One Will be Wrong; The Other Will be Less Wrong There is less than a week to go before election day, and the most unifying trait shared between the two parties is a mutual desire for outcome certainty. The 2016 race proved that there are no sure things in politics. Low turnout, low Democratic enthusiasm, and a proportionally high share of undecided voters tipped an apparent Clinton lead into a narrow Trump victory. Now, fast forward to the eve of the final ballot getting cast in 2020, and seemingly the only thing higher than Joe Biden's poll position is the share of the electorate that believes that pre-election day polls are meaningless. According to the FiveThirtyEight election model forecast, Joe Biden currently has an 89% chance of winning the electoral college vs. President Trump's 11%. The advent of political betting markets, such as PredictIt, allows researchers to quantify just how much skepticism surrounds polling data. Implied betting odds instead suggest that Biden has roughly a 62% chance of victory compared to Trump's 38%— a net difference of 27% between the two models. Using history and theory as a guide, we try to make sense of these disparities and offer a sober take on which method better represents reality. Across the Fifty U.S. States, the District of Columbia, two congressional districts in Maine, and three congressional districts in Nebraska, a total of 56 simultaneous races combine to award electors and select the Presidential winner. Assigning equal weighting to all 56 races, the FiveThirtyEight model, on average, gives Joe Biden an additional win probability of 5.6% per contest. However, differences are more appreciable in key battleground states. Topping the list of probability discrepancies is Pennsylvania, where FiveThirtyEight estimates that President Trump has just over a 14% chance of re-election, 25% lower than the implied PredictIt betting odds of 39%. As of this writing, eleven states have estimated probability differences over 10%, all of which have higher Trump re-election odds on PredictIt than FiveThirtyEight. These include Pennsylvania, Michigan, Wisconsin, Florida, Minnesota, Arizona, North Carolina, Nevada, Georgia, New Hampshire, Ohio. Unsurprisingly, the above list reads a lot like a final-week campaign itinerary for both candidates. There are three conceivable explanations for why the probability differences might vary as much as they do in battleground states: Sampling / Polling Error FiveThirtyEight Modelling Error PredictIt Pricing Inefficiencies / Investor Herding While we won’t know which is the best explanation until after November 3rd (or possibly later), peering back to see how these platforms predicted the 2016 race might give clues about their predictive accuracy in the week ahead. 2016 In Review In the final FiveThirtyEight 2016 election forecast, the model estimated that then-candidate Trump had a little less than a 29% chance of winning the electoral college. For the national popular vote forecast, the FiveThirtyEight forecast had Clinton capturing 48.5% of the voting public vs. 44.9% for Trump. Despite perceptions of inaccuracy, these models came very close to the final tally (well within a single standard deviation). In total, Hillary Clinton won 48.0%, and Donald Trump won 45.9% of all votes cast— both landed within one percentage point of the FiveThirtyEight model. PredictIt, on the other hand, had final implied electoral winning probabilities for Clinton and Trump at just above 79% and slightly below 21%, respectively. While both platforms had Trump as the clear underdog, FiveThirtyEight's model suggested the likelihood of an Election Day upset was higher than the betting odds would reflect. FiveThirtyEight's model tended to better account for state-level surprises as well. In Pennsylvania and Wisconsin, FiveThirtyEight gave candidate Trump an additional 2.3% and 3.0% in winning probability, respectively. However, PredictIt's implied betting odds did give Trump a slightly higher chance in Michigan, measuring 2.7% higher win probability than FiveThirtyEight's model. The state with the starkest difference was Florida. Betting odds had the possibility of a Trump win in the Sunshine State at just below 33%. FiveThirtyEight's model instead had Florida in the tossup category, giving candidate Trump about a 45% chance of winning. Analysis Though neither platform ever reflected a 2016 Trump victory as a probabilistically likely event, at both the state-level and national races, the FiveThirtyEight model routinely did a better job of identifying competitive races that pundits (and campaigns for that matter) had otherwise taken for granted. Returning to the three possible explanations of why there are wide forecast margins in battleground states, the relative outperformance of the FiveThirtyEight prediction model makes it a less likely causal candidate. Polling data, by most accounts, missed the mark in 2016. It wasn't just that the polls were off; it's that they tended to be wrong in similar ways to one another. After four years of conducting statistical autopsies and recalibrating methodologies, pollsters claim to have made significant adjustments in 2020. One change is the widescale adoption of education-level weighted sampling, among others. Whether these succeed in reducing sampling correlations and, by extension, making pre-election polls more accurate remains to be seen. Market herding, or the tendency for many investors to think in similar ways and distort prices, is likely a partial cause for the battleground forecast margins. Four years ago, PredictIt markets overweighted the likelihood of a Clinton victory by significant margins in Florida and at the national level. Further, market functionality relies on depth. A wide range of investors with varying opinions of fair value is a necessary pre-condition to efficient risk pricing. The relative size of PredictIt's state-level election markets, which all tend to be less than $2.5 million in total market cap, makes it more vulnerable to a material mispricing. Come election night, and it will be clear whether statistical- or market-based prediction models had a better pulse on the realities of the 2020 electorate. We here at Chandan will follow up with an addendum to this piece in the days to follow, analyzing where forecast models were most and least accurate. Until then, we leave you with the wisdom of George Box: "all models are wrong, but some are useful."

The U.S. economy remains 10.7 million jobs below its pre-pandemic level. The CARES Act, a stimulus measure passed by Congress that has aided the recovery, has seen most of its funds run it out. Washington remains deadlocked in approving a new package, leaving swaths of the American workforce in limbo. Anxiety hit US markets this week as negotiations on a new stimulus package appeared to fizzle out. On Tuesday, the White House signaled that they would wait until after election day to move forward on further stimulus measures. However, this move was quickly (albeit partially) walked-back on Wednesday shortly after equity markets responded with, to put it mildly, a severe lack of enthusiasm. It now appears that the talks may restart— aided in part by the chorus of economists and business leaders emphasizing the need for government support to avoid severe damage to the economy. The supplemental unemployment benefits included in the CARES act, the initial stimulus package, expired on July 31st. As large employers such as American Airlines threaten mass layoffs, there is an even greater urgency for lawmakers and the administration to consider as they move forward with talks. As the pandemic rages into its seventh month, so does the US Recession. Since February, over 10.7 million[1] people have lost their jobs and remain unemployed. The downturn thrust roughly two-million more workers into part-time work. Meanwhile, millions more who have lost their jobs have dropped out of the Labor Force altogether. For many, news of a rarely seen Washington compromise on a fiscal stimulus bill in March was lifesaving— literally. The bill included: Provisions aimed at addressing supply shortages and access to health services. U.S. Treasury-backed loans to Small Businesses and States, including Airlines. Direct stimulus relief to American workers, including supplemental unemployment benefits. As the recovery began in June, many of these policies played a hand in sustaining growth. Emergency loans lent out to Small Businesses helped them adjust to COVID-related restrictions, supplemental unemployment benefits helped support those still jobless, while a moratorium on evictions kept vulnerable tenants from losing their homes. Regrettably, most of these provisions lapsed at the start of August, and distressed signals from American Industries have already started flashing. Airlines furloughed nearly 32,000 workers this past week, citing a lack of financial support from as the primary factor. Before the bailout sirens ring, let's contextualize the Airlines and other sectors around the economy's current set of circumstances. These are large industries that employ and provide medical coverage for numerous workers, who have had their consumer demand base directly dampened by COVID-19. The pullback in economic activity extends way beyond vacations and business trips. Citing Google Mobility data, trips to workplaces, transit hubs, and to retail and recreation places remain significantly below pre-pandemic levels. The impact stretches to adjacent places of commerce, such as restaurants, bars, and shopping districts. Office restrictions have led to layoffs in custodial and security work, while school closures continue to create a complicated series of choices for families to make, as seen in the astounding number of women who have dropped out of the workforce entirely during the pandemic. In many ways, the US economy needs relief over stimulus. COVID-19 cases show the signs of a second wave, while consumer sentiment remains far below the highs seen before the pandemic. Without an inflection in both of these trends, the demand cushions needed to restart economic growth will simply fail to materialize. While the Federal Reserve has extended its monetary policy tools to address the crisis by committing to low real interest rates for the foreseeable future, such mechanisms are limited in scope. Some economists have raised concerns about the potential for a K-shaped without targeted fiscal assistance to American workers, a development that could exacerbate growing wealth inequality. On Tuesday, Minneapolis Federal Reserve President Neel Kashkari cautioned that “If we don’t support people who have lost their jobs, then they can’t pay their bills and then it ripples through the economy and the downturn is much worse than it needs to be.”[2] As Washington struggles to cut a deal under the cloud of an election season, the US economy is left to weather the storm. Like all recessions, some businesses and jobs will be permanently lost due to Covid-19. No measure can prevent this entirely. Still, with support from the federal government, millions of households can stay afloat until there is a plausible and safe path of returning to normal. While enumerable aspects of the ongoing public health crisis remain beyond congressional control, the task of assisting in-need American workers sits at the cross-section of achievable and required. [1] Bureau of Labor Statistics [2] Retrieved from CNBC on October 7th, 2020

Seattle tops the Arbor-Chandan Opportunity Matrix. Phoenix and Austin follow closely behind, benefiting from resilient labor markets. Texas metros, led by Dallas and Houston, continue to capture an outsized share of large multifamily investment activity. OVERVIEW The U.S. economy was gliding into 2020 along a path of consistent yet unspectacular growth. After more than a decade of expansion, multifamily asset pricing remained exceptionally tight, with investors searching for yield in secondary and tertiary markets. When the full magnitude of the COVID-19 pandemic hit the U.S. in March, it became readily apparent that the multifamily sector would see a recessionary reshuffling of fundamentals, in a pace unrivaled in recent history. This report develops an analytical framework to predict where large multifamily investment opportunities will be most abundant in the year ahead. Through the use of an opportunity matrix, the top 50 U.S. metros are ranked based on a composite blend of performance metrics. The analysis pays specific attention to how local economies have fared during the pandemic and how multifamily investment activity performed. The top 50 metros are based on population estimates. All metros are reported at the Metropolitan Statistical Area (MSA) level. For the full analysis, visit the Arbor Research Page on the Arbor Realty Trust website.

While returns near elections are lower on average, there is no observable causal relationship. When controlling for recessions, the impact of an election falls to near zero. Multifamily asset price growth has slowed with COVID-19 but performance has held up compared to the Great Recession. OVERVIEW As a follow up to our recent Chatter blog piece, highlighting lower returns and increased volatility for public equities near U.S. presidential elections, this article narrows in on multifamily asset prices. Reviewing Real Capital Analytics data from 2001 through the present, we find that multifamily prices tend to grow less quickly near general elections. However, with the limited number of general elections over the sample period and the deep recession of 2008, slumping asset prices near election day may prove to be more happenstance rather than resulting from an independent causal relationship. In this limited study, when controlling for the effects of a recession, the impact of an election on multifamily prices falls to near zero. For the full analysis, visit Arbor Chatter at the Arbor Realty Trust website.

Omaha registered labor force gains from a year ago, reinforcing renter demand. Unemployment in the country's least impacted labor market, Louisville, rose by 2.0% year-over-year. Boston experienced the nation’s highest unemployment percentage gain, up 12.6% from a year ago. OVERVIEW Year-over-year employment growth turned negative in each of the top 50 largest U.S. metros, according to the Bureau of Labor Statistics’ latest Metropolitan Jobs Report. While no city went unscathed, the scale of damage to labor markets differed depending on location. Smaller cities like Louisville, Omaha and Salt Lake City made it through July with only a moderate uptick in unemployment. However, larger metros, including Boston, Los Angeles and Las Vegas, were less fortunate. Chandan Economics analyzed the latest metro-level job numbers, examining where there’s more to the story than meets the eye. For the full analysis, visit Arbor Chatter at the Arbor Realty Trust website.

Monthly stock market returns fall an average of 0.72% to 0.32%. Equity REIT monthly return volatility rises by approximately 77%. Monthly equity returns fall from an average of 1.06% to 0.55%. OVERVIEW The 2020 calendar year has already seen more than its fair share of volatility provoking events. Rising tensions in both trade and foreign policy relations provided markets with heightened anxiety even before the onset of the COVID-19 pandemic. Looking ahead to the upcoming U.S. presidential election, divisiveness and potential volatility seem likely to ensnare the country during the next few months. This article attempts to quantify the observable market effects of the U.S. presidential elections throughout recent history. With specific attention to measures of volatility and returns across real estate and other public equities, this limited study finds evidence of lower average returns and higher levels of volatility in the immediate few weeks before and after general elections. For the full analysis, visit Arbor Chatter at the Arbor Realty Trust website.

Personal mobility data serves as a measure of COVID-19 urban recovery. Madison, Wisconsin ranks as the most improved metro for walking and driving. The San Francisco Bay Area ranks as the least recovered metro, using mobility criteria. OVERVIEW Cities across the U.S. are attempting to balance economic activity against coronavirus transmission risks. While the goals remain uniform, the levels of success are not. This article analyzes personal mobility data provided by Apple Maps. Data was gathered for driving, transit, and walking trips, between mid-January and late-August. While the numbers are not a perfect proxy for economic activity, they provide a gauge of COVID-19 urban recovery, reflecting where life most and least resembles pre-pandemic levels of normality. For the full analysis, visit Arbor Chatter at the Arbor Realty Trust website.

Small multifamily cap rates barely budged in Q2 2020, narrowing by 5 bps. Risk premiums for small multifamily rose to a record-high as 10-year Treasury yields fell. Confidence in ability to pay rent varies with income level. OVERVIEW National average cap rates for small multifamily properties narrowed by 5 bps in the second quarter of 2020, reaching 5.8%. In the first quarter, cap rates widened by 21 bps, and there was some concern that they would further inch up as the economic downturn continued. However, small multifamily cap rates held steady due to a pricing tug of-war. Falling risk-free interest rates have the effect of reducing cap rates, while increased operational risks have the effect of widening cap rates. When both phenomena happen at the same time, the net result is cap rate stability. For the full analysis, visit Arbor Chatter at the Arbor Realty Trust website.

Tenant performance measures have taken a marginal hit but have stabilized, beating early predictions. Headwinds are still forecasted and multifamily continues to weather the storm. Low-interest rates have led to a refinancing surge but multifamily LTVs fell to 2016-level lows. OVERVIEW Concerns are rising about whether the multifamily sector, including its renters, can sustain economic security as new daily COVID-19 cases remain stubbornly high. Even as the economy stumbled through much of the spring and summer, the apartment sector generally stayed on its feet. While tenant performance measures have taken a marginal hit, they have stabilized and held up better than early predictions had suggested. Rent collections have ranged between 76.6% and 82.2% since April, according to Census Bureau estimates. In turn, both property-level cash flows and cap rates have remained resilient. However, as coronavirus cases surged across the U.S. in July, it became apparent that the country’s speedy return to normalcy would not occur. For the full analysis, visit Arbor Chatter at the Arbor Realty Trust website.

Lower-income renter confidence in paying rent dipped as the CARES Act expirations approached. The performance gap between higher- and lower-income renters widened. The course of the pandemic and governmental assistance will continue to affect market stability. OVERVIEW Second to only the toll on public health, the economic fallout remains one of the most pressing effects of the ongoing pandemic. Starting first in major metros across the Northeast, namely New York and Boston, through March and April, the virus spread throughout urban centers and their surrounding areas like wildfire. Employers shuttered their offices and sent staff home, while states implemented restrictions on commercial activity, drawing consumers away from the economic and lifestyle interactions that give cities their value. In New York City alone, more than one million residents have lost employment, while the demand-driving gears of the city have shifted into neutral. Concerns over how the shutdown would impact apartment markets in cities like New York and elsewhere quickly bubbled. But the swell of stimulus and tenant protections introduced in the federal CARES Act proved effective in slowing the threat. For the full analysis, visit Arbor Chatter at the Arbor Realty Trust website.

Ben Metcalf from the Terner Center for Housing Innovation at the University of California, Berkeley (formerly Deputy Assistant Secretary of the U.S. Department of Housing and Urban Development) and Noerena Limón from the National Association of Hispanic Real Estate Professionals (formerly with the Consumer Financial Protection Bureau and the White House Office of Political Affairs under President Obama) join Sam Chandan to discuss the increasing challenges facing small apartment building owners and the communities they serve. Metcalf and Limón review the findings of a new joint survey, which shows one in four small landlords has borrowed to cover expenses since the beginning of the pandemic, and posit that further deterioration in rent collections following the expiration of enhanced unemployment benefits threatens property owner' capacity to meet operating expenses as well as mortgage payments and property taxes. Listen to this episode of the Urban Lab on Apple Podcasts. For more information about the NAHREP-Terner Center survey, visit NAHREP and the Terner Center on the web.

Sam Chandan, Silverstein Chair at the NYU Schack Institute of Real Estate and Chairman of Chandan Economics, writes in Forbes: The resilience of the professionally managed segment of the apartment market, where rent payment rates remain close to pre-pandemic levels, threatens to obscure the importance of renewed protections and the enormous challenges confronting the most income-constrained renters. As of mid-July, 30% of households with incomes below $25,000 had missed or deferred the prior month’s rent. Across all renting households, more than 20% missed their last rent payment, a significantly higher share than for the subset of professionally managed properties, which are typically newer, more urban, larger in unit count, and offered at higher price points. ... Absent the financial support of the CARES Act or clear timing for new benefits, millions of families that met their rent obligations in the initial months of the pandemic will join the ranks of families seeking relief or at risk of eviction. The impact on broader housing market stability may be substantial. Of immediate concern, small landlords face an uphill battle in fulfilling their mortgage and property tax commitments, irrespective of whether they negotiate rent deferments and discounts or pursue evictions in an environment of weakened apartment demand. For the full column, visit the Forbes website.