Housing Market and Cycles
Friday, Jan. 3, 2020 8:00 AM - 10:00 AM (PDT)
- Chair: Sophia Gilbukh, City University of New York-Baruch College
Procyclical Price-Rent Ratios: Theory and Implications
AbstractProcyclical changes in price-rent ratios lead procyclical changes in housing prices, expected appreciation, and speculation. Procyclical volatilities of prices and price-rent ratios differ across categories of housing. Speculation during expansions is necessary for equilibrium. These properties and others are predicted by a simple, two-state switching model and its easy extensions. In the model the housing market cycles randomly between expansions and contractions. Housing prices are forward-looking, while short-term rents are not. Risk-neutral investors have no behavioral biases, capital constraints, private information or trading frictions.
‘Memory’ in the Middle: Housing Price - Macroeconomic Interactions in the United States
AbstractMacroeconomic variations and housing price fluctuations are tightly interlinked. In this paper,
we study the impact of (system) long-memory on a model of dynamic interactions between a
housing market and a macroeconomy. We characterize housing price equilibrium via identification
and quantification of distinct demand and supply responses to changes in macroeconomic
conditions. We argue that the actual disequilibrium error corrections in the interactive
system are slow and nonlinear inducing an undesirable interplay of many economy-wide
shocks so many so that the expected dynamic stability of the system becomes a difficult objective
to achieve. To resolve this issue, using a quarterly data (1975Q1-2016Q1) for the US,
our fractionally cointegrated vector autoregressive estimations demonstrate that the housing
market adjusts gradually towards the market clearing, while shocks in the system are featured
with a long-memory, further indicating informational inefficiency in the housing market. We
quantify memory-driven impacts of macroeconomic variables, and find that the impacts can
be transmitted not only through either the housing demand or supply channel exclusively, but
also through both the channels simultaneously. Overall impacts of macroeconomic variables
are eventually derived by aggregating their possible impacts from both the channels. We conclude
that a failure to identify the distinct demand and supply effect-transmission channels
could result in an estimation bias of macroeconomic effects; disregarding the memory pattern
of shocks in the system further leads to a mis-representation of macroeconomic policy effectiveness
in an environment with persistent policy uncertainty. A forecasting exercise confirms
the predictive power of the FCVAR model, and robustness checks support our baseline results.
Is the Behavior of Sellers with Expected Gains and Losses Relevant to Cycles in House Prices?
AbstractWe examine anchoring to the price paid at purchase on each phase of an important cycle in the Connecticut housing market, 2000-2017, using a repeat sales model that allows the negotiated prices of sellers with expected gains to differ from those with expected losses: expected market values are based on a standard hedonic model and a new simulation model for unobserved quality. We exploit differences between actual and counterfactual house price indices by analyzing negotiated premiums and discounts multiplied by the magnitudes of those quantities and by the proportion of sales with expected losses and gains: all variables are adjusted for unobserved quality. Results suggest that anchoring was associated with large increases in the observed change in house prices during the boom (2004-2006) as sellers with gains reduced their discounts. Counterfactuals suggest that anchoring reduced the amount of decline during the bust (2007-2012) when the behavior of those with losses dominate; losses bargained for substantial premiums per dollar loss. Our models of second sales price formation, together with univariate and bivariate analysis, support search and bargaining models of sellers who are responding to expected losses and gains. This extends the anchoring literature which has focused on individual behavior, and it supports new stylized facts associated with housing market cycles.
Federal Reserve Bank of Boston
Federal Reserve Bank of Cleveland
Federal Reserve Bank of Philadelphia
- R3 - Real Estate Markets, Spatial Production Analysis, and Firm Location
- E3 - Prices, Business Fluctuations, and Cycles