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

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 1.There are several reasons to suspect that the linkage between housing wealth and consumption may have increased in the 2000s relative to previous decades. 2.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. 3.The results appear to be largely driven by populations that are traditionally considered credit constrained. 4.These results could have potentially important implications for the outlook of the U.S. economy.

Outline 1. Motivation 2. 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 3. Data Two regional-level panel datasets One individual-level dataset 4. 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’d 5.Implications 6.Future work

1. Motivation

One way to extract equity

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 ….. A.Relaxation of credit constraints on existing homeowners Increased the share of equity that could be withdrawn Increased LTVs on new purchases Increased LTVs on refis May have allowed a small fraction of households to extract very large proportions of housing equity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 reasons ….. B. Change in the composition of households

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

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

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. Data Regional datasets 1.New motor vehicle retail sales in over 180 U.S. markets (DMAs) from 1989q1 to 2007Q3 2.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. 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. Data Time-Series Variance Across DMAs for Key Variables

3. Data Time-Series Variance Across CA MSAs for Key Variables

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

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 Results

4. Empirical Results: Taxable Sales

4. Empirical Results: Motor Vehicle Sales

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 Sales

4. Empirical Results: Motor Vehicle Sales

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

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: SIPP

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

5. Implications

6. Future work Forecast errors Symmetry –Extending our datasets Identification PSID Labor supply and wealth shocks

1. Motivation

3. Data Designated Market Areas (DMAs)