The aim of the New York City Recovery Index is to create a practical, weekly pulse-check of the economic progress of the city. We built the overall city recovery index from components that align with the lives of New Yorkers:
- Transport and mobility
- Real estate and housing
- Restaurant reservations
As New York recovers from COVID-19, the shutdowns, and their economic repercussions, we expect each of these elements to return to pre-pandemic levels. To measure the pace of the recovery, we report indices for each component and sum them into an overall recovery index.
We required the following criteria for a dataset to be included in the overall index:
- Availability: The data must be reported reliably, at least weekly, and available to the public. There are multiple measures that we would have liked to include, but are only reported monthly or quarterly, and often with a significant delay.
- Relevancy: For each individual measure, a return to the baseline (accounting for seasonality) should reasonably reflect the normal functioning of the city. For example, a consistently crowded rush-hour train would suggest that both commuters and tourists have returned to their previous habits.
Data Collection & Analysis
New York City Recovery Index
The New York City Recovery Index is calculated from an un-weighted average of each of the six sub-indices. A reading of 100 is considered "normal”.
COVID-19 Hospitalizations Index
The COVID-19 Hospitalizations Index represents the impact of the pandemic on the functioning of the city.
The daily count of hospitalizations which tested positive for COVID-19 are taken from New York City’s Department of Health and Mental Hygiene. We choose to base our health index on hospitalization counts rather than positive COVID-19 tests or deaths. Testing data is dependent on the number of people who can be tested, which fluctuates based on testing supplies available, distribution of those supplies to medical professionals, and testing facilities open to the public, as well as individuals’ decision to be tested. Hospitalizations are a more stable measure over time, as they track those people who are admitted with COVID-19. We don't choose to use death counts because the number of deaths often considerably lags the spread of the virus.
This is a complex index. The aim of this index is to understand where we are in the progress of containing the virus, as it relates to the functioning of New York City. Therefore, we built the index to reflect the following: When there are no daily hospitalizations, the index will be 100. In April, when New York City passed 1,500 hospitalizations a day and the city was under a hard lockdown, the index should be close to its minimum possible value (0). Therefore, like the initial unemployment claims index, as the number of hospitalizations goes down, the index will increase back to 100.
The spread of COVID-19 was exponential before measures were taken to contain the spread, and a second wave could progress similarly. Therefore we are concerned with the rate of increase (whether or not hospitalizations double week over week) as much as the total number.
To construct the index, we took the trailing 7-day average of daily hospitalizations beginning on February 29, 2020. We based the index on the log of the number of hospitalizations to ensure the index directly tracks the rate of change of hospitalizations. By basing the index on the log of the number, it reflects that even with 20-30 hospitalizations per day, the city is still not in a position to return to normal.
This aligns with the measures that health officials advise for social distancing practices and thus the viability of much economic activity in New York City. Even though as of July 24th, 2020 we are in phase 4 of reopening, indoor dining, gyms, theaters and cinemas remain closed. So the health index must reflect that we are still some distance away from the city running at full capacity. Only when daily hospitalizations approach 0, will the index reflect the ability of New York City to fully return back to normal. Because there were no recorded hospitalizations earlier in the year, we set the baseline for early weeks to ‘100’.
Subway Mobility Index
The New York City Subway is a powerful indicator of movement around the city, and the physical reality of this mode of transportation also means ridership has been especially impacted by the pandemic.
The index is composed of MTA data showing day-by-day ridership numbers for the New York City subway. The MTA calculates the percent change against the ridership/traffic volume from the equivalent day in 2019 (generally the same day of week during the same week), with data beginning on March 1, 2020. For index values occurring before March 1, we used MTA data showing the number of turnstile entries and exits made each week by customers in each station of the New York City Subway. The counts are aggregated from readings occurring every 4 hours from turnstiles in subway stations across the city. The MTA publishes these observations on a weekly basis, covering seven-day periods beginning on the Saturday two weeks prior to the posting date and ending on the following Friday.
To construct the index, we take the 7-day trailing average of the daily year-over-year percent change in subway ridership as reported by the MTA. These values are converted to whole numbers for the Subway Mobility Index. The MTA reports these daily ridership numbers going back to March 1.
To construct the index prior to March 1, weekly turnstile reports were downloaded and compiled into one file. Observations were compiled starting in December of 2018 in order to establish a historical baseline. We used the cumulative entries and calculated the daily change to arrive at total daily entries by station and turnstile. We then summed across all the stations to get daily subway entries for all of New York City since late December 2018. Using this, we take the 7-day trailing average.
For the component score, the Subway Mobility Index is divided by six to be equally weighted alongside the other components.
Home Sales Index
Housing is a time-honored preoccupation for New Yorkers. We believe home sales to be a measure of economic confidence in the city on behalf of homeowners. Furthermore, home sales often involve large financial products which bear significance beyond the individuals transacting the sale.
As a measure, the Home Sales Index represents the volume of pending home sales in a given week in New York City.
The index is created from StreetEasy data showing weekly pending home sales. The data are collected from StreetEasy’s online home listings for the city of New York. A sale is marked as pending by the listing agent when the seller has accepted an offer from a buyer, however it does not indicate a closed deal. Deals may not close for a variety of reasons including inspection, financing, etc. however, an agent need not necessarily report a closed deal, or may choose to do so long after the deal has closed. We have chosen to use pending sales data as pending sales are recorded on the StreetEasy platform in a timely and reliable way.
We calculate the year-over-year percent change in pending home sales in NYC from weekly reports provided by StreetEasy. Year-over-year percentages are converted to whole numbers for the Home Sales Index. For the component score, the Home Sales Index is divided by six to be equally weighted alongside the other components.
Rental Inventory Index
While pending home sales are an indicator of the economic confidence of buyers, rental inventory reflects the lived conditions of the majority of New Yorkers for whom buying a home is cost-prohibitive. We use rental inventory because the number of available units reflects the changing conditions of the market before landlords adjust the asking price.
As a measure, the Rental Inventory Index is derived from the number of homes available for rent in a given week in compared to the baseline number of homes available to rent in an average month, seasonally adjusted. The data are provided by StreetEasy and collected from online rental listings for the city of New York.
There are two extremes when the rental market indicates a bad situation for the NYC economy. If there are too many apartments for rent, there is a lack of demand, suggesting people are leaving the city. If there are too few apartments to rent, prices jump and people are priced out of the rental market. To account for both scenarios, the rental index measures how far the rental inventory is, in any given week, from the optimal range.
For this index we track weekly 2020 rental inventory figures against a predictive seasonal model created from 2010-2019 monthly data. To create the model, we used rental inventory numbers from the past 10 years to determine typical monthly variation from January including each year’s overall growth or decline.
We then compare the model to 2020 by measuring the growth/decline of each week relative to the 2020 January average. The difference between the predictive model and the 2020 data allows us to see how unusual each week in 2020 is compared to the past 10 years. The index is then created to reflect the difference between the model and the 2020 weekly figures, and higher index values are intended to reflect better market conditions. So, if there are too few rentals the index goes down, and if there are too many rentals the index also goes down.
Unemployment Claims Index
The Unemployment Claims Index represents the employment health of New York City. As a measure, we use initial claims for unemployment insurance to track the economic recovery of the city. Rise in initial unemployment claims, seen as a decrease in the index, begins in the week of March 21, a little over one week after a COVID-19 national emergency is declared.
An unemployment claim is an application for cash benefits that an employee makes after being laid off or being unable to work for other covered reasons, such as the COVID-19 pandemic. Since the onset of the pandemic in March 2020, New York State began publishing detailed weekly reports with data on Unemployment Insurance claims in New York State.
Weekly reports provided by the NYSDOL Division of Research and Statistics provide the year-over-year percent change in initial claims by region of the state. We collected the figures reported for New York City from every report released since weekly reporting began in March, 2020. Data for December 14th through March is estimated from the weekly year-over-year percent increase numbers reported by the Federal Reserve Bank of St. Louis for New York State.
The index takes the inverse of the year-over-year percentage change of unemployment claims. For example, if unemployment claims this year are 2X higher than last year (200% year-over-year), then the index is 50 (100/2). If initial claims are 4X higher than last year (400% year-over-year), the index is 25 (100/4). If initial claims are the same as last year, the index will be 100 (100/1). So as initial claim numbers trend back to the level seen in 2019, the index will increase back up to 100.
Restaurant Reservations Index
Restaurants are a key indicator of the economic life of New York City. While not all restaurants take reservations, OpenTable hosts reservations for over 36,000 restaurants in the New York area.
Restaurant reservations began to show a decline the week of March 14th, 3 days before NYC would order a mandatory shut down of bars and restaurants. With the phase 3 reopening in early July, some restaurants have started taking reservations for outdoor seating.
The index is created from OpenTable data showing daily year-over-year seated diners. The data are collected from a sample of restaurants on the OpenTable network including online reservations, phone reservations, and walk-ins. Day-of-week fluctuations are accounted for by comparing the same day of the week from the same week in the previous year. OpenTable data reporting begins on February 18. A baseline index of 100 is assumed for January 1st-February 17th.
Year-over-year percentages are converted to whole numbers for the Restaurant Reservations Index. For the component score, the Restaurant Reservations Index is divided by six to be equally weighted alongside the other components.
NOTE: We will no longer be incorporating New Business License data as a part of our index, as it is not a consistent or sufficiently broad indicator.
The Department of Consumer and Worker Protection licenses more than 75,000 businesses in more than 50 industries (overview of businesses licensed by DCWP) . Notably, these businesses do not include barbershops, nail salons, restaurants and bars, which are not required to obtain a DCWP license. Legislation determines which businesses require a DCWP license.
We take the data for all new licenses granted, by day, since 2010. Using the historical data, we found that numbers of new licenses vary consistently through each year. We use data from 2010-2019 to build a model of daily seasonality in a normal year, and then apply that model to 2020 to predict the expected number of applications in an 7-day period.
For example: if there are 40% fewer new small business licenses in a 7-day period in 2020 than the seasonal model predicts, the index will be 60 (100-40).
Current Limitations and Areas for Future Exploration
There are countless ways of measuring the economic impact COVID-19. The New York City Recovery Index is not meant to be a complete portrait of every economic aspect of an economy as diverse and multi-layered as New York City. There were several measures not included that fit our objectives, but are not consistently or publicly available. Also, this index is not meant to serve as a predictive measure of how long it will take for the city to recover, rather, it's a weekly readout of where the city is compared to normal. It is possible that the future economic landscape of New York City differs from its past, and requires new indicators to better reflect that reality.
The NYC Recovery Index does not reflect the economic recovery of any individual. The iniquitous distribution of wealth, as well as the disproportionate impact of the pandemic on Black and Latino residents results in a broad range of lived experiences not reflected in the index. We hope to be able to supplement our weekly reporting with richer data on a monthly basis.
We further acknowledge that with each data set there are limitations. Initial Unemployment Insurance claims, for example, do not reflect the job losses of undocumented individuals who represent an important and especially vulnerable segment of the workforce in New York City. Additionally, the accuracy of initial COVID-19 hospitalization data were impacted by the availability of tests in the early days of the pandemic, among other factors.
This index was created by the Investopedia Research Team with direction from Jon Roberts, Ph.D., and in collaboration with Alexandra Kerr, Caleb Silver, and Dylan Zurawell.
Special thanks to Spectrum News New York 1 for their collaboration and partnership. Special thanks also to StreetEasy for providing weekly pending home sales, historically and by borough, and for their collaboration and clarification in the use of their data.