The Origins of the Columbus Metro’s Domestic Migration




Top 30 Largest Net Domestic In-Migration Origins (Ohio Counties and States)

Numbers are based on estimates. Updated 1/24/2018 with 2011-2015 data.

2006-2010————————2009-2013—————————-2011-2015
1. Cuyahoga: 1602———-1. Cuyahoga: 1905————–1. Cuyahoga: 1842
2. Montgomery: 1020——-2. Michigan: 1425—————-2. Michigan: 1239
3. Michigan: 893————-3. Montgomery: 1123————3. Montgomery: 1088
4. Maryland: 745————-4. Summit: 744——————–4. Summit: 764
5. Lorain: 740—————–5. Lorain: 715———————-5. Lucas: 626
6. Virginia: 636—————6. Indiana: 694———————6. New Jersey: 608
7. Mahoning: 603————7. Lucas: 569———————–7. New York: 575
8. Stark: 584——————8. Maryland: 512——————-8. Medina: 572
9. Lucas: 554—————–9. Hamilton: 504——————–9. Stark: 484
10. Summit: 531————-10. Clermont: 466—————–10. Trumbull: 465
11. Highland: 499———–11. Stark: 466———————–11. Maryland: 464
12. New Jersey: 497——-12. Arizona: 463——————–12. Allen: 406
13. Hamilton: 483———–13. Alabama: 431——————-13. Washington (state): 399
14. New York: 419———-14. Trumbull: 401——————-14. Erie: 386
15. Allen: 384—————-15. Mahoning: 387——————15. Indiana: 386
16. Tennessee: 375——–16. Fayette: 354———————16. Massachusetts: 384
17. Logan: 328—————17. Washington (state): 353—–17. Pennsylvania: 371
18. Trumbull: 325————18. Coshocton: 346—————-18. Kentucky: 368
19. Coshocton: 310———19. Medina: 322——————–19. W. Virginia: 339
20. Jefferson: 290———–20. Allen: 302————————20. Lake: 316
21. Scioto: 259—————21. Erie: 290————————-21. Belmont: 314
22. Belmont: 254————22. Highland: 270——————-22. Wayne: 298
23. Huron: 245—————23. Puerto Rico: 265—————23. Fayette: 290
24. Darke: 217—————24. Adams: 260———————24. Mahoning: 289
25. Lake: 212—————-25. Warren: 260———————25. New Hampshire: 288
26. Tuscarawas: 202——-26. Massachusetts: 259———-26. Alaska: 282
27. Iowa: 200—————–27. Wayne: 259———————27. Alabama: 280
28. Shelby: 199————–28. Morgan: 255——————–28. Lorain: 277
29. Medina: 196————-29. Tuscarawas: 253————–29. Tuscarawas: 277
30. Massachusetts: 192—30. Ashtabula: 244—————–30. Geauga: 261

Top 30 Largest Net Domestic Out-Migration Destinations (Ohio counties and States)

2006-2010——————————-2009-2013—————————-2011-2015
1. Texas: -1371———————-1. Georgia: -1024—————-1. Florida: -1333
2. Knox: -942————————-2. Florida: -1013——————2. Missouri: -703
3. North Carolina: -782————3. Greene: -524——————-3. Georgia: -680
4. Georgia: -718———————4. Missouri: -516——————4. Athens: -607
5. Athens: -679———————-5. Colorado: -448—————–5. Knox: -506
6. Kentucky: -516——————-6. California: -436—————–6. Tennessee: -442
7. South Carolina: -499———–7. South Carolina: -431———-7. Colorado: -435
8. California: -364——————-8. Knox: -418———————-8. California: -391
9. Florida: -360———————-9. North Carolina: -417———-9. Greene: -388
10. Wood: -351———————10. Wisconsin: -395————–10. South Carolina: -362
11. Richland: -344——————11. Athens: -336——————11. Marion: -329
12. Greene: -239——————–12. Minnesota: -308————-12. Hamilton: -312
13. West Virginia: -236————13. Utah: -290———————13. Logan: -306
14. Missouri: -219——————-14. Richland: -266—————14. Utah: -300
15. Crawford: -209——————15. Portage: -265—————–15. Wood: -282
16. Hardin: -179———————16. Kentucky: -257—————16. Scioto: -249
17. Noble: -177———————-17. Logan: -242——————-17. Seneca: -183
18. Muskingum: -175—————18. Pennsylvania: -242———18. Champaign: -174
19. Butler: -173———————-19. Tennessee: -200————19. Oregon: -158
20. Holmes: -163——————–20. Oregon: -187—————-20. New Mexico: -157
21. Marion: -138———————21. Wood: -166——————21. Meigs: -150
22. Portage: -134——————-22. Sandusky: -157————–22. Mississippi: -146
23. Ottawa: -131——————–23. Mississippi: -151————-23. Portage: -142
24. Sandusky: -124—————-24. Jefferson: -127—————24. Idaho: -137
25. Oregon: -120——————-25. Kansas: -98——————-25. Minnesota: -125
26. Indiana: -116——————-26. Delaware (state): -88——-26. North Dakota: -112
27. Idaho: -115———————27. Idaho: -74———————-27. Wisconsin: -111
28. Utah: -103———————- 28. Crawford: -73—————–28. Darke: -103
29. Fayette: -93———————29. Hardin: -68——————–29. Texas: -95
30. Kansas: -90———————30. Seneca: -66——————-30. Hardin: -87

Top 25 Largest Positive Swings Between 2006-2010 and 2011-2015
1. Texas: +1276
2. North Carolina: +982
3. Kentucky: +884
4. West Virginia: +575
5. Indiana: +502
6. Washington (state): +466
7. Knox: +436
8. Richland: +406
9. Butler: +395
10. Fayette: +383
11. Medina: +376
12. Alaska: +364
13. Michigan: +346
14. Alabama: +298
15. Clinton: +282
16. Erie: +263
17. New Hampshire: +261
18. Lawrence: +241
19. Cuyahoga: +240
20. Summit: +233
21. Wayne: +226
22. Crawford: +221
23. Muskingum: +211
24. Clermont: +198
25. Nevada: +197

Top 25 Largest Negative Swings Between 2006-2010 and 2011-2015
1. Florida: -973
2. Tennessee: -817
3. Hamilton: -795
4. Logan: -634
5. Colorado: -598
6. Scioto: -508
7. Highland: -491
8. Missouri: -484
9. Lorain: -463
10. Virginia: -437
11. Darke: -320
12. Mahoning: -314
13. Champaign: -310
14. Jefferson: -301
15. Maryland: -281
16. New Mexico: -261
17. Minnesota: -249
19. Coshocton: -233
20. Washington (county): -208
21. Ashland: -202
22. Utah: -197
23. Marion: -191
24. Seneca: -181
25. Iowa: -158

Total Counts By Period
Positive Ohio Counties
2006-2010: 53
2009-2013: 57
2011-2015: 50

Positive States, including DC and Puerto Rico
2006-2010: 21
2009-2013: 24
2011-2015: 28

Total Net In-Migration
Ohio
2006-2010: +8,008
2009-2013: +11,366
2011-2015: +7,895

Outside Ohio
2006-2010: -1,158
2009-2013: -466
2011-2015: +1,598

Ohio and Outside Ohio
2006-2010: +6,850
2009-2013: +10,900
2011-2015: +9,493

All these figures show that the Columbus metro has net positive domestic migration. While the majority of that comes from within the state, Columbus’ previously negative net total from outside the state has more recently become positive as well. For a long time, Columbus’ relative success was not well-known outside of the state, but perhaps word is finally getting out.

For a lot more Columbus demographic information, check out: Columbus Demographics




Where Racial Groups are Growing Fastest in Franklin County




The US Census recently released updated estimates for 2016 for smaller-area designations like tracts and blocks. Looking at them, I wanted to see where individual racial groups were growing the fastest at that level.
The first map is based on the % change from 2010 to 2016.

What’s interesting about this map is that it is such a hodgepodge. No single part of the county is dominated by growth in any specific racial group. However, a few things can be generally determined. For example, almost all of the tracts where the White population is growing the fastest are within I-270, and the majority of those within the eastern half of the Columbus in what have long been dominated by Black majority populations. These areas include parts of Linden, the Near South and Near East sides. That said, the White population was growing the fastest in just 30 census tracts by % change. This compared to 53 for the Black population, 83 for the Asian population and 107 for the Hispanic population.

The next map takes a slightly different approach, measuring the TOTAL change in population, rather than by %.

Again, a hodgepodge, but much less so than before. Instead of being the fastest-growing in just 30 tracts, the White population rockets up to 108 tracts. This shows that, while Asian and Hispanic populations have respectable % growth, this is largely based on comparatively small population bases. Still, non-White populations are clearly making inroads throughout Franklin County.

For more information on demographics, go to: Columbus Demographics
And for Franklin County racial and economic maps, go to: Census Tract and Zip Code Maps




Historic Halloween Weather

1981-2010 Halloween Averages
High: 61
Low: 41
Mean: 51
Precipitation: 0.09″
Snowfall: 0.0″

Top 20 Warmest Halloween Highs
1. 1950: 83
2. 1974: 80
3. 1900: 79
4. 1927, 1933: 78
5. 1979, 2003: 75
6. 1882, 1901, 1982, 1999: 74
7. 1909, 1990: 73
8. 1888, 1935, 1944, 1987: 72
9. 1915, 1971: 71
10. 1919: 70
11. 1891, 1986, 2001, 2008: 69
12. 1929, 1956, 1978, 1981, 1985, 2005, 2007: 68
13. 1903, 1943, 1946, 1952, 1991, 2013: 67
14. 1902, 1916, 1953, 1965, 1984: 66
15. 1942, 1958, 1961, 1968, 1969, 2009: 65
16. 1896, 1897, 1912, 1921, 1922, 1940, 1945, 1964, 1997, 2004: 64
17. 1914, 1941, 1970, 2000, 2006: 63
18. 1886, 1924, 1934, 1959, 1977, 1983, 1998: 62
19. 1892, 1938, 1989, 1995: 61
20. 1963, 1966, 1994: 60

Top 20 Coldest Halloween Highs
1. 1906: 38
2. 1993: 39
4. 1878, 1895: 40
5. 1913, 1923, 1954: 41
6. 1885, 1917, 2012: 42
7. 1890: 43
8. 1908, 1925, 1926: 44
9. 1898, 1905: 45
10. 1930, 1976: 46
11. 1879, 1931, 2002: 47
12. 1894, 1918, 1939: 48
13. 1955, 1962, 1996, 2014: 49
14. 1880, 1972: 50
15. 1973, 2010, 2011: 52
16. 1907, 1951, 1988: 53
17. 1887, 1893, 1932, 2015: 54
18. 1883, 1928, 1949, 1975: 55
19. 1899, 1967: 56
20. 1884, 1911, 1937, 1957, 1980, 1992: 57

Top 20 Warmest Halloween Lows
1. 1919: 61
2. 1882: 60
3. 2003: 59
4. 1927, 1929: 58
5. 1900, 1956, 2013: 57
6. 1921, 1941, 1982: 56
7. 1950: 55
8. 1959, 1979: 54
9. 1971: 53
10. 1881, 1891, 1933, 1946, 1974, 1991: 52
11. 1901, 1984, 1998: 51
12. 1947: 50
13. 1961, 2001: 49
14. 1935, 1948, 1960, 1967, 1985, 1995, 1999: 48
15. 1889, 1896, 1909, 1943, 1977: 47
16. 1924, 1945, 2004, 2016: 46
17. 1899, 1911, 1916: 45
18. 1903, 1940, 2015: 44
19. 1965, 1970, 1973, 1981, 1989, 1990, 1992, 1997, 2009: 43
20. 1888, 1942, 1957, 1994, 2006: 42

Top 20 Coldest Halloween Lows
1. 1887: 20
2. 1962, 1988: 25
3. 1923: 27
4. 1908, 1925: 28
5. 1885, 1893, 1913, 1917, 1953, 1975: 29
6. 1904, 1906: 30
7. 1878, 1938, 1954, 1968: 31
8. 1928, 1934, 1949, 1958, 1964, 1976, 1980, 2000: 32
9. 1879, 1926, 1930: 33
10. 1890, 1936, 1951, 1966, 1969, 1993, 1996, 2002, 2008, 2010: 34
11. 1892, 1920: 35
12. 1894, 1895, 1932, 1955, 1978: 36
13. 1910, 1983: 37
14. 1886, 1898, 1914, 1939, 1944, 1963, 2007, 2012, 2014: 38
15. 1884, 1905, 1918, 1937, 2005, 2011: 39
16. 1880, 1883, 1907, 1952, 1972, 1986, 1987: 40
17. 1897, 1902, 1912, 1915, 1922, 1931: 41
18. 1888, 1942, 1957, 1994, 2006: 42
19. 1965, 1970, 1973, 1981, 1989, 1990, 1992, 1997, 2009: 43
20. 1903, 1940, 2015: 44

Top 20 Wettest Halloweens
1. 1932: 1.44″
2. 2009: 1.21″
3. 2013: 0.98″
4. 1941: 0.97″
5. 1919: 0.91″
6. 1942: 0.51″
7. 1960: 0.45″
8. 1905, 1973: 0.43″
9. 2006: 0.36″
10. 1989: 0.34″
11. 1976: 0.32″
12. 1993: 0.31″
13. 1972: 0.29″
14. 1994: 0.26″
15. 1895: 0.23″
16. 1959: 0.20″
17. 1948: 0.18″
18. 1889: 0.17″
19. 1921, 1951: 0.15″
20. 1963, 1967, 2012, 2014: 0.11″

Snowiest Halloweens
1. 1993: 1.0″
2. 1954: 0.2″
3. 1906, 1917, 1926, 1930, 1951, 2012: Trace

Greatest Snow Depth
1. 1954: Trace

Fall Weather Correlation to Winter Severity?




As we go into the winter season, it’s time to talk about how this one might end up. There’s a belief that fall weather is a good sign of how cold or warm winter will be. How true is that for Columbus? Also, what might any correlation mean for the winter of 2017-2018?

First, let’s just look at October temperatures.
The October normal mean temperature for Columbus is 55 degrees.

Between 1878 and 2016, there have been 47 Octobers that featured a mean temperature of 53.9 degrees or lower, what we’re considering a Cold October for the purposes of this comparison.
Of those 47 Octobers, 27 of the 47 had following winters that were colder than normal, or 57.4%, 13 had average temperature winters, or 27.7%, and the remaining 7 were warmer than normal, or 14.9%.
Interestingly, this category contains both the warmest winter on record- 1889-1890 and the coldest on record- 1976-1977- as shown by the chart below.

Next, we look at Normal Octobers, which are +/- 1 degree of the 1981-2010 Average of 55 degrees.
Between 1878 and 2016, there were 45 normal Octobers. Of those, 21 had colder than normal following winters, or 46.7%. 11 were followed by normal winters, or 24.4%, and 13 had warmer than normal winters, or 28.9%.

Finally, let’s look at warm Octobers, which are those with means of 56.1 degrees or higher. There were 46 Octobers with warmer than normal means since 1878. Of those, 18 featured following winters that were colder than normal, or 39.1%. Another 18, or 39.1%, were followed by average winters. The final 10 winters were warmer than normal. Here’s the graph.

So just based on the October mean temperature, Octobers that are colder than normal have a 47% higher chance of having a colder than normal winter than warmer than normal Octobers do. But is October a better indicator than November, a month that is closer to actual winter?

Colder than normal Novembers- 43.3 degrees or lower- included 78 Novembers since 1878. Of those, 38 or 48.7% had colder than normal winters. 21 (26.9%) had normal winters and 19 (24.4) had warmer than normal winters.

With the 38 normal Novembers, 43.4 to 45.4 degrees, there were 18 that had colder than normal winters, or 47.4%, with 11 normal winters (28.9%) and 9 warmer than normal winters (23.7%).

Finally, there were 24 warmer than normal Novembers since 1878- 45.5 degrees or higher. Only 6, or 25%, were followed by cold winters. An additional 9 (37.5%) were normal, while the last 9 (37.5%) were warmer than normal.

To reiterate, here are the ranked percentages of cold winters by the preceding October or November.
1. Cold Octobers: 57.4%
2. Cold Novembers: 48.7%
3. Normal Novembers: 47.4%
4. Normal Octobers: 46.7%
5. Warm Octobers: 39.1%
6. Warm Novembers: 25.0%

It should be no surprise that cold Octobers and Novembers have a stronger correlation to the following winters also being colder, with colder winters becoming increasingly unlikely as those months warm. Cold Octobers have a higher correlation than Cold Novembers, as well as Warm Octobers, but Normal Novembers have a slight advantage over Normal Octobers. Based on this, October actually has a stronger correlation to the following winter’s temperature mean than does November.

Going further, though, what about bi-monthly combinations?

Rank of Bi-Monthly Combinations and the percentage of colder than normal following winters, along with total years in sample:
Normal October/Normal November: 87.5% 8 Years
Cold October/Warm November: 57.1% 7 Years
Cold October/Cold November: 53.8% 26 Years
Normal October/Cold November: 48.1% 27 Years
Warm October/Cold November: 44.0% 25 Years
Cold October/Normal November: 38.5% 13 Years
Warm October/Warm November: 28.6% 7 Years
Warm October/Normal November: 26.7% 15 Years
Normal October/Warm November: 0.0% 8 Years

So a normal fall is clearly the best, but the sample size is not particularly high. Normal to Warm is unanimously warm, but again, it has a small sample size.

October 2017 has been overwhelmingly warm. While this wouldn’t normally bode well for a cold winter, each year is influenced by a multitude of factors.

For more general Columbus weather records, go here: Columbus All-Time Weather




The Midwest Beat the South in Regional Domestic Migration in 2016

For years, if not decades, we’ve been hearing a familiar tale- that anyone and everyone is moving from the Midwest and Northeast to the South and West. This trend began during and after the collapse of Northern manufacturing, and as higher cost of living began to make the lower-cost South more attractive in particular. However, a lot of the South’s growth over the years- indeed a majority- never had anything to do with region-to-region migration. Instead, it was due largely to natural growth (births vs. deaths) and international migration, particularly from Central America. What received all the attention, though, was the belief that people were packing up and moving to the South from places like Ohio and other struggling Northern states. While that may have been true for a while, that is increasingly looking like it is no longer the case.

The Midwest, especially, has been derided as the region no one wants to live in. Despite its growing population approaching 66 million people, the common refrain was that its colder winters, flailing economies and questionable demographic future meant that it was simply a region being left behind by the booming Southern states.

Recently, the US Census released estimates for 2015-2016 geographic mobility, and they tell a very different story altogether.

First, let’s look at the total domestic migration moving to the Midwest from other regions.
South to Midwest: +309,000
West to Midwest: +72,000
Northeast to Midwest: +61,000
Total to Midwest: +442,000

And then compare that to the total that the Midwest sends to other regions.
Midwest to South: -254,000
Midwest to West: -224,000
Midwest to Northeast: -34,000
Total from Midwest: -512,000

Net difference by region.
Midwest vs. South: +55,000
Midwest vs. West: -152,000
Midwest vs. Northeast: +27,000
Total Net: -70,000

So while the Midwest is seeing and overall net domestic migration loss, it is entirely to the Western states.

This could just be an off year, as almost all recent years showed losses to the South, but then again, maybe not. The South has been in a boom for several decades now, and in that time, the region still lags the other 3 in almost every quality of life metric used. All booms end eventually, and the South’s 2 biggest perceived advantages, low cost of living and business-friendly climate, have been gradually eroding over time. As Census surveys show, people don’t actually move for a change in weather, so it’s the economic factors that are going to make the biggest impacts long-term. The Midwest now has many cities and several states that are doing well economically, including Columbus, and perhaps they are becoming more attractive than they have in many years. Time will tell, but last year, the narrative of an unattractive Midwest vs. South was at least temporarily shelved.