In this paper, we utilize a spatial two-dimensional regression discontinuity (RD) design to study how Tokyo's property market evaluates information on seismic hazard risk. This approach is superior to the conventional one-dimensional RD design as it is able to account for spatially heterogeneous treatment effects and reduce small-sample biases. Our data consists of residential property transactions from the 23-ward area of Tokyo. Our results show that the unit prices of residential properties in low-risk zones were between 13,970-17,380 JPY higher than those in high-risk zones depending on the type of seismic hazard risk. In addition, we find that information on seismic hazard risk does not significantly affect the prices of newly constructed apartments, which are more resistant to earthquake damage than older residences. (C) 2015 Elsevier B.V. All tights reserved.

In this paper, we consider the estimation of partially linear additive quantile regression models where the conditional quantile function comprises a linear parametric component and a nonparametric additive component. We propose a two-step estimation approach: in the first step, we approximate the conditional quantile function using a series estimation method. In the second step, the nonparametric additive component is recovered using either a local polynomial estimator or a weighted Nadaraya-Watson estimator. Both consistency and asymptotic normality of the proposed estimators are established. Particularly, we show that the first-stage estimator for the finite-dimensional parameters attains the semiparametric efficiency bound under homoskedasticity, and that the second-stage estimators for the nonparametric additive component have an oracle efficiency property. Monte Carlo experiments are conducted to assess the finite sample performance of the proposed estimators. An application to a real data set is also illustrated.

This study proposes the use of semiparametric varying-coefficient methods to estimate the preference heterogeneity within stated choice data. Semiparametric varying-coefficient methods have the potential to overcome the disadvantages of conventional random parameter models and latent class models. For binary probit models with varying coefficients, in particular, this study proposes an easy-to-compute local iterative least squares (LILS) approach, based on the expectation-maximization algorithm. The finite sample properties of the LILS estimator are assessed using Monte Carlo experiments. In order to demonstrate the practical usefulness of semiparametric varying-coefficient methods, we present an empirical study, conducting an economic valuation of a landscape with dichotomous choice contingent valuations.

This study investigates the identification of parameters in semiparametric binary response models of the form y=1(x'β+v+ε&gt
0) when there are nonignorable nonresponses. We propose an estimation procedure for the identified set, the set of parameters that are observationally indistinguishable from the true value β, based on the special regressor approach of Lewbel (2000). We show that the estimator for the identified set is consistent in the Hausdorff metric. © 2013 Elsevier B.V.

Effective and efficient planning and development of residential environments require clarifying the nature of residential preferences. In reality, residential preferences are heterogeneous, so the standard econometric models that assume only one type of preference are not optimal. In this study, conjoint choice experiment methods are employed with a mixed logit approach. The findings reveal significant heterogeneity with regard to some residential attributes. The determinants of preference heterogeneity were also investigated by conducting regression analyses on the attributes that were valued heterogeneously. Overall, the relationships observed between the explanatory variables and the heterogeneity in the valuations were understandable. However, coefficient of determination values for each model were low, indicating that the bulk of preference heterogeneity results from unobservable factors.

The hedonic price method was used to estimate the influence of parks on the rental prices of single-room dwellings in Setagaya Ward, Tokyo, Japan. A simple least squares method is not optimal when the data set contains spatial autocorrelation. To improve the accuracy of estimates, we employed spatial autoregression and kriging models, resulting in a higher validity of the spatial models compared to the least squares model. Kriging models were superior to others particularly in terms of prediction accuracy, indicating that these models should be employed if the objective is superior prediction rather than estimation. The results showed that the effect of parks on property values varied with the buffer distance and park size.