Since 1970, housing’s relative price, share of expenditure, and “unaffordability” have all grown. We estimate housing demand parameters using compensated and uncompensated frameworks over space and time, testing restrictions imposed by demand theory and household mobility. The data support the hypothesis that housing demand is both income and price inelastic, and that housing demand has exhibited a secular increase over time. We estimate an ideal cost-of-living index that demonstrates how the poor are impacted disproportionately in high-rent cities, and how rising rents amplified increases in real income inequality. Rising rents and inequality both help explain why housing has become less affordable.
Keywords: Housing demand, housing affordability, cost-of-living, inflation, non-homothetic preferences, consumer economics, income shares.
JEL Numbers: D12, E31, R21
1 Introduction
Food, clothing, and shelter are all considered to be basic needs for life, yet their consumption patterns have differed widely over the past several decades. In the United States since 1959, the fraction of Personal Consumption Expenditures (PCE) devoted to food and clothing fell from 27.4 percent to 10.6 percent, while expenditures on housing and utilities actually rose from 16.1 to 18.1 percent.1 In the American Housing Survey and the Consumer Expenditure Survey, the rise has been even more dramatic, about 7 percentage points since 1970 (see Figure 1B). The increase has been even sharper for renters, even while the homeownership rate has not risen appreciably (Figure 1C). The percentage of renting households facing “moderate” or “extreme” affordability burdens more than 30 or 50 percent of their income spent on housing has risen by 20 and 15 percentage points. These trends support the recent claim by the Secretary of Housing and Urban Development that, “We are in the midst of the worst rental affordability crisis that this country has known” (Olick 2013).2
The increasing share of expenditures on housing appears to contradict the view that housing is a necessity, as incomes have risen over time (see Figure 1D). One possible resolution to this apparent paradox may be that the price of housing (or shelter) services has risen 35 over percent relative to other goods since 1970 according to the Consumer Price Index (CPI). If demand is price inelastic, this rising price could have caused housing’s share to rise, even as incomes grew.
Figure 2 graphs these ideas using a production possibility frontier (PPF) and indifference curves, for housing and non-housing goods. It seems that the PPF has expanded further in the direction of non-housing goods, as many of these may be traded internationally and subject to greater technological improvements. With this expansion, both income effects (illustrated by the movement from point A to point B in the figure) and substitution effects (illustrated by the move- ment from point B to point C) lead households to increase their consumption of non-housing goods
1Food here is defined as “Food and beverages purchased for off-premises consumption,” while clothing corresponds
to “Clothing and footwear.”
2The Joint Center for Housing Studies of Harvard University (JCHS, 2013) documents that from 2000 to 2012, the median share of renters’ incomes devoted to contract rent rose nearly five percentage points to 27.4 percent, and that 28 percent of renting households now spend more than half of their incomes on rent.
more than of housing. The income effect causes housing’s share to fall (compare points B and D), but the rise in the relative price, determined by the slope of the PPF, causes housing’s share to rise if substitution response is limited (compare C and E).
Demand may have changed for other reasons. In particular, demographic changes have led to smaller families and households, also seen in Figure 1D. Housing consumption has a somewhat public, or non-congestible, component. Thus, having fewer members per household should raise demand as a fraction of consumption (Barten, 1964; Deaton and Paxson, 1998). Alternatively, tastes for housing may have simply grown, perhaps as a demand for privacy.
Below, we investigate housing demand using a novel, but intuitive framework. In section 3 demonstrate that cross-sectional data lends itself to estimating compensated (Hicksian) housing demand functions, as mobility equalizes the utility households receive from living in different locations. On the other hand, time-series data lends itself only to estimating uncompensated (Mar- shallian) demand. Unlike previous authors, we use data on non-housing prices to test restrictions imposed by demand theory, which serve as a check on the validity of our empirical methodology. Under such restrictions, we integrate a demand equation into non-homothetic utility and expendi- ture functions with a constant elasticity of substitution. These functions are useful to researchers interested in housing consumption behavior, or in how changes in cost-of-living affect welfare. Our measures improve on typical measures of “housing affordability” by separately accounting for income and substitution effects. The analysis also provides an unconventional examination of demand theory by using spatial variation, rather than more conventional temporal variation (e.g. Deaton 1986, Blundell et al. 1993).
CS 340 Milestone One Guidelines and Rubric Overview: For this assignment, you will implement the fundamental operations of create, read, update,
Retail Transaction Programming Project Project Requirements: Develop a program to emulate a purchase transaction at a retail store. This
7COM1028 Secure Systems Programming Referral Coursework: Secure
Create a GUI program that:Accepts the following from a user:Item NameItem QuantityItem PriceAllows the user to create a file to store the sales receip
CS 340 Final Project Guidelines and Rubric Overview The final project will encompass developing a web service using a software stack and impleme
