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The Annals of Regional Science

Erschienen in:

01.10.2013 | Original Paper

Differences in quality of life estimates using rents and home values

verfasst von: John V. Winters

Erschienen in: The Annals of Regional Science | Ausgabe 2/2013

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Abstract

Quality of life differences across areas can be measured by differences in “real wages,” where real wages are computed as nominal wages adjusted for the cost of living. Computing cost of living differences involves several important issues, including how housing prices should be measured. Previous researchers typically have used some combination of rental payments and homeowner housing values, but housing values are forward-looking and may not reflect current user costs. This paper examines differences in quality of life estimates for US metropolitan areas using, alternatively, rents and housing values. We find that the two measures of quality of life are highly correlated. Value-based estimates, however, are considerably more dispersed than rent-based estimates, likely because of the recent housing bubble and because housing values often provide an imperfect measure of the present user cost of housing.

Vorheriger Artikel Understanding the impact of trails on residential property values in the presence of spatial dependence

Nächster Artikel Core–periphery dynamics in the Portland, Oregon, region: 1982–2006

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Gabriel and Rosenthal (2004) also develop a method to rank the quality of the business environment across areas.

Alternatively, Gyourko and Tracy (1991) treat non-housing prices as an amenity in wage and housing hedonic regressions.

A few previous studies have also measured housing prices using either only rents or only housing values instead of a combination of the two, but no previous study has examined differences in quality of life rankings that result from measuring housing prices by only rents or only housing values.

Greenwood et al. (1991) find that although a few areas may be out of equilibrium at a given point in time, spatial equilibrium is not an unreasonable assumption for the most part. Oswald and Wu (2010, 2011), however, find that subjective responses about individual well-being differ across the United States even after controlling for individual characteristics. This may reflect heterogeneous preferences for locations (Krupka and Donaldson 2013) or decreased responsiveness to area-specific demand shocks

(Partridge et al. 2012). This could affect our ability to accurately estimate implicit amenity prices and rank quality of life across areas.

Alternatively, we could have defined the expenditure function and used Shephard’s Lemma to obtain an equivalent result as in Albouy (2008).

For non-workers, the implicit price to live in a high quality of life area depends only on the cost of living and not on wages. Thus, we would expect retirees and other non-workers to be attracted to areas where amenity values are capitalized more into wages than prices (Chen and Rosenthal 2008).

See Gyourko et al. (1999) for a review of the literature on quality of life and amenity valuation. Stover and Leven (1992) also discuss a number of important issues related to estimating quality of life.

One potentially important limitation is that high skilled workers may sort into high wage or high quality of life areas, which would bias the estimates. We include detailed controls for observable worker characteristics, but some worker characteristics (e.g., innate ability) remain unobservable.

Gabriel and Rosenthal (2004) and Chen and Rosenthal (2008) follow a related approach by computing quality of life estimates in levels (as opposed to logs) by subtracting the local wage premium from the local housing cost premium. Measuring QOL in levels requires assumptions about household labor supply and housing consumption decisions based on sample means. Their approach does not account for non-housing prices, and doing so would require some assumption about non-housing consumption presumably based on income or housing consumption.

A few small CBSAs are not identified and are not included in this study.

Pretax hourly wages \(({w}_{ij} )\) are estimated by dividing annual wage income by the number of weeks worked times the usual hours worked per week. Federal income taxes are estimated using the federal tax schedule and based on several assumptions. We assume that all married couples file jointly and receive two personal exemptions, and non-married persons have a filing status of single and receive one personal exemption. Itemized deductions are assumed to equal 20 % of annual income, but taxpayers take the standard deduction if it is more than their itemized deductions. Deductions and exemptions are subtracted from annual earnings to estimate taxable income. Tax schedules are then used to compute federal tax liabilities. We next compute the average tax rate for each taxpayer \(({\tau _{ij}})\) and then multiply the pretax hourly wage by one minus the average tax rate to compute after-tax hourly wages. \(({W_{ij}})=w_{ij} (1-\tau _{ij})\).

Social security contributions could be easily estimated, but estimating social security benefits is much more difficult. Adjusting wages for state and local income taxes would also require accounting for other taxes and the benefits from public spending that these taxes make possible.

Rents are measured to include certain utilities but exclude a portion of rents attributable to property tax payments based on the effective tax rates of owner-occupied housing. Removing property taxes from rents is based on the assumption that higher property taxes are offset by lowering other state and local taxes (e.g., income, sales, etc.). If this assumption holds, then including property taxes in rents to construct quality of life estimates would cause areas that heavily rely on property taxes to have higher QOL values than they should. As a practical matter, excluding property taxes has only a small effect on QOL estimates for most areas.

Note that this expenditure share for housing differs from official reports of the CES expenditure share for both “Housing” and “Shelter.” The housing share based on gross rents used herein includes certain utilities but excludes others and also excludes expenditures for household operations, housekeeping, and household furnishings. The housing share of 0.29 also differs from the official CES tabulations in that homeowner housing expenditures are measured by implicit rents and not by out-of-pocket expenses such as mortgage interest.

An elasticity of substitution less than (greater than) one would imply a higher (lower) expenditure share for housing in areas with relatively high housing prices.

A related problem is that prices are measured at the sub-metropolitan level, but data are not generally available for all areas within a metropolitan area. If sub-metropolitan areas for which prices are reported are not representative of places in the same metro for which prices are not reported, the average price level for the metro area will be measured with error.

We also examined how the quality of life measures would vary if we assumed that non-housing prices were constant across metro areas. In general, the results are a bit less dispersed but still highly though imperfectly correlated with the measures that do include non-housing prices. For example, the correlation

between our baseline rent-based quality of life measure and the corresponding measure assuming equal non-housing prices was 0.85 for 2000 and 0.74 for 2007. The results are highly correlated because places with high non-housing prices also tend to have high housing prices. However, completely ignoring non-housing prices creates a bias toward less expensive areas.

State College is home to Pennsylvania State University, and Morgantown is the home of the University of West Virginia. Winters (2011b) also shows that college towns are growing faster than other metropolitan areas and suggests that it is because recent student in-migrants often develop friendships, relationships with local employers, and a taste for local amenities and decide to stay in the area after their education is complete.

Winters (2011a) also finds mixed results for some other common (dis)amenities such as crime, pollution, commute times, population density, and city size.

The correlation for the estimates themselves is also very high at 0.752.

We are not aware of any other rankings for 2007, so we only compare rankings for 2000.

Lee and You (2011) also document that the value-income ratio is higher in high quality of life areas, but they do not focus on changes over time.

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Titel: Differences in quality of life estimates using rents and home values
verfasst von: John V. Winters
Publikationsdatum: 01.10.2013
Verlag: Springer Berlin Heidelberg
Erschienen in: The Annals of Regional Science / Ausgabe 2/2013
Print ISSN: 0570-1864
Elektronische ISSN: 1432-0592
DOI: https://doi.org/10.1007/s00168-013-0551-7

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