Search

In this study we use a large database of real estate transactions to assess the magnitude of measurement error associated with using popular house price indices (HPIs) to value individual properties. In the 4 large U.S. counties that we analyze, we find that the bias associated with using these HPIs to value individual homes increased from near zero in 2005 to between 26% and 113% in 2010. In the second part of the analysis, we use data from Florida to demonstrate that forecast combination methods can be used to improve the accuracy of property-level valuations, in some cases reducing the estimated bias by more than a factor of 3. We find that even the simplest forecast combination method – a simple average – has the potential to significantly improve value estimates. [ABSTRACT FROM AUTHOR]

Although a broad-based increase in house prices has been observed over the past year, not everyone is convinced the rise of house prices will persist and lead to a steady recovery of the economy. The main reason for this skepticism is uncertainty about the 'shadow inventory': foreclosed homes held by investors or as REOs, which have not yet hit the market but likely will as market prices rise. The volume of shadow inventory itself in local markets is largely unknown, as is its impact on the housing market. This study quantifies the size of the shadow inventory and investigates the spatial impact of the out-flow of shadow inventory. The scope of our study is a set of housing markets (AZ, CA, and FL) that vary in both their historic housing price volatility as well as institutional factors - such as foreclosure law statutes - that may influence the relationship between the shadow inventory and house price dynamics. To address the endogeneity that characterizes the spatial interaction of house prices and the out-flow of the shadow inventory, we utilize a simultaneous equation system of spatial autoregressions (SESSAR). The model is estimated using measures of the shadow inventory derived from DataQuick's national transaction history database and county-level house price indices provided by Black Knight. Lastly, because our estimate - as well as all other existing estimates - of the shadow inventory relies upon string matching algorithms to identify entry into and exit out of REO status, we validate the accuracy of our measures of REOs using loss mitigation data from the OCC Mortgage Metrics database. [ABSTRACT FROM AUTHOR]

Abstract: Applying the rational expectations hypothesis, this essay models the current value of a house as the conditional expectation of the discounted stream of housing services accruing to the owner of the house. The value of housing services is determined by neighborhood effects as well as the physical attributes of the property itself. In the existing hedonic literature, future transactions have not been utilized to describe neighborhood effects. The rational expectations asset pricing model in this study accounts for expected future neighborhood effects as well as observed current neighborhood effects. The reduced form of the rational expectations model is a spatial autoregressive (SAR) model with two spatial lags. After employing the generalized method of moments (GMM) in estimating the spatial asset pricing model, I find that both expected future transactions and prior transactions in the neighborhood are significant. The inclusion of expected future transaction prices in the neighborhood takes into account the influence of expected changes in the community and factors these potential changes into the current house price. This is consistent with forward-looking households. The forward-looking model generates superior out-of-sample prediction performance relative to both the conventional hedonic model without considering neighborhood effects or the standard spatial hedonic model including only past transactions. [Copyright &y& Elsevier]