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Hedonic property value analysis is one of the leading methods of environmental valuation. This non-market technique uses variation in home sales to infer the values of amenities or disamenities. While there have been numerous studies about air quality and hazardous waste, the number of papers focusing on water quality is much smaller. Consequently, there are still many unanswered questions about the proper handling of water quality through hedonic methods. Furthermore, estimates from hedonic property price analyses are rarely used in government cost benefit analyses. This dissertation investigates several important hedonic issues in a large analysis of water quality in central Florida. The first chapter of this paper explores the extent of water quality benefits. Almost all past studies have focused exclusively on waterfront homes. The present paper includes non-waterfront homes and investigates three hypotheses about the marginal impact of water quality. The first hypothesis is that non-waterfront homes are positively affected by water quality, but by a smaller amount than waterfront homes. The second hypothesis is about the effect of lake distance on the relationship between water quality and property prices: this relationship should be negative. The third hypothesis states that properties near larger lakes have a higher implicit price for water quality than homes around smaller lakes, all else constant. These three hypotheses are investigated in each chapter of the dissertation, and provide a unifying theme to the paper. Results from Chapter 1 support all three hypotheses. Most importantly, the empirical estimates indicate that water quality benefits extend beyond the waterfront in a declining gradient. Excluding non-lakefront homes from the analysis can therefore substantially underestimate the total benefits of a water quality improvement. Estimates of the total property price benefits from a one foot increase in water quality were found to double with the addition of non-waterfront homes. The second chapter examines the sensitivity of results to several spatial specifications. Spatial issues can be a problem in analyses of real estate data because of spatially correlated variables, unobservable neighborhood codes and covenants, identical or similar builders, and property appraisal valuation techniques. The focus of the chapter is on the spatial weights matrix (SWM). Six different SWM's are constructed, which are based on popular specifications encountered in the current spatial hedonic literature. An out-of-sample forecasting exercise is used to compare multiple spatial specifications. Results indicate that certain spatial models may be sensitive to the specification of the weights matrix. Furthermore, many popular models currently used in the literature could be improved by allowing more non-zero elements in the SWM. The third chapter investigates the definition of "water quality" and uses several additional quality indicators. Choosing the proper pollution indicator is an issue that has plagued many areas of the valuation literature. While clarity indicators have become popular in hedonic property price analysis, they are not used for the purposes of regulation by many state environmental departments. This chapter uses several indicators that are used by the state of Florida to classify lakes and implement policy. Implicit prices are computed for all of the indicators and issues of benefit extent and total benefits are explored. Instead of finding an optimal indicator for all situations, results indicate that the use of at least two types of indicators may capture a larger range of the true total benefits. The final chapter uses a repeat sales model to address potential problems with omitted variable bias. Due to the size of the data set in this paper, there are a substantial number of homes that have sold more than once. The repeat sales model analyzes differences in property sales prices for the same home over time. The three hypotheses of the first chapter are explored in this alternative model. The implicit price obtained from the repeat sales model is much larger than the regular hedonic model. However, there are some concerns with the smaller population of repeat sales.

No description available.

No description available.

No description available.

No description available.

This paper uses data for 255 NUTS-2 European regions over the period 1995-2003 to test the relative explanatory performance of two important rival theories seeking to explain variations in the level of economic development across regions, namely the neoclassical model originating from the work of Solow (1956) and the so-called Wage Equation, which is one of a set of simultaneous equations consistent with the short-run equilibrium of new economic geography (NEG) theory, as described by Fujita, Krugman and Venables (1999). The rivals are non-nested, so that testing is accomplished both by fitting the reduced form models individually and by simply combining the two rivals to create a composite model in an attempt to identify the dominant theory. We use different estimators for the resulting panel data model to account variously for interregional heterogeneity, endogeneity, and temporal and spatial dependence, including maximum likelihood with and without fixed effects, two stage least squares and feasible generalised spatial two stage least squares plus GMM; also most of these models embody a spatial autoregressive error process. These show that the estimated NEG model parameters correspond to theoretical expectation, whereas the parameter estimates derived from the neoclassical model reduced form are sometimes insignificant or take on counterintuitive signs. This casts doubt on the appropriateness of neoclassical theory as a basis for explaining cross-regional variation in economic development in Europe, whereas NEG theory seems to hold in the face of competition from its rival. (authors' abstract)

La présence de dépendance spatiale des prix immobiliers impose aux méthodes d’estimation de prendre en compte cet élément. Les deux approches de la statistique spatiale sont l’économétrie spatiale et la géostatistique. La géostatistique estime directement la matrice de variance-covariance en supposant que la covariance entre les observations dépend inversement de la distance séparant leur localisation. L’économétrie spatiale définit et intègre la matrice d’interaction spatiale dans un modèle de régression hédonique. Si ces deux méthodes sont possibles pour étudier la dépendance spatiale des prix immobiliers dans des contextes variés, il n’existe cependant pas de règles très claires quant au choix de la méthode à sélectionner. Cette thèse procède à un examen détaillé de ces deux approches afin de pouvoir en distinguer les ressemblances et les différences, les avantages et les inconvénients. Des exemples d’application de chaque approche dans une étude immobilière sont présentés. La géostatistique est utilisée pour analyser la stationnarité du variogramme, ainsi que la sensibilité du variogramme aux paramètres de l’estimation hédonique. Le modèle d’économétrie spatiale est utilisé pour tenter d’identifier économétriquement le quartier dominant du marché immobilier d’une ville / Geostatistics and spatial econometrics are two spatial statistical approaches used to deal with spatial dependence. Geostatistics estimates directly the variance-covariance matrix by assuming that the covariance among observations depends inversely on the distance between their locations, called the covariogram. Spatial econometrics defines and integrates the spatial interaction matrix in a hedonic regression model. In real estate, price estimation should take into account these spatial characteristics because property prices are correlated. Hence, these two approaches are commonly used to study the spatial dependence of the real estate prices in many contexts. However, a definite rule in selection these statistic approaches has not been established. This thesis examined these two approaches in order to distinguish the similarities, differences, advantages, and disadvantages of each methodology. Some examples of their applications in a real estate study. The geostatistics is used to analyze the stationarity of the variogram and its sensitivity depending on the parameters added in hedonic estimation. The spatial econometric is used to define econometrically the real estate market dominant area

Ao longo das últimas décadas, um número cada vez maior de pesquisadores tem argumentado que simples índices como crescimento do PIB ou exportações são insuficientes para descrever o bem estar da população de um país. Contudo, apesar de estudos recentes adicionarem variáveis como desigualdade de renda, infraestrutura e inovação tecnológica às análises, a maioria dos autores falham na consideração de aspectos espaciais, tais quais os efeitos de vizinhança para estratégias governamentais de infraestrutura e desenvolvimento sócio-econômico. Nesse sentido, o presente trabalho tem como principal objetivo analisar a relação entre crescimento econômico, infraestrutura de transportes, gastos em inovação tecnológica e desenvolvimento sócio-econômico nos estados federativos brasileiros e seus efeitos de spillover (difusão), por meio de técnicas de econometria espacial. Os principais resultados demonstram a infraestrutura rodoviária como maior promotor de spillovers positivos para crescimento e desenvolvimento. Quanto à inovação, os resultados indicaram spillovers negativos, tanto para crescimento como para desenvolvimento, sendo significante somente para desenvolvimento. / Over the past decades, an increasing number of researchers have argued that simple indices such as GDP growth and exports are insufficient to describe the welfare of a country. However, in spite of recent studies add variables such as income inequality, infrastructure and technological innovation to the analysis, most authors fail to consider spatial aspects, as is the neighborhood effects for government strategies on infrastructure and socio-economic development. Accordingly, the present work aims to examine the relationship between economic growth, capital investment in public transport infrastructure, spending on technological innovation, and socio-economic development in the Brazilian federal states and their spillover effects, using spatial econometrics techniques. The main results show the road infrastructure as the biggest promoter of positive spillovers for growth and development. As for innovation, results indicated negative spillovers, both for growth and for development, being significant only for development.