For a household, home ownership provides necessary shelter, potential investment returns associated with property appreciation and a hedge against increased housing related cash outlays. In addition to potential appreciation, individual households benefit over time from a housing dividend defined as the difference between the market rent for the individual household’s housing unit and the household’s actual house ownership costs. The purchase of a house can substantially fix a household’s recurring housing related expenditures and generates a hedge (implied housing dividend) that increases with ownership tenure. This expenditure hedge (dividend) to home ownership is documented using pooled, cross-year samples from the Consumer Expenditure Survey (CEX). The housing dividend delivers a non-trivial effect on household non-housing expenditures after controlling for housing value, housing equity, financial assets and income.
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We do not want to get caught up in semantics. There is a difference between consumption and expenditures, especially at the household level and for durable goods and durable services. Consumption is a theoretical concept in economic models dated at least as early as in Friedman (1957), and expenditure is an empirical measure that is used to approximate consumption. At the macro-level the distinction may be less relevant given the specific question of interest. It is acknowledged that some consumption items may be purchased once upfront, but their produced utility streams will be spread over time. The literature of consumption studies using micro household data usually concentrates on expenditures on food or non-durable goods and non-durable services (see the superb summary and references listed in Deaton (1992), and more recently, Vissing-Jørgensen (2002), Malloy et al. (2009), Aguiar and Hurst (2013), Aguila et al. (2011), Attanasio et al. (2012), Blundell et al. (2012), among others). In these expenditure categories, consumption and expenditures are less likely to diverge drastically from each other. Whenever possible, we use “consumption” in a theoretical discussion setting, and “expenditure” in an empirically oriented context. We also highlight that at the household level we are modeling expenditures, or where the household obtains and spends its resources or cash flows.
Engelhardt (1996) and Skinner (1996) show that the consumption response to housing capital gains, especially positive ones, is rather small. A homeowner can benefit from rising housing wealth by pledging the house as collateral to borrow to fund consumption. By doing this, the consumer retains or increases access to cash and liquid assets that can be used to pay for expenditures. Cooper (2013) shows that this borrowing channel is significant, particularly for those households who might be liquidity constrained. Gan (2010) provides similar evidence for Hong Kong.
Research testing for housing market efficiency makes progress without a particular need for a clarified definition of the housing dividend. Cho (1996) surveys related empirical studies.
In contrast, Gan (2010) uses credit card spending to measure consumption, which presumably includes spending on a subset of both nondurable and durable goods and services, but excludes all others where a credit card is not used.
Beracha and Johnson (2012) provide an example of the constructs related to the buy versus rent literature which includes debt assumptions, cash flow assumptions and housing quality assumptions across rental and owner based markets.
Case et al. (2011) update their previous analysis by extending the sample to more recent years, and find that new results only reinforce their previous conclusions.
These studies also estimate elasticities of consumption to income and to financial wealth by including them as additional regressors. Results vary. The elasticity estimates of income are around 0.10 to 0.30. The elasticity estimates of financial wealth are in the range of 0.01 to 0.10. Except Cooper (2013), all of the cited studies above find a larger housing wealth elasticity than the financial wealth elasticity.
Finding different consumption propensities for dividends versus capital gains is potentially justified by the duration dependent net return structure generated by capital gains taxation (Balcer and Judd 1987; Dammon et al. 2001). Mental accounting is another behavioral finance explanation for investors’ preference for cash dividends (Shefrin and Statman 1984). Meanwhile, it is well-known that equity dividend incomes are concentrated in high-income households. These households’ consumption behavior likely differs from the average household, and, in particular, may be less sensitive to cash flow considerations (Poterba and Slemrod 2007). Hence, it is difficult to extrapolate Baker et al.’s (2007) estimated propensity to consume to the general population.
The essence of the model remains the same should income and more assets be allowed for, but the derivation will be considerably more cumbersome.
Due to the fact that the financial wealth information is only available in the fifth interview of CUs in CEX, we cannot exploit the quarterly panel nature of CEX.
1995:Q3-Q4 and 2004:Q3-Q4 observations are omitted due to a sample frame change that occurred in 1996 and 2005, which makes it impossible to track a small fraction of the consumer units for the full year. Observations from years prior to 1988 as well as 1991:Q2-Q4 and 1992 are omitted due to unavailability of detailed expenditure files on mortgage payments and house operational costs.
NBER CEX files (cleaned and streamlined by Ed Harris and John Sabelhaus) condensed hundreds of expense categories into a much smaller set of summary categories (the documentation can easily be accessed at http://www.nber.org/data/ces_cbo.html) and annualized the data. The BLS version of the CEX samples also includes some summary categories that may overlap with those in the NBER extract version, but they are presented on a quarterly basis. Whenever possible, we use what is already available in the NBER version; if a particular variable or spending category is opaque, missing, or limited in construction in the NBER version, we turn to the family (FMLY) or detailed expenditure files of BLS version. For example, the variable of owned housing principal reduction in the NBER version includes the reduction of mortgage principal on vacation properties, whereas the BLS version distinguishes between these two. For the post-2007 data for which NBER version is unavailable, we follow Harris and Sabelhaus’ procedure to create those summary-level expenditure variables to be consistent.
Which means, for instance, a homeowner who rents a property elsewhere to live in is discarded from the sample.
In general, goods are commodities that can be stored and perhaps used over time, and services are commodities that cannot be stored and have to be consumed at the time and location of purchase. For an item that has both a goods component and a services component, the classification is based on the predominant component (e.g., spending on alcoholic beverages consumed away from home is classified as services). Durable goods have an average useful life of at least 3 years, including motor vehicles and parts, furnishings and durable household equipment, recreational goods and vehicles, and other durable goods. Nondurable goods have an average useful life of less than 3 years, including food and beverages purchased for off-premises consumption, clothing and footwear, gasoline and other energy goods.
In the 2009 revision of the NIPA Personal Consumption Expenditures classification system, purchased meals and beverages at food services and drinking places are counted as services (Bureau of Economic Analysis 2012), thus spending at restaurants is now counted as service expenditures, in contrast to as non-durable goods as before. However, the food expenditure used in this paper covers both food purchased for home production as well as consumed at restaurants, and therefore is a mix of components from non-durable goods and services categories.
This variable is to be distinguished from another variable — special or lump sum mortgage payments, which may or may not be related with refinancing or property transactions. As a robustness check, we exclude all observations that report non-zero values for lump sum mortgage payments, or non-zero values in purchase or selling prices of properties, and find virtually no difference in results.
Francois (1989) lists a number of reasons why a hedonic model using market rents would underestimate the true underlying rents.
Heston and Nakamura (2009) note the possibility that homeowners might place above market values when assessing equivalent rents if units have special features.
It is possible that the noise in equivalent rents is non-random, correlated with household characteristics or even expenditures in one way or the other. Explicit evidence on this, especially for the CEX self-reported rent data, is elusive. If the literature on the quality of homeowners’ self-assessed house value is of any guidance, no consensus has been attained. Goodman and Ittner (1992) and the earlier studies cited therein, and more recently, Kiel and Zabel (1999) and Agarwal (2007), find homeowners mostly overestimated their house value by several percentage points relative to an outside benchmark. Ihlanfeldt and Martinez-Vazquez (1986) find the discrepancy between homeowners’ self-assessed house value and appraisal value is related to owners’ race and age. Agarwal (2007) finds that whether a homeowner is an overestimator or an underestimator depends on a host of household socioeconomic characteristics. In contrast, Kain and Quigley (1972), Goodman and Ittner (1992) and Kiel and Zabel (1999) find the errors in these estimates are not statistically significantly related to characteristics of the owners. The discrepancy in the findings of studies cited above may well rest on the different data sources these authors use. Regardless, owners’ assessed house value is heavily employed in empirical work. In theory, how a consumer plans his or her consumption depends justifiably on the perception of wealth and income level he or she has given the information set at that point of time. Agarwal (2007) provides evidence supporting this view.
In addition, the limitations of CEX data on housing characteristics variables prevent us from taking a cross-hedonic estimation to obtain predicted equivalent rents for these households, as done in Goodman (1988). To assess the impact of over-estimating rents, we discount each household’s reported rent value by as large as 10 % and still find the housing dividend effect is comparable to the home value wealth effect in terms of MPCs.
This probably reflects the top-coding of some of the variables in CEX. In Table 7, we provide the estimates from the median regressions, which are less likely to be affected by top-coding, to show that they are similar to those yielded by linear regressions.
These estimates are comparable to other studies using different sources of micro data and different estimation schemes. For example, by exploring a large panel data set of Hong Kong households with various econometric specifications, Gan (2010) estimates a housing wealth effect of about 0.10-0.19 measured by the elasticity of consumption.
For aggregate data, within the representative agent framework, the coefficients from the regression of the log of consumption on the log of wealth components may be interpreted as wealth shares, given the co-integration relationship between these variables arising from the budget constraint (Ludvigson 2007).
The estimated elasticities for the conventional wealth components are generally similar to those in the literature (see the referred studies in Section 2). For example, when we look at the total expenditure, the estimates are in line with existing studies: the home value elasticity is 0.0962 with a MPC of 0.02, the financial wealth elasticity is 0.0178 with a MPC of 0.09 and the log of after-tax income coefficient is 0.2484 with a MPC of 0.12. All are statistically significant at the 0.01 level.
Recall that service expenditure includes food at restaurants. See Section 4 for related discussion.
We also note that the goodness of fit for the regression of durable expenditure is substantially lower than other expenditures.
In addition, to address the concerns of top-coding for wealth and income variables in the CEX (Poterba and Slemrod 2007), we conduct median regressions with the same set of dependent and independent variables and obtain similar results.
We provide only the description and do not investigate causal relations related to mortgage lending changes and price appreciation.
Existing literature highlights a mismatch in the house price-to-income ratio and price-to-rent ratio for the boom period. For example, see Beracha and Johnson (2012).
While full investigations are beyond the scope of this paper, the results suggest that a differential between housing-service cost and rent could be a factor in mortgage default. A household might continue to service debt even with little or no equity if rental options for similar properties are priced such that the cost of the rental option (inclusive of moving costs) from a cash flow standpoint is equal or higher.
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The authors would like to acknowledge Thesia Garner, Robert McClelland, Jonathan Skinner, participants at the 2014 Consumer Expenditure Survey Microdata Users’ Workshop at the Bureau of Labor Statistics, seminar participants at the Congressional Budget Office, and session participants at the 2014 Global Chinese Real Estate Conference in Nanjing, China.
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Guo, S., Hardin, W.G. Financial and Housing Wealth, Expenditures and the Dividend to Ownership. J Real Estate Finan Econ 54, 58–96 (2017). https://doi.org/10.1007/s11146-015-9540-1
- Imputed rent
- Wealth effect