NEARFP12-17 SECTION J. Appendix A Candidate Indicators.pdf

Appendix A Candidate Indicators

States, cities and foundations are increasingly establishing indicators and benchmarks by which to monitor the effectiveness of place-based (“neighborhood”) programs and initiatives. The NEA has hypothesized that its creative placemaking interventions will primarily impact communities in the following ways:

Creative placemaking will have a positive effect on artists and the surrounding arts community Residents’ attachment to communities will be affected by creative placemaking Creative placemaking will improve the quality of life for neighborhood residents Local economic conditions, including local business and property values will be positively affected

Through a previous contract, the NEA created this document as a first cut at developing a set of indicators drawn from national-level datasets that would reflect these dimensions of livability. While this is not the final set of indicators that the NEA wishes the contractor to examine under this RFP, this first cut outlines the NEA’s general thinking on the subject and gives a good sense of the kinds of indicators in which the NEA will ultimately be interested in validating through this contract.

Data and Methodology

Approach This indicators system blends primary and secondary data in order to evaluate these hypotheses. To monitor whether target places are positively impacted, the indicators system will use a battery of core indicators of neighborhood change and will track them in the 51 sites using standard data sources as discussed below.

Researchers will supplement these indicators with primary, site‐specific, data collection.

This blended approach – using both primary and secondary data—is necessary because primary data collection is extremely resource‐intensive. Fortunately analysts have cast about in the plethora of secondary data in an effort to find the right such data with which to capture the environment in which local place‐based programs operate. A community of practice has now emerged among those interested in secondary data for small‐area description and analysis. The indicators system is built upon and extends the foundation of these existing practices.

Note that although in our discussion below we may touch upon the possible inclusion of measures built on local data, such data are not as yet comparable across places or are not readily available for the diverse levels of geography in which Our Town projects are operating. This is an important note. Given that there are 51 extremely varied projects, interesting analysis that would require more detailed data, such as hedonic modeling of home values, are not possible at this initial stage of Our Town. We touch on these possible data points in the discussion below, but do not recommend that the NEA pursue resource‐intensive local or individual‐level data at the outset.

Thus our final recommendations focus on a set of quantitative data that can be pulled from national data sets to allow for:

• Sustainability of the source

• Data feeding into the indicators system cannot rely on local municipalities that may or may not be able to provide the requested data for the local geography. Although more cities have adopted open data practices, the “open gov” movement is not mature enough to ensure that municipal‐level data will be secure in the foreseeable future.

• Comprehensiveness and comparability

• Inasmuch as the Our Town grantees and projects are located across the nation and involve a host of geographic levels, the data must be nationally accessible for large to small geographies and be comparable across all Our Town places.

• Cost effectiveness

• Although higher‐cost options were considered and are discussed here, our decisions were based on low‐cost (primarily public) options for reasons of ease and sustainability. The indicators system draws heavily on the US Census American Community Survey 5‐year estimates.

• Replicability

• The indicators system is designed so that it can be easily replicated by any of a number of organizations in the future.

Conceptual Issues Place‐based Interventions often have what might be viewed as conceptually competing desired outcomes. For example, stability and attachment to community for current residents may be adversely affected by increased attractiveness and improved economic conditions that may attract new residents to the area (that is, increased vitality might engender gentrification). Organizations dedicated to community improvement make a distinction between investments that focus on people (e.g., job training, financial literacy) vs. investments that focus on place (e.g., housing development, business strip renovation). The difference is significant because public and non‐profit sector programs frequently want to improve places by helping individual people, but when people develop more human, social, or cultural capital they become enabled to move to a new place – taking the value of the investment with them.1 The aim of the Our Town place‐based initiative is to support investments that will sustainably improve the quality of life in targeted places and with current residents.

To address this, the indicators system includes indicators of neighborhood change that are meant to capture positive effects of Our Town initiatives as well as what we are calling framing indicators that may signal undesirable neighborhood “churn” or other indicators of gentrification such as rise in median incomes. In addition, primary data collection will supplement the secondary analysis partially in an effort to measure whether there is local resident turnover.

Data Analysis We must caution against collapsing the measures discussed below into one or more indices. We believe that the NEA is in agreement with us that indices, in that they tend to discard variance and are difficult to interpret for actionable information, should be avoided. Two approaches to the use of the measures included in the indicators system are called for. Instead, factor analysis should be undertaken in order to establish like communities. Through identifying latent commonalities in the measures examined below, communities could be assigned to a typology and their trajectory followed along with other communities included in their typology. This would allow a descriptive analysis and reveal communities that should be examined further – those that are “off track” or off‐ trajectory may be either positively or negatively exceptional. While factor analysis and the creation of a typology will allow for tracking, it does not easily lend itself to an understanding of what underlying conceptual measures are taking the community “off track.”2

For that, we believe that a descriptive strands analysis should be employed. This is a relatively elegant but recently‐pioneered visualization process3 that allows for quick comparison across a number of indicators and across geographic groups (a sample appears in Appendix I). It is a decision‐making tool in that it easily suggests where major differentials among communities lie. We suggest that the next phase of the development of the indicators system “tinker” with the optimal use of visualizing strands.

We recommend that the nation be used as a benchmark and all local measures by taken as a difference from the national norm. This will allow maximum comparability among indicators, especially since the indicators are not uniformly calculated. For example “length of residence” is a mean of grouped data, while commute time to work is a median. Comparability as to their relative positive or negative trajectory can be engendered by taking them as a difference from the national measure.

1 A great review is given in Bolton, Roger. Place Prosperity vs. People Prosperity' Revisited: An Old Issue with a New Angle.” Urban Studies April 1992 vol. 29 no. 2 185-203 2 Weissbourd, Robert, Ricardo Bodini and Michael He. Dynamic Neighborhoods: New Tools for Community and Economic Development. RW Ventures, September 2009.

3 Concept developed for MCIC by Rutter Design.

A note on hedonic modeling. Compelling arguments for the use of hedonic analysis to measure the impact of cultural undertakings exist.4 However, at this stage in the development of an Our Town Indicators system, a hedonic analysis is not feasible. The amount of point‐level and geographically specific data required for a hedonic analysis is prohibitive at this point for the indicators system, as discussed briefly above. One need only review the data collection suggested or undertaken by previous hedonic studies, whether for cultural interventions,5 brownfield clean‐up efforts,6 or the negative impacts of unhealthy land uses7 to understand the resource‐intensive nature of hedonic analysis.

Data Visualization We suggest the NEA explore the use of an open source mapping tool called “Weave.” It is an open source tool that was developed by the Open Indicators Consortium.8 From their website: “Weave … is a new web‐based visualization platform designed to enable visualization of any available data by anyone for any purpose. Weave is an application development platform supporting multiple levels of user proficiency – novice to advanced – as well as the ability to integrate, disseminate and visualize data at nested levels of geography.” It has been successfully implemented in a number of local instances and is unique in that it allows data to be visually linked by pairing graphical representation with mapping visualizations.9

Impact on art communities and artists

We anticipate the effect of Our Town initiatives to be felt in the development of artists and artistic communities.

Operationalizing and measuring these concepts proves difficult. Artistic activity is both informal and formal, where the formal (primarily in the form of economic activity) is more easily measured with available data. With the interest in cultural activity and participation growing in the 1990s as part of a recognition of the role that informal arts plays in community development, researchers have turned to local information sources for evidence of this activity. These information sources include primary data collection at cultural events as well as data from local governments about informal events such as block parties and street festivals. The indicators system will collect data on informal activities through primary data collection.

Local sources are also needed for information on some parts of formal activity: data on arts educators within schools, after‐school programs, etc. Local data, although considered extremely useful for modeling the impact of both informal and formal arts on local communities10 are not presently consistently available at scale for researchers. Cross‐city comparisons may become available as the appetite for “local open data” initiatives grows.11

Much of formal activity is captured in economic data that measures activities by cultural establishments or firms and by activities of individuals in creative or cultural occupations. Data on establishments and firms will be available at the “place of work” level – that is, activity is counted where the activity takes place. For example, a

4 Sheppard, Stephen. Measuring the Impact of Culture Using Hedonic Analysis. Center for Creative Community Development, October 2010.

5 Sheppard, ibid.

6 DeSousa, Christopher A., Changshan Wu and Lynne Westphal. “Assessing the Effect of Publicly Assisted Brownfield Redevelopment on Surrounding Property Values.” Economic Development Quarterly Vol 23: 2 (May 2009), pps. 95-110.

7 Nelson, Jon P. “Meta-Analysis of Airport Noise and Hedonic Property Values: Problems and Prospects.” Journal of Transportation Economics and Policy, July 2003.

8 Discussed at http://oicweave.org/ and at http://flowingdata.com/2012/02/07/weave-for-visualization-development/ 9 Oram, Andy. Update on WEAVE government data visualization software” O’Reilly Media.

http://radar.oreilly.com/2010/11/update-on-weave-government-dat.html 10 Reeves, Michelle. Measuring the Economic and Social Impact of the Arts: A Review. The Arts Council of England, May 2002.

11 Lathrop, Daniel and Laurel Ruma. Open Government: Collaboration, Transparency, and Participation in Practice. OReilly Media, 2010.

http://oicweave.org/

“downtown” symphony orchestra may consist of seventy‐five musicians from a regional labor shed, but the activity is counted in the official statistics as seventy‐five musicians employed in one central location. Establishment data are therefore limited in their ability to capture the “diffusion” of the activities of individuals apart from the places where they work.

An associated challenge is the intertwining of cultural production and cultural consumption. Markusen et al.

observe that research on cultural activities often conflate the two.12 Our Town Initiative creative placemaking projects are designed to contribute toward the livability of communities and help transform them into places with the arts at their core. In this, there is no distinction made between places that are invigorated because of increased participation either as producers or consumers. For example, does increased attendance at a street festival “count” toward arts‐related revitalization even if the attendees themselves are not artists? The placemaking answer is likely “yes,” but broadly‐available secondary sources tend to conflate the two and thus might be less‐than desirable.13

Secondary Data Sources Indicator: Median Earnings of Residents Employed in Entertainment‐Related Industries Category: Impact on Art Communities and Artists

Description: Median earnings for residents employed in entertainment‐related industries (arts, design, entertainment, sports, and media occupations).

How calculated: Median earnings is calculated by and released for major occupational categories.

Data Source: American Community Survey 5‐year Estimates (ACS); Table B24011: Occupation by Median Earnings in the Past 12 Months for the Civilian Employed Population 16 Years and Over.

Level of Geography: Census Tract, Municipality, County, State, Nation. For small geographies, data may be suppressed to protect individuals’ confidentiality

Frequency: ACS rolling 5‐year estimates, as described above.

Statistical Limitations of the Data Source: ACS sampling error, as described above. Since this measure is a median, it may not be possible to use this measure where “communities” are defined through a compilation of lower‐level geographies. In this case, the measure might best be converted from a measure of central tendency (median) to a measure of dispersion (range).

Uses and Limitations of Indicator: The use of an occupational measure allows us to capture creative activity by place of residence, and thus perhaps brings us closer to our goal of using measures that capture community‐level activity more closely linked to the Our Town initiative. However, it may contain some occupations not as closely aligned with Our Town goals, such as sports announcers, news reporters and public relations specialists.

Nevertheless it includes self‐employed individuals that are missed by most other records of economic activity as they primarily rely upon businesses that are subject to FICA regulations, as discussed below.

Indicator: Number of Employees of Art Organizations Category: Impact on Art Communities and Artists

Description: The Number of Employees of Art Organizations indicator is a measure of the percentage of total employment that is concentrated in the Arts.

12 Markusen, Ann, Gregory H. Wassall, Douglas DeNatale and Randy Cohen. “Defining the Creative Economy:

Industry and Occupational Approaches.” Economic Development Quarterly, Val 22 No 1 (February 2008), pp. 24- 45.

13 The Consumer Expenditure Survey product by the US Bureau of Labor Statistics is a valuable source of where consumers spend their resources, but it is only available for large cities and broad regions.

How calculated: The number of employees of arts organizations is considered those employees classified at the two digit code 71, “Arts, entertainment and recreation.” To determine the concentration of the workforce employed in this field, this number is taken as percentage of the total number of employees in the region. All of these figures are available through County Business Patterns.

Data Source: County Business Patterns (CBP), US Census Bureau

Level of Geography: Zip Code or County. Data may be withheld to avoid disclosing data of individual companies;

data are included in higher level totals.

Frequency: County Business Patterns data are updated annually.

Statistical Limitations of the Data: Payroll and employment data are tabulated from administrative records for single‐unit companies and a combination of administrative records and survey‐collected data for multi‐unit companies. They are not subject to sampling error, but are subject to nonsampling errors, which can be attributed to several sources: inability to identify all cases that should be in the universe; definition and classification difficulties; errors in recording or coding the data obtained; and other errors of coverage, processing, and estimation for missing or misreported data.

Uses and Limitations of Indicator: As with the majority of secondary data sets that track employment by institutions (whether private or non‐profit), County Business Patterns data invariably undercount self‐employed individuals and other kinds of contract workers. Businesses not required to pay FICA fees (Federal Insurance Contributions Act) are not generally covered by County Business Patterns. Businesses operating without an Employer Identification Number (EIN), and businesses with an EIN but without employees (called nonemployer businesses), are excluded from the County Business Patterns and ZIP Business Patterns universe. Also missed are individuals who do contract work on a free‐lance basis.

However, CBP data may be more closely aligned to the goals of indicators than are the occupational data discussed above. The Arts, Entertainment, and Recreation sector is comprised of (1) establishments that are involved in producing, promoting, or participating in live performances, events, or exhibits intended for public viewing; (2) establishments that preserve and exhibit objects and sites of historical, cultural, or educational interest; and (3) establishments that operate facilities or provide services that enable patrons to participate in recreational activities or pursue amusement, hobby, and leisure‐time interests. It does not include newspapers, advertising and public relations establishments.

Indicator: Payroll at Art Organizations Category: Impact on Art Communities and Artists

Description: The payroll at art organizations measures what percentage of income dollars in the local economy are distributed to art and art related workers. Studies have shown that across the arts, earnings are highly differential, with some securing higher levels of earnings than workers with a similar education, while others were poorly paid.14 This indicator will show how local artists are earning compared to the larger labor force.

How calculated: The payroll at arts organizations is classified at the two digit code 71, “Arts, entertainment and recreation.” To determine the concentration of the number of dollars paid in this field, this number is taken as percentage of the total payroll for the region. All of these figures are available through the County Business Patterns.

Data Source: County Business Patterns (CBP), US Census Bureau.

Level of Geography: Zip Code or County

Frequency: The County Business Patterns data is updated annually.

Statistical Limitations of the Data: Payroll and employment data are tabulated from administrative records for single‐unit companies and a combination of administrative records and survey‐collected data for multi‐unit companies. They are not subject to sampling error, but are subject to nonsampling errors, which can be attributed

14 Reeves, op. cit.

to several sources: inability to identify all cases that should be in the universe; definition and classification difficulties; errors in recording or coding the data obtained; and other errors of coverage, processing, and estimation for missing or misreported data.

Uses and Limitations of Indicator: As with the majority of secondary data sets that track employment by institutions (whether private or non‐profit), County Business Patterns data invariably undercount self‐employed individuals and other kinds of contract (“1099”) workers. Businesses operating without an Employer Identification Number (EIN), and businesses with an EIN but without employees, are excluded from the County Business Patterns and ZIP Business Patterns universe. However, as discussed in the notes to the indicator above, CBP data may be more closely aligned to the goals of indicators than are occupational data.

Attachment to Community Attachment to Community has primarily been operationalized in individual’s relationships to housing. For example, it has been demonstrated that a primary indicator of community attachment is the length of time a resident has resided in the community. Beginning with Kasarda and Janowitz’ seminal work in 1974 that compared the effects of urban density with length of residency on community attachment, research has consistently demonstrated that length of residence plays a primary role in the creation of attachment to community.15 Length of residence has been viewed as a key factor influencing community behavior and attitudes, and has been found to play an important role in the assimilation of households into the social fabric of local communities.16

A survey of the literature also finds that, due to the reduced mobility and financial stake of homeowners, they exhibit a heightened concern for the quality of their community, and these households are more socially involved in community affairs than their renting counterparts.17 18 Further, research has found a positive correlation between homeownership and contact with neighbors, as well as propensity to volunteer in their community.19

What this suggests is that housing tenure is an important determinant of community attachment, but that the importance of the overall length of neighborhood residency should not be dismissed. One might argue in fact that a long‐term renter exhibits a similar level of neighborhood attachment as his or her shorter‐termed owner counterpart. The trade‐off between tenure (owners vs. renters) and length of residency is further compounded by the reality that neighborhoods will vary with respect to the amount of housing that is actually available for ownership. We thus propose to use a measure that approximates a neighborhood’s potential for homeownership as well as measures of homeownership and length of residency.

The indicators system thus contains four secondary housing‐related measures of attachment to community:

Capacity for Homeownership, Owner Occupancy, Length of Residency, and Occupied Housing Units. It also contains a fifth measure related to travel time to work.

Secondary Data Sources Indicator: Neighborhood Capacity for Home Ownership Category: Community Attachment

15 Kasarda, John D. and Morris Janowitz. “Community Attachment in Mass Society.” American Sociological Review, Vol. 39, No. 3 (June 1974): 328-339.

16 Theodori, Gene L. “Exploring the Association between Length of Residence and Community Attachment: A Research Note.” Southern Rural Sociology Vol. 20, No. 1 (2004): 107-122.

17 Dietz, Robert D. “The Social Consequences of Homeownership.” Center for Urban and Regional Analysis. June 18, 2003.

18 Rohe, William M. and Leslie S. Stewart. “Homeownership and Neighborhood Stability.” Housing Policy Debate Vol. 7, No. 1 (1996): 37 – 81.

19 Blum, Terry C. and Paul William Kingston. “Homeownership and Social Attachment.” Sociological Perspectives, Vol. 27, No. 2 (Apr., 1984), pp. 159-180 .

http://www.jstor.org/stable/1389016

Description: Percent of all structures which are single units as a percent of all units.

How calculated: Number of structures that are single detached, single attached, mobile homes and RVs as a percent of all units.

Data Source: American Community Survey 5‐year Estimates (ACS); Table B25024, UNITS IN STRUCTURE The ACS data are released for ordinal categories of structure type: single detached, single attached, two, three or four, five to nine, 10 to 19, 20 to 49, 50 or more, mobile home; and Boat, RV, van, etc.

Level of Geography: Census Tract, Municipality, County, State, Nation. For small geographies, data may be suppressed to protect individuals’ confidentiality.

Frequency: The ACS 5‐year estimates are updated annually. The estimates are calculated using data collected over a 5 year (60 month) period. The ACS surveys approximately 2.9 million housing unit addresses annually.

Statistical Limitations of the Data: The ACS estimates are based on data from a sample of housing units and people in the population, not the full populations. For this reason, ACS estimates have a degree of uncertainty associated with them, called standard errors. The ACS calculates standard errors for each estimate produced and publishes the 90 percent confidence level margins of error (the Census Bureau standard). You can be 90 percent confident that the interval within the margin of error from the estimate includes the true value.

Uses and Limitations of Indicator: Nationally 67% of all housing units are owner‐occupied. This ranges from a low of 10% for units in structures with 10‐19 units to a high of 86% for single‐detached units. Other structures mirror that for single family: single attached units (69%), mobile homes (75%) and RVs (68%). (Author’s calculations using 2005‐2009 American Community Survey 5‐Year Estimates). Using these national rates as a benchmark we can count these structure types among those that “carry the capacity” for home ownership in a neighborhood. The amount of these types of structures is an indicator of the potential for home ownership.

At best this indicator is an approximation of a community’s homeownership potential from the supply side – the built environment. It does not account for residents’ appetite or financial capacity for homeownership. This measure can be used as a point in time and over time to track changes in capacity, with potential to speak to changing economic conditions. In addition this indicator, together with “length of residency” below avoids the potential for “penalizing” places where in fact fewer ownership options exist but attachment is nonetheless high.

20 21 It would be “experimental” in that measures of regional or neighborhood capacity have appeared primarily in research on innovation, entrepreneurship and the like, but have not been widely used in neighborhood research.

Indicator: Length of Residence Category: Community Attachment

Description: Length of Residence approximates the number of years the householder has lived at his or her current location. These are ordinal data measured at approximately 5‐year intervals.

How calculated: Length of Residence is calculated from the ACS field “Tenure by Year Householder Moved into Unit” (B25038). The question is collected only from occupied housing units, and refers to the year of the latest move by the householder. This is an ordinal measure and thus a mean for grouped data will be calculated for both renters and homeowners.

Data Source: American Community Survey 5‐year Estimate (Table GCT2504, percent of occupied housing units that were moved into in 2005 or later).

Level of Geography: Census Tract, Municipality, County, State, Nation. For small geographies, data may be suppressed to protect individuals’ confidentiality.

20 Haurin, Donald R., Dietz, Robert D. and Weinberg, Bruce, A. “The impact of Neighborhood Homeownership Rates: A Review of the Theoretical and Empirical Literature.” Journal of Housing Research, Vol. 13, No. 2 (2003):

119-151.

21 Rohe, William M. and Leslie S. Stewart. “Homeownership and Neighborhood Stability.” Housing Policy Debate Vol. 7, No. 1 (1996): 37 – 81.

Frequency: ACS rolling 5‐year estimates, as described above.

Statistical Limitations of the Data Source: ACS sampling error, as described above.

Uses and Limitations of Indicator: The use of “Length of Residence” as an indicator of community attachment is well‐accepted in research on neighborhood vitality, as is indicated by the number of references used throughout this section. Yet in the ACS, the measure is given at the ordinal level with a final bottom‐coded category (for 2009, this is “1969 and earlier”) limits variance.

Indicator: Owner Occupancy Category: Community Attachment

Description: A housing unit is considered owner occupied if the owner or co‐owner lives in the unit, either through a mortgage, other debt source, or free and clear. If a unit is not owner occupied, it is considered renter occupied, whether or not the occupant pays rent.

How calculated: Owner occupancy is calculated by dividing the number of housing units that are owner‐occupied by the total number of occupied housing units. The total universe of occupied housing units includes both owner and renter occupied units.

Data Source: American Community Survey 5‐year Estimates (ACS); Table B25003, Tenure

Level of Geography: Census Tract, Municipality, County, State, Nation. For small geographies, data may be suppressed to protect individuals’ confidentiality.

Frequency: ACS rolling 5‐year estimates, as described above.

Statistical Limitations of the Data Source: ACS sampling error, as described above.

Uses and Limitations of Indicator: While owner occupancy is often used as a measure of neighborhood stability and residents’ attachment to community, increasing levels of owner occupancy over time can indicate “gentrification” and replacement of long‐term residents. This is especially true if multi‐family housing stock is being converted from rental to condominiums or co‐ops. By combining this indicator with “ownership capacity” described above, and with benchmark indicators such as income levels, we can evaluate whether increasing homeownership is evidence of gentrification.

Indicator: Percent Occupied Category: Community Attachment

Description: A housing unit is vacant if no one is living in it at the time of the interview, unless its occupants are only temporarily absent. In addition, a vacant unit may be one which is entirely occupied by persons who have a usual residence elsewhere. New units not yet occupied are classified as vacant housing units if construction has reached a point where all exterior windows and doors are installed and final usable floors are in place.

How calculated: Percent occupancy is calculated by dividing the number of housing units that are occupied by the total number of housing units. The total universe of housing units includes both units for rent and units for sale.

Data Source: American Community Survey 5‐year Estimates (ACS); Table B25002, Occupancy Status

Level of Geography: Census Tract, Municipality, County, State, Nation. For small geographies, data may be suppressed to protect individuals’ confidentiality.

Frequency: ACS rolling 5‐year estimates, as described above.

Statistical Limitations of the Data Source: ACS sampling error, as described above.

Uses and Limitations of Indicator: Percent occupied may be an operationalization of overall neighborhood attractiveness more so than attachment. For example, it could be that very few housing units are occupied but those that are, have long‐standing occupants. It is thus important to include this indicator so that we do not over‐ emphasize the role of long‐standing residency. Long residency length may indicate that a household is “stuck” in an undesirable neighborhood.

Quality of Life

Quality of life may be thought of as a concept affected in a complex way by how individual characteristics such as physical health, social relationships, and personal beliefs interact with salient features of the built environment.

Data limitations in the availability of secondary data about individual behaviors and outcomes related to quality of life (comparable data about everyday activities, health circumstances, etc.) are not available for the various levels of geography called for in the Our Town Indicators system. This initial indicator system thus focuses on attributes of the built environment.

Many measures of “quality of life” are included in the categories above. However, there are two important categories of neighborhood stability and vitality that can be grouped under the general rubric of Quality of Life:

Access to jobs and amenities, and neighborhood conditions.

Secondary Data Sources Indicator: Mean Commute Time to Work Category: Quality of life, Access to Work and Amenities

Description: Mean commute time to work is the average length of time a worker, from the centroid of a place, commutes to his or her place of work.

How calculated: Mean commute time for all workers with a fixed location of work outside the home.

Data Source: Longitudinal Employer‐Household Dynamics Data. A combination of federal and state administrative data on employers and employees with core Census Bureau censuses and surveys

Level of Geography: Census Tract, Zip Code, Municipality, County, State, Nation. For small geographies, data may be suppressed to protect individuals’ confidentiality.

Frequency: Annual, with a two‐year lag.

Statistical Limitations of the data: LED data rely upon administrative records and thus are not sample‐designed data.

Uses and Limitations of Indicator: Studies consistently show that people prioritize leisure time over time commuting to work.22 Long commutes are associated with a lower quality of life and are indicative of a lack of nearby job opportunities. 23

Indicator: Retail Businesses Category: Quality of life, Access to Work and Amenities

Description: The presence of retail amenities in a neighborhood activates street activity, leading to more “eyes on the street” (less crime) and a more supportive local environment for resident’s everyday needs.

How calculated: Number of retail businesses, per 1,000 persons

Data Source: EASI estimates and projections of businesses and demographics. EASI's standard library of data contains over 1,900 variables created for eight separate geographies.

22 Emrath, Paul and Natalia Siniavskaia. “Household Type, Housing Choice, and Commuting Behavior.” National Association of Home Builders, Special Studies, December 7, 2009.

23 Many other scholarly articles exist, but for an informal review, see Jonah Lehrer’s (author of How We Decide) Frontal Cortex blog posting: “Commuting” (March 2010).

Level of Geography: Census Block, Census Tract, Zip Code, Municipality, County and States

Frequency: annual

Statistical Limitations of the data: Depending on the variable of interest, EASI estimates and projections can be overly sensitive to the latest estimates from the Federal Statistical System (Bureau of Labor Statistics, Census Bureau, etc. depending on the variable). Since the data are built primarily from federal secondary sources, local conditions are not well integrated into the data and there is some lag in capturing local change.

Uses and Limitations of Indicator: Studies have demonstrated that it is the number of businesses located in a neighborhood that create street‐level activity that initially signals vitality. In the long run, the size of the establishments in the form of either payroll or number of employees may be more important. In that instance, we would see more movement in the Economic Conditions indicator. Presence of Retail Establishments is meant to measure the initial appearance of new “life”. In addition, EASI data may be expensive.

Indicator: Walkscore Category: Quality of life, Access to Work and Amenities

Description: Overall, Americans’ ideal communities have a mix of housing and amenities within an easy walk, according to a 2011 Community Preference Survey. Nearly six in ten adults prefer to live in a neighborhood with a mix of houses, stores, and businesses as opposed to communities with only housing where they would need to drive to get to businesses.24

How Calculated: Street Smart Walk Score calculates a score by mapping out the walking distance to amenities in 9 different amenity categories. These categories are grocery, restaurants, shopping, coffee, banks, parks, schools, books, and entertainment. In amenity categories where depth of choice is important, we count multiple amenities in a given category. Categories are also weighted according to their importance (details below). The distance to a location, counts, and weights determine a base score of an address, which is then normalized to a score from 0 to

100. After this, an address may receive a penalty for having poor pedestrian friendliness metrics, such as having long blocks or low intersection density.

Data Source: Walkscore.com

Level of Geography: Point Level

Frequency: Data are updated in real time.

Statistical Limitations of the data: There are a number of factors that contribute to walkability that are not part of the walkscore.com algorithm, including street design, safety, pedestrian‐friendly community design, topography, and weather. Walkscore.com is an approximation, but not a direct measure, of walkability.

Uses and Limitations of Indicator: Walkscore is calculated at point‐level and thus cannot be used for Our Town projects that are not address‐based.

Indicator: Crime Category: Quality of Life, Neighborhood Conditions

Description: Numerous studies have shown that crime lowers the quality of life for residents in a neighborhood, reflecting lower property values, negative neighborhood perception, and the desire to move.25

24 Belden Russonello Strategists. “The 2011 Community Preference Survey: What Americans are looking for when deciding where to live.” March 2011.

http://www.realtor.org/wps/wcm/connect/a0806b00465fb7babfd0bfce195c5fb4/smart_growth_comm_survey_result s_2011.pdf?MOD=AJPERES 25 Diener, Ed and Eunkook Suh. “Measuring quality of life: Economic, social and subjective indicators.” Social Indicators Rsearch. (1997). 40:1 p.189-216.

Taylor, B Ralph. “The Impact of Crime on Communities.” The Annals of the American Academny of Political and Social Science. (1995). 539:1 p 28-45.

How calculated: Rate of violent offences includes all reported murder and non‐negligent manslaughter, forcible rape, robbery, and aggravated assault within a jurisdiction. Rate of property offenses includes all burglary – breaking or entering, larceny‐theft, motor vehicle theft, and arson within a jurisdiction. Both rates are calculated per 100,000 population as calculated by the U.S. Census Bureau (the UCR Program estimates population in non‐ census years).

Data Source: FBI, Uniform Crime Reports as prepared by the National Archive of Criminal Justice Data

Level of Geography: Police Department Level

Frequency: The FBI Uniform Crime Reporting (UCR) data is updated annually.

Statistical Limitations of the data: Only data for city law enforcement agencies 10,000 and over in population and county law enforcement agencies 25,000 and over in population are available. Variations in population coverage and reporting practices may cause differences in reporting from year to year. Crime rates are not available for agencies that report data for less than 12 months of a year.

Uses and Limitations of Indicator: Crime in this sense is reported crime. “Crime” may actually go up in a neighborhood initially as more residents begin to feel “attached” to their community and thus are more apt to report crime. Movement in crime statistics will thus have to be followed closely. Following convention, we suggest that crime be cleaved into two categories: property crime and personal crime, which are linked but not correlated.

(Property crime is de‐linked in more affluent neighborhoods.) 26

Indicator: Addresses not Collecting Mail Category: Quality of Life, Neighborhood Conditions

Indicator: Addresses Not Collecting Mail for 90+ Days

Category: Community Attachment

Description: Addresses Not Collecting Mail for 90+ Days measures the number of vacant residential units in a community. Vacancy rates are frequently are associated with falling property values, deterioration of the physical condition of the area, and increases safety concerns. 27

Data Source: United States Postal Service (USPS)

Level of Geography: Census Tract

How it’s calculated: Addresses Not Collecting Mail for 90+ Days is calculated by quarterly aggregate data on addresses identified by the USPS as having been "vacant" or "No‐Stat" in the previous quarter. The vacancy rate for the neighborhood is equal to the addresses not collecting mail for 90+ days as a percentage of total number of residential addresses. Both of these figures are available through the USPS.

Frequency: The USPS data are updated quarterly through 2010; data updates for 2011 have been delayed while HUD and the postal service determine how to disseminate the data.

Limitations: There are some issues with the data classifications that may lead to errors in determining the vacancy rates. In urban areas, addresses whose residents collect mail at P.O. boxes instead of through regular delivery, are counted as no‐stats, even if there may be someone living there. Addresses on rural routes that use P.O. boxes instead of delivery are not counted as addresses. Additionally, it can be very difficult to calculate a vacancy rate because technically some no‐stats are vacant too. No‐stats are addresses that have been more or less abandoned or addresses that are under construction and are not yet ready to be occupied. There is no real way to tell the two apart in the dataset.

26 Hope, Tim, Jane Bryan, Alan Trickett and Denise R. Osborn. “The Phenomena of Multiple Victimization. The Relationship between Personal and Property Crime Risk.”

27 Galster, George, Chris Hayes and Jennifer Johnson. “Identifying Robust, Parsimonious Neighborhood Indicators.”

Journal of Planning Education and Research 2005 24 (3) p. 265 -280.

Indicator: Anchor Institutions Category: Quality of life, Neighborhood Conditions

Description: Community research has focused on ways that place‐based amenities, anchor institutions, and social patterns help overcome negative neighborhood features.28 29

How calculated: The number of anchor institutions, defined as places of worship, community centers and social service agencies per 1,000 population.

Data Source: EASI estimates and projections of businesses and demographics. EASI's standard library of data contains over 1,900 variables created for eight separate geographies.

Level of Geography: Census Block, Census Tract, Zip Code, Municipality, County and States

Statistical Limitations of the data: Depending on the variable of interest, EASI estimates and projections can be overly sensitive to the latest estimates from the Federal Statistical System (Bureau of Labor Statistics, Census Bureau, etc. depending on the variable). Since the data are built primarily from federal secondary sources, local conditions are not well integrated into the data and there is some lag in capturing local change.

28 Taub,R., D. Garth Taylor and Jan Dunham, 1984. Paths of Neighborhood Change: Race and Crime in Urban America, Chicago: University of Chicago.

29 Weissbourd, Robert, Ricardo Bodini and Michael He. Dynamic Neighborhoods: New Tools for Community and Economic Development. RW Ventures, September 2009.

Economic conditions

Secondary Data Sources Indicator: Loan Amounts for Housing Property Sales Category: Economic Impact

Description: Property Values are used to measure the desirability of the property as well as the stability of the community over time. Studies have shown that there is a positive correlation between the location of artists, art and cultural districts, and art centers with residential property values, as people are commonly attracted to living near cultural resources.30

How Calculated: Property values are calculated using “Amount of Loan” field in the HMDA data. From these data, the median home purchase loan amount for the geography is calculated.

Data Source: Home Mortgage Disclosure Act (HMDA)

Level of Geography: Census Tract

Frequency: The HMDA data are updated annually.

Statistical Limitations of the Data: HMDA reporting requirements do not mandate certain lenders report all lending activity. For example, depository institutions with less than $40 million in assets, those that do not have a home or branch office in a metropolitan statistical area or a metropolitan division are not required to report under HMDA, which may lead to underrepresentation of loans in non metropolitan areas.

Uses and Limitations of Indicator: HMDA data track loan data for recent property sales which are not directly a measure of property values. It must also be noted that it is a loan amount, not the sales price of the property; the loan amount will vary with the amount of down payment made by the buyer.31

Indicator: Median Income Category: Economic Impact

Description: Median income

How Calculated: Median income is the amount which divides the income distribution into two equal groups, half having incomes above the median, half having incomes below the median. The medians for people are based on people 15 years old and over with income.

Data Source: American Community Survey 5‐year Estimates (ACS), B19013, Median income in the past 12 months.

Level of Geography: Census Tract, Municipality, County, State, Nation. For small geographies, data may be suppressed to protect individuals’ confidentiality.

Frequency: ACS rolling 5‐year estimates, as described above.

Statistical Limitations of the Data Source: ACS sampling error, as described above. Since this measure is a median, it may not be possible to use this measure where “communities” are defined through a compilation of lower‐level geographies. In this case, the measure might best be converted from a measure of central tendency (median) to a measure of dispersion (range).

Uses and Limitations of Indicator: Median income is a measure to be employed both in terms of tracking increasing economic vitality among people living in the place. However, it also can serve as an important framing indicator in that we can track the potential displacement and gentrification.

30 Sheppard Stephen C. Culture and Revitalization: The Economic Effects of MASS MoCA on its Community.

Center for Creative Community Development. C3D Report NA3.2006.

31 Pettit, Kathryn and Audrey E. Droesch. A Guide to Home Mortgage Disclosure Act Data. DataPlace by KnowledgePlex, 2008.

Indicator: Total number of Jobs Category: Economic Impact

Description: The total number of jobs measure the size of the overall economy, and as rate can represent the proportion of the population earning income. More jobs also may indicate more local spending.

How calculated: The total number of jobs is calculated as a rate per 1,000 people.

Data Source: Longitudinal Employer‐Household Dynamics (LEHD)

Level of Geography: Census Block, Census Tract, Zip Code, Municipality, County

Frequency: The Longitudinal Employer‐Household Dynamics data is updated annually, but has a significant lag (one to two years).

Statistical Limitations of the Data: For small geographies, data may be suppressed to protect individuals’ confidentiality. LED data rely upon a match of employer and employee administrative records and thus are not sample‐designed data.

Uses and Limitations of Indicator: Total number of jobs in a community does not necessarily mean that the people living in that community hold those jobs. Studies of the geographic mis‐match between residents’ location and appropriate job opportunities have consistently found that low‐income workers commute longer than their more well‐off counterparts, (thus implying they work outside their community). This is especially true of neighborhoods near re‐energizing downtown cores, such as Portland and Chicago.32

Indicator: Number of In‐Service Business Addresses Category: Economic Impact

Description: The Number of In‐Service Business Addresses indicator is a measure of the economic vitality of the community.

How Calculated: The USPS gathers data every three months on the number of business addresses to which it delivers. To normalize by size, this number is then taken as a percentage of total business addresses, whether or not they receive mail. This gives a percent of what we might call “in service” business addresses.

Data Source: United States Postal Service (USPS)

Level of Geography: Census Tract

Frequency: The USPS data are updated quarterly.

Statistical Limitations of the Data: There are a few issues with the data classifications that may lead to errors in determining the number of business addresses. In urban areas, addresses whose residents collect mail at P.O.

boxes instead of through regular delivery, are counted as no‐stats, even if there may be someone living there.

Addresses on rural routes that use P.O. boxes instead of delivery are not counted as addresses.

32 For a historical review of this issue, see Kain, John F. “The Spatial Mismatch Hypothesis: Three Decades Later.”

Housing Policy Debate.