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 PlanA comprehensive plan is a guiding document that provides a framework for city policies and priorities regarding the physical development of the city. It is a long-range vision of what we want our city to become and is a tool for making decisions about how that vision should be achieved. It outlines strategic steps to make the vision a reality and provides targeted and strategic planning of the physical development of the city. 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 (infillDevelopment of vacant land within previously built areas. These areas are already served by public infrastructures, such as transportation and utilities. Parks and open space are also considered infill, since they are permanent uses for vacant parcels. 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.
| 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
| 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
| 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
| 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 master planA plan for the development of a portion of the city that contains proposed land uses, a generalized transportation system, and the relationship of the area included in the plan to surrounding property., 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
| 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
| 2015 | 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
| 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
| 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
| 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
| 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
| 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
| 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
| 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
| 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)
| 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)
| 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)
| 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
| 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
| 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
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). One complicating factor is that special districts account for an increasing share of sometimes equivalent parks funding.
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
| 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
| 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®
| 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
| 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 |
