Long-Run Equilibrium Shift and Short-Run Dynamics of U.S. Home Price Tiers during the Housing Bubble

We use vector error correction models to examine the interdependence between the high and the low price tiers during the latest housing market boom and bust. For 118 of the 364 US statistical areas analyzed, the tiered price indexes are bound by a long-run relationship. In general, low tier homes appreciated more than high tier homes in the past two decades. In contrast to previous periods of high volatility, however, low tier homes appreciated more during the boom and lost more value during the bust of the market. We find a shift in the long-run equilibrium during the bubble —the cointegration parameter that ties the tiers together is greater in absolute value during the bubble period compared to the periods of more moderate appreciation and depreciation rates. Moreover, the shift in the long-run equilibrium can be explained by differences in subprime originations across housing markets. We also find that short run price dynamics is driven by momentum in both segments of the market

Dynamic Oligopoly Pricing: Evidence from the Airline Industry

We explore how pricing dynamics in the European airline industry vary with the competitive environment. Our results highlight substantial variations in pricing dynamics that are consistent with a theory of intertemporal price discrimination. First, the rate at which prices increase towards the scheduled travel date is decreasing in competition, supporting the idea that competition restrains the ability of airlines to price-discriminate. Second, the sensitivity to competition is substantially increasing in the heterogeneity of the customer base, reflecting further that restraints on price discrimination are only relevant if there is initial scope for price discrimination. These patterns are quantitatively important, explaining about 83 percent of the total within-flight price dispersion, and explaining 17 percent of the observed cross-market variation of pricing dynamics

The Choice of Airport, Airline, and Departure Date and Time: Estimating the Demand for Flights

This paper estimates the demand for flights in an international air travel market using a unique dataset with detailed information not only on flight choices but also on contemporaneous prices and characteristics of all the alternative non-booked flights. The estimation strategy employs a simple discrete choice random utility model that we use to analyze how choices and its response to prices depend on the departing airport, the identity of the carrier, and the departure date and time. The results show that a 10\% increase in prices in a 100-seat aircraft throughout a 100-period selling season decreases quantity demanded by 7.7 seats. We also find that the quantity demanded is more responsive to prices for Delta and American, during morning and evening flights and that the response to prices changes significantly over different departure dates

A Time Series Test to Identify Housing Bubbles

In this paper we propose a new time series empirical test to identify housing bubble periods. Our test estimates the beginning and the burst of bubbles as structural breaks in the difference between the appreciation rates of the Case-Shiller price tiers. We identify the relevant periods by exploiting the common characteristic that lower-tier house prices tend to rise faster during the boom and fall more precipitously during the bust. We implement our test on 15 U.S. Metropolitan Statistical Areas during the most recent housing bubble

SEPARATING BETWEEN UNOBSERVED CONSUMER TYPES: EVIDENCE FROM AIRLINES

We propose an alternative approach to identify unobserved consumer types and assess whether firms price discriminate. Unlike other screening schemes that rely on quantity discounts or product differentiation, in our finite mixture structure individuals have unit demands and the product is homogeneous. We implement the model using an original U.S. airlines data set. The results support the existence of two demand types. The high-type “business” traveler is less price sensitive, has a higher valuation, and pays a higher price than the low type “tourist.” The proportion of high types also increases as the departure date nears. (JEL C23, L93, R41