D.C. lost at least 17,000 more people during the pandemic than in the prior year, according to USPS data on net moves. At least 9,000 of the loss appears to be permanent.

When the pandemic restrictions were put in place last Spring, there was plenty of anecdotal evidence that people were leaving D.C. as white-collar workers became remote, others were laid off, and schools shut down. Data from the United States Postal Service (USPS), which records changes of address, provides a clue as to how many people left D.C. and where they went. This data gives us an insight into peoples’ housing choices when work and school are decoupled from where you live, something important to understand if an increase in remote work extends beyond the pandemic.

The USPS data clearly shows more people moved out than in during 2020 than 2019, with moves accelerating after COVID-19 restrictions began in March 2020. In 2019, USPS recorded 11,480 net moves out of the city, while in 2020 that number increased to 29,362, an increase of 17,882 net moves out (or 2.6 times more).(1) Net moves out is the number of moves out of the city that exceed the number of moves in and is a proxy for population loss. The number of moves reflects change of address forms filed by both individuals and families.(2) While there was some decrease in the number of moves into DC in 2020, about 90% of the increase in net moves out of the city results from additional people moving out of the city.(3)

Not all of the moves in 2020 were permanent. When someone submits a change of address form they mark the move as permanent or temporary(4) and the data clearly shows a higher portion of moves than usual were temporary in 2020. Out of the increase of 17,882 net moves out between 2019 and 2020, we estimate 9,335 were permanent and 8,547 were temporary.(5)

The data shows more people moving out of the city than into it in both 2019 and 2020, despite the decennial Census count showing strong population growth between 2010 and 2020. However, D.C.’s population increase from moves within the United States has steadily decreased the last several years, and 2018 and 2019 IRS data and 2019 and 2020 Census population estimates show D.C. lost more people to other parts of the country than it gained. The last couple years, any population increase in D.C. has been entirely driven by births and international migration, according to these estimates.

The USPS data includes at least some, perhaps even most, moves out of D.C to international locations,(6) but does not include moves in from international locations. While USPS tells us that the number of international moves in the dataset for the nation as a whole, from which we extracted data for D.C., is “insignificant”, international migrants make up a sizeable portion of people moving to and from the District. The absence of incoming international moves in the USPS data likely leads to an overcount of net moves out of D.C. each year, but the increasein net moves out between 2019 and 2020 (roughly 17,000, or 9,000 permanent) will not be overcounted unless international migration into D.C. increased between 2019 and 2020, which seems unlikely due to the pandemic. In our end notes we adjust the USPS data with estimates of the missing international moves and take into account families being counted as only one mover to illustrate how the USPS data might translate into net numbers of people moving out of D.C. in 2019 and 2020.(7)

What parts of the city did people leave?

The zip codes close to the downtown core, especially those close to the west end of downtown, appear to have had the biggest population loss during the COVID-19 period, even when accounting for population differences between zip codes. The map below shows this pattern. Specifically, it shows the increase in net outmigration from March-December 2019 to March-December 2020, per every 1,000 residents in each zip code.

The zip codes with the largest population loss due to COVID-19 were 20036 (south side of Dupont/Golden Triangle), 20009 (Adams Morgan, Columbia Heights, 14th/U St NW, Dupont), 20024 (Southwest Waterfront) and 20005 (south side of Logan Circle/Franklin Square). All of these zip codes had at least 60 more net moves out per 1,000 people in Mar-Dec 2020 than Mar-Dec 2019.(8) To put those numbers in perspective, 20009, the largest zip code of the bunch with about 52,000 people, lost about 1,700 people from Mar-Dec 2019, even after accounting for people moving in. That number increased to 5,500 people from Mar-Dec 2020. These zipcodes have relatively high numbers of multifamily buildings, indicating that perhaps apartment dwellers were more likely to relocate during the pandemic than those in single family homes.

Which zip code fared the best during the pandemic? Zip codes 20012 (Shepherd Park, Takoma) and 20015 (Chevy Chase, Friendship Heights, Barnaby Woods) had nearly no change in migration between Mar-Dec 2019 and Mar-Dec 2020. In fact, zip code 20015 was the only one where USPS data showed more residential moves in than out during both time periods.

Where did people go?

The data we received from USPS on the destinations of people moving from D.C. was highly redacted for privacy reasons, limiting the conclusions we can make. (This data was received via Freedom Of Information Act (FOIA) request, whereas the data used for the graphs above is publicly available.) Still, with the data we have, we can see that a large portion of movers–at least 31%, but likely many more–moved to another location within the D.C. metro region between March and December 2020. This aligns with other research(9) showing a large majority of moves during COVID-19 were not to far-flung places but instead to locations within the same metro region.

Moves to the D.C. suburbs

For moves within the D.C. metro region, we had a complete dataset for 10 zip codes located in the close-in suburbs of Bethesda, Chevy Chase, Oxon Hill, Silver Spring, Alexandria, and Arlington. This data shows that in a typical year (Mar-Dec 2019), D.C. lost more people to these zip codes than it gained from them. In 2020 this trend accelerated. From Mar-Dec 2020 these suburban zip codes gained, on net, 3 times as many people from D.C. than during the same period in the prior year. (Net moves is the difference between moves in and out).

We can expand the suburban zip codes for which we have complete data if we look only at moves out of  D.C. to the suburbs without accounting for people moving from the suburbs to D.C. Looking at only moves out from D.C., we see that suburbs to the north and west of the city seemed to have had the largest increases in moves from D.C. during the pandemic (Mar-Dec 2020) compared to a year prior (Mar-Dec 2019). The Bethesda zip code of 20817 recorded the highest percent increase in moves from D.C. (63%). That zip code received 246 movers from D.C. between March and December 2019 and 400 between March and December of 2020.

The other zip codes to have an increase in movers from D.C. of 50% or higher during the pandemic were 22203 and 22206, both in Arlington.

Moves out of the D.C. region

The redacted USPS data only shows moves from D.C. to places outside the D.C. region for destination zip codes that received more than 10 people from D.C. in a single month. Because this data only shows a relatively large number of people moving from D.C. to a particular place at a particular time, we refer to these moves as “high volume moves.” The USPS data shows 1,608 high volume moves from D.C. to places outside the region between March and December 2020 and 673 of these moves during the same period in 2019.

One distinctive feature of high volume moves in the data we reviewed is that there were simply more high volume move destinations in 2020 than 2019 for locations outside the D.C. region. The map below shows the high volume move destinations outside the region in Mar-Dec 2020 that were not high volume destinations in Mar-Dec 2019. The big surprise from the data is the Delaware beaches were in the top destinations in 2020.

Columbia, MD, and Charlottesville, VA, were the next most common destinations outside the region after Rehoboth Beach and Lewes, DE, that had high volume moves in Mar-Dec 2020 but not Mar-Dec 2019. Notably, these are both small to mid-sized cities not too far outside the boundaries of the D.C. metro region. Annapolis and Baltimore were the top high volume move destinations in both 2020 and 2019.

Moves within the District

In addition to capturing moves in and out of the city, the USPS data shows moves within D.C. The number of moves within the city is close to the number of moves out of the city. From Mar-Dec 2019 USPS data shows 43,074 intracity moves. During the same period in 2020 there were 49,632 intracity moves.

As the map below shows, the general pattern of intracity moves is for people to move from neighborhoods close to downtown, especially those to the north and west of downtown, to outer neighborhoods. Areas east of the Anacostia River also lose residents to other parts of the city. The pattern of people moving from downtown to outer neighborhoods strengthened during the pandemic.

From Mar-Dec 2019 the neighborhoods that lost the most residents to other parts of the city were 20005 (south side of Logan Circle/Franklin Square), 20036 (south side of Dupont/Golden Triangle), 20037 (West End, Foggy Bottom), and 20001 (LeDroit Park, Shaw, Mt Vernon Triangle), all of which had a net population loss of more than 10 movers per 1,000 residents to other parts of the city. Zip codes 20005 and 20036 had the highest loss at 33 and 32 movers per 1,000, respectively. The zipcodes that gained the most population from intracity moves during this time were 20018 (Brentwood, Langdon, Woodridge, Fort Lincoln), 20017 (Brookland), 20015 (Chevy Chase, Friendship Heights, Barnaby Woods), and 20003 (Navy Yard, south side of Capitol Hill). These zip codes had net gains ranging from 11 to 17 movers per 1,000 residents, with 20018 gaining the most people.

Migration patterns within the city looked much the same during the height of the pandemic (Mar-Dec 2020). The differences that stand out are that during the pandemic several of the zip codes that had gained people in 2019 gained even more people, especially 20003 (Navy Yard, south side of Capitol Hill) and 20015 (Chevy Chase, Friendship Heights, Barnaby Woods), which saw the largest increases. Likewise, many of the zipcodes that lost people in 2019 lost even more during the pandemic. Zip codes 20036 (south side of Dupont/Golden Triangle) and 20009 (Adams Morgan, Columbia Heights, 14th/U St NW, Dupont) had the biggest jumps in movers lost per 1,000 residents. Notably, zip code 20008 (Connecticut Ave. corridor) went from losing people pre-pandemic to gaining people during the pandemic.

It may come as a surprise that zip code 20003, which includes Navy Yard, a neighborhood with many multifamily buildings, had the largest influx of people from other parts of the city during the pandemic, since other areas with a lot of multifamily housing had some of the largest population losses. New apartments coming online and offering incentives for moving in may be why Navy Yard and adjacent areas were able to attract so many residents from other parts of the city.

Note: zip codes 20004, 20006, 20052, 20057, and 20064 do not appear in the table due to insufficient data
Source: D.C. OCFO analysis of USPS change of address data obtained through a FOIA request

Will people return?

The question policymakers across the country are asking is will people return to cities they left during the pandemic. Presumably those who filed a temporary change of address request will return, as well as some who left temporarily but filed a permanent move, such as apartment dwellers returning to a different unit. For those who truly moved permanently, it remains to be seen if the city can attract people to replace them, something that could depend on telework policies post pandemic. The good news is that as of May 2021 the USPS data shows net moves out of the city have returned to 2019 levels. But for the city to regain the population it lost, we would need to see an influx of residents into the city at levels we have not seen in several years.

-Ginger Moored, Fiscal Analyst

Thank you to Susan Steward for sharing her knowledge of the FOIA process and to Norton Francis, Lori Metcalf, Daniel Mohammed, and Steve Swaim for their edits and feedback.

End Notes

(1) USPS redacted data for zip codes with 10 or fewer moves of individuals or families in a given month. While the vast majority of data was left intact, a few smaller zip codes had data redacted for a few months. To test the accuracy of our conclusion that moves in 2020 were 2.6 times those in 2019, we redid the analysis excluding all data for zip codes where some data was missing. That analysis shows 2020 moves were 2.5 times higher than those in 2019.

(2) Families can file a single change of address form for multiple people if all of those people have the same last name and have the same origin and destination. Most change of address forms filed in D.C. are for individuals.

(3) The table below shows the data behind our net move calculations.

(4) A temporary move is valid for up to six months and can be renewed for up to another six months; USPS confirms that renewals are not counted in the data and temporary moves converted to permanent moves are not counted twice.

(5) The USPS dataset gives the percentage of all moves, including business moves, that were temporary and permanent. To estimate the number of permanent residential moves, we applied this percentage to residential moves only. Residential moves greatly outnumber businesses moves in the data. It is possible that some people who filed a permanent move do have plans to return to the city; for instance, a renter who left temporarily might have filed a permanent move since they would be moving back to a different apartment unit upon return.

(6) While domestic movers can file a change of address online, international movers must print out a paper form and bring it to their local post office, which could have an effect on the number of forms filed by international movers. USPS accepts change of address forms for people moving from the U.S. to another country, but not for people moving from another country to the U.S.

(7) Census PUMS data shows an estimated 10,606 people moving from an international location to D.C. in 2019. If we add these people to the USPS data for both 2019 and 2020 and assume all families counted in the USPS data have two family members, the net number of people moving out of D.C., or population loss due to moves, would be 2,881 in 2019 and 23,555 in 2020, meaning D.C. lost an additional 20,674 people in 2020. Adjusting for redacted data in the USPS dataset (see End Note #1) would bring that loss down to approximately 19,443 people (12,898 permanent). The actual loss will be higher if the average USPS “family” has more than 2 people and if international migration to DC in 2020 were less than in 2019.

(8) Downtown zip codes 20004, 20005, and 20036 had insufficient data on moves of families due to the low number of these types of moves. Therefore, data for these zip codes in the map “DC Zip Codes that Lost the Most People” reflects only moves of individuals. Data for all other zip codes reflects moves for both individuals and families. Limiting the data in this way did not change the ranking of the top four zip codes with the largest increase in moves out mentioned in our narrative, nor did it change the conclusion that they all had an increase of at least 60 net moves out between Mar-Dec 2019 and Mar-Dec 2020.

(9) See “More Americans Are Leaving Cities, But Don’t Call It an Urban Exodus”, published by Bloomberg CityLab (Patino, Kessler, Holder, Gu, and Rojanasakul) on April 26, 2021, available here: https://bloom.bg/2TLvvDC

What exactly is this data?

The number of net moves out of D.C., including the number of net moves by D.C. zip code, comes from a publicly available dataset on the USPS FOIA (Freedom of Information Act) website. You can access that data here. This dataset separates moves into three categories: business, family, and individual. This analysis looks only at residential moves, which is the sum of family and individual moves. A move is counted as a family move if one form is submitted for multiple people with the same last name. Individual moves are filed for just one person. The data also separates moves into those that are temporary and permanent. A temporary move is valid for up to six months and can be renewed for up to another six months; USPS confirms that renewals are not counted in the data and temporary moves converted to permanent moves are not counted twice.

D.C movers’ destinations and intracity moves by zip code come from a dataset the Office of Revenue Analysis obtained through a FOIA request for USPS data. You can access that data here. This dataset includes residential moves only but does not separate out moves into individual and family or temporary and permanent.

The “D.C. metro region” refers to the Core-Based Statistical Area (CBSA). We matched zip codes to the D.C. CBSA using a crosswalk available on the website of the U.S. Department of Housing and Urban Development. The crosswalk is available here.  

Zip code populations come from the 2019 5-year American Community Survey data published by the Census Bureau. That data can be accessed here.

The Impact of the Subsidized Rents on the City Tenure of Low-Income Renters

According to the U.S. Census, 58 percent of the 292,000 occupied housing units in the District of Columbia in 2019 were rental units. Thus, rental housing is the preferred housing type for most in the city and plays an important role in the city’s overall housing market. However, rapidly rising costs of even this type of housing (relative to home ownership) is a growing concern for many. In this context, one may suppose the high income of many renters would be positively correlated with their city tenure (i.e. presumably a greater ability to pay). But, this appears not be the case. By way of a closer examination of the role of different rent levels on the city tenure of renters, we find the subsidized monthly rents for respective residents are a significant factor contributing to their relative longer average city tenure, and the very high monthly rents of Class A residents are a significant factor contributing to their shorter average city tenure.

To better understand the role of housing costs in the city’s growth and development, the Office of Revenue Analysis (ORA) conducted a study (see here) to see if different rent levels had any effect on the length of city tenure (i.e. the years in the city) of apartment tenants in the city. This study examined this issue from two perspectives. First, the study assessed the effect of subsidized apartment rents (via Housing Production Trust Fund housing) on respective tenants’ tenure in the city relative to apartment tenants with market rents in comparable Class B and C buildings. And second, the study assessed the effect of relatively high apartment rents (i.e. in large Class A multifamily buildings) on the respective tenant’s tenure in the city relative to apartment tenants with market rents in comparable Class B buildings. Class A buildings are the newest and highest quality buildings built within the last 15 years with top amenities. Class B buildings are generally older buildings and more likely to have lower income tenants. And, Class C buildings are the oldest buildings in the market and tend to be in need of major renovation but have the lowest rental rates.

The Housing Production Trust Fund

The Housing Production Trust Fund (HPTF) is a revenue fund administered by the D.C. Department of Housing and Community Development (DHCD). The HPTF provides gap financing for new and renovated residential projects meant to be affordable for low- to moderate-income households. The goal of the HPTF program is to make sure that “every resident in the District can afford a place to call home.” Since 2001, more than 9,000 affordable housing units have been produced using the Fund’s resources. At the end of fiscal year 2019 the HPTF had an available balance of $142.9 million. [Annually, 15% of deed recordation and transfer revenue from property transactions are dedicated to the Fund. In 2019, that amount was $77.2 million and in 2020, $68.1 million.]

Segmenting the District’s Apartment Rental Market

We compared city tenure of income tax filers who lived in HPTF multifamily buildings for at least one year between 2008 and 2012 to income tax filers who resided solely in market-rate (i.e. non-subsidized rent) Class B or Class C buildings during the same years and in the same neighborhoods. And, in efforts to be more comprehensive in understanding the city’s rental market, we compared city tenures of income tax filers that were residents of large Class A multifamily buildings which have higher rents for at least one year between 2008 and 2012 to income tax filers that were solely residents of market-rate Class B buildings for the same time period also in the same neighborhoods.

The Role of Apartment Rent Levels

Figure 1 shows that income tax filers who only lived in market rate Class B large multifamily buildings, and controlling for other factors, remained city residents an average of six years. However, income tax filers who lived in an HPTF building tended to remain city residents for almost 2 years longer, and filers who lived in large Class A multifamily buildings tended to remain city residents a little less than 5 years.

Figure 1

The study found that HPTF residents (relative to market rate Class B rental unit residents) followed by head of household tax filers appear to be the strongest factors identified contributing to longer city tenures as shown in Figure 2. In contrast, Figure 3 shows that for income tax filers who resided in Class A rental units in large multifamily buildings, filing as head of household (relative to filing as married or single) was the strongest factor identified contributing to longer city tenures while residing in a Class A rental unit was also strongly correlated with shorter city tenures. In the models, “being a HPTF resident” is our proxy for having low housing costs (i.e. subsidized monthly rents), “being a Class A resident” is a proxy for enduring some of the city’s highest monthly rents, and “being a Class B resident” is our reference group.

Figure 2

Figure 3

The Role of Income

The study also found that the average income of residents of HPTF rental housing in 2016 was $22,978, while the average income of residents of Class A rental housing was more than 3 times higher at $75,326. However, the effect of average income growth on increasing city tenure was 3.5 times stronger for HPTF residents than Class A residents. This might suggest that, as HPTF residents experience income growth, their subsidized rents may become even more valuable to them when considering the other residential alternatives, including migrating out of the city. Hence, income growth for low income earners appears to be positively correlated with their longer city tenures but has little correlation with longer city tenures by Class A tenants.

Tenants with the Lowest (Highest) Incomes have the Longest (Shortest) Tenures

Because of the city’s high housing costs, one may suppose that high income would be positively correlated with city tenure (i.e. presumably a greater ability to pay). But, the above findings indicate the opposite. We find that the low monthly rents for HPTF residents is a significant factor contributing to their longer average city tenure, and the very high monthly rents of Class A residents are a significant factor contributing to their shorter average city tenure. (While it is probable that at least some outmigrant Class A residents move into homeownership outside the city, such data were not analyzed for this study.) And while there are many factors that influence city tenure, this study focused primarily on the role of rent levels on the city tenure of city residents.

HPTF: Helping to Retain an Economically Diverse City

Since 2006, the District of Columbia has reversed the trend of decline and has experienced significant population growth. However, there continues to be a simultaneous and persistent pattern of out-migration of residents particularly at the lower levels of income. This study finds the level of apartment rents faced by the city renters appears to influence their length of tenure in the city. It also appears that HPTF supported housing is providing an increasing number of affordable housing units to low-to moderate- income residents, which, in turn, is helping to counter the more general pattern of out-migration of low-income residents caused, in part, by the lack of affordable housing and gentrification.

What is this data?

This study utilized administrative tax data that includes District of Columbia individual income tax data for 2001 to 2016, property tax data for 2001 to 2016, and federal individual income tax data for city residents for years 2006 to 2016. The tenure of tenants is based on the cumulative number of years tenants filed their income taxes with the city from their particular residences. Also, HPTF housing data was collected from the DHCD Open Data database and HPTF Annual Reports.

The key model focused on income tax filers that were residents of HPTF multifamily buildings for at least one year between 2008 and 2012 (the treatment group). Accordingly, income tax filers who resided solely in market-rate (i.e. non-subsidized rent) Class B or Class C buildings between 2008 and 2012 are the control group. The use of tenants’ income tax data for years 2001 to 2007 and 2013 to 2016 helped to more accurately identify the start and ending year of city residency and tenure of all tax filers in the study. All tax filers were between the ages of 22 and 70 when they first moved into any of the buildings in the study.

An Evaluation of the District’s Tax Increment Financing: Is it a net fiscal gain to the District?

Tax Increment Financing in the District

Tax Increment Financing (TIF) is an economic development policy tool used by state and local governments to stimulate economic development in a specific area. Its popularity stems from the notion that a TIF project is self-financing, that is, that the tax revenue generated by the project covers the bond payments for the publicly subsidized loan needed to implement the project. The District of Columbia implemented its first TIF project in 2002 and that was followed by seven additional large TIF projects. (There are some smaller TIF projects but these eight projects in our analysis are the largest, most important ones.) Our recent study (See here) is a retrospective evaluation of the economic and fiscal performance of these District TIF projects.  The study’s objective was to answer two questions: 1) does each project produce a positive net fiscal gain for the city; and 2) does the entire TIF program produce a positive net fiscal gain for the city?

The study of the eight projects finds that five were indeed self-financing, while three were not (see Table 1). We also found the net tax revenue from the five positive projects (property and sales taxes minus debt service) covers the shortfalls in the three lagging projects so that the District’s TIF program in the aggregate appears to be a net fiscal gain to the city.

A Review of the District’s TIF Projects

By and large, tax increment financing in the District of Columbia is generally used to help produce large but unconventional development projects in specific locations of the city that otherwise, arguably, would not happen. The actual amount of a project’s TIF subsidy is the principal amount plus interest of the TIF debt service. Typically, this subsidy directly finances all or a portion of the total development costs of the project. Theoretically, the TIF subsidy is justified as the amount needed to overcome some stated economic impediment for example, environmental damage, keeping a site location from achieving its highest and best use. But in the District of Columbia, it appears that TIFs are applied for a slightly different reason.

The District of Columbia is a relatively small city with over half of its land area prohibited from being developed by the private markets, largely because of the federal presence. The city also has a vibrant economy that generates steady growth in jobs and population. The result is that development of one sort or the other is happening in almost all areas of the city. As such, TIFs in the District are used to facilitate development projects in specific neighborhoods that, possibly, might not see that exact type of development, usually to achieve important socio-economic goals beyond just job and income growth. For example, the Gallery Place and Mandarin sites may have likely been developed as predominately Class A office space without TIF. But Gallery Place is now the entertainment and retail hub of the city’s central business district, and the Mandarin is a 4-star international hotel that was the first to bring upscale hotel and retail activity to the Southwest waterfront. DC USA may have likely been a predominately residential development without TIF.  But, it was the first new large-scale retail complex in a residential neighborhood anchored by one of the nation’s leading national retail chains. And the Convention Center Hotel site may have also likely been a predominately Class A office space or even a hotel development (with only a fraction of hotel rooms, thus precluding it from being classified and marketed as a convention center hotel). Instead, it is now the city’s largest hotel with 1,175 rooms and an underground concourse connecting it to the Convention Center (See here).  So, we can view TIF subsidies in the District of Columbia as the amount needed to overcome the additional project costs above and beyond the costs of a project that would be the highest and best use of land in that location. The subsidy is justified as a means of achieving important socio-economic goals for the city (e.g., grocery stores to eliminate food deserts in certain neighborhoods, broadening the market for business travel to the District, and increasing affordable housing).

The District of Columbia implemented its inaugural TIF project in 2002, and that was followed by seven additional large TIF projects up until 2010. District TIF projects have been used to facilitate retail, residential, hotel, and other mixed-use developments in the city (Table 1).

Measuring the benefits: TIF vs. A privately financed alternative project

One of the key assumptions in the analysis conducted by many state and local governments to decide whether to greenlight a potential TIF project is that the location site for the TIF project has prohibitively high economic costs that precludes a purely private sector financed development of the project (the so-called “but for” test).

The model used for this analysis does not make such an assumption. Our model assumes each actual TIF location would eventually be developed by the private sector absent a TIF but as a project much more in line with the conventional economic and social characteristics of the existing neighborhood (i.e., less investment risk).

We assume the privately financed alternative for each project starts sometime between the TIF start date and 2019 and that the 2019 real property value of the alternative project is 75 percent of the TIF project’s real property value in 2019.[1]

For example, the property value of the Gallery Place TIF project grew from $6.9 million in 2002 to $596.6 million in 2019.  We assume the property value of the counterfactual at the Gallery place location grows such that it reaches 75 percent of the Gallery Place project’s real property value (that is, $447.2 million) in 2019 but for the public financial support.

Earlier TIF projects were self-financing, but later ones were not

We use the model to evaluate each TIF project and present the results in Table 2. The table shows that the Gallery Place project achieved a positive cash flow in year 4 of the 25-year debt service schedule and reached its breakeven point in year 8. The table also shows that when we divide total cumulative net tax revenue for the 25 years period by the TIF bond amount (in 2019 dollars), the District is estimated to achieve a 67 percent return on investment (ROI) in year 25 of the debt payment schedule (adjusted for inflation). Also, five years after the project was delivered to the market, the TIF bond amount was 21 percent of the project site’s total property value.

When the estimated ROI in year 25 of each project’s debt service schedule is greater than zero for any project, we conclude the project will ultimately be a net positive fiscal gain for the city. Consequently, the District’s first five TIF projects produced a net positive cash flow, a financial breakeven point (all within eight years), and ultimately a positive fiscal gain (ROI) for the District. However, the Capper Carrollsburg, Convention Center Hotel and Rhode Island Row projects are not expected to produce a positive ROI over the life of the TIF loan as indicated by the negative ROI in column 5.

Based on a closer examination of the model results and the actual specifics of each project, it appears that the first five TIF projects achieved sound financial standing and an early breakeven point primarily because the total debt for each project was relatively low and the net tax revenue generated at each site was relatively high. In contrast, despite their important socio-economic roles, the last three projects (Capper Carrollsburg, which subsidizes senior housing; Rhode Island Row, which subsidizes affordable housing; and the Convention Center Hotel, which was developed to land more and larger annual conventions to fully utilize the Washington Convention Center), are not expected to generate enough net tax revenue over 25 years to service the TIF debt. (See study here)

What is the net fiscal impact of all the District’s TIF program so far?

When we aggregate all eight TIF projects between the date of the first TIF bond issuance (2002) and when the model estimates that the last debt service payment is due (2034), we can calculate the annual total debt service (including interest) and the total annual net tax generated at each TIF site. We find that the entire District’s TIF program achieves a positive cash flow starting in 2005. The model results indicate that the Gallery Place and Mandarin Hotel projects are the primary sources of excess net tax revenue for the program and, hence, are the source of cross subsidizing the seemingly uneconomic projects of Capper Carrollsburg, Convention Center Hotel, and Rhode Island Row.

The District’s TIF fiscal balancing act

According to the analysis, five TIF projects generated a net fiscal gain, and 3 did not. We conclude that while the financials of each new potential TIF in the future must continue to be highly scrutinized, policy makers should also remain cognizant of the financial health of the District’s entire TIF portfolio. The study finds that, to date, several large projects with high ROIs are cross subsidizing those seemingly uneconomic projects. The cross subsidy enables the “uneconomic” projects to achieve very important socio-economic goals for the District (e.g. affordable housing and broadening the market for business travel to the District) without jeopardizing the District’s overall fiscal health.

What is this data?

The model used in this study is designed to determine if each TIF project produced or is likely to produce enough incremental tax revenue to cover its debt service. We define incremental tax revenue as the tax revenue from the TIF project in excess of tax revenue from a comparable (a counterfactual) but totally privately financed project in that location. The model assumes a 25-year loan (i.e. TIF) at a 6 percent interest rate applied to each TIF project but for the actual TIF loan amount for each project. The interest rate for fixed rate debts range from 4 to 7.5 percent, and the actual median interest rate for the seven projects was 6.1 percent.

This analysis is based on annual real property assessment values and real property tax collections for years 2002 through 2019 for each TIF project. The analysis for three projects (Gallery Place, DC USA (Target) and Rhode Island Row) include retail sales activity (sales taxes), and four projects (the Mandarin, Capitol Hill Towers, Embassy Suites, and Convention Center Hotels) include hotel sales activity (hotel taxes). In the model, that actual property, retail and hotel taxes generated at each site is also used to finance each respective project’s TIF debt.

[1] 1 Given that most TIF projects are in prime locations and the city has experience significant property development in nearly all areas of the city, we assume the estimated 2019 real property valuation of each counterfactual would range between 50 and 100 percent of the actual 2019 TIF property valuation. Hence, we assume an average 75 percent 2019 real property valuation of each counterfactual in the model.

DC’s Property Sales Market: Trends and Fluctuations – 2000 to 2020

An Overview of the City’s Property Market

Between 2000 and 2019, the District of Columbia property market experienced remarkable growth. According to annual District property assessment data, the total value of all taxable property grew from $46.6 billion in 2000 to $224.4 billion (382.4 percent higher) in 2019 (Figure 1). The value of the residential sector grew at an annual average rate of 9.5 percent over the period, and the commercial sector grew at a slightly slower annual average rate of 8.7 percent. The higher growth rate for the residential sector caused the share of all residential value to increase to 56.5 percent in 2019 and the commercial sector share to decline to 43.5 percent (Figure 2).

Whereas most of the value of all taxable real property is attributed to the residential sector, most of the value of property sales subject to total deed taxation is commercial property. Based on annual deed tax data from the District Recorder of Deeds, we estimate that the total property value subject to total deed taxation grew from $4.1 billion in 2000 to $17.4 billion in 2019 (Figure 3). The value of the residential property sold grew at an annual average rate of 7.3 percent over the period, and the value of the commercial property grew at an annual average rate of 8.7 percent. And while there are many factors (nationally and locally) that contributed to the rapid appreciation of property prices and values, the 10-year treasury rate also fell from 6.05 percent in 2000 to 2.05 percent in 2019. And in contrast to the rapid growth in property values, the national consumer price index only grew at an annual average rate of 2.1 percent between 2000 and 2019.

Additionally, the faster growth of property sale values in the commercial sector caused the share of all commercial value to increase to 51.1 percent in 2019 and the residential sector share to decline to 48.9 percent (Figure 4).

Figure 5 shows that commercial property sold as a share of all commercial property in the city was 13.4 percent in 2001, and residential property sold as a share of all residential property in the city was 13.4 percent also in 2001. The slight but general downward trend in the ratios over the years in the figure reflect the robust annual growth on the total value of both sectors since 2000. In 2005, a record high of 23.2 percent of the total commercial property value in the city was sold. This reflects the brief period of very rapid expansion in commercial office space in the city prior the Great Recession. A record of 50 investment grade office buildings were sold in 2005 (Delta Associates). 

Annual Fluctuations in the Property Sales Market

The above figures may suggest that the property sales market has grown in a relatively smooth upward trending fashion, but that is not the case. Figure 6 shows that more recently the Great Recession caused deed recordation taxes to decline 35.4 percent in 2009. It appears that major broad-based cuts to federal spending (or threatened cuts) in 2012, 2013 and 2015 contributed to deed taxes declining significantly in those years.  The figure also shows that the current COVID-19 pandemic (and resultant national recession) also caused deed tax collections to decline 23.6 percent in 2020. (Coincidentally, the District increased its deed recordation and transfer tax rates from 1.45% to 2.50% for commercial properties valued at $2 million or higher in October 2019. We estimate absent that tax rate increase, deed tax collections would have declined approximately 38 percent in 2020 compared to 2019, making it the largest decline in deed tax collections in over 20 years.)

Figure 7 shows the annual percent change in deed tax collections by class.  In years of major decline, there tends to be a greater reduction in commercial sales activity than in residential sales activity. The greater contraction in commercial sales activity caused the value of all residential sales to account for 50 percent or more of all value sold in those same years (Figure 8). (Interestingly, the commercial office sales activity in the city began to decline in 2006, three years before the Great Recession of 2009. Hence, this sector may at times be a leading indicator of the broader economy and not just a lagging indicator.) But more generally, figures 7 and 8 suggest that annual residential property sales are the bedrock of annual deed tax collections, and the level of sales or notably lack of sales of commercial property causes the greatest declines/swings in annual deed tax collections activity.

Residential Property Sales in 2020

There were 9,270 residential homes sold in 2019 but only 8,469 (8.6 percent less) sold in 2020. The lower number of homes sold in 2020 is largely attributed to the 23.7 percent drop in single-family attached homes (Figure 9). Interestingly, neither of these three subsectors of the residential market experienced a decline in median sale prices in 2020 (Figure 10).

In addition to single family home sales, the residential property sales sector also includes the sale of multifamily (rental) properties. When we examined the sales of the largest multifamily properties in years 2014 to 2020, we find that there were fewer sales in 2020 than in 2018 and 2019. While this subsector tended to produce an average of $594 million in transaction volume in years 2014 to 2019, only 35 percent of that average was generated in 2020. This appears to be a major factor in the 27 percent decline in deed tax collections from the residential sector in 2020 as shown in Figure 7. Consequently, the total value of residential property sales as share of the total value of all residential property value in city dropped from 6.0 percent in 2019 to 4.3 percent in 2020. The properties shown in Figures 11 and 12 are large Class A & B multifamily residential properties with more than 100 rental units that were built or renovated after 2000. The average sale price for the over 2 million square feet of large multifamily building space sold in 2019 was $373 per square foot (CoStar).

Commercial Office Building Sales in 2020

The city’s commercial sector experienced a greater decline in deed tax activity than the residential sector in 2020. In the large commercial office building subsector, there were also fewer sales and lower transaction volume in 2020 (Figure 13). Although the average annual transaction volume in this subsector for years 2014 to 2019 was about $4 billion, 2020 saw only about 42 percent of that average amount (Figure 14). Assuming 2020 would have been relatively similar to the prior years, this suggests that approximately $2.3 billion in sales transactions did not occur in 2020, which is likely a major factor in the 42.7 percent decline in deed tax collections in the commercial sector in 2020 (Figure 7). Consequently, the total value of commercial property sales as share of the total value of all commercial property value in city dropped from 8.9 percent in 2019 to 5.0 percent in 2020. The average sale price for the over 7 million square feet of large commercial office space sold in 2019 was $516 per square foot (Delta Associates).

An Interpretation of Recent Trends and Fluctuations

Over the past 21 years, DC’s property market experienced not only remarkable growth but also major market fluctuations.  The greatest annual fluctuations appear to be correlated with national recessions, major broad-based cuts (or threatened cuts) to federal spending and the COVID-19 pandemic.  From the perspective of annual deed tax collections, it appears that these national economic shocks took a greater toll on the city’s large commercial office building sales sector than on the city’s residential sales sector. Also, the years in which such shocks occurred were promptly followed by strong rebounds in deed tax activity in both sectors. This suggest that these shocks caused a significant slowdown in sales activity (or even a postponement of a considerable number of sale transactions) for that year and maybe the following year with a relatively prompt return to more normal sales levels resuming shortly thereafter, particularly in the large office building subsector. 

The residential and commercial sectors of the city property sales market each account for about half of all taxable property value sold on an annual basis. The residential sector tends to grow healthily and is the relatively less volatile sector of the city’s property market. Hence, it can be considered the bedrock of annual deed tax collections. The commercial sector, on the other hand, has been growing faster on average and appears to be responsible for most of the volatility in annual deed tax collections activity.

LEED-certified buildings lower operating expenses, commands higher rents for residential units

Tackling climate change and advancing environmental sustainability is a challenge that is emerging as a key urban policy issue by an increasing number of U. S. cities, and the District of Columbia is a national leader on this front. The District of Columbia was the first city in the nation to pass major environmental sustainability legislation. In 2006, the city enacted the Green Building Act (GBA), which requires that nearly all new privately-owned commercial buildings meet the standards of the United States Green Building Council’s Leadership in Energy and Environmental Design (LEED) green building rating system. LEED is a leading design standard for green buildings across the country.

The city has consistently led the nation in the number of LEED-certifications and was designated the world’s first LEED Platinum city in 2017 (see here). In 2018, the District had 145 certified building projects with 37.1 million LEED-certified gross square feet (see here). Because of the legislative mandate, it is currently presumed that nearly all large commercial buildings built after 2009, when the law was fully enacted, are green in certain respects. Even though the legislation targeted commercial buildings, surprisingly, an increasing number of newly built large residential buildings have obtained LEED certification. In fact, 71 percent of all new apartment units in large multi-family residential buildings delivered in the city since 2014 are LEED-certified. This suggests that some residential developers have discovered considerable additional economic value in building LEED-certified buildings. To begin to understand this issue better, we conducted a study (see here) to assess the effects of LEED-certification on the operating expenses, particularly utility expenses, and rents of both large commercial office buildings and large multi-family residential buildings. 

Comparing LEED-certified to non-LEED buildings

The city’s GBA mandates all new private commercial development projects that are 50,000 square feet or larger meet, at minimum, the “Certified” level of LEED certification standards. Therefore, all commercial buildings built after 2009 presumably meet LEED-certified standards (per city building regulations). To discern between the office buildings that meet LEED-certified standards and those that do not, this study analyzed the 2018 rents, utility expenses and operating expenses for both older and newer buildings. A group of 35 large commercial office buildings built between 1990 and 1997 were considered “older” buildings and were presumed to not meet LEED-certified standards. These buildings were compared to 30 large commercial office buildings built between 2009 and 2018. These buildings were presumed to meet LEED-certified standards and are referred to as the “newer” buildings.

This study also compared the 2018 rents, utility expenses and operating expenses of 27 LEED-certified large multi-family residential buildings in the District to 26 non-LEED certified buildings of similar size, age and submarket locations. These 53 residential buildings comprised a total of 15,663 residential units in the District of which 7,299 were LEED units.

The impact of LEED certification on operating expenses and rents

The study found that operating expenses was on average $2.53 per square foot (7.43 percent) lower for the newer, LEED-certified commercial buildings and utility expenses were $0.80 per square foot (9.4 percent) lower than for older non-LEED-certified commercial buildings (see Figure 1). LEED certification was found not to have an appreciable effect on the average rent per square foot for newer buildings vis-à-vis older buildings.

For multi-family residential buildings, LEED certification lowered operating expenses by $1.39 per square foot (17.3 percent); utility expenses were $0.45 per square foot (7.8 percent) lower than for non-LEED buildings. LEED buildings were found to command rental rates $0.30 per square foot (10.2 percent) higher than comparable non-LEED buildings.

Figure 1

What does these findings mean for sustainability and housing affordability?

The study also found evidence that tenants in residential LEED-buildings have higher incomes than tenants in non-LEED, but otherwise comparable, buildings. This finding is related to the previously discussed finding that LEED buildings fetch about 10 percent higher rents (in an average of 27.2 square feet or 3.4 percent smaller space) and suggests that renters are willing to pay a premium for the sustainable living that LEED buildings provide. One might presume that this rent effect would generally preclude lower income households as tenants.

However, based on a closer review of the Office of Tax and Revenue’s 2018 building income and expense data (BI&E) for all income earning properties in the city, we found that at least 500 affordable housing units were Inclusionary Zoning (IZ) units in LEED-certified residential buildings. The city’s IZ program was enacted into law in 2006 and is one of many policy tools used by the city to provide more affordable housing units to low-income residents. The program requires a minimum of 8 –10 percent of the residential floor area of all new residential projects (including LEED projects) be set aside as units for low-income households.

This closer review of the data also revealed that at least 400 additional LEED units (exclusive of the 500 IZ units mentioned above) are in some type of affordable housing program (i.e. subsidized rent). Consequently, at least 900 low-income households in 2018 were afforded housing in some of the city’s newest, more expensive LEED residential developments, and thus participating in the District’s sustainable living movement.

A cross analysis between OTR’s individual income tax data and the real property tax BI&E data also revealed that 6 percent of tenants in LEED buildings filed income taxes as head of householders whereas there were only 2 percent of such tenants in non-LEED buildings. (Head of household tax filers are unmarried working adults with one or more dependents and tend to have lower annual income on average than single and married tax filers.) Furthermore, the study found that head of household tax filers in the more expensive LEED buildings earned on average $25,538 (39.1 percent) less than their counterparts in non-LEED buildings. This finding suggests that the District has found a way to achieve its twin, and what at first seem conflicting, policy goals of sustainable living and housing affordability.

The future of LEED in the District

The District of Columbia’s Green Building Act of 2006 required that only commercial buildings meet LEED standards. Yet a large and increasing number of residential buildings are LEED-certified (comprising 7,300 housing units in 2018), suggesting that there is a strong and vibrant market for LEED-certified buildings even in the absence of a legislative requirement.  This means that LEED buildings are not only environmentally sustainable but also make good economic and financial sense. The study also shows that LEED buildings do not necessarily compromise the District’s affordable housing goals; rather, the study shows that good affordable housing policy can be complementary to sustainable living. In any case, the growing number of both commercial and residential buildings in the city that meet LEED-certification standards will help the District of Columbia lower its overall carbon footprint more rapidly than anticipated at the enactment of the GBA in 2006.

What is this data?

The study used several types of microlevel administrative data from the District of Columbia Office of Tax and Revenue (OTR), including the 2018 Building Income and Expense data, 2018 Real Property Assessment data, and 2016 Individual Income Tax (IIT) data. The IIT data was used to analyze annual income for tenants of residential buildings.

The Mystery of the District’s Self-Employed

When you have eliminated the impossible, whatever remains, no matter how improbable, must be the truth.

-Sherlock Holmes

When the CARES Act passed in March, self-employed persons were granted eligibility to apply for and receive unemployment benefits for the first time in history through the Pandemic Unemployment Assistance (PUA) program.  The self-employed have always been a part of the economy, but the CARES Act marked a turning point for this business type with formal acknowledgement by legislators to the importance of supporting them. But who are the “self-employed”? The identity of the self-employed has long been a mystery. And like the solution to any mystery, one must ask the right questions, find the right clues, and piece together those clues to arrive at the truth.

With the self-employed people two narratives exist. In some cases, self-employed data is lumped in with businesses, and in others they are an employee for themselves. Even among policymakers, the overarching question of, “who and how many are self-employed?” is often debated. Are the self-employed only those people in the “gig economy” or are they established firms that we may frequent daily without knowing they are self-employed? To solve this mystery, we dug through publicly available federal data from the Census for the District of Columbia to gain a better perspective on this significant group. For the sake of simplicity, the self-employed referenced here are “single-person firms” where the owner is the only employee of the firm.

The Tale of the Self-Employed Establishment
From the business perspective, how many businesses within the District are considered one-person self-employed organizations? Using the Statistics on U.S. Businesses and Non-Employer Statistics Data from the Census for 2007 through 2017, Figure 1 reports that in the past ten years self-employed establishments have grown to account for 70 percent of all establishments in the District. Over this same period, the self-employed share of total establishments grew by 4.1 percentage points from 65.9 percent to 70 percent in 2007 and 2017, respectively.

Figure 1: Share of Establishments by Type, 2007 vs. 2017

Source: Census, Statistics of U.S. Businesses and Non-Employer Statistics

As of 2017, 54,965 establishments within the District were registered as self-employed. While these establishments make a significant impact in terms of the number of establishments, their contribution to District total wages and salaries is limited. Traditional businesses with an owner and employees will often budget anywhere from 15 to 30 percent of sales for payroll. Assuming an average of 22.5 percent for payroll as a share of revenues, ORA estimated from sales receipts data the payroll size of self-employed versus employer establishments and compared their contribution to total District payroll.

From the Employee Narrative
In the self-employed establishment, payroll expense varies depending on the share after expenses remaining. As noted in Figure 2, the self-employed account for approximately 1.0% of all payroll earned within the District between 2007 and 2017.

Figure 2: Share of Payroll by Establishment Type, 2007 vs. 2017 (in billions)

Source: Census, Statistics of U.S. Businesses and Non-Employer Statistics, ORA

The share of payroll accounting for those self-employed as employees modestly grew to 1.1% in 10 years, but overall remains marginal compared to the traditional employer establishments. Despite the total establishments that are self-employed, the core fact remains that they are still an employee and when thinking about payroll, this would only account for 60,000 potential employees within the District. Whereas employer establishments employed 527,004 employees according to the data in 2017. Thus, while the self-employed make a significant share of total establishments, as individual employees they are vastly outnumbered by those in traditional employment in the District.   

The Red Herring of the Self-employed and the App-service Employment
Contrary to popular belief, self-employed individuals are not relegated to only ridesharing or other app service employment. As Figure 3 shows, the majority of the self-employed are to be found in the Professional, scientific and technical services sector, one of the District’s largest employment sectors, and a major driver of District economic growth in recent years.

Figure 3: Top 10 Industries for Self-Employed (Total Establishments), 2017

Source: Census, Non-Employer Statistics

According to the Census data, in 2017 the total sales for self-employed establishments was $1.9 billion. In context, that was the going rate if you wanted to buy both the Tampa Bay Rays ($825 million) and Miami Marlins ($940 million) in 2017.. Looking back at Figure 3, Professional, scientific, and technical services accounted for 30 percent of the total self-employed establishments. Jobs within this sector include consultants, lawyers, and computer programmers. In terms of sales, the sector accounted for nearly 43 percent, or $821 million, of the total self-employed sales that same year. Real estate and rental and leasing sector, which includes real estate agents and property management companies, although a smaller share of total establishments accounted for an estimated 10 percent of the total sales in 2017. Combined with Professional, scientific, and technical services, the two sectors account for nearly 35 percent of the total establishments, and 53 percent of all self-employed establishment sales in 2017 or $1.0 billion. While many self-employed could be from ride-sharing, the data indicates a larger portion may be in more lucrative industries.

Concluding Remarks
The mystery of the self-employed has eluded policymakers for decades. Dueling narratives of self-employed as establishments or employees have complicated the issue even further. As establishments, the level of newly self-employed within the District has grown continuously since 2007 to become 70 percent of all establishments by 2017. Viewed as  employees alone underestimates the significance of self-employed as an important driver of District economic growth, given the share payroll in 2017 accounted for by self-employed was estimated to be 1%. Contrary to popular thought, the self-employed are not restricted to retail or transportation, but are prominent in well-established skill-based sectors of the District economy. Overall, their contributions are not limited to tip-based income but have amassed to $1.9 billion in sales annually. However, there is no easy solution to the mystery of the self-employed, only more questions. But, the Census data may be the cipher we need to begin unraveling this mystery and answering those questions in the future.

For an interactive experience, check out our blog dashboard companion piece in Tableau, “The Mystery of the District’s Self-Employed”

Using high-frequency data for real-time tracking of the economic impact of COVID-19 on the District of Columbia

The COVID-19 pandemic has wreaked havoc on US national, state, and local economies. One of the difficulties in assessing the extent of the economic impact is the lack of timely data. While measures to control the pandemic rapidly destroyed jobs in the retail and hospitality and leisure sectors, official US Bureau of Labor Statistics figures on jobs at the national level were not available until a week after the end of the month when the jobs were lost.  And at the state and local level, the jobs numbers are available two are more weeks after the end of the month.  Lag times for personal income and gross domestic product (GDP) measures are even longer. Initial estimates of national GDP are not available until a month after the end of a quarter, and it is even longer for state and local governments. For example, the US Bureau of Economic Analysis (BEA) published 1st quarter personal income figures for states and the District of Columbia on June 23, almost three months after the end of the quarter, and first quarter GDP figures for states and the District will not be available until July 30th.

To track the unfolding impact of the pandemic, we have used various high-frequency data, including daily and weekly unemployment claims, smartphone counts in the District of Columbia, and credit card data on expenditures. Below we described the trends for the smartphone counts and credit card spending before and after the start of the lockdown to control the pandemic.


Daytime District population, which includes commuters and tourists and almost doubles the resident population, drives the economic activity in the District. So, tracking the impact of the pandemic on daytime population is a key measure of the pandemic’s impact on DC’s economy. Figure 1 below shows the smartphone counts in the District from January 2017 through June 2020. With schools and businesses closed, DC saw its lowest daytime population numbers, by a wide margin, since the data was first collected in January 2017. The data, provided to the District by Thasos, shows a 69% drop in the number of people in DC compared to the same period the year prior (April 1-June 1). To put that in perspective, the drop from the historically highest average week for DC (first week in April AKA spring break), to the lowest average week (winter holidays/New Year’s Day), is 37%.

Figure 1: DC Average Daytime Population by Week

daytime pop

Tableau Link

When looking at specific days, blizzards and Christmas in DC have traditionally been the lowest days at 430k-450k daytime population, and the COVID-19 lockdowns have seen days in the 300k range.

Note: The data received by the District is one aggregate number per day and is therefore completely anonymized. The data pulls from a geofence that is aimed at capturing commuters and while it captures most of the city, it does not account for every person in DC every day.

Credit Card Data

Credit card spending data from Earnest Research, which is aggregated and stripped of any personal identification information, give a real-time view of how spending has declined during COVID-19. As Figure 2 shows, in DC total spending has fallen by about 30% since April 15, 2020 compared to the same period 1 year before.

Figure 2: DC Credit Card Spending, All Categories

DC All Spending

Tableau Link

As Figures 3-5 shows, some sectors had larger declines than others. Two of the hardest hit sectors are restaurants and hotels. Figure 3 shows that restaurant spending for the week of June 17 is 46% below the same period last year, while Figure 4 shows that hotel spending is almost 90% below the same period last year. On the other hand, Figure 5 shows that grocery spending soared at the beginning of the lockdown, rising as high as 45% in the first week of April compared to the same period previous year. Grocery spending has gradually fallen back to just above pre-pandemic lockdown levels.

Figure 3: DC Credit Card Spending, Restaurants

dc restaurant spending

Tableau Link

Figure 4: DC Credit Card Spending, Lodging

dc hotel spending

Tableau Link

Figure 5: DC Credit Card Spending, Grocery

dc grocery spending

Tableau Link

DC Spending Relative to US and other Jurisdictions

National credit card spending data has basically recovered to what it was in 2019. Cities are experiencing much larger declines due to loss of commuters and having a different mix of businesses compared to suburbs/rural areas. Figure 6 shows DC compared to a select group of cities. NYC has seen the largest total spending decline of any large city with a ~50% drop. DC has been down approximately 30% and Los Angeles down ~10% during this period.

Figure 6: DC Credit Card Spending Compared to Other Jurisdictions

dc compared

Tableau Link