PlanCOS Appendix F: Keystone Indicators | City of Colorado Springs

Note: These keystone indicator profiles are expected to be reviewed and refined annually to keep their data and methodologies current.

The date of this version is: February 2019

Overview

How do we know if we are achieving the PlanCOS vision? The Comprehensive Plan is shaped by the vision and a set of goals that state the community’s aspirations for the future. Keystone Indicators are established to further describe the community’s desired direction, and help monitor performance and progress towards achieving the Plan’s vision and goals.
Indicators help track and communicate progress, and can also serve as alerts to emerging problems or challenges. Characteristics of effective indicators include the following:

Relevant to the Plan’s vision and goals to track meaningful desired outcomes;
Clear and understandable and do not rely on overly complex definitions or calculations;
Defensible and grounded in quality data that can be regularly reported and can be consistently and accurately tracked over time;
Useful in making decisions that affect the community, reflecting topics the community directly or indirectly addresses through local plans, policies or implementation programs;
Interdisciplinary in that the same indicator can be used across different chapters in this Plan in conjunction with other City plans and programs:
Comparable to other regional, municipal, state or national benchmarks

These Indicator Profiles are intended to provide an overview of the general approach to the calculation and use of each indicator. As experience is gained, and new data sources or techniques become available, it is expected that adaptations to these methodologies will be made over time in order to maximize the ongoing effectiveness and value of these indicators as measures of PlanCOS progress.

How are they used?

Regular tracking of indicators can help the city staff, leaders, and community members assess whether or not PlanCOS is leading the community toward its vision and goals. While no singular indicator can paint a complete picture of progress, a suite of carefully-selected indicators can help present a compelling story of achievements and challenges related to the Comprehensive Plan vision, goals, policies and strategies. To ensure that the City is making progress toward achieving our vision and goals, the indicators are expected to be used by City staff in annual reporting along with more frequently updated online “dashboard” reporting on progress being made to achieve plan success.

A summary of each indicator is provided on the following pages. Data availability varies by indicator, and as such, the baseline years shown on the indicator graphics include the most recent year for which data are available. The trajectory, amount of change and variability of progress over time, will be different for each indicator. Depending on the indicator, the degree of direct influence on a measure will also vary. Although regular reporting on these measures is important, the overall goal is to show progress over the longer term.

Methodology

Capacity Calculations Methodology

The future development capacity of Colorado Springs was projected to identify how many households and how many square feet of employment development might exist within the current city limits at densities allowed by the current zoning code. This analysis takes into consideration environmentally sensitive areas and high density zoning overlays.

Development capacity for vacant land was calculated based on expected zoning densities for three vacant land types; Banning Lewis Ranch area, other greenfield areas, and vacant areas within the city core (infill areas).

Redevelopment capacity was calculated in three steps; assessing areas for potential accessory dwelling units, including expected densities from current Urban Renewal Plans, and adding in the maximum entitled densities for the areas of the city with the highest likelihood to change or redevelop.

It is recognized that the future densities and the mix of uses for some properties and areas are especially susceptible to uncertainty. In particular, the capacity for larger greenfield areas such as Banning Lewis Ranch should be expected to require substantial recalibration as plans and entitlements for these properties evolve and actual development patterns take shape. Similarly, for larger areas and corridors with a capacity for change (such as Downtown and mature commercial centers and corridors) recalibration will also need to occur as patterns emerge and development plan are established. At a more site and project specific level, it should also be noted that this methodology effectively averages or smooths the capacity assumptions for particular properties.
Therefore, it is recommended that this capacity analysis be recalculated on a periodic basis (at least every five years), using a recalibrated methodology that best reflects emerging trends, pattern and conditions.

Vacancy
Category	Acres	Dwelling Units	Square Feet (commercial/office/industrial)
Existing Development	N/A	192,000	78,078,000
Vacant Capacity in Banning Lewis Ranch	22,000	65,000	41,677,000
Vacant Capacity in other greenfield areas	6,000	13,000	9,607,000
Vacant Capacity in core (infill) areas	6,700	16,000	15,153,000
Total Vacant Land Capacity	34,700	94,000	66,437,000
Single Family Housing Accessory Dwelling Unit Density Increase	N/A	3,800	N/A
Redevelopment Capacity in Urban Renewal Areas	300	2,000	6,606,000
Redevelopment Capacity in Areas of Change	5,000	4,600	2,415,000
Total Redevelopment Capacity	5,300	10,400	0,021,000
Total Additional Capacity	40,000	104,400	75,458,000
Total Capacity		296,400	153,536,000

The Indicators

1. New Residential Net Density

This indicator will track the density of residential dwelling units added to the city each year compared with average net density of all existing residential properties in the city. This measure is important because it gets to the heart of the PlanCOS density vision by answering whether or not new developments are contributing positively to density. This measure is intended to account for most types of added units including those in established and newly developing areas. Because only residential parcels are included in the analysis, this net measurement approach will largely avoid concerns with accounting for other uses of property including non-residential buildings, street right-of-way and parks and open space. This indicator is intended primarily to be used as a citywide measure but may also be used to track activity and progress in priority areas identified by the City. It is also helpful to compare with the net density of all residential areas across the city.

Units of measure

Dwelling units per acre (du/ac) of land with an Assessor’s residential land use code. Comparison of densities for added new unit with existing averages.

Existing Citywide Condition

6.5 du/ac (through 2020) – Net density of all residential development
8.04 du/ac (2020) – Net density of new residential development

Goal/Trajectory

Increase over time subject to cyclical market fluctuations

Source

Assessor’s parcel data, combined with building permit data

Methodology

For the city-wide base density calculation, sum all units on parcels with a residential assessor use code and divide by the acreage of the residential parcels. For each new year added to the trend analysis, building permit data for that year will be used in lieu of the assessors use code because this yields annual better results. All residential building permits for units added are geo-coded to a parcel. In the case where multifamily units have been permitted on a larger parcel with units from prior years, a distinct polygon will be created to account for just the newly developed part of the site. This method excludes rights-of-way, parks, and non-residential development so as not to decrease density calculations when a mix of land uses or open space amenities are within the neighborhood. A more detailed methodology will be documented to assure year-over-year consistency.

Frequency of data collection and lag time for reporting

This data can be prepared annually, with a few months required to perform the analysis and quality assurance

Timeline and areas expected for change

Density trends are anticipated to vary from year to year with the expectation of longer term trends becoming evident in 5 year intervals or after major development or redevelopment projects are completed. There is expected to be some annual volatility in this measure based on fluctuations in market demand for housing types, and on the timing of building permits for larger projects.

Scale of Application

Municipal, and major subareas of the city

Statistical Confidence

100% of the city sampled for existing density. Any parcels with a building permit for added residential dwelling units can be captured annually for density changes. Dependent on careful and consistent correlation between building permit data and the Assessor’s database. Requires careful QA/QC to verify geo-referencing of building permits is accurate

Level of Effort

Some calculation required. Can be completed immediately at the end of each year using building permit and parcel data along with the related parcel improvement table. Effort to create the data and maps for each year will be considerable. However, the data for one year only has to be calculated once, and it will then be available for additional (e.g. sub-area) analysis.

Relevant Chapters

Chapter 2: Vibrant Neighborhoods

Chapter 3: Unique Places

Table: Net Density of New Residential Development

Table: Net density of new residential development
	2015	2016	2017	2018	2019	2020
Number of new units	2375	3586	3230	3585	3220	4561
Acres of property within new units	333.6	383.8	401.3	426.3	400.6	508.6
New Net Density	7.12	9.34	8.05	8.41	8.04	8.97

Table: Net Density of All Residential Development

Table: Net density of all residential development
	2015	2016	2017	2018	2019	2020
Number of units	190,496	194,082	197,312	200,897	204,117	208,678
Acres of property within residential units	29,966.8	30,350.58	30,751.88	31,178.2	31,578.78	32,087.38
All Net Density	6.36	6.39	6.42	6.44	6.5	6.5

Graph: Residential Net Density

2. Net City Lane Miles Added Compared with Development and Redevelopment

The total lane miles of streets maintained by the City are an important barometer of the efficiency of our land use patterns. By reducing the amount of new street pavement added to the city compared to the additional development activity the system serves, future street maintenance costs will be reduced because there will be less pavement to maintain per person. Environmental impacts (such as from storm water) will become more manageable. Positively affected areas of the city should become more livable at a human scale. PlanCOS ideas and priorities that contribute to this indicator include increased density in targeted activity centers and corridors, infill and redevelopment, use of technology to enhance existing transportation capacity, and recommendations for narrow local street profiles. This indicator is intended primarily to be used as a citywide measure but may also be used to track activity and progress in sub-areas of the city.

Units of Measure

Lane Miles per Dwelling Unit; Lane Miles added compared with Dwelling Units added

Relevant Chapters

Chapter 2: Vibrant Neighborhoods

Chapter 3: Unique Places

Chapter 5: Strong Connections

Existing Citywide Condition

0.25 land miles per added dwelling units (2020 permits only)

0.03 lane miles per dwelling unit overall citywide (2020)

Goal/Trajectory

Decrease in proportion of lane miles to dwelling units

Source

Colorado Springs Cartograph OMS database

Methodology

Annually request a year-end report of total lane miles from existing data base. Compare with prior year to calculate annual change. Query Assessor’s data base at the end of each year to determine total number of dwelling units added from prior year. Compare ratios. There are 6,453 square yards in an eleven foot lane one mile long.

Frequency of data collection and lag time for reporting

This data can be obtained annually, at the beginning of the year. Historical data is not available since the data was not previously tracked in the Cartograph OMS database. New street inventories are updated in the database efficiently and timely. Although there is essentially no lag obtaining queries from this database, there may be a lag in inclusion of land miles in the database.

Timeline and areas expected for change

Mid to long-term (5-10+). This measure is particularly susceptible to short term fluctuations depending on development phasing and the timing of acceptance of roadway by the City. Considering the timeline to complete developments, new or changing trends in development patterns will only start to appear after a few years.

Scale of Application

Municipal, and subareas of the city

Statistical confidence

Each segment of pavement is hand measured by an inspector and recorded as square yards.

Level of Effort

Some calculation required to convert total square yards to total lane miles.

Table: Net City Lane Miles Added

Table: Net City Lane Miles Added
	2017	2018	2019	2020
Total Lane Miles	5,849.45	5,972.75	6133.13	6232.051
Added Lane Miles	TBD	123.30	160.13	99.371
Total Dwelling Units	197,312	200,897	204,118	208,678
New DU in Infill and Redevelopment	3,230	3,858	1,227	920
Added Dwelling Units	3,230	3,585	3,221	4561
Lanes Miles Per Added Dwelling Unit		0.034	0.05	0.02
Lane Miles Per Dwelling Unit	0.030	0.030	0.03	0.03

3. Number of High Priority Neighborhood Plans Completed

High quality, targeted, responsive and representative neighborhood planning is acknowledged as essential to the success of PlanCOS because these plans provide the level of area-specific attention necessary to effectively apply the broad principles the Plan to the individual and unique neighborhoods throughout the city. Rather than keep track of how much of the city has an associated land use , the recommended indicator is the level of progress being made on plans for only those neighborhoods identified through a community and city leadership process.

Relevant Chapters

Chapter 2: Vibrant Neighborhoods

Units of Measure

Number of new or updated neighborhood plans completed

Existing Citywide Condition

1 plan was adopted in 2018, with a another anticipated to be adopted in early 2019

Goal/Trajectory

Increase over time

Source

Colorado Springs Planning Department

Methodology

Count the number of neighborhood plans adopted annually. As part of the annual reporting, more specifics can be provided on the particulars of any plans completed, or in process, as well as on other progress or programs aligned with the neighborhood planning goal.

Frequency of data collection and lag time for reporting

Immediate availability of data.

Timeline and areas expected for change

Short to Mid-term (3-5 years). High priority neighborhoods. Due to low expected numbers, and timing/resource considerations, multi-year trends will be most important.

Scale of application

Neighborhood and Municipal

Statistical confidence

High

Level of effort

Low. Plans should be readily accessible to track once adopted.

Table: Number of High Priority Neighborhood Plans Completed

Table: Number of High Priority Neighborhoods
	2015	2016	2017	2018	2019	2020
New Neighborhood Plans	1	1	1	1	2	2

4. Infill and Redevelopment Activity

Infill and redevelopment activity is identified as a key indicator because it extends across many of the themes and ideas that are priorities for this Plan. This incorporates a combination of reduced vacant acreage in core areas of the city combined with evidence of increasing comparative development activity (i.e. building permit value) in these areas. In addition to being applied to the entire core area of the city, this combined indicator can also be used to evaluate sub-areas within the overall infill area as well as to support specific infill projects or initiatives. The detailed components of this indicator are described in the Appendix.

Units of Measure

Remaining vacant acres in overall infill area
Building permit value in infill area

Relevant Chapters

Chapter 3: Unique Places

Chapter 4: Thriving Economy

Remaining Vacant Acres in Infill Area

Existing Citywide Condition

5,217 remaining vacant acres of infill (2020)

Goal/Trajectory

Decrease

Source

Colorado Springs Parcel Data

Methodology

Annual calculation in coordination with City IT/GIS. IT/GIS performs an established annual process to determine total vacant parcels in City using the Assessor’s land use codes as a beginning. Based on prior year’s data and additional review, the results then need to be “scrubbed” to remove political subdivision and other parcels with an Assessor’s designation of vacant, but with a clear other use (e.g. stormwater pond or dedicated/restricted open space). Then adjusted again using Pikes Peak Regional Building Department’s building codes for building starts and related platted parcels. This is followed by a simple query of remaining vacant acres in the established infill area polygon.

Frequency of data collection and lag time for reporting

Annual calculation usually performed mid-year for the prior year; Some lag time in determining most current status of parcels related to available air photography and the lag in the Assessor’s process of updating use codes in their data base. Source: City GIS Department with Planning & Community Development Department

Timeline and areas expected for change

Except in the event of unlikely large scale demolitions that convert previously developed property to vacant status, continuous progress is anticipated. However the rate of progress will be contingent on how robust the overall development market is. Therefore, short term fluctuations in the rate of annual change should be expected. Also, adaptive reuse projects and the highest density infill projects will have the least impact on this measure

Scale of Application

City-wide and sub-area, such as infill areas

Statistical Confidence

Relatively high over the long term; however parcel-specific choices to include or not include as vacant can have a substantial impact if these choices pertain to large parcels (for example a conversion of a large infill area parcel from a vacant to a dedicated open space designation could imply more infill progress than was really evident in a given year). Good confidence for overall data and for larger parcels. Lower confidence for smaller parcels because they are not reviewed based on level of effort. Because the database is always being improved, year over year trends may not be fully reflective of near term trends.

Level of Effort

Significant for QA/QC on the initial results of the query, effort focused on larger parcels that will have more impact on the overall result.

Table: Remaining Vacant Infill Areas

Table: Remaining Vacant Infill Areas
	2016	2017	2018	2019	2020
Vacant Infill Acres	7,333	6,564	6,295	5,564	5,217
Vacant Acres Citywide	37,661	36,013	35,062	32,401	31,207
Vacant Acres Banning Lewis Ranch	22,299	22,124	22,002	21,766	21,617

Building Permit Value in Infill Areas

Existing Citywide Condition

507 M (2020)

Goal/Trajectory

Higher or steady proportions of total permit value in infill areas, noting that overall city-wide permit value is expected to fluctuate significantly due to economic cycles.

Source

Pikes Peak Regional Building Department

Methodology

Pikes Peak Regional Building Department, GPS coordinate building permit data for residential and excludes small residential alteration permit values. Residential includes single-family and multi-family building permits valuations of each project. Commercial building permits includes electric, plumbing, HVAC, but excludes the cost of materials, labor, demolition and elevator building permit valuations for the project. Adjustments to these methods are subject to refinement.

Frequency of data collection and lag time for reporting

Annual; very little data lag

Timeline and areas expected for change

Annual fluctuations should be expected based on the overall development market. Downtown data could be susceptible to the timing of major building permit issuance. Longer term trend will be important, including proportional comparisons with the entire City.

Scale of Application

Citywide and for infill area; Downtown Partnership also collects data for downtown

Statistical Confidence

High level of confidence based on RBD data; but limited to values as reported to RBD, and subject to some geo-coding errors

Level of Effort

Low

Table: Building Permit Values in Infill Areas

Table: Building Permit Values in Infill Areas
	2016	2017	2018	2019	2020
Residential Building Permit Value in Infill Area	$264,122,374	$171,510,827	$261,248,880	$264,413,584	$217,127,471
Commercial Building Permit Value in Infill Area	$160,038,712	$198,350,522	$143,150,493	$207,686,242	$289,562,031
Total Valuation	$424,161,086	$369,861,349	$404,399,373	$472,099,826	$506,689,502

5. Housing Attainability

Improving housing affordability over time is identified and addressed as one of the cornerstone challenges and priorities in PlanCOS. This recommended indicator includes overall median single-family and multifamily housing affordability along with total homeless population counts. Together this combination of measures is intended to provide an important and helpful general barometer for progress based on the broad averages and overall counts at different levels along the economic spectrum. It will be important to also be attentive to impacts on sub-groups of housing consumers, whose needs and experience may not be fully represented by measures that focus on overall median housing costs. Likewise, although changes in the overall homeless populations provide an important measure in that area, the status of sub-groups within that overall number will be important.

Units of Measure

Single Family Home Ownership Affordability Index
Apartment Rental Affordability Index
Total Homeless Populations in El Paso County

Relevant Chapters

Chapter 2: Vibrant Neighborhoods

Chapter 3: Unique Places

Single Family Home Ownership Affordability Index/Housing Opportunity Index

Existing Citywide Condition

60.1 (2020 Q3)

Goal/Trajectory

Increase

Source

National Association of Home Builders(NAHB) and Wells Fargo

Methodology

The NAHB has established a Housing Opportunity Index (HOI) with data maintained and provided for Metropolitan Statistical Areas nation-wide, The MSA for Colorado Springs includes all of El Paso and Teller Counties. Colorado Springs will use the calculations provided as a reasonable proxy for the City. The HOI is defined at the share of homes sold in that area that are affordable to a family earning the local median income based on standard mortgage underwriting criteria. Includes new and existing homes.

For income, NAHB uses the annual median family income estimates for metropolitan areas published by the Department of Housing and Urban Development. NAHB assumes that a family can afford to spend 28 percent of its gross income on housing; this is a conventional assumption in the lending industry. That share of median income is then divided by twelve to arrive at a monthly figure.

Annual time series date will be provided to depict the change in this index over time. The City’s index can also be compared with other municipalities. The City may also calculate indices based on other AMI levels in order to evaluate changes by income level and employment sector.

Frequency of data collection and lag time for reporting

Data are available quarterly, subject to a lag time of about six weeks. It is anticipated that this data will be reported annually

Timeline and areas of expected change

Attention to annual trends will be important, although trends over a longer period will be most important. As a standard calculation, this measure will be influenced by national as well as local trends and decisions.

Scale of Application

Citywide

Statistical Confidence

High

Level of Effort

Very Low

Single Family Ownership Affordability Index

Table: Single Family Ownership Affordability Index
	2015	2016	2017	2018 (3Q)	2019	2020 Q3
Median Price	235,000	25,000	275,000	300,000	$322,000	$360,000
Housing Opportunity Index	77	76	70	61.2	69.8	60.1
Median Income	$73,000	$71,000	$73,600	$77,700	$81,400	$75,800
National Rank	97	96	127	139	136	153
Regional Rank	9	11	13	12	12	15
Affordability Index	77.4	75.7	73.6	61.2	69.8	60.1

Graph: Single Family Ownership Affordability Index

Apartment Rental Affordability Index

Existing Citywide Condition

1.26 (2020)

Goal/Trajectory

Decrease

Source

Methodology created by City staff (Community Development HUD Program Manager) using available published data sources from federal and State agencies.

Methodology

50% AMI for 3 person household obtained from federal sources; affordable rent calculated based on 30% of this monthly income. Average rent based on Apartment Association Vacancy and Rental Report. Simple calculation of ratio.

Frequency of data collection and lag time for reporting

Annual, with some of the data used in the calculation, based on prior surveys and calculations.

Timeline and areas expected for change

Although annual changes will be important, the longer term trends (.i.e. 5-years) will be most important.

Scale of Application

Citywide

Statistical Confidence

High based on formally accepted data; but only representative of the averages for one category of renter, and not necessarily reflective of the full continuum of rental affordability. Average rents are stated asking rents, and may not be fully reflective of discounts and/or leasing.

Level of Effort

no entry

Apartment Rental Affordability Index

Table: Apartment Rental Affordability Index
	2015	2016	2017	2018	2019	2020 Q3
50% AMI 3 person household	$32,850	$31,950	$33,150	$35,00	$36,650	$36,750
2 BR 1 Bath Affordable Rent	$821	$799	$829	$875	$916	$919
2BR 1 Bath Average Rent	$859	$942	$1,024	$1,070	$1,128	$1,159
Affordability Index	1.05	1.18	1.24	1.22	1.23	1.26

Graph: Apartment Rental Affordability Index

Total Homeless Population in El Paso County

Existing Citywide Condition

1,339 homeless (January 2020)

Goal/Trajectory

Decrease

Source

El Paso County, Pikes Peak Continuum of Care. Point in Time data

Methodology

Rely on existing Point in Time count which has an established methodology

Frequency of data collection and lag time for reporting

Data collected in January of every year. The report is generally made available in May of that same year.

Timeline and areas expected for change

Mid-term (around 5 years). The numbers fluctuate annually, in part based on the conditions associated with each year’s survey. and are subject to many factors including policy decisions and funding.

Sale of Application

County, State, National

Statistical Confidence

Methodology is highly replicable, and considerable resources are applied to the survey. However, results can vary based on relative resources and conditions in any given year, and there is always the potential for missing of double counting persons.

Level of Effort

Low. Data is collected by Pikes Peak Continuum of Care.

Number of Homeless People in El Paso County

Table: Number of Homeless People in El Paso County
	2015	2016	2017	2018	2019	2020
Sheltered total	830	991	958	1,038	1,562	981
Sheltered Emergency	496	591	536	652	679	621
Sheltered Transitional	334	400	422	386	439	360
Unsheltered Persons	243	311	457	513	444	358
Total Persons - HUD Count	1,073	1,302	1,415	1,551	1,562	1,339

Graph: Total Persons in HUD Count

6. Existing Downtown Measures

Progress toward making Downtown an economic and cultural center of the region will be critical to the overall success of PlanCOS. In this case, the recommended indicators are those already in place and being measured by the organizations responsible for managing Downtown program, and funding initiatives (currently coordinated through the Downtown Partnership).

Units of Measure

New residential units added annually
Value of building permit activity compared with prior years and with the overall city

Relevant Chapters

Chapter 3: Unique Places

Chapter 4: Thriving Economy

New Residential Units Added in Downtown

Existing Citywide Condition

214 new units (2020)

Goal/Trajectory

Increase or ongoing strong trends

Source

Downtown Partnership

Methodology

Developed and applied by Downtown Partnership. For Downtown dwelling units the Downtown Partnership keeps track of RBD units started and units completed within ¼ mile of the DDA boundary.

Frequency of data collection and lag time in reporting

Annual; limited lag time; somewhat dependent on when the Downtown Partnership produces the numbers

Timeline and areas expected for change

Specific to the Downtown area; Annual trends will be important; however annual numbers are expected fluctuate depending on the timing of building permit issuance for larger projects and/or the exact date of certificates of occupancy

Scale of Application

Limited to Downtown area; but comparable to city-wide numbers

Statistical Confidence

High based on RBD and limited data points that can be cross checked

Level of Effort

Very low assuming Downtown Partnership continues to collect this data

Table: Residential Units Build Downtown

Table: Residential Units Build Downtown
	2015	2016	2017	2018	2019	2020
Unit Starts	31	29	172	276	205	235
Units Delivered	-	53	195	241	0	214

Graph Residential Units Built Downtown

City Proportion of County New Residential Units

Value of Build Permit Activity in Downtown

Existing Citywide Condition

$207,555,870 (2020)

Goal/Trajectory

increase

Source

Downtown Partnership

Methodology

Building permit data is obtained from RBD by Downtown Partnership for the 80903 Zip Code and not the downtown boundaries. Plancheck valuations are the estimated cost of the project in entirety including permits, cost of materials and labor.

Frequency of data collection and lag time in reporting

Annual, subject to some potential delay based on priorities of the Downtown Partnership

Timeline and areas expected for change

Annual trends will be important, subject to cyclical economic fluctuations similar to those noted for residential units added

Scale of Application

80903 Zip Code, but comparable to city or county-wide numbers

Statistical Confidence

high

Level of Effort

Low

Total Downtown (80903 zip code) Building Permit Valuations

Table: Total Downtown Building Permit Valuations
	2015	2016	2017	2018	2019	2020
Total Plancheck Valuations	$15,024,011	$187,278,854	$112,286,927	$119,307,772	$125,472,799	$207,555,870

Graph: Total Downtown Building Permit Valuations

7. Economic Indicators

The economic indicators for PlanCOS include the following measures, each of which are available from existing data sources and are easily comparable with other jurisdictions:

These measures are chosen because together they reflect a combination of the economic outcomes PlanCOS is intended to support as well as the economic activity that will be needed to allow many of the recommendations in the Plan to be fiscally sustainable with private and public sector resources. From another perspective, many of the other recommendations of PlanCOS are intended to encourage the conditions that will be necessary to attract the economic development and workforce that will contribute to a sustainably strong economy. Although the importance of these interrelationships between high quality and attractive physical development, and a strong economy are implicitly understood, we also recognize that it will be challenging to directly tie progress with economic indicators to progress related to physical development.

Units of Measure

New residential units added annually
New jobs added that are at or above the median salary for the region.
Unemployment Rate
Median Wages Compared with State

Relevant Chapters

Chapter 4: Thriving Economy

New jobs added that are at or above the median salary for the region

Existing Citywide Condition

Data Acquisition in Process

Goal/Trajectory

increase

Source

Bureau of Labor Statistics

Methodology

Bureau of Labor Statistics reports hourly and annual 25th, median, 75th, and 90th percentile wages and the employment percent relative standard error.

The percentile wage estimate is the value of a wage below which a certain percent of workers fall. The Occupational Employment Statistics (OES) survey is a semiannual survey measuring occupational employment and wage rates for wage and salary workers in nonfarm establishments in the United States. OES estimates are constructed from a sample of about 1.2 million establishments and weighted sampled employment. Personal Wage reported for all occupations

High-paying jobs are defined as those within industries where the average earnings are above average. Utilizing the wage data and number of employees calculate the total percentage of workers in all sectors that earn above average. For 2017, these industries include: professional, scientific, and technical services; finance and insurance; manufacturing; construction; information; public administration; wholesale trade; utilities; management; and mining, oil and gas.

Frequency of data collection and lag time in reporting

To Be Determined

Timeline and areas expected for change

Mid-term (5-10 years); This measure is susceptible to influence by national economic trends.

Citywide, however based on how industries have been grouped into typologies, Spinoffs and Startups has the highest proportion of high-paying jobs, and therefore can expect the most change.

Scale of Application

Municipal, Regional, State, National

Statistical Confidence

Jobs are reported by employers, estimates from surveys, and weighted.

Level of Effort

Some effort to collect, aggregate, and calculate utilizing multiple datasets.

Table: Number of Jobs Above the Median Salary

Table: Number of Jobs Above Median Salary
	2015	2016	2017	2018	2019	2020
At or Above Median Salary (50th	Data Acquisition in Process

City-wide New Residential Units Added Annually

Existing Citywide Condition

4561Total residential units added (2020)

3080 Single Family (2020)

1481 Multi Family (2020)

Goal/Trajectory

Increase or maintenance of proportion of new units added in city compared with overall County increase; overall long term increase in dwelling units in City.

Source

Pikes Peak Regional Building Department

Methodology

Obtain annual GPS coordinate permit data for added residential units from the Pikes Peak Regional Building Department; QA/QC the data points for building codes, residential use, and city boundaries; select related parcels; prepare and maintain maps of distribution of units; perform calculations.

Frequency of data collection and lag time in reporting

Data are available monthly, but annual calculations are proposed in part because of the need to perform quality control on the data.

Timeline and areas expected for change

Annual; citywide with most activity occurring in greenfield areas, followed by redevelopment areas including downtown; Because numbers can be expected to fluctuate along with state and national trends, a proportional comparison with the County will also be important.

Scale of Application

Municipal, County, State, National

Statistical Confidence

Fairly High subject to QA/QC concern with addressing and geocoding.

Level of Effort

Low. Relatively easy to calculate.

Table: New Residential Units Added Annually

Table: New Residential Units Added Annually
	2016	2017	2018	2019	2020
Total Added County side Residential Units	4954	4854	5585	5388	6808
Total Added City Residential Units	3586	3230	3585	3220	4561
Single-family City	1952	2121	2208	2110	3080
Multifamily City	1634	1109	1377	1110	1481
City Proportion of County (of total added units)	69%	67%	64%	60%	67%

Graph: Residential Units Added

Unemployment Rate

Existing Citywide Condition

6.95% annual average (2020 Jan- Nov)

Goal/Trajectory

Maintain Low

Source

US Bureau of Labor Statistics

Methodology

The Bureau of Labor Statistics produces a monthly unemployment rate and an annual average for past years. 2018 not seasonally adjusted

Frequency of data collection and lag time in reporting

Monthly unemployment rates are reported with a lag time of 1-2 months. Official annual averages are reported in April the following year.

Timeline and areas expected for change

Mid-term (around 5 years). The numbers fluctuate annually, and are subject to many external factors.

Scale of Application

Municipal, Regional, State, National

Statistical Confidence

Each year, historical estimates from the Local Area Unemployment Statistics (LAUS) program are revised to reflect new population controls from the Census Bureau, updated input data, and re-estimation. The data for model-based areas also incorporate new seasonal adjustment, and the unadjusted estimates are controlled to new census division and U.S. totals. Sub-state area data subsequently are revised to incorporate updated inputs, re-estimation, and controlling to new statewide totals.

Level of Effort

Minimal. No calculation is necessary.

Table: Unemployment Rate (Annual Average)

Table: Unemployment Rate
	2014	2015	2016	2017	2018	2019 (Nov)	2020 (Jan)
COS Annual Unemployment Rate	6.0%	4.6%	3.7%	3.3%	3.9%	3.35%	6.79%
Colorado Unemployment Rate	5.0%	3.9%	3.3%	2.9%	3.5%	3.04%	6.95%

Graph: Unemployment Rate (Annual Average)

Median and Mean Wages Compared with State

Existing Citywide Condition

$81,629 Colorado Springs (2020) – HUD Median Household Income

No CO – HUD Median Household Income data

$19.11 Colorado Springs (2019) – BLS Median Hourly Wage

$21.28 State of Colorado (2019) – BLS Median Hourly Wage

Goal/Trajectory

Increase

Source

The source for wage data has been updated. The Housing and Urban Development (HUD) for median household income and the Bureau of Labor and Statistics (BLS) for median hourly wage. We are not using American Community Survey (US Census) – ACS 2013-2017, five year estimate at this time due to the data becoming outdated and less accurate between Censuses.

Methodology

HUD - A special tabulation of Median Family Income (MFI) estimates from the 2013-2017 5-year ACS was prepared by the U.S. Census Bureau and used by HUD as the basis for calculating HUD's FY2020 MFIs. Estimates of MFI from this tabulation are used if they are determined to be statistically reliable. For FY2020, the test for reliability is whether the margin of error for the estimate is less than 50% of the estimate itself and whether the ACS estimate is based on at least 100 survey cases.

BLS - The Occupational Employment Statistics (OES) survey is a semiannual survey measuring occupational employment and wage rates for wage and salary workers in nonfarm establishments in the United States. OES estimates are constructed from a sample of about 1.2 million establishments and weighted sampled employment. Personal Wage reported for all occupations

American Community Survey (ACS), Current Population Survey (CPS) and Annual Social and Economic (ASEC) Supplement. The CPS is a joint effort between the Bureau of Labor Statistics and the Census Bureau. Household income reported

Frequency of data collection and lag time in reporting

For Bureau of Labor and Statistics OES survey, panels and estimates are reporting semiannually.

For Census data, it is best to update the metric at the 10 year Census interval to re-calibrate. American Community Survey data is built off of a sample size while the 10 year census number attempts to survey all citizens. For either data set, there is a processing lag time of 2+ years.

Timeline and areas expected for change

Annual trends are important, but long , Colorado Springs income growth has been statistically slower than the State

Scale of Application

Municipal, Regional, State, National

Statistical Confidence

Occupational Employment Statistics are based on survey responses, panels and are adjusted. Percent relative standard error is 1.1%

American Community Survey shows a margin of error of 1%

Level of Effort

Minimal. Readily available at American Fact Finder or other census websites.

Mean Median Household Income (HUD)

Table: Mean Median Household Income
	2016	2017	2018	2019	2020
COS Mean Income	$ 70,954	$ 73,608	$77,675	$81,376	$81,629
Colorado Mean Income	No data

Graph: Median Household Income

Median Hourly Wage (ACS)

Table: Median Hourly Wage
	2016	2017	2018	2019
COS Median Wage	$17.90	$18.15	$18.61	$19.11
Coloardo Median Wage	$19.09	$19.66	$20.34	$21.28

Graph: Median Hourly Wage

8. Renowned Culture Indicators

When considered together, these renowned culture indicators provide a measure of the ongoing activity that is indicative of a rich culture throughout the city.

Creative Vitality Index
Number of Creative Jobs
Creative Industry Earnings

Existing Citywide Condition

Creative Vitality Index (2019) = 0.89

Number of Creative Jobs (2019) = 10,650

Total Industry Earnings (2019) = $751.1M

Goal/Trajectory

Increase

Source

Creative Vitality Suite (cvsuite.org)

Methodology

WESTAF © Creative VitalityTM Suite 2019, zip codes within the Colorado Springs city limits used to run regional snapshot.

The Creative Vitality Index (CVI) is an index that provides a value for the relative economic health of a region’s creative activity. The Creative Vitality Index compares the per capita concentration of creative activity in two regions. Data on creative industries, occupations, and cultural nonprofit revenues are indexed using a population-based calculation. The resulting CVI Value shows a region’s creative vitality compared to another region. The US CVI benchmark value is 1.0.

Data Sources: Economic Modeling Specialists International, National Assembly of State Arts Agencies, National Center for charitable Statistics.

Frequency of data collection and lag time in reporting

Can be computed upon request, annually, with a lag time of two or more months.

Timeline and areas expected for change

Two to five years with steady growth. Redeveloping areas of the city will expect to see growth in this sector.

Scale of Application

Citywide, or for subareas

Statistical Confidence

Based on regional economic, occupation, and non-profit reporting.

Level of Effort

Low assuming access to this tool remains available and can be coordinated with the Downtown Partnership

Relevant Chapters

Chapter 6: Renowned Culture

Creative Vitality Suite

Table: Creative Vitality Suite
	2016	2017	2018	2019	2020
Creative Vitality Index	0.90	0.99	1.08	0.89
Number of Creative jobs	10,960	11,068	11,320	10,650
Total Industry Earnings	$681,700,000	$677,700,00	$685,500,000	$751,100,000
Total Industry Sales	$2,900,000,000	$2,800,000,000	$2,600,000,000	$2,700,000,000
Cultural Nonprofit Revenues	$32,000,000	$68,300,000	$68,300,000	$27,500,000

9. Majestic Landscapes Indicators

Although it is recognized that additional factors need to be evaluated as part of a more complete measurement of the progress made toward the city’s Majestic Landscapes goals, together, these two measures provide a good sense for the level of access residents and visitors have, along with how well we are taking care of our investment in green infrastructure.

Units of Measure

Percent of City Population Within ½ Mile of a Park
Per Capita Total Funding for Parks Operations
Miles of Developed Urban and Park Trails
Percent of City Population, Area, and Employment Within ½ Mile of a Park, Trail, or Accessible Open Space Area

Relevant Chapters

Chapter 7: Majestic Landscapes

Chapter 2: Vibrant Neighborhoods

Chapter 3: Unique Places

Percent of City Population, Area, and Employment within ½ Mile of a Park, Trail, or Accessible Open Space Area

Existing Citywide Condition

74% population within 0.5 miles of only a Park (2020)

Goal/Trajectory

Increase

Source

The Trust for Public Land Park Score

Methodology

Annually municipal publications form The Trust for Public Land table. Information is sourced from the US Census Bureau, ESRI’s 2017 Demographic Forecasts, and the Trust for Public Land’s annual survey of all public and non-profit park agencies and groups in the 100 largest US cities. (Trust for Public Land)

Percent Population and Percent Employment that live and or work within 0.5 miles of park land, trail or accessible open space is not a readily available resource. A methodology would need to be created.

Frequency of data collection and lag time in reporting

Annually; this will require a rigorous GIS analysis

Timeline and areas expected for change

Expected increased access in gap areas and newly developed areas; On a City-wide basis the expectation is that there will be slow progress toward improving this measure. This is a function of existing development patterns coupled with the fact that the resources required to add facilities of this nature.

Scale of Application

Municipal, Regional; could also be evaluated for sub-areas of the City

Statistical Confidence

Relatively high

Level of Effort

low

Populations within 1/2 Mile of a Park or 10 minute Walk

Table: Populations within 1/2 mile of a park or 10 minute walk
	2015	2016	2017	2018	2019	2020
Park Land as % City Area	9.3%	9%	9%	9.3%	9.4%	9.4%
Percent Population within 1/2 Mile	70%	69%	70%	69%	74%	74%

Population, Area and Employment within ½ Mile of a Park, Trail, or Accessible Open Space Area

Table: Data Aquisition in Process

Per Capita Total Funding for Parks Operations

Existing Citywide Condition

$79.53 per capita spent annually on park operations (2020)

Goal/Trajectory

Increase

Source

Trust for Public Lands Park Score

Methodology

Frequency of data collection and lag time in reporting

Annually with little lag time, data is available by 3rd quarter of the year following the year of reporting

Timeline and areas expected for change

Short to Mid-term with a clear ability to consider year over year trends; with additional attention to longer term trends

Scale of Application

Municipal and comparable municipalities

Statistical Confidence

Medium, some discrepancies in data appear between table and fact sheets, the table was used.

Level of Effort

Low

Dollars Spent on Park Operations per Person

Table: Dollars spent on park operations per person
	2015	2016	2017	2018	2019	2020
Colorado Springs	$51	$58	$76	$81	$86.00	$79.53
Denver	$117	$109	$115	$121	$115.00	$130.01
Aurora	$103	$132	$139	$143	$145.00	$149.33
Austin, TX	$83	$92	$103	$108	$119.00	$147.03
Omaha, NE	$74	$74	$76	$79	$80.00	$78.59
Albuquerque, NM	$68	$59	$58	$62	$65.00	$62.15

Miles of Trails

On and off-street trails not only provide opportunities for active transportation alternatives (biking, walking etc.), but they also encourage additional passive recreation and access to natural landscapes throughout Colorado Springs. Tracking the miles of trails is a good indicator and benchmark for recreation access and can easily be compared to other cities and metropolitan regions.

Units of Measure

Miles of Developed Urban and Park Trails

Existing Citywide Condition

147 miles of Urban Trails (2020)

135 miles of Park Trails (2020)

Goal/Trajectory

Increase

Source

Colorado Springs trail data measured in a GIS environment.

Methodology

To measure only city-owned trails, sum all developed Tier 1, 2, 3, 4 trails in the GIS database including Cheyenne Mountain State Park trails. This may include trails that are technically outside the city limits.

Frequency of data collection and lag time in reporting

Trail data is regularly updated and available to the City.

Timeline and areas expected for change

Short to Mid-term. Attention to annual added increments will be important)

There are a number of trails that the City is already planning on developing in the coming years. Many of the large trail additions will be seen in Emerging Neighborhoods in north Colorado Springs and Banning Lewis Ranch. Additional connections are planned in Mountain Shadow, Pinecliff, and Pulpit Rock neighborhoods, and connecting the Broadmoor neighborhoods north-south.

Scale of Application

Municipal, State, National

Statistical Confidence

GIS trail data should be reasonably accurate.

Level of Effort

Readily available data

Miles of Trails

Table: Miles of Trails
	2015	2016	2017	2018	2019	2020
Urban Trails	125	125	125	138	138	147
Park Trails	135	135	135	135	135	135

10. Citywide Pedestrian, Bicycle, and Transit Infrastructure

Improving walkability and throughout the city is a cornerstone goal of PlanCOS. Increasing bicycle infrastructure and safety is also a major objective, as is taking transit to the next level especially in key activity centers and corridors. Walkscore® and its related Bikescore® and Transitscore® are nationally recognized measures for walkability and bicycle and transit access, in communities. These scores can be calculated citywide, or for areas of focus, and can be compared with other communities. However, because these measures are primarily based on a calculation of land use proximity, and do not account for the quality and design of walkable infrastructure, care should be taken in interpreting the results. This indicator can also be coupled with tracking the number of miles of bike lanes and bicycle infrastructure.

Units of Measure

Walkscore®
Bikescore®
Transitscore®
Bike Lanes, Routes, and Boulevards

Relevant Chapters

Chapter 2: Vibrant Neighborhoods

Chapter 3: Unique Places

Chapter 5: Strong Connections

Walkscore®, Bikescore®, and Transitscore®

Existing Citywide Condition

Walkscore® = 35 (2020)

Bikescore® = 45 (2020)

Transitscore® = 19 (2020)

Goal/Trajectory

Increase

Source

Walkscore.com

Methodology

Measures on a scale from 0 – 100.

Walk Score analyzes hundreds of walking routes to nearby amenities. Points are awarded based on the distance to amenities in each category. Amenities within a 5 minute walk (.25 miles) are given maximum points. A decay function is used to give points to more distant amenities, with no points given after a 30 minute walk. Walk Score also measures pedestrian friendliness by analyzing population density and road metrics such as block length and intersection density. Data sources include Google, Education.com, Open Street Map, the U.S. Census, Localeze, and places added by the Walk Score user community.

Transit Score is a patented measure of how well a location is served by public transit. Transit Score is based on data released in a standard format by public transit agencies. To calculate a Transit Score, we assign a "usefulness" value to nearby transit routes based on the frequency, type of route (rail, bus, etc.), and distance to the nearest stop on the route. The "usefulness" of all nearby routes is summed and normalized to a score between 0 - 100.

Bike Score measures whether an area is good for biking. For a given location, a Bike Score is calculated by measuring bike infrastructure (lanes, trails, etc.), hills, destinations and road connectivity, and the number of bike commuters. These component scores are based on data from the USGS, Open Street Map, and the U.S. Census.

(Walkscore.com)

Frequency of data collection and lag time in reporting

The data can be tracked annually, at the beginning of the year. Historical data is not available, however annually tracking will be part of the PlanCOS updates building historical patterns from 2018 forward.

Timeline and areas expected for change

Trends for Walkscore in particular will occur over substantial periods of time this score is contingent on citywide development patterns that will take a long time to change. The Bikescore and Transitscore measures have some potential for more rapid change if service or facilities were extended.

Scale of Application

Municipal and subareas - will differ in range

Statistical Confidence

A ubiquitous measure in urban planning.

Level of Effort

Low

Table: Walkscore®, Bikescore®, and Transitscore®

Table: Walkscore, Bikescore, and Transitscore
	2018	2019	2020	2021
Walkscore	36/100	36/100	35/100
Bikescore	42/100	42/100	45/100
Transitscore	19/100	19/100	19/100

Miles of Bike Lanes, Routes and Boulevards

Existing Citywide Condition

557 miles of bike lanes, routes, and boulevards (2020)

Goal/Trajectory

Increase

Source

Colorado Springs bike facility data, provided by Bike Plan

Methodology

Isolate and sum only miles of bike lanes, bike routes, bike boulevards, buffered bike lanes, shared lane marking, protected bike lanes and contra-flow bike lanes. Each segment is assigned a field and measured in GIS environment.

Frequency of data collection and lag time in reporting

Regularly updated in the city’s open data platform, data.coloradospring.gov

Timeline and areas expected for change

Gained access in gap areas and continued connections in Colorado Springs Bike Plan Vision Network. (2-5 years): Annual increments will be important to pay attention to as trends.

Scale of Application

Municipal, Regional

Statistical Confidence

Based on GIS bike infrastructure database calculations, relatively high

Level of Effort

Medium

Table: Miles of Bike Lanes, Routes, and Boulevards

Table: Miles of Bike Lanes, Routes, and Boulevards
	2017	2018	2019	2020
Bike Lanes	226.6	244	246.6	253.6
Bike Routes	212.4	214	214	209.9
Bike Boulevards	3	3	3.27	3.0
Buffered Bike Lanes	7.7	16	19.4	22.8
Shared Lane Marking	2.3	5.7	5.7	5.7
Protected Bike Lane	0	0.6	0.6	0.6
Contra-flow Bike Lane	0.4	0.4	0.4	0.6
Miles of Mike Lanes, Routes and Boulevards	455	484.6	545	557