A Spatial Autocorrelation Approach for Examining the Effects of Urban Greenspace on Residential Property Values

This paper presents spatially explicit analyses of the greenspace contribution to residential property values in a hedonic model. The paper utilizes data from the housing market near downtown Los Angeles. We first used a standard hedonic model to estimate greenspace effects. Because the residuals were spatially autocorrelated, we implemented a spatial lag model as indicated by specification tests. Our results show that neighborhood greenspace at the immediate vicinity of houses has a significant impact on house prices even after controlling for spatial autocorrelation. The different estimation results from non-spatial and spatial models provide useful bounds for the greenspace effect. Greening of inner city areas may provide a valuable policy instrument for elevating depressed housing markets in those areas.     

Spatial modelling of claim frequency and claim size in non-life insurance

In this paper, models for claim frequency and average claim size in non-life insurance are considered. Both covariates and spatial random effects are included allowing the modelling of a spatial dependency pattern. We assume a Poisson model for the number of claims, while claim size is modelled using a Gamma distribution. However, in contrast to the usual compound Poisson model, we allow for dependencies between claim size and claim frequency. A fully Bayesian approach is followed, parameters are estimated using Markov Chain Monte Carlo (MCMC). The issue of model comparison is thoroughly addressed. Besides the deviance information criterion and the predictive model choice criterion, we suggest the use of proper scoring rules based on the posterior predictive distribution for comparing models. We give an application to a comprehensive data set from a German car insurance company. The inclusion of spatial effects significantly improves the models for both claim frequency and claim size, and also leads to more accurate predictions of the total claim sizes. Further, we detect significant dependencies between the number of claims and claim size. Both spatial and number of claims effects are interpreted and quantified from an actuarial point of view.     

Factor Copula Approaches for Assessing Spatially Dependent High-Dimensional Risks

In this article, we propose an innovative approach for modeling spatial dependence among losses from various geographical locations. The proposed model converts the challenging task of modeling complex spatial dependence structures into a relatively easier task of estimating a continuous function, of which the arguments can be the coordinates of the locations. The approach is based on factor copula models, which can capture various linear and nonlinear dependence. We use radial basis functions as the kernel smoother for estimating the key function that models all the spatial dependence structures. A case study on a thunderstorm wind loss dataset demonstrates the analysis and the usefulness of the proposed approach. Extensions to spatiotemporal models and to models for discrete data are briefly introduced, with an example given for modeling loss frequency with excess zeros.     

A Spatial Cross-Sectional Credibility Model with Dependence Among Risks

A Bühlmann-Straub type credibility model with dependence structure among risk parameters and conditional spatial cross-sectional dependence is studied. Predictors of future losses for the model under both types of dependence are derived by minimizing the expected quadratic loss function, and nonparametric estimators of structural parameters are considered in the spatial statistics context. Predictions and estimations made for the proposed model are examined and compared to other models in an application with crop insurance data and in a simulation study.     

A Spatio-Temporal Autoregressive Model for Multi-Unit Residential Market Analysis*

By splitting the spatial effects into building and neighborhood effects, this paper develops a two order spatio-temporal autoregressive model to deal with both the spatio-temporal autocorrelations and the heteroscedasticity problem arising from the nature of multi-unit residential real estate data. The empirical results based on 54,282 condominium transactions in Singapore between 1990 and 1999 show that in the multi-unit residential market, a two order spatio-temporal autoregressive model incorporates more spatial information into the model, thus outperforming the models originally developed in the market for single-family homes. This implies that the specification of a spatio-temporal model should consider the physical market structure as it affects the spatial process. It is found that the Bayesian estimation method can produce more robust coefficients by efficiently detecting and correcting heteroscedasticity, indicating that the Bayesian estimation method is more suitable for estimating a real estate hedonic model than the conventional OLS estimation. It is also found that there is a trade off between the heteroscedastic robustness and the incorporation of spatial information into the model estimation. The model is then used to construct building-specific price indices. The results show that the price indices for different condominiums and the buildings within a condominium do behave differently, especially when compared with the aggregate market indices.This paper was presented at the Singapore–Hong Kong International Real Estate Research Symposium, organized by the Department of Real Estate, National University of Singapore, from 18 to 19 July, 2003.     

Spatial Distribution of Retail Sales

We examine the distribution of sales for a retail chain in the Houston market using a spatial gravity model. Unlike previous empirical studies, our approach models spatial dependencies among both consumers and retailers. The results show that both forms of spatial dependence exert statistically and economically significant impacts on the estimates of parameters in retail gravity models. Contrary to the suggestions of (Gautschi, D. A. (1981). “Specification of Patronage Models for Retail Center Choice,” Journal of Marketing Research 18, 162–174.) as well as (Eppli, M. J., and J. D. Shilling. (1996). “How Critical Is a Good Location to a Regional Shopping Center?” Journal of Real Estate Research 12, 459–468.), our results show the importance of the distance parameter in retail gravity models may be greatly understated. Thus, ignoring spatial dependence may lead to overestimation of the deterministic extent of trade areas, and underestimate the importance of good locations.     

Implied volatilities,stochastic interest rates,and currency futures options valuation: an empirical investigation

Different models of pricing currency call and put options on futures are empirically tested. Option prices are determined using different models and compared to actual market prices. Option prices are determined using historical as well as implied volatility. The different models tested include both constant and stochastic interest rate models. To determine if the model prices are different from the market prices, regression analysis and paired t-tests are performed. To see which model misprices the least, root mean square errors are determined. It is found that better results are obtained when implied volatility is used. Stochastic interest rate models perform better than constant interest rate models.     

Housing Price Dynamics in Time and Space: Predictability,Liquidity and Investor Returns

It is widely accepted that aggregate housing prices are predictable, but that excess returns to investors are precluded by the transactions costs of buying and selling property. We examine this issue using a unique data set—all private condominium transactions in Singapore during an eleven-year period. We model directly the price discovery process for individual dwellings. Our empirical results clearly reject a random walk in prices, supporting mean reversion in housing prices and diffusion of innovations over space. We find that, when house prices and aggregate returns are computed from models that erroneously assume a random walk and spatial independence, they are strongly autocorrelated. However, when they are calculated from the appropriate model, predictability in prices and in investment returns is completely absent. We show that this is due to the illiquid nature of housing transactions. We also conduct extensive simulations, over different time horizons and with different investment rules, testing whether better information on housing price dynamics leads to superior investment performance.     

Additive Hedonic Regression Models with Spatial Scaling Factors: An Application for Rents in Vienna

This paper is motivated by two common challenges in hedonic price modeling: nonlinear price functions, which require flexible modeling approaches, and the inherent spatial heterogeneity in real estate markets. We apply additive mixed regression models (AMM) to estimate hedonic price equations for rents in Vienna. Non-linear effects of continuous covariates as well as a smooth time trend are modeled non-parametrically through P-splines. Unobserved district-specific heterogeneity is modeled in two ways: First, by location specific intercepts with the postal code serving as a location variable. Second, in order to permit spatial variation in the nonlinear price gradients, we introduce multiplicative scaling factors for nonlinear covariates. This allows highly nonlinear implicit price functions to vary within a regularized framework, accounting for district-specific spatial heterogeneity, which leads to a considerable improvement of model quality and predictive power. Our findings provide insight into the spatially heterogeneous structure of price gradients in Vienna, showing substantial spatial variation. Accounting for spatial heterogeneity in a very general way, this approach permits higher accuracy in prediction and allows for location-specific nonlinear rent index construction.     

On Spatial Public Finance Empirics

This paper focuses on the empirical specification of theoretical models of strategic interaction that give rise to a spatial pattern in local government expenditures and revenues. It shows that estimation of a reduced form inter-jurisdictional reaction function might not by itself allow to discriminate among competing strategic interaction theories. A review of the recent empirical literature suggests that exploring in more depth the specific empirical implications of alternative theoretical models, as well as fully exploiting the institutional features of multi-tiered government structures and local electoral systems, can help identify the structural model generating the observed spatial auto-correlation in policy variables.JEL Code: H71, H72, H77