Background

The Brice-Tussing area of Columbus was once a vibrant and viable retail district on the far east side of the city.  Over the years, retail activity shifted even further east and suddenly the area found itself out of favor with retailers of all sizes.  The preponderance of big box retail made the decline even more noticeable when preferences began to shift.

The Task

Tired of watching the Brice-Tussing area languish, the City engaged an enterprising team of consultants to study the area and identify opportunities for redevelopment.  During the study process, the team met with a variety of stakeholders including several local area commissions, residents, commercial realtors, local business leaders and potential investors.  Data was poured over and parcels were scrutinized for highest and best use as well as optimal land use and zoning.

Info

The plan is currently in the final stages of development.  In the interim, here is a page dedicated to the project and maintained by the City of Columbus.  Here you can read a midterm draft of the plan as well as view several display boards that were generated for the various open houses.

“Creating successful urban retail districts is a goal of planners and community leaders alike. But as Robert J. Gibbs points out in Principles of Urban Retail Planning and Development(2012; Wiley; 272 pp.; $80), planners may be hampered in that task by an overly romantic view of an ideal shopping area. Even in the best planned new urbanist developments, he points out, retail components often fail to live up to expectations.

….(Gibbs) explodes various myths about what makes a successful retail district and lists some of the common mistakes made by planners, business owners, and community leaders — failing to begin a project with a professional market analysis, for instance. He shies away from easy answers. While clearly in favor of the walkable retail districts that planners typically espouse, for instance, he concedes that they don’t always succeed financially.

Gibbs includes plenty of useful information on specifics such as parking. His book will be most useful to private-sector planners and those who work with public-private partnerships. But the material it contains will also be helpful to public planners dealing with zoning issues. — Ryan Smith”

Several of us here at UDG, have at one point in our lives, worked for real estate market analysts (in fact, we have several current clients that are in this line of business).  This is where we learned the value of conducting a market analysis for planning and development purposes.  Our backgrounds in GIS and Urban Planning provide us with a unique perspective on the concept of the market analysis.  We have seen more than our fair share of good and bad examples of market analysis.  If you are a city conducting a land use or comprehensive plan, it is in your best interest to include market analysis as part of the planning process.  In addition, you should thoroughly vet the analyst to make sure they understand what the goals and objectives of the plan are.  Traditional, boilerplate market analysis is not going to suffice.  Cities, and the spaces within a city, are unique.   The market analyst must be willing to approach their task as part of the entire planning team, which means they must be engaged in the process from start to finish.

Traditional market analysis does not address the goals of a land use or comprehensive plan.  There are two basic questions planners need to answer with respect to the market analysis:  1.  Is there a market and 2. how “much” should we plan for? Further, planners (and the public in general) may ask questions regarding “what it takes” to achieve the critical mass required to achieve the desired results.  For example, “how many households do we need to add, at varying income levels, to achieve the critical mass required to support a medium-sized grocery store?”

Geographic Information Systems (GIS) are the perfect tool for conducting this type of analysis and far too few analysts invest the time and money to employ a robust GIS to help them answer these spatial questions.  A GIS makes it much easier to visualize the current conditions as well as visualize future conditions – which is at the heart of the concept of planning.

Urban Decision Group has been fine tuning this very type of analysis into a service we call “Planning Market Analytics“.  Planning Market Analytics is specifically designed for informing  comprehensive or land-use plans.  Like a traditional market analysis, field observations are required but the observations must be targeted and focused on the goals at hand.  Our service focuses on a data-driven GIS model to produce predictive analytics via established methods such as Huff Modeling.

The Planning Market Analytics service is usually expensive because of its intended audience.  The audience for a traditional market analysis generally consists of developers and  financiers.  That group is looking for very specific price points, rents, and lease rates for defined product types like town homes or 2 bedroom apartments.  The planning audience, on the other hand, is focused on the larger picture.  They need to  know if a project has a  chance at being successful (is there a market?), how much space should be allocated, what infrastructure improvements will be necessary, etc.  Two different audiences require two difference approaches.

So if you’re a city, county, region or state that is engaging in city or regional planning, I agree with the letter writer above.  Do you your homework first.  It’s a nominal portion of the project cost that can literally save you millions on the back end.

If you would like more information on Planning Market Analytics and you live in North America, contact Urban Decision Group at 614-383-8447 or email Rick Stein at rstein at urbandecisiongroup.com.

Cincinnati, OH:

Cleveland, OH:

Columbus, OH:

Detroit, MI:

Indianapolis, IN:

Kansas City, MO:

Louisville, KY:

Milwaukee, WI:

Pittsburgh, PA:

St. Louis, MO:

After deciding on the cities I wanted to research, my next step was to figure out which variables to use for my ranking system. I was somewhat limited by data availability and time constraints, but I was able to narrow it down to ten variables that give a pretty idea about the quality of life in each downtown:

• Overall Pop. Change 2000-2012 (ESRI Estimated)
• Age 20-34 Pop. Change 2000-2012 (ESRI Estimated)
• Age 65+ Pop. Change 2000-2012 (ESRI Estimated)
• Housing Occupancy Rate 2012 (ESRI Estimated)
• Median Income 2012 (ESRI Estimated)
• Median Rent 2010 (2006-2010 ACS)
• Disposable Income 2012 (ESRI Estimated)
• Population Density 2012 (ESRI Estimated)
• Percentage of Workers Using Public Transit (2006-2010 American Community Survey)
• Walk Score

While there are many more variables we could look at to judge quality of life, I chose these ten subjectively because they provide a good snapshot of a downtown’s health and the data was somewhat convenient to gather. I chose the two age categories because they are good indicators of the attractiveness of living downtown. The 20-34 year old cohort typically have more disposable income than any other age group and are thus more likely to put more of their money back into the downtown economy, similar to the 65+ age cohort. I also wanted to look at Walk Score and public transit use to get an idea of the commuting patterns of each downtown and median income and median rent to measure affordability.

After collecting all of the data, I ranked each city 1-10 based on each category, with 1 being the best score and 10 being the worst. I then totaled up all ten categories to get the final rankings. They are as follows:

1. Pittsburgh- 43
2. Milwaukee- 46
3. Kansas City- 49
4. Cincinnati- 51
5. Cleveland- 51
6. St. Louis- 51
7. Louisville- 59
8. Columbus- 62
9. Indianapolis- 66
10. Detroit- 72

And the winner is…Pittsburgh! Given how much the effort the city has put in to transform its downtown as an attractive location for the creative class, I am not really shocked. While our estimates show downtown Pittsburgh losing population from 2000-2012, the latest numbers from the 2007-2011 American Community Survey show an addition of another 1,000 people, bringing it closer to the 5,000 figure from the 2000 census. What puts Pittsburgh ahead of Milwaukee in the total score is its top ranking in median income, disposable income, and population 65+. It also has the highest median rent at $935 in 2010.

On the other end of the spectrum Detroit had very high population decline, losing 20.9% of its downtown population from 2000-2012. It also has the second lowest median income at $16,736, but the fourth highest median rent, meaning that many people are likely burdened with a high rent/income ratio and have little disposable income to put back in the city. They also have the highest percentage of people using public transit to get to work at 23%, which is both an indicator of a good transit system and a lack people that cannot afford their own means of transportation.

So where does Columbus fall in all of this? Unfortunately we are towards the bottom. We rank third lowest in both median and disposable income and last in population density. Columbus is also very low in the percentage of people using public transit to get to work at just 5%, but we have the fourth highest WalkScore. One bright spot for Columbus is that we rank second highest in population 65+ at 10.9%, only behind Pittsburgh at 12.1%. It seems that more older people like this nice couple are finding it feasible to sell their house in the suburbs and move to the city where almost everything is within walking distance. I believe that this trend says a lot about the attractiveness and sense of security of downtown Columbus and why it appeals to people of all ages.

What does these rankings say about the overall health of the downtowns in the Midwest? A little or a lot depending on how much weight you put into each variable. For the purposes of this analysis each category was weighted equally, but for instance some may have feel that a city’s WalkScore is not as important as median income. There are also many economic factors that go into creating a vibrant downtown that I did not get into in this analysis and It is important to remember that there are many neighborhoods in each city that are more attractive to residential living than the central business district. It isn’t necessarily every city’s goal to create a full service live/work/play environment downtown, but it should would be nice to see. Also, in case you are interested, here are the complete Downtown Rankings.

So Franklin and Delaware Counties are bright spots in a state where most counties lose population overall, but what other information can we infer from these growth rates? A friend asked if these growth trends might indicate that individuals in Central Ohio are starting to prefer urban environments over the suburban, with Franklin County representing urbanity and Delaware County the suburbs. It’s a good question, but the short answer is… not really.

Beyond the fact that one year is not enough information to establish any sort of statistically relevant trend, that more people are moving into Franklin County matters a little less than where in Franklin County they are moving (as some have already discussed). After all, there’s a big difference between greenfield development outside of Columbus City limits and moving into the city center. It’s also important to separate natural population changes from migration. Natural population growth includes births and deaths, and the remainder are individuals moving in and out of an area.  This type of voluntary population change would obviously be the more interesting demographic for exploring a preference for urban or suburban environments.

But getting back to my friend’s question, there are some basic ways we can look at this data to get a rough idea of where things stand. Again the really interesting question isn’t whether people are moving into Franklin County, it’s where they’re going. We can get an idea of exactly where people are going in the county by disaggregating the Census data from the entire county to census block groups. Since there’s no question on the US Census about a preference for urban or suburban communities, the next step is picking some kind of proxy. I decided to use population density. Population density is usually expressed by the number of people per square mile or square kilometer. Basically, it is the ratio of people to space: a small area with a large number of individuals has a higher population density than a large area with few individuals. Higher population densities, therefore, correlate with more urban environments and lower population densities with suburban or rural areas. My thought was that if people in central Ohio were starting to prefer urban communities, then the population density of Columbus proper should increase over time. Sounds like a perfect excuse to make a few maps.

Before I get to the maps, I have to emphasize that this is a quick survey exercise. I’m an academic at heart and I’d feel horrible if I didn’t point out that I didn’t triple check my numbers, I didn’t account for natural vs. migratory population changes, and I didn’t account for the growth rate (percentage increase) of each census block, or any number or time consuming things that would better validate these results. This is a sketch of population patterns in Franklin County, Ohio, and it’s a game anyone can play. If you have some kind of access to a mapping program, I encourage you to download this free data from American FactFinder and explore on your own. It’s a fun nerdy time.

First we have Franklin County’s population density in the year 2000.  I calculated population density by dividing the population of each census block group by its area.  If I were making this map again I would probably leave the numbers in the population density scale, but at the time I thought the numbers were too confusing because the census block groups can be such small areas.  I’d also retain labels for the highways and major roads to make the map easier to understand.  Regardless, I think this map largely reflects what you’d expect if you’re familiar with Columbus or Franklin County.  The areas with the highest population density are generally within I-270 (the circular outerbelt highway) and are further concentrated immediately south and north of downtown (downtown is smack in the middle of the map within the rectangle of highways).

Next is Franklin County’s population density in 2012 using the census estimates.  You can’t see it on the map, but I decided to keep the numeric range behind the density scale (lowest, low, middle, high, highest) the exact same as in the 2000 map because I wanted to see exactly how the population densities did or did not change.  My theory was that, given the same scale, an influx of people into the city (a preference for urban living) would result in more dark blue areas around the core.

It might be a bit difficult to see at first, but instead of a steady gain in population density in the heart of the city, it seems that the population densities spread out a bit throughout the county, meaning there was actually some density loss in Columbus, particularly in the German Village/South Side area.  (It would be interesting to go through foreclosure data to see if this area was particularly affected by the housing crash – perhaps that can be a future map series.)  North of downtown in the Short North/University area held pretty steady and density gains are apparent in the communities that surround Columbus proper.

After making these maps to demonstrate why an increase in Franklin County’s population doesn’t necessarily mean an increased desire for urban living, I started to wonder if I was over complicating the issue.  Maybe it would be better to simply see how areas lost or gained population over the twelve years.  I decided to make another map that just looked at whether each census block group lost population, gained population, or held steady from 2000 – 2012.  Again, I did not differentiate between natural and migratory population change.

The green areas in this map represent a net population gain since the year 2000 while the grey displays a net population loss. This map largely confirms the 2012 population density map in that the areas that lost density experienced a net loss of population over the same time period.  It also suggests in simple binary terms that areas outside of Columbus seem to have experienced population growth in equal or greater terms as the city proper.  To double check I made one more map highlighting Columbus’s city limits.

The opaque areas of the map are Columbus proper.  This makes it a bit easier to see that although the city itself has experienced a healthy amount of growth over its entire geographic area, there were losses in eastern portion of the city and much of the growth is in the periphery.

At the end of the day, these maps confirmed my suspicions: Sorry, friend.  Franklin County’s growth rate doesn’t really mean that more people are choosing an urban life style.  However, the last map brought a bit of unexpected optimism.  Columbus may not be the densest urban environment, it may have even lost some population density since 2000, but it has experienced positive growth downtown and downtown’s surrounding neighborhoods.  I can’t help but feel that this is a good sign; if you’ve seen the development boom in central Columbus lately, you might agree.  From the mixed-use development underway at High and Hubbard in the Short North, to the construction of apartments around Columbus Commons, to the the grand opening of the downtown Hills Market, it certainly feels like there’s a renewed momentum for the central area of the city.    Perhaps in another ten or so years we’ll look at the census data and see, thanks to present day efforts, that individuals are in fact expressing a preference for urban living in Central Ohio.