ECONOMETRIC ANALYSIS OF HOUSING TRAIT PRICES IN A THIRD WORLD CITY*

ABSTRACT. A first step in the process of economic analysis of housing markets in Third World cities is the econometric analysis of housing trait prices. The information on market price of housing is basic to the derivation/estimation of other market parameters such as housing demand and supply elasticities. In addition, housing trait prices constitute invaluable inputs into the analysis of effects of government housing programs. This paper presents estimates of housing trait prices in a Third World city housing market, the city of Jos in Nigeria. Nonlinear stochastic specification of a policy constrained hedonic price function is presented as an unbiased estimator of housing trait prices. The Box-Cox statistical procedure was employed in the paper to obtain hedonic regression coefficients which are the parameters needed to compute the average prices evaluated both at the mean of each trait and at their margins. The potential uses of housing trait prices for policy analysis are discussed briefly.     

Parameterized Seismic Fragility Curves for Curved Multi-frame Concrete Box-Girder Bridges Using Bayesian Parameter Estimation

This paper addresses the application of a Bayesian parameter estimation method to a regional seismic risk assessment of curved concrete bridges. For this purpose, numerical models of case-study bridges are simulated to generate multiparameter demand models of components, consisting of various uncertainty parameters and an intensity measure (IM). The demand models are constructed using a Bayesian parameter estimation method and combined with limit states to derive the parameterized fragility curves. These fragility curves are used to develop bridge-specific and bridge-class fragility curves. Moreover, a stepwise removal process in the Bayesian parameter estimation is performed to identify significant parameters affecting component demands.     

ESTIMATION AND HYPOTHESIS TESTING FOR NONPARAMETRIC HEDONIC HOUSE PRICE FUNCTIONS

ABSTRACT In contrast to the rigid structure of standard parametric hedonic analysis, nonparametric estimators control for misspecified spatial effects while using highly flexible functional forms. Despite these advantages, nonparametric procedures are still not used extensively for spatial data analysis due to perceived difficulties associated with estimation and hypothesis testing. We demonstrate that nonparametric estimation is feasible for large datasets with many independent variables, offering statistical tests of individual covariates and tests of model specification. We show that fixed parameterization of distance to the nearest rapid transit line is a misspecification and that pricing of access to this amenity varies across neighborhoods within Chicago.     

BAYESIAN MODEL AVERAGING IN THE CONTEXT OF SPATIAL HEDONIC PRICING: AN APPLICATION TO FARMLAND VALUES

ABSTRACT Specification uncertainty arises in spatial hedonic pricing models because economic theory provides no guide in choosing the spatial weighting matrix and explanatory variables. Our objective in this paper is to investigate whether we can resolve uncertainty in the application of a spatial hedonic pricing model. We employ Bayesian Model Averaging in combination with Markov Chain, Monte Carlo Model Composition. The proposed methodology provides inclusion probabilities for explanatory variables and weighting matrices. These probabilities provide a clear indication of which explanatory variables and weighting matrices are most relevant, but they are case specific.     

Bayesian Analysis of Knowledge Spillovers in European Regions*

Abstract. This paper estimates the effects of knowledge spillovers on patent growth rates across 335 European regions over the 1989–1999 period. We propose a dynamic model based on an innovation production function. A Bayesian approach is used to take into account area‐specific innovation and spatial spillovers. The estimation of the model proceeds via Markov Chain Monte Carlo simulation. The results show significant positive and negative spatial effects on innovative activity. The model allows for a rich spatial specification, which we illustrate by incorporating transport proximity measured by transportation time between regions to augment the typical spatial proximity measure of connectivity between regions. Doing this produces more pronounced spatial spillovers that exhibit a more polarized spatial pattern than a model relying on spatial proximity alone.     

Bayesian Estimation of Regional Production for CGE Modeling

Abstract Computable general equilibrium (CGE) models are often criticized for using restrictive functional forms and relying on external sources for parameter values in their calibration. CGE modelers argue that in many instances reliable econometric estimates of important model parameters are unavailable because they must be estimated using small numbers of time‐series observations. To address these criticisms, this paper uses a Bayesian approach to estimate the parameters of a translog production function in a regional computable general equilibrium model. Using priors from more reliable national estimates, and parameter restrictions required by neoclassical production theory, estimation is done by Markov chain Monte Carlo simulation. A stylized regional CGE model is then used to contrast policy responses of a Cobb‐Douglas specification with those from the estimated translog equation.     

A Bayesian Approach to Modeling Binary Data: The Case of High‐Intensity Crime Areas

This paper reports the fitting of a number of Bayesian logistic models with spatially structured or/and unstructured random effects to binary data with the purpose of explaining the distribution of high‐intensity crime areas (HIAs) in the city of Sheffield, England. Bayesian approaches to spatial modeling are attracting considerable interest at the present time. This is because of the availability of rigorously tested software for fitting a certain class of spatial models. This paper considers issues associated with the specification, estimation, and validation, including sensitivity analysis, of spatial models using the WinBUGS software. It pays particular attention to the visualization of results. We discuss a map decomposition strategy and an approach that examines properties of the full posterior distribution. The Bayesian spatial model reported provides some interesting insights into the different factors underlying the existence of the three police‐defined HIAs in Sheffield.     

SPATIAL MULTIPLIERS IN HEDONIC ANALYSIS: A COMMENT ON “SPATIAL HEDONIC MODELS OF AIRPORT NOISE,PROXIMITY, AND HOUSING PRICES”*

ABSTRACT This note draws upon the spatial‐hedonic analysis of Cohen and Coughlin to clarify the role of spatial multipliers in regression specification and benefits measurement, and to demonstrate the appropriate calculation of these benefits from dummy‐variable coefficients in semi‐logarithmic spatial‐lag models.     

LEAST SQUARES ESTIMATES OF THE REGIONAL EMPLOYMENT MULTIPLIER: AN APPRAISAL*

Basically, we have attempted to show the following in the course of setting out the algebra of regression analysis of selected regional employment multiplier models: (1) When the basic features of the model are shaped by the assumption of an unlagged response of local employment to changes in export employment, the least squares estimates of the multipliers are highly sensitive to the export coefficients vector A, given the sample observation matrix X. In a completely disaggregated model such as Equation (21), the multipliers are solely determined by the export coefficients and thus are entirely independent of sample observations. However, this independence does not hold in the case of a partially disaggregated model. The identity relation is also destroyed when a lag relationship is introduced into a completely disaggregated model. (2) A simple lag model produces results bascially different from those obtained by an unlagged model if the overall differences between current and lagged observations are significant. (3) Given a matrix of sample observations on employment, it is possible to estimate the upper limits of a least-squares aggregate multiplier and its variance simply from knowledge of the export coefficients (4) The export coefficients vector has also an important bearing upon the correlation coefficient. The correlation is unity if and only if the export coefficients vector is proportional to the local employment coefficients vector, while it is zero if and only if the export coefficients vector is a vector all of whose elements are one. Also, the correlation coefficient is equal to one when a completely disaggregated model is used. There is finally the question of what these results mean in terms of the formulation of a multiplier model. First of all, in view of the crucial importance of the export coefficients and the difficulties of estimating them, most of the existing models do not seem to offer promising results. Furthermore, all the models examined here have made some simplifying assumption with respect to the constancy of the export coefficients. It remains highly uncertain whether these coefficients are reasonably stable over time. Of course, it would be theoretically more acceptable to relax the assumption of the invariance of export coefficients and to obtain such coefficients at different points of time for each industry. However, this would be accomplished only at the cost of increased difficulties of estimating larger numbers of export coefficients. In addition, there is some doubt as to the validity of the assumption that export employment is proportional to export sales. Since a lag relationship is important not only in terms of attempts to formulate multiplier models more realistically, but also in terms of its significant effect on the multiplier values obtained, the nature and the form of a lagged response and its estimation problems need to be investigated in depth. Finally, problems of least squares bias and efficiency, inference, and prediction which may arise in the context of various models presented here remain to be investigated. A detailed analysis of such problems must be the subject of further investigation.     

Sequential, Bayesian Geostatistics: A Principled Method for Large Data Sets

The principled statistical application of Gaussian random field models used in geostatistics has historically been limited to data sets of a small size. This limitation is imposed by the requirement to store and invert the covariance matrix of all the samples to obtain a predictive distribution at unsampled locations, or to use likelihood-based covariance estimation. Various ad hoc approaches to solve this problem have been adopted, such as selecting a neighborhood region and/or a small number of observations to use in the kriging process, but these have no sound theoretical basis and it is unclear what information is being lost. In this article, we present a Bayesian method for estimating the posterior mean and covariance structures of a Gaussian random field using a sequential estimation algorithm. By imposing sparsity in a well-defined framework, the algorithm retains a subset of " basis vectors " that best represent the " true " posterior Gaussian random field model in the relative entropy sense. This allows a principled treatment of Gaussian random field models on very large data sets. The method is particularly appropriate when the Gaussian random field model is regarded as a latent variable model, which may be nonlinearly related to the observations. We show the application of the sequential, sparse Bayesian estimation in Gaussian random field models and discuss its merits and drawbacks.     

Balancing Spatial and Non-Spatial Variation in Varying Coefficient Modeling: A Remedy for Spurious Correlation

This study discusses the importance of balancing spatial and non-spatial variation in spatial regression modeling. Unlike spatially varying coefficients (SVC) modeling, which is popular in spatial statistics, non-spatially varying coefficients (NVC) modeling has largely been unexplored in spatial fields. Nevertheless, as we will explain, consideration of non-spatial variation is needed not only to improve model accuracy but also to reduce spurious correlation among varying coefficients, which is a major problem in SVC modeling. We consider a Moran eigenvector approach modeling spatially and non-spatially varying coefficients (S&NVC). A Monte Carlo simulation experiment comparing our S&NVC model with existing SVC models suggests both modeling accuracy and computational efficiency for our approach. Beyond that, somewhat surprisingly, our approach identifies true and spurious correlations among coefficients nearly perfectly, even when usual SVC models suffer from severe spurious correlations. It implies that S&NVC model should be used even when the analysis purpose is modeling SVCs. Finally, our S&NVC model is employed to analyze a residential land price data set. Its results suggest existence of both spatial and non-spatial variation in regression coefficients in practice. The S&NVC model is now implemented in the R package spmoran.     

Designs for Detecting Spatial Dependence

The aim of this article is to find optimal or nearly optimal designs for experiments to detect spatial dependence that might be in the data. The questions to be answered are: how to optimally select predictor values to detect the spatial structure (if it is existent) and how to avoid to spuriously detect spatial dependence if there is no such structure. The starting point of this analysis involves two different linear regression models: (1) an ordinary linear regression model with i.i.d. error terms—the nonspatial case and (2) a regression model with a spatially autocorrelated error term, a so-called simultaneous spatial autoregressive error model. The procedure can be divided into two main parts: The first is use of an exchange algorithm to find the optimal design for the respective data collection process; for its evaluation an artificial data set was generated and used. The second is estimation of the parameters of the regression model and calculation of Moran's I , which is used as an indicator for spatial dependence in the data set. The method is illustrated by applying it to a well-known case study in spatial analysis.     

HEDONIC ANALYSIS OVER TIME AND SPACE: THE CASE OF HOUSE PRICES AND TRAFFIC NOISE

One consequence of the expanding road network and its associated traffic is increased levels of traffic noise. While the hedonic literature has consistently found a negative relationship between real estate prices and noise levels, research in the United States has typically relied on crude measures of traffic noise. Here, we reduce the measurement error of traffic noise exposure through a detailed model of noise propagation over the landscape. We then estimate the hedonic relationship between noise and single family house prices using over 40,000 transactions throughout the St. Paul, Minnesota, urban area from 2005 to 2010. We implement spatially and temporally flexible local regression techniques and find significant nonstationarity in the hedonic function over time and space.     

BAYSIAN INFERENCE FOR ORDERED RESPONSE DATA WITH A DYNAMIC SPATIAL‐ORDERED PROBIT MODEL

ABSTRACT Many databases involve ordered discrete responses in a temporal and spatial context, including, for example, land development intensity levels, vehicle ownership, and pavement conditions. An appreciation of such behaviors requires rigorous statistical methods, recognizing spatial effects and dynamic processes. This study develops a dynamic spatial‐ordered probit (DSOP) model in order to capture patterns of spatial and temporal autocorrelation in ordered categorical response data. This model is estimated in a Bayesian framework using Gibbs sampling and data augmentation, in order to generate all autocorrelated latent variables. It incorporates spatial effects in an ordered probit model by allowing for interregional spatial interactions and heteroskedasticity, along with random effects across regions or any clusters of observational units. The model assumes an autoregressive, AR(1), process across latent response values, thereby recognizing time‐series dynamics in panel data sets. The model code and estimation approach is tested on simulated data sets, in order to reproduce known parameter values and provide insights into estimation performance, yielding much more accurate estimates than standard, nonspatial techniques. The proposed and tested DSOP model is felt to be a significant contribution to the field of spatial econometrics, where binary applications (for discrete response data) have been seen as the cutting edge. The Bayesian framework and Gibbs sampling techniques used here permit such complexity, in world of two‐dimensional autocorrelation.     

Exploring a Relationship Between Aggregate and Individual Levels Spatial Data Through Semivariogram Models

Analysis of social data is frequently done using aggregate-level data. There may not be a direct interest in spatial relationships in the data, but the presence of spatial interdependence may still need to be taken into account. This article explores the aggregation effect from a spatial perspective by assuming nonzero covariance for individual data from two different groups. We investigate the bias associated with aggregate-level data for semivariogram analysis. We show that the bias mainly arises from the average of the semivariogram within the groups. It is also shown how aggregated-level data may be used to estimate parameters of an individual-level semivariogram model. A nonlinear regression method is proposed to carry out this estimation procedure and a simulation is done to clarify the results.     

Bayesian Model Averaging for Spatial Econometric Models

We extend the literature on Bayesian model comparison for ordinary least-squares regression models to include spatial autoregressive and spatial error models. Our focus is on comparing models that consist of different matrices of explanatory variables. A Markov Chain Monte Carlo model composition methodology labeled MC ³ by Madigan and York is developed for two types of spatial econometric models that are frequently used in the literature. The methodology deals with cases where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. Estimates and inferences are produced by averaging over models using the posterior model probabilities as weights, a procedure known as Bayesian model averaging. We illustrate the methods using a spatial econometric model of origin–destination population migration flows between the 48 U.S. states and the District of Columbia during the 1990–2000 period.     

HEDONIC PRICES,IDENTIFICATION, AND THE DEMAND FOR PUBLIC SAFETY*

In this paper, we use a two-stage intercity hedonic model to estimate household demand for public safety. This approach is shown to readily address the identification problem inherent in the hedonic model. Data from the 1980 Public Use Microdata Sample are used to estimate a willingness-to-pay function for the public-safety good. Income is found to be the primary determinant of willingness to pay. Indeed, the influence of income outweighs the combined impact of family life-cycle considerations.     

A Bayesian Updating Applied to Earthquake Ground-Motion Prediction Equations for Iran

This article presents new ground-motion prediction equations for three distinct seismic regions of Iran via updating the previous global model using observed data for each region by means of Bayesian updating. The Bayesian theory has the advantage that it results in more accurate results even in situations when little data is available. This leads the way for updating global models to obtain new local models for seismotectonic regions with little available data like Iran. The proposed updated model was compared against currently available models for Iran and the results reveal the overall stability and quality performance of the proposed model.     

NOTE ON THE INCLUSION OF SITE CLASSIFICATION INFORMATION IN EQUATIONS TO ESTIMATE STRONG GROUND MOTIONS

Two main regression methods have been proposed for using site category information within ground motion prediction equations, these are: (a) joint estimation of the site category coefficients and the magnitude and distance coefficients; or (b) estimation of site category coefficients by using the residuals from the equation derived without considering soil conditions. Method (a) requires each record be assigned a site category whereas for method (b), because it relies on residuals, site information can be missing for some records. This short note finds that if the mean of the transformed distances within each site category is the same then the two methods give the same site coefficients. If, however, these means are significantly different then method (b) can yield incorrect site coefficients.