Franklin County Gentrification Trends 1990-2015




**Note: This was originally posted on March 8, 2016. However, the data went to 2014 rather than 2015 and I actually posted it without adding all the maps and other information intended.

I saw this post (http://www.citylab.com/housing/2016/03/mapping-the-resegregation-of-diverse-neighborhoods-in-4-us-cities/472086/) the other day about changing neighborhood demographics in certain cities, particularly when it comes to racial segregation and gentrification. Surprisingly, of all the maps and posts I’ve done on demographics, I hadn’t thought to do one like this. Well, now I have.

A bit of an explanation is needed for the color coding:
-For those categories marked “Steady”, the demographic listed has been the majority throughout the period, with little to no change of other demographics.
-For those mixed categories of one decline and one rise, it means that the majority demographic has declined at least 5 percentage points, while a secondary demographic has risen at least 5 percentage points.
-For the category of recent or steady integration, there are at least 2 demographics at 10% or more of the total population, as well as a 3rd demographic reaching at least 5% of the population.

A few things that stand out to me: The eastern half of the county is in much greater flux than the western half, and integration is respectable county-wide. These neighborhoods of demographic equilibrium are largely the result of increasing Hispanic and Asian populations, particularly on the Northeast and West Sides, as well as the Whitehall area. In the center core, almost all of the High Street corridor has remained Steady White, suggesting that other demographics have, so far, been unable to tap into the building boom along and adjacent to this corridor. One other thing I notice is that there are FAR more tracts with a growing black population than there are with a growing White population, suggesting that perhaps the idea of Whites moving into neighborhoods and displacing residents is not quite as big of an issue as some might believe.

Here are the integrated tracts by year, based the above criteria, and their racial breakdown.

Top 10 Tracts with the Highest White Population

1990
1. 7205: 99.6%
2. 98: 99.1%
3. 7207: 98.9%
4. 120, 9240: 98.8%
5. 7201, 7203, 80: 98.7%
6. 7922, 9440, 9752: 98.6%
7. 9751, 10601: 98.5%
8. 110, 8141, 8821, 9711, 9740: 98.4%
9. 9450, 9800: 98.3%
10. 6230, 7210: 98.2%
2015
1. 65: 98.7%
2. 6810: 97.4%
3. 6822, 9712: 97.0%
4. 98: 96.0%
5. 6721, 6950: 95.9%
6. 220: 95.8%
7. 9497: 95.6%
8. 66: 95.5%
9. 6330: 94.8%
10. 7394: 94.7%

Breakdown of # of Tracts by % of White Population
1990
95% or Higher: 80
90%-94.9%: 73
80%-89.9%: 64
70%-79.9%: 10
60%-69.9%: 11
50%-59.9%: 6
Total Majority White Tracts: 244
40%-49.9%: 7
30%-39.9%: 9
20%-29.9%: 5
10%-19.9%: 9
0.1%-9.9%: 9
0%: 0
Total Minority White Tracts: 39
2015
95% or Higher: 11
90%-94.9%: 35
80%-89.9%: 62
70%-79.9%: 52
60%-69.9%: 30
50%-59.9%: 19
Total Majority White Tracts: 209
40%-49.9%: 11
30%-39.9%: 17
20%-29.9%: 25
10%-19.9%: 15
0.1%-9.9%: 6
0%: 0
Total Minority White Tracts: 74

Top 10 Tracts with the Highest Black Population
1990
1. 730: 94.2%
2. 5420: 93.4%
3. 15, 28: 92.3%
4. 36: 91.8%
5. 5410: 91.4%
6. 7551: 91.1%
7. 7512: 90.9%
8. 23: 89.0%
9. 2520: 87.4%
10. 29: 87.2%
2015
1. 7512: 88.1%
2. 9337: 87.7%
3. 730: 84.9%
4. 7511: 83.6%
5. 23: 82.2%
6. 15: 81.9%
7. 55: 81.4%
8. 5420, 9332: 81.0%
9. 29: 80.9%
10. 8813: 79.1%

Breakdown of # of Tracts by % of Black Population
1990
95% or Higher: 0
90%-94.9%: 7
80%-89.9%: 10
70%-79.9%: 4
60%-69.9%: 8
50%-59.9%: 6
Total Majority Black Tracts: 35
40%-49.9%: 7
30%-39.9%: 10
20%-29.9%: 9
10%-19.9%: 32
0.1%-9.9%: 190
0%: 0
Total Minority Black Tracts: 248
2015
95% or Higher: 0
90%-94.9%: 0
80%-89.9%: 9
70%-79.9%: 8
60%-69.9%: 28
50%-59.9%: 9
Total Majority Black Tracts: 52
40%-49.9%: 20
30%-39.9%: 17
20%-29.9%: 24
10%-19.9%: 44
0.1%-9.9%: 126
0%: 0
Total Minority Black Tracts: 231

Top 10 Tracts with the Highest Asian Population
1990
1. 7820: 23.3%
2. 1122: 11.2%
3. 1110: 10.8%
4. 105: 9.0%
5. 1810: 8.2%
6. 6372: 7.6%
7. 6384: 7.3%
8. 1121: 7.2%
9. 6386: 6.9%
10. 6395: 6.8%
2015
1. 7820: 34.1%
2. 7721: 26.8%
3. 6230: 26.7%
4. 1122: 21.9%
5. 7830: 17.0%
6. 1110: 16.6%
7. 105: 16.2%
8. 6395: 15.5%
9. 6372: 15.3%
10. 6386: 14.9%

Breakdown of # of Tracts by % of Asian Population
1990
95% or Higher: 0
90%-94.9%: 0
80%-89.9%: 0
70%-79.9%: 0
60%-69.9%: 0
50%-59.9%: 0
Total Majority Asian Tracts: 0
40%-49.9%: 0
30%-39.9%: 0
20%-29.9%: 1
10%-19.9%: 2
0.1%-9.9%: 273
0%: 7
Total Minority Asian Tracts: 283
2015
95% or Higher: 0
90%-94.9%: 0
80%-89.9%: 0
70%-79.9%: 0
60%-69.9%: 0
50%-59.9%: 0
Total Majority Asian Tracts: 0
40%-49.9%: 0
30%-39.9%: 1
20%-29.9%: 4
10%-19.9%: 17
0.1%-9.9%: 215
0%: 46
Total Minority Asian Tracts: 283

Top 10 Tracts with the Highest Hispanic Population
1990
1. 7820: 2.9%
2. 1122, 7209: 2.5%
3. 1810, 30: 2.3%
4. 8163, 9323, 9336: 2.1%
5. 6352, 7830: 2.0%
6. 1110, 1121, 2750: 1.9%
7. 10, 32, 40, 42, 7533: 1.8%
8. 12, 17, 1901, 6353, 7041, 7199: 1.7%
9. 6, 1820, 6945, 7531, 7551, 7721, 9326, 99: 1.6%
10. 13, 2710, 6933, 7120, 7532, 8164, 8230, 8730, 103: 1.5%
2015
1. 8230: 39.3%
2. 8164: 28.7%
3. 8163: 26.4%
4. 26: 24.2%
5. 9321: 22.7%
6. 8210: 22.6%
7. 99: 21.4%
8. 9230: 21.0%
9. 7043: 19.8%
10. 6945: 18.9%

Breakdown of # of Tracts by % of Hispanic Population
1990
95% or Higher: 0
90%-94.9%: 0
80%-89.9%: 0
70%-79.9%: 0
60%-69.9%: 0
50%-59.9%: 0
Total Majority Hispanic Tracts: 0
40%-49.9%: 0
30%-39.9%: 0
20%-29.9%: 0
10%-19.9%: 0
0.1%-9.9%: 278
0%: 5
2015
95% or Higher: 0
90%-94.9%: 0
80%-89.9%: 0
70%-79.9%: 0
60%-69.9%: 0
50%-59.9%: 0
Total Majority Hispanic Tracts: 0
40%-49.9%: 0
30%-39.9%: 1
20%-29.9%: 7
10%-19.9%: 33
0.1%-9.9%: 241
0%: 9

Integrated Tracts By Year
1990: 2
2015: 98

Most Integrated Tract by Year
1990
1122: White: 76.6% Black: 9.6% Asian: 11.2% Hispanic: 2.5%
2015
7721: White: 33.9% Black: 31.2% Asian: 26.8% Hispanic: 10.1%

All in all, the data shows that the county is much less racially stratified/segregated now than it was in 1990, and that it doesn’t appear that gentrification is really affecting many areas in terms of forcing out one racial group for another.

The Recovery of Ohio’s 3-C Downtowns: Revisit

A little more than 4 years ago, I posted numbers on how Columbus, Cincinnati and Cleveland were recovering in their urban cores. See that post here: http://allcolumbusdata.com/?p=289 and here: http://allcolumbusdata.com/?p=312 That post has proven to be one of the site’s most popular. I figured it was time to take a look at their continuing changes.

You can see by the chart for the 1950 Boundary population, the urban core of each city, that all 3-Cs suffered population losses post-1950. However, the rate of losses gradually declined, and 2 of the cities, Columbus and Cincinnati, appear to be growing in this boundary since at least 2010. Cleveland continues to lose.

This is shown further by the chart below.

As far as the actual Downtowns of each, here are the population trends.

For the most part, population declines in the 3-Cs peaked around 1980, give or take a decade. Since then, all of them have seen increases, with Cleveland seeing the most rapid increase and Cincinnati the least. Columbus has seen steady, but increasingly rapid growth with each subsequent decade since 1980.

Here is the chart for Downtown growth by decade.

Census Tract Population Density 2015

The US Census recently released population data for census tracts. I figured midway through the decade would be a good point to update where these stand because they give greater insight in smaller-scale population changes. I looked at all the census tracts in Franklin County and came up with the following map series.

First, the population in 2015.

Next, the population density of tracts in 2010, as reference.

And now 2015.

On the surface, it’s difficult to see the changes, but put side by side, you can tell there have been a lot of increases across the county. To make this more visible, I made the following maps.

You can see that some of the strongest density increases occurred around Downtown and the Short North, New Albany, parts of the Campus area, and Dublin.

The map above gives a straightforward look at where the density increased and decreased. As you can see, the increases FAR outweighed the decreases. Most of the latter were scattered except across the Far South Side and parts of the Whitehall area.

Here were the top 20 most dense census tracts in 2015.
1. 1810: 29,508.2 South Campus/Victorian Village
2. 1121: 25,287.9 Main Campus
3. 13: 21,961.4 Campus/Indianola Terrace
4. 1110: 18168.6 North Campus/Tuttle Park
5. 10: 17386.3 Campus/SoHud
6. 12: 16,981.9 Campus/Iuka Ravine
7. 20: 13,030.5 Short North/Victorian Village
8. 17: 12,872.3 Weinland Park
9. 6: 12,153.6 Old North Columbus
10. 21: 10,853.5 Short North/High Street
11. 8163: 10,255.3 Lincoln Village/Southwest Columbus
12: 4810: 9,557.4 South Central Hilltop
13. 47: 9,492.7 North Central Hilltop
14. 6352: 9,434.0 Northwest Columbus/Henderson Road
15. 57: 9,257.4 Brewery District/South German Village
16. 5: 9,177.9 Old North Columbus
17. 6933: 9,090.9 Forest Park East
18. 16: 8,980.5 Weinland Park
19. 4620: 8,928.6 North Central Hilltop
20. 1820: 8743.3 Victorian Village

It’s obvious that the High Street corridor is the most dense of the city, racking up most of the top 20.

Now here are the 20 tracts with the largest density increases 2010-2015.
1. 1121: 4,375.9
2. 6: 2,178.5
3. 21: 1,934.9
4. 22: 1,478.1
5. 40: 1,107.7 South Downtown
6. 1820: 1,044.1
7. 20: 921.7
8. 38: 904.3 Old Towne East
9. 5: 861.2
10. 210: 833.9 Clintonville
11. 32: 751.1 Arena District West/West Victorian Village
12. 730: 736.9
13. 7551: 656.0 Somerset/South Easton
14. 7951: 610.4 West Columbus
15. 6372: 574.6 Hayden Falls/Sawmill Road
16. 7209: 514 New Albany
17. 7395: 497.6 Blacklick/East Broad
18. 10: 492.8
19. 8230: 449.3 Westland
20. 710: 447.3 West-Central Linden

And finally, the top 20 largest declines 2010-2015.
1. 13: -2,964.3
2. 12: -1,625.1
3. 42: -1,620.8 Scioto Peninsula/East Franklinton
4. 920: -902.2 Northeast Linden
5. 17: -775.4
6. 50: -554.4 Franklinton
7. 61: -485.7 South High Street
8. 59: -441.9 Near South Side/Deshler Park
9. 4620: -380.4
10. 720: -380.2
11. 4610: -335.4
12. 820: -305.4 North Linden
13. 7721: -305.2 North Linden
14. 45: -258.1 North Hilltop
15. 60: -253.2 Vassor Village
16. 810: North Central Linden
17. 7532: -240.3 Morse Road/Easton
18. 2520: -240.1 Near East Side/King-Lincoln
19. 47: -206.6
20. 9333: -194.9 Linwood

So there you have it.

Census Tract Income 2010-2015

The US Census recently released demographic information for census tracts for 2015. Here are some quick maps for Franklin County for median household income.

First, median household income for both 2010 and 2015.

And the % change between 2010-2015.

As can be seen, a lot of the greatest improvements over the 5-year period were around Downtown, the Near East Side, North High, South High and around some of the higher-income suburbs like Upper Arlington and the New Albany area.

Housing Trends of Columbus

***Originally Posted May 23, 2014, updated with 2014 data 9/18/2015 and again on 5/29/2016 with 2015 data***

I posted a graph recently showing housing permits for Franklin County to show how construction was trending. Today, I found more long-term data for both the city and county that continue to show some interesting trends.

First, let’s look at just the city of Columbus.

The chart above goes back through the mid-1990s. The first thing to notice is the housing boom from 1999-2002. Both single-family and multi-family construction was booming. The very good economic conditions, or seemingly good ones, during the 1999-2000 period is probably most responsible for this. What’s most interesting is that the boom seemed to last through at least part of the mild recession experienced in 2001-2002. After that, housing of both types started to decline through the late 2000s. This shows that construction in the city began to decline as early as 2002-2003, before the peak of the general housing boom in the mid-2000s.

Another interesting fact is at the end of the period. Multi-family units have recovered and are back in boom territory. This boom, however, is much different than the one that occurred more than a decade ago, as shown by the below chart.

During the 1999-2002 housing boom, multi-family housing averaged 59.3% of all the units constructed. In the current boom, which began in 2012, multi-family housing has averaged 81.4% of all the units constructed. The average difference between the types 1999-2002 was just 18.6 points. In the current boom, the difference is almost 63 points! In that regard, there really is no comparison between the housing boom a decade ago and the current one. Multi-family construction is in MUCH higher relative demand now than it was at any time in the last 20 years, including during the last housing boom.

But what does this tell us about where the housing is actually being constructed? Well, for that, we have to look at the entirety of Franklin County. Is the county also seeing a similar multi-family boom, or has single-family construction recovered there more than in the city?

This chart, in some aspects, is the opposite of the one for the city. While in the city, multi-family units consistently outnumbered single-family, the opposite is true for the county as a whole. This is likely because the county takes into account all the suburban areas, most of which are dominated by single-family housing. In only a few instances did multi-family housing units outnumber single-family before 2010. After 2010, it’s clear that the multi-family boom is hitting the rest of the county and not just Columbus itself. This may actually represent an even greater shift in housing construction. While it appeared that single-family construction was gradually rising since 2011, it once again fell off some in 2015 while multi-family went up. It appears that the new reality is, at least for now, holding steady.

Here’s the % of total chart for the county.

So it’s also clear that the county is seeing most of its construction in recent years be multi-family units.