Property Condition Disclosure Law: Does 'Seller Tell All' Matter in Property Values?

At the time when at least two-thirds of the US states have already mandated some form of seller's property condition disclosure statement and there is a movement in this direction nationally, this paper examines the impact of seller's property condition disclosure law on the residential real estate values, the information asymmetry in housing transactions and shift of risk from buyers and brokers to the sellers, and attempts to ascertain the factors that lead to adoption of the disclosur law. The analytical structure employs parametric panel data models, semi-parametric propensity score matching models, and an event study framework using a unique set of economic and institutional attributes for a quarterly panel of 291 US Metropolitan Statistical Areas (MSAs) and 50 US States spanning 21 years from 1984 to 2004. Exploiting the MSA level variation in house prices, the study finds that the average seller may be able to fetch a higher price (about three to four percent) for the house if she furnishes a state-mandated seller's property condition disclosure statement to the buyer.

The Impact of Property Condition Disclosure Laws on Housing Prices: Evidence from an Event Study using Propensity Scores

We examine the impact of seller's Property Condition Disclosure Law on the residential real estate values. A disclosure law may address the information asymmetry in housing transactions shifting of risk from buyers and brokers to the sellers and raising housing prices as a result. We combine propensity score techniques from the treatment effects literature with a traditional event study approach. We assemble a unique set of economic and institutional attributes for a quarterly panel of 291 US Metropolitan Statistical Areas (MSAs) and 50 US States spanning 21 years from 1984 to 2004 is used to exploit the MSA level variation in house prices. The study finds that the average seller may be able to fetch a higher price (about three to four percent) for the house if she furnishes a state-mandated seller.s property condition disclosure statement to the buyer. When we compare the results from parametric and semi-parametric event analyses, we find that the semi-parametric or the propensity score analysis generals moderately larger estimated effects of the law on housing prices.

Some Identification Issues in Nonparametric Linear Models with Endogenous Regressors

In applied work economists often seek to relate a given response variable y to some causal parameter mu* associated with it. This parameter usually represents a summarization based on some explanatory variables of the distribution of y, such as a regression function, and treating it as a conditional expectation is central to its identification and estimation. However, the interpretation of mu* as a conditional expectation breaks down if some or all of the explanatory variables are endogenous. This is not a problem when mu* is modelled as a parametric function of explanatory variables because it is well known how instrumental variables techniques can be used to identify and estimate mu*. In contrast, handling endogenous regressors in nonparametric models, where mu* is regarded as fully unknown, presents di±cult theoretical and practical challenges. In this paper we consider an endogenous nonparametric model based on a conditional moment restriction. We investigate identification related properties of this model when the unknown function mu* belongs to a linear space. We also investigate underidentification of mu* along with the identification of its linear functionals. Several examples are provided in order to develop intuition about identification and estimation for endogenous nonparametric regression and related models.

Performance Evaluation of the New Connecticut Leading Employment Index Using Lead Profiles and BVAR Models

Dua and Miller (1996) created leading and coincident employment indexes for the state of Connecticut, following Moore's (1981) work at the national level. The performance of the Dua-Miller indexes following the recession of the early 1990s fell short of expectations. This paper performs two tasks. First, it describes the process of revising the Connecticut Coincident and Leading Employment Indexes. Second, it analyzes the statistical properties and performance of the new indexes by comparing the lead profiles of the new and old indexes as well as their out-of-sample forecasting performance, using the Bayesian Vector Autoregressive (BVAR) method. The new indexes show improved performance in dating employment cycle chronologies. The lead profile test demonstrates that superiority in a rigorous, non-parametric statistic fashion. The mixed evidence on the BVAR forecasting experiments illustrates the truth in the Granger and Newbold (1986) caution that leading indexes properly predict cycle turning points and do not necessarily provide accurate forecasts except at turning points, a view that our results support.