Housing Wealth and Consumption: Did the Linkage Increase in the 2000s

1
Housing Wealth and Consumption Did the Linkage
Increase in the 2000s?

• Mark Doms
• Federal Reserve Bank of San Francisco
• Wendy Dunn
• Board of Governors
• Daniel Vine
• Board of Governors
• Household Indebtedness, House Prices and the
  Economy,
• September 19-20, 2008
• Sveriges Riksbank

2
Thanks to,

• Tack till Riksbanken
• Martin who received a draft so late
• Great research assistants

3
Usual caveat

• The results presented here do not necessarily
  reflect the views of the Federal Reserve Bank of
  San Francisco or the Board of Governors of the
  Federal Reserve System.

4
Summary

• There are several reasons to suspect that the
  linkage between housing wealth and consumption
  may have increased in the 2000s relative to
  previous decades.
• Using 3 different datasets, 2 of which are new,
  and using equations similar to those used to
  forecast consumption, we find support for this
  idea.
• The results appear to be largely driven by
  populations that are traditionally considered
  credit constrained.
• These results could have potentially important
  implications for the outlook of the U.S. economy.

5
Outline

• Motivation
• Possible reasons why the linkage between
  housing wealth and consumption may have increased
• Relaxation of credit constraints
• On existing homeowners
• Change in the composition of homeowners
• Changes in attitudes/behaviors

6
Outline, contd

• Data
• Two regional-level panel datasets
• One individual-level dataset
• Estimates
• Estimate a large variety of models
• Test whether the linkage between consumption and
  house prices increased in the 2000s
• To the extent possible, which areas/people had
  the largest changes.

7
Outline, contd

• Implications
• Future work

8
1. Motivation
9
1. Motivation
10
1. Motivation
11
1. Motivation

• One way to extract equity

12
2. Possible reasons why the linkage between
housing wealth and consumption may have increased

• A. Relaxation of credit constraints on existing
  homeowners
• Reduction in costs of extracting equity
• As a result of large investments made in IT, the
  cost of extracting equity from homes has fallen
  significantly since the 1990s home equity
  lines of credit, refis, reverse mortgages

13
2. Possible reasons ..

• Relaxation of credit constraints on existing
  homeowners
• Increased the share of equity that could be
  withdrawn
• Increased LTVs on new purchases
• Increased LTVs on refis
• May have allowed a small fraction of households
  to extract very large proportions of housing
  equity

14
2. Possible reasons ..

• B. Change in the composition of households

15
2. Possible reasons ..

• C. Behavioral changes
• Consumers may have increased their expectations
  about the longer-run rate of return from housing
  in response to long, sustained increases in house
  prices, and hype

16
Figure 5 Example of Changes in Future House
Price Appreciation
17
2. Possible reasons ..

• C. Behavioral changes, continued
• During the 2000s, consumers may have learned
  about the relative virtues of home equity lines
  of credit
• Attitudes towards extracting equity may have
  changed
• Both of these could have been, in part, the
  result from a massive advertising campaign

18
Figure 4 Examples of Home Equity Advertisements
19
Figure 4 Examples of Home Equity Advertisements
20
3. Data

• Micro datasets with good measures of consumption
  are difficult to come by for the U.S.
• We develop 2 regional panel datasets with measure
  of consumption and the measures of other
  variables typically used in consumption models
• 1 individual-level dataset

21
3. Data

• Regional datasets
• New motor vehicle retail sales in over 180 U.S.
  markets (DMAs) from 1989q1 to 2007Q3
• Quarterly taxable sales in 28 California
  metropolitan statistical areas (MSAs) from 1990Q1
  to 2007Q1.
• We merge measures of personal income,
  unemployment rate, housing wealth, house prices,
  financial wealth, transfer income . into both
  datasets

22
3. Data
23
3. Data

• The second covers quarterly taxable sales in 28
  California metropolitan statistical areas (MSAs)
  from 1990Q1 to 2007Q1
• Construct other variables in the same way as for
  the motor vehicle/DMA dataset
• Not as many observations as the DMA dataset, but
  covers a larger portion of consumption

24
3. Data

• Time-Series Variance Across DMAs for Key
  Variables

25
3. Data

• Time-Series Variance Across CA MSAs for Key
  Variables

26
4. Empirical Results

• Identification
• Although there may be a bias, we do not believe
  that the bias would necessarily increase over
  time.
• Second, we do not believe that it would increase
  more for some segments of the population than
  others

27
4. Empirical Results

• Estimate a wide variety of models, well
  show two main classes with our datasets
• Growth rates on growth rates versus levels
  (error-correction)
• Split our sample by time, credit scores, to
  see, to some extent, how our results align with
  others
• How are variables measured

28
4. Empirical Results

• Growth rates on growth rates (a la Case, Quigley,
  and Shiller Gan Campbell and Cocco)

29
4. Empirical Results

• On a quarterly basis, most of variance in the log
  change in housing wealth arrives from changes in
  house prices.
• We examine unadjusted and adjusted changes in
  house prices

30
4. Empirical Results
31
4. Empirical Results Taxable Sales
32
4. Empirical Results Taxable Sales
33
4. Empirical Results Motor Vehicle Sales
34
4. Empirical Results Motor Vehicle Sales
35
4. Empirical Results

• For what groups?
• Split the sample in many ways
• Income
• Rapid/not rapid house price increases
• .
• Measures that might be related to credit
  constraints
• Denial rates
• Average credit scores

36
4. Empirical Results Taxable Sales
37
4. Empirical Results Motor Vehicle Sales
38
4. Empirical Results

• Levels (error correction model) (Davis and
  Palumbo, ABHL)
• Measures are in logs
• Stock-Watson procedure dynamic OLS
• DMA/MSA fixed effects
• Time effects--sometimes

39
4. Empirical Results Levels, Motor Vehicles
40
4. Empirical Results
41
4. Empirical Results SIPP
Survey of Income and Program Participation
(SIPP) complicated survey structure Did a
family buy a new car over the past year? Examine
only those families that did not move in
consecutive years. Control for existing car
stock, income, age, and log change in house
value. Results not as robust as in the other
datasets.
42
4. Empirical Results SIPP
43
4. Empirical Results SIPP
44
5. Implications

• Do these results help in forecasting
• How much of a drag will the decline in house
  prices have on the economy

45
5. Implications
46
6. Future work

• Forecast errors
• Symmetry
• Extending our datasets
• Identification
• PSID
• Labor supply and wealth shocks

47
(No Transcript)
48
1. Motivation
49
1. Motivation
50
3. Data
Designated Market Areas (DMAs)