We draw a distinction between the concepts of purchase affordability (whether a household is able to borrow enough funds to purchase a house) and repayment affordability (the burden imposed on a household of repaying the mortgage). We operationalize this distinction in the context of a new methodology for constructing affordability measures that draws on the value-at-risk concept and takes account of the whole distribution of household income and house prices rather than just the median. Empirically we find that the distinction between purchase and repayment affordability can be pronounced.In the Sydney prime mortgage market over the period 1996 to 2006, repayment affordability deteriorated very significantly while purchase affordability remained quite stable. This difference can be attributed to the loosening of credit constraints in the mortgage market which it seems has carried through primarily into higher house prices.We also consider how median house-price-to-income ratio measures of affordability can be extended to take account of the whole distribution of income and house prices. We propose a new quantile based measure which indicates that the housing affordability problem may be systematically worse than suggested by standard median measures.(JEL. C43, E25, E64, R31)

SUMMARYWe construct a copula from the skew t distribution of Sahu et al. (2003). This copula can capture asymmetric and extreme dependence between variables, and is one of the few copulas that can do so and still be used in high dimensions effectively. However, it is difficult to estimate the copula model by maximum likelihood when the multivariate dimension is high, or when some or all of the marginal distributions are discrete-valued, or when the parameters in the marginal distributions and copula are estimated jointly. We therefore propose a Bayesian approach that overcomes all these problems. The computations are undertaken using a Markov chain Monte Carlo simulation method which exploits the conditionally Gaussian representation of the skew t distribution. We employ the approach in two contemporary econometric studies. The first is the modelling of regional spot prices in the Australian electricity market. Here, we observe complex non-Gaussian margins and nonlinear inter-regional dependence. Accurate characterization of this dependence is important for the study of market integration and risk management purposes. The second is the modelling of ordinal exposure measures for 15 major websites. Dependence between websites is important when measuring the impact of multi-site advertising campaigns. In both cases the skew t copula substantially outperforms symmetric elliptical copula alternatives, demonstrating that the skew t copula is a powerful modelling tool when coupled with Bayesian inference.

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We construct a copula from the skew t distribution of Sahu, Dey & Branco (2003). This copula can capture asymmetric and extreme dependence between variables, and is one of the few copulas that can do so and still be used in high dimensions effectively. However, it is difficult to estimate the copula model by maximum likelihood when the multivariate dimension is high, or when some or all of the marginal distributions are discrete-valued, or when the parameters in the marginal distributions and copula are estimated jointly. We therefore propose a Bayesian approach that overcomes all these problems. The computations are undertaken using a Markov chain Monte Carlo simulation method which exploits the conditionally Gaussian representation of the skew t distribution. We employ the approach in two contemporary econometric studies. The first is the modeling of regional spot prices in the Australian electricity market. Here, we observe complex non-Gaussian margins and nonlinear interregional dependence. Accurate characterization of this dependence is important for the study of market integration and risk management purposes. The second is the modeling of ordinal exposure measures for 15 major websites. Dependence between websites is important when measuring the impact of multi-site advertising campaigns. In both cases the skew t copula substantially out-performs symmetric elliptical copula alternatives, demonstrating that the skew t copula is a powerful modeling tool when coupled with Bayesian inference.

This article examines the optimal selling mechanism problem in real estate market using mean‐variance analysis and downside risk analysis. When sellers can choose between accepting the first offer above a reservation price or auctions (waiting an optimal and fixed time), sellers having higher risk aversion choose auctions and wait a fixed time while sellers having lower risk aversion choose an optimal reservation price and wait a random time. Positive auction discounts are compensated by reduced risks, and there exists a connection between liquidity risk and conditional auction discount. More (Fewer) sellers will choose to sell their houses through auctions in a hot (cold) market or when holding cost increases (decreases). When sellers choose auctions, sellers having higher risk aversion who have lower holding cost wait longer and obtain higher sale price. Loss‐averse sellers unanimously choose the mechanism of setting an optimal reservation price.

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