This link shows a time lapse of metros across the US from 1999 to 2014 and how total jobs changed over the months and years. You can click on a metro for more individual details.
Want to know how many jobs are within a 30-minute walk to transit? Check out this link, which includes Columbus.
For 50 years now, the story has been how the South has been booming while the Midwest has languished in perpetual decline. Nearly every day, a new ranking or story comes about how great the South is in relation to its Northern neighbors, but the more I’ve looked at the numbers, the more I realize that the hype is built upon lies, half-truths and cherry-picking data.
The first data point we’re going to look at is Gross Domestic Product, or GDP, a measure of the total economic output, for the Midwest vs. the South.
So on this measure, the South is doing pretty well vs. the Midwest… or so it seems at first. The advantage the South has, however, is Texas. Without the behemoth state, the South has had growth on pace with the Midwest, though the recession did knock the Midwest down a bit from a fairly wide gap. Even so, the Midwest is ahead of the South without Texas, making it pretty clear that Texas is a HUGE reason for the South’s growth. All by itself, it nearly double’s the region’s GDP. The Midwest has no such massively dominant state. So does this mean that the South has Texas to thank for all the attention it gets? More light will be shed on this as we go.
Now let’s look at GDP growth by decade for the regions.
Create a graph
Again, on the surface the South does well. The 2000s were especially kind to the South, while the Midwest declined some, likely due to the double recessions that occurred. However, during the 2010s so far, the Midwest has been growing a bit faster than the South (without Texas), something which hasn’t happened since the 1970s. Once more, Texas shows up as being the main contributor by FAR vs. all other Southern states combined.
Taking GDP further, what does it look like per-capita for the regions?
graph and charts
First of all, the data only goes back to 1987, and the 1997 jump is because the data collection sources changed. In any case, the Midwest region is the leader here. The South has been stagnant for the last decade or so, while the Midwest, aside from during the recession, has seen a steady rise. Since the recession, the pace of per-capita GDP growth has accelerated, and the gap between the region and the South has widened. The Midwest has reached the US average, while the South, with or without Texas, is well below it and not catching up. What does this mean? Well, that despite relatively healthy GDP total growth in the South, it has simply not been fast enough to keep pace with either the national average or the Midwest. The Midwest has a much stronger economic output per its population than the South does, by almost $10,000 per person.
What about income?
graph and charts
I know this chart is a bit hard to see, but it runs from 1930-2013. What it shows is that the Midwest has long had the highest per-capita income of the two regions. In fact, the gap between the two has grown steadily wider over years, and has accelerated in the last 5. The Midwest, while just below the national average now, is ahead of the South as a whole, Texas alone and the South without Texas.
To illustrate the income change over the 1930-2013 period further, let’s look at % growth by decade.
Make a graph
This chart actually shows that the South generally performed much better by rate of growth from the 1970s and earlier. Since then, the rate of growth between the regions has been much closer, and in the 1990s and 2010s, the Midwest grew faster. What this seems to indicate is that the long term growth rate in income is gradually turning more strongly towards the Midwest after a long period where the South had faster growth. The Midwest has also seen faster growth than the national average since the 1980s, not exactly an indication of some kind of sustained decline.
So far, the picture is not quite as one-sided as we’ve been told.
In Part 2, I will look at job growth and employment stats.
Date: July 9, 1913
Event Type: Weather
A severe hailstorm hit parts of the South Side on that Wednesday afternoon. In an issue of The Democratic Banner out of Mt. Vernon, the headline on July 11th read “Streets Covered with Ice Boulders: Destruction Wrought by Hail Storm at Columbus.”
The article went on:
This city was visited by probably the most disastrous hailstorm in its history. The damage to crops and buildings in this immediate vicinity is estimated conservatively at $125,000. (About $3.1 million today).
South Side florists alone report losses of approximately $50,000. In hundreds of houses practically every window was broken. The ground on the South Side was covered by a thick layer of “ice boulders” for an hour or two afterwards. As a midsummer phenomenon it probably was without a parallel in this state.
Accompanied by 45 mph winds, the hail that fell was reported to be about 3″ in diameter. The hail was large enough to hit and fracture the wrist of J.W. Sprouse, a teamster, and shattered glass from a greenhouse was driven through the arm of William Bernard, a florist.
For this update, I’m going to do things a bit differently. In previous updates, I have done long ranking lists and it got to be a bit overwhelming. So starting with this update, I’m going to do more charts instead.
In any case, August continued the year-long trend of home sales being down, with the month coming in at more than 11% off from the same time last year. Prices, however, were up more than 6% to reach a monthly record. There continues to be a supply problem, which is the main mechanism driving both lower sales and higher prices.
Now on to the charts!
March 1913 Flood
This video is mostly photos, but still quite interesting.
A video about Columbus being a test market (something that is still true somewhat today) and the impact of Reader’s Digest on Columbus businesses.
Images from OSU Campus, Downtown and more.
Check out this relocation video from when AEP moved its headquarters to Columbus from New York. Total cheese fest. The focus on suburban malls is interesting considering their decline today.
Who could forget this event? It was supposed to be a defining event for the city, but ended up very overhyped and not nearly the success that was promised.
May 11, 1995
A Channel 4 news report on gas prices. Ironic that the report is that prices are too high, but I bet everyone would love to see these prices again.
Over the last few decades, much attention has been given to the fact that domestic migration has heavily favored the “Sun Belt”, states made up of the Southeast west to the West Coast. While Northern states weren’t all losing people, the region as a whole sent far more people to the Sun Belt than they retained. This helped fuel the respective Southern boom, and media story after media story over the years have made sweeping predictions of this growing powerhouse region, often centered around the idea that the boom had no foreseeable end. The irony with these predictions is that they ignored history. For more than 2 centuries, the North was where people moved. Its states and cities saw massive influxes of population. As recently as the decade of the 1950s, Ohio grew by nearly 2 million alone. Economic conditions in decline, job losses, particularly in the manufacturing industry, increases in the cost of living and other factors ended the boom and helped to bring about the rise of the South, so to speak. Since at least the 1960s, the story has been about the Sun Belt/West.
The US Census does state migration estimates every year, and there are some interesting things going on in the data that may indicate that the boom in the South is faltering while the North’s fortunes are not looking as grim as they once did.
First, what are the regions?
South: Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia and West Virginia.
North: Connecticut, Delaware, Washington D.C., Illinois, Indiana, Iowa, Kansas, Maine, Maryland, Massachusetts, Michigan, Minnesota, Missouri, Nebraska, New Hampshire, New Jersey, New York, North Dakota, Ohio, Pennsylvania, Rhode Island, South Dakota, Vermont and Wisconsin.
West: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington and Wyoming.
Let’s next look at the states by rank of domestic migration in 2005, the earliest available year for the state data, and compared it to 2012, the most recent year available. This period covers the period just before and just after the Great Recession.
Domestic Migration Rank, 2005 vs. 2012, by Total
1. Florida: +188,035__________________________1. Florida: +108,823
2. Arizona: +131,501_________________________2. Texas: +105,565
3. Texas: +124,522__________________________3. Colorado: +43,530
4. Georgia: +88,250__________________________4. Washington: +37,187
5. North Carolina: +51,575_____________________5. North Carolina: +34,846
6. Tennessee: +43,901________________________6. South Carolina: +34,149
7. Oregon: +43,360___________________________7. Nevada: +25,835
8. Washington: +38,093________________________8. Arizona: +25,615
9. South Carolina: +32,312______________________9. Georgia: +25,204
10. Arkansas: +30,765_________________________10. Missouri: +20,176
11. Nevada: +26,839__________________________11. North Dakota: +14,254
12. Idaho: +20,308____________________________12. Tennessee: +13,255
13. Colorado: +16,963_________________________13. Virginia: +12,110
14. Oklahoma: +16,372_________________________14. Arkansas: +11,981
15. Alabama: +14,501__________________________15. Oregon: +10,742
16. New Mexico: +13,714_______________________16. New Hampshire: +10,711
17. Delaware: +12,561_________________________17. Delaware: +10,610
18. Virginia: +11,121___________________________18. Kentucky: +8,899
19. Kentucky: +7,451___________________________19. Mississippi: +6,569
20. Missouri: +6,338____________________________20. Oklahoma: +6,402
21. Iowa: +5,406_______________________________21. Utah: +5,717
22. Montana: +4,185____________________________22. Vermont: +4,375
23. Pennsylvania: +2,868________________________23. South Dakota: +3,578
24. Maine: +2,447______________________________24. Montana: +3,410
25. Hawaii: +2,388______________________________25. Idaho: +3,400
26. West Virginia: +998__________________________26. Wisconsin: +1,468
27. New Hampshire: +497________________________27. Iowa: +275
28. South Dakota: +360__________________________28. Ohio: -105
29. Wyoming: -366______________________________29. West Virginia: -300
30. Minnesota: -1,154____________________________30. Wyoming: -639
31. Kansas: -2,244______________________________31. Maryland: -2,821
32. North Dakota: -2,553__________________________32. Rhode Island: -2,948
33. Wisconsin: -2,756____________________________33. Louisiana: -4,741
34. Vermont: -3,580_____________________________34. Kansas: -4,850
35. Nebraska: -5,128____________________________35. Nebraska: -5,174
36. Utah: -5,639________________________________36. Hawaii: -6,364
37. Connecticut: -6,536__________________________37. Connecticut: -6,712
38. Mississippi: -7,120___________________________38. Washington D.C.: -7,470
39. Indiana: -9,222______________________________39. New Mexico: -9,228
40. Maryland: -9,718____________________________40. Alabama: -9,431
41. Washington D.C.: -12,872_____________________41. Indiana: -10,460
42. Rhode Island: -15,037________________________42. Maine: -11,025
43. New Jersey: -22,051_________________________43. Minnesota: -14,904
44. Alaska: -23,567_____________________________44. Massachusetts: -15,579
45. Ohio: -40,841______________________________45. Pennsylvania: -21,656
46. Massachusetts: -52,726______________________46. Michigan: -41,761
47. Michigan: -53,852___________________________47. Alaska: -49,250
48. Illinois: -55,932_____________________________48. Illinois: -68,356
49. Louisiana: -99,684__________________________49. California: -73,345
50. New York: -239,848_________________________50. New Jersey: -89,666
51. California: -266,243_________________________51. New York: -135,149
So in 2005, the breakdown was as follows:
12 of 14 Southern states had positive domestic migration. The only 2 that did not, Louisiana and Mississippi, were heavily influenced in 2005 by Hurricane Katrina, which caused large numbers of displaced residents to leave the states entirely.
7 of 24 Northern states has positive domestic migration. The 7 states were mixed between the Midwest and the Northeast/Mid-Atlantic. Just one Great Lakes State had positive domestic migration in 2005.
9 of 13 Western states had positive domestic migration. Only California and a few Mountain West states had negative numbers.
The 2005 numbers show the overall domestic migration picture as it had been for at least the last few decades, if not much longer. The South and West were the dominant net gainers of domestic migration, while most of the North sent people to those regions.
In 2012, the breakdown was as follows:
11 of 14 Southern states had positive domestic migration. Even with Katrina-hit state Mississippi having net gains in 2012, the overall number of states with positive gains declined.
8 of 24 Northern states had positive domestic migration, a slight improvement over 2005.
8 of 13 Western states had positive domestic migration, a slight decline over 2005.
But the breakdowns don’t tell us the whole story. When trying to compare the two years, trends are very important, and the trends are far more revealing.
Total Change 2005-2012 By Rank
1. California: +192,898
2. New York: +104,699
3. Louisiana: +94,943
4. Ohio: +40,736
5. Massachusetts: +37,147
6. Colorado: +26,567
7. North Dakota: +16,807
8. Missouri: +13,838
9. Mississippi: +13,689
10. Michigan: +12,091
11. Rhode Island: +12,089
12. Utah: +11,356
13. New Hampshire: +10,214
14. Vermont: +7,955
15. Maryland: +6,897
16.Washington, D.C.: +5,402
17. Wisconsin: +4,224
18. South Dakota: +3,218
19. South Carolina: +1,837
20. Kentucky: +1,448
21. Virginia: +989
22. Nebraska: -46
23. Connecticut: -176
24. Wyoming: -273
25. Montana: -775
26. Washington: -906
27. Nevada: -1,004
28. Indiana: -1,238
29. West Virginia: -1,298
30. Delaware: -1,951
31. Kansas: -2,606
32. Iowa: -5,131
33. Hawaii: -8,752
34. Oklahoma: -9,970
35. Illinois: -12,424
36. Maine: -13,472
37. Minnesota: -13,750
38. North Carolina: -16,729
39. Idaho: -16,908
40. Arkansas: -18,784
41. Texas: -18,957
42. New Mexico: -22,942
43. Alabama: -23,932
44. Pennsylvania: -24,524
45. Alaska: -25,683
46. Tennessee: -30,646
47. Oregon: -32,618
48. Georgia: -63,046
49. New Jersey: -67,615
50. Florida: -79,212
51. Arizona: -105,886
5 of 14 Southern states improved their domestic migration rates 2005-2012.
13 of 24 Northern states improved their domestic migration rates 2005-2012.
3 of 13 Western states improved their domestic migration rates 2005-2012.
Ohio had the 4th best improvement over the period, a huge change. But some might ask, is it really a change when the rates may still be positive or negative like they were before? Well, yes and no. 7 years is not that long, and we’re talking about decades-long patterns here. Those won’t change like flipping a switch. It will take time. The point is more that for many states that have faced negative numbers for a long time, there is positive momentum now that they did not have before. Another question some may ask, however, is if the recession during the period reduced mobility. In some cases, I’m sure that it did, but if so, that reduction seems to have been centered on the South. A reduction in mobility would only indicate that migration rates would reduce to levels around 0, neither particularly positive nor negative. That reduction would NOT necessarily support switches from positive to negative or increases in negative or positive rates that already exist. Meaning that reduced mobility would mean that positive would become less positive as fewer people moved in, and negative would become less negative as fewer people left. On a state and regional basis, there is a wide range of results that do not support that geographic mobility alone is the culprit, or even a primary factor.
In Part 2, we’ll look more closely at how regions and individual states are performing relative to each other.
The Columbus Land Bank got started back in 1994 to address vacant land and properties, but more specifically, the worst of the worst. Over the years, the number of properties on the list has grown into the hundreds as the city bought the properties to either renovate what could be renovated, or to demolish those that could not be saved and were contributing to the decline of surrounding neighborhoods.
The city provides a few links where these properties can be searched for and purchased. The properties are in various stages of decline and are being sold only to those qualified to renovate the properties or replace them with new development. Many of them are in urban locations, and most of the houses are old, with many retaining elements of their original architecture. In most cases, they need major to moderate rehabs, however. Given the rise of urban living lately and the rapid pace of revitalization happening throughout urban Columbus, these properties maintain some inherent value despite what their overall condition may be.
The first link is an interactive map where you can search for properties. It’s a great resource where you can search by address, street or area. You can also apply to buy properties if you are so inclined.
The second link is a list of for-sale property highlights. This list is updated through the last 90 days.
Take a look!
June’s housing data for the Columbus region was just released. The month continued the same story as the previous 5 months, with home sales down due to a lack of inventory. Prices continued to rise and the number of days to sell a home continued to fall in relation to this problem. As always, I looked at 21 major areas of the Columbus region, both urban and suburban. Here is how those areas performed.
Top 10 June 2014 Sales Totals
1. Columbus: 1,045
2. Upper Arlington: 109
3. Dublin: 108
4. Clintonville: 83
5. Westerville: 77
6. Gahanna: 63
7. Grove City: 62
8. Reynoldsburg: 59
9. New Albany: 38
Top 10 June 2014 Sales Increases over June 2013
1. Downtown: +72.0%
2. Grove City: +14.5%
3. Gahanna: +12.7%
4. German Village: +10.5%
5. Grandview Heights: +6.3%
6. Canal Winchester: +3.8%
7. Hilliard: 0.0%
8. Minerva Park: 0.0%
9. Columbus: -1.8%
10. Reynoldsburg: -5.1%
Top 10 Year-to-Date Sales Through June 2014
1. Columbus: 4,803
2. Dublin: 362
3. Grove City: 332
4. Clintonville: 323
5. Westerville: 299
6. Upper Arlington: 294
7. Reynoldsburg: 265
8. Hilliard: 258
9. Gahanna: 221
10. Pickerington: 136
Top 10 Year-to-Date Increases Through June 2014 Over 2013
1. Obetz: +42.1%
2. Downtown: +13.4%
3. Grove City: +11.0%
4. New Albany: +2.8%
5. Reynoldsburg: -0.4%
6. Westerville: -3.2%
7. Pataskala: -3.4%
8. German Village: -3.9%
9. Columbus: -5.3%
10. Clintonville: -5.8%
Average Sales June 2014
Urban without Columbus: 30
Average % Change June 2014 vs. June 2013
Urban without Columbus: -6.0%
Average YTD Sales Through June 2014
Urban without Columbus: 119.2
Average YTD % Change YTD Through June 2014
Urban without Columbus: -4.7%
Top 10 Average Sales Price June 2014
1. New Albany: $659,186
2. Upper Arlington: $389,575
3. Bexley: $382,496
4. Dublin: $377,541
5. German Village: $307,753
6. Downtown: $300,582
7. Worthington: $283,209
8. Grandview Heights: $246,271
9. Hilliard: $230,396
10. Gahanna: $229,845
Top 10 Average Sales Price % Change June 2014 vs. June 2013
1. Whitehall: +55.6%
2. Worthington: +17.7%
3. New Albany: +15.8%
4. Bexley: +15.6%
5. Columbus: +12.2%
6. Clintonville: +9.3%
7. Hilliard: +8.5%
8. Canal Winchester: +7.4%
9. Pickerington: +6.7%
10. Gahanna: +6.4%
Top 10 Average Sales Prices YTD Through June 2014
1. New Albany: $541,077
2. Dublin: $360,202
3. Upper Arlington: $348,160
4. Bexley: $334,491
5. Downtown: $305,215
6. German Village: $302,117
7. Worthington: $261,659
8. Grandview Heights: $245,946
9. Hilliard: $225,849
10. Gahanna: $213,782
Top 10 Average YTD Sales Price % Change Through June 2014 vs. 2013
1. Obetz: +24.7%
2. Grandview Heights: +15.5%
3. Worthington: +11.3%
4. Pataskala: +10.8%
5. Columbus: +10.6%
6. Pickerington: +9.9%
7. Canal Winchester: +9.5%
8. Downtown: +9.5%
9. Dublin: +9.2%
10. Westerville: +8.5%
Average Sales Price June 2014
Urban without Columbus: $240,917
Average Sales Price Change June 2014 vs. June 2013
Urban without Columbus: +7.1%
Average Sales Price YTD Through June 2014
Urban without Columbus: $229,327
Average Sales Price % Change YTD Through June 2014
Urban without Columbus: +6.2%
Top 10 Fastest Selling Markets June 2014 (Based on Average # of Days for Listings to Sell)
1. Worthington: 21
2. Upper Arlington: 36
3. Clintonville: 38
4. Gahanna: 39
5. Hilliard: 39
6. Dublin: 40
7. German Village: 42
8. Obetz: 45
9. Bexley: 46
10. Pataskala, Westerville: 47
Top 10 Fastest Selling Markets YTD Through June 2014
1. Worthington: 39
2. Minerva Park: 44
3. Upper Arlington: 44
4. Obetz: 46
5. Hilliard: 47
6. Clintonville: 52
7. Westerville: 53
8. Grandview Heights: 54
9. Dublin: 57
10. Bexley: 59
Average # of Days Before Sale, June 2014
Urban without Columbus: 47.3
Average # of Days Before Sale YTD Through June 2014
Urban without Columbus: 57.4
Top 10 Lowest Market Housing Supplies June 2014 (Based on # of Months to Sell all Listings)
1. Grandview Heights: 1.0
2. Worthington: 1.4
3. Westerville: 2.0
4. Clintonville: 2.1
5. German Village: 2.1
6. Gahanna: 2.3
7. Hilliard: 2.5
8. Minerva Park: 2.7
9. Obetz: 2.7
10. Upper Arlington: 2.7
A healthy housing supply is considered to be around 5-6 months. Anything less than 3 months is considered very low. Grandview’s 1 month is ridiculously low and the lowest reading I’ve seen for any area.
Average # of Months to Sell All Listings, June 2014
Urban without Columbus: 2.6
Average % Change of Single-Family Home Sales June 2014 vs. June 2013
Urban without Columbus: +36.8%
Average % Change of Single-Family Home Sales YTD Through June 2014 vs. YTD 2013
Urban without Columbus: -8.9%
Average % Change of Condo Sales June 2014 vs. June 2013
Urban without Columbus: -5.0%
Average % Change of Condo Sales YTD Through June 2014 vs. YTD 2013
Urban without Columbus: +21.0%