A retail price index including the shadow price of owner occupied housing

How do house price changes affect the cost of living? The retail price index in the UK does not directly incorporate house price changes. In- stead it uses mortgage interest to capture the cost of owning a home. This is a useful method from many perspectives. However, from a con- sumer welfare perspective, while mortgage interest does capture the cost of a particular service, it does not capture the cost of housing services. The shadow price of housing captures the welfare cost to a household of changes in housing prices. In this paper we create a new shadow price index using RPI data and the shadow price of housing and investigate how replacing the mortgage interest with the shadow price of housing affects measures of the cost of living

Dynamic housing expenditures and household welfare

In this paper we develop a measure of current "expenditures" on housing services for owner-occupiers. Having such a measure is important for measuring the relative welfare of households, especially when comparing renters and owners and for measuring inflation. From a theoretical perspective expenditures equal the "shadow price" of housing services (the marginal rate of substitution between housing services and non-durable consumption) multiplied by the quantity of housing services consumed. In an idealised world, two simple measures of the shadow price are available; the user cost of housing capital and the rental price of an equivalent rental house. However, imperfect capital markets, risk aversion, the tax system, moving costs and systematic differences between houses available in the rental and owner-occupied sectors drive a wedge between the shadow price of housing and these other two measures. This paper contributes to previous research by calibrating a lifecycle model of housing investment and consumption to data from the UK Family Expenditure Survey and by developing measures of the shadow price of housing that take into account uncertainty in house prices, interest rates and incomes, dynamic life cycle choices, and liquidity constraints that depend on both income and house value

Identification and estimation of hedonic models

This paper considers the identification and estimation of hedonic models. We establish that in an additive version of the hedonic model, technology and preferences are generically nonparametrically identified from data on demand and supply in a single hedonic market. The empirical literature that claims that hedonic models estimated on data from a single market are fundamentally underidentified is based on arbitrary linearizations that do not use all the information in the model. The exact economic model that justifies linear approximations is unappealing. Nonlinearities are generic features of equilibrium in hedonic models and a fundamental and economically motivated source of identification

Hedonic Pricing Functions

Hedonic price functions describe the equilibrium relationships between characteristics of products and their prices. They are used to predict prices of new goods, to adjust for quality change in price indexes, and to measure consumer and producer valuations of differentiated products. They emerge as market outcomes from both competitive and non-competitive markets. The functional form is determined by the distribution of buyers and their preferences, the distribution of sellers and their costs, and the structure of competition in the market

Using penalized likelihood to select parameters in a random coefficients multinomial logit model

This paper is about estimating a random coefficients logit model in which the distribution of each coefficient is characterized by finitely many parameters, some of which may be zero. The paper gives conditions under which, with probability approaching 1 as the sample size increases, penalized maximum likelihood (PML) estimation with the adaptive LASSO (AL) penalty distinguishes correctly between zero and non-zero parameters. The paper also gives conditions under which PML reduces the asymptotic mean-square estimation error of any continuously differentiable function of the model’s parameters. The paper describes a method for computing PML estimates and presents the results of Monte Carlo experiments that illustrate their performance. It also presents the results of PML estimation of a random coefficients logit model of choice among brands of butter and margarine in the British groceries market

Sparse demand systems: corners and complements

We propose a demand model where consumers simultaneously choose a few different goods from a large menu of available goods, and choose how much to consume of each good. The model nests multinomial discrete choice and continuous demand systems as special cases. Goods can be substitutes or complements. Random coefficients are employed to capture the wide variation in the composition of consumption baskets. Non-negativity constraints produce corners that account for different consumers purchasing different numbers of types of goods. We show semiparametric identification of the model. We apply the model to the demand for fruit in the United Kingdom. We estimate the model’s parameters using UK scanner data for 2008 from the Kantar World Panel. Using our parameter estimates, we estimate a matrix of demand elasticities for 27 categories of fruit and analyze a range of tax and policy change scenarios

The Housing Stock, Housing Prices, and User Costs: The Roles of Location, Structure and Unobserved Quality

Which housing characteristics are important for understanding homeownership rates? How are housing characteristics priced in the rental and owner-occupied markets? And what can the answers to the previous questions tell us about economic theories of homeownership? Using the English Housing Survey, we estimate a selection model of the allocation of properties to the owner-occupied and rental sectors. Structural characteristics and unobserved quality are important for selection. Location is not. Accounting for selection is important for estimates of rent-to-price ratios and can explain some puzzling correlations between rent-to-price ratios and homeownership rates. These patterns are consistent with, among others, hypotheses of contracting frictions in the rental market likely related to housing maintenance