Housing wealth and consumption did the linkage increase in the 2000s
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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.

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

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Housing wealth and consumption did the linkage increase in the 2000s

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


Thanks to

Thanks to,

  • Tack till Riksbanken

  • Martin who received a draft so late

  • Great research assistants


Usual caveat

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.


Summary

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.


Outline

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


Outline cont d

Outline, cont’d

  • 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.


Outline cont d1

Outline, cont’d

  • Implications

  • Future work


1 motivation

1. Motivation


1 motivation1

1. Motivation


1 motivation2

1. Motivation


1 motivation3

1. Motivation

One way to extract equity


2 possible reasons why the linkage between housing wealth and consumption may have increased

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


2 possible reasons

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


2 possible reasons1

2. Possible reasons …..

B. Change in the composition of households


2 possible reasons2

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


Figure 5 example of changes in future house price appreciation

Figure 5: Example of Changes in Future House Price Appreciation


2 possible reasons3

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


Figure 4 examples of home equity advertisements

Figure 4: Examples of Home Equity Advertisements


Figure 4 examples of home equity advertisements1

Figure 4: Examples of Home Equity Advertisements


3 data

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


3 data1

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


3 data2

3. Data


3 data3

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


3 data4

3. Data

Time-Series Variance Across DMAs for Key Variables


3 data5

3. Data

Time-Series Variance Across CA MSAs for Key Variables


4 empirical results

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


4 empirical results1

4. Empirical Results

Estimate a wide variety of models, we’ll 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


4 empirical results2

4. Empirical Results

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


4 empirical results3

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


4 empirical results4

4. Empirical Results


4 empirical results taxable sales

4. Empirical Results: Taxable Sales


4 empirical results taxable sales1

4. Empirical Results: Taxable Sales


4 empirical results motor vehicle sales

4. Empirical Results: Motor Vehicle Sales


4 empirical results motor vehicle sales1

4. Empirical Results: Motor Vehicle Sales


4 empirical results5

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


4 empirical results taxable sales2

4. Empirical Results: Taxable Sales


4 empirical results motor vehicle sales2

4. Empirical Results: Motor Vehicle Sales


4 empirical results6

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


4 empirical results levels motor vehicles

4. Empirical Results: Levels, Motor Vehicles


4 empirical results7

4. Empirical Results


4 empirical results sipp

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.


4 empirical results sipp1

4. Empirical Results: SIPP


4 empirical results sipp2

4. Empirical Results: SIPP


5 implications

5. Implications

  • Do these results help in forecasting

  • How much of a drag will the decline in house prices have on the economy


5 implications1

5. Implications


6 future work

6. Future work

  • Forecast errors

  • Symmetry

    • Extending our datasets

  • Identification

  • PSID

  • Labor supply and wealth shocks


1 motivation4

1. Motivation


1 motivation5

1. Motivation


3 data6

3. Data

Designated Market Areas (DMAs)


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