual growth in Indianapolis has been climbing in the double digits for eight months, and rose 12.8% in March to $167,000, the fastest of any major market. Atlanta’s median home value grew 10.7% in March to $220,000, which puts it just shy of the $226,700 national median. And Las Vegas posted 10% median home value growth in March to reach $280,600. While most major metros have by now reclaimed their housing bubble peaks, it will still be a while before Las Vegas reclaims its June 2006 peak of $316,800.

Rents keep climbing

The median rent nationwide rose 2.5% ($36) in March to $1,474 a month. Among major metros, rents climbed fastest year-over-year in Las Vegas, where they rose 7.6% ($98) to $1,396; Phoenix, where they were up 6.7% ($91) to $1,446; and Orlando, Fla., where they grew 6.5% ($93) to $1,531.

Rents rose 2% or less in nine of the 35 largest metro areas. Baltimore grew the slowest, climbing 1% from March 2018 ($18) to $1,753. It was followed by San Jose, Calif., and the New York metro area, where rents rose just 1.5 percent from a year ago (to $3,553 in San Jose and $2,419 in New York).

‘Lazy’ Millennials Do More Work When Buying, Selling Homes

‘Lazy’ Millennials Do More Work When Buying, Selling Homes

An overhyped stereotype about millennials is that they’re entitled narcissists who can’t be bothered to do homework, legwork or even stash a few dollars in the bank (see avocado toast). That caricature can be taken apart in many ways – including by research from the Zillow Group Consumer Housing Trends Report that shows millennial home buyers and sellers are extremely motivated: They go on more tours, give more open houses, do more research on real estate professionals, and fix up their homes at higher rates than older generations.

Tours, tours, tours!

When buying a home, millennials go on more tours than their older counterparts. The average millennial goes on 4.4 tours — slightly more than Gen X and baby boomers — and outdoes the average of 2.7 for the silent generation.[1] They also attend more open houses: 42.7 percent of millennials go to at least two – a higher share of buyers than Gen X (30.4 percent), boomers (24.9 percent), and the silent generation (16.3 percent).

When millennials use an agent, they still do more themselves. Among millennials that use an agent, 20.2 percent go on tours themselves, higher than the 12.2 percent of Gen X, 10.4 percent of boomers, and 3 percent of the silent generation who do the same. Millennials selling their homes also give more tours on their own before getting their agents involved: 30.3 percent of them give tours to potential buyers before engaging an agent, compared to 18.1 percent of Gen X, 8.5 percent of boomers and 10.1 percent of the silent generation.

Millennials do their homework

Millennial buyers also do more research throughout the process. Among those who enlist the help of an agent at some point in their search, 37 percent of them preview or screen homes themselves, compared to 28.3 percent of Gen X, 29.6 percent of boomers and 14.5 percent of silent generation buyers. More millennial buyers also identify the homes they consider: 42.6 percent, compared to 32.7 percent of Gen X, 29.5 percent of boomers, and 10.3 percent of the silent generation.

When hiring the many professionals that play a part in the buying and selling processes, millennials are more likely to research and evaluate agents, contractors, inspectors, and other professionals. When looking for an agent, the average Millennial seller contacts 2.5 agents before settling on one – more than the 1.7 agents that Gen X and baby boomer sellers contact and more than the 1.4 that silent generation sellers reach out to.

When searching for an agent to help them buy a home, 81.2 percent of Millennials do at least one of the following to evaluate them:

  • Read online reviews
  • Visit their brokerage website
  • Look up their past sales history
  • Ask a friend or family member about their experience with the agent or broker
  • Figure out their market knowledge / how well they know the area
  • Interview agent(s) or broker(s)

Older generations also research their agents, but at lower rates than millennials: 75.9 percent of Gen X buyers do at least one of the above, as do 68.7 percent of Boomers and 71.1 percent of the Silent Generation.

Among buyers who use an agent, millennials are more likely to find their own inspector than older generations: 22.5 percent of millennials find their own, compared to 17.2 percent of Gen X, 11.7 percent of Boomers and 11.6 percent of the Silent Generation.

The average millennial buyer also outdoes other generations when it comes to contacting lenders: Millennials contact an average of 2.8 lenders before choosing one, more than the 1.7 lenders contacted by Gen X, 1.8 by Boomers and 1.3 by the Silent Generation.

Younger sellers are more likely to fix up before selling

Millennials are more likely than all older generations to fix up their homes for sale. They outdo baby boomers and the silent generation when it comes to painting, redecorating, landscaping, replacing or buying new furniture, and kitchen and bathroom improvements. Ninety percent of millennials do some sort of improvement, compared to 84.6 percent of Gen X, 69.1 percent of boomers and 58.8 percent of the silent generation.

You might think millennials are doing more work because of the kinds of houses they own: If the houses are older, for example, they might need more repairs. But the data show that millennials sold homes that are on average about eight years newer than homes sold by older generations.

The DIY Generation flexes tech skills

Millennial sellers that use an agent are also more likely than older generations to do a lot of the work that agents often handle. For example, millennials are more likely to have photographs taken of their home: 31.7 percent do, compared to 18 percent of Gen X sellers, 11.4 percent of boomer sellers and 4.3 percent of silent generation sellers. In addition to photos, millennials also make print ads and have video or other media taken of their homes at higher rates. Given how tech savvy they are, it’s no surprise that they’re big on promoting their homes on real estate sites (22.7 percent) at nearly triple the rate of older generations (8.5 percent) and on social media (38.0 percent compared to 15.5 percent for older sellers).

Younger sellers learn as they go

Seventy-eight percent of millennial sellers are doing so for the first time; this is their first rodeo. The fact that they’re overwhelmingly learning the ropes for the first time may partially explain why they are doing more work: 58.1 percent of them have at least one offer fall through, compared to 37.9 percent of Gen X sellers, 30 percent of boomer sellers and 22 percent of silent generation sellers. Because a large proportion are first timers, they also are less likely to have an established network of professionals to rely on, which means they have to do more research to find a team.

Even so, they are more eager than older generations to do work themselves. When asked whether they prefer to take the lead themselves or rely on guidance from professionals, 57.5 percent of millennial sellers say they are more inclined to take the lead themselves – a higher percentage than older generations. Among Gen X sellers, 40 percent report taking the lead, compared to 29.5 percent for boomers and 24 for the silent generation. This preference may explain why millennials often outdo older generations when it comes to the homework, fixing up and other jobs associated with the home selling process.

[1] Millennials refers to people between the ages of 24 and 38. Gen X is 39 to 53. Baby boomers are 54-73. Silent generation refers to people age 74 and up.

For more helpful tips, visit the Zillow blog!