Housing, Economics and People in the U.S. and Wisconsin
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1 Housing, Economics and People in the U.S. and Wisconsin Morris A. Davis, Ph. D. Academic Director, James A. Graaskamp Center for Real Estate Associate Professor, Department of Real Estate and Urban Land Economics Wisconsin School of Business, University of Wisconsin Madison February, 2014
2 corr = New Starts in 000s (left) Y/Y Growth Rate, Real House Prices (right) New Starts (000s) = *Y/Y Growth in House Prices R2 = 0.71
3 800 Error from Regression :Q2 value = Starts are lower than predicted by 747K units. Starts in 2013:Q3 are 882K, predicted is 1.6M.
4 According to model, starts too low by 747K How did past gaps like this close? Statistical evidence Starts and house prices correct the gap Starts may be too low But house prices may be too high
5 4 Real House Price Growth Even if house price growth tapers
6 1300 Housing Starts Forecast 1.2 million starts by end of 2014
7 Why would we expect continued strength to house prices?
8 Gross Rent-Price Ratio, U.S. Stock of Owner-Occupied Housing 6.50% 6.00% 5.50% 5.00% 4.50% 4.00% 3.50% 3.00% 2.50% 2.00% CSW FHFA Housing fairly valued. Prices and rents to incr. at same rate
9 Taken from Goldman Sachs US Housing Monitor Vacancy rates may be low given we are exiting a recession
10 What do other forecasts look like? What about demographics? What about single family vs multi family?
11 Rappaport (2013) based on demographics The Demographic Shift from Single Family to Multifamily Housing by Jordan Rappaport. Economic Review, Federal Reserve Bank of Kansas City, 4 th Quarter, 2013
12
13 Rappaport (2013) Starts Projections SF MF total , , , , , , , , , , ,900
14 Notes on Wisconsin
15 12 Real Growth in House Prices 8 Percent corr = Wisconsin U.S. Wisconsin isn t that different from the US
16 12 Real Growth in House Prices 8 Percent appleton eauclaire fonddulac greenbay janesville lacrosse madison milwaukee oshkosh racine sheboygan wausau And many places around WI have similar trends
17 corr = Real WI House Price Growth (left) New WI Permits (right, log scale) Permits and Prices are Highly Correlated in WI
18 2 Real House Price Growth, WI Even if house price growth slowly recovers
19 20000 Housing Permits, WI Permits and activity should increase quickly
20 permit = 1 job 2 permits = 1 job WI Construction Building Employment (left) WI New WI SF Permits (right) 1,500 4,000 new const (building) jobs by 2015?
21 Notes on Unemployment and Employment
22 12 Unemployment Rate US Unemployment Rate: Expect a Further Decline WI
23 This paper was written in 2004
24 68 Labor Force Participation Rate US Data Aaronson et al Forecast US Participation Rate might continue to decline
25 2014 forecast assuming 1%/year pop growth
26 Wisconsin is a bit older than the median state in terms of the age structure of the population No reason to believe we will escape forecasted decline in labor force participation Recently have had slower than 1% per year population growth and its old population
27 Ratio of to 50+. Wisconsin is 1.15 (29 th ) Author s calculations based on the American Community Survey
28 WI population, Minimal increase category. Author s calculations based on the American Community Survey
29 2014 WI forecast with 0% 0.5% pop growth
30 In migration is very important for job growth How can we get new population to WI? We should target the young, especially year old college grads
31 Author s calculations based on the American Community Survey
32 Where do the most likely movers live? (as a percent of the population in the state)
33 Percent of State that is a High Prob Mover (Orange) WI = 33%. If Wisconsin added 3% = 121,828 people. Author s calculations based on the American Community Survey
34 What are current migration patterns?
35 21+. Total inflows to WI were 130,000 per year Total outflows from WI were 141,000 per year Author s calculations based on the American Community Survey
36 These are net flows of people, ages 21+ Author s calculations based on the American Community Survey
37 Are our net outflows retirement related?
38 Ages Author s calculations based on the American Community Survey
39 WISCONSIN net migration age <= HS SOME COL >= COL ,411 2,740 6, , , , , >= ,276 total by edu 2, ,039 total 10,932 Annual averages, Data derived from American Community Survey
40 Compare to our trading partners for people
41 MINNESOTA net migration age <= HS SOME COL >= COL ,876 4, , , , , , , >= total by edu 3,379 5,181 2,944 total 5,616
42 ILLINOIS net migration age <= HS SOME COL >= COL ,074 3,901 12, , ,891 1,715 7, , , ,954 1, , , ,631 4, ,594 2,036 2, , , ,167 1,443 2,304 >=70 3,478 1,070 2,320 total by edu 23,775 16,896 19,844 total 60,515 MICHIGAN net migration age <= HS SOME COL >= COL ,073 12, ,432 4,030 7, ,125 1,626 6, ,448 1,379 3, ,579 2,048 2, ,963 1,412 2, ,268 1,583 2, , , , , , ,786 >=70 3,072 1,764 1,673 total by edu 22,220 17,387 46,087 total 85,694 Author s calculations based on the American Community Survey
43 CALIFORNIA net migration age <= HS SOME COL >= COL ,549 4,497 19, ,080 7,723 15, ,179 5,003 4, ,362 5,450 1, ,377 3,373 1, ,908 5, ,266 2,305 1, ,161 6,841 1, ,012 3,801 4, ,875 2, >= total by edu 70,307 47,565 27,922 total 89,950 TEXAS net migration age <= HS SOME COL >= COL ,539 6,010 16, ,170 9,083 15, ,538 5,770 9, ,685 4,484 8, ,103 3,905 3, ,109 3,195 4, ,231 2,350 3, ,273 3, , , , >=70 6,330 1,247 2,467 total by edu 64,595 39,129 69,293 total 173,017 Author s calculations based on the American Community Survey
44 To have any job growth we need more people Recruit year olds w/ college degree? Most likely to move Least drain on the NPV of our retirement system We are currently LOSING this age group Where does this group want to live and why?
45 Thanks
46 Extra slides on Migration data WI MSA level house prices and permits
47 WISCONSIN inmigration outmigration net migration age <= HS SOME COL >= COL <= HS SOME COL >= COL <= HS SOME COL >= COL ,671 9,196 9,158 7,260 6,456 15,864 1,411 2,740 6, ,084 5,301 13,980 5,574 5,348 15,994 1, , ,829 3,929 8,928 5,053 4,420 9, ,216 2,870 6,380 3,576 3,885 6, , ,453 2,820 3,820 3,711 2,340 3, ,683 2,392 2,899 4,051 2,932 3, ,141 1,912 2,293 2,507 2,136 3, ,259 2,005 1,969 2,179 1,693 3, , ,072 1,194 1,540 1,648 1,511 2, , , , >=70 3, , , ,276 total by edu 44,079 32,875 52,811 41,340 32,507 66,850 2, ,039 total 129, ,697 10,932
48 MICHIGAN inmigration outmigration net migration age <= HS SOME COL >= COL <= HS SOME COL >= COL <= HS SOME COL >= COL ,091 8,872 10,346 12,446 10,945 22, ,073 12, ,276 7,383 16,042 12,708 11,413 23,630 3,432 4,030 7, ,329 4,508 11,109 9,454 6,134 17,492 1,125 1,626 6, ,342 4,484 7,924 7,790 5,863 11,109 1,448 1,379 3, ,652 2,906 4,421 8,231 4,954 7,415 2,579 2,048 2, ,918 3,475 3,002 6,881 4,887 5,170 1,963 1,412 2, ,602 2,515 3,004 6,870 4,098 5,556 2,268 1,583 2, ,143 2,679 2,655 7,143 3,240 4,993 3, , ,958 2,489 2,114 4,840 2,830 5,263 1, , ,662 1,650 1,064 3,758 2,220 2,850 1, ,786 >=70 6, ,075 9,472 2,551 3,748 3,072 1,764 1,673 total by edu 67,373 41,748 63,756 89,593 59, ,843 22,220 17,387 46,087 total 172, ,571 85,694
49 ILLINOIS inmigration outmigration net migration age <= HS SOME COL >= COL <= HS SOME COL >= COL <= HS SOME COL >= COL ,872 13,613 34,961 15,946 17,514 22,353 4,074 3,901 12, ,602 12,402 41,390 15,149 12,884 43, , ,989 7,804 22,840 11,880 9,519 30,702 1,891 1,715 7, ,538 6,042 14,854 10,056 6,956 17,071 2, , ,882 4,294 8,622 8,694 7,248 10, ,954 1, ,678 4,651 5,400 8,918 4,673 8,837 2, , ,545 3,663 4,458 7,072 5,294 8, ,631 4, ,690 2,546 3,952 6,284 4,582 6,696 2,594 2,036 2, ,108 2,240 2,757 8,035 2,968 6,146 3, , ,162 1,156 1,552 4,329 2,599 3,856 2,167 1,443 2,304 >=70 7,066 1,403 2,124 10,544 2,473 4,444 3,478 1,070 2,320 total by edu 83,132 59, , ,907 76, ,754 23,775 16,896 19,844 total 285, ,371 60,515
50 MINNESOTA inmigration outmigration net migration age <= HS SOME COL >= COL <= HS SOME COL >= COL <= HS SOME COL >= COL ,882 7,982 15,780 7,510 10,858 11, ,876 4, ,620 8,076 16,706 6,151 7,666 15, ,483 3,643 10,222 3,688 3,926 10,164 1, ,962 2,194 4,566 3,628 2,710 6, , ,478 2,706 4,353 3,811 2,844 3,218 1, , ,240 2,068 3,374 2,804 2,163 2, ,771 1,908 2,521 3,159 1,667 3, ,842 1,259 3,155 2,348 1,864 2, , ,065 1,156 1,147 1,958 1,914 2, , , ,511 2,194 1,074 1, >=70 3, ,768 3,565 1,158 2, total by edu 37,437 32,663 65,103 40,816 37,844 62,159 3,379 5,181 2,944 total 135, ,819 5,616
51 CALIFORNIA inmigration outmigration net migration age <= HS SOME COL >= COL <= HS SOME COL >= COL <= HS SOME COL >= COL ,123 32,433 49,340 45,672 36,930 29,798 9,549 4,497 19, ,632 28,191 80,902 43,712 35,914 65,116 11,080 7,723 15, ,696 18,249 52,605 28,875 23,252 48,320 8,179 5,003 4, ,716 13,570 33,903 26,078 19,020 35,583 9,362 5,450 1, ,035 10,768 22,724 24,412 14,141 24,490 10,377 3,373 1, ,665 8,522 16,418 18,573 14,225 16,990 5,908 5, ,760 10,049 14,514 20,026 12,354 16,184 8,266 2,305 1, ,931 6,445 13,386 14,092 13,286 15,076 3,161 6,841 1, ,924 5,657 9,245 11,936 9,458 13,826 2,012 3,801 4, ,856 3,866 7,043 8,731 6,803 7,586 2,875 2, >=70 19,041 7,144 9,845 18,579 7,076 9, total by edu 190, , , , , ,003 70,307 47,565 27,922 total 645, ,148 89,950
52 TEXAS inmigration outmigration net migration age <= HS SOME COL >= COL <= HS SOME COL >= COL <= HS SOME COL >= COL ,295 27,931 36,915 33,756 21,921 20,758 4,539 6,010 16, ,672 31,997 57,808 31,502 22,914 41,905 13,170 9,083 15, ,490 22,432 40,617 22,952 16,662 31,014 9,538 5,770 9, ,753 17,982 30,525 18,068 13,498 21,721 7,685 4,484 8, ,105 14,461 19,882 16,002 10,556 16,228 9,103 3,905 3, ,180 12,851 15,376 17,071 9,656 11,222 3,109 3,195 4, ,637 10,270 13,587 13,406 7,920 9,823 4,231 2,350 3, ,419 7,605 12,032 11,719 6,332 8, ,273 3, ,900 6,043 8,419 8,568 5,111 6,923 3, , ,580 4,404 4,592 5,722 3,524 4,411 2, >=70 18,617 5,414 7,770 12,287 4,167 5,303 6,330 1,247 2,467 total by edu 255, , , , , ,230 64,595 39,129 69,293 total 664, , ,017
53 milwaukee Growth in House Prices Permits (Red) and Forecast (Blue)
54 madison Growth in House Prices Permits (Red) and Forecast (Blue)
55 greenbay Growth in House Prices Permits (Red) and Forecast (Blue)
56 appleton Growth in House Prices Permits (Red) and Forecast (Blue)
57 racine Growth in House Prices Permits (Red) and Forecast (Blue)
58 oshkosh Growth in House Prices Permits (Red) and Forecast (Blue)
59 eauclaire Growth in House Prices Permits (Red) and Forecast (Blue)
60 janesville Growth in House Prices Permits (Red) and Forecast (Blue)
61 lacrosse Growth in House Prices Permits (Red) and Forecast (Blue)
62 wausau Growth in House Prices Permits (Red) and Forecast (Blue)
63 sheboygan Growth in House Prices Permits (Red) and Forecast (Blue)
64 fonddulac Growth in House Prices Permits (Red) and Forecast (Blue)
65 Starts equation (R2 of 0.14) Δstarts t = 0.15*Δstarts t *ΔHPgrowth t *error t 1 predicted increase of 98K starts (to 980K) in 2013:Q4 House Price equation (R2 of 0.12) ΔHPgrowth t = 0.34* ΔHPIgrowth t *error t 1 predicted decrease of 0.60 Y/Y HP growth. 2.8% for 2013:Q4
66 Price HOUSING MARKET STABLE Supply P Demand Q Quantity
67 Supply shocks: Changes in Price and Quantity are Negatively Correlated
68 Price HOUSING BOOM (?) FROM SUPPLY Old Supply New Supply P old P new Demand Q old Q new Quantity
69 Demand shocks: Changes in Price and Quantity are Positively Correlated
70 Price HOUSING BOOM FROM DEMAND Supply P new P old New Demand Old Demand Q old Q new Quantity
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