An Assessment of the Expected Impacts of City-Level Parking Cash-Out and Commuter Benefits Ordinances

Chapter 3. City and Scenario Selection

Several cities and scenarios were considered for inclusion in the analysis. This section describes the process (including the factors considered) for selection of the five core scenarios (with a few extensions) and nine cities analyzed.

Scenarios

The research team developed a range of potential policy scenarios to consider and used this initial list to select five policy scenarios for the final analyses. Appendix B. Additional Scenarios Considered for Analysis provides a discussion of the initial long list of potential policy scenarios, which may be of value to cities considering various policy options to reduce congestion, parking demand, and related externalities.

The following were the five scenarios selected for analysis:

1. Monthly parking cash-out
2. Monthly commuter benefit (employer-paid transit/vanpool benefit for employees with subsidized parking)
3. Monthly parking cash-out and pre-tax transit benefit for employees without subsidized parking
4. Daily parking cash-out and pre-tax transit benefit for employees without subsidized parking
5. Requirement to eliminate subsidized parking benefit and provide universal $5 per day employer-paid non-SOV commute benefit

Scenario 1: Monthly Parking Cash-Out

This is an ordinance that requires employers offer employees the option to cash-out their parking on a monthly basis. Employees must commit in advance to not use their parking space for the entire month. The cash-out value is equal to the monthly parking market rate, adjusted to account for the fact that some employees would accept the full cash-out value as taxable cash, and others would accept a tax-free transit benefit for a portion of the value with the remainder as taxable cash.

Scenario 2: Monthly Commuter Benefit (Employer-Paid Transit/Vanpool Benefit)

This is an ordinance that requires employers providing free/subsidized parking to offer employees a transit or vanpool benefit paid by the employer. These benefits are exempt from payroll taxes and employee income taxes, including transit and vanpool benefits up to the maximum allowed by law for each commuter, but not in excess of the value of the parking benefit.

Scenario 3: Monthly Parking Cash-Out and Pre-Tax Transit Benefit for Employees Without Subsidized Parking

In addition to requiring that employers that subsidize parking offer a monthly parking cash-out option (same as Scenario 1), all other employers must make pre-tax transit benefits available to all of their employees. That is, employers must allow their employees to set aside their own income on a pre-tax basis for transit¹² costs. This scenario applies a requirement to all worksites—those that provide free or subsidized parking and those that currently do not. Both employers and employees save money on taxes when the employee sets aside income on a pre-tax basis.

Scenario 4: Daily Parking Cash-Out and Pre-Tax Transit Benefit for Employees Without Subsidized Parking

This scenario is the same as Scenario 3 with the difference that the parking cash-out must be offered as a daily cash-out option, rather than monthly. In addition to requiring that employers that subsidize parking offer a daily parking cash-out option, all other employers must make pre-tax transit benefits available to all of their employees. That is, employers must allow their employees to set aside their own income on a pre-tax basis for transit⁷ costs. This scenario applies a requirement to all worksites—those that provide free or subsidized parking and those that currently do not. Both employers and employees save money on taxes when the employee sets aside income on a pre-tax basis.

Scenario 5: Requirement to Eliminate Subsidized Parking Benefit + Provide Universal $5 Per Day Employer-Paid Non-SOV Commute Benefit

This is an ordinance that requires employers that are offering their employees subsidized parking to cease offering it and for all employers to offer employer-paid non-SOV commute benefits of $5 per commute day. This could come in the form of a “transportation wallet” to pay for non-SOV commute trips that incur discrete charges (including transit, vanpool, and pay-per-use bikeshare and e-scooter trips). Cash compensation would also be provided so that the total benefit for non-SOV travel is $5 per commute day (if the “transportation wallet” expense would otherwise be less), and the full $5 would be paid for non-SOV trips that do not incur discrete per-use charges (e.g., cycling using a personal bicycle or annual bikeshare membership, walking, and carpool commutes). Non-SOV trips would need to be verified, such as through a smartphone application or by providing other evidence (e.g., the employee sharing with the employer bikeshare trip data that is available online, parking a personal bicycle at a worksite which the employer could see, or providing evidence of a home address within a walkable 1.25 miles of work and signing a declaration of having walked to work). The non-SOV commute benefits would be exempt from taxes to the extent allowed by law for eligible (transit and vanpool) modes.

Scenario Extensions and Adjustments

The research team also examined the following extensions and adjustments of core scenarios:

• VMT for Affected Commuters: Scenarios 1 and 2 apply only to a subset of all commuters. As such, the changes in affected commuter VMT will be more prominent than those in citywide VMT.
• Scenarios 1A and 3A: These scenarios entail re-runs of Scenarios 1 and 3, respectively, but are limited to employers with fewer than 20 employees.
• Partial Subsidy Impacts: The research team conducted a sample analysis to examine the VMT impacts of partial parking subsidies.
• Telecommuting Impacts for All Scenarios: The research team scaled impacts on congestion and emissions to reflect expectations in teleworking after the COVID-19 pandemic.

Cities

Criteria were established to guide city selection. The study team was searching for cities with large employment bases and a large number of drive-alone commuters where cash-out policies would have the highest potential impacts. In addition, data quality and geographical diversity were considered in city selection. Site selection criteria included:

• Size of drive-alone employee population: Larger drive-alone employee populations will yield higher absolute results; cities with larger commute driving populations were preferred for the analysis.
• Price of parking: Higher priced parking is an incentive for employees to cash-out their parking; cities with higher market rate parking were preferred for the analysis.
• Mode share indicators: The potential for employees to switch to transit as an alternative to driving alone is helpful to the decision to cash-out parking. Cities with high transit mode shares indicate that transit is a viable alternative.
• Data to support: Availability of city-specific data is essential for the analysis.

Drive-alone employee population and monthly parking rates were prioritized when selecting cities. Table 2 shows the 26 cities considered for analysis in order of their drive-alone population size. Cities that have better data availability are noted with a check mark.

Table 2. Employee populations, mode shares, and parking rates by city.

Cities	Employee Population*	Percent Drive Alone*	Drive-Alone Employee Population*	Percent Public Transit*	Daily Parking Rates**	Monthly Parking Rates**	Data Availability
Houston, TX	1,873,491	81%	1,525,543	3%	$19	$118	✓
Los Angeles, CA	2,154,978	71%	1,521,020	9%	$24	$137
New York, NY	4,733,695	23%	1,080,909	58%	$47	$655	✓
Chicago, IL	1,530,905	47%	717,309	33%	$31	$242
San Diego, CA	926,419	77%	713,003	4%	$20	$138	✓
Dallas, TX	893,716	80%	712,217	4%	$15	$122
San Antonio, TX	876,905	80%	701,772	3%	-	-
Phoenix, AZ	898,950	77%	691,544	3%	-	-
Charlotte, NC	606,473	80%	486,238	3%	$17	-
Austin, TX	615,370	77%	473,835	3%	$20	$153
Miami, FL	543,145	86%	465,121	6%	$24	-
Indianapolis, IN	547,906	85%	464,911	1%	$19	-	✓
Atlanta, GA	606,657	74%	450,884	9%	-	-	✓
Columbus, OH	527,281	82%	433,622	3%	-	-
Jacksonville, FL	519,393	83%	428,888	2%	-	-
Fort Worth, TX	498,101	83%	412,923	1%	-	$125
Philadelphia, PA	784,744	51%	399,384	27%	$25	$258	✓
Denver, CO	569,707	69%	392,562	10%	$20	$173
Memphis, TN	419,731	85%	358,112	0%	-	-
Washington, DC	840,050	42%	351,616	37%	$23	$273	✓
San Jose, CA	446,527	76%	341,524	4%	$24	-
Boston, MA	827,852	41%	340,247	38%	$34	$337	✓
Portland, OR	504,277	62%	312,370	13%	$15	$192
Seattle, WA	663,761	46%	307,169	28%	$23	$231	✓
Baltimore, MD	392,680	71%	277,741	11%	$17	$152
San Francisco, CA	794,514	30%	235,789	44%	$27	$297

*Source for employee population and mode shares is American Community Survey (ACS) 2019 5-Year Estimates, Modeshare by Workplace City (Place Geography) Table B08601: Means of Transportation to Work by Workplace Geography
**Source for city parking costs is Parkopedia’s 2019 North America Parking Index (Parkopedia 2020)

While not originally considered, cities with the most expensive parking and robust transit systems, which were prioritized for analysis, typically had lower rates of employers subsidizing parking than other cities. This limited the potential benefits, as most of the policies explored were triggered by employers subsidizing employee parking. The rationale for attempting to choose cities where impacts would be greatest is the same as for attempting to choose policies where the impacts would be substantial. Namely, city leaders are most likely interested in policies that yield the biggest impacts in their cities. Nevertheless, there was diversity in the types of cities selected for analysis, allowing many cities that were not analyzed to get a sense of what impacts the policies would have in their cities by looking at the results from one or more similar cities. Even better, the spreadsheet model developed for this analysis could be populated with data from cities that were not originally analyzed to produce such analysis; potential development of such a model was identified as a key future work activity.

Upon review of the criteria and in coordination with the peer review group, the following cities were selected for analysis:

• Boston/Cambridge, MA
• Chicago, IL
• Houston, TX
• Indianapolis, IN
• Los Angeles, CA
• New York, NY
• Philadelphia, PA
• San Diego, CA
• Washington, DC

Data and Methods Summary

For each scenario described above, the research team developed and tested various approaches to calculate their impacts on vehicle commute travel. Because of the availability of varying research and analysis approaches, each with distinct advantages and disadvantages, that could be applied to each scenario, in most cases the research team used the two best calculation approaches and then developed a midpoint estimate of results for a given policy.

Outputs. The primary direct output of the analysis is the estimated reduction in VMT. The reduction in VMT was then used to estimate reduction in driving-related externalities by applying per-mile factors to the vehicle travel metrics. The analysis focused on traffic congestion, emissions, and safety, as these impacts are of concern to State, regional, and local governments. Key outputs included:

• Reduction in vehicle travel
  • Reduction in average daily commute VMT, determined using reduction in vehicle trips, trip lengths, and vehicle occupancies
• Reduction in driving-related externalities
  • Reduction in congestion, in terms of average delay
  • Reduction in criteria air pollutant emissions
  • Reduction in greenhouse gas (GHG) emissions
• Reduction in crashes

Inputs. Key inputs were generally unique to each city, to the extent local data were available, and include:

• Employee populations
  • Total number of employees working in the city
  • Share of employees with access to free or subsidized parking from their employer (used to estimate the number of employees subject to a cash-out ordinance)
  • Share of employees with access to subsidized transit commuter benefits
• Employee commute characteristics
  • Citywide mode share
  • Mode share of employees with access to free or subsidized parking from their employers (used to estimate number of drivers eligible for cash-out)
  • Average commute distance for automobile commuters in/into the analysis city
• Travel cost factors
  • Average monthly market cost of parking in the analysis city, converted to daily rates
  • Average monthly cost of a transit pass in the analysis city, converted to daily rates
• Driver responses
  • Elasticity of VMT with respect to parking costs
  • Elasticity of transit ridership with respect to transit costs

VMT reduction estimates were primarily derived using two analysis strategies for each scenario. Citywide mode shares were estimated, averaged between the two strategies, and then applied to total employee, trip distance, and vehicle occupancy data to estimate VMT reductions. The two strategies were:

1. Using TRIMMS, a sketch planning tool for analyzing many types of strategies at a regional or sub-area scale, the first-round outputs were calculated. The tool is Microsoft® Excel-based, and preloaded with metropolitan-specific data, including employment and travel data, and travel elasticities and cross elasticities derived from national research. The user can adjust the parameter values and price elasticities. Default parameters were adjusted for starting commuter mode shares (to reflect the population receiving fully subsidized parking).
2. The second-round outputs were garnered by directly applying a travel price elasticity of -0.30 for the change in vehicle travel in relation to the driving costsas derived from the research team’s literature review and conversations with the study’s peer review group.

Outputs from these two strategies were averaged, and the averages are reported as the results in this study. This analysis relied on several data sources for input data, including data from the U.S. Census Bureau, local employer or employee surveys, and input from the peer review group convened for this analysis. Based on data availability, the analysis uses the most recent (pre-pandemic) available data for employment, driving patterns, emissions rates, and parking cost characteristics wherever available, rather than attempting to forecast these figures to a specific future year. With respect to Scenario 4, the impacts of daily cash-out were estimated to facilitate an additional 16 percent shift away from driving alone compared to monthly cash-out based on results from a study of daily cash-out in Minneapolis (Lari et al. 2014). A transit elasticity reflecting changes in ridership with respect to transit costs of -0.15 was used in applicable scenarios based on the research team’s literature review and conversations with the study’s peer review group.

Raw VMT reductions were scaled to account for telework expectations (between 1.4x and 3.2x pre-pandemic conditions, with the “most likely” scenario being 2x pre-pandemic conditions ¹³) in a post-pandemic near-future time. Citywide VMT reduction estimates were reported for the “most likely” telework scenario, or 2x pre-pandemic rates. Reported reductions in this analysis reflect reductions in commute VMT resulting from the modeled scenarios. Teleworkers are essentially, then, excluded from the analysis as “non-commuters.” Because of this, where raw VMT reduction results are reported as a range based on various telework expectations, larger reductions in commute VMT are expected with lower rates of telework, given a larger starting number of commuters impacted. Although not the focus of this analysis, it is important to note that teleworking also has positive benefits for reductions in VMT, congestion, emissions, and crashes.¹⁴

Impacts on congestion, emissions, and crash reduction estimates were calculated using factors relating these metrics to VMT, where the raw VMT reductions accounted for the “most likely” telework scenario. With respect to congestion, the research team evaluated three possible methods for estimating delay impacts resulting from the policy scenarios. One approach used baseline delay measures specific to each city to estimate congestion impacts from VMT linearly. Another approach applied an area-size (but non-city) specific elasticity that assessed delay in a more sophisticated and logical (i.e., non-linear) manner. Ultimately, the chosen approach used both the city-specific baseline and area-size-based elasticity to estimate changes in delay for the modeled scenarios. Presented changes in delay are relative to all peak-time VMT (not just commute VMT). Additional details on data and methodologies, including related to the selected congestion estimation process, are provided in Appendix C. Data and Analysis Methodology.

General Assumptions

The analysis of the five scenarios includes a number of assumptions that reflect, among other things, limited data and experience with voluntary and mandatory parking cash-out and a desire to serve the study objective to produce useful and comparable results across scenarios and cities. These general assumptions are described below.

Full Adoption and Compliance. The approach calculates impacts at the point of full adoption and compliance by all affected employers. The analysis does not account for changes in the adoption of parking cash-out policies that may occur over time (e.g., time for roll-out of the ordinance requirements) but presents results assuming full compliance. This document does not provide strategies for implementation and enforcement. However, Appendix A. Implementation Resources provides a brief discussion and example resources.

Free Versus Partially Subsidized Parking. Due to data limitations and sometimes vague descriptions of parking subsidies in the referenced sources, the analysis primarily considers employees with free parking as eligible for parking cash-out. Evaluating impacts of partial subsidies would require information about the percentage of employees in each city receiving partial parking subsidies of different amounts, so that cost estimates could be plugged into TRIMMS and elasticity analysis methods appropriately. Because of the focus on free parking, actual results may vary depending on the extent of partial subsidies in a given location. Additional discussion is provided in Appendix D. Additional Results, including about estimating impacts of partial subsidies for two different scenarios in the two cities where there is sufficient data to support such analysis.

Near-Term Conditions. Based on data availability, the analysis uses the most recent (pre-pandemic) available data for employment, driving patterns, emissions rates, and parking cost characteristics wherever available, rather than attempting to forecast these figures to a specific future year. As discussed, raw VMT reduction, congestion, emissions, and crash reduction estimates were scaled to account for telework expectations in a post-pandemic near-future time. However, even the near-future rates of telework are difficult to predict as the pandemic recovery evolves.

Some factors are expected to change in future years; specifically, pollutant emission rates will decrease as the current vehicle fleet is gradually replaced by cleaner vehicles, so it is important to recognize that per-vehicle emissions benefits 5 or 10 years in the future may be lower than estimated in this analysis (although other factors, such as rising employment population, would partially offset this). Other benefits, such as those related to reduced travel delays, may be higher since traffic congestion would otherwise continue to grow over time, and the greater the level of congestion, the greater the benefit of VMT reductions on congestion.

Market Parking Rates. The market parking rates used in the analysis reflect an estimate based on available parking data in the CBD. CBD parking rates were taken from the Parkopedia’s North American Parking Index (Parkopedia, 2020), which provides rates for “all publicly available paid off-street and on-street parking locations in a city center.” Because the index is focused on parking in CBDs, these prices are likely on the high end of the range for a city, especially for expansive cities such as Los Angeles and Houston, where there may be areas with low or even no parking costs. In low-cost non-CBD areas, the parking costs and corresponding cash-out amount would be lower. Cities could, as was modeled, require that cash-out values in non-CBD areas equal the average daily price of transit. In some cities, monthly CBD parking rates are high (e.g., $655 in New York City, equivalent to $34.48 per day); and assuming a non-CBD parking rate equal to a round-trip average transit cost results in a much lower parking rate or cash-out value figure for the non-CBD area (in this case, an average of $7.05 in New York). The actual non-CBD market parking rate, for which the study team did not have reliable data, could be lower or higher (parking outside the CBD in New York could still be quite expensive, for example). An average city-wide parking rate was then calculated for each city based on the weighted average of the rates reflective of the share of employment in the city in the CBD and non-CBD areas.

CBD Parking Benefits Offered at Full Market Value. The analysis assumes that employers subsidizing parking are doing so at the full market value of the parking, even though for many cities, the average CBD market value is significantly higher than the $280 per month that is allowed as exempt from payroll taxes and employee income taxes. Survey data support this assumption, as full-value employer parking subsidies were found to be much more common than partial subsidies.

No Transit Capacity Restrictions. The analysis does not account for capacity or operational restrictions that may, for example, challenge transit to accommodate significant new demand. Some regional transit systems would probably not be able to accommodate some of the increases in ridership predicted by the analysis results in this study, except if they instigated peak-shoulder travel incentives, such as those being tested by the Bay Area Rapid Transit System, to spread out peak travel. Carpooling, though, would not require special accommodations, although high-occupancy vehicle and high-occupancy toll lanes could help. It is not clear whether roadway or transit capacity constraints will be more of a limitation in the future and how that might impact mode choice.

Responsiveness to Pricing. As described in Chapter 2. Literature Review, there is a wide range of travel behavior responses to changes in trip and parking costs. Because most of the scenarios envision “all or nothing” price changes with respect to parking, and it is not possible to calculate or use elasticity if one of the prices is zero, this analysis focused on finding the price elasticity of travel, incorporating parking costs within travel costs, instead of price elasticity of parking. After reviewing many studies, the team selected an elasticity of -0.3 as the value best supported in the literature. The elasticity value and other decisions made for this study were in part validated for Scenario 1 as the results were in the range of those found in parking studies conducted in Los Angeles by Donald Shoup.

The peer review group expressed some concern that the driving reductions would not be as large as the analysis forecasts because of an “endowment effect.” This effect, shown to exist in the literature, results in people placing a higher value (and requiring a higher payment) to give up that which they possess than they would be willing to pay for the same thing in the first place. Initially, employees may not respond to a new cash-out offer the same as they would if they were forced to pay cash for parking because of the endowment effect; however, in the long term, more people will face making a decision to take a cash-out offer or parking when they are newly employed, prior to actually “possessing” the workplace parking. In this instance, forfeiting a higher wage in exchange for parking is more likely to be perceived as an actual cost.

Responsiveness to Daily Cash-Out. There is very limited research on which to base calculations of employee response to a daily cash-out offer. Indeed, the analysis here is based on only a single, small study in Minneapolis that showed a monthly parking pass with a pro-rated rebate for forfeited parking days yielding about a 24 percent reduction in parking days, which in this analysis was assumed to reflect a 16 percent mode shift (with the other 8 percent, or one-third of this reduction, assumed to have resulted from teleworking). A daily cash-out is likely to encourage more participants than a monthly cash-out because of the increased flexibility, but participants are likely to skip driving only once or twice a week. A few employees who otherwise would take the monthly cash-out might choose daily cash-out and then drive more than under the monthly option, although even without this option, those cashing out monthly could still occasionally purchase daily parking on their own.

Responsiveness to Monthly Cash-Out as Related to Daily Parking Costs. The response to a monthly cash-out offer may depend in part on the market rate for daily parking in a city in relation to monthly parking. If the price of daily parking is moderate relative to monthly parking (e.g., about 1/20th the cost of monthly parking), then employees may be more drawn to accepting a monthly cash-out offer, as the price of driving on any given day would be modest. If, on the other hand, the cost of daily parking is high relative to monthly parking (e.g., if monthly parking is discounted significantly), then employees may be reluctant to accept a monthly cash-out offer.

Crashes Scale Linearly with VMT. After consulting the peer review group convened for this research, the team believed it reasonable to assume reductions in crashes would scale linearly with reductions in VMT for this sketch-level analysis. Deviations from this standard trend during the COVID-19 pandemic might be attributed to higher levels of speeding during this time (Litman, 2022b), which is expected to dissipate as the causes start to recede (e.g., near-empty roadways and pandemic-inspired antisocial behavior).

For interested readers, Appendix C. Data and Analysis Methodology provides information on compiled data and analysis approaches (including additional discussion on congestion methods considered) for each scenario in detail.