US GDP Growing Continuing Recovery In the Global Recession, with Output in the US Reaching a High in Feb 2020 (https://www.nber.org/cycles.html), in the Lockdown of Economic Activity in the COVID-19 Event, Mediocre Cyclical United States Economic Growth in the Lost Economic Cycle of the Global Recession with Economic Growth Underperforming Below Trend Worldwide, Real Private Fixed Investment, Corporate Profits, United States Terms of International Trade, Destruction of Household Nonfinancial Wealth with Stagnating Total Real Wealth, United States Housing, United States House Prices, World Inflation Waves, World Cyclical Slow Growth, and Government Intervention in Globalization
Carlos M. Pelaez
© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021.
IA Mediocre Cyclical United States Economic Growth
IA1 Stagnating Real Private Fixed Investment
IA2 Swelling Undistributed Corporate Profits
IID United States Terms of International Trade
IIA United States Housing Collapse
IIA1 Sales of New Houses
IIA2 United States House Prices
II Destruction of Household Nonfinancial Wealth with Stagnating Total Real Wealth
I World Inflation Waves
IA Appendix: Transmission of Unconventional Monetary Policy
IB1 Theory
IB2 Policy
IB3 Evidence
IB4 Unwinding Strategy
IC United States Inflation
IC Long-term US Inflation
ID Current US Inflation
IE Theory and Reality of Economic History, Cyclical Slow Growth Not Secular Stagnation and Monetary Policy Based on Fear of Deflation
III World Financial Turbulence
IV Global Inflation
V World Economic Slowdown
VA United States
VB Japan
VC China
VD Euro Area
VE Germany
VF France
VG Italy
VH United Kingdom
VI Valuation of Risk Financial Assets
VII Economic Indicators
VIII Interest Rates
IX Conclusion
References
Appendixes
Appendix I The Great Inflation
IIIB Appendix on Safe Haven Currencies
IIIC Appendix on Fiscal Compact
IIID Appendix on European Central Bank Large Scale Lender of Last Resort
IIIG Appendix on Deficit Financing of Growth and the Debt Crisis
II I United States Housing Collapse. Data and other information continue to provide depressed conditions in the US housing market in a longer perspective, with recent improvement at the margin. Table IIB-1 shows sales of new houses in the US at seasonally adjusted annual equivalent rate (SAAR). The US Census Bureau revised all seasonally adjusted new house sales from 2014 to 2019 with the report for Apr 2019 on May 23, 2019 (https://www.census.gov/construction/nrs/pdf/newressales.pdf). The US Census Bureau revised all seasonally adjusted new house sales from 2015 to 2020 with the report of Apr 2020 on May 26, 2020 (https://www.census.gov/construction/nrs/pdf/newressales.pdf). The US Census Bureau revised all seasonally adjusted new house sales from 2016 to 2021 with the report for Apr 2021 on May 25, 2021 (https://www.census.gov/construction/nrs/pdf/newressales.pdf). There is significant oscillation of monthly house sales. Recovery from the global recession after 2007 was inadequate. New house sales dropped 14.7 percent in Mar 2020 and decreased 6.6 percent in Apr 2020 in the global recession with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event. Recovery was quite sharp with a SAARs jumping to 993 thousand in Jan 2021. Sales eased to 769 thousand in May 2021.
Table IIB-1, US, Sales of New Houses Seasonally Adjusted Annual Rate (SAAR), Thousands and %
| SAAR Thousands | ∆% | |
| Dec-2010 | 326 | 13.6 |
| Jan-2011 | 307 | -5.8 |
| Feb-2011 | 270 | -12.1 |
| Mar-2011 | 300 | 11.1 |
| Apr-2011 | 310 | 3.3 |
| May-2011 | 305 | -1.6 |
| Jun-2011 | 301 | -1.3 |
| Jul-2011 | 296 | -1.7 |
| Aug-2011 | 299 | 1.0 |
| Sep-2011 | 304 | 1.7 |
| Oct-2011 | 316 | 3.9 |
| Nov-2011 | 328 | 3.8 |
| Dec-2011 | 341 | 4.0 |
| Jan-2012 | 335 | -1.8 |
| Feb-2012 | 366 | 9.3 |
| Mar-2012 | 354 | -3.3 |
| Apr-2012 | 354 | 0.0 |
| May-2012 | 370 | 4.5 |
| Jun-2012 | 360 | -2.7 |
| Jul-2012 | 369 | 2.5 |
| Aug-2012 | 375 | 1.6 |
| Sep-2012 | 385 | 2.7 |
| Oct-2012 | 358 | -7.0 |
| Nov-2012 | 392 | 9.5 |
| Dec-2012 | 399 | 1.8 |
| Jan-2013 | 446 | 11.8 |
| Feb-2013 | 447 | 0.2 |
| Mar-2013 | 444 | -0.7 |
| Apr-2013 | 441 | -0.7 |
| May-2013 | 428 | -2.9 |
| Jun-2013 | 470 | 9.8 |
| Jul-2013 | 375 | -20.2 |
| Aug-2013 | 381 | 1.6 |
| Sep-2013 | 403 | 5.8 |
| Oct-2013 | 444 | 10.2 |
| Nov-2013 | 446 | 0.5 |
| Dec-2013 | 433 | -2.9 |
| Jan-2014 | 443 | 2.3 |
| Feb-2014 | 420 | -5.2 |
| Mar-2014 | 405 | -3.6 |
| Apr-2014 | 403 | -0.5 |
| May-2014 | 451 | 11.9 |
| Jun-2014 | 418 | -7.3 |
| Jul-2014 | 402 | -3.8 |
| Aug-2014 | 456 | 13.4 |
| Sep-2014 | 470 | 3.1 |
| Oct-2014 | 476 | 1.3 |
| Nov-2014 | 442 | -7.1 |
| Dec-2014 | 497 | 12.4 |
| Jan-2015 | 515 | 3.6 |
| Feb-2015 | 540 | 4.9 |
| Mar-2015 | 480 | -11.1 |
| Apr-2015 | 502 | 4.6 |
| May-2015 | 502 | 0.0 |
| Jun-2015 | 480 | -4.4 |
| Jul-2015 | 506 | 5.4 |
| Aug-2015 | 518 | 2.4 |
| Sep-2015 | 456 | -12.0 |
| Oct-2015 | 482 | 5.7 |
| Nov-2015 | 504 | 4.6 |
| Dec-2015 | 546 | 8.3 |
| Jan-2016 | 505 | -7.5 |
| Feb-2016 | 517 | 2.4 |
| Mar-2016 | 532 | 2.9 |
| Apr-2016 | 576 | 8.3 |
| May-2016 | 571 | -0.9 |
| Jun-2016 | 557 | -2.5 |
| Jul-2016 | 628 | 12.7 |
| Aug-2016 | 575 | -8.4 |
| Sep-2016 | 558 | -3.0 |
| Oct-2016 | 575 | 3.0 |
| Nov-2016 | 571 | -0.7 |
| Dec-2016 | 561 | -1.8 |
| Jan-2017 | 578 | 3.0 |
| Feb-2017 | 601 | 4.0 |
| Mar-2017 | 643 | 7.0 |
| Apr-2017 | 604 | -6.1 |
| May-2017 | 627 | 3.8 |
| Jun-2017 | 612 | -2.4 |
| Jul-2017 | 553 | -9.6 |
| Aug-2017 | 550 | -0.5 |
| Sep-2017 | 622 | 13.1 |
| Oct-2017 | 625 | 0.5 |
| Nov-2017 | 718 | 14.9 |
| Dec-2017 | 658 | -8.4 |
| Jan-2018 | 610 | -7.3 |
| Feb-2018 | 644 | 5.6 |
| Mar-2018 | 680 | 5.6 |
| Apr-2018 | 658 | -3.2 |
| May-2018 | 680 | 3.3 |
| Jun-2018 | 598 | -12.1 |
| Jul-2018 | 600 | 0.3 |
| Aug-2018 | 582 | -3.0 |
| Sep-2018 | 584 | 0.3 |
| Oct-2018 | 546 | -6.5 |
| Nov-2018 | 618 | 13.2 |
| Dec-2018 | 566 | -8.4 |
| Jan-2019 | 628 | 11.0 |
| Feb-2019 | 675 | 7.5 |
| Mar-2019 | 721 | 6.8 |
| Apr-2019 | 689 | -4.4 |
| May-2019 | 619 | -10.2 |
| Jun-2019 | 711 | 14.9 |
| Jul-2019 | 636 | -10.5 |
| Aug-2019 | 677 | 6.4 |
| Sep-2019 | 706 | 4.3 |
| Oct-2019 | 703 | -0.4 |
| Nov-2019 | 700 | -0.4 |
| Dec-2019 | 733 | 4.7 |
| Jan-2020 | 756 | 3.1 |
| Feb-2020 | 730 | -3.4 |
| Mar-2020 | 623 | -14.7 |
| Apr-2020 | 582 | -6.6 |
| May-2020 | 704 | 21.0 |
| Jun-2020 | 839 | 19.2 |
| Jul-2020 | 972 | 15.9 |
| Aug-2020 | 977 | 0.5 |
| Sep-2020 | 971 | -0.6 |
| Oct-2020 | 969 | -0.2 |
| Nov-2020 | 865 | -10.7 |
| Dec-2020 | 943 | 9.0 |
| Jan-2021 | 993 | 5.3 |
| Feb-2021 | 823 | -17.1 |
| Mar-2021 | 886 | 7.7 |
| Apr-2021 | 817 | -7.8 |
| May-2021 | 769 | -5.9 |
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
There is additional information of the report of new house sales in Table IIB-2. The stock of unsold houses fell from rates of 6 to 8 percent of sales in 2011 to 4 to 5 percent in 2013 and 5.1 percent in May 2021. Robbie Whelan and Conor Dougherty, writing on “Builders fuel home sale rise,” on Feb 26, 2013, published in the Wall Street Journal (http://professional.wsj.com/article/SB10001424127887324338604578327982067761860.html), find that inventories of houses have declined as investors acquire distressed houses of higher quality. Median and average house prices oscillate. In May 2021, median prices of new houses sold not seasonally adjusted (NSA) increased 2.5 percent after increasing 4.9 percent in
Apr 2021. Average prices increased 2.3 percent in May 2021 and increased 3.1 percent in Apr 2020. Between Dec 2010 and May 2021, median prices increased 55.2 percent, with increases of 6.0 percent in Feb 2016, 4.9 percent in Nov 2015, 2.2 percent in Sep 2015, 13.6 percent in Oct 2014, 4.0 percent in Aug 2014, 4.0 percent in May 2014 and 5.2 percent in Mar 2014. Average prices increased 47.6 percent between Dec 2010 and Apr 2021, with increases of 5.1 percent in Mar 2016, 4.0 percent in Sep 2015, 4.4 percent in Jul 2015 and 18.3 percent in Oct 2014. Between Dec 2010 and Dec 2012, median prices increased 7.1 percent and average prices increased 2.6 percent. Price increases concentrated in 2012 with increase of median prices of 18.2 percent from Dec 2011 to Dec 2012 and of average prices of 13.8 percent. Median prices increased 16.7 percent from Dec 2012 to Dec 2014, with increase of 13.6 percent in Oct 2014, while average prices increased 24.7 percent, with increase of 18.3 percent in Oct 2014. Median prices decreased 1.5 percent from Dec 2014 to Dec 2015 while average prices fell 5.5 percent. Median prices increased 10.1 percent from Dec 2015 to Dec 2016 while average prices increased 8.5 percent. Median prices increased 5.0 percent from Dec 2016 to Dec 2017 while average prices increased 5.3 percent. Median prices decreased 4.0 percent from Dec 2017 to Dec 2018 while average prices decreased 5.2 percent. Median prices decreased 0.1 percent from Dec 2018 to Dec 2019 while average prices decreased 1.1 percent. Median prices increased 10.9 percent from Dec 2019 to Dec 2020 while average prices increased 6.4 percent. Median prices increased 18.1 percent from May 2020 to May 2021 while average prices increased 16.8 percent. Robbie Whelan, writing on “New homes hit record as builders cap supply,” on May 24, 2013, published in the Wall Street Journal (http://online.wsj.com/article/SB10001424127887323475304578500973445311276.html?mod=WSJ_economy_LeftTopHighlights), finds that homebuilders are continuing to restrict the number of new homes for sale. Restriction of available new homes for sale increases prices paid by buyers.
Table IIB-2, US, New House Stocks and Median and Average New Homes Sales Price
| Unsold* | Median | Month | Average New House Sales Price USD | Month | |
| Dec-2010 | 7 | 241200 | 9.8 | 291,700 | 3.5 |
| Jan-2011 | 7.3 | 240100 | -0.5 | 275,700 | -5.5 |
| Feb-2011 | 8.1 | 220100 | -8.3 | 262,800 | -4.7 |
| Mar-2011 | 7.2 | 220500 | 0.2 | 260,800 | -0.8 |
| Apr-2011 | 6.7 | 224700 | 1.9 | 268,900 | 3.1 |
| May-2011 | 6.6 | 222000 | -1.2 | 262,700 | -2.3 |
| Jun-2011 | 6.6 | 240200 | 8.2 | 273,100 | 4.0 |
| Jul-2011 | 6.7 | 229900 | -4.3 | 270,300 | -1.0 |
| Aug-2011 | 6.5 | 219600 | -4.5 | 259,300 | -4.1 |
| Sep-2011 | 6.3 | 217000 | -1.2 | 255,400 | -1.5 |
| Oct-2011 | 6 | 224800 | 3.6 | 258,300 | 1.1 |
| Nov-2011 | 5.7 | 214300 | -4.7 | 250,000 | -3.2 |
| Dec-2011 | 5.3 | 218600 | 2.0 | 262,900 | 5.2 |
| Jan-2012 | 5.3 | 221700 | 1.4 | 265,700 | 1.1 |
| Feb-2012 | 4.8 | 239900 | 8.2 | 274,000 | 3.1 |
| Mar-2012 | 4.9 | 239800 | 0.0 | 283,600 | 3.5 |
| Apr-2012 | 4.9 | 236400 | -1.4 | 287,900 | 1.5 |
| May-2012 | 4.7 | 239200 | 1.2 | 280,900 | -2.4 |
| Jun-2012 | 4.8 | 232600 | -2.8 | 271,800 | -3.2 |
| Jul-2012 | 4.6 | 237400 | 2.1 | 282,300 | 3.9 |
| Aug-2012 | 4.6 | 253200 | 6.7 | 305,500 | 8.2 |
| Sep-2012 | 4.5 | 254600 | 0.6 | 297,700 | -2.6 |
| Oct-2012 | 4.9 | 247200 | -2.9 | 285,400 | -4.1 |
| Nov-2012 | 4.6 | 245000 | -0.9 | 290,700 | 1.9 |
| Dec-2012 | 4.5 | 258300 | 5.4 | 299,200 | 2.9 |
| Jan-2013 | 4 | 251500 | -2.6 | 306,900 | 2.6 |
| Feb-2013 | 4.1 | 265100 | 5.4 | 312,500 | 1.8 |
| Mar-2013 | 4.2 | 257500 | -2.9 | 300,200 | -3.9 |
| Apr-2013 | 4.4 | 279300 | 8.5 | 337,000 | 12.3 |
| May-2013 | 4.6 | 263700 | -5.6 | 314,000 | -6.8 |
| Jun-2013 | 4.1 | 259800 | -1.5 | 306,100 | -2.5 |
| Jul-2013 | 5.5 | 262200 | 0.9 | 329,900 | 7.8 |
| Aug-2013 | 5.5 | 255300 | -2.6 | 310,800 | -5.8 |
| Sep-2013 | 5.4 | 269800 | 5.7 | 321,400 | 3.4 |
| Oct-2013 | 4.9 | 264300 | -2.0 | 335,700 | 4.4 |
| Nov-2013 | 5 | 277100 | 4.8 | 335,600 | 0.0 |
| Dec-2013 | 5.2 | 275500 | -0.6 | 321,200 | -4.3 |
| Jan-2014 | 5.1 | 269800 | -2.1 | 337,300 | 5.0 |
| Feb-2014 | 5.3 | 268400 | -0.5 | 325,900 | -3.4 |
| Mar-2014 | 5.6 | 282300 | 5.2 | 331,500 | 1.7 |
| Apr-2014 | 5.7 | 274500 | -2.8 | 325,100 | -1.9 |
| May-2014 | 5.2 | 285600 | 4.0 | 323,500 | -0.5 |
| Jun-2014 | 5.7 | 287000 | 0.5 | 338,100 | 4.5 |
| Jul-2014 | 6.2 | 280400 | -2.3 | 345,200 | 2.1 |
| Aug-2014 | 5.4 | 291700 | 4.0 | 356,200 | 3.2 |
| Sep-2014 | 5.4 | 261500 | -10.4 | 319,100 | -10.4 |
| Oct-2014 | 5.3 | 297000 | 13.6 | 377,500 | 18.3 |
| Nov-2014 | 5.7 | 298300 | 0.4 | 348,900 | -7.6 |
| Dec-2014 | 5.1 | 301500 | 1.1 | 373,200 | 7.0 |
| Jan-2015 | 4.8 | 292000 | -3.2 | 348,300 | -6.7 |
| Feb-2015 | 4.5 | 286600 | -1.8 | 346,300 | -0.6 |
| Mar-2015 | 5.1 | 286600 | 0.0 | 349,300 | 0.9 |
| Apr-2015 | 4.9 | 294500 | 2.8 | 340,400 | -2.5 |
| May-2015 | 5 | 287500 | -2.4 | 336,200 | -1.2 |
| Jun-2015 | 5.4 | 285100 | -0.8 | 326,900 | -2.8 |
| Jul-2015 | 5.2 | 292300 | 2.5 | 341,200 | 4.4 |
| Aug-2015 | 5 | 293000 | 0.2 | 343,300 | 0.6 |
| Sep-2015 | 5.9 | 299500 | 2.2 | 357,200 | 4.0 |
| Oct-2015 | 5.6 | 298000 | -0.5 | 368,900 | 3.3 |
| Nov-2015 | 5.5 | 312600 | 4.9 | 373,200 | 1.2 |
| Dec-2015 | 5.1 | 297100 | -5.0 | 352,500 | -5.5 |
| Jan-2016 | 5.6 | 288400 | -2.9 | 361,200 | 2.5 |
| Feb-2016 | 5.5 | 305800 | 6.0 | 341,700 | -5.4 |
| Mar-2016 | 5.5 | 303200 | -0.9 | 359,000 | 5.1 |
| Apr-2016 | 5 | 318300 | 5.0 | 369,300 | 2.9 |
| May-2016 | 5.1 | 295200 | -7.3 | 349,700 | -5.3 |
| Jun-2016 | 5.3 | 311200 | 5.4 | 357,800 | 2.3 |
| Jul-2016 | 4.5 | 297400 | -4.4 | 353,000 | -1.3 |
| Aug-2016 | 5 | 298900 | 0.5 | 355,100 | 0.6 |
| Sep-2016 | 5.2 | 314800 | 5.3 | 366,100 | 3.1 |
| Oct-2016 | 5.2 | 302800 | -3.8 | 352,200 | -3.8 |
| Nov-2016 | 5.2 | 315000 | 4.0 | 363,400 | 3.2 |
| Dec-2016 | 5.4 | 327000 | 3.8 | 382,500 | 5.3 |
| Jan-2017 | 5.3 | 315200 | -3.6 | 357,700 | -6.5 |
| Feb-2017 | 5.2 | 298000 | -5.5 | 370,500 | 3.6 |
| Mar-2017 | 4.9 | 321700 | 8.0 | 384,400 | 3.8 |
| Apr-2017 | 5.3 | 311100 | -3.3 | 365,800 | -4.8 |
| May-2017 | 5.2 | 323600 | 4.0 | 378,400 | 3.4 |
| Jun-2017 | 5.4 | 315200 | -2.6 | 370,600 | -2.1 |
| Jul-2017 | 6 | 322900 | 2.4 | 372,400 | 0.5 |
| Aug-2017 | 6.2 | 314200 | -2.7 | 369,200 | -0.9 |
| Sep-2017 | 5.5 | 331500 | 5.5 | 379,300 | 2.7 |
| Oct-2017 | 5.5 | 319500 | -3.6 | 394,000 | 3.9 |
| Nov-2017 | 4.8 | 343400 | 7.5 | 388,500 | -1.4 |
| Dec-2017 | 5.3 | 343300 | 0.0 | 402,900 | 3.7 |
| Jan-2018 | 5.7 | 329600 | -4.0 | 377,800 | -6.2 |
| Feb-2018 | 5.5 | 327200 | -0.7 | 373,600 | -1.1 |
| Mar-2018 | 5.2 | 335400 | 2.5 | 369,200 | -1.2 |
| Apr-2018 | 5.5 | 314400 | -6.3 | 385,100 | 4.3 |
| May-2018 | 5.3 | 316700 | 0.7 | 372,600 | -3.2 |
| Jun-2018 | 6.2 | 310500 | -2.0 | 370,100 | -0.7 |
| Jul-2018 | 6.3 | 327500 | 5.5 | 392,300 | 6.0 |
| Aug-2018 | 6.6 | 321400 | -1.9 | 380,900 | -2.9 |
| Sep-2018 | 6.7 | 328300 | 2.1 | 386,400 | 1.4 |
| Oct-2018 | 7.3 | 328300 | 0.0 | 394,900 | 2.2 |
| Nov-2018 | 6.5 | 308500 | -6.0 | 367,100 | -7.0 |
| Dec-2018 | 7.3 | 329700 | 6.9 | 381,800 | 4.0 |
| Jan-2019 | 6.6 | 305400 | -7.4 | 361,100 | -5.4 |
| Feb-2019 | 6 | 320800 | 5.0 | 383,600 | 6.2 |
| Mar-2019 | 5.6 | 310600 | -3.2 | 372,700 | -2.8 |
| Apr-2019 | 5.8 | 339000 | 9.1 | 385,400 | 3.4 |
| May-2019 | 6.5 | 312700 | -7.8 | 379,100 | -1.6 |
| Jun-2019 | 5.6 | 311800 | -0.3 | 361,900 | -4.5 |
| Jul-2019 | 6.2 | 308300 | -1.1 | 373,500 | 3.2 |
| Aug-2019 | 5.8 | 327000 | 6.1 | 392,700 | 5.1 |
| Sep-2019 | 5.5 | 315700 | -3.5 | 372,100 | -5.2 |
| Oct-2019 | 5.5 | 322400 | 2.1 | 380,300 | 2.2 |
| Nov-2019 | 5.5 | 328000 | 1.7 | 384,400 | 1.1 |
| Dec-2019 | 5.3 | 329500 | 0.5 | 377,700 | -1.7 |
| Jan-2020 | 5.1 | 328900 | -0.2 | 384,000 | 1.7 |
| Feb-2020 | 5.3 | 331800 | 0.9 | 386,200 | 0.6 |
| Mar-2020 | 6.3 | 328200 | -1.1 | 375,400 | -2.8 |
| Apr-2020 | 6.6 | 310100 | -5.5 | 360,300 | -4.0 |
| May-2020 | 5.3 | 317100 | 2.3 | 368,700 | 2.3 |
| Jun-2020 | 4.3 | 341100 | 7.6 | 382,200 | 3.7 |
| Jul-2020 | 3.6 | 329800 | -3.3 | 379,100 | -0.8 |
| Aug-2020 | 3.5 | 325500 | -1.3 | 386,300 | 1.9 |
| Sep-2020 | 3.5 | 344400 | 5.8 | 405,100 | 4.9 |
| Oct-2020 | 3.5 | 346900 | 0.7 | 394,600 | -2.6 |
| Nov-2020 | 4 | 350800 | 1.1 | 396,100 | 0.4 |
| Dec-2020 | 3.8 | 365300 | 4.1 | 401,700 | 1.4 |
| Jan-2021 | 3.6 | 373200 | 2.2 | 418,600 | 4.2 |
| Feb-2021 | 4.5 | 362000 | -3.0 | 407,500 | -2.7 |
| Mar-2021 | 4.2 | 348300 | -3.8 | 408,300 | 0.2 |
| Apr-2021 | 4.6 | 365300 | 4.9 | 420,900 | 3.1 |
| May-2021 | 5.1 | 374400 | 2.5 | 430,600 | 2.3 |
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
The depressed level of residential construction and new house sales in the US is evident in Table IIB-3 providing new house sales not seasonally adjusted in Jan-May of various years. New house sales increased 25.9 percent from Jan-May 2020 to Jan-May 2021 in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event. There is an ongoing boom in real estate acquisitions. Comparisons are strong relative to current house sales in contrast with weakness in earlier periods after the global recession from IVQ2007 to IIQ2009. New house sales increased 27.2 percent from Jan-May 2019 to Jan-May 2021. New house sales increased 28.5 percent from Jan-May 2018 to Jan-May 2021. New house sales increased 38.5 percent from Jan-May 2017 to Jan-May 2021. Sales of new houses are higher in Jan-May 2020 relative to Jan-May 2016 with increase of 54.5 percent. Sales of new houses are higher in Jan-May 2020 relative to Jan-May 2015 with increase of 66.2 percent. Sales of new houses in Jan-May 2021 were substantially lower than in many years between 1996 and 2019 except for the years from 2008 to 2019. There are several other increases of 97.9 percent relative to 2014, 94.8 percent relative to Jan-May 2013, 139.7 percent relative to Jan-May 2012, 189.9 percent relative to Jan-May 2011, 142.9 percent relative to Jan-May 2010, and 149.3 percent relative to Jan-May 2009. New house sales in Jan-May 2020 are 56.5 percent higher than in Jan-May 2008. Sales of new houses in Jan-May 2020 are lower by 0.5 percent relative to Jan-May 2007. Sales of new houses are lower by 23.2 percent relative to Jan-May 2006, 33.7 percent relative to 2005 and 30.5 percent relative to 2004. The housing boom peaked in 2005 and 2006 when increases in fed funds rates to 5.25 percent in Jun 2006 from 1.0 percent in Jun 2004 affected subprime mortgages that were programmed for refinancing in two or three years on the expectation that price increases forever would raise home equity. Higher home equity would permit refinancing under feasible mortgages incorporating full payment of principal and interest (Gorton 2009EFM; see other references in http://cmpassocregulationblog.blogspot.com/2011/07/causes-of-2007-creditdollar-crisis.html). Sales of new houses in Jan-May 2021 relative to the same period in 2003 fell 16.5 percent and decreased 9.7 percent relative to the same period in 2002. Similar percentage declines are also for 2020 relative to years from 2000 to 2004. Sales of new houses in Jan-May 2021 decreased 2.6 per cent relative to the same period in 1998. The population of the US was 179.3 million in 1960 and 281.4 million in 2000 (Hobbs and Stoops 2002, 16). Detailed historical census reports are available from the US Census Bureau at (http://www.census.gov/population/www/censusdata/hiscendata.html). The estimate of the US population is 418.8 million in 2015. The US population increased by 133.6 percent from 1960 to 2015. The final row of Table IIB-3 reveals catastrophic data: sales of new houses in Jan-May 2021 of 374 thousand units are higher by 1.6 percent relative to 368 thousand units of houses sold in Jan-May 1977, which is fifteenth year when data become available in 1963. The civilian noninstitutional population increased from 122.416 million in 1963 to 259.175 million in 2019, or 111.7 percent (https://www.bls.gov/data/) and to 260.329 million in 2020 or 112.7 percent. The Bureau of Labor Statistics (BLS) defines the civilian noninstitutional population (http://www.bls.gov/lau/rdscnp16.htm#cnp): “The civilian noninstitutional population consists of persons 16 years of age and older residing in the 50 States and the District of Columbia who are not inmates of institutions (for example, penal and mental facilities and homes for the aged) and who are not on active duty in the Armed Forces.” Note: there are two equal total new houses sold in 2015 of 84 (39 in Jan and 45 in Feb) and 84 in 2016 (39 in Jan and 45 in Feb). There are two other equal total new houses sold of 68 in 2013 (32 in Jan and 36 in Feb) and 68 in 2014 (33 in Jan and 35 in 2014).
Table IIB-3, US, Sales of New Houses Not Seasonally Adjusted, Thousands and %
| Jan-May 2021 | 374 |
| Jan-May 2020 | 297 |
| ∆% Jan-May 2021/Jan-May 2020 | 25.9 |
| Jan-May 2019 | 294 |
| ∆% Jan-May 2021/Jan-May 2019 | 27.2 |
| Jan-May 2018 | 291 |
| ∆% Jan-May 2021/Jan-May 2018 | 28.5 |
| Jan-May 2017 | 270 |
| ∆% Jan-May 2021/Jan-May 2017 | 38.5 |
| Jan-May 2016 | 242 |
| ∆% Jan-May 2021/Jan-May 2016 | 54.5 |
| Jan-May 2015 | 225 |
| ∆% Jan-May 2021/Jan-May 2015 | 66.2 |
| Jan-May 2014 | 189 |
| ∆% Jan-May 2021/Jan-May 2014 | 97.9 |
| Jan-May 2013 | 192 |
| ∆% Jan-May 2021/Jan-May 2013 | 94.8 |
| Jan-May 2012 | 156 |
| ∆% Jan-May 2021/ | 139.7 |
| Jan-May 2011 | 129 |
| ∆% Jan-May 2021/ | 189.9 |
| Jan-May 2010 | 154 |
| ∆% Jan-May 2021/ | 142.9 |
| Jan-May 2009 | 150 |
| ∆% Jan-May 2021/ | 149.3 |
| Jan-May 2008 | 239 |
| ∆% Jan-May 2021/Jan-May 2008 | 56.5 |
| Jan-May 2007 | 376 |
| ∆% Jan-May 2021/Jan-May 2007 | -0.5 |
| Jan-May 2006 | 487 |
| ∆% Jan-May 2021/Jan-May 2006 | -23.2 |
| Jan-May 2005 | 564 |
| ∆% Jan-May 2021/ | -33.7 |
| Jan-May 2004 | 538 |
| ∆% Jan-May 2021/ | -30.5 |
| Jan-May 2003 | 448 |
| ∆% Jan-May 2021/ | -16.5 |
| Jan-May 2002 | 414 |
| ∆% Jan-May 2021/Jan-May 2002 | -9.7 |
| Jan-May 2001 | 415 |
| ∆% Jan-May 2021/Jan-May 2001 | -9.9 |
| Jan-May 2000 | 388 |
| ∆% Jan-May 2021/Jan-May 2000 | -3.6 |
| Jan-May 1998 | 384 |
| ∆% Jan-May 2020/Jan-May 1998 | -2.6 |
| Jan-May 1977 | 368 |
| ∆% Jan-May 2021/Jan-May 1977 | 1.6 |
*Computed using unrounded data
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
The revised level of 306 thousand new houses sold in 2011 is the lowest since 560 thousand in 1963 in the 56 years of available data while the level of 368 thousand in 2012 is only higher than 323 thousand in 2010. The level of sales of new houses of 437 thousand in 2014 is the lowest from 1963 to 2009 with exception of 412 thousand in 1982 and 436 thousand in 1981. The population of the US increased 129.4 million from 179.3 million in 1960 to 308.7 million in 2010, or 72.2 percent. The estimate of the US population is 418.8 million in 2015. The US population increased 133.6 percent from 1960 to 2015. The civilian noninstitutional population increased from 122.416 million in 1963 to 259.175 million in 2019, or 111.7 percent (https://www.bls.gov/data/) and to 260.329 million in 2020 or 112.7 percent. The Bureau of Labor Statistics (BLS) defines the civilian noninstitutional population (https://www.bls.gov/lau/rdscnp16.htm#cnp): “The civilian noninstitutional population consists of persons 16 years of age and older residing in the 50 States and the District of Columbia who are not inmates of institutions (for example, penal and mental facilities and homes for the aged) and who are not on active duty in the Armed Forces.”
Table IIB-4, US, New Houses Sold, NSA Thousands
| Period | Sold During Period |
| 1963 | 560 |
| 1964 | 565 |
| 1965 | 575 |
| 1966 | 461 |
| 1967 | 487 |
| 1968 | 490 |
| 1969 | 448 |
| 1970 | 485 |
| 1971 | 656 |
| 1972 | 718 |
| 1973 | 634 |
| 1974 | 519 |
| 1975 | 549 |
| 1976 | 646 |
| 1977 | 819 |
| 1978 | 817 |
| 1979 | 709 |
| 1980 | 545 |
| 1981 | 436 |
| 1982 | 412 |
| 1983 | 623 |
| 1984 | 639 |
| 1985 | 688 |
| 1986 | 750 |
| 1987 | 671 |
| 1988 | 676 |
| 1989 | 650 |
| 1990 | 534 |
| 1991 | 509 |
| 1992 | 610 |
| 1993 | 666 |
| 1994 | 670 |
| 1995 | 667 |
| 1996 | 757 |
| 1997 | 804 |
| 1998 | 886 |
| 1999 | 880 |
| 2000 | 877 |
| 2001 | 908 |
| 2002 | 973 |
| 2003 | 1,086 |
| 2004 | 1,203 |
| 2005 | 1,283 |
| 2006 | 1,051 |
| 2007 | 776 |
| 2008 | 485 |
| 2009 | 375 |
| 2010 | 323 |
| 2011 | 306 |
| 2012 | 368 |
| 2013 | 429 |
| 2014 | 437 |
| 2015 | 501 |
| 2016 | 561 |
| 2017 | 613 |
| 2018 | 617 |
| 2019 | 683 |
| 2020 | 822 |
Source: US Census Bureau https://www.census.gov/construction/nrs/index.html
Chart IIB-1 of the US Bureau of the Census shows the sharp decline of sales of new houses in the US. Sales rose temporarily until about mid 2010 but then declined to a lower plateau followed by increase, stability and new oscillating increase. There is decrease in the final segment followed by marginal increase. There is renewed decline and stabilization with recovery in oscillations in May-2020-May 2021.
Source: US Census Bureau
https://www.census.gov/construction/nrs/img/c25_curr.gif
Between 1991 and 2001, sales of new houses rose 78.4 percent at the average yearly rate of 6.0 percent, as shown in Table IB-5. Between 1995 and 2005 sales of new houses increased 92.4 percent at the yearly rate of 6.8 percent. There are similar rates in all years from 2000 to 2005. The boom in housing construction and sales began in the 1980s and 1990s. The collapse of real estate culminated several decades of housing subsidies and policies to lower mortgage rates and borrowing terms (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009b), 42-8). Sales of new houses sold in 2019 fell 9.8 percent relative to the same period in 1996 and fell 46.8 percent relative to 2005. Sales of new houses increased 20.4 percent from 2019 to 2020 in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event.
| ∆% | Average Yearly % Rate | |
| 1963-2019 | 22.0 | 0.4 |
| 1991-2001 | 78.4 | 6.0 |
| 1995-2005 | 92.4 | 6.8 |
| 2000-2005 | 46.3 | 7.9 |
| 1996-2019 | -9.8 | NA |
| 2000-2019 | -22.1 | NA |
| 2005-2019 | -46.8 | NA |
| 2019-2020 | 20.4 | NA |
NA: Not Applicable
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
IMPORTANT NOTE: Charts IIB-2 through IIB-2A cannot be updated because of the discontinuance of support of the Adobe Flash Player (https://www.adobe.com/products/flashplayer/end-of-life.html).
Chart IIB-2, US, New Single-family Houses Sold, NSA, 1963-2020
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-2A shows NSA sales of new single-family homes in the United States from 2019 to 2020. There is sharp decrease in Mar-Apr 2020 in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event followed by vigorous recovery in May-Aug 2020 and decline in Sep 2020 followed by mild increase in Oct 2020 and decline in Nov 2020.
Chart IIB-2A, US, New Single-family Houses Sold, NSA, 2019-2020
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-2A, US, New Single-family Houses Sold, SA, 2019-2020
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
The available historical annual data of median and average prices of new houses sold in the US between 1963 and 2020 is in Table IIB-6. On a yearly basis, median and average prices reached a peak in 2007 and then fell substantially. There is recovery in 2012-2018 followed by decline in 2019. Prices recovered in 2020 in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event.
Table IIB-6, US, Median and Average Prices of New Houses Sold, Annual Data
| Period | Median | Average |
| 1963 | 18,000 | 19,300 |
| 1964 | 18,900 | 20,500 |
| 1965 | 20,000 | 21,500 |
| 1966 | 21,400 | 23,300 |
| 1967 | 22,700 | 24,600 |
| 1968 | 24,700 | 26,600 |
| 1969 | 25,600 | 27,900 |
| 1970 | 23,400 | 26,600 |
| 1971 | 25,200 | 28,300 |
| 1972 | 27,600 | 30,500 |
| 1973 | 32,500 | 35,500 |
| 1974 | 35,900 | 38,900 |
| 1975 | 39,300 | 42,600 |
| 1976 | 44,200 | 48,000 |
| 1977 | 48,800 | 54,200 |
| 1978 | 55,700 | 62,500 |
| 1979 | 62,900 | 71,800 |
| 1980 | 64,600 | 76,400 |
| 1981 | 68,900 | 83,000 |
| 1982 | 69,300 | 83,900 |
| 1983 | 75,300 | 89,800 |
| 1984 | 79,900 | 97,600 |
| 1985 | 84,300 | 100,800 |
| 1986 | 92,000 | 111,900 |
| 1987 | 104,500 | 127,200 |
| 1988 | 112,500 | 138,300 |
| 1989 | 120,000 | 148,800 |
| 1990 | 122,900 | 149,800 |
| 1991 | 120,000 | 147,200 |
| 1992 | 121,500 | 144,100 |
| 1993 | 126,500 | 147,700 |
| 1994 | 130,000 | 154,500 |
| 1995 | 133,900 | 158,700 |
| 1996 | 140,000 | 166,400 |
| 1997 | 146,000 | 176,200 |
| 1998 | 152,500 | 181,900 |
| 1999 | 161,000 | 195,600 |
| 2000 | 169,000 | 207,000 |
| 2001 | 175,200 | 213,200 |
| 2002 | 187,600 | 228,700 |
| 2003 | 195,000 | 246,300 |
| 2004 | 221,000 | 274,500 |
| 2005 | 240,900 | 297,000 |
| 2006 | 246,500 | 305,900 |
| 2007 | 247,900 | 313,600 |
| 2008 | 232,100 | 292,600 |
| 2009 | 216,700 | 270,900 |
| 2010 | 221,800 | 272,900 |
| 2011 | 227,200 | 267,900 |
| 2012 | 245,200 | 292,200 |
| 2013 | 268,900 | 324,500 |
| 2014 | 288,500 | 347,700 |
| 2015 | 294,200 | 352,700 |
| 2016 | 307,800 | 360,900 |
| 2017 | 323,100 | 384,900 |
| 2018 | 326,400 | 385,000 |
| 2019 | 321,500 | 383,900 |
| 2020 | 336,900 | 391,900 |
Source: US Census Bureau https://www.census.gov/construction/nrs/index.html
Prices rose sharply between 2000 and 2005 as shown in Table IIB-7. In fact, prices in 2019 are higher than in 2000. Between 2006 and 2019, median prices of new houses sold increased 30.4 percent and average prices increased 25.5 percent. Between 2018 and 2019, median prices decreased 1.5 percent and average prices decreased 0.3 percent. Median prices increased 35.1 percent from 2006 to 2020 while average prices increased 26.8 percent. Median prices increased 3.6 percent from 2019 to 2020 while average prices increased 1.0 percent in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event.
Table IIB-7, US, Percentage Change of New Houses Median and Average Prices, NSA, ∆%
| Median New | Average New Home Sales Prices ∆% | |
| ∆% 2000 to 2003 | 15.4 | 19.0 |
| ∆% 2000 to 2005 | 42.5 | 43.5 |
| ∆% 2000 to 2019 | 90.2 | 85.5 |
| ∆% 2005 to 2019 | 33.5 | 29.3 |
| ∆% 2000 to 2006 | 45.9 | 47.8 |
| ∆% 2006 to 2019 | 30.4 | 25.5 |
| ∆% 2009 to 2019 | 48.4 | 41.7 |
| ∆% 2010 to 2019 | 45.0 | 40.7 |
| ∆% 2011 to 2019 | 41.5 | 43.3 |
| ∆% 2012 to 2019 | 31.1 | 31.4 |
| ∆% 2013 to 2019 | 19.6 | 18.3 |
| ∆% 2014 to 2019 | 11.4 | 10.4 |
| ∆% 2015 to 2019 | 9.3 | 8.8 |
| ∆% 2016 to 2019 | 4.5 | 6.4 |
| ∆% 2017 to 2019 | -0.5 | -0.3 |
| ∆% 2018 to 2019 | -1.5 | -0.3 |
| ∆% 2006 to 2020 | 36.7 | 28.1 |
| ∆% 2019 to 2020 | 4.8 | 2.1 |
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
IMPORTANT NOTE: Charts IIB-3 through IIB-4A cannot be updated because of the discontinuance of support of the Adobe Flash Player (https://www.adobe.com/products/flashplayer/end-of-life.html).
Chart IIB-3 of the US Census Bureau provides the entire series of new single-family sales median prices from Jan 1963 to Nov 2020. There is long-term sharp upward trend with few declines until the current collapse. Median prices increased sharply during the Great Inflation of the 1960s and 1970s and paused during the savings and loans crisis of the late 1980s and the recession of 1991. Housing subsidies throughout the 1990s caused sharp upward trend of median new house prices that accelerated after the fed funds rate of 1 percent from 2003 to 2004. There was sharp reduction of prices after 2006 with recovery recently above earlier prices.
Chart IIB-3, US, Median Sales Price of New Single-family Houses Sold, US Dollars, NSA, 1963-2020
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-3A of the US Census Bureau provides the entire series of new single-family sales median prices from Jan 2019 to Nov 2020. There is sharp decline of prices in Mar-Apr 2020 in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event followed by vigorous recovery in May-Jun 2020 with decline in Jul-Aug 2020, recovery in Sep 2020 and decline in Oct-Nov 2020.
Chart IIB-3A, US, Median Sales Price of New Single-family Houses Sold, US Dollars, NSA, 2019-2020
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-4 of the US Census Bureau provides average prices of new houses sold from the mid-1970s to Nov 2020. There is similar behavior as with median prices of new houses sold in Chart IIB-3. The only stress occurred in price pauses during the savings and loans crisis of the late 1980s and the collapse after 2006 with recent recovery, interrupted in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event.
Chart IIB-4, US, Average Sales Price of New Single-family Houses Sold, US Dollars, NSA, 1975-2020
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-4A of the US Census Bureau provides average prices of new houses sold from Jan 2019 to Nov 2020. Prices declined in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event with vigorous recovery in May-Jun 2020 followed by decline in Jul 2020. There is sharp recovery in Sep-Aug 2020 followed by decline in Oct 2020 and mild increase in Nov 2020.
Chart IIB-4A, US, Average Sales Price of New Single-family Houses Sold, US Dollars, NSA, 2019-2020
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-5 of the Board of Governors of the Federal Reserve System provides the rate for the 30-year conventional mortgage, the yield of the 30-year Treasury bond and the rate of the overnight federal funds rate, monthly, from 1954 to 2016. All rates decline throughout the period from the Great Inflation of the 1970s through the following Great Moderation and until currently. In Apr 1971, the fed funds rate was 4.15 percent and the conventional mortgage rate 7.31 percent. In November 2012, the fed funds rate was 0.16 percent, the yield of the 30-year Treasury 2.80 percent and the conventional mortgage rate 3.35. The final segment shows an increase in the yield of the 30-year Treasury to 3.61 percent in July 2013 with the fed funds rate at 0.09 percent and the conventional mortgage at 4.37 percent. The final data point shows marginal decrease of the conventional mortgage rate to 3.60 percent in May 2016 with the yield of the 30-year Treasury bond at 2.63 percent and overnight rate on fed funds at 0.37 percent. The recent increase in interest rates if sustained could affect the US real estate market. Shayndi Raice and Nick Timiraos, writing on “Banks cut as mortgage boom ends,” on Jan 9, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702303754404579310940019239208), analyze the drop in mortgage applications to a 13-year low, as measured by the Mortgage Bankers Association. Nick Timiraos, writing on “Demand for home loans plunges,” on Apr 24, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702304788404579522051733228402?mg=reno64-wsj), analyzes data in Inside Mortgage Finance that mortgage lending of $235 billion in IQ2014 is 58 percent lower than a year earlier and 23 percent below IVQ2013. Mortgage lending collapsed to the lowest level in 14 years. In testimony before the Committee on the Budget of the US Senate on May 8, 2004, Chair Yellen provides analysis of the current economic situation and outlook (http://www.federalreserve.gov/newsevents/testimony/yellen20140507a.htm): “One cautionary note, though, is that readings on housing activity--a sector that has been recovering since 2011--have remained disappointing so far this year and will bear watching.”
Chart IIB-5, US, Thirty-year Conventional Mortgage, Thirty-year Treasury Bond and Overnight Federal Funds Rate, Monthly, 1954-2016
Source: Board of Governors of the Federal Reserve System
http://www.federalreserve.gov/releases/H15/default.htm
Chart IIB-5A of the Board of Governors of the Federal Reserve System provides the yield of the 30-year Treasury bond and the rate of the overnight federal funds rate, monthly, from 1977 to 2021. The Board of Governors of the Federal Reserve System discontinued the conventional mortgage rate in its data bank. The final data point is 0.06 percent for the fed funds rate in May 2021 and 2.32 percent for the thirty-year Treasury bond resulting from the massive unconventional monetary policy in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event. The conventional mortgage rate stood at 2.96 percent in May 2021.
Chart IIB-5A, US, Thirty-year Treasury Bond and Overnight Federal Funds Rate, Monthly, 1977-2021
Source: Board of Governors of the Federal Reserve System
https://www.federalreserve.gov/releases/H15/default.htm
Table IIB-8, US, Fed Funds Rate, Thirty Year Treasury Bond and Conventional Mortgage Rate, Monthly, Percent per Year, Dec 2012 to Jan 2020
| Fed Funds Rate | Yield of Thirty-Year Constant Maturity | Conventional Mortgage Rate | |
| 2012-12 | 0.16 | 2.88 | 3.35 |
| 2013-01 | 0.14 | 3.08 | 3.41 |
| 2013-02 | 0.15 | 3.17 | 3.53 |
| 2013-03 | 0.14 | 3.16 | 3.57 |
| 2013-04 | 0.15 | 2.93 | 3.45 |
| 2013-05 | 0.11 | 3.11 | 3.54 |
| 2013-06 | 0.09 | 3.4 | 4.07 |
| 2013-07 | 0.09 | 3.61 | 4.37 |
| 2013-08 | 0.08 | 3.76 | 4.46 |
| 2013-09 | 0.08 | 3.79 | 4.49 |
| 2013-10 | 0.09 | 3.68 | 4.19 |
| 2013-11 | 0.08 | 3.8 | 4.26 |
| 2013-12 | 0.09 | 3.89 | 4.46 |
| 2014-01 | 0.07 | 3.77 | 4.43 |
| 2014-02 | 0.07 | 3.66 | 4.3 |
| 2014-03 | 0.08 | 3.62 | 4.34 |
| 2014-04 | 0.09 | 3.52 | 4.34 |
| 2014-05 | 0.09 | 3.39 | 4.19 |
| 2014-06 | 0.1 | 3.42 | 4.16 |
| 2014-07 | 0.09 | 3.33 | 4.13 |
| 2014-08 | 0.09 | 3.2 | 4.12 |
| 2014-09 | 0.09 | 3.26 | 4.16 |
| 2014-10 | 0.09 | 3.04 | 4.04 |
| 2014-11 | 0.09 | 3.04 | 4 |
| 2014-12 | 0.12 | 2.83 | 3.86 |
| 2015-01 | 0.11 | 2.46 | 3.67 |
| 2015-02 | 0.11 | 2.57 | 3.71 |
| 2015-03 | 0.11 | 2.63 | 3.77 |
| 2015-04 | 0.12 | 2.59 | 3.67 |
| 2015-05 | 0.12 | 2.96 | 3.84 |
| 2015-06 | 0.13 | 3.11 | 3.98 |
| 2015-07 | 0.13 | 3.07 | 4.05 |
| 2015-08 | 0.14 | 2.86 | 3.91 |
| 2015-09 | 0.14 | 2.95 | 3.89 |
| 2015-10 | 0.12 | 2.89 | 3.8 |
| 2015-11 | 0.12 | 3.03 | 3.94 |
| 2015-12 | 0.24 | 2.97 | 3.96 |
| 2016-01 | 0.34 | 2.86 | 3.87 |
| 2016-02 | 0.38 | 2.62 | 3.66 |
| 2016-03 | 0.36 | 2.68 | 3.69 |
| 2016-04 | 0.37 | 2.62 | 3.61 |
| 2016-05 | 0.37 | 2.63 | 3.6 |
| 2016-06 | 0.38 | 2.45 | 3.57 |
| 2016-07 | 0.39 | 2.23 | 3.44 |
| 2016-08 | 0.4 | 2.26 | 3.44 |
| 2016-09 | 0.4 | 2.35 | 3.46 |
| 2016-10 | 0.4 | 2.5 | 3.47 |
| 2016-11 | 0.41 | 2.86 | 3.77 |
| 2016-12 | 0.54 | 3.11 | 4.2 |
| 2017-01 | 0.65 | 3.02 | 4.15 |
| 2017-02 | 0.66 | 3.03 | 4.17 |
| 2017-03 | 0.79 | 3.08 | 4.2 |
| 2017-04 | 0.9 | 2.94 | 4.05 |
| 2017-05 | 0.91 | 2.96 | 4.01 |
| 2017-06 | 1.04 | 2.8 | 3.9 |
| 2017-07 | 1.15 | 2.88 | 3.97 |
| 2017-08 | 1.16 | 2.8 | 3.88 |
| 2017-09 | 1.15 | 2.78 | 3.81 |
| 2017-10 | 1.15 | 2.88 | 3.9 |
| 2017-11 | 1.16 | 2.8 | 3.92 |
| 2017-12 | 1.3 | 2.77 | 3.95 |
| 2018-01 | 1.41 | 2.88 | 4.03 |
| 2018-02 | 1.42 | 3.13 | 4.33 |
| 2018-03 | 1.51 | 3.09 | 4.44 |
| 2018-04 | 1.69 | 3.07 | 4.47 |
| 2018-05 | 1.7 | 3.13 | 4.59 |
| 2018-06 | 1.82 | 3.05 | 4.57 |
| 2018-07 | 1.91 | 3.01 | 4.53 |
| 2018-08 | 1.91 | 3.04 | 4.55 |
| 2018-09 | 1.95 | 3.15 | 4.63 |
| 2018-10 | 2.19 | 3.34 | 4.83 |
| 2018-11 | 2.2 | 3.36 | 4.87 |
| 2018-12 | 2.27 | 3.10 | 4.64 |
| 2019-01 | 2.40 | 3.04 | 4.46 |
| 2019-02 | 2.40 | 3.02 | 4.37 |
| 2019-03 | 2.41 | 2.98 | 4.27 |
| 2019-04 | 2.42 | 2.94 | 4.14 |
| 2019-05 | 2.39 | 2.82 | 4.07 |
| 2019-06 | 2.38 | 2.57 | 3.80 |
| 2019-07 | 2.40 | 2.57 | 3.77 |
| 2019-08 | 2.13 | 2.12 | 3.62 |
| 2019-09 | 2.04 | 2.16 | 3.61 |
| 2019-10 | 1.83 | 2.19 | 3.69 |
| 2019-11 | 1.55 | 2.28 | 3.70 |
| 2019-12 | 1.55 | 2.30 | 3.72 |
| 2020-01 | 1.55 | 2.22 | 3.62 |
| 2020-02 | 1.58 | 1.97 | 3.47 |
| 2020-03 | 0.65 | 1.46 | 3.45 |
| 2020-04 | 0.05 | 1.27 | 3.31 |
| 2020-05 | 0.05 | 1.38 | 3.23 |
| 2020-06 | 0.08 | 1.49 | 3.16 |
| 2020-07 | 0.09 | 1.31 | 3.02 |
| 2020-08 | 0.10 | 1.36 | 2.94 |
| 2020-09 | 0.09 | 1.42 | 2.89 |
| 2020-10 | 0.09 | 1.57 | 2.83 |
| 2020-11 | 0.09 | 1.62 | 2.77 |
| 2020-12 | 0.09 | 1.67 | 2.68 |
| 2021-01 | 0.09 | 1.82 | 2.74 |
| 2021-02 | 0.08 | 2.04 | 2.81 |
| 2021-03 | 0.07 | 2.34 | 3.08 |
| 2021-04 | 0.07 | 2.30 | 3.06 |
| 2021-05 | 0.06 | 2.32 | 2.96 |
Source: Board of Governors of the Federal Reserve System
https://www.federalreserve.gov/releases/H15/default.htm
http://www.freddiemac.com/pmms/pmms30.html
IIB2 United States House Prices. The Federal Housing Finance Agency (FHFA), which regulates Fannie Mae and Freddie Mac, provides the FHFA House Price Index (HPI) that “is calculated using home sales price information from Fannie Mae and Freddie Mac-acquired mortgages” (http://fhfa.gov/webfiles/24216/q22012hpi.pdf 1). The Federal Housing Finance Agency (FHFA), which regulates Fannie Mae and Freddie Mac, provides the FHFA House Price Index (HPI) that “is calculated using home sales price information from Fannie Mae and Freddie Mac-acquired mortgages” (http://fhfa.gov/webfiles/24216/q22012hpi.pdf 1). Table IIA2-1 provides the FHFA HPI for purchases only, which shows behavior similar to that of the Case-Shiller index but with lower magnitudes. House prices catapulted from 2000 to 2003, 2005 and 2006. From IVQ2000 to IVQ2006, the index for the US as a whole rose 55.0 percent, with 62.1 percent for New England, 72.0 percent for Middle Atlantic, 71.2 percent for South Atlantic but only by 33.1 percent for East South Central. Prices fell relative to 2014 for the US and all regions from 2006 with exception of increase of 2.6 percent for East South Central. Prices for the US increased 4.9 percent in IVQ2014 relative to IVQ2013 and 12.9 percent from IVQ2012 to IVQ2014. From IVQ2000 to IVQ2014, prices rose for the US and the four regions in Table IIA2-1.
Table IIA2-1, US, FHFA House Price Index Purchases Only NSA ∆%
| United States | New England | Middle Atlantic | South Atlantic | East South Central | |
| IVQ2000 | 24.0 | 40.6 | 35.8 | 25.9 | 11.0 |
| IVQ2000 | 50.5 | 65.0 | 67.6 | 62.9 | 25.4 |
| IVQ2000 to | 55.0 | 62.1 | 72.0 | 71.2 | 33.1 |
| IVQ2005 to | -1.5 | -8.7 | -2.3 | -7.4 | 8.9 |
| IVQ2006 | -4.4 | -7.1 | -4.8 | -11.9 | 2.6 |
| IVQ2007 to | -1.9 | -5.1 | -5.0 | -8.6 | 0.7 |
| IVQ2011 to | 18.9 | 7.3 | 6.9 | 19.9 | 11.8 |
| IVQ2012 to | 12.9 | 6.8 | 5.7 | 13.8 | 8.6 |
| IVQ2013 to IVQ2014 | 4.9 | 2.5 | 2.2 | 5.1 | 4.2 |
| IVQ2000 to | 48.3 144.27 | 50.6 138.40 | 63.7 127.30 | 50.9 140.28 | 36.6 146.07 |
Source: Federal Housing Finance Agency
http://www.fhfa.gov/KeyTopics/Pages/House-Price-Index.aspx
Data of the FHFA HPI for the remaining US regions are in Table IIA2-2. Behavior is not very different from that in Table IIA2-1 with the exception of East North Central. House prices in the Pacific region doubled between 2000 and 2006. Although prices of houses declined sharply from 2005 and 2006 to 2014 with exception of West South Central and West North Central, there was still appreciation relative to 2000.
Table IIA2-2, US, FHFA House Price Index Purchases Only NSA ∆%
| West South Central | West North Central | East North Central | Mountain | Pacific | |
| IVQ2000 | 11.1 | 18.3 | 14.7 | 18.9 | 44.6 |
| IVQ2000 | 23.9 | 31.0 | 23.8 | 58.0 | 107.7 |
| IVQ2000 to IVQ2006 | 31.6 | 33.7 | 23.7 | 68.6 | 108.7 |
| IVQ2005 to | 26.6 | 4.7 | -5.4 | -2.6 | -14.7 |
| IVQ2006 | 19.1 | 2.6 | -5.4 | -8.7 | -15.1 |
| IVQ2007 to | 15.2 | 3.2 | -2.1 | -5.6 | -6.0 |
| IVQ2011 to | 18.1 | 13.5 | 14.2 | 32.9 | 37.6 |
| IVQ2012 to | 12.1 | 8.9 | 11.1 | 17.9 | 24.4 |
| IVQ2013 to IVQ2014 | 5.9 | 4.0 | 4.6 | 5.5 | 7.3 |
| IVQ2000 to IVQ2014 | 56.8 145.53 | 37.1 158.59 | 17.1 155.13 | 53.9 172.46 | 77.1 132.21 |
Source: Federal Housing Finance Agency
http://www.fhfa.gov/KeyTopics/Pages/House-Price-Index.aspx
Monthly and 12-month percentage changes of the FHFA House Price Index are in Table IIA2-3. Percentage monthly increases of the FHFA index were positive from Apr to Jul 2011 with exception of declines in May and Aug 2011 while 12-month percentage changes improved steadily from around minus 6.0 percent in Mar to May 2011 to minus 4.5 percent in Jun 2011. The house price index increased 0.5 percent in Jan 2019 and increased 5.5 percent in 12 months. House prices increased 0.4 percent in Feb 2019 and increased 5.1 percent in 12 months. The house price index increased 0.2 percent in Mar 2019 and increased 5.0 percent in 12 months. House prices increased 0.8 percent in Apr 2019 and increased 5.3 percent in 12 months. The house price index increased 0.5 percent in May 2019 and increased 5.2 percent in 12 months. House prices increased 0.3 percent in Jun 2019 and increased 4.9 percent in 12 months. The house price index increased 0.5 percent in Jul 2019 and increased 5.1 percent in 12 months. House prices increased 0.1 percent in Aug 2019 and increased 4.7 percent in 12 months. The house price index increased 0.7 percent in Sep 2019 and increased 5.4 percent in 12 months. House prices increased 0.5 percent in Oct 2019 and increased 5.5 percent in 12 months. The house price index increased 0.4 percent in Nov 2019 and increased 5.3 percent in 12 months. House prices increased 0.9 percent in Dec 2019 and increased 5.9 percent in 12 months. The house price index increased 0.6 percent in Jan 2020 and increased 6.0 percent in 12 months. House prices increased 0.7 percent in Feb 2020 and increased 6.4 percent in 12 months. The house price index increased 0.2 percent in Mar 2020 and increased 6.4 percent in 12 months. House prices increased 0.5 percent in Apr 2020 and increased 5.9 percent in 12 months. The house price index decreased 0.2 percent in May 2020 and increased 5.2 percent in 12 months. House prices increased 1.1 percent in Jun 2020 and increased 6.1 percent in 12 months. The house price index increased 1.2 percent in Jul 2020 and increased 6.8 percent in 12 months. House prices increased 1.6 percent in Aug 2020 and increased 8.3 percent in 12 months. The house price index increased 1.7 percent in Sep 2020 and increased 9.4 percent in 12 months. House prices increased 1.4 percent in Oct 2020 and increased 10.4 percent in 12 months. The house price index increased 1.1 percent in Nov 2020 and increased 11.2 percent in 12 months. House prices increased 1.2 percent in Dec 2020 and increased 11.5 percent in 12 months. The house price index increased 1.0 percent in Jan 2021 and increased 12.1 percent in 12 months. House prices increased 1.1 percent in Feb 2021 and increased 12.5 percent in 12 months. The house price index increased 1.4 percent in Mar 2021 and increased 13.9 percent in 12 months.
Table IIA2-3, US, FHFA House Price Index Purchases Only SA. Month and NSA 12-Month ∆%
| Month ∆% SA | 12-Month ∆% NSA | ||||
| 3/1/2021 | 1.4 | 13.9 | |||
| 2/1/2021 | 1.1 | 12.5 | |||
| 1/1/2021 | 1.0 | 12.1 | |||
| 12/1/2020 | 1.2 | 11.5 | |||
| 11/1/2020 | 1.1 | 11.2 | |||
| 10/1/2020 | 1.4 | 10.4 | |||
| 9/1/2020 | 1.7 | 9.4 | |||
| 8/1/2020 | 1.6 | 8.3 | |||
| 7/1/2020 | 1.2 | 6.8 | |||
| 6/1/2020 | 1.1 | 6.1 | |||
| 5/1/2020 | -0.2 | 5.2 | |||
| 4/1/2020 | 0.5 | 5.9 | |||
| 3/1/2020 | 0.2 | 6.4 | |||
| 2/1/2020 | 0.7 | 6.4 | |||
| 1/1/2020 | 0.6 | 6.0 | |||
| 12/1/2019 | 0.9 | 5.9 | |||
| 11/1/2019 | 0.4 | 5.3 | |||
| 10/1/2019 | 0.5 | 5.5 | |||
| 9/1/2019 | 0.7 | 5.4 | |||
| 8/1/2019 | 0.1 | 4.7 | |||
| 7/1/2019 | 0.5 | 5.1 | |||
| 6/1/2019 | 0.3 | 4.9 | |||
| 5/1/2019 | 0.5 | 5.2 | |||
| 4/1/2019 | 0.8 | 5.3 | |||
| 3/1/2019 | 0.2 | 5.0 | |||
| 2/1/2019 | 0.4 | 5.1 | |||
| 1/1/2019 | 0.5 | 5.5 | |||
| 12/1/2018 | 0.3 | 5.7 | |||
| 11/1/2018 | 0.5 | 5.8 | |||
| 10/1/2018 | 0.4 | 5.9 | |||
| 9/1/2018 | 0.1 | 5.9 | |||
| 8/1/2018 | 0.5 | 6.1 | |||
| 7/1/2018 | 0.4 | 6.2 | |||
| 6/1/2018 | 0.4 | 6.4 | |||
| 5/1/2018 | 0.6 | 6.5 | |||
| 4/1/2018 | 0.4 | 6.4 | |||
| 3/1/2018 | 0.3 | 6.9 | |||
| 2/1/2018 | 0.8 | 7.4 | |||
| 1/1/2018 | 0.7 | 7.2 | |||
| 12/1/2017 | 0.4 | 6.4 | |||
| 11/1/2017 | 0.6 | 6.5 | |||
| 10/1/2017 | 0.5 | 6.4 | |||
| 9/1/2017 | 0.4 | 6.3 | |||
| 8/1/2017 | 0.7 | 6.5 | |||
| 7/1/2017 | 0.6 | 6.2 | |||
| 6/1/2017 | 0.4 | 6.1 | |||
| 5/1/2017 | 0.4 | 6.4 | |||
| 4/1/2017 | 0.8 | 6.5 | |||
| 3/1/2017 | 0.7 | 6.2 | |||
| 2/1/2017 | 0.7 | 6.4 | |||
| 1/1/2017 | -0.1 | 5.8 | |||
| 12/1/2016 | 0.6 | 6.2 | |||
| 11/1/2016 | 0.5 | 6.0 | |||
| 10/1/2016 | 0.5 | 6.0 | |||
| 9/1/2016 | 0.6 | 5.9 | |||
| 8/1/2016 | 0.4 | 5.9 | |||
| 7/1/2016 | 0.5 | 5.6 | |||
| 6/1/2016 | 0.5 | 5.6 | |||
| 5/1/2016 | 0.4 | 5.5 | |||
| 4/1/2016 | 0.5 | 5.8 | |||
| 3/1/2016 | 0.8 | 5.7 | |||
| 2/1/2016 | 0.2 | 5.2 | |||
| 1/1/2016 | 0.4 | 5.9 | |||
| 12/1/2015 | 0.4 | 5.4 | |||
| 11/1/2015 | 0.5 | 5.6 | |||
| 10/1/2015 | 0.5 | 5.5 | |||
| 9/1/2015 | 0.6 | 5.6 | |||
| 8/1/2015 | 0.2 | 5.1 | |||
| 7/1/2015 | 0.5 | 5.3 | |||
| 6/1/2015 | 0.4 | 5.2 | |||
| 5/1/2015 | 0.6 | 5.4 | |||
| 4/1/2015 | 0.4 | 5.1 | |||
| 3/1/2015 | 0.3 | 5.1 | |||
| 2/1/2015 | 0.9 | 5.1 | |||
| 1/1/2015 | 0.0 | 4.6 | |||
| 12/1/2014 | 0.7 | 4.9 | |||
| 11/1/2014 | 0.4 | 4.8 | |||
| 10/1/2014 | 0.6 | 4.3 | |||
| 9/1/2014 | 0.1 | 4.1 | |||
| 8/1/2014 | 0.4 | 4.5 | |||
| 7/1/2014 | 0.4 | 4.4 | |||
| 6/1/2014 | 0.6 | 4.7 | |||
| 5/1/2014 | 0.2 | 4.7 | |||
| 4/1/2014 | 0.3 | 5.4 | |||
| 3/1/2014 | 0.3 | 5.7 | |||
| 2/1/2014 | 0.4 | 6.3 | |||
| 1/1/2014 | 0.4 | 6.5 | |||
| 12/1/2013 | 0.6 | 6.8 | |||
| 11/1/2013 | 0.0 | 6.7 | |||
| 10/1/2013 | 0.3 | 7.1 | |||
| 9/1/2013 | 0.5 | 7.4 | |||
| 8/1/2013 | 0.3 | 7.3 | |||
| 7/1/2013 | 0.6 | 7.7 | |||
| 6/1/2013 | 0.6 | 7.3 | |||
| 5/1/2013 | 0.9 | 7.1 | |||
| 4/1/2013 | 0.5 | 6.9 | |||
| 3/1/2013 | 1.0 | 7.0 | |||
| 2/1/2013 | 0.6 | 6.6 | |||
| 1/1/2013 | 0.8 | 6.2 | |||
| 12/1/2012 | 0.5 | 5.0 | |||
| 11/1/2012 | 0.4 | 4.8 | |||
| 10/1/2012 | 0.5 | 4.8 | |||
| 9/1/2012 | 0.4 | 3.8 | |||
| 8/1/2012 | 0.7 | 4.0 | |||
| 7/1/2012 | 0.2 | 3.1 | |||
| 6/1/2012 | 0.4 | 3.1 | |||
| 5/1/2012 | 0.6 | 3.0 | |||
| 4/1/2012 | 0.5 | 2.1 | |||
| 3/1/2012 | 0.8 | 1.8 | |||
| 2/1/2012 | 0.2 | -0.3 | |||
| 1/1/2012 | -0.4 | -1.4 | |||
| 12/1/2011 | 0.3 | -1.5 | |||
| 11/1/2011 | 0.4 | -2.5 | |||
| 10/1/2011 | -0.6 | -3.3 | |||
| 9/1/2011 | 0.6 | -2.5 | |||
| 8/1/2011 | -0.3 | -4.0 | |||
| 7/1/2011 | 0.2 | -3.8 | |||
| 6/1/2011 | 0.3 | -4.5 | |||
| 5/1/2011 | -0.2 | -5.9 | |||
| 4/1/2011 | 0.2 | -5.7 | |||
| 3/1/2011 | -1.1 | -5.9 | |||
| 2/1/2011 | -0.9 | -5.1 | |||
| 1/1/2011 | -0.6 | -4.5 | |||
| 12/1/2010 | -0.7 | -3.9 | |||
| 12/1/2009 | -1.0 | -2.0 | |||
| 12/1/2008 | -0.3 | -10.4 | |||
| 12/1/2007 | -0.5 | -3.4 | |||
| 12/1/2006 | 0.0 | 2.3 | |||
| 12/1/2005 | 0.6 | 9.8 | |||
| 12/1/2004 | 0.9 | 10.2 | |||
| 12/1/2003 | 0.8 | 8.0 | |||
| 12/1/2002 | 0.6 | 7.7 | |||
| 12/1/2001 | 0.6 | 6.7 | |||
| 12/1/2000 | 0.6 | 7.1 | |||
| 12/1/1999 | 0.5 | 6.1 | |||
| 12/1/1998 | 0.5 | 5.9 | |||
| 12/1/1997 | 0.3 | 3.3 | |||
| 12/1/1996 | 0.3 | 2.7 | |||
| 12/1/1995 | 0.4 | 3.0 | |||
| 12/1/1994 | 0.0 | 2.5 | |||
| 12/1/1993 | 0.5 | 3.1 | |||
| 12/1/1992 | -0.1 | 2.3 |
Source: Federal Housing Finance Agency
https://www.fhfa.gov/DataTools
The bottom part of Table IIA2-3 provides 12-month percentage changes of the FHFA house price index since 1992 when data become available for 1991. Table IIA2-4 provides percentage changes and average rates of percent change per year for various periods. Between 1992 and 2019, the FHFA house price index increased 168.7 percent at the yearly average rate of 3.7 percent. In the period 1992-2000, the FHFA house price index increased 39.0 percent at the average yearly rate of 4.2 percent. The average yearly rate of price increase accelerated to 7.5 percent in the period 2000-2003, 8.5 percent in 2000-2005 and 7.4 percent in 2000-2006. At the margin, the average rate jumped to 10.0 percent in 2003-2005 and 7.4 percent in 2003-2006. House prices measured by the FHFA house price index increased 25.8 percent at the average yearly rate of 1.8 percent between 2006 and 2019 and 28.7 percent between 2005 and 2019 at the average yearly rate of 1.8 percent. The FHFA house price index increased 11.5 percent from 2019 to 2020.
| Dec | ∆% | Average ∆% per Year |
| 1992-2019 | 168.7 | 3.7 |
| 1992-2020 | 199.7 | 4.0 |
| 1992-2000 | 39.0 | 4.2 |
| 2000-2003 | 24.1 | 7.5 |
| 2000-2005 | 50.2 | 8.5 |
| 2003-2005 | 21.0 | 10.0 |
| 2005-2019 | 28.7 | 1.8 |
| 2005-2020 | 43.7 | 2.4 |
| 2000-2006 | 53.6 | 7.4 |
| 2003-2006 | 23.8 | 7.4 |
| 2006-2019 | 25.8 | 1.8 |
| 2006-2020 | 40.3 | 2.4 |
| 2019-2020 | 11.5 | 11.5 |
Source: Federal Housing Finance Agency
https://www.fhfa.gov/DataTools
The explanation of the sharp contraction of household wealth can probably be found in the origins of the financial crisis and global recession. Let V(T) represent the value of the firm’s equity at time T and B stand for the promised debt of the firm to bondholders and assume that corporate management, elected by equity owners, is acting on the interests of equity owners. Robert C. Merton (1974, 453) states:
“On the maturity date T, the firm must either pay the promised payment of B to the debtholders or else the current equity will be valueless. Clearly, if at time T, V(T) > B, the firm should pay the bondholders because the value of equity will be V(T) – B > 0 whereas if they do not, the value of equity would be zero. If V(T) ≤ B, then the firm will not make the payment and default the firm to the bondholders because otherwise the equity holders would have to pay in additional money and the (formal) value of equity prior to such payments would be (V(T)- B) < 0.”
Pelaez and Pelaez (The Global Recession Risk (2007), 208-9) apply this analysis to the US housing market in 2005-2006 concluding:
“The house market [in 2006] is probably operating with low historical levels of individual equity. There is an application of structural models [Duffie and Singleton 2003] to the individual decisions on whether or not to continue paying a mortgage. The costs of sale would include realtor and legal fees. There could be a point where the expected net sale value of the real estate may be just lower than the value of the mortgage. At that point, there would be an incentive to default. The default vulnerability of securitization is unknown.”
There are multiple important determinants of the interest rate: “aggregate wealth, the distribution of wealth among investors, expected rate of return on physical investment, taxes, government policy and inflation” (Ingersoll 1987, 405). Aggregate wealth is a major driver of interest rates (Ibid, 406). Unconventional monetary policy, with zero fed funds rates and flattening of long-term yields by quantitative easing, causes uncontrollable effects on risk taking that can have profound undesirable effects on financial stability. Excessively aggressive and exotic monetary policy is the main culprit and not the inadequacy of financial management and risk controls.
The net worth of the economy depends on interest rates. In theory, “income is generally defined as the amount a consumer unit could consume (or believe that it could) while maintaining its wealth intact” (Friedman 1957, 10). Income, Y, is a flow that is obtained by applying a rate of return, r, to a stock of wealth, W, or Y = rW (Ibid). According to a subsequent restatement: “The basic idea is simply that individuals live for many years and that therefore the appropriate constraint for consumption decisions is the long-run expected yield from wealth r*W. This yield was named permanent income: Y* = r*W” (Darby 1974, 229), where * denotes permanent. The simplified relation of income and wealth can be restated as:
W = Y/r (1)
Equation (1) shows that as r goes to zero, r →0, W grows without bound, W→∞.
Lowering the interest rate near the zero bound in 2003-2004 caused the illusion of permanent increases in wealth or net worth in the balance sheets of borrowers and also of lending institutions, securitized banking and every financial institution and investor in the world. The discipline of calculating risks and returns was seriously impaired. The objective of monetary policy was to encourage borrowing, consumption and investment but the exaggerated stimulus resulted in a financial crisis of major proportions as the securitization that had worked for a long period was shocked with policy-induced excessive risk, imprudent credit, high leverage and low liquidity by the incentive to finance everything overnight at close to zero interest rates, from adjustable rate mortgages (ARMS) to asset-backed commercial paper of structured investment vehicles (SIV).
The consequences of inflating liquidity and net worth of borrowers were a global hunt for yields to protect own investments and money under management from the zero interest rates and unattractive long-term yields of Treasuries and other securities. Monetary policy distorted the calculations of risks and returns by households, business and government by providing central bank cheap money. Short-term zero interest rates encourage financing of everything with short-dated funds, explaining the SIVs created off-balance sheet to issue short-term commercial paper to purchase default-prone mortgages that were financed in overnight or short-dated sale and repurchase agreements (Pelaez and Pelaez, Financial Regulation after the Global Recession, 50-1, Regulation of Banks and Finance, 59-60, Globalization and the State Vol. I, 89-92, Globalization and the State Vol. II, 198-9, Government Intervention in Globalization, 62-3, International Financial Architecture, 144-9). ARMS were created to lower monthly mortgage payments by benefitting from lower short-dated reference rates. Financial institutions economized in liquidity that was penalized with near zero interest rates. There was no perception of risk because the monetary authority guaranteed a minimum or floor price of all assets by maintaining low interest rates forever or equivalent to writing an illusory put option on wealth. Subprime mortgages were part of the put on wealth by an illusory put on house prices. The housing subsidy of $221 billion per year created the impression of ever increasing house prices. The suspension of auctions of 30-year Treasuries was designed to increase demand for mortgage-backed securities, lowering their yield, which was equivalent to lowering the costs of housing finance and refinancing. Fannie and Freddie purchased or guaranteed $1.6 trillion of nonprime mortgages and worked with leverage of 75:1 under Congress-provided charters and lax oversight. The combination of these policies resulted in high risks because of the put option on wealth by near zero interest rates, excessive leverage because of cheap rates, low liquidity because of the penalty in the form of low interest rates and unsound credit decisions because the put option on wealth by monetary policy created the illusion that nothing could ever go wrong, causing the credit/dollar crisis and global recession (Pelaez and Pelaez, Financial Regulation after the Global Recession, 157-66, Regulation of Banks, and Finance, 217-27, International Financial Architecture, 15-18, The Global Recession Risk, 221-5, Globalization and the State Vol. II, 197-213, Government Intervention in Globalization, 182-4).
There are significant elements of the theory of bank financial fragility of Diamond and Dybvig (1983) and Diamond and Rajan (2000, 2001a, 2001b) that help to explain the financial fragility of banks during the credit/dollar crisis (see also Diamond 2007). The theory of Diamond and Dybvig (1983) as exposed by Diamond (2007) is that banks funding with demand deposits have a mismatch of liquidity (see Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 58-66). A run occurs when too many depositors attempt to withdraw cash at the same time. All that is needed is an expectation of failure of the bank. Three important functions of banks are providing evaluation, monitoring and liquidity transformation. Banks invest in human capital to evaluate projects of borrowers in deciding if they merit credit. The evaluation function reduces adverse selection or financing projects with low present value. Banks also provide important monitoring services of following the implementation of projects, avoiding moral hazard that funds be used for, say, real estate speculation instead of the original project of factory construction. The transformation function of banks involves both assets and liabilities of bank balance sheets. Banks convert an illiquid asset or loan for a project with cash flows in the distant future into a liquid liability in the form of demand deposits that can be withdrawn immediately.
In the theory of banking of Diamond and Rajan (2000, 2001a, 2001b), the bank creates liquidity by tying human assets to capital. The collection skills of the relationship banker convert an illiquid project of an entrepreneur into liquid demand deposits that are immediately available for withdrawal. The deposit/capital structure is fragile because of the threat of bank runs. In these days of online banking, the run on Washington Mutual was through withdrawals online. A bank run can be triggered by the decline of the value of bank assets below the value of demand deposits.
Pelaez and Pelaez (Regulation of Banks and Finance 2009b, 60, 64-5) find immediate application of the theories of banking of Diamond, Dybvig and Rajan to the credit/dollar crisis after 2007. It is a credit crisis because the main issue was the deterioration of the credit portfolios of securitized banks as a result of default of subprime mortgages. It is a dollar crisis because of the weakening dollar resulting from relatively low interest rate policies of the US. It caused systemic effects that converted into a global recession not only because of the huge weight of the US economy in the world economy but also because the credit crisis transferred to the UK and Europe. Management skills or human capital of banks are illustrated by the financial engineering of complex products. The increasing importance of human relative to inanimate capital (Rajan and Zingales 2000) is revolutionizing the theory of the firm (Zingales 2000) and corporate governance (Rajan and Zingales 2001). Finance is one of the most important examples of this transformation. Profits were derived from the charter in the original banking institution. Pricing and structuring financial instruments was revolutionized with option pricing formulas developed by Black and Scholes (1973) and Merton (1973, 1974, 1998) that permitted the development of complex products with fair pricing. The successful financial company must attract and retain finance professionals who have invested in human capital, which is a sunk cost to them and not of the institution where they work.
The complex financial products created for securitized banking with high investments in human capital are based on houses, which are as illiquid as the projects of entrepreneurs in the theory of banking. The liquidity fragility of the securitized bank is equivalent to that of the commercial bank in the theory of banking (Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 65). Banks created off-balance sheet structured investment vehicles (SIV) that issued commercial paper receiving AAA rating because of letters of liquidity guarantee by the banks. The commercial paper was converted into liquidity by its use as collateral in SRPs at the lowest rates and minimal haircuts because of the AAA rating of the guarantor bank. In the theory of banking, default can be triggered when the value of assets is perceived as lower than the value of the deposits. Commercial paper issued by SIVs, securitized mortgages and derivatives all obtained SRP liquidity on the basis of illiquid home mortgage loans at the bottom of the pyramid. The run on the securitized bank had a clear origin (Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 65):
“The increasing default of mortgages resulted in an increase in counterparty risk. Banks were hit by the liquidity demands of their counterparties. The liquidity shock extended to many segments of the financial markets—interbank loans, asset-backed commercial paper (ABCP), high-yield bonds and many others—when counterparties preferred lower returns of highly liquid safe havens, such as Treasury securities, than the risk of having to sell the collateral in SRPs at deep discounts or holding an illiquid asset. The price of an illiquid asset is near zero.”
Gorton and Metrick (2010H, 507) provide a revealing quote to the work in 1908 of Edwin R. A. Seligman, professor of political economy at Columbia University, founding member of the American Economic Association and one of its presidents and successful advocate of progressive income taxation. The intention of the quote is to bring forth the important argument that financial crises are explained in terms of “confidence” but as Professor Seligman states in reference to historical banking crises in the US, the important task is to explain what caused the lack of confidence. It is instructive to repeat the more extended quote of Seligman (1908, xi) on the explanations of banking crises:
“The current explanations may be divided into two categories. Of these the first includes what might be termed the superficial theories. Thus it is commonly stated that the outbreak of a crisis is due to lack of confidence,--as if the lack of confidence was not in itself the very thing which needs to be explained. Of still slighter value is the attempt to associate a crisis with some particular governmental policy, or with some action of a country’s executive. Such puerile interpretations have commonly been confined to countries like the United States, where the political passions of democracy have had the fullest way. Thus the crisis of 1893 was ascribed by the Republicans to the impending Democratic tariff of 1894; and the crisis of 1907 has by some been termed the ‘[Theodore] Roosevelt panic,” utterly oblivious of the fact that from the time of President Jackson, who was held responsible for the troubles of 1837, every successive crisis had had its presidential scapegoat, and has been followed by a political revulsion. Opposed to these popular, but wholly unfounded interpretations, is the second class of explanations, which seek to burrow beneath the surface and to discover the more occult and fundamental causes of the periodicity of crises.”
Scholars ignore superficial explanations in the effort to seek good and truth. The problem of economic analysis of the credit/dollar crisis is the lack of a structural model with which to attempt empirical determination of causes (Gorton and Metrick 2010SB). There would still be doubts even with a well-specified structural model because samples of economic events do not typically permit separating causes and effects. There is also confusion is separating the why of the crisis and how it started and propagated, all of which are extremely important.
In true heritage of the principles of Seligman (1908), Gorton (2009EFM) discovers a prime causal driver of the credit/dollar crisis. The objective of subprime and Alt-A mortgages was to facilitate loans to populations with modest means so that they could acquire a home. These borrowers would not receive credit because of (1) lack of funds for down payments; (2) low credit rating and information; (3) lack of information on income; and (4) errors or lack of other information. Subprime mortgage “engineering” was based on the belief that both lender and borrower could benefit from increases in house prices over the short run. The initial mortgage would be refinanced in two or three years depending on the increase of the price of the house. According to Gorton (2009EFM, 13, 16):
“The outstanding amounts of Subprime and Alt-A [mortgages] combined amounted to about one quarter of the $6 trillion mortgage market in 2004-2007Q1. Over the period 2000-2007, the outstanding amount of agency mortgages doubled, but subprime grew 800%! Issuance in 2005 and 2006 of Subprime and Alt-A mortgages was almost 30% of the mortgage market. Since 2000 the Subprime and Alt-A segments of the market grew at the expense of the Agency (i.e., the government sponsored entities of Fannie Mae and Freddie Mac) share, which fell from almost 80% (by outstanding or issuance) to about half by issuance and 67% by outstanding amount. The lender’s option to rollover the mortgage after an initial period is implicit in the subprime mortgage. The key design features of a subprime mortgage are: (1) it is short term, making refinancing important; (2) there is a step-up mortgage rate that applies at the end of the first period, creating a strong incentive to refinance; and (3) there is a prepayment penalty, creating an incentive not to refinance early.”
The prime objective of successive administrations in the US during the past 20 years and actually since the times of Roosevelt in the 1930s has been to provide “affordable” financing for the “American dream” of home ownership. The US housing finance system is mixed with public, public/private and purely private entities. The Federal Home Loan Bank (FHLB) system was established by Congress in 1932 that also created the Federal Housing Administration in 1934 with the objective of insuring homes against default. In 1938, the government created the Federal National Mortgage Association, or Fannie Mae, to foster a market for FHA-insured mortgages. Government-insured mortgages were transferred from Fannie Mae to the Government National Mortgage Association, or Ginnie Mae, to permit Fannie Mae to become a publicly-owned company. Securitization of mortgages began in 1970 with the government charter to the Federal Home Loan Mortgage Corporation, or Freddie Mac, with the objective of bundling mortgages created by thrift institutions that would be marketed as bonds with guarantees by Freddie Mac (see Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 42-8). In the third quarter of 2008, total mortgages in the US were $12,057 billion of which 43.5 percent, or $5423 billion, were retained or guaranteed by Fannie Mae and Freddie Mac (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 45). In 1990, Fannie Mae and Freddie Mac had a share of only 25.4 percent of total mortgages in the US. Mortgages in the US increased from $6922 billion in 2002 to $12,088 billion in 2007, or by 74.6 percent, while the retained or guaranteed portfolio of Fannie and Freddie rose from $3180 billion in 2002 to $4934 billion in 2007, or by 55.2 percent.
According to Pinto (2008) in testimony to Congress:
“There are approximately 25 million subprime and Alt-A loans outstanding, with an unpaid principal amount of over $4.5 trillion, about half of them held or guaranteed by Fannie and Freddie. Their high risk activities were allowed to operate at 75:1 leverage ratio. While they may deny it, there can be no doubt that Fannie and Freddie now own or guarantee $1.6 trillion in subprime, Alt-A and other default prone loans and securities. This comprises over 1/3 of their risk portfolios and amounts to 34% of all the subprime loans and 60% of all Alt-A loans outstanding. These 10.5 million unsustainable, nonprime loans are experiencing a default rate 8 times the level of the GSEs’ 20 million traditional quality loans. The GSEs will be responsible for a large percentage of an estimated 8.8 million foreclosures expected over the next 4 years, accounting for the failure of about 1 in 6 home mortgages. Fannie and Freddie have subprimed America.”
In perceptive analysis of growth and macroeconomics in the past six decades, Rajan (2012FA) argues that “the West can’t borrow and spend its way to recovery.” The Keynesian paradigm is not applicable in current conditions. Advanced economies in the West could be divided into those that reformed regulatory structures to encourage productivity and others that retained older structures. In the period from 1950 to 2000, Cobet and Wilson (2002) find that US productivity, measured as output/hour, grew at the average yearly rate of 2.9 percent while Japan grew at 6.3 percent and Germany at 4.7 percent (see Pelaez and Pelaez, The Global Recession Risk (2007), 135-44). In the period from 1995 to 2000, output/hour grew at the average yearly rate of 4.6 percent in the US but at lower rates of 3.9 percent in Japan and 2.6 percent in the US. Rajan (2012FA) argues that the differential in productivity growth was accomplished by deregulation in the US at the end of the 1970s and during the 1980s. In contrast, Europe did not engage in reform with the exception of Germany in the early 2000s that empowered the German economy with significant productivity advantage. At the same time, technology and globalization increased relative remunerations in highly-skilled, educated workers relative to those without skills for the new economy. It was then politically appealing to improve the fortunes of those left behind by the technological revolution by means of increasing cheap credit. As Rajan (2012FA) argues:
“In 1992, Congress passed the Federal Housing Enterprises Financial Safety and Soundness Act, partly to gain more control over Fannie Mae and Freddie Mac, the giant private mortgage agencies, and partly to promote affordable homeownership for low-income groups. Such policies helped money flow to lower-middle-class households and raised their spending—so much so that consumption inequality rose much less than income inequality in the years before the crisis. These policies were also politically popular. Unlike when it came to an expansion in government welfare transfers, few groups opposed expanding credit to the lower-middle class—not the politicians who wanted more growth and happy constituents, not the bankers and brokers who profited from the mortgage fees, not the borrowers who could now buy their dream houses with virtually no money down, and not the laissez-faire bank regulators who thought they could pick up the pieces if the housing market collapsed. The Federal Reserve abetted these shortsighted policies. In 2001, in response to the dot-com bust, the Fed cut short-term interest rates to the bone. Even though the overstretched corporations that were meant to be stimulated were not interested in investing, artificially low interest rates acted as a tremendous subsidy to the parts of the economy that relied on debt, such as housing and finance. This led to an expansion in housing construction (and related services, such as real estate brokerage and mortgage lending), which created jobs, especially for the unskilled. Progressive economists applauded this process, arguing that the housing boom would lift the economy out of the doldrums. But the Fed-supported bubble proved unsustainable. Many construction workers have lost their jobs and are now in deeper trouble than before, having also borrowed to buy unaffordable houses. Bankers obviously deserve a large share of the blame for the crisis. Some of the financial sector’s activities were clearly predatory, if not outright criminal. But the role that the politically induced expansion of credit played cannot be ignored; it is the main reason the usual checks and balances on financial risk taking broke down.”
In fact, Raghuram G. Rajan (2005) anticipated low liquidity in financial markets resulting from low interest rates before the financial crisis that caused distortions of risk/return decisions provoking the credit/dollar crisis and global recession from IVQ2007 to IIQ2009. Near zero interest rates of unconventional monetary policy induced excessive risks and low liquidity in financial decisions that were critical as a cause of the credit/dollar crisis after 2007. Rajan (2012FA) argues that it is not feasible to return to the employment and income levels before the credit/dollar crisis because of the bloated construction sector, financial system and government budgets.
Table IIA-2 shows the euphoria of prices during the housing boom and the subsequent decline. House prices rose 62.2 percent in the US national home price index between Mar 2000 and Mar 2005. Prices rose 80.1 percent in the US national index from Mar 2000 to Mar 2006. House prices rose 27.3 percent between Mar 2003 and Mar 2005 for the US national propelled by low fed funds rates of 1.0 percent between Jul 2003 and Jul 2004. Fed funds rates increased by 0.25 basis points at every meeting of the Federal Open Market Committee (FOMC) from Jun 2004 until Jun 2006, reaching 5.25 percent. Simultaneously, the suspension of auctions of the 30-year Treasury bond caused decrease of yields of mortgage-backed securities with intended increase in mortgage rates. Similarly, between Mar 2003 and Mar 2006 the US national increased 41.3 percent. House prices have increased from Mar 2006 to Mar 2021 by 33.3 percent for the US national. Measuring house prices is quite difficult because of the lack of homogeneity that is typical of standardized commodities. In the 12 months ending in Mar 2021, house prices increased 13.2 percent in the US national. Table IIA-1 also shows that house prices increased 140.1 percent between Mar 2000 and Mar 2021 for the US national. House prices are close to the lowest level since peaks during the boom before the financial crisis and global recession. The US national increased 32.0 percent in Mar 2021 from the peak in Jun 2006 and increased 32.0 percent from the peak in Jul 2006. The final part of Table II-2 provides average annual percentage rates of growth of the house price indexes of Standard & Poor’s Case-Shiller. The average rate for the US national was 3.8 percent from Dec 1987 to Dec 2020 and 3.6 percent from Dec 1987 to Dec 2000. Although the global recession affecting the US between IVQ2007 (Dec) and IIQ2009 (Jun) caused decline of house prices of slightly above 30 percent, the average annual growth rate between Dec 2000 and Dec 2020 was 3.9 percent for the US national.
Table IIA-1, US, Percentage Changes of Standard & Poor’s Case-Shiller National Home Price Indices, Not Seasonally Adjusted, ∆%
| US National | |
| ∆% Mar 2000 to Mar 2003 | 27.4 |
| ∆% Mar 2000 to Mar 2005 | 62.2 |
| ∆% Mar 2003 to Mar 2005 | 27.3 |
| ∆% Mar 2000 to Mar 2006 | 80.1 |
| ∆% Mar 2003 to Mar 2006 | 41.3 |
| ∆% Mar 2005 to Mar 2021 | 48.0 |
| ∆% Mar 2006 to Mar 2021 | 33.3 |
| ∆% Mar 2009 to Mar 2021 | 66.3 |
| ∆% Mar 2010 to Mar 2021 | 69.7 |
| ∆% Mar 2011 to Mar 2021 | 76.8 |
| ∆% Mar 2012 to Mar 2021 | 79.3 |
| ∆% Mar 2013 to Mar 2021 | 64.7 |
| ∆% Mar 2014 to Mar 2021 | 51.2 |
| ∆% Mar 2015 to Mar 2021 | 44.9 |
| ∆% Mar 2016 to Mar 2021 | 37.9 |
| ∆% Mar 2017 to Mar 2021 | 30.6 |
| ∆% Mar 2018 to Mar 2021 | 22.7 |
| ∆% Mar 2019 to Mar 2021 | 18.4 |
| ∆% Mar 2020 to Mar 2021 | 13.2 |
| ∆% Mar 2000 to Mar 2021 | 140.1 |
| ∆% Peak Jun 2006 to Mar 2021 | 32.0 |
| ∆% Peak Jul 2006 to Mar 2021 | 32.0 |
| Average ∆% Dec 1987-Dec 2020 | 3.8 |
| Average ∆% Dec 1987-Dec 2000 | 3.6 |
| Average ∆% Dec 1992-Dec 2000 | 4.5 |
| Average ∆% Dec 2000-Dec 2020 | 3.9 |
Monthly house prices increased sharply from Feb 2013 to Jan 2014 for both the SA and NSA national house price, as shown in Table IIA-3. In Jan 2013, the seasonally adjusted national house price index increased 0.9 percent and the NSA increased 0.3. House prices increased at high monthly percentage rates from Feb to Nov 2013. The most important seasonal factor in house prices is school changes for wealthier homeowners with more expensive houses. With seasonal adjustment, house prices fell from Dec 2010 throughout Mar 2011 and then increased in every month from Apr to Jul 2011 but fell in every month from Aug 2011 to Feb 2012. The not seasonally adjusted index registers increase in Mar 2012 of 1.4 percent. Not seasonally adjusted house prices increased 1.9 percent in Apr 2012 and at high monthly percentage rates through Aug 2012. House prices not seasonally adjusted stalled from Oct 2012 to Dec 2012 and surged from Feb to Sep 2013, decelerating in Oct 2013-Jan 2014. House prices grew at fast rates in Mar-Jul 2014. The SA national house price index increased 1.5 percent in Mar 2021 and the NSA index increased 2.0 percent. Declining house prices cause multiple adverse effects of which two are quite evident. (1) There is a disincentive to buy houses in continuing price declines. (2) More mortgages could be losing fair market value relative to mortgage debt. Another possibility is a wealth effect that consumers restrain purchases because of the decline of their net worth in houses. Table IIA-2, US, Monthly Percentage Change of S&P Corelogic Case-Shiller National Home Price Indices, Seasonally Adjusted and Not Seasonally Adjusted, ∆%
| ∆% SA | ∆% NSA | ||||
| December 2010 | -0.1 | -0.8 | |||
| January 2011 | -0.4 | -1.1 | |||
| February 2011 | -0.8 | -0.9 | |||
| March 2011 | -0.3 | 0.0 | |||
| April 2011 | 0.0 | 1.0 | |||
| May 2011 | -0.1 | 1.1 | |||
| June 2011 | 0.0 | 0.9 | |||
| July 2011 | -0.1 | 0.3 | |||
| August 2011 | -0.3 | -0.4 | |||
| September 2011 | -0.5 | -1.1 | |||
| October 2011 | -0.5 | -1.3 | |||
| November 2011 | -0.6 | -1.3 | |||
| December 2011 | -0.3 | -1.1 | |||
| January 2012 | -0.1 | -0.7 | |||
| February 2012 | -0.1 | -0.1 | |||
| March 2012 | 1.0 | 1.4 | |||
| April 2012 | 0.9 | 1.9 | |||
| May 2012 | 0.7 | 1.9 | |||
| June 2012 | 0.6 | 1.5 | |||
| July 2012 | 0.5 | 0.8 | |||
| August 2012 | 0.4 | 0.3 | |||
| September 2012 | 0.4 | -0.2 | |||
| October 2012 | 0.5 | -0.3 | |||
| November 2012 | 0.7 | 0.0 | |||
| December 2012 | 0.6 | -0.1 | |||
| January 2013 | 0.9 | 0.3 | |||
| February 2013 | 0.6 | 0.6 | |||
| March 2013 | 1.5 | 1.9 | |||
| April 2013 | 1.0 | 2.0 | |||
| May 2013 | 0.9 | 1.9 | |||
| June 2013 | 0.9 | 1.7 | |||
| July 2013 | 0.9 | 1.2 | |||
| August 2013 | 0.9 | 0.7 | |||
| September 2013 | 0.8 | 0.2 | |||
| October 2013 | 0.6 | -0.1 | |||
| November 2013 | 0.5 | -0.1 | |||
| December 2013 | 0.6 | -0.1 | |||
| January 2014 | 0.6 | 0.1 | |||
| February 2014 | 0.4 | 0.3 | |||
| March 2014 | 0.3 | 0.8 | |||
| April 2014 | 0.2 | 1.1 | |||
| May 2014 | 0.2 | 1.1 | |||
| June 2014 | 0.2 | 0.9 | |||
| July 2014 | 0.3 | 0.6 | |||
| August 2014 | 0.4 | 0.2 | |||
| September 2014 | 0.4 | -0.1 | |||
| October 2014 | 0.4 | -0.2 | |||
| November 2014 | 0.4 | -0.1 | |||
| December 2014 | 0.4 | -0.1 | |||
| January 2015 | 0.4 | -0.1 | |||
| February 2015 | 0.3 | 0.2 | |||
| March 2015 | 0.4 | 0.9 | |||
| April 2015 | 0.3 | 1.1 | |||
| May 2015 | 0.3 | 1.1 | |||
| June 2015 | 0.3 | 0.9 | |||
| July 2015 | 0.4 | 0.6 | |||
| August 2015 | 0.5 | 0.3 | |||
| September 2015 | 0.5 | 0.1 | |||
| October 2015 | 0.6 | 0.0 | |||
| November 2015 | 0.5 | 0.1 | |||
| December 2015 | 0.5 | 0.0 | |||
| January 2016 | 0.4 | 0.0 | |||
| February 2016 | 0.2 | 0.1 | |||
| March 2016 | 0.3 | 0.8 | |||
| April 2016 | 0.3 | 1.1 | |||
| May 2016 | 0.4 | 1.0 | |||
| June 2016 | 0.4 | 0.9 | |||
| July 2016 | 0.4 | 0.6 | |||
| August 2016 | 0.6 | 0.3 | |||
| September 2016 | 0.5 | 0.2 | |||
| October 2016 | 0.5 | 0.0 | |||
| November 2016 | 0.5 | 0.1 | |||
| December 2016 | 0.5 | 0.1 | |||
| January 2017 | 0.6 | 0.1 | |||
| February 2017 | 0.3 | 0.2 | |||
| March 2017 | 0.4 | 0.8 | |||
| April 2017 | 0.4 | 1.1 | |||
| May 2017 | 0.5 | 1.1 | |||
| June 2017 | 0.5 | 0.9 | |||
| July 2017 | 0.5 | 0.7 | |||
| August 2017 | 0.6 | 0.4 | |||
| September 2017 | 0.5 | 0.2 | |||
| October 2017 | 0.5 | 0.1 | |||
| November 2017 | 0.5 | 0.2 | |||
| December 2017 | 0.6 | 0.2 | |||
| January 2018 | 0.6 | 0.1 | |||
| February 2018 | 0.5 | 0.4 | |||
| March 2018 | 0.4 | 0.8 | |||
| April 2018 | 0.5 | 1.0 | |||
| May 2018 | 0.4 | 0.9 | |||
| June 2018 | 0.4 | 0.8 | |||
| July 2018 | 0.3 | 0.4 | |||
| August 2018 | 0.4 | 0.2 | |||
| September 2018 | 0.3 | 0.0 | |||
| October 2018 | 0.3 | 0.0 | |||
| November 2018 | 0.2 | -0.1 | |||
| December 2018 | 0.2 | -0.2 | |||
| January 2019 | 0.2 | -0.2 | |||
| February 2019 | 0.2 | 0.1 | |||
| March 2019 | 0.2 | 0.7 | |||
| April 2019 | 0.4 | 0.9 | |||
| May 2019 | 0.3 | 0.8 | |||
| June 2019 | 0.2 | 0.6 | |||
| July 2019 | 0.2 | 0.4 | |||
| August 2019 | 0.4 | 0.2 | |||
| September 2019 | 0.3 | 0.1 | |||
| October 2019 | 0.3 | 0.0 | |||
| November 2019 | 0.4 | 0.1 | |||
| December 2019 | 0.4 | 0.1 | |||
| January 2020 | 0.5 | 0.1 | |||
| February 2020 | 0.5 | 0.4 | |||
| March 2020 | 0.5 | 0.9 | |||
| April 2020 | 0.5 | 1.0 | |||
| May 2020 | 0.1 | 0.6 | |||
| June 2020 | 0.3 | 0.6 | |||
| July 2020 | 0.7 | 0.8 | |||
| August 2020 | 1.3 | 1.1 | |||
| September 2020 | 1.4 | 1.2 | |||
| October 2020 | 1.5 | 1.3 | |||
| November 2020 | 1.4 | 1.1 | |||
| December 2020 | 1.2 | 0.8 | |||
| January 2021 | 1.2 | 0.8 | |||
| February 2021 | 1.3 | 1.2 | |||
| March 2021 | 1.5 | 2.0 |
Table IIA-4 summarizes the brutal drops in assets and net worth of US households and nonprofit organizations from 2007 to 2008 and 2009. Total assets fell $8.8 trillion or 10.4 percent from 2007 to 2008 and $8.0 trillion or 9.4 percent to 2009. Net worth fell $9.1 trillion from 2007 to 2008 or 12.9 percent and $7.7 trillion to 2009 or 11.0 percent. Subsidies to housing prolonged over decades together with interest rates at 1.0 percent from Jun 2003 to Jun 2004 inflated valuations of real estate and risk financial assets such as equities. The increase of fed funds rates by 25 basis points until 5.25 percent in Jun 2006 reversed carry trades through exotic vehicles such as subprime adjustable-rate mortgages (ARM) and world financial markets. Short-term zero interest rates encourage financing of everything with short-dated funds, explaining the SIVs created off-balance sheet to issue short-term commercial paper to purchase default-prone mortgages that were financed in overnight or short-dated sale and repurchase agreements (Pelaez and Pelaez, Financial Regulation after the Global Recession, 50-1, Regulation of Banks and Finance, 59-60, Globalization and the State Vol. I, 89-92, Globalization and the State Vol. II, 198-9, Government Intervention in Globalization, 62-3, International Financial Architecture, 144-9).
Table IIA-4, Difference of Balance Sheet of Households and Nonprofit Organizations, Billions of Dollars from 2007 to 2008 and 2009
| 2007 | 2008 | Change to 2008 | 2009 | Change to 2009 | |
| A | 84,734.3 | 75,908.4 | -8,825.9 | 76,772.6 | -7,961.7 |
| Non | 30,625.8 | 28,152.7 | -2,473.1 | 26,130.0 | -4,495.8 |
| RE | 25,834.2 | 23,238.5 | -2,595.7 | 21,199.4 | -4,634.8 |
| FIN | 54,108.6 | 47,755.7 | -6,352.9 | 50,642.6 | -3,466.0 |
| LIAB | 14,502.0 | 14,398.4 | -103.6 | 14,276.1 | -225.9 |
| NW | 70,232.3 | 61,151.0 | -9,081.3 | 62,496.5 | -7,735.8 |
Source: Board of Governors of the Federal Reserve System. 2021. Flow of funds, balance sheets and integrated macroeconomic accounts: first quarter 2021. Washington, DC, Federal Reserve System, Jun 10. https://www.federalreserve.gov/releases/z1/current/default.htm
The apparent improvement in Table IIA-4A is mostly because of increases in valuations of risk financial assets by the carry trade from zero interest rates to leveraged exposures in risk financial assets such as stocks, high-yield bonds, emerging markets, commodities and so on. Zero interest rates also act to increase net worth by reducing debt or liabilities. The net worth of households has become an instrument of unconventional monetary policy by zero interest rates in the theory that increases in net worth increase consumption that accounts for 68.3 percent of GDP in IQ2021 (https://cmpassocregulationblog.blogspot.com/2021/05/us-gdp-growing-at-saar-64-percent-in_29.html and earlier https://cmpassocregulationblog.blogspot.com/2021/05/us-gdp-growing-at-saar-64-percent-in.html), generating demand to increase aggregate economic activity and employment. There are neglected and counterproductive risks in unconventional monetary policy. Between 2007 and IQ2021, real estate increased in value by $11,723.5 billion and financial assets increased $55,453.6 billion, explaining most of the increase in net worth of $66,684.8 billion obtained by deducting the increase in liabilities of $2741.6 billion from the increase of assets of $69,426.4 billion (with minor rounding error). Net worth increased from $70,232.3 billion in IVQ2007 to $136,917.1 billion in IQ2021 by $66,684.8 billion or 94.9 percent. The US consumer price index for all items increased from 210.036 in Dec 2007 to 264.877 in Dec 2020 (https://www.bls.gov/cpi/data.htm) or 26.1 percent. Net worth adjusted by CPI inflation increased 54.6 percent from 2007 to IQ2021. Real estate assets adjusted for CPI inflation increased 15.3 percent from 2007 to IQ2021. There are multiple complaints that unconventional monetary policy concentrates income on wealthier individuals because of their holdings of financial assets while the middle class has gained less because of fewer holdings of financial assets and higher share of real estate in family wealth. There is nothing new in these arguments. Interest rate ceilings on deposits and loans have been commonly used. The Banking Act of 1933 imposed prohibition of payment of interest on demand deposits and ceilings on interest rates on time deposits. These measures were justified by arguments that the banking panic of the 1930s was caused by competitive rates on bank deposits that led banks to engage in high-risk loans (Friedman, 1970, 18; see Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 74-5). The objective of policy was to prevent unsound loans in banks. Savings and loan institutions complained of unfair competition from commercial banks that led to continuing controls with the objective of directing savings toward residential construction. Friedman (1970, 15) argues that controls were passive during periods when rates implied on demand deposit were zero or lower and when Regulation Q ceilings on time deposits were above market rates on time deposits. The Great Inflation or stagflation of the 1960s and 1970s changed the relevance of Regulation Q. Friedman (1970, 26-7) predicted the future:
“The banks have been forced into costly structural readjustments, the European banking system has been given an unnecessary competitive advantage, and London has been artificially strengthened as a financial center at the expense of New York.”
In short, Depression regulation exported the US financial system to London and offshore centers. What is vividly relevant currently from this experience is the argument by Friedman (1970, 27) that the controls affected the most people with lower incomes and wealth who were forced into accepting controlled-rates on their savings that were lower than those that would be obtained under freer markets. As Friedman (1970, 27) argues:
“These are the people who have the fewest alternative ways to invest their limited assets and are least sophisticated about the alternatives.” Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. US economic growth has been at only 2.0 percent on average in the cyclical expansion in the 47 quarters from IIIQ2009 to IQ2021 and in the global recession with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event. Boskin (2010Sep) measures that the US economy grew at 6.2 percent in the first four quarters and 4.5 percent in the first 12 quarters after the trough in the second quarter of 1975; and at 7.7 percent in the first four quarters and 5.8 percent in the first 12 quarters after the trough in the first quarter of 1983 (Professor Michael J. Boskin, Summer of Discontent, Wall Street Journal, Sep 2, 2010 http://professional.wsj.com/article/SB10001424052748703882304575465462926649950.html). There are new calculations using the revision of US GDP and personal income data since 1929 by the Bureau of Economic Analysis (BEA) (https://apps.bea.gov/iTable/index_nipa.cfm) and the third estimate of GDP for IQ2021 (https://www.bea.gov/sites/default/files/2021-06/gdp1q21_3rd_1.pdf). The average of 7.7 percent in the first four quarters of major cyclical expansions is in contrast with the rate of growth in the first four quarters of the expansion from IIIQ2009 to IIQ2010 of only 2.8 percent obtained by dividing GDP of $15,557.3 billion in IIQ2010 by GDP of $15,134.1 billion in IIQ2009 {[($15,557.3/$15,134.1) -1]100 = 2.8%], or accumulating the quarter on quarter growth rates (Section I and earlier https://cmpassocregulationblog.blogspot.com/2021/05/us-gdp-growing-at-saar-64-percent-in_29.html). The expansion from IQ1983 to IQ1986 was at the average annual growth rate of 5.7 percent, 5.3 percent from IQ1983 to IIIQ1986, 5.1 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.6 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990, 4.4 percent from IQ1983 to IIQ1990, 4.3 percent from IQ1983 to IIIQ1990, 4.0 percent from IQ1983 to IVQ1990, 3.8 percent from IQ1983 to IQ1991, 3.8 percent from IQ1983 to IIQ1991, 3.8 percent from IQ1983 to IIIQ1991, 3.7 percent from IQ1983 to IVQ1991, 3.7 percent from IQ1983 to IQ1992, 3.7 percent from IQ1983 to IIQ1992, 3.7 percent from IQ1983 to IIIQ1992, 3.8 percent from IQ1983 to IVQ1992, 3.7 percent from IQ1983 to IQ1993, 3.6 percent from IQ1983 to IIQ1993, 3.6 percent from IQ1983 to IIIQ1993, 3.7 percent from IQ1983 to IVQ1993, 3.7 percent from IQ1983 to IQ1994, 3.7 percent from IQ1983 to IIQ1994, 3.7 percent from IQ1983 to IIIQ1994 and at 7.9 percent from IQ1983 to IVQ1983 (Section I and earlier https://cmpassocregulationblog.blogspot.com/2021/05/us-gdp-growing-at-saar-64-percent-in_29.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://apps.bea.gov/iTable/index_nipa.cfm). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IQ2021 and in the global recession with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event would have accumulated to 47.9 percent. GDP in IVQ2020 would be $23,318.7 billion (in constant dollars of 2012) if the US had grown at trend, which is higher by $4232.3 billion than actual $19,086.4 billion. There are more than four trillion dollars of GDP less than at trend, explaining the 28.1 million unemployed or underemployed equivalent to actual unemployment/underemployment of 16.2 percent of the effective labor force with the largest part originating in the global recession with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event (https://cmpassocregulationblog.blogspot.com/2021/06/increase-in-may-2021-of-nonfarm-payroll.html and earlier https://cmpassocregulationblog.blogspot.com/2021/05/increase-in-apr-2021-of-nonfarm-payroll.html). Unemployment is decreasing while employment is increasing in initial adjustment of the lockdown of economic activity in the global recession resulting from the COVID-19 event (https://www.bls.gov/covid19/employment-situation-covid19-faq-may-2021.htm). US GDP in IQ2021 is 18.2 percent lower than at trend. US GDP grew from $15,762.0 billion in IVQ2007 in constant dollars to $19,086.4 billion in IQ2021 or 21.1 percent at the average annual equivalent rate of 1.5 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.0 percent per year from May 1919 to May 2021. Growth at 3.0 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 106.8161 in Dec 2007 to 158.8071 in May 2021. The actual index NSA in May 2021 is 98.4433 which is 38.0 percent below trend. The underperformance of manufacturing in Mar-Aug 2020 originates partly in the earlier global recession augmented by the current global recession with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19. Manufacturing grew at the average annual rate of 3.3 percent between Dec 1986 and Dec 2006. Growth at 3.3 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 106.8161 in Dec 2007 to 165.1264 in May 2021. The actual index NSA in May 2021 is 98.4433, which is 40.4 percent below trend. Manufacturing output grew at average 1.7 percent between Dec 1986 and May 2021. Using trend growth of 1.7 percent per year, the index would increase to 133.9244 in May 2021. The output of manufacturing at 98.4433 in May 2021 is 26.5 percent below trend under this alternative calculation. Using the NAICS (North American Industry Classification System), manufacturing output fell from the high of 108.5167 in Jun 2007 to the low of 84.7321 in May 2009 or 21.9 percent. The NAICS manufacturing index increased from 84.7321 in Apr 2009 to 99.0088 in May 2021 or 16.8 percent. The NAICS manufacturing index increased at the annual equivalent rate of 3.5 percent from Dec 1986 to Dec 2006. Growth at 3.5 percent would increase the NAICS manufacturing output index from 104.6868 in Dec 2007 to 166.0895 in May 2021. The NAICS index at 99.0088 in May 2021 is 40.4 below trend. The NAICS manufacturing output index grew at 1.7 percent annual equivalent from Dec 1999 to Dec 2006. Growth at 1.7 percent would raise the NAICS manufacturing output index from 104.6868 in Dec 2007 to 131.2547 in May 2021. The NAICS index at 99.0088 in May 2021 is 24.6 percent below trend under this alternative calculation.
Table IIA-4A, US, Difference of Balance Sheet of Households and Nonprofit Organizations Billions of Dollars from 2007 to 2019 2020 and IQ20201
| Value 2007 | Change to 2019 | Change to 2020 | Change to IQ2021 | |
| Assets | 84,734.3 | 49,564.6 | 64,242.0 | 69,426.4 |
| Nonfinancial | 30,540.4 | 9,422.5 | 12,900.5 | 14,058.1 |
| Real Estate | 25,834.2 | 7,729.0 | 10,755.1 | 11,723.5 |
| Financial | 54,108.6 | 40,227.5 | 51,426.8 | 55,453.6 |
| Liabilities | 14,502.0 | 1,918.7 | 2,554.0 | 2,741.6 |
| Net Worth | 70,232.3 | 47,645.9 | 61,688.0 | 66,684.8 |
Notes: Deposits: Total Time and Savings Deposits FL15303005; Net Worth = Assets – Liabilities
Source: Board of Governors of the Federal Reserve System. 2021. Flow of funds, balance sheets and integrated macroeconomic accounts: first quarter 2021. Washington, DC, Federal Reserve System, Jun 10. https://www.federalreserve.gov/releases/z1/current/default.htm
IIA Destruction of Household Nonfinancial Wealth with Stagnating Total Real Wealth. The valuable report on Financial Accounts of the United States formerly Flow of Funds Accounts of the United States provided by the Board of Governors of the Federal Reserve System (https://www.federalreserve.gov/releases/z1/default.htm https://www.federalreserve.gov/apps/fof/) is rich in important information and analysis. Table IIA-1, updated in this blog for every new quarterly release, shows the balance sheet of US households combined with nonprofit organizations in 2007, 2019, 2020 and IQ2021. Assets increased to $134.3 trillion in 2019 by $49.6 trillion or 58.5 percent. Assets increased to $149.0 trillion in 2020 by $64.2 trillion or 75.8 percent. Assets increased to $154.2 trillion in IQ2021 by $69.4 trillion or 81.9 percent. Liabilities increased from $14.5 trillion in 2007 to $16.4 trillion in 2019, by $1918.7 billion or increase of 13.2 percent. Liabilities increased $1902.6 billion or 13.1 percent from 2007 to 2019. Liabilities increased $2554.0 billion or 17.6 percent from 2007 to 2020. Liabilities increased $2741.6 billion from 2007 to IQ2021 or 18.9 percent. Net worth increased from $70,232.3 billion in 2007 to $136,917.1 billion in IQ2021 by $66,684.8 billion or 94.9 percent. The US consumer price index for all items increased from 210.036 in Dec 2007 to 264.877 in Mar 2021 (https://www.bls.gov/cpi/data.htm) or 26.1 percent. Net worth adjusted by CPI inflation increased 54.6 percent from 2007 to IQ2021. Nonfinancial assets increased $12,114.2 billion from $30,625.8 billion in 2007 to $44,598.5 billion in IQ2021 or 45.6 percent. There was increase from 2007 to IQ2021 of $11,723.5 billion in real estate assets or by 45.4 percent. Real estate assets adjusted for CPI inflation increased 15.3 percent between 2007 and IQ2021. The National Association of Realtors estimated that the gains in net worth in homes by Americans were about $4 trillion between 2000 and 2005 (quoted in Pelaez and Pelaez, The Global Recession Risk (2007), 224-5). Net worth increased 67.8 percent from IVQ2007 to IVQ2019 or 37.2 percent adjusting for inflation, 58.6 percent from IVQ2007 to IQ2020 or 29.0 percent adjusting for inflation, 70.3 percent from IVQ2007 to IIQ2020 or 38.8 percent adjusting for inflation, 76.4 percent from IVQ2007 to IIIQ2020 or 42.3 percent adjusting for inflation, 87.8 percent from IVQ2007 to IVQ2020 or 51.5 percent adjusting for inflation and 94.9 percent from IVQ2007 to IQ2021 or 54.6 percent adjusting for inflation. Net worth increased 16.2 percent from IVQ2019 to IQ2021 or 12.7 percent adjusting for inflation. Net worth decreased by $6,523.0 billion from IVQ2019 to IQ2020 or by 5.5 percent in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event. Net worth increased $8,271.2 billion from IQ2020 to IIQ2020 or 7.4 percent. Net worth increased 1.5 percent from IVQ2019 to IIQ2020. Net worth increased $5,978.4 billion from IVQ2019 to IIIQ2020 or 5.1 percent. Net worth increased $14,042.1billion from IVQ2019 to IVQ2020 or 11.9 percent.Net worth increased $19,038.9 billion from IVQ2019 to IQ2021 or 16.2 percent and 12.7 adjusting for inflation. Real estate increased $3,994.5 billion from IVQ2019 to IQ2021 or 11.9 percent. Financial assets increased $15,226.1 billion from IVQ2019 to IQ2021 or 16.1 percent. Stock markets recovered in Apr 2020 to Mar 2021. Corporate equities increased $7,424.2 billion from IVQ2019 to IQ2021 or 35.6 percent.
Table IIA-1, US, Balance Sheet of Households and Nonprofit Organizations, Billions of Dollars Outstanding End of Period, NSA
| 2007 | 2019 | 2020 | IQ2021 | |
| Assets | 84,734.3 | 134,298.9 | 148,976.3 | 154,160.7 |
| Nonfinancial | 30,625.8 | 39,962.9 | 43,440.9 | 44,598.5 |
| Real Estate | 25,834.2 | 33,563.2 | 36,589.3 | 37,557.7 |
| Durable Goods | 4,473.9 | 5,750.1 | 6,179.9 | 6,361.9 |
| Financial | 54,108.6 | 94,336.1 | 105,535.4 | 109,562.2 |
| Deposits | 5,916.5 | 10,194.2 | 10,807.7 | 11,187.6 |
| Debt Secs. | 3,520.7 | 5,653.1 | 5,116.9 | 4,639.7 |
| Mutual Fund Shares | 4,535.9 | 10,044.1 | 11,223.4 | 11,661.1 |
| Equities Corporate | 9757.7 | 20,860.6 | 25,890.9 | 28,284.8 |
| Equity Noncorporate | 8,409.9 | 12,116.2 | 12,908.1 | 13,113.9 |
| Pension | 16,414.9 | 27,729.3 | 29,466.2 | 29,875.9 |
| Liabilities | 14,502.0 | 16,420.7 | 17,056.0 | 17,243.6 |
| Home Mortgages | 10,625.0 | 10,482.4 | 10,924.7 | 11,041.5 |
| Consumer Credit | 2,609.5 | 4192.6 | 4,187.8 | 4163.2 |
| Net Worth | 70,232.3 | 117,878.2 | 131,920.3 | 136,917.1 |
Notes: Deposits: Total Time and Savings Deposits FL15303005; Net Worth = Assets – Liabilities
Source: Board of Governors of the Federal Reserve System. 2021. Flow of funds, balance sheets and integrated macroeconomic accounts: first quarter 2021. Washington, DC, Federal Reserve System, Jun 10. https://www.federalreserve.gov/releases/z1/current/default.htm
The explanation of the sharp contraction of household wealth can probably be found in the origins of the financial crisis and global recession. Let V(T) represent the value of the firm’s equity at time T and B stand for the promised debt of the firm to bondholders and assume that corporate management, elected by equity owners, is acting on the interests of equity owners. Robert C. Merton (1974, 453) states:
“On the maturity date T, the firm must either pay the promised payment of B to the debtholders or else the current equity will be valueless. Clearly, if at time T, V(T) > B, the firm should pay the bondholders because the value of equity will be V(T) – B > 0 whereas if they do not, the value of equity would be zero. If V(T) ≤ B, then the firm will not make the payment and default the firm to the bondholders because otherwise the equity holders would have to pay in additional money and the (formal) value of equity prior to such payments would be (V(T)- B) < 0.”
Pelaez and Pelaez (The Global Recession Risk (2007), 208-9) apply this analysis to the US housing market in 2005-2006 concluding:
“The house market [in 2006] is probably operating with low historical levels of individual equity. There is an application of structural models [Duffie and Singleton 2003] to the individual decisions on whether or not to continue paying a mortgage. The costs of sale would include realtor and legal fees. There could be a point where the expected net sale value of the real estate may be just lower than the value of the mortgage. At that point, there would be an incentive to default. The default vulnerability of securitization is unknown.”
There are multiple important determinants of the interest rate: “aggregate wealth, the distribution of wealth among investors, expected rate of return on physical investment, taxes, government policy and inflation” (Ingersoll 1987, 405). Aggregate wealth is a major driver of interest rates (Ibid, 406). Unconventional monetary policy, with zero fed funds rates and flattening of long-term yields by quantitative easing, causes uncontrollable effects on risk taking that can have profound undesirable effects on financial stability. Excessively aggressive and exotic monetary policy is the main culprit and not the inadequacy of financial management and risk controls.
The net worth of the economy depends on interest rates. In theory, “income is generally defined as the amount a consumer unit could consume (or believe that it could) while maintaining its wealth intact” (Friedman 1957, 10). Income, Y, is a flow that is obtained by applying a rate of return, r, to a stock of wealth, W, or Y = rW (Ibid). According to a subsequent restatement: “The basic idea is simply that individuals live for many years and that therefore the appropriate constraint for consumption decisions is the long-run expected yield from wealth r*W. This yield was named permanent income: Y* = r*W” (Darby 1974, 229), where * denotes permanent. The simplified relation of income and wealth can be restated as:
W = Y/r (1)
Equation (1) shows that as r goes to zero, r →0, W grows without bound, W→∞.
Lowering the interest rate near the zero bound in 2003-2004 caused the illusion of permanent increases in wealth or net worth in the balance sheets of borrowers and also of lending institutions, securitized banking and every financial institution and investor in the world. The discipline of calculating risks and returns was seriously impaired. The objective of monetary policy was to encourage borrowing, consumption and investment but the exaggerated stimulus resulted in a financial crisis of major proportions as the securitization that had worked for a long period was shocked with policy-induced excessive risk, imprudent credit, high leverage and low liquidity by the incentive to finance everything overnight at close to zero interest rates, from adjustable rate mortgages (ARMS) to asset-backed commercial paper of structured investment vehicles (SIV).
The consequences of inflating liquidity and net worth of borrowers were a global hunt for yields to protect own investments and money under management from the zero interest rates and unattractive long-term yields of Treasuries and other securities. Monetary policy distorted the calculations of risks and returns by households, business and government by providing central bank cheap money. Short-term zero interest rates encourage financing of everything with short-dated funds, explaining the SIVs created off-balance sheet to issue short-term commercial paper to purchase default-prone mortgages that were financed in overnight or short-dated sale and repurchase agreements (Pelaez and Pelaez, Financial Regulation after the Global Recession, 50-1, Regulation of Banks and Finance, 59-60, Globalization and the State Vol. I, 89-92, Globalization and the State Vol. II, 198-9, Government Intervention in Globalization, 62-3, International Financial Architecture, 144-9). ARMS were created to lower monthly mortgage payments by benefitting from lower short-dated reference rates. Financial institutions economized in liquidity that was penalized with near zero interest rates. There was no perception of risk because the monetary authority guaranteed a minimum or floor price of all assets by maintaining low interest rates forever or equivalent to writing an illusory put option on wealth. Subprime mortgages were part of the put on wealth by an illusory put on house prices. The housing subsidy of $221 billion per year created the impression of ever increasing house prices. The suspension of auctions of 30-year Treasuries was designed to increase demand for mortgage-backed securities, lowering their yield, which was equivalent to lowering the costs of housing finance and refinancing. Fannie and Freddie purchased or guaranteed $1.6 trillion of nonprime mortgages and worked with leverage of 75:1 under Congress-provided charters and lax oversight. The combination of these policies resulted in high risks because of the put option on wealth by near zero interest rates, excessive leverage because of cheap rates, low liquidity because of the penalty in the form of low interest rates and unsound credit decisions because the put option on wealth by monetary policy created the illusion that nothing could ever go wrong, causing the credit/dollar crisis and global recession (Pelaez and Pelaez, Financial Regulation after the Global Recession, 157-66, Regulation of Banks, and Finance, 217-27, International Financial Architecture, 15-18, The Global Recession Risk, 221-5, Globalization and the State Vol. II, 197-213, Government Intervention in Globalization, 182-4).
There are significant elements of the theory of bank financial fragility of Diamond and Dybvig (1983) and Diamond and Rajan (2000, 2001a, 2001b) that help to explain the financial fragility of banks during the credit/dollar crisis (see also Diamond 2007). The theory of Diamond and Dybvig (1983) as exposed by Diamond (2007) is that banks funding with demand deposits have a mismatch of liquidity (see Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 58-66). A run occurs when too many depositors attempt to withdraw cash at the same time. All that is needed is an expectation of failure of the bank. Three important functions of banks are providing evaluation, monitoring and liquidity transformation. Banks invest in human capital to evaluate projects of borrowers in deciding if they merit credit. The evaluation function reduces adverse selection or financing projects with low present value. Banks also provide important monitoring services of following the implementation of projects, avoiding moral hazard that funds be used for, say, real estate speculation instead of the original project of factory construction. The transformation function of banks involves both assets and liabilities of bank balance sheets. Banks convert an illiquid asset or loan for a project with cash flows in the distant future into a liquid liability in the form of demand deposits that can be withdrawn immediately.
In the theory of banking of Diamond and Rajan (2000, 2001a, 2001b), the bank creates liquidity by tying human assets to capital. The collection skills of the relationship banker convert an illiquid project of an entrepreneur into liquid demand deposits that are immediately available for withdrawal. The deposit/capital structure is fragile because of the threat of bank runs. In these days of online banking, the run on Washington Mutual was through withdrawals online. A bank run can be triggered by the decline of the value of bank assets below the value of demand deposits.
Pelaez and Pelaez (Regulation of Banks and Finance 2009b, 60, 64-5) find immediate application of the theories of banking of Diamond, Dybvig and Rajan to the credit/dollar crisis after 2007. It is a credit crisis because the main issue was the deterioration of the credit portfolios of securitized banks as a result of default of subprime mortgages. It is a dollar crisis because of the weakening dollar resulting from relatively low interest rate policies of the US. It caused systemic effects that converted into a global recession not only because of the huge weight of the US economy in the world economy but also because the credit crisis transferred to the UK and Europe. Management skills or human capital of banks are illustrated by the financial engineering of complex products. The increasing importance of human relative to inanimate capital (Rajan and Zingales 2000) is revolutionizing the theory of the firm (Zingales 2000) and corporate governance (Rajan and Zingales 2001). Finance is one of the most important examples of this transformation. Profits were derived from the charter in the original banking institution. Pricing and structuring financial instruments was revolutionized with option pricing formulas developed by Black and Scholes (1973) and Merton (1973, 1974, 1998) that permitted the development of complex products with fair pricing. The successful financial company must attract and retain finance professionals who have invested in human capital, which is a sunk cost to them and not of the institution where they work.
The complex financial products created for securitized banking with high investments in human capital are based on houses, which are as illiquid as the projects of entrepreneurs in the theory of banking. The liquidity fragility of the securitized bank is equivalent to that of the commercial bank in the theory of banking (Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 65). Banks created off-balance sheet structured investment vehicles (SIV) that issued commercial paper receiving AAA rating because of letters of liquidity guarantee by the banks. The commercial paper was converted into liquidity by its use as collateral in SRPs at the lowest rates and minimal haircuts because of the AAA rating of the guarantor bank. In the theory of banking, default can be triggered when the value of assets is perceived as lower than the value of the deposits. Commercial paper issued by SIVs, securitized mortgages and derivatives all obtained SRP liquidity on the basis of illiquid home mortgage loans at the bottom of the pyramid. The run on the securitized bank had a clear origin (Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 65):
“The increasing default of mortgages resulted in an increase in counterparty risk. Banks were hit by the liquidity demands of their counterparties. The liquidity shock extended to many segments of the financial markets—interbank loans, asset-backed commercial paper (ABCP), high-yield bonds and many others—when counterparties preferred lower returns of highly liquid safe havens, such as Treasury securities, than the risk of having to sell the collateral in SRPs at deep discounts or holding an illiquid asset. The price of an illiquid asset is near zero.”
Gorton and Metrick (2010H, 507) provide a revealing quote to the work in 1908 of Edwin R. A. Seligman, professor of political economy at Columbia University, founding member of the American Economic Association and one of its presidents and successful advocate of progressive income taxation. The intention of the quote is to bring forth the important argument that financial crises are explained in terms of “confidence” but as Professor Seligman states in reference to historical banking crises in the US, the important task is to explain what caused the lack of confidence. It is instructive to repeat the more extended quote of Seligman (1908, xi) on the explanations of banking crises:
“The current explanations may be divided into two categories. Of these the first includes what might be termed the superficial theories. Thus it is commonly stated that the outbreak of a crisis is due to lack of confidence,--as if the lack of confidence was not in itself the very thing which needs to be explained. Of still slighter value is the attempt to associate a crisis with some particular governmental policy, or with some action of a country’s executive. Such puerile interpretations have commonly been confined to countries like the United States, where the political passions of democracy have had the fullest way. Thus the crisis of 1893 was ascribed by the Republicans to the impending Democratic tariff of 1894; and the crisis of 1907 has by some been termed the ‘[Theodore] Roosevelt panic,” utterly oblivious of the fact that from the time of President Jackson, who was held responsible for the troubles of 1837, every successive crisis had had its presidential scapegoat, and has been followed by a political revulsion. Opposed to these popular, but wholly unfounded interpretations, is the second class of explanations, which seek to burrow beneath the surface and to discover the more occult and fundamental causes of the periodicity of crises.”
Scholars ignore superficial explanations in the effort to seek good and truth. The problem of economic analysis of the credit/dollar crisis is the lack of a structural model with which to attempt empirical determination of causes (Gorton and Metrick 2010SB). There would still be doubts even with a well-specified structural model because samples of economic events do not typically permit separating causes and effects. There is also confusion is separating the why of the crisis and how it started and propagated, all of which are extremely important.
In true heritage of the principles of Seligman (1908), Gorton (2009EFM) discovers a prime causal driver of the credit/dollar crisis. The objective of subprime and Alt-A mortgages was to facilitate loans to populations with modest means so that they could acquire a home. These borrowers would not receive credit because of (1) lack of funds for down payments; (2) low credit rating and information; (3) lack of information on income; and (4) errors or lack of other information. Subprime mortgage “engineering” was based on the belief that both lender and borrower could benefit from increases in house prices over the short run. The initial mortgage would be refinanced in two or three years depending on the increase of the price of the house. According to Gorton (2009EFM, 13, 16):
“The outstanding amounts of Subprime and Alt-A [mortgages] combined amounted to about one quarter of the $6 trillion mortgage market in 2004-2007Q1. Over the period 2000-2007, the outstanding amount of agency mortgages doubled, but subprime grew 800%! Issuance in 2005 and 2006 of Subprime and Alt-A mortgages was almost 30% of the mortgage market. Since 2000 the Subprime and Alt-A segments of the market grew at the expense of the Agency (i.e., the government sponsored entities of Fannie Mae and Freddie Mac) share, which fell from almost 80% (by outstanding or issuance) to about half by issuance and 67% by outstanding amount. The lender’s option to rollover the mortgage after an initial period is implicit in the subprime mortgage. The key design features of a subprime mortgage are: (1) it is short term, making refinancing important; (2) there is a step-up mortgage rate that applies at the end of the first period, creating a strong incentive to refinance; and (3) there is a prepayment penalty, creating an incentive not to refinance early.”
The prime objective of successive administrations in the US during the past 20 years and actually since the times of Roosevelt in the 1930s has been to provide “affordable” financing for the “American dream” of home ownership. The US housing finance system is mixed with public, public/private and purely private entities. The Federal Home Loan Bank (FHLB) system was established by Congress in 1932 that also created the Federal Housing Administration in 1934 with the objective of insuring homes against default. In 1938, the government created the Federal National Mortgage Association, or Fannie Mae, to foster a market for FHA-insured mortgages. Government-insured mortgages were transferred from Fannie Mae to the Government National Mortgage Association, or Ginnie Mae, to permit Fannie Mae to become a publicly-owned company. Securitization of mortgages began in 1970 with the government charter to the Federal Home Loan Mortgage Corporation, or Freddie Mac, with the objective of bundling mortgages created by thrift institutions that would be marketed as bonds with guarantees by Freddie Mac (see Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 42-8). In the third quarter of 2008, total mortgages in the US were $12,057 billion of which 43.5 percent, or $5423 billion, were retained or guaranteed by Fannie Mae and Freddie Mac (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 45). In 1990, Fannie Mae and Freddie Mac had a share of only 25.4 percent of total mortgages in the US. Mortgages in the US increased from $6922 billion in 2002 to $12,088 billion in 2007, or by 74.6 percent, while the retained or guaranteed portfolio of Fannie and Freddie rose from $3180 billion in 2002 to $4934 billion in 2007, or by 55.2 percent.
According to Pinto (2008) in testimony to Congress:
“There are approximately 25 million subprime and Alt-A loans outstanding, with an unpaid principal amount of over $4.5 trillion, about half of them held or guaranteed by Fannie and Freddie. Their high risk activities were allowed to operate at 75:1 leverage ratio. While they may deny it, there can be no doubt that Fannie and Freddie now own or guarantee $1.6 trillion in subprime, Alt-A and other default prone loans and securities. This comprises over 1/3 of their risk portfolios and amounts to 34% of all the subprime loans and 60% of all Alt-A loans outstanding. These 10.5 million unsustainable, nonprime loans are experiencing a default rate 8 times the level of the GSEs’ 20 million traditional quality loans. The GSEs will be responsible for a large percentage of an estimated 8.8 million foreclosures expected over the next 4 years, accounting for the failure of about 1 in 6 home mortgages. Fannie and Freddie have subprimed America.”
In perceptive analysis of growth and macroeconomics in the past six decades, Rajan (2012FA) argues that “the West can’t borrow and spend its way to recovery.” The Keynesian paradigm is not applicable in current conditions. Advanced economies in the West could be divided into those that reformed regulatory structures to encourage productivity and others that retained older structures. In the period from 1950 to 2000, Cobet and Wilson (2002) find that US productivity, measured as output/hour, grew at the average yearly rate of 2.9 percent while Japan grew at 6.3 percent and Germany at 4.7 percent (see Pelaez and Pelaez, The Global Recession Risk (2007), 135-44). In the period from 1995 to 2000, output/hour grew at the average yearly rate of 4.6 percent in the US but at lower rates of 3.9 percent in Japan and 2.6 percent in the US. Rajan (2012FA) argues that the differential in productivity growth was accomplished by deregulation in the US at the end of the 1970s and during the 1980s. In contrast, Europe did not engage in reform with the exception of Germany in the early 2000s that empowered the German economy with significant productivity advantage. At the same time, technology and globalization increased relative remunerations in highly-skilled, educated workers relative to those without skills for the new economy. It was then politically appealing to improve the fortunes of those left behind by the technological revolution by means of increasing cheap credit. As Rajan (2012FA) argues:
“In 1992, Congress passed the Federal Housing Enterprises Financial Safety and Soundness Act, partly to gain more control over Fannie Mae and Freddie Mac, the giant private mortgage agencies, and partly to promote affordable homeownership for low-income groups. Such policies helped money flow to lower-middle-class households and raised their spending—so much so that consumption inequality rose much less than income inequality in the years before the crisis. These policies were also politically popular. Unlike when it came to an expansion in government welfare transfers, few groups opposed expanding credit to the lower-middle class—not the politicians who wanted more growth and happy constituents, not the bankers and brokers who profited from the mortgage fees, not the borrowers who could now buy their dream houses with virtually no money down, and not the laissez-faire bank regulators who thought they could pick up the pieces if the housing market collapsed. The Federal Reserve abetted these shortsighted policies. In 2001, in response to the dot-com bust, the Fed cut short-term interest rates to the bone. Even though the overstretched corporations that were meant to be stimulated were not interested in investing, artificially low interest rates acted as a tremendous subsidy to the parts of the economy that relied on debt, such as housing and finance. This led to an expansion in housing construction (and related services, such as real estate brokerage and mortgage lending), which created jobs, especially for the unskilled. Progressive economists applauded this process, arguing that the housing boom would lift the economy out of the doldrums. But the Fed-supported bubble proved unsustainable. Many construction workers have lost their jobs and are now in deeper trouble than before, having also borrowed to buy unaffordable houses. Bankers obviously deserve a large share of the blame for the crisis. Some of the financial sector’s activities were clearly predatory, if not outright criminal. But the role that the politically induced expansion of credit played cannot be ignored; it is the main reason the usual checks and balances on financial risk taking broke down.”
In fact, Raghuram G. Rajan (2005) anticipated low liquidity in financial markets resulting from low interest rates before the financial crisis that caused distortions of risk/return decisions provoking the credit/dollar crisis and global recession from IVQ2007 to IIQ2009. Near zero interest rates of unconventional monetary policy induced excessive risks and low liquidity in financial decisions that were critical as a cause of the credit/dollar crisis after 2007. Rajan (2012FA) argues that it is not feasible to return to the employment and income levels before the credit/dollar crisis because of the bloated construction sector, financial system and government budgets.
Table IIA-2 shows the euphoria of prices during the housing boom and the subsequent decline. House prices rose 62.2 percent in the US national home price index between Mar 2000 and Mar 2005. Prices rose 80.1 percent in the US national index from Mar 2000 to Mar 2006. House prices rose 27.3 percent between Mar 2003 and Mar 2005 for the US national propelled by low fed funds rates of 1.0 percent between Jul 2003 and Jul 2004. Fed funds rates increased by 0.25 basis points at every meeting of the Federal Open Market Committee (FOMC) from Jun 2004 until Jun 2006, reaching 5.25 percent. Simultaneously, the suspension of auctions of the 30-year Treasury bond caused decrease of yields of mortgage-backed securities with intended increase in mortgage rates. Similarly, between Mar 2003 and Mar 2006 the US national increased 41.3 percent. House prices have increased from Mar 2006 to Mar 2021 by 33.3 percent for the US national. Measuring house prices is quite difficult because of the lack of homogeneity that is typical of standardized commodities. In the 12 months ending in Mar 2021, house prices increased 13.2 percent in the US national. Table IIA-1 also shows that house prices increased 140.1 percent between Mar 2000 and Mar 2021 for the US national. House prices are close to the lowest level since peaks during the boom before the financial crisis and global recession. The US national increased 32.0 percent in Mar 2021 from the peak in Jun 2006 and increased 32.0 percent from the peak in Jul 2006. The final part of Table II-2 provides average annual percentage rates of growth of the house price indexes of Standard & Poor’s Case-Shiller. The average rate for the US national was 3.8 percent from Dec 1987 to Dec 2020 and 3.6 percent from Dec 1987 to Dec 2000. Although the global recession affecting the US between IVQ2007 (Dec) and IIQ2009 (Jun) caused decline of house prices of slightly above 30 percent, the average annual growth rate between Dec 2000 and Dec 2020 was 3.9 percent for the US national.
Table IIA-1, US, Percentage Changes of Standard & Poor’s Case-Shiller National Home Price Indices, Not Seasonally Adjusted, ∆%
| US National | |
| ∆% Mar 2000 to Mar 2003 | 27.4 |
| ∆% Mar 2000 to Mar 2005 | 62.2 |
| ∆% Mar 2003 to Mar 2005 | 27.3 |
| ∆% Mar 2000 to Mar 2006 | 80.1 |
| ∆% Mar 2003 to Mar 2006 | 41.3 |
| ∆% Mar 2005 to Mar 2021 | 48.0 |
| ∆% Mar 2006 to Mar 2021 | 33.3 |
| ∆% Mar 2009 to Mar 2021 | 66.3 |
| ∆% Mar 2010 to Mar 2021 | 69.7 |
| ∆% Mar 2011 to Mar 2021 | 76.8 |
| ∆% Mar 2012 to Mar 2021 | 79.3 |
| ∆% Mar 2013 to Mar 2021 | 64.7 |
| ∆% Mar 2014 to Mar 2021 | 51.2 |
| ∆% Mar 2015 to Mar 2021 | 44.9 |
| ∆% Mar 2016 to Mar 2021 | 37.9 |
| ∆% Mar 2017 to Mar 2021 | 30.6 |
| ∆% Mar 2018 to Mar 2021 | 22.7 |
| ∆% Mar 2019 to Mar 2021 | 18.4 |
| ∆% Mar 2020 to Mar 2021 | 13.2 |
| ∆% Mar 2000 to Mar 2021 | 140.1 |
| ∆% Peak Jun 2006 to Mar 2021 | 32.0 |
| ∆% Peak Jul 2006 to Mar 2021 | 32.0 |
| Average ∆% Dec 1987-Dec 2020 | 3.8 |
| Average ∆% Dec 1987-Dec 2000 | 3.6 |
| Average ∆% Dec 1992-Dec 2000 | 4.5 |
| Average ∆% Dec 2000-Dec 2020 | 3.9 |
Monthly house prices increased sharply from Feb 2013 to Jan 2014 for both the SA and NSA national house price, as shown in Table IIA-3. In Jan 2013, the seasonally adjusted national house price index increased 0.9 percent and the NSA increased 0.3. House prices increased at high monthly percentage rates from Feb to Nov 2013. The most important seasonal factor in house prices is school changes for wealthier homeowners with more expensive houses. With seasonal adjustment, house prices fell from Dec 2010 throughout Mar 2011 and then increased in every month from Apr to Jul 2011 but fell in every month from Aug 2011 to Feb 2012. The not seasonally adjusted index registers increase in Mar 2012 of 1.4 percent. Not seasonally adjusted house prices increased 1.9 percent in Apr 2012 and at high monthly percentage rates through Aug 2012. House prices not seasonally adjusted stalled from Oct 2012 to Dec 2012 and surged from Feb to Sep 2013, decelerating in Oct 2013-Jan 2014. House prices grew at fast rates in Mar-Jul 2014. The SA national house price index increased 1.5 percent in Mar 2021 and the NSA index increased 2.0 percent. Declining house prices cause multiple adverse effects of which two are quite evident. (1) There is a disincentive to buy houses in continuing price declines. (2) More mortgages could be losing fair market value relative to mortgage debt. Another possibility is a wealth effect that consumers restrain purchases because of the decline of their net worth in houses. Table IIA-2, US, Monthly Percentage Change of S&P Corelogic Case-Shiller National Home Price Indices, Seasonally Adjusted and Not Seasonally Adjusted, ∆%
| ∆% SA | ∆% NSA | ||||
| December 2010 | -0.1 | -0.8 | |||
| January 2011 | -0.4 | -1.1 | |||
| February 2011 | -0.8 | -0.9 | |||
| March 2011 | -0.3 | 0.0 | |||
| April 2011 | 0.0 | 1.0 | |||
| May 2011 | -0.1 | 1.1 | |||
| June 2011 | 0.0 | 0.9 | |||
| July 2011 | -0.1 | 0.3 | |||
| August 2011 | -0.3 | -0.4 | |||
| September 2011 | -0.5 | -1.1 | |||
| October 2011 | -0.5 | -1.3 | |||
| November 2011 | -0.6 | -1.3 | |||
| December 2011 | -0.3 | -1.1 | |||
| January 2012 | -0.1 | -0.7 | |||
| February 2012 | -0.1 | -0.1 | |||
| March 2012 | 1.0 | 1.4 | |||
| April 2012 | 0.9 | 1.9 | |||
| May 2012 | 0.7 | 1.9 | |||
| June 2012 | 0.6 | 1.5 | |||
| July 2012 | 0.5 | 0.8 | |||
| August 2012 | 0.4 | 0.3 | |||
| September 2012 | 0.4 | -0.2 | |||
| October 2012 | 0.5 | -0.3 | |||
| November 2012 | 0.7 | 0.0 | |||
| December 2012 | 0.6 | -0.1 | |||
| January 2013 | 0.9 | 0.3 | |||
| February 2013 | 0.6 | 0.6 | |||
| March 2013 | 1.5 | 1.9 | |||
| April 2013 | 1.0 | 2.0 | |||
| May 2013 | 0.9 | 1.9 | |||
| June 2013 | 0.9 | 1.7 | |||
| July 2013 | 0.9 | 1.2 | |||
| August 2013 | 0.9 | 0.7 | |||
| September 2013 | 0.8 | 0.2 | |||
| October 2013 | 0.6 | -0.1 | |||
| November 2013 | 0.5 | -0.1 | |||
| December 2013 | 0.6 | -0.1 | |||
| January 2014 | 0.6 | 0.1 | |||
| February 2014 | 0.4 | 0.3 | |||
| March 2014 | 0.3 | 0.8 | |||
| April 2014 | 0.2 | 1.1 | |||
| May 2014 | 0.2 | 1.1 | |||
| June 2014 | 0.2 | 0.9 | |||
| July 2014 | 0.3 | 0.6 | |||
| August 2014 | 0.4 | 0.2 | |||
| September 2014 | 0.4 | -0.1 | |||
| October 2014 | 0.4 | -0.2 | |||
| November 2014 | 0.4 | -0.1 | |||
| December 2014 | 0.4 | -0.1 | |||
| January 2015 | 0.4 | -0.1 | |||
| February 2015 | 0.3 | 0.2 | |||
| March 2015 | 0.4 | 0.9 | |||
| April 2015 | 0.3 | 1.1 | |||
| May 2015 | 0.3 | 1.1 | |||
| June 2015 | 0.3 | 0.9 | |||
| July 2015 | 0.4 | 0.6 | |||
| August 2015 | 0.5 | 0.3 | |||
| September 2015 | 0.5 | 0.1 | |||
| October 2015 | 0.6 | 0.0 | |||
| November 2015 | 0.5 | 0.1 | |||
| December 2015 | 0.5 | 0.0 | |||
| January 2016 | 0.4 | 0.0 | |||
| February 2016 | 0.2 | 0.1 | |||
| March 2016 | 0.3 | 0.8 | |||
| April 2016 | 0.3 | 1.1 | |||
| May 2016 | 0.4 | 1.0 | |||
| June 2016 | 0.4 | 0.9 | |||
| July 2016 | 0.4 | 0.6 | |||
| August 2016 | 0.6 | 0.3 | |||
| September 2016 | 0.5 | 0.2 | |||
| October 2016 | 0.5 | 0.0 | |||
| November 2016 | 0.5 | 0.1 | |||
| December 2016 | 0.5 | 0.1 | |||
| January 2017 | 0.6 | 0.1 | |||
| February 2017 | 0.3 | 0.2 | |||
| March 2017 | 0.4 | 0.8 | |||
| April 2017 | 0.4 | 1.1 | |||
| May 2017 | 0.5 | 1.1 | |||
| June 2017 | 0.5 | 0.9 | |||
| July 2017 | 0.5 | 0.7 | |||
| August 2017 | 0.6 | 0.4 | |||
| September 2017 | 0.5 | 0.2 | |||
| October 2017 | 0.5 | 0.1 | |||
| November 2017 | 0.5 | 0.2 | |||
| December 2017 | 0.6 | 0.2 | |||
| January 2018 | 0.6 | 0.1 | |||
| February 2018 | 0.5 | 0.4 | |||
| March 2018 | 0.4 | 0.8 | |||
| April 2018 | 0.5 | 1.0 | |||
| May 2018 | 0.4 | 0.9 | |||
| June 2018 | 0.4 | 0.8 | |||
| July 2018 | 0.3 | 0.4 | |||
| August 2018 | 0.4 | 0.2 | |||
| September 2018 | 0.3 | 0.0 | |||
| October 2018 | 0.3 | 0.0 | |||
| November 2018 | 0.2 | -0.1 | |||
| December 2018 | 0.2 | -0.2 | |||
| January 2019 | 0.2 | -0.2 | |||
| February 2019 | 0.2 | 0.1 | |||
| March 2019 | 0.2 | 0.7 | |||
| April 2019 | 0.4 | 0.9 | |||
| May 2019 | 0.3 | 0.8 | |||
| June 2019 | 0.2 | 0.6 | |||
| July 2019 | 0.2 | 0.4 | |||
| August 2019 | 0.4 | 0.2 | |||
| September 2019 | 0.3 | 0.1 | |||
| October 2019 | 0.3 | 0.0 | |||
| November 2019 | 0.4 | 0.1 | |||
| December 2019 | 0.4 | 0.1 | |||
| January 2020 | 0.5 | 0.1 | |||
| February 2020 | 0.5 | 0.4 | |||
| March 2020 | 0.5 | 0.9 | |||
| April 2020 | 0.5 | 1.0 | |||
| May 2020 | 0.1 | 0.6 | |||
| June 2020 | 0.3 | 0.6 | |||
| July 2020 | 0.7 | 0.8 | |||
| August 2020 | 1.3 | 1.1 | |||
| September 2020 | 1.4 | 1.2 | |||
| October 2020 | 1.5 | 1.3 | |||
| November 2020 | 1.4 | 1.1 | |||
| December 2020 | 1.2 | 0.8 | |||
| January 2021 | 1.2 | 0.8 | |||
| February 2021 | 1.3 | 1.2 | |||
| March 2021 | 1.5 | 2.0 |
Table IIA-4 summarizes the brutal drops in assets and net worth of US households and nonprofit organizations from 2007 to 2008 and 2009. Total assets fell $8.8 trillion or 10.4 percent from 2007 to 2008 and $8.0 trillion or 9.4 percent to 2009. Net worth fell $9.1 trillion from 2007 to 2008 or 12.9 percent and $7.7 trillion to 2009 or 11.0 percent. Subsidies to housing prolonged over decades together with interest rates at 1.0 percent from Jun 2003 to Jun 2004 inflated valuations of real estate and risk financial assets such as equities. The increase of fed funds rates by 25 basis points until 5.25 percent in Jun 2006 reversed carry trades through exotic vehicles such as subprime adjustable-rate mortgages (ARM) and world financial markets. Short-term zero interest rates encourage financing of everything with short-dated funds, explaining the SIVs created off-balance sheet to issue short-term commercial paper to purchase default-prone mortgages that were financed in overnight or short-dated sale and repurchase agreements (Pelaez and Pelaez, Financial Regulation after the Global Recession, 50-1, Regulation of Banks and Finance, 59-60, Globalization and the State Vol. I, 89-92, Globalization and the State Vol. II, 198-9, Government Intervention in Globalization, 62-3, International Financial Architecture, 144-9).
Table IIA-4, Difference of Balance Sheet of Households and Nonprofit Organizations, Billions of Dollars from 2007 to 2008 and 2009
| 2007 | 2008 | Change to 2008 | 2009 | Change to 2009 | |
| A | 84,734.3 | 75,908.4 | -8,825.9 | 76,772.6 | -7,961.7 |
| Non | 30,625.8 | 28,152.7 | -2,473.1 | 26,130.0 | -4,495.8 |
| RE | 25,834.2 | 23,238.5 | -2,595.7 | 21,199.4 | -4,634.8 |
| FIN | 54,108.6 | 47,755.7 | -6,352.9 | 50,642.6 | -3,466.0 |
| LIAB | 14,502.0 | 14,398.4 | -103.6 | 14,276.1 | -225.9 |
| NW | 70,232.3 | 61,151.0 | -9,081.3 | 62,496.5 | -7,735.8 |
Source: Board of Governors of the Federal Reserve System. 2021. Flow of funds, balance sheets and integrated macroeconomic accounts: first quarter 2021. Washington, DC, Federal Reserve System, Jun 10. https://www.federalreserve.gov/releases/z1/current/default.htm
The apparent improvement in Table IIA-4A is mostly because of increases in valuations of risk financial assets by the carry trade from zero interest rates to leveraged exposures in risk financial assets such as stocks, high-yield bonds, emerging markets, commodities and so on. Zero interest rates also act to increase net worth by reducing debt or liabilities. The net worth of households has become an instrument of unconventional monetary policy by zero interest rates in the theory that increases in net worth increase consumption that accounts for 68.3 percent of GDP in IQ2021 (https://cmpassocregulationblog.blogspot.com/2021/05/us-gdp-growing-at-saar-64-percent-in_29.html and earlier https://cmpassocregulationblog.blogspot.com/2021/05/us-gdp-growing-at-saar-64-percent-in.html), generating demand to increase aggregate economic activity and employment. There are neglected and counterproductive risks in unconventional monetary policy. Between 2007 and IQ2021, real estate increased in value by $11,723.5 billion and financial assets increased $55,453.6 billion, explaining most of the increase in net worth of $66,684.8 billion obtained by deducting the increase in liabilities of $2741.6 billion from the increase of assets of $69,426.4 billion (with minor rounding error). Net worth increased from $70,232.3 billion in IVQ2007 to $136,917.1 billion in IQ2021 by $66,684.8 billion or 94.9 percent. The US consumer price index for all items increased from 210.036 in Dec 2007 to 264.877 in Dec 2020 (https://www.bls.gov/cpi/data.htm) or 26.1 percent. Net worth adjusted by CPI inflation increased 54.6 percent from 2007 to IQ2021. Real estate assets adjusted for CPI inflation increased 15.3 percent from 2007 to IQ2021. There are multiple complaints that unconventional monetary policy concentrates income on wealthier individuals because of their holdings of financial assets while the middle class has gained less because of fewer holdings of financial assets and higher share of real estate in family wealth. There is nothing new in these arguments. Interest rate ceilings on deposits and loans have been commonly used. The Banking Act of 1933 imposed prohibition of payment of interest on demand deposits and ceilings on interest rates on time deposits. These measures were justified by arguments that the banking panic of the 1930s was caused by competitive rates on bank deposits that led banks to engage in high-risk loans (Friedman, 1970, 18; see Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 74-5). The objective of policy was to prevent unsound loans in banks. Savings and loan institutions complained of unfair competition from commercial banks that led to continuing controls with the objective of directing savings toward residential construction. Friedman (1970, 15) argues that controls were passive during periods when rates implied on demand deposit were zero or lower and when Regulation Q ceilings on time deposits were above market rates on time deposits. The Great Inflation or stagflation of the 1960s and 1970s changed the relevance of Regulation Q. Friedman (1970, 26-7) predicted the future:
“The banks have been forced into costly structural readjustments, the European banking system has been given an unnecessary competitive advantage, and London has been artificially strengthened as a financial center at the expense of New York.”
In short, Depression regulation exported the US financial system to London and offshore centers. What is vividly relevant currently from this experience is the argument by Friedman (1970, 27) that the controls affected the most people with lower incomes and wealth who were forced into accepting controlled-rates on their savings that were lower than those that would be obtained under freer markets. As Friedman (1970, 27) argues:
“These are the people who have the fewest alternative ways to invest their limited assets and are least sophisticated about the alternatives.” Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. US economic growth has been at only 2.0 percent on average in the cyclical expansion in the 47 quarters from IIIQ2009 to IQ2021 and in the global recession with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event. Boskin (2010Sep) measures that the US economy grew at 6.2 percent in the first four quarters and 4.5 percent in the first 12 quarters after the trough in the second quarter of 1975; and at 7.7 percent in the first four quarters and 5.8 percent in the first 12 quarters after the trough in the first quarter of 1983 (Professor Michael J. Boskin, Summer of Discontent, Wall Street Journal, Sep 2, 2010 http://professional.wsj.com/article/SB10001424052748703882304575465462926649950.html). There are new calculations using the revision of US GDP and personal income data since 1929 by the Bureau of Economic Analysis (BEA) (https://apps.bea.gov/iTable/index_nipa.cfm) and the third estimate of GDP for IQ2021 (https://www.bea.gov/sites/default/files/2021-06/gdp1q21_3rd_1.pdf). The average of 7.7 percent in the first four quarters of major cyclical expansions is in contrast with the rate of growth in the first four quarters of the expansion from IIIQ2009 to IIQ2010 of only 2.8 percent obtained by dividing GDP of $15,557.3 billion in IIQ2010 by GDP of $15,134.1 billion in IIQ2009 {[($15,557.3/$15,134.1) -1]100 = 2.8%], or accumulating the quarter on quarter growth rates (Section I and earlier https://cmpassocregulationblog.blogspot.com/2021/05/us-gdp-growing-at-saar-64-percent-in_29.html). The expansion from IQ1983 to IQ1986 was at the average annual growth rate of 5.7 percent, 5.3 percent from IQ1983 to IIIQ1986, 5.1 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.6 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990, 4.4 percent from IQ1983 to IIQ1990, 4.3 percent from IQ1983 to IIIQ1990, 4.0 percent from IQ1983 to IVQ1990, 3.8 percent from IQ1983 to IQ1991, 3.8 percent from IQ1983 to IIQ1991, 3.8 percent from IQ1983 to IIIQ1991, 3.7 percent from IQ1983 to IVQ1991, 3.7 percent from IQ1983 to IQ1992, 3.7 percent from IQ1983 to IIQ1992, 3.7 percent from IQ1983 to IIIQ1992, 3.8 percent from IQ1983 to IVQ1992, 3.7 percent from IQ1983 to IQ1993, 3.6 percent from IQ1983 to IIQ1993, 3.6 percent from IQ1983 to IIIQ1993, 3.7 percent from IQ1983 to IVQ1993, 3.7 percent from IQ1983 to IQ1994, 3.7 percent from IQ1983 to IIQ1994, 3.7 percent from IQ1983 to IIIQ1994 and at 7.9 percent from IQ1983 to IVQ1983 (Section I and earlier https://cmpassocregulationblog.blogspot.com/2021/05/us-gdp-growing-at-saar-64-percent-in_29.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://apps.bea.gov/iTable/index_nipa.cfm). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IQ2021 and in the global recession with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event would have accumulated to 47.9 percent. GDP in IVQ2020 would be $23,318.7 billion (in constant dollars of 2012) if the US had grown at trend, which is higher by $4232.3 billion than actual $19,086.4 billion. There are more than four trillion dollars of GDP less than at trend, explaining the 28.1 million unemployed or underemployed equivalent to actual unemployment/underemployment of 16.2 percent of the effective labor force with the largest part originating in the global recession with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event (https://cmpassocregulationblog.blogspot.com/2021/06/increase-in-may-2021-of-nonfarm-payroll.html and earlier https://cmpassocregulationblog.blogspot.com/2021/05/increase-in-apr-2021-of-nonfarm-payroll.html). Unemployment is decreasing while employment is increasing in initial adjustment of the lockdown of economic activity in the global recession resulting from the COVID-19 event (https://www.bls.gov/covid19/employment-situation-covid19-faq-may-2021.htm). US GDP in IQ2021 is 18.2 percent lower than at trend. US GDP grew from $15,762.0 billion in IVQ2007 in constant dollars to $19,086.4 billion in IQ2021 or 21.1 percent at the average annual equivalent rate of 1.5 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.0 percent per year from May 1919 to May 2021. Growth at 3.0 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 106.8161 in Dec 2007 to 158.8071 in May 2021. The actual index NSA in May 2021 is 98.4433 which is 38.0 percent below trend. The underperformance of manufacturing in Mar-Aug 2020 originates partly in the earlier global recession augmented by the current global recession with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19. Manufacturing grew at the average annual rate of 3.3 percent between Dec 1986 and Dec 2006. Growth at 3.3 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 106.8161 in Dec 2007 to 165.1264 in May 2021. The actual index NSA in May 2021 is 98.4433, which is 40.4 percent below trend. Manufacturing output grew at average 1.7 percent between Dec 1986 and May 2021. Using trend growth of 1.7 percent per year, the index would increase to 133.9244 in May 2021. The output of manufacturing at 98.4433 in May 2021 is 26.5 percent below trend under this alternative calculation. Using the NAICS (North American Industry Classification System), manufacturing output fell from the high of 108.5167 in Jun 2007 to the low of 84.7321 in May 2009 or 21.9 percent. The NAICS manufacturing index increased from 84.7321 in Apr 2009 to 99.0088 in May 2021 or 16.8 percent. The NAICS manufacturing index increased at the annual equivalent rate of 3.5 percent from Dec 1986 to Dec 2006. Growth at 3.5 percent would increase the NAICS manufacturing output index from 104.6868 in Dec 2007 to 166.0895 in May 2021. The NAICS index at 99.0088 in May 2021 is 40.4 below trend. The NAICS manufacturing output index grew at 1.7 percent annual equivalent from Dec 1999 to Dec 2006. Growth at 1.7 percent would raise the NAICS manufacturing output index from 104.6868 in Dec 2007 to 131.2547 in May 2021. The NAICS index at 99.0088 in May 2021 is 24.6 percent below trend under this alternative calculation.
Table IIA-4A, US, Difference of Balance Sheet of Households and Nonprofit Organizations Billions of Dollars from 2007 to 2019 2020 and IQ20201
| Value 2007 | Change to 2019 | Change to 2020 | Change to IQ2021 | |
| Assets | 84,734.3 | 49,564.6 | 64,242.0 | 69,426.4 |
| Nonfinancial | 30,540.4 | 9,422.5 | 12,900.5 | 14,058.1 |
| Real Estate | 25,834.2 | 7,729.0 | 10,755.1 | 11,723.5 |
| Financial | 54,108.6 | 40,227.5 | 51,426.8 | 55,453.6 |
| Liabilities | 14,502.0 | 1,918.7 | 2,554.0 | 2,741.6 |
| Net Worth | 70,232.3 | 47,645.9 | 61,688.0 | 66,684.8 |
Notes: Deposits: Total Time and Savings Deposits FL15303005; Net Worth = Assets – Liabilities
Source: Board of Governors of the Federal Reserve System. 2021. Flow of funds, balance sheets and integrated macroeconomic accounts: first quarter 2021. Washington, DC, Federal Reserve System, Jun 10. https://www.federalreserve.gov/releases/z1/current/default.htm
The comparison of net worth of households and nonprofit organizations in the entire economic cycle from IQ1980 (and from IVQ1979) to IIIQ1994 and from IVQ2007 to IVQ2019 is in Table IIA-5. There is also the comparison of net worth from IV2019 to IQ2021 in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event. The data reveal the following facts for the cycles in the 1980s:
- IVQ1979 to IIIQ1994. Net worth increased 200.8 percent from IVQ1979 to IIIQ1994, the all items CPI index increased 94.8 percent from 76.7 in Dec 1979 to 149.4 in Sep 1994 and real net worth increased 54.4 percent.
- IQ1980 to IVQ1985. Net worth increased 66.7 percent, the all items CPI index increased 36.5 percent from 80.1 in Mar 1980 to 109.3 in Dec 1985 and real net worth increased 22.2 percent.
- IVQ1979 to IVQ1985. Net worth increased 70.3 percent, the all items CPI index increased 42.5 percent from 76.7 in Dec 1979 to 109.3 in Dec 1985 and real net worth increased 19.5 percent.
- IQ1980 to IQ1989. Net worth increased 121.5 percent, the all items CPI index increased 52.7 percent from 80.1 in Mar 1980 to 122.3 in Mar 1989 and real net worth increased 45.1 percent.
- IQ1980 to IIQ1989. Net worth increased 126.0 percent, the all items CPI index increased 54.9 percent from 80.1 in Mar 1980 to 124.1 in Jun 1989 and real net worth increased 45.9 percent.
- IQ1980 to IIIQ1989. Net worth increased 131.8 percent, the all items CPI index increased 56.1 percent from 80.1 in Mar 1980 to 125.0 in Sep 1989 and real net worth increased 48.6 percent.
- IQ1980 to IVQ1989. Net worth increased 136.2 percent, the all items CPI index increased 57.4 from 80.1 in Mar 1980 to 126.1 in Dec 1989 and real net worth increased 50.0 percent.
- IQ1980 to IQ1990. Net worth increased 137.2 percent, the all items CPI index increased 60.7 percent from 80.1 in Mar 1980 to 128.7 in Mar 1990 and real net worth increased 47.6 percent.
- IQ1980 to IIQ1990. Net worth increased 140.1 percent, the all items CPI index increased 62.2 percent from 80.1 in Mar 1980 to 129.9 in Jun 1990 and real net worth increased 48.0 percent
- IQ1980 to IIIQ1990. Net worth increased 138.3 percent, the all items CPI index increased 65.7 percent from 80.1 in Mar 1980 to 132.7 in Jun 1990 and real net worth increased 43.9 percent.
- IQ1980 to IVQ1990. Net worth increased 143.3 percent, the all items CPI index increased 67.0 percent from 80.1 in Mar 1980 to 133.8 in Dec 1990 and real net worth increased 45.7 percent. The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://apps.bea.gov/iTable/index_nipa.cfm). This new cyclical contraction explains the contraction of net worth in IIIQ1990
- IQ1980 to IQ1991. Net worth increased 149.3 percent, the all items CPI index increased 68.5 percent from 80.1 in Mar 1980 to 135.0 in Mar 1991 and real net worth increased 47.9 percent.
- IQ1980 to IIQ1991. Net worth increased 149.9 percent, the all items CPI index increased 69.8 percent from 80.1 in Mar 1980 to 136.0 in Jun 1991 and real net worth increased 47.2 percent.
- IQ1980 to IIIQ1991. Net worth increased 153.2 percent, the all items CPI index increased 71.3 percent from 80.1 in Mar 1980 to 137.2 in Sep 1991 and real net worth increased 47.8 percent.
- IQ1980 to IVQ1991. Net worth increased 159.4 percent, the all items CPI index increased 72.2 percent from 80.1 in Mar 1980 to 137.9 in Dec 1991 and real net worth increased 50.7 percent.
- IQ1980 to IQ1992. Net worth increased 160.2 percent, the all items CPI index increased 73.9 percent from 80.1 in Mar 1980 to 139.3 in Mar 1992 and real net worth increased 49.6 percent.
- IQ1980 to IIQ1992. Net worth increased 161.4 percent, the all items CPI index increased 75.0 percent from 80.1 in Mar 1980 to 140.2 in Jun 1992 and real net worth increased 49.3 percent.
- IQ1980 to IIIQ1992. Net worth increased 165.1 percent, the all items CPI index increased 76.4 percent from 80.1 in Mar 1980 to 141.3 in Sep 1992 and real net worth increased 50.3 percent.
- IQ1980 to IVQ1992. Net worth increased 171.5, the all items CPI index increased 77.2 percent from 80.1 in Mar 1980 to 141.9 in Dec 1992 and real net worth increased 53.3 percent.
- IQ1980 to IQ1993. Net worth increased 175.2 percent, the all items CPI increased 79.3 percent from 80.1 in Mar 1980 to 143.6 in Mar 1993 and real net worth increased 53.5 percent.
- IQ1980 to IIQ1993. Net worth increased 177.9 percent, the all items CPI increased 80.3 percent from 80.1 in Jun 1980 to 144.4 in Jun 1993 and real net worth increased 54.2 percent.
- IQ1980 to IIIQ1993. Net worth increased 182.3 percent, the all items CPI increased 81.1 percent from 80.1 in Jun 1980 to 145.1 in Sep 1993 and real net worth increased 55.8 percent.
- IQ1980 to IVQ1993. Net worth increased 187.0 percent, the all items CPI increased 82.0 percent from 80.1 in Jun 1980 to 145.8 in Dec 1993 and real net worth increased 57.7 percent.
- IQ1980 to IQ1994. Net worth increased 188.7 percent, the all items CPI increased 83.8 percent from 80.1 in Jun 1980 to 147.2 in Mar 1994 and real net worth increased 57.1 percent.
- IQ1980 to IIQ1994. Net worth increased 190.7 percent, the all items CPI increased 84.8 percent from 80.1 in Jun 1980 to 148.0 in Jun 1994 and real net worth increased 57.3 percent.
- IQ1980 to IIIQ1994. Net worth increased 194.6 percent, the all items CPI increased 86.5 percent from 80.1 in Jun 1980 to 149.4 in Sep 1994 and real net worth increased 57.9 percent.
There is comparatively weaker performance in the economic cycle of the global recession of 2007:
- IVQ2007 to IVQ2019. Net worth increased 67.8 percent, the all items CPI increased 22.3 percent from 210.036 in Dec 2007 to 256.974 in Dec 2019 and real net worth increased 37.2 percent.
There is sharp contraction followed by recovery in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event:
- IVQ2019 to IQ2021. Net worth increased 16.2 percent, the all items CPI increased 3.1 percent from 256.974 in IVQ2019 to 264.877 in IQ2021 and real net worth increased 12.7 percent. Net worth decreased by $6,523.0 billion from IVQ2019 to IQ2020 or by 5.5 percent in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event. Net worth increased $8,271.2 billion from IQ2020 to IIQ2020 or 7.4 percent. Net worth increased 1.5 percent from IVQ2019 to IIQ2020. Net worth increased $5,978.4 billion from IVQ2019 to IIIQ2020 or 5.1 percent. Net worth increased $14,042.1billion from IVQ2019 to IVQ2020 or 11.9 percent.Net worth increased $19,038.9 billion from IVQ2019 to IQ2021 or 16.2 percent and 12.7 adjusting for inflation. Real estate increased $3,994.5 billion from IVQ2019 to IQ2021 or 11.9 percent. Financial assets increased $15,226.1 billion from IVQ2019 to IQ2021 or 16.1 percent. Stock markets recovered in Apr 2020 to Mar 2021. Corporate equities increased $7,424.2 billion from IVQ2019 to IQ2021 or 35.6 percent.
The explanation is partly in the sharp decline of wealth of households and nonprofit organizations and partly in the mediocre growth rates of the cyclical expansion beginning in IIIQ2009. Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. US economic growth has been at only 2.0 percent on average in the cyclical expansion in the 47 quarters from IIIQ2009 to IQ2021 and in the global recession with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event. Boskin (2010Sep) measures that the US economy grew at 6.2 percent in the first four quarters and 4.5 percent in the first 12 quarters after the trough in the second quarter of 1975; and at 7.7 percent in the first four quarters and 5.8 percent in the first 12 quarters after the trough in the first quarter of 1983 (Professor Michael J. Boskin, Summer of Discontent, Wall Street Journal, Sep 2, 2010 http://professional.wsj.com/article/SB10001424052748703882304575465462926649950.html). There are new calculations using the revision of US GDP and personal income data since 1929 by the Bureau of Economic Analysis (BEA) (https://apps.bea.gov/iTable/index_nipa.cfm) and the third estimate of GDP for IQ2021 (https://www.bea.gov/sites/default/files/2021-06/gdp1q21_3rd_1.pdf). The average of 7.7 percent in the first four quarters of major cyclical expansions is in contrast with the rate of growth in the first four quarters of the expansion from IIIQ2009 to IIQ2010 of only 2.8 percent obtained by dividing GDP of $15,557.3 billion in IIQ2010 by GDP of $15,134.1 billion in IIQ2009 {[($15,557.3/$15,134.1) -1]100 = 2.8%], or accumulating the quarter on quarter growth rates (Section I and earlier https://cmpassocregulationblog.blogspot.com/2021/05/us-gdp-growing-at-saar-64-percent-in_29.html). The expansion from IQ1983 to IQ1986 was at the average annual growth rate of 5.7 percent, 5.3 percent from IQ1983 to IIIQ1986, 5.1 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.6 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990, 4.4 percent from IQ1983 to IIQ1990, 4.3 percent from IQ1983 to IIIQ1990, 4.0 percent from IQ1983 to IVQ1990, 3.8 percent from IQ1983 to IQ1991, 3.8 percent from IQ1983 to IIQ1991, 3.8 percent from IQ1983 to IIIQ1991, 3.7 percent from IQ1983 to IVQ1991, 3.7 percent from IQ1983 to IQ1992, 3.7 percent from IQ1983 to IIQ1992, 3.7 percent from IQ1983 to IIIQ1992, 3.8 percent from IQ1983 to IVQ1992, 3.7 percent from IQ1983 to IQ1993, 3.6 percent from IQ1983 to IIQ1993, 3.6 percent from IQ1983 to IIIQ1993, 3.7 percent from IQ1983 to IVQ1993, 3.7 percent from IQ1983 to IQ1994, 3.7 percent from IQ1983 to IIQ1994, 3.7 percent from IQ1983 to IIIQ1994 and at 7.9 percent from IQ1983 to IVQ1983 (Section I and earlier https://cmpassocregulationblog.blogspot.com/2021/05/us-gdp-growing-at-saar-64-percent-in_29.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://apps.bea.gov/iTable/index_nipa.cfm). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IQ2021 and in the global recession with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event would have accumulated to 47.9 percent. GDP in IVQ2020 would be $23,318.7 billion (in constant dollars of 2012) if the US had grown at trend, which is higher by $4232.3 billion than actual $19,086.4 billion. There are more than four trillion dollars of GDP less than at trend, explaining the 28.1 million unemployed or underemployed equivalent to actual unemployment/underemployment of 16.2 percent of the effective labor force with the largest part originating in the global recession with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event (https://cmpassocregulationblog.blogspot.com/2021/06/increase-in-may-2021-of-nonfarm-payroll.html and earlier https://cmpassocregulationblog.blogspot.com/2021/05/increase-in-apr-2021-of-nonfarm-payroll.html). Unemployment is decreasing while employment is increasing in initial adjustment of the lockdown of economic activity in the global recession resulting from the COVID-19 event (https://www.bls.gov/covid19/employment-situation-covid19-faq-may-2021.htm). US GDP in IQ2021 is 18.2 percent lower than at trend. US GDP grew from $15,762.0 billion in IVQ2007 in constant dollars to $19,086.4 billion in IQ2021 or 21.1 percent at the average annual equivalent rate of 1.5 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.0 percent per year from May 1919 to May 2021. Growth at 3.0 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 106.8161 in Dec 2007 to 158.8071 in May 2021. The actual index NSA in May 2021 is 98.4433 which is 38.0 percent below trend. The underperformance of manufacturing in Mar-Aug 2020 originates partly in the earlier global recession augmented by the current global recession with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19. Manufacturing grew at the average annual rate of 3.3 percent between Dec 1986 and Dec 2006. Growth at 3.3 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 106.8161 in Dec 2007 to 165.1264 in May 2021. The actual index NSA in May 2021 is 98.4433, which is 40.4 percent below trend. Manufacturing output grew at average 1.7 percent between Dec 1986 and May 2021. Using trend growth of 1.7 percent per year, the index would increase to 133.9244 in May 2021. The output of manufacturing at 98.4433 in May 2021 is 26.5 percent below trend under this alternative calculation. Using the NAICS (North American Industry Classification System), manufacturing output fell from the high of 108.5167 in Jun 2007 to the low of 84.7321 in May 2009 or 21.9 percent. The NAICS manufacturing index increased from 84.7321 in Apr 2009 to 99.0088 in May 2021 or 16.8 percent. The NAICS manufacturing index increased at the annual equivalent rate of 3.5 percent from Dec 1986 to Dec 2006. Growth at 3.5 percent would increase the NAICS manufacturing output index from 104.6868 in Dec 2007 to 166.0895 in May 2021. The NAICS index at 99.0088 in May 2021 is 40.4 below trend. The NAICS manufacturing output index grew at 1.7 percent annual equivalent from Dec 1999 to Dec 2006. Growth at 1.7 percent would raise the NAICS manufacturing output index from 104.6868 in Dec 2007 to 131.2547 in May 2021. The NAICS index at 99.0088 in May 2021 is 24.6 percent below trend under this alternative calculation.
Table IIA-5, Net Worth of Households and Nonprofit Organizations in Billions of Dollars, IVQ1979 to IIIQ1994 and IVQ2007 to IQ2021
| Period IQ1980 to IIIQ1994 | |
| Net Worth of Households and Nonprofit Organizations USD Millions | |
| IVQ1979 IQ1980 | 9,104.3 9,297.0 |
| IVQ1985 IIIQ1986 IVQ1986 IQ1987 IIQ1987 IIIQ1987 IVQ1987 IQ1988 IIQ1988 IIIQ1988 IVQ1988 IQ1989 IIQ1989 IIIQ1989 IVQ1989 IQ1990 IIQ1990 | 15,501.6 16,563.8 17,135.5 17,758.4 18,049.3 18,459.4 18,403.8 18,911.2 19,344.0 19,661.2 20,163.7 20,594.0 21,015.5 21,553.0 21,956.8 22,048.5 22,318.7 |
| III1990 | 22,157.4 |
| IV1990 | 22,621.3 |
| I1991 | 23,177.9 |
| IIQ1991 | 23,231.6 |
| IIIQ1991 | 23,536.3 |
| IVQ1991 | 24,117.8 |
| IQ1992 | 24,189.4 |
| IIQ1992 | 24,300.2 |
| IIIQ1992 | 24,647.8 |
| IVQ1992 | 25,244.3 |
| IQ1993 | 25,582.2 |
| IIQ1993 | 25,841.0 |
| IIIQ1993 | 26,242.7 |
| IVQ1993 | 26,681.8 |
| IQ1994 | 26,839.8 |
| IIQ1994 | 27.024.2 |
| IIIQ1994 | 27,388.4 |
| ∆ USD IVQ1979 to IVQ1985 IVQ1979 to IIIQ1994 IQ1980-IVQ1985 IQ1980-IIIQ1986 IQ1980-IVQ1986 IQ1980-IQ1987 IQ1980-IIQ1987 IQ1980-IIIQ1987 IQ1980-IVQ1987 IQ1980-IQ1988 IQ1980-IIQ1988 IQ1980-IIIQ1988 IQ1980-IVQ1988 IQ1980-IQ1989 IQ1980-IIQ1989 IQ1980-IIIQ1989 IQ1980-IVQ1989 IQ1980-IQ1990 IQ1980-IIQ1990 | +6,397.3 ∆%70.3 R∆19.5 +18,284.1 ∆%200.8 R∆%54.4 +6,204.6∆%66.7 R∆%22.2 +7,266.8 ∆%78.2 R∆%29.5 +7,838.5 ∆%84.3 R∆%33.6 +8,461.4 ∆%91.0 R∆%36.5 +8,752.3 ∆%94.1 R∆%37.0 +9,162.4 ∆%98.6 R∆%38.3 +9106.8 ∆%98.0 R∆%37.4 +9614.2 ∆%103.4 R∆%39.9 10,047.0 ∆%108.1 R∆%41.2 +10,364.2 ∆%111.5 R∆%41.4 +10,866,7 ∆%116.9 R∆%44.2 +11297.0 ∆%121.5 R∆%45.1 +11,718.5 ∆%126.0 R∆% 45.9 +12,256.0∆%131.8 R∆% 48.6 +12,659.8 ∆%136.2 R∆%50.0 +12,751.5 ∆%137.2 R∆%47.6 +13,021.7 ∆%140.1 R∆%48.0 |
| IQ1980-IIIQ1990 | +12,860.4∆%138.3 R∆%43.9 |
| IQ1980-IVQ1990 | +13,324.3 ∆%143.3 R∆%45.7 |
| IQ1980-IQ1991 | +13,880.9 ∆%149.3 R∆%47.9 |
| IQ1980-IIQ1991 | +13,934.6 ∆%149.9 R∆%47.2 |
| IQ1980-IIIQ1991 | +14,239.3 ∆%153.2 R∆%47.8 |
| IQ1980-IVQ1991 | +14,820.8 ∆%159.4 R∆%50.7 |
| IQ1980-IQ1992 | +14,892.4 ∆%160.2 R∆%49.6 |
| IQ1980-IIQ1992 | +15,003.2 ∆%161.4 R∆%49.3 |
| IQ1980-IIIQ1992 | +15,350.8 ∆%165.1 R∆%50.3 |
| IQ1980-IVQ1992 | +15,947.3 ∆%171.5 R∆%53.3 |
| IQ1980-IQ1993 | +16,285.2 ∆%175.2 R∆%53.5 |
| IQ1980-IIQ1993 | +16,544.0 ∆%177.9 R∆% 54.2 |
| IQ1980-IIIQ1993 | +16,945.7 ∆%182.3 R∆% 55.8 |
| IQ1980-IVQ1993 | +17,384.8 ∆%187.0 R∆% 57.7 |
| IQ1980-IQ1994 | +17,542.8 ∆%188.7 R∆% 57.1 |
| IQ1980-IIQ1994 | +17,727.2 ∆%190.7 R∆% 57.3 |
| IQ1980-IIIQ1994 | +18,091.4 ∆%194.6 R∆% 57.9 |
| Period IVQ2007 to IVQ2019 | |
| Net Worth of Households and Nonprofit Organizations USD Millions | |
| IVQ2007 | 70,232.3 |
| IVQ2019 | 117,878.2 |
| ∆ USD Billions | +47,645.9 ∆%67.8 R∆% 37.2 |
| Period IVQ2019 to IQ2021 | |
| IVQ2019 | 117,878.2 |
| IQ2021 | 136,917.1 |
| IVQ2019 to IQ2021 | |
| ∆ USD Billions | +18,038.9 ∆%16.2 R∆%12.7 |
Net Worth = Assets – Liabilities. R∆% real percentage change or adjusted for CPI percentage change.
Notes: Deposits: Total Time and Savings Deposits FL15303005; Net Worth = Assets – Liabilities
Source: Board of Governors of the Federal Reserve System. 2021. Flow of funds, balance sheets and integrated macroeconomic accounts: first quarter 2021. Washington, DC, Federal Reserve System, Jun 10. https://www.federalreserve.gov/releases/z1/current/default.htm
Chart IIA-1 of the Board of Governors of the Federal Reserve System provides US wealth of households and nonprofit organizations from IVQ2007 to IQ2021. There is remarkable stop and go behavior in this series with two sharp declines and two standstills in the 46 quarters of expansion of the economy beginning in IIIQ2009. The increase in net worth of households and nonprofit organizations is the result of increases in valuations of risk financial assets and compressed liabilities resulting from zero interest rates. Wealth of households and nonprofits organization increased 54.6 percent from IVQ2007 to IQ2021 when adjusting for consumer price inflation. Net worth of households and nonprofit organizations fell 3.4 percent from 109,016.2 billion in IIIQ2018 to 105,350.0 billion in IVQ2018 or $3,666.2 billion. Financial assets decreased 4.5 percent from 87,233.8 billion in IIIQ2018 to 83,318.4 billion in IVQ2018 or $3915.4 billion. Corporate equities fell 13.9 percent from $18,929.9 billion in IIIQ2018 to $16,301.7 billion in IVQ2018 or $2,628.2 billion. These are the revised data in the report of Jun 10, 2021, for IQ2021. Net worth increased 67.8 percent from IVQ2007 to IVQ2019 or 37.2 percent adjusting for inflation, 58.6 percent from IVQ2007 to IQ2020 or 29.0 percent adjusting for inflation, 70.3 percent from IVQ2007 to IIQ2020 or 38.8 percent adjusting for inflation, 76.4 percent from IVQ2007 to IIIQ2020 or 42.3 percent adjusting for inflation, 87.8 percent from IVQ2007 to IVQ2020 or 51.5 percent adjusting for inflation, and 94.9 percent from IVQ2007 to IQ2021 or 54.6 percent when adjusting for inflation. Net worth increased 16.2 percent from IVQ2019 to IQ2021 or 12.7 percent adjusting for inflation.
Net worth decreased by $6,523.0 billion from IVQ2019 to IQ2020 or by 5.5 percent in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event. Net worth increased $8,271.2 billion from IQ2020 to IIQ2020 or 7.4 percent. Net worth increased 1.5 percent from IVQ2019 to IIQ2020. Net worth increased $5,978.4 billion from IVQ2019 to IIIQ2020 or 5.1 percent. Net worth increased $14,042.1billion from IVQ2019 to IVQ2020 or 11.9 percent.Net worth increased $19,038.9 billion from IVQ2019 to IQ2021 or 16.2 percent and 12.7 adjusting for inflation. Real estate increased $3,994.5 billion from IVQ2019 to IQ2021 or 11.9 percent. Financial assets increased $15,226.1 billion from IVQ2019 to IQ2021 or 16.1 percent. Stock markets recovered in Apr 2020 to Mar 2021. Corporate equities increased $7,424.2 billion from IVQ2019 to IQ2021 or 35.6 percent.
Chart IIA-1, Net Worth of Households and Nonprofit Organizations in Millions of Dollars, IVQ2007 to IQ2021
Source: Board of Governors of the Federal Reserve System. 2021. Flow of funds, balance sheets and integrated macroeconomic accounts: first quarter 2021. Washington, DC, Federal Reserve System, Jun 10. https://www.federalreserve.gov/releases/z1/current/default.htm
Chart IIA-2 of the Board of Governors of the Federal Reserve System provides US wealth of households and nonprofit organizations from IVQ1979 to IIIQ1994. There are changes in the rates of growth of wealth suggested by the changing slopes but there is smooth upward trend. There was significant financial turmoil during the 1980s. Benston and Kaufman (1997, 139) find that there was failure of 1150 US commercial and savings banks between 1983 and 1990, or about 8 percent of the industry in 1980, which is nearly twice more than between the establishment of the Federal Deposit Insurance Corporation in 1934 through 1983. More than 900 savings and loans associations, representing 25 percent of the industry, were closed, merged or placed in conservatorships (see Pelaez and Pelaez, Regulation of Banks and Finance (2008b), 74-7). The Financial Institutions Reform, Recovery and Enforcement Act of 1989 (FIRREA) created the Resolution Trust Corporation (RTC) and the Savings Association Insurance Fund (SAIF) that received $150 billion of taxpayer funds to resolve insolvent savings and loans. The GDP of the US in 1989 was $5641.6 billion (https://apps.bea.gov/iTable/index_nipa.cfm), such that the partial cost to taxpayers of that bailout was around 2.66 percent of GDP in a year. The Bureau of Economic Analysis estimates US GDP in 2019 at $21,433.2 billion, such that the bailout would be equivalent to cost to taxpayers of about $570.1 billion in current GDP terms and $556.9 relative to GDP in 2020 of $20,936.6 billion. A major difference with the Troubled Asset Relief Program (TARP) for private-sector banks is that most of the costs were recovered with interest gains whereas in the case of savings and loans there was no recovery. Money center banks were under extraordinary pressure from the default of sovereign debt by various emerging nations that represented a large share of their net worth (see Pelaez 1986). Net worth of households and nonprofit organizations increased 194.6 percent from IQ1980 to IIIQ1994 and 57.9 percent when adjusting for consumer price inflation. The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://apps.bea.gov/iTable/index_nipa.cfm). This new cyclical contraction explains the contraction followed by stability of net worth in the final segment followed by mild increase and then rising trend in Chart IIA-2.
Chart IIA-2, Net Worth of Households and Nonprofit Organizations in Millions of Dollars, IVQ1979 to IIIQ1994
Source: Board of Governors of the Federal Reserve System. 2021. Flow of funds, balance sheets and integrated macroeconomic accounts: first quarter 2021. Washington, DC, Federal Reserve System, Jun 10. https://www.federalreserve.gov/releases/z1/current/default.htm
Chart IIA-3 of the Board of Governors of the Federal Reserve System provides US wealth of households and nonprofit organizations from IVQ1945 at $806.6 billion to IQ2021 at $136,917.1 billion or increase of 16,874.6 percent. The consumer price index not seasonally adjusted was 18.2 in Dec 1945 jumping to 264.877 in IQ2021 or increase of 1,355.4 percent. There was a gigantic increase of US net worth of households and nonprofit organizations over 75.25 years with inflation-adjusted increase from $44.319 in dollars of 1945 to $516.908 in IQ2021 or 1066.3 percent. In a simple formula: {[($136,917.1÷806.6)÷(264.877/18.2)-1]100 = 1066.3%}. Wealth of households and nonprofit organizations increased from $806.6 billion at year-end 1945 to $136,917.1 billion at the end of IQ2021 or 16,874.6 percent. The consumer price index increased from 18.2 in Dec 1945 to 264.877 in Mar 2021 or 1,355.4 percent. Net wealth of households and nonprofit organizations in dollars of 1945 increased from $44.319 in 1945 to $516.908 in IQ2021 or 1066.3 percent at the average yearly rate of 3.3 percent. US real GDP grew at the average rate of 2.9 percent from 1945 to 2019 and at 2.8 percent from 1945 to 2020 (https://apps.bea.gov/iTable/index_nipa.cfm). The combination of collapse of values of real estate and financial assets during the global recession of IVQ2007 to IIIQ2009 caused sharp contraction of net worth of US households and nonprofit organizations. Recovery has been in stop-and-go fashion during the worst cyclical expansion in the 75.25 years when US GDP grew at 2.0 percent on average in the forty-seven quarters between IIIQ2009 and IQ2021 (https://cmpassocregulationblog.blogspot.com/2021/05/us-gdp-growing-at-saar-64-percent-in_29.html and earlier https://cmpassocregulationblog.blogspot.com/2021/05/us-gdp-growing-at-saar-64-percent-in.html). US GDP was $228.0 billion in 1945 and net worth of households and nonprofit organizations $806.6 billion for ratio of wealth to GDP of 3.54. The ratio of net worth of households and nonprofits of $70,232.3 billion in 2007 to GDP of $14,451.9 billion was 4.86. The ratio of net worth of households and nonprofits of $117,878.2 billion in 2019 to GDP of $21,433.2 billion was 5.50. The ratio of net worth of households and nonprofits of $131,920.3 billion in IVQ2020 to GDP of 20,936.6 was 6.30. The final data point in Chart IIA-3 is net worth of household and nonprofit institutions at $136,917.1 billion in IQ2021 for increase of 16,874.6 percent relative to $806.6 billion in IVQ1945. CPI adjusted net worth of household and nonprofit institutions increased from $44.319 in IVQ1945 to $516.908 in IQ2021 or 1066.3 percent at the annual equivalent rate of 3.3 percent. Net worth of households and nonprofit organizations fell 3.4 percent from 109,016.2 billion in IIIQ2018 to 105,350.0 billion in IVQ2018 or $3,666.2 billion. Financial assets decreased 4.5 percent from 87,233.8 billion in IIIQ2018 to 83,318.4 billion in IVQ2018 or $3915.4 billion. Corporate equities fell 13.9 percent from $18,929.9 billion in IIIQ2018 to $16,301.7 billion in IVQ2018 or $2,628.2 billion. These are the revised data in the report of Jun 10, 2021, for IQ2021. Net worth increased 68.4 percent from IVQ2007 to IVQ2019 or 37.7 percent adjusting for inflation, 58.8 percent from IVQ2007 to IQ2020 or 29.2 percent adjusting for inflation, 70.4 percent from IVQ2007 to IIQ2020 or 38.8 percent adjusting for inflation, 75.6 percent from IVQ2007 to IIIQ2020 or 41.7 percent adjusting for inflation and 85.7 percent from IVQ2007 to IVQ2020 or 49.8 percent adjusting for inflation. Net worth decreased by $6,523.0 billion from IVQ2019 to IQ2020 or by 5.5 percent in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event. Net worth increased $8,271.2 billion from IQ2020 to IIQ2020 or 7.4 percent. Net worth increased 1.5 percent from IVQ2019 to IIQ2020. Net worth increased $5,978.4 billion from IVQ2019 to IIIQ2020 or 5.1 percent. Net worth increased $14,042.1billion from IVQ2019 to IVQ2020 or 11.9 percent.Net worth increased $19,038.9 billion from IVQ2019 to IQ2021 or 16.2 percent and 12.7 adjusting for inflation. Real estate increased $3,994.5 billion from IVQ2019 to IQ2021 or 11.9 percent. Financial assets increased $15,226.1 billion from IVQ2019 to IQ2021 or 16.1 percent. Stock markets recovered in Apr 2020 to Mar 2021. Corporate equities increased $7,424.2 billion from IVQ2019 to IQ2021 or 35.6 percent.
Chart IIA-3, Net Worth of Households and Nonprofit Organizations in Millions of Dollars, IVQ1945 to IQ2021
Source: Board of Governors of the Federal Reserve System. 2021. Flow of funds, balance sheets and integrated macroeconomic accounts: first quarter 2021. Washington, DC, Federal Reserve System, Jun 10. https://www.federalreserve.gov/releases/z1/current/default.htm
Federal Reserve System, Mar 11. https://www.federalreserve.gov/releases/z1/current/default.htm
Table IIA-6 provides percentage changes of nonfinancial domestic sector debt. Households increased debt by 10.5 percent in 2006 but reduced debt from 2010 to 2011. Households have increased debt moderately since 2012. Financial repression by zero fed funds rates or negative interest rates intends to increase debt and reduce savings. Business had not been as exuberant in acquiring debt and has been increasing debt benefitting from historically low costs while increasing cash holdings to around $2 trillion by swelling undistributed profits because of the uncertainty of capital budgeting. The key to growth and hiring consists in creating the incentives for business to invest and hire. States and local government were forced into increasing debt by the decline in revenues but began to contract in IQ2011, decreasing again from IQ2011 to IVQ2011, increasing at 2.1 percent in IIQ2012 and decreasing at 0.2 percent in IIIQ2012 and 2.6 percent in IVQ2012. State and local government increased debt at 1.9 percent in IQ2013 and decreased at 1.1 percent in IIQ2013. State and local government decreased debt at 3.0 percent in IIIQ2013 and at 2.8 percent in IVQ2013. State and local government reduced debt at 1.7 percent in IQ2014 and decreased at 0.4 percent in IIQ2014. State and local government reduced debt at 2.7 percent in IIIQ2014 and increased at 0.7 percent in IVQ2014. State and local government increased debt at 1.6 percent in IQ2015 and increased at 0.2 percent in IIIQ2015. State and local government decreased debt at 0.9 percent in IVQ2015. State and local government increased debt at 0.7 percent in IQ2016 and increased at 2.4 percent in IIQ2016. State and local government increased debt at 0.7 percent in IIIQ2016. Opposite behavior is for the federal government that has been rapidly accumulating debt but without success in the self-assigned goal of promoting economic growth. Financial repression constitutes seigniorage of government debt (http://cmpassocregulationblog.blogspot.com/2011/05/global-inflation-seigniorage-financial.html http://cmpassocregulationblog.blogspot.com/2011/05/global-inflation-seigniorage-monetary.html).
Table IIA-6, US, Percentage Change of Nonfinancial Domestic Sector Debt
| Total | Households | Business | Federal | State & | |
| IQ2021 | 5.8 | 6.5 | 4.4 | 6.5 | 3.8 |
| IVQ2020 | 6.3 | 6.2 | 1.1 | 10.9 | 1.6 |
| IIIQ2020 | 5.4 | 5.6 | 0.7 | 9.1 | 5.5 |
| IIQ2020 | 25.6 | 0.1 | 15.2 | 59.2 | 3.5 |
| IQ2020 | 10.8 | 3.7 | 19.1 | 11.4 | 0.9 |
| IVQ2019 | 3.3 | 3.6 | 2.2 | 4.2 | 2.9 |
| IIIQ2019 | 5.9 | 3.3 | 5.9 | 8.9 | 0.9 |
| IIQ2019 | 3.5 | 4.3 | 4.2 | 3.1 | -1.8 |
| IQ2019 | 5.9 | 2.2 | 6.7 | 9.6 | -0.7 |
| IVQ2018 | 3.4 | 2.7 | 4.6 | 4.1 | -2.7 |
| IIIQ2018 | 3.9 | 3.6 | 4.6 | 4.4 | -1.2 |
| IIQ2018 | 4.3 | 3.5 | 3.3 | 6.7 | 0.2 |
| IQ2018 | 6.7 | 3.1 | 3.9 | 14.3 | -3.2 |
| IVQ2017 | 3.8 | 5.1 | 4.8 | 1.8 | 4.0 |
| IIIQ2017 | 4.5 | 2.6 | 6.1 | 6.0 | -0.6 |
| IIQ2017 | 4.6 | 4.2 | 6.4 | 4.4 | -1.0 |
| IQ2017 | 3.3 | 3.8 | 6.0 | 1.7 | -2.2 |
| IVQ2016 | 2.0 | 2.6 | 1.8 | 2.1 | -0.3 |
| IIIQ2016 | 5.2 | 4.4 | 6.0 | 6.1 | 0.7 |
| IIQ2016 | 4.5 | 3.7 | 4.2 | 6.0 | 2.4 |
| IQ2016 | 5.5 | 2.4 | 9.2 | 6.2 | 0.7 |
| IVQ2015 | 8.0 | 4.1 | 5.9 | 15.6 | -0.9 |
| IIIQ2015 | 2.1 | 1.3 | 5.3 | 0.6 | 0.3 |
| IIQ2015 | 4.7 | 3.8 | 8.2 | 3.4 | 0.2 |
| IQ2015 | 3.0 | 2.2 | 7.5 | -0.3 | 1.6 |
| IVQ2014 | 3.7 | 2.3 | 6.4 | 3.1 | 0.7 |
| 2020 | 12.5 | 3.9 | 9.2 | 24.1 | 2.9 |
| 2019 | 4.7 | 3.4 | 4.8 | 6.6 | 0.3 |
| 2018 | 4.7 | 3.3 | 4.3 | 7.6 | -1.6 |
| 2017 | 4.2 | 3.9 | 6.1 | 3.7 | 0.0 |
| 2016 | 4.4 | 3.1 | 5.1 | 5.6 | 1.0 |
| 2015 | 4.4 | 2.2 | 7.0 | 5.0 | 0.4 |
| 2014 | 3.8 | 1.2 | 6.8 | 5.4 | -2.5 |
| 2013 | 4.2 | 2.3 | 4.9 | 6.7 | -0.2 |
| 2012 | 4.7 | 0.5 | 5.7 | 10.1 | -0.3 |
| 2011 | 3.7 | 0.1 | 2.5 | 10.8 | -1.0 |
| 2010 | 4.5 | -0.4 | -0.7 | 18.5 | 3.0 |
| 2009 | 3.7 | 0.5 | -3.9 | 20.4 | 4.7 |
| 2008 | 5.7 | 0.0 | 5.7 | 21.4 | 1.4 |
| 2007 | 8.1 | 7.1 | 12.5 | 4.7 | 6.2 |
| 2006 | 8.4 | 10.5 | 9.8 | 3.9 | 4.4 |
Note: Quarterly data for IQ2016 and earlier and annual data for 2007 and earlier are from prior reports.
Net Worth = Assets – Liabilities. R∆% real percentage change or adjusted for CPI percentage change.
Notes: Deposits: Total Time and Savings Deposits FL15303005; Net Worth = Assets – Liabilities
Source: Board of Governors of the Federal Reserve System. 2021. Flow of funds, balance sheets and integrated macroeconomic accounts: first quarter 2021. Washington, DC, Federal Reserve System, Jun 10. https://www.federalreserve.gov/releases/z1/current/default.htm
Table IIA-7 provides wealth of US households and nonprofit organizations since 2005 in billions of current dollars at the end of period, NSA. Wealth fell from $70,232 billion in 2007 to $62,496 billion in 2009 or 11.0 percent and to $66,855 billion in 2011 or 4.8 percent. Wealth increased 94.9 percent from 2007 to IQ2021, increasing 54.6 percent after adjustment for inflation, primarily because of bloating financial assets while nonfinancial assets declined/stagnated cyclically in real terms. Net worth of households and nonprofit organizations fell 3.4 percent from 109,016.2 billion in IIIQ2018 to 105,350.0 billion in IVQ2018 or $3,666.2 billion. Financial assets decreased 4.5 percent from 87,233.8 billion in IIIQ2018 to 83,318.4 billion in IVQ2018 or $3915.4 billion. Corporate equities fell 13.9 percent from $18,929.9 billion in IIIQ2018 to $16,301.7 billion in IVQ2018 or $2,628.2 billion. These are the revised data in the report of Jun 10, 2021, for IQ2021. Net worth increased 68.4 percent from IVQ2007 to IVQ2019 or 37.7 percent adjusting for inflation, 58.8 percent from IVQ2007 to IQ2020 or 29.2 percent adjusting for inflation, 70.4 percent from IVQ2007 to IIQ2020 or 38.8 percent adjusting for inflation, 75.6 percent from IVQ2007 to IIIQ2020 or 41.7 percent adjusting for inflation and 85.7 percent from IVQ2007 to IVQ2020 or 49.8 percent adjusting for inflation. Net worth decreased by $6,523.0 billion from IVQ2019 to IQ2020 or by 5.5 percent in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event. Net worth increased $8,271.2 billion from IQ2020 to IIQ2020 or 7.4 percent. Net worth increased 1.5 percent from IVQ2019 to IIQ2020. Net worth increased $5,978.4 billion from IVQ2019 to IIIQ2020 or 5.1 percent. Net worth increased $14,042.1billion from IVQ2019 to IVQ2020 or 11.9 percent.Net worth increased $19,038.9 billion from IVQ2019 to IQ2021 or 16.2 percent and 12.7 adjusting for inflation. Real estate increased $3,994.5 billion from IVQ2019 to IQ2021 or 11.9 percent. Financial assets increased $15,226.1 billion from IVQ2019 to IQ2021 or 16.1 percent. Stock markets recovered in Apr 2020 to Mar 2021. Corporate equities increased $7,424.2 billion from IVQ2019 to IQ2021 or 35.6 percent.
Table IIA-7, US, Net Worth of Households and Nonprofit Organizations, Billions of Dollars, Amounts Outstanding at End of Period, NSA
| Quarter | Net Worth |
| IQ2021 | 136,917 |
| IVQ2020 | 131,920 |
| IIIQ2020 | 123,857 |
| IIQ2020 | 119,626 |
| IQ2020 | 111,355 |
| IVQ2019 | 117,878 |
| IIIQ2019 | 114,304 |
| IIQ2019 | 113,446 |
| IQ2019 | 111,132 |
| IVQ2018 | 105,350 |
| IIIQ2018 | 109,016 |
| IIQ2018 | 107,123 |
| IQ2018 | 105,620 |
| IVQ2017 | 104,554 |
| IIIQ2017 | 101,896 |
| IIQ2017 | 99,987 |
| IQ2017 | 98,265 |
| IVQ2016 | 95,799 |
| IIIQ2016 | 95,106 |
| IIQ2016 | 93,286 |
| IQ2016 | 91,846 |
| IVQ2015 | 90,667 |
| IIIQ2015 | 89,111 |
| IIQ2015 | 90,103 |
| IQ2015 | 89,557 |
| IVQ2014 | 87,699 |
| IIIQ2014 | 85,817 |
| IIQ2014 | 85,213 |
| IQ2014 | 83,296 |
| IVQ2013 | 81,605 |
| IIIQ2013 | 79,535 |
| IIQ2013 | 76,965 |
| IQ2013 | 75,817 |
| IVQ2012 | 72,925 |
| 2020 | 131,920 |
| 2019 | 117,878 |
| 2018 | 105,350 |
| 2017 | 104,553 |
| 2016 | 95,799 |
| 2015 | 90,667 |
| 2014 | 87,699 |
| 2013 | 81,605 |
| 2012 | 72,925 |
| 2011 | 67,900 |
| 2010 | 66,855 |
| 2009 | 62,496 |
| 2008 | 61,510 |
| 2007 | 70,232 |
| 2006 | 68,375 |
| 2005 | 63,683 |
Net Worth = Assets – Liabilities. R∆% real percentage change or adjusted for CPI percentage change.
Notes: Deposits: Total Time and Savings Deposits FL15303005; Net Worth = Assets – Liabilities
Source: Board of Governors of the Federal Reserve System. 2021. Flow of funds, balance sheets and integrated macroeconomic accounts: first quarter 2021. Washington, DC, Federal Reserve System, Jun 10. https://www.federalreserve.gov/releases/z1/current/default.htm
© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021.
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