Presentation Transcript

1. Homeownership, Consumption and the 2006-2008 Housing Price CollapsePreliminary – July 14th 2009

   Jon Skinner (Dartmouth and NBER) Victor Stango (UC Davis) Jon Zinman (Dartmouth)
2. Changes in House Prices and the Collapse Source: Standard and Poor’s Press Release, May 26 2009
3. Monthly Per Capita Consumption, 2007-2008(seasonally adjusted; income price deflator)
4. Research Question How does a rapid and substantial decline in housing wealth affect high-frequency consumption? Up-to-date and rich transaction-level data covering checking, savingsand credit card accounts for a panel of several thousand individuals, 2007-2008 Our primary focus: relative changes in expenditure between homeowners and non-homeowners Roadmap: Data description and preliminary results first. Additional tests/theory later.
5. Our Data Administrative data containing (potentially complete) set of household deposit account and credit card transactions Compiled by market research firm Lightspeedtm Pays participants in other consumer panels $20 on average for allowing Lightspeed online access to account statements Panelists must register 2+ accounts Set of accounts is fixed at registration Data for 2007-2008 (soon, 2006 and 2009) Transaction characteristics: dollar amount, payment medium (check, debit card, elec.), “transaction description” revealing merchant, category of exp. Currently working on cleaning all of this up
6. Panelist/Sample Characteristics At registration, panelists provide demographic/financial information: Age, education, income, homeownership, financial market participation, etc. Compared to national averages: Younger, more-educated, higher-income, more female More creditworthy (conditional on age) Use internet for banking Not a balanced panel Both entry and attrition
7. Measuring Consumption Deposit account consumption: Debit card purchases, checks, fees paid, bills paid, automatic deductions Not counted: transfers from one account to another Mismeasured: savings that are not transfers, payments to credit cards Credit card consumption: purchase transactions Not counted: fees/interest paid “Consumption”=deposit consumption + credit card consumption – credit card repayment Some measurement issues, as with any data set Lots of advantages in HH finance relative to, e.g., the SCF
8. Subsample and Data For this Paper Aggregate data up to panelist-month Use a subsample of panelist-month observations Averageconsumption>$1000/month Accounts not “dying” in the data Observed account inflows (“income”) close to self-reported income at registration Discard anomalous changes in C (probably reflect scraping/coding errors) Homeownership status observed (at registration) Yields roughly 25,000 panelist/months, 2007-2008 Roughly 3000 panelists
9. Empirical Model: Base Simple model estimating within-panelist changes in consumption spending, 2007-2008: Unit of observation: panelist (i) & month (t) Income control: ln[monthly income] Have used higher-order terms Dt : Month/year effects Fixed panelist effects fi Cluster se’s at panelist level
10. Main Results: Monthly effects
11. Empirical Model II: Homeownership Identical to base model, but allowing monthly changes to vary for homeowners and non-homeowners Hi= dummy variable for homeownership Ln[income] interacted with Hi Coefficients on the month/year shifts vary for homeowners and non-homeowners
12. Homeowner / Non-Homeowner Shifts by Month
13. Homeowner / Non-Homeowner: Relative Difference by Month
14. Homeowner / Non-Homeowner Difference by Month: Adjusted
15. Alternative Explanations for Consumption Drop Credit market contraction associated with housing collapse could lead to consumption decline (e.g., Aron and Muellbauer, 2006) A third factor caused both the decline in housing wealth and in consumption (e.g., Attanasio, et. al., 2005), such as stock market movements or changes in expected future income
16. Credit Limits For any panelist/month with one or more active credit cards (~80% of observations), we observe the total credit limit across all cards Can observe month-to-month (or even daily) variation in this piece of access to credit Probably correlated with changes in access to other credit But perhaps not proportionally First, examine relative change in ln[total credit line] for homeowners and non-homowners
17. Homeowner and Non-homeowner Credit Lines
18. Relative Changes in Credit Line
19. Rel. Consumption Shift Adjusted for Credit Lines
20. The Shift in Consumption for Homeowners Mechanical: mortgage-related expenditure? Relative shift is if anything bigger on small-ticket (<$500) items Can be much more precise about identifying mortgage payments in the future Equities Timing is difficult to disentangle We have some data on financial market participation, asset holdings – plan to exploit this, geographic variation in housing bubble/collapse
21. Other Cross-sectional Differences Can also ask whether homeowner vs. non-homeowner effect operates differently in the cross-section Income Age Parsimonious and (overly) simple tests: Look at HO/non-HO trend parameter Preliminary results: Decline is greater for age 30-49 than 50+ (Attanasio, et al; Campbell and Cocco? (their “old” age 40+) Decline is greater for income >$40k than <$40k More data (2006, 2009) will help with this
22. From Empirics to Theory What theory might be consistent with our (and others’) empirical puzzles? Theoretical magnitude too small in standard life cycle model. A $1 increase in housing wealth affects consumption by (MPC x $1)/(1+r)n x (fraction of house downsized in year n) Empirical estimates typically larger (e.g., Carroll, Otsuka, & Slacalek)
23. A Precautionary Saving/Buffer Stock Model with Housing…. House is hedge against rental price hikes (Sinai and Souleles (2005) In practice, elderly households downsize only when they have to because of a health shock, loss of spouse, or income decline (Venti and Wise, 2005; Choi, 2001; Davidoff, 2009; Skinner, 1996). Middle-aged households unleashed housing wealth with home equity loans (Mian and Sufi, 2009), but just 8% of 65+ have a home equity loan (Gallup, 2007) – and elderly optimally eschew reverse mortgages (Davidoff, 2009) Implication of two-period precautionary saving model with impatient consumers: magnitude of wealth effects still large (housing wealth needed in “bad” state of the world) but MPC larger for young than elderly (Skinner, 1996)
24. Strong effects of housing price changes – appear to be distinct from influence of income and credit card line availability Stronger effects for middle-aged (30-50) than for older (50+) households Future work: add 2006, 2009 data (2009 when saving rates really begin to rise) and additional zip or local economic conditions. More work to disentangle changes in consumption by type/size of expenditure. Calibrate model Conclusions
25. Additional Slides
26. Preliminary Findings for Homeownership Appear to be persistent seasonal differences in spending across homeowners, non-homeowners Controlling for these, a simple linear trend in relative consumption spending fits the homeowner vs. non-homeowner difference over time reasonably well Over the period 1/07 to 12/08, the linear trend shows a decline in homeowner spending of roughly 7%, relative to non-homeowners
27. Driven by the Supply Side? For any panelist/month with one or more active credit cards (~80% of observations), we observe the total credit limit across all cards Can observe month-to-month (or even daily) variation in this piece of access to credit Probably correlated with changes in access to other credit But perhaps not proportionally First, examine relative change in ln[total credit line] for homeowners and non-homowners
28. Credit Limit Results Credit card lines fall for homeowners relative to non-homeowners Changes in credit card lines do not explain the homeowner vs. non-homeowner relative change in consumption Did lines of credit on homes change differently from lines of credit on credit cards? Need to push on this more in future work
29. Housing Prices The evolution of housing prices (certainty model)…. Period 2 housing prices in steady-state θ is the perceived long-term growth rate in housing – it shifted from a positive robust number 1980-2007 to something closer to zero in 2008. ρi price of housing services in period i Pi price of housing εi white noise error term
30. Research Questions How does a (rapid and substantial) decline in housing wealth affect high-frequency consumption? Up-to-date and rich transaction-level data covering deposit and credit accounts for a panel of several thousand individuals, 2007-2008 Our primary focus: simple difference between homeowners and non-homeowners Looking ahead: Other variation in cross-section (old/young, etc.) Disaggregation: what components of C change or do not? What theory could explain the patterns we see?
31. Previous Literature (Highly Selective!) Attanasio et al, 2007 Campbell and Cocco: Larger for “old” (Age 40+) than young; also see Case et al., 2005 Mian and Sufi, 2009: Housing appreciation for younger homeowners translates into home equity loans & consumption Liquidity constraints may play a role (Campbell and Cocco, 2007) Housing wealth affects consumption gradually (Carroll et al, 2009)